Search Results (11,792)

Promoting consumers’ organic food purchases is an important way to drive the green transformation of the demand side of food consumption and achieve sustainable development. This study, through two studies, examines the interactive effects and psychological mechanisms of different types of AI shopping assistants (analytic AI vs. empathic AI) and algorithmic recommendation cues (item-referent cues vs. user-referent cues) on consumers’ organic food purchase intention. The findings indicate that the two have an interaction effect, with analytic AI combined with item-referent cues being more effective, while empathic AI matched with user-referent cues is more effective. Further analysis revealed that perceived information validity and green trust each play a mediating role, forming a chain mediation path. Specifically, perceived information validity positively influences green trust, and green trust further positively influences purchase intention. This work reveals the matching mechanism between AI source characteristics and algorithmic recommendation cues, expands the application of Cue Utilization Theory and Signaling Theory in AI-driven organic food consumption contexts, and provides practical insights for companies to develop differentiated AI assistant configurations, recommendation information design, and green trust cultivation strategies. Full article

Generative artificial intelligence (AI) chatbots are increasingly prominent in higher education, but temporal evidence on student perceptions remains limited in English-medium transnational STEM education. This repeated cross-sectional study surveyed students at a Sino-British engineering institution in Spring 2023 (Wave 1; N = 165) and Spring 2025 (Wave 2; N = 297), and found that familiarity (37.6% vs. 67.3%; OR = 3.43), willingness (55.8% vs. 77.8%; OR = 2.78), comfort (63.6% vs. 74.4%; OR = 1.66), and perceived overall learning enhancement (46.1% vs. 69.0%; OR = 2.61) were higher in 2025. In a respondent-level model combining Wave 1 with the Spring four-programme subset (analytic N = 426), survey wave remained associated with willingness after adjustment for programme and year (OR = 3.51, 95% CI 2.17–5.69, p < 0.001). In the Wave 2 Spring concern checklist (N = 297), accuracy (51.5%) and deduplicated over-reliance (50.2%) were the leading concerns; because the concern blocks differed in format and options, specific concerns cannot be shown to have increased or matured. The findings indicate higher population-level adoption readiness, not individual progression or routine use. “Concern maturation” remains a hypothesis requiring invariant instruments and panel designs. Full article

Campylobacter jejuni and Campylobacter coli are major foodborne pathogens whose accurate species-level discrimination is important for outbreak investigations as well as rapidly assessing putative antimicrobial resistance profiles and the pathogenic potential of the bacterium. To facilitate species differentiation, a novel assay that integrates the nucleic acid-sensing capability of the oligo-Alpha with the multiplexing capacity of the AlphaPlex bead chemistries was developed. This wash-free system (designated as oligo-Plex) enables the detection and differentiation of C. jejuni and C. coli within a single reaction and can be completed in approximately 75 min. It works by using custom oligonucleotides modified for bead attachment, which hybridize sequentially along Campylobacter’s glyA gene and ultimately bridge the donor and acceptor beads. Improvements in assay stringency were made by increasing incubation temperatures, thus allowing the resolution of target from non-target. Comparisons of FITC–europium and DIG–terbium labeling systems revealed superior performance by the FITC–europium pair and suggested that helical positioning and steric accessibility likely influence donor–acceptor efficiency. Maximized signal separation was seen when using terbium for the detection of C. coli and europium for the detection of C. jejuni. Testing was performed in a Tris-based buffer and milk to confirm matrix tolerance, with potential areas for further optimization identified. The oligo-Plex presented here establishes a streamlined, adaptable platform suitable for high-throughput screening of multiple nucleic acid analytes that is readily extendable to a diverse array of pathogens through appropriate oligo selection. Full article

Against the dual background of ICH (intangible cultural heritage) revitalization and urban stock space renewal, the renovation of traditional craft workshops has shifted from limited workshop repair to comprehensive space creation that balances craft protection, cultural dissemination, and sustainable operation. While existing scholarship has explored the functional composition and qualitative design strategies of ICH workshops, there remains a notable research gap in quantitative spatial performance evaluation frameworks tailored to craft production constraints, and the actual contribution of spatial design to living heritage transmission lacks objective measurement tools. This study takes craftsmanship-oriented ICH workshops as the core research object. Based on field investigations, multi-subject questionnaires, and expert consultations, 14 tertiary indicators are selected from three dimensions: production and safeguarding, experience and dissemination, and operation and development, to construct a spatial performance evaluation system for living heritage transmission. The Analytic Hierarchy Process (AHP) is adopted to determine the weight of each indicator. Taking the Yuezhou Fan ICH workshop as an empirical case, this study conducts a quantitative comparison of spatial performance before and after renovation. The results show that the comprehensive performance score of the workshop after renovation has increased by approximately 109.7% compared with that before renovation, among which the production and safeguarding dimension have the most significant improvement, verifying the rationality and practicability of the evaluation system. Theoretically, this study extends the application scope of built environment performance evaluation to the field of craft heritage spaces and establishes a closed-loop logic of “quantitative diagnosis—deficiency identification—targeted optimization” for workshop renovation. Based on the evaluation results, this paper proposes a progressive optimization path of “consolidating production baseline—upgrading experience scenarios—empowering diversified operation”, which provides a generalizable quantitative framework and practical reference for the spatial renovation and performance evaluation of similar craftsmanship-oriented ICH workshops. Full article

Conventional furniture design predominantly hinges on empirical expertise and subjective appraisal, absent systematic quantitative validation of users’ visual cognition. This research establishes a multimodal quantitative evaluation framework for round-back armchairs that integrates subjective weighting via AHP and objective eye tracking measurements. Four ocular physiological metrics are aggregated using the CRITIC–entropy composite weighting approach to characterize visual attention allocation. PCA distills three core perceptual dimensions, and Quantification Theory Type I constructs a mapping function linking component morphology to perceptual responses. Notable systematic divergence is identified between expert aesthetic preferences and users’ fixation patterns. Backrests govern volume perception, chair rings dominate concise shape perception, and foot stretchers determine stylistic elegance. Low-relief backrests, three-segment curved chair rings and inward horse hoof stretchers yield superior perceptual performance, whereas gooseneck front posts and connecting balusters exhibit trivial marginal optimization gains. A model-derived candidate morphological configuration, A2-B3-C1-D1-E2-F2, is identified with corresponding effect size estimation. This study provides an exploratory analytical framework and methodological reference for promoting the transition from experience-oriented to data-driven traditional furniture design. Full article

The rapid growth of organisational textual data has increased the value of Natural Language Processing (NLP) and semantic analytics for strategic decision support. However, many employees still lack the knowledge and skills needed to evaluate AI-generated information critically. This study develops an explainable Management Information Systems (MIS) framework that integrates NLP-based semantic analytics with PLS-SEM to examine the relationship between AI illiteracy and strategic decision quality. A convergent mixed-methods design with sequential analytical integration was used with a valid sample of 200 knowledge workers from public organisations in Saudi Arabia across six industries. The sample included employees from Saudi Arabia, Egypt, Jordan, Sudan, Syria, India, and the Philippines. Quantitative data were analysed using PLS-SEM, while textual data were analysed using Sentence-BERT, BERTopic, semantic network analysis, and Aspect-Based Sentiment Analysis. The results showed that higher AI illiteracy was negatively associated with strategic decision quality and positively associated with automation bias, uncritical trust in AI, and cognitive offloading. Digital proficiency and AI governance awareness weakened the negative association between AI illiteracy and decision quality, while functional-background differences were examined through multigroup analysis. The semantic analysis identified six themes: AI competency, decision trust, AI governance, decision support, organisational learning, and risk awareness. Sentiment analysis showed positive views of productivity and decision support, together with concerns about algorithmic bias, explainability, transparency, and AI governance. The study contributes an integrated human–AI decision vulnerability framework in which semantic evidence complements structural modelling and provides a clearer understanding of AI-related competency, reliance, governance, and decision-support issues. Full article

Background: School violence is a major public health concern, with bullying alone affecting up to 50% of adolescents worldwide. Life skills-based programs have been proposed as a promising preventive approach, yet evidence for their effectiveness in secondary education remains limited. This systematic review and meta-analysis aims to synthesize the existing evidence on the effectiveness of life skills-based violence prevention programs among adolescents aged 12–18. Methods: A systematic literature search was conducted across five databases (PubMed, Scopus, ERIC, CINAHL, and PsycInfo) in accordance with PRISMA 2020 guidelines (INPLASY202650168). Interventional studies published between 2005 and 2025 that targeted secondary school students were eligible. Risk of bias was assessed using the Cochrane RoB2 (for RCTs/cRCTs) and ROBINS-I tools. A three-level random-effects meta-analytic model with Robust Variance Estimation was employed to pool Hedges’ g effect sizes. Results: Nineteen studies met the eligibility criteria, with 18 contributing 56 effect sizes to the meta-analysis. The pooled effect size was statistically significant (g = −0.2652, 95% CI [−0.4879, −0.0425], p = 0.0196), indicating an overall small reduction in violent behaviors. However, a high degree of variation in effects was observed across studies (Q(55) = 2263.58, p < 0.001; I² = 99.23%). None of the examined moderators—including outcome type, study design, and risk-of-bias tier—significantly explained this heterogeneity. The sensitivity analysis, excluding one highly influential study, yielded a smaller pooled effect (g = −0.1422, 95% CI [−0.2246, −0.00598]) while maintaining high heterogeneity (I² = 92.82%). Conclusions: Although life skills-based programs demonstrate a statistically significant overall average effect in reducing school violence among adolescents, this finding must be interpreted with extreme caution given the high degree of variation in effect sizes across contexts and the fact that prediction intervals span across zero. Because moderator analyses revealed no significant differences across intervention types, study designs, or outcome categories, no specific program type can be identified as consistently more effective. Future primary research must prioritize prospectively registered trials with multi-informant assessments to establish true intervention effectiveness. Full article

Historical art materials have always been a starting point for better understanding artists’ artwork composition, the long-term stability of used formulations and for designing appropriate strategies for conservation. It is important to consider how artists have always been involved over the centuries in the selection and preparation of the materials they used. This changed with the advent of industrial formulations, which were produced and marketed on a large scale, leading to significant changes from the 19th century onwards. In this study, the study of a set of 30 green paint tubes from the Munch Museum collection (Oslo) is presented. The selected investigation method allows for a progressive acquisition of complementary and increasingly detailed information through three multi-analytical steps, involving elemental, spectroscopic and chromatographic techniques, with the aim of characterising these materials in their various inorganic and organic components. Results achieved allow the identification of green pigments’ chemical composition and classification of them in distinct groups, presence of inert additives and characterisation of organic binding media. Degradation processes were also highlighted through the detection of metal soap formations. Full article

Electrochemical sensor arrays that perform simultaneous multi-technique (SMT) measurements within a shared electrolyte are essential for continuous, multi-analyte detection in microfluidic platforms for environmental and healthcare monitoring. This review examines the potentiostat architectures and electrode geometries relevant to SMT operation. Traditional single channel and multi-electrode potentiostat topologies are surveyed, and their suitability for multi-cell shared-electrolyte environments is evaluated. Additionally, crosstalk mechanisms in shared electrolytes are classified into chemical, electrical, and a newly identified category termed stability-based interference, which arises from conflicting feedback loops in conventional grounded working electrode instrumentation. A survey of existing multi-cell platforms reveals that most reported systems either avoid true SMT operation or address crosstalk primarily through electrode geometry without systematic evaluation of instrumentation effects. Based on this analysis, we introduce an instrumentation and electrode geometry co-design framework that provides a unified design pathway toward continuous multi-analyte microfluidic sensors for wearable and point-of-care applications. Full article

Large classes in Chinese College English programmes make frequent analytic assessment of student writing difficult. Large language models (LLMs) may support more frequent formative assessment, but their scores may vary across queries and be systematically harsher or more lenient than local teacher ratings. Using a corpus-based, five-fold cross-validated comparative rater-evaluation design, this study examined whether statistical calibration could make LLM-assisted scores more interpretable for College English writing assessment and where their use should remain limited. Data comprised 414 timed argumentative essays written by Chinese non-English majors at one applied undergraduate institution. Two trained College English teachers independently rated the essays on a seven-dimension analytic rubric informed by China’s Standards of English Language Ability, providing the local reference standard. Three LLMs rated each essay–dimension pair on five occasions. Under five-fold cross-validation, uncalibrated scores were compared with location–scale correction, isotonic calibration, and equipercentile linking, using quadratic weighted kappa, Spearman correlation, mean absolute error, signed bias, and half-point tolerance accuracy. Agreement between models did not imply agreement with teachers: two models showed inter-model kappa values of 0.70–0.78 but an average kappa of only 0.15 with teacher ratings while rating the essays about one band more severely. Calibration removed most of this severity difference and raised pooled kappa to 0.61–0.70 depending on the method (0.63–0.64 under equipercentile linking), compared with a teacher–teacher agreement benchmark of 0.747. The three methods differed little, and the improvement mainly reflected closer alignment of score distributions rather than better judgement of writing quality. Agreement was higher for vocabulary, syntax, and grammar but remained low for cohesion and conventions. The findings suggest that LLM-assisted scoring may support low-stakes formative feedback when calibrated to local teacher standards and used under teacher supervision, while teachers retain responsibility for judging content, coherence, argumentation, and communicative quality. Full article

Grid-forming converters are pivotal for stability support in modern power systems, where their interactive behavior is significantly determined by control parameters. However, prevalent impedance-based analysis is confined to individual control schemes, lacking a unified basis for comparative assessment and generalized parameter impact analysis. To bridge this gap, this paper develops a generalized sequence impedance model that consolidates major multi-loop GFM control strategies, structurally mapping each control loop to specific impedance components. Utilizing this generalized representation, the interactive effects of inner-loop parameters are analytically disentangled. Based on the found effects, a general parameter-tuning rule is proposed to improve the interactive stability of the GFM converter connected to different grids. Experimental validation confirms the model’s accuracy and demonstrates its utility for the systematic, stability-oriented design of GFM converters under diverse grid conditions. Full article

Inductive power transfer (IPT) is a promising charging approach for rail trams because their trajectories are fixed, lateral displacement is constrained by the rails, and charging infrastructure can be installed at predetermined locations. Nevertheless, the long vehicle body, high power demand, variable air gap, and dynamic operating conditions of rail trams impose stringent requirements on magnetic-coupler design. This study proposes a bibliometric-assisted analytic hierarchy process (AHP) framework for the comprehensive performance evaluation of magnetic couplers used in rail–tram IPT systems. The framework considers five performance dimensions: power-efficiency characteristics, spatial characteristics, power density, time characteristics, and energy-transfer capability. Bibliometric keyword-occurrence statistics are introduced as an external source of evidence to support the initial construction of AHP judgment matrices, thereby reducing the exclusive dependence of conventional AHP on the judgments of a small expert group. A 2M2T low-floor tram is used as a case study, and two magnetic-coupler configurations, namely the $2\times 1$ and $3\times 1$ configurations, are evaluated using electromagnetic and circuit-simulation results. The case study illustrates the application of the proposed framework to the comparison of magnetic-coupler configurations under the operating and installation constraints of the investigated tram. The present validation is limited to simulation-based analysis of one tram platform and two configurations; further experimental and multi-configuration validation is required. Full article

Triboelectric nanogenerators (TENGs) are promising for harvesting low-frequency mechanical energy, but rotating TENGs (R-TENGs) driven by low-speed water flow remain constrained by limited driving torque, sliding-contact losses, and rotating-system stability. Here, a three-dimensional (3D) riveted complementary-structure rotating triboelectric nanogenerator (RCSR-TENG) is proposed for low-speed water-flow energy harvesting. A semi-analytical formulation incorporating a force-dependent real-contact fraction is developed to describe the coupled relationships among output voltage, transferred charge, rotation angle, and contact force. Because the contact parameters were not independently calibrated, the formulation is used for sensitivity and trend analysis rather than as a quantitatively validated predictive model. For the single prototype tested for each configuration, at 1000 rpm under the fixed effective measurement load of 9 MΩ, the RCSR-TENG produced a peak output power of 544 μW, compared with 304 μW for the flat R-TENG, representing an increase of approximately 79%. The same RCSR-TENG prototype maintained a stable voltage amplitude of over 150,000 rotation cycles. When coupled to a fully passive flapping-foil collector in a 0.55 m s⁻¹ water flow, the system generated periodic electrical output with a peak area-normalized power exceeding 5000 μW m⁻². These results demonstrate the structural-performance advantage of the riveted complementary design and its proof-of-concept applicability to low-speed water-flow energy harvesting. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, with five-year survival below 5% for metastatic small cell lung cancer (SCLC) and below 10% for metastatic non-small cell lung cancer (NSCLC). Immune checkpoint inhibitors have improved outcomes, but primary and acquired resistance, driven by tumor microenvironment immunosuppression, antigen heterogeneity, and T-cell exhaustion, leaves a substantial unmet need. T-cell engagers (TCEs), bispecific antibodies that redirect cytotoxic T-cells to tumor cells independent of MHC-I-restricted antigen presentation, offer a mechanistically distinct approach. Methods: We conducted a structured narrative review, without formal PRISMA methodology or meta-analytic pooling, of PubMed, Embase, and ClinicalTrials.gov through June 2026, supplemented by conference abstracts from ASCO, ESMO, AACR, and ATS, covering clinical, translational, and preclinical evidence for TCEs across established and emerging targets in thoracic malignancy. Results: Tarlatamab, a DLL3/CD3 bispecific TCE, received full FDA approval in November 2025 based on DeLLphi-304 data showing a median overall survival benefit of 13.6 versus 8.3 months over chemotherapy (HR 0.60; p < 0.001), establishing proof-of-concept for the TCE platform in lung cancer and NCCN Category 1 status in ES-SCLC. Beyond DLL3, an expanding pipeline of targets, including Claudin-18.2, TROP-2, FOLR1, CD70, and HER2, is under active TCE development; several of these antigens have independently validated tumor-selective expression through approved or late-stage antibody-drug conjugates (ADCs), providing target-level clinical de-risking for TCE development, though the two modalities have distinct requirements for antigen density and internalization that must be independently validated. Novel tri-specific constructs incorporating costimulatory domains and combination strategies with checkpoint inhibitors are in early clinical development. Conclusions: Tarlatamab approval validates the TCE platform in lung cancer, but overcoming TME-mediated resistance, antigen heterogeneity, and class-specific toxicities including cytokine release syndrome remains the central challenge. Rational TCE design, incorporating costimulatory signaling, antigen selection informed by parallel ADC validation data, and evidence-based combination strategies, offers the most credible path toward expanding this platform’s impact in metastatic lung cancer. Full article

We investigate the potential of technology-based attention training (AT), particularly virtual reality (VR), as a stress-management tool. Mental stress is rising globally, and researchers increasingly use immersive technologies, wearable sensors, and machine learning (ML) for its detection and control. This feasibility study examines the impact of fully immersive VR-based AT on mental stress using electroencephalogram (EEG) signals and automated classification. We designed virtual exercises targeting different attention types and analyzed EEG responses with an ML framework; the resulting dataset, collected at the Arab Academy for Science and Technology (Alexandria, Egypt), is publicly available. For an unbiased estimate, we adopt a leakage-free evaluation in which the train/test split is performed by time, before segmentation into overlapping windows, so neighboring windows cannot appear in both sets. Subject-specific tree-based classifiers detected stress with a mean accuracy of about 97%, whereas leave-one-subject-out (LOSO) validation yielded about 67%, indicating strongly individual stress signatures and motivating a subject-specific strategy. Using these models, we compared the number of classifier-predicted stress segments before and after AT and visualized the feature space with T-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). Under subject-specific models the number of stress-predicted segments decreased after AT (Wilcoxon signed-rank $p<0.05$); however, because this reduction was corroborated neither by a non-circular (LOSO) detector nor by a centroid-separation measure, we interpret it as an exploratory, classifier-predicted effect rather than independently validated stress reduction. The results highlight the promise—and the current limits—of integrating immersive VR with EEG-guided analytics for mental-health support. Full article