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A significant advancement in artificial intelligence is the development of large language models (LLMs). Despite opposition and explicit bans by some authorities, LLMs continue to play a transformative role, particularly in education, by improving language understanding and generation capabilities. This study explores LLMs' types, history, and training processes, alongside their application in education, including digital and higher education settings. A novel theoretical framework is proposed to guide the integration of LLMs into education, addressing key challenges such as personalization, ethical concerns, and adaptability. Furthermore, the study presents practical case studies and solutions to barriers, such as data privacy and bias, offering insights into their role in enhancing the teaching–learning process. By providing a systematic analysis and proposing a structured framework, this study advances current knowledge and highlights the significant potential of LLMs in revolutionizing education. [ABSTRACT FROM AUTHOR]

This study presents a learning‐based iterative model predictive control (MPC) scheme for unknown (Lipschitz continuous) nonlinear dynamical systems. The proposed method begins by learning the unknown part of the controlled system using a Gaussian process (GP), which helps derive multi‐step reachable sets that are guaranteed to encompass the actual system states. At each time step in each iteration, the MPC controller calculates a sequence of control inputs that robustly satisfy state and control constraints, as well as terminal constraints based on the GP‐based reachable sets. Then only the first control input is applied to the system. After the iteration, the initial state is reset, and the same procedure is executed with the MPC optimization problem defined by the updated terminal set and cost. As iteration goes on, improvement of the control performance is expected since more data is obtained and the environment is progressively explored. The proposed method provides properties such as recursive feasibility and input to state stability of the goal region under certain assumptions. Moreover, bound on the performance cost in each iteration associated with the implementation of the proposed MPC scheme is also analyzed. The results of the simulation study show that the proposed control scheme can iteratively improve the control performance. [ABSTRACT FROM AUTHOR]

This article proposes an online universal self‐learning control (USLC) algorithm based on a physical performance policy‐optimization neural network, which aims to solve the problem of universal self‐learning optimal control laws for nonlinear systems with various uncertain dynamics. As a key system characterization, this algorithm predicts the discrepancy between the optimal and current control laws by evaluating overall performance in each iterative learning cycle, leveraging an offline‐trained universal policy network. This approach is universal, as it does not rely on an exact system model and can adaptively control performance preferences across various tasks by customizing the physical performance cost weights. Using the established control law‐performance surface and contraction Lyapunov function, the necessary assumptions and proofs for the stable convergence of the system within a three‐dimensional manifold space are provided. To demonstrate the universality of USLC, simulation experiments are conducted on two different systems: a low‐order circuit system and a high‐order variable‐span aircraft attitude control system. The stable control achieved under varying initial values and boundary conditions in each system illustrates the effectiveness of the proposed method. Finally, the limitations of this study are discussed. [ABSTRACT FROM AUTHOR]

This study examines strategies to transform Al-Kharj region in Saudi Arabia into a smart city, specifically in the healthcare, education and employment (HEE) sectors. Employing a dynamic panel model from 2010 to 2023, encompassing 121 organizations and firms, we examine the influence of investments in infrastructure, technology adoption, sustainability initiatives and citizen engagement on implementing innovative practices in HEE. Our findings underscore the significant impact of these four factors. Additionally, we conducted surveys among policy-makers and residents to pinpoint the challenges hindering the adoption of these strategies. Based on the surveys' outcomes, we formulate policy implications and recommendations to assist the Al-Kharj region in transitioning to an intelligent city status. [ABSTRACT FROM AUTHOR]

In today's evolving landscape of video surveillance, our study introduces SuspAct, an innovative ensemble model designed to detect suspicious activities in real time swiftly. Leveraging advanced Long-term Recurrent Convolutional Networks (LRCN), SuspAct represents a significant advancement in intelligent surveillance technology. By combining insights from various LRCN models through the Majority Voting ensemble technique, SuspAct enhances its overall robustness, outperforming traditional surveillance methods. Through rigorous experimentation on large-scale datasets, we demonstrate SuspAct's superiority in proactive crime prevention, showcasing its potential to revolutionize security protocols and contribute substantially to public safety. Our work addresses the challenges posed by the escalating volume of video data and lays a strong foundation for future advancements in intelligent video surveillance technology. [ABSTRACT FROM AUTHOR]

Abstract A significant advancement in artificial intelligence is the development of large language models (LLMs). Despite opposition and explicit bans by some authorities, LLMs continue to play a transformative role, particularly in education, by improving language understanding and generation capabilities. This study explores LLMs’ types, history, and training processes, alongside their application in education, including digital and higher education settings. A novel theoretical framework is proposed to guide the integration of LLMs into education, addressing key challenges such as personalization, ethical concerns, and adaptability. Furthermore, the study presents practical case studies and solutions to barriers, such as data privacy and bias, offering insights into their role in enhancing the teaching–learning process. By providing a systematic analysis and proposing a structured framework, this study advances current knowledge and highlights the significant potential of LLMs in revolutionizing education.

Abstract This article studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature. A unified data‐driven CACC design is proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and human‐driven vehicles (HVs). The CACC leverages online‐collected sufficient data samples of vehicle accelerations, spacing, and relative velocities. The data‐driven control design is formulated as a semidefinite program that can be solved efficiently using off‐the‐shelf solvers. Efficacy of the proposed CACC are demonstrated on a platoon of pure AVs and mixed platoons with different penetration rates of HVs using a representative aggressive driving profile. Advantage of the proposed design is also shown through a comparison with the classic adaptive cruise control (ACC) method.

Abstract The integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial‐temporal hybrid convolutional‐transformer (CT‐Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT‐Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT‐Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT‐Transformer's potential for accurate RES and load forecasting and PSF evaluation.

Abstract The complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding‐based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding‐based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks.

Abstract Trained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed‐loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed‐loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear‐quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL‐controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non‐linear system and then tested on the corresponding real‐world hardware platform without any fine‐tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU).

This article studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature. A unified data‐driven CACC design is proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and human‐driven vehicles (HVs). The CACC leverages online‐collected sufficient data samples of vehicle accelerations, spacing, and relative velocities. The data‐driven control design is formulated as a semidefinite program that can be solved efficiently using off‐the‐shelf solvers. Efficacy of the proposed CACC are demonstrated on a platoon of pure AVs and mixed platoons with different penetration rates of HVs using a representative aggressive driving profile. Advantage of the proposed design is also shown through a comparison with the classic adaptive cruise control (ACC) method. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial‐temporal hybrid convolutional‐transformer (CT‐Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT‐Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT‐Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT‐Transformer's potential for accurate RES and load forecasting and PSF evaluation. [ABSTRACT FROM AUTHOR]

To address the variable initial state and trail length this paper first presents a robust PD‐type open‐closed‐loop iterative learning control (ILC) law for a multiple‐input‐multiple‐output (MIMO) linear discrete‐time system. It is demonstrated that the convergence condition is dependent on the PD‐type feed‐forward learning gains, while an appropriate feedback learning gain can improve the ILC convergence performance. As a special case of PD‐type open‐closed‐loop ILC law, P‐type and D‐type open‐closed‐loop ILC laws are deduced. The three developed ILC laws ensure that as the number of iterations approaches infinity, the expectation of ILC tracking error will be constrained within a limited range, where the boundary is proportional to the initial state variation. Through a numerical simulation, the effectiveness of the proposed ILC laws is illustrated. [ABSTRACT FROM AUTHOR]

The complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding‐based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding‐based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks. [ABSTRACT FROM AUTHOR]

As a prosthesis made to compensate for the residual loss of the amputee's limb, the shoulder disarticulation upper limb prosthesis replaces the missing arm function of the shoulder amputee to a certain extent. However, the current upper limb prosthesis mainly interacts with the outside world through the prosthetic hand for grasping and gripping, and the interaction between other parts and the environment is often neglected, which is not in line with the use habits of the human arm. To address this problem, this paper proposes a reinforcement learning–based method for controlling the forearm interaction of a shoulder-disconnected upper limb prosthesis, and analyzes and solves the forces during the interaction, reducing the impact of uncertainty on interaction actions and accelerating training while ensuring the stability of handheld items. We evaluated the performance of the control method during the interaction between the upper limb prosthesis and the external environment through simulation experiments. After the training, the bionic arm was able to push the object into the target range for different objects and pushing distance requirements, which showed the good control effect of the method. Also, the control method can be applied to improve the interaction between the robotic arm and the environment. [ABSTRACT FROM AUTHOR]

Trained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed‐loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed‐loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear‐quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL‐controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non‐linear system and then tested on the corresponding real‐world hardware platform without any fine‐tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU). [ABSTRACT FROM AUTHOR]

Recommender systems -RS- have experienced exponential growth in various fields, especially in the tourism sector, improving tourism activities’ accuracy, personalization, and experience, thus strengthening indicators such as promotion. However, some challenges and opportunities exist to overcome, such as the lack of data on emerging destinations wishing to adopt these solutions. This manuscript presents a literature review of the current trends in RS applied to the tourism industry, including categories associated with their use and emerging techniques. Likewise, it presents a pathway for implementing an RS when insufficient data are available for a destination. The SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and used the WoS, Science Direct, and Scopus databases. The results show that the hybrid RS integrates deep learning algorithms, data analytics, and optimisation techniques with collaborative tourism features to provide innovative solutions in terms of performance, accuracy, and personalisation of recommendations, thus achieving the management of tourist destinations or tourism-oriented services. Emerging destinations that lack RS data in tourism should use various data sources generated by tourists on social media, tourism portals, and through their interaction with tour operators. New tourism recommender system solutions can emerge following trends integrating new technologies based on user experience, collaboration, and the integration of multiple data sources.

This letter addresses the output consensus problem for a class of two‐dimensional (2D) multi agent systems (MASs) described by Fornasini–Marchesini model. By transforming the 2D agent into a compact form, an adaptive variable, which adjusted by the tracking errors of itself and the neighbour agents, is designed to approximate the unknown varying coefficient. Then, based on the approximated coefficient and the iteration‐varying reference surfaces, the distributed adaptive iterative learning control strategy is obtained. The output consensus of the 2D multi agent is proved. Simulations are included to verify the effectiveness of the investigated distributed adaptive ILC for 2D MASs with random variations on initial condition and reference surface. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the use of one-way analysis of variance ANOVA as an effective tool for ranking the features calculated from diagnostic signals and evaluates their impact on the accuracy of the machine learning system's classification of displacement pump wear.The first part includes a review of contemporary diagnostic systems and a description of typical damage of multi-piston displacement pumps and Its causes. The work also contains description of a diagnostic experiment which was conducted in order to obtain the matrix of vibration signals and the matrix of pressures measured at selected locations on the pump housing and at the pump pressure line. The measured signals were subjected to time–frequency analysis. The features of signals calculated in the time and frequency domains were ranked using the ANOVA. The next step involved the use the available classifiers in pump wear evaluation, conducting tests and assessing their effectiveness in terms of the ranking of features and the origin of diagnostic signals.

Intravenous cannulation (IV) is a common technique used in clinical infusion. This study developed a mixed reality IV cannulation teaching system based on the Hololens2 platform. The paper integrates cognitive‐affective theory of learning with media (CATLM) and investigates the cognitive engagement and willingness to use the system from the learners' perspective. Through experimental research on 125 subjects, the variables affecting learners' cognitive engagement and intention to use were determined. On the basis of CATLM, three new mixed reality attributes, immersion, system verisimilitude, and response time, were introduced, and their relationships with cognitive participation and willingness to use were determined. The results show that high immersion of mixed reality technology promotes students' higher cognitive engagement; however, this high immersion does not significantly affect learners' intention to use mixed reality technology for learning. Overall, cognitive and emotional theories are effective in mixed reality environments, and the model has good adaptability. This study provides a reference for the application of mixed reality technology in medical education. [ABSTRACT FROM AUTHOR]

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem. [ABSTRACT FROM AUTHOR]

This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.

The digital competences of university students are developed using a digital learning ecosystem that integrates: 1) virtual learning environments; 2) digital learning tools; and 3) learning methodologies. This research followed the methodology of systematic literature mapping and review, searching the WoS and Scopus databases and obtaining a total of 5,652 articles between 2001 and 2023. Inclusion and exclusion criteria were then applied to reduce the number of selected articles and carry out a systematic literature mapping and review. Among the relevant results of the literature mapping and systematic review, the geographic distribution of scientific publications, the educational areas in which they have been worked on, and the universities were identified. Educational methodologies, technological tools, and virtual learning environments used to develop university students’ digital competences holistically were also determined. This study is useful as it provides a comprehensive, general, and detailed overview of scientific production and its main contributions regarding the methodologies, tools, and environments that contribute to developing students’ the digital competences in higher education.

Recognizing the increasing relevance of entrepreneurship skillsets to engineers, CASS launched an initiative to educate on entrepreneurship with technology solutions aligned with CAS Society’s visions. In this special issue, this article introduces the motivation, course setup, and the results of an intensive six-week hybrid course stimulating entrepreneurial thinking. The course was based on a novel iterative and agile innovation framework designed to lead up to the BIOCASS 2023 conference in Toronto, inviting the best proposals for an in-person presentation. The learners confirmed a changed mindset towards innovation generation. [ABSTRACT FROM AUTHOR]

Abstract Concerning the problems that the traditional Convolutional Neural Network (CNN) ignores contextual semantic information, and the traditional Recurrent Neural Network (RNN) has information memory loss and vanishing gradient, this paper proposes a Bi‐directional Encoder Representations from Transformers (BERT)‐based dual‐channel parallel hybrid neural network model for text sentiment analysis. The BERT model is used to convert text into word vectors; the dual‐channel parallel hybrid neural network model constructed by CNN and Bi‐directional Long Short‐Term Memory (BiLSTM) extracts local and global semantic features of the text, which can obtain more comprehensive sentiment features; the attention mechanism enables some words to get more attention that highlights important words and improves the model's sentiment classification ability. Finally, the dual‐channel output features are fused for sentiment classification. The experimental results on the hotel review datasets show that the Accuracy of the proposed model in sentiment classification reaches 92.35% and the F1 score reaches 91.59%.

Introduction: The COVID‐19 pandemic revealed numerous barriers to effectively managing public health crises, including difficulties in using publicly available, community‐level data to create learning systems in support of local public health decision responses. Early in the COVID‐19 pandemic, a group of health care partners began meeting to learn from their collective experiences. We identified key tools and processes for using data and learning system structures to drive equitable public health decision making throughout different phases of the pandemic. Methods: In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID‐19. The theoretical drivers were explored qualitatively through a series of nine 45‐min telephonic interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future practices, tools, and systems. In addition to the interviews, partners in Dallas and Cincinnati reflected on their own COVID‐19 experiences. Results: Interview responses fell broadly into four themes that contribute to effective, community driven responses to COVID‐19: real‐time, accessible data that are mindful of the tension between community transparency and individual privacy; a continued fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment and goals. These themes and partner experiences helped us revise our preliminary theory of change around the importance of community collaboration and trust building and also helped refine the development of the Community Protection Dashboard tool. Conclusions: There was broad agreement amongst public health and community leaders about the key elements of the data and learning systems required to manage public health responses to COVID‐19. These findings may be informative for guiding the use of data and learning in the management of future public health crises or population health initiatives. [ABSTRACT FROM AUTHOR]

This is a virtual dialog between Jeffrey C. Carver and Daniel S. Katz on how people learn programming languages. It's based on a talk Jeff gave at the first US-RSE Conference (US-RSE'23), which led Dan to think about human languages versus computer languages. Dan discussed this with Jeff at the conference, and this discussion continued asynchronous, with this column being a record of the discussion. [ABSTRACT FROM AUTHOR]

Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful, and some can deteriorate the classification performance. Sparsity-based methods have been widely used to address multi-label feature selection due to their efficiency and effectiveness. However, most (if not all) existing methods do not consider the three forms of interactions simultaneously, which could hinder their ability to achieve good performance. Moreover, most existing methods are gradient-based, which are prone to getting stuck at local optima. This paper proposes a new sparsity-based feature selection approach that can simultaneously consider all three forms of interactions. Furthermore, this paper develops a novel sparse learning method based on particle swarm optimisation that can avoid local optima. The proposed method is compared against the state-of-the-art multi-label feature selection methods in terms of multi-label classification performance. The results show that our method performed significantly better in selecting high-quality feature subsets with respect to various feature subset sizes. [ABSTRACT FROM AUTHOR]

In real hyperspectral scenes, heterogeneous spatial details and noises make a single subspace assumptions unrealistic. In this letter, a novel transferable multiple tensor subspace learning scheme is proposed for super-resolution enhancement of hyperspectral image (HSI). The intrinsic assumption is that the nonlocal patch tensors extracted from HSIs are derived from multiple tensor low-rank subspaces, which is compatible with practical data distribution and may better characterize the complex structures underlying HSIs. The transferable subspace structures are embedded into both nonblind and semi-blind HSI super-resolution. The alternating direction method of multipliers (ADMMs) algorithm is derived for model learning. The superiority of our method is demonstrated by comprehensive experiments on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a novel ensemble learning-based adaptive stochastic model predictive control (SMPC) algorithm for constrained linear systems with unknown nonlinear terms and random disturbances. The ensemble network combining a feedforward neural network and a Bayesian network is used to offline learn the nonlinear dynamics and disturbance distribution parameters. Then, the mixed-tube scheme is designed to cope with input constraints and state chance constraints while decreasing computational demands and conservativeness. The reliability of the stochastic tube is guaranteed using the Hoeffding inequality-based verification mechanism, which results in a chance constraint with double probabilities. The feasibility and exponential stability of the SMPC are rigorously proven. A numerical example verifies the merits of the proposed algorithm in terms of the control performance and the feasible domain. [ABSTRACT FROM AUTHOR]

Brief historical background on the establishment and activities of the Institute of Control Sciences (IPU RAS). The paper presents key findings of the Institute (obtained mainly in the 20th century) in the field of pattern recognition and of related analysis of complex data. It focuses on four areas of research including (a) the method of potential functions, (b) the theory of learning and self-learning systems, (c) the generalized portrait method and recovery of dependences based on empirical data, and (d) automatic classification methods and expert classification analysis. Relations between these areas are studied. The pioneers in the field are named (M.A. Aizerman, E.M. Braverman, L.I. Rozonoer, Ya.Z. Tsypkin, V.N. Vapnik, A.Ya. Chervonenkis, I.B. Muchnik, and A.A. Dorofeyuk among others) and brief biographical notes on the life and scientific work of these scientists are presented. The follow-ups of the results thus obtained are shown. The bibliography of publications by the Institute's researchers in leading journals of Russia on pattern recognition problems and related complex data analysis tasks is provided. [ABSTRACT FROM AUTHOR]

Background The presence of artificial intelligence (AI) in many areas of social life is becoming widespread. The advantages of AI are being observed in medicine, commerce, automobiles, customer service, agriculture and production in factory settings, among others. Workers first encountered robots in the work environment in the 1960s. Since then, intelligent systems have become much more advanced. The expansion of AI functionality in the work environment exacerbates human health risks. These can be physical (lack of adequate machine control, accidents) or psychological (technostress, fear, automation leading to job exclusion, changes in the labour market, widening social differences). Material and Methods The purpose of this article is to identify, based on selected literature, possible applications of AI and the potential benefits and risks for humans. Results The main area of interest was the contemporary work environment and the health consequences associated with access to smart technologies. A key research area for us was the relationship between AI and increased worker control. Conclusions In the article, the authors emphasize the importance of relevant EU legislation that guarantees respect for the rights of the employed. The authors put forward the thesis that the new reality with the widespread use of AI, requires an analysis of its impact on the human psycho-social and health situation. Thus, a legal framework defining the scope of monitoring and collection of sensitive data is necessary. Med Pr. 2023;74(3):227–33

The relevance of the declared subject of this research work is determined by the need to develop and implement software for users of various skill levels, as well as to create effective multi-scenario systems for describing processes occurring in multiple environments. The primary purpose of this scientific research is to study the principles of modeling the functioning of multi-scenario systems. The basis of the methodological approach in this scientific study is a combination of methods of system analysis of the principles of creating models for the functioning of multi-scenario systems with an analytical study of the prospects for building monolithic architectures of online learning systems. In the course of this scientific research, results were obtained that describe the principles of modeling a multi-scenario online learning system, as well as illustrating the features of the interaction of individual subsystems within a single multi-scenario system, taking into account the effectiveness of each of the subsystems performing the functions assigned to it within a single multi-scenario system. The results obtained reflect the fundamental principles of building the operation of multi-scenario systems in the conditions of the need to process a large amount of data, taking into account the difference in user characteristics, their levels of preparedness, as well as the variety of user requests that have a significant impact on the process of creating a multi-scenario system model and its functioning in constantly changing environments. External conditions. The practical significance of the results obtained in this scientific study, as well as the conclusions formulated on their basis, lies in the possibility of their application in the development of information presentation systems, the operation of which is based on the principle of multi-scenario, in order to provide the option of choosing modes of use and their automatic adjustment.

Ahmad H Alwashmi Department of Orthopedic Surgery, College of Medicine, Qassim University, Buraydah, 52571, Saudi ArabiaCorrespondence: Ahmad H Alwashmi, Department of Orthopedic Surgery, College of Medicine, Qassim University, Buraydah, Qassim, 52571, Saudi Arabia, Email a.alwashmi@qu.edu.saBackground: Residents in training must employ a variety of study strategies, as they not only participate in academic studies but also interact with patients. This study aimed to evaluate the study practices and factors affecting those practices among Saudi Arabian physical medicine and rehabilitation residents during their residency program.Methods: In this cross-sectional study, a previously used questionnaire was distributed to Saudi Arabian physiatry residents from July 1 to August 15, 2022, via a social media platform and completed using a Google Forms survey. A Microsoft Excel spreadsheet was used to collect, clean, and import the data before IBM SPSS Statistics for Windows, version 22.0 was utilized for statistical analysis.Results: The data of 94.91% of respondents were included in the analysis. Individuals who were female, unmarried or divorced, and without children predominated. Only 17.9% (n = 10) of the residents believed that their training program effectively prepared them to pass the board examination, which was the most strongly motivating factor for studying for 85.7% of respondents. Over two-thirds of the residents mentioned that they regularly exercise. Residents who studied more than 11 hours per week had a significantly lower score in the category of factors that negatively affect examination performance (M = 12.33 ± 2.82, F = 2.794, P < 0.05). Females, final-year residents, and Riyadh residents studied more than their counterparts.Conclusion: Our study is the first to investigate how Saudi physiatrists study, with the finding that current physiatry residents employ a combination of traditional and contemporary learning strategies. This information can help stakeholders to understand current training challenges, improve the quality of training for physiatry residents, and create an ideal learning environment.Keywords: cross-sectional study, questionnaire, performance assessment, students, learning systems, physiatry