Search Results (220)

To improve the stability and safety of electric vehicles during medium-to-high-speed cornering, this paper investigates torque differential control for dual rear-wheel hub motor drive systems, extending beyond traditional speed control based on the Ackermann steering model. A nonlinear three-degree-of-freedom vehicle dynamics model incorporating the Dugoff tire model was established. By introducing the maximum correntropy criterion, an unscented Kalman filter was developed to estimate longitudinal velocity, sideslip angle at the center of mass, and yaw rate. Building upon the speed differential control achieved through Ackermann steering model-based rear-wheel speed calculation, improvements were made to the conventional exponential reaching law, while a novel switching function was proposed to formulate a new sliding mode controller for computing an additional yaw moment to realize torque differential control. Finally, simulations conducted on the Carsim/Simulink platform demonstrated that the maximum correntropy criterion unscented Kalman filter effectively improves estimation accuracy, achieving at least a 22.00% reduction in RMSE metrics compared to conventional unscented Kalman filter. With torque control exhibiting higher vehicle stability than speed control, the RMSE values of yaw rate and sideslip angle at the center of mass are reduced by at least 20.00% and 4.55%, respectively, enabling stable operation during medium-to-high-speed cornering conditions. Full article

This study examines the influence of ethical leadership on corporate sustainability and financial performance, highlighting the moderating effect of firms’ commitment to the United Nations Sustainable Development Goals (SDGs). Utilizing panel data from 420 automotive companies spanning 2015 to 2024, the analysis applies the System Generalized Method of Moments (GMM) to control for endogeneity and unobserved heterogeneity. All data were gathered from the Refinitiv Eikon Platform (LSEG) and annual reports. Panel GMM regression is used to estimate the relationship to deal with the endogeneity problem. The results reveal that ethical leadership significantly improves corporate sustainability performance—measured by ESG scores from Refinitiv Eikon and Bloomberg—as well as financial indicators like Return on Assets (ROA) and Tobin’s Q. Additionally, firms that demonstrate breadth (the range of SDG-related themes addressed), concentration (the distribution of non-financial disclosures across SDGs), and depth (the overall volume of SDG-related information) in their SDG disclosures gain greater advantages from ethical leadership, resulting in enhanced ESG performance and higher market valuation. This study offers valuable insights for corporate leaders, policymakers, and investors on how integrating ethical leadership with SDG alignment can drive sustainable and financial growth. Full article

The intentional yaw offset of wind turbines has shown potential to redirect wakes, enhancing overall plant power production, but it may increase fatigue loading on turbine components. This study analyzed fatigue loads on the NREL 5 MW reference wind turbine under varying yaw offsets using blade element momentum theory, dynamic blade element momentum, and the converging Lagrange filaments vortex method, all implemented in OpenFAST. Simulations employed yaw angles from −40° to 40°, with turbulent inflow generated by TurbSim, an OpenFAST tool for realistic wind conditions. Fatigue loads were calculated according to IEC 61400-1 design load case 1.2 standards, using thirty simulations per yaw angle across five wind speed bins. Damage equivalent load was evaluated via rainflow counting, Miner’s rule, and Goodman correction. Results showed that the free vortex method, by modeling unsteady aerodynamic forces, yielded distinct differences in damage equivalent load compared to the blade element method in yawed conditions. The free vortex method predicted lower damage equivalent load for the low-speed shaft bending moment at negative yaw offsets, attributed to its improved handling of unsteady effects that reduce load variations. Conversely, for yaw offsets above 20°, the free vortex method indicated higher damage equivalent for low-speed shaft torque, reflecting its accurate capture of dynamic inflow and unsteady loading. These findings highlight the critical role of unsteady aerodynamics in fatigue load predictions and demonstrate the free vortex method’s value within OpenFAST for realistic damage equivalent load estimates in yawed turbines. The results emphasize the need to incorporate unsteady aerodynamic models like the free vortex method to accurately assess yaw offset impacts on wind turbine component fatigue. Full article

The stochastic correlation for Brownian motions is the integrand in the formula of their quadratic covariation. The estimation of this stochastic process becomes available from the temporally localized correlation of latent price driving Brownian motions in stochastic volatility models for asset prices. By analyzing this process for Apple and Microsoft stock prices traded minute-wise, we give statistical evidence for the roughness of its paths. Moment scaling indicates fractal behavior, and both fractal dimensions (approx. 1.95) and Hurst exponent estimates (around 0.05) point to rough paths. We model this rough stochastic correlation by a suitably transformed fractional Ornstein–Uhlenbeck process and simulate artificial stock prices, which allows computing tail dependence and the Herding Behavior Index (HIX) as functions in time. The computed HIX is hardly variable in time (e.g., standard deviation of 0.003–0.006); on the contrary, tail dependence fluctuates more heavily (e.g., standard deviation approx. 0.04). This results in a higher correlation risk, i.e., more frequent sudden coincident appearance of extreme prices than a steady HIX value indicates. Full article

The readiness of businesses to address global climate change is pivotal for achieving sustainable development. However, the dynamics of business development remain underexplored, thereby limiting the depth and scope of research in this area. To this aim, the study examines the relationship between CO₂ emissions and new business density (NBD) in the top 14 countries with the highest NBD (Hong Kong, Cyprus, New Zealand, Estonia, Malta, United Kingdom, Australia, Botswana, Iceland, Latvia, Mauritius, Norway, Sweden, and Georgia) from 2006 to 2020, within the framework of Schumpeter’s theory and the environmental Kuznets curve (EKC) hypothesis, incorporating control variables such as renewable energy consumption (REC) and population size. To estimate the relationships between variables, we employ the novel Method of Moments Quantile Regression (MMQR) approach. The findings suggest that higher NBD is associated with increased CO₂ emissions. The results support the EKC hypothesis, positing an inverted U-shaped relationship between economic growth and environmental degradation, and highlight the mitigating effects of REC and population growth on CO₂ emissions. These findings emphasize the need for countries to align labor legislation with sustainable development objectives and to promote strategies grounded in environmental principles, green economic practices, and eco-friendly technologies. Full article

The Sylhet Division of Bangladesh, highly susceptible to monsoon flooding, requires effective flood risk management to reduce socio-economic losses. Flood frequency analysis is an essential aspect of flood risk management and plays a crucial role in designing hydraulic structures. This study applies regional flood frequency analysis (RFFA) using L-moments to identify homogeneous hydrological regions and estimate extreme flood quantiles. Records from 26 streamflow gauging stations were used, including streamflow data along with corresponding physiographic and climatic characteristic data, obtained from GIS analysis and ERA5 respectively. Most stations showed no significant monotonic trends, temporal correlations, or spatial dependence, supporting the assumptions of stationarity and independence necessary for reliable frequency analysis, which allowed the use of cluster analysis, discordancy measures, heterogeneity tests for regionalization, and goodness-of-fit tests to evaluate candidate distributions. The Generalized Logistic (GLO) distribution performed best, offering robust quantile estimates with narrow confidence intervals. Multiple Non-Linear Regression models, based on catchment area, elevation, and other parameters, reasonably predicted ungauged basin peak discharges (R² = 0.61–0.87; RMSE = 438–2726 m³/s; MAPE = 41–74%) at different return periods, although uncertainty was higher for extreme events. Four homogeneous regions were identified, showing significant differences in hydrological behavior, with two regions yielding stable estimates and two exhibiting greater extreme variability. Full article

Background/Objectives: Stroke survivors often develop asymmetric gait patterns that may lead to abnormal knee joint loading and potentially increased risk of osteoarthritis. This study aimed to investigate differences in knee joint loading between paretic and non-paretic limbs during walking in individuals post-stroke. Methods: Twenty-one chronic stroke survivors underwent three-dimensional gait analysis. A modified musculoskeletal model with a specialized knee mechanism was used to estimate medial and lateral tibiofemoral contact forces during the stance phase. Statistical parametric mapping was used to identify significant differences in joint kinematics, kinetics, and contact forces between limbs. Stepwise regression analyses examined relationships between knee moments and compartmental contact forces. Results: Significant differences in knee loading were observed between limbs, with the non-paretic limb experiencing higher medial compartment forces during early stance (6.7–15.1%, p = 0.001; 21.9–30.7%, p = 0.001) and late stance (72.3–93.7%, p < 0.001), and higher lateral compartment forces were recorded during pre-swing (86.2–99.0%, p < 0.001). In the non-paretic limb, knee extensor moment was the primary predictor of first peak medial contact force (R² = 0.573), while knee abductor moment was the primary predictor in the paretic limb (R² = 0.559). Conclusions: Musculoskeletal modeling revealed distinct asymmetries in knee joint loading between paretic and non-paretic limbs post-stroke, with the non-paretic limb experiencing consistently higher loads, particularly during late stance. These findings suggest that rehabilitation strategies should address not only paretic limb function but also potentially harmful compensatory mechanisms in the non-paretic limb to prevent long-term joint degeneration. Full article

This study developed diameter distribution models using the Weibull function for Korean red pine (Pinus densiflora), Korean white pine (P. koraiensis), and Japanese larch (Larix kaempferi). The study data were collected from 49 Korean red pine stands, 54 Korean white pine stands, and 49 Japanese larch stands located in national forests in Gangwon and North Gyeongsang Provinces, South Korea. To identify the optimal method for modeling the diameter distribution of these three species, parameter recovery methods and parameter prediction methods were analyzed. To identify the optimal parameter recovery method for presenting the diameter distribution of these three species, ten parameter recovery methods were compared using moment-based, percentile-based, and hybrid approaches. For parameter prediction methods, major stand characteristics were used as independent variables to develop the models for the parameters a, b, and c of the Weibull function. For estimating the Weibull parameters, two methods—the estimated parameter recovery method and the parameter prediction method—were compared and analyzed. The optimal parameter recovery method was the one using the minimum DBH, the mean DBH, and the DBH variance. The coefficient of determination (R²) for the models predicting the minimum DBH, the mean DBH, and the DBH variance ranged from 0.7186 to 0.9747, and the R² for the models directly predicting parameters ranged from 0.7032 to 0.9374, indicating high explanatory power and unbiased results. When comparing the two methods, the parameter prediction method showed higher accuracy and lower bias. In addition, paired t-tests were conducted to assess differences from the observed Weibull parameters. The results showed a significant difference for the estimated parameter recovery method, whereas no significant difference was found for the parameter prediction method, further supporting its reliability. Full article

Robust robot manipulation hinges on effective state estimation. The VRT 6 robot leverages an inertia measurement unit with triaxial gyroscopes, magnetometers, and accelerometers, as well as a position sensor, but these sensors are plagued by noise that demands rigorous filtering. To tackle this, an adaptively scaled unscented Kalman filter was employed. The filter’s scaling parameter was meticulously optimized using density- and moment-based techniques, as both system properties and estimated state impact this crucial parameter. A Maximum Likelihood Estimation (ML) substantiates the enhanced quality of the estimated velocity and acceleration, on par with the position estimate. Minimizing measurement prediction error (MMPE) also shows better results with less RMSE when compared to fixed-kappa values, and the quality of position estimates is higher with the increase in the domain of the scaling parameter. By carefully selecting the adaptive scaling parameters’ range to minimize sigma point weights and ensure the positive definiteness of the covariance matrix, this enhanced UKF method achieved markedly superior state estimates compared to standard UKF implementations. Full article

Background: The adoption of information and communication technology (ICT) is transforming supply chains in the European Union, affecting logistical performance, economic integration and sustainability. This study examines the extent to which ICT adoption affects logistics efficiency in the 27 EU Member States. Methods: Using panel data from the World Bank and UNCTAD (2008–2018), the analysis applies the Arellano–Bond Generalized Method of Moments estimator to assess the impact of ICT indicators, broadband penetration, mobile connectivity and digital skills on logistics performance. GDP per capita and trade openness are included as control variables. Results: The results show that a 1% increase in ICT usage correlates with a 0.12-point increase in the Logistics Performance Index. Higher ICT usage leads to more efficient supply chains, lower costs and higher customer satisfaction. However, there are still differences in digitalization: the ICT usage rate of SMEs is 28% in Bulgaria and 27% in Romania, compared to the EU average of 59%. Conclusions: Bridging the digital divide requires targeted investments in ICT infrastructure, harmonized regulatory frameworks and stronger public–private cooperation to foster regional economic cohesion. This study provides policy recommendations to drive digital transformation, strengthen the resilience of logistics and improve the sustainability of supply chains in the EU. Full article

There is now a vast body of literature on ARMA and VARMA models with time-dependent or time-varying coefficients. A large part of it is based on local stationary processes using time rescaling and assumptions of regularity with respect to time. A recent paper has presented an alternative asymptotic theory for the parameter estimators based on several distinct assumptions that seem difficult to verify at first look, especially for time-dependent VARMA or tdVARMA models. The purpose of the present paper is to detail several examples that illustrate the verification of the assumptions in that theory. These assumptions bear on the moments of the errors, the existence of the information matrix, but also how the coefficients of the pure moving average representation of the derivatives of the residuals (with respect to the parameters and evaluated at their true value) behave. We will do that analytically for two bivariate first-order models, an autoregressive model, and a moving average model, before sketching a generalization to higher-order models. We also show simulation results for these two models illustrating the analytical results. As a consequence, not only the assumptions can be checked but the simulations show how well the small sample behavior of the estimators agrees with the theory. Full article

Security issues surrounding deep learning models weaken their application effectiveness in various fields. Studying attacks against deep learning models contributes to evaluating their security and improving it in a targeted manner. Among the methods used for this purpose, adversarial example generation methods for deep learning models have become a hot topic in academic research. To overcome problems such as extensive network access, high attack costs, and limited universality in generating adversarial examples, this paper proposes a generic algorithm for adversarial example generation based on improved DE-C&W. The algorithm employs an improved differential evolution (DE) algorithm to conduct a global search of the original examples, searching for vulnerable sensitive points susceptible to being attacked. Then, random perturbations are added to these sensitive points to obtain adversarial examples, which are used as the initial input of C&W attack. The loss functions of the C&W attack algorithm are constructed based on these initial input examples, and the loss function is further optimized using the Adaptive Moment Estimation (Adam) algorithm to obtain the optimal perturbation vector. The experimental results demonstrate that the algorithm not only ensures that the generated adversarial examples achieve a higher success rate of attacks, but also exhibits better transferability while reducing the average number of queries and lowering attack costs. Full article

In the seismic design of steel moment-resisting frames (MRFs), the panel zone region can significantly affect overall ductility and energy-dissipation capacity. This study investigates the influence of panel zone flexibility on the seismic response of steel MRFs by comparing two modeling approaches: one with a detailed panel zone representation and the other considering fixed beam-column connections. A total of 30 2D steel MRFs (15 frames incorporating panel zone modeling and 15 frames without panel zone modeling) are subjected to nonlinear time–history analyses using four suites of ground motions compatible with Eurocode 8 (EC8) soil types (A, B, C, and D). Structural performance is evaluated at three distinct performance levels, namely, damage limitation (DL), life safety (LS), and collapse prevention (CP), to capture a wide range of potential damage scenarios. Based on these analyses, the study provides information about the seismic response of these frames. Also, lower-bound, upper-bound, and mean values of behavior factor (q) for each soil type and performance level are displayed, offering insight into how panel zone flexibility can alter a frame’s inelastic response under seismic loading. The results indicate that neglecting panel zone action leads to an artificial increase in frame stiffness, resulting in higher base shear estimates and an overestimation of the seismic behavior factor. This unrealistically increased behavior factor can compromise the accuracy of the seismic design, even though it appears conservative. In contrast, including panel zone flexibility provides a more realistic depiction of how forces and deformations develop across the structure. Consequently, proper modeling of the panel zone supports both safety and cost-effectiveness under strong earthquake events. Full article

This paper presents a novel multivariate mean-reverting jump-diffusion model that incorporates correlated jumps and seasonal effects to capture the complex dynamics of commodity prices. The model also accounts for the interplay between price volatility and convenience yield, offering a comprehensive framework for commodity futures pricing. By leveraging the Feynman–Kac theorem, we derive a partial integro-differential equation for the conditional moment generating function of the log price, enabling an analytical solution for pricing commodity futures. This solution is validated against Monte Carlo simulations, demonstrating high accuracy and computational efficiency. The model is empirically applied to historical futures prices of natural rubber from the Thailand Futures Exchange. Key parameters—including commodity price dynamics, convenience yields, and seasonal factors—are estimated, revealing the critical role of jumps and seasonality in influencing market behavior. Notably, our findings show that convenience yields are negative, reflecting higher inventory costs, and tend to increase with rising spot prices. These results provide actionable insights for traders, risk managers, and policymakers in commodity markets, emphasizing the importance of correlated jumps and seasonal patterns in pricing and risk assessment. Full article

This research explores the relationship between credit risk and bank value within the framework of horizontal mergers and acquisitions (M&A), employing a quantile regression approach to analyze how horizontal M&A activities moderate this relationship across 110 operational Bank Holding Companies (BHCs) over 23 years. This paper stands out from previous studies by extending the scope beyond linear approaches and using the Quantiles via Moments estimator to address potential endogeneity concerns. The results demonstrate a significant negative link between credit risk and bank value, which decreases in magnitude as moving higher in the value distribution. Conversely, there is a consistent positive connection between M&A activities and bank value that is stable across different quantiles of value. Mergers and acquisitions worsen the negative impact of credit risk on bank value, affecting banks with both low and high values similarly. The findings provide useful information for investors, practitioners, and policymakers in the banking industry. Investors may use credit risk and value proposition assessments to make well-informed investment decisions, or to construct well-diversified portfolios, and identify appropriate institutions for mergers and acquisitions to enhance value. It is recommended that practitioners prioritize efficient credit risk management, especially before engaging in M&A activities and aligning them with the bank’s value proposition. Policymakers should develop guidelines to regulate M&A transactions, using established dynamic credit risk standards that correspond to banks’ value propositions, to promote financial stability and drive industry expansion. Full article