Search Results (4,680)

Diaphragm springs, as critical components in commercial vehicle clutch assemblies, directly determine the clutch’s working performance. The design of diaphragm springs, which possess a distinct symmetrical structure that underpins their mechanical behavior, centers on obtaining the large-end nonlinear load–displacement curve—a typical large deformation-induced nonlinear problem. Traditional design relies on the A-L formula, but studies show finite element analysis (FEA) yields results closer to actual measurements. This study established an FEA model of the diaphragm spring’s disc spring (excluding separation fingers) and validated its correctness by comparing it with the A-L formula. Then, using FEA on models with separation fingers, it analyzed factors influencing the large-end load–displacement characteristics. Leveraging the inherent symmetry of the diaphragm spring structure, particularly the symmetrical distribution of separation fingers, the analysis process efficiently captures uniform mechanical responses during deformation, while this symmetric arrangement also ensures balanced load distribution during clutch operation, a critical factor for stabilizing the load–displacement curve. Results indicate the separation finger root is a key factor, with larger root holes, square holes (compared to circular ones), and more separation fingers reducing stiffness to effectively adjust the curve; in contrast, the tip and length of separation fingers have little impact, making the latter unsuitable for design adjustments. Full article

Prior economic research emphasized land, labor and physical capital as the primary drivers of growth, but contemporary work highlights the pivotal role of human capital. Investments in education, health and governance are now regarded as central to sustainable development; yet important questions remain regarding their effectiveness and context-specific impact. This study investigates how human capital investment influences labor force participation and income growth within the ASEAN nine economies for the period from 2000 to 2022 which provides a rich example of contrast in economic and governance outcomes within a single geographic region. Impacted units of measurement of labor force participation and income growth are evaluated using the Bayesian Additive Regression Trees model to select the most important variables, the Bayesian Dynamic Nonlinear Multivariate panel model to estimate regional effects, and the Time-varying Seemingly Unrelated Regression Equations model to evaluate country-specific dynamics, which considers not just the influence of investments in health and education but also the context of rule, law, and governance. The findings indicate that human capital investments exhibit heterogenous effects across economic tiers and the need for strategies and future study of preconditions to improve returns particularly in low-tier economies. Accordingly, mid-tier, emerging economies exhibit the greatest benefit from human capital investments while top-tier exhibit the probable impact of the law of diminishing returns as their human capital development is already well underway. Despite the limited scope, this study still has the potential to draw constructive theoretical and practical implications. Full article

Background/Objectives: The diagnostic value of Quantitative Flow Ratio (QFR) with respect to Fractional Flow Reserve (FFR) in real-world settings is not well described, and neither are the factors influencing the bias of QFR versus FFR well understood. The learning curve associated with QFR calculation has not been thoroughly investigated. Hence, we sought to evaluate the association between the QFR and FFR, to investigate the influence of clinical parameters on both values and their difference, and to analyze the learning curve associated with QFR measurement in a real-world setting. Methods: All patients who underwent FFR and QFR measurements in 2023 at our tertiary-care center were included. The bias was characterized using a Bland–Altman plot and multivariable regression was used to uncover its potential predictors. Results: QFR calculation was successful in 73% of 595 patients with 778 vessels with FFR measurement results. Median bias of QFR was 0.011, but in 7% of the cases, the difference between the two exceeded 0.10. A good correlation was found between the two indices. Receiver operating characteristic curve analysis showed that the area under the curve of QFR for predicting FFR ≤ 0.80 was 0.912. FFR and QFR values were lower in the left anterior descending artery; acute coronary syndrome indication was associated with higher QFR values. Right coronary artery localization was associated with a greater bias of QFR, whereas female gender and aortic stenosis were associated with a lower bias of QFR. Both measurement time and bias decreased in a non-linear fashion with increasing experience. Conclusions: Clinical and angiographic factors affect the bias of QFR versus FFR. QFR has a short learning curve with growing experience leading to shorter measurement time and less bias. Full article

Land use efficiency (LUE) serves as a crucial nexus between economic development and sustainable resource management, directly influencing urban production–consumption systems. While economic development stages (EDSs) reflect a region’s environmental carrying capacity and profoundly affect LUE, the specific mechanisms governing this relationship remain unclear. In this study, we combined multi-source data to portray the spatiotemporal patterns of EDSs and LUE in 276 Chinese cities from 1995 to 2020, and we identified the nonlinear effects of EDSs on LUE. Based on the fine-scale LUE, it is confirmed that the older the age of urban land generation, the higher the LUE, laying a theoretical foundation for subsequent research. Simultaneously, the EDS continues to be upgraded, with approximately 70% of cities reaching the post-industrialization stage or higher by 2020. The results of partial dependency plots (PDPs) revealed that the EDS has a positive impact on LUE. From the perspective of different urban scales, the higher the EDSs of supercities, type I large cities, type II large cities, and type II small cities, the greater the positive impact on LUE, whereas the impact patterns at other urban scales follow an inverted U-shape. These findings carry important implications for sustainable spatial development, particularly in optimizing land resource allocation to assist the shift to more efficient production systems and responsible consumption patterns. Full article

The rock strata traversed by frozen shafts in coal mines located in western regions are predominantly composed of weakly cemented, water-rich sandstones of the Cretaceous system. Investigating the rheological damage behavior of saturated sandstone under frozen conditions is essential for evaluating the safety and stability of these frozen shafts. To explore the damage evolution and creep characteristics of Cretaceous sandstone under the coupled influence of low temperature and in situ stress, a series of triaxial creep tests were conducted at a constant temperature of −10 °C, under varying confining pressures (0, 2, 4, and 6 MPa). Simultaneously, acoustic emission (AE) energy monitoring was employed to characterize the damage behavior of saturated frozen sandstone under stepwise loading conditions. Based on the experimental findings, a fractional-order creep constitutive model incorporating damage evolution was developed to capture the time-dependent deformation behavior. The sensitivity of model parameters to temperature and confining pressure was also analyzed. The main findings are as follows: (1) Creep deformation progressively increases with higher confining pressure, and nonlinear accelerated creep is observed during the final loading stage. (2) A fractional-order nonlinear creep model accounting for the coupled effects of low temperature, stress, and damage was successfully established based on the test data. (3) Model parameters were identified using the least squares fitting method across different temperature and pressure conditions. The predicted curves closely match the experimental results, validating the accuracy and applicability of the proposed model. These findings provide a theoretical foundation for understanding deformation mechanisms and ensuring the structural integrity of frozen shafts in Cretaceous sandstone formations of western coal mines. Full article

Received Signal Strength Indicator (RSSI) prediction is valuable for network planning and optimization as it helps determine the optimal placements of wireless access points and enables better coverage planning. It is also crucial for efficient handover management between cells or access points, reducing dropped connections and improving service quality. Additionally, RSSI prediction supports indoor positioning systems, power management optimization, and cost-efficient network deployment. Path loss models have historically served as the foundation for RSSI prediction, providing a theoretical framework for estimating signal strength degradation. However, modern machine learning approaches have emerged as a revolutionary solution for network optimization, providing more versatile and data-driven methods to enhance wireless network performance. In this paper, an adaptive machine learning framework integrating environmental sensing parameters such as temperature, relative humidity, barometric pressure, and particulate matter for RSSI prediction is proposed. Performance analysis reveals that RSSI values are influenced by environmental factors through complex, non-linear interactions, thereby challenging the conventional linear assumptions of traditional path loss models. The proposed model demonstrates improved predictive accuracy over the baseline, with relative increases in variance explained of 6.02% and 2.04% compared to the baseline model excluding and including environmental parameters, respectively. Additionally, the root mean squared error is reduced to 1.40 dB. These results demonstrate that cognitive methods incorporating environmental data can substantially enhance RSSI prediction accuracy in wireless communications. Full article

Numerous machine learning models are viewed as an important means for evaluating the built environment (BE) features and travel behavior. However, most of them ignore the interaction effects of the BE and geographic locations. To strengthen their spatial interpretability, the study combines the random forest and GeoShapley method to scrutinize the nonlinear and spatial interaction effects of the BE features on ride-hailing demand using multi-source data from Nanjing, China. The results indicate that the land use mixture, the interaction between the distance to city center and geographic locations, and geographic locations are the most essential factors influencing ride-hailing demand. All BE features exhibit nonlinear effects on ride-hailing demand. Moreover, Among the BE features, distance to city center, land use mixture, and distance to metro stop demonstrate significant interaction effects with geographic locations. The findings indicate the necessity of incorporating geospatial analysis into the relationships and offer implications for implementing location-specific strategies. Full article

Avian influenza (AI), particularly the H5 subtypes, poses a significant and persistent threat globally. While the influence of environmental factors on AI seasonality is recognized, a comprehensive understanding of the hierarchical and interactive effects of multi-scale drivers in a vast and ecologically diverse country like China remains limited. We developed an interpretable machine learning framework (XGBoost with SHAP) to analyze the spatiotemporal risk of 1800 H5 AI outbreaks in mainland China from 2000 to 2023. We integrated multi-source data, including dynamic poultry density, Köppen climate classifications, Important Bird and Biodiversity Areas (IBAs), and daily meteorological variables, to identify key drivers and quantify their nonlinear and synergistic effects. The model demonstrated high predictive accuracy (5-fold cross-validation $R^2$ = 0.776). Our analysis revealed that macro-scale ecological contexts, particularly poultry density and specific Köppen climate zones (e.g., Cwa), and strong seasonality were the most dominant drivers of AI risk. We identified significant nonlinear relationships, such as a strong inverse relationship with temperature, and a critical synergistic interaction where high temperatures substantially amplified risk in areas with high poultry density. The final predictive map identified high-risk hotspots primarily concentrated in eastern and southern China. Our findings indicate that H5 AI risk is governed by a hierarchical interplay of multi-scale environmental drivers. This interpretable modeling approach provides a valuable tool for developing targeted surveillance and early warning systems to mitigate the threat of avian influenza. Full article

This study focuses on the adaptive tracking control issue for nonlinear multi-agent systems (MASs) under the influence of asymmetric input constraints and external disturbances. Firstly, an auxiliary system is proposed, which can ensure flexible prescribed performance under input saturation conditions. Meanwhile, by introducing a transformation function, the distributed errors are freed from initial constraints. Employing the backstepping method, the adaptive technique, and a neural network approximation technology, a finite-time prescribed performance adaptive tracking control algorithm is designed, enabling the tracking errors to stably converge within the prescribed performance bounds. Secondly, a composite disturbance observer is developed to estimate and mitigate the combined disturbances, which include external perturbations and approximation errors from radial basis function neural networks (RBF NNs). It not only achieves effective disturbance compensation but also further suppresses the approximation errors of RBF NNs. Finally, stability analysis using the Lyapunov function demonstrates that all closed-loop signals remain uniformly ultimately bounded (UUB), with adaptive tracking errors converging to a compact region within a finite time. Simulation results and comparative studies confirm the proposed method’s effectiveness and advantages, providing a basis for its practical use in distributed control applications. Full article

With the development of precision livestock farming, in order to achieve the goal of fine management and improve the health and welfare of dairy cows, research on dairy cow motion monitoring has become particularly important. In this study, considering the problems surrounding a large amount of model parameters, the poor accuracy of multi-target tracking, and the nonlinear motion of dairy cows in dairy farming scenes, a lightweight detection model based on improved YOLO v11n was proposed and four tracking algorithms were compared. Firstly, the Ghost module was used to replace the standard convolutions in the YOLO v11n network and a more lightweight attention mechanism called ELA was replaced, which reduced the number of model parameters by 18.59%. Then, a loss function called SDIoU was used to solve the influence of different cow target sizes. With the above improvements, the improved model achieved an increase of 2.0 percentage points and 2.3 percentage points in mAP@75 and mAP@50-95, respectively. Secondly, the performance of four tracking algorithms, including ByteTrack, BoT-SORT, OC-SORT, and BoostTrack, was systematically compared. The results show that 97.02% MOTA and 89.81% HOTA could be achieved when combined with the OC-SORT tracking algorithm. Considering the demand of equipment in lightweight models, the improved object detection model in this paper reduces the number of model parameters while offering better performance. The OC-SORT tracking algorithm enables the tracking and localization of cows through video surveillance alone, creating the necessary conditions for the continuous monitoring of cows. Full article

This study investigated how specific sustainability topics disclosed in standalone sustainability reports influence firm value and whether third-party assurance moderates this relationship. Drawing on signaling, agency, stakeholder, and legitimacy theories, we applied latent Dirichlet allocation (LDA) to extract latent topics from U.S. corporate sustainability reports. We analyzed their impact on Tobin’s Q using panel regressions and supplement our findings with discrete Bayesian networks (DBNs) and Shapley additive explanations (SHAP) to capture non-linear patterns. We identified six core topics: environmental impact, sustainable consumption, daily necessities, socio-economic impact, healthcare, and operations. The results revealed that topics of healthcare and daily necessities have immediate and sustained positive effects on firm value, while environmental and socio-economic impact topics demonstrate lagged effects, primarily two years after disclosure. The presence of assurance, however, produces mixed outcomes: it enhances credibility in some cases, but reduces firm value in others, especially when applied to environmental and socio-economic disclosures. This suggests a dual signaling effect of assurance, potentially increasing investor scrutiny when gaps in performance are highlighted. Our findings underscore the importance of topic selection, consistency in reporting, and strategic application of assurance in ESG communications to maintain stakeholder trust and market value. Full article

To address the negative sample selection bias and limited interpretability of traditional debris-flow event susceptibility models, this study proposes a framework that enhances generalization by integrating negative sample screening via a stacking ensemble model with an interpretable random forest. Using Wenchuan County, Sichuan Province, as the study area, 19 influencing factors were selected, encompassing topographic, geological, environmental, and anthropogenic variables. First, a stacking ensemble—comprising logistic regression (LR), decision tree (DT), gradient boosting decision tree (GBDT), and random forest (RF)—was employed as a preliminary classifier to identify very low-susceptibility areas as reliable negative samples, achieving a balanced 1:1 ratio of positive to negative instances. Subsequently, a stacking–random forest model (Stacking-RF) was trained for susceptibility zonation, and SHAP (Shapley additive explanations) was applied to quantify each factor’s contribution. The results show that: (1) the stacking ensemble achieved a test-set AUC (area under the receiver operating characteristic curve) of 0.9044, confirming its effectiveness in screening dependable negative samples; (2) the random forest model attained a test-set AUC of 0.9931, with very high-susceptibility zones—covering 15.86% of the study area—encompassing 92.3% of historical debris-flow events; (3) SHAP analysis identified the distance to a road and point-of-interest (POI) kernel density as the primary drivers of debris-flow susceptibility. The method quantified nonlinear impact thresholds, revealing significant susceptibility increases when road distance was less than 500 m or POI kernel density ranged between 50 and 200 units/km²; and (4) cross-regional validation in Qingchuan County demonstrated that the proposed model improved the capture rate for high/very high susceptibility areas by 48.86%, improving it from 4.55% to 53.41%, with a site density of 0.0469 events/km² in very high-susceptibility zones. Overall, this framework offers a high-precision and interpretable debris-flow risk management tool, highlights the substantial influence of anthropogenic factors such as roads and land development, and introduces a “negative-sample screening with cross-regional generalization” strategy to support land-use planning and disaster prevention in mountainous regions. Full article

With the advancement of global climate governance, public attention—an emerging form of social capital—has played an increasingly important role in the carbon emission effects of green technological innovation. Based on panel data from 267 prefecture-level cities in China from 2012 to 2022, this study employed a two-way fixed-effects model to identify the nonlinear relationship between green innovation and carbon emissions, incorporated interaction terms to examine the moderating effect of public attention, and applied a spatial Durbin model to analyze the spatial spillover effects of green innovation. The results reveal an inverted U-shaped relationship between green innovation and carbon emissions, with the inflection point corresponding to 8.58 authorized green patents per 10,000 people—a threshold that most cities have yet to reach. Public attention significantly altered the shape of the carbon effect curve by making it steeper; in cities with a higher share of secondary industry, it delayed the inflection point, whereas in cities dominated by the tertiary industry, the turning point appeared earlier. In addition, green innovation had significant spatial spillover effects, and its impact on carbon emissions in neighboring cities displayed a U-shaped pattern. This paper proposes an analytical framework of “socially empowered innovation” to reveal the nonlinear moderating mechanism through which public attention influences the carbon effects of green innovation. The findings offer important policy implications: efforts should focus on long-term innovation, promote regional coordination, guide rational public participation, and avoid short-sighted and unsustainable mitigation practices. Full article

This study develops an integrated analytical framework to examine the interplay of urbanization, energy consumption, and CO₂ emissions at the city level in China. Utilizing the Entropy-TOPSIS method for multidimensional urbanization measurement, the GM_Combo model for spatial spillover analysis, and Random Forest for identifying emission drivers, we analyze data from 282 Chinese cities from 2006 to 2020. Results reveal significant hierarchical differences in urbanization, with K-means clustering identifying high, medium, and low urbanization groups reflecting diverse regional development pathways. Energy consumption increasingly drives emissions, while urbanization’s influence declines, indicating partial decoupling. Strong spatial spillovers highlight the need for regional coordination. Ecological assets provide moderate mitigation effects. These findings contribute to the literature by introducing a multidimensional urbanization index, uncovering nonlinear energy–emissions dynamics, and quantifying intercity spillovers, offering empirical support for tailored low-carbon policies and sustainable urban governance. Full article

Flood risk assessment is an effective tool for disaster prevention and mitigation. As land use is a key factor influencing flood disasters, studying the impact of different land use patterns on flood risk is crucial. This study evaluates flood risk in the Chang-Zhu-Tan (CZT) urban agglomeration by selecting 17 socioeconomic and natural environmental factors within a risk assessment framework encompassing hazard, exposure, vulnerability, and resilience. Additionally, the Patch-Generating Land Use Simulation (PLUS) and multilayer perceptron (MLP)/Bayesian network (BN) models were coupled to predict flood risks under three future land use scenarios: natural development, urban construction, and ecological protection. This integrated modeling framework combines MLP’s high-precision nonlinear fitting with BN’s probabilistic inference, effectively mitigating prediction uncertainty in traditional single-model approaches while preserving predictive accuracy and enhancing causal interpretability. The results indicate that high-risk flood zones are predominantly concentrated along the Xiang River, while medium-high- and medium-risk areas are mainly distributed on the periphery of high-risk zones, exhibiting a gradient decline. Low-risk areas are scattered in mountainous regions far from socioeconomic activities. Simulating future land use using the PLUS model with a Kappa coefficient of 0.78 and an overall accuracy of 0.87. Under all future scenarios, cropland decreases while construction land increases. Forestland decreases in all scenarios except for ecological protection, where it expands. In future risk predictions, the MLP model achieved a high accuracy of 97.83%, while the BN model reached 87.14%. Both models consistently indicated that the flood risk was minimized under the ecological protection scenario and maximized under the urban construction scenario. Therefore, adopting ecological protection measures can effectively mitigate flood risks, offering valuable guidance for future disaster prevention and mitigation strategies. Full article