Search Results (7,556)

Fatigue assessment of floating offshore wind turbines (FOWTs) remains challenging because fatigue-sensitive regions may occur outside conventional predefined hotspots. This study applies a previously numerically verified full-field fatigue-screening workflow combining Unit Load Response, submodeling, and Virtual Test Rig concepts to the TaidaFloat semi-submersible FOWT under Taiwan Strait environmental conditions. Reconstructed nodal stress histories are used to map hull fatigue and evaluate occurrence-weighted contributions from 182 environmental bins, including operational and typhoon conditions. The results identify fatigue-sensitive regions not only at conventional column–bracing and column–pontoon connections but also in the upper main column and along the turbine–hull load path. Upper column fatigue is mainly associated with turbine-induced bending, whereas lower column and waterline-adjacent regions are more sensitive to wave-induced global hull bending. Frequently occurring near-rated operational conditions dominate the occurrence-weighted hull fatigue contribution, while selected typhoon conditions produce high short-term damage but limited long-term contributions within the four-year dataset. Approximately 94.6% of hull fatigue damage is captured by 28% of the bins, and a common hull–mooring set captures 97.0% of both contributions using 62% of the bins. These findings support hotspot screening and environmental-bin prioritization rather than detailed or certification-level fatigue life prediction. Full article

Background/Objectives: Peripheral arterial disease (PAD) and diabetes mellitus (DM) may produce different and overlapping plantar thermal patterns. This study descriptively characterized whole-foot temperature-distribution patterns in clinically defined healthy, PAD, DM, and combined PAD_DM cohorts. Methods: This single-center, exploratory, cross-sectional observational study included 73 participants: healthy controls (n = 21), isolated PAD (n = 13), isolated DM (n = 19), and combined PAD_DM (n = 20). Right- and left-foot temperature distributions were processed separately for each participant and interpreted as one paired bilateral thermal footprint using patient-level histograms, representative bilateral kernel density estimation (KDE) profiles, and a cohort-derived deterministic rule-based model. The model was developed from the present cohort and was not evaluated as an independent diagnostic classifier. Results: Among healthy controls, 11 of 21 participants (52.4%) were assigned to healthy and 8 of 21 (38.1%) to borderline healthy categories. In the DM cohort, 7 of 19 participants (36.8%) were assigned to DM, 3 of 19 (15.8%) to borderline healthy/suspected DM or controlled DM, 3 of 19 (15.8%) to DM suspected PAD, and 5 of 19 (26.3%) to PAD_DM. In the combined PAD_DM cohort, 15 of 20 participants (75.0%) were assigned to PAD_DM. The isolated PAD cohort was the most heterogeneous: 6 of 13 participants (46.2%) were assigned to PAD_DM, while none were assigned to the isolated PAD output. Conclusions: This exploratory, cross-sectional study demonstrated that whole-foot plantar temperature distributions can descriptively characterize differences among healthy individuals, patients with isolated DM, and those with combined PAD and DM. While healthy controls generally demonstrated symmetric thermal footprints, the DM cohort frequently exhibited globally elevated and compact temperature-distribution profiles, while mixed asymmetric patterns were common in participants with combined PAD and DM. In contrast, the isolated PAD cohort was characterized by pronounced thermal heterogeneity. Full article

Photosynthetically active radiation (PAR) is a fundamental driver of terrestrial photosynthesis and a key input for light use efficiency-based estimates of gross primary productivity (GPP). However, existing PAR products are typically designed for regional to global applications and often remain spatially mismatched with the finer-resolution land surface variables now commonly derived from optical satellite observations. In this study, we present a semi-empirical framework for deriving daily clear-sky and all-sky PAR from Sentinel-2 Level-2A imagery. The approach combines solar geometry, daily extraterrestrial radiation, and simplified atmospheric transmittance parameterizations using Sentinel-2 aerosol, water vapor, and scene classification information to estimate clear-sky PAR, and further extends this formulation to all-sky conditions through a cloud-transmission factor derived from cloud probability to generate a spatially explicit PAR product aligned with Sentinel-2 observations. The resulting estimates are evaluated against flux tower observations from 172 AmeriFlux sites across North and South America for the period 2017–2024 and compared with MODIS MCD18, VIIRS VNP18, and CERES SYN1deg PAR products. The clear-sky Sentinel-2 formulation showed a moderate positive bias of 6.38 W m⁻², while the all-sky cloud adjustment reduced the mean bias to −1.44 W m⁻² with an RMSE of 23.53 W m⁻² and correlation of r = 0.87. The largest improvements occurred in spring and summer seasons, when atmospheric attenuation has the strongest influence on the clear-sky estimates. MODIS and CERES all-sky PAR products achieved lower overall errors with RMSE of 17.60 W m⁻² and 15.56 W m⁻², respectively, but at substantially coarser spatial resolution. The proposed framework therefore provides a practical high-resolution approximation of daily PAR that is spatially consistent with Sentinel-2 observations. Rather than replacing dedicated radiative transfer-based products, the method is intended to support analyses in which PAR needs to be evaluated together with Sentinel-2 bands, vegetation indices, and other Sentinel-2-derived variables within a common observational framework. Full article

Occluded person re-identification (Re-ID) is a challenging task, as non-target pedestrians or surrounding obstacles often interfere with the visual cues of the target person, making it difficult for models to effectively learn discriminative feature representations. Most existing methods focus on salient body parts via spatial partitioning or external cues; however, they are either limited in capturing diverse semantic information or tend to introduce additional network complexity. To address these issues, we propose a Diversity Feature Learning Network (DFLNet). Specifically, a Scene-Level Occlusion (SLO) strategy is designed to automatically simulate two common occlusion scenarios by modeling the relative spatial relationships between the target person and surrounding occluders in real-world scenes. Subsequently, multiple class tokens are introduced to capture diverse representations of the target identity. A Token Diversity Constraint (TDC) loss is further imposed on these class tokens to encourage the learning of discriminative and diverse feature embeddings. Finally, we design a Diversity Feature Fusion (DFF) module, which facilitates the interaction and integration of dual-branch features by modeling global feature correlations and optimizing inter-feature distribution distances. Extensive experiments on occluded, partial, and holistic Re-ID datasets demonstrate the effectiveness of the proposed DFLNet. Full article

Rice leaf diseases significantly affect crop health and yield potential, creating a need for accurate and timely disease diagnosis. Many existing approaches still rely on single-stream feature extraction architectures, which may limit the ability to simultaneously capture global contextual information and fine-grained disease characteristics. Moreover, limited interpretability and decision-support capability hinder their practical deployment in real-world rice farming. To address these limitations, we employed a framework consists of two parallel feature extraction streams designed to capture different characteristics of disease patterns. The first stream uses a Swin Transformer to learn global contextual information and long-range spatial relationships across the leaf image. The second stream employs ConvNeXt to extract local texture features, including lesion details, spots, and color variations. By combining these complementary representations, the proposed framework effectively integrates global semantic information with local disease-specific features for improved classification performance. The extracted features from the two streams are fused through concatenation followed by an attention-based feature refinement module, enabling adaptive weighting of discriminative features. The refined representation is then used by a fully connected classifier for disease prediction. To enhance model interpretability, Grad-CAM visualization is incorporated to highlight disease-relevant regions and provide visual explanations for the model decisions. Furthermore, an LLM-based advisory module is integrated as a post-diagnosis decision-support component to provide contextualized disease management information and suggestions based on the predicted disease category. The generated suggestions are intended to support, rather than replace, expert agronomic recommendations and should be validated by agricultural professionals before practical application. The proposed framework was evaluated on two rice leaf disease datasets, achieving accuracies of 99.55% and 97.06% on Dataset-1 and Dataset-2, respectively, which are higher than those reported in previous studies. Additionally, five-fold cross-validation on Dataset-1 achieved an average accuracy of 98.91% ± 0.47, demonstrating the stability of the proposed approach. Cross-dataset evaluation using nine common disease classes across both datasets achieved 90.80% accuracy, indicating improved generalization across different data distributions. The proposed framework provides an accurate and explainable approach for rice leaf disease diagnosis in smart agriculture applications. Full article

Soil erosion, structural degradation, and inefficient nitrogen (N) use jointly constrain dryland agriculture on the Loess Plateau—a globally recognized ecologically fragile region. A common concern is that soil conservation practices may reduce short-term crop productivity. This study evaluated whether conservation tillage based on no-tillage and straw mulching, particularly when coupled with controlled-release N, could enhance soil aggregate stability, apparent nutrient availability, soybean productivity, and N-use efficiency. A two-year field experiment was conducted in 2022–2023 at the Shenmu Erosion and Environment Research Station, Shaanxi Province, China. Five treatments were compared: conventional tillage without N application (CTCK), conventional tillage with normal N application (CTU), conservation tillage without N application (SMCK), conservation tillage with normal N application (SMU), and conservation tillage with controlled-release N (SMS). SMS consistently increased the proportion of >0.25 mm water-stable macroaggregates, mean weight diameter, and geometric mean diameter. It also increased soil organic carbon and its particulate and mineral-associated fractions in the 0–30 cm profile, improved total N, mineral N, total P, and available P, and produced the highest soybean yield. Across two years, SMS yielded 1960.57 kg ha⁻¹, 162.26% higher than CTCK and 66.66% higher than CTU. The treatment also achieved the highest agronomic efficiency despite reduced N input. These findings indicate that integrating conservation tillage with controlled-release N can enhance soil structural stability, apparent nutrient availability, and soybean productivity. It confirms that soil conservation and high crop output are mutually reinforcing rather than conflicting. Given the ecological sensitivity of the Loess Plateau, this integrated strategy offers a promising pathway toward more resilient dryland farming systems. Full article

Ornamental plants constitute an important sector of the agricultural market, with global relevance. The impacts of climate change also affect flowering plants, increasing susceptibility to stress conditions. The most common effects are related to germination capacity and uniformity, which can indirectly compromise aesthetic value. Seed priming represents a promising environmentally friendly strategy to improve germination performance and stress tolerance in ornamental plants. This review gives an overview of the most recent advances in priming applied to ornamental species, highlighting the physiological, biochemical, and molecular impacts of the technique. Priming approaches can differ (e.g., hydro-, osmotic, hormonal, nano-based), with the common aim of enhancing germination efficiency, seed vigor, and promoting greater uniformity and quality of plantlets under stress conditions. Up to now, a bottleneck in the use of this promising tool has been represented by different responses related to species and protocol standardization. Molecular evaluation is required to monitor the long-term effectiveness of priming. The use of priming represents a sustainable and low-cost tool to enhance the adaptability of ornamental species in stress conditions, supporting the transition to new sustainable solutions for the future of ornamental plant production. Full article

Background/Objectives: Sleep disturbances are common but frequently underrecognized in multiple sclerosis (MS), independently predicting reduced quality of life and worsening fatigue, cognitive impairment, and depression. While pain, nocturia, fatigue, and mood symptoms are established contributors, cardiometabolic, and inflammatory factors remain largely unexplored despite their known links to poor sleep in other populations. This study used interpretable machine learning to determine the relative contribution of disease-related, cardiometabolic, and inflammatory parameters to sleep quality, assessed by the PSQI, in patients with MS. Methods: This cross-sectional, observational, single-center cohort study enrolled adult patients with MS. Sleep quality (PSQI), daytime sleepiness (ESS), and restless legs syndrome severity (IRLS) were assessed alongside clinical, anthropometric, hemodynamic, and laboratory parameters, including inflammatory and metabolic indices. Three regression models, Support Vector Regression (SVR), Random Forest, and XGBoost, were trained on 28 predictors to predict PSQI global scores and evaluated on an internal test set, with feature contributions examined using SHAP analysis. Results: A total of 173 patients with MS were included (mean age 39.66 ± 11.86 years, 69.9% female, 90.2% RRMS), with 48.0% classified as poor sleepers (PSQI > 5) and a mean PSQI score of 6.06 ± 3.47. XGBoost achieved the best predictive performance (test R² = 0.451). SHAP analysis identified IRLS severity as the strongest predictor of PSQI across all three models, followed by EDSS score and depression in the Random Forest and XGBoost models, while daytime sleepiness (ESS) ranked consistently among the top predictors. Cardiometabolic and inflammatory parameters contributed inconsistently, with several showing effects opposite to physiological expectation. Conclusions: Sleep impairment in MS was most strongly associated with restless legs syndrome severity and neurological disability, with depression also contributing in the two best-performing models. Disease-related and symptomatic factors outweighed cardiometabolic and inflammatory contributions. These findings support the value of interpretable machine learning for identifying clinically relevant correlates of sleep quality in MS, though the exploratory design and modest sample size warrant confirmation in larger, independent cohorts. Full article

Effective conservation of the Near Threatened (NT) black-necked crane (Grus nigricollis) requires standardized frameworks for organizing multi-scale biodiversity data. This study proposes the Essential Biodiversity Variables (EBV) system as an optimal structure for archiving and sharing biodiversity data such as Habitat Suitability Index (HSI). Developed by the Group on Earth Observations Biodiversity Observation Network (GEO BON), the EBV framework is an emerging system offering a robust solution for standardizing data exchange. Based on 483,592 valid location records of 106 black-necked cranes using satellite telemetry, we apply species distribution models and demonstrate how the multi-dimensional EBV architecture accommodates distinct life-stage preferences: breeding sites favor mid-elevations modulated by temperature; migration staging relies on precipitation regimes; and wintering grounds are driven by moisture availability and the avoidance of human-modified landscapes. The EBV NetCDF (Network Common Data Form) format functions as a self-describing hypercube that captures spatial, temporal, and life-stage dimensions while ensuring metadata transparency. This integration facilitates critical applications, including the identification of priority conservation areas and climate vulnerability assessments, thereby bridging the gap between species-specific modeling and global biodiversity monitoring standards. Full article

Fatigue and balance disorders are common in people with multiple sclerosis (PwMS), but the neural mechanisms linking them during postural control remain unclear. This cross-sectional study aims to examine associations between fatigue, balance impairment, and task-evoked cortical hemodynamics/connectivity. Sixteen PwMS (relapsing-remitting MS; EDSS < 3.5) and 19 healthy controls (HC) were enrolled. Based on the Modified Fatigue Impact Scale (MFIS), PwMS were classified as fatigued (f_PwMS, MFIS ≥ 38; n = 10) or non-fatigued (nf_PwMS, n = 6). Participants performed five 30 s trials of static stance on foam with eyes closed. fNIRS recorded frontal, temporal, and occipital cortical activity, while stabilometry measured center-of-pressure sway. In PwMS, MFIS showed a trend toward association with sway (r = 0.49; p = 0.06). Sway was higher in f_PwMS than nf_PwMS and HC but not significantly different (ANOVA p = 0.341). Compared with HC, PwMS showed increased bilateral prefrontal and reduced occipital HR. Stratified analyses showed greater HR in f_PwMS versus HC and nf_PwMS in bilateral BA10 and in temporo-parietal regions, with a right-hemisphere predominance. Global coherence did not differ across groups, but exploratory BA10 analysis showed higher prefrontal coherence in f_PwMS than nf_PwMS (p = 0.02). Fatigued PwMS exhibit amplified, right prefrontal and temporo-parietal activation and focal prefrontal hyper-connectivity, consistent with compensatory top–down control to maintain balance. Full article

This paper investigates inverse spectral problems for a class of discrete Sturm–Liouville operators with multiple transmission interfaces. The presence of several interfaces generates a coupled spectral structure in which the reconstruction of the operator coefficients is determined by a common generalized spectral function. A generalized spectral framework adapted to the multi-interface setting is introduced, and explicit reconstruction formulas for the associated tridiagonal coefficient matrix are derived. Using the corresponding Hankel moment determinants, it is shown that the interface coefficients are spectrally linked through the same moment sequence, producing a globally coupled reconstruction mechanism. In contrast to the single-interface case, the reconstruction cannot be decomposed into independent local procedures. A constructive recovery algorithm for the operator coefficients is obtained, and the role of the transmission parameters in the spectral representation is analyzed. An example illustrating the reconstruction process for multiple interfaces is also presented. Full article

Background: Lead (Pb) exposure remains a major global health issue, and Pb stable isotope ratio analysis is a powerful tool for identifying potential exposure sources. However, traditional whole blood collection is difficult to implement in remote or resource-limited settings. We developed and validated a novel method for measuring Pb isotopic composition in dried blood spots (DBS) using a multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS) and applied it in a pilot study of DBS collected from children in Suriname, South America. Methods: Method accuracy was evaluated by comparing isotopic ratios (²⁰⁸Pb/²⁰⁶Pb and ²⁰⁷Pb/²⁰⁶Pb) measured in laboratory-spiked DBS (15 µL of blood per DBS) with those from matched whole blood samples (1 mL) across a range of Pb concentrations (2.1–28.9 µg/dL). The method was then applied to DBS collected from 18 children, ages 6 months to 5 years, in Suriname. Potential local exposure sources, including soil, shotgun pellets, and cooking utensils, were also analyzed for lead isotopic composition and compared with DBS isotope ratios to assess source concordance. Results: DBS isotopic ratios accurately reflected whole blood values when Pb concentrations exceeded ~315 pg/punch (~2.1 µg/dL), with greater accuracy observed above 5 µg/dL. In the Suriname DBSs, blank-corrected ²⁰⁸Pb/²⁰⁶Pb ratios were consistent across regions (mean 2.12 ± 0.018; n = 15) and showed no significant geographic stratification (p = 0.11). DBS signatures (isotope ratios) most closely aligned with shotgun pellets and soil samples, whereas those from cooking utensils did not match DBS signatures. A positive correlation was observed between estimated blood total Pb and soil Pb concentrations (ρ = 0.60, p = 0.20). Conclusions: Overall, DBS provided reliable Pb isotopic ratio measurements above ~2–5 µg/dL, spanning the current CDC reference value of 3.5 µg/dL at which children’s exposures warrant investigation. These findings support DBS as a minimally invasive, scalable tool for exposure source identification in environmental epidemiology, particularly in remote settings where traditional venipuncture is impractical. What this study adds: This study establishes the first validated method for high-precision lead (Pb) stable isotopic ratio analysis using dried blood spots (DBS). By implementing a sample blank correction, we demonstrate that DBS can accurately resolve isotopic signatures even at low blood lead concentrations. Applied in DBS collected in Suriname, the method successfully linked DBS signatures to local shotgun pellets and soils, illustrating a common exposure profile across diverse regions. This work expands the utility of DBS beyond simple screening, offering a minimally invasive, scalable tool for source attribution in global health research and environmental epidemiology in remote regions. Full article

Gout is one of the most common inflammatory arthritides worldwide, and its burden is rising in Southeast Asia alongside rapid demographic and socioeconomic transitions. This region, characterized by diverse healthcare systems, cultural practices, and levels of economic development, presents a unique context in which the epidemiology and management of gout are evolving. Despite the availability of effective urate-lowering therapy (ULT) and well-established treat-to-target (T2T) strategies, real-world outcomes remain suboptimal. Barriers to optimal gout care operate at multiple levels. Physician-related factors include clinical inertia and limited awareness of evidence-based recommendations. Patient-related factors include limited understanding of gout and its treatment, compounded by socioeconomic disadvantages. System-level challenges include inequitable access to healthcare services, limited health insurance coverage, and resource-strained health systems. The current evidence base in Southeast Asia remains limited and heterogeneous, raising uncertainty regarding the applicability of global data to local populations. Nevertheless, emerging studies suggest that multidisciplinary, context-oriented approaches can improve gout outcomes. The objective of this review is to explore the evolving epidemiological landscape and barriers to gout management in Southeast Asia. Full article

Prostate cancer (PC) is one of the most common cancers in men globally and there is an urgent need for new immune-based approaches because many traditional therapies, including chemotherapy, radiotherapy, and anti-androgens, have limitations. Due to their distinct physicochemical and biological features, gold nanoparticles (AuNPs) are emerging as a potential nanoplatform for the development of strategies in prostate cancer therapy. These features include tunable size, shape, and surface plasmon resonance (SPR) and high surface-to-volume ratio, which result in enhanced drug loading, targeted delivery and improved bioavailability. In addition, AuNPs can also be functionalized for active targeting to promote selective accumulation in tumors while minimizing systemic toxicity. In addition, the intrinsic optical and photothermal properties of these nanoparticles allow them to serve for PTT, radiosensitization and multimodal imaging, i.e., CT (computed tomography) and photoacoustic imaging. These have shown potential in pre-clinical and clinical evaluation but face issues with long-term toxicity, biodistribution and large-scale manufacturing. In this review, we summarize the organizing features, functional properties, therapeutic activities and translational potentials of AuNPs in prostate cancer treatment, with emphasis on their contributions towards precision nanomedicine. Full article

The rapid integration of Distributed Generation (DG) units is transforming traditional passive radial distribution networks into active grids with bidirectional power flow. Determining the optimal allocation and physical hosting capacity of DG units is essential for maintaining grid stability while maximizing technical gains. This study presents a multi-objective optimization framework using empirical peak-load field data from an active medium-voltage distribution network in Türkiye. A custom Particle Swarm Optimization (PSO) algorithm was developed in MATLAB to simultaneously minimize active and reactive power losses while improving the Voltage Deviation Index (VDI). The baseline network model and load flow calculations established in DIgSILENT PowerFactory were rigorously cross-validated with MATPOWER, demonstrating complete mathematical agreement with a negligible deviation (<0.001%). Simulation results under both capacity-constrained ($10 \mathrm{MW}$ limit) and unconstrained Hosting Capacity Discovery scenarios demonstrate that single-objective optimization causes negative trade-offs across other operational metrics. Conversely, multi-objective hybrid approaches provide a balanced, globally common optimum operating point best suited to the grid’s natural electrical structure, consistently identifying a reliable ultimate hosting capacity threshold of $21.78 \mathrm{MW}$ at Busbar 4. These findings offer a concrete, empirical decision-support framework for active distribution network planning and modernization. Full article