Eng. Proc., 2023, RAiSE-2023

The International Conference on Recent Advances in Science and Engineering, RAiSE-2023, organized by the Department of Mechanical & Industrial Engineering at Manipal Institute of Technology, MAHE, Manipal, India, in collaboration with the School of Engineering and IT at MAHE Dubai, UAE, on 4 and 5 October 2023 in hybrid mode at MAHE Dubai was a significant milestone in the scientific and engineering community [...] Full article

Web applications are widely used, and the applications deployed on the web do not always satisfy all the security policies. This may arise due to less secure configurations, less knowledge in security configurations, or due to insecure coding practices. Even though a lot of practices are available, a lot of security loopholes are still available for hackers to steal information. A secure web application framework is discussed here which incorporates solutions to major security loopholes that attackers may use for stealing information or compromising systems. The security framework proposed here ensures an encrypted data transfer making the data safe and server-side vulnerability detection and avoidance for major attacks like SQLinjection (SQLi) and Cross Site Scripting (XSS). The client side of the framework is responsible for validations, encryption, and session management through a JavaScript module. The server side of the framework is responsible for decryption and validation, data management, and URL management. The framework deployed with PHP showed a good outcome when tested with the Arachni web application security scanner. The framework will be further studied for performance with huge workloads. Further, the work will be extended to cover other attacks. Full article

This research paper unveils an all-encompassing literature exploration into “nudging” in digital platforms and its profound impact on the user experience. This study delved into various sources spanning academic research papers, corporate reports, books, and online publications, acquired through a thorough four-step approach. The methodology entailed unearthing pertinent sources via diverse academic databases and industry networks, and a diligent review process to estimate their relevance and calibre. Data extraction from each selected source focused on the employed nudge techniques, underlying behavioural principles, and their repercussions on the user experience. The findings were subsequently synthesised to unearth the existing literature’s prevalent themes, disparities, and prospective gaps. The paper underscores the importance of nudging as a potent driver of user actions while safeguarding their autonomy. We employed a comprehensive approach to explore nudging application and influences on digital platforms, including academic database searches, corporate reports, and web blogs. We thoroughly extracted data on platform types, nudging strategies, behavioural theories, and user experience influences and impacts. Our study deliberates on potential future research trajectories, encompassing ethical considerations and personalised nudging methodologies. Ultimately, this study underscores how applying nudge techniques in the architecture of digital platforms can elevate user experiences and confer value upon both users and providers. However, the findings acknowledge the inherent limitations that accompany any literature review and may not encapsulate every facet of the subject matter. Full article

Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging. CNNs are particularly adept in automatically identifying complex patterns and features in pictures like X-rays, CT scans, and MRIs. They accomplish this by capturing hierarchical information utilizing layers of convolutional and pooling processes. By enabling precise disease diagnosis, anatomical structure segmentation, and even patient outcomes’ prediction, CNNs have transformed medical imaging. In this review paper, we examine how crucial CNNs are for improving diagnostic effectiveness and efficiency across a range of medical imaging applications. This review details how Convolutional Neural Networks (CNNs) are used, focusing on the development and use of 3D CNNs for processing and categorizing multidimensional and moving images. The paper discusses how critical 3D CNNs are in areas like analyzing surveillance videos and, especially, in the field of medical imaging to find pathological tissues. With this method, pathologists can segment the layers of the bladder with a lot more accuracy, which cuts down on the time they have to spend looking over them by hand. CNNs use specific filters to find spatial and temporal relationships in images, making understanding and interpreting them easier. CNNs are better at fitting image datasets because they have fewer parameters and weights that can be used more than once. This makes the network better able to understand complex images. This thorough review shows how 3D CNNs could improve the speed and accuracy of processing and analyzing medical images and how far they have already come. Full article

The current scenario demands the usage of alternate materials with lower stress and stress–strain energy deformation for applications in gas turbines, chassis of automobiles, and biomedical instruments. The work on Inconel-718 can be carried out as it is a new material; it can be used for many applications in addition to its usage in automobiles. Inconel-718 is a superalloy of nickel (Ni) and chromium (Cr). Inconel-718 is corrosion resistant and oxidation resistant when subjected to extreme temperature conditions. But when applying tensile and compressive load, it bends, causing the formation of Luder’s band. The work analyses the formation of Luder’s band in Inconel-718. The methodology for detecting Luder’s band is based on the material structure. It also depends on the material’s rigidity modulus and the shear stress ratio to shear strain. The stress, harmonic, and thermal analyses were carried out using ANSYS to find the red-hot zone for the formation of Luder’s band. The results demonstrate that Luder’s band is mainly formed at the middle point of the frame. The stress for Inconel-718 is in the range of 0.064454 MPa to 81.514 MPa, whereas the frequency varies from 0.71157 to 475.87 Hz under vibration load. Conversely, while heating Inconel-718, the temperature varies from 1.6009 × 10⁵ to 112.83 °C. The analysis shows that Inconel-718 is a better material for designing automobile parts. Full article

In crucial applications, sensor node coverage of the objective zone must be stabilized in Wireless Sensor Networks (WSN). A network with holes in coverage is more susceptible to node failures or malicious attacks. According to the total number of hops used to transport data, nodes may calculate their distance from the sink node. A coverage hole may be present if a node notices a much higher hop count than its neighbors. The network becomes more robust and resilient to diverse problems by proactively recognizing and correcting coverage holes. Coverage hole identification aids in the efficient use of network resources. By identifying places with poor coverage, resources such as electricity and bandwidth may be efficiently deployed to increase coverage in specific areas or extend the network lifetime overall. However, some node sensors die while the network operates due to energy restrictions, which may disturb the inclusion of the objective zone, resulting in a coverage hole. Due to limited battery life, the existence of impediments and physical damage to sensor nodes, coverage holes may emerge in sensor networks. Early identification of coverage holes enables prompt maintenance and troubleshooting, which minimizes the need for future major and expensive replacements or reconfigurations. The loss on the region of interest may be calculated by locating the coverage holes and identifying the malfunctioning node that created it. This article discusses many coverage-hole-detecting methods, classification approaches, and different performance comparison assessments. Compared to conventional techniques for detecting coverage holes, the investigated methods contribute to the universal viewpoint on holes and compute the number of holes quite precisely. Full article

The increasing environmental concerns associated with conventional composites, made using glass-fiber-reinforced polymers (GFRP) and carbon-fiber-reinforced polymers (CFRP), have shifted attention to bio-based composites. These environmentally responsible alternatives offer performance without sacrificing biodegradability. The present study examines the biodegradability of a novel bio-based epoxy composite reinforced with Musa acuminata (banana) fibers. Two composite variants were compared: one with 2.5% Montmorillonite (MMT) nanoclay and one without. While previous research has demonstrated an enhancement in mechanical and physical properties of polymer matrix composites with the addition of MMT nanoclay, it was hypothesized in this study that nanoclay addition would not significantly impact the composites’ biodegradability. To confirm this, we conducted standard biodegradability tests and an SEM analysis. The SEM results revealed a uniform distribution of MMT nanoclay within the bio-based polymer matrix, in addition to strong interfacial adhesion and decreased void crater sizes. The inclusion of nanoclay did not significantly impact the composites’ biodegradability, according to the statistical analysis provided in the present study. The present study also developed regression models to predict biodegradability over time to facilitate the determination of the timespan required for 100 percent biodegradability of the tested bio-based composite. Thus, this study is a significant benchmark for advancing eco-friendly composite materials. Full article

Early in the 1990s, recommender systems emerged to assist users in dealing with the cognitive overload caused by the internet. Since then, similar systems have expanded into many more capacities, such as assisting users in exploration, enhancing decision making, or even providing entertainment. Understanding the user task and how to modify the advice to assist it are made possible by these features. Recommender systems for education have been proposed in related research. These recommender systems assist students in locating the learning materials that best suit their requirements. One of the primary requirements of the online social platform is to engage the user in an effective way. For this purpose, online media starts to use gamification to improve the user participants. The reward system for online media widely uses gamification elements such as points, badges, etc. Thereby, in a badge-based system, an unachieved badge highly influences the gamification system. In this paper, unachieved and achievable badges were recommended using item-based collaborative filtering recommendation model. This enables us to gather information from the candidates and make accurate predictions about the jobs that might suit them. This is also durable in the sense that any missing data about the candidate does not affect the algorithm as a whole as it is capable of making assumptions regarding the missing data based on similar data already stored in the database. Beyond this, this algorithm can be employed to host courses on the website. The empirical observation shows that the proposed model has recommended the badge with 70 percent accuracy. Full article

Geolocation and Geographic Information Systems (GIS) are becoming essential tools in several sectors. Clustering-based geopositional data analysis has enormous potential for helping the citizens of a given city. The insights gained from this kind of study can assist inhabitants and tourists in making better-educated decisions and improve overall quality of life by shedding light on numerous facets of the city’s infrastructure, services, and facilities. Due to its capacity to combine databases and display geographic data, GIS has proven important in a variety of industries. City planners and other stakeholders may learn a lot about the requirements of the city’s residents by clustering geopositional data. Making wise judgments based on this knowledge will raise the standard of living for everyone who lives, works, and visits the city. The purpose of this research is to use k-means clustering to identify the best houses to live in for immigrants according to their expectations, amenities, price, and proximity to the workplace or educational institution, and provide them with the best accommodation suggestions. After gathering the geolocational data of the city to which the immigrants have moved, the details will be cleaned and the data will be analyzed using different data pre-processing and data exploratory techniques. At last, the data will be clustered using the k-means clustering algorithm. It is computationally efficient and operates perfectly when clusters are spherical and comparable in size. It is essential to handle data privacy and security properly while working with geopositional data. The quality of life for those who live in cities can be improved by utilizing clustering algorithms to analyze geopositional data. Full article

The pseudoscience known as iridology makes the unsubstantiated claim that it can identify medical disorders by examining the iris, the colored portion of the eye. Iridology does not provide a reliable means of diagnosis, and there is no scientific proof to back up its claims. To find patterns that are connected to particular medical conditions, computerized iris analysis software may need to examine thousands of iris images. A method of iridology known as Computer-Aided Iridology (CAI) uses software to study the iris. CAI still is not a medically accepted diagnostic technique and is not any more trustworthy than conventional iridology. Applying technology in medical science had a great impact on diagnosing diseases. Decision making is the most critical task in computer-aided applications. Computer vision and deep learning make this task more accurate and are widely used in many applications, mainly in diagnosing diseases. The methodologies, data acquisition source, and volume of data used for both training and testing in the pre-diagnosis of human organs utilizing iris patterns are thoroughly studied. Understanding its limitations allows researchers to concentrate on creating and evaluating improvements in technology that could boost its accuracy and usefulness. Iridology has been considered as having no use for years and becomes effective when combined with technology. This study includes various technical factors used in iridology for the pre-diagnosing of diseases. Recognizing the limitations of iridology allows healthcare providers to avoid errors in diagnosis and prevent individuals from undergoing redundant procedures or therapies based solely on iridology assessments. Full article

Alzheimer’s disease (AD) is the most common form of dementia in senior individuals. It is a progressive neurological ailment that predominantly affects memory, cognition, and behavior. An early AD diagnosis is essential for effective disease management and timely intervention. Due to its complexity and heterogeneity, AD is, however, difficult to diagnose precisely. This paper investigates the integration of disparate machine learning algorithms to improve AD diagnostic accuracy. The used dataset includes instances with missing values, which are effectively managed by employing appropriate imputation techniques. Several feature selection algorithms are applied to the dataset to determine the most relevant characteristics. Moreover, the Synthetic Minority Oversampling Technique (SMOTE) is employed to address class imbalance issues. The proposed system employs an Ensemble Classification algorithm, which integrates the outcomes of multiple predictive models to enhance diagnostic accuracy. The proposed method has superior disease prediction capabilities in comparison to existing methods. The experiment employs a robust AD dataset from the UCI machine learning repository. The findings of this study contribute significantly to the field of AD diagnoses and pave the way for more precise and efficient early detection strategies. Full article

Cloud computing offers more advantages to clients and associations regarding capital uses and working cost investment funds. This study gives an ontological model of the cloud fabricating space to help with the data trade between the cloud-producing assets. The ideas of the proposed model depend on a writing survey of models of the cloud and models of assembling. In the research article, the problem addressed is how cloud brokers are providing cloud services in an efficient way to cloud users. It is the main prologue to an ontology-based, process-situated, and specialist framework that is autonomous of a society that permits most associations to utilize it. The rising number of cloud providers, the nonappearance of interoperability, and the heterogeneity in current open cloud stages lead to the requirement for creative frameworks to track down the foremost fitting cloud resource plan as successfully and mechanized as may be anticipated. In this paper, we depicted the building arrangement of a cloud organization made of two agreeable modules. The Cloud Agency’s objective is to naturally secure assets from suppliers on the premise of SLA evaluation rules and find the foremost reasonable cloud supplier that fulfills users’ prerequisites, and the Semantic Motor’s objective is to make a rationalist depiction of assets based on users’ benefit prerequisites and a brokering framework. Full article

A speculative exhibit for distributed computing organizations is implied as a haze of mists joining different parceled mists into a solitary fluid mass for on-request tasks. Fundamentally put, the mist between clouds would ensure that a cloud could use resources outside of its run using current understandings with other cloud benefit providers. Edge processing is a growing registering perspective that brings several frameworks and devices to or near the client. The edge consists in handling data closer to where they are being created, dealing with additional important rates and volumes and resulting in a more conspicuous activity drove happening in real time. These centers perform continuous planning of the data that they receive within a millisecond response time. The center points discontinuously send logical summary information to the cloud. An example of an edge computer is a smartphone connected to a cloud system. Haze computing is more like a “gateway” to insights and control over handling. A haze computer connects to multiple edge computers simultaneously, resulting in a specialized set of devices for more efficient data handling and capacity. There are cutoff points to the actual resources and the geographic reach of any cloud. A cloud cannot help its customers if all its computational and storage capacities are used up. An inter-cloud system addresses situations in which one cloud gains access to other clouds’ frameworks for computing, capacity, or other assets. Full article

Computed tomography urography (CTU) is a specialized radiological procedure that produces finely detailed pictures of the urinary system, comprising the kidneys, ureters, and bladder, using computed tomography (CT) scans. This diagnostic procedure’s main goal is to assess disorders that impact these vital organs, such as stones in the kidneys, tumors, UTIs, and morphological anomalies. CTU has benefits like the capacity to deliver a personalized therapeutic strategy via radiomics and artificial intelligence technologies, as well as extra knowledge about abdominal anatomy. This comprehensive article looks at how computed tomography urography (CTU) is used and how it can be changed to evaluate the urinary system, especially the kidneys, bladder, and ureters. The most important part of this review is the discussion on 3D kidney segmentation and reconstruction from urographic images, which has helped doctors a lot with the accurate diagnosis and planning of treatment for kidney diseases. Even though 3D convolution networks have been used a lot in medical picture segmentation, it can be hard to adapt them to clinical data from different modalities that have not been seen before. The review gives an in-depth look at the current research on how an unsupervised domain adaptation or translation method can be used with 2D networks, especially for accurate kidney segmentation in urographic images. Through this thorough study, we want to show how these techniques can be used in medical imaging and how they might change in the future. Full article

Mining technologies depend upon their outcomes, focusing only on certain data features within the database. They select only certain features related to the process from diverse integrated data resources and transform them into a form suitable for mining tasks. Different implementations of mining techniques run on data sources, which may be of considerable volume, to extract different knowledge outcomes suitable for various analyses and decision-making processes. The proposed study provides the design and development of the Clustering of Non-Associated Items set (CNAIS) within a transactional database. The development of the algorithm and its application to the data set are described and the results are noted. Comparisons with state-of-the-art methods show that CNAIS exhibits better performance. Full article

In the education domain, the significance of student feedback and other stakeholders for raising educational standards has received more attention in recent years. As a result, numerous instruments and strategies for obtaining student input and assessing faculty performance, as well as other facets of education, have been developed. There are two main methods to collect feedback from students, as follows: the direct and indirect methods. In the direct method, feedback is collected by distributing a questionnaire and taking their responses. The limitation of this method is that the true experience of students is not revealed, and there is room for bias in the collection and assessment of such a questionnaire. To overcome this limitation, the indirect method can be followed where social media posts can be used to collect feedback from students as they are active on social media and use it to express their opinions as posts. To address the problem of the manual annotation of large volumes of data, this paper proposes a machine learning method that uses the sentiment 140 dataset as the training set to automate the process of annotations of tweets. The same method can be used to label any qualitative data. In total, 5000 tweets were scraped and considered for this study. Various pre-processing methods, including byte-order-mark removal, hashtag removal, stop word removal, and tokenization, were applied to the data. The term frequency-inverse document frequency (TF-IDF) trigrams technique was then used to process the cleaned data. The TF-IDF technique using trigrams captures negation for sentiment analysis. The vectorized data are then processed using various machine learning algorithms to classify the polarity of tweets. Performance parameters such as the F1-score, recall, accuracy, and precision are compared. With a 94.16% F1-score, 94% precision, 94% recall, and 95.16% accuracy, the Ridge Classifier performed better than the others. Full article

A chatbot is a computer program that uses general rules and Artificial Intelligence techniques to simulate human conversation. This paper highlights the different scenarios of human-computer interaction and the journey it has gone through from evolution to evolvement to innovation to the development of the technical era. Here, the main focus is on the ways humans interact with the computer and how it has changed day-to-day life and reduced human efforts in performing everyday activities. There is an impact of HCI (Human–Computer Interaction) on people and has consequences in the form of both advantages and disadvantages of this interaction. The various innovations and machines have given birth to human–computer interaction as well as technology interaction. The main objective is to style the interface amongst men as well with Personal Computers (PCs) as usual as the interface amid beings. The user can interact in this system using text or voice. As per way as interaction is concerned direct, indirect, and strategic interaction of humans with computers and the latest gadgets is possible. Dynamic intelligence makes it like real-time communication with an individual. It can handle the user request and offer relevant information that can be used as a friend one would seek for knowledge. The proposed system is developed using the Rasa of an open-source platform. Further, the article focuses on the features and role of chatbots in an educational context. High precision in sentence analysis is attained with the aid of the proposed method up to a 91% hit ratio. The hit rate for the similarity computation is high. The system can handle a broader variety of requests as a consequence of its ability to recognize many ways to phrase the same inquiry and map them to related results. Full article

Machine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. This work offers a practical introduction to the core concepts and principles of bagging decision trees used for breast cancer diagnosis. In this article, three main algorithms, viz. linear regression (LR), decision tree (DT), and random forest, were used. The random forest method used bagging techniques for selecting data points, and feature optimization was also carried out. Through our experiments, it has been found that the results obtained with the bagging trees algorithm outperform the result obtained with the best decision tree parameters. A feature optimization scheme was also introduced in the selection of data points during the training phase, which effectively increased accuracy. Full article

A mobile adhoc network (MANET) is a network that comprises mobile devices positioned in various places functioning without any central administration. Routing in MANET plays a vital role when the data packet (DP) is sent from source to destination. In order to improve the routing stability in MANET, resource utilization (i.e., energy and bandwidth) has to be controlled. An effective firefly resource-optimized routing (FFROR) technique controls resource utilization and improves routing stability during data packet (DP) transmission in MANET. Initially, in FFROR, the firefly resource optimization (FFRO) algorithm generates the population of fireflies (i.e., mobile nodes). It calculates the light intensity of every firefly based on objective functions (i.e., minimum energy consumption and minimum bandwidth utilization). The FFRO algorithm ranks fireflies according to the light intensity and finds the best resource-optimized mobile node (MN) to send the DP to the destination. This, in turn, helps in finding the resource-optimized mobile nodes and choosing the route path for sending the DP to the destination. The proposed FFROR technique uses the FFRO algorithm to increase routing stability and throughput. The simulation is carried out to analyze the performance of proposed FFROR techniques with parameters such as energy consumption, bandwidth availability, routing stability, and throughput. Full article

To be effective, evidence-driven disaster risk management (DRM) relies on a wide variety of data types, information sources, and models. Weather modeling, the rupture of earthquake fault lines, and the creation of dynamic urban exposure measures all require extensive data collection from a variety of sources in addition to complex science. There are various methodologies to utilize AI to recognize necessities and asset accessibility by the likes of Twitter; however, the foremost broadly recognized and exact strategies remain cloudy. Within the occurrence of a catastrophe, machine learning apparatuses for designating assets are required to instantly help those in need. This overview appears to be necessary for additional examination with respect to an assertion on endorsed methods for calculation to demonstrate assurance, benchmarking datasets, crisis word references, word embedding techniques, and evaluation methods. As fiascos of all sorts become more common, these devices have the potential to improve real-time crisis administration over all stages of a catastrophe. This study aims to provide readers, including data scientists, with a clear and uncomplicated reference on how disaster risk management systems can benefit from machine learning. There are numerous sources of information on this set of technologies, which are both complicated and constantly changing. The volume of sensor data that can be analyzed has increased exponentially because of enormous increases in computational speed and capacity over the past few decades. Full article

Smart agricultural monitoring is the use of cutting-edge technology to manage all elements impacting plants and lowering crop yield quality. The main objective of smart crop monitoring and management is to guarantee farmers optimal productivity. Additionally, the market for worldwide smart crop management is expanding continuously as a result of the rising need for smart agricultural techniques. Machine learning techniques have the potential to be utilized to provide intelligent agricultural yield suggestions that will assist farmers in increasing their crop yields and profitability. Machine learning algorithms are used to analyze massive collections containing previous yield statistics, meteorological data, soil data, and other parameters in order to discover patterns and associations that might be used to predict agricultural yields. The methodology used in this system is that the farmer must enter the details of conditions in the field. Once entered into the system, the data are analyzed. This predicts the state of environmental conditions and predicts the crop that is suitable under these situations to give a greater yield. A web application is also built here for the farmer to analyze the information regarding their crops and to generate relevant reports. To find better crops under various conditions, the k-nearest neighbor (KNN) technique is used. Finally, the farmer achieves better results based on the conditions in the field, enabling them to plant the crop that is appropriate to those conditions. The proposed system helps a huge number of farmers by using IoT (Internet of Things) devices and web applications for smart irrigation. Full article

Piezoelectric smart materials have gained significant attention in various technological applications due to their ability to convert mechanical energy into electrical energy and vice versa. These materials have diverse energy harvesting, sensing, actuation, and biomedical engineering applications. Research investigations on piezoelectric smart materials encompass many areas, including material development, characterization, modeling, device design, and manufacturing techniques. Computational material science is crucial in advancing these materials’ understanding, design, and optimization. This research paper aims to provide an overview of the computational approaches employed in piezoelectric smart materials. The state-of-the-art computational techniques used for modeling piezoelectric materials are reviewed, and their applications in device design are explored along with performance optimization. This comprehensive review highlights the potential of computational material science in shaping the future of piezoelectric smart materials. It is observed that density functional theory and molecular dynamics are commonly used techniques. At the same time, finite element and phase field methods are employed for specific applications requiring continuum modeling or phase evolution simulations. Further exploration reveals that computational material science optimizes existing smart materials’ structural and compositional parameters through modeling and simulation. This improves properties such as enhanced performance, increased durability, and greater functionality. In addition, computational material science is employed to design and predict the properties of new piezoelectric materials by utilizing advanced modeling techniques, enabling the discovery and development of materials with tailored piezoelectric properties for specific applications. Recent research advancements in piezoelectric smart materials have contributed to developing materials with improved properties, advanced fabrication techniques, and expanded application possibilities. These advancements have paved the way for the realization of innovative devices and systems that harness the unique capabilities of piezoelectric materials. Full article

The people of India sought digital modes of payment during the demonetization period in India (2016); with the increasing growth of the internet, electronic commerce (e-commerce) websites have become imperative for securely accessing payment gateways, encouraging the growth of digital payment processes and payment app development. During the pandemic, there was an exponential increase in mobile payments using smartphones. The usage of mobiles and their market penetration with government schemes such as ‘Digital India’ accelerated the use of mobile payments by a large percentage of customers in the coastal hinterland (Manipal) of India. This study aimed to analyze the critical factors influencing digital payments in the university town of Manipal. From the literature, 13 regressors were shortlisted, and their effect was measured against a behavioral intention to use mobile payments. A structured and validated questionnaire is used as a research tool for data collection that is analyzed using structural equation modeling. The structure equation modeling included using smart partial least squares (SPLS), in which path coefficients, t-statistics, and consistency tests were conducted. The investigation found that ease of use, social influence, perceived behavioral control, rewards and offers, credibility, compatibility, perceived cost, impact on the environment, and government schemes have a positive influence on m-payments. Social influence has a strong influence on m-payments and is a direct enabler of technology acceptance. The critical factors were identified by using smart PLS as being ease of use and social influence, which were identified as the critical factors concerning m-payments. Full article

To create a more immersive experience, electronic content developers utilize hardware solutions that not only display images and produce sounds but also manipulate the viewer’s real environment. These devices can control visual effects like lighting variations and fog, emit scents, simulate liquid effects, and provide vibration or locomotion sensations, such as moving the viewer’s chair. The goal is to emulate additional sensations for the viewers and engender the belief that they are truly present within the virtual environment. These devices are typically found in specially designed cinemas referred to as xD cinemas, such as 4D, 5D, 9D, etc., where each effect is treated as an additional dimension, enhancing the overall experience. Currently, all of these effects are triggered by timers. The system determines which effect to play based on timers. This approach is problematic, for it requires programming each device for each movie. In this research, we address this problem by introducing the idea of Special Effect Tags (SETs) that can be added in the subtitle files. The SETs aim to serve as a standard that will allow the various devices to know when each artificial phenomenon should be triggered. They are generic and can support infinite artificial phenomena, also known as dimensions. This paper introduces the idea of a common special effect framework and a generic architecture of a special effects player that is independent of any specific hardware solutions. Full article

Airfoil noise due to pressure fluctuations impacts the efficiency of aircraft and has created significant concern in the aerospace industry. Hence, there is a need to predict airfoil noise. This paper uses the airfoil dataset published by NASA (NACA 0012 airfoils) to predict the scaled sound pressure using five different input features. Diverse Random Forest and Gradient Boost Models are tested with five-fold cross-validation. Their performance is assessed based on mean-squared error, coefficient of determination, training time, and standard deviation. The results show that the Extremely Randomized Trees algorithm exhibits the most superior performance with the highest Coefficient of Determination. Full article

The development of a knowledge- and information-based society can be aided by higher education. Through research and extension efforts, higher education institutions must perform a variety of functions, including building an intelligent human resource pool, gaining new skills, and creating new knowledge. As a result, the development of skilled workers with the ability to think critically, creatively, and logically is the primary focus of higher education institutions. However, there are some significant obstacles in the way of offering quality education, such as how to identify low-performing students and their causes. Predicting student performance has become challenging as a result of the vast quantity of data in educational databases. The lack of a developed system for assessing and monitoring student achievement is also not being considered. There are primarily two causes for this kind of situation. Initially, there was inadequate study of the various prediction techniques to select the ones that would best predict students’ success in educational environments. The second is the lack of investigation into the courses. In this research work, efforts have been made to identify low-performing students through the proposed Back Propagation Neural Network for Student Performance Analysis (BPNN-SPA) model, which generates more accurate, efficient, and dependable results as compared to some of the existing techniques and models. The performance of the proposed model is compared with the Support Vector Machine and Random Decision algorithms and evaluated by four significant performance metrics, namely, sensitivity, specificity, accuracy, and the F-measure. Based on performance measures, the proposed BPNN-SPA achieved better accuracy than existing algorithms. Full article

In the aviation industry, the usage of composites is increasing because of their unique feature in terms of damage tolerance and high structural integrity. It inspires the researcher to focus on dynamic behaviour of subsonic aircraft hat-shaped CFRP composite panels using the LS-Dyna tool to prove its excellent impact behaviour with the help of spherical, ogival and conical impactors. High-velocity impact simulations conducted in this research work duplicate the Foreign Object Damage on aircraft panels. The impact load locations are identified from the literature and notable damage features such as stresses, delamination length, internal energy absorbed, resultant force, delamination zone, resultant acceleration and resultant velocity are compared for the chosen impactor shapes. The elastic and plastic failure zones are displayed very clearly in the results to avoid any further damage in the future. All results help to understand the composite shell behaviour and different damage patterns. Full article

This research focuses on analyzing the microstructural and mechanical characteristics of SS 347 and DSS 2205 stainless steel dissimilar welds. This is achieved by altering the weld parameters, welding current and heating cycle at three different levels each. In total, nine experimental trials were conducted and the welded sheets were applied to macrograph studies and a tensile shear test for analyzing the nugget quality and mechanical strength. The welded specimens were placed for observation under a scanning electron microscope (SEM) to observe the microstructure of the weldments. Specimen 9 was subjected to a microhardness test. The macrograph study revealed that the nugget size grows proportionally to the rise in the welding current and heating cycle. When the current exceeds 7.5 kA, the size of the nugget exceeds the threshold value of 4√t, where ‘t’ is the sheet metal thickness. The tensile shear test results clearly indicate that as the nugget size grows, the tensile force also rises. Sample 9 possesses a maximum tensile force of 18 kN and the mode of failure observed is influenced by the welding current and heating cycles. The failure mode of sample 9 was pulled out and the microhardness was maximum at the fusion zone with 320 HV. Full article

In today’s age of social networking, web news inconsistencies have become a pressing concern. These discrepancies can mislead individuals when making important purchase decisions. Despite the existing research in this area, there is a need for more empirical and rigorous investigation into the inconsistencies reported in reviews. False reporting and disinformation on social media platforms can significantly impact societal stability and peace. Fake news is frequently disseminated on social media and can easily influence and deceive populations and governments. Many researchers are working toward distinguishing fake news from genuine news on social media platforms. The practical and timely identification of fake news can help prevent its spread. Our study focuses on how machine learning and deep learning algorithms are used to detect fraudulent data. The most fundamental and practical techniques deployed over recent years are investigated, classified, and defined in numerous datasets in an extended review model. Additionally, simulation media and recorded indicators of performance are reviewed in detail. The review, as mentioned above, provides a comprehensive analysis of key research findings, delving into pertinent issues that may impact individuals in the academic and professional realms interested in augmenting the reliability of automated FND models. Full article

Cloud brokers play a crucial role in providing an effective service by utilizing cloud computing. The middleware known as cloud brokers aids in the provision of effective cloud services to cloud users. There are a lot of cloud brokers who offer cloud services to cloud users on a reservation-in-advance basis so that they do not have to rush to use the cloud. A cloud organization agent is IT work and an arrangement of activities in which an organization or other component improves at least one cloud organization for the better and at least one buyer of that help through three basic employments: counting combination, joining, and customization trade. Since cloud innovation offers a Cloud Benefit Brokerage stage for them to run their delicate and basic operations, it has gotten to be exponentially acknowledged by businesses all over the world. A cloud organization provider has to provide a profitable strategy for restricting the induction to the distinctive components of the cloud that the board organizes. The clients should be able to safely construct, create, and spare their claim reports, and there should be a centralized database detailing information from different lives of clients. Utilizing the proposed methodology system, users are provided with the reserved services that best suit their needs. Effective services were the subject of this research paper. The proposed system performs best due to its complexity. Full article

Presently, there has been a huge rise in the demand for power owing to increases in population and commercial organizations. Traditional power plants are not able to keep up with the increasing needs of customers. Finding a different way to meet consumers’ needs is the main problem in the current situation. Most RESs (renewable energy sources) like wind, solar, hydro/water sources and fuel cells are environmentally beneficial. The number of available resources has no bearing on how much electricity can be produced using RESs. Due to differences in natural resources, there are constant fluctuations in the availability of RESs. In this technical study, two significant RE (Renewable Energy) power sources—PV (photovoltaic) cells and WES (wind energy systems)—are studied in various weather scenarios. First, a cutting-edge intelligent controller system was created, which aids in tracking the peak power point. Due to the unpredictable nature of weather, a MPPT (maximum power point tracking) controller is required for RES. This work aims to present IDNN- (improved deep neural network) and MPPT-based unique methods for power generation using solar and winds. When a hybrid PV/WES system is integrated into MG s(microgrids), power quality may be improved and THD values can be reduced. It was confirmed from the results of the simulation that the proposed IDNN system yields better performance in different operating situations by means of lower MSE (mean square error) rates, lower THD (total harmonic distortion) and lower computational complexity than the existing method. Full article

The widespread adoption of the Internet of Things (IoT) across various domains has ushered numerous applications into our daily lives. Ensuring the security of sensitive data, including wirelessly transmitted private information and images generated by IoT devices is paramount. However, IoT devices are often termed “constraint devices” due to their limited computational resources like CPU power or memory capacity. Also, ensuring the integrity of IoT devices and networks is imperative in fostering trust in the capabilities and benefits of IoT technology. Addressing data tampering, device vulnerabilities, and network weaknesses through proactive security measures is essential in realizing the full potential of the IoT while safeguarding against potential risks and disruptions. Traditional encryption approaches prove inadequate, as they demand excessive computational power; this is a challenge for IoT devices. To address this, a novel and less intrusive encryption method has been proposed, leveraging the inherent unpredictability of DNA nucleotide sequences. This approach is tailored to accommodate the resource constraints of IoT devices. By harnessing the intrinsic randomness of DNA sequences, a robust secret key is generated, significantly bolstering resilience against attackers. The key is crafted through uncomplicated substitution techniques and transposition operations. Upon satisfying the computational requisites of IoT devices and safeguarding image security, a DNA-based key comes into play for photo encryption. Rigorous testing has demonstrated its effectiveness, showcasing its superior attributes in terms of key size, encryption speed, and distortion minimization when compared to alternative encryption techniques. This innovative encryption paradigm not only upholds the integrity of IoT-generated data but does so without overwhelming the devices’ limited computing capabilities. Full article

In recent years, transparent conducting oxide semiconductor materials have found applications in both science and technology, especially in the areas of semiconductors, optoelectronics, and a wide range of energy efficiency devices. These TCO materials are the building blocks of various optoelectronic devices, such as transparent thin-film transistors, solar cells, and light-emitting diodes. This work concentrates on the structure, morphology, and optical properties of ZnO and Zn_0.95La_0.05O thin films at 673 K using a chemical spray technique. The polycrystalline nature and wurtzite structure of ZnO were confirmed by using XRD analysis with preferred growth along the (1 0 1) plane. The Zn_0.95La_0.05O deposits showed maximum crystallinity of 15.4 nm and a strain value of 2.4 × 10⁻³. The lattice constants increased for lanthanum-doped ZnO thin films due to the ionic radii mismatch of the doping material, which causes lattice expansion. Fibrous morphology was observed for ZnO, and a mixed structure of grains and fibers was observed for Zn_0.95La_0.05O films, which confirms the insertion of La³⁺ into the Zn²⁺ position. The Zn_0.95La_0.05O deposits showed transmittance above 80% due to the increased crystalline quality and a bandgap of 3.32 eV. The photoluminescence spectra showed peaks corresponding to e-h recombination, zinc defects (Zn_i and O_zn), and oxygen vacancy (O_i and V_o). The lanthanum-doped ZnO films showed increased band-edge emission and decreased defect-related peaks due to the increased crystalline quality. Hence, the doping of La³⁺ ions into a ZnO lattice enhances the crystalline quality and increases the transparency of the host ZnO matrix, which is suitable for optoelectric device applications. Full article

IoT security, also known as Internet of Things security, is an innovation component that focuses on protecting connected devices and systems on the Internet of Things (IoT). There are several fields which relate to the IoT framework such as computers, mechanical and computerized machines, objects, creatures, and people. Each thing has a unique identifier and the ability to transfer data across an organization. The Internet of Things organizations help to obtain a practical advantage by taking care of the hardships of consolidating wearables, sensors, associations, cloud, and applications without choosing security. Development is stressed over partner contraptions with each other to work with the correspondence between them. The devices that are related will really need to share the information that can be used as a commitment by any contraption that is dependent upon various contraptions for input. It is known as the Trap of Things, like the Internet. This development requires certifications to sort out among contraptions. Different industry-unequivocal data and IoT development expertise cover firmware improvement, transportability, conveyed registering, and data assessment, for making the market space an impressive range for end clients. The end clients receive soft assembled decisions concerning solid data assessment in IoT organizations. Nowadays, many IoT applications, computations, and organizations are utilizing services over the Internet. These are the most important applications that need security from the cyber web. If cyberattacks are going on in IoT devices, security is a must for the end users. Full article

India is the world’s leading producer and exporter of turmeric. Indian turmeric is known as the best in the world because of its natural medicinal properties. Different turmeric varieties have different amounts of nutritional value, which results in variations in their cost and quality. The quality assessment of turmeric aids in evaluating and determining its quality, and it helps to promote its marketing. Hence, the identification of turmeric cultivars is of great importance. But it requires manual inspection by human experts, generates subjective results and is time-consuming. Machine vision will provide a more accurate and faster way to identify different agricultural products and their varieties. This study presents an automated system to identify turmeric rhizome varieties by extracting morphological, color and texture features. The classification of different rhizome types is carried out by using image processing techniques followed by K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) and Linear Discriminant Analysis (LDA) classifiers. The proposed work shows promising results for the identification of turmeric rhizome varieties. Full article

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must thus be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. Full article

Machine learning (ML) helps the Internet of Things (IoT) become widely used by automatically identifying data patterns and extracting important insights from the vast pool of observed data. To efficiently serve corporations, governments, and individual consumers, the Internet of Things (IoT) needs machine learning (ML). The IoT gathers environmental data and automates decision-making using sophisticated methods based on human judgement. Data, application, and industry perspectives are used to organise and assess machine learning–IoT literature. We discuss how machine learning and the Internet of Things can make our surroundings smarter by reviewing relevant research. Our analysis includes many cutting-edge methods. We also discuss pandemic control, networked-enabled cars, distributed computing, trivial deep learning, and the Internet of Things. Technological, personal, commercial, and societal concerns face the Internet of Things. Learning how to use the IoT can improve society’s well-being and longevity. We also examine a case study to find comparative results among various machine learning methods integrated with the IoT. Full article

An emerging topic that has the potential to enhance user experience, reduce crime, and target advertising is human emotion recognition, utilizing DeepFace and Artificial Intelligence (AI). The same feeling may be expressed differently by many individuals. Accurately identifying emotions can be challenging, in light of this. It helps to understand an emotion’s significance by looking at the context in which it is presented. Depending on the application, one must decide which AI technology to employ for detecting human emotions. Because of things like lighting and occlusion, using it in real-world situations can be difficult. Not every human emotion can be accurately detected by technology. Human–machine interaction technology is becoming more popular, and machines must comprehend human movements and expressions. When a machine recognizes human emotions, it gains a greater understanding of human behavior and increases the effectiveness of work. Text, audio, linguistic, and facial movements may all convey emotions. Facial expressions are important in determining a person’s emotions. There has been little research undertaken on the topic of real-time emotion identification, utilizing face photos and emotions. Using an Artificial Intelligence-based DeepFace approach, the proposed method recognizes real-time feelings from facial images and live emotions of persons. The proposed module extracts the facial features from an active shape DeepFace model by identifying 26 facial points to recognize human emotions. This approach recognizes the emotions of frustration, dissatisfaction, happiness, neutrality, and wonder. The proposed technology is unique, in that it implements emotion identification in real-time, with an average accuracy of 94% acquired from actual human emotions. Full article

Struvite is a substance that can be extracted from wastewater and has the potential to replace conventionally manufactured fertilizers and reduce environmental issues. A slow-release fertilizer can more effectively be used by matching the nutrient requirements of plants through the growing period and gradually supplying N and P for crop growth. Struvite is an ecologically friendly fertilizer because of its gradual fertilizer treatment and high quality. Existing research indicates that the solubility and absorption of struvite by plants are equivalent to those of artificial phosphorus fertilizers such as triple superphosphate or potassium phosphate. Struvite is recognized to be an effective fertilizer for grass, tree seedlings, ornamental plants, vegetables, and flower beds. Struvite precipitation removes phosphorus and nitrogen from sewage water, hence alleviating phosphorus shortages from non-renewable phosphorus sources and water eutrophication. Struvite would also be useful in the grasslands and woods where fertilizers are used. However, the agricultural utility of struvite has not been thoroughly investigated. As a result, this work is reported as a pot experiment designed to assess the fertilizer value of struvite. Experimental settings were created, and pot experiments were conducted to establish the optimal amount of struvite based on two factors. The initial pH for struvite synthesis was 9. The formulated struvite fertilizers were compared to standard phosphorus fertilizers in the pot trials. Fourier-transform spectroscopy and Scanning Electron Microscopy (SEM) with Energy-Dispersive X Ray Spectroscopy (EDAX) were employed to support the quantitative findings. To summarize, struvite precipitation is a desirable and effective method for removing phosphate and nitrogen from domestic sewage water and using them as fertilizers. Full article

Plant diseases can result in significant yield losses, posing a threat to food security and economic stability. Deep neural networks, particularly Convolutional Neural Networks (CNNs), have shown exceptional success in image classification tasks, often surpassing human-level performance. However, conventional methods for leaf disease detection relied on manual inspection by agricultural experts, leading to limited scalability and precision. To tackle these challenges, this research introduces a novel approach called the Leaky Rectilinear Residual Network (LRRN) for plant leaf disease detection. The LRRN model comprises three key modules—data pre-processing, feature extraction, and classification. It integrates ResNet architecture with the Leaky ReLU activation function to classify plant diseases. Experimental evaluations were performed on affected plant leaf disease images from the Plant Village dataset, utilizing performance evaluation metrics to assess the proposed model. The achieved results were compared to state-of-the-art techniques, demonstrating superior accuracy (94.56%), precision (93.48%), F1-scores (92.83%), recall (93.12%), and specificity (92.58%). These findings substantiate the effectiveness of the proposed LRRN method of plant leaf disease detection. Full article

In scientific literature, the impacts of heat, light, and ventilation on indoor settings have been extensively studied. It shows how important it is to consider a building’s HLV characteristics in the context of its surroundings. These elements have a direct impact on a building’s comfort level, energy effectiveness, and general sustainability. Many studies have investigated the effects of heat, light, and ventilation individually, rather than in combination with each other. This is because these factors have complex and dynamic interactions with each other, making it challenging to study them comprehensively. However, not many studies in this area have been made considering Indian geographical conditions. It can be challenging for a customer to find an apartment in a high rise building that meets their needs. Thus, using DesignBuilder tools at four different locations in India, a simulation was made and an analysis on the effects of HLV was performed for a symmetrical 10-storey building with adjacent buildings. An in-depth discussion of the air change rate of the building, daylighting performance in relation to different floors, and the difference between the indoor and outdoor temperatures of the building has been performed in this study. The criteria for choosing an apartment in a high rise building in accordance with the client’s requirements have also been derived from these results. Analysis on the effect of heat shows that the higher-density and taller surrounding buildings have a more pronounced effect on reducing the temperature difference. In the analysis of light, the height and distance of the surrounding buildings play a significant role in casting shadows on the main building. Ventilation analysis showed that higher floors have better ventilation compared to the lower floors and an increase in distance of the surrounding building increases the air change rate. The energy consumption analysis highlights that when the main building is surrounded by multiple buildings, energy consumption tends to decrease. The results indicate that as the building distance increases, energy consumption increases. Similar patterns are shown in all of the locations which were simulated, but the energy consumption load depends on the climatic condition of each location. Ahmedabad has the highest energy consumption load followed by Delhi, Guwahati, and Bangalore, irrespective of the distance and height of the surrounding buildings from the main building. Based on these findings, the guidelines were drawn for the selection of a suitable flat based on the requirement of the customer. Full article

High data transmission rates over wide regions and to clients in locations where broadband service is not accessible are provided by WiMAX, based on IEEE 802.16 standards for Broadband Wireless Access (BWA). The use of several antennas for sending and receiving data is a common feature of MIMO systems in wireless communications. WiMAX-MIMO devices are designed to improve WiMAX system performance. An analysis of MIMO-WiMAX systems using various modulations and coding rates in a Rayleigh fading channel is presented in this work. Matlab software version (R2018a) is used to examine the relationship between bit error rates and signal-to-noise ratios with various cyclic prefixes and single/multiple transceivers. The codes of Alamouti STBC are used to examine the BER performance of MIMO-WiMAX. Full article

Low-voltage (LV) network assets, although they do not play a significant role in reliability indices compared to medium-voltage (MV) assets like the transformer and switchgears, are required to be designed in a way that would mitigate the risk of sporadic failures, hence incurring an R&M cost. LV assets like LV cables, distribution panels, molded-case circuit breakers (MCCBs), and miniature circuit breakers (MCBs) generally do not have a planned maintenance (PM) schedule and are procured based on the run-to-failure concept in view of the huge volume. These assets are exposed to the harshest of environmental and operation conditions. Hence, it is imperative that we take the necessary measures during the design stage such that they are able to cater to their stringent duties, which include frequent short circuits, exposure to the environment, and thermal overloads. It is also important to periodically review the product design based on site feedback and product performance to re-calibrate the product and its associated processes. Through this technical paper, several case studies are presented wherein special terminal connectors with shear bolts were designed to mitigate the thermal hotspot issues causing frequent fire and failures—i.e., vertical fuse switch disconnectors (VFSDs) and miniature circuit breaker (MCBs). A case study on condition monitoring through a substation inspection schedule is also presented, through which potential failures were averted in time. The observations and measurements are mapped in an SAP system for trend analysis. With the adoption of effective product and process design, AEML has reduced asset failures. Full article

Recent trends have shown a greatly increasing number of users in the digital world, so there is a need for a large number of resources. To handle these resources, there is the need to manage and schedule in an optimized manner using artificial intelligence (AI) systems. These systems deal with the business-common method of managing offerings. Ordinary models consolidate inbound deals, outbound bargains, account-based offerings, or a mix of diverse models. An organization model may gather multiple choices that an organization makes over a long period of time, considering a system, cycle, or trade. In our approach, computational resources are treated as commodities that can be bought and sold in a decentralized marketplace. Agents representing AI tasks or workloads participate in resource auctions, competing for the resources they need. The allocation of resources is determined through competitive bidding, where the highest bidder secures the required resources. This approach encourages efficient resource utilization and fair distribution based on the tasks’ priorities and value. Our sales-based models for resource management and scheduling offer a promising solution for optimizing AI systems’ resource allocation. By applying principles from auction theory and market dynamics, AI systems can become more adaptive, responsive, and efficient in managing computational resources, ultimately leading to improved performance and resource utilization. Full article

R2U-Net, or Recurrent Residual U-Net, is a U-Net extension that includes both residual and recurrent connections for image segmentation tasks. R2U-Net is an image segmentation task-focused network that mixes residual and recurrent connections to boost performance and manage sequential data. Semantic segmentation algorithms based on deep learning (DL) have demonstrated state-of-the-art performance recently. Specifically, these methods have proven effective for tasks like medical image segmentation, classification, and detection. U-Net is one of the most prominent deep learning techniques for these applications. These proposed structures for segmentation problems have various advantages. In addition, better feature representation for segmentation tasks is provided by accumulating features using recurrent residual convolutional layers. Moreover allows us to design a more effective U-Net architecture for medical picture segmentation using the same amount of network parameters. The experimental results reveal that the model outperforms analogous models such as R2U-Net on segmentation tasks. The accuracy of the R2UNet model was 95.6%, while the FF + (AlexResNet + R2Unet) result was more than 97%, with an accuracy (%) of 97.4, AUC (%) of 97.35, precision (%) of 97.4, F1-score (%) of 95.26, and recall (%) of 97.16. The employment of these segmentation approaches in the identification and diagnosis of breast cancer produced outstanding results. Our proposed method could provide a more precise diagnosis of breast cancer, perhaps improving patient outcomes. Full article

Camouflaged objects are masked within an existing image or video under similar patterns. This makes it tedious to detect target objects post classification. The pattern distributions are monotonous due to similar pixels and non-contrast regions. In this paper, a distribution-differentiated target detection scheme (DDTDS) is proposed for segregating and identifying camouflaged objects. First, the image is segmented using textural pixel patterns for which the linear differentiation is performed. Convolutional neural learning is used for training the regions across pixel distribution and pattern formations. The neural network employs two layers for linear training and pattern differentiation. The differentiated region is trained for its positive rate in identifying the region around the target. Non-uniform patterns are used for training the second layer of the neural network. The proposed scheme pursues a recurrent iteration until the maximum segmentation is achieved. The metrics of positive rate, detection time, and false negatives are used for assessing the proposed scheme’s performance. Full article

A brain tumor diagnosis is a complex and difficult task that requires accurate and efficient data analysis. In past years, deep learning has emerged as a promising tool for improving the accuracy of mental health diagnoses. This research article presents a review of various in-depth studies and models for mental health diagnosis and examines the performance of convolutional neural networks (CNNs), VGG16, and other deep learning models on multistate data in the brain. The results show that deep learning models can provide high accuracy and efficiency in brain tumor detection beyond imaging techniques to also discuss the clinical applications of these models, including assisting radiologists in brain diagnosis and improving patient outcomes. Overall, this work raises awareness of deep learning’s application in medicine and offers insights into the future of brain tumor research. Full article

All associations use on-premises data focus. An on-premises data focus suggests that an association maintains all locally required IT systems. An on-premises data focus consolidates everything from the servers that support Web and email access to the provision of gear and communicates related data back to the organization to establish features like uninterruptible control. Data focus organization is not confined to ensuring that an establishments and program strategies are helpful. Data focus chiefs are also responsible for the security of their circumstances. Establishing a data community office is a sensible idea. Most do not have outside windows and, by and large, only a few entrances. Security staff surveil the inside of the structure, screening for dubious activity using footage from observation cameras positioned along the perimeter. This integrates the use of strong security measures, like two-factor confirmation, for all clients. It is also suggested to encrypt all data in movement, both inside the data center and between the data community and any external structures. The components of data centers must be safeguarded against physical threats. A data center’s physical security controls include a secure location, physical access controls for the building, and monitoring systems. As organizations relocate on-premises IT frameworks to cloud specialist co-ops, cloud information capacity, cloud foundations, and cloud applications, it is vital to comprehend the safety strategies they implement and the service-level arrangements they have set up. Full article

Flow rate measurement is a challenging task in the industry as there is no general-purpose measuring instrument for all appliances. However, orifice plates with multiple holes can be employed to measure the flow rate accurately. A computational fluid dynamics (CFD)-based numerical study was conducted to investigate the flow separation characteristics caused by the flow of water in multiple-hole orifice plates using ANSYS FLUENT R15.0 software. The study included single- and multiple-hole orifice plates, with orifices with a 36% area ratio, an equivalent diameter ratio (β-ratio) of 0.6, and hole number configurations of 1H, 4H, 9H, 16H, and 25H. The discharge coefficient for flow through multiple-hole orifices was obtained and compared for holes distributed in circular and square configurations. The significant parameters considered for the analysis were the hole number, distribution of holes, pressure drop, and reattachment points. A k-ε turbulence model was employed to study velocity fields, reattachment length, and discharge coefficient. We discuss the effects of hole numbers and their allocation on the reattachment length and discharge coefficient. Results are presented in the form of pressure variation comparisons, downstream recovery distance plots, recirculation zone plots, and percentage change in the coefficient of discharge. The study revealed that the number of holes in the plate significantly affects the pressure drop across the plate, the recirculation zone, and the orifice’s discharge coefficient. Full article

Skin diseases are a prevalent and diverse group of medical conditions that affect a significant portion of the global population. One critical drawback includes difficulty in accurately diagnosing certain skin conditions, as many diseases can share similar symptoms or appearances. In this paper, we propose a Weighted Ensemble Region-based Convolutional Network (WERCNN) methodology that consolidates a Mask R-CNN (Mask Region-based Convolutional Neural Network) with the weighted average ensemble technique to enhance the performance of segmentation tasks. A skin disease image dataset obtained from kaggle is utilized to segment the skin disease image. This study investigates the utilization of a Mask R-CNN in skin disease segmentation, where it is prepared on a skin disease image dataset of dermatological pictures. The weighted average ensemble model is utilized to optimize the weights of the Mask R-CNN model. The performance metrics accuracy, precision, recall, specificity, and F1-score are to be employed; this can achieve the values of 94.7%, 93.6%, 93.9%, 92.6%, and 93.7%, respectively. With regard to skin disease segmentation, the WERCNN has shown extraordinary in accurately segmenting the impacted regions of skin images by providing valuable insights to dermatologists for diagnosis and treatment planning. Full article

Security is currently the main boundary for cloud-based administrations. It is not adequate to just consolidate the cloud by adding a couple of additional controls or component answers for your current organization security programming. Businesses must utilize both virtual and physical information center security frameworks to keep them secure. The objective is to defend it from dangers that may jeopardize the secrecy, judgment, or openness of mental property or commerce data resources. These are the fundamental central focuses of all assigned attacks, and in this way, they require a high degree of security. Hundreds to thousands of physical and virtual servers are partitioned up into information centers agreeing to sort applications, information classification zones, and other criteria. To protect applications, frameworks, information, and clients, information center security takes on the workload over physical information centers and multi-cloud situations. It also applies to open cloud data centers. All server ranches ought to protect their applications and data from a rising number of refined threats and around-the-world ambushes. Each organization is at risk of assault, and numerous organizations have been compromised without being mindful of it. An evaluation of your resources and business necessities is important to improve a spotless way to deal with your way of life and cloud security technique. To deal with a strong mixture of multi-cloud wellbeing program, you should lay out perceivability and control. You can consolidate incredible controls, organize responsibility dispersion, and lay out fantastic gambles on the board with the assistance of safety items and experts. Full article

Image processing-based pattern recognition applications often use texture features to identify structural characteristics. Existing algorithms, including statistical, structural, model-based, and transform-based, lack expertise for specialized features extracted around potentially defective regions. This paper proposes an attention-guided deep-learning texture feature extraction algorithm that can learn features at various regions with varying complexities, addressing the lack of expertise in existing techniques. This approach can be used for applications such as minor fabric defects and hairline faults in PCB manufacturing. Full article

Electrical discharge machining (EDM) and its variant methods are used to fabricate three-dimensional and complex geometrical features from micro level to nano dimensions. Researchers have successfully experimented with high-strength alloys and composite materials, finding wide applications in defense, automobile, and medical industries to shape precision micro-grooves (straight, tapered, and angular-based). Motion-type EDM methods (when the tool electrode is moving) utilize capabilities to rotate the tool electrode or work material to manufacture grooves (applications included in the micro-electronics sector, aircraft engines, and diffraction gratings). In the present investigation, experimental studies were performed to fabricate the grooves of high-strength NI-based alloy using the EDM electrode (cylindrical in shape) using Taguchi’s L-18 orthogonal array. SEM studies were performed at different magnifications to check and analyze the recast layer formation on the surface of the groove at different parametric settings. The analysis of the effect of input parameters was tested on machine performance responses viz. MRR, EWR, and surface roughness. This was revealed, and the optimum levels of process parameters were analyzed, showing the best surface finish with a maximum metal removal rate after analyzing using SEM. The MRR was found to increase with an increase in the thickness of the disk electrode (0.1–0.6) at all parametric settings. Also, roughness increased with an increase in the current settings from 6 to 12 A. SEM analysis depicts that groove thick ness at the bottom (565 µm) and top of the groove (1.14 mm). Full article

Present work focusses on investigating the effect of process parameters such as feed rate and spindle speed on quality characteristics of the hole, i.e., surface roughness (Ra) and circularity at entry and exit in the drilling of aluminium (Al) 6061 reinforced with different volume fraction of silicon nitride (Si₃N₄). Optimum parameters for Ra and circularity of hole at entry and exit are obtained as feed rate at 0.125 mm/rev, spindle speed at 300 rpm, diameter of drill at 8 mm, and % Vol. of Si₃N₄ at 5%. Using Analysis of Variance (ANOVA), we observed that spindle speed is the most influential parameter followed by feed rate. Full article

Rust is a modern programming language that addresses the drawbacks of earlier languages by providing features such as memory safety at compilation and high performance. Rust’s memory safety features include ownership and borrowing, which makes it an ideal choice for systems programming, where memory safety is critical. Forth is a stack-based programming language that is widely used for low-level system programming due to its simplicity and ease of use. This research paper aims to explore the combination of Forth and Rust programming languages to create a more robust and efficient solution for low-level system programming. The primary objective is to demonstrate the implementation of essential Forth operations, including addition, subtraction, assignment, comparison, and if-else statements, while demonstrating loops, push operations, and dump operations in Rust. The implementation of these operations in Rust is demonstrated using code from actual implementation. This research paper also discusses the advantages of using Rust for low-level system programming. Rust’s memory safety features, coupled with its high performance, make it an ideal choice for systems programming, where memory safety and performance are critical. The combination of Forth and Rust provides a more efficient and safer solution for low-level system programming, making the implementation more robust. Our implementation tries to leverage these properties of both languages to make a memory-safe and low-level system programming language. This research paper also includes code snippets to provide a practical demonstration of how the Forth operations can be implemented in Rust. Full article

The study of a drug’s absorption, distribution, metabolization, and excretion by the body is known as pharmacokinetics (PK). In pharmacokinetics, the two-compartment model is used to understand the distribution and elimination of drugs. The two-compartment model represents the body as two distinct compartments: the central compartment (such as the blood) and the peripheral compartment (such as tissues). This work aims to enhance the understanding of drug kinetics inside the human body by comparing different mathematical models. The important focus of this study is to compare the distribution patterns of the drugs methadone, buprenorphine, tramadol, and tapentadol when administered intravenously using a two-compartment model. To mathematically describe the distribution of drugs in the body, a system of nonlinear ordinary differential equations is employed. These equations capture the dynamics of drug concentration in the different compartments over time. The roots are obtained by solving this system of equations using numeric analysis techniques. The study determines the duration of the drugs to attain the minimum effective concentration in the blood by analyzing the obtained results. Furthermore, the study also determines the time it takes for these drugs to be eliminated from the body. This data is significant for understanding the drug’s clearance rate and its potential duration of action. By comparing the distribution patterns and elimination rates of methadone, buprenorphine, tramadol, and tapentadol, the study provides insights into the differences between these drugs in terms of their pharmacokinetic properties. Healthcare professionals can utilize this information to optimize drug therapy, ensuring that the drugs are administered in accurate amounts and at precise intervals to target the desired therapeutic effect. Overall, this study provides a comprehensive analysis of drug kinetics, aiding in a better understanding of drug behavior within the human body and facilitating informed decision making in clinical settings. Full article

This paper presents a comprehensive dynamic analysis of a 650 W vertical-axis wind turbine (VAWT) rotor system, focusing on the impact of radial permanent magnet bearings (PMBs) on its performance. Through optimization of PMB capacity and stiffness using multi-ring radially magnetized stack structures, the study explores their influence on modal frequency, vibration amplitude, and system stability. The research progresses through steps, initially analyzing the rotor system with deep groove ball bearings (DGBs), considering the bearing span length, and transitioning to a hybrid bearing set (HBS) with PMBs. Ultimately, the rotor system entirely relies on radial PMBs, as investigated through finite element analysis (FEA). The results reveal significant improvements in critical speeds (5.75–9.81 percent higher than operational speeds), emphasizing the influence of bearing stiffness on system dynamics and stability. The study’s insights offer valuable contributions to the understanding and design optimization of VAWT rotor systems supported by PMBs, enhancing the efficiency and reliability of wind energy conversion systems. Full article

In recent years, the security of containers has become a crucial aspect of modern software applications’ security and integrity. Containers are extensively used due to their lightweight and portable nature, allowing swift and agile deployment across different environments. However, the increasing popularity of containers has led to unique security risks, including vulnerabilities in container images, misconfigured containers, and insecure runtime environments. Containers are often built using public repository images and base image vulnerability is inherited by containers. Container images may contain outdated components or services, including system libraries and dependencies and known vulnerabilities from these components can be exploited. Images downloaded from untrusted sources may include malicious code that compromises other containers running in the same network or the host system. Base images may include unnecessary software or services that increase the attack surface and potential vulnerabilities. Several security measures have been implemented to address these risks, such as container image scanning, container orchestration security, and runtime security monitoring. Implementing a solid security policy and updating containers with the latest patches can significantly improve container security. Given the increasing adoption of containers, organizations must prioritize container security to protect their applications and data. This work presents automated, robust security techniques for continuous integration and continuous development pipelines, and the added overhead is empirically analyzed. Then, we nail down specific research and technological problems the DevSecOps community encounters and appropriate initial fixes. Our results will make it possible to make judgments that are enforced when using DevSecOps techniques in enterprise security and cloud-native applications. Full article

The gaming industry is one of the most important and innovative subfields in the field of technology, which boasts a staggering USD 200 billion in annual revenue and stands as a behemoth. It has an immense effect on popular culture, social networking, and the entertainment industry. Continuous advances in technology are the primary factor fueling the industry’s expansion, and these innovations are also revolutionizing the design of games and improving the overall gaming experience for players. The growing number of people who have access to the internet, the widespread use of smartphones, and the introduction of high-bandwidth networks such as 5G have all contributed to an increase in the demand for gaming around the world. It is essential to perform consumer feedback analysis if one wants to appreciate market requirements, evaluate game performance, and realize the effect that games have on players. On the other hand, short-text reviews frequently lack grammatical syntax, which makes it difficult for standard natural language processing (NLP) models to effectively capture underlying values and, as a result, compromises the accuracy of these models. This research focuses on determining which natural language processing model is the most accurate at forecasting five-star ratings of video games based on brief reviews. We make use of natural language processing (NLP) to avoid the constraints that are imposed on us by the linguistic structure of short-text reviews. The findings of our research have led to several important contributions, one of which is the creation of an innovative model for reviewing and grading short writings. The accuracy is improved by employing different machine learning models, which enables game creators and other industry stakeholders to identify patterns about the behavior and preferences of the users. Full article

Elliptic-curve cryptography (ECC) has become a robust cryptographic technique that ensures secure data transmission with comparatively small key sizes. In this context, this research introduces a novel approach to ECC-key-pair generation by utilizing genetic algorithms (GAs). GAs have proven effective in solving optimization problems by mimicking the principles of natural selection and genetics. The proposed genetic algorithm-based ECC-key generation process involves several stages: chromosome initialization, fitness evaluation, selection, uniform crossover, and mutation. Chromosomes representing points on an elliptic curve are initialized randomly, evaluated for their proximity to a predefined target point using a fitness function, and subjected to tournament selection to determine parents for the next generation. Uniform crossover and mutation operators then create offspring, inheriting traits from their parents while introducing diversity. The generated ECC-key pair comprises private and public keys derived from the GA-driven process. The private key is chosen randomly within the constraints of the elliptic curve’s parameters, while the public key is generated through the GA procedure. The study evaluates the efficiency and effectiveness of the proposed ECC-GA approach through an empirical analysis of execution time, key size, and the size of the search space. The outcomes of this research highlight the potential of genetic algorithms in ECC-key generation, offering a promising alternative for enhancing the security and efficiency of cryptographic systems, especially in resource-limited environments. The exploration of key size and search space may assist in understanding the security implications and computational complexity associated with the proposed method. Overall, the ECC-GA approach opens avenues for further research in innovative key-generation techniques for modern cryptographic applications. Full article

In the era of rapid technological advancement, understanding and confronting the challenges posed by AI systems are imperative. The concept of Superintelligence denotes the potential for AI to surpass the intellectual capacities of even the most brilliant human minds. As AI capabilities outpace human intellect and continually evolve, achieving such Superintelligence could lead to a point of no return—technological singularity—with uncontrollable repercussions, risking humanity’s existence. The proposed Waning Intellect theory suggests placing a finite lifespan on AI models to prevent unchecked evolution. Waning Intellect anticipates potential diminishing AI capabilities due to increased neural network complexity, posing risks to reliability, safety, and ethical concerns. Upholding ethical standards, human–AI collaboration, and robust regulatory frameworks are pivotal in leveraging AI’s potential while ensuring responsible deployment and mitigating risks. Full article

The Internet of Things is essential for business. It makes it possible to gather and analyze huge amounts of data in real time. IoT devices also encourage computerization. They enable individuals to gain greater control over their circumstances, well-being, and safety. As a rule, there are two principal sorts of asset the executives move toward that concern framework and applications. All improvement groups that work with cloud situations will be influenced by the modern approaches to cloud administration. Utilization checking, asset assignment to applications and administrations based on their prerequisites, and capacity administration— all components of asset administration—guarantee that assets are utilized successfully. It might, for instance, utilize robotized apparatuses to screen how its servers are being utilized, donate more assets to administrations that are in great demand, and cut back on administrations that are not in great demand. The Internet of Things makes it conceivable to computerize regular undertakings that commonly consume a ton of assets and labor supply; thus, trading settings considering brief environment or use is one model. This opens a great deal of assets, permitting the organization to focus on development and a bigger vision of the business. It provides information to encourage better choices and tracks down holes in tasks, cycles, and business arrangements. It likewise makes an extraordinary association between the production line floor and the business. This implies expanded efficiency, even while reducing expenses and energy use. Full article

Heat exchangers of high effectiveness are generally sought by the thermal industry for the efficient utilization of heat energy. The present study focuses on the enhancement of the effectiveness of a single-tube heat exchanger by attaching equilateral triangular-prism roughness elements on the peripheral heat transfer surface. The forced convection in a circular tube is analyzed using ANSYS-fluent considering air as the working fluid in the Reynolds number (Re) range of 10,000 to 18,000. The geometric parameters (the cross-section and the roughness element height) are fixed. The effects of longitudinal pitch, angular pitch and the orientation of triangular-prism roughness elements on the heat transfer and the frictional energy loss are studied. The presence of roughness elements on the heat transfer surface is found to increase turbulence and fluid mixing. Up to a 23% increase in heat transfer performance is seen in the Nusselt number values of the roughened tube over the smooth tube. The presence of roughness elements on the tube surface also increases the frictional losses; however, this increase is found to be gradual with the reduction in both longitudinal and angular pitch. Full article

Recently, machine learning-based task automation framework have been gaining attention in human resource management of Multi-National Companies (MNCs). Task automation framework helps MNCs to automate repetitive HR tasks, analyse data quickly and accurately, forecast workforce, and recognize employees. MNCs are now beginning to use ML algorithms in combination with Artificial Intelligence (AI) to streamline the HR processes. Most MNCs have large-scale operations and decentralized organization structures which put additional pressure on HR teams to carry out intricate and tedious manual processes. To ease the process, ML-based task automation framework facilitates HR teams to leverage the power of AI and perform HR management tasks in a more effective and efficient manner. The ML-based task automation framework utilizes automation bots which can simulate all processes of HR management such as recruitment, time attendance, tracking employee records, scheduling calendar, and office administration tasks. The machine learning-based task automation framework utilizes predictive analytics to identify trends, patterns, behaviour, anomalies, and important insights from the large volumes of structured and unstructured data. Full article

This study aims to identify the sustainability programs introduced in their supply chains by the Indian retailing (FMCG and Pharma) sector and the various problems encountered in managing their supply chains. The researchers collected the opinions of 200 companies from the FMCG and pharma sectors after checking the questionnaire’s internal consistency and validity using Cronbach’s α and Kaiser–Meyer–Olkin (KMO) tests. After data collection, the data were summarized, coded, and controlled using R Studio and Microsoft Excel. The hypotheses were analyzed using the Kruskal–Wallis (K-W) hypothesis technique. Manufacturers emphasized that their supply chains impact toxic waste and pollution, that wholesalers and retailers are highly influenced by poor cost control and management, that there is a difficulty in forecasting demand, and that there are supply related problems. Full article

Blood cancer diagnosis is a critical medical procedure, yet difficult and expensive for clinical personnel to perform accurately. Artificial neural networks have been shown to be effective in diagnosing a range of diseases, due to their powerful ability to identify and classify patterns in data. Here, we present a study that employed one such ANN to diagnose blood cancer from data gathered from network sensors. First, a sensor network was placed in an animal model to capture various physiological data, including cardiac and respiratory rates, body temperature, and blood pressure. This data was then sent to an ANN which used a classification system based on the type of cancer for diagnostic analysis. Our results showed that the ANN was able to accurately diagnose a blood cancer with an accuracy of 92.1% and that its accuracy improved with the addition of more data. Our study demonstrates that ANNs can be successfully used to accurately diagnose blood cancer using data from network sensors, which could reduce costs and provide faster results in clinical settings. Full article

Solar energy is gaining immense significance as a renewable energy source owing to its environmentally friendly nature and sustainable attributes. Crystalline silicon solar cells are the prevailing choice for harnessing solar power. However, the efficiency of these cells is greatly influenced by their configuration and temperature. This research aims to explore the current–voltage (I−V) characteristics of individual, series, and parallel configurations in crystalline silicon solar cells under varying temperatures. Additionally, the impact of different temperature conditions on the overall efficiency and Fill Factor of the solar cell was analyzed. With the aid of a solar simulator and required conditions, the I−V characteristics of each configuration—individual, series, and parallel—were obtained. The solar panel was subjected to various temperature settings, and I−V characteristics were obtained for each configuration to calculate the maximum power and Fill Factor for each case. In addition to this, the results showed that the parallel configuration has a larger power output, followed by the individual and series configurations. Additionally, the temperature of the solar panel had a significant effect on the output power of the solar cells. The maximum output power is also affected by temperature variation. The Fill Factor, on the other hand, was observed to be dependent on the configuration but had no significant variation with respect to the temperature. The effect of solar irradiance was also observed in a configuration with a definite temperature. This research offers valuable insights into the ideal configuration and optimal temperature for achieving maximum efficiency in crystalline silicon solar cells. Hence, a definite configuration with optimum temperature yields maximum power output and helps in attaining maximum efficiency. Full article

Carbon fiber-reinforced aluminum laminates, known as CARALL, have wide applications in aircraft structures. However, numerous holes must be processed to assemble these structures, which is conventionally practiced through drilling. However, the drilling process exhibits certain limitations when utilized for hole-making in heterogeneous materials. In the recent past, helical milling has positioned itself as an alternative to the drilling process. However, helical milling performance examination during hole-making in CARALL is scant and needs further evaluation. The present study compares the milling process to the drilling process considering important performance indices, including cutting forces, surface roughness, chip morphology, machining temperature, and burr size. Additionally, microscopic characterization of the boreholes is performed to verify the presence of surface damage and delamination defects. Helical milling successfully lowered the thrust and radial forces and restrained the machining temperature below the levels attained via drilling. The diametrical deviation is higher at entry and lower at exit for both processes; however, helical milling produced holes with much higher accuracy. Helical milling developed smaller sized holes in comparison to drilling. Moreover, rougher surfaces due to the abrasion of continuous chips were observed in drilling, while a smoother finish was noted in helically milled holes. Based on the comprehensive comparative analysis, helical milling positions itself as an acceptable alternative to conventional drilling for machining fiber metal laminates. Full article

Sentiment analysis involves contextually examining text to identify and extract subjective information from source material. It aids businesses in comprehending the public sentiment surrounding their brand, product, or service while monitoring online discussions. Nevertheless, analyzing social media content is often limited to basic sentiment analysis and simple count-based metrics. Devices that allow the collection of huge amounts of unstructured, opinionated data are becoming increasingly connected with humans. Everyday-activity-related comments and evaluations have been obtained as a result of the advances in Internet-based services like social media platforms and blogs. This study supplies a comprehensive assessment of sentiment analysis approaches to provide academics with a global perspective on the analysis of feelings and its associated domain, applications, and challenges. To comprehend the applications of sentiment analysis, this article provides a detailed explanation of the technique for performing this activity. To comprehend the benefits and drawbacks of each method, they are then evaluated, compared, and discussed. To establish future perspectives, the difficulties of sentiment analysis are finally evaluated. Full article

Agriculture is pivotal in Bangladesh, with maize being a central crop. However, leaf diseases significantly threaten its productivity. This study introduces deep learning models for enhanced disease detection in maize. We developed an unique datasets of 4800 maize leaf images, categorized into four health conditions: Healthy, Common Rust, Gray Leaf Spot, and Blight. These images underwent extensive Pre-processing and data augmentation to improve robustness. We explored various deep learning models, including ResNet50GAP, DenseNet121, VGG19, and a custom Sequential model. DenseNet121 and VGG19 showed exceptional performance, achieving accuracies of 99.22% and 99.44% respectively. Our research is novel due to the integration of transfer learning and image augmentation, enhancing the models’ generalization capabilities. A hybrid model combining ResNet50 and VGG16 features achieved a remarkable 99.65% accuracy, validating our approach. Our results indicate that deep learning can significantly impact agricultural diagnostics, offering new research directions and applications. This study highlights the potential artificial intelligence in advancing agricultural practices and food security in Bangladesh, emphasizing the need for model interpretability to build trust in machine learning solutions. Full article

The wide range of biological processes and functions that benzimidazole moieties can be used for makes them very interesting synthetic molecules. A novel class of scaffolds for benzimidazole heterocycles has been successfully constructed in the present study and synthesized by using the starting material of O-phenylenediamine derivatives (1a–c). The 1-methyl-2-(methylthio)-1H-benzo[d]imidazole derivatives (3a–c) have been synthesized as intermediate compounds by treating the precursors (1a–c) with carbon disulfide followed by N- and S-methylation with iodomethane and anhydrous potassium carbonate. In the latter step, the intermediate molecules were converted into benzimidazole-containing methyl-piperazine (4a–c), piperazin-ol tethered benzimidazoles (5a–c), and phenylpiperazine holding benzimidazoles (6a–c). The structures assigned to target compounds have been analyzed and confirmed via IR, NMR, and MS analysis. The antibacterial, anthelmintic, and anticancer properties of the target compounds were examined. The biological study showed that the compounds 6b, 4c, and 5a emerge as excellent antibacterial, antifungal, and anthelmintic agents, respectively, whereas heterocycle 6a showed excellent anticancer activity against hepatocyte-derived cell line HUH7, as well as the MCF7 breast cancer cell line with IC₅₀ values of 6.41 and 9.70 µg/mL, respectively. The discovery of a novel class of hetero compounds with multiple hetero moieties that may aid in medication design is also highlighted in this study. Full article

A balanced operating power system with all elements carrying normal currents and bus voltages within the prescribed limits can be disrupted due to faults within the system. Overhead transmission networks are vulnerable to the vagaries of the atmosphere and, therefore, statistically have the highest probability of fault occurrence. Quick and accurate fault detections assist in timely remedial action, offering significant economic and operational benefits. Maintaining continuous and uninterrupted supply functionality is one of the critical objectives of electric utilities for a reliable system operation. Also, identifying and locating faults is crucial to address them in time to avert the risk of cascading failures. During faults, fast electromagnetic transients associated with the current and voltage waveforms can provide valuable insights into identifying abnormal operating conditions. To analyze these non-stationary signals in both the time and frequency domains, wavelet transform (WT) has become an indispensable tool. Thanks to its ability to adapt to variable window sizes, WT provides a more accurate and detailed resolution, making it a highly useful technique for signal analysis. In this context, this paper presents the application of WT-based intelligent technique to detect and classify power system faults accurately. The transient disturbances caused by various faults are subjected to wavelet transform analysis to analyze the detail coefficients of phase currents. The maximum detail coefficients of phase currents, which differ significantly when the system experiences a fault, served as the distinguishing feature to identify different power system faults. The phase current signals are analyzed with one of the wavelets from the Daubechies 4 (db4) family to obtain detail coefficients, thus enabling the categorization of the faults. Extensive simulation tests for fault types have been conducted on the standard IEEE 5-Bus system to demonstrate the technique’s effectiveness and fault detection capability, allowing utilities to take timely protective actions. Full article

Hybrid metal matrix composites (MMCs) are increasingly important in aviation, marine, automotive, and industrial manufacturing due to their ability to enhance the mechanical and chemical properties of composites. The study aimed to understand the fabrication, mechanical properties and microstructural properties of LM6/B4C/Gr composites. An aluminium alloy (LM6) is the base metal, having properties of less weight, medium strength, and excellent castability. The addition of B4C and Gr enhanced the tensile strength, hardness, and wear resistance of the composites, while maintaining good ductility. Boron carbide is a lightweight and extremely hard material with excellent wear resistance and high thermal stability. It has a specific modulus that is almost two times higher than that of aluminium, meaning it can provide superior stiffness and strength while maintaining a low weight such as drive shafts, housings, and structural supports. The addition of graphite improves the lubrication properties of the composites. Composites were successfully fabricated through a stir casting process, with the uniform dispersion of boron carbide and graphite particles in the aluminium LM6 matrix. The hybrid metal matrix composites are fabricated by five different combinations of B4C (1, 2, 3, 4, 5 wt%) with constant wt% of graphite (1 wt%).The fabricated samples of hybrid composites used to find the mechanical properties and microstructure analysis. The test results reveal that the tensile strength and hardness of the composites increased with an increase in the weight percentage of reinforcements, and the percentage of elongation decreases with increasing the reinforcement particles. The boron carbide (B₄C) and graphite (Gr)particles in a matrix material are analyzed by a scanning electron microscope (SEM). Energy dispersive X-ray analysis (EDX) is used to evaluate the microstructure and chemical composition of the composites, providing valuable insights for their design and optimization. Full article

The Internet of Things (IoT) and machine learning provide insights that would otherwise be hidden in data for quicker, automated responses and improved decision-making. By ingesting images, videos, and audio, machine learning for the Internet of Things can be used to predict future trends, identify anomalies, and enhance intelligence. The IoT organic framework comprises millions of sharp objects, and to form these sharp objects to communicate and work suitably, asset tasks are necessary. Protection of the quality of service (QoS) is one of the diverse reasons that resource tasks ought to be performed. These techniques aid accomplices in choosing tasks resulting in preeminent regard and impact. Prebuilt software-as-a-service (SaaS) applications, called IoT Cleverly applications, can use dashboards to analyze and display data from IoT sensors. If one of the devices is hacked, the security of every other device in this chain is compromised. This can possibly result in second thoughts about a security plans. A user can see key execution indicators and measure the time between data entries by using IoT dashboards and alarms. Calculations based on machine learning can find peculiarities in equipment, notify customers, and even start robotized repairs or proactive countermeasures. AI and Profound Learning resemble managing a real workplace issue such as marking by combining a few innovations that enable constant naming. Full article

The current work investigates the comfort of two-wheeler riders and compares the footrest vibration between an internal combustion-engine-based and electric two-wheeler. The Retrofit Hero Honda CD-100 two-wheeler is considered for the study and is further converted into the electric mode in the laboratory. Electric two-wheelers, even though they have fewer moving parts than internal combustion engine-based two-wheelers, encounter vibrations that emerge from road excitations. Cracks, potholes, and irregular humps on the road are the major influencers of these vibrations. These vibrations, when they transfer to the human body, have been reported to cause major injuries to the human body in the long run. By performing several trials on actual road conditions, with both the rider as well as pillion, the vibration dose value is calculated at the footrest. Different scenarios, such as a random speed test, a 20 kmph speed test and a 30 kmph speed test, are conducted on the two-wheeler. The vibration dose value (VDV) method is used to analyze the rider’s comfort. A comparison is made between the internal combustion engine-based and electric-based two-wheeler to determine its comfort level at the footrest. It is found that the VDV as well as the RMS acceleration decreased considerably in the case of the electric two-wheeler when compared to the internal combustion engine-based vehicle. However, it is found that as the speed is increased, the vibrations increased as well. Hence, further scope is found for the improvement and inculcation of vibration damping at the locations where the vibrations are pronounced in order to improve the overall riding experience of a two-wheeler rider. Full article

Non-geographic routing protocols are inefficient when applied to large-scale mobile networks composed of hundreds of nodes. On the other hand, geographic routing protocols have the disadvantage of needing a location sensor. The goal is to address the challenges of efficient content retrieval and routing scalability in NDN-based networks by leveraging the benefits of both NDN and Bloom Filter technologies. In this article we propose a routing protocol for mobile networks, which is scalable to networks composed of hundreds of nodes. The protocol does not require any localization equipment and is adapted for devices with limited memory and/or processing resources. This goal is achieved through the use of Bloom Filters to efficiently store and spread topological information. In the methodology followed, nodes do not forward messages with topological information to other nodes. To make the process efficient, each node aggregates the topological information it receives from its direct neighbors with its own and only the result of this operation is transmitted to the remaining nodes. Several simulations were carried out in the Qualnet network simulator in order to validate the algorithm proposed by the Hybrid Routing Algorithm with NDNs (HRAN). The obtained results were compared with other non-geographic protocols for mobile networks. HRAN seems to be a routing protocol designed for MANETs, utilizing Bloom Filters to manage topological information. A Bloom Filter is a data structure used to test whether an element is a member of a set. It uses a bit array and multiple hash functions to determine if an element is present in the set. This type of data structure allows storing a large amount of binary information in an efficient way, reducing the resources required by the routing protocol. Full article

Chemical industries are generating unprecedented effluent, including toxic aromatic compounds such as phenol, which poses severe environmental risks. This study explores the acute and prolonged effects of phenol, which range from the death of animals, birds, and fish to reduced plant growth, reproductive problems, and changes in appearance and behaviour. Additionally, oral exposure to phenol can be toxic to humans. Meanwhile, the agricultural sector faces challenges in finding salvage value for increasing amounts of waste. To address this issue, our research analyzes organic materials with no market value and investigates the feasibility of achieving efficient adsorption using their char. We specifically examine pine nuts, an abundantly available waste material. Our objective is to synthesize an organic adsorbent material that meets specific criteria: organic, readily available at zero cost, derived from waste with no other utility, native to the area, abundantly accessible, possessing a large surface area, and demonstrating superior adsorption capabilities. This research employs chemical activation using four acids (nitric acid, sulfuric acid, hydrochloric acid, and orthophosphoric acid) and involves drying and heating the samples at different elevated temperatures. The selection of the optimal adsorbent is based on an analysis of the BET (Brunauer–Emmett–Teller) surface area and pore volume, ensuring its efficacy. The adsorption efficiency was also tested with the help of a UV spectrophotometer to assess its efficiency using Beer–Lambert’s law. The study also goes through an ultimate analysis to measure the amount of carbon content in our adsorbent. Through this study, we aim to develop sustainable waste management practices by utilizing pine nut waste as a valuable resource for effective phenol removal. Full article

The recent trend of Infrastructure as a Service is a service that provides IT components, like computing and storage, on a pay-as-you-go basis over the web. Today, IaaS has endless applications related to the businesses using it. After conducting a contextual analysis, we note that organizations have moved most of their activities to the cloud. For the most part, this implies that they presently use Software as a Service (SaaS) applications rather than authorized on-prem applications and that they have moved their restrictive programming and frameworks from a server farm to IaaS providers. For years, cloud experts have discussed whether there is truly such an amazing concept as a confidential cloud in IaaS, that is, an on-premises cloud in the client’s server farm. IaaS has undergone an extensive transformation from conventional equipment server farms to a virtualized and cloud-based framework. By eliminating the connection between equipment and working programs and middleware, companies found that they could scale data requirements rapidly and effectively to fulfill their responsibilities. To utilize IaaS, a business can buy a particular resource from a cloud computing supplier to reorganize its computing framework so clients can concentrate on tasks like acquiring and overseeing their own computer programs. This involves incorporating things like computer servers, applications for websites, and versatile gadgets. Along these lines, an enterprise can organize its own equipment infrastructure while having the required assets to carry out a plan of action. Full article

The emergence of Machine Learning (ML) strategies within the scope of community security has led to principal advances in improving clever Artificial Intelligence (AI) primarily based on intrusion detection structures. Intrusion Detection Systems (IDSs) are used to locate malicious conduct in conversation systems and the internet. A smart AI-based IDS comprises some additives that enable it to provide an automatic and green safety solutions for high-density verbal exchange structures. Present IDS stumble on intrusions and anomalies that are primarily based on predefined guidelines and signature patterns, whereas clever AI primarily based on IDS uses ML methods to gather significant volumes of information from both external and internal sources to hit upon anomalies that could imply a safety breach. Smart AI-based total IDS combines diverse ML methods which are inclusive of supervised studying, unsupervised learning, deep studying, neural networks, and reinforcement-gaining knowledge to create a holistic security solution. Full article

Structures as well as components are generated by depositing filaments on one another via the technique of additive manufacturing. Among the various processes of printing, 4D printing combines the technology of 3D printing with the passage of time, resulting in additively generated parts that are responsive to stimuli from the outside via modifications of their form, volume, size, or mechanical qualities. Thus, the materials of shape memory are used in 4D printing and respond to environmental factors including temperature, pH, and humidity. Shape memory polymers (SMPs) are materials with a shape memory effect that are best suited for additive manufacturing. Contrarily, the method named fused filament fabrication (FFF) is employed most frequently among all additive manufacturing methods. In this regard, the objective of the present study is to evaluate all investigations that have been conducted on 4D-FFF materials’ mechanical properties. The study offers an unparalleled overview that highlights the possibilities of 4D FFF printing across multiple applications in engineering while keeping the end structure’s or component’s structural integrity in consideration. Full article

Businesses require ways to check asset use in order not to disregard Service-Level Agreements and guarantee that assets are efficiently distributed to specific departments. A method of allocating, managing, and monitoring cloud resources is provided by cloud resource management systems. They permit one to make and oversee pools of assets, allocate those assets to explicit clients or applications, and track how they are being utilized. Users are able to request and provision resources as needed through a self-service interface provided by a good cloud resource management system. When using a cloud provider, businesses that manage their own resources frequently achieve greater efficiency. A portion of the ways in which IT robotization helps organizations deal with their assets involves setting boundaries for the greatest and least number of virtual machines (VMs), setting look-ahead times for VMs to appear, and halting VMs when they are inactive and, at that point, not needed for operations. Moreover, IT organizations might profit from developing a structure of warnings to further develop perceivability and control over asset utilization. Cloud computing is a model used to enable omnipresent, helpful, on-request network admittance to a common pool of configurable processing assets that can be quickly provisioned and delivered with negligible administrative exertion and without specialist organizations. Distributed computing is a financial model for huge corporations, as it removes the requirement for beginning interest in capital or framework costs. Full article

The goal of Feature Extraction Algorithms (FEAs) is to combat the dimensionality curse, which renders machine learning algorithms ineffective. The most representative FEAs are investigated conceptually and experimentally in our work. First, we discuss the theoretical foundation of a variety of FEAs from various categories like supervised vs. unsupervised, linear vs. nonlinear and random-projection-based vs. manifold-based, show their algorithms and compare these methods conceptually. Second, we determine the finest sets of new features for various datasets, as well as in terms of statistical significance, evaluate the eminence of the different types of transformed feature spaces and power analysis, and also determine the FEA efficacy in terms of speed and classification accuracy. Full article

Juvenile Idiopathic Arthritis (JIA) is a widespread and chronic condition that affects children and adolescents worldwide. The person suffering from JIA is characterized by chronic joint inflammation leading to pain, swelling, stiffness, and limited body movements. Individuals suffering from JIA require ongoing treatment for their lifetime. Beyond inflammation, JIA patients have expressed concerns about various factors and the lack of responsive services addressing their challenges. The implementation of smart garments offers a promising solution to assist individuals with Juvenile Idiopathic Arthritis in performing their daily activities. These garments are designed to seamlessly integrate technology and clothing, providing not only physical support but also addressing the psychological and emotional aspects of living with a chronic condition. By incorporating sensors, these smart garments can monitor joint movement, detect inflammation, and provide real-time feedback to both patients and healthcare providers. To tackle these comprehensive challenges, the research aims to offer a solution through the design of a smart garment, created with a holistic approach. This smart garment is intended to improve the overall well-being of JIA patients by enhancing their mobility, comfort, and overall quality of life. The integration of technology into clothing can potentially revolutionize the way JIA is managed, allowing patients to better manage their condition and minimize its impact on their daily lives. The synergy between healthcare and technology holds great potential in addressing the multifaceted challenges posed by Juvenile Idiopathic Arthritis patients. Through innovation and empathy, this research aims to pave the way for a brighter future for individuals living with Juvenile Idiopathic Arthritis. Full article

Graphene oxide (GO) and reduced graphene oxide (rGO) are well known for their exceptional characteristics in a variety of applications. Reduced graphene oxide differs from graphene oxide in terms of morphological aspects, quality, functionalized groups, and crystallinities. Several attempts to synthesize GO and rGO have been documented in studies. The paper discussed the numerous ways to synthesize GO and rGO, and a literature review revealed that Hummers’ technique stands out as the most commonly used. Graphite is mixed with potassium permanganate, sodium nitrate, and strong sulfuric acid to make GO. Notably, Hummers’ technique has the advantage of faster synthesis and higher GO quality. The paper discusses several investigations, including the morphological and structural characteristics, chemical bonding information, and mechanical properties of GO and rGO. Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and the Vickers Hardness Tester are generally used to study these characteristics. The FTIR analysis revealed that the most common peaks in both GO and rGO were found to be associated with the O-H, C=O, C-OH, and C-O functional groups. XRD examination, on the other hand, revealed a diffraction peak at 2θ = 10.2°, indicating oxidized graphite in the case of GO, as well as a graphitic peak at 2θ = 26.3°, indicating graphitic graphite. Furthermore, the addition of GO and rGO into ceramics or polymers was discovered to cause significant changes in their mechanical characteristics, such as tensile strength, Young’s modulus, and others. This demonstrates the revolutionary potential of graphene in improving the performance of composite materials. Full article

Aluminum Nitride (AlN) is a promising piezoelectric material for microelectromechanical systems owing to its attractive physical and chemical properties and CMOS compatibility. It has a moderate piezo response compared to its rival material bound to its wide application. This obstacle can be overcome by doping or alloying. Sc alloying increases the piezo response of AlN up to four-fold; it also increases the electromechanical coupling coefficient, which is a prominent figure of merit for any MEMS device application. Sc doping induces elastic softening in wurtzite AlN, enhances polarization, and increases piezoelectric constants. However, the possibility of phase separation at higher Sc concentrations, and the wurtzite phase of AlN, which is responsible for piezoelectricity, becomes negligible. Therefore, knowing the optimum concentration of Sc for device applications is necessary. In this work, using density functional theory, we calculated the lattice parameter, band and density of states along with the physical properties such as Young’s modulus, the bulk modulus, Poisson’s ratio, and elastic constants of pristine AlN and Sc doped AlN. The DFT calculations show that the geometrical optimized lattice parameters agree with the literature. As a function of increased Sc concentration, the calculated Young’s modulus and elastic constants decrease, indicating a decrease in hardness and elastic softening, respectively. Meanwhile, the bulk modulus and Poisson’s ratio increase with an increase in Sc concentration, representing an increase in the crystal cell parameters and elastic deformation. AlN and AlScN thin films were grown on Si (111) substrate using magnetron sputtering to study the structural properties experimentally. The deposited films show the required c-axis (002) preferential crystallographic orientation. The XRD peaks of Sc doped AlN thin films have shifted to a lower angle than pristine AlN, indicating elastic softening/tensile stress in grown thin films. So, from our observation, we can conclude that Sc doping induces elastic softening in AlN and deposited films have a preferential crystallographic orientation that can be applied in MEMS devices. Full article

Maximizing the efficiency of solar energy in Industry 4.0 requires an automated AI-powered system. The AI-powered system relies on intelligent algorithms to identify the most efficient energy sources for the industry’s needs and adjust them accordingly while learning from every task it is given. To increase the efficiency of solar energy, the system first utilizes sensors to monitor the environment and conditions of the solar cells, optimizes the solar energy for the machines running in the industrial setting, and then stores the energy in a reserve storage system. By continually monitoring and adjusting the energy settings, the AI-powered system efficiently maximizes the efficiency of the solar energy generated for industrial use. Furthermore, the system can be customized according to your industry’s changing needs and requirements, providing the ability to reduce costs in energy usage while improving the efficiency and productivity of machines. Full article

The utilization of composite materials in structural components has been on the rise in the aerospace, automotive, and marine industries. Although these materials offer numerous benefits, they can be damaged by various sources, such as low-velocity drop-weight impacts. Debris on a runway or tools falling onto composites can cause this type of impact, which has led to extensive research on crashworthiness and impact damage assessment. This study aimed to assess the response of woven E-glass/epoxy composite laminates under low-velocity drop-weight impacts. Tests were conducted using experimental methods and numerical simulations with a drop-weight impact-testing machine and the explicit finite element software LS-DYNA. The experimental tests were performed according to ASTM standards, with varied magnitudes of initial impact energy ranging from 7.85 J to 23.54 J and a specimen thickness of 4 mm. Force–time, energy–time, and force–displacement histories, obtained through the experiments and numerical analyses along with images of the damaged specimens, were examined. The effective stress contours are also illustrated to gain a deeper comprehension of the stress distribution in the laminates. The findings demonstrated that the impact energy significantly influences the impact response of the specimens, and both the experimental and numerical analyses yielded similar results, validating the modeling approach for the impact problem in composite materials. The study provides insight into the damage mechanism of woven E-glass/epoxy composite laminates under drop-weight impacts and is expected to contribute to a better understanding of their response in low-velocity drop-weight impact events. Full article

Underwater acoustic sensor networks involve deploying sensors underwater in order to establish a wireless network framework aimed at discovering new resources, detecting targets, and monitoring pollution. However, the primary challenge in these networks lies in enhancing energy efficiency and extending the sensor’s lifespan, as manually recharging batteries deep within the sea or ocean is not feasible. To address this, we have employed a dynamic network model for target sensing. In an effort to enhance the energy, transmission, and overall lifespan of the Underwater Acoustic Sensor Network (UASN), we have devised a Heuristic Search Algorithm called the Multi-population Harmony Search Algorithm. Additionally, a Dynamic Routing Technique has been developed to dynamically determine whether a given set of sensors should operate or enter sleep mode, with the objective of effectively covering the specified targets. Full article

In this research, the gas tungsten arc welding method was used to join 201 and 316L austenitic stainless steels using various filler electrodes (316L, 309L and 309LMo), resulting in dissimilar welds, and its various properties, namely, microstructural evolution, mechanical behavior and corrosion behavior were investigated. The ferrite–austenite solidification mode was attained, and therefore, different types of ferrite (lathy ferrite and skeletal ferrite) were formed in the austenite matrix in all of the filler electrode weldments’ weld zones, however, the variation in content of ferrite was observed. A ferritoscope was used to estimate the ferrite content in the weld zone, and for E316L, E309L and E309LMo filler electrodes, the ferrite number observed were 8.78, 9.05 and 12.69 units, respectively. Hence, the 316L filler electrode exhibited the lowest ferrite content, while the 309LMo filler electrode weldment displayed a higher ferrite content ascribed to the variation in the chemical composition of filler electrodes (different chemical composition of ferrite stabilizer elements, namely, chromium, molybdenum, etc.). Further, the mechanical characteristics, including microhardness and tensile characteristics, were determined to be higher in the 309LMo filler electrode weldment, followed by the 309L and 316L filler electrode weldments, primarily due to the increased ferrite content. All the welds exhibited failure in the ductile mode. Moreover, higher sensitization was observed in the 309LMo filler electrode weldment, with the 309L and 316L filler electrode weldments following suit, which is ascribed to the higher ferrite content. This higher ferrite content resulted in higher interphase regions of ferrite/austenite, thus resulting in higher sensitization. Full article

Localization technology is essential for making wireless sensor networks(WSN)’s information processing and information collecting applications actually feasible. The beacon information is made available to the unknown nodes using the route exchange protocol. These data are more useful for determining the coordinates of neighboring nodes. Consequently, it was discovered that the algorithm for localizing nodes always has a flaw. Consequently, a brand-new metaheuristic termed Bat with simulated annealing is proposed to fix the flaw in the WSN standard node localization technique. The overall effectiveness of identifying the nodes is enhanced as a result of the large reduction in localization errors. The most popular localization estimation methods are the distance vector hop (DV-Hop) technique and approximate point-in-triangulation (APIT), which have high node localization accuracy and simple deployment in real-time environments. The primary benefits and their disadvantages, which give it a slight disadvantage in preference, are presented in this work. Both strategies are compared for their conventional performance and efficiency when combined with the Bat-SA algorithm. Full article

In modern manufacturing industries with Industry 4.0 capabilities, the automated identification and classification of machined surfaces based on their texture will play a crucial role. Texture analysis through computer vision, image processing, classification using artificial neural networks (ANN), and various machine learning techniques have been prominent research areas in recent decade. Tamura features are very popular in selecting optimum textural features from an image, especially in the medical domain. These textural features correspond to human visual perception and play a significant role in identifying and shortlisting the best features from the photographs. Despite the popularity of Tamura features in the medical domain, their usage in extracting the features from machined surface photographs is seldom reported. Hence, the present study investigates the feasibility of using Tamura features to classify machined surface images produced using turning, milling, grinding, and shaping operations in manufacturing. Photographs of the surfaces produced are obtained using smartphone cameras. Further, the photographs are preprocessed and divided into sixteen different portions. Then, Tamura features are extracted and are given as input to ANN, support vector machines (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). The result shows that each machine learning (ML) algorithm performs differently while classifying the same set of machined surface images. Amongst the ML algorithms considered in the study, RF classified the photographs of surfaces machined using different machining operations with the highest accuracy. On the other hand, SVM performed poorly. Full article

In science and technology, the application of mathematics and mathematical modelling is crucial. A more conceptual and axiomatic approach has been taken in developing the narrative from geometry in the enormous history of mathematics. Mathematics is distinct from all other topics due to its use of theorems, proofs, axioms, corollaries, examples, results, and analysis. Applications of mathematics can be found, among others, in management sciences, biosciences, chemical technology, computer sciences, information technology, and the medical industry. Differentiation and its extensions are among the most frequently used branches in mathematics. Different curves are created when a plane connects with the surface of a cone. They are called conic sections. Conic sections have uses in physics and architecture, among other fields. In this study, differential equations are used to determine the conic section’s type and locate its center. The effectiveness of conventional and innovative teaching strategies is compared using Bayesian inference. The Bayesian method is employed to update the prior assumptions regarding the relative efficacy of the two approaches. Data on student performance in four different types of classes are gathered for the analysis. Full article

Thiadiazole (THD) derivatives are famous for their exceptional chemical properties and versatile biological activities. In this work, we report computational investigations of the structure, reactivity, and binding affinity of three 1,3,4-THD derivatives (THDs) toward the SARS-CoV-2 main protease (Mpro). Hirshfeld surface (HS) analyses are carried out in conjunction with topological calculations in the context of the quantum theory of atoms in molecules (QTAIM) and reduced density gradient (RDG) to unravel the nature and magnitude of noncovalent interactions that contribute to maintaining these THDs. The three approaches consistently indicate that the titled THDs are mainly stabilized by weak intramolecular H…H, C-H…π, C-H…N, and N-H..H interactions in their monomeric forms, while their dimers also exhibit intermolecular π…π stacking and T-shaped contacts. In addition, Hirshfeld atomic charges, frontier molecular orbitals (FMOs), Fukui functions, and molecular electrostatic potential (MEP) reveal that the pyrrolic H atom (ring F) and the imidazole N atom (ring E) are the preferred binding sites for nucleophilic and electrophilic attacks, respectively. Finally, docking and molecular dynamics simulations demonstrate the remarkable binding profile of THDs toward the Mpro, which can be related to potential inhibitory activity. Full article

In the present work, the behavior of parallel plate capacitors filled with different dielectric materials and having varied gaps between the plates is developed and analyzed. The capacitor model’s capacitance and energy storage characteristics are estimated numerically and analytically. The simulation results of the model developed in the Multiphysics simulation package show that the capacitance of the capacitor decreases with an increase in the gap between the plates. Similarly, energy storage capacity increases with the material’s dielectric constant, with PVDF showing enhanced storage capacity. Further, the results of both analytical and numerical methods were in good agreement. Thus, the developed model was validated. The findings can potentially advance the design and optimization of capacitor-based systems, enabling the development of improved sensors, actuators, and efficient energy storage applications. Full article

Sign languages are important for the deaf and hard-of-hearing communities, as they provide a means of communication and expression. However, many people outside of the deaf community are not familiar with sign languages, which can lead to communication barriers and exclusion. Each country and culture have its own sign language, and some countries have multiple sign languages. Indian Sign Language (ISL) is a visual language used by the deaf and hard-of-hearing community in India. It is a complete language, with its own grammar and syntax, and is used to convey information through hand gestures, facial expressions, and body language. Over time, ISL has evolved into its own distinct language, with regional variations and dialects. Recognizing hand gestures in sign languages is a challenging task due to the high variability in hand shapes, movements, and orientations. ISL uses a combination of one-handed and two-handed gestures, which makes it fundamentally different from other common sign languages like American Sign Language (ASL). This paper aims to address the communication gap between specially abled (deaf) people who can only express themselves through the Indian sign language and those who do not understand it, thereby improving accessibility and communication for sign language users. This is achieved by using and implementing Convolutional Neural Networks on our self-made dataset. This is a necessary step, as none of the existing datasets fulfills the need for real-world images. We have achieved 0.0178 loss and 99% accuracy on our dataset. Full article

In any manufacturing setup, reaming operation is always prominent and present because of ever increasing demands for improved quality of the manufactured products. At the same time, new engineering materials make the process challenging. Further, reaming is the highly sought-after operation to achieve specified tolerance for specified applications to satisfy the rising demand for high-quality and precision-engineered products. Hence, accurate prediction of reaming torque is of utmost necessity, as it gives rise to uneven cutting forces, thereby affecting the surface finish of the reamed hole. High torque produces high-cutting forces, resulting in uneven surface finish and oversized holes. In this regard, the ability of traditional statistical tools to identify intricate correlations and patterns in reaming operation data is limited. To overcome these issues, machine learning methods such as the Artificial Neural Network (ANN) provide reliable options. The present study compares the use of ANN and Random Forest to analyze the data from reaming operations to predict the torque and compares it with those of the Random Forest method and the polynomial regression model. The model is trained and tested using a well-structured dataset that includes multiple input parameters (e.g., material, tool radius, and rotation angle) and the related reaming outputs (e.g., torque) in the suggested supervised learning method. An interconnected single layer of artificial neurons is used to create the ANN model. A comparison is made between the ANN and the Random Forest algorithm, a well-liked ensemble learning technique based on decision trees, to assess the performance of the ANN. The same dataset is used to train both ANN and Random Forest algorithms. The result showed that ANN gave better performance when compared to the other models, with testing accuracy of 94.4% and 61% for ANN and Random Forest, respectively. Full article

This research paper explains how, in the last twenty years, the study and research of product service systems (PSSs) in the automobile sphere has experienced high growth due to its unique approach in enriching customer value and experience, strengthening product competence for both customers and providers, and facilitating improved control and management over the product lifecycle. The review of the literature for the automobile sphere is classified into four categories: (i) numbers of publications per year, (ii) journal-specific publications, (iii) year-wise publications, and (iv) growth of research based on applied techniques. The integration of additional services proves instrumental in improving product design, optimizing operations, and offering innovative new services. Additionally, this paper explains how the product service system (PSS) plays an important role in enriching outcomes from customers, efficiency of the product, and lifecycle management. Its future scope lies in integration and fostering innovative ideas, improved customer service, and performance-based contracts because PSSs can drive long-term relationships, trust, and growth in the industry. The paper emphasizes the importance of user-centered design and innovative business models. The primary objective of this review paper is to provide a comprehensive analysis of product service systems (PSSs) in the automobile industry, focusing on progress, challenges, and opportunities, with the aim of responding to the evolution of consumer behavior through innovative innovations such as digitalization and sustainability integration. Full article

Machine learning is an emerging area in research. Nowadays, researchers are utilizing machine learning across all domains to find optimal solutions. Machine learning facilitates the growth of an intrusion detection system (IDS) in the context of cyber security. These systems are proposed to identify and classify cyber-attacks on the network. However, an exhaustive assessment and performance evolution of various machine learning algorithms remains unavailable. In this study, we introduce a framework designed to nurture a versatile and efficient IDS adept at identifying and categorizing unexpected and evolving cyber threats. This is achieved through the use of Recursive Feature Elimination (RFE). In RFE, the algorithm is run recursively until a selected number of features are identified to enhance efficiency and reduce computational cost. The rapid detection of these attacks can facilitate the identification of potential intruders, and the damage will be lowered. We attained remarkable accuracies, with an average rate between 98% and 99% across all the classifiers and against all four types of attacks. The random forest and decision tree models stood out, each achieving peak accuracies of 99% in both KDD-99 and NSL-KDD Datasets. Full article

Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to simplify an image’s representation for analysis and interpretation, making it easier for a computer to comprehend and extract meaningful information from visual data. Adaptive K-means clustering is a variant of the classic K-means clustering algorithm in which the number of clusters (K) is continuously adjusted during the clustering process. Unlike classic K-means, which requires you to choose the number of clusters before executing the algorithm, adaptive K-means identifies the best number of clusters based on the features of the data. The proposed model works as follows. Firstly, pre-processing is performed by acquiring all the input images. Secondly, adaptive k-means clustering is employed for segmentation. Thirdly, important features are automatically extracted from X-ray images by making use of a feature-based image registration technique. Then, the detection of bone fractures is automatically carried out. The results are compared with those of existing studies, and it is observed that this model provides better results. Full article

Non-orthogonal multiple access (NOMA) is a potential technology to support high network density, while satisfying the quality of service (QoS) demands. To maximize the attainable sum rate of individual users and minimize outage, the power allocation (PA) factors must be optimized. In the proposed work, a differential evolution (DE) algorithm is implemented to optimize the power factors assigned to users. The proposed optimization maximizes the sum rate by $\approx 5.87\mathrm{\%}$ to $\approx 12.65\mathrm{\%}$ compared to random PA. The near user requires $\approx 8.24\mathrm{d}\mathrm{B}\mathrm{m}$ to $\approx 14.13\mathrm{d}\mathrm{B}\mathrm{m}$ less transmit power, whereas the cell-edge user requires $\approx 6.56\mathrm{d}\mathrm{B}\mathrm{m}$ to $\approx 9.18\mathrm{d}\mathrm{B}\mathrm{m}$ less transmit power compared to random PA to attain an outage probability of ${10}^{-3}$. Full article

The paper proposes a new approach to AI, called Cognitive Artificial Intelligence (CAI), which is inspired by the human brain. CAI aims to develop AI systems that can think and act rationally, closely mirroring human cognition. The proposed approach, called Knowledge-Expanding System (KES), enables AI systems to acquire profound knowledge and evolve over time, surpassing the intellectual capacities of even the most gifted human minds. The realization of Transcendent Intelligence in AI systems has profound implications for humanity, necessitating thoughtful consideration of ethical aspects and the responsible development and deployment of Transcendent AI for the benefit of humanity. Full article

The 5G communication systems are rapidly becoming integral in numerous areas, such as user experience, productivity, and performance, due to increased bandwidth, lower latencies, and superior signal coverage. As such, ensuring a high-performance 5G network has become more important than ever before. To this end, different performance metrics, such as throughput, latency, and packet error rate, must be measured and monitored on a regular basis. Time series analysis has emerged as a promising tool to measure, diagnose, and predict the performance of 5G communication systems. By considering the time dimension of metrics such as latency, throughput, and packet error rate, time series analysis provides a comprehensive view of the system and can potentially uncover patterns that are otherwise hidden in isolated metrics. Moreover, this type of analysis can also be used to fine-tune system parameters to improve system performance, detect faults, and identify trends in the system. In this way, time series analysis is an ideal tool for understanding, optimizing, and maintaining 5G communication systems. Full article

Rubber-based materials play an important role in various engineering and healthcare applications. Numerous hyperelastic models have been proposed in the long line of literature to model these nonlinear elastic materials. Due to the need to balance simplicity with accuracy, purely invariant I₁-based models have been proposed, which possess certain limitations with respect to the accurate description of their mechanical behaviors. In this paper, we improve the Yeoh model, a classical and popular I₁-based hyperelastic model with high versatility. The Yeoh model is modified by adding a generalized power-law type term. The model’s capabilities are analyzed under homogeneous deformation modes, such as uniaxial tensile, biaxial tensile and pure shear loading conditions. Experimental data pertaining to rubber-based materials are applied to the proposed hyperelastic model. Also, the interesting phenomenon of thin balloon expansion is investigated by applying the model to relevant experimental data on elastomeric balloons available in the literature. A genetic algorithm-based least squares optimization routine is carried out to determine the material constants while applying the reported experimental data. The results of curve fitting to experimental data pertaining to rubber-based materials showed the capability of the model to describe such multiaxial loading responses with acceptable accuracy (R² ≥ 0.95). The model also showed the capability to describe both the limit-point instability and the strain stiffening in thin rubber balloons, demonstrating its versatility and suitability for modeling rubber-like materials under various applications. The model’s performance can be further extended in the future by coupling terms related to anisotropy, compressibility, damage, etc., according to requirements. Full article

The Cloud-Based Secured Connection Management Model (CS-CMM) for high-density fog networks is a novel approach that leverages cloud resources and the proliferation of computing power at the edge of networks. The model seeks to address the challenges encountered when managing large FoNets of numerous devices. The proposed model uses encrypted and secure connections between devices and the cloud infrastructure. This allows for comprehensive and secure management of nodes, devices, and links. The proposed model utilizes shared communication channels to allow for optimal utilization of connectivity resources, and to reduce the latency of communication. The model also utilizes secure protocols for distributed computing and secure communication, ensuring end-to-end security for all nodes. The proposed model employs self-organizing algorithms and adaptive techniques to enable rapid adaptation to changes in network density and topology. This model provides a secure, efficient, and reliable means of managing high-density fog networks. Full article

The global energy shift towards sustainability and renewable power sources is pressing. Large-scale electric vehicles (EVs) play a pivotal role in accelerating this transition. They significantly curb carbon emissions, especially when charged with renewable energy like solar or wind, resulting in near-zero carbon footprints. EVs also enhance grid flexibility, acting as mobile energy storage, stabilizing power supply. Integrating EVs into renewable systems offers demand response programs, optimizing energy use. However, extensive infrastructure development, particularly charging networks, is a significant challenge. Collaboration among governments, utility companies, and private sectors is crucial to ensure a smooth transition to electric mobility. Full article

The Blockchain-based information security framework for health care big data environments is a framework designed for the secure storage, access, and transmission of health care data in big data environments. It combines the privacy and security advantages of encryption and decentralized networks offered by Blockchain technology with the scalability of distributed systems to provide an effective secure platform for big data applications. The framework is based on the principles of confidentiality and immutability to ensure the security and privacy of health care data. The framework is designed to support a wide range of information sources and use cases including patient records, clinical research, medical imaging, genomic data, and pharmaceutical trials. It is also designed to be compatible with existing distributed computing and data querying technologies such as Hadoop and Spark, which will help organizations to improve the accessibility of health care data. The Blockchain-based framework will also provide an audit trail, allowing hospitals and other organizations to better monitor and control access to their data. This will enable organizations to ensure compliance with HIPAA and other regulations, while providing enhanced confidentiality and privacy to users and patients. Full article

This study aimed to detect trace amounts of lead using Na₃BiO₄-Bi₂O₃ mixed oxide nanostructures. Scanning electron microscopy (SEM) showed the presence of nanoplates with an average thickness of 90 nm. X-ray diffraction (XRD indicated the presence of poly-crystalline Na₃BiO₄ and Bi₂O₃ in the ratio 1:4. The chemical structure of the prepared samples was also studied through X-ray photoelectron spectroscopy. These nanostructured electrodes are highly sensitive to Pb²⁺ ions with a limit of detection of 68 ppt (0.32 nM). Full article

In the present investigation, bending stress of a profile corrected altered tooth-sums gear train, for a constant center distance, is estimated. The number of teeth altered by ±4% is considered and bending stress is estimated for 25° and 20° pressure angle gears. Since the stress concentration depends on the type of fillet radius, in this work, the bending stresses are computed in the tooth for various fillet radii generated by rack cutters such as; sharp corner tip, rounded corner tip, protuberance tip, and fully rounded tip. It is found that the bending stress is less in the tooth radius generated by a fully rounded tip cutter. Full article

Class II cavity preparation was conducted on 84 recently extracted premolars. Group I consisted of teeth restored with composite and without adhesive, (n = 12). Group II consisted of teeth restored with composite using prime and bond universal adhesive (n = 36). Group III teeth were restored with composites with Scotchbond universal adhesive (n = 36). Group II and III (n = 18 each) were further subdivided into chitosan pretreated and non-pretreated groups and named as II a, II b, III a, and III b, respectively. Microleakage was tested using the fluid filtration model. The mean microleakage was least for the prime and bond universal groups pretreated with chitosan (0.00145 and 0.00205) at both time periods. This was followed by the Scotchbond universal group pretreated with chitosan group (0.00149 and 0.00203). This was followed by the prime and bond without pretreatment with chitosan groups (0.00229 and 0.00225). Full article

The proliferation of internet services in various industries, especially the financial sector, has increased financial fraud. Fraud detection and prevention are critical to protecting both individuals and organizations from significant financial loss. However, the lack of publicly available datasets containing fraud is a major challenge. This study aims to address these issues using advanced machine learning techniques. Known for their ability to provide insight into data, decision trees are used for real-time fraud detection. In addition, deep learning techniques and artificial neural networks (ANN) are used to detect complex fraud patterns, while logistic regression is used to model the probability of fraudulent events. The accuracy of these methods, including decision trees, logistic regression, and ANN, is fully evaluated, with accuracies of 99.8%, 99.9%, and 99.94%, respectively. These findings provide valuable guidance for companies on choosing effective anti-fraud strategies and shed light on the adaptability of algorithms to real financial contexts, contributing to machine learning-based fraud detection. Full article

Micro-electrical discharge machining (μ-EDM) has come up as an effective material removal process for the manufacturing of miniaturized components in modern industries. The performance and quality of the μ-EDM process mainly depend on the combination of process parameters selected. This paper attempts to demonstrate the applicability of three well-known multi-criteria decision-making (MCDM) techniques, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), multi-attributive border approximation area comparison (MABAC), and complex proportional assessment (COPRAS) methods, separately hybridized with the grey wolf optimization (GWO) algorithm. The proposed hybrid optimization approaches are applied to find the optimal parametric setting of a μ-EDM process during machining on a stainless steel shim as the work material. Feed rate, capacitance, and voltage were selected as the machining control parameters, while material removal rate, surface roughness, and tool wear ratio were selected as the responses. The polynomial regression (PR) meta-models are observed as the inputs to these hybrid optimizers. The results obtained are further compared to the traditional weighted sum multi-objective optimization (WSMO) approach, which suggests that all the considered MCDM-PR-GWO approaches outperform traditional PR-WSMO-GWO approaches in obtaining better machining performance measures. Full article

This research work presents a study on the flow behaviour in the lid-driven cavity (LDC) flows with triangular blocks using computational fluid dynamics techniques. The LDC flow is a widely studied problem that remains a standard for viscous incompressible fluid flows, with a range of parameters, including the Reynolds number, being explored. The finite volume method was used to discretize the domain, and simulations were computed using ANSYS FLUENT 2021 R1. The fluid flow started when the top wall is moved in the +X direction, whereas the other three walls are kept stationary. A grid independence test was performed to determine the optimum grid size and to obtain a grid-independent solution. Quantitative elements of the 2D flows in lid-driven cavities were explored for Reynolds numbers ranging from 1000 to 8000, and the results were validated against the existing literature. The consequence of different values of the Reynolds number (Re) were analyzed and examined through vorticity, streamline patterns, and kinetic energy contours. The velocity profile at the centerline was enhanced, and the vortex number and size increased with an increase in Re. The behaviour of the isolines of the vortices and the kinetic energy contours was also analyzed. The kinetic energy contours show that the high velocity of the fluid particles close to the upper wall is a significant factor affecting the maximum kinetic energy values. As the Reynolds number increased, the kinetic energy gradually increased at the boundary. This suggests that the Re considerably affects the energy values. Overall, this study provides valuable insights into the flow behaviour of lid-driven cavities and the effects of obstacles on flow patterns, contributing to the existing literature and being useful for researchers and engineers working in the field of fluid dynamics. Full article

Early detection of cancer is important for successful treatment and improved survival of many cancer types. Technological advances have enabled researchers to develop more precise and reliable methods of cancer detection that go beyond traditional methods, such as biopsy and imaging. Through methods such as blood tests, MRI scans, and gene expression profiling, it is now possible to quickly and accurately diagnose many types of cancer. Early detection of cancer can lead to improved outcomes for patients and can even help save lives. Time series analysis is a data mining technique used to identify and analyze the temporal patterns in datasets. The proposed model reached 91.30% accuracy, 90.11% precision, 92.46% recall, and a 90.12% F1-score. This enhanced version of time series analysis incorporates multiple layers of data sources and uses advanced machine learning algorithms to identify patterns that could signal the presence of a tumor. Innovations in time series analysis for cancer detection can have a significant impact on modern healthcare. Time series analysis is a mathematical method used to analyze trends in data over multiple periods. It can be used to identify patterns that may indicate early signs of cancer. Full article

The end user has a cost-effective and cloud-based method of storing and retrieving their personal information through remote access using some kind of network connectivity. The user may view the data at any time and from any location they want. However, the data that are stored on the cloud may not always stay in a safe state. As the data can only be accessed by the end user via the intervention of a third party, the authenticity and integrity of the data are at risk of being compromised. It is possible for many people to utilize different Web access at the same time to access and recover their information stored on the cloud. As a consequence, a user’s sensitive data are exposed, leaked, or lost in several locations. Cryptographic methods, such as the Elliptic Curve Cryptography, have been used in the development of a great deal of different algorithms and protocols, all with the intention of preserving the confidentiality and authenticity of the data (ECC). In this research, we present a safe and efficient method for exchanging data via the cloud, while simultaneously preserving both the data’s security and their integrity. The suggested system primarily operates by combining the Elliptic Curve Cryptography (ECC) technique with the Advanced Encryption Standard (AES) method to guarantee verification and maintain the solidarity of data. The findings of the experiments demonstrate that the strategy that was presented is effective and produces superior outcomes when compared to other ways that are already in use. Full article

Autonomous vehicles (AVs) have the potential to increase safety while reducing energy use, pollution, and traffic congestion, to name a few positive effects. Industries, however, are having trouble implementing AVs. The goal of this study was to pinpoint and analyze the obstacles preventing the widespread use of AVs. To do this, a comprehensive review of the literature was conducted to identify the barriers, which were later confirmed by a panel of experts. There were five issues that needed to be addressed: a lack of infrastructure, funding limitations for manufacturing, low customer acceptance, security breach concerns, and potential employment effects. After these barriers were decided upon, the Decision-Making Trial and Evaluation Laboratory method was chosen to model them. The DEMATEL approach makes use of the expertise of groups and experts while relying on matrix tools and graph theory. It develops a visual framework that emphasizes the causal connections between various factors. A DEMATEL digraph is also presented which helps to identify which barrier is the most crucial barrier. Priority ranking was applied to the identified barriers and categorization of barriers was also performed in this study. Two categories were formed, namely, cause and effect barrier categories. Based on the results of DEMMATEL, the lack of funds for manufacturing AVs and the lack of infrastructure are the most crucial barriers to AV adoption. Industries should focus on the cause group barriers first as they run the system. By eliminating cause group barriers, the impact of effect barriers can be reduced. Implications and future directions were provided in the current study. Full article

A company’s most valuable resource is its workforce, which includes each worker. Because of the crucial role that employees play in the success of an organization, measuring employee turnover rate has become one of the most important metrics that businesses are concentrating on in the modern era. Attrition may occasionally arise owing to unavoidable circumstances such as moving to a distant place, retirement, etc. But when attrition begins creating holes in the pockets of an organization, it is necessary to monitor the situation closely. When hiring new staff, a company must use a significant quantity of its available resources. The process of rehiring employees needs to be eliminated, and a strong workforce needs to be maintained, so it is necessary to adapt the analysis of systematic machine learning models. From these models, a suitable model that gauges the risk of attrition may then be selected. This not only helps an organization save money by preserving its resources but also assists in preserving the status quo of its staff. Full article

The Internet of Things (IoT) based structural algorithm for automatic agriculture refers to the system of using powerful real-time data collected from a variety of sensors with software and analytics to autonomously manage agro-ecosystems. This algorithm can be used to monitor environments, analyze data and use this knowledge to take specific actions to help farmers and producers maximize their production and profitability. This algorithm provides an unprecedented level of precision, accuracy and control over the agricultural environment, allowing greater efficiency and optimization in farming practices. It enables monitoring, scheduling, and control of different agro-ecosystem components, such as water, soil, fertilizer, light, humidity, temperature, soil pH and crop growth. The algorithm can also point to general trends and patterns in the environment, as well as offer timely advice to farmers in response to real-time conditions. The algorithm is also capable of automatically diagnosing and responding to unexpected problems, which can help prevent costly mistakes and excessive waste of water, fertilizer, energy, etc. Full article

AISI-4140 alloy steel is a widely used a material in various fields of engineering like automotive, aerospace, oil and gas industries, construction, and structural engineering. Its application in diverse industries is mainly due to its excellent combination of strength, toughness, and wear resistance properties. Understanding the fracture toughness of AISI-4140 alloy steel is essential for ensuring the structural integrity and reliability of engineering components made from this material. The current study focuses on the experimental evaluation of fracture toughness due to its critical role in determining the material’s resistance to fracture and crack propagation. In this research, the plane-strain fracture toughness of AISI-4140 alloy steel material is investigated. Five compact tension specimens (CT) with three different thicknesses 12.5 mm, 20 mm, and 25 mm, are prepared as per ASTM standard E 399-90. The initial pre-crack in these specimens is introduced using fatigue loading. The fracture toughness test is conducted as per established procedures, and the specimens are subjected to controlled loading until a fracture occurs. Scanning electron microscopy analysis of the fractured surfaces allowed for a detailed examination of the fracture features, including crack propagation paths and microstructural characteristics. The average fracture toughness value for the AISI-4140 alloy steel is determined as 44.8 MPa $\sqrt{m}$. Full article

Water pollution, particularly through industrial effluents, is a significant environmental challenge. The present study explores the synthesis, characterization, and photocatalytic application of zinc oxide nanoparticles (ZnO NPs) for the degradation of Methylene Blue (MB) dye. ZnO NPs were synthesized via the hydrothermal method, and their structural and morphological features were examined using X-ray diffraction, Transmittance Electron Microscopy, and FESEM techniques. A systematic study was carried out to investigate the effects of catalyst mass dosage, initial dye concentration, and light intensity on photocatalytic degradation efficiency. Results show that the synthesized ZnO NPs are effective in MB dye degradation, and the process adheres to first-order kinetics. This work not only demonstrates the potential of ZnO NPs in addressing industrial dye pollution but also contributes valuable insights toward the development of cost-effective and environmentally sustainable water treatment solutions. Full article

Registration involves aligning two or more images by transforming one image into the coordinate system of another. Registration of histopathological slide images is a critical step in many image analysis applications including disease detection, classification, and prognosis. It is very useful in Computer-Aided Diagnosis (CAD) and allows automatic analysis of tissue images, enabling more accurate detection and prognosis than manual analysis. Due to the complexity and heterogeneity of histopathological images, registration is challenging and requires the careful consideration of various factors, such as tissue deformation, staining variation, and image noise. There are different types of registration and this work focuses on feature-based image registration specifically. A qualitative analysis of different feature detection and description methods combined with different outlier rejection methods is conducted. The four feature detection and description methods experimentally analyzed are Oriented FAST and rotated BRIEF (ORB), Binary Robust Invariant Scalable Key points (BRISK), KAZE, and Accelerated KAZE, and the three outlier rejection methods examined are Random Sample Consensus (RANSAC), Graph cut RANSAC (GC-RANSAC), and Marginalizing Sample Consensus (MAGSAC++). The results are visually and quantitively analyzed to select the method that gives the most accurate and robust registration of the histopathological dataset at hand. Several evaluation metrics, the number of key points detected, and a number of inliers are used as parameters for evaluating the performance of different feature detection–description methods and outlier rejection algorithm pairs. Among all the combinations of methods analyzed, BRISK paired with MAGSAC++ generates the most optimal registration results. Full article

This paper concentrates on the development of a closed-loop tracking of the sun that precisely follows the sun’s trajectory, allowing photovoltaic panels to capture the maximum amount of solar energy. Azimuthal and elevation-tracking mechanisms are included in the proposed system, and a feedback controller based on sensors monitors the brightness of the sun continuously as a reference signal. The controller generates a signal to operate the tracking motor with two axes, orienting the PV panel towards the sun, when the intensity exceeds a set threshold. In both east–west (E-W) and north–south (N-S) directions, the solar tracking system (STS) tracks the sun’s position independently. A dual-axis solar tracking system (DAST) was made of three 335-watt panels (each generating 1 kilowatt of power) in a PV system. Three 335-watt panels were used to successfully execute the dual-axis solar tracking system, with each panel contributing to the PV system’s overall power generation of 1 kilowatt. Overall, the PV system integration of a dual-axis solar tracking system with three 335-watt panels shows the potential for higher power output and energy efficiency. This configuration offers a viable means of maximizing the advantages of renewable energy sources and efficiently harnessing solar energy. Full article

In independent renewable energy systems (RESs), one of the primary concerns needing to be addressed is the maintaining of power balances between supplies and requirements that are cost-optimized in residences linked to these systems. The amount of power generated through RESs has substantially risen, with solar and wind being the two primary sources in RESs. In modern power systems, small-scale distributed networks are growing at a rapid pace and distributed generation (DG) plays an important role. Micro grids are very recent additions to electrical infrastructures. Power management is primarily required for smooth operation, maintaining consistency, and robustness, as well as controlling the actual and reactive power of independent DG. However, the batteries are expensive; moreover, during the charging and discharging process, huge amounts of power are lost, characterizing important problems which have to be averted. This paper introduces the weighted particle swarm optimization (WPSO) method for controlling energy systems and grid hybrid energy systems that comprise photovoltaic (PV), wind turbine, batteries, and diesel generators. By maximizing the power derived from RES and reducing battery power usage, energy is preserved, and the cost of energy consumption (energy of diesel) is reduced. Meteorological data from Spain were used in this study’s simulations. The method depends on the data forecast of renewable energy one day in advance and the everyday load power consumption profile. The results of the simulation show that WPSO outperforms existing algorithms in terms of energies, costs, and battery lives. Full article

Data localization, data explosion, data security, data protection, and data acceleration are important driving forces in India’s datacenter revolution, which has raised a demand for datacenter expansion in the country. In addition, the pandemic has pushed the need for technology adoption, digitization across industries, and migration to cloud-based services across the globe. The launch of 5G services, digital payments, big data analytics, smartphone usage, digital data access, IoT services, and other technologies like AI (artificial intelligence), AR (augmented reality), ML (machine learning), 5G, VR (virtual reality), and Blockchain have been a strong driving force for datacenter investments in India. However, the rapid expansion of these datacenters presents unique challenges, particularly in predicting and managing their power requirements. This abstract focuses on understanding the power prediction and demand aspects specific to hyperscale datacenters in India. The study aims to analyze historical power consumption data from existing hyperscale datacenters in India and develop predictive models to estimate future power requirements. Factors such as server density, workload patterns, cooling systems, and energy-efficient technologies will be considered in the analysis. Datacenter negatively impacts the environment because of the large consumption of power sources and 2% of the global contribution of greenhouse gas emissions. Given the increasing cost of power, datacenter players are naturally encouraged to save energy, as power is a high datacenter operational expenditure cost. Additionally, this research will explore the impact of renewable energy integration, backup power solutions, and demand–response mechanisms to optimize energy usage and reduce reliance on conventional power sources. Many datacenter providers globally have started using power from renewable energy like solar and wind energy through Power Purchase Agreements (PPA) to reduce these carbon footprints and work towards a sustainable environment. In addition, today’s datacenter industry constantly looks for ways to become more energy-efficient through real innovation to reduce its carbon footprint. Full article