Search Results (13,691)

Lightweight artificial aggregates (LWAs) are key materials for sustainable construction, offering reduced structural self-weight, improved thermal performance, and enhanced resource efficiency. However, their production remains geographically concentrated and largely dependent on virgin raw materials, while significant volumes of industrial waste continue to be landfilled. This study addresses these challenges by developing regional LWAs through the incorporation of high levels of porcelain polishing residue (PPR) into red clay matrices, promoting waste valorisation within a circular economy framework. Four mixtures were produced with 20, 40, 60, and 80 wt.% PPR replacing red clay and sintered at 1220 °C and 1240 °C. Raw materials were characterized by laser granulometry, X-ray fluorescence, and X-ray diffraction, while the produced aggregates were evaluated in terms of bloating index, mass loss, bulk density, water absorption, modulus of deformation, crushing strength, and visual morphology. A full factorial experimental design coupled with analysis of variance (ANOVA) was applied to quantify the effects of mixture composition, firing temperature, and aggregate size. All formulations exhibited significant bloating (>35%), with expansion intensifying as PPR content and firing temperature increased, reaching up to 140.6% for mixtures with 80% PPR at 1240 °C. Bulk density values ranged from 0.53 to 1.14 g/cm³, and water absorption remained below 20% for all compositions, confirming their classification as lightweight aggregates. Mechanical performance was strongly dependent on the balance between expansion and matrix densification. The mixture containing 40% red clay and 60% PPR sintered at 1220 °C showed the most favourable performance, achieving crushing strengths of approximately 5.00 MPa while maintaining low density, outperforming commercial reference aggregates. Statistical analysis identified mixture composition and firing temperature as the dominant factors governing expansion and density. The results demonstrate that porcelain polishing residue is a technically viable and sustainable raw material for high-performance LWA production, enabling regional manufacturing routes with reduced environmental impact and strong potential for structural and non-structural construction applications. Full article

Background: Existing XR simulators for enteral feeding rely mainly on self-reported learning outcomes and procedural checklists. As a result, they offer limited opportunities to capture objective behavioral data or to present dynamic patient reactions. This two-stage pilot study evaluated an XR-based gastrostomy tube-feeding simulator (ESTE-TF) that integrates sensor-derived performance metrics and two biological-reaction presentation modalities (projection mapping and tablet display). Methods: In Experiment 1, nursing students completed pre- and post-experience questionnaires assessing perceived learning across seven domains, alongside sensor-based measurements of feeding-start timing, dosing-rate characteristics, and total procedure time. Experiment 2 employed a tablet-based version with four learning items assessed for students and post-experience evaluations collected from registered nurses. Participants also compared the two XR presentation methods. Results: Students demonstrated perceived learning gains of small-to-large magnitude across both experiments (Experiment 1: d = 0.455–0.974; Experiment 2: d = 0.014–0.886), with wide 95% confidence intervals reflecting the exploratory nature of this pilot work. Sensor-derived data showed greater dosing-rate variability and longer procedure times among students than nurses. Participants reported that projection mapping offered a more embodied experience, whereas tablet displays provided clearer visibility. Conclusions: These findings indicate the feasibility and preliminary educational potential of integrating sensing technologies with XR-based biological-reaction presentation for gastrostomy-feeding training. Given the small samples and non-validated measures, results should be interpreted as exploratory. Future research will refine sensor accuracy, establish standardized performance metrics, and evaluate learning outcomes using validated instruments and controlled study designs. Full article

Twenty-five years after the inception of Positive Psychology, its principles are still perceived to be underutilized in addressing working conditions or providing care to healthcare professionals. This article aims to synthesize evidence on the use of Positive Psychology in supporting healthcare workers. A detailed data search and analysis strategy was employed to identify scientific evidence that could answer the following guiding question: What empirical evidence is available regarding the effects of applying Positive Psychology principles or interventions to the mental health and well-being of healthcare professionals? Ten articles were selected and analyzed using the META-CORE model (Meta-level Conceptual, Operational, Reflective Evaluation). Based on the main findings of the ten reviewed studies, three themes were developed: (1) promotion of emotional and psychological well-being, gathering evidence that reflects subjective changes among healthcare professionals; (2) strengthening of personal resources and virtues through a process of self-perception and self-assessment within the work context; and (3) professional appreciation and a positive organizational climate. This scoping review contributes to strengthening the theoretical foundation of current attention to the situation of healthcare workers, aligning more clearly with the conceptual bases of Positive Psychology and its concern with mental health and the growing imperative to address contemporary challenges. Full article

In precision aquaculture, feeding automation becomes particularly challenging when the dispenser operates on a non-fixed platform, as its dynamic behavior introduces perturbations that hinder accurate balance measurement and complicate dispenser control. To address this problem, this work proposes the integration of a weight estimator with robust control strategies. Two control approaches are evaluated: (i) a fuzzy proportional controller, where the fuzzy sets are generated using the fuzzy C-means clustering algorithm, and (ii) a self-tuning regulator (STR) based on based on an Autoregressive with Exogenous Input (ARX) model of the dispenser. In addition, the weight estimator employs a model of additive components dependent on the kinematics of the oscillating platform, with its hyperparameters experimentally optimized through cost function minimization. The proposal was experimentally validated using a compact prototype of an automatic dispenser mounted on an oscillating platform with pelletized feed, demonstrating robust performance and good dispensing accuracy, especially when employing the fuzzy-based control. Full article

Although extensive research has been conducted on the regenerants for unmodified and SBS-modified asphalt, in-depth studies on the activation of regenerants to restore the SBS cross-linked network while preserving their diffusion performance have not yet been reported. This study quantitatively evaluated the activation effect of self-healing regenerants on SBS cross-linked networks by testing the activation degree of 6%, 8%, and 10% cross-linked networks with self-healing regenerants; the phase structure of SBS-modified asphalt before and after regeneration was examined using fluorescence microscopy (FM); the underlying mechanism of the reactive regenerant was elucidated by Fourier Transform Infrared Spectroscopy (FTIR) and Gel Permeation Chromatography (GPC); furthermore, the rheological response characteristics of the reactive regenerant and conventional regenerant were comparatively analyzed. The findings indicated that the SBS cross-linked network self-healing regenerant exhibited a more pronounced activation effect on aged asphalt. Specifically, when the dosage of the regenerant reaches 8%, its repairing effect on the cross-linked network becomes particularly significant. Reconstructing the cross-linked network structure of SBS-modified asphalt enabled the recovery of the viscoelastic properties of the recycled asphalt. Nevertheless, an excessive dosage of the regenerant failed to further enhance the cross-linked structure in a meaningful way and might even exert an adverse impact on the high-temperature performance of the recycled asphalt. Full article

The RAG agentic architecture has demonstrated its ability to transform large language models (LLMs) into agents capable of planning, reasoning, and executing subtasks using external tools or APIs. In the financial sector, one of the main priorities when implementing new technologies—especially in systems like chatbots—is the protection of customer data and the need to maintain customer trust, making the challenges significant. This research presents a robust banking chatbot system that integrates RAG agentic architecture with specialized financial components, setting a new standard in the digital banking sector by prioritizing security, transparency, and functionality. The contributions of this work include the implementation of RAG agentic reasoning and self-correction financial components, and, primarily, the empirical study of the impact of a semantic firewall with online learning in financial RAG agentic systems, evaluated using public benchmarks and standard ranking metrics. Full article

Wireless sensor networks (WSNs) are integral components of smart environments. These allow monitoring and communication to take place autonomously across distributed sensor nodes. Nevertheless, they suffer from constrained resources that make them susceptible to routine-layer attacks. These specifically involve blackhole, flooding, selective forwarding attack traffic and normal traffic. The conventional machine learning and deep learning methods employed are effective in catering to these attacks, yet they have generalization issues when the network conditions are dynamic. The models are generally trained on the local features that make them more dependable and less interpretable. To overcome these issues, this paper proposes an attention-driven transformer encoder for tabular WSN traffic, designed for robust and interpretable intrusion detection in WSNs. The model represents the WSN features as sequential tokens and employs multi-head self-attention to capture global and local dependencies among sensor attributes and employs a multi-head self-attention for capturing the local and global dependencies among the sensor attributes. The framework integrated several components, including normalization, chi-square-based feature selection, and positional embedding. These are followed by multi-layer transformer encoding blocks for the feature fusion and subsequent classification. The framework has been evaluated on the publicly available WSN dataset. Results have been shown to attain an accuracy of 99.37%, which makes it outperform the traditional deep learning baseline models. The comparative analysis has shown the model to be superior in terms of generalization and reduced convergence time. It further offers enhanced interpretability that makes it a good fit to be deployed in real-world scenarios where resources can be constrained. Full article

Background/Objectives: Evaluation of students in practicums is essential in their training process. Mobile technologies enable formative assessments in training, enhance feedback, and improve students’ clinical competence and self-efficacy. Nevertheless, in the absence of previous evidence, their effects on clinical learning must be evaluated with rigor and caution. We aimed to evaluate the improvement in nursing students’ clinical competence and self-efficacy during their clinical practicums using the Evaluation Nursing Student (EVENS) application. Methods: A quasi-experimental design with non-equivalent control and intervention groups was adopted. Participants were not randomly assigned. The inclusion criterion was enrolment for the Supervised Practicum II course in the Nursing degree course at University X. Students agreeing to use the EVENS application during their Supervised Practicum II were assigned to the intervention group. The primary outcomes were student competence and self-efficacy, and the secondary outcome was the usability of the application. The analysis included a comparison of the pre- and post-intervention means of the intervention and control groups using Student’s t-tests. Results: One hundred and forty-nine mostly female (n = 137, 91.9%) students participated in the study. Forty-eight were assigned to the intervention group and 101 to the control group. No statistically significant differences regarding clinical competence or self-efficacy were found between the groups. Tutors rated the application’s usability with an average of 3.8 out of 5. Conclusions: The use of the EVENS application did not improve the primary outcomes. Although it was positively received by tutors as supportive of their role in training students engaged in clinical practicums. Full article

As a key technology for achieving building heating electrification and decarbonization, the air-source heat pump (ASHP) has long been constrained by outdoor heat exchanger frosting in cold and humid regions. Frosting leads to increased thermal resistance, a sharp rise in air-side pressure drop, and the attenuation of heating capacity, while traditional active defrosting methods, such as reverse-cycle defrosting, suffer from high energy consumption and heating interruption. This review aims to systematically present the recent research progress of superhydrophobic surfaces (SHSs) as a highly efficient passive anti-frosting strategy. First, the complex phase-change dynamics of frosting and key influencing factors such as environment and surface characteristics are deeply analyzed. Second, it elucidates how superhydrophobic surfaces achieve delayed frosting and sloughing off defrosting by delaying nucleation, promoting droplet self-removal, and reducing ice adhesion. Furthermore, fabrication processes suitable for complex fin structures are systematically reviewed from the perspectives of subtractive manufacturing, in situ growth, and additive coatings, and their industrialization prospects are compared. Finally, the practical effects of this technology in improving heat transfer coefficients, reducing fan energy consumption, and improving defrosting efficiency are evaluated. Although superhydrophobic technology has significant energy-saving potential, it still faces challenges such as poor long-term durability, wettability failure under extreme conditions, and residual micro-droplets. Future research should focus on the development of highly durable materials, the matching design of micro–nano structures with macro flow channels, and active–passive synergistic anti-frosting strategies. Full article

Critical Thinking (CT) is recognized as a foundational competency for professional readiness, innovation, and ethical reasoning in higher education, enabling students to analyze information, evaluate evidence, and make reasoned decisions in complex environments. The rapid integration of Generative Artificial Intelligence (GenAI) tools, such as large language models, presents new opportunities and risks for CT development. This study conducts a systematic literature review to synthesize empirical evidence on the pedagogical implications and cognitive impact of GenAI on students’ CT. Following PRISMA guidelines, and search terms around GenAI Tools, Critical Thinking And Higher Education, on five major education research databases—Web of Science; Scopus; EBSCOhost (Education Source, ERIC, and APA PsycInfo); and Compendex and Inspec (Elsevier)—63 empirical studies published between January 2023 and April 2025 were analyzed across higher education contexts, disciplines, and intervention designs. Results indicate that GenAI offers notable cognitive affordances, including scaffolding reflective reasoning, promoting self-regulation, and facilitating iterative dialogue and argument evaluation. Pedagogical strategies clustered into four primary integration typologies: AI-based feedback prompts, dialogue simulation and reflection, AI-supported peer review, and critical engagement with AI-generated content. Nearly half of the studies reported statistically significant CT improvements, particularly when GenAI use was guided by structured prompts, reflective activities, and performance-based assessment. However, multiple risks persist, including cognitive offloading, uncritical acceptance of AI outputs, and diminished intellectual autonomy, especially in unguided or surface-level usage. This review highlights the need for intentional pedagogical design, validated CT assessment tools, and longitudinal studies to ensure GenAI acts as a catalyst rather than a substitute for human reasoning. By identifying effective integration strategies and outlining potential pitfalls, this study provides evidence-informed guidance for educators and institutions aiming to responsibly leverage GenAI to strengthen students’ CT skills. Full article

Pediatric gait abnormalities are closely intertwined with musculoskeletal dysfunctions and heightened injury risk, underscoring the urgency of early and accessible screening tools. Here, we develop and validate a video-based semi-supervised Abnormal Gait Recognition Module (AGRM) to address unmet needs in pediatric gait assessment, with a focus on diagnostic performance and clinical interpretability. The AGRM is built on a 3D ResNet backbone, synergistically integrated with a Mean Teacher Module (MTM) to mitigate the limitations of limited labeled clinical data, and a Spatial Hierarchical Pooling Module (SHPM) for robust multiscale spatiotemporal feature extraction—two core innovations tailored to gait dynamics. We trained and validated the model on a hybrid dataset combining self-collected pediatric gait videos and the public CASIA-B dataset, evaluating its performance in binary (normal vs. abnormal) and three-class (normal, genu varum, genu valgum) classification tasks using accuracy, macro-precision, macro-recall, and macro-F1 score. Ablation studies quantified the incremental contributions of MTM and SHPM, while Grad-CAM visualization was employed to enhance model interpretability. In the three-class classification task, the AGRM achieved a 70.5% accuracy, 72.1% macro-precision, 71.5% macro-recall, and a macro-F1 score of 0.718; in the binary task, it yielded a 80.3% precision and 79.2% recall. SHPM significantly augmented spatiotemporal feature aggregation, capturing fine-grained gait dynamics, whereas MTM improved model generalization under constrained labeled data scenarios—findings corroborated by ablation experiments. Grad-CAM visualization confirmed the model’s targeted attention to lower extremity regions, particularly the knee joints, aligning with the pathological loci of gait abnormalities. Collectively, our AGRM demonstrates robust performance and generalization in identifying pediatric gait abnormalities, while effectively capturing key pathological gait characteristics. This video-based intelligent approach offers a promising tool for early gait screening in both clinical and community settings, addressing barriers to accessible pediatric musculoskeletal assessment. Full article

Sorting defective cocoons is a critical procedure in the silk reeling industry to ensure the quality of raw silk products. Currently, this process relies heavily on manual inspection, which is labor-intensive, subjective, and inefficient. While automated sorting based on machine vision offers a promising alternative, existing object detection algorithms struggle to balance accuracy and computational complexity, particularly when detecting tiny surface defects or distinguishing morphologically similar cocoons in dense scenarios. To address these challenges, this paper proposes an efficient silkworm cocoon defect detection model named LMD-YOLO, based on the YOLOv10 architecture. In this model, we introduce three key improvements to enhance feature extraction and multi-scale perception. First, we integrate a Large Separable Kernel Attention (LSKA) module into the C2f structure (C2f-LSKA) of the backbone. This design decomposes large kernels to capture global shape features with minimal computational cost, effectively distinguishing double cocoons from normal ones. Second, we replace standard upsampling with a DySample module in the neck, which utilizes dynamic point sampling to recover fine-grained texture details of tiny defects like surface stains. Third, a Multi-Scale Dilated Attention (MSDA) mechanism is embedded before the detection heads to aggregate semantic information across different scales, improving robustness against background interference. YOLOv10 was selected as the baseline due to its NMS-free characteristic, which mitigates the latency caused by post-processing in high-speed sorting tasks. Evaluations on a self-constructed multi-category dataset indicate that LMD-YOLO surpasses established detectors, including YOLOv8n and Faster R-CNN. Relative to the YOLOv10n baseline, our method improves mAP@0.5 by 3.11%, achieving 94.46%. Notably, Precision and Recall are increased by 3.50% and 2.97%, reaching 89.98% and 93.61%, respectively. With a compact size of 2.68 M parameters and an inference speed of 115 FPS, the proposed model offers a practical trade-off between accuracy and latency for real-time cocoon defect detection. Full article

Background and aim: Traditional marker-based optical motion capture systems are costly, time-consuming to operate, and constrained by laboratory environments, limiting their broader adoption in clinical practice and naturalistic settings. Markerless motion capture based on a sums-of-Gaussians (SoG) body model is a potential alternative; however, its metrological properties for kinematic assessment during walking and slow running remain insufficiently validated. Using a conventional marker-based Vicon system as the reference, this study evaluated the reliability and concurrent validity of an SoG-based markerless system (MocapGS) for bilateral lower-limb joint range of motion (ROM) during gait. Methods: Thirty-six healthy adults completed self-selected-pace speed walking and slow running tasks while both systems synchronously acquired bilateral lower-limb kinematics. The intraclass correlation coefficient (ICC), standard error of measurement (SEM), SEM percentage (SEM%), minimal detectable change (MDC), MDC percentage (MDC%), and root mean square error (RMSE) were used to assess reliability. Concurrent validity was evaluated using the Pearson correlation coefficient, paired-sample t-tests, and the concordance correlation coefficient (CCC) to compare the ROM. Results: Vicon showed moderate-to-high reliability for ROM in most joints across both tasks. By contrast, the MocapGS achieved acceptable ICC values mainly for the sagittal-plane ROM at the hip and knee. The CCC analysis showed no significant agreement between the two systems. Bland–Altman plots showed systematic biases with spatially heterogeneous random errors. During walking, MocapGS systematically overestimated ROM relative to Vicon at several joint axes; the widest limits of agreement (LOA) occurred at the left knee X-axis and right hip Z-axis. During running, overestimation was consistent across all bilateral joints at the X-axis and the right hip at the Y-axis, while the widest LOA were found at the bilateral hip X-axes. These specific discrepancies highlighted the joint–axis combinations with the greatest measurement variance. In walking, the test–retest reliability of the knee flexion–extension ROM measured by the MocapGS approached that of Vicon; however, the SEM% and MDC% were generally larger for MocapGS than for Vicon. The RMSE exceeded 5 degrees for ROM in most joint planes, especially in the frontal and transverse planes and at distal joints; errors increased further during slow running. Conclusions: MocapGS may be used for coarse monitoring of large-magnitude changes in sagittal-plane kinematics during gait; however, it is currently unlikely to replace Vicon for clinical decision-making or detecting subtle gait changes, and its outputs should be interpreted with caution, particularly for ankle kinematics and non-sagittal-plane motion. Full article

Background/Objectives: Family caregivers play a central role in the provision of long-term home-based care and often provide unpaid support over extended periods. This role is associated with substantial psychological, physical, social, and financial burden. Despite high support needs, access to psychosocial services remains limited for many family caregivers. Against this background, AI-based care companions are discussed as a potential low-threshold supplement to existing support structures. The objective of this study was to explore subjectively perceived family caregiver burden and to examine expectations, acceptance conditions, and concerns regarding a hypothetical AI-based care companion, rather than to evaluate effectiveness. Methods: An exploratory mixed-methods study was conducted using an anonymous online survey. Perceived family caregiver stress was assessed using self-developed, non-validated ordinal items, including a single-item global burden rating and categorical stress domains. The questionnaire combined closed-ended items (Likert-scale and multiple-choice) with one open-ended question to assess perceived stress, experiences with psychosocial support, and attitudes toward a hypothetical AI care companion. Participants were recruited via an online caregiving course platform. Data collection was voluntary and anonymous and took place in Germany between October and November 2025. Quantitative data were analyzed descriptively and exploratorily, and qualitative responses were analyzed using thematic analysis. Results: Fifty-five family caregivers participated in the survey. Overall, perceived family caregiver burden was high, with psychological stress most frequently identified as the dominant stress domain. Difficulties in accessing psychosocial support were reported by 58% of the respondents. Willingness to consider using an AI-based care companion varied by degree of acceptance: 36% reported clear willingness, 31% expressed conditional or tentative willingness, and 33% indicated reluctance or rejection. The most frequently selected expected functions included emotional support, early detection of overload, and caregiving-related information. Data protection, professional reliability, and concerns regarding incorrect advice were identified as the most relevant perceived risks. Conclusions: The findings reflect family caregivers’ perceived burden and anticipated needs, highlighting persistent gaps in psychosocial support. From the perspective of respondents, a hypothetical AI-based care companion could represent a complementary support option if it provides personalized, non-judgmental, and reliable assistance. These results describe perceived potential and acceptance conditions, not verified efficacy. Further research, including prototype development, usability testing, and pilot studies, is required to examine feasibility, ethical implications, and real-world impact. Full article

In this paper, we propose a signal intelligence (SIGINT) drone swarm system with a three-dimensional (3-D) volumetric self-complementary array configuration. In the proposed system, multiple drones form two array layers separated along the boresight direction of the system, providing sufficient spacing between drones mounting an antenna element. The antenna elements in one array layer are arranged in a complementary manner to fill empty spaces in the other layer, allowing the system to maximize the number of drones deployed within the aperture area. As a result, the effective electrical spacing at 300 MHz is reduced from 1.7λ and 0.9λ to 0.85λ and 0.45λ along the x- and y-axes, respectively. The array gains of the proposed system are 3.96 dBi, 6.40 dBi, and 15.3 dBi at 100 MHz, 200 MHz, and 300 MHz, and the side-lobe levels (SLLs) are −13.0 dB, −12.7 dB, and −13.0 dB. In addition, the proposed drone swarm SIGINT system is evaluated in a practical SIGINT environment that considers terrain features, and then the detection performance is compared with those of conventional ground-based and airborne SIGINT systems. In this SIGINT scenario, the proposed system can detect signals over an extended detection range of 150 km than those of ground-based and airborne systems. Full article