Search Results (160)

The rapid integration of Industry 4.0 technologies into non-computer engineering curricula presents a significant pedagogical challenge: avoiding a “one-size-fits-all” approach. While Project-Based Learning (PBL) is widely advocated for teaching Internet of Things (IoT), little research addresses how students from different engineering branches—specifically Industrial, Environmental, and Electrical—respond to identical technical requirements. This study evaluates the deployment of ESP32-based IoT solutions for local agriculture and beekeeping problems in the Peruvian Andes, analyzing the performance and perception of three distinct student cohorts (Total N = 95). Results indicate a significant divergence in learning outcomes and satisfaction. The cohort predominantly composed of Industrial Engineering students (NRC-33563) demonstrated lower adherence to technical code modularization (88% vs. 97%) and lower overall course recommendation rates compared to the mixed cohorts (NRC-33562/33561), who reported higher engagement with the hardware implementation. These findings suggest that while Environmental and Electrical engineering students naturally align with the sensing and actuation layers of IoT, Industrial engineering students may require a curriculum that emphasizes process optimization and data analytics over raw firmware development. We propose a differentiated pedagogical framework to maximize engagement and competency acquisition across diverse engineering disciplines. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

Deep reinforcement learning (DRL) has shown great potential in path planning tasks. However, in sparse reward environments, DRL still faces significant challenges such as low training efficiency and a tendency to converge to suboptimal policies. Traditional reward shaping methods can partially alleviate these issues, but they typically rely on hand-crafted designs, which often introduce complex reward coupling, make hyperparameter tuning difficult, and limit generalization capability. To address these challenges, this paper proposes Curriculum-guided Learning with Adaptive Reward Shaping for Deep Q-Network (CLARS-DQN), a path planning algorithm that integrates Adaptive Reward Shaping (ARS) and Curriculum Learning (CL). The algorithm consists of two key components: (1) ARS-DQN, which augments the DQN framework with a learnable intrinsic reward function to reduce reward sparsity and dependence on expert knowledge; and (2) a curriculum strategy that guides policy optimization through a staged training process, progressing from simple to complex tasks to enhance generalization. Training also incorporates Prioritized Experience Replay (PER) to improve sample efficiency and training stability. CLARS-DQN outperforms baseline methods in task success rate, path quality, training efficiency, and hyperparameter robustness. In unseen environments, the method improves task success rate and average path length by 12% and 26%, respectively, demonstrating strong generalization. Ablation studies confirm the critical contribution of each module. Full article

Background: The World Health Organization and the Australian physical activity guidelines, in line with contemporary research, recommend regular muscle-strengthening activities for optimal muscular fitness in children and adolescents. However, the extent to which muscle-strengthening or muscular fitness receives curricular emphasis is unknown in Australia. Objectives: To examine to what extent the Australian Health and Physical Education Curriculum, Foundation to Year 10 (AHPEC; F–10) addresses and/or promotes muscular fitness. Methods: This study involved a mixed-methods content analysis of the AHPEC F–10 using: (i) conceptual analysis to identify muscular fitness-related terms; and (ii) relational analysis to examine alignment between muscular fitness content and curriculum rationale/aims. A search of national and international physical activity guidelines and school-based muscular fitness intervention literature generated a keyword set to guide abstraction from the AHPEC. Curriculum aim, rationale, level descriptions, achievement standards and content were coded to determine the extent to which muscular fitness was embedded. Intercoder reliability was established via consensus meetings. Muscular fitness content coverage was quantified as the proportion of directly aligned muscular fitness relevant content points per stage and aggregated primary (F–6), secondary (7–10), and F–10 scores. Results: A review of 32 national and one international physical activity guidelines identified 88 muscular fitness activities in total, with some activities appearing in multiple guidelines; 53.1% of national guidelines did not provide explicit muscular fitness examples, and where examples existed, they emphasised accessible modes (e.g., climbing, bodyweight tasks, jumping, and lifting). Additionally, analysis of school-based muscular fitness intervention literature identified 22 distinct muscular fitness activities to guide abstraction. Muscular fitness was absent in the AHPEC rationale and aims, was largely inferred in primary years level description and achievement standards and became more explicit in secondary achievement standards. Direct alignment of content with muscular fitness was non-existent or low across stages of learning (Foundation = 0%, Stage 1 = 0%, Stage 2 = 6.1%, Stage 3 = 9.1%, Stage 4 = 8.6%, Stage 5 = 8.8%). Overall, muscular fitness content coverage averaged 3.8% in primary, 8.7% in secondary, and 5.4% across F–10. Conclusions: The AHPEC treats muscular fitness as a low priority in primary schooling and a minor content area in secondary, yielding developmental messaging that is less aligned with contemporary evidence and physical activity guidelines. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

This study examines the evolution and main trends in the number of students, teaching staff and graduates in Romania’s pre-university education system over the period 1990–2024 (and 1990–2023 for graduates), employing ARIMA models to generate forecasts up to the year 2027. The research is grounded in the premise of profound structural transformations within the Romanian educational system, driven by demographic decline, external migration, recurrent reforms, and shifts in resource allocation. The descriptive analysis highlights a pronounced downward trend for all three variables (students, teaching staff and graduates), reflecting the continuous reduction in the school-age population and the restructuring of the educational network. The statistical tests employed, such as Shapiro–Wilk, Augmented Dickey–Fuller, Durbin–Watson, Breusch–Godfrey and ARCH, validate the selected optimal ARIMA models: ARIMA(1,1,1) for teaching staff, ARIMA(4,1,3) for students, and ARIMA(3,1,5) for graduates. The forecasting results indicate that this declining trend is expected to persist through 2027: the number of teaching staff is estimated to decrease to approximately 178,700 individuals, the number of students is estimated to decrease to around 2.78 million, and the number of graduates is projected to fall until 2026, followed by a potential slight stabilization in 2027. The Spearman correlation analysis indicates strong associations among all variables, suggesting that their dynamics are predominantly shaped by demographic and migratory factors. Granger causality analysis shows that changes in birth rates lead to rapid adjustments in teaching staff within 2–3 years. No significant short-term causality is found for the number of students or graduates, though demographic effects appear after 5–6 years for students, indicating long-term impacts on the school population. This study underscores the importance of econometric methods in informing educational policy, particularly in the context of the marked contraction of the school-age population. It also highlights the need for strategic planning regarding human resources in education, per-student funding, the reorganization of the school network, and curriculum adaptation. Full article

As artificial intelligence (AI) technologies and Virtual Exchange (VE) become increasingly embedded in higher education, AI-driven digital humans have begun to feature in design-oriented virtual international workshops, providing a novel context for examining learner behaviour. This study develops a structural model to examine the links between system support, interaction processes, self-efficacy, satisfaction, and international learning intention. Specifically, it investigates how perceived AI support, system ease of use, and interaction intensity influence students’ continuous participation in international learning through the mediating roles of learning self-efficacy, interaction quality, and satisfaction. Data were collected through an online questionnaire administered to undergraduate and postgraduate students who had participated in an AI-driven digital human–supported online international design workshop, yielding 611 valid responses. Reliability and validity analyses, as well as structural equation modelling, were conducted using SPSS 22 and AMOS v.22.0. The results show that perceived AI support, system ease of use, and interaction intensity each have a significant positive effect on learning self-efficacy and interaction quality. Both self-efficacy and interaction quality, in turn, significantly enhance learning satisfaction, which subsequently increases students’ intentions for sustained participation in international learning. Overall, the findings reveal a coherent causal chain: AI-driven digital human system characteristics → learning process experience → learning satisfaction → sustained participation intention. This study demonstrates that integrating AI-driven digital humans can meaningfully improve learners’ process experiences in virtual international design workshops. The results provide empirical guidance for curriculum design, pedagogical strategies, and platform optimization in AI-supported, design-oriented virtual international learning environments. Full article

Personalized face image generation is essential for Artificial Intelligence-Generated Content (AIGC) applications such as personalized digital avatars and user-customized media creation. However, existing diffusion-based approaches still suffer from insufficient identity consistency and limited text editability. In this work, we propose CoFaDiff, a diffusion-based face generation framework designed for coordinating identity consistency and text-driven editability. To enhance identity consistency, our method integrates a dual-encoder scheme that jointly leverages CLIP and ArcFace to capture both semantic and discriminative facial features, incorporates a progressive curriculum learning strategy to obtain more robust identity representations, and adopts a hybrid IdentityNet–IPAdapter architecture that explicitly models facial location, pose, and corresponding identity embeddings in a unified manner. To enhance text-driven editability, we introduce three complementary optimization strategies: First, long-prompt fine-tuning is employed to reduce the model’s dependency on identity conditions. Second, a semantic alignment loss is incorporated to regularize the influence of identity embeddings within the semantic space of the pretrained diffusion model. Third, during classifier-free guided sampling, we modulate the strength of the identity condition by stacking different numbers of zero-valued identity tokens, enabling users to flexibly balance identity consistency and text editability according to their needs. Experiments on FFHQ and IMDB-WIKI demonstrate that CoFaDiff achieves superior identity consistency and text editability compared to recent baselines. Full article

(1) Background: Interprofessional supervision is an emerging approach in health professions education that strengthens collaborative practice competencies while maintaining profession-specific expertise. Understanding current evidence regarding supervision models, outcomes, and implementation factors is crucial for advancing this field. (2) Methods: This narrative review analyzed 28 studies, including quantitative, qualitative, mixed-methods studies, and systematic reviews. Studies were analyzed for supervision models, outcome measures, evidence of effectiveness, and implementation factors. (3) Results: Six categories of interprofessional supervision models were identified: clinical practice-based, group supervision, competency-based training, skills training, case-based learning, and mentorship/coaching. Across models, interprofessional supervision consistently enhanced collaborative competencies, professional development, clinical skills, and organizational outcomes. Organizational support, structured curricula, interprofessional leadership, and individual readiness facilitated implementation success. Barriers included limited resources, professional silos, and challenges in curriculum integration. (4) Conclusions: Interprofessional supervision shows consistently positive outcomes across diverse models and settings, though more rigorous research designs and standardized outcome measures are needed. Successful implementation requires systematic attention to multiple factors at multiple levels, from organizational support to individual readiness. Interprofessional supervision is positioned for significant advancement through the application of implementation science frameworks and continued research on optimal model characteristics and implementation strategies. Full article

We present a differentiable, curriculum-based optimization workflow for the engineering-oriented design of large-aperture reflective optical systems. The method integrates physics-informed differentiable ray tracing with a progressive, two-stage optimization strategy that evolves from simple Ritchey–Chrétien (RC) foundations to complex four-mirror architectures. Without relying on pretrained models or large datasets, the workflow optimizes geometric and physical parameters under field-weighted RMS, focal length, and dynamic obscuration constraints while maintaining minimal perturbation to primary and secondary mirrors. Validated through Zemax-based analysis, the optimized systems achieve high imaging quality with improved RMS uniformity and stable convergence across varying aperture scales. This approach provides a practical and scalable pathway for the design and optimization of reflective optical instruments, offering strong robustness and adaptability for diverse imaging applications. Full article

Business Economics lacks coherent theoretical foundations despite its prominence in business education. This paper critiques conventional equilibrium-based curricula that begin with ceteris paribus assumptions, proposing instead a systems-based evolutionary framework integrating macro–meso–micro perspectives. Through conceptual analysis, we demonstrate how traditional approaches fail to capture dynamic business realities. Our evolutionary framework incorporates seven pillars: variation–selection–retention dynamics, multi-level integration, dynamic capabilities, institutional networks, complexity theory, organizational form evolution, and behavioral insights. The paper provides curriculum guidelines (12-week structure) that maintain economic literacy while teaching students to reason through feedback loops, uncertainty, and systemic change. This repositioning represents the need for a paradigm shift from static optimization toward understanding businesses as adaptive systems, better preparing students for navigating continuous change in complex environments. Full article

Not all knowledge is equally important in teaching and learning, yet identifying important knowledge remains a fundamental challenge in curriculum design. Traditional methods rely on expert judgment and lack systematic, reproducible criteria. This study demonstrates that node-level concept network analysis can provide operational definitions for important knowledge by deconstructing their characteristics into quantifiable structural features. Using a mathematics concept network, we analyzed both Big Ideas and International Baccalaureate (IB) key concepts, examining whether their characteristics can be systematically deconstructed into network indicators. The analysis successfully deconstructs important knowledge characteristics into structural features, with the deconstruction process demonstrating stability across independent optimization algorithms. While specific indicators (such as degree centrality) emerge from the analysis, the fundamental contribution is establishing the analytical framework itself. This research illustrates the potential of concept network analysis for addressing fundamental challenges in curriculum design and knowledge-focused educational research. Full article

Deploying deep reinforcement learning (DRL) in safety-critical nuclear control is limited less by raw performance than by the absence of licensable, audit-ready evidence. We introduce a Deterministic Assurance Framework (DTAF) that converts controller behavior into licensing-grade proof by combining the following: (i) deterministic licensing gates tied to formal safety and performance limits (e.g., Total Time Unsafe (TTU) = 0; bounded Transient Severity Score (TSS); and minimum Grid Load-Following Index (GLFI)); (ii) a portfolio of adversarial stress tests representative of off-nominal operation; and (iii) a traceability and explainability package that renders every evaluated action auditable. The DTAF is demonstrated on a high-fidelity pressurized-water-reactor (PWR) simulation model used as a software-in-the-loop testbed. Three governor architectures are evaluated under identical, fixed scenarios: a curriculum-trained Soft Actor–Critic (SAC) agent, and Differential-Evolution-optimized Proportional–Integral–Derivative (PID-DE) and Fuzzy-Logic (FLC-DE) Controllers. Performance is assessed deterministically via gate-aligned metrics—TTU, TSS, GLFI, cumulative control effort (CE_sum), valve-reversal count (V_rev), and speed overshoot (OS_ω). Across the adversarial portfolio, the SAC controller meets the predeclared licensing gates in single-run evaluations, whereas the strong conventional baselines violate gates in specific high-severity cases; where all methods remain within the safe envelope, the SAC delivers a higher GLFI and lower CE_sum, with fewer reversals and reduced overshoot. All licensing conclusions derive from deterministic single-run tests; a small, fixed-seed check (three seeds with descriptive intervals) is reported separately as non-licensing supplementary analysis. By producing transparent, reproducible artifacts, the DTAF offers a regulator-oriented pathway for qualifying DRL controllers in grid-interactive nuclear operations. Full article

The aerial game environment is complex. To enhance mission success rates, UAVs must comprehensively consider threats from various directions and distances, as well as autonomous evasion maneuver decision-making methods for multiple UAV platforms, rather than solely focusing on threats from specific directions and distances or decision-making methods for fixed UAV platforms. Accordingly, this study proposes an autonomous missile evasion maneuver decision-making method for UAVs, suitable for multi-scenario and multi-platform transferable mission requirements. A three-dimensional UAV-missile pursuit-evasion model is established, along with state-space, hierarchical maneuver action space and reward function models for autonomous missile evasion. The auto-regressive multi-hybrid proximal policy optimization (ARMH-PPO) algorithm is proposed for this model, integrating autoregressive network structures and utilizing long short-term memory (LSTM) networks to extract temporal features. Drawing on exploration curriculum learning principles, temporal fusion of process and event reward functions is implemented to jointly guide the agent’s learning process through human experience and strategy exploration. Additionally, a proportion integration differentiation (PID) method is introduced to control the UAV’s maneuver execution, reducing the coupling between maneuver control quantities and the simulation object. Simulation experiments and result analysis demonstrate that the proposed algorithm ranks first in both average reward value and average evasion success rate metrics, with the average evasion success rate approximately 8% higher than the second-ranked algorithm. In the three initial scenarios where the missile is positioned laterally, head-on, and tail-behind the UAV, the UAV’s missile evasion success rates are 95%, 70%, and 85%, respectively. Multi-platform simulation results demonstrate that the decision model constructed in this paper exhibits a certain degree of multi-platform transferability. Full article

In human daily-life scenarios, humanoid robots need not only to stand up smoothly but also to autonomously sit down for rest, energy management, and interaction. This capability is crucial for enhancing their autonomy and practicality. However, both sitting and standing involve complex dynamics constraints, diverse initial postures, and unstructured terrains, which make traditional hand-crafted controllers insufficient for multi-scenario demands. Reinforcement Learning (RL), with its generalization ability across high-dimensional state spaces and complex tasks, offers a promising solution for automatically generating motion control policies. Nevertheless, policies trained directly with RL often produce abrupt motions, making it difficult to balance smoothness and stability. To address these challenges, we propose a two-stage reinforcement learning framework: In the first stage, we focus on exploration and train initial policies for both sitting and standing, with relatively weak constraints on smoothness and joint safety, and without introducing noise. In the second stage, we refine the policies by tracking the motion trajectories obtained in the first stage, aiming for smoother transitions. We model the tracking problem as a bi-level optimization, where the tracking precision is dynamically adjusted based on the current tracking error, forming an adaptive curriculum mechanism. We apply this framework to a 1.7 m adult-scale humanoid robot, achieving stable execution in two representative real-world scenarios: sitting down onto a chair, stand up from a chair. Our approach provides a new perspective for the practical deployment of humanoid robots in real-world scenarios. Full article