Search Results (379)

Driven by the large-scale deployment of renewable electricity, water electrolysis has emerged as a leading pathway for high-efficiency hydrogen production. Under practical operating conditions, gas evolution and gas–liquid two-phase flow inside electrolyzers substantially reshape electrode interfacial states and the in-cell mass transfer environment and have been reported to cause performance losses on the order of 10–30% under unfavorable conditions. This review summarizes the evolution of electrode-generated bubbles during nucleation, growth, detachment, and coalescence, and consolidates the fundamental features of two-phase hydrodynamics and phase-distribution patterns in electrolyzer channels. Progress and limitations of major two-phase modeling approaches are then assessed with respect to their capability to resolve the relevant interfacial and transport processes. The impacts of gas evolution and two-phase flow on electrochemical performance, stability, and durability are subsequently discussed. Finally, recent advances in two-phase-flow management—through flow-field organization and structural design, as well as the introduction of external physical fields—are reviewed, together with experimental and diagnostic methods used to quantify bubble behavior and phase distributions. This review aims to provide a coherent understanding of the governing behaviors, research tools, and performance implications of gas evolution and two-phase flow in water electrolysis, and to inform electrode/transport-layer design, flow-field management, and the development of predictive numerical models. Full article

Background: Multimorbidity frequently involves overlapping neuro-psychic, cardiometabolic, and renal disturbances, yet clinical assessment often relies on diagnosis-based comorbidity counts that may not fully capture cumulative physiological stress. We developed the Neuro–Cardio–Renal Stress Index (NCR-SI) as a pragmatic composite framework to describe multisystem burden using routinely available clinical data. Methods: This cross-sectional study analyzed electronic medical record data from adult patients with chronic conditions. NCR-SI integrates three domains: neuro-psychic burden (text-derived indicators and psychotropic medication use), cardiometabolic stress (triglyceride–glucose index and cardiometabolic diagnoses), and renal function (MDRD-estimated eGFR staging). Importantly, this study is not intended to demonstrate incremental predictive value over individual components or established comorbidity indices. Rather, it presents NCR-SI as a transparent, domain-based descriptive framework and reports its internal coherence and distribution across clinically recognizable multimorbidity contexts. Results: A total of 148 patient records were screened; 143 patients met complete-case criteria and were included in the main NCR-SI analyses. NCR-SI ranged from 0 to 10 (median 5). Higher scores were observed in renometabolic profiles. NCR-SI showed expected structural associations with declining renal function (eGFR; ρ ≈ −0.71), moderately with the TyG index (ρ ≈ 0.42), and weakly with medication burden. Correlation with age-adjusted CCI was minimal (ρ ≈ 0.09), indicating limited overlap with diagnosis-based comorbidity counts. Domain-specific correlations were consistent with predefined score construction rules, particularly between the renal domain and eGFR, and between the cardiometabolic domain and TyG. Conclusions: NCR-SI provides a pragmatic, integrative descriptor of neuro-cardio-renal stress using routinely collected clinical data. Rather than replacing established comorbidity indices, NCR-SI may complement them by summarizing multidimensional physiological burden patterns. NCR-SI is proposed as a research-oriented, hypothesis-generating descriptive framework. External validation in independent cohorts and longitudinal evaluation against clinically meaningful outcomes (e.g., hospitalization, mortality, functional status, healthcare utilization) are required before any claims of clinical performance can be made. Full article

Accurate monitoring of crop phenology is essential for precision agriculture and yield forecasting. However, satellite-derived time series often suffer from inherent noise, such as residual atmospheric effects and mixed pixels, as well as a frequent lack of ground-truth data in agriculture. In response, this study proposes an algorithm to define the type of spectral signatures for the principal phenological stages of crops, using them as the foundation for training supervised machine learning classification models. The algorithm was developed using Fuzzy C-Means (FCM) clustering to identify the spectral signature reference groups in winter wheat across the Burgos region (Spain) during the 2020 and 2021 growing seasons. To enhance cluster independence and biological coherence, a multi-step filtering process was implemented, including spectral purity (membership degree, SAM, and SAMder) and temporal coherence filters. The filtered and labeled dataset (80% original Burgos dataset) was used to train supervised classification models (KNN and XGBoost). The models’ reliability was verified through three wheat tests (remaining 20%), labeled using other clustering techniques, and an independent barley dataset from diverse geographic locations (Valladolid and Soria). The filtering process significantly improved cluster stability by removing outliers and transition spectral signatures. The supervised models demonstrated exceptional performance; the KNN model slightly outperformed XGB, achieving a mean Accuracy of 0.977, a Kappa of 0.967, and an F1-score of 0.977 in the wheat external test. Furthermore, the model showed, when applied to barley, that its phenological spectral signatures are equivalent in shape to those of wheat, with an Accuracy of 0.965 and an F1-score of 0.974. In addition, it was verified that the type spectral signatures remain the same regardless of the location. This study presents a robust classification tool capable of labeling four key phenological stages (tillering, stem elongation, ripening, and senescence) without ground truth. By effectively removing inherent satellite noise, the proposed methodology produces organized, cleaned datasets. This structured foundation is critical for future research integrating spectral signatures with harvester data to develop high-precision yield prediction models. Full article

Analyzing large-scale student feedback is critical for higher education quality assurance, yet manual analysis is inefficient and subjective. This paper proposes an integrated framework that unifies supervised classification, unsupervised clustering, and retrieval-augmented generation (RAG) to produce evidence-grounded and actionable insights. Ensemble-based supervised models perform thematic classification, while multi-encoder embedding fusion enables unsupervised discovery of coherent feedback clusters. A multi-stage RAG module integrates category predictions and cluster structure to retrieve representative evidence and generate transparent summaries with citation traceability. The framework is evaluated on student feedback collected from a Central Asian university and two public benchmarks, EduRABSA and Coursera course reviews, covering seven thematic categories. The supervised ensemble achieves 83.0% accuracy and 0.829 Macro-F1 on the primary dataset, while unsupervised clustering attains a silhouette score of 0.271 under the best fusion strategy. Independent evaluation on external benchmarks yields ensemble accuracy of 81.1% on EduRABSA and 49.8% on Coursera, confirming the framework’s adaptability across diverse educational contexts. By leveraging supervised labels and unsupervised structure, the proposed framework enables evidence-grounded, category-aware LLM-based summaries that faithfully reflect the diversity and distribution of student feedback and support actionable educational decision-making. Full article

Quantitative microbial risk assessment (QMRA) has become a central framework for evaluating foodborne microbial hazards by integrating microbiological data, exposure assessment, dose–response modelling, and probabilistic simulation. Over the past three decades, its rapid expansion has created challenges in obtaining a coherent overview of the field’s structure, dominant themes, and research trajectories. This study presents a bibliometric and systematic review of QMRA research in food safety. Bibliographic data were retrieved from the Scopus database (search conducted in January 2026), including peer-reviewed articles published in English between 1995 and 2024, and analysed using performance analysis and science mapping techniques to assess publication trends, influential contributors, collaboration patterns, and thematic evolution. Risk of bias assessment was not applicable due to the bibliometric nature of the study. The results indicate steady long-term growth of QMRA research, based on a final dataset of 186 articles across multiple journals and countries, with a concentrated influence structure dominated by a limited number of specialised journals, institutions, and research groups. International collaboration is particularly strong within European networks. Thematic analysis identifies probabilistic exposure assessment, Monte Carlo simulation, predictive microbiology, and dose–response modelling as the methodological core, with a primary focus on major foodborne pathogens such as Campylobacter, Salmonella, Listeria monocytogenes, and Escherichia coli. Persistent emphasis on uncertainty, cross-contamination, and dose–response relationships highlights key methodological challenges. Limitations include reliance on a single database and potential exclusion of studies using alternative terminology. These findings provide a structured overview of the QMRA landscape and identify priorities for methodological refinement and future application in food safety risk assessment. This study received no external funding and was not prospectively registered. Full article

Background: The phase of electroencephalogram (EEG) signals critically influences cortical reactivity to external inputs. Phase-dependent effects and their sensitivity to stimulation intensity have been observed at suprathreshold levels, while subthreshold transcranial magnetic stimulation (TMS) cannot induce motor evoked potentials (MEPs), resulting in limited research on phase-dependent responses under subthreshold stimulation. In this study, we used a combined transcranial magnetic stimulation and electroencephalography (TMS–EEG) approach to examine how the ongoing EEG phase influences cortical responses at subthreshold intensity and to characterize these responses in terms of temporal, spatial, and spectral features. Methods: Thirty-four healthy adults received subthreshold single-pulse TMS at the motor hotspot during 64-channel EEG recording. The mu-phase at the time of TMS delivery was estimated using autoregression-based forward prediction and categorized into four bins (0°, 90°, 180°, and 270°). The cortical responses were assessed using inter-trial phase coherence (ITPC), TMS-evoked potentials (TEPs), global mean field power (GMFP), and event-related spectral perturbation (ERSP). Results: Phase estimation reliably distinguished four mu-phase bins. Subthreshold TMS–EEG responses showed clear phase dependence: early ITPC and several TEP components (N15, P30, N45, P60, and N100) differed significantly across phases, with 180° and 270° often eliciting stronger responses. GMFP revealed robust phase effects at mid-latency components, and TMS-induced mu-rhythms were the greatest at 180°. Conclusions: Our results showed that the EEG phase significantly modulates cortical reactivity at subthreshold stimulation levels, supporting mu-phase-based closed-loop TMS as a promising strategy for precise neuromodulation. Full article

Background and Objectives: Serum biomarker discovery in resectable pancreatic ductal adenocarcinoma (PDAC) remains a critical unmet need, as over 80% of patients present with unresectable disease. Serum proteomics offers a promising approach for identifying circulating biomarkers associated with early-stage disease; however, clinical translation has been limited by inconsistent validation and the absence of clinically relevant comparator populations. Materials and Methods: We performed a discovery-phase study using data-independent acquisition mass spectrometry-based serum proteomics in 35 patients with resectable, non-metastatic PDAC and 34 non-cancer controls without hepato-biliary-pancreatic disease. Following quality filtering (≥80% detection threshold), 407 proteins were retained for analysis. Differential abundance was assessed using Welch’s t-test with Benjamini–Hochberg correction (FDR < 0.01, |FC| ≥ 1.5). Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis and logistic regression with repeated stratified 5-fold cross-validation (100 repetitions) and bootstrap resampling (1000 iterations). Functional enrichment analysis was performed using g:Profiler. Results: Ninety proteins were significantly altered in PDAC (50 increased, 40 decreased). Inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) demonstrated the highest individual diagnostic performance (AUC = 0.90), followed by coagulation factor XIII A chain (F13A1; AUC = 0.89) and ferritin light chain (FTL; AUC = 0.86). Functional enrichment revealed significant overrepresentation of secretory granule lumen components (adjusted p = 0.001) and complement/coagulation pathways (adjusted p < 0.001). An enrichment-guided three-protein panel (ITIH3, F13A1, and FTL) achieved an AUC of 0.98 (95% CI: 0.95–1.00), with a cross-validated mean AUC of 0.96, sensitivity of 83% (95% CI: 66.4–93.4%), and specificity of 100% (95% CI: 89.7–100%) within the discovery cohort. Conclusions: This discovery-phase study identifies a biologically coherent serum protein signature enriched for secretory granule lumen components in resectable PDAC. The three-protein panel demonstrates strong internal validation performance; however, these estimates may be optimistic due to feature selection performed prior to cross-validation. External validation in independent cohorts—including chronic pancreatitis controls and parallel CA19-9 assessment—will be essential to determine clinical applicability. Full article

We propose a novel scheme that is used for the enhancement of phase sensitivity. The SU(1,1) interferometer with a two-mode squeezed coherent state input, using balanced homodyne detection (BHD) and intensity detection (ID), is shown. Our results demonstrate that the phase sensitivity achieved via BHD outperforms that of ID. The optimal phase sensitivity via BHD surpasses the Heisenberg limit (HL) and approaches the quantum Cramér–Rao bound. A larger photon number and parameter strength can make the phase sensitivity better. Furthermore, we show the effects of internal and external losses on phase sensitivity in detail. When external loss reaches 10%, the phase sensitivity can reach the HL. Next, we have a detailed discussion on the impact of the squeezing parameter and photon number on phase sensitivity, which shows that our scheme has better phase sensitivity and enhanced robustness. This interferometer system thus holds significant potential for applications in quantum precision measurement. Full article

We propose Trust Triangle, a Bridging Methodology that establishes evidential reliability through multi-attribution consensus, ensures external validity via statistical hypothesis testing, and enables controlled generation with RAG-anchored LLMs to transform black-box predictions into trustworthy, auditable explanations. This framework is instantiated for credit card fraud detection by integrating multi-method feature attributions with rigorous statistical validation. The resulting reliability-validity-verified insights are synthesized with high-relevance domain knowledge (relevance score > 0.7) retrieved from a real-world corpus via Retrieval-Augmented Generation (RAG). A structured Chain-of-Thought (CoT) prompt then guides an LLM to produce coherent, audit-ready case reports. Our contributions are threefold: (1) a verifiable framework for quantifying attribution reliability and validity, (2) a demonstrated end-to-end pipeline from robust prediction to semantically grounded explanation, and (3) a generalizable paradigm for Trustworthy ML in high-stakes domains. Experiments on a highly imbalanced dataset (fraud rate: 8.74%) demonstrate robust performance (PR-AUC = 0.7867), successfully identify statistically significant predictive features, and generate audit-ready reports, thereby advancing a rigorous, evidence-based pathway from model output to decision-ready support. Full article

Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchain–MAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identified—basic, intermediate, and advanced—distinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: Blockchain–MAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered. Full article

Multi-hop question answering (MHQA) requires models to retrieve and reason over evidence distributed across multiple documents, which remains challenging for conventional retrieval-augmented generation (RAG) approaches. Although RAG improves factual grounding by incorporating external knowledge, flat retrieval strategies often struggle to maintain coherent reasoning chains when implicit dependencies among entities and documents are involved. This paper presents a query-driven dual-layer graph retrieval framework for MHQA. The framework operates on a unified heterogeneous graph integrating entities, relations, and supporting texts, and dynamically constructs candidate subgraphs through joint retrieval over entities and relations, complemented by lexical retrieval signals. Reasoning paths are refined by combining structural strength modeling with contrastive learning-based path scoring, and an adaptive pruning strategy is employed to regulate evidence scale according to query complexity and path score distributions. Experiments on HotpotQA and 2WikiMultihopQA show that the proposed framework achieves higher EM and F1 scores than existing RAG and graph-based retrieval methods, particularly in complex multi-hop scenarios. These results indicate the importance of structured and query-adaptive evidence organization for multi-hop reasoning. Full article

Generative AI enables rapid visualization of sustainable urban design scenarios, yet the question of whether these outputs encode sustainability as operable spatial logic, rather than merely depicting it as a visual impression, remains underexplored. This study proposes a two-level assessment framework that scores the same sustainability dimensions at both the visual-representation level and the spatial-logic level, treating the systematic decoupling between the two as a form of visual greenwashing: system-induced representational distortion rather than deliberate misrepresentation. Using AI-workflow reports from two site-based urban design studios (47 students, 12 teams, 36 coded scenes), the framework integrates rubric-based scoring with qualitative process tracing of breakdown–repair logs. Results show that image-level scores consistently outperform logic-level scores across all five dimensions, with the gap most severe in mobility hierarchy and walkability and smallest in green/blue infrastructure. Case analysis reveals that breakdowns arise from failures in program encoding, urban-scale coherence, functional-boundary demarcation, and relational-condition matching, and that students deploy multi-stage repair pipelines, including prompt restructuring, tool switching, reference injection, and external-source compositing, to re-inject collapsed spatial logic. These findings reframe AI-assisted urban design as repair-centered workmanship rather than automated production. The study proposes three guardrails to prevent visual sustainability from substituting for spatial-logic sustainability: image–logic paired submission, design audit trail formalization, and gap-based red-flag review. Full article

Background/Objectives: Irritable bowel syndrome (IBS) is a prevalent gastrointestinal disorder with implications for individual quality of life and society. Patients with IBS suffer a variety of symptoms but have few treatment options. The level of satisfaction with IBS treatment is low, stressing the need to expand the IBS treatment toolbox. The aim of this study is to describe drivers and barriers to the implementation of time-restricted eating (TRE) as a treatment alternative for patients with IBS. Methods: A convenience sample of 14 informants was drawn from a pool of 97 successful participants in an eight-week 16:8 TRE intervention. The informants partook in audio-recorded semi-structured in-depth interviews. Recordings were processed by a computer language model and interview transcripts were generated automatically. The transcripts were proofread, structured and analysed with a reflexive inductive thematic analysis approach. Results: The analysis generated six main themes consisting of 18 sub-themes in total. One main theme describes drivers of implementation concerning domains such as motivation, supporting factors, mentality, behaviour and determinants of sustainability. The results from this study are largely coherent with the findings from earlier feasibility studies conducted on other populations, but several key differences related to population characteristics emerged. Conclusions: Overall, the analysis suggests that TRE can be a feasible treatment option for IBS, but successful implementation is dependent on individual ability, external support and symptom relief. Full article

Background: Kitesurfing is a wind-propelled water sport performed in highly variable environmental conditions. Scientific evidence describing internal load under standardized ecological sea constraints remains limited. Aim: This study aimed to characterize cardiovascular and perceptual responses during a standardized kitesurfing session and to examine associations among heart rate-based internal load indices, session rating of perceived exertion, and global navigation satellite system-derived external output variables. Methods: A total of 112 male recreational kitesurfers (32.1 ± 6.8 years) completed a 40–50 min standardized session under monitored wind conditions (17–22 knots) along a predefined approximately 800 m course. Heart rate was continuously recorded, and session rating of perceived exertion (Borg Category-Ratio 10 scale) was collected 30 ± 5 min post-session. Training impulse, mean percentage of maximal heart rate, and session rating of perceived exertion load were calculated. Pearson correlation analyses with bootstrapping (1000 resamples) and five percent trimming were performed, with statistical significance set at 0.05. Results: Sessions were performed at 78.4 ± 9.1 percent of maximal heart rate. Training impulse and mean percentage of maximal heart rate were strongly associated (correlation coefficient = 0.90, probability value < 0.001), reflecting the shared heart rate-based structure of these metrics. Training impulse showed a moderate association with session rating of perceived exertion load (correlation coefficient = 0.46, probability value < 0.001). No significant associations were observed between internal load indices and global navigation satellite system-derived mean speed (correlation coefficient = −0.14, probability value = 0.149) or distance (correlation coefficient = 0.06, probability value = 0.555). Sensitivity analyses confirmed the stability of the observed associations. Conclusions: Under standardized ecological sea conditions, kitesurfing sessions were characterized by sustained high submaximal cardiovascular intensity. Heart rate-based and perceptual measures showed consistent associations within this protocol, whereas global navigation satellite system-derived external outputs were not significantly related to internal load indices. Within the limits of this cross-sectional ecological design, the combined use of one heart rate-based indicator and session rating of perceived exertion offers a coherent and practically interpretable description of session internal load in open-water kitesurfing. Full article

Sustainability literacy is increasingly invoked in construction and planning research, yet it is most often framed as an educational construct concerned with awareness, knowledge, and attitudes. This framing provides limited explanatory power for understanding how sustainability values are translated into in real-world planning decisions, particularly under conditions of uncertainty and value conflict. In parallel, artificial intelligence (AI) has been introduced into planning practice largely as an optimization-driven analytical tool, reinforcing instrumental conceptions of rationality. This study reconceptualizes sustainability literacy as a decision capability and develops an AI-enabled theoretical framework that positions AI as a cognitive partner in sustainability-oriented construction planning. Methodologically, the study adopts a conceptual research design grounded in a systematic interdisciplinary literature synthesis spanning planning theory, circular economy, social sustainability, and AI-enabled decision support, combined with theory-building and framework development procedures. The proposed framework clarifies how human judgment can be cognitively augmented through AI-supported interpretation, trade-off exploration, and value-informed deliberation, thereby reframing sustainability as an internal driver of planning judgment rather than an external performance criterion. By conceptualizing human–AI collaboration as an iterative, reflective process, the study establishes a coherent theoretical basis for context-sensitive sustainability planning in the built environment, with implications for decision-support system design, planning practice, and professional education. Full article