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In the field of paper conservation and archival research, identifying the raw materials of paper is important to elucidate its history and culture. As the most basic element of the raw materials for paper, fibers have not been sufficiently investigated. In this study, we propose a nondestructive method for estimating paper fibers from macro photographs ( $4000\times3000$ pixels) captured using a digital camera. The proposed method consists of background patch (500 pixels per side) detection (BPD), wherein background regions with no text are identified; patch fiber classification (PFC), wherein background patches obtained after BPD are analyzed for fiber classification; and paper fiber estimation (PFE), wherein macro images obtained after PFC are analyzed for fiber estimation. BPD and PFC are employed to perform patch-based classification on segmented macro images, which are reconstructed during PFE to obtain the final fiber estimation results. We performed experiments using 1337 macro images (64176 patches) to evaluate the fiber estimation accuracy for kozo, mitsumata, and gampi via three-fold cross-validation. The average fiber classification accuracy for patch images was observed to be 79.1%; accordingly, the average fiber estimation accuracy for macro images was 85.8%. Experimental results indicated that PFE can be realized in a nondestructive manner on macro images of paper captured using a digital camera.

Computer scientists often publish their source code accompanying their publications, prominently using code repositories across various domains. Despite the concurrent existence of scholarly articles and their associated official code repositories, explicit references linking the two are often missing. Traditionally, identifying whether scholarly content and code repositories pertain to the same research project requires manual inspection, a time-consuming task. This paper proposes a deep learning-based algorithm for automatically matching scholarly articles with their corresponding official code repositories. Our findings indicate that the most common linking information includes the paper title and BibTeX entries, typically found in the repository’s readme document. In this study, we employed SPECTER for vector embedding of paper and repository metadata. Utilizing these embedding representations with the Light Gradient Boosting Machine (LGBM), our method achieved an F1 score of 0.94. Moreover, combining our best model with a rule-based approach improved performance by 5.31%. This study successfully delineates a connection between academic papers and associated official code repositories, minimizing reliance on explicit bibliographic information in repositories.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

Grid-forming (GFM) converter control technology and synchronous condensers (SCs) have both been reported to improve the overall stability of a power system. GFM technology is being considered for offshore wind power plants (OF WPPs) due to their stabilising effect, and SCs are being integrated into OF WPPs as they increase the short circuit level of the system. This paper investigates the effect of SCs on the small-signal stability of an OF WPP with different converter control strategies, namely grid-following (GFL) and grid-forming (GFM). Primarily, the effect of SCs can be two-fold: (1) overall stability enhancement of the WPP by providing reactive power support, and (2) contribution to the effective short circuit ratio (SCR) of the WPP by fault current support. Therefore, this paper focuses on studies concerning these effects on an aggregated model of a WPP connected to the grid. This paper also tries to compare the stabilising effects of GFM converters and SCs by comparing the small-signal stability of the test system with GFL control and SC and with GFM control. To that end, a state-space model of the test system is developed for small-signal stability assessment and the SC’s effect on its stability. In addition, a mathematical explanation of SCR enhancement with an SC is provided and is verified against the fault MVA calculations done on time-domain simulation models.

To enhance the effective use of teaching video information in digital campuses, provide timely and accurate assessment of students’ classroom learning status, and offer technical support for teaching management, this paper introduces an improved SSD (Single Shot MultiBox Detector) object detection algorithm for intelligent analysis of students’ real classroom behavior. This article examines classroom teaching videos from four months of a semester at a certain university, collecting over 10,446 1080p video frames to create a dataset for annotation and training. To address the poor detection performance of the SSD model when identifying four behavior states—listening, sleeping, playing, and writing—of students in combined classes due to object occlusion and complex classroom environments, this paper introduces ResNet residual modules and a dual attention mechanism into the VGG-16 (Visual Geometry Group-16) network. This enhancement improves the model’s ability to detect student states in video frames under conditions of object occlusion. An FPN (Feature Pyramid Network) fusion model is constructed to address the low recognition accuracy of the SSD model for small objects in the back rows, thereby improving image recognition accuracy. The experimental results show that the improved SSD model has improved feature extraction ability and small object recognition accuracy compared to the original SSD model, with mAP increasing from 51.62% to 63.75%. The research results of this project can provide real-time information reference for university teaching management, help teachers understand students’ learning status promptly, and adjust teaching methods on time to improve teaching quality and classroom effectiveness.

In this paper, a novel design of X-shaped slot antenna, whose input impedance is approximately to dipole slot antenna, is proposed for use in the private 5G of Sub-6 GHz band. The proposed X-shaped slot antenna is truncated from the infinite shape of self-complementary antenna. The proposed X-shaped slot antenna has the input impedance around $50~\Omega $ to $60~\Omega $ and has wideband and high gain characteristics, which is the crucial contribution of the design for planar array configuration. The X-shaped slot antenna is connected to a $50~\Omega $ input port through microstrip line which is electromagnetically coupled to the center portion of the X-shaped slot antenna and terminated by an opened circuit line. In this paper, antenna characteristics are simulated by using Finite Element Method (FEM) in commercial simulation software EMPro (Keysight). From simulation results, the X-shaped slot antenna has magnitude of reflection coefficient |S $_{11}\vert $ less than −9.7 dB (VSWR

WiFi fingerprinting is one of the most widely used techniques for indoor positioning systems. However, existing fingerprinting datasets came in different shapes and forms with varying levels of information without any standardised format. They were also dispersed across multiple platforms, making it challenging for new researchers to identify and access a suitable dataset to evaluate their own positioning systems. To address this challenge, this paper provides a comprehensive review of more than 50 publicly available WiFi fingerprinting datasets. We examine the most critical elements for fingerprinting, including the size and location of the testbed, the WiFi signal input, the number of locations, the temporal and spatial intervals of data collection, the positioning performance, and more. Surprisingly, it was observed that a large number of reference and access points, the use of 3D coordinates, denser sampling grid, and higher data collection frequencies do not always guarantee improved performance as often reported in the literature. The paper also outlines current challenges, and proposes guidelines for creating new WiFi fingerprint datasets.

This paper proposes a comprehensive review of orthodontic archwire bending robots for fixed appliances, focusing on their key technologies. Orthodontic archwire bending robots can significantly reduce the workload of orthodontists and enhance the efficiency of orthodontic treatment with compared with the orthodontist’s manual bending of orthodontic archwires. Due to the high degree of customization required for orthodontic archwires and the challenge of springback during bending, research on robotic archwire bending has concentrated on developing advanced robotic systems, optimizing archwire bending plans, and implementing springback compensation methods. This paper comprehensively reviews the advancements in design and control of orthodontic archwire bending robots for fixed appliances. It delves into orthodontic archwire bending planning and springback compensation methods, providing a categorized introduction and detailed summaries. Finally, it evaluates and discusses the challenges and future directions of orthodontic archwire bending robots and their core technologies.

This paper introduces a new method for analyzing electrical signals useful to track harmonics amplitude through the use of a simple linear Kalman Filter with high accuracy and computational efficiency. The key idea in this paper is based on the vector extraction properties of the orthogonal base decomposition of the electrical signal which usually correlates with noise and different signal modes depending from its physical origin. This vector extraction preserves a proportional time-frequency energy relation for each harmonic’s magnitude, from the which we can propose a discriminating criterion related to this method accuracy, validated here through The Parseval’s Theorem.

While Industry 4.0 improves human productivity, it also raises sustainability and social challenges. Industry 5.0, as a supplement and logical continuation of the Industry 4.0 paradigm, focuses on the development of a human-centric, sustainable, and resilient manufacturing system. This paper reviews the existing literature. First, it discusses the definition and implementation framework for Industry 5.0. Then, it expounds the application status of Industry 5.0 in the field of intelligent manufacturing from four perspectives: digital manufacturing and intelligence, human-centric intelligent manufacturing and production process management, decentralized and resilient production, and sustainable production. It summarizes the role of Industry 5.0 technology in various intelligent manufacturing scenarios, as well as the challenges it faces, and concludes by analyzing Industry 5.0’s potential development direction and future technologies. This paper believes that the application of Industry 5.0 technology will effectively improve the production capacity of intelligent manufacturing systems and promote the development of intelligent manufacturing systems in a safe, efficient, sustainable and resilient direction; the development of Industry 5.0 will focus on giving full play to human creativity, avoiding repetitive labor through human-robot collaboration, and thereby realizing human value.

Millimeter-wave (mmWave) frequencies ranging from 30 to 300 GHz offer vast bandwidth and high data transmission rates, making them ideal for high-throughput applications and the expanding Internet of Things (IoT). However, mmWave implementation faces challenges such as narrow beams, susceptibility to blockage, and rapid channel fluctuations due to user mobility. To address these issues, non-orthogonal multiple access (NOMA) is employed, enhancing spectral efficiency by allowing multiple users to share the same frequency resources at different power levels. This paper focuses on Power-Domain NOMA (PD-NOMA), a variant of NOMA that allocates different power levels to users sharing the same frequency resources. Although other forms of NOMA, such as Code-Domain NOMA (CD-NOMA) and Cooperative NOMA, exist, our discussion will primarily focus on PD-NOMA due to its practical application in mmWave-NOMA networks. The integration of mmWave and NOMA presents both significant opportunities and complex challenges, particularly in resource management. This combination aims to leverage mmWave’s high bandwidth and NOMA’s efficient resources utilization to overcome physical layer limitations and enhance network performance. Traditional methods struggle with optimizing those resources like power levels, bandwidth, beam directions, and user pairing. Deep learning (DL) presents a promising solution by learning optimal resource allocation from data. This paper reviews current and future DL applications in five key areas: power allocation, energy efficiency, user association, bandwidth allocation, and subcarrier allocation in mmWave-NOMA networks. It also highlights available open-source datasets, code, and DL frameworks supporting these developments and discusses key research directions. The structure of the paper is organized as follows: first, background knowledge on mmWave and NOMA systems is presented, followed by an in-depth review of DL applications for resource management. Finally, we conclude with challenges and future research directions in this evolving field.

In order to improve the signal-to-noise ratio (SNR) of spaceborne synthetic aperture radar for wide swath imaging, the technique SCan-On-REceive (SCORE) is always adopted to form a digital beam to track pulse echoes so as to improve the receiving gain. The prerequisite for the effective SCORE is that the information of topography profile is well known in advance, otherwise the beam center of the digital receiving beam will mismatch with the real Direction-Of-Arrival (DOA) of echoes, leading to the loss of receiving gain and that of consequent SNR. Furthermore, the Range Ambiguity-to-Signal Ratio (RASR) will also deteriorate due to this mismatch. To deal with this problem, this paper brings forward a novel approach based on Generalized Pencil-Of-Function (GPOF) that can efficiently and precisely estimate the topography profile from the raw data of received echoes. Compared with estimation approaches using spatial spectrum, our scheme does not have to implement the range compression at first, and thus the requirements on the onboard computation resources can be eased to a large extent. This paper, for the first time, presents the complete estimation procedures to evaluate the topography profile of scene, which consists of topography modelling, echo processing and the final topography reconstruction. By comparing with the real topography profile, the effectiveness of reconstruction result has been validated.

A vast number of Muslims come to Makkah, Saudi Arabia every year to perform Hajj and Umrah, and the kingdom plans to reach 30 million annually by Vision 2030 through the incorporation of artificial intelligence (AI) technologies. Enhancing Hajj and Umrah rituals presents several challenges, including incidents such as suicides in the holy mosque, stampedes in the holy places, terrorist threats, sunstrokes, pandemics like COVID-19 and the spread of contagious diseases, particularly respiratory tract infections. Some of these challenges have prompted strict instructions, including wearing face masks and adhering to regular cleaning and sanitization. A number of studies have surveyed the application of AI in Hajj and Umrah research, yet none have been comprehensive. In this paper, I categorize research papers that utilize AI technologies to enhance Hajj and Umrah rituals into nine categories. Also, I identify eleven AI technologies employed by researchers to improve these rituals and organize the research papers based on these technologies for easier access. Furthermore, I discuss open challenges and propose new AI technologies that could address these challenges. This paper demonstrates that the use of AI in Hajj and Umrah has significantly enhanced the pilgrims’ experience, and simplified the process for them to perform various rituals. Despite challenges occurring from the ethical and societal implications of using AI technologies, particularly in terms of impacting pilgrims’ spiritual experiences and cultural sensitivities, AI has greatly improved crowd management and transportation. Additionally, the services provided during Hajj rituals, including waste management, accommodation (especially during Mina days), supplies such as water, food, and medications, and reducing emergency response time, have benefited from AI integration. Furthermore, the use of AI technologies has played a major role in improving the infrastructure of the holy cities, including energy and electricity supplies.

The accurate estimation of Li-ion battery (LIB) states such as State of Charge (SOC), State of Health (SOH), and State of Power (SOP) plays a pivotal role in the efficient operation of Electric Vehicles (EVs). These parameters can impact the battery’s health, driving range, and overall vehicle performance. Transformer-based artificial neural networks have shown impressive results in natural language processing (NLP) and estimation problems of many other domains. This paper presents an intensive study on the capabilities of various Transformer-based models in estimating the SOC and SOH of LIBs, the SOP is obtained based on the estimated SOC. This paper provides the following key original contributions: 1) the application of the Informer and Reformer variants of the Transformer model for the first time for SOH estimation of LIBs in EVs, 2) studying the effect of inverted embedding of iTransformers, a modified architecture of the transformers, on SOC and SOH estimation, inversion is performed on the Informer and Reformer as well; 3) applying a simple feature extraction method using partial discharge cycles for SOH estimation with Transformer-based models; 4) a new robust method is proposed for SOC estimation based on a 2-Encoder-Transformer with a one-dimensional convolutional neural network (1D-CNN) architecture; 5) the various architectures are trained, validated and tested on two real-world datasets comprising various driving scenarios and battery conditions. Comparative analysis with various deep learning architectures show impressive accuracy for estimating the SOC and SOH, leading to better SOP calculation.

In today’s power electronic devices, power converters are widely applied in daily life, with increasing power demands. Consequently, the dynamic performance of power converters has become significant, as the compensators directly impact the system’s transient and dynamic response. This paper aims to design a type-III compensator for non-ideal buck converters to improve dynamic performance over a wide input voltage range. This paper introduces the non-ideal buck converter’s small signal model with state space averaging method, provides the guidelines for pole and zero positions of a type-III compensator using Matlab/Simulink Sisotool software, and presents simulation and experimental results to verify a design example and the performance and feasibility of the type-III compensator. Additionally, the transient and dynamic performance under different input voltages, load transients, and phase margins are compared with the K-factor and pole/zero compensation methods, providing a comprehensive reference when designing the compensator circuit for power converters. The results demonstrate that the designed type-III compensator effectively enhances stability and transient response, making it a valuable tool for engineers seeking to optimize power converter performance in various operational scenarios.

The integration of the Renewable Energy Directive II (REDII) into the legislation of European Union member states and the subsequent proliferation of Renewable Energy Communities (REC) has highlighted the need for software platforms to support the administrative, financial, and technical operations of these communities. However, the rise of RECs also introduces significant cybersecurity risks, including financial losses, privacy violations, and broader threats to the stability of the distribution grid. Despite these risks, RECs present a unique opportunity to address cybersecurity concerns from inception, ensuring that security measures are integrated into their design, thereby reducing the complexity and cost of later countermeasures. This paper proposes an IEC 62443-based framework for the secure-by-design development of RECs; after analyzing the risks, the paper proposes a set of guidelines for its mitigation, and validates the proposed approach showing how a REC that respects these guidelines would be compliant with the IEC 62443 standard.

The rapid expansion of online learning platforms presents both challenges and opportunities for educational institutions. Online learning analytics has emerged as a critical tool to derive insights from student learning behaviors, offering flexible educational access and promoting lifelong learning. This paper provides a comprehensive review of recent advancements in online learning analytics, with a focus on personalized and adaptive learning systems. We systematically analyze keyword co-occurrence, identifying key research clusters that highlight the evolution of methodologies, particularly in areas such as natural language processing and multimodal learning analytics. Furthermore, we explore contributions from leading authors, institutions, and countries, demonstrating the global collaboration driving this field. The paper emphasizes the implications of these advancements for educators and instructional designers, suggesting that integrating AI-driven, data-rich systems into educational environments will enhance personalized learning experiences. Our findings highlight emerging trends, including the application of sophisticated algorithms for predictive analytics, learner behavior modeling, and adaptive educational frameworks. These trends suggest future research opportunities to address the challenges of personalized education, aiming to inspire ongoing innovation in both the academic and practical domains of online learning analytics.

In this paper, both the achievement and practical implementation of a fractional number of servers in queueing systems is proposed. The Erlang-B traffic model is considered to demonstrate how the fractional number of servers can be physically realized and incorporated into teletraffic analysis. While mathematical extensions of the Erlang-B formula to consider fractional number of servers have been explored in the literature, neither the practical realization nor the insensitivity property of such models has been extensively studied. In this paper, an approach is presented to effectively achieve fractional number of servers by considering, upon the arrival of service requests, the existence of $\lfloor s\rfloor +1$ servers at times, and only $\lfloor s\rfloor $ servers at other times. The strength of this approach lies in its easy integration into the Call Admission Control (CAC) strategy of real queueing systems. Utilizing a fractional number of servers enables precise adjustment of blocking probability, facilitating a fair and adequate performance comparison among different resource management strategies and evaluation scenarios. Additionally, the insensitivity property of the considered Erlang-B model with fractional number of servers is demonstrated. Finally, to illustrate the practical benefits and advantages of considering a fractional number of servers in the CAC and performance evaluation, numerical examples of the proposed concepts are provided and discussed, specifically within the context of a cellular system with VoIP traffic.

The global drive towards a net-zero world and commitments from advanced countries towards a significant cut of greenhouse gases by 2030 are objectives that have compelled a new technological paradigm that must align with new global initiatives towards meeting agreed objectives towards the net-zero world. The Internet-of-Things (IoT) is a technology that has shown strong viability in assisting nations and corporations in meeting global commitments to the net-zero goal. To be effective, the operation of IoT devices must meet very tight constraints; consequently, the design of the electronics, and applications that run on IoT devices must be optimal especially in power utilization considerations. It is in this regard that this paper presents the design of a power prediction model for a Cortex M processor. The design was achieved through a unique methodology in which an instruction dissector played a central role in determining the exact instructions that were executed, rather than using a generalized set of instructions from an instruction grouping. Multivariate Adaptive Regression Splines (MARS) was used as the machine learning technique for deriving the prediction models. A key feature of using MARS is that the predictor variables having the greatest effect on the models were identified as the MVN and SBC instructions. A performance comparison of the design in this paper was made with similar designs where it was shown that the approach of the design in this work yielded more robust models with a reported error rate of 0.000629193 for logic power and 0.023096787 for data power.

Linearization is a powerful tool that enables the analysis and control of nonlinear systems by simplifying their dynamics and allowing the use of well-established linear control techniques. This paper presents a new methodology for the linearization of dynamic nonlinear systems that can be applied without the need to know their internal structure and parameters solely from measured input/output data. The proposed technique, based on fuzzy logic, simplifies a higher-order nonlinear system into a first-order nonlinear fuzzy system. This enables the design of control systems based on an inverse fuzzy model in the vicinity of the chosen operating point. The properties of the proposed methodology were first verified for less complex systems by simulation in MATLAB. This paper presents the application of this method for torque control of an induction motor, which is a complex nonlinear system of the fifth order. The results were experimentally validated by measurements using an IC inverter-induction motor system. The experimental measurements confirmed the correctness and quality of the proposed methodology. Compared to traditional methods, it does not require any knowledge of the mathematical model of the nonlinear system, use of complex differential algebra, or other additional transformations. The proposed non-analytical linearization technique is primarily designed for a class of mechatronic systems with electric drives. Its simple structure and design methodology minimize the complexity of implementation, thereby enabling it to be widely used in industrial practice for other nonlinear systems with comparable structures.

This paper tackles issues with controller synthesis of disturbed robot manipulators using adaptive sliding mode control (ASMC) and time-delay control (TDC) schemes. For robust control of dynamic system consisting of complex nonlinear functions, this paper proposes a novel ASMC method that uses a novel sliding variable-based adaptive law. The proposed adaptive law employs both function-based and rate-based decreasing law near the sliding surface to improve tracking performance while maintaining or even reducing chattering. The proposed sliding variable also incorporates additional terms for the position error and their time derivatives to compensate for time-delay estimation (TDE) errors induced from TDC methods. For the stability analysis of the robot manipulator controlled by the proposed ASMC, this paper proves uniformly ultimately bounded (UUB) stability of the sliding variable. Numerical simulation and experimental results demonstrate the effectiveness of the proposed ASMC in terms of tracking performance.

In this paper, we propose CNFET-OCL, the first open-source cell libraries for 5-nm and 7-nm carbon nanotube field-effect transistor (CNFET) technologies. Our CNFET-OCL is designed to emulate the predictive 5-nm and 7-nm CNFET technologies presented in a published paper. We achieved this by performing a number of SPICE simulations based on an open-source CNFET SPICE model and making certain assumptions used in previous work. Each of our cell libraries includes two types of delay model (i.e., the composite current source and nonlinear delay model), each having 56 typical standard cells, which is sufficient to design various VLSI circuits. CNFET-OCL fully supports both logic synthesis and timing-driven place and route design in the Cadence design flow. Our experimental results demonstrate that CNFET-OCL can achieve performance levels comparable to those reported in previous studies on CNFETs. Consequently, CNFET-OCL can serve as an effective evaluation tool for the CNFET research community.

This paper investigates classes of decision tables (DTs) with 0-1-decisions that are closed under the removal of attributes (columns) and changes to the assigned decisions to rows. For tables from any closed class (CC), the authors examine how the minimum complexity of deterministic decision trees (DDTs) depends on the minimum complexity of a strongly nondeterministic decision tree (SNDDT). Let this dependence be described with the function $F_{\Psi ,A}(n)$ . The paper establishes a condition under which the function $F_{\Psi , A}(n)$ can be defined for all values. Assuming $F_{\Psi , A}(n)$ is defined everywhere, the paper proved that this function exhibits one of two behaviors: it is bounded above by a constant or it is at least n for infinitely many values of n. In particular, the function $F_{\Psi , A}(n)$ can grow as an arbitrary nondecreasing function $\varphi (n)$ that satisfies $\varphi (n) \geq n$ and $\varphi ({0}) = 0$ . The paper also provided conditions under which the function $F_{\Psi , A}(n)$ remains bounded from above by a polynomial in n.

Proton Exchange Membrane Fuel Cell (PEMFC) systems represent a crucial clean energy component, offering a sustainable alternative to traditional power sources. However, ensuring the dependable and optimal performance of PEMFC systems is essential for their widespread adoption and integration into various applications, ranging from transportation to stationary power generation. To overcome the challenges associated with maintaining the reliability and efficiency of PEMFC systems, there is a growing reliance on Artificial Intelligence (AI)-based diagnosis techniques. AI has emerged as a transformative tool in analyzing complex data patterns, detecting anomalies, and facilitating autonomous decision-making. In the context of PEMFC systems, AI-driven methodologies offer innovative solutions for diagnosing faults, predicting system performance, and optimizing operational parameters. By using AI techniques such as Machine Learning (ML), Neural Networks (NN), expert systems, and data-driven approaches, researchers aim to enhance the reliability, efficiency, and durability of PEMFC systems while minimizing downtime and maintenance costs. The application of AI in PEMFC system diagnosis involves the development of sophisticated algorithms capable of analyzing various data sources, including sensor readings, system parameters, and environmental conditions. These algorithms can detect anomalies, diagnose faults, and provide recommendations for remedial actions in real-time, ensuring continuous and reliable operation of PEMFC systems. While previous papers may have addressed aspects of PEMFC system diagnosis, this comprehensive survey aims to systematically synthesize existing literature to identify shared concepts and research gaps. This extensive survey encompasses a spectrum of AI methodologies, Machine Learning (ML) algorithms, Neural Networks (NN), expert systems, and data-driven approaches associated with diagnosing PEMFC systems. We reviewed over 750 papers to identify shared concepts and real-world implementation issues for future research directions.

This paper presents a novel data-driven approach to fault diagnosability analysis for linear discrete-time systems. Current methods rely heavily on single evaluation functions based on distance similarity, which may not fully utilize directional information in different sets of data. To address this limitation, the paper proposes a comprehensive set of quantitative evaluation indices that consider both distance and direction similarity. This approach introduces data-driven residuals and reformulates the fault diagnosability evaluation as an assessment of the differences between the residuals in fault-free and faulty cases. Specifically, the paper presents detectability measures that provide the relative size of the residual evaluation function caused by faults and noise. Additionally, a set of isolability evaluation indices is proposed by combining distance and direction similarity between the residuals in different faulty cases. These indices can potentially serve as a reference for optimizing system configuration and designing fault diagnosis systems. To verify the effectiveness of the proposed method, we analyze the diagnosability of gas regulators using data collected from an experimental platform. The proposed detectability indices are 0.560, 0.780, and 1.067, while the integrated isolability measures are $4.5\times 10^{-7}$ , 0.0046, and 0.0044, providing more detailed and precise information than existing methods.

The integration of IoT with fuzzy logic provides innovative solutions for various medical applications. IoT enables seamless connectivity between medical devices, real-time monitoring, and data collection. Fuzzy logic has the ability to handle uncertainty and imprecision, thereby enhancing decision-making, medical diagnosis, and treatment. By combining IoT with fuzzy logic, medical providers can develop an intelligent system that improves the quality of healthcare systems. Moreover, the integration of IoT with fuzzy logic enables healthcare providers with powerful predictive analytics capabilities, enabling early detection of diseases and proactive interventions. Several research papers in the literature have investigated the use of IoT with fuzzy logic in medical applications. This review focuses on the use of IoT with fuzzy logic in medical applications. The contributions of this paper are to review the current state of the literature and the potential direction of the medical application of fuzzy system with IoT, and propose a novel scheme that combines Generative Artificial Intelligence (GAI) such as ChatGPT with a fuzzy-based IoT framework. The benefits, challenges, power management, and privacy are also discussed in this paper. The publications are classified according to the type of medical applications. In addition, they are classified according to the type of fuzzy logic used into four categories. We present this review to show the current status in this field. We used Scopus and Google Scholar databases as the basis for presenting this review. The number of publications, number of citations, and other information are discussed to show the achievements in this field. The related research is examined, compared, and classified, focusing on the advantages, challenges, and future research areas.

The increasing integration of cyber-physical systems (CPS) in critical infrastructures has heightened the importance of ensuring their security against various cyber threats. Game theory has emerged as a powerful analytical tool to model and analyze the strategic interactions between attackers and defenders in CPS. This survey provides a comprehensive review of the state-of-the-art research on the application of game theory in the security of CPS, with a specific focus on attack models and security analysis. We employ machine learning algorithms to classify existing research papers based on the attack target and the types of attacks they address. Our classification reveals significant trends and gaps in the current literature, offering insights into the effectiveness of different game-theoretic approaches and the prevalence of various attack models. By synthesizing the findings from over 800 research papers, we highlight the strengths and limitations of existing methodologies and propose directions for future research. This survey aims to serve as a valuable resource for researchers and practitioners seeking to enhance the security of CPS through game-theoretic frameworks and machine learning techniques.

Recommendation systems play an important role in creating personalized content for consumers, improving their overall experiences across several applications. Providing the user with accurate recommendations based on their interests is the recommender system’s primary goal. Collaborative filtering-based recommendations with the help of matrix factorization techniques is very useful in practical uses. Owing to the expanding size of the dataset and as the complexity increases, there arises an issue in delivering accurate recommendations to the users. The efficient functioning of the recommendation system undergoes the scalability challenge in controlling large and varying datasets. This paper introduces an innovative approach by integrating matrix factorization techniques and community detection methods where the scalability in recommendation systems will be addressed. The steps involved in the proposed approach are: 1) The rating matrix is modeled as a bipartite network. 2) Communities are generated from the network. 3) Extract the rating matrices that belong to the communities and apply MF to these matrices in parallel. 4) Merge the predicted rating matrices belonging to the communities and evaluate root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In our paper different matrix factorization approaches like basic MF, NMF, SVD++, and FANMF are taken along with the Louvain community detection method for dividing the communities. The experimental analysis is performed on five different diverse datasets to enhance the quality of the recommendation. To determine the method’s efficiency, the evaluation metrics RMSE, MSE, and MAE are used, and the time required to evaluate the computation is also computed. It is observed in the results that almost 95% of our results are proven effective by getting lower RMSE, MSE, and MAE values. Thus, the main aim of the user will be satisfied in getting accurate recommendations based on the user experiences.

In recent years, deep learning has been widely applied in diagnosing failures in oil and gas pipelines due to its powerful feature representation capabilities. However, in practical applications, the diagnostic accuracy falls short of meeting actual requirements due to complex environmental interference, coupled with existing methods that focus solely on spatial feature relationships. To improve diagnostic performance, this paper eliminates the influence of complex backgrounds through optimal neural-immune domain in the image preprocessing stage. Subsequently, we propose an Immune Depth Presentation Network that can effectively capture inter-feature correlations in the channel dimension. This network is integrated with a conventional convolutional neural network to construct the Immune Depth Presentation Convolutional Neural Network model. To validate the effectiveness and stability of the model, we compared the proposed algorithm in this paper with classic algorithms such as BPNN, KNN, VGG16, VGG19, Resnet34, and Resnet50. Our experiments show that the proposed algorithm achieved the highest average testing accuracy, demonstrating stable performance.

Gastrointestinal (GI) diseases are most common worldwide and the death rate can be reduced by early detection. Endoscopy is widely regarded as the gold standard for diagnosing and managing digestive disorders, affecting both the upper and lower GI tracts. Endoscopy is performed to uncover biopsy tissues used to check the presence of cancerous or benign cells, Helicobacter pylori (H. pylori) infection, or perform colonoscopy in case of the removal of polyps. A systematic review was conducted on databases like PubMed, Scopus, Google Scholar, and IEEE Explore, including research papers published up to May 2023, through the systematic search, 33 papers were identified. This review offers valuable insights to physicians and technological guidance to future researchers by examining GI tract diseases. It provides a detailed analysis of Machine learning (ML) techniques like preprocessing, segmentation, feature extraction, and classification. Additionally, Deep Learning (DL) approaches like transfer learning (TL), Convolution Neural Networks (CNN), optimization, transformer, and reinforcement learning have been analyzed for GI diagnosis. The DL approach has increased its use in GI diseases and CNN was the most commonly used architecture. Lastly, the review highlights the research published in the specialized GI fields and provides technological suggestions and insights for future research prospects. Overall, this study broadens the body of knowledge regarding the existing Artificial Intelligence (AI) techniques in gastroenterology as a manual for creating and assessing AI models.

Bike-sharing systems (BSSs) have been adopted in various places, and many primary studies have been conducted to design and establish station locations. However, there has been no attempt to compile and summarize these in a systematic literature review (SLR) related to long-term BSS design. This paper presents an SLR that identifies and interprets studies published from January 1st, 2001, to January 1st, 2023. The main research question this paper seeks to answer is “What key factors are used to design BSSs?” those factors are supported by criteria such as methods for interpreting and using data from several sources. In this context, both automatic and manual searches for literature were conducted, resulting in 96 studies related to the design of a BSS being included in this review. The methodology used objectively assesses the research topic in a reliable, repeatable, and replicable manner. It allows us to determine what factors, methods, and data were used in the strategic planning of the BSS design. The review results show that although many solutions to problems have been devised, gaps need to be filled in this research field. This study aims to help researchers detect weaknesses and gaps and open new horizons regarding the BSS long-term design.

Ten years ago, the concept of the hyperloop vacuum train promised to revolutionize transportation by offering a fast, inexpensive, and eco-friendly alternative to traditional modes of travel. The key components of the hyperloop are a vacuum tube, magnetic levitation, and linear electric propulsion technology, which are envisaged to achieve surface velocities approaching the speed of sound. This paper presents the functionalities of an ideal hyperloop transportation system (HTS) with a low-cost track and lightweight hyperloop capsule. It is shown that this ideal system is indeed difficult to achieve in reality. Despite the potential benefits, hyperloop technology lacks experimental evidence at subsonic speeds to reach a higher level of technological readiness. Taking one step back, the hyperloop has lessons to learn from the maglev research and experiments in the 1970s. In fact, there are many unresolved challenges associated with maglev technologies, even at moderate speeds, that need to be recognized before reaching the whole way into the subsonic speed domain. This paper provides a status update after ten years of hyperloop research and development.

Advancements in deep learning enable sophisticated algorithms to learn from vast data sets, driving innovation and efficiency in power systems. The global push for electrification has increased focus on deep learning, leading to numerous recent publications in this field. This paper presents a bibliometric analysis and future trends of deep learning in power systems, aiming to identify its fundamental characteristics and summarize the research hot topics and future trends. This study used the Scopus database with the keywords “deep learning” and “power systems”, yielding 20,202 publications from 1970 to 2023. Over 37,600 authors contributed, averaging 12 citations per paper. Keyword trends show traditional deep learning techniques like LSTMs and CNNs are widely used in power systems, while newer methods like advanced reinforcement learning, graph neural networks, and physics-informed neural networks are emerging, promising advancements in optimal power flow, V2G integrations, and grid resilience. Further analysis highlights ubiquitous deep learning applications like load forecasting and power quality analysis in power systems. Emerging topics include microgrid optimization and electric vehicle charging demand prediction, with growing interest in IoT management, digital twins, and cybersecurity. Future research will focus on self-healing grids, optimal power flow, and energy trading for improved reliability and security.

In recent years, the application of deep learning in recommendation systems has achieved breakthrough progress. Neural networks have captured the complex nonlinear relationships between user behaviors and item characteristics. As indispensable elements, ID (Identity) features are widely used to construct user-item association models. The embedding of ID features plays a crucial role in neural network-based recommendation models, enhancing the model’s learning ability by converting them into one-hot vectors as input to the deep learning network. ID features are discrete and high-dimensional sparse, containing critical information for reasoning in recommendation systems. However, most models use the same embedding method to encode all features, leading to inaccurate representation of ID features and affecting the model’s generalization ability. This paper proposes a Contrastive Learning and Deep Fusion Recommendation Model based on ID features (CDM-ID) to address this issue. This model independently encodes ID features first, then accurately re-encodes ID and attribute features through a contrastive learning module, generating high-quality inputs for subsequent feature interaction modules. Finally, it achieves feature fusion through an attention mechanism, significantly improving the performance of the recommendation model. Experiments conducted on three public datasets demonstrate that CDM-ID achieves an average improvement of 8.95% and 5.17% in AUC and F1 scores, respectively, compared to the various baseline models. Additionally, it reduces MAE and RMSE by an average of 9.62% and 12.75%, respectively, further validating the effectiveness and superiority of the proposed model. The contributions of this paper lie in optimizing ID feature embeddings, enhancing embedding accuracy, generating multi-order interaction features, and applying an attention mechanism for feature fusion, thereby significantly improving the performance and prediction accuracy of recommendation systems.

The operation of the electricity system faces the difficulty of minimizing ice damage to transmission lines. Due to the uncertainty of transmission line icing thickness, accurate prediction of icing thickness is very important to guide transmission line planning and power grid anti-icing design effectively. Scientific and reasonable early warning assessment of conductor icing is also helpful to make timely and accurate response measures to the possible freezing disaster risk, so as to effectively ensure the safe and stable operation of power grid and reduce the occurrence of the freezing disaster. Based on this, this paper proposes a multi-factor prediction model based on GM (1, 6) grey theory, which considers the transmission line conductor icing model under the influence of multiple meteorological factors. Based on the conductor icing model, the conductor icing degree can be predicted in real time according to meteorological parameters, so as to realize the purpose of transmission line conductor icing disaster risk early warning. It is worth noting that the paper improves the traditional grey model by adding random data functions, which makes the model solve the problem of inaccurate prediction of small samples. Finally, a case study is carried out, and the icing disaster risk is divided into five levels. It is found that the maximal prediction error of icing thickness based on GM (1, 6) grey theory multi-factor prediction model is 10.06%(The average value is only 4.22%), and the accuracy of transmission line conductor icing disaster risk early warning is 88.9%. In addition, a certain safety margin value is added to the predicted value near the critical value of icing thickness, reducing the probability of judging the high-risk level as low-risk. Applying risk early warning method in ice areas can guide the anti-ice work of transmission line.

This paper analyzes the security and performance of the isogeny-based hash functions. The isogeny-based hash function was first proposed by Charles, Goren, and Lauter, and is referred to as the CGL hash function. However, Lauter and Petit demonstrated that a collision could be found if the endomorphism ring of the starting curve is known. Subsequently, isogeny hash functions that mitigate the Lauter-Petit attack have been proposed. This paper analyzes three methods that counter the Lauter-Petit attack: the methods proposed by Panny, Zaman and Min, and Larsson. In particular, we propose a new isogeny-based hash function SHH, which exploits Panny’s method with Hessian curves. We then analyze the security and performance of the proposed SHH along with the methods by Zaman and Min (SCH) and Larsson (SLH). More specifically, the security was analyzed in the context of the Lauter-Petit attack, collision resistance, and fault tolerance. The analysis in this paper shows that all three algorithms can counter the Lauter-Petit attack. However, for SCH, we demonstrated that collision pairs could be found with carelessly chosen parameters. This paper also provides guidelines for selecting parameters to make SCH collision-resistant. From a fault-tolerance perspective, SCH and SLH are not fault-tolerant. We also present the results of implementing the three algorithms in SageMath. The implementation results show that at the 128-bit security level, hashing a 256-bit message takes 0.130s for SCH, 0.125s for projective-SLH, and 0.162s for SHH. As can be seen from the implementation results, projective-SLH is the most efficient, while SHH performs slower due to the use of a larger finite field.

This paper proposes a new reduced switch-count power cell for a regenerative Cascaded H-Bridge (CHB) motor drive that reduces the number of switching devices per power cell from ten switches to six switches. The proposed power cell employs a Four-Switch Three Phase Inverter (FSTPI) at the front-end and a half-bridge inverter at the output of the power cell. The new configuration introduces four challenges: cell input current unbalance, input low order harmonics, DC-link voltage capacitor balancing, and switching harmonics. This paper analyzes and addresses these issues to ensure proper operation, control, and compliance with grid connection standards. The solutions are verified through simulation studies of a 3kV CHB motor drive and experimental validation with a single power cell setup. The results show the possibility of replacing the traditional power cell with a proposed cell, effectively reducing the required number of switches from ten to six.

Administrative divisions are regional divisions of the state for the purpose of hierarchical administration. With the development of urbanization process, a concern arising from the changing urban dynamics is that whether current administrative division accords with urban development? Following the concept of urban space formed by human activities, existing studies have sought proxies for human activities and then delineated urban boundaries formed by human activities from a data-driven perspective. However, their lack of exploration into the implicit human activity patterns behind the proxies results in weak interpretability and rationality of methods and results. This paper studies the delineation of urban interior boundaries respecting to human mobility patterns. Specifically, this paper first uses human mobility as a proxy to construct a network that represents spatial interaction in urban regions. Then we explore the region-based human crowd mobility patterns to reveal the mechanisms behind the formation of urban space through human activities. Finally, we employ the community detection technology to naturally delimit the urban interior boundaries formed by human mobility, and make a comparison with the official urban boundaries. Taking Xi’an in Shaanxi Province of China as an example, we conduct the delineation on the spatial interaction network based on the real mobility information of 24,770,715 mobile phone users in the whole city. We find that human mobility can establish a stable correlation between regions (or capture the objective correlations between regions), and the human crowd patterns are applicable for mining unusual urban regions from the perspective of anomaly detection, which are of great significance for understanding the urban spatial structure based on empirical evidence. In addition, in the final delineation result, some unexpected communities that are closely linked due to human activities appear from the results, and these findings help the urban planners re-examine the administrative division.

Insider threats cause greater losses than external attacks, prompting organizations to invest in detection systems. However, there exist challenges: 1) Security and privacy concerns prevent data sharing, making it difficult to train robust models and identify new attacks. 2) The diversity and uniqueness of organizations require localized models, as a universal solution could be more effective. 3) High resource costs, delays, and data security concerns complicate building effective detection systems. This paper introduces FedITD, a flexible, hierarchy, and federated framework with local real-time detection systems, combining Large Language Models (LLM), Federated Learning (FL), Parameter Efficient Tuning (PETuning), and Transfer Learning (TF) for insider threat detection. FedITD uses FL to protect privacy while indirect integrating client information and employs PETuning methods (Adapter, BitFit, LoRA) with LLMs (BERT, RoBERTa, XLNet, DistilBERT) to reduce resource use and time delay. FedITD customizes client models and optimizes performance via transfer learning without central data transfer, further enhancing the detection of new attacks. FedITD outperforms other federated learning methods and its performance is very close to the best centrally trained method. Extensive experiment results show FedITD’s superior performance, adaptability to varied data, and reduction of resource costs, achieving an optimal balance in detection capabilities across source data, unlabeled local data, and global data. Alternative PETuning implementations are also explored in this paper.

Time synchronization among edge devices is crucial for ensuring seamless operation in delay-sensitive networks like IoT, smart cities, and industrial automation. These systems cannot reliably support various operations such as distributed data logging, algorithm coordination, or event-driven actions without accurate time coordination. Existing synchronization methods are resource-intensive as they often rely on multiple timestamp exchanges to correct clock skew. This paper introduces a novel approach that addresses these challenges named as Resource-Limited Skew Estimation and Correction (RLSEC) method. RLSEC minimizes the need for multiple exchanges by using a single Resource-Limited Timestamp (RLT) to correct both clock skew and phase offsets. This method not only simplifies the synchronization process but also improves energy efficiency, making it particularly suitable for resource constrained edge devices. The paper provides a detailed mathematical analysis of RLSEC and demonstrates its practical effectiveness through experiments using Texas Instruments TMS320C6713 DSP Starter Kits (DSK). The results confirm RLSEC’s potential to enhance synchronization efficiency while conserving computational resources in edge networks.

While recognition of sign language alphabets and isolated words has matured in recent years, recognition of sign language sentences, or continuous signing, is still a research topic of interest in computer vision, especially in signer independent mode of recognition. Existing state-of-the-art solutions in the continuous Arabic Sign Language Recognition (ArSLR) are promising; however, when implemented in signer independent mode, the accuracies drop noticeably. In this paper, we propose a solution for recognizing continuous Arabic signing in signer dependent and independent modes through the use of motion images with positioning information. Initially, sign videos are converted into several motion images, each emphasizing a different segment of the sentence. This is achieved by a weighted sum of residual images after applying optical flow and motion compensation. Each motion image is composed of the whole sentence video, hence putting the emphasized segment into context in terms of previous and successive sign words. Thereafter, hand-crafted features are calculated from each resultant image, including numerical summaries of the horizontal, vertical and diagonal profiles. With such features, the architecture used for model generation and testing is simplified, where it consists of a single Bi-LSTM layer followed by dropout, softmax, and classification layers. This paper makes use of a recent Arabic Sign Language dataset known as ArabSign. The dataset is composed of 6 signers and 93 words arranged into 50 sentences with 30 repetitions each. Experimental results revealed that the proposed solution is suitable for signer dependent and signer independent modes of continuous sign language recognition. Using a Leave-One-Signer-Out policy, the proposed solution achieved word-recognition rates of 99.8% and 75.3%, respectively. These results noticeably surpass relevant state-of-the-art solutions.

With advancements in technology, deep learning has become increasingly widespread, particularly in fields like robot control, computer vision, and autonomous driving. In these areas, obtaining pose information of target objects, especially their spatial location, is crucial for robot grasping tasks. Although many effective implementations of six degrees of freedom (6D) pose estimation methods based on RGB images exist, challenges in this domain persist. This paper provides a comprehensive review of traditional 6D pose estimation methods, deep learning approaches, and point cloud techniques by analyzing their advantages and disadvantages. It also discusses evaluation metrics and performance on common datasets for 6D pose estimation. Furthermore, the paper offers a theoretical foundation for robot grasping and explores future directions for 6D pose estimation. Finally, it summarizes the current state and development trends of 6D pose estimation, aiming to help researchers better understand and learn about this field.

As society advances globally, the pursuit of gender equity transcends geographical borders and maintains its historical significance. This paper examines the persistent underrepresentation of women in STEM fields, with a focus on engineering, both nationally and globally. Using data from the Enade in Brazil from 2004 to 2019, it explores the reasons behind this disparity and the influence of differences in interests between men and women in STEM careers. The study highlights the ongoing male predominance in engineering courses, despite a gradual increase in female participation. It observes gender variations in engineering disciplines and across regions in Brazil, emphasizing the need to enhance female representation. The paper underscores the importance of recognizing gender expectations, stereotypes, and socioeconomic inequalities while promoting the inclusion of all gender identities to create equitable environments. It proposes strategies to promote gender equality in STEM, including safe environments, combatting sexism, female leadership, and adjustments to educational policies.

This paper presents a novel method for power system reliability studies that combines graph neural networks with reinforcement learning. Monte Carlo methods are the backbone of probabilistic power system reliability analyses. Recent efforts from the authors indicate that optimal power flow solvers could potentially be replaced with the policies of deep reinforcement learning agents, to obtain significant speedups of Monte Carlo simulations while retaining close to optimal accuracies. However, a limitation of that reinforcement learning approach was that the training of the agent is tightly connected to the specific case being analyzed, and the agent cannot be used as is in new, unseen cases. In this paper, we seek to overcome these issues by representing the state and actions in the power reliability environment by features in a graph, where the adjacency matrix can vary from time step to time step. By combining this with a message-passing graph neural network-based reinforcement agent, we are able to train an agent where the agent model is independent of the power system grid structure. For the actor part of this architecture, we have implemented both a deterministic agent being a variant of the Twin Delayed DDPG-algorithm, and a stochastic agent with similarities to the Soft Actor Critic-algorithm. We show that the agent can solve small extensions of a test case without having seen the new parts of the power system during training. In all of our reliability Monte Carlo simulations using this graph neural network agent, the simulation time is competitive with that based on optimal power flow, while still retaining close to optimal accuracy.

This paper presents a digital image encryption scheme based on an amnesic super chaotic system. First, the performance of the hyperchaotic system with the addition of a memristor is analyzed, such as phase track map, Lyapunov exponential map, Pangaea cross-section, and other tests. The test results prove that the system has better hyper-chaotic properties. The encryption system proposed in this paper generates the initial value of the system through the SHA3-256 function to realize ‘one map, one secret.’ Chaotic sequences are generated by the memristive hyperchaotic system and used to encrypt the image. The encryption process, goes through row, column disruption, Arnold disruption, DNA encryption, and improved heterogeneous or diffusion encryption. The encrypted image is analyzed by Matlab. It is verified that the pixel dis-tribution of the encrypted image is uniform through a 3D pixel distribution graph, correlation coefficient graph, and histogram. The NPCR, UACI values of the system are obtained experimentally are close to the ideal values 99.6064%, 33.4635%, which verifies the key sensitivity of the system. The encryption system has been tested to have good ability against anti-shearing attack and noise attacks. Encrypting images of different sizes, the entropy of the image information after encryption by the system is close to 8, which verifies that the system has good encryption effect. Finally, the data loss problem of the encryption system is experimentally analyzed. The experiments show that the encryption system proposed in this paper has good anti-data loss performance. Compared to the existing techniques, the encryption of hyper chaotic systems has a high degree of complexity and unpredictability, as well as sensitivity to initial values and parameters to obtain encrypted images with better confidentiality performance.

Diabetes, especially type 2 diabetes (T2D), is a chronic disease affecting millions of people worldwide. The increasing prevalence of T2D, coupled with the complex interplay between genetic, environmental, and lifestyle factors, presents a major challenge for effective disease management. The traditional methods for predicting T2D progression and determining appropriate treatment strategies are often subjective and less accurate, resulting in treatment delays. Therefore, artificial intelligence (AI) based prediction models become crucial, as they offer a more objective and data-driven approach to T2D management. By leveraging advanced statistical techniques and machine learning algorithms, AI-based prediction models can better identify patients at high risk for T2D progression and predict responses to different treatment options. This can ultimately lead to improved outcomes for patients suffering from T2D. Therefore, this paper aims to review the existing research articles published from 2018 to 2022 using a systematic literature review (SLR) approach. From 40 selected articles, a taxonomy of the most common techniques for developing a prediction model in diabetes progression is drawn in three approaches: mathematical, machine learning (ML), and deep learning (DL). In addition, the best practices of dataset characteristics, pre-processors, and evaluation metrics of the existing algorithms are also provided, focusing on the context of diabetes progression prediction. The findings found that the majority of the selected papers employed ML, specifically the RF model, proven to have superiority in performance. This review also discusses current challenges faced in building prediction models for diabetes progression and proposes future research directions to overcome these challenges. The promising directions drawn include 1) incorporating feature reduction or importance tools to explore the relationship between variables, 2) developing an interpretable predictive model to provide analytical results that are understandable to clinicians, and 3) validating the model with multiple large-sample size datasets and seeking clinical advice from experts.

Due to large-scale integration of variable renewable generation and accelerating electrification of transportation and heating sectors as well as industrial processes, increased amount of flexibility is needed from different voltage levels to fulfill simultaneously the flexibility needs of local distribution system operators (DSOs) and system-wide transmission system operator (TSO). This paper develops further the coordinated and frequency level-dependent distributed energy resources (DERs) control and adaptive on-load-tap-changers (OLTCs) management scheme which enables prioritized flexibility services provision for the DSOs and TSO. The key idea of the scheme is that the coordination of flexibility utilisation between DSOs and TSOs is done depending on the severity of the situation from the whole power system’s viewpoint regarding frequency deviation level. During smaller frequency deviations DSOs’ needs are prioritized and during larger frequency deviations priority is on TSO’s needs to support momentarily the stability of the whole power system. The overall target of the studied management scheme is also to increase DERs’, especially photovoltaics’ (PVs’) and electric vehicles’ (EVs’), hosting capacity in the DSOs’ networks and increase DERs availability for the flexibility services provision to the TSO during smaller frequency deviations. In this paper, enhanced OLTC control principles are presented including novel interconnection transformer OLTC control scheme for larger medium-voltage (MV) network-connected DER units. This MV DER OLTC control logic can enable, for example, same DER active power response with 7.7% smaller current or 8.5% higher DER unit active power response with the same current value. In addition, this paper studies the effect of different OLTC control inputs, DER reactive power control settings, LV feeder length and large-scale integration of EVs on low-voltage (LV) network with multiple PSCAD simulations. At the end, further improvement possibilities of the studied management scheme are also briefly described.

Unmanned Aerial Vehicles (UAVs) are increasingly utilized across various industries, necessitating high reliability to ensure safety and reduce operational costs. This paper introduces a systematic methodology for physics-based reliability modeling tailored for UAV control applications, focusing on adaptive control allocation to optimize reliability. The study addresses the limitations of conventional degradation-independent behavior factor-based models, which often rely on inaccurate degradation models due to the lack of parameterization data sources and methods. By replacing time with stress-derived variables in the reliability function, this approach enables the combination of any time-dependent reliability functions with any stress-life relationships, allowing for real-time physics-based reliability assessments. The approach is demonstrated through the development and application of models for electronic speed controllers within a hexarotor UAV, using a virtual prototype for flight simulation. Simulation results reveal that physics-based models improve reliability prediction accuracy compared to conventional proportional hazard models, particularly due to their reliance on published and manufacturer’s data for parameterization. The paper concludes by highlighting the need for future research to simplify the integration of stress factor online measurements, addressing the complexity and data requirements inherent in physics-based models.