Search Results (1,726)

Wireless Sensor Networks (WSNs) are widely deployed for long-term monitoring in environments characterized by nonstationary sensing dynamics, intermittent connectivity and continuously evolving network topologies, while reliable, fine-grained labeled data capturing faults and adversarial behaviors remain scarce. This survey systematically reviews and synthesizes recent research that integrates autoencoder-based representation learning with self-supervised learning (SSL) objectives to enhance anomaly detection under these practical constraints. We structure the existing literature through a unified taxonomy encompassing autoencoder variants, self-supervised pretext tasks, spatio-temporal encoding mechanisms and the increasing use of graph-structured autoencoders for topology-aware modeling. Across distinct methodological categories, SSL-augmented frameworks consistently demonstrate improved robustness and stability compared to purely reconstruction-driven baselines, particularly in heterogeneous, dynamic and temporally drifting WSN environments. Nevertheless, this review also highlights several unresolved challenges that hinder real-world adoption, including uncertain scalability to large-scale networks, limited model interpretability, nontrivial energy and memory overheads on resource-constrained sensor nodes and a lack of standardized evaluation protocols and reporting practices. By consolidating publicly available datasets, experimental configurations and comparative performance trends, we derive concrete design requirements for robust and resource-aware anomaly detection in operational WSNs and outline promising future research directions, emphasizing lightweight model architectures, explainable learning mechanisms and federated AE–SSL paradigms to enable adaptive, privacy-preserving monitoring in next-generation IoT sensing systems. Full article

Nanofertilizers have attracted increasing attention as an approach to improve the low nutrient use efficiency of conventional fertilizers, in which only a limited fraction of applied nitrogen, phosphorus, and potassium is ultimately taken up by crops. Beyond their capacity to minimize nutrient losses, nanofertilizers have attracted increasing attention for their possible role in addressing environmental issues, including soil eutrophication and the contamination of groundwater systems. Owing to their nanoscale characteristics, including large specific surface area and enhanced adsorption capacity, these materials enable more precise nutrient delivery to the rhizosphere and sustained release over extended periods, while also influencing soil–plant–microbe interactions. In this review, nanofertilizers are classified into six major categories—macronutrient-based, micronutrient-based, organic, controlled-release, composite, and nano-enhanced formulations—and representative examples and preparation routes are summarized, including green synthesis approaches and conventional chemical methods. The agronomic mechanisms associated with nanofertilizer application are discussed, with emphasis on enhanced nutrient uptake, modification of soil physicochemical properties, and shifts in microbial community composition. Reported studies indicate that nanofertilizers can increase crop yield across different crop species and formulations, while also contributing to improved nutrient cycling. Despite these advantages, several limitations continue to restrict their broader adoption. These include uncertainties regarding long-term environmental behavior, relatively high production costs compared with conventional fertilizers, and the absence of well-defined regulatory and safety assessment frameworks in many regions. Overall, this review highlights both the opportunities and challenges associated with nanofertilizer application and points to the need for further development of cost-effective formulations and standardized evaluation systems that account for their distinct environmental interactions. Full article

As artificial intelligence (AI) becomes increasingly integrated into organizational processes to enhance efficiency, decision-making, and innovation, aligning AI systems with human teams remains a major challenge to realizing their full potential. Although academic interest is growing, the conceptual landscape of human–AI relationships remains fragmented. This study employs a bibliometric co-word analysis of 4093 peer-reviewed documents indexed in Scopus to map the intellectual structure of the field. Using a strategic diagram, we assess the relevance and maturity of five major thematic clusters identified in the field. Results highlight the structural dominance of Human–AI Interactions (Centrality: 1595), Human–AI Collaboration (1150), and Teaming and Augmentation (1131) as foundational themes, while Conversational AI (655), and Ethics and Responsibility (431) emerge as specialized domains. Based on the analysis, we propose a conceptual framework that classifies human–AI relationships into four categories—symbiotic, augmented, assisted, and substituted intelligence—according to the level of AI autonomy and human involvement. Rather than providing prescriptive guidance for practitioners, this framework is intended primarily as a scholarly contribution that clarifies the conceptual landscape and supports future theoretical and empirical work. While potential implications for organizational contexts can be inferred, these are secondary to the study’s main goal of offering a research-based synthesis of the field. Ultimately, our work contributes to academic consolidation by offering conceptual clarity and highlighting opportunities for future research, while underscoring the critical need for ethical alignment and interdisciplinary dialogue to guide future AI adoption. Full article

Carbon fibre-reinforced polymer (CFRP) bonded steel structures are increasingly adopted in offshore floating structures, yet their interfacial performance is highly susceptible to corrosion in marine environments. Corrosion-induced degradation of the CFRP–steel interface can significantly affect load transfer mechanisms and long-term structural reliability. This paper reports an experimental study on corrosion-induced degradation mechanisms and bond–slip behaviour of CFRP–steel double-strap joints. Controlled corrosion damage was generated using an accelerated electrochemical technique calibrated to ISO 9223 corrosivity categories. Tension tests were performed to examine the effects of corrosion degree, CFRP bond length, and the inclusion of glass fibre sheets (GFS) in the adhesive layer on failure modes, ultimate load capacity, and effective bond length. Digital image correlation (DIC) was employed to obtain strain distributions along the CFRP plates and to establish a bond–slip model for corroded interfaces. The results indicate that corrosion promotes a transition from CFRP delamination to steel–adhesive interface debonding, reduces interfacial shear strength to 17.52 MPa and fracture energy to 5.49 N/mm, and increases the effective bond length to 130 mm. Incorporating GFS mitigates corrosion-induced bond degradation and enhances joint performance. The proposed bond–slip model provides a basis for more reliable durability assessment and design of bonded joints in corrosive environments. Full article

Quantifying e-waste in Sub-Saharan Africa remains constrained by scarce data, weak institutional reporting, and the dominance of informal sector activity. We present the first nationwide assessment of e-waste generation and Random Forest-based national forecasting in Sierra Leone. A mixed-methods survey administered 6000 questionnaires across all 16 districts, targeting households, institutions, enterprises, and informal actors. The study documented devices in use, storage, and disposal across the following six categories: ICT, appliances, lighting, batteries, medical, and other electronics. Population growth and device adoption simulations were combined with lifespan distributions and a Random Forest model trained on survey and simulated historical data to construct e-waste flows and forecast quantities through to 2050, including disposal fate probabilities for repurposing versus discarding. The results showed sharp spatial disparities, with Western Urban (Freetown) averaging about 10 kg per capita compared to 1.8 kg per capita in rural areas. Long-term district patterns were highly concentrated: 50-year annual averages indicated that Western Area Urban contributes 15.3% of national totals, followed by Bo (12.7%) and Western Area Rural (12.1%), with the top five districts contributing 59.1%. By 2050, total national e-waste entering reuse and disposal pathways was projected to reach 23.4 kilo tons per year (kt yr⁻¹) with a 95% uncertainty interval (UI) of 11–42 kt yr⁻¹ (and a 99% interval extending to 50 kt yr⁻¹), corresponding to 0.9–3.4 kg/capita/year. Household appliances dominated total mass, ICT devices exhibited high reuse rates, and batteries showed minimal reuse despite high hazard potential. These findings provide critical evidence for e-waste policy, regulation, and infrastructure planning in data-scarce regions. Full article

Autonomous driving perception systems are vulnerable to physical adversarial attacks. Existing defenses largely adopt loosely coupled architectures where visual and kinematic cues are processed in isolation, thus failing to exploit physical spatiotemporal consistency as a structural prior and often struggling to balance adversarial robustness, transferability, accuracy, and efficiency under realistic attacks. We propose a physics-aware trajectory–appearance consistency defense that detects and corrects spatiotemporal inconsistencies by tightly coupling visual semantics with physical dynamics. The module combines a dual-stream spatiotemporal encoder with endogenous feature orchestration and a frequency-domain kinematic embedding, turning tracking artifacts that are usually discarded as noise into discriminative cues. These inconsistencies are quantified by a Trajectory–Appearance Mutual Exclusion (TAME) energy, which supports a physics-aware switching rule to override flawed visual predictions. Operating on detector backbone features, outputs, and tracking states, the defense can be attached as a plug-in module behind diverse object detectors. Experiments on nuScenes, KITTI, and BDD100K show that the proposed defense substantially improves robustness against diverse categories of attacks: on nuScenes, it improves Correction Accuracy (CA) from 86.5% to 92.1% while reducing the computational overhead from 42 ms to 19 ms. Furthermore, the proposed defense maintains over 71.0% CA when transferred to unseen detectors and sustaining 72.4% CA under adaptive attackers. Full article

The issue of overpressure at fire doors in escape passage is often overlooked in traditional tunnel design. Current design approaches tend to overemphasize maintaining positive pressure inside the passage for smoke prevention, which results in excessive resistance when opening fire doors. This can hinder emergency evacuation efficiency and pose a threat to personnel safety. This study focused on a typical 1000-m-long straight escape passage to investigate the overpressure problem of fire doors in highway tunnels from both theoretical and empirical perspectives. Traditional pressure calculations for tunnel escape passages adopt relevant guiding designs from the building category, which may lead to certain errors. Therefore, on this basis, this paper employs pressure calculation equations based on the specific pipeline characteristics of smoke control systems. By solving the pressure calculation equations for the fire doors in escape passages, the thrust required to open the doors in the closed state was analyzed. Results show that the force needed to open a fire door can reach up to 168 N under fire conditions, which far exceeds the allowable limits stipulated in relevant design standards. Furthermore, the results indicate that the maximum allowable length of the escape passage should not exceed 3200 m within acceptable pressure limits through numerical simulation. A mathematical relationship between passage length and fire door pressure was also established, confirming the accuracy of the maximum allowable passage length. This study analyzed the hazards of overpressure in escape passages and proposes a method for determining the maximum permissible passage length, aiming to balance the requirements of smoke control with the safety of personnel evacuation. Full article

Background/Objective: Lifestyle behaviors evolve with age and are driven by biological requirements (e.g., growth and development) and environmental changes (e.g., living and working situations), and they interact bidirectionally with health. Few studies have tracked these behaviors from emerging adulthood into later adulthood. This study examines changes in lifestyle behavior patterns from precollege to adulthood and their association with weight trajectories. Methods: Between 1998 and 2007, 4783 incoming undergraduate students at a northeastern US university completed a health survey. In 2018, 970 completed a follow-up alumni survey. Latent class analysis (LCA) was used to categorize respondents into five lifestyle patterns: stable healthy, stable moderately healthy, stable minimally healthy, worsened, or improved. BMI trajectories were similarly classified into five weight status patterns. Associations between LCA lifestyle patterns and weight were examined using ANCOVA. Results: The most common lifestyle pattern was stable moderately healthy (36.7%). Over 11–20 years, 31.7% of respondents experienced a decline in lifestyle behaviors, and 18.6% improved. During this period, the prevalence of overweight more than doubled (12% to 26%), and obesity quadrupled (2% to 8%). Transitioning to a higher BMI category was noted in 34.9% of those with a stable minimally healthy lifestyle compared with 15.9% among those with a stable healthy lifestyle. Conclusions: Early lifestyle behaviors have long-term implications for weight status. Initiatives that promote the adoption and maintenance of healthy behaviors from precollege through adulthood might reduce obesity risk. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

Growing concerns over greenhouse gas (GHG) emissions have positioned hydrogen fuel cell buses (HFCBs) as a promising alternative for sustainable urban mobility. By eliminating tailpipe emissions and enabling significant reductions in well-to-wheel GHG intensities when hydrogen is sourced from renewables, HFCBs can contribute to improved urban air quality, energy diversification, and alignment with climate goals. Despite these benefits, large-scale adoption faces challenges related to production costs, hydrogen infrastructure, and efficiency improvements across the supply chain. Life cycle assessment (LCA) provides a valuable framework to assess these trade-offs holistically, capturing environmental, economic, and social dimensions of HFCB deployment. However, inconsistencies in system boundaries, functional units, and impact categories highlight the need for more standardized and comprehensive methodologies. This paper examines the potential of hydrogen buses by synthesizing evidence from peer-reviewed studies and identifying opportunities for integration into urban fleets. Findings suggest that when combined with robust LCA approaches, hydrogen buses offer a pathway toward decarbonized, cleaner, and more resilient public transport systems. Strategic adoption could not only enhance environmental performance but also foster innovation, infrastructure development, and long-term economic viability, positioning HFCBs as a cornerstone of sustainable urban transportation transitions. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

Large language models (LLMs) offer new opportunities for agricultural education and decision support, yet their adoption is limited by domain-specific terminology, ambiguous retrieval, and factual inconsistencies. This work presents AgroLLM, a domain-governed agricultural knowledge system that integrates structured textbook-derived knowledge with Retrieval-Augmented Generation (RAG) and a Domain Knowledge Processing Layer (DKPL). The DKPL contributes symbolic domain concepts, causal rules, and agronomic thresholds that guide retrieval and validate model outputs. A curated corpus of nineteen agricultural textbooks was converted into semantically annotated chunks and embedded using Gemini, OpenAI, and Mistral models. Performance was evaluated using a 504-question benchmark aligned with four FAO/USDA domain categories. Three LLMs (Mistral-7B, Gemini 1.5 Flash, and ChatGPT-4o Mini) were assessed for retrieval quality, reasoning accuracy, and DKPL consistency. Results show that ChatGPT-4o Mini with DKPL-constrained RAG achieved the highest accuracy (95.2%), with substantial reductions in hallucinations and numerical violations. The study demonstrates that embedding structured domain knowledge into the RAG pipeline significantly improves factual consistency and produces reliable, context-aware agricultural recommendations. AgroLLM offers a reproducible foundation for developing trustworthy AI-assisted learning and advisory tools in agriculture. Full article

Social media is a valuable resource in many spheres of life in the 21st century; however, excessive, uncontrolled use is associated with various adverse health conditions. In this study, we used a quantitative approach, an observational design, and a comparative scope to compare levels of academic commitment and social media addiction, and their respective dimensions, grouping participants according to various sociodemographic and educational criteria. A total of participants was 1200 students (65.3% female) with an average age of 21.4 years, from the Faculty of Health Sciences at the National University of Chimborazo, Ecuador, and data were collected using the Ultrecht Academic Commitment Scale and Social Media Addiction Questionnaire. When grouped by major, statistically significant differences were found only for dedication (p = 0.038), lack of control over social media use (p = 0.016), and excessive social media use (p = 0.002). When grouped by social media use, there were statistically significant differences in all the dependent variables, with p-values ranging from 0.000 to 0.011. Regarding the frequency of social media use, no significant differences were found in academic engagement (p ≥ 0.05), while the opposite was observed for social media use. A comparative analysis identified categories with significant differences. The results enabling an accurate diagnosis and the adoption of the most appropriate educational strategies; also serves as a theoretical and methodological basis for further research on the subject. Full article

Farmers’ perceptions of improved rice varieties represent a critical initial step in their adoption as climate change adaptation strategies. This study examined farmers’ perceptions by integrating on-farm adaptive research, which compared the agronomic performance of rice varieties, with participatory approaches to capture farmers’ evaluation of improved varieties. A total of 81 farmers from climate-affected areas of Batang Regency were purposively selected as respondents. Data was collected through structured interviews and questionnaires administered during the evaluation of field demonstrations. Farmers’ perception levels were assessed using a Guttman scale and classified into three categories: high, medium, and low. Logistic regression analysis was subsequently employed to examine the relationship between farmers’ socio-demographic characteristics and their acceptance of improved rice varieties. The results indicate that, overall, farmers exhibited a low perception of improved rice varieties. Among the evaluated opinions, Inpari 32 HDB received the highest perception scores across all agronomic attributes. The regression results show that farm size and age significantly influence variety acceptance. The odds ratio for farm size (0.117) suggests that each additional hectare of cultivated land area reduces the likelihood of adopting improved rice varieties by approximately 88.3%, holding other factors constant. In contrast, the odds ratio for age (1.080) indicates that each additional year of age increases the probability of adoption by about 8%. Full article

Blended learning is widely adopted in higher education, yet little is known about how students experience its motivational and instructional features. In this study, we examined undergraduate students’ experiences regarding blended learning by integrating Herzberg’s two-factor theory with the TPACK framework. Semi-structured interviews were conducted with 24 undergraduates at a large Vietnamese university. A theory-informed qualitative content analysis approach was used to identify codes, categories, and themes. These were then mapped onto the pedagogical content knowledge (PCK), technological content knowledge (TCK), and technological pedagogical knowledge (TPK) intersections of the TPACK framework. The findings showed that hygiene factors included unengaging teaching practices, inadequate digital infrastructure, and limited online interaction. These factors often produced frustration and reduced engagement. Motivator factors included active and relevant pedagogical strategies, engaging and accessible digital resources, and technology-facilitated autonomous, expressive, and creative learning work. These factors encouraged deeper learning and stronger motivation. It is concluded that blended learning design must address both hygiene and motivator factors to improve student engagement. Integrating these factors with the TPACK intersections offers a practical model for improved course structures, enhanced digital resources, and the design of more interactive technology-supported pedagogy. The findings provide actionable implications for higher education institutions seeking to improve the quality of blended learning. Full article