Appl. Syst. Innov., Volume 8, Issue 2 (April 2025) – 31 articles

Brain activity plays a fundamental role in science and technology, particularly in the advancement of cognitive process therapies. Gaining a deeper understanding of brain function can contribute to the development of more effective therapeutic strategies aimed at enhancing cognitive performance and mental well-being. Advances in technological innovation in the health sector have allowed the creation of portable wireless electroencephalogram (EEG) devices, which make recordings in contexts outside the laboratory or clinical area. This work aims to design, manufacture, and acquire data on the Chameleon-1 helmet used by young and adult people people in different health states. The data acquisition of the EEG signals is carried out using two electrodes positioned at points $F_3$ and $F_4$, which are placed with the international 10–20 system. Tests were performed on several university participants. The recorded results show reliable, precise, and stable data in each patient with an average concentration of 91%. Excellent results were obtained from patients with different health conditions. In these records, the efficiency and robustness of the Chameleon-1 helmet were verified in adapting to any skull and with good data precision without noise alteration. Full article

The protection of personally identifiable information (PII) is being increasingly demanded by customers and governments via data protection regulations. Private and public organizations store and exchange through the Internet a large amount of data that include the personal information of users, employees, and customers. While discovering PII from a large unstructured text corpus is still challenging, a lot of research work has focused on identifying methods and tools for the detection of PII in real-time scenarios and the ability to discover data exfiltration attacks. In those research attempts, natural language processing (NLP)-based schemas are widely adopted. Our work combines NLP with deep learning to identify PII in unstructured texts. NLP is used to extract semantic information and the syntactic structure of the text. This information is then processed by a pre-trained Bidirectional Encoder Representations from Transformers (BERT) algorithm. We achieved high performance in detecting PII, reaching an accuracy of 99.558%. This represents an improvement of 7.47 percentage points over the current state-of-the-art model that we analyzed. However, the experimental results show that there is still room for improvement to obtain better accuracy in detecting PII, including working on a new, balanced, and higher-quality training dataset for pre-trained models. Our study contributions encourage researchers to enhance NLP-based PII detection models and practitioners to transform those models into privacy detection tools to be deployed in security operation centers. Full article

Bike-sharing initiatives play a crucial role in sustainable urban transportation, addressing vehicular congestion, air quality issues, and sedentary lifestyles. However, the connection between bike-sharing facilities and the advantages perceived by users remains insufficiently explored particular in post-industrial regions, such as Silesia, Poland. This study develops a multidimensional framework linking infrastructure elements—such as station density, bicycle accessibility, maintenance standards, and technological integration—to perceived benefits. Using a mixed-methods approach, a survey conducted in key Silesian cities combines quantitative analysis (descriptive statistics, factor analysis, and regression modelling) with qualitative insights from user feedback. The results indicate that the most valuable benefits are health improvements (e.g., improved physical fitness and mobility) and environmental sustainability. However, infrastructural deficiencies—disjointed bike path systems, uneven station placements, and irregular maintenance—substantially hinder system efficiency and accessibility. Inadequate bike maintenance adversely affects efficiency, safety, and sustainability, highlighting the necessity for predictive upkeep and optimised services. This research underscores innovation as a crucial factor for enhancing systems, promoting seamless integration across multiple modes, diversification of fleets (including e-bikes and cargo bikes), and the use of sophisticated digital solutions like real-time tracking, contactless payment systems, and IoT-based monitoring. Furthermore, the transformation of post-industrial areas into cycling-supportive environments presents strategic opportunities for sustainable regional revitalisation. These findings extend beyond the context of Silesia, offering actionable insights for policymakers, urban mobility planners, and Smart City stakeholders worldwide, aiming to foster inclusive, efficient, and technology-enabled bike-sharing systems. Full article

This paper presents a novel Human Activity Recognition (HAR) framework using wearable sensors, specifically targeting applications in gait rehabilitation and assistive robots. The new methodology includes the usage of an open-source dataset. This dataset includes surface electromyography (sEMG) and inertial measurement units (IMUs) signals for the lower limb of 22 healthy subjects. Several activities of daily living (ADLs) were included, such as walking, stairs up/down and ramp walking. A new framework for signal conditioning, denoising, filtering, feature extraction and activity classification is proposed. After testing several signal conditioning approaches, such as Wavelet transform (WT), Principal Component Analysis (PCA) and Empirical Mode Decomposition (EMD), an autocepstrum analysis (ACA)-based approach is chosen. Such a complex and effective approach enables the usage of supervised classifiers like K-nearest neighbor (KNN), neural networks (NN) and random forest (RF). The random forest classifier has shown the best results with an accuracy of 97.63% for EMG signals extracted from the soleus muscle. Additionally, RF has shown the best results for IMU signals with 98.52%. These results emphasize the potential of the new framework of wearable HAR systems in gait rehabilitation, paving the way for real-time implementation in lower limb assistive devices. Full article

This research utilizes machine learning (ML), and especially deep learning (DL), techniques for efficient feature extraction of intrusion attacks. We use DL to provide better learning and utilize machine learning multilayer perceptron (MLP) as an intrusion detection (IDS) and intrusion prevention (IPS) system (IDPS) method. We deploy DL and MLP together as DLMLP. DLMLP improves the high detection of all intrusion attack features on the Internet of Things (IoT) device dataset, known as the CICIoT2023 dataset. We reference the CICIoT2023 dataset from the Canadian Institute of Cybersecurity (CIC) IoT device dataset. Our proposed method, the deep learning multilayer perceptron intrusion detection and prevention system model (DLMIDPSM), provides IDPST (intrusion detection and prevention system topology) capability. We use our proposed IDPST to capture, analyze, and prevent all intrusion attacks in the dataset. Moreover, our proposed DLMIDPSM employs a combination of artificial neural networks, ANNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Consequently, this project aims to develop a robust real-time intrusion detection and prevention system model. DLMIDPSM can predict, detect, and prevent intrusion attacks in the CICIoT2023 IoT dataset, with a high accuracy of above 85% and a high precision rate of 99%. Comparing the DLMIDPSM to the other literature, deep learning models and machine learning (ML) models have used decision tree (DT) and support vector machine (SVM), achieving a detection and prevention rate of 81% accuracy with only 72% precision. Furthermore, this research project breaks new ground by incorporating combined machine learning and deep learning models with IDPS capability, known as ML and DLMIDPSMs. We train, validate, or test the ML and DLMIDPSMs on the CICIoT2023 dataset, which helps to achieve higher accuracy and precision than the other deep learning models discussed above. Thus, our proposed combined ML and DLMIDPSMs achieved higher intrusion detection and prevention based on the confusion matrix’s high-rate attack detection and prevention values. Full article

In recent times, gas is becoming one of the most significant resources utilised worldwide. The continuous increase in demand requires an increase in the production and preparation of gas for further utilisation. Conventional sources cannot satisfy this need, so it is necessary to resort to alternative methods of obtaining raw materials; one of the most promising is the development of unconventional reservoirs. The study considers a structurally complex gas-bearing reservoir; due to the peculiarities of the structure, the use of traditional approaches to gas production causes a number of difficulties and significantly reduces efficiency. A structurally inhomogeneous reservoir is considered a distributed object; a pressure field control system is synthesised. As a result, the efficiency of the system is evaluated, and its scalability is analysed. Full article

Transformers are essential in electrical networks, and their failure can lead to the shutdown of a section or the entire grid. This study proposes a combination of techniques for early fault detection, distinguishing between small load imbalances and incipient interturn short circuits. An experimental setup was developed using a three-phase transformer bank with three single-phase dry-type transformers. One transformer was modified to create controlled short circuits of two and four turns and to simulate a load imbalance by reducing the winding by four turns. The main contribution of this research is the development of a combined diagnostic approach using instantaneous space phasor (ISP) spectral analysis and infrared thermal imaging to differentiate between load imbalances and incipient interturn short circuits in transformers. This method enhances early fault detection by identifying distinctive electrical and thermal signatures associated with each condition. The results could improve transformer monitoring, reducing the risk of failure and enhancing grid reliability. Full article

In this study, we conducted experimental and numerical investigations of a Darrieus rotor with asymmetrical blades, which has two structural configurations—with and without horizontal parallel plates. Experimental tests were conducted in a wind tunnel at various air flow velocities (ranging from 3 m/s to 15 m/s), measuring rotor rotation frequency, torque, and thrust force. The computational simulation used the ANSYS 2022 R2 Fluent software package, where CFD simulations of air flow around both rotor configurations were performed. The calculations employed the Realizable k-ε turbulence model, while an unstructured mesh with local refinement in the blade–flow interaction zones was used for grid generation. The study results showed that the rotor with horizontal parallel plates exhibits higher aerodynamic efficiency at low wind velocities compared to the no-plates rotor. The experimental findings indicated that at wind speeds of 3–6 m/s, the rotor with plates demonstrates 18–22% higher torque, which facilitates the self-start process and stabilizes turbine operation. The numerical simulations confirmed that horizontal plates contribute to stabilizing the air flow by reducing the intensity of vortex structures behind the blades, thereby decreasing aerodynamic drag and minimizing energy losses. It was also found that the presence of plates creates a directed flow effect, increasing the lift force on the blades and improving the power coefficient (Cp). In the case of the rotor without plates, the CFD simulations identified significant low-pressure zones and high turbulence regions behind the blades, leading to increased aerodynamic losses and reduced efficiency. Thus, the experimental and numerical modeling results confirm that the Darrieus rotor with horizontal parallel plates is a more efficient solution for operation under low and variable wind conditions. The optimized design with plates ensures more stable flow, reduces energy losses, and increases the turbine’s power coefficient. These findings may be useful for designing small-scale wind energy systems intended for areas with low wind speeds. Full article

This study investigated the predictive performance of three regression models—Gradient Boosting (GB), Random Forest (RF), and XGBoost—in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. Utilizing a dataset encompassing air pollution metrics and socio-economic indices, the models were trained and tested to evaluate their accuracy and robustness. Performance was assessed using metrics such as coefficient of determination ($r^2$), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability ($r^2$ = 0.55, MAE = 0.17, and RMSE = 0.05). To further interpret the results, SHAP (SHapley Additive exPlanations) analysis was applied to the best-performing model to identify the most influential features driving mortality predictions. The analysis highlighted the critical roles of specific pollutants, including benzene and socio-economic factors such as life quality and instruction, in influencing mortality rates. These findings underscore the interplay between environmental and socio-economic determinants in health outcomes and provide actionable insights for policymakers aiming to reduce health disparities and mitigate risk factors. By combining advanced machine learning techniques with explainability tools, this research demonstrates the potential for data-driven approaches to inform public health strategies and promote targeted interventions in the context of complex environmental and social determinants of health. Full article

This article considers the numerical solution of the direct and inverse problems of the gas lift process in oil production, described by a system of hyperbolic equations. The inverse problem is reduced to an optimal control problem, where the control is the initial velocity of the gas. To minimize the quadratic objective functional, the gradient method is used, in which the gradient is determined using the conjugate equation method. The latter involves constructing a conjugate problem based on the Lagrange identity and the duality principle. Solving the conjugate problem allows us to obtain an analytical expression for the gradient of the functional and effectively implements the Landweber iterative method. A numerical experiment was carried out that confirmed the effectiveness of the proposed method in optimizing the parameters of the gas lift process. Full article

Pixel circuits are key components of flat panel displays, including liquid crystal displays (LCDs), organic light-emitting diode displays (OLEDs), and micro light-emitting diode displays (micro-LEDs). Depending on the active layer material of the thin film transistor (TFT), pixel circuits are categorised into amorphous silicon (a-Si) technology, low-temperature polycrystalline silicon (LTPS) technology, metal oxide (MO) technology, and low-temperature polycrystalline silicon and oxide (LTPO) technology. In this review, we outline the fundamental display principles and four major TFT technologies, covering conventional single-gated TFTs to novel two-gated TFTs. We focus on novel pixel circuits for three glass-based display technologies with additional mention of pixel circuits for silicon-based OLED and silicon-based micro-LED. Full article

The increasing number and complexity of cyber–physical systems in vehicles necessitate a rigorous approach to identifying functional safety and cybersecurity hazards during the concept phase of product development. This study establishes a systematic method for identifying safety and security requirements for E/E components in the automotive sector, utilizing the SysML language within the CAMEO environment. The method’s activities and work products are grounded in the ISO 26262:2018 and ISO/SAE 21434:2021 standards. Comprehensive requirements were defined for the method’s application environment and activities, including generic methods detailing the creation of work products. The method’s metamodel was developed using the MagicGrid framework and validated through an application example. Synergies between the two foundational standards were identified and integrated into the method. The solution generation was systematically described by detailing activities for result generation and the production of standard-compliant work products. To facilitate practical implementation, a method template in SysML was created, incorporating predefined stereotypes, relationships, and tables to streamline the application and enhance consistency. Full article

Peristaltic pumps represent a fundamental component of hemodialysis machines. They facilitate the transfer of fluids, particularly in the collection and treatment of blood. This study aims to improve pump precision and reliability by reducing steady-state error and optimizing flow consistency, measured in milliliters per minute. A detailed characterization established the relationship between revolutions per minute (RPM) and flow rate (mL/min), with redundant mass and volume measurements supporting accuracy. To model the system’s behavior, two non-linear functions and one linear function were compared, with the polynomial model proving the most accurate and revealing the pump’s inherently non-linear flow behavior. A proportional–integral (PI) controller was then applied, and optimized through step input and non-linear least squares fitting. A key aspect of this study is a comparative validation against a commercial hemodialysis machine, configured identically with the same blood circuit diameter, tubing brand, and filter, in order to ensure equivalency in conditions. Results showed a maximum flow rate error of 0.5296%, highlighting the integration of control and characterization methods that enhance system precision, dependability, and reproducibility—critical factors for ensuring the safety and effectiveness of hemodialysis treatments. Full article

This paper describes a proposed method for preserving tangible cultural heritage by reconstructing a 3D model of cultural heritage using 2D captured images. The input data represent a set of multiple 2D images captured using different views around the object. An image registration technique is applied to configure the overlapping images with the depth of images computed to construct the 3D model. The automatic 3D reconstruction system consists of three steps: (1) Image registration for managing the overlapping of 2D input images; (2) Depth computation for managing image orientation and calibration; and (3) 3D reconstruction using point cloud and stereo-dense matching. We collected and recorded 2D images of tangible cultural heritage objects, such as high-relief and round-relief sculptures, using a low-cost digital camera. The performance analysis of the proposed method, in conjunction with the generation of 3D models of tangible cultural heritage, demonstrates significantly improved accuracy in depth information. This process effectively creates point cloud locations, particularly in high-contrast backgrounds. Full article

The goal of this study was to construct a mathematical and computational model that accurately represents the complex, flexible movements and mechanics of an elephant’s trunk. Rather than serving as a biological study, the elephant trunk model was used as an application to demonstrate the effectiveness of a proposed rigid–flexible coupling framework. This model has broader applications beyond understanding the mechanics of an elephant trunk, including its potential use in designing flexible robotic systems and prosthetics, as well as contributions to the fields of biomechanics and animal locomotion. An elephant’s trunk, a highly flexible and muscular organ without bones, is best modeled using continuum mechanics to capture the dynamic behavior of its motion. Given the rigid body nature of an elephant’s head movement and the highly flexible nature of the trunk, a robust geometric framework for the rigid–flexible interface is crucial to accurately capture the complex interactions, force transmission, and dynamic behavior arising from their distinct motion characteristics and differing coordinate representations. Under the umbrella of flexible multibody dynamics, this study introduced a hybrid coordinate system, integrating the Natural Coordinates Formulation (NCF) and the Absolute Nodal Coordinates Formulation (ANCF), to establish the geometric constraints governing the interaction between the rigid body (the head) and the highly flexible body (the trunk). Moreover, the model illustrates how forces and moments are transmitted between these components in both direct and inverse scenarios. Various finite elements were evaluated to identify suitable elements for modeling the elephant’s trunk. The model’s accuracy was validated through simulations of bending, twisting, compression, and other characteristic trunk movements. The solution method is presented alongside the simulation analysis for various motion scenarios, providing a comprehensive framework for understanding and replicating the trunk’s complex dynamics. Full article

Data-driven organizations aim to control business decisions based on data. However, despite significant investments in digitalization, studies show that many organizations continue to face challenges in fully realizing the benefits of data. Existing maturity models for digital transformation, data management, and data-driven organizations lack a comprehensive, industry-agnostic, and practically validated approach to addressing industry challenges. This work introduces a refined data management maturity model developed using De Bruin’s maturity model assessment methodology. The model aims to incorporate all key elements of a data-driven organization, emphasizing the interdependencies required to evaluate maturity levels and provide targeted recommendations for addressing data-related challenges during the transition to a data-driven organization. An initial validation with 31 industry experts confirmed the model’s feasibility and practical applicability. As the next step, we plan to validate the model further by deploying the full questionnaire and deriving the maturity of each process dimension, along with its weighting, through assessments with industry partners from various sectors, including automotive, aviation, consumer goods/manufacturing, pharma, and media. Preliminary findings also underscored the importance of a deeper focus on the organization dimension, particularly in the context of Industry 5.0. Future research will refine the model through iterative development phases to address this critical area. Full article

In the era of Industry 5.0, human-centric manufacturing necessitates deep integration between workers and intelligent workshop scheduling systems. However, the inherent variability in worker efficiency due to learning and forgetting effects poses challenges to human–machine–logistics collaboration, thereby complicating multi-resource scheduling in smart workshops. To address these challenges, this study proposes a real-time task-driven human–machine–logistics collaborative framework designed to enhance multi-resource coordination in smart workshops. First, the framework incorporates a learning-forgetting model to dynamically assess worker efficiency, enabling real-time adjustments to human–machine–logistics resource states. Second, a task-driven self-organizing approach is introduced, allowing human, machine, and logistics resources to form adaptive groups based on task requirements. Third, a task slack-based matching method is developed to facilitate real-time, adaptive allocation of tasks to resource groups. Finally, the proposed method is validated through an engineering case study, demonstrating its effectiveness across different order scales. Experimental results indicate that, on average, completion time is reduced by no less than 10%, energy consumption decreases by at least 8%, and delay time is reduced by over 70%. These findings confirm the effectiveness and adaptability of the proposed method in highly dynamic, multi-resource production environments. Full article

Access to high-quality dental healthcare remains a challenge in many countries due to limited resources, lack of trained professionals, and time-consuming report generation tasks. An intelligent clinical decision support system (ICDSS), which can make informed decisions based on past data, is an innovative solution to address these shortcomings while improving continuous patient support in dental healthcare. This study proposes a viable solution with the aid of multimodal artificial intelligence (AI) and large language models (LLMs), focusing on their application for generating orthopantomography radiology reports and answering questions in the dental domain. This work also discusses efficient adaptation methods of LLMs for specific language and application domains. The proposed system primarily consists of a Blip-2-based caption generator tuned on DPT images followed by a Llama 3 8B based LLM for radiology report generation. The performance of the entire system is evaluated in two ways. The diagnostic performance of the system achieved an overall accuracy of 81.3%, with specific detection rates of 87.9% for dental caries, 89.7% for impacted teeth, 88% for bone loss, and 81.8% for periapical lesions. Subjective evaluation of AI-generated radiology reports by certified dental professionals demonstrates an overall accuracy score of 7.5 out of 10. In addition, the proposed solution includes a question-answering platform in the native Sinhala language, alongside the English language, designed to function as a chatbot for dental-related queries. We hope that this platform will eventually bridge the gap between dental services and patients, created due to a lack of human resources. Overall, our proposed solution creates new opportunities for LLMs in healthcare by introducing a robust end-to-end system for the automated generation of dental radiology reports and enhancing patient interaction and awareness. Full article

Integrating Artificial Intelligence (AI) and Extended Reality (XR) technologies into agriculture presents a transformative opportunity to modernize education and sustainable food production. Traditional agriculture training remains resource-intensive, time-consuming, and geographically restrictive, limiting scalability. This study explores an AI-driven Digital Twin (DT) system embedded within a gamified XR environment designed to enhance decision-making, resource management, and practical training in viticulture as well as woody crop management. A survey among stakeholders in the viticultural sector revealed that participants are increasingly open to adopting Virtual Reality (VR) combined with AI-enhanced technologies, signaling a readiness for digital learning transformation in the field. The survey revealed a 4.48/7 willingness to adopt XR-based training, a 4.85/7 interest in digital solutions for precision agriculture, and a moderate climate change concern of 4.16/7, indicating a strong readiness for digital learning transformation. Our findings confirm that combining AI-powered virtual educators with DT simulations provides interactive, real-time feedback, allowing users to experiment with vineyard management strategies in a risk-free setting. Unlike previous studies focusing on crop monitoring or AI-based decision support, this study examines the potential of combining Digital Twins (DTs) with AI-driven personal assistants to improve decision-making, resource management, and overall productivity in agriculture. Proof-of-concept implementations in Unity and Oculus Quest 3 demonstrate how AI-driven NPC educators can personalize training, simulate climate adaptation strategies, and enhance stakeholder engagement. The research employs a design-oriented approach, integrating feedback from industry experts and end-users to refine the educational and practical applications of DTs in agriculture. Furthermore, this study highlights proof-of-concept implementations using the Unity cross game engine platform, showcasing virtual environments where students can interact with AI-powered educators in simulated vineyard settings. Digital innovations support students and farmers in enhancing crop yields and play an important role in educating the next generation of digital farmers. Full article

This study enhances the music-listening experience and promotes Thai artists. It provides users easy access to Thai songs that match their current moods and situations, making their music journey more enjoyable. The system analyzes users’ emotions through text input, such as typing their current feelings, and processes this information using machine learning to create a playlist that resonates with their feelings. This study focuses on building a tool that caters to the preferences of Thai music listeners and encourages the consumption of a wider variety of Thai songs beyond popular trends. This study develops a tool that successfully creates personalized playlists by analyzing the listener’s emotions. Phrase and keyword recognition detect the listener’s emotions, generating playlists tailored to their feelings, thus improving their music-listening satisfaction. The classifiers employed in this study achieved the following accuracies: random forest (0.94), XGBoost (0.89), decision tree (0.85), logistic regression (0.79), and support vector machine (SVM) (0.78). Full article

The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) heavily rely on the lifetime of sensor nodes, which is inversely proportional to transmission power. Nodes with greater separation demand higher transmission power, while those closer together require less power. In practice, node placement varies significantly due to diverse terrain and contours, making power transmission configuration a critical and challenging issue in WSNs. This paper introduces an Enhanced Grey Wolf Optimization (EGWO) algorithm designed to optimize power transmission in WSN environments. Traditional Grey Wolf Optimization (GWO) employs a parameter that decreases linearly with iterations to regulate exploitation. In contrast, the proposed EGWO adopts a concave decline in the exploitation rate, allowing for more precise optimization in areas under exploration. The enhancement utilizes a cosine function that gradually decreases from 1 to 0, providing a smoother and more controlled transition. The experimental results demonstrate that EGWO outperforms other optimization algorithms. The proposed method achieves the lowest fitness value of −4.21, compared to 1.22 for standard GWO, −2.81 for PSO, and 2.86 for BESO, indicating its superiority in optimizing power transmission in WSNs. Full article

Accurate skin cancer classification is essential for early diagnosis and effective treatment planning, enabling timely interventions and improved patient outcomes. In this paper, the performance of four pre-trained models—two convolutional neural networks (ResNet50 and VGG19) and two vision transformers (ViT-b16 and ViT-b32)—is evaluated in distinguishing malignant from benign skin cancers using a publicly available dermoscopic dataset. Among these models, ResNet50 achieved the highest performance across all the evaluation metrics, with accuracy, precision, and recall of 89.09% and an F1 score of 89.08%, demonstrating its ability to effectively capture complex patterns in skin lesion images. While the other models produced competitive results, ResNet50 exhibited superior robustness and consistency. To enhance model interpretability, two eXplainable Artificial Intelligence (XAI) techniques, Local Interpretable Model-Agnostic Explanations (LIME) and integrated gradients, were employed to provide insights into the decision-making process, fostering trust in automated diagnostic systems. These findings underscore the potential of deep learning for automated skin cancer classification and highlight the importance of model transparency for clinical adoption. As AI technology continues to evolve, its integration into clinical workflows could improve diagnostic accuracy, reduce the workload of healthcare professionals, and enhance patient outcomes. Full article

We study the throughput and losses of a buffer with stochastically dependent service times. Such dependence occurs not only in packet buffers within TCP/IP networks but also in many other queuing systems. We conduct a comprehensive, time-dependent analysis, which includes deriving formulae for the count of packets processed and lost over an arbitrary period, the temporary intensity of output traffic, the temporary intensity of packet losses, buffer throughput, and loss probability. The model considered enables mimicking any packet interarrival time distribution, service time distribution, and correlation between service times. The analytical findings are accompanied by numerical computations that demonstrate the influence of various factors on buffer throughput and losses. These results are also verified through simulations. Full article

The adoption of Information Technologies in organizations is a crucial decision for growth, productivity, competitiveness, and even survival in an increasingly competitive market. It highlights the growing importance of automation solutions such as Robotic Process Automation to achieve or maintain competitiveness. Although there is research on Robotic Process Automation, most of it focuses on technology, and what it can provide, rather than on the effective contribution to the better performance of organizations, which depends on adoption and use. This work studies the propensity to the adoption and usage of Robotic Process Automation. As a basis for the conceptual model of this research, the Diffusion of Innovation and Technology Organization Environment theoretical models were used in order to evaluate the propensity for adoption and use of Robotic Process Automation from an organizational perspective. This research uses mixed methods. Initially, in the exploratory phase, interviews were carried out to complement the information collected in the literature with a view to developing a model for assessing the propensity to use Robotic Process Automation, and, subsequently, hypotheses were made based on the existing literature and combined with the exploratory phase results; in addition, data from surveys collected from 141 organizations were utilized to evaluate the suggested model, as well as the underlying hypotheses. The findings suggest that it is in the technological context that the antecedents prove to be significant in the propensity for the adoption and use of Robotic Process Automation, namely Compatibility and Relative Advantage. The implications of these findings are discussed from a practical and research perspective. Full article

Split air conditioning units are crucial for ensuring the thermal comfort of buildings. Conventional scheduling or pre-timed system activities result in high consumption and wasted energy. This study proposes an intelligent control system for automatic setpoint adjustment in an educational building based on real-time indoor and outdoor environmental and room occupancy data. Principal component analysis was used to identify energy consumption components in ramp-up and steady-state power usage scenarios. K-means clustering was then used to categorize environmental scenarios and occupancy patterns to identify operational states, predict power consumption and environmental variables, and generate fuzzy inference system rules. The application of rough set theory eliminated rule redundancy by at least 99.27% and enhanced computational speed by 96.40%. After testing using real historical data from an uncontrolled environment and occupant thermal comfort satisfaction surveys reflecting a range of ACU setpoints, the enhanced inference system achieved daily average power savings of 25.56% and a steady-state power period at 63.24% of the ACU operating time, as compared to conventional and variable setpoint operations. The proposed technique provides a basis for dynamic and data-driven decision-making, enabling sustainable energy management in smart building applications. Full article

This article presents a method for solving the inverse problem of computed tomography using an incomplete dataset. The problem focuses on reconstructing spatial objects based on the data collected from transmitters and receivers (referred to as projection vectors). The novelty of the proposed approach lies in combining two types of algorithms, namely heuristic and deterministic. Specifically, Artificial Bee Colony (ABC) and Jellyfish Search (JS) algorithms were utilized and compared as heuristic methods, while the deterministic methods were based on the Hooke–Jeeves (HJ) and Nelder–Mead (NM) approaches. By merging these techniques, a hybrid algorithm was developed, integrating the strengths of both heuristic and deterministic algorithms. The proposed hybrid algorithm proved to be approximately five to six times faster than an approach relying solely on metaheuristics while also providing more accurate results. In the worst-case test, the fitness function value for the hybrid algorithm was approximately 22% lower than that of the purely metaheuristic-based approach. Experimental tests further demonstrated that the hybrid algorithm, whether based on Hooke–Jeeves or Nelder–Mead, was stable and well suited for solving the considered problem. The article includes experimental results that confirm the effectiveness, accuracy, and efficiency of the proposed method. Full article

This study adopted a socio-technical approach to optimizing key factors for implementing the ETHICS (Effective Technical and Human Implementation of Computer-based Systems) framework in long-term care. Accurate record-keeping by nurse aides is essential, and deploying suitable information technology solutions can greatly improve operational efficiency. To achieve a comprehensive understanding of system requirements and information needs, the researchers combined the Fuzzy Delphi method, FAHP (Fuzzy Analytic Hierarchy Process), and TISM (Total Interpretive Structural Modeling), addressing both human and technical dimensions. The findings highlighted that the efficient allocation of human resources, a consultative and participative work environment, and adequate time to deliver high-quality care are crucial for enhancing record-keeping practices and overall operational efficiency. This improvement will ultimately lead to a higher care quality, cost savings, and better resource utilization. Additionally, adapting to changes in technology, regulations, economic conditions, demographics, industry standards, and organizational practices remains critical. By promoting a balanced integration of technical capabilities with human factors, this approach supports the effective design of socio-technical systems in long-term care settings. Full article

This work introduces a model for lead-acid battery health monitoring in automobiles, focusing on detecting degradation before complete failure. With the proliferation of electronic modules and increasing power demands in vehicles, along with enhanced sensor data availability, this study aims to investigate battery lifespan. Dead batteries often lead to customer dissatisfaction and additional expenses due to inadequate diagnosis. This study seeks to enhance predictive diagnostics and provide drivers with timely warnings about battery health. The proposed method employs the Detrended Cross-Correlation Analysis Coefficient for end-of-life detection by analyzing the cross-correlation of voltage signals from batteries in different states of health. The results demonstrate that batteries with a good state of health exhibit a coefficient consistently within the statistically significant cross-correlation zone across all time scales, indicating a strong correlation with reference batteries over extended time scales. In contrast, batteries with a deteriorated state of health compute a coefficient below 0.3, often falling within the non-significant cross-correlation zone, confirming a clear decline in correlation. The method effectively distinguishes batteries nearing the end of their useful life, offering a low-computational-cost alternative for real-time battery monitoring in automotive applications. Full article

Federated learning (FL) enables deep learning models to be trained locally on devices without the need for data sharing, ensuring data privacy. However, when clients have uneven or imbalanced data distributions, it leads to data heterogeneity. Data heterogeneity can appear in different ways, often due to variations in label, data distributions, feature variations, and structural inconsistencies in the images. This can significantly impact FL performance, as the global model often struggles to achieve optimal convergence. To enhance training efficiency and model performance, a common strategy in FL is to exclude clients with limited data. However, excluding such clients can raise fairness concerns, particularly for smaller populations. To understand the influence of data heterogeneity, a self-evaluating federated learning framework for heterogeneity, Fed-Hetero, was designed to assess the type of heterogeneity associated with the clients and provide recommendations to clients to enhance the global model’s accuracy. Fed-Hetero thus enables the clients with limited data to participate in FL processes by adopting appropriate strategies that enhance model accuracy. The results show that Fed-Hetero identifies the client with heterogeneity and provides personalized recommendations. Full article

As graphical user interfaces continue to become more complex; it is becoming increasingly important for user interface (UI) and user experience (UX) designers to understand how design elements influence user attention. This study investigates the impact of button shape on user perception, focusing on shape preferences, attention distribution, and perceived pleasantness. To isolate the effect of shape, buttons with five different corner radii (completely angular to completely curved) were presented without contextual influences in a pairwise comparison. The research combined eye-tracking technology with digital questionnaires to collect both objective and subjective data. The results obtained revealed a preference for buttons with moderate corner radii, while buttons with completely angular corners received the least attention and were the least favored. Notably, discrepancies emerged between subjective preferences and objective attention rankings, particularly for wireframe buttons. This research demonstrates the effectiveness of eye-tracking in UI/UX design studies and provides valuable insights into the relationship between attention and preference for abstract design elements. The findings offer fundamental theory for creating more intuitive and effective graphical user interfaces, while also highlighting the limitation and importance of examining design elements within relevant contexts in future studies. Full article