Search Results (2,777)

Background: Hydration and electrolyte strategies are critical in mountain ultra-endurance events, yet field-based evidence from trail races remains limited. This study examined the relationship between fluid intake, sodium consumption, and body mass changes in trail runners competing under real environmental conditions. Methods: A cross-sectional field study was conducted during La Misión Brasil 2024. Athletes of both sexes competing in the endurance race (35 km; EG: n = 15; age = 37.0 [29.5–46.0] years; 12 men and 3 women) and the ultra-endurance race (80 km; UEG: n = 13; age = 42.0 [37.0–46.0] years; 11 men and 2 women) were included in the study. Pre- and post-race body mass were assessed, and in-race fluid and food intake were collected using an adapted 24-h dietary recall. Water and sodium intake were expressed as total (L and mg, respectively) and per-hour (mL/h and mg/h, respectively) values. Environmental temperature and humidity were obtained from a local weather station. Group comparisons were performed using the Mann–Whitney U test, and associations were examined with Spearman’s correlation (p < 0.05). Results: EG (n = 15) and UEG (n = 13) showed similar absolute and relative body mass changes (2.6% to −3.0%; p > 0.05). EG runners presented greater weight loss rate (−270 vs. −115 g/h; p = 0.002), while UEG consumed higher total water (7.11 vs. 4.14 L; p = 0.008) and sodium (5789 vs. 2857 mg; p = 0.003). Water intake per hour was higher in EG (626 vs. 427 mL/h; p = 0.017). Body Mass Index was negatively correlated with hourly weight loss (r = −0.605; p < 0.001). Water and sodium intake per hour were positively correlated (r = 0.607; p < 0.001), though neither predicted hourly weight loss. Conclusions: Hydration responses may differ according to environmental stress and pacing demands. Changes in body mass may not necessarily reflect hydration adequacy, suggesting a possible multifactorial nature of hydroelectrolyte balance during mountain endurance events. Full article

Background/Objectives: Daily sleep patterns are associated with cognitive health and Alzheimer’s disease (AD). However, it remains unclear how suboptimal irregular sleep manifests in AD from the preclinical stage to dementia. This study aimed to establish the dose–response association between sleep irregularity and psychometrically defined stage-specific AD as well as executive dysfunction, among adults with subjective cognitive and sleep issues. Methods: Cross-sectional data were obtained from 532 Japanese adults (mean age = 63.9 years) between March 2023 and April 2024. Sleep irregularity was quantified using the Sleep Regularity Index (SRI) with 24/7 accelerometer data. A modified Poisson regression with cubic splines was performed to establish the dose–response association. Results: This study identified novel non-linear associations. The prevalence ratios of cognitive impairment, defined as being in the preclinical and more advanced stages of AD, significantly declined beyond a median SRI of 60. Participants within this SRI range also showed significantly lower prevalence ratios of poorer Trail Making Test B performance. All results were independent of age, sleep duration, and risk of depression. Conclusions: Maintaining balanced-to-regular daily sleep patterns might be optimal for AD progress from its preclinical stages, with a potential benchmark at SRI of 60, especially for those individuals at risk for cognitive decline and sleep disorders. Further research is needed to replicate this benchmark in diverse populations and to evaluate the effect of rigid sleep regularity on cognitive health. Full article

Background/Objectives: We investigated whether ingestion of caffeine (~1 h before) was beneficial to subsequent morning (07:30 h), mood, strength and cognitive measures. Methods: Fourteen recreationally active males were recruited and completed six sessions: (i) one repetition maximum (1RM) for bench press and back squat; (ii) two familiarization sessions of strength measures; (iv) three experimental conditions administered in a double-blinded, randomized counterbalanced design order, either caffeine (Caffeine [CAFF], 300 mg or 2.8–4.3 mg/kg body weight), placebo (Placebo [PLAC]) ingested at 06:30 h, or no-pill control (No Pill [NoPill]). For each experimental session, on arrival at the laboratory, rectal and skin temperature were measured as well as a battery of cognitive performance through a battery of tests (trail-making test, Rey’s auditory verbal learning test, and Stroop word–colour interference test). Thereafter, maximum voluntary contraction on an isometric chair (MVC) without and with stimulation was conducted, and three repetitions were performed at 40, 60 and 80% of 1RM for bench press and back squat. Average power (AP), average velocity (AV), peak velocity (PV), mean propulsive velocity (MPV), average acceleration (RDV), displacement (D) and time-to-peak velocity (tPV) were recorded using MuscleLab linear encoders. Rating of perceived exertion and effort was asked after each set (RPE). The data was analysed using a general linear model with repeated measures. Results: MVC peak-force values with and without stimulation showed a significant increase in the CAFF condition compared to values for NoPill and with stimulation PLAC conditions (stim: Δ9.0 and 8.7%; no stim: 8.3%; p < 0.05; η²_p = 0.33 and 0.42). Greater muscle % activation was achieved for the CAFF than the other conditions (~6%, p ≤ 0.042; η²_p = 0.33). In the non-stimulated MVC, RPE was perceived as easier (4.8%, p = 0.04). AV and MPV values were higher in both bench press (Δ3.3 and 4.6%) and back squat (Δ7.7 and 9.2%) in CAFF than the PLAC condition (p = 0.031; η²_p = 0.24 and 0.23 and 0.24 and 0.32). CAFF improved auditory total recall compared to NoPill (9.5%, p = 0.040; η²_p = 0.22). Conclusions: Early morning ingestion of caffeine improved MVC to levels observed by others in the evening, as well as some aspects of bench press, back squat and recall performance. Caffeine ingestion had no effect on core temperature, mood, tiredness, alertness or other measures of cognitive performance. Full article

Flood risk maps play a critical role in land-use regulation, infrastructure planning, and community preparedness, which are key components of sustainable and climate-resilient urban development. Their production, however, remains costly, labor-intensive, and time-demanding as it relies on simulation-driven workflows that combine hydrodynamic modeling with expert interpretation and extensive validation. To address this issue from a sustainability perspective, we develop a novel, practical, and near-real-time large language model (LLM)-based framework to support property-level flood risk assessment. This framework, which synthesizes geospatial, hydrological, infrastructural, and historical flood information, extends existing research and explores novel risk estimation methods for use in planning practice. Using Brisbane, Australia, as a case study, we develop Flood-LLM, a multi-agent system that transforms multi-source urban datasets into structured textual representations, models diverse neighborhood conditions, and fine-tunes a reasoning model using expert-assessed risk classifications. The results show that Flood-LLM can reproduce official flood risk labels for creek, river, storm tide, and overland-flow hazards with reasonable accuracy, outperforming classical machine learning, deep learning, and untuned LLM baselines. Visual and quantitative analyses indicate that the framework demonstrates a qualitatively nuanced capability to capture salient spatial patterns present in the official maps, while generating a textual chain-of-thought providing a transparent audit trail for its labeling decisions. These findings suggest that such LLM-based approaches can produce potential complementary tools to expert-reviewed planning classifications and support more sustainable, adaptive flood risk management by enabling timely map production and updates that facilitate informed decision-making in rapidly changing environmental conditions. 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

The combined control method of dimples and Gurney flaps has proven effective in enhancing the power coefficient of Vertical Axis Wind Turbines (VAWTs). However, the adaptability of this combined control structure to different airfoil geometries remains unclear. This paper investigates the aerodynamic characteristics of the Toward-Outside Dimple-Gurney Flap (TO-DGF) on three typical airfoils: NACA0021, NACA0012, and S1046. A dynamic flow field prediction model was established using the Lattice Boltzmann Method (LBM) combined with Wall-Modeled Large Eddy Simulation (WMLES). The Taguchi experimental design was employed to analyze the sensitivity of aerodynamic performance to airfoil type, Gurney flap position, and Gurney flap height. The results indicate that the airfoil type is the most critical factor affecting the power coefficient $C_P$, contributing significantly to the performance variance. Specifically, the NACA0021 airfoil demonstrated optimal performance in suppressing dynamic stall. Furthermore, the optimal DGF position varies with the tip speed ratio (TSR): placing the structure at 0.05C and 0.15C from the trailing edge yields the best aerodynamic performance for low (TSR = 1.5) and medium (TSR = 2.4) TSRs, respectively. This study provides a valuable reference for the structural design of high-efficiency VAWT blades within the investigated TSR range. Full article

The trailing-edge design of a wind turbine airfoil is critical for balancing the aerodynamic performance and structural robustness of a wind turbine blade. In this paper, the S809 airfoil and its blunt trailing-edge variant, the S809-100 airfoil, are taken as the research objects. The aerodynamic and flow-field characteristics of both airfoils are analyzed by computational fluid dynamics, which is validated by U.S. National Renewable Energy Laboratory experiments and wind tunnel particle image velocimetry. The results show that the S809-100 airfoil achieves a higher lift coefficient across the entire angle of attack ($\alpha$) range 0–18°, with a superior lift-to-drag ratio within 8–12°. Three distinct states of aerodynamic response are identified for both airfoils, based on time series and spectral features of lift and drag coefficients, and flow-field structures: steady convergence state, periodic fluctuation state, and irregular fluctuation state. The two airfoils differ significantly in aerodynamic response transition with respect to $\alpha$: for the S809 airfoil, the aerodynamic response remains in a steady convergence state up to $\alpha =16°$ before shifting to a periodic fluctuation state, while for the S809-100 airfoil, it exhibits a periodic fluctuation state from $\alpha =0°$ and transitions to an irregular fluctuation state beyond $\alpha =14.2°$. This difference stems from trailing-edge thickening, which induces flow unsteadiness in the S809-100 airfoil. This shift in the aerodynamic response from the periodic fluctuation state to the irregular fluctuation state is attributed to the transition from single-frequency large-scale vortex shedding to a multi-scale vortex interaction, confirmed via spectral and flow-field analyses. This study focuses on the correlated flow structures of wind turbine airfoils and deepens the understanding of unsteady aerodynamic responses; the combined analysis of enhanced aerodynamic performance and induced unsteady fluctuation due to trailing-edge thickening offers a valuable reference for wind turbine blade design. Full article

There is no approved drug therapy for schwannomas associated with NF2-related schwannomatosis (NF2-SWN). Neither life-saving surgical resection or radiation are curative and can compound the debilitating neurological effects of the schwannomas. We previously identified fimepinostat, a dual histone deacetylase (HDAC)/phosphoinositide-3 kinase (PI3K) inhibitor, as a promising drug candidate with pro-apoptotic effects on NF2-related schwannomas. This preclinical study used the pharmaceutical formulation of fimepinostat to confirm its efficacy in schwannomas and identify pro-apoptotic signaling pathways. Fimepinostat was tested in human schwannoma model cells, patient-derived primary vestibular and non-vestibular schwannoma cells, and in a sciatic nerve allograft model. The signaling pathways leading to caspase-3-dependent apoptosis were elucidated using immune assays, flow cytometry, imaging, proteome, and acetylome analysis. Acute exposure to fimepinostat led to p21-dependent cell cycle inhibition, upregulation of tumor necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL R2), and downregulation of tumor necrosis factor receptor 1 (TNFR1), Yes-associated protein (YAP), and inhibitors of apoptosis. Moreover, fimepinostat downregulated cytokine and chemokine secretion increased by merlin loss in schwannoma cells. Fimepinostat is a promising new drug intervention for NF2-SWN patients with the potential to promote tumor regression. Full article

Objectives: This systematic review and meta-analysis compared freehand and navigation-assisted spine surgery with robot-assisted techniques, focusing on radiation dose, fluoroscopy time, and factors influencing these outcomes. Methods: Following the PRISMA and PROSPERO protocols, we searched major databases for comparative studies on radiation exposure or fluoroscopy duration. Non-comparative, cadaveric, and animal studies were excluded. Bias was assessed with RoB 2 and MINORS. The data were pooled using random-effects models, with subgroup, meta-regression, sensitivity, and publication-bias analyses. Results: Twenty-eight studies (3205 patients) were included. Compared with freehand surgery, robotic assistance did not significantly reduce radiation dose (SMD −0.81; p = 0.07) or fluoroscopy time (SMD −0.56; p = 0.06), with substantial heterogeneity. Subgroup analyses revealed lower exposure with specific robotic systems (e.g., Tianji^®), in degenerative and trauma indications, and at cervical, lumbar, and thoracolumbar levels. No differences were observed between robotic-assisted and navigation-assisted techniques. A meta-regression showed increasing an fluoroscopy time and radiation dose in more recent freehand studies, while trends were stable in robotic cohorts. No publication bias was detected. Conclusions: Robotic-assisted surgery was not associated with statistically significant reductions in overall radiation dose or fluoroscopy time compared with freehand techniques. Effects may vary by robotic platform, indication, and anatomical level; however, substantial heterogeneity limits certainty. Further randomized controlled trails with standardized reporting are warranted. Full article

The global maritime industry, a critical pillar of international trade, continues to face persistent challenges in ensuring the integrity, security, and transparency of containerized cargo data, particularly during ocean transport. Traditional container tracking systems at sea often lack the reliability and resilience required to prevent data tampering, cyber threats, and operational inefficiencies. As supply chains become more complex and interconnected, the demand for robust, end-to-end data security solutions becomes more pressing. A promising technological advancement in this area is the convergence of smart containers, equipped with Internet of Things (IoT) sensors for real-time condition monitoring, and blockchain technology (BCT) for secure data validation. These IoT devices facilitate continuous tracking of critical parameters such as location, temperature, humidity, tilt, and the like. However, the data they generate remains vulnerable to cyberattacks, signal disruptions, and unauthorized alterations. Blockchain’s decentralized and tamper-evident architecture addresses these vulnerabilities by enabling secure data immutability, transparent audit trails, and enhanced stakeholder trust. Despite its potential, the practical integration of blockchain with smart container systems in maritime logistics remains largely underexplored. To bridge this gap, this paper proposes a blockchain-enabled smart container monitoring system that combines container real-time data with secure physical tracking. Furthermore, to ensure scalability and efficient in data storage, hybrid on/off-chain architecture is introduced, balancing blockchain integrity with performance and resource optimization. Full article

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy is highly sensitive to panel discretization strategies, which are often determined heuristically. This study proposes a bio-inspired optimization framework for VLM panel discretization and evaluates it through a systematic case study on a representative wing geometry. A grid-convergence analysis was initially carried out to ensure solution independence across various spanwise-to-chordwise panel ratios. Subsequently, a novel Hybrid Response Surface Methodology (HRSM), integrating Box–Behnken and Central Composite experimental designs, was employed to enable a more comprehensive exploration of the factor space while quantifying the effects of clustering parameters at the leading-edge, trailing-edge, root, and tip regions of the wing. The HRSM dataset was further utilized to train Ensemble Machine-Learning surrogate models, which were coupled with bio-inspired Crayfish and Aquila optimization algorithms, alongside a classical Genetic Algorithm (GA) as a performance benchmark, to identify the optimal discretization strategy and to enable a comparative assessment of their convergence behavior and robustness against the numerical noise of the ensemble-based landscape. Compared to base (i.e., uniform) panel distribution, the optimally clustered discretization enhanced overall aerodynamic prediction accuracy by approximately 33%, particularly at low angles of attack, while maintaining robust performance at higher angles. Both algorithms converged to similar minima; however, the Aquila algorithm achieved higher solution consistency, whereas the Crayfish algorithm exhibited greater dispersion despite faster convergence, revealing a multimodal optimization landscape. The variance decomposition revealed that trailing-edge clustering dominated aerodynamic accuracy at low angles of attack, contributing up to 90% of the total variance, whereas tip clustering became increasingly influential at higher angles, exceeding 30%, highlighting the need for adaptive discretization strategies to ensure reliable VLM-based aerodynamic analyses. Full article

Algorithmic management systems increasingly coordinate work, allocate resources, and support decisions in corporate, public sector, and research environments. Yet many such systems remain opaque: they optimize and score effectively but struggle to communicate rationales that are contextual, auditable, and defensible under emerging governance expectations. Large language models (LLMs) can help bridge this gap by translating quantitative signals into human-readable explanations and enabling interactive clarification. However, LLM integration also introduces new risks—hallucinated rationales, bias amplification, prompt-based security failures, and automation dependence—that must be governed rather than merely engineered. This article proposes a governance-oriented architecture for LLM-augmented algorithmic management. The model combines the following elements: an algorithmic decision core; an LLM-based cognitive interface for explanation and dialogue, and a verification and governance layer that enforces policy constraints, provenance, audit trails, and human-in-command oversight. The framework is developed through targeted conceptual synthesis and normative alignment with key governance instruments (e.g., the EU AI Act, GDPR, and ISO/IEC 42001). It is illustrated through cross-domain scenarios and complemented by a demonstrative synthetic-trace simulation that highlights transparency–latency trade-offs under verification controls. Using the demonstrative simulation (n = 120 decision events), the framework illustrates a mean baseline latency of 100.3 ms and a mean LLM-augmented latency of 115.8 ms (≈15.5% increase), a mean explanation validity proxy of 85.6%, and a simulated constraint-satisfaction rate of 94.2% (113/120 events), with failed cases routed to review. These values are presented as design-level indicators of operational plausibility and governance trade-offs, not empirical performance benchmarks or state-of-the-art comparisons. The paper contributes a conceptual and governance-oriented architectural blueprint for integrating generative AI into organisational decision systems without sacrificing accountability, compliance, or operational reliability. Full article

Ecotourism in protected areas creates a conservation paradox: tourism revenue funds protection, yet tourism infrastructure simultaneously degrades the wildlife it protects. We examined this paradox in white-faced capuchins (Cebus imitator) in Tortuguero National Park, comparing behaviour across a high-tourism accommodation site (2152 monthly guests) and a strictly regulated terrestrial trail. Using focal animal and sweep sampling methods, we recorded 477 behavioural units across 261 min, analysing locomotion, feeding, and agonistic behaviours through generalized linear models. Primates in accommodation areas exhibited significantly reduced high substrate use (p = 0.005), showed a trend toward increased anthropogenic food reliance (p = 0.070), and higher—but not statistically significant—rates of agonistic behaviours (p > 0.05). The negative correlation between natural foraging and active food supply (r = −0.31) is consistent with anthropogenic provisioning that may alter primate ecological functions. These findings demonstrate that effective conservation in tourism contexts requires integrated management addressing three interconnected challenges: (1) habituation to human presence, (2) food provisioning with cascading consequences, and (3) ecosystem-level degradation through altered primate functions. We recommend evidence-based interventions including secured waste management, enforcement of wildlife feeding prohibitions, and environmental education programs with community participation. Ecotourism sustainability requires managing human–wildlife interactions and integrating local stakeholder perspectives to preserve animal welfare and ecosystem functions essential for conservation. Full article

To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. Full article

Modern organizations increasingly rely on heterogeneous database environments that combine relational, document-oriented, and key-value storage systems to optimize performance for diverse application requirements. However, this technological diversity creates significant challenges for implementing consistent data governance policies, regulatory compliance, and access control across disparate systems. Traditional governance approaches that operate within individual database silos fail to provide unified policy enforcement and create compliance gaps that expose organizations to regulatory and operational risks. This paper presents a novel API-driven architecture that enables unified data governance across heterogeneous database environments without requiring database-specific modifications or vendor lock-in. The proposed framework implements a centralized governance layer that coordinates policy enforcement across PostgreSQL, MongoDB, and Amazon DynamoDB systems through RESTful API interfaces. Key architectural components include differentiated access control through hierarchical API key management, automated compliance workflows for regulatory requirements such as GDPR, real-time audit trail generation, and comprehensive data quality monitoring with automated improvement mechanisms. Comprehensive experimental evaluation demonstrates the framework’s effectiveness across multiple operational dimensions. The system achieved 95.2% accuracy in access control enforcement across different data classification levels, while automated GDPR compliance workflows demonstrated 98.6% success rates with average processing times of 2.9 h. Performance evaluation reveals acceptable overhead characteristics with linear scaling patterns for PostgreSQL operations (R² = 0.89), consistent sub-20ms response times for MongoDB logging operations, and sustained throughput rates ranging from 38.9 to 142.7 requests per second across the integrated system. Data quality improvements ranged from 16.1% to 34.3% across accuracy, completeness, consistency, and timeliness dimensions over a 12-week monitoring period, with accuracy improving by 17.8 percentage points, completeness by 13.2 percentage points, consistency by 19.7 percentage points, and timeliness by 24.5 percentage points. The duplicate detection system achieved 94.6% precision and 95.6% recall across various duplicate types, including cross-database redundancy identification. The results demonstrate that API-driven governance architectures can effectively address the persistent challenges of policy fragmentation in multi-database environments while maintaining operational performance and enabling measurable improvements in data quality and regulatory compliance. The framework provides a practical migration path for organizations seeking to implement comprehensive governance capabilities without replacing existing database infrastructure investments. Full article