Search Results (675)

Anxiety disorders pose an increasing challenge to the mental health of individuals, particularly in regions with limited healthcare access. This study investigated the potential of integrating a brain–computer interface for processing electroencephalography (EEG) data with deep learning models to accurately classify anxious and non-anxious states. In the first phase, a convolutional neural network (CNN) was developed and validated on the public GAMEEMO dataset, achieving a classification accuracy of 95.72%. In the second phase, we conducted a separate experimental validation with seven participants (aged 18–60 years) using a within-subjects design. The protocol comprised a custom Stroop test to elicit acute cognitive stress and anxiety-related arousal, followed by a guided 4–7–8 breathing exercise to induce relaxation. EEG data from this experiment were used to classify anxious versus non-anxious states with the same CNN architecture after domain adaptation. On this self-collected dataset, the CNN achieved an accuracy of 86.58%. These results demonstrate proof-of-concept transferability while highlighting the performance gap between controlled benchmark data and real-world, small-sample recordings. The deep learning model can subsequently be coupled with neurofeedback techniques to manage anxiety levels. Overall, the findings support the potential of the developed automated system for detecting stress-induced anxious states, with possible future integration into neurofeedback-based management systems. Full article

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article

The research aims to design and develop learning materials to be used by Agri-Fishery students. It aims to incorporate modern techniques, making it easier for the frontline to deliver the information included in the material. The use of flipbooks, PDF formats, and Canva, a free web-based visual communication and design platform, with embedded actual field demonstration videos were made available in the developed instructional material. The ADDIE model was used to develop instructional material, highlighting the processes it employs. As the processes progressed, the learning materials were evaluated by experts in the field using the adapted Instructional Material Evaluation Checklist (IMEC). The evaluation results obtained a mean of 3.31 and an SD of 0.80, with a satisfactory remark of ‘High Evidence’. The content prevailed with the least mean of 3.22 and a standard deviation (SD) of 1.01, indicating Sufficient Evidence. The numbers from the content evaluation showed how the curriculum linked its requirements to explicit self-directed learning and outcome-based learning capabilities. Full article

Rurbanization and peri-urbanization are among the most dynamic territorial processes affecting metropolitan regions in Morocco, particularly within the Casablanca–Settat region. These transformations, driven by rapid urban growth, demographic pressure, and socio-economic change, generate complex transitional spaces between rural and urban environments. In this context, the present study proposes a hybrid methodology for detecting, classifying, and analyzing the rural–urban continuum by using remote sensing data and artificial intelligence techniques. The approach integrates Sentinel-2 satellite imagery, spectral indices, Global Human Settlement Layer datasets, and socio-demographic indicators derived from the Moroccan census. Two models, Self-Organizing Maps (SOM) and Graph Neural Networks (GNN), were applied to classify territories into four categories: urban, peri-urban, rurban, and rural. Model outputs were combined with expert-based decision rules to improve classification robustness and interpretability. The SOM model achieved up to $89.3\%$ agreement with expert classifications and a Cohen’s Kappa coefficient of $0.842$, demonstrating strong interpretability and consistency, while the GNN model reached $53\%$ agreement and effectively modeled spatial dependencies and neighborhood interactions. Diachronic analysis between 2014 and 2024 revealed a $54\%$ increase in peri-urban municipalities, a $24\%$ decrease in rurban territories, and a decline in rural municipalities, highlighting intensified urban sprawl and fragmentation of agricultural landscapes. Beyond its scientific contribution, this study provides a valuable decision-support framework for urban planners, environmental agencies, and policy makers involved in territorial governance and sustainable development. It can support land-use planning, monitoring of urban sprawl, protection of agricultural lands, and the implementation of adaptive territorial policies aimed at improving the resilience and sustainability of rurban environments. Full article

Deep learning has significantly improved the quality of infrared and visible image fusion. However, existing mainstream deep fusion networks often come with complex architectures and a large number of parameters. While general lightweight techniques can effectively reduce model complexity, they often weaken feature interactions during the lightweighting process, resulting in the loss of complementary texture and thermal information in fused images and making it difficult to balance fusion performance and model efficiency. To address these issues, this paper constructs an infrared and visible image fusion network based on a self-compensating lightweight convolution mechanism, named LWC-DenseFuse. The core of the network lies in a self-compensating lightweight convolution module, which goes beyond conventional convolution replacement and explicitly addresses feature degradation introduced by lightweight design. The module decouples spatial and channel correlations of standard convolution through depthwise convolution and pointwise convolution, while incorporating a channel attention mechanism to adaptively enhance salient features. Additionally, channel shuffle technology is employed to promote information exchange between groups, thereby enhancing feature interaction and compensating for the loss of critical information caused by lightweight design. To further improve the representation capability of the lightweight network during optimization, a staged training strategy with progressive loss weighting is introduced. Experimental evaluations demonstrate that the proposed fusion network significantly reduces the number of model parameters while ensuring real-time inference performance. Meanwhile, it effectively alleviates the performance degradation typically associated with lightweight architectures, as evidenced by improvements in information entropy and visual fidelity. Full article

The problem of path following control for underactuated Unmanned Surface Vehicles (USVs) is tackled in this work, and a scheme based on Predicted Adaptive Line-of-Sight (PALOS) is put forward. At the guidance level, prediction techniques and adaptive mechanisms are incorporated to eliminate the inherent assumption of small sideslip angle in the conventional LOS methods, enabling online estimation and dynamic feedforward compensation of time-varying sideslip angles. On the control side, radial basis function neural networks are combined with virtual parameter learning techniques to achieve online approximation of the lumped uncertainties, which include modeling inaccuracies and external disturbances. An adaptive control scheme based on lifelong learning mechanisms is developed, wherein the historical knowledge is constructed and preserved through feedback terms to achieve knowledge retention and on-demand reuse, thereby enhancing control efficiency and mitigating catastrophic forgetting. Additionally, a self-triggered mechanism acts as a knowledge transfer instrument, reducing communication overhead, relaxing transmission conditions, and rigorously precluding Zeno behavior. Through theoretical derivations, one can prove that all closed-loop signals are uniformly ultimately bounded. Comprehensive numerical simulations based on the 1:70 CyberShip II scale-model ship dynamics under complex sea conditions verify the proposed approach to be both effective and practical. Full article

This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational safety. The primary objective of the study is to evaluate the synergy between renewable energy sources (solar and wind energy) and emerging propulsion technologies in aviation (hydrogen and electrification) from the perspective of safety and operational stability. The methodology is based on a systematic review of 78 scientific studies identified in the Scopus and Web of Science databases. The analysis identifies critical technical and operational barriers, including electromagnetic interference caused by wind turbines, optical hazards associated with photovoltaic systems, and stability challenges in airport microgrids under peak loads resulting from the charging of electric aircraft. Particular attention is given to the safety of hydrogen infrastructure, where findings from the literature indicate the need to revise separation distances and highlight the potential reduction of airport stand capacity by 5% to 16%. The study synthesizes these findings into a strategic framework for “Smart Green Airports”, proposing solutions such as adaptive infrastructure design, the deployment of predictive models based on artificial intelligence, and the implementation of inherently safe energy storage systems. The paper concludes that achieving airport energy self-sufficiency while maintaining the integrity of flight operations is feasible only through the holistic integration of technical measures, simulation-based planning, and strict compliance with updated safety regulations. Full article

The triboelectric nanogenerator (TENG), which converts mechanical energy into electrical energy through the coupled effect of triboelectrification and electrostatic induction, has garnered significant interest among researchers due to its portability and self-powered characteristics. Despite its evident development potential, TENG continues to face challenges, including the necessity to enhance its triboelectric performance through the optimization of structures, materials, and manufacturing techniques to improve energy conversion efficiency. Additionally, its environmental stability and durability also need to be improved. TENGs designed inspired by non-human animals and plants offer feasible solutions to address these limitations. These bio-inspired TENGs optimize the structural design of TENGs and the materials of the triboelectric layers by imitating the structures, functions, and behaviors of organisms, thereby further improving the energy conversion efficiency, sensitivity, wear resistance, adaptability to special environments, biocompatibility, and wearing comfort of TENGs. This paper expounds on the progress of TENGs inspired by non-human animals and plants applied in environmental energy harvesting, human health and motion monitoring. It also discusses the current challenges, with a view to providing insights for the interdisciplinary integration and development of bionics and TENGs. Full article

The emergence of artificial intelligence (AI) tools, particularly generative models, in the last five years has fundamentally transformed the framework and methodologies of learning in higher education. Students are integrating AI for producing new ideas, assisted and personalized search, academic writing, advanced data analysis, and personalized learning. For this reason, an update of the theoretical and conceptual framework regarding the adoption of technologies in the educational environment is required. Based on traditional Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology (TAM/UTAUT) models, we propose a new Partial Least Squares Structural Equation Modeling (PLS-SEM) model developed for the context of AI in higher education. The novelty of the model lies in the integration of the mediating relationship through trust (trust in AI outputs, TAIO) between perceived academic integrity risk (PAIR) and behavioral intention to use (BI), while anchoring perceived learning utility (PUL) and perceived effort expectancy (PEE) in AI literacy-specific self-efficacy (AILSE). The model is tested using a sample of 339 higher education students from economics and computer science specializations and validated using the R environment and the SEMinR package as specific software tools. Our proposed research hypotheses consider six reflective latent constructs and a mediating relationship, which we analyze using validated PLS-SEM techniques. All items included in the model constructs are formulated for use in university educational contexts and are adapted to specific AI tools for learning in the university environment. Full article

Curved photovoltaic (PV) systems provide greater architectural form adaptability for building-integrated photovoltaic (BIPV) applications. However, the combined effects of external shading and self-shading result in degradation in power output. In this work, the effectiveness of static reconfiguration techniques for performance optimization of curved BIPV systems under complex partial shading conditions was comparatively evaluated. Employing a 6 × 6 total-cross-tied (TCT) curved PV system with a 120° central angle as the case study, this work simulated the curved irradiance distribution and the corresponding I–V/P–V characteristics through an experimentally proven simulation model. A comparative investigation was performed to evaluate the performance enhancement achieved by three static reconfiguration strategies under the complex combined self-shading and external shading conditions. The results indicate that, compared with the original TCT topology without reconfiguration, the proposed static reconfiguration strategies increased the maximum power output up to 58% by effectively mitigating current mismatch under complex shading conditions. Different static reconfiguration strategies exhibit differentiated advantages when addressing specific shading patterns. Overall, static reconfiguration is demonstrated to be a viable optimization approach for curved BIPV systems without introducing additional electrical complexity, and the selection of specific strategies should be determined by the local shading conditions. Full article

This paper aims to explore non-traveling fractal solutions to an extended Hirota–Satsuma–Ito equation (gHSI) that contains several well-known equations arising in fluid dynamics. Our approach is based on the application of a new variable-separation technique that transfers the governing equation into several solvable forms. Some of these equations can also be solved with standard analytical methods. We employ the modified generalized exponential rational function method (mGERFM), resulting in a varied set of exact analytical solutions. These solutions exhibit a wide range of structural types, such as periodic, rational, hyperbolic, and hybrid configurations. A notable feature of our solutions is that the obtained solutions include several free functions, which provide a systematic way to modify the structure of the waveforms in the solutions. By appropriately selecting these free functions, several categories of dromion-type solutions are introduced. These non-traveling fractal solutions appear to be the first of their kind derived for this equation. The analytical findings are supported by illustrations that demonstrate the complex temporal and spatial dynamics that are characteristic of these solutions. The proposed approach opens a systematic path to non-traveling waves in higher-dimensional systems, where functional flexibility gives rise to self-similar fractal structures, and could be adapted to other equations in physics and engineering. Full article

With the widespread adoption of multiple-input–multiple-output (MIMO) technology, channel state information (CSI) prediction has become a crucial technique for enhancing the performance of wireless communication systems. Traditional channel prediction methods face performance bottlenecks under high-speed mobility and complex channel conditions, making it difficult to meet the requirements of modern communication systems. To address this issue, this paper proposes a fully complex-valued cross-domain modeling framework, termed a complex-valued multi-scale transformer with time–frequency cross-attention network (CMTF-Net), for MIMO CSI prediction. CMTF-Net integrates a learnable multi-scale short-time Fourier transform (LMS-STFT), complex-valued multi-head self-attention (C-MHSA), and bidirectional cross-domain attention for complex-valued sequences (BCDA-CVS). These modules are designed to preserve amplitude–phase consistency, adapt time–frequency representations to CSI evolution, and enable information interaction between temporal and spectral features. On the simulated Overall Test set, CMTF-Net achieves the lowest MAE of 0.000032 and the highest Corr. ($\rho$) of 0.8230 among the compared methods, while maintaining competitive SE and BER values of 0.4240 and 0.2411 at SNR = 10 dB. On the DICHASUS measured datasets, CMTF-Net also shows favorable Test-ID and Test-OOD performance. For example, on DICHASUS-2186, it obtains Corr. ($\rho$)/SE/BER values of 0.8367/0.4935/0.2243 on Test-ID and 0.8061/0.4697/0.2351 on Test-OOD. These results indicate that CMTF-Net provides a balanced performance profile across prediction accuracy, spatial alignment, and communication-oriented evaluation. Full article

Diabetes Mellitus is a chronic metabolic disorder characterized by elevated blood glucose due to defects in insulin secretion or action, or both, leading to serious short- and long-term complications if not effectively managed. However, there is limited qualitative evidence exploring how youths diagnosed with Type 1 Diabetes Mellitus (T1DM) experience disease onset, management, complications, emotional adaptation, and education within the South African public healthcare system. The study aims to investigate the lived experiences of youths living with T1DM in a selected public hospital in Limpopo province, South Africa. The objectives were to explore and describe the lived experiences of youths living with T1DM. A qualitative, explorative, descriptive, and contextual design was used to gain a thorough understanding of the experiences of youths living with T1DM. A non-probability sampling technique was used to select 12 participants using a pre-determined criterion. Data were collected through individual semi-structured interviews using an interview guide. The data were analyzed using Colaizzi’s method, where themes and sub-themes were developed with the inclusion of an independent coder. Measures to ensure trustworthiness and ethical considerations were adhered to throughout the study. The findings revealed that, despite the participants sharing the same diagnosis, they experience multiple interrelated barriers that significantly hindered effective self-care management, such as limited access to diabetic diet, glucometers and supplies, treatment and informational-related barriers, school-related challenges, transportation constraints and inadequate social support. Furthermore, the findings highlighted gaps in early recognition of symptoms, standardized diabetes education, psychosocial support, and continuity of care. The study recommends the need for holistic, patient-centred, and contextualized interventions that do not only address medical management but the socioeconomic, educational, and psychological needs of youths. Full article

Accurate multi-step building load forecasting is critical for optimizing energy management in smart grids and reducing operational costs. However, existing forecasting methods struggle with complex temporal dependencies, seasonal variations, and robust performance under noisy conditions. This paper proposes R-SATNet (Robust Self-Attention Transformer Network), a novel deep learning architecture that integrates multi-head self-attention mechanisms with robust optimization techniques for enhanced building load prediction. The proposed framework incorporates temporal feature extraction modules, adaptive noise suppression layers, and multi-scale attention blocks to capture both short-term fluctuations and long-term seasonal patterns. Extensive experiments on real-world building load datasets demonstrate that R-SATNet achieves superior forecasting accuracy with 15.7% lower RMSE and 12.3% improved MAPE compared to state-of-the-art methods. The model maintains robust performance under various noise conditions and provides reliable multi-step predictions up to 24 h ahead, making it highly suitable for practical smart energy system deployments. The proposed framework is validated across six diverse building datasets spanning commercial, residential, industrial, campus, mixed-use, and healthcare facilities, confirming its generalizability and practical applicability in heterogeneous smart energy environments. Full article

Structural materials in nuclear energy, aerospace, and electronics face long-term irradiation by high-energy particles, triggering microscopic defect evolution and macroscopic performance degradation that limits service safety. This review provides a systematic overview of irradiation damage mechanisms, with particular emphasis on the role of surfaces. The discussion traces the evolution from initial defect generation through energy deposition and displacement cascades to the migration and aggregation of defects toward surfaces, culminating in their interactions with near-surface microstructures. A comparative analysis of damage behaviors in metals, ceramics, silicon-based materials, and polymers is presented, elucidating how distinct mechanisms arise from fundamental differences in crystal structure and chemical bonding. The integration of multiscale simulation techniques with advanced in situ characterization is highlighted as a critical approach for deciphering the cross-scale processes. Current strategies for enhancing radiation resistance including composition optimization, microstructure regulation, and interface design are summarized. Finally, the review outlines key challenges such as multi-field coupling damage characterization and long-term predictive modeling. Future research directions are foreseen to emphasize closer simulation–experiment integration and the design of smart, self-adapting materials, thereby providing comprehensive theoretical and technical support for the development of next-generation radiation-tolerant materials. Full article