Search Results (192,132)

The rapid expansion of wind power as a key component of global renewable energy systems has led to the widespread deployment of wind turbines in environments exposed to diverse natural hazards. While hazard effects are often investigated individually, real wind turbine systems frequently experience concurrent or sequential hazards over their operational lifetime, giving rise to interaction effects that are not adequately captured by conventional design approaches. This paper presents Part 2 of a comprehensive review on natural hazards affecting wind turbine performance, combining bibliometric keyword co-occurrence analysis with a critical synthesis of recent technical studies. The review focuses on earthquakes, sea waves, and extreme wind events, while also highlighting other hazard types that have received comparatively limited attention in the literature, examining their effects on wind turbine systems and the mitigation strategies reported to address associated risks. Rather than treating hazards in isolation, their impacts are synthesised through cross-hazard interaction pathways and component-level failure modes. The findings indicate that wind turbine vulnerability under multi-hazard conditions is governed not only by load magnitude but also by hazard-induced changes in system properties and operational state. Key research gaps are identified, emphasising the need for state-aware, mechanism-consistent multi-hazard assessment frameworks to support the resilient design and operation of future wind energy systems. Full article

Electron transfer (ET) is a foundational biogeochemical process in paddy soils, distinctively molded by alternating anaerobic-aerobic conditions from flooding-drainage cycles. Despite extensive research on heavy metal(loid) (denoted as “HM”, e.g., As, Cd, Cr, Hg) dynamics in paddies, ET has not been systematically synthesized as a unifying regulatory mechanism, and the trade-offs of ET-based mitigation strategies remain unclear. These critical gaps have drastically controlled HMs’ mobility, which further modulates bioavailability and subsequent accumulation in rice (Oryza sativa L., a staple sustaining half the global population), posing substantial food safety risks. Alongside progress in electroactive microorganism (EAM) research, extracellular electron transfer (EET) mechanism delineation, and soil electrochemical monitoring, ET’s role in orchestrating paddy soil HM dynamics has garnered unparalleled attention. This review explicitly focuses on the linkage between ET processes and HM biogeochemistry in paddy ecosystems: (1) elucidates core ET mechanisms in paddy soils (microbial EET, Fe/Mn/S redox cycling, organic matter-mediated electron shuttling, rice root-associated electron exchange) and their acclimation to flooded conditions; (2) systematically unravels how ET drives HM valence transformation (e.g., As(V) to As(III), Cr(VI) to Cr(III)), speciation shifts (e.g., exchangeable Cd to oxide-bound Cd), and mobility changes; (3) expounds on ET-regulated HM bioavailability by modulating soil retention capacity and iron plaque formation; (4) synopsizes ET-modulated HM accumulation pathways in rice (root uptake, xylem/phloem translocation, grain sequestration); (5) evaluates key factors (water management, fertilization, straw return) impacting ET efficiency and associated HM risks. Ultimately, we put forward future avenues for ET-based mitigation strategies to uphold rice safety and paddy soil sustainability. Full article

Background/Objectives: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to pose a significant global health challenge due to its high transmissibility and potential for severe clinical outcomes. Early identification of patients at risk of hospitalization is essential for effective triage in emergency departments and for the optimal allocation of healthcare resources. Methods: This prospective study included 84 patients aged over 18 years who presented to the emergency department on 23 December 2020, with suspected SARS-CoV-2 infection. Initially, 100 patients were evaluated, and 16 were excluded based on predefined exclusion criteria. The mean age of the participants was 53.65 ± 13.62 years, and 39 (46.4%) were women. Results: At admission, the mean signal peptide, CUB domain, EGF (SCUBE-1) level among SARS-CoV-2 patients was 0.16 ± 0.08 ng/mL. There was no significant difference in SCUBE-1 levels between patient and control groups (n = 59 vs. 25), but levels differed significantly between hospitalized and home-treated patients (n = 37 vs. 22; p = 0.001). Neutrophil count (p = 0.001) and NLR (p = 0.010) were higher in patients than controls and also higher in hospitalized than home-treated patients (p = 0.003 and p = 0.015). ROC analysis revealed that SCUBE-1 predicted hospitalization with 84.6% sensitivity and 88.9% specificity. A positive correlation was observed between SCUBE-1 levels and length of hospital stay (p = 0.007, r = 0.554), with a median stay of 9.0 (5.0–11.0) days. Conclusions: SCUBE-1 levels were significantly associated with disease severity in SARS-CoV-2 patients and may serve as a promising biomarker to support clinical decision-making for hospitalization versus home-based management. Full article

Circulating irisin, a myokine implicated in energy expenditure and adipose tissue regulation, has been increasingly studied as a potential biomarker of metabolic dysfunction. This study evaluated the relationship between serum irisin and metabolic indices, including the atherogenic index of plasma (AIP), the lipid accumulation product (LAP), and hypertriglyceridemic-waist (HTGW) phenotype in individuals with prediabetes (PreDM) and newly diagnosed type 2 diabetes mellitus (T2DM). A total of 138 participants (48 PreDM, 90 T2DM) were assessed for anthropometric, glycemic, and lipid parameters. Serum irisin levels were measured by enzyme-linked immunosorbent assay (ELISA) and correlated with insulin resistance indices (Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Quantitative Insulin Sensitivity Check Index (QUICKI)), glycemic control (glycosylated hemoglobin A1c (HbA1c)), and composite lipid markers (total triglycerides-to-high-density lipoprotein cholesterol (TG/HDL-C)). Group differences were evaluated using non-parametric tests; two-way ANOVA assessed interactions between phenotypes and markers; multiple linear regression (MLR) and logistic regression models explored independent associations with metabolic indices and HTGW; receiver operating characteristic (ROC) analyses compared global and stratified model performance. Serum irisin was significantly lower in T2DM than in PreDM (median 140.4 vs. 230.7 ng/mL, p < 0.0001). Irisin levels remained comparable between males and females in both groups. Post hoc analysis shows that lipid indices and irisin primarily distinguish HTGW phenotypes, especially in T2DM. In both groups, irisin correlated inversely with HOMA-IR, AIP, and TG/HDL-C, and positively with QUICKI, indicating a possible compensatory role in early insulin resistance. MLR analyses revealed no independent relationship between irisin and either AIP or LAP in PreDM, while in T2DM, waist circumference remained the strongest negative predictor of irisin. Logistic regression identified age, male sex, and HbA1c as independent predictors of the HTGW phenotype, while irisin contributed modestly to overall model discrimination. ROC curves demonstrated good discriminative performance (AUC = 0.806 for global; 0.794 for PreDM; 0.813 for T2DM), suggesting comparable predictive accuracy across glycemic stages. In conclusion, irisin levels decline from prediabetes to overt diabetes and are inversely linked to lipid accumulation and insulin resistance but do not independently predict the HTGW phenotype. These findings support irisin’s role as an integrative indicator of metabolic stress rather than a stand-alone biomarker. Incorporating irisin into multi-parameter metabolic panels may enhance early detection of cardiometabolic risk in dysglycemic populations. Full article

Background and Objectives: Despite substantial advances in treatment strategies for patients with type 2 diabetes mellitus (T2DM), its complication, particularly sarcopenia, has emerged as a global healthcare challenge. Pay-for-performance (P4P), an incentive-based payment scheme, has long been used to improve the quality of care; however, few studies have explored its effect on sarcopenia prevention. Therefore, we conducted a nested case–control study to investigate the association between P4P participation and the risk of sarcopenia among patients with T2DM. Materials and Methods: Using a large claims dataset, we identified individuals aged 20–70 years with newly diagnosed T2DM between 2001 and 2010 in Taiwan. All enrollees were followed up until 2013 to determine the occurrence of sarcopenia. For each case, we randomly matched two controls without sarcopenia. The risk of sarcopenia in relation to P4P participation was estimated by fitting conditional logistic regression to yield the adjusted odds ratio (aOR) and corresponding 95% confidence interval (CI). Results: A total of 3475 individuals with sarcopenia and 6948 matched controls were included. Patients enrolled in the P4P program had a lower risk of sarcopenia than their matched counterparts (aOR = 0.66; 95% CI: 0.61–0.74). Earlier P4P enrollment (within 1 year of T2DM diagnosis) and high-intensity P4P participation were associated with additional reductions in sarcopenia risk. Conclusions: Integrating P4P into routine T2DM care may help prevent sarcopenia, highlighting the importance of interdisciplinary collaboration and timely program implementation. Full article

Monitoring of antimicrobial resistance (AMR) in the environment is needed, but standardized methods are lacking. This study evaluated a cost-efficient, kit-based approach for detecting antibiotic-resistant Escherichia coli (AR-E. coli) and predicting multidrug-resistant E. coli (MDR-E. coli) in concentrated animal feeding operations (CAFO)-impacted surface waters in Tulare, California, and Clayton, Michigan. Eighteen Tulare and six Clayton samples were analyzed using modified IDEXX Colilert-18 kits with selective antibiotics. In Round 1 of the study, 702 isolates were tested against 12 antibiotics using disk diffusion to identify ideal additions. No and low resistance to AMP was seen at the unimpacted and less-impacted sites, respectively, while up to 34% of isolates at impacted sites were resistant to AMP. The percentage of isolates resistant to erythromycin was 16% or lower at less-impacted sites, but it ranged up to 64% at impacted sites. In Round 2, 1002 isolates (1704 total) were characterized to compare modified IDEXX results with culture-based results. Resistance to ampicillin (AMP-E. coli-col) detected by IDEXX strongly correlated with MDR prevalence in Clayton (R² = 0.71, p = 0.00197), while extended-spectrum beta-lactamase-producing E. coli (ESBL-E. coli-col) correlated with MDR prevalence in Tulare (R² = 0.70, p < 0.0001). This observation appears to be site-specific. Findings suggest both IDEXX- and culture-based methods can serve as AMR screening tools at CAFO-impacted sites. This work supports community-based monitoring and informs standardized approaches for tracking AMR in inland waters. Full article

The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the absolute necessity for deterministic real-time response, and the rigorous demands of safety certification protocols. Meanwhile, traditional geometry-based lane detection pipelines continue to exhibit limited robustness under adverse illumination conditions, including intense backlighting, low-contrast nighttime scenes, and heavy rainfall. Motivated by these constraints, this work re-examines geometry-based lane perception from a sensor-level viewpoint and introduces a Binary Line Segment Filter (BLSF) that leverages the inherent structural regularity of lane markings in bird’s-eye-view (BEV) imagery within a computationally lightweight framework. The proposed BLSF is integrated into a complete pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and a simplified Hough-style sliding-window fitting scheme combined with RANSAC. Experiments on a self-collected dataset of 297 challenging frames show that the inclusion of BLSF significantly improves robustness over an ablated baseline while sustaining real-time performance on a 2 GHz ARM CPU-only platform. Additional evaluations on the Dazzling Light and Night subsets of the CULane and LLAMAS benchmarks further confirm consistent gains of approximately 6–7% in F₁-score, together with corresponding improvements in IoU. These results demonstrate that interpretable, geometry-driven lane feature extraction remains a practical and complementary alternative to lightweight learning-based approaches for cost- and safety-critical ADAS applications. Full article

Background: Vitreoretinal lymphoma (VRL) often presents with features resembling uveitis and is commonly associated with central nervous system lymphoma (CNSL). Intravitreal methotrexate (IVMTX) is widely used as local therapy; however, objective markers for treatment response and prognosis remain limited. This study investigated choroidal structural changes after IVMTX via enhanced depth imaging optical coherence tomography (EDI-OCT) and explored prognostic indicators for subsequent CNSL development. Methods: This retrospective study included 18 patients (27 eyes) with VRL treated with IVMTX at Tokushima University Hospital between 2006 and 2021. EDI-OCT was conducted at baseline and at 1 and 3 months after IVMTX. Choroidal thickness and luminal and stromal areas were quantified through image binarization. The stromal/choroidal area (S/C) ratio and its association with CNSL onset were statistically analyzed. Results: The mean number of IVMTX injections administered over 3 months was 5.9 ± 1.3. Foveal retinal thickness did not significantly change, whereas foveal choroidal thickness significantly decreased from 275.8 ± 15.8 µm at baseline to 257.5 ± 14.7 µm at 1 month (p < 0.01). Total choroidal and stromal areas, particularly in the outer choroidal layer, were significantly decreased after IVMTX (p < 0.0001), whereas the luminal area in the inner layer modestly reduced (p < 0.05). The S/C ratio significantly declined at 1 month post-treatment (p < 0.001). Patients who developed CNSL within 2 years of VRL onset demonstrated higher baseline S/C ratios (p < 0.05). Conclusions: IVMTX induces measurable reductions in choroidal areas and stromal proportion, indicating decreased inflammatory infiltration. The baseline S/C ratio observed on EDI-OCT is a potential noninvasive biomarker of VRL activity and a prognostic indicator for early CNSL development. Full article

Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral–entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen’s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R² up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems. Full article

Because of its strong bearing capacity and large size, a thick and hard roof is the main source of strong ground pressure in a stope, and its breaking and migration mechanism and effective control are very important for realizing safe and efficient mining in coal mines. In this paper, by constructing a numerical model that fully considers the actual occurrence conditions of such a roof, the control law of the occurrence conditions of a thick and hard roof on its fracture law and strata behavior is systematically studied, and the control mechanism of the movement and hydraulic fracturing of this kind of roof is revealed. The results show that (1) the fracture process of a thick hard roof is characterized by three stages—crack initiation, extension, and overall instability—and the “pressure arch” structure formed by the overlying huge hard rock stratum is the fundamental force source leading to strong ground pressure; (2) the roof thickness and horizon significantly control the stress distribution and fracture behavior of coal and rock mass, and the peak stress of coal and rock mass is positively correlated with the roof thickness, but negatively correlated with its horizon; (3) with the increase in roof thickness, the dominant fracture mechanism changes from tension type to tension–shear composite type, which leads to a significant increase in fracture step. Hydraulic fracturing technology can effectively cut off the “pressure arch” structure and optimize the stress field of surrounding rock. After fracturing, the first weighting step and weighting strength are reduced by 36% and 38.1%, respectively. An industrial test shows that a fracturing treatment realizes timely and orderly roof caving and achieves the controllable weakening and safe promotion of the thick and hard roof. This study provides a solid theoretical basis and a successful engineering practice model for roof disaster prevention and control under similar geological conditions. Full article

Tartary buckwheat has increasingly become the focus of people’s attention due to its powerful health benefits. Golden buckwheat is a traditional Chinese medicine. People have begun to utilize it through wide hybridization to further enhance the health benefits of Tartary buckwheat. To study the genetic stability of the interspecific hybrids of Tartary buckwheat with golden buckwheat, and to provide scientific basis for the interspecific cross breeding of buckwheat, the mitotic chromosomes of two buckwheat double lines and their interspecific hybrids with golden buckwheat were subjected to observe the karyotypes. The results showed as follows: (1) The two autotetraploid Tartary buckwheat lines (Long Black-4T and Daku-1) have chromosome number 2n = 32. The karyotype formula of 2n = 4x = 32 consisted of 16 pairs of metacentric chromosomes for Long Black-4T (TTTT) while Daku-1 (TTTT) has 1sm + 7m Gui Jinqiao 4 with 2n = 32 has a karyotype formula of 2n = 4x = 32 that consisted 1sm + 6m + 1M (genome M) and 2sm + 5m + 1M (genome M’). The normal fertile tetraploid hybrid F1 plants between Long Black-4T and Gui Jinqiao 4 has 2n = 4x = [1sm + 7m (M), 1sm + 7m (M’), 14m + 2M (TT)]. The normal fertile variety Gui Jinku 1 from the above hybrid progenies shows 2n = 4x = [3sm + 5m (M), 2sm + 6m (M’), 16m (TT)], indicating an increment of sm chromosomes by rearrangements of chromosome structure in the M and M’ genomes. The above parents and their hybrids with the MM’TT genome show fertility. A plant from F2 of the above cross, showing highly infertility, has 2n = 3x= [1sm + 7m (M), 1sm + 7m (M’), 8m (T)]; and back cross progeny plant from Daku 1/Gui Jinqiao 4 F2//Gui Jinqiao 2 golden buckwheat has 2n = 4x = [16m (MM), 5sm + 3m (M’), 1sm + 7m (T)], showed high infertility, which is caused by genome aneuploidy and non-even ploidy. The above shows that there are obvious variations of genome karyotypes from the same parent, indicated by certain chromosome structural rearrangements in genomes T, M, and M’. Full article

(1) Background: Improving nitrogen use efficiency in peanuts is essential for achieving a high yield with reduced nitrogen fertilizer input. This study investigates the role of the fungal endophyte Phomopsis liquidambaris in regulating nitrogen utilization throughout the entire growth cycle of peanuts. (2) Methods: Field pot experiments and a two-year plot trial were conducted. The effects of Ph. liquidambaris colonization on the rhizosphere microbial community, soil nitrogen forms, and peanut physiology were analyzed. (3) Results: Colonization by Ph. liquidambaris significantly suppressed the abundance of ammonia-oxidizing archaea (AOA) and bacteria (AOB) in the rhizosphere at the seedling stage. This led to a transient decrease in nitrate and an increase in ammonium availability, which enhanced nodulation-related physiological responses. Concurrently, the peanut-specific rhizobium Bradyrhizobium sp. was enriched in the rhizosphere, and the root exudates induced by the fungus further stimulated nodulation activity. These early-stage effects promoted the establishment of peanut–Bradyrhizobium symbiosis. During the mid-to-late growth stages, the fungus positively reshaped the composition of key functional microbial groups (including diazotrophs, AOA, and AOB), thereby increasing rhizosphere nitrogen availability. (4) Conclusions: Under low nitrogen fertilization, inoculation with Ph. liquidambaris maintained yield stability in long-term monocropped peanuts by enhancing early nodulation and late-stage rhizosphere nitrogen availability. This study provides a promising microbe-based strategy to support sustainable legume production with reduced nitrogen fertilizer application. Full article

Biofuels have emerged as a promising alternative to conventional fuels, offering improved environmental sustainability. Nevertheless, inadequate control of their physicochemical properties can lead to increased emissions and potential engine damage. Existing methods for regulating these properties depend on costly and sophisticated laboratory equipment, which poses significant challenges for integration into industrial production processes. Three-dimensional printing technology provides a cost-effective alternative to traditional fabrication methods, offering particular benefits for the development of low-cost designs for detecting liquid properties. In this work, we present a sensor system for assessing biofuel solutions. The presented device employs piezoelectric sensors integrated with 3D-printed, liquid-filled cells whose structural design is refined through experimental validation and novel optimization strategies that account for sensitivity, recovery and resolution. This system incorporates discrete electronic circuits and a microcontroller, within which artificial intelligence algorithms are implemented to correlate sensor responses with fluid viscosity and density. The proposed approach achieves calibration and resolution errors as low as 0.99% and $1.48\times {10}^{-2}$ mPa·s for viscosity, and 0.00485% and $1.9\times {10}^{-4}$ g/mL for density, enabling detection of small compositional variations in biofuels. Additionally, algorithmic methodologies for dimensionality reduction and data treatment are introduced to address temporal drift, enhance sensor lifespan and accelerate data acquisition. The resulting system is compact, precise and applicable to diverse industrial liquids. Full article

Accurate trajectory tracking remains a central challenge in differential-drive mobile robots (DDMRs), particularly when operating under real-world conditions. Model Predictive Control (MPC) provides a powerful framework for this task, but its performance degrades when the robot deviates significantly from the nominal path. To address this limitation, robust recovery mechanisms are required to ensure stable and precise tracking. This work presents an experimental validation of an MPC controller applied to a four-wheel DDMR, whose odometry is corrected by a SLAM algorithm running in ROS 2. The MPC is formulated as a quadratic program with state and input constraints on linear (v) and angular ($\omega$) velocities, using a prediction horizon of $N_p=15$ future states, adjusted to the computational resources of the onboard computer. A novel dynamic look-ahead re-entry strategy is proposed, which activates when the robot exits a predefined lateral error band ($\delta =0.05$ m) and interpolates a smooth reconnection trajectory based on a forward look-ahead point, ensuring gradual convergence and avoiding abrupt re-entry actions. Accuracy was evaluated through lateral and heading errors measured via geometric projection onto the nominal path, ensuring fair comparison. From these errors, RMSE, MAE, $P_{95}$, and in-band percentage were computed as quantitative metrics. The framework was tested on real hardware at 50 Hz through 5 nominal experiments and 3 perturbed experiments. Perturbations consisted of externally imposed velocity commands at specific points along the path, while configuration parameters were systematically varied across trials, including the weight R, smoothing distance $L_{\mathrm{smooth}}$, and activation of the re-entry strategy. In nominal conditions, the best configuration (ID 2) achieved a lateral RMSE of 0.05 m, a heading RMSE of 0.06 rad, and maintained 68.8% of the trajectory within the validation band. Under perturbations, the proposed strategy substantially improved robustness. For instance, in experiment ID 6 the robot sustained a lateral RMSE of 0.12 m and preserved 51.4% in-band, outperforming MPC without re-entry, which suffered from larger deviations and slower recoveries. The results confirm that integrating MPC with the proposed re-entry strategy enhances both accuracy and robustness in DDMR trajectory tracking. By combining predictive control with a spatially grounded recovery mechanism, the approach ensures consistent performance in challenging scenarios, underscoring its relevance for reliable mobile robot navigation in uncertain environments. Full article