Search Results (2,803)

This study investigates how the integration of artificial intelligence reshapes the professional identity of university English teachers within higher education. Against the backdrop of global digital transformation, AI presents both disruptive potential and a significant ‘adaptation crises’ for educators. Using Bronfenbrenner’s Ecological Systems Theory as an analytical framework, this research systematically reviews the existing literature to address three objectives: (1) identify directions and typologies of teacher identity transformation; (2) analyse multilayered ecological influential factors; and (3) examine core challenges teachers face and their corresponding coping strategies. The findings indicate that professional identity is dynamically reconstructed across nested ecosystems, from micro-level classroom interactions to chronological level sociocultural contexts. This study advances an integrative perspective on the complex technology–teacher relationship, highlighting that successful identity transformation requires coordinated support across all ecological levels. Theoretical and practical implications are discussed to facilitate sustainable teacher development in the face of AI. Full article

This study investigates the educational potential of instructor-designed Generative Pre-trained Transformers (GPTs) in a university-level Japanese course, drawing on self-determination theory (SDT) and the noticing hypothesis. Using a mixed-methods design, we examined how the continuous use of an instructor-developed Artificial Intelligence tutoring system (basic Japanese GPTs) relates to learners’ psychological needs satisfaction, cognitive noticing, and perceptions of Artificial Intelligence (AI)-assisted learning among 74 undergraduate students at a South Korean university. Quantitative data were analyzed using descriptive statistics and Pearson correlation; qualitative data from open-ended items and reflective writing underwent systematic content analysis. The findings revealed three key patterns. First, learners reported relatively high levels of satisfaction across all three SDT needs—autonomy, competence, and relatedness—particularly in relation to self-directed reviews and affective safety. Second, qualitative analysis identified three distinct noticing experiences: AI-supported clarification of linguistic form, noticing through intentional error generation and AI feedback, and metacognitive regulation of learning strategies. Third, the learners perceived the instructor-designed GPTs not merely as a convenience tool but as a structured learning environment that supported output-oriented, interaction-based practice. These findings suggest that the educational effectiveness of generative AI in foreign language education is not determined by frequency of use alone but also by the quality of pedagogical design underlying its deployment. This study contributes a practice-based model for AI integration in general education language courses while acknowledging limitations related to its single-course scope and reliance on self-reported data. Full article

Artificial intelligence (AI) data centers are creating large, power-dense loads, often faster than transmission lines, substations, transformers, and grid interconnections can be expanded. This review assesses whether solid oxide fuel cells (SOFCs) can provide dependable on-site power during these grid delivery constraints and remain competitive after grid capacity becomes available. We critically synthesized evidence on AI electricity demand, competing power supply options, SOFC efficiency and durability, commercial deployments, environmental impacts, thermal and electrical integration, and hybrid SOFC–battery–grid systems. We also performed a screening-level levelized cost of electricity sensitivity analysis covering natural gas prices, carbon costs, stack replacement, grid electricity prices, and the avoided cost of delayed grid access. The evidence indicates that commercial SOFC systems can achieve approximately 50–60% net electrical efficiency and scale modularly from 325 kW units to a planned deployment of up to 2.45 GW. A nominal 100 MW installation would require approximately 308 such modules and at least 3600 m² of direct equipment area, excluding auxiliary systems and safety setbacks. However, multi-year durability targets of about 40,000 h, fuel and carbon price exposure, slow transient response, lifecycle methane emissions, and limited opportunities to use high temperature exhaust heat remain important constraints. The economic analysis indicates that avoided grid delay costs can justify SOFCs as bridge assets, whereas long-term retention requires competitiveness without this temporary benefit. SOFCs are therefore most suitable for sites that prioritize rapid access to firm power, modularity, reliability, and low local air pollutant emissions, rather than as a universal alternative to grid expansion. Full article

Background: Large loop excision of the transformation zone (LLETZ) is an established surgical procedure for cervical intraepithelial neoplasia; however, secondary postoperative bleeding may lead to unplanned outpatient visits, hospitalization, or surgical intervention. This study aimed to evaluate the incidence and risk factors of postoperative bleeding after LLETZ. Methods: We conducted a retrospective single-center cohort study comprising 1493 patients who underwent LLETZ for the treatment of cervical dysplasia between January 2008 and May 2023 at the Medical University of Vienna. Postoperative bleeding was defined as an unscheduled outpatient presentation for bleeding from the operative site within eight weeks after LLETZ requiring additional treatment. Clinical, surgical and histopathological parameters were analyzed. Univariable logistic regression was performed to identify factors associated with postoperative bleeding, with additional stratified analyses according to the use of intraoperative cervical infiltration. Results: Postoperative bleeding occurred in 56 patients (3.8%). The median time to bleeding was 13.5 days (IQR 9.0–17.0). Among patients with postoperative bleeding, 20 (35.7%) required hospitalization, 10 (50.0%) underwent operative intervention, and 2 (10.0%) required blood transfusion. In logistic regression, increasing cone length was associated with higher odds of postoperative bleeding (OR 1.83 per cm, 95% CI 1.06–3.18, p = 0.031). Anticoagulation and/or antiplatelet therapy was also associated with an increased bleeding risk (OR 3.61, 95% CI 1.46–8.96, p = 0.006). Higher BMI was associated with lower odds of postoperative bleeding (BMI per +5 kg/m²: OR 0.62, 95% CI 0.43–0.90, p = 0.011). Conclusions: LLETZ is associated with a low risk of clinically relevant postoperative bleeding, with an unplanned outpatient re-presentation rate of 3.8% and a re-operation rate of 0.7%. Increased cone length and anticoagulation and/or antiplatelet therapy were associated with higher bleeding rates. Larger prospective studies are needed to validate these findings and improve individualized perioperative risk assessment. Full article

Financial development can influence carbon emissions through capital allocation, technological support, and policy transmission. To investigate the inherent association between grassroots inclusive financial institutions and territorial green transformation, this study establishes a city-level panel dataset of the Yangtze River Delta urban agglomeration covering the period from 2010 to 2022. Within the analytical framework of the Spatial Durbin Model, this research decomposes the baseline effect, functional transmission pathways, and cross-sectional heterogeneity of the impact of the development of small loan company (SLC) providers on urban carbon intensity. The results show that SLC expansion significantly increases local carbon emission intensity and produces spatial spillover effects across neighboring cities. Mechanism analysis indicates that SLCs increase emissions mainly by supporting the expansion of small- and micro-sized enterprises in energy-intensive manufacturing sectors, while their role in promoting green technological innovation remains limited. Further analysis shows that local government willingness to pursue green transition weakens the carbon-increasing effect of SLCs, whereas digital inclusive finance strengthens it. The effect also varies by location and regulatory environment, with stronger effects in medium-distance cities and under lower regulatory intensity. These findings reveal how grassroots inclusive financial institutions affect regional carbon outcomes and offer policy implications for aligning inclusive finance with green transition goals. This paper innovatively transcends the conventional low-carbon research paradigm focusing on macro-finance and large formal financial institutions, and instead takes SLCs, a typical micro-level inclusive finance entity, to explore their unique paths affecting regional carbon emissions, and clarifies their action boundaries from multiple dimensions including government governance and digital finance empowerment, which enriches interdisciplinary research literature integrating inclusive finance and low-carbon economy. But this study has limitations: its sample is limited to the Yangtze River Delta urban agglomeration, so the universality of the conclusion needs further verification. This research provides theoretical support and policy reference for regulating the sustainable development of the small loan industry, promoting the integration of inclusive finance and green low-carbon transformation, and advancing high-quality regional low-carbon development. Full article

Underprediction of high electrical demand can be more operationally consequential than an equally sized overprediction, yet standard point forecasting models are optimized primarily for average error. This case study evaluates a multivariate quantile Transformer as a safety-oriented next observation forecasting layer for a university laboratory. A timestamp-level audit identified 33,374 native measurements collected from 22 April to 12 December 2024 at a median interval of approximately 10 min. The final leakage-free pipeline uses only real observations, performs the chronological split before sequence generation, fits all scalers on training data only, and rejects windows containing gaps greater than 30 min. Persistence, fixed-order SARIMA, LSTM, GRU, CNN–LSTM, and an MSE-trained Transformer were evaluated on the same 6595-sample test period. GRU achieved the best deterministic accuracy (MAE 0.017744 kW; RMSE 0.023278 kW), whereas the proposed τ = 0.90 Transformer intentionally traded point accuracy (MAE 0.033830 ± 0.001133 kW) for asymmetric risk control. Across five independent runs, it achieved a pinball loss of 0.004453 ± 0.000069 kW, empirical coverage of 87.95 ± 1.01%, and a peak underprediction rate of 26.64 ± 5.32%, compared with 72.94–100% for the conventional benchmark outputs. Additional τ = 0.75 and τ = 0.95 experiments demonstrate the expected accuracy–safety trade-off. MAPE is not used as a primary metric because near-zero loads make percentage errors unstable. The results support the proposed model as a complementary upper quantile forecasting layer for this small, dynamic facility; they do not establish general performance at feeder or system scale. Full article

Background: Corporate energy transition can create efficiency, financing, and valuation benefits, but it also exposes firms to implementation, information, and transition risks. This study examines whether digital transformation helps firms convert energy-transition strategies into financial value. Unlike prior studies that mainly treated digitalization or sustainability as broad direct predictors, this study examines an implementation-based, multidimensional digital capability as a boundary condition across three distinct energy-transition strategies and both accounting- and market-based financial outcomes. Methods: Using an unbalanced panel of 30 firms associated with Indonesia’s LQ45 Low Carbon Leaders Index (120 firm years, 2020–2025), we construct a 30-item implementation-based Digital Transformation Index and estimate two-way fixed-effects models with firm-level wild-cluster-bootstrap inference, conditional marginal effects, false-discovery-rate adjustment, and prespecified robustness checks. Results: Clean energy use is positively associated with return on assets, return on equity, and Tobin’s Q. Low-carbon operational efficiency is most clearly associated with return on assets, whereas renewable energy use is primarily reflected in Tobin’s Q. Digital transformation is positively associated with all three outcomes and selectively strengthens the financial effects of the three transition strategies. Conclusions: Digital transformation is not a universal performance amplifier. It functions as a strategy- and outcome-specific value-conversion and risk-management capability that improves the monitoring, coordination, financing, verification, and communication of energy-transition investments. Full article

Ensuring the safety and reliability of photovoltaic (PV) installations requires accurate electrical arc fault detection. This work presents a computational arc fault detection framework that combines fixed-length windowing, Fast Fourier Transform (FFT)-based features, and supervised machine learning classifiers. Data were acquired using an Arc Fault Circuit Interrupter (AFCI) test bench developed based on IEC 63027. Current and voltage signals were partitioned into 200-sample windows, DC-offset corrected, and Hann-windowed signals. Each window generated 204 statistical and spectral attributes used to train and evaluate Random Forest (RF), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN) models. Hyperparameters were tuned by grid search with TimeSeriesSplit cross-validation, comparing min–max normalization and Z–Score standardization. On a 15% hold-out test set, CNN with Z–Score achieved F1 = 0.9982 and recall = 0.9975, followed by MLP (F1 = 0.9957) and RF (F1 = 0.9821). Amortized per-window inference latencies were ≈ 0.0035 ms for RF, ≈ 0.0016 ms for MLP with Z–Score, and ≈ 0.032 ms for CNN. These classifier-stage timings indicate computational compatibility with edge-oriented implementation but do not constitute an end-to-end IEC 63027 AFCI compliance assessment. The framework targets integration into PV inverters at Mackenzie Presbyterian University’s solar plant. Full article

A control that cannot be audited is a control that does not yet exist operationally. Commercial off-the-shelf (COTS) Internet of Things (IoT) devices in Ambient Assisted Living (AAL) environments expose this problem sharply: they export raw behavioural telemetry rather than the security-auditable event records required by the logging and monitoring controls of ISO/IEC 27001:2022. This study formalises that deficit as the Admissibility Gap, a weighted, field-level measure of the mismatch between native device output and the evidentiary requirements of Annex A. An audit of four publicly available AAL datasets (CASAS, SPHERE, UCI HAR, OPPORTUNITY) confirms that identity attribution, integrity, firmware version, and privacy-minimisation governance fields are universally absent, establishing that the gap is systemic. To close it, this study proposes the Compliance Transformation Layer, an edge middleware applying three rules: LDAP-based identity attribution, keyed HMAC-SHA256 integrity sealing with firmware baseline injection, and privacy-preserving GPS truncation. An experimental campaign on 10,000 synthetic records reduced the weighted Admissibility Gap deficit from 57.0% to 4.7% (an illustrative figure under the authors’ weight vector; because the transformation rules apply deterministically, this is a demonstration of sufficiency rather than an independent validation, and its direction is robust to the weighting), with outputs mapped to the Microsoft Sentinel Common Event Format schema and the BSI IT-Grundschutz OPS.1.1.5 logging requirements. Benchmarking on Raspberry Pi 4 hardware yielded a mean per-record latency of 1.574 ms at idle, demonstrating that audit-ready logging is achievable from the edge gateway inward on commodity hardware without hardware or firmware modification. The integrity and identity guarantees are enforced from the point of gateway ingestion; the device-to-gateway segment is treated as a declared trust boundary. Full article

The growing demand for healthier beverages has boosted the development of fruit-based functional drinks, but their real potential depends not only on initial bioactive content but also on the fraction that remains available after digestion (bioaccessibility). This study evaluated the antioxidant capacity and in vitro bioaccessibility of bioactive compounds in a beverage formulated from pineapple, apple, ginger, and cinnamon extracts, with inulin and myo-inositol as complementary ingredients. Three formulations were prepared: F1 (fruit–spice extract), F2 (F1 + inulin), and F3 (F1 + inulin + myo-inositol). Physicochemical parameters (pH, °Brix, reducing sugars, dietary fiber), total phenolic content (Folin–Ciocalteu), and antioxidant capacity (ABTS and FRAP) were measured before and after an adapted INFOGEST gastrointestinal digestion. Bioaccessibility was calculated from the intestinal supernatant. All formulations had pH 3.61, but soluble solids increased from F1 to F3 (6.17–8.97 °Brix). Folin–Ciocalteu values decreased after digestion in all formulations, but F1 retained the highest bioaccessibility (95.05 ± 5.82%), while F2 and F3 showed much lower values (33.28 ± 2.01% and 39.17 ± 5.52%, respectively). ABTS values increased after digestion in all formulations, with bioaccessibility exceeding 100% (125–143%). In contrast, FRAP values dropped sharply after digestion (bioaccessibility 4.59–11.00%), with F2 and F3 showing slightly higher retention than F1. The antioxidant response after digestion is strongly method-dependent and influenced by the beverage matrix. The formulation without inulin and myo-inositol (F1) better preserved the reducing capacity measured by Folin–Ciocalteu, while enriched formulations showed higher FRAP retention. ABTS bioaccessibility increased universally, suggesting release or transformation of radical-scavenging compounds during digestion. This study supports the need to include bioaccessibility assays when evaluating the functional potential of complex fruit-based beverages. Full article

Environmental risks associated with climate change, biodiversity loss, and sustainability transitions extend beyond the allocation of financial losses. Research recognises insurance’s role in environmental risk management and sustainable finance. It also considers climate adaptation and governance, but remains fragmented across disciplines and analytical levels. This study develops an integrative conceptual framework distinguishing risk transfer, established insurance incentive and portfolio effects, and risk transformation. Drawing on a theory-informed synthesis of the literature, it connects insights from environmental economics and principal–agent theory. Signalling and institutional theory provide complementary perspectives. Risk transformation is defined as an outcome in which insurance-related mechanisms make a demonstrable contribution to sustained and independently verifiable reductions in underlying environmental risk or its alterable drivers. Such transformation may occur within a single insured organisation; cross-level transmission may broaden or amplify its effects but is not a defining condition. The framework identifies four potential economic, behavioural, informational, and institutional pathways, which may operate separately or jointly, with capital allocation incorporated into the economic pathway. A context-specific operational protocol specifies ex ante magnitude, persistence, attribution, and verification criteria without imposing a universal scale across heterogeneous risks. The framework also identifies pathway complementarity, conflict, and single-path failure and specifies the boundary conditions that determine whether the pathways generate substantive risk reduction. It explains when effects remain confined to compensation, conventional incentives, insured-loss reduction, risk selection, portfolio de-risking, or symbolic compliance. The study concludes by formulating categorised research propositions and corresponding empirical strategies. Full article

Contemporary society grapples with diverse values and socio-economic structural differences. These differences extend beyond subtle contextual nuances and strategic considerations, intruding into one’s first-person account and preferences. From its first-order mooring, these divergences ascend to higher-order manifestations, expressing themselves in distinct political perspectives, cultural practices, and religious beliefs. While such differences inevitably give rise to conflicts, a deeper analysis reveals that the core of rising conflict often lies in sectarian differences. Although various conflict resolution mechanisms have been employed to settle disputes, they often fail to yield enduring results. In this regard, the paper proposes interfaith dialogue as a promising approach for conflict resolution and reconciliation. The paper argues that interfaith dialogue is neither a debate nor a negotiation, but a sustained endeavour to break down the walls of difference. Furthermore, to ensure substantive outcomes, the dialogue must be firmly grounded in philosophical foundations that are value-laden and universally acknowledged. Against this backdrop, the paper introduces the Jaina philosophical tradition, specifically the principle of ‘non-absolutism’ (anekāntavāda) and the doctrine of ‘qualified assertion’ (syādvāda), as a coherent framework for dialogical engagements. Focusing on epistemic decentring, conditional articulation, and non-violent involvement, the Jaina framework pushes interfaith dialogue beyond conflict resolution towards conflict transformation. Full article

Dissolved gas analysis (DGA) is widely used for power transformer fault diagnosis, but many learning-based studies still treat gas concentrations and derived ratios as flat input features. KGRAT is positioned here as an IEC-informed graph representation with a relation-conditioned graph attention learner, rather than as a universally strong predictor. Gas, symptom, and fault entities are linked by four standards-informed relation types, and relation-conditioned attention is learned over this fixed graph. On a six-class benchmark of 589 samples evaluated with stratified 10-fold cross-validation, KGRAT achieved 0.7233 accuracy and 0.7089 Macro-F1. In this single-seed evaluation, it scored above IEC Three-Ratio, Duval Triangle, raw-feature SVM, raw-feature MLP, and a complete-graph GAT ablation; the dependent-fold Holm diagnostic supported the complete-graph contrast within that run but is not seed-robust inference. Feature-engineered tree ensembles were stronger, with GBDT using ratio/symptom features reaching 0.8283 Macro-F1. A filtered four-label Cliango/DGA evaluation is reported only as a constrained stress test over common labels, not as six-class external validation. The evidence therefore supports KGRAT as a standards-aligned, relation-level inspectable representation for DGA modeling, not as a deployment-ready diagnostic system or a substitute for stronger feature-engineered tree ensembles on this dataset. Full article

Artificial intelligence (AI) is rapidly transforming higher education, requiring universities to prepare graduates who can use these technologies critically, ethically, and responsibly. However, socially oriented professions remain comparatively underexplored. This study examined preparedness for AI-mediated professional practice among Social Education students through a convergent mixed-methods design integrating perceived AI use, self-reported learning competencies, and professional knowledge requirements. Participants were 49 undergraduate students enrolled in a Social Education degree programme. Quantitative data were collected using a single self-report item assessing perceived AI use and the abbreviated Basic Learning Competencies Scale (COMPES), while qualitative data were obtained through an open-ended question analysed using the Reinert method with IRAMUTEQ. Participants predominantly reported low to moderate perceived AI use. The association between perceived AI use and the overall COMPES score was small and imprecise, r = 0.16, 95% CI [−0.13, 0.42], precluding firm conclusions. Lexical analysis identified six classes that reflected practical applications, professional knowledge, educational considerations, and ethical concerns related to AI-mediated socioeducational practice. The findings suggest that preparedness for AI-mediated practice may involve perceived AI use, self-reported learning competencies, ethical-professional judgement, and professional knowledge requirements. The study provides a preliminary integrative interpretation with implications for curriculum development in Social Education. Full article

In the context of an increasingly tense relationship between environmental and economic goals, green innovation is of crucial importance for the global green transformation. However, its dual external effects often render a single policy tool ineffective, which makes the implementation of a policy mix necessary. In this study, according to a three-dimensional policy mix framework, four pilot policies in the fields of environment, innovation, and finance were selected and combined in pairs. Panel data from 276 Chinese cities (from 2006 to 2021) were used to compare the differences in the effects of different types of policy mixes on green innovation using the difference-in-differences method and to verify the mediating effects of green finance and university–industry collaboration. The synergy test results show that the cross-domain mix with the differentiation of tool types and the compatibility of mechanisms is the best. The policy effects were more evident in cities outside the Yangtze River Economic Belt and in non-central cities. These findings provide theoretical and practical insights for designing an effective policy mix to promote green transformation. Full article