Search Results (4,201)

Predicting the degradation and lifespan of embedded computers relies critically on the accurate evaluation of key parameter degradation within computing systems. Accelerated high-temperature tests are frequently employed as an alternative to ambient-temperature degradation tests, primarily due to the excessive duration and cost of ambient-temperature testing. However, the scarcity of effective methodologies for correlating degradation trends across distinct temperature conditions persists as a prominent challenge. This study addresses this gap by leveraging adversarial learning to generate low-temperature degradation sequences from high-temperature datasets. The adversarial learning framework enables feature transfer across diverse operating conditions and facilitates domain adaptation learning. This empowers the model to extract features invariant to degradation trends across multiple temperature conditions. Furthermore, soft dynamic time warping (SDTW) is utilized to precisely align the generated low-temperature sequences with their real-world counterparts. This alignment methodology enables elastic matching of time series data exhibiting nonlinear temporal variations, thereby ensuring accurate comparison and synchronization of degradation sequences. Compared with prior methodologies, our proposed approach delivers superior performance on computer degradation data. It offers a more accurate and reliable solution for the degradation analysis and lifespan prediction of embedded computers, thereby advancing the reliability of computational systems. Full article

The coastal environment is a dynamic system shaped by both natural processes and human activities. In recent decades, increasing anthropogenic pressure and climate change—manifested through sea-level rise and more frequent extreme events—have accelerated coastal retreat, highlighting the need for improved management strategies and standardized tools for coastal risk assessment. Existing approaches remain highly heterogeneous, differing in structure, input data, and the range of factors considered. To address this gap, this study proposes an index-based methodology of general validity designed to quantify coastal erosion risk through the combined analysis of hazard, vulnerability, and exposure factors. The approach was developed for multi-scale and multi-risk applications and implemented across 54 representative sites along the Calabrian coast in southern Italy, demonstrating strong adaptability and robustness for regional-scale assessments. Results reveal marked spatial variability in coastal risk, with the Tyrrhenian sector exhibiting the highest values due to the combined effects of energetic wave conditions and intense anthropogenic pressure. The proposed framework can be easily integrated into open-access GIS platforms to support evidence-based planning and decision-making, offering practical value for public administrations and stakeholders, and providing a flexible, accessible tool for integrated coastal risk management. Full article

Indonesia’s beef cattle sector plays a central role in achieving food security, enhancing rural livelihoods, and fostering economic resilience. However, it faces fragmented governance, import dependence, and persistent challenges of low productivity levels. To capture the evolving evidence base, this study conducted a scoping review of 61 peer-reviewed publications (2019–2025) drawn from six major databases. This study employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review Protocol and Arksey and O’Malley’s framework. Key patterns, advances, and gaps, along with evidence and research recommendations, were identified using the PAGER analytical approach. The dominant themes include production efficiency, environmental sustainability, policy, market linkages, and technological innovation. The results show that most studies employed quantitative or system modelling designs. In the global literature, multidimensional sustainability frameworks have shifted away from production-centric ones, with regional studies highlighting different emphases, such as carbon metrics in South America and market access and livelihood resilience in Asia and Africa. Integrated crop, livestock, and forestry systems; legume-based nutrient management; genotype-specific feeding and breeding; and enabling policies within inclusive markets were revealed through the synthesis of the PAGER framework as four calculated levers for sustainable transformation. However, actors inadequately integrate feed, genetic, climate interactions, and governance mechanisms. According to this review, technological innovation must align with adaptive governance. Climate-resilient, low-carbon beef systems also require the development of inclusive institutional frameworks. Indonesia’s experience demonstrates the benefits of integrating science, policy, and the market to improve productivity, resource stewardship, and equity in tropical livestock systems, thereby enhancing a resilient agri-food supply chain in Indonesia. Full article

Academic adaptation is widely recognized as a critical challenge for international students, with direct implications for their academic success and performance. While existing research has established a positive correlation between academic adaptation and performance, it has not adequately explored this relationship in the context of international students in China. Moreover, the potential mediating role of student engagement warrants further empirical investigation. To address this gap, this study employs a cross-sectional survey of 427 international students in China. The findings confirm a significant positive relationship between academic adaptation and academic performance. Moreover, student engagement was identified as a significant, albeit limited, mediator in this relationship. This result indicates that the effect of student engagement on academic performance may be more immediate, whereas its effect on academic adaptation may be prior. By elucidating this complex mediating pathway, this study advances our understanding of the processes linking adaptation to performance. It offers practical insights for educators seeking to enhance the international student experience. Full article

This study investigates how Australian Zennials (born 1993–1999) navigate slow fashion consumption in a market dominated by fast fashion and affordability challenges. Using semi-structured interviews with 20 participants, it explores their motivations, barriers, and adaptive strategies. Findings reveal that Zennials are driven by ethical values, environmental awareness, and a preference for quality design, yet face constraints such as cost, limited access to sustainable brands, and skepticism toward greenwashing. Rather than a simple value–action gap, participants demonstrate creative solutions, most notably, strategic engagement with the second-hand market. This enables them to practice slow fashion ideals of durability, longevity, and mindful consumption in a cost-effective way. The study reframes the attitude–behavior gap by identifying Perceived Behavioral Control (PBC) as a key enabler, supported by knowledge, repair skills, and peer norms. These insights offer practical implications for brands, designers, and policymakers, positioning the second-hand economy as the central mechanism that operationalizes Zennial engagement with sustainable fashion. Full article

Predicting the Remaining Useful Life (RUL) of engine oil is critical for proactive maintenance and fleet reliability. However, irregular and noisy single-point sampling presents challenges for conventional prognostic models. To address this, a hierarchical physics-informed transfer learning (TL) framework is proposed that reconstructs nonlinear degradation trajectories directly from non-time-series data. The method uniquely integrates Arrhenius-type oxidation kinetics and thermochemical laws within a multi-level TL architecture, coupling fleet-level generalization with engine-specific adaptation. Unlike conventional approaches, this framework embeds physical priors directly into the transfer process, ensuring thermodynamically consistent predictions across different equipment. An integrated uncertainty quantification module provides calibrated confidence intervals for RUL estimation. Validation was conducted on 1760 oil samples from dump trucks, dozers, shovels, and wheel loaders operating under real mining conditions. The framework achieved an average R² of 0.979 and RMSE of 10.185. This represents a 69% reduction in prediction error and a 75% narrowing of confidence intervals for RUL estimates compared to baseline models. TL outperformed the asset-specific model, reducing RMSE by up to 3 times across all equipment. Overall, this work introduces a new direction for physics-informed transfer learning, enabling accurate and uncertainty-aware RUL prediction from uncontrolled industrial data and bridging the gap between idealized degradation studies and real-world maintenance practices. Full article

Background: Many biological drugs have a rival version produced from different cell lines by other manufacturers; these drugs are referred to as biosimilars. By providing accurate information, encouraging patient and medical community acceptance, and advocating for their appropriate usage, pharmacists can play a crucial role in supporting the uptake of biosimilar medicines. Aim: This study aimed to assess pharmacists’ knowledge and attitudes toward biosimilar medicines in the Aseer region in Saudi Arabia. Methods: The study employed a descriptive, cross-sectional design using an anonymous online self-administered questionnaire. The questionnaire was developed by adapting a previously validated instrument and consisted of three sections: demographic data, knowledge about biosimilars, and attitudes toward biosimilars. Two non-probability sampling approaches, i.e., convenience and snowball sampling, were using for data collection. Results: A total of 298 pharmacists participated in the current study. Overall, a total of 135 (45.3%) demonstrated good knowledge of biosimilar medicines, while 163 (54.7%) exhibited poor knowledge. The median knowledge score among the study participants was 5 (5–6). Only 26.2% of pharmacists in the current study correctly identified that biosimilars were not generics and not interchangeable with reference biologics. More experienced pharmacists and those working in industry-related sectors demonstrated greater knowledge of biosimilars (p < 0.05). Pharmacists in the current study demonstrated generally favorable attitudes toward biosimilar medicines. Conclusions: The current study revealed knowledge gaps regarding biosimilar medicines among pharmacists. Targeted educational initiatives, continuing professional development opportunities, and enhanced curricular content could be implemented to address these gaps. Full article

Background: This scoping review systematically maps and synthesises contemporary literature on the biomechanics of active below-knee prosthetic devices, focusing on gait kinematics, kinetics, energy expenditure, and muscle activation. It further evaluates design advancements, including powered ankle–foot prostheses and variable impedance systems, that seek to emulate physiological ankle function and enhance mobility outcomes for transtibial amputees. Methods: This review followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted on ScienceDirect, PubMed and IEEE Xplore for studies published between 2013 and 2023. Search terms were structured according to the Population, Intervention, Comparator, and Outcome (PICO) framework. From 971 identified articles, 27 peer-reviewed studies were found to meet the inclusion criteria between January 2013 and December 2023. Data were extracted on biomechanical parameters, prosthetic design characteristics, and participant demographics to identify prevailing trends and research gaps. This scoping review was registered with Research Registry under the following registration number: reviewregistry 2055. Results: The reviewed studies demonstrate that active below-knee prosthetic systems substantially improve gait symmetry and ankle joint range of motion compared with passive devices. However, compensatory trunk and pelvic movements persist, indicating that full restoration of natural gait mechanics remains incomplete. Metabolic efficiency varied considerably across studies, influenced by device design, control strategies, and user adaptation. Notably, the literature exhibits a pronounced gender imbalance, with only 10.7% female participants, and a reliance on controlled laboratory conditions, limiting ecological validity. Conclusions: Active prosthetic technologies represent a significant advancement in lower-limb rehabilitation. Nevertheless, complete biomechanical normalisation has yet to be achieved. Future research should focus on long-term, real-world evaluations using larger, more diverse cohorts and adaptive technologies such as variable impedance actuators and multi-level control systems to reduce asymmetrical loading and optimise gait efficiency. Full article

Green buildings increasingly couple electrical, thermal, and hydrogen subsystems, yet these assets are typically monitored and controlled through separate standards and protocols. The resulting heterogeneous information models and communication stacks hinder millisecond-level coordination, plug-and-play integration, and resilient operation. To address this gap, we develop a unified information model and a cross-protocol real-time interaction mechanism based on extensions of IEC 61850. At the modeling level, we introduce new logical nodes and standardized data objects that describe electrical, thermal, and hydrogen devices in a single semantic space, supported by a global unit system and knowledge-graph-based semantic checking. At the communication level, we introduce a semantic gateway with adaptive mapping bridges IEC 61850 and legacy building protocols, while fast event messaging and 5G-enabled edge computing support deterministic low-latency control. The approach is validated on a digital-twin platform that couples an RTDS-based multi-energy system with a 5G test network. Experiments show device plug-and-play within 0.8 s, cross-protocol response-time differences below 50 ms, GOOSE latency under 5 ms, and critical-data success rates above 90% at a bit-error rate of 10⁻³. Under grid-fault scenarios, the proposed framework reduces voltage recovery time by about 60% and frequency deviation by about 70%, leading to more than 80% improvement in a composite resilience index compared with a conventional non-unified architecture. These results indicate that the framework provides a practical basis for interoperable, low-carbon, and resilient energy management in green buildings. Full article

The Hadamengou gold deposit, located on the northern margin of the North China Craton, represents one of the region‘s most significant gold mineralization clusters. However, exploration in its deeper and peripheral sectors is constrained by ecological protection policies and the structural complexity of the ore-forming systems. Multivariate analysis combined with multi-model integration provides an effective mathematical approach for interpretating geochemical datasets and guiding mineral exploration, yet, its application in the Hadamengou region has not been systematically investigated. To address this research gap, this study developed a pilot framework in the key Buerhantu area, on the periphery of the Hadamengou metallogenic cluster, applying and adapting a multivariate-multimodel methodology for mineral prediction. The goal is to improve exploration targeting, particularly for concealed and deep-seated mineralization, while addressing the methodological challenges of mathematical modeling in complex geological conditions. Using 1:10,000-scale lithogeochemical data, we implemented a three-step workflow. First, isometric log-ratio (ILR) and centered log-ratio (CLR) transformations were compared to optimize data preprocessing, with a reference column (YD) added to overcome ILR constraints. Second, principal component analysis (PCA) identified a metallogenic element association (Sb-As-Sn-Au-Ag-Cu-Pb-Mo-W-Bi) consistent with district-scale mineralization patterns. Third, S-A multifractal modeling of factor scores (F1–F4) effectively separated noise, background, and anomalies, producing refined geochemical maps. Compared with conventional inverse distance weighting (IDW), the S-A model enhanced anomaly delineation and exploration targeting. Five anomalous zones (AP01–AP05) were identified. Drilling at AP01 confirmed the presence of deep gold mineralization, and the remaining anomalies are recommended for surface verification. This study demonstrates the utility of S-A multifractal modeling for geochemical anomaly detection and its effectiveness in defining exploration targets and improving exploration efficiency in underexplored areas of the Hadamengou district. Full article

Large language models (LLMs) increasingly serve as decision-support systems across linguistically diverse populations, yet whether they reason consistently across languages remains underexplored. We investigate whether LLMs exhibit language-dependent preferences in distributive justice scenarios and whether domain persona prompting can reduce cross-linguistic inconsistencies. Using six behavioral economics scenarios adapted from canonical social preferences research, we evaluate Gemini 2.0 Flash across English and Korean in both baseline and persona-injected conditions, yielding 1,201,200 observations across ten professional domains. Results reveal substantial baseline cross-linguistic divergence: five of six scenarios exhibit significant language effects (9–56 percentage point gaps), including complete preference reversals. Domain persona injection reduces these gaps by 62.7% on average, with normative disciplines (sociology, economics, law, philosophy, and history) demonstrating greater effectiveness than technical domains. Systematic boundary conditions emerge: scenarios presenting isolated ethical conflict resist intervention. These findings parallel human foreign-language effects in moral psychology while demonstrating that computational agents are more amenable to alignment interventions. We propose a compensatory integration framework explaining when professional framing succeeds or fails, providing practical guidance for multilingual LLM deployment, and establishing cross-linguistic consistency as a critical alignment metric. Full article

Background/Objectives: Community health workers (CHWs) play a critical role in advancing health equity by bridging gaps in care for underserved populations. However, limited institutional support, inconsistent training, and lack of integration contribute to high rates of burnout. The Lunch and Learn program was launched in Maryland in fall 2023 as a virtual continuing education and peer-support initiative designed to foster professional development, enhance connections among CHWs, and align with Maryland state CHW certification requirements. This article describes the program’s first year of implementation as a proof-of-concept and model for scalable CHW workforce support. Methods: The program offered twice-monthly, one-hour virtual sessions that included expert-led presentations, Q&A discussions, and dedicated peer-support time. Participant engagement was assessed using attendance metrics, post-session surveys, and annual feedback forms to identify trends in participation, learning outcomes, and evolving professional priorities. Results: Participation increased over time with the program’s listserv expanding from 29 to 118 members and average session attendance more than doubling. CHWs highlighted the program’s value in meeting both educational and emotional support needs. Conclusions: The Lunch and Learn program demonstrates a promising model for addressing burnout through education and community connection. As an adaptable, CHW-informed initiative, it supports both professional growth and well-being. Ongoing development will focus on expanding access, incorporating experiential learning assessments, and advocating for sustainable funding to ensure long-term program impact and CHW workforce stability. Full article

The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, neglecting broader links between methodological evolution, technological maturity, and industrial readiness. To address this gap, this study presents a bibliometric review mapping the development of RL and Deep Reinforcement Learning (DRL) research in Industrial Automation and robotics. Following the PRISMA 2020 protocol to guide the data collection procedures and inclusion criteria, 672 peer-reviewed journal articles published between 2017 and 2026 were retrieved from Scopus, ensuring high-quality, interdisciplinary coverage. Quantitative bibliometric analyses were conducted in R using Bibliometrix and Biblioshiny, including co-authorship, co-citation, keyword co-occurrence, and thematic network analyses, to reveal collaboration patterns, influential works, and emerging research trends. Results indicate that 42% of studies employed DRL, 27% focused on Multi-Agent RL (MARL), and 31% relied on classical RL, with applications concentrated in robotic control (33%), process optimization (28%), and predictive maintenance (19%). However, only 22% of the studies reported real-world or pilot implementations, highlighting persistent challenges in scalability, safety validation, interpretability, and deployment readiness. By integrating a review with bibliometric mapping, this study provides a comprehensive taxonomy and a strategic roadmap linking theoretical RL research with practical industrial applications. This roadmap is structured across four critical dimensions: (1) Algorithmic Development (e.g., safe, explainable, and data-efficient RL), (2) Integration Technologies (e.g., digital twins and IoT), (3) Validation Maturity (from simulation to real-world pilots), and (4) Human-Centricity (addressing trust, collaboration, and workforce transition). These insights can guide researchers, engineers, and policymakers in developing scalable, safe, and human-centric RL solutions, prioritizing research directions, and informing the implementation of Industry 5.0–aligned intelligent automation systems emphasizing transparency, sustainability, and operational resilience. Full article

Astronomy provides an exceptional context for developing data literacy, critical thinking, and computational skills in education. This paper presents a project-based learning (PBL) framework that integrates exploratory data analysis (EDA), fuzzy logic, and explainable artificial intelligence (XAI) to teach students how to extract and interpret scientific knowledge from real astronomical data. Using open-access resources such as NASA’s JPL Horizons and ESA’s Gaia DR3, together with Python libraries like Astroquery and Plotly, learners retrieve, process, and visualize dynamic datasets of comets, asteroids, and stars. The methodology follows the full data science pipeline, from acquisition and preprocessing to modeling and interpretation, culminating with the application of the FAS-XAI framework (Fuzzy-Adaptive System for Explainable AI) for pattern discovery and interpretability. Through this approach, students can reproduce astronomical analyses and understand how data-driven methods reveal underlying physical relationships, such as orbital structures and stellar classifications. The results demonstrate that combining EDA with fuzzy clustering and explainable models promotes deeper conceptual understanding and analytical reasoning. From an educational perspective, this experience highlights how inquiry-based and computationally rich activities can bridge the gap between theoretical astronomy and data science, empowering students to see the Universe as a laboratory for exploration, reasoning, and discovery. This framework thus provides an effective model for incorporating artificial intelligence, open data, and reproducible research practices into STEM education. Full article

Foundation models excel on general benchmarks but often underperform in clinical settings due to domain shift between internet-scale pretraining data and medical data. Multimodal deep learning, which jointly leverages medical images and clinical text, is promising for diagnosis, yet it remains unclear whether domain adaptation is better achieved by fine-tuning large vision–language models or by training lighter, task-specific architectures. We address this question by introducing PairDx, a balanced dataset of 22,665 image–caption pairs spanning six medical document classes, curated to reduce class imbalance and support fair, reproducible comparisons. Using PairDx, we develop and evaluate two approaches: (i) PairDxCLIP, a fine-tuned CLIP (ViT-B/32), and (ii) PairDxFusion, a custom hybrid model that combines ResNet-18 visual features and GloVe text embeddings with attention-based fusion. Both adapted models substantially outperform a zero-shot CLIP baseline (61.18% accuracy) and a specialized model, BiomedCLIP, which serves as an additional baseline and achieves 66.3% accuracy. Our fine-tuned CLIP (PairDxCLIP) attains 93% accuracy and our custom fusion model (PairDxFusion) reaches 94% accuracy on a held-out test set. Notably, PairDxFusion achieves this high accuracy with 17 min, 55 s of training time, nearly four times faster than PairDxCLIP (65 min, 52 s), highlighting a practical efficiency–performance trade-off for clinical deployment. The testing time also outperforms the specialized model—BiomedCLIP (0.387 s/image). Our results demonstrate that carefully constructed domain-specific datasets and lightweight multimodal fusion can close the domain gap while reducing computational cost in healthcare decision support. Full article