Search Results (1,103)

(1) Background: In today’s digital society, challenges like cyberbullying, harmful social media use, and unhealthy digital habits demand innovative and inclusive educational responses. This study investigates the potential of service-learning (SL) and electronic service-learning (e-SL) as experiential approaches to enhance digital well-being among youth. By actively engaging students, educators, and community stakeholders in co-designed projects, SL/e-SL promotes critical awareness, digital citizenship, and prosocial values while addressing digital risks. (2) Methods: This review offers a literature-based analysis of existing programs and good practices that apply experiential education to encourage responsible digital engagement. It explores SL and e-SL experiences across various educational settings. (3) Results: The findings show that SL and e-SL can be effective educational tools, creating meaningful opportunities for youth to participate in tackling digital issues and building inclusive spaces where students, faculty, and communities collaborate to foster digital literacy and well-being. The analysis also led to the development of quality standards for SL and e-SL practices that promote digital well-being. (4) Conclusions: This study highlights key implications for teaching, underscoring the value of integrative pedagogies that connect experiential learning to digital challenges, promoting a more inclusive and responsible digital culture. Full article

Non-cooperative spatial target detection plays a vital role in enabling autonomous on-orbit servicing and maintaining space situational awareness (SSA). However, due to the limited computational resources of onboard embedded systems and the complexity of spaceborne imaging environments, where spacecraft images often contain small targets that are easily obscured by background noise and characterized by low local information entropy, many existing object detection frameworks struggle to achieve high accuracy with low computational cost. To address this challenge, we propose YOLO-GRBI, an enhanced detection network designed to balance accuracy and efficiency. A reparameterized ELAN backbone is adopted to improve feature reuse and facilitate gradient propagation. The BiFormer and C2f-iAFF modules are introduced to enhance attention to salient targets, reducing false positives and false negatives. GSConv and VoV-GSCSP modules are integrated into the neck to reduce convolution operations and computational redundancy while preserving information entropy. YOLO-GRBI employs the focal loss for classification and confidence prediction to address class imbalance. Experiments on a self-constructed spacecraft dataset show that YOLO-GRBI outperforms the baseline YOLOv8n, achieving a 4.9% increase in mAP@0.5 and a 6.0% boost in mAP@0.5:0.95, while further reducing model complexity and inference latency. Full article

The detection of faint, fast-moving objects such as space debris, in optical data is a major challenge due to their low signal-to-background ratio and short visibility time. This work addresses this issue by implementing the Stack-CNN algorithm, originally designed for offline analysis, on an FPGA-based platform to enable real-time triggering capabilities in constrained space hardware environments. The Stack-CNN combines a stacking method to enhance the signal-to-noise ratio of moving objects across multiple frames with a lightweight convolutional neural network optimized for embedded inference. The FPGA implementation was developed using a Xilinx Zynq Ultrascale+ platform and achieves low-latency, power-efficient inference compatible with CubeSat systems. Performance was evaluated using both a physics-based simulation framework and data acquired during outdoor experimental campaigns. The trigger maintains high detection efficiency for 10 cm-class targets up to 30–40 km distance and reliably detects real satellite tracks with signal levels as low as 1% above background. These results validate the feasibility of on-board real-time debris detection using embedded AI, and demonstrate the robustness of the algorithm under realistic operational conditions. The study was conducted in the context of a broader technology demonstration project, called DISCARD, aimed at increasing space situational awareness capabilities on small platforms. Full article

As human–robot shared service environments become increasingly common, autonomous navigation in narrow space environments (NSEs), such as indoor corridors and crosswalks, becomes challenging. Mobile robots must go beyond reactive collision avoidance and interpret surrounding risks to proactively select safer routes in dynamic and spatially constrained environments. This study proposes a deep reinforcement learning (DRL)-based navigation framework that enables mobile robots to interact with pedestrians while identifying and traversing open and safe spaces. The framework fuses 3D LiDAR and RGB camera data to recognize individual pedestrians and estimate their position and velocity in real time. Based on this, a human-aware occupancy map (HAOM) is constructed, combining both static obstacles and dynamic risk zones, and used as the input state for DRL. To promote proactive and safe navigation behaviors, we design a state representation and reward structure that guide the robot toward less risky areas, overcoming the limitations of traditional approaches. The proposed method is validated through a series of simulation experiments, including straight, L-shaped, and cross-shaped layouts, designed to reflect typical narrow space environments. Various dynamic obstacle scenarios were incorporated during both training and evaluation. The results demonstrate that the proposed approach significantly improves navigation success rates and reduces collision incidents compared to conventional navigation planners across diverse NSE conditions. Full article

Device-to-device (D2D) communication enhances network efficiency by enabling direct, low-latency links between nearby users or devices. While most existing research assumes that D2D connections occur with the nearest neighbor, this assumption often fails in real-world scenarios—such as dense indoor environments, smart buildings, and industrial IoT deployments—due to factors like channel variability, physical obstructions, or limited user participation. In this paper, we investigate the performance implications of connecting to the n-th nearest neighbor in a cellular network supporting underlay D2D communication. Using a stochastic geometry framework, we derive and analyze key performance metrics, including the coverage probability and average data rate, for both D2D and cellular links under proximity-aware connection strategies. Our results reveal that non-nearest-neighbor associations are not only common but sometimes necessary for maintaining reliable connectivity in highly dense or constrained spaces. These findings are directly relevant to IoT-enhanced localization systems, where fallback mechanisms and adaptive pairing are essential for communication resilience. This work contributes to the development of proximity-aware and spatially adaptive D2D frameworks for next-generation smart environments and 5G-and-beyond wireless networks. Full article

In the digital environment, grooming—classified as a communication-based risk—has shown a steadily increasing frequency in recent years. In Hungary, increasing attention has been directed to the protection of children’s rights in the digital space in alignment with ensuring their online safety, with both parents and the state playing crucial roles in ensuring a safe digital presence. Within this context, the state bears a particular responsibility to educate not only children but also parents. This study explores how public policies and institutional programs in Hungary address the prevention of grooming and the reactive management of this harm through parental awareness. It examines existing measures aimed at expanding knowledge related to prevention and response, based on a qualitative analysis of the normative foundations of the state’s educational obligations and the relevant academic literature. The study relies on questionnaire data collected from parents of children aged 7 to 18 to examine the effectiveness of state measures and parents’ perceptions of them. The findings of the empirical research may support the development of state-led parental education programs and identify current gaps. As such, it can play a guiding role in shaping the direction of a future, large-scale investigation. Full article

In recommender systems research, the data sparsity problem has driven the development of hybrid recommendation algorithms integrating multimodal information and the application of graph neural networks (GNNs). However, conventional GNNs relying on homogeneous Euclidean embeddings fail to effectively model the non-Euclidean geometric manifold structures prevalent in real-world scenarios, consequently constraining the representation capacity for heterogeneous interaction patterns and compromising recommendation accuracy. As a consequence, the representation capability for heterogeneous interaction patterns is restricted, thereby affecting the overall representational power and recommendation accuracy of the models. In this paper, we propose a hyperbolic graph neural network model with contrastive learning for rating–review recommendation, implementing a dual-graph construction strategy. First, it constructs a review-aware graph to integrate rich semantic information from reviews, thus enhancing the recommendation system’s context awareness. Second, it builds a user–item interaction graph to capture user preferences and item characteristics. The hyperbolic graph neural network architecture enables joint learning of high-order features from these two graphs, effectively avoiding the embedding distortion problem commonly associated with high-order feature learning. Furthermore, through contrastive learning in hyperbolic space, the model effectively leverages review information and user–item interaction data to enhance recommendation system performance. Experimental results demonstrate that the proposed algorithm achieves excellent performance on multiple real-world datasets, significantly improving recommendation accuracy. Full article

The collection grid represents a relevant share of the total initial investments into a wind power plant. Planning/optimizing collection grids is a task that grows severely in complexity according to the size of the analyzed plant, i.e., its number of wind turbines. This paper enhances a well-known mixed-integer linear programming formulation based on the types of cables available for installation and meant for radial grids. More specifically, this work proposes valid constraints that tighten the search space and enable faster convergence. Results indicate that small wind power plants do not benefit from the novel constraints, whereas the time to solve medium and large plants can significantly decrease. However, comparisons against an alternative algorithm based on the flowing power reveal that the proposed enhancement to the cable type-based formulation does not make it the most computationally efficient. In studies regarding Thanet, a wind power plant with 100 wind turbines, the mean convergence time has decreased from 18% up to 85% for different cases when applying the proposed constraints to the cable type-based formulation. Nonetheless, such durations are 2 to 15 times more extensive than what is required by the power-based algorithm. Thus, this paper seeks to raise awareness regarding the assessed algorithms and aid in more efficient inter-array cabling optimization studies. Full article

As cities grow faster and food systems grow more fragile, architects and planners are increasingly challenged to design spaces that not only house people but also support environmental and social well-being. This study investigates how vertical farming can be integrated into residential building facades in Egypt as a strategy to promote local food production and sustainable design. Focusing on a government housing project in Port Said, three façade-based design options were developed and assessed through structured surveys targeting two stakeholder groups: experts and residents. This research revealed a strong interest and awareness across both samples. While users prioritized benefits such as esthetics, air quality, and the ease of use, experts emphasized feasibility concerns, maintenance needs, and policy barriers. Both groups favored the second design option as the most balanced and applicable solution. By foregrounding stakeholder input, this study fills a gap in the existing literature on building-integrated agriculture and provides design and policy recommendations grounded in the local context. It advocates for inclusive design thinking, where technical viability and community values are considered together. While limited to single case and visual assessment methods, this research offers a foundation for further applied studies and broader sustainable design frameworks. Full article

Space debris is an increasingly severe problem in the space industry. According to projections, the number of satellites will increase from the current 10,000 to 100,000 by 2030, specially in LEO orbits. This significant rise in the number of satellites threatens space sustainability, forcing satellites to perform more maneuvers to avoid impacts or leading to the production of more and more space debris due to collisions (Kessler Syndrome). Consequently, substantial efforts have been made to detect and track space debris, leading to the development of the current catalogs. However, with existing technology, detecting and tracking small debris remains challenging. In order to improve the current system, several proposals of Space-Based Situational Awareness (SBSA) have been made. These proposals involve satellites equipped with telescopes to detect space debris and determine their orbits. Unlike prior works, focused primarily on detection rates, this research aims to quantify their accuracy in orbit determination as a function of observation duration, the number of observers, and sensor precision. The Unscented Kalman Filter (UKF) is employed as the core estimation algorithm, leveraging both simulated single-case analyses and Monte Carlo simulations to evaluate system performance under various configurations and uncertainties. The results indicate that a constellation of at least three observers with high-precision instruments and sub-kilometer positioning accuracy can reliably estimate debris orbits within an observation period of 4–7 min, with the mean error in position and velocity obtained being 2.2–3 km and 3–4 m/s, respectively. These findings offer critical insights for designing future SBSA constellations and optimizing their operational parameters to address the growing challenge of orbital debris. Full article

The discovery and design of high-entropy alloys (HEAs) faces significant challenges due to the vast combinatorial design space and uncertainties in thermodynamic data. This work presents a modular, uncertainty-aware computational framework with the primary objective of accelerating the discovery of solid-solution HEA candidates. The proposed pipeline integrates ensemble thermodynamic modeling, Monte Carlo-based estimation, and a structured three-phase pruning algorithm for efficient search space reduction. Key quantitative results are achieved in two main areas. First, for binary alloy thermodynamics, a Bayesian Neural Network (BNN) ensemble trained on domain-informed features predicts mixing enthalpies with high accuracy, yielding a mean absolute error (MAE) of 0.48 kJ/mol—substantially outperforming the classical Miedema model (MAE = 4.27 kJ/mol). These probabilistic predictions are propagated through Monte Carlo sampling to estimate multi-component thermodynamic descriptors, including $\Delta H_{\mathrm{mix}}$ and the $\mathrm{\Omega }$ parameter, while capturing predictive uncertainty. Second, in a case study on the Al-Cu-Fe-Ni-Ti system, the framework reduces a 2.4 million (2.4 M) candidate pool to just 91 high-confidence compositions. Final selection is guided by an uncertainty-aware viability metric, $P\left(\mathrm{HEA}\right)$, and supported by interpretable radar plot visualizations for multi-objective assessment. The results demonstrate the framework’s ability to combine physical priors, probabilistic modeling, and design heuristics into a data-efficient and interpretable pipeline for materials discovery. This establishes a foundation for future HEA optimization, dataset refinement, and adaptive experimental design under uncertainty. Full article

Background: Eco-anxiety and solastalgia are psychological responses to environmental degradation and climate change. This study examines how these concepts are represented in Spanish-language digital media, considering both emotional dimensions and the profiles of content producers. Methods: We conducted an inductive qualitative content analysis of 120 Spanish-language items (online news articles and selected posts from digital platforms) published between October 2023 and March 2024. Items were identified using a Boolean search strategy and initially filtered by LIWC to detect high emotional-and-anxiety term density; final coding followed grounded-theory procedures, resulting in four thematic categories. Results: The most frequent theme was environmental activism (41%), followed by catastrophic thinking (29%), coping strategies (25%), and loss of meaningful places (6%). Among content producers, citizen participants represented 40%, youth activists 25%, and scientists 15%. Digital media function both as sources of anxiety-inducing content and as spaces for awareness-raising and support. Conclusions: While eco-anxiety is not a clinical diagnosis, it exerts a significant psychological impact—particularly on youth and vulnerable groups. Spanish-language digital platforms play an ambivalent role, amplifying distress yet enabling resilience and collective action. Future interventions should leverage these channels to foster environmental awareness, emotional resilience, and civic engagement. Full article

In HPC systems, job scheduling plays a critical role in determining resource allocation and task execution order. With the continuous expansion of computing scale and increasing system complexity, modern HPC scheduling faces two major challenges: a massive decision space consisting of tens of thousands of computing nodes and a huge job queue, as well as complex temporal dependencies between jobs and dynamically changing resource states.Traditional heuristic algorithms and basic reinforcement learning methods often struggle to effectively address these challenges in dynamic HPC environments. This study proposes a novel scheduling framework that combines GTrXL with PPO, achieving significant performance improvements through multiple technical innovations. The framework leverages the sequence modeling capabilities of the Transformer architecture and selectively filters relevant historical scheduling information through a dual-gate mechanism, improving long sequence modeling efficiency compared to standard Transformers. The proposed SECT module further enhances resource awareness through dynamic feature recalibration, achieving improved system utilization compared to similar attention mechanisms. Experimental results on multiple datasets (ANL-Intrepid, Alibaba, SDSC-SP2) demonstrate that the proposed components achieve significant performance improvements over baseline PPO implementations. Comprehensive evaluations on synthetic workloads and real HPC trace data show improvements in resource utilization and waiting time, particularly under high-load conditions, while maintaining good robustness across various cluster configurations. Full article

In an increasingly interconnected world, educational systems must meet the needs of diverse learners from varying cultural, linguistic, and socioeconomic backgrounds. This study aims to explore the principles and practices of designing inclusive digital learning environments tailored to global classrooms, where diversity in language, learning styles, accessibility, and technological resources presents unique challenges and opportunities. This study also explores how leveraging digital tools, artificial intelligence, and adaptive learning technologies can create environments that are responsive to individual learners’ needs and sensitive to cultural nuances. Research on inclusive instructional design was compiled, highlighting methods such as localized content adaptation, multi-language support, and flexible learning pathways. Furthermore, the role of collaborative learning platforms was assessed to foster a sense of community across geographic and cultural boundaries. Case studies were conducted from diverse educational perspectives to propose effective strategies for inclusive digital design, highlighting successful approaches and areas for improvement. Ultimately, a roadmap was constructed for educators, designers, and policymakers to create accessible and culturally aware digital learning spaces to support the academic and social development of all learners, regardless of their background. The results of this study underscore the importance of inclusivity in digital education, contributing to a more equitable and connected global learning landscape. Full article

This study tackles scheduling challenges in multi-product assembly within distributed manufacturing, where components are produced simultaneously at dedicated factories (single capacity per site) and assembled centrally upon completion. To minimize makespan and maximum tardiness, we design a symmetry-exploiting enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) integrated with Q-learning. Our approach systematically explores the solution space using dual symmetric variable neighborhood search (VNS) strategies and two novel crossover operators that enhance solution-space symmetry and genetic diversity. An ε-greedy policy leveraging maximum Q-values guides the symmetry-aware search toward optimality while enabling strategic exploration. We validate an MILP model (Gurobi-implemented) and present our symmetry-refined algorithm against six heuristics. Multi-scale experiments confirm superiority, with Friedman tests demonstrating statistically significant gains over benchmarks, providing actionable insights for efficient distributed manufacturing scheduling. Full article