Technologies, Volume 11, Issue 5 (October 2023) – 38 articles

Green electrospinning harnesses the potential of renewable biomaterials to craft biodegradable nanofiber structures, expanding their utility across a spectrum of applications. In this comprehensive review, we summarize the production, characterization and application of electrospun cellulose, collagen, gelatin and other biopolymer nanofibers in tissue engineering, drug delivery, biosensing, environmental remediation, agriculture and synthetic biology. These applications span diverse fields, including tissue engineering, drug delivery, biosensing, environmental remediation, agriculture, and synthetic biology. In the realm of tissue engineering, nanofibers emerge as key players, adept at mimicking the intricacies of the extracellular matrix. These fibers serve as scaffolds and vascular grafts, showcasing their potential to regenerate and repair tissues. Moreover, they facilitate controlled drug and gene delivery, ensuring sustained therapeutic levels essential for optimized wound healing and cancer treatment. Biosensing platforms, another prominent arena, leverage nanofibers by immobilizing enzymes and antibodies onto their surfaces. This enables precise glucose monitoring, pathogen detection, and immunodiagnostics. In the environmental sector, these fibers prove invaluable, purifying water through efficient adsorption and filtration, while also serving as potent air filtration agents against pollutants and pathogens. Agricultural applications see the deployment of nanofibers in controlled release fertilizers and pesticides, enhancing crop management, and extending antimicrobial food packaging coatings to prolong shelf life. In the realm of synthetic biology, these fibers play a pivotal role by encapsulating cells and facilitating bacteria-mediated prodrug activation strategies. Across this multifaceted landscape, nanofibers offer tunable topographies and surface functionalities that tightly regulate cellular behavior and molecular interactions. Importantly, their biodegradable nature aligns with sustainability goals, positioning them as promising alternatives to synthetic polymer-based technologies. As research and development continue to refine and expand the capabilities of green electrospun nanofibers, their versatility promises to advance numerous applications in the realms of biomedicine and biotechnology, contributing to a more sustainable and environmentally conscious future. Full article

This paper contributes to the field of museum and visitor experience in terms of atmosphere by discussing the “museum digital atmosphere” or MDA, a notion that has been introduced and found across museums in Greece. Research on museum atmospherics has tended to focus on physical museum spaces and exhibits. By “atmosphere”, we mean the emotional state that is a result of public response adding to the overall museum experience. The MDA is therefore studied as the specific emotional state caused by the use of digital applications and technologies. The stimulus–organism–response or SOR model is used to define the MDA, so as to confirm and reinforce the concept. To that end, a qualitative methodological approach is used; we conduct semi-structured interviews and evaluate findings via content analysis. The sample consists of 17 specialists and professionals from the field, namely museologists, museographers, museum managers, and digital application developers working in Greek museums. Ultimately, this research uses the SOR model to reveal the effect of digital tools on the digital atmosphere in Greek museums. It also enriches the SOR model with additional concepts and emotions taken from real-life situations, adding new categories of variables. This research provides the initial data and knowledge regarding the concept of the MDA, along with its importance. Full article

In this article, we introduce an innovative hybrid quantum search algorithm, the Robust Non-oracle Quantum Search (RNQS), which is specifically designed to efficiently identify the minimum value within a large set of random numbers. Distinct from the Grover’s algorithm, the proposed RNQS algorithm circumvents the need for an oracle function that describes the true solution state, a feature often impractical for data science applications. Building on existing non-oracular quantum search algorithms, RNQS enhances robustness while substantially reducing running time. The superior properties of RNQS have been demonstrated through careful analysis and extensive empirical experiments. Our findings underscore the potential of the RNQS algorithm as an effective and efficient solution to combinatorial optimization problems in the realm of quantum computing. Full article

Packet classification based on rules of packet header fields is the key technology for enabling software-defined networking (SDN). Ternary content addressable memory (TCAM) is a widely used hardware for packet classification; however, commercially available TCAM chips have only limited storage. As the number of supported header fields in SDN increases, the number of supported rules in a TCAM chip is reduced. In this work, we present a novel scheme to enable packet classification using TCAM with entries that are narrower than rules by storing the most representative field of a ruleset in TCAM. Due to the fact that not all rules can be distinguished using one field, our scheme employs a TCAM-based multimatch packet classification technique to ensure correctness. We further develop approaches to reduce redundant TCAM accesses for multimatch packet classification. Although our scheme requires additional TCAM accesses, it supports packet classification upon long rules with narrow TCAM entries, and drastically reduces the required TCAM storage. Our experimental results show that our scheme requires a moderate number of additional TCAM accesses and consumes much less storage compared to the basic TCAM-based packet classification. Thus, it can provide the required scalability for long rules required by potential applications of SDN. Full article

Silk fibroin (SF) holds promise for the preparation of matrices for tissue engineering and regenerative medicine or for the development of drug delivery systems. Regenerated fibroin from Bombyx mori cocoons is water-soluble and can be processed into scaffolds of various forms, such as fibrous matrices, using the electrospinning method. In the current study, we studied the correlation between concentrations of fibroin aqueous solutions and their properties, in order to obtain electrospun mats for tissue engineering. Two methods were used to prevent solubility in fibroin-based matrices: The conversion of fibroin to the β-conformation via treatment with an ethanol solution and chemical cross-linking with genipin (Gp). The interaction of Gp with SF led to the appearance of a characteristic blue color but did not lead to the gelation of solutions. To speed up the cross-linking reaction with Gp, we propose using chitosan-containing systems and modifying fibrous materials via treatment with a solution of Gp in 80% ethanol. It was shown that the composition of fibroin with chitosan contributes to an improved water resistance, reduces defective material, and leads to a decrease in the diameter of the fibers. The electrospun fiber matrices based on regenerated fibroin modified by cross-linking with genipin in water–alcohol solutions were shown to promote cell adhesion, spreading, and growth and, therefore, could hold promise for tissue engineering. Full article

In the realm of scaffold-free cell therapies, there is a questto develop organotypic three-dimensional (3D) tissue surrogates in vitro, capitalizing on the inherent ability of cells to create tissues with an efficiency and sophistication that still remains unmatched by human-made devices. In this study, we explored the properties of scaffolds obtained by the electrospinning of a thermosensitive copolymer, poly(N-isopropylacrylamide-co-N-tert-butylacrylamide) (P(NIPAM-co-NtBA)), intended for use in such therapies. Two copolymers with molecular weights of 123 and 137 kDa and a content of N-tert-butylacrylamide of ca. 15 mol% were utilized to generate 3D scaffolds via electrospinning. We examined the morphology, solution viscosity, porosity, and thickness of the spun matrices as well as the mechanical properties and hydrophobic–hydrophilic characteristics of the scaffolds. Particular attention was paid to studying the influence of the thermosensitive polymer’s molecular weight and dispersity on the resultant scaffolds’ properties and the role of electroforming parameters on the morphology and mechanical characteristics of the scaffolds. The cytotoxicity of the copolymers and interaction of cells with the scaffolds were also studied. Our findings provide significant insight into approaches to optimizing scaffolds for specific cell cultures, thereby offering new opportunities for scaffold-free cell therapies. Full article

Environmental pollution poses a pressing global challenge, demanding innovative solutions for effective pollutant removal. Photocatalysts, particularly titanium dioxide (TiO₂), are renowned for their catalytic prowess; however, they often require ultraviolet light for activation. Researchers had turned to doping with metals and non-metals to extend their utility into the visible spectrum. While this approach shows promise, it also presents challenges such as material stability and dopant leaching. Co-doping, involving both metals and non-metals, has emerged as a viable strategy to mitigate these limitations. Inthe fieldof adsorbents, carbon-based materials doped with nitrogen are gaining attention for their improved adsorption capabilities and CO₂/N₂ selectivity. Nitrogen doping enhances surface area and fosters interactions between acidic CO₂ molecules and basic nitrogen functionalities. The optimal combination of an ultramicroporous surface area and specific nitrogen functional groups is key to achievehigh CO₂ uptake values and selectivity. The integration of photocatalysis and adsorption processes in doped materials has shown synergistic pollutant removal efficiency. Various synthesis methods, including sol–gel, co-precipitation, and hydrothermal approaches had been employed to create hybrid units of doped photocatalysts and adsorbents. While progress has been made in enhancing the performance of doped materials at the laboratory scale, challenges persist in transitioning these technologies to large-scale industrial applications. Rigorous studies are needed to investigate the impact of doping on material structure and stability, optimize process parameters, and assess performance in real-world industrial reactors. These advancements are promising foraddressing environmental pollution challenges, promoting sustainability, and paving the way for a cleaner and healthier future. This manuscript provides a comprehensive overview of recent developments in doping strategies for photocatalysts and adsorbents, offering insights into the potential of these materials to revolutionize environmental remediation technologies. Full article

The Ising model is defined by an objective function using a quadratic formula of qubit variables. The problem of an Ising model aims to determine the qubit values of the variables that minimize the objective function, and many optimization problems can be reduced to this problem. In this paper, we focus on optimization problems related to permutations, where the goal is to find the optimal permutation out of the $n!$ possible permutations of n elements. To represent these problems as Ising models, a commonly employed approach is to use a kernel that applies one-hot encoding to find any one of the $n!$ permutations as the optimal solution. However, this kernel contains a large number of quadratic terms and high absolute coefficient values. The main contribution of this paper is the introduction of a novel permutation encoding technique called the dual-matrix domain wall, which significantly reduces the number of quadratic terms and the maximum absolute coefficient values in the kernel. Surprisingly, our dual-matrix domain-wall encoding reduces the quadratic term count and maximum absolute coefficient values from $n^3-n^2$ and $2n-4$ to $6n^2-12n+4$ and 2, respectively. We also demonstrate the applicability of our encoding technique to partial permutations and Quadratic Unconstrained Binary Optimization (QUBO) models. Furthermore, we discuss a family of permutation problems that can be efficiently implemented using Ising/QUBO models with our dual-matrix domain-wall encoding. Full article

An approach is presented to enhance the isolation of a two-port Multiple Input Multiple Output (MIMO) antenna using a decoupling structure and a common defected ground structure (DGS) that physically separates the antennas from each other. The antenna operates in the 24 to 40 GHz frequency range. The innovation in the presented MIMO antenna design involves the novel integration of two arc-shaped symmetrical elements with dimensions of 35 × 35 × 1.6 mm³ placed perpendicular to each other. The benefits of employing an antenna with elements arranged perpendicularly are exemplified by the enhancement of its overall performance metrics. These elements incorporate a microstrip feed featuring a quarter-wave transformer (QWT). This concept synergizes with decoupling techniques and a defected ground structure to significantly enhance isolation in a millimeter wave (mm wave) MIMO antenna. These methods collectively achieve an impressively wide bandwidth. Efficient decoupling methodologies have been implemented, yielding a notable increase of 5 dB in isolation performance. The antenna exhibits 10 dB impedance matching, with a 15 GHz (46.87%) wide bandwidth, excellent isolation of more than 28 dB, and a desirable gain of 4.6 dB. Antennas have been analyzed to improve their performance in mm wave applications by evaluating diversity parameters such as envelope correlation coefficient (ECC) and diversity gain (DG), with achieved values of 0.0016 and 9.992 dB, respectively. The simulation is conducted using CST software. To validate the findings, experimental investigations have been conducted, affirming the accuracy of the simulations. Full article

Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around conventional manufacturing constraints, and thermal performance has plateaued as a result. While 3D printing offers the design freedom to address these limitations, there has been a notable lack in high thermal conductivity materials beyond aluminium alloys. Recently, the 3D printing of pure copper to sufficiently high densities has finally taken off, due to the emergence of commercial-grade printers which are now equipped with 1 kW high-power lasers or short-wavelength lasers. Although the capabilities of these new systems appear ideal for processing pure copper as a bulk material, the performance of advanced thermal management devices are strongly dependent on topology-optimised filigree structures, which can require a very different processing window. Hence, this article presents a broad overview of the state-of-the-art in various additive manufacturing technologies used to fabricate pure copper functional filigree geometries comprising thin walls, lattice structures, and porous foams, and identifies opportunities for future developments in the 3D printing of pure copper for advanced thermal management devices. Full article

With the rapid expansion of the Internet of Things (IoT), the necessity for lightweight communication is also increasing due to the constrained capabilities of IoT devices. This paper presents the design of a novel lightweight protocol called the Enhanced Lightweight Security Gateway Protocol (ELSGP) based on a distributed computation model of the IoT layer. This model introduces a new type of node called a sub-server to assist edge layer servers and IoT devices with computational tasks and act as a primary gateway for dependent IoT nodes. This paper then introduces six features of ELSGP with developed algorithms that include access token distribution and validation, authentication and dynamic interoperability, attribute-based access control, traffic filtering, secure tunneling, and dynamic load distribution and balancing. Considering the variability of system requirements, ELSGP also outlines how to adopt a system-defined policy framework. For fault resiliency, this paper also presents fault mitigation mechanisms, especially Trust and Priority Impact Relation for Byzantine, Cascading, and Transient faults. A simulation study was carried out to validate the protocol’s performance. Based on the findings from the performance evaluation, further analysis of the protocol and future research directions are outlined. Full article

An environmentally friendly alkaline electrolyte of silicate and borate, which contained the addition of carbohydrates (lactose, starch, and dextrin), was applied to produce micro-arc oxidation (MAO) coatings on AZ31B magnesium alloy surfaces in constant current mode. The effects of the carbohydrates on the performance of the MAO coatings were investigated using a scanning electron microscope (SEM), an X-ray diffractometer (XRD), energy-dispersive spectroscopy (EDS), the salt spray test, potentiodynamic polarization curves, and electrochemical impedance spectroscopy (EIS). The results show that the carbohydrates effectively inhibited spark discharge, so the anodized growth process, surface morphology, composition, and corrosion resistance of the MAO coatings were strongly dependent on the carbohydrate concentration. This is ascribed to the surface adsorption layer formed on the surface of the magnesium alloy. When the carbohydrate concentration was 10 g/L, smooth, compact, and thick MAO coatings with excellent corrosion resistance on the magnesium alloy were obtained. Full article

The advent of Artificial Intelligence (AI) has had a broad impact on life to solve various tasks. Building AI models and integrating them with modern technologies is a central challenge for researchers. These technologies include wearables and implants in living beings, and their use is known as human augmentation, using technology to enhance human abilities. Combining human augmentation with artificial intelligence (AI), especially after the recent successes of the latter, is the most significant advancement in their applicability. In the first section, we briefly introduce these modern applications in health care and examples of their use cases. Then, we present a computationally efficient AI-driven method to diagnose heart failure events by leveraging actual heart failure data. The classifier model is designed without conventional models such as gradient descent. Instead, a heuristic is used to discover the optimal parameters of a linear model. An analysis of the proposed model shows that it achieves an accuracy of 84% and an $F_1$ score of 0.72 with only one feature. With five features for diagnosis, the accuracy achieved is 83%, and the $F_1$ score is 0.74. Moreover, the model is flexible, allowing experts to determine which variables are more important than others when implementing diagnostic systems. Full article

In this work, the effects of weight concentration on the properties of poly(lactide-co-glycolide) polymeric scaffolds prepared by electrospinning are investigated, using four different weight concentrations of poly(lactide-co-glycolide) for the electrospinning solutions (2, 3, 4, 5 wt.%). With increasing concentration of poly(lactide-co-glycolide) in the electrospinning solutions, their viscosity increases significantly. The average fiber diameter of the scaffolds also increases with increasing concentration. Moreover, the tensile strength and maximum elongation at break of the scaffold increase with increasing electrospinning concentration. The prepared scaffolds have hydrophobic properties and their wetting angle does not change with the concentration of the electrospinning solution. All poly(lactide-co-glycolide) scaffolds are non-toxic toward fibroblasts of the cell line 3T3-L1, with the highest numbers of cells observed on the surface of scaffolds prepared from the 2-, 3- and 4-wt.% electrospinning solutions. The results of the analysis of mechanical and biological properties indicate that the poly(lactide-co-glycolide) scaffolds prepared from the 4 wt.% electrospinning solution have optimal properties for future applications in skin tissue engineering. This is due to the fact that the poly(lactide-co-glycolide) scaffolds prepared from the 2 wt.% and 3 wt.% electrospinning solution exhibit low mechanical properties, and 5 wt.% have the lowest porosity values, which might be the cause of their lowest biological properties. Full article

Nanotechnologies are being increasingly widely used in advanced energy fields. Triboelectric nanogenerators (TENGs) represent a class of new-type flexible energy-harvesting devices with promising application prospects in future human societies. As one of the most important parts of TENG devices, triboelectric materials play key roles in the achievement of high-efficiency power generation. Conventional polymer tribo-negative materials, such as polytetrafluoroethylene (PTFE), polyvinylidene difluoride (PVDF), and the standard polyimide (PI) film with the Kapton^® trademark based on pyromellitic anhydride (PMDA) and 4,4′-oxydianiline (ODA), usually suffer from low output performance. In addition, the relationship between molecular structure and triboelectric properties remains a challenge in the search for novel triboelectric materials. In the current work, by incorporating functional groups of trifluoromethyl (–CF₃) with strong electron withdrawal into the backbone, a series of fluorine-containing polyimide (FPI) negative friction layers have been designed and prepared. The derived FPI-1 (6FDA-6FODA), FPI-2 (6FDA-TFMB), and FPI-3 (6FDA-TFMDA) resins possessed good solubility in polar aprotic solvents, such as the N,N-dimethylacetamide (DMAc) and N-methyl-2-pyrrolidone (NMP). The PI films obtained via the solution-casting procedure showed glass transition temperatures (T_g) higher than 280 °C with differential scanning calorimetry (DSC) analyses. The TENG prototypes were successfully fabricated using the developed PI films as the tribo-negative layers. The electron-withdrawing trifluoromethyl (–CF₃) units in the molecular backbones of the PI layers provided the devices with an apparently enhanced output performance. The FPI-3 (6FDA-TFMDA) layer-based TENG devices showcased an especially impressive open-circuit voltage and short-circuit current, measuring 277.8 V and 9.54 μA, respectively. These values were 4~5 times greater when compared to the TENGs manufactured using the readily accessible Kapton^® film. Full article

The persistent threat posed by infectious pathogens remains a formidable challenge for humanity. Rapidly spreading infectious diseases caused by airborne microorganisms have far-reaching global consequences, imposing substantial costs on society. While various detection technologies have emerged, including biochemical, immunological, and molecular approaches, these methods still exhibit significant limitations such as time-intensive procedures, instability, and the need for specialized operators. This study presents an innovative solution that harnesses the potential of surface acoustic wave (SAW) sensors for the detection of airborne microorganisms. The research involves the establishment of a sensor model within the framework of COMSOL Multiphysics, utilizing a predefined piezoelectric multi-physics interface and employing a 2D modeling approach. Chitosan, selected as the sensing film for the model, interfaces with lithium niobate (LiNbO3), the chosen piezoelectric material responsible for detecting airborne pathogens. The analysis of microbe presence centers on solid displacement and electric potential frequencies, operating within the 850–900 MHz range. Notably, the first and second resonant frequencies are identified at 856 and 859 MHz, respectively. To enhance understanding, this study proposes a novel mathematical model grounded in Stokes’ Law and mass balance equations. This model serves to analyze microbe concentration, offering a fresh perspective on quantifying the presence of airborne pathogens. Through these endeavors, this research contributes to advancing the field of airborne microorganism detection, offering a promising avenue for addressing the challenges posed by infectious diseases. Full article

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification. Full article

Malware propagation is a growing concern due to its potential impact on the security and integrity of connected devices in Internet of Things (IoT) network environments. This study investigates parameter estimation for Susceptible-Infectious-Recovered (SIR) and Susceptible–Infectious–Recovered–Susceptible (SIRS) models modeling malware propagation in an IoT network. Synthetic data of malware propagation in the IoT network is generated and a comprehensive comparison is made between two approaches: algorithms based on Monte Carlo methods and Physics-Informed Neural Networks (PINNs). The results show that, based on the infection curve measured in the IoT network, both methods are able to provide accurate estimates of the parameters of the malware propagation model. Furthermore, the results show that the choice of the appropriate method depends on the dynamics of the spreading malware and computational constraints. This work highlights the importance of considering both classical and AI-based approaches and provides a basis for future research on parameter estimation in epidemiological models applied to malware propagation in IoT networks. Full article

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems. Full article

The intricate neuroinflammatory diseases multiple sclerosis (MS) and neuromyelitis optica (NMO) often present similar clinical symptoms, creating challenges in their precise detection via magnetic resonance imaging (MRI). This challenge is further compounded when detecting the active and inactive states of MS. To address this diagnostic problem, we introduce an innovative framework that incorporates state-of-the-art machine learning algorithms applied to features culled from MRI scans by pre-trained deep learning models, VGG-NET and InceptionV3. To develop and test this methodology, we utilized a robust dataset obtained from the King Abdullah University Hospital in Jordan, encompassing cases diagnosed with both MS and NMO. We benchmarked thirteen distinct machine learning algorithms and discovered that support vector machine (SVM) and K-nearest neighbor (KNN) algorithms performed superiorly in our context. Our results demonstrated KNN’s exceptional performance in differentiating between MS and NMO, with precision, recall, F1-score, and accuracy values of 0.98, 0.99, 0.99, and 0.99, respectively, using leveraging features extracted from VGG16. In contrast, SVM excelled in classifying active versus inactive states of MS, achieving precision, recall, F1-score, and accuracy values of 0.99, 0.97, 0.98, and 0.98, respectively, using leveraging features extracted from VGG16 and VGG19. Our advanced methodology outshines previous studies, providing clinicians with a highly accurate, efficient tool for diagnosing these diseases. The immediate implication of our research is the potential to streamline treatment processes, thereby delivering timely, appropriate care to patients suffering from these complex diseases. Full article

The academic and research communities are showing significant interest in the modern and highly promising technology of wireless mesh networks (WMNs) due to their low-cost deployment, self-configuration, self-organization, robustness, scalability, and reliable service coverage. Multicasting is a broadcast technique in which the communication is started by an individual user and is shared by one or multiple groups of destinations concurrently as one-to-many allotments. The multicasting protocols are focused on building accurate paths with proper channel optimization techniques. The forwarder nodes of the multicast protocol may behave with certain malicious characteristics, such as dropping packets, and delayed transmissions that cause heavy packet loss in the network. This leads to a reduced packet delivery ratio and throughput of the network. Hence, the forwarder node validation is critical for building a secure network. This research paper presents a secure forwarder selection between a sender and the batch of receivers by utilizing the node’s communication behavior. The parameters of the malicious nodes are analyzed using orthogonal projection and statistical methods to distinguish malicious node behaviors from normal node behaviors based on node actions. The protocol then validates the malicious behaviors and subsequently eliminates them from the forwarder selection process using secure path finding strategies, which lead to dynamic and scalable multicast mesh networks for communication. Full article

In the field of eHealth, several works have proposed telemonitoring systems focused on patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) treatment. Nevertheless, no secondary study presents a comparative analysis of these works regarding the technology readiness level (TRL) framework. The TRL scale goes from 1 to 9, with 1 being the lowest level of readiness and 9 being the highest. This paper analyzes works that propose telemonitoring systems focused on patients with CKD undergoing PD treatment to determine their TRL. We also analyzed the requirements and parameters that the systems of the selected works provide to the users to perform telemonitoring of the patient’s treatment undergoing PD. Fourteen works were relevant to the present study. Of these works, eight were classified within TRL 9, two were categorized within TRL 7, three were identified within TRL 6, and one within TRL 4. The works reported with the highest TRL partially cover the requirements for appropriate telemonitoring of patients based on the specialized literature; in addition, those works are focused on the treatment of patients in the automated peritoneal dialysis (APD) modality, which limits the care of patients undergoing the continuous ambulatory peritoneal dialysis (CAPD) modality. Full article

Lung disease is a respiratory disease that poses a high risk to people worldwide and includes pneumonia and COVID-19. As such, quick and precise identification of lung disease is vital in medical treatment. Early detection and diagnosis can significantly reduce the life-threatening nature of lung diseases and improve the quality of life of human beings. Chest X-ray and computed tomography (CT) scan images are currently the best techniques to detect and diagnose lung infection. The increase in the chest X-ray or CT scan images at the time of training addresses the overfitting dilemma, and multi-class classification of lung diseases will deal with meaningful information and overfitting. Overfitting deteriorates the performance of the model and gives inaccurate results. This study reduces the overfitting issue and computational complexity by proposing a new enhanced kernel convolution function. Alongside an enhanced kernel convolution function, this study used convolution neural network (CNN) models to determine pneumonia and COVID-19. Each CNN model was applied to the collected dataset to extract the features and later applied these features as input to the classification models. This study shows that extracting deep features from the common layers of the CNN models increased the performance of the classification procedure. The multi-class classification improves the diagnostic performance, and the evaluation metrics improved significantly with the improved support vector machine (SVM). The best results were obtained using the improved SVM classifier fed with the features provided by CNN, and the success rate of the improved SVM was 99.8%. Full article

Audio guidance is a common means of helping visually impaired individuals to navigate, thereby increasing their independence. However, the differences between different guidance modalities for locating objects in 3D space have yet to be investigated. The aim of this study was to compare the time, the hand’s path length, and the satisfaction levels of visually impaired individuals using three automatic cueing modalities: pitch sonification, verbal, and vibration. We recruited 30 visually impaired individuals (11 women, average age 39.6 ± 15.0), who were asked to locate a small cube, guided by one of three cueing modalities: sonification (a continuous beep that increases in frequency as the hand approaches the cube), verbal prompting (“right”, “forward”, etc.), and vibration (via five motors, attached to different locations on the hand). The three cueing modalities were automatically activated by computerized motion capture systems. The subjects separately answered satisfaction questions for each cueing modality. The main finding was that the time to find the cube was longer using the sonification cueing (p = 0.016). There were no significant differences in the hand path length or the subjects’ satisfaction. It can be concluded that verbal guidance may be the most effective for guiding people with visual impairment to locate an object in a 3D space. Full article

This article introduces a novel asynchronous full adder that operates in an input–output mode (IOM), displaying both monotonicity and an early output characteristic. In a monotonic asynchronous circuit, the intermediate and primary outputs exhibit similar signal transitions as the primary inputs during data and spacer application. The proposed asynchronous full adder ensures monotonicity for processing data and spacer, utilizing dual-rail encoding for inputs and outputs, and corresponds to return-to-zero (RtZ) and return-to-one (RtO) handshaking. The early output feature of the proposed full adder allows the production of sum and carry outputs based on the adder inputs regardless of the carry input when the spacer is supplied. When utilized in a ripple carry adder (RCA) architecture, the proposed full adder achieves significant reductions in design metrics, such as cycle time, area, and power, compared to existing IOM asynchronous full adders. For a 32-bit RCA implementation using a 28 nm CMOS technology, the proposed full adder outperforms an existing state-of-the-art high-speed asynchronous full adder by reducing the cycle time by 10.4% and the area by 15.8% for RtZ handshaking and reduces the cycle time by 9.8% and the area by 15.8% for RtO handshaking without incurring any power penalty. Further, in terms of the power-cycle time product, which serves as a representative measure of energy, the proposed full adder yields an 11.8% reduction for RtZ handshaking and an 11.2% reduction for RtO handshaking. Full article

The use of robotics in rehabilitating motor functions has increased exponentially in recent decades. One of the most used robotic tools is undoubtedly the Armeo^® Power, which has proved to have excellent qualities as a rehabilitation tool. However, none of these studies has investigated the ability of Armeo^® Power to assess the upper limb by correlating the data resulting from the software with patient-reported outcome measures (PROMs). The present study aims to evaluate the variability between the standardized PROMs, Stroke Upper Limb Capacity Scale (SULCS), Fugl–Meyer upper limb assessment (FMA-UL), and the Armeo^® Power measurements. To evaluate the correlation between SULCS and FMA-UL and the strength and joint assessments obtained with the Armeo^® Power, Pearson’s correlation coefficient was used. A total of 102 stroke survivors were included in this cross-sectional study, and all participants finished the study. The results showed many statistically significant correlations between PROM items and Armeo^® Power data. In conclusion, from this study, it can be stated that Armeo^® Power, based on the analysis of the data collected, can be an objective evaluation tool, which can be combined with the operator-employee traditional evaluation techniques, especially when compared to a patient-reported outcome measures (PROMs). Full article

The rise of online social networks has revolutionized the way businesses and consumers interact, creating new opportunities for customer word-of-mouth (WoM) and brand advocacy. Understanding and managing customer advocacy in the online realm has become crucial for businesses aiming to cultivate a positive brand image and engage with their target audience effectively. In this study, we propose a framework that leverages the pre-trained XLNet- (bi-directional long-short term memory) BiLSTM- conditional random field (CRF) architecture to construct a Knowledge Graph (KG) for social customer advocacy in online customer engagement (CE). The XLNet-BiLSTM-CRF model combines the strengths of XLNet, a powerful language representation model, with BiLSTM-CRF, a sequence labeling model commonly used in natural language processing tasks. This architecture effectively captures contextual information and sequential dependencies in CE data. The XLNet-BiLSTM-CRF model is evaluated against several baseline architectures, including variations of BERT integrated with other models, to compare their performance in identifying brand advocates and capturing CE dynamics. Additionally, an ablation study is conducted to analyze the contributions of different components in the model. The evaluation metrics, including accuracy, precision, recall, and F1 score, demonstrate that the XLNet-BiLSTM-CRF model outperforms the baseline architectures, indicating its superior ability to accurately identify brand advocates and label customer advocacy entities. The findings highlight the significance of leveraging pre-trained contextual embeddings, sequential modeling, and sequence labeling techniques in constructing effective models for constructing a KG for customer advocacy in online engagement. The proposed framework contributes to the understanding and management of customer advocacy by facilitating meaningful customer-brand interactions and fostering brand loyalty. Full article

The thermal effect and heat dissipation have a significant impact on three-dimensional stacked chips, and the positional layout of the chip’s three-dimensional layout directly affects the internal temperature field. One effective way is to plan the overall layout of three-dimensional integrated circuits by considering the thermal effect and layout utilization. In this paper, an ant colony algorithm is used to search for the most planned paths and achieve the overall layout optimization by considering the effects of power, temperature, and location on the thermal layout and using feedback optimization of pheromone concentration. The simulation results show that the optimization of the thermal layout of 3D integrated circuits can be well realized by adjusting the algorithm parameters. The maximum temperature, temperature gradient, and layout scheme verify reliability and practicability. It improves the utilization rate of chips, optimizes the layout, realizes energy conservation, and reduces resource waste. Full article

Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks. Full article