Search Results (50)

Precise spatiotemporal assessment of habitat suitability is essential for crop pest and disease risk warning and food security. However, most existing approaches focus on disease occurrence, overlook spatial heterogeneity and time series information, and therefore, struggle to capture the habitat dynamics from occurrence to epidemic. We propose a dynamic framework for rice sheath blight (RSB) habitat suitability assessment that integrates rice phenology and disease time series to reveal fine-grained intra-annual spatiotemporal variability via a spatiotemporally adaptive strategy beyond the reach of traditional static models. Remote sensing and meteorological time series data, together with RSB survey data and crowdsourced records from southern China, are integrated in this study. First, the study area is partitioned into sub-regions and sensitive time windows (STWs) based on climate and rice-cropping systems. MaxEnt, combined with natural breaks, is then used to assess RSB occurrence suitability and delineate multi-level suitable areas. Geographical and temporal weighted regression (GTWR) and the coefficient of variation (CV) are finally applied within moderate-to-high occurrence-suitability areas to reconstruct time series of intra-STW epidemic potential dynamics and quantify their temporal variation. Results indicate the optimal phenology-based scheme yields one STW for single-cropping sub-regions and three STWs for double- and mixed-cropping sub-regions. MaxEnt AUC ranges from 0.610 to 0.768, with natural-break thresholds at 0.312 and 0.473. GTWR produces generally robust intra-STW fits (most local R² > 0.4), and the CV highlights localized, time-varying high-fluctuation zones within occurrence-suitable areas that static maps do not reveal. Overall, our method extends crop disease habitat suitability assessment from a static paradigm to a spatiotemporally adaptive, dynamic one, providing useful habitat background constraints for monitoring, early warning, and forecasting of crop pests and diseases under complex cropping systems. Full article

Understanding vessel energy demand requires knowledge of vessel motion relative to the surrounding water, rather than motion relative to the Earth’s surface. However, direct measurements of speed through water (STW) are rarely accessible beyond individual vessels, as they rely on onboard instrumentation and are not publicly available. As a result, studies of vessel energy consumption and zero-emission transition pathways in inland waterway transport (IWT) often rely on Automatic Identification System (AIS) data, which provides speed over ground (SOG) but does not account for environmental current effects. The objective of this study is to develop an inferential method to estimate STW using sparse publicly available data. The proposed method combines AIS-derived vessel trajectories with a modelled environmental current field derived from tidal elevation data to resolve the component of the current acting along the vessel’s direction of travel. Vessel motion is reconstructed from AIS position data, and the along-track current component is obtained through vector projection onto the vessel trajectory. Combining this with observed SOG enables estimation of STW without onboard measurements. The method is evaluated using representative vessel case studies on the tidal River Thames, where estimated STW is compared with independent Doppler-based measurements. A detailed validation is presented for one representative vessel, with additional validation undertaken across multiple vessel types operating under different conditions. Across the validation cases, the methodology shows strong agreement with measured STW, demonstrating that STW can be estimated with acceptable accuracy using widely available data. This establishes the physical foundation required for subsequent propulsion power and energy-demand assessment in data-constrained environments. Full article

This research investigates the home health care routing and scheduling problem with soft time windows and perishable medicines (HHCRSP-STW-PM), where care crews must serve patients over multiple periods while accounting for skill-based assignments, medicine perishability, and flexible service times. The objective minimizes total travel and penalty costs for early or late services. We formulate a mixed integer linear program (MILP) model to optimally solve small instances and develop an effective greedy randomized adaptive search procedure (GRASP) to address large instances. GRASP includes a tailored construction heuristic with problem-specific local search operators in the local search phase. Numerical experiments conducted on newly generated instances demonstrate that while the MILP model provides optimal solutions for small instances, only GRASP is able to handle large-scale instances within a reasonable computational time. Sensitivity analyses allow us to examine the impact of the approach’s parameters, perishability of medicine, soft time windows, and care crew resources. Full article

Sit-to-walk (STW) is a short-duration, high-load, multijoint transition requiring rapid lower-limb neuromuscular coordination across seat-off, load transfer, and gait initiation. Surface electromyography (sEMG)-based prediction of knee and ankle joint moments may support motor function evaluation and inform future assistive-control applications, but existing models remain limited in modeling cross-muscle sEMG feature interactions and mitigating phase-dependent prediction errors. This study developed an anatomy-informed framework for sEMG-driven moment prediction during STW. The model encoded sEMG channels from thigh and shank muscles into separate anatomical branches. Cross-Attention was used to model task-relevant intersegmental interactions, and BiLSTM was applied to capture short-term temporal dependencies. Eighteen healthy participants performed STW trials while sEMG, three-dimensional kinematics, and ground reaction forces were synchronously collected. Knee and ankle moments were calculated using inverse dynamics and used as reference targets. Among six models, the Cross-Attention model achieved the lowest test-set overall error, with an Overall nRMSE Fixed of 4.51%; the knee peak error in the P3 unloading phase was 16.17%. Ablation experiments indicated that Cross-Attention, BiLSTM temporal modeling, anatomical branch separation, and joint-specific output mapping contributed to prediction performance. This framework provides an interpretable approach for sEMG-driven multijoint moment prediction in complex non-stationary movements. Full article

Quantifying metabolic cost during short transitional movements is challenging because conventional metabolic measurements have limited temporal resolution. This proof-of-concept study examined whether model-derived metabolic cost during the sit-to-walk (STW) transition could be exploratorily stratified using interpretable ensemble learning. Forty-nine healthy adults completed the STW phase of the Timed Up and Go task with synchronized three-dimensional kinematics, ground reaction forces, and eight-channel surface electromyography. Individually scaled OpenSim gait2392 models and the Umberger metabolic model were used to estimate metabolic power from seat-off to the end of the first complete gait cycle. Window-averaged metabolic power was stratified into low-, medium-, and high-cost levels. Window-level biomechanical features were extracted from kinematic, kinetic, and muscle-state time series. Seven classifiers were trained using a subject-level 7:3 train–test split and stratified five-fold cross-validation within the training set, and their probability outputs were integrated through TOPSIS-weighted classifier fusion. SHapley Additive exPlanations were used for class-specific feature attribution. The fused ensemble achieved an AUC of 0.870, F1 score of 0.703, accuracy of 0.705, and specificity of 0.853 on the independent test set. Discrimination was stronger for the low- and high-cost levels than for the medium-cost level. SHAP-based attribution highlighted force-related changes and knee-angle variability and amplitude measures as prediction-relevant biomechanical features. These findings support a model-derived, interpretable workflow for extending STW assessment from task performance to task cost, while indicating the need for further validation in larger and clinical datasets. Full article

Permanent magnet synchronous motors (PMSMs) are utilized in demanding conditions and applications requiring precision and accuracy, such as servo systems. Especially at low speeds, the effects of cogging torque, current measurement and offset errors, improper controller gains, mechanical resonance, and torque fluctuations caused by load torque and flux result in fluctuations at various frequencies in the motor output speed. This study, motivated by two factors, proposes an extended state observer (ESO)-based multivariable fast response extremum-seeking (FESC) interval type-2 fuzzy PID (IT2FPID) controller to improve dynamic response and reduce speed ripple at low speeds in situations where all these negative factors could arise. This approach enables the real-time adaptation of parameters to counteract the decline in controller performance caused by the nonlinear characteristics of PMSMs and parameter fluctuations while also optimizing disturbance rejection in the speed response under varying operating conditions and existing speed ripple. The experimental results from the prototype setup validate that the proposed control mechanism is functional, valid, and precise in diminishing speed ripples during low-speed operations. The simulation and test outcomes of the control scheme show that speed noise at low speeds is reduced from 26% to 3% compared to traditional proportional-integral (PI) controller and supertwisting (STW) sliding mode controller (SMC) responses and that the scheme exhibits a 16–23% reduction in undershoot amplitude and faster recovery in the presence of load torque variations. Full article

The high-speed rotor electric drive system in control moment gyroscopes (CMGs) is essential for precise spacecraft attitude control. Rigorous testing of this system is critical for ensuring reliability and longevity throughout orbital missions. However, conventional test bench methods exhibit numerous limitations. In contrast, the electric motor emulator (EME) provides a flexible and efficient alternative for power-level testing of the CMG high-speed rotor brushless DC motor drive system. To address the challenges of trapezoidal back-electromotive force (back-EMF) emulation and insufficient square-wave current tracking accuracy in existing brushless DC motor emulator (BLDCME) implementations, this paper proposes a sliding time window-based dynamic current compensation control (STW-DCCC) strategy for the CMG high-speed rotor BLDCME. First, based on the VSC single-conversion-circuit topology, the BLDCME basic control strategy based on the motor port current and the current change rate is implemented to achieve a tracking control of the square-wave current and emulation of the trapezoidal back-EMF. Building upon this foundation, a sliding time window-based RMS current compensation optimization strategy for the BLDCME is designed to provide dynamic compensation for system disturbances and thereby enhance the tracking accuracy of the square-wave current. Furthermore, by incorporating fault information, the proposed STW-DCCC strategy can also emulate the resistance unbalance fault of the brushless DC motor. Finally, through experiments, a comparative analysis is conducted between the basic control strategy and the proposed STW-DCCC strategy under normal operating conditions, parameter mismatch operating conditions, and resistance unbalance fault conditions, thereby validating the effectiveness of the proposed method. Full article

Irritable Bowel Syndrome (IBS) affects 4–15% of the global population, and the limited efficacy of existing pharmacologic therapies has driven growing interest in plant-based therapeutic options among both patients and clinicians. A comprehensive assessment of all plant extracts investigated in IBS is therefore essential, given the limited effectiveness of conventional treatments and the increasing interest in complementary approaches. Evidence from recent systematic reviews and meta-analyses consistently indicates that peppermint oil is the most effective botanical agent, particularly for reducing abdominal pain and overall IBS symptom severity. Iberogast (STW-5 and STW-5 II) has also demonstrated clinical improvements across multiple trials, while curcumin shows mechanistic and preliminary clinical potential by modulating several IBS-related pathways. In contrast, extracts such as Curcuma xanthorrhiza, Fumaria officinalis, and various Ayurvedic formulations have not shown significant clinical benefit. Other agents, including Aloe vera, flavonoids, St John’s wort, and ginger, exhibit mixed or inconsistent results, reflecting heterogeneity in study designs and underlying mechanisms. A review of international guidelines reveals that peppermint oil is the only plant-based therapy consistently acknowledged across adult and pediatric recommendations. The aim of this review is to summarize, compare, and critically evaluate all plant extracts studied for the prevention and treatment of IBS, integrating mechanistic pathways, clinical evidence, and current international guideline recommendations to clarify their therapeutic relevance for clinical practice. Full article

Background: Flexible flatfoot is a common musculoskeletal disorder in adolescents, which is characterized by a collapsed longitudinal arch. A common surgery like subtalar arthroereisis depends on the implant in sinus tarsi. Optimal match between them can potentially avoid postoperative pain and obtain improved prognosis. Investigations into anatomical morphology of sinus tarsi by weight-bearing CT (WBCT) may unveil the pathogenesis and facilitate the treatment of flexible flatfoot. Methods: This retrospective study included 28 control cases and 42 flatfoot cases. The sinus tarsi length (STL), the sinus tarsi width (STW), the angle between its long axis and the horizontal line (ST-H angle), the sinus tarsi angle (ST angle), and the tibial width were measured. We also calculated two ratios (STL/tibia width and STW/tibia width) to standardize individual differences. Data analysis was conducted via mean/median comparisons and subsequent linear regression. Results: The STL and the STL/tibia width were significantly greater in the flatfoot group (25.73 ± 3.50 vs. 23.09 ± 3.77 mm, p = 0.004; 0.90 ± 0.15 vs. 0.81 ± 0.14, p = 0.009). The ST angle was significantly smaller in the flatfoot group by an average of 4.63° (13.20° vs. 17.83°, p < 0.001). Linear regression revealed that female gender and smaller ST angle were significantly correlated with higher Meary angle, while smaller ST angle and greater STL/tibia width were significantly correlated with lower Pitch angle (p = 0.002, p = 0.007; p = 0.003, p = 0.004). No statistical predictive effects were observed for the other variables. Conclusions: The ST angle and STL/tibia width may serve as auxiliary parameters for implant selection in subtalar arthroereisis to improve sizing match within the sinus tarsi. Full article

Textile dyeing effluents are characterized by recalcitrant organics and high salinity, requiring robust pretreatments prior to biological polishing. The heterogeneous Fenton-type (HFT) oxidation over Prussian Blue nanoparticles supported on γ-alumina (PBNP/γ-Al₂O₃) was investigated in a liquid batch-recycle packed-bed reactor treating a synthetic textile wastewater (STW) reproducing an industrial dye bath with the Reactive Black 5 (RB5) dye, together with simplified RB5 and RB5 + NaCl matrices. Hydrogen peroxide decay followed pseudo-first-order kinetics. Using fixed initial doses (11, 20, 35 mmol L⁻¹), the catalyst exhibited an early adaptation phase and then reproducible operation: from the fourth reuse onward, both the H₂O₂ decomposition rate constant and DOC removal varied by <10% under identical conditions. Among matrices, STW exhibited the highest oxidant efficiency. With an initial H₂O₂ dose of 11 mmol L⁻¹, the treatment enabled complete discoloration and produced effluents with negligible toxicity. Increasing the initial dose to 20 or 35 mmol L⁻¹ did not improve treatment and led to a decrease in the hydrogen peroxide decomposition rate with reuses and loss of PB ν(C≡N) Raman bands, indicating surface transformation. Overall, PBNP/γ-Al₂O₃ demonstrated reproducible activity and structural resilience in saline, dyeing-relevant matrices at H₂O₂ doses that preserve catalytic integrity, confirming its feasibility as a stable and reusable pretreatment catalyst for saline dyeing effluents, and supporting its integration into hybrid AOP–biological treatment schemes for dyeing wastewater. Full article

Spread-tow woven fabrics (STWs) have attracted considerable attention owing to their thin-layered characteristics, high fiber strength utilization rate and superior designability, finding wide application in the aerospace field. To meet the application requirements for materials with high specific strength/specific modulus in the aerospace field, this study designed an anti-sandwich structured composite with high specific load-bearing capacity. Herein, the core layer was a load-bearing structure composed of STW, while the surface layers were hybrid lightweight structures made of STW and nonwoven (NW) felt. Repeated impact test results showed that increasing the thickness ratio of the core layer enhanced the impact resistant stiffness of the overall structure, whereas increasing the proportion of NW felt in the surface layers improved the energy absorption of the composites but reduced their load-bearing stiffness and strength. The composite exhibited superior repeated impact resistance, achieving a peak impact load of 17.43 kN when the thickness ratio of the core layer to the surface layers was 2:1 and the hybrid ratio of the surface layers was 3:1. No penetration occurred after 20 repeated impacts at the 50 J or 3 repeated impacts at 100 J. Meanwhile, both the maximum displacement and impact duration increased, whereas the bending stiffness declined as the number of impacts increased. The failure mode was mainly characterized by progressive interfacial cracking in the surface layers and fracture in the core layer. Full article

Vertical ground reaction force (vGRF) plays an important role in the study of running-related injuries (RRIs). This study explores the synchronization method between inertial measurement unit (IMU) and vGRF data of running and develops ANN models to accurately predict vGRF. Fifteen runners participated in this study. Acceleration data and vGRF values of eight rearfoot strikers and seven forefoot strikers running at 12, 14, and 16 km/h were collected by a single IMU and an instrumented treadmill. The sliding time window synchronization (STWS) algorithm was developed to sync IMU data with vGRF data. The wavelet neural network model (WNN) and feed-forward neural network model (FFNN) were adapted to predict vGRF using three-axis or sagittal-axis acceleration data in the stance phase, respectively. One rearfoot striker and one forefoot striker were randomly selected as a test set, while the other participants formed training sets. After synchronization, mean absolute errors for stride time of the IMU and vGRF data were less than 11.2 ms. The coefficient of multiple correlations for vGRF measured curves and predicted curves was more than 0.97. The normalized root mean square errors (NRMSEs) between two curves were 4.6~9.2%, and R² was 0.93~0.99. For peak vGRF, the NRMSEs were 1.6~8.2%, except for rearfoot strike runners at 16 km/h using the FFNN model (10.7% and 11.1%). The Bland–Altman plots indicate that the errors for both the WNN and FFNN models are within acceptable limits. The STWS algorithm can effectively achieve the data synchronization between the IMU and the force plate during running. Both WNN and FFNN models demonstrated good accuracy and agreement in predicting vGRF. Using sagittal-axis acceleration data may be an ideal model with good prediction accuracy and less input data. This work provides direction for developing ANN models of personalized monitoring of lower limb load. Full article

Access to clean and safe water is crucial for community well-being. Water samples from storage tank water (STW) and municipal tap water (MTW) were aseptically collected, and total bacterial and coliform counts were determined. Isolates were Gram-stained, and conventional biochemical tests were conducted. Antibiotic susceptibility testing was performed using Kirby–Bauer’s disk diffusion technique. Selected isolates were confirmed through Sanger sequencing of amplified 16S rRNA genes. Polymerase chain reaction and gel electrophoresis techniques were used to determine the presence of quinolone and beta-lactam resistance genes. A total of 50 water samples were analyzed. The mean total coliform counts (TCCs) were 5.75 for STW and 5.5 for MTW. In total, 43 and 13 bacterial isolates were recovered from STW and MTW, respectively, with Gram-negative bacteria being more prevalent 58.14% (25/43) in STW and 81.82% (9/11) in MTW. The isolates appeared to belong to seven different presumptive bacterial genera on biochemical tests. The 16S rRNA gene amplicon Sanger sequencing of 38 isolates revealed 15 different species. A total of 38 isolates tested for resistance genes revealed that 47.37%, 31.58%, 21.05%, 10.53%, 28.95%, and 13.16% harbored gyrB, parC, gyrA, parE, blaSHV, and blaTEM genes, respectively. Antibiotic susceptibility profiling revealed a predominance of multidrug-resistant (MDR) strains among the bacterial isolates from both water sources. Regular monitoring and enhanced water treatment are critical to protect the public health and reduce the spread of potential pathogenic and antibiotic-resistant bacterial strains in household water systems. Full article

Portuguese oyster (Magallana angulata) is one of the most important shellfish species worldwide. Although significant improvements in growth have been achieved through artificial selection breeding, the genetic basis underlying these traits remains unclear. Thus, this study aimed to (i) estimate variation and heritability for growth-related traits and (ii) identify SNPs and candidate genes associated with growth traits in Portuguese oyster. Five growth-related traits, including shell height (SH), shell length (SL), shell width (SW), whole weight (WW), and soft tissue weight (STW), were measured and analyzed in 114 one-year-old individuals from a cultivated population in Fujian Province, China. Through whole-genome sequencing and genotyping, we obtained 8,183,713 high-quality SNPs. Based on the genomic relationship matrix, heritability for the five traits was estimated, ranging from 0.071 to 0.695. Through genome-wide association analysis (GWAS), a total of nine SNPs were identified as significantly or suggestively associated with one of the growth-related traits, each explaining phenotypic variation ranging from 14.13% to 18.56%. Differentially expressed genes (DEGs) between individuals with extreme phenotypes were identified using comparative transcriptome analysis, ranging from 868 to 2274 for each trait. By combining GWAS and comparative transcriptome analysis, a total of seven candidate genes were identified, with biological functions related to growth inhibition, stress response, cell cycle regulation, and immune defense. The associations between the candidate genes and the growth-related traits were validated by using single-marker association analysis in other populations. Based on SNPs in these candidate genes, 16 haplotypes associated with growth-related traits were obtained. This study contributes to a deeper understanding of the genetic mechanisms of growth traits, and provides a theoretical basis and genetic markers for the breeding of fast-growing strains of the Portuguese oyster. Full article

The energy consumption of air-jet looms mainly comes from air compressors. Predicting the air consumption of air-jet looms for the upcoming period is significant for the variable frequency adjustment of air compressors, thereby aiding in energy saving and reducing fabric costs. This paper proposes an innovative method that combines Sliding Time Windows (STW), feature analysis, and incremental learning to improve the accuracy of short-term air consumption prediction. First, the STW method is employed during the data collection phase to enhance data reliability. Through feature analysis, significant factors affecting the air consumption of air-jet looms, beyond traditional research, are explored and incorporated into the prediction model. The experimental results indicate that the introduction of new features improved the model’s R² from 0.905 to 0.950 and reduced the MSE from 32.369 to 16.239. The STW method applied to the same random forest model increased the R² from 0.906 to 0.950 and decreased the MSE from 32.244 to 16.239. The decision tree method, compared to the linear regression model, improved the R² from 0.928 to 0.950 and reduced the MSE from 23.541 to 16.239, demonstrating significant predictive performance enhancement. After establishing the optimal model, incremental learning is used to continuously improve the reliability and accuracy of short-term predictions. Experiments show that the incremental learning method, compared to static models, offers better resilience and reliability when new data are collected. The proposed method significantly improves the accuracy of air consumption prediction for air-jet looms, providing strong support for the variable frequency adjustment of air compressors, and contributes to the goals of energy saving and cost reduction. The research results indicate that this method not only enhances prediction accuracy but also provides new insights and methods for future energy-saving research. Full article