Search Results (3,220)

School Attendance Problems (SAPs) are multidimensional phenomena with significant short- and long-term effects not only for students’ socio-emotional and cognitive development but also for the broader social welfare of the country. Grounded in Self-Determination Theory (SDT), this study examined the associations between self-perceived competence and distinct non-attendance motivations among adolescents at risk for SAPs. In Study 1, Exploratory and Confirmatory Factor Analyses (EFA: n = 391; CFA: n = 593) supported a refined 13-item four-factor structure of the Italian version of the Adolescent Reasons for School Non-Attendance Scale (ARSNA), with satisfactory fit indices (CFI = 0.90; TLI = 0.87; RMSEA = 0.07), demonstrating its suitability for use with Italian adolescents. In Study 2 (n = 183 at-risk adolescents), hierarchical regression analyses, controlling age, gender, academic achievement, and failed subjects, revealed that lower self-perceived academic competence was associated with Truancy-related reasons (β = −0.27, p < 0.01), whereas School Refusal-related reasons were associated with lower self-perceived general (β = −0.23, p < 0.01) and academic competence (β = −0.18, p < 0.05). These findings provide the first Italian validation of the ARSNA and highlight competence-related processes as central mechanisms underlying Truancy and School refusal in at-risk adolescents, with direct implications for early identification and targeted intervention. Full article

In this study, we present a novel adaptive algorithm, social learning-enhanced deep reinforcement learning (SLDRL), which integrates social learning mechanisms into deep reinforcement learning (DRL) to improve agent performance in both discrete and continuous state-space environments. The proposed hybrid control architecture enables agents to autonomously decide when and how to exploit socially acquired behaviors, balancing social learning with individual exploration through an entropy-based intrinsic motivation mechanism. The framework incorporates online imitation and enactment mechanisms that allow agents to observe and selectively reuse behavioral sequences acquired from other agents during training. We evaluate SLDRL in a sparse-reward discrete grid-based foraging task and in the dense-reward continuous-state/discrete-action CartPole problem. In both domains, SLDRL agents outperform baseline DRL agents, achieving faster learning and higher cumulative rewards. The results show that socially acquired behaviors are utilized adaptively throughout training, with the balance between imitation and individual learning emerging dynamically according to the structure of the environment and the agent’s experience. Comparisons with a behavioral cloning baseline further indicate that selectively integrating observed behaviors can yield more robust long-term learning than direct imitation of demonstration trajectories. Overall, the results demonstrate that SLDRL can effectively leverage online social learning in diverse environments. Full article

The widespread adoption of voice-based authentication systems has been accompanied by an escalating threat from deep learning-based synthetic speech generation techniques. This study presents a comparative and experimental investigation of cepstral feature extraction methods for deepfake speech detection. Specifically, Mel-Frequency Cepstral Coefficients (MFCC), Linear-Frequency Cepstral Coefficients (LFCC), and Constant-Q Cepstral Coefficients (CQCC) are systematically evaluated with respect to their frequency scaling characteristics, spectral resolution properties, and capacity to capture artifacts specific to synthetic speech production. Experiments were conducted on 5571 audio samples drawn from the ASVspoof 2021 Logical Access evaluation partition, with all methods assessed under identical classification conditions using a linear Support Vector Machine. Results indicate that CQCC attains the highest numerical performance, achieving 83.59% accuracy, 89.15% ROC-AUC, and 15.83% Equal Error Rate (EER); however, the performance difference between MFCC and CQCC does not reach statistical significance (p = 0.202). Five-fold cross-validation corroborates this finding (CQCC: 87.89% ± 0.81%). McNemar’s test confirms that the performance difference between LFCC and CQCC is statistically significant (p = 0.036). A fine-grained attack-wise analysis across 13 spoofing systems reveals that no single feature representation consistently outperforms the others across all attack types; CQCC achieves the highest accuracy on 6 out of 13 systems, while MFCC remains competitive on several attack categories. The overall findings indicate that deepfake detection performance is highly sensitive not only to the classifier architecture but also to the choice of frequency scale, cepstral transformation design, and data conditions. Empirical motivation is provided that multi-feature strategies integrating complementary frequency representations may offer more robust and generalizable detection solutions. Full article

Detecting rare structural damage without labeled fault data remains a critical unsolved challenge in structural health monitoring (SHM). Prestressed concrete catenary poles are key elements of high-speed railway infrastructure, and undetected degradation can compromise safety and service reliability. This paper introduces BcDCGAN, a Bayesian conditional deep convolutional generative adversarial network designed for unsupervised anomaly detection in multivariate vibration time series from three in-service catenary poles. Trained exclusively on healthy acceleration signals with wind-speed conditioning, the model learns the normal structural dynamics and produces an uncertainty-based anomaly score that combines reconstruction quality, adversarial evaluation, and epistemic uncertainty into a single decision function. An adaptive, data-driven threshold estimate from healthy validation data enables practical deployment without damage labels. On a real 2017 catenary pole dataset (1606 signals, 70/10/20 split) with injected, physically motivated damage-like patterns, BcDCGAN achieves high anomaly recall with interpretable uncertainty signals and clear separation between normal and anomalous latent representations. The results suggest that Bayesian conditional GANs can support risk-aware monitoring of railway infrastructure under varying environmental and operational conditions. Full article

The rapid evolution of Padel in Saudi Arabia (SA) has positioned the sport as a popular recreational and social activity, mainly among young adults. However, limited research has examined how different forms of sport motivation are associated with mental health outcomes in this emerging context. Drawing on Self-Determination Theory (SDT), this study investigated the associations between intrinsic and extrinsic motivation and depression, stress, and anxiety among Padel players in SA. A quantitative, cross-sectional online survey was conducted with a sample of 475 players, the majority of whom were aged 17–35 and held at least a bachelor’s degree. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to evaluate the relationships between multidimensional motivation factors and mental health symptoms. The findings revealed a nuanced, at times paradoxical, pattern of relationships. Intrinsic motivation to experience stimulation (engaging in an activity because of the positive sensations, excitement, enjoyment, or stimulation that the activity itself provides, rather than for external rewards or pressures) was consistently associated with lower levels of depression, stress, and anxiety, suggesting that enjoyment-driven involvement is associated with better mental health outcomes. In contrast, intrinsic motivation to accomplish was positively correlated with all three mental health indicators, indicating that achievement-oriented engagement might intensify emotional pressure. Among extrinsic motivations, external regulation was significantly associated with poorer mental health outcomes. In contrast, introjected regulation unexpectedly displayed a negative association with psychological distress, demonstrating a potentially adaptive role in this setting. Identified regulation, however, was not significantly associated with any mental health symptoms. These results underscore the “double-edged” nature of sport motivation, showing that not all internal or external motives yield uniformly positive consequences. The study contributed to the growing literature by providing a context-specific understanding of how motivational dynamics function within a rapidly growing sport in Saudi Arabia. In practice, the findings suggested that enjoyment-based involvement was associated with more favourable mental health outcomes, whereas performance-related pressures might be associated with less favourable outcomes. Full article

The present study aims to analyze the development of achievement motivation and self-efficacy belief in the context of elite sports schools. A total of 658 athletes (349 female, 309 male) from Year 5 onwards participated in the central trials and performance assessments in track and field for elite sports schools between 2016 and 2025. In addition to the analysis of physical and athletic performance, the following aspects were also documented: achievement motivation, need for achievement motives and general self-efficacy beliefs. Firstly, differences between the genders were measured in terms of fear of failure and confidence, exhibiting a small effect size ranging from 0.175 to 0.25 and a significance of 0.001 and 0.026. A subsequent analysis of the Kruskal–Wallis test, pertaining to the various groups with differing performance levels, revealed significant disparities in self-discipline (p = 0.010), goal setting (p = 0.013) and confidence (p = 0.029). The effect sizes for these differences ranged from 0.08 to 0.14, indicating a modest magnitude of impact. The psychological profile of the top athletes, which is based on the psychological determinants of the study, differs significantly from that of the other groups of athletes at time t₁ (p = 0.001). It is recommended that appropriate training and guidance from coaches and sports psychologists be provided, given that confidence and self-efficacy expectations are key predictors of physical and athletic performance. Full article

This paper presents the design, implementation, and assessment approach of the Blended Intensive Program (BIP) “Towards Enhanced Pedestrian Safety”, developed under the Erasmus+ framework at the University of Beira Interior in collaboration with four partner universities from Slovenia, Poland, and Croatia. The study applies the EPIC framework (Embedded, Pluralistic, Internationalized, and Connected) to the design and evaluation of a short-term, hybrid, and international learning format in Civil Engineering, addressing a gap in the literature regarding the limited availability of structured and empirically grounded approaches for the design and assessment of Blended Intensive Programs in engineering education. The research presents an applied case study design in higher education supported by a mixed-methods approach, combining quantitative and qualitative data from a cohort of 25 participants across different academic levels. Data collection includes performance-based assessment (tests and project evaluation), structured surveys, and qualitative feedback. The program combined digital technologies, including GIS, machine learning, and video image analysis, with problem-based and collaborative learning in a hybrid format comprising 7 h online and 35 h in person. The BIP fostered both technical competences, such as road safety diagnosis and data-driven modelling, and transversal skills, including teamwork, communication, leadership, and intercultural competence. Results from student assessment and satisfaction surveys indicate high levels of engagement, motivation, and learning achievement. The findings provide empirical support for the applicability of the EPIC framework in the design and implementation of BIPs, demonstrating its adaptability to short-term, hybrid, and international learning contexts. The study highlights the pedagogical value of integrating international collaboration, digital tools, and active learning strategies, while aligning with SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). Full article

This paper presents a miniaturized ultra-wideband high-pass filter integrated with a notch function based on low-temperature co-fired ceramic (LTCC). The design motivation is to realize continuous wideband high-pass transmission while rejecting a narrow in-band interference/leakage component in compact RF front-end modules. The proposed design employs a cascaded structure of a seventh-order quasi-elliptic HPF and a three-section $\lambda /4$ stub notch filter in a single multilayer LTCC chip. Multiple transmission zeros (TZs) are introduced to improve the lower-stopband selectivity, while the three-section coupled-line NF produces a tunable localized rejection band. The LTCC implementation further integrates multilayer capacitors, three-dimensional helical inductors, shielded strip-line coupling stubs, a grounding compensation capacitor, and an isolation wall to balance compactness, impedance matching, and parasitic suppression. The fabricated chip achieves an ultra-wide bandwidth of 2.35 octaves, a notch 20 dB FBW of 8.5%, an insertion loss below 2 dB, a 60 dB roll-off rate of 154.1 dB/GHz within the lower stopband, and a voltage standing wave ratio (VSWR) less than 2. Experimental results validate that the proposed compact chip meets communication requirements and is suitable for 5G base stations, radar systems, and other applications. The chip dimensions are 4.5 mm × 3.2 mm × 2.5 mm. Full article

Decision-making in cyber defense exercises (CDX) is shaped by technical, emotional, motivational, and collaborative human factors under uncertainty and time pressure. This study proposes a human-centered Adaptive Neuro-Fuzzy Inference System (ANFIS) framework to model and predict Counterfactual Decision Reflection (CDR) outcomes in CDX environments. Two complementary datasets representing technical, emotional, motivational, and teamwork-related dimensions were collected from the international Lithuanian Armed Forces cyber defense exercise Amber Mist 2024 and analyzed using Spearman correlation, 3D regression surface modeling, fuzzy rule extraction, and ANFIS prediction to investigate the relationship between human factors and CDR. The results demonstrated that teamwork, communication, and collaboration have a stronger influence on decision stability than isolated technical competencies. Baseline ANFIS evaluation indicated that triangular membership functions provided the best generalization, while generalized bell functions achieved the lowest training errors. To improve model robustness, multiple synthetic data augmentation methods were evaluated. The augmented ANFIS models substantially improved predictive performance, reducing testing error values significantly. The findings confirm that synthetic-data-enhanced neuro-fuzzy modeling provides an effective and interpretable framework for analyzing human-centered cybersecurity decision-making processes in cyber defense exercises. Full article

Graph Neural Networks (GNNs) have achieved strong performance on graph-structured data via neighborhood message passing. Recent studies on GNNs suggest that not all feature dimensions benefit equally from message passing, motivating preference-guided feature splitting rather than uniform aggregation. Empirically, the splitting criterion is affected by class-boundary nodes with label-inconsistent neighborhoods, which confound the estimation of which dimensions should be propagated. Moreover, conducting propagation on the original topology may amplify feature–topology mismatch, causing messages to be passed along incompatible edges. To address these issues, we propose a plug-and-play architecture called Core–Shell Adaptive augmentation with dual-branch Polarized Topology Refinement (CSA-PTR), through simultaneous consideration of clearer feature splitting and a more ideal topology for GNNs to better satisfy the selective propagation criterion. Specifically, CSA-PTR consists of three modules. Core–shell adaptive augmentation stabilizes node representations by a purity-aware clustering algorithm, which reduces the ambiguity in feature-preference estimation. Then, graph feature splitting allocates feature dimensions into a propagation branch and a feature-only branch based on learned preferences. Finally, Dual-branch Polarized Topology Refinement exploits these branches as complementary views to learn polarized weights, yielding a more desirable topology and improving information flow. Extensive experiments on diverse benchmarks show that CSA-PTR achieves competitive performance across the evaluated settings, while consistently improving several representative GNN backbones. Full article

Student mental health, stress, and success are interconnected, yet the mechanisms linking them remain insufficiently understood. Drawing on Stress Optimization and Self-Regulated Learning (SRL) theories, this study examined how stress appraisals and learning practices jointly contribute to student mental health and academic functioning in post-secondary students, supporting a view of student success that comprises both feeling well psychosocially and functioning well academically. Using a sample of 226 university students, the study replicated prior work on the predictive roles of coping self-efficacy (CSE) and stress mindset (SM) across indicators of student success, including flourishing mental health, motivation-related challenges, social-emotional challenges, and GPA. It extended this work by testing whether metacognitive monitoring and adaptation, and academic social engagement, mediated these relationships. Results showed that neither CSE nor SM significantly predicted GPA, suggesting that stress appraisals alone may be insufficient to explain academic achievement. However, both CSE and SM significantly predicted flourishing mental health, and CSE was additionally associated with fewer motivation-related and social-emotional challenges. Mediation analyses indicated that metacognitive monitoring partially explained the relationship between CSE and reduced motivation challenges, while academic social engagement mediated relationships between stress appraisals and social-emotional challenges and mental health. Findings underscore the value of integrating psychosocial and educational perspectives in promoting student success. Full article

Two natural materials, pozzolan from Aguelmous (Middle Atlas, Morocco) and local gravel (3–6 mm), were selected and compared as permeable layers in a multi-soil layering (MSL) system treating domestic wastewater. This comparison was motivated by the need to evaluate a widely used conventional material in Morocco (gravel) against a highly porous volcanic pozzolan expected to improve pollutant removal due to its superior structural and physicochemical properties. The study combined physicochemical characterization, adsorption experiments using methylene blue (MB) and methyl orange (MO), and pilot-scale multi-soil layering (MSL) system tests under vertical-flow conditions. Pozzolan exhibited a highly porous structure with a specific surface area of 13.77 m²·${\mathrm{g}}^{-1}$ and a total porosity of 70.12%, compared with 1.92 m²·${\mathrm{g}}^{-1}$ and 45.49% for gravel, respectively. These properties resulted in higher adsorption performance, with MB removal efficiencies of 66.60% for pozzolan versus 45.35% for gravel at 16.5 mg·${\mathrm{L}}^{-1},$ and MO removal efficiencies of 69.12% versus 58.55% at 10 mg·${\mathrm{L}}^{-1}$. Correspondingly, pozzolan showed higher adsorption capacities for MB (9.976 mg·g⁻¹) and MO (1.534 mg·g⁻¹) than gravel (0.829 and 0.750 mg·g⁻¹, respectively). In pilot-scale operation, pozzolan-based filters achieved higher removal efficiencies for BOD₅ (82.01%), COD (86.14%), ammoniacal nitrogen (89.48%), and total phosphorus (66.30%) compared with gravel (75.56%, 78.24%, 61.56%, and 50.42%, respectively). The enhanced ammoniacal nitrogen removal in filter F2 (6–15 mm) is attributed to favorable aerobic conditions within the system, which likely promoted nitrification due to improved oxygen availability. However, the absence of sufficient anoxic zones limited denitrification, leading to nitrate (${\mathrm{N}\mathrm{O}}_3^{-}$) accumulation. Overall, the results indicate that natural pozzolan is a highly suitable permeable material for enhanced pollutant removal, making it a promising alternative to conventional gravel in decentralized MSL wastewater treatment systems. Full article

To address the prevalent issues of slow convergence, low exploration efficiency, and large value estimation bias in traditional Deep Q-Networks for intelligent vehicle path planning, this paper proposes an improved Dueling Double Deep Q-Network (D3QN) path-planning method guided by the Dynamic Window Approach (DWA) heuristic. The Dueling Double DQN architecture decouples state value and action advantage representations, while the dual estimator of Double DQN mitigates Q-value overestimation. A Prioritized Experience Replay (PER) mechanism samples transitions non-uniformly based on Temporal Difference error with importance sampling correction, improving the reuse of critical samples and training stability. DWA evaluation criteria are transformed into dense heuristic reward signals, enabling the agent to receive continuous multi-dimensional guidance during exploration without executing online trajectory optimization. The environment augments the sparse navigation objective with a Chebyshev goal-progress term motivated by potential-based reward shaping theory together with auxiliary DWA-style channels. The policy-invariance property of potential-based shaping is referenced only for the goal term added to the sparse task reward rather than for the full composite training return. A continuous Ackermann steering kinematic model with a pure-pursuit path-tracking controller is adopted for deployment to ensure executable trajectories under non-holonomic constraints. The proposed method (DWA-D3QN) is systematically evaluated against sparse-reward D3QN, PBRS-guided D3QN, DQN, DDQN, Dueling DQN, APF-DQN, PPO, SAC, TD3, A*, and classical DWA in a grid map environment with static and dynamic obstacles. Results are reported with statistical significance over multiple random seeds. Under complex difficulty, DWA-D3QN achieves a success rate of 94.1 ± 3.4% with a collision rate of 5.9 ± 3.4% over 15 seeds, representing improvements of 64.1 and 8.4 percentage points over the sparse-reward and PBRS-guided D3QN baselines, respectively. Ablation experiments reveal the differentiated contributions of clearance, heading, and velocity shaping terms: clearance awareness provides the strongest single contribution, heading alignment reinforces directional guidance, and velocity regularization refines trajectory quality under the joint constraints of the former two. The full composite reward achieves the lowest variance among all evaluated DRL methods, confirming enhanced training stability. Comparisons with PPO, SAC, and TD3 confirm the statistically significant advantages of the proposed framework (PPO: $p=0.0010$, SAC: $p=0.0007$, TD3: $p=0.0024$). ROS/Gazebo validation with an Ackermann-steered vehicle achieves a success rate of 96.0% with a collision rate of 4.0% over 50 trials, further confirming the applicability of the learned policy in continuous-state environments with realistic vehicle kinematics. Full article

Deep saline aquifers are among the most promising formations for long-term geological CO₂ storage due to their extensive distribution and large storage capacity. Accurate estimation of the CO₂ diffusion coefficient in brine is essential for modeling dissolution trapping, one of the most long-term reliable CO₂ sequestration mechanisms. However, laboratory measurements under reservoir conditions are costly and time-intensive, motivating the development of efficient predictive tools. This study develops machine learning (ML) frameworks for predicting bulk and porous media CO₂ diffusivity by combining two data augmentation strategies—Conditional Tabular Generative Adversarial Networks (CTGAN) and pseudo-labeling (PL)—with four ML algorithms: Random Forest Regression (RFR), XGBoost Regression (XGBR), Natural Gradient Boosting (NGBoost), and Gene Expression Programming (GEP). The database consists of 186 bulk diffusivity and 47 porous media diffusivity observations compiled from experimental and molecular dynamics studies, covering pressures of 0.1–30 MPa, temperatures of 286–673 K, salinities up to 300,000 ppm, and permeabilities of 0.05–2500 Darcy. Data augmentation increased dataset density by approximately 40%, resulting in hybrid datasets of 260 and 68 samples for bulk and porous media diffusivity, respectively. Results show that PL consistently outperforms CTGAN augmentation by preserving physically meaningful relationships and improving predictive accuracy. NGBoost achieved the best performance, with RMSE values of 0.33 and 0.61 for bulk and porous media diffusivity, respectively. Feature-importance analysis identified temperature as the dominant control on diffusivity, followed by pressure and salinity, while permeability exhibited limited influence. The developed framework provides a computationally efficient alternative to extensive laboratory measurements and offers a reliable tool for reservoir simulation, CO₂-EOR studies, and geological carbon storage design under data-limited conditions. Full article

Real-time, free-breathing, ungated cardiac magnetic resonance imaging (CMR) is a clinically valuable alternative to conventional breath-held, ECG-gated cine imaging for patients who cannot sustain breath holds or produce reliable cardiac rhythms, including pediatric, arrhythmic, and respiratory-compromised populations. Achieving diagnostic image quality in this setting requires aggressive k-space undersampling and sophisticated reconstruction. Because no fully sampled reference exists for such acquisitions, supervised deep learning is not directly applicable, motivating unsupervised, subject-specific methods. Existing approaches typically rely on low-dimensional continuous latent spaces, which can limit their capacity to represent concurrent cardiac and respiratory motions as distinct states and may suffer from posterior collapse. We introduce VQ-SToRM (Vector-Quantized Smoothness Regularization on Manifolds), an unsupervised framework that adapts the Vector-Quantized Variational Autoencoder to real-time CMR by replacing the continuous latent manifold of prior existing methods with a learned discrete codebook. The encoder, decoder, and codebook are trained jointly on the undersampled non-Cartesian k-t space data of a single subject. On free-breathing, ungated spiral acquisitions from healthy volunteers, VQ-SToRM accurately resolved cardiac and respiratory motion across all phases of the cardiac cycle. A systematic ablation study identified a compact configuration—a codebook of only five embeddings of dimension ten—as optimal, indicating that a small discrete codebook is sufficient to represent the dominant cardiac and respiratory motion content. Compared with V-SToRM and Time-DIP, VQ-SToRM achieved smoother frame-to-frame transitions and comparable or superior signal-to-noise and contrast-to-noise ratios with lower variance across frames and datasets, offering a promising path toward clinically practical real-time CMR. Full article