Search Results (12,466)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

The evaporation dynamics of droplets on smooth and inclined micro-pillar surfaces were experimentally investigated. The surface temperature was increased from 50 °C to 120 °C, with the inclination angles being 0°, 30°, 45°, and 60° respectively. The dynamic parameters, including contact area, nucleation density, bubble stable diameter, and droplet asymmetry, were recorded using two high-speed video cameras, and the corresponding evaporation performance was analyzed. Experimental results showed that the inclination angle had a significant influence on the evaporation of micro-pillar surfaces than smooth surfaces as well as a positive correlation between the enhancement performance of the micro-pillars and increasing inclination angles. This angular dependence arises from surface inclination-induced tail elongation and the corresponding asymmetry of droplets. With definition of the one-dimensional asymmetry factor (ε) and volume asymmetry factor (γ), it was proven that although the asymmetric thickness of the droplets reduces the nucleation density and bubble stable diameter, the droplet asymmetry significantly increased the heat exchange area, resulting in a 37% improvement in the evaporation rate of micro-pillar surfaces and about a 15% increase in its enhancement performance to smooth surfaces when the inclination angle increased from 0°to 60°. These results indicate that asymmetry causes changes in heat transfer conditions, specifically, a significant increase in the wetted area and deformation of the liquid film, which are the direct enhancement mechanisms of inclined micro-pillar surfaces. Full article

As a typical porous medium, unsaturated loess demonstrates critical hydro-mechanical coupling properties that fundamentally influence geohazard mitigation, groundwater resource evaluation, and foundation stability in geotechnical engineering. This investigation develops a novel theoretical framework to overcome the limitations of existing models in converting electrical resistivity tomography (ERT) profiles into water content distributions for unsaturated loess through quantitative inversion modeling. Systematic laboratory investigations on remolded loess specimens with controlled density and water content conditions revealed distinct resistivity–water interaction mechanisms. A characteristic two-stage decay pattern was identified: resistivity exhibited an exponential decrease from 420 Ω·m (water saturation (S_w = 10%)) to 90 Ω·m (S_w = 40%), followed by asymptotic stabilization at S_w ≥ 40%. The derived quantitative correlation provides a robust mathematical basis for water content profile inversion. Field validation through integrated ERT and borehole data demonstrated exceptional predictive accuracy in shallow strata (<20 m depth), achieving mean absolute errors of <5%. However, inversion reliability decreased with depth (>20 m), primarily attributed to density-dependent charge transport mechanisms. This underscores the necessity of incorporating coupled thermo-hydro-mechanical processes for deep-layer characterization. This study provides a robust framework for engineering applications of ERT in loess terrains, offering significant advancements in geotechnical monitoring and geohazard prevention. Full article

Soil salinization in oasis areas of arid regions is recognized as a dynamic and multifaceted environmental threat influenced by both natural processes and human activities. In this study, 13 spatiotemporal predictors derived from field surveys and remote sensing are utilized to construct a spatial probabilistic model of salinization. A Bayesian Belief Network is integrated with spline interpolation in ArcGIS to map the likelihood of salinization, while Partial Least Squares Structural Equation Modeling (PLS-SEM) is applied to analyze the interactions among multiple drivers. The test results of this model indicate that its average sensitivity exceeds 80%, confirming its robustness. Salinization risk is categorized into degradation (35–79% probability), stability (0–58%), and improvement (0–48%) classes. Notably, 58.27% of the 1836.28 km² Keriya Oasis is found to have a 50–79% chance of degradation, whereas only 1.41% (25.91 km²) exceeds a 50% probability of remaining stable, and improvement probabilities are never observed to surpass 50%. Slope gradient and soil organic matter are identified by PLS-SEM as the strongest positive drivers of degradation, while higher population density and coarser soil textures are found to counteract this process. Spatially explicit probability maps are generated to provide critical spatiotemporal insights for sustainable oasis management, revealing the complex controls and limited recovery potential of soil salinization. Full article

This paper presents a comparative study of data-driven modeling approaches for vanadium redox flow batteries (VRFBs), utilizing Multiple Linear Regression (MLR) and Random Forest (RF) algorithms. Experimental voltage–capacity datasets from a 1 kW/1 kWh VRFB system were digitized, processed, and used for model training, validation, and testing. The MLR model, built using eight optimized features, achieved a mean error (ME) of 0.0204 V, a residual sum of squares (RSS) of 8.87, and a root mean squared error (RMSE) of 0.1796 V on the test data, demonstrating high predictive performance in stationary operating regions. However, it exhibited limited accuracy during dynamic transitions. Optimized through out-of-bag (OOB) error minimization, the Random Forest model achieved a training RMSE of 0.093 V and a test RMSE of 0.110 V, significantly outperforming MLR in capturing dynamic behavior while maintaining comparable performance in steady-state regions. The accuracy remained high even at lower current densities. Feature importance analysis and partial dependence plots (PDPs) confirmed the dominance of current-related features and SOC dynamics in influencing VRFB terminal voltage. Overall, the Random Forest model offers superior accuracy and robustness, making it highly suitable for real-time VRFB system monitoring, control, and digital twin integration. This study highlights the potential of combining machine learning algorithms with electrochemical domain knowledge to enhance battery system modeling for future energy storage applications. Full article

This study proposes an integrated framework that combines nonlinear stochastic vibration analysis with reliability assessment to address the safety issues of cable systems under damage conditions. First of all, a mathematical model of the damaged cable is established by introducing damage parameters, and its static configuration is determined. Using the Pearl River Huangpu Bridge as a case study, the accuracy of the analytical solution for the cable’s sag displacement is validated through the finite difference method (FDM). Furthermore, a quantitative relationship between the damage parameters and structural response under stochastic excitation is developed, and the nonlinear stochastic dynamic equations governing the in-plane and out-of-plane motions of the damaged cable are derived. Subsequently, a Gaussian Radial Basis Function Neural Network (GRBFNN) method is employed to solve for the steady-state probability density function of the system response, enabling a detailed analysis of how various damage parameters affect structural behavior. Finally, the First-Order and Second-Order Reliability Method (FORM/SORM) are used to compute the reliability index and failure probability, which are further validated using Monte Carlo simulation (MCS). Results show that the severity parameter η shows the highest sensitivity in influencing the failure probability among the damage parameters. For the system of the Pearl River Huangpu bridge, an increase in the damage extent δ from 0.1 to 0.4 can reduce the reliability-based service life of by approximately 40% under fixed values of the damage severity and location, and failure risk is highest when the damage is located at the midspan of the cable. This study provides a theoretical framework from the point of stochastic vibration for evaluating the response and associated reliability of mechanical systems; the results can be applied in practice with guidance for the engineering design and avoid potential damages of suspended cables. Full article

This research aimed to evaluate the impact of using brick waste powder (BWP) and varying lengths of polyester fibers (PFs) on the performance properties of asphalt concrete (AC) mixtures. BWP was utilized as a replacement for traditional limestone powder (LS) filler, while PFs of three lengths (3 mm, 8 mm, and 15 mm) were introduced. The study employed the response surface methodology (RSM) for experimental design and analysis of variance (ANOVA) to identify the influence of BWP and PF on the selected performance indicators. These indicators included bulk density, air voids, voids in the mineral aggregate, voids filled with asphalt, Marshall stability, Marshall flow, Marshall quotient, indirect tensile strength, wet tensile strength, and the tensile strength ratio. The findings demonstrated that BWP improved moisture resistance and the mechanical performance of AC mixes. Moreover, incorporating PF alongside BWP further enhanced these properties, resulting in superior overall performance. Using multi-objective optimization through RSM-based empirical models, the study identified the optimal PF length of 5 mm in combination with BWP for achieving the best AC properties. Validation experiments confirmed the accuracy of the predicted results, with an error margin of less than 8%. The study emphasizes the intriguing prospect of BWP and PF as sustainable alternatives for improving the durability, mechanical characteristics, and cost-efficiency of asphalt pavements. Full article

This study examines the effect of temperature during pyrolysis on the capacity of cedar wood-derived biochar to be employed as a sustainable electrode material for supercapacitors. Cedar wood-derived biochars were produced at different temperatures of 800 °C, 900 °C, 1000 °C and 1100 °C and fully characterized in terms of their structural, physicochemical and electrochemical properties, including specific surface area, hydrophobicity, electrical conductivity, and surface functional groups. The results indicated that the cedar wood biochar obtained through pyrolysis at 900 °C (BC900) provided optimal electrical conductivity, hydrophobicity, and porosity characteristics relative to the other cedar wood biochars produced by pyrolysis at 800 °C to 1100 °C. Specifically, when compared to commercial activated carbon (AC), BC900 provided half the specific capacitance at a current density of 1 A g⁻¹ and indicated that there is more potential for improvement with further activation and doping. The influence of the binder (either polyvinylidene fluoride (PVDF) or chitosan) in combination with conductive carbon black (CB) was also examined. Electrodes fabricated with PVDF binder showed higher specific capacitance, while biochar electrodes made from CB and chitosan (BC900/CB/chitosan) showed better electrical conductivity, wettability, and good electrochemical stability with >95% capacity retention even after 10,000 cycles. Full article

Improving the corrosion resistance of magnesium (Mg) alloys is a long-term challenge, especially when cost-effectiveness is taken into account. In this work, Mg-1Bi and Mg-1Sn extruded alloys are prepared, and the effects of cost-effective Bi and Sn on the corrosion behavior of Mg alloys are comparatively studied. The corrosion resistance of the Mg-1Sn alloy (P_H: 2.83 ± 0.19 mm y⁻¹) is better than that of the Mg-1Bi alloy (P_H: 13.75 ± 1.12 mm y⁻¹), being about five times greater. In addition to the relatively low dislocation density in Mg-1Sn alloy, the difference in corrosion resistance is mainly attributed to two aspects of influence brought about by the addition of Sn and Bi. The Mg₂Sn phase introduced by the addition of Sn has a potential difference (PD) of ~30 mV, which is significantly lower than that (~90 mV) of the Mg₃Bi₂ phase introduced by adding Bi, thereby weakening the micro-couple corrosion tendency of the Mg-1Sn alloy. The addition of Bi has little effect on the corrosion film, while the addition of Sn makes the corrosion film on the Mg-1Sn alloy contain SnO₂, improving the compactness of the corrosion film and thereby enhancing the corrosion protection effect. Full article

This study investigates the mechanical, tribological, and electrochemical behaviour of a novel precipitation-hardenable martensitic alloy produced by selective laser melting (SLM). The alloy was specifically engineered with an optimised composition, free from cobalt and molybdenum, and featuring reduced nickel content (7 wt.%) and 8 wt.% chromium. It has been developed as a cost-effective and sustainable alternative to conventional maraging steels, while maintaining high mechanical strength and a refined microstructure tailored to the steep thermal gradients inherent to the SLM process. Several ageing heat treatments were assessed to evaluate their influence on microstructure, hardness, tensile strength, retained austenite content, dislocation density, as well as wear behaviour (pin-on-disc test) and corrosion resistance (polarisation curves in 3.5%NaCl). The results indicate that ageing at 540 °C for 2 h offers an optimal combination of hardness (550–560 HV), tensile strength (~1700 MPa), microstructural stability, and wear resistance, with a 90% improvement compared to the as-built condition. In contrast, ageing at 600 °C for 1 h enhances ductility and corrosion resistance (Rp = 462.2 kΩ; Ecorr = –111.8 mV), at the expense of a higher fraction of reverted austenite (~34%) and reduced hardness (450 HV). This study demonstrates that the mechanical, surface, and electrochemical performance of this novel SLM-produced alloy can be effectively tailored through controlled thermal treatments, offering promising opportunities for demanding applications requiring a customised balance of strength, durability, and corrosion behaviour. Full article

Forest trees have emerged as a promising passive solution for mitigating low-frequency ground vibrations generated by railway operations, offering ecological and cost-effective advantages. This study proposes a three-dimensional semi-analytical method developed for evaluating the dynamic responses of the coupled track–ground–tree system. The thin-layer method is employed to derive an explicit Green’s function corresponding to a har-monic point load acting on a layered half-space, which is subsequently applied to couple the foundation with the track system. The forest trees are modeled as surface oscillators coupled on the ground surface to evaluate the characteristics of multiple scattered wavefields. The vibration attenuation capacity of forest trees in mitigating railway-induced ground vibrations is systematically investigated using the proposed method. In the direction perpendicular to the track on the ground surface, a graded array of forest trees with varying heights is capable of forming a broad mitigation frequency band below 80 Hz. Due to the interaction of wave fields excited by harmonic point loads at multiple locations, the attenuation performance of the tree system varies significantly across different positions on the surface. The influence of variability in tree height, radius, and density on system performance is subsequently examined using a Monte Carlo simulation. Despite the inherent randomness in tree characteristics, the forest still demonstrates notable attenuation effectiveness at frequencies below 80 Hz. Among the considered parameters, variations in tree height exert the most pronounced effect on the uncertainty of attenuation performance, followed sequentially by variations in density and radius. Full article

Background/Objectives: Cannabinoid use is rising among patients undergoing spinal fusion, yet its influence on bone healing is poorly defined. The endocannabinoid system (ECS)—through cannabinoid receptors 1 (CB1) and 2 (CB2)—modulates skeletal metabolism. We reviewed preclinical, mechanistic and clinical evidence to clarify how individual cannabinoids affect fracture repair and spinal arthrodesis. Methods: PubMed, Web of Science and Scopus were searched from inception to 31 May 2025 with the terms “cannabinoid”, “CB1”, “CB2”, “spinal fusion”, “fracture”, “osteoblast” and “osteoclast”. Animal studies, in vitro experiments and clinical reports that reported bone outcomes were eligible. Results: CB2 signaling was uniformly osteogenic. CB2-knockout mice developed high-turnover osteoporosis, whereas CB2 agonists (HU-308, JWH-133, HU-433, JWH-015) restored trabecular volume, enhanced osteoblast activity and strengthened fracture callus. Cannabidiol (CBD), a non-psychoactive phytocannabinoid with CB2 bias, accelerated early posterolateral fusion in rats and reduced the RANKL/OPG ratio without compromising final union. In contrast, sustained or high-dose Δ⁹-tetrahydrocannabinol (THC) activation of CB1 slowed chondrocyte hypertrophy, decreased mesenchymal-stromal-cell mineralization and correlated clinically with 6–10% lower bone-mineral density and a 1.8–3.6-fold higher pseudarthrosis or revision risk. Short-course or low-dose THC appeared skeletal neutral. Responses varied with sex, age and genetic background; no prospective trials defined safe perioperative dosing thresholds. Conclusions: CB2 activation and CBD consistently favor bone repair, whereas chronic high-THC exposure poses a modifiable risk for nonunion in spine surgery. Prospective, receptor-specific trials stratified by THC/CBD ratio, patient sex and ECS genotype are needed to establish evidence-based cannabinoid use in spinal fusion. Full article

Local site conditions can significantly influence the amplitude, duration, and frequency content of seismic recordings, making the characterization of subsoil properties a critical component in seismic hazard assessment. However, despite extensive research, standardized methodologies for assessing site effects are still lacking. This study presents preliminary steps in the site characterization of a small area of Campi Flegrei caldera (Italy), with the aim of enhancing understanding of local lithology and seismic wave propagation. The analysis focuses on the broad-band seismic station CSTH, installed in 2021, and incorporates data from a temporary 2D array of five short-period sensors deployed around the station. These sensors recorded both ambient noise and seismic events associated with caldera dynamics. To improve the robustness of the characterization, data from two additional permanent broad-band stations (CPIS and CSOB) of the Istituto Nazionale di Geofisica e Vulcanologia—Osservatorio Vesuviano’s monitoring network, also located nearby a hydrothermal field, were included. Spectral analyses such as Power Spectral Density (PSD), Horizontal-to-Vertical (H/V) spectral ratios, and f-k array technique were performed to evaluate the frequency-dependent response of the site and to support the development of a comprehensive seismic site model. Full article

This study examines whether regional infrastructure can predict economic resilience in European regions, focusing on resistance, recovery, and reorientation during the COVID-19 crisis. While infrastructure is widely recognized as a key factor influencing regional resilience, its explicit role has been underexplored in the European context. Using a comprehensive literature review and spatial econometric models applied to NUTS-2 level data from 2017 to 2024, we investigate the direct and spatial spillover effects of various infrastructure types—transportation, healthcare, tourism, education, and digital access—on regional resilience outcomes. We apply OLS and four spatial models (SEM, SLX, SDEM, SDM) under 29 spatial weighting matrices to account for spatial autocorrelation. Results show that motorway density, early school leaving, and healthcare infrastructure in neighbouring regions significantly affect resistance. For recovery, railway density and GDP per capita emerge as key predictors, with notable spatial spillovers. Reorientation is shaped by population structure, railway density, and tourism infrastructure, with both positive and negative spatial dynamics observed. The findings underscore the importance of infrastructure not only in isolation but also within regional systems, revealing complex interdependencies. We conclude that policymakers must consider spatial externalities and coordinate infrastructure investments to enhance regional economic resilience across interconnected Europe. Full article

This study aims to clarify the nonlinear pressure loss patterns of the pneumatic system in a pneumatic seeder under varying pipeline structures and airflow parameters, and to develop a rapid prediction equation for the main pipe’s pressure loss. The studied multi-branch pipeline system consists of a main pipe, a header, and ten branch pipes. The main pipe is vertically installed at the center of the header in a straight-line configuration. The ten branch pipes are symmetrically and evenly spaced along the axial direction of the header, distributed on both sides of the main pipe. The outlet directions of the branch pipes are arranged in a 180° orientation opposite to the inlet direction of the main pipe, forming a symmetric multi-branch configuration. Firstly, this study investigated the flow characteristics within the multi-branch pipeline of the pneumatic system and elaborated on the mechanism of flow division in the pipeline. The key geometric factors affecting airflow were identified. Secondly, from a microscopic perspective, CFD simulations were employed to analyze the fundamental causes of pressure loss in the multi-branch pipeline system. Finally, from a macroscopic perspective, a dimensional analysis method was used to establish an empirical equation describing the relationship between the pressure loss (P) and several influencing factors, including the air density (ρ), air’s dynamic viscosity (μ), closed-end length of the header (Δl), branch pipe 1’s flow rate (Q), main pipe’s inner diameter (D), header’s inner diameter (γ), branch pipe’s inner diameter (d), and the spacing of the branch pipe (δ). The results of the bench tests indicate that when 0.0018 m³·s⁻¹ ≤ Q ≤ 0.0045 m³·s⁻¹, 0.0272 m < d ≤ 0.036 m, 0.225 m < δ ≤ 0.26 m, 0.057 m ≤ γ ≤ 0.0814 m, and 0.0426 m ≤ D ≤ 0.0536 m, the prediction accuracy of the empirical equation can be controlled within 10%. Therefore, the equation provides a reference for the structural design and optimization of pneumatic seeders’ multi-branch pipelines. Full article