Search Results (2,158)

A new organic salt of thiabendazole with p-toluenesulfonic acid was successfully synthesized by mechanochemistry. Notably, the same crystalline form and morphology were obtained both through neat grinding and liquid-assisted grinding using 4-methyltetrahydropyran, a sustainable solvent not yet commonly employed in mechanochemical processes. The resulting salt crystallizes as a hydrate with impressive physical stability for up to 18 months under four storage conditions, including 40 °C. Comprehensive solid-state characterization (PXRD, DSC, TGA, HSM, SEM) confirmed the phase identity, purity, and thermal behavior of the material, while FTIR spectroscopy provided insight into the intermolecular interactions driving salt formation and stabilizing the crystalline water. In comparison to pure thiabendazole, the hydrate salt exhibited a remarkable ~70-fold increase in solubility and significantly improved intrinsic dissolution rate over the entire study period. Importantly, the in vivo evaluation in the Heligmosomoides polygyrus mouse model of the salt and the pure drug revealed similar moderate reductions in worm burden, indicating that salt formation does not compromise anthelmintic efficacy. Full article

In this study, a laboratory-scale slag–steel reaction experiment was conducted to systematically evaluate the influence of the initial MgO content (3–7 wt.%) in LF refining slag on the cleanliness of GCr15 bearing steel. The assessment was performed from multiple perspectives by comparing the total oxygen content (T[O]) in molten steel, the inclusion area fraction, and the inclusion number density after 30 min of slag–steel interaction. To further elucidate the thermodynamic driving forces and kinetic mechanisms governing inclusion capture by slag, a predictive slag adsorption model was developed using an in-house computational code coupled with FactSage 8.1. Under conditions of slag basicity R (CaO/SiO₂) ranging from 4.0 to 8.0, MgO content varying from 0 to 7 wt.%, and a constant Al₂O₃ content of 32 wt.%, the chemical driving force ΔC (the mass-fraction difference between slag components and inclusions), the slag viscosity η, and the combined parameter ΔC/η were calculated at 1600 °C for three representative inclusion types: Al₂O₃, MgO·Al₂O₃, and MgO. In addition, the model was employed to quantitatively characterize the adsorption capacity of slag toward Mg–Al binary inclusions under varying MgO levels. Both experimental observations and model calculations demonstrate that the slag–steel reaction markedly enhances inclusion removal, as evidenced by pronounced decreases in T[O], inclusion number density, and inclusion area fraction after reaction. With increasing MgO content in slag, T[O] and inclusion-related indices exhibit a consistent trend of first decreasing and then increasing, reaching minimum values at an MgO level of 5 wt.%. Further analysis reveals a positive correlation between the apparent inclusion-removal rate constant ko and ΔC/η corresponding to MgO·Al₂O₃ inclusions. Moreover, the slag’s adsorption capacity toward Mg–Al binary inclusions decreases overall as the MgO fraction in inclusions increases. Notably, when the MgO content in inclusions exceeds 29 wt.%, the adsorption capacity undergoes an abrupt drop, indicating a pronounced cliff-like attenuation behavior. Full article

The article presents issues related to fossil fuel energy consumption and CO₂ emissions from motor vehicles. It identifies the main areas of research in this field in the context of motor vehicles, namely driver behavior, fuel consumption, and OBD systems. The research sample consisted of experimental data containing records of a series of test drives conducted with a passenger vehicle equipped with a gasoline-powered internal combustion engine, collected via an OBD diagnostic interface. Three subsets related to engine operation and energy demand patterns were distinguished for the study: during vehicle start-up and low-speed driving (vehicle start-up mode), during urban driving, and during extra-urban driving. Multiple regression models were constructed for the analyzed subsets to predict CO₂ emissions based on engine energy output parameters (power, load) and vehicle kinematic parameters. The developed models were subjected to detailed evaluation and mutual comparison, taking into account their predictive performance and the interpretability of the results. The analysis made it possible to identify the variables with the most substantial impact on CO₂ emissions and fuel energy consumption. The models allow individual drivers to monitor and optimize vehicle energy efficiency in real-time. The extra-urban driving model achieved the highest predictive accuracy, with a mean absolute error (MAE) of 19.62 g/km, which makes it suitable for real-time emission monitoring during highway driving. Full article

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments. Full article

Reducing dependency on chemical pesticides is a core ambition of the European Green Deal, yet adoption of low-input practices remains uneven. This systematic review synthesizes evidence on the behavioural determinants of European farmers’ knowledge, attitudes, and practices (KAP) regarding sustainable pesticide use and evaluates the role of digital tools in facilitating Integrated Pest Management (IPM). Following PRISMA 2020 guidelines, we analysed 65 peer-reviewed articles published between 2011 and 2025, which were identified through Scopus and Web of Science. The synthesis reveals that while pro-environmental attitudes drive the intention to change, actual behaviour is frequently inhibited by loss aversion, ‘clean field’ social norms, and perceived economic risks. Digital tools—specifically Decision Support Systems (DSSs) and precision technologies—demonstrate technical potential to reduce pesticide loads but are constrained by the same behavioural barriers: a lack of trust in models, perceived complexity, and costs. Consequently, we propose a Psycho-Digital Integration Framework which posits that digital innovation acts as a catalyst only when embedded in systemic enablers—specifically green insurance schemes and independent advisory networks. These mechanisms are critical to redistribute perceived agricultural risk and bridge the gap between technical potential and behavioral adoption. Full article

The increasing proliferation of new energy vehicles and autonomous vehicles has led to the formation of mixed traffic flows characterized by diverse driving behaviors, posing new challenges for intersection signal control. To address this issue, this study proposes a multi-class customer feedback queuing network (MCFFQN) model that incorporates state-dependent road capacity and congestion propagation mechanisms to accurately capture the stochastic and dynamic nature of mixed traffic flows. An evaluation framework for intersection performance is established based on key indicators such as vehicle delay, the energy consumption of new energy vehicles, and the fuel consumption and emissions of conventional vehicles. A recursive solution algorithm is developed and validated through simulations under various traffic demand scenarios. Building on this model, a signal timing optimization model aimed at minimizing total costs—including delay and environmental impacts—is formulated and solved using the Mesh Adaptive Direct Search (MADS) algorithm. A case study demonstrates that the optimized signal timing scheme significantly enhances intersection performance, reducing vehicle delay, energy consumption, fuel consumption, and emissions by over 20%. The proposed methodology provides a theoretical foundation for sustainable traffic management under mixed traffic conditions. Full article

Shale oil is primarily hosted within nanopores, where its flow behavior exhibits significant deviations from classical Darcy flow. The combined influences of nanoscale confinement and interfacial interactions represent key scientific challenges that hinder efficient shale oil recovery. The results show that under 25 °C and 1 MPa, the displacement distances of shale oil within 12 s in 100, 200, and 300 nm channels were 2.88, 5.67, and 11.01 mm, respectively. As pore size decreases, flow capacity drops sharply, and the displacement–time relationship evolves from quasi-linear to strongly nonlinear, indicating pronounced nanoscale non-Darcy behavior. By incorporating an equivalent resistance coefficient into the plate-channel flow model, the experimental data were accurately fitted, enabling quantitative evaluation of the additional flow resistance induced by nanoconfinement and interfacial adsorption. The equivalent resistance coefficient increases markedly with decreasing pore size but decreases progressively with increasing temperature and driving pressure. Increasing temperature and pressure partially mitigates nanoconfinement effects. In 200 nm channels, the equivalent resistance coefficient decreases from 1.87 to 1.20 as temperature rises from 25 to 80 °C, while in 100 nm channels it decreases from 2.43 to 1.65 as driving pressure increases from 1 to 6 MPa. Nevertheless, even under high-temperature and high-pressure conditions, shale-oil flow does not fully recover to ideal Darcy behavior. This work establishes a nanofluidic-based prediction and evaluation framework for shale oil flow, offering theoretical guidance and experimental reference for unconventional reservoir development and the optimization of enhanced oil recovery strategies. Full article

As a novel traffic security facility to improve the environment of tunnels, the influence of tunnel sidewall decoration on drivers has been highly controversial. To analyze the impact of the multi-factor coupling of sidewall decoration effects on driving safety, eight combination schemes with different pattern elements and pattern spacings were designed to create a driving simulation environment. Twenty-seven drivers were recruited to obtain fine-grained driving behavior indicators via driving simulation experiments. The velocity following ratio, steering wheel angle, maximum deceleration, and accelerator power were selected to construct an index system. The visual information load of drivers was quantified by the landscape color quantified theory. Based on the analysis of the influence of the singular factor of the pattern element or pattern spacing on driving behavior, a coupling coordination degree model is introduced to quantify the relationship between the complexity of the pattern elements, the pattern spacing, and the coupling coordination degree, and a reasonable combination of their complexities is selected. The results show that the element complexity and pattern spacing of tunnel sidewall decoration have significant effects on driving behavior. Among the schemes considered in this study, the coupling effect of an element complexity of 562.1 and a pattern spacing of 5.5 m was found to be the optimal combination. The coupling coordination degree should be more than 0.8 as the threshold, and the model analysis results indicated that when the pattern spacing was fixed at about 10 m, the ideal element complexity was between 135.6–564.7. This study offers both theoretical and technical support for enhancing traffic safety through tunnel sidewall decoration. By defining optimal thresholds for information density and pattern spacing, it lays a solid foundation for the development of a standardized guideline on decoration content. Full article

Energy-efficient driving is essential for reducing the environmental impacts of road transport, especially for electric passenger vehicles. This research aims to build a data-driven behavioral analysis and energy-consumption evaluation model. The model relies on sensor data from the vehicle’s on-board communication network, primarily the CAN (Controller Area Network) bus. We analyze patterns of key powertrain and battery parameters—such as current, voltage, state of charge (SoC), and power—in relation to driver inputs, such as the accelerator pedal position. In the first stage, we review the literature with a focus on machine learning and clustering methods used in behavioral and energy analysis. We also examine the role of on-board telemetry systems. Next, we develop a controlled measurement architecture. It defines reference consumption maps from dynamometer data across operating points and environmental variables, including SoC, temperature, and load. The longer-term goal is a multidimensional behavioral map and profiling framework that can predict energy efficiency from real-time driver inputs. This work lays the foundation for a future system with adaptive, feedback-based driver support. Such a system can promote intelligent, sustainable, and behavior-oriented mobility solutions. Full article

Spatial equity of healthcare services is a critical concern in social equity and spatial justice research. Despite the availability of various methods to measure this equity, few studies have integrated the supply–demand coupling perspective with the analysis of impacts of residents’ travel behaviors’ on equity. This study develops and applies a Travel Behavior-based Coupling Coordination Degree (TB-CCD) method to assess the spatial equity of healthcare services in the Xi’an region. The results show the following: (1) Traditional single-mode models may fail to accurately assess this equity, whereas the TB-CCD model provides a more realistic evaluation. (2) Public transportation and driving provide a more equitable distribution of healthcare services compared to walking and cycling modes. The spatial equity of healthcare services exhibits a distinct core–periphery pattern, where accessibility and equity levels are significantly higher in city centers than in suburban areas. (3) The distribution of inequity ‘deserts’ and ‘oases’ in healthcare services is found to be travel-mode dependent, with the walking and public transportation modes exhibiting the highest incidence of these classifications. These findings provide valuable insights for urban planners and policymakers to formulate strategies and spatial plans aimed at enhancing equity in healthcare services. Full article

The rising levels of Co(II) in aquatic environments present considerable risks, thereby necessitating the development of effective remediation strategies. This study introduces an innovative pre-hydration method for synthesizing carbonated calcined clay dolomite (CCCD) to efficiently remove Co(II) from contaminated water. This pre-hydration treatment successfully reduced the complete carbonation temperature of the material from 500 °C to 400 °C, significantly enhancing energy efficiency. The Co(II) removal performance was systematically investigated by varying key parameters such as contact time, initial Co(II) concentration, pH, and solid/liquid ratio. Optimal removal was achieved at 318 K with pH of 4 and a solid/liquid ratio of 0.5 g·L⁻¹. Continuous flow column experiments confirmed the excellent long-term stability of CCCD, maintaining a consistent Co(II) removal efficiency of 99.0% and a stable effluent pH of 8.5 over one month. Isotherm and kinetic models were used to empirically describe concentration-dependent and time-dependent uptake behavior. The equilibrium data were best described by the Langmuir model, while kinetics followed a pseudo-second-order model. An apparent maximum removal capacity of 621.1 mg g⁻¹ was obtained from Langmuir fitting of equilibrium uptake data. Mechanistic insights from Visual MINTEQ calculations and solid phase characterizations (XRD, XPS, and TEM) indicate that Co(II) removal is dominated by mineral water interface precipitation. The gradual hydration of periclase (MgO) forms Mg(OH)₂, which creates localized alkaline microenvironments at particle surfaces and drives Co(OH)₂ formation. Carbonate availability further favors CoCO₃ formation and retention on CCCD. Importantly, this localized precipitation pathway maintains a stable, mildly alkaline effluent pH (around 8.5), reducing downstream pH adjustment demand and improving operational compatibility. Overall, CCCD combines high Co(II) immobilization efficiency, strong long-term stability, and an energy-efficient preparation route, supporting its potential for scalable remediation of Co(II) contaminated water. Full article

Radial piston motors are expected to expand their applications in hydraulic drive systems due to their high torque density and mechanical robustness. However, its volumetric efficiency can be significantly affected by the multi-stroke operating characteristics and leakage occurring in the micro-clearances of the valve plate. In this study, a detailed modeling procedure for a multi-stroke radial piston motor is proposed using the 1D system simulation software Amesim. In particular, the dynamic interaction between the ports and pistons inside the motor is formulated using mathematical function-based expressions, enabling a more precise representation of the driving behavior and torque generation process. Furthermore, to characterize the leakage flow occurring in the micro-clearance between the fluid distributor and cylinder housing, the commercial CFD software Simerics MP+ was employed to analyze the three-dimensional flow characteristics within the leakage gap. Based on these CFD results, a leakage-path function was constructed and implemented in the Amesim model. As a result, the developed model exhibited strong agreement with reference data from an actual motor in terms of overall operating performance, including volumetric and mechanical efficiencies while consistently reproducing the leakage behavior observed in the CFD analysis. The simulation approach presented in this study demonstrates the capability to reliably capture complex fluid–mechanical interactions at the system level, and it can serve as an effective tool for performance prediction and optimal design of hydraulic motors. Full article

To ensure the safety of vehicles and occupants under failures or functional limitations of ego vehicles, a minimum risk maneuver (MRM) has been proposed as a key automated driving system (ADS) function. However, executing an MRM may pose certain potential risks when sensor failures occur. This study proposed an MRM strategy designed to enhance highway-driving safety during MRM execution under multiple sensor-failure conditions. A hazard and operability study analysis, based on an ADS behavior model, is conducted to systematically identify hazards, determine potential hazardous events, and categorize the associated safety risks arising from sensor failures. Within the proposed strategy, virtual objects are generated to account for potential hazards and support risk assessments. Adaptive MRM behavior is determined in real time by analyzing surrounding objects and evaluating time-to-collision and time headway. The strategy is verified by using a MATLAB–CARLA co-simulation environment across three representative highway scenarios with combined sensor failures. The result demonstrates that the proposed MRM strategy can mitigate collision risk in hazardous scenarios while effectively leveraging the remaining functional sensors to guide the ego vehicle toward an appropriate minimum risk condition during MRM execution. Full article

An equivalent circuit model (ECM) is a highly practical tool for simulating Li-ion battery behavior. There are many relevant studies which compare different ECM variants or suggest algorithms to extract model parameters from the experimental data. However, little attention has been given to the battery tests used for identification of the ECM parameters. Therefore, here the influence of experimental test pulse characteristics on the parameterized ECM accuracy was systematically studied. The test pulse duration was varied in a wide range from 9 s to about 2.5 min. The portion of the relaxation phase data used by the parameter optimization algorithm was also varied in an even wider range. Total 168 ECM parameter sets were obtained. Each parameter set was validated using nine diverse current profiles representing different battery operation conditions, including one based on Urban Dynamometer Driving Schedule (UDDS). The validation results prove that the impact of the test pulse choice on the parameterized ECM accuracy is great to the point that it can overshadow the use of a higher-order Thevenin model. By choosing the optimal parameter set, the simulated voltage root mean square error (RMSE) was reduced to as low as 3.0 mV and 1.2 mV for first- and second-order ECM, respectively, while the second-order model based on arbitrary chosen test pulse on average yields RMSE value above 5 mV. Full article

Miniature commercial trucks constitute a critical component of urban freight systems but face elevated crash risk due to distinctive driving patterns, frequent operation, and variable loads. This study quantifies how long-term and short-term driving behaviors jointly shape crash economic loss levels and identifies factors most strongly associated with severe claims. A driver-level dataset linking multi-source running behavior indicators, vehicle attributes, and insurance claims is constructed, and an enhanced Wasserstein generative adversarial network with Euclidean distance is employed to synthesize minority crash samples and alleviate class imbalance. Crash economic loss levels are modeled using a random-effects generalized ordinal logit specification, and model performance is compared with a generalized ordered logit benchmark. Marginal effects analysis is used to evaluate the influence of pre-collision driving states (straight, turning, reversing, rolling, following closely) and key behavioral indicators. Results indicate significant effects of inter-provincial duration and count ratios, morning and empty-trip frequencies, no-claim discount coefficients, and vehicle age on crash economic loss, with prolonged speeding duration and fatigued mileage associated with major losses, whereas frequent speeding and fatigue episodes are primarily linked to minor claims. These findings clarify causal patterns for miniature commercial truck crashes with different economic losses and provide an empirical basis for targeted safety interventions and refined insurance pricing. Full article