Search Results (5,930)

Research on mobile-assisted language learning has mainly focused on teacher-initiated learning, instead of student-initiated learning outside of class. In self-directed language learning with mobile technology, students’ satisfaction with and persistence in learning are conditionafor making self-directed learning effective. This study examined how university learners’ persistence and satisfaction towards self-directed language learning using mobile technology are predicted by mobile readiness, teacher support, and engagement. Survey data from 446 language learners in different disciplines attending Chinese universities were analyzed using structural equation modeling. Learners’ satisfaction was found to be significantly and positively related to their mobile readiness and persistence to both mobile readiness and engagement. Additionally, learners’ mobile readiness was found to make a strongly significant contribution to engagement in self-directed learning using mobile technology. And teacher support was significantly and positively linked to learners’ mobile readiness yet negatively to learners’ engagement. However, the findings showed an indirect and positive impact on learners’ engagement with a mediating role for mobile readiness. Considering the importance of learners’ mobile readiness and the critical impact of teacher support in our context, further research should explore learners’ characteristics and teacher support in mobile self-directed learning settings. Full article

Temporal Knowledge Graph Inference (TKGI) is a cornerstone for intelligent decision-making in dynamic scenarios, but existing models face critical bottlenecks, including inadequate complex-context modeling, a lack of entity importance quantification, insufficient novel-event reasoning accuracy, and weak domain adaptability. To address these issues, this study proposes a semantics-enhanced model (LLM-DSaR) integrating Large Language Models (LLMs), temporal attention networks, and optimized contrastive learning. Specifically, a two-stage LLM semantic enhancement (LLM1 + LLM2) framework first generates structured semantic analysis reports via adaptive prompt engineering, and then extracts domain-specific semantic embeddings from the last-layer hidden states through pooling and linear projection, which are further fused with TransE-based structural embeddings; meanwhile, LLM2 mitigates data sparsity in novel-event reasoning; a dynamic weight fusion (DWF) framework adaptively assigns feature weights to achieve deep feature synergy; an LLM-enhanced contrastive-learning module strengthens event clustering and discrimination. Experiments on five public datasets and a self-constructed Robotics Temporal Knowledge Graph (RTKG) show LLM-DSaR outperforms 16 baselines: on RTKG, its MRR is 10.35 percentage points higher than GCR, and Hits@10 reaches 88.87%. Ablation experiments validate core modules’ effectiveness, confirming LLM-DSaR adapts to professional scenarios like robot maintenance prediction, providing a novel technical paradigm for complex-domain TKG reasoning. Full article

Through the combined lenses of ecofeminism, masculinity studies, and critical animal studies, this article examines the cultural functions of animal metamorphosis in two Walt Disney animated feature films, Beauty and the Beast and The Princess and the Frog. It argues that animality operates as a narrative and symbolic space in which dominant gender norms and human–animal hierarchies are temporarily destabilized and reconfigured. Drawing on film analysis, this study shows how the animal figure enables the emergence of alternative masculinities—sensitive, relational, and ecologically attuned—while simultaneously exposing the structural limits of this apparent subversion. Although these films challenge toxic masculinity and propose more egalitarian interspecific relationships, their narrative resolutions ultimately reinstate anthropocentric and heteronormative frameworks by reasserting human centrality and normative romantic closure. By situating Disney’s representations within broader Western dualistic logics of domination (culture/nature, masculine/feminine, human/animal), I demonstrate that animality functions less as an autonomous mode of existence than as a transitional narrative device facilitating human self-transformation. In doing so, this article contributes to current discussions on how culturally mediated representations of animals shape human social imaginaries, ethical frameworks, and understandings of interspecies relationships. Full article

Antimicrobial resistance represents one of the most formidable global health crises of the 21st century, driven by the diminishing efficacy of conventional antibiotics due to bacterial adaptation and biofilm formation. In response, antimicrobial nanoformulations have emerged as a transformative therapeutic paradigm, offering multifaceted and innovative mechanisms to combat resistant pathogens. This comprehensive review delineates the broad scope and distinct novelty of nano-enabled antimicrobial strategies, moving beyond the single-target limitations of traditional drugs. We systematically explore the diverse architectural classes of nanoformulations—including metallic, polymeric, and self-assembling nanostructures—and elucidate their unique mechanistic actions. These encompass (1) physical disruption of microbial membranes via electrostatic interactions; (2) catalytic generation of reactive oxygen and nitrogen species to induce an ‘oxidative storm’; (3) intracellular sabotage of essential metabolic pathways; (4) the ‘Trojan horse’ strategy for enhanced drug delivery and bioavailability; (5) efflux pump bypass to counteract a major resistance mechanism; (6) penetration and eradication of resilient biofilms; and (7) disarming pathogens through quorum sensing and virulence inhibition. Furthermore, this review highlights the immunomodulatory potential of nanoformulations; their activity beyond bacteria against fungi, viruses, and parasites; and the critical role of the nano-bio interface defined by surface physicochemistry. We also address the translational pathway, considering challenges in nanotoxicology, scalability, and regulatory approval, alongside the ecological impact and economic horizon of these technologies. This sector is projected to reach USD 5.4 to 8.96 billion by 2033 to 2034, with compound annual growth rates of 11 to 21% across antimicrobial nanomaterials, nanocoatings, and nanomedicine applications. By integrating insights from computational modeling and in silico design, this review underscores how nanoformulations leverage synergistic, multi-target approaches to overcome resistance, enhance therapeutic efficacy, and represent a significant leap forward in the future of infectious disease management. The novelty lies in the holistic and mechanistic synthesis of how nanotechnology is redefining antimicrobial warfare, offering a promising arsenal to avert a post-antibiotic era. Full article

The strong non-linearity and multi-scale coupling characteristics of massive heterogeneous components in modern power systems pose severe challenges to traditional numerical simulation methods, rendering them inadequate for urgent online real-time assessment. This paper systematically reviews state-of-the-art hybrid transient stability simulation technologies that deeply integrate physics and data. It first dissects the critical bottlenecks of traditional numerical simulations—specifically computational inefficiency, convergence fragility, and model fidelity gaps—to elucidate the necessity of evolving toward a new physics–data integration paradigm. Subsequently, the review categorizes current methodologies into three technical dimensions: artificial intelligence (AI)-enhanced numerical solvers, AI-based surrogate modeling, and physics-embedded AI modeling. These approaches are synthesized to demonstrate their unique advantages in breaking through computational speed limits, enhancing numerical robustness, and effectively bridging the fidelity gap between simulation models and physical reality. Finally, addressing existing limitations regarding physical consistency and generalization, the paper proposes future research directions, including constructing network architectures with hard physical constraints, enhancing adaptability to complex grid scenarios, and developing self-evolving intelligent simulation frameworks to ensure future grid security. Full article

Background: Sustainability-oriented higher education research has increasingly foregrounded staff wellbeing, motivational practice, and instructional continuity as central to long-term educational effectiveness. However, empirical evidence remains limited for university English language teaching (ELT) instructors operating within fragile, post-conflict, and regionally uneven systems such as Libya. In particular, little is known about whether job satisfaction is translated into motivational teaching behaviour through teacher self-efficacy, or how such relationships vary across demographic and institutional contexts. Addressing this gap is critical for understanding human-capital sustainability in higher education systems facing structural instability. Methods: A quantitative cross-sectional online survey was conducted using Google Forms and regionally stratified convenience sampling across Libya’s Western, Eastern, Central, and Southern regions. The final sample comprised 385 eligible university ELT instructors, including both full-time and part-time staff. Data were collected using three validated instruments: an adapted Teacher Job Satisfaction Questionnaire (21 items), a teacher self-efficacy scale (12 items), and a motivational teaching strategies scale (18 items). All measures demonstrated satisfactory internal consistency. Data analysis was performed using IBM SPSS Statistics v29, applying descriptive statistics, Pearson correlation analysis, regression-based mediation analysis with bootstrapping, and group comparisons using independent-samples t-tests and one-way ANOVA. Results: The sample included 57.14% male and 42.86% female instructors, with 62.86% employed full-time and the majority reporting 6–10 years of teaching experience (51.95%). Mean scores indicated moderate levels of job satisfaction (M = 3.32, SD = 0.94) and teacher self-efficacy (M = 3.03, SD = 0.68), alongside high levels of motivational teaching strategies (M = 4.15, SD = 0.87). Job satisfaction was positively associated with motivational teaching strategies (r = 0.61, p < 0.001) and teacher self-efficacy (r = 0.49, p < 0.001), while teacher self-efficacy was also positively related to motivational strategies (r = 0.53, p < 0.001). Mediation analysis revealed a significant partial mediating effect of teacher self-efficacy (indirect effect = 0.19, 95% CI [0.12, 0.28]). Significant differences were observed across demographic variables (age, gender, teaching experience) and institutional characteristics (employment status and university region). Conclusions: The findings indicate that sustainable teaching practice in Libyan higher education has been jointly shaped by organisational satisfaction and teachers’ capability beliefs. These results underscore the importance of context-sensitive institutional policies that support both structural working conditions and psychological resources. Future research could extend this evidence through longitudinal and mixed-methods designs to deepen understanding of sustainability-oriented teaching dynamics in fragile higher education systems. Full article

The prevalence of osteoporosis, a skeletal disorder characterized by reduced bone mass, microarchitectural deterioration, and increased fracture risk, poses a substantial global healthcare burden. Although animal models and two-dimensional cell cultures have been used to advance bone research, they do not completely replicate the multicellular interactions, extracellular matrix organization, and biomechanical environment of human bone, limiting their translational relevance. This review provides a critical synthesis of recent advances in bone organoid technology, emphasizing biological complexity, technical innovation, and relevance to osteoporosis modeling. Beyond summarizing progress, we distinguish validated capabilities from aspirational claims and identify the methodological gaps that must be addressed before bone organoids can reliably support drug screening, regenerative medicine, and precision approaches. Advances in stem cell biology, tissue engineering, and three-dimensional culture systems have enabled the use of self-organizing, multicellular organoids that reproduce key physiological and pathological features of bone. These systems model estrogen-deficiency-induced bone loss, glucocorticoid-associated osteoporosis, aging-related degeneration, and genetic susceptibility. By integrating osteogenic and endothelial components within biomimetic matrices, bone organoids can support mechanistic studies and pharmacological testing. However, their incomplete vascularization, limited mechanical fidelity, instability, and lack of standardized benchmarks restrict their translational readiness. Overcoming these barriers requires technological refinement, quantitative metrics, and regulatory alignment. Full article

Background: This structured narrative review summarizes and critically appraises evaluated childhood obesity prevention programs implemented in European countries and published between 2015 and 2024. Methods: Systematic searches for PubMed, EBSCOhost, and Google Scholar, complemented by research registries, were conducted year-by-year and independently screened by two reviewers. Results: Five multinational/international programs were identified alongside multiple national initiatives delivered in family, school, community, healthcare, and digital settings. Overall, interventions consistently improved intermediate outcomes—such as selected dietary behaviors, physical activity participation, knowledge, and parental self-efficacy—more than anthropometric endpoints. Effects on BMI/BMI z-score or overweight/obesity prevalence were heterogeneous and frequently small or non-significant, especially for short-duration, single-setting educational interventions. More favorable anthropometric outcomes were commonly reported in long-term, population-scaled physical activity or community-based programs as well as in multidisciplinary healthcare-supported approaches; however, these strategies were typically resource-intensive and sometimes showed differential effectiveness across socioeconomic or cultural groups. Conclusions: The evidence indicates that single-setting or short-term interventions may improve selected behavioral outcomes but are generally insufficient to produce sustained effects on anthropometric measures without integration into broader, multi-level strategies. It is needed to integrate families, schools, communities, and health services with explicit attention to sustainability and equity. Technology-supported tools may strengthen reach and continuity when embedded within comprehensive prevention frameworks. Full article

This quasi-experimental study compared GenAI-supported flipped learning (AI-FL) with reading-based flipped learning (R-FL) in an 11-week preservice chemistry course. Two intact classes completed the same topics and identical in-class activities, differing only in pre-class preparation through guided GenAI-based interactive learning or assigned readings. The study examined lesson-design performance, learning attitude, self-regulated learning, and critical thinking awareness. After controlling for pretest scores, the reading-based flipped learning group showed stronger lesson-design performance, whereas the GenAI-supported group reported more positive learning attitudes. No significant group differences were observed for self-regulated learning or critical thinking awareness. These findings suggest that, in this course context, GenAI-supported pre-class learning may enhance learners’ attitudes but does not necessarily improve rubric-aligned lesson-design performance compared with reading-based preparation. Full article

The deployment of Wireless Sensor Networks (WSNs) remains challenging and time consuming due to the manual commissioning, configuration, and maintenance of resource-constrained Internet of Things (IoT) devices. Achieving precise network-wide time synchronization in such systems further increases this deployment complexity. This paper presents a novel Real-Time Operating System (RTOS)-integrated time synchronization method that distributes an absolute Coordinated Universal Time (UTC) reference across the network using a single Global Navigation Satellite System (GNSS)-enabled host. The method extends the semantics of the RTOS tick count by directly linking it to a global time reference. Consequently, sensor nodes obtain a notion of UTC time and can execute time-critical tasks at precisely defined moments without requiring a dedicated Real-Time Clock (RTC) or GNSS module on each sensor node. This design reduces both hardware cost and overall system complexity. Experimental results obtained on custom-developed hardware running FreeRTOS demonstrate a task synchronization error below ±30 μs between the GNSS reference and a sensor node operating at a clock frequency of 32 MHz. Such precise network-wide synchronization enables more efficient channel utilization, reduces power consumption, and improves the accuracy of both local and coordinated task execution across multiple devices in WSNs. It therefore serves as a key enabler for self-deployable WSNs. Full article

The deterioration of mechanical properties and seepage issues in fractured rock masses represent critical technical bottlenecks in the field of geotechnical engineering. Traditional remediation techniques suffer from drawbacks such as environmental pollution, poor filling effects in microfissures, and susceptibility to secondary cracking, making it difficult to meet the requirements for long-term effectiveness and environmental compatibility in fractured rock mass reinforcement. Microbially induced calcium carbonate precipitation (MICP) technology, which drives the formation of calcium carbonate crystals through microbial metabolic activities, achieves fracture filling and rock mass reinforcement. This technology offers several advantages, including environmental friendliness, high permeability, and excellent compatibility; thus, it represents a cutting-edge direction for green remediation in geotechnical engineering. In this paper, the core mineralization mechanisms of MICP technology, key influencing factors, and engineering applications in fractured rock masses are systematically analysed. Research has indicated that MICP can significantly increase the compressive strength, impermeability, and liquefaction resistance of fractured rock masses, enabling both self-healing of rock fractures and precise filling of existing fissures. Compared with traditional techniques, it demonstrates superior environmental compatibility and remediation efficacy. This review aims to serve as a reference for theoretical research and engineering applications of MICP in fractured rock mass reinforcement. Full article

Wind turbine blade surface defect detection remains challenging due to large variations in defect scales, blurred edge textures, and severe interference from complex backgrounds, which often lead to insufficient detection accuracy and high false and missed detection rates. To address these issues, this paper proposes an improved RTDETR-based detection framework, termed CEA-DETR, for wind turbine blade surface defect inspection. First, a Cross-Scale Multi-Edge feature Extraction (CSME) backbone is designed by integrating multi-scale pooling and edge-enhancement units with a dual-domain feature selection mechanism, enabling effective extraction of fine-grained texture and edge features across different scales. Second, an Efficient Multi-Scale Feature Fusion Network (EMSFFN) is constructed to facilitate deep cross-level feature interaction through adaptive weighted fusion and multi-scale convolutional structures, thereby enhancing the representation of multi-scale defects. Furthermore, an adaptive sparse self-attention mechanism is introduced to reconstruct the AIFI module, strengthening global dependency modeling and guiding the network to focus on critical defect regions under complex background conditions. Experimental results demonstrate that CEA-DETR achieves mAP50 and mAP50:95 of 89.4% and 68.9%, respectively, representing improvements of 3.1% and 6.5% over the RT-DETR-r18 baseline. Meanwhile, the proposed model reduces computational cost (GFLOPs) by 20.1% and parameter count by 8.1%. These advantages make CEA-DETR more suitable for deployment on resource-constrained unmanned aerial vehicles (UAVs), enabling efficient and real-time autonomous inspection of wind turbine blades. Full article

Background/Objectives: Midwifery workforce sustainability faces critical challenges including high burnout and turnover rates threating the service quality and the maternal health outcomes. While self-leadership and self-compassion represent promising psychological resources, their roles relative to organizational factors remain underexplored. This study examined associations between self-leadership, self-compassion, and workforce outcomes (job satisfaction, turnover intention, performance) among Turkish midwives. Methods: A cross-sectional survey was conducted with 346 midwives working in diverse healthcare settings across Turkey from May 2021 to April 2022. Data were collected through an online self-report questionnaire using validated scales for self-leadership and self-compassion as well as measures of job satisfaction, turnover intention, and job performance, and including demographic and organizational items. Descriptive statistics, one-way ANOVA (with Eta-squared [η²] calculated to determine effect size), and correlation analyses were conducted, followed by hierarchical multiple regression and binary logistic regression to examine predictive relationships, with organizational factors entered before psychological resources. Results: Self-leadership and self-compassion demonstrated a moderate positive correlation (r = 0.342, p < 0.01). Self-leadership strongly predicted job performance (OR = 2.497, p = 0.001), particularly through natural reward strategies emphasizing intrinsic motivation (OR = 1.970, p < 0.001). However, neither psychological resource significantly predicted job satisfaction or turnover intention when organizational factors were included. Work schedule, healthcare setting, professional position, and income emerged as primary predictors of satisfaction and retention. Work experience predicted increased psychological distress (OR = 1.073, p = 0.003). Conclusions: Psychological resources demonstrate domain-specific effects on workforce outcomes in midwifery: self-leadership strategies strongly enhance job performance, whereas job satisfaction and turnover intention are influenced primarily by organizational conditions. These findings highlight the need for multi-level strategies to support the sustainability of the midwifery workforce. Full article

Hematocrit (HCT) fluctuations and ambient temperature variations are two critical interference factors limiting the accuracy of electrochemical glucose test strips in self-monitoring of blood glucose (SMBG). In this study, a high-precision screen-printed glucose sensor incorporating in situ impedance-based HCT correction and temperature compensation was developed. The system employs a time-division multiplexing strategy, integrating a normalized thermodynamic model and an in situ impedance-based HCT correction algorithm, to achieve synergistic decoupling and precise compensation of temperature and HCT interferences. Experimental results demonstrate that after multi-parameter synergistic correction, the system exhibits excellent stability across a wide temperature range (10–35 °C) and a broad HCT range (10–70%). The accuracy indicators significantly surpass ISO 15197:2013 standards. In contrast, uncorrected measurements showed deviations ranging from approximately −80% to +30% due to HCT fluctuations. This multiple correction strategy effectively resolves systematic errors in whole blood testing without increasing electrode complexity or requiring pretreatment steps, providing a robust technical solution for high-precision, low-cost personal glucose monitoring. Full article

The twin-blade planetary mixer is critical for processing highly viscous materials in the chemical and polymer industries, yet optimizing its mixing characteristics alongside energy efficiency remains challenging. This study investigates the twin-blade planetary mixer, using computational fluid dynamics simulation methods to analyze the operating parameters and multi-objective optimization of performance in viscous systems. First, the multi-axis stirring process was simulated numerically based on the Planetary Motion Method, revealing the working process at the cross-section and of the blades, thereby unveiling a mixing mechanism driven by cyclic transitions between local shear-intensive kneading and global convective circulation. Then, through orthogonal experiments and ANOVA, the dominant role of the hollow blade’s self-rotation speed on performance was clarified. Furthermore, based on Kriging and NSGA-II, with LINMAP employed for decision making, an optimal parameter combination, specifically a hollow blade self-rotation speed of 94.86 rpm, a speed ratio of 0.063, and a blade-to-bottom height of 2.79 mm, successfully achieved an 8.15% reduction in power consumption, a 20.03% increase in global axial flow, and a 5.01% enhancement in maximum kneading pressure. Full article