Search Results (1,932)

A 7224C high-speed angular contact ball bearing used in a multi-wire sawing machine is selected as the research object to investigate the cage dynamic characteristics under oil-lubricated operating conditions. First, in order to determine the oil-phase volume fraction on the cage surface, a fluid-domain model of the bearing cavity is established, and numerical simulations are performed using the VOF multiphase-flow method coupled with the RNG k-ε turbulence model. The effects of the guiding clearance, pocket clearance, and rotational speed are analyzed, and a regression equation for the cage-surface oil-phase volume fraction is developed based on a uniform test design. Subsequently, a bearing dynamic model is constructed, in which lubrication-related parameters are determined based on the regression equation, and the force balance and equations of motion for each component are derived. Finally, using the slip ratio and the deviation ratio of the cage-centroid whirl velocity as evaluation indices, the influences of multiple parameters on cage stability are examined. The results indicate that increasing the clearances and rotational speed leads to a higher slip ratio, whereas increasing the axial and radial loads reduces the slip ratio. Moreover, enlarging the guiding clearance and increasing the axial load improve cage stability, while a larger pocket clearance and an excessively high radial load deteriorate cage stability. Full article

Non-built-up green areas are essential for preserving the ecological functions of cities and fostering sustainable growth. Focusing on Shanghai, we developed a comprehensive framework of driving forces that integrates socioeconomic, natural, policy, and financial indicators. To assess the spatial-temporal changes in regional green space configurations and their underlying mechanisms between 2000 and 2020, we utilized stepwise regression alongside Geographically Weighted Regression (GWR) techniques. The results show that regional green space exhibited a clear stage-dependent evolution, with the total area decreasing from 580.56 km² in 2000 to 506.43 km² in 2005 and then increasing continuously to 905.70 km² in 2020. Forest land consistently expanded and became the dominant land type, while wetland showed a “decrease–increase” pattern and grassland experienced an early decline followed by partial recovery. The primary elements driving these changes underwent substantial transformations over the study period. During the initial phase, socioeconomic variables, particularly real estate investments (β = −0.296), demonstrated pronounced adverse impacts. Conversely, post-2005, financial allocations for landscaping and policy interventions emerged as the main favorable drivers (β = 0.598). Furthermore, environmental aspects like NDVI and waterway density provided a continuous positive influence on green space enlargement. Certain socioeconomic indicators, notably population density, transitioned from exerting adverse impacts to having beneficial effects during the latter periods. The primary drivers demonstrated considerable spatial variation; socioeconomic impacts were largely localized in regions undergoing urban growth, whereas environmental and policy variables exerted broader and more consistent influences. Overall, these outcomes highlight a shift from a socioeconomic-dominated evolutionary process to one governed by a synergy of multiple factors. This offers a theoretical foundation for refining urban ecological strategies and harmonizing city expansion with ecological conservation. Full article

Heritage rammed earth is a special soil material formed by manually selecting and ramming locally available Quaternary surface deposits layer by layer. However, the quantitative influence of material sourcing and rammed technology on the compressive properties of heritage rammed earth remains insufficiently understood, which limits the mechanical assessment and conservation planning of rammed earth sites. In this study, undisturbed rammed earth from 15 Ming Great Wall sites in Northwest China was investigated. Field 3D scanning, particle-size analysis, uniaxial compression testing, mesoscopic structural observation, and DEM analysis were combined to evaluate the effects of material characteristics and rammed technology on the compressive properties of heritage rammed earth. The results show clear regional differences in material characteristics and rammed technology parameters across the 15 sites. Across the five occurrence regions from the Extremely Arid Area to the Semi-Humid Area, dry density, silt fraction, curvature coefficient, and ramming pit distribution area ratio generally decreased, whereas clay and colloidal particle fraction, $d_{60}$, $C_u$, and rammed modulus generally increased. These variations were accompanied by changes in internal fabric, including aggregate proportion, coordination-number difference, high-stress particle proportion, and force-chain particle proportion. The peak stress and failure strain ranged from 0.48 to 1.01 MPa and from 0.03 to 0.07, respectively. Both parameters showed a decreasing regional trend from the extremely arid area to the semi-humid area, following the sequence: extremely arid area, arid area, semi-arid area, cold and humid area, and semi-humid area. From the Extremely Arid Area to the Semi-Humid Area, the shear failure mode changed from single-fork to mixed double-fork and then to intersecting double-fork. Regression analysis further showed that material and rammed technology parameters were closely related to mesoscopic structural parameters, with R² values generally greater than 0.75. These findings suggest that the regional differences in compressive behavior were closely associated with variations in material sourcing, rammed technology, internal fabric, and the load-bearing structure of rammed earth. Full article

Background: Respiratory muscle strength (RMS) is a critical factor influencing athletic performance, particularly in high-intensity or prolonged activities. RMS encompasses inspiratory (IMs) and expiratory muscles (EMs), which differ in anatomical structure, fiber composition, and responsiveness to training. Methods: This pilot interventional within-subject study investigated the effects of two resistive respiratory muscle training (RMT) protocols on RMS and small airway function in eight physically active adults (two females, six males). Maximal inspiratory (MIP) and expiratory pressures (MEP), along with pulmonary function tests (PFTs), were measured using the Airofit PRO™ device and spirometry before and after two consecutive 7-day training protocols, with a 2-day break between interventions. The workload was progressively increased by lengthening the duration of forced inhalation and exhalation, while keeping the air resistance constant. Results: Results demonstrated significant improvements in MEP across both protocols and after a 10-day washout period (p < 0.001–0.03), whereas MIP showed no significant changes (p = 0.19–0.66). Moderate transient improvements were observed in small airway flow (MEF25%) following the first protocol (ES = 0.62), which regressed after the second. Conclusions: These outcomes suggest differential responsiveness of respiratory muscles to RMT; EMs, characterized by a higher proportion of fast-twitch type II fibers and a predominantly passive role in normal breathing, respond rapidly to short-duration, high-intensity forced expiration training through neuromuscular adaptations. Conversely, IMs, dominated by slow-twitch type I fibers, require longer-duration, higher-load training to elicit meaningful adaptations, explaining the limited changes in MIP. Small airway function appeared minimally trainable due to structural and physiological constraints, with short-term improvements likely reflecting effort-dependent factors rather than lasting adaptations. Finally, RMT can selectively enhance EM performance through appropriately designed short-duration, high-intensity interventions, while IMs may necessitate prolonged or higher-load stimuli. The findings highlight the importance of targeted training strategies, individualized to muscle fiber composition and functional demands, to optimize respiratory performance. Future research should investigate longer interventions, larger diverse cohorts, and precise measurement methods to further elucidate RMT’s effects on both respiratory muscles and small airway function. Full article

Spirometry is widely regarded as the clinical gold standard for quantifying lung function. It plays a central role in the diagnosis and management of cardiopulmonary disorders, including asthma and chronic obstructive pulmonary disease (COPD). However, the procedure relies on a forceful and often stressful expiratory maneuver that may cause patient discomfort and require substantial effort, frequently necessitating active coaching and trained personnel to ensure reproducible measurements. In this paper, we present the Digital Spirometry Patch (DSP), a soft, flexible, wearable patch capable of estimating lung function parameters by utilizing low-effort breathing maneuvers. Eighteen participants performed low-effort and forceful breathing maneuvers while wearing the DSP to collect tracheal sound and chest movement signals for spirometric parameter estimation using elastic net and simple linear regression. Using leave-one-subject-out cross-validation, the elastic net models achieved RMSEs of 0.668 L, 0.224 L, and 0.428 L/s for FVC, FEV₁, and PEF, respectively, using low-effort breathing maneuvers, and 0.499 L, 0.304 L, and 0.891 L/s using forceful exhalation maneuvers. These results demonstrate the potential of the DSP as a wearable, low-effort alternative for estimating lung function outside of conventional spirometry settings. Full article

Accurately predicting crop yield and its spatiotemporal variability is crucial for precision agriculture. This study developed a prior knowledge-guided remote sensing yield estimation framework at Youyi Farm in China. Based on multi-source data from 2016 to 2025, a Yield-Formation Key Dataset (YFKD) was constructed by integrating Meteorological, Eco-physiological, Phenological, and Soil features. Combined with Boruta feature selection, MLR (Multiple Linear Regression), RF (Random Forest), and XGBoost (Extreme Gradient Boosting) models were compared, and SHAP (Shapley Additive Explanations) was utilized for spatiotemporal driving force analysis. The results showed that the YFKD-XGBoost model achieved the optimal performance ($R^2=0.865$, RMSE = 1491 kg/ha), improving accuracy by up to 17.7% compared to the baseline model. Global SHAP analysis revealed that Soil Spectral Reflectance provided the highest contribution. Temporally, the period from late July to mid-September (especially mid-August) served as the critical monitoring window. Spatially, based on the area share of the dominant negative SHAP contributor, Meteorological Background was the most widespread limiting factor (34.8% of the constrained area), Soil Conditions constraints showed localized clustering (16.4%), while Phenological and Eco-physiological constraints dominated intra-field spatial differentiation. This study validated the feasibility of this framework for high-precision yield estimation and the analysis of yield formation driving factors under the constraints of a limited regional dataset (n = 233), providing reliable support for regional differentiated agricultural management. Full article

Background/Objectives: Preoperative pulmonary function tests (PFTs) contain numerous physiologic parameters, yet surgeons typically rely on forced expiratory volume in one second (FEV₁) and diffusing capacity of the lung for carbon monoxide (DLCO) to assess surgical risk. This study aimed to evaluate whether artificial intelligence (AI) could utilize more PFT data to predict the occurrence of prolonged air leak (PAL) following lung resection. Methods: An optical character recognition (OCR) model was used to extract structured data from PFT reports. These data were combined with clinical and demographic features from our institutional Society of Thoracic Surgeons General Thoracic Surgery Database (STS-GTSD) between 2016 and 2023. A feature selection algorithm was used to select the most predictive features, and a neural network was trained and tested on an internal validation cohort to predict PAL. Model performance was compared to previously published models. Results: There were 410 patients undergoing lung resection who had PFTs successfully digitized by the OCR system. A total of 76 available PFT features were extracted per patient. The final AI model included 10 key input variables, including three PFTs and seven clinical variables. On validation, the model achieved a specificity of 73%, sensitivity of 60%, overall accuracy of 72%, and an area under the curve of 0.74. This performance exceeded most existing PAL prediction models. Conclusions: AI-driven models using structured PFT and clinical data can enhance prediction of prolonged air leak after lung resection and outperform conventional regression-based models. Further research may focus on external validation and integration into clinical workflows. Full article

Background/Objectives: Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that increases the risk for cardiovascular disease. Although gait impairments are documented in older adults with MetS, few studies have examined gait biomechanics or the potential for gait-related measures to differentiate metabolic syndrome status in young adults. This study examined whether gait biomechanics, functional gait performance, and muscle strength are associated with MetS risk factors in young adults, and whether these measures predict MetS classification. Methods: Twenty-four young adults meeting criteria for metabolic syndrome (MetS+) and 24 participants without MetS (MetS−) completed cardiometabolic assessments, gait analysis, functional gait testing, and lower extremity isometric strength testing. Multiple linear regression examined associations between gait velocity and MetS risk factors, and binary logistic regression assessed the ability of biomechanical, functional, and strength variables to differentiate MetS status. Results: Compared with matched controls, MetS+ participants demonstrated slower gait velocity, longer stance time, and lower propulsive ground reaction forces. Regression models examining MetS risk factors did not significantly explain variance in gait velocity. Logistic regression indicated that spatiotemporal gait parameters and GRF variables could differentiate MetS classification with fair predictive ability, whereas functional gait performance and strength measures showed limited classification performance. Conclusions: Young adults with MetS demonstrated modest differences in select gait variables, but the MetS risk factors did not show strong relationships with gait velocity in regression analyses. Spatiotemporal gait parameters differentiated MetS+ and MetS− groups but offered limited predictive value. These findings suggest that subtle biomechanical differences may be present early in the progression of MetS, although stronger functional impairments may not yet be detectable in young adults. Full article

This study investigates the direct impact of financial repression on economic growth in the Central African Economic and Monetary Community (CEMAC) and the West African Economic and Monetary Union (UEMOA) using a lagged composite repression index and panel fixed-effects regressions. Contrary to theoretical expectations, lagged repression exhibits a significantly positive association with GDP growth in the main model, with robustness checks confirming no negative direct effect. The findings suggest that in pegged currency unions, repression may support growth through public channels or forced savings, offsetting private crowding-out, while capital formation remains a key driver. This effect, contrasting with repression’s negative impact on investment, highlights union-specific resilience and calls for calibrated reforms to balance stability with deepening. Full article

This study investigates ultrasonic vibration-assisted (UV) CNC milling of hardened 90CrSi steel cylindrical surfaces, with emphasis on ultrasonic horn design, experimental analysis, and multi-objective optimization of machining parameters, addressing the need for an integrated framework combining system design, experimental validation, and multi-objective optimization. A quarter-wavelength ultrasonic horn was designed and experimentally validated to operate at a frequency of 20 kHz. By adjusting the horn–workpiece system, stable vibration amplitudes were achieved to enable effective ultrasonic-assisted milling of cylindrical surfaces. Milling experiments based on a Box–Behnken design were conducted to examine the effects of vibration amplitude, cutting speed, feed rate, and radial depth of cut on material removal rate (MRR) and surface roughness (Ra). Surrogate models using response surface methodology (RSM) and Gaussian process regression (GPR) were developed to predict machining performance. A GPR-assisted NSGA-II algorithm was then applied to simultaneously maximize MRR and minimize Ra, resulting in a well-defined Pareto front that reveals the trade-off between machining productivity and surface quality. Furthermore, an AHP-based decision-making approach was employed to select preferred machining conditions from the Pareto-optimal solutions. The GPR models demonstrated high predictive accuracy (R² > 0.98), and validation experiments confirmed the reliability of the predicted optimal results, with deviations below 5%. In addition, a comparative analysis between ultrasonic-assisted and conventional milling showed that MRR increased by 10.81–40.17%, Ra decreased by 27.11–44.44%, and cutting force was reduced by 14.2–42.65%, providing direct experimental evidence of improved machinability. The results demonstrate that the proposed integrated framework provides an effective strategy for optimizing ultrasonic vibration-assisted milling processes and improving the machinability of hardened 90CrSi cylindrical surfaces. Overall, the proposed framework provides a practical and cost-effective strategy for enhancing machining performance and offers a robust approach for multi-objective optimization of ultrasonic vibration-assisted milling processes. Full article

Investigating the influence of women’s family status on farmers’ adoption of digital and intelligent production transformation holds significant value in bridging the gender gap in research on modern agricultural production transformation and in facilitating the digital and intelligent transformation of the agricultural sector. Drawing on survey data from Henan Province collected through a household survey conducted in July 2024 by the research team, which employed a combination of stratified and random sampling, and focusing on farmers’ adoption of plant protection drone technology, this paper employs the Triple-Hurdle model to examine the impact of women’s family status on farmers’ digital and intelligent production transformation decisions and the underlying mechanisms. The baseline regression results show that the improvement of women’s family status facilitates farmers’ digital and intelligent production transformation decisions. Specifically, it enhances farmers’ willingness to adopt digital and intelligent production transformation, promotes their adoption behavior of plant protection drone technology, and increases the degree of adoption of such technology. The mechanism analysis reveals that the improvement of women’s family status promotes farmers’ digital and intelligent production transformation decisions by increasing their satisfaction with the institutional environment. The heterogeneity analysis of household characteristics indicates that women’s family status has a greater facilitating effect on the willingness of farmers with lower female labor force participation and those with heavier child or elderly dependency burdens to undergo digital and intelligent production transformation. The heterogeneity analysis of village environmental characteristics shows that women’s family status has a greater facilitating effect on the willingness and behavior of farmers in villages with a larger number of technical personnel to undergo digital and intelligent production transformation. Additionally, it has a greater facilitating effect on the willingness of farmers in villages with a stronger culture of gender equality to undergo such transformation. Using plant protection drone adoption as an example, this paper provides preliminary evidence of the positive impact of women’s family status on the digital and intelligent transformation of agriculture. However, due to the inherent limitations of cross-sectional data, our exploration of the dynamic process of transformation remains inadequate. Therefore, future research is warranted to employ longitudinal panel data to further validate the findings of this study. Full article

With the implementation of China’s strategy to build a maritime power, new-quality marine productive forces have emerged as an important driver of high-quality development in the marine economy. Based on panel data from 11 coastal provinces in China covering the period 2013–2022, this study constructs a comprehensive evaluation index system for both new-quality marine productive forces and the high-quality development of the marine economy. It employs the entropy method to calculate a composite development index and uses panel models, mediation effect models, and threshold regression models to examine the mechanism through which new-quality marine productive forces influence the high-quality development of the marine economy. The study finds the following: (1) New-quality marine productive forces are positively associated with the high-quality development of the marine economy. (2) They are also positively associated with marine science and technology innovation, which in turn is associated with the high-quality development of the marine economy, suggesting a partial mediating role. (3) The level of economic development plays a nonlinear moderating role: the positive association is not significant at lower levels of economic development, strengthens at moderate levels, and weakens at higher levels. Full article

A probabilistic data-driven framework for voltage quality forecasting in distribution transformers based on Gaussian process regression and high-resolution field measurements is presented. Voltage time series acquired under real operating conditions were modeled using composite covariance functions designed to capture long-term trends and stochastic multi-scale fluctuations. The proposed approach enables simultaneous prediction and uncertainty quantification, allowing direct compliance assessment with voltage quality standards. The additive Gaussian process models achieved coefficients of determination above 0.75 and produced statistically uncorrelated residuals, indicating an adequate representation of the intrinsic temporal structure. However, the predictive intervals exhibit a certain level of undercoverage, indicating that, while uncertainty is effectively quantified, there is still room for improvement in calibration. The selected kernel structures revealed distinct physical regimes in the voltage dynamics, including smooth steady operation, moderately irregular behavior associated with localized disturbances, and multi-scale stochastic variability. For benchmarking purposes, results were compared with those obtained from a stochastic damped harmonic oscillator with restoring force, a naive model, a seasonal naive model and an Autoregressive Integrated Moving Average model. The oscillator model, the naive model, the seasonal naive model, and the Autoregressive Integrated Moving Average model generated strongly autocorrelated residuals, whereas the Gaussian process models yielded consistent white-noise residuals that outperformed all the other models. These findings demonstrate that probabilistic Gaussian process modeling provides an interpretable, scalable, and uncertainty-aware alternative for predictive voltage quality assessment in modern distribution systems. Full article

Food waste valorization represents a critical challenge and opportunity for sustainable food systems. This study investigated the reuse of sauce production by-products through two approaches: (i) solvent-free recovery of an oil-rich fraction and (ii) development of polymeric films for potential edible or biodegradable packaging. Centrifugation recovered approximately 10 g per 100 g of by-product. The recovered oil was characterized for total polyphenols and fatty acid composition, showing a profile consistent with vegetable oils (mainly olive oil), with minor contributions attributable to cheese and meat components. A full factorial design was used to prepare and test films and to study the effects of the three ingredients used, namely pectin, carvacrol, and sauce by-products, on their mechanical, surface, and antibacterial properties. Chemometric analysis based on principal component analysis (PCA) and regression-based modeling (multiple linear regression and response surface analysis) was applied to identify the relationships among the responses and the most influential factors. Among the tested formulations, N3 (low pectin and by-product; high carvacrol) showed the most favorable overall balance, combining the strongest antibacterial activity (mean inhibition halo diameter of 14.8 mm and 17.8 mm against Escherichia coli ATCC 11229 and Staphylococcus aureus ATCC 6538, respectively) with favorable mechanical performance, including the highest maximum force (0.53 ± 0.01 MPa) and elastic modulus, (6.8 ± 0.01 MPa) and intermediate elongation (12 ± 3%) and work at maximum force (11.9 ± 0.9 N mm). Full article

Relief is an important driver in the spatial differentiation of river runoff and its regime both in the mountains and on the plains, which is most evident in arid and semi-arid regions of the Earth’s land. Based on 22 small and medium-sized rivers of the zones of forest–steppe and steppe in the temperate climate zone of the eastern part of the East European (Russian) Plain, within the Middle Volga region and the eastern part of the Don River basin, the role of this factor in the spatiotemporal changes in various key runoff parameters (annual average runoff (Q), annual maximum runoff (Q_max), and annual minimum runoff for both cold (Q_min-CP) and warm (Q_min-WP) seasons) between two baseline climatic periods of the Anthropocene (1961–1990 and 1991–2020) is considered using the average elevation of river basin (H) as its quantitative indicator and statistical procedures of regression and correlation analysis. It is found that in the interperiod trends of the Anthropocene, the H factor was the leading (and statistically significant) cause of spatial variability in the changes in Q_max in the forest–steppe zone, through its inverse relationship with H (with a 70% contribution of influence), Q_min-CP in the forest–steppe and steppe zones (with a 55–75% contribution of influence), and Q_min-WP in the steppe zone (with a 64% contribution of influence), through their direct relationships with H. It is also shown that H acted as an important factor (with a 47% contribution of influence) of statistically significant strengthening of the spatiotemporal coherence of Q and Q_max values between the studied periods, but only in the river basins of the Middle Volga region: during the period of the most active climate warming (1991–2020), the region’s upland rivers turned out to be more coherent in these two runoffs than the lowland ones. An ambiguous influence of H on the mutual correlation of all the examined runoff parameters over the baseline periods was revealed. The identified patterns are a consequence of the reaction of the complex altitudinal zoning of the plain’s landscapes mainly to climate changes, especially during the cold season (frequent thaws, decreasing soil freezing depth, etc.). The achieved results are intended to contribute to the understanding of the role of so-called “passive” driving forces in contemporary regional changes in river runoff. Full article