Search Results (9,769)

Out-of-hospital cardiac arrest (OHCA) remains one of the leading causes of death worldwide, with patient survival strongly dependent on rapid defibrillation. Although unmanned aerial vehicles (UAVs) have recently been investigated for Automated External Defibrillator (AED) delivery, the existing solutions are predominantly based on low-speed multirotor platforms with limited operational range and cruise velocity. This paper presents the conceptual development and preliminary analyses of a high-speed VTOL tailsitter UAV intended for rapid AED delivery missions. The proposed flying-wing platform combines vertical take-off and landing capability, hover flight during payload deployment, and cruise flight at 160 km/h. The work includes initial design assumptions, mission profile definition, and preliminary propulsion-related analyses forming part of a broader UAV development project conducted at Wrocław University of Science and Technology. The presented study addresses the technological gap in high-speed medical UAV platforms capable of combining efficient forward flight with precise hover capability for emergency-response applications. Full article

Identifying new drivers of urban green technology innovation is important for sustainable development. Drawing on Marx’s theory of productive forces, this study examines how data-element development relates to the quantity and quality of urban green technology innovation, using a balanced panel of 297 Chinese prefecture-level and above cities over 2011–2023, a city-level data-element development index based on the entropy weighting method, and two-way fixed-effects models. Data-element development is positively associated with both dimensions, and this association holds across a series of robustness checks. Mechanism analysis links data-element development to lower resource misallocation, which raises both dimensions, and to stronger university–industry research collaboration, with a relatively larger contribution to quality. Environmental regulation intensity reinforces the effect on both dimensions; the interaction with government internet-service responsiveness is significant over 2015–2023, the period for which the measure is consistently recorded, and is reported as supplementary evidence. Heterogeneity tests show larger effects in less-developed cities for both dimensions; across the Hu Huanyong Line and by population size, the between-group differences are significant for the quantity dimension. The results are consistent with the effect depending on whether a city has the complementary conditions in place that make data usable rather than on economic development level alone. Full article

The psychological processes linking polarization experiences across different online interaction contexts to university students’ value-related judgments and choices warrant further investigation. This study distinguishes experienced polarization within online communities (EPC) from experienced polarization in open online discussions (EPD) and examines their associations with university students’ value-related judgments and choices, including parallel indirect associations through cognitive dissonance response (CDR) and emotional contagion response (ECR). Data from 2105 valid cross-sectional self-report questionnaires collected at a university in China were randomly divided into two subsamples: 770 cases for exploratory factor analysis and 1335 cases for confirmatory factor analysis and structural equation modeling. Indirect effects were estimated using 5000 bootstrap resamples. After adjustment for seven covariates covering demographics, internet use, and online participation, both forms of polarization experience were significantly and positively associated with CDR and ECR. Both psychological responses were also significantly and positively associated with value judgment difficulty (VJD) and value choice susceptibility (VCS). All four direct paths from the two forms of polarization experience to VJD and VCS were significant. Standardized estimates for the eight specific indirect effects ranged from 0.032 to 0.198, and all 95% bias-corrected confidence intervals excluded zero. Together, the predictors in the respective regression equations explained 44.4% of the variance in VJD and 54.5% in VCS. These findings provide statistical support for the hypothesized mediation relationships, indicating a pattern in which direct and indirect associations coexist. By distinguishing polarization experiences in specific interaction contexts, the study jointly connects cognitive and emotional responses to judgment formation and the reference points informing choices. It thereby provides empirical evidence for understanding how university students respond to online opinions and forms the basis for their evaluations and choices. Full article

Background: Immunotherapy has transformed cancer treatment and is widely used in the Chinese mainland. Though advances have been made, immune checkpoint inhibitor-related liver injury (ICILI) remains a significant clinical challenge. Existing risk models commonly lack time-specific risk stratification for ICILI. The present study aimed to develop and validate interpretable machine learning models to predict grade 2 or higher ICILI at multiple time points in patients with gastrointestinal cancer (GC). Methods: This retrospective cohort study encompassed GC patients who commenced their initial ICI medication between January 2019 and June 2023 at Zhongshan Hospital, Fudan University. Five machine learning algorithms, including Logistic Regression, Random Forest, Extreme Gradient Boosting (XGBoost), Gradient Boosting (GradientBoost), and Adaptive Boosting (AdaBoost), were utilized to develop predictive models for grade ≥ 2 ICILI at specific intervals of 3 months, 6 months, and 12 months. The evaluation of model performance was conducted using the area under the curve (AUC), accuracy, precision, recall, and F1-score. The Shapley Additive exPlanations (SHAP) method was employed to assess feature importance and interpret the final model. Results: A total of 1337, 849, and 401 patients were enrolled in the follow-up groups at 3 months, 6 months, and 12 months. The final model for grade ≥ 2 ICILI was developed with GradientBoost and achieved an AUC of 0.769 (95% CI: 0.732–0.806), with a test set accuracy of 0.834. XGBoost yielded AUCs of 0.671 (95% CI: 0.636–0.706) at 3-month indication, 0.678 (95% CI: 0.638–0.718) at 6 months, and 0.644 (95% CI: 0.589–0.699) at 12-month follow-up in the 5-fold cross-validation. The DCA curve demonstrated solid clinical benefit, whereas the calibration curve indicated good predictive reliability. SHAP analysis identified several parameters as predictive features at different intervals, which suggested that the ICILI determinants varied from acute inflammatory to host-related characteristics. Conclusions: A temporal stratification prediction model for grade ≥ 2 ICILI in GC patients was developed and validated at various intervals using ML algorithms with SHAP interpretability. This methodology facilitated early recognition of varying parameters across different treatment phases, enhancing clinical management and elevating treatment outcomes. Full article

As generative artificial intelligence (GAI) becomes increasingly integrated into undergraduate medical education, its relationship with students’ learning engagement warrants further investigation. Guided by self-determination theory, this cross-sectional questionnaire study surveyed 498 medical undergraduates at a medical university in China and used structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to examine average associations and configurational patterns among GAI use, basic psychological need satisfaction, and learning engagement. SEM results showed that GAI use was positively but weakly associated with basic psychological need satisfaction, whereas basic psychological need satisfaction was more strongly associated with learning engagement. The direct association between GAI use and learning engagement was not statistically significant, while the statistical indirect association through basic psychological need satisfaction was significant but small. The fsQCA results showed that high GAI use was not a necessary condition for high learning engagement. The configuration combining autonomy, competence, and relatedness need satisfaction had the highest coverage and showed relatively stable results across threshold adjustments, with competence need satisfaction as a core condition. These findings suggest that the educational relevance of GAI use may be better understood in relation to students’ psychological need conditions than to use frequency alone. Full article

Reduced nicotinamide adenine dinucleotide phosphate (NADPH) sustains reductive biosynthesis, antioxidant defense, oxidase activity, and redox signaling; nevertheless, studies framed as “NADPH imaging” often interrogate biochemically non-equivalent variables and analytically distinct endpoints. To resolve that ambiguity, this review organizes current methods by analyte-defining event, optical transduction or readout, acquisition modality, and deployment context. Extraction and enzyme-coupled assays can define recovered NADPH, oxidized nicotinamide adenine dinucleotide phosphate (NADP⁺), total NADP(H), or a derived ratio, albeit at the cost of subcellular information. By contrast, protein-based, genetically encoded, and chemigenetic systems enable reversible, compartment-addressable measurements of sensor-accessible cofactor binding, ligand-dependent assembly, relay output, or NADPH/NADP⁺ balance; however, quantitative interpretation remains contingent on affinity, sensor abundance, pH, maturation, calibration, and sensor-induced buffering. Reaction-based probes offer the broadest spectral and imaging flexibility—including ratiometric, near-infrared, two-photon, and photoacoustic formats—yet most rely on hydride-transfer chemistry shared by reduced nicotinamide adenine dinucleotide (NADH) and NADPH. Absent matched kinetics and simultaneous mixed-cofactor experiments, NAD(P)H-responsive remains the most defensible designation for these platforms. Label-free autofluorescence, fluorescence lifetime imaging microscopy (FLIM), and phasor analysis preserve native spatial context; even so, they report composite intensity, binding-state, or metabolic contrast rather than a universal absolute NADPH concentration. Across modalities, rigorous interpretation requires physiologically relevant concentration ranges, product or binding-mechanism verification, matrix- and organelle-specific controls, time-resolved calibration, and orthogonal measurements of pool size or flux. By aligning each signal-generating event with the endpoint it can legitimately support, the review establishes a mechanistic basis for platform selection and for interpreting NADPH-related optical changes across purified systems, cells, tissues, and biofluids. Full article

LaNi_1-xFe_xO_3-δ (LNF) perovskite oxides are promising Sr- and Co-free materials for solid oxide fuel cell (SOFC) cathodes and energy-related catalytic applications. The Ni/Fe ratio strongly influences their electronic structure, defect chemistry, transport properties, reducibility, and structural stability. This review examines how these composition-dependent properties affect LNF synthesis and performance across different applications. For SOFC cathodes, LaNi_0.6Fe_0.4O₃ is an important reference composition because of its favorable balance of conductivity, thermal compatibility, and stability. In oxygen evolution electrocatalysis and other applications, however, the preferred Ni/Fe ratio varies substantially with the required reaction properties and operating conditions. Synthesis, microstructure, surface reconstruction, reduction, and exsolution further affect the functional state of LNF. Therefore, no universally optimal Ni/Fe ratio exists. Composition should instead be selected for the intended application, followed by appropriate synthesis and surface/interface engineering to further improve performance. Full article

Artificial intelligence (AI) is rapidly transforming veterinary education and practice, increasing the need for AI literacy among future veterinary professionals. However, evidence regarding veterinary students’ knowledge, use, and perceptions of AI remains limited. This cross-sectional study evaluated AI awareness, usage patterns, attitudes, and educational expectations among undergraduate veterinary students at the University of Las Palmas de Gran Canaria (Spain). A semi-structured online questionnaire was completed by 189 students (43% of the target population), and data were analysed using descriptive statistics and tests of association, with Monte Carlo procedures when appropriate and false discovery rate adjustment for multiple comparisons. Most students reported frequent use of generative AI tools, particularly ChatGPT, mainly for information retrieval and academic support. Participants generally perceived AI as a valuable educational resource but expressed concerns regarding the reliability of AI-generated information, ethical issues, data privacy, and the potential impact on critical thinking. Despite widespread AI use, most respondents reported limited formal training and insufficient institutional guidance, while strongly supporting the integration of AI-related competencies into the veterinary curriculum. These findings highlight a gap between the rapid adoption of AI and the development of formal AI literacy in veterinary education. Veterinary curricula should promote not only technical proficiency but also critical evaluation, ethical awareness, and the responsible use of AI in professional practice. Full article

For sustainability education to support societal transformation, it is necessary to understand how pre-service teachers conceptualize sustainable development and which Sustainable Development Goals (SDGs) they prioritize. This study examined pre-service teachers’ metaphorical perceptions of sustainable development, their 2030 SDG priorities, and convergence/divergence between the two. A qualitatively driven convergent parallel mixed-methods design was used with 93 pre-service teachers from six teacher education programs at a public university who had completed a sustainability-related course. Data were collected through a metaphor elicitation form and an SDG priority-ranking form. Content analysis produced 226 valid metaphor units grouped into six overarching themes and 17 subthemes. Metaphors centered mainly on future and intergenerational transfer, environmental protection and responsibility, and sustaining life and continuity. Zero Hunger, Good Health and Well-being, Clean Water and Sanitation, No Poverty, and Quality Education received the highest priority. Participants conceptualized sustainable development positively, holistically, and with a future orientation, yet prioritized immediate basic human needs. Environmental protection, systems integrity, and intergenerational responsibility were prominent metaphorically, whereas Climate Action, Responsible Consumption and Production, ecosystem-related goals, and Partnerships for the Goals ranked relatively low. The findings highlight a need for interdisciplinary, practice-oriented teacher education that makes the interdependence of the SDGs more explicit. Full article

The growing use of Generative Artificial Intelligence (GenAI) in higher education has increased the need to understand the factors associated with instructors’ intention to use these technologies in teaching. This study examined how instructors’ GenAI-related knowledge and competencies and perceptions of institutional support jointly relate to their intention to use GenAI. Using a cross-sectional survey design, data were collected from 110 faculty members at a large public university. Pearson correlation analyses and Hayes’ PROCESS macro (Model 4) were used to examine relationships among institutional support, Intelligent-TPACK, and intention to use GenAI. The findings revealed significant positive associations among all three variables and a significant indirect association between institutional support and intention through Intelligent-TPACK. By examining institutional support and Intelligent-TPACK within a single model, this study highlights the joint relevance of institutional and instructor-level factors in understanding instructors’ intention to use GenAI. The study may inform institutional efforts to support faculty development and responsible GenAI integration, with broader implications for quality and sustainable education in alignment with Sustainable Development Goal 4 (SDG 4). Full article

Background/Objectives: University students face academic, social, and lifestyle-related demands with the potential to influence affective functioning. Mood states provide information on everyday emotional profiles and may be associated with physical activity (PA) and sleep. This study analysed mood states and examined their relationship with physical activity, sleep quality, and sleep duration, considering sociodemographic and academic variables. Methods: A cross-sectional study was conducted with 1469 university students from Granada, Spain. Mood states were assessed using the Profile of Mood States, physical activity with the International Physical Activity Questionnaire–Short Form, and sleep quality and duration with the Pittsburgh Sleep Quality Index. Mann–Whitney U tests, Kruskal–Wallis H tests, and Spearman’s rho correlations were performed. Results: Poor sleep quality was observed in 81.4% of participants, while mean sleep duration was 6.96 ± 1.18 h/night. Students with poor sleep quality showed higher scores in depression, fatigue, anger, and tension than those with good sleep quality (all p < 0.001), whereas students with good sleep quality showed higher vigour (13.79 ± 5.39 vs. 11.59 ± 5.36; p < 0.001). Inactive/low-PA students showed higher depression and fatigue scores than more active groups, whereas the high-PA group showed the highest vigour scores. Conclusions: Mood-state profiles were associated with sleep quality, PA, and psychosocial and academic factors. Health promotion strategies in higher education should prioritise sleep quality, accessible PA, recovery, time management, and support for vulnerable profiles. Full article

Background/Objectives: Interprofessional collaboration (IPC) between physicians and registered nurses (RNs) is essential for managing increasingly complex patient care. Although IPC is associated with improved coordination and patient outcomes, hierarchical dynamics and organisational constraints may limit RNs’ meaningful participation. This study explored physicians’ perceptions of RNs’ professional roles and contributions within IPC in hospital settings. Methods: This qualitative descriptive study used nine semi-structured focus group interviews with 42 physicians from medical units across three university hospitals in the Capital Region of Denmark, recruited through convenience sampling. Interviews focused on everyday collaborative practices, including interprofessional meetings and ward rounds. Data were analysed using inductive qualitative content analysis. Results: Physicians’ accounts of IPC with RNs were captured in four themes: RNs’ contributions are central to IPC; Cultural conditions enabling IPC; Misaligned workflows and competing responsibilities shaping IPC; and Clinical partnership and expectations of support in IPC. Conclusions: From the participating physicians’ perspectives, IPC was shaped by RNs’ experience, relational culture, and organisational conditions. Physicians described relying particularly on RNs’ sharing of information and clinical assessments that they considered important for safe and coordinated care, which they perceived as reflective of RNs’ experience. However, they perceived organisational constraints as limiting the integration of nursing perspectives into decision-making. These findings indicate areas for further investigation and potential organisational development related to RNs’ participation in IPC. Full article

Wearable single-lead electrocardiogram (ECG) devices are increasingly used for out-of-clinic rhythm monitoring, but the rigour with which their signal-processing pipelines are validated varies widely across the literature, with most published reports relying on either a single clinical comparison or a single algorithmic benchmark. We propose and apply a multi-tier validation methodology—combining clinical reference comparison, expert-annotated database benchmarking, and a deliberate morphological stress-test—and demonstrate it on the SmartGuard single-lead wearable ECG device and its signal-processing pipeline. The three tiers comprise (i) a within-subject clinical comparison in 31 healthy adult volunteers against a Bionet Cardio 7 limb-lead Lead I reference with cardiologist-confirmed measurements (a two-electrode limb configuration approximating Lead I, not a full diagnostic 12-lead ECG), (ii) algorithmic benchmarking on the Lobachevsky University ECG Database (LUDB, 200 records, expert annotations), and (iii) a robustness stress-test on the QT Database (QTDB, 103 records, hybrid expert annotations). The pipeline integrates discrete wavelet transform-based PQRST delineation with a derivative-based QRS-width refinement, a tangent-method T-wave end estimator, and median-absolute-deviation robust averaging. In the clinical tier, mean within-subject biases were negligible for heart rate, QT, QTc and RR (all p > 0.6) and borderline for QRS duration (+7.3 ms, p = 0.066), with a systematic R-wave amplitude underestimation (−0.15 mV, p < 0.0001). On LUDB, heart-rate and RR-interval agreement were excellent (Pearson r > 0.95) and QRS duration good (r = 0.73, MAE 13.8 ms). On QTDB, performance degraded in line with the increased morphological heterogeneity of the dataset, particularly for QT-related metrics. Despite this minimal mean bias at the population level, individual-record QT and QTc errors showed high dispersion, precluding accurate clinical QT interpretation from single wearable traces in individual patients. Moreover, the resting, healthy-adult design of the clinical tier means its findings cannot be extrapolated to arrhythmic conditions or exercising states. The protocol is described in sufficient detail to serve as a template for evaluating other wearable single-lead ECG devices. Full article

Sustainability assessment increasingly requires consumption-based indicators that attribute environmental pressures to final demand rather than only to the place of production; however, the total ecological footprint also includes carbon uptake land, creating potential accounting overlap with energy use. This study estimates conditional associations between the consumption-based ecological footprint per capita, income, aggregate energy use per capita, trade openness, and broad institutional quality in a balanced 44-country panel from 2003 to 2022. A parsimonious static Common Correlated Effects Mean Group (CCEMG) model is the reference levels specification, complemented by first-difference CCEMG, Augmented Mean Group, two-way fixed effects with Driscoll–Kraay inference, and Dynamic Common Correlated Effects (CCE). The static basic energy coefficient is 0.2673 (standard error [SE] = 0.0923, p = 0.0059), while the regulatory-quality extension gives 0.2988 (SE = 0.1175, p = 0.0146). The basic first-difference estimate is 0.2269 (p = 0.0145), whereas the fully augmented Dynamic CCE estimate is 0.1470 (p = 0.3520). New finite-T diagnostics show that all country designs are full rank, but the median standardized condition number rises from 75.15 in the basic static model to 214.42 in the fully augmented dynamic model as residual degrees of freedom fall from 12 to 6. Excluding the least-precise quartile of country slopes does not weaken the positive static mean, and leave-one-year-out estimates remain positive in every omission. Replacing regulatory quality with Worldwide Governance Indicators (WGI) rule of law yields an energy coefficient of 0.3294 (p = 0.0075). Exploratory Spearman tests do not link national energy coefficients to the available country-average structural covariates. Westerlund evidence remains mixed, formal U-shape tests do not support an Environmental Kuznets Curve, and national coefficients are widely dispersed. The evidence is therefore sign-consistent but statistically specification-sensitive and does not identify a causal, non-carbon, or universal policy elasticity. For sustainability research, these results show that energy-related ecological pressure should be assessed through consumption-based accounting and country-specific heterogeneity rather than translated into a universal energy-reduction target. Full article

This integrative literature review examines how generative artificial intelligence is shaping human thinking, cognitive processes and writing practices, with a focus on education across schools, universities, adult learning and professional settings. It considers how AI-supported composing alters the ways writers plan, generate, organise, revise and evaluate text, and how these changes reshape cognition, agency, authorship and learning. Rather than treating generative AI simply as a tool for productivity, the review approaches it as a relational technology that mediates meaning-making and textual production, while reserving the language of intentional agency for human actors. Thirty-six publications, identified through a structured search of seven information sources reported in accordance with PRISMA 2020 and appraised using criteria matched to each form of evidence, were synthesised thematically. Albert Bandura’s social cognitive theory provides the analytical lens, particularly triadic reciprocal causation, self-efficacy, observational learning, self-regulation and human agency. The discussion develops a critical account that recognises clear benefits while flagging risks of cognitive offloading, uncritical trust, homogenised expression and weakened metacognitive control. Claims are calibrated to the type of evidence supporting them, and findings drawn from preprints, small samples and self-report designs are presented as preliminary. The review advances a hybrid account in which human and machine contributions to composing are entwined and argues that this entwinement makes deliberate human participation more rather than less necessary. It concludes by proposing guidelines for educators across sectors, together with the assessment conditions under which those guidelines can be verified. Full article