Search Results (64,991)

Aiming at the multi-objective solution problem of the deployment optimization of the hybrid communication network based on PLC, wireless and dual-mode collaborative networking, this paper proposes an algorithm design based on comprehensive performance optimization with business benefits as the orientation. Firstly, according to the non-ideal channel conditions and the low latency service requirements, the cross-layer modeling of the physical layer and MAC layer is adopted. Then, a dynamic weighting mechanism based on different service levels is defined, and a hybrid communication network adaptive access model considering the constraints of business benefits, network performance, and networking costs is designed. The hybrid communication network deployment and access algorithm design based on K-mean clustering and the improved NSGA-II are realized. Finally, the algorithm performance simulation and comparative analysis are carried out. The simulation results show that the proposed algorithm design can effectively balance the two objectives of network benefits and deployment costs under various network constraints and provide diversified deployment strategies in a targeted manner. Full article

Low-field benchtop nuclear magnetic resonance (NMR) spectroscopy has emerged as an accessible analytical platform for rapid, routine, and application-oriented analysis. However, its broader analytical adoption remains constrained by intrinsic limitations, including reduced spectral resolution, severe signal overlap, and lower sensitivity compared with conventional high-field instruments. To address these limitations, artificial intelligence (AI), including machine learning and deep learning approaches, has increasingly been explored alongside conventional chemometric strategies to enhance information extraction from low-field spectral data. This review examines recent developments in AI-assisted benchtop NMR across three major application domains: classification and authentication, quantitative analysis, and spectral processing or automated interpretation. Current evidence suggests that classification and authentication currently represent the most mature application area, whereas quantitative analysis shows promising but often condition-dependent performance. In contrast, spectral reconstruction and automated interpretation remain comparatively early-stage and exploratory, despite their potential long-term relevance for addressing intrinsic information limitations. Key challenges, including limited dataset diversity, poor model transferability, validation pitfalls, limited interpretability, and the lack of benchmarking and standardized workflows, are critically discussed. Future progress will likely depend not only on advances in AI algorithms, but also on the development of robust, reproducible, and analytically meaningful workflows. Overall, AI-assisted benchtop NMR is evolving from proof-of-concept applications toward a more structured analytical framework for extracting chemically meaningful information from spectrally constrained low-field data. Full article

The development of digital learning technologies has introduced innovative tools to enhance science and chemistry education, including PhET simulations. This study aims to evaluate the effectiveness of PhET simulations on students’ learning outcomes through a systematic literature review following the PRISMA 2020 guidelines. A systematic search of Scopus and Crossref databases was conducted (last search: January 2026) using predefined keywords. Eligible studies were empirical research published between 2020 and 2026 that investigated PhET simulations in science-related education and reported learning outcomes, while non-empirical studies and non-Scopus-indexed articles were excluded. Risk of bias was assessed using an adapted Joanna Briggs Institute critical appraisal tool. Due to heterogeneity in study designs and outcome measures, the results were synthesized using a narrative approach. A total of 14 studies across elementary to higher education levels were included. The findings indicate that PhET simulations consistently improve learning outcomes, particularly academic achievement and conceptual understanding, with effects generally favoring simulation-based instruction over traditional methods. However, higher-order skills and affective outcomes such as motivation and attitude remain less frequently investigated. The evidence is limited by variability in study designs, incomplete reporting of non-cognitive outcomes, and the absence of quantitative synthesis. Overall, PhET simulations demonstrate strong potential as an effective interactive learning medium, although their impact depends on instructional design, teacher facilitation, and technological accessibility. Full article

Background/Objectives: Neoadjuvant cisplatin-based chemotherapy (NAC) followed by radical cystectomy (RC) is a standard treatment for cisplatin-eligible patients with muscle-invasive bladder cancer (MIBC), yet baseline tools to refine prognostic stratification remain limited. We aimed to develop and internally validate a clinicopathological nomogram integrating the neutrophil-to-lymphocyte ratio (NLR) to estimate event-free survival (EFS) in patients with MIBC treated with NAC. Methods: We retrospectively analyzed 210 patients with cT2–T4aN0–1M0 MIBC treated with cisplatin-based NAC at two Spanish institutions between 2010 and 2021. Candidate predictors included demographic, clinicopathological, and routine laboratory variables. A multivariable Cox model with backward selection based on the Akaike information criterion (AIC) was used to derive the final model, and internal validation was performed using 1000 bootstrap resamples. Results: Sex, age, prior non–muscle-invasive bladder cancer (NMIBC), and NLR were retained in the final nomogram. The model showed moderate discrimination, with a Harrell’s c-index of 0.60 and an optimism-corrected c-index of 0.58. The nomogram stratified patients into low-, intermediate-, and high-risk groups, with median EFS not reached, 47.5 months, and 18.0 months, respectively. High-risk patients also showed lower pathological complete response (pCR) rates. Conclusions: This exploratory nomogram integrates an accessible systemic inflammatory marker with baseline clinical variables to identify patients with poorer outcomes despite NAC. External validation in contemporary cohorts is warranted before clinical implementation. Full article

Traumatic fractures are common in small animal emergency care, yet subtle fracture lines may be difficult to identify accurately using routine three-dimensional reconstruction workflows, particularly when access to specialized software is limited. This study describes the use of the open-source platform Three-Dimensional Slicer for computed tomography-based reconstruction and three-dimensional printing in a small dog with cranial trauma, with emphasis on documenting a practical and reproducible workflow through voxel resampling. Imaging data were imported into the software, bone structures were segmented using a rapid workflow, voxel spacing was resampled for smoother surface visualization by volume resampling, and the reconstructed model was processed for physical printing. Digital models of different resolutions were generated within minutes, and a life-size skull model was successfully fabricated using fused deposition modeling in less than three hours at a material cost of under one United States dollar. The enhanced model provided an intuitive representation of fracture morphology and spatial relationships compared with routine reconstruction alone. These findings demonstrate that open-source software combined with low-cost printing can provide a rapid, affordable, and user-friendly approach for practical skeletal reconstruction in small animals, with practical value for fracture assessment, preoperative planning, and broader use in resource-limited veterinary settings. Full article

Traditional Mediterranean grapevine landraces represent irreplaceable reservoirs of adaptive diversity, yet many regional germplasm pools remain poorly characterized, limiting conservation strategies and climate-resilient breeding. This study presents the first comparative genetic assessment of 154 local Vitis accessions from three historically interconnected but genomically underrepresented Mediterranean regions: Greece, Morocco, and Slovenia. Using 12 highly polymorphic nuclear SSR markers, we detected substantial genetic diversity (168 alleles; mean heterozygosity He = 0.881) with distinct regional signatures. Moroccan accessions exhibited the highest allelic richness and 11 private alleles, reflecting diverse agroecological adaptation. Slovenian germplasm formed a cohesive, genetically stable cluster with high effective allele numbers. Greek accessions exhibited the highest observed heterozygosity and 14 private alleles, consistent with the Aegean’s role as a major diversification hotspot. Despite >90% of variance occurring within individuals, AMOVA and pairwise FST (0.050–0.061) revealed low to moderate but significant geographic differentiation. Multivariate analyses (PCA, UPGMA) and Bayesian clustering (sNMF, K = 3) consistently resolved three regional genetic groups with varying admixture levels, consistent with a mosaic domestication model, as previously proposed for the Mediterranean basin, shaped by recurrent introductions, wild introgression, and region-specific selection. Our results show that peripheral Mediterranean germplasm harbors meaningful, regionally distinctive, substantial, non-redundant diversity not fully represented in surveys focused on climate adaptation, disease resistance breeding, and long-term genetic resource conservation. These findings challenge simplistic diffusion models and emphasize the strategic importance of geographically comprehensive sampling in grapevine conservation programs. Full article

University orientation programmes are a primary mechanism through which new students become familiar with campus facilities, academic spaces, and institutional procedures. However, many orientation activities are delivered as single in-person sessions, limiting opportunities for students to revisit spatial and procedural information after the event. To help address this constraint, a digital twin-inspired metaverse orientation application, the Digital Twin Metaverse Orientation (DTMO), was designed in Unity and hosted on Spatial.io as a spatially faithful virtual replica of a faculty environment. An exploratory pilot evaluation was conducted with 30 university students from multiple faculties after a facilitator-guided orientation session. The System Usability Scale (SUS), Net Promoter Score (NPS), and two open-ended questions were used to examine perceived usability, recommendation intention, and the reasons underpinning recommendation decisions. The application obtained a mean SUS score of 86.83, corresponding to an excellent perceived-usability rating, and an NPS of 53.33, indicating positive immediate recommendation intention. Qualitative responses suggested that participants valued the DTMO for engagement, accessibility, ease of navigation, and support for spatial familiarisation, while some participants emphasised that it should complement rather than replace physical orientation. These pilot findings indicate promising user reception in a small, guided-session sample, but they do not establish orientation effectiveness, learning transfer, wayfinding performance, retention, belonging, institutional integration, or sustained use. Further research with broader samples and outcome-based measures is therefore needed. Full article

The high dropout rate in massive open online courses (MOOCs) continues to limit their potential in promoting inclusive and sustainable learning. Although many prediction models have been used to identify potential dropouts, most studies view dropout as a static classification problem and fail to clearly reveal the dynamic trajectory of learner participation over time. Therefore, this study introduces a phased analysis perspective, treating MOOC dropout as a process that continuously evolves at different stages. On the basis of the KDDCUP2015 dataset, we constructed behavioral characteristics at three time points: the first week, the third week, and the fifth week. By combining robust feature analysis and interpretable models, we systematically examined the changing patterns of dropout modes. The results revealed significant differences across the different stages. In the early stage of the course, dropout was related mainly to the unstable interaction behaviors of learners, such as restricted access to resources and irregular participation rhythms. In the middle and late stages, task-oriented behaviors, especially those related to video-based learning activities, gradually became key factors. Notably, high-frequency video participation does not always reduce the risk of dropout; when video activity is high but the overall interaction rate is low, it is more likely to indicate an increase in the risk of dropout. These results indicate that the combination of behaviors is more crucial than mere activity levels. By revealing the changing characteristics of behaviors at different stages, this study helps support the design of more practical early warning methods. Full article

Background: Dedicated artificial intelligence (AI) systems for fracture detection already exist, yet general-purpose multimodal models are increasingly accessible to clinicians despite not being developed or formally validated as medical devices. Their behavior in focused orthopedic imaging tasks remains insufficiently characterized. Purpose: To characterize how two accessible general-purpose multimodal models interpret AP pelvis radiographs with hip fractures, focusing on context dependence, overconfidence, and complementary error patterns within a surgically confirmed positive-only cohort. This was a behavioral characterization study of a fracture-positive cohort, not a diagnostic accuracy evaluation. Methods: In April 2026, we retrospectively studied 214 surgically confirmed hip fractures on AP pelvis radiographs using two general-purpose multimodal models under six prompting conditions. In runs A–D, the models were explicitly told that a hip fracture was present and were asked to classify it; in runs E–F, they were not told whether a hip fracture was present. Each image was rerun de novo in a separate chat session through vendor APIs using a fixed base prompt and no image preprocessing. We recorded hip-fracture detection, correct laterality, coarse fracture pattern, intracapsular displacement, AO/OTA grading, subtrochanteric identification, and self-reported confidence. Because the cohort contained hip fractures only, we report fracture-detection rates and classification performance within a positive-only cohort rather than full diagnostic-accuracy metrics. Results: Using the more conservative endpoint of hip-fracture detection with correct laterality, GPT-5.4 was correct in 79.0% and 86.4% of cases in runs E and F, whereas Gemini was correct in 80.4% and 93.5%, respectively. When outputs from both models were combined, this endpoint reached 89.7% in run E and 96.7% in run F, indicating complementary rather than redundant error patterns. Incorrect laterality cues markedly degraded performance, from 90.7% to 66.4% in GPT-5.4 and from 97.7% to 57.0% in Gemini. Performance remained limited for treatment-relevant subtyping, particularly AO/OTA grading and subtrochanteric identification. Both models frequently remained highly confident when wrong, and self-reported confidence did not reliably distinguish correct from incorrect outputs. Conclusions: Accessible general-purpose multimodal models showed partial capability for coarse hip-fracture interpretation, but they remained context-sensitive, unreliable for treatment-relevant subtyping, and highly confident even when incorrect. Their complementary error patterns are hypothesis-generating rather than evidence of clinical readiness. On the basis of these findings, we do not support unvalidated or uncontrolled clinical use of such models. As access to these tools expands, explicit usage boundaries, minimum performance expectations, repeated local revalidation, and sustained human oversight become increasingly necessary. Full article

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity (ε* = ε′ – jε″) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ε′ and ε″ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ε″ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ε′ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios. Full article

This study examines how the 4Es framework—Experience, Exchange, Everyplace, and Evangelism—influences online cosmetics purchase behavior in Thailand, addressing the growing importance of digital consumer engagement in emerging markets. A mixed-methods approach was employed, combining quantitative data from a structured survey with qualitative insights from semi-structured interviews. Binary logistic regression analysis was used to assess the effects and predictive power of the 4Es and selected demographic and behavioral variables. The results indicate that all four dimensions significantly influence purchase behavior, with Evangelism emerging as the strongest predictor, followed by Experience, Everyplace, and Exchange. The model demonstrates strong predictive capability, highlighting the importance of behavioral factors such as platform usage, purchase frequency, and social media engagement, while several demographic variables show limited influence. Qualitative findings further support these results, revealing that consumers place strong emphasis on social influence, emotional engagement, and convenience in their online shopping experiences. The study concludes that online cosmetics purchase behavior is shaped by a combination of experiential, relational, and socially driven factors, with social influence playing a dominant role. These findings demonstrate that the 4Es framework remains highly relevant in digitally mediated consumer environments, where purchase decisions are increasingly influenced by interactive experiences, omnichannel accessibility, value co-creation, and consumer advocacy. By integrating quantitative and qualitative evidence, the study extends the application of the 4Es framework beyond traditional marketing contexts and demonstrates its value as a comprehensive model for understanding consumer engagement and online purchasing behavior in contemporary digital marketplaces. The mixed-methods approach provides both generalizable and contextually grounded insights, offering theoretical contributions to digital marketing literature and practical guidance for marketers seeking to strengthen consumer engagement and brand advocacy in increasingly competitive online markets. Full article

The transition to university is a critical period of academic, social, and emotional adjustment. Social media plays a central yet complex role, offering opportunities for connection while also posing risks to wellbeing, productivity, and social integration. This study explores how first-year undergraduates experience and interprets their social media use during this transition. Using a constructivist grounded theory approach, nine focus groups were conducted with 40 first-year students at a UK university. Analysis generated a model conceptualising social media as a ‘double-edged sword’ within the broader social transition. Five interrelated themes were identified: social transition, polarised attitudes, quality of social connection, wellbeing support, and quantity and form of use. Social media supported early connection-building, maintaining peer ties, and accessing support, yet excessive or passive use intensified social comparison, fear of missing out, and academic distraction. Students recognised problematic use themselves, often prompting peer intervention to restore balance. Form, purpose, and perceived impact shaped experiences more than time spent. Findings highlight the importance of student agency, peer networks, and institutional support, offering implications for promoting healthy social media practices, wellbeing, and smoother transitions to university life. Full article

Respiratory mortality in Europe is associated with interacting environmental, infrastructural, climatic, and energy-related conditions. This study investigates country–year patterns of respiratory disease mortality by integrating panel-data econometrics, clustering analysis, and machine-learning prediction. The econometric results indicate that agricultural land use and coal-based electricity generation are positively associated with respiratory mortality, while access to electricity and freshwater withdrawals show negative associations. Cooling degree days capture a heat-related environmental-health dimension, although some coefficients become weaker under robust specifications. Sanitation and renewable energy display heterogeneous and specification-sensitive patterns, suggesting that they may partly reflect broader development gradients, infrastructure transitions, and regional heterogeneity rather than direct causal mechanisms. Hierarchical clustering identifies 10 country–year environmental-health profiles, highlighting differentiated combinations of energy systems, land use, infrastructure, climatic exposure, and respiratory mortality. This approach avoids treating countries as fixed homogeneous units and allows environmental-health profiles to vary over time. The selected hierarchical solution provides a balanced and interpretable structure relative to more polarized clustering alternatives. Machine-learning models are used as a complementary predictive exercise rather than as substitutes for econometric inference. Within the adopted validation framework, K-nearest neighbors achieves the strongest predictive performance. Additional stability checks and local additive explanations improve transparency regarding model tuning and prediction behavior, while confirming that machine-learning outputs should be interpreted as predictive rather than causal evidence. Overall, the findings support integrated and region-sensitive policy approaches combining air-quality management, infrastructure resilience, energy transition, climate adaptation, and public-health planning. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Dermatophytosis remains one of the most prevalent superficial fungal infections worldwide and is increasingly encountered as a persistent or difficult-to-treat syndrome. A major clinical problem is that apparent treatment failure is often attributed to antifungal resistance, although many cases are instead driven by diagnostic uncertainty, corticosteroid-modified disease, reinfection, inadequate exposure, poor adherence, and limited drug delivery within keratinized tissues. This narrative review was developed to clarify the distinction between true antifungal resistance and clinical recalcitrance, with particular attention to terbinafine-resistant Trichophyton species, Trichophyton indotineae, tinea incognito, onychomycosis, dermatophytoma, and high-barrier skin and nail infections. We synthesized peer-reviewed literature and guideline-level evidence addressing epidemiology, molecular mechanisms of resistance, clinical phenotypes of recalcitrance, diagnostic escalation, therapeutic decision-making, and antifungal delivery in keratinized tissues. The review contributes a dermatology-centered conceptual framework in which persistent dermatophytosis is interpreted through both microbiological resistance and modifiable recalcitrance drivers. This approach emphasizes confirmation of fungal disease when indicated, phenotypic and anatomic classification, avoidance of inappropriate corticosteroid combinations, optimization of dose, duration, vehicle, and adherence, measures to improve drug access and reduce protected fungal burden in high-barrier disease, and prevention of reinfection from reservoirs. The proposed framework may support more rational antifungal use and reduce unnecessary escalation; however, it is based on narrative synthesis rather than a systematic review or prospective validation. Additional studies are needed to determine how such structured clinical approaches affect clinical outcomes, relapse rates, antifungal exposure, and resistance emergence in real-world dermatology practice. Full article