Search Results (1,025)

Soil salinization represents one of the most severe abiotic constraints on global forest productivity. Populus, the most widely cultivated fast-growing timber tree and a premier model woody plant, exhibits striking intrageneric variation in salt tolerance—from the extremely halophytic Populus euphratica to highly salt-sensitive cultivated clones. Understanding the molecular basis of this variation has profound implications for saline–alkali land reclamation and salt-tolerant variety breeding. This review systematically synthesizes current knowledge on Populus salt-stress responses, covering three primary injury mechanisms (osmotic stress, ionic toxicity, and oxidative damage) and the corresponding physiological countermeasures. We further survey functional genes across four major categories: ion transporters, osmotic-adjustment enzymes, antioxidant-defense components, and transcription factors. Crucially, we extend beyond the herbaceous-plant paradigm by examining salt-tolerance strategies that are specific to the woody architecture of Populus: long-distance radial and axial Na⁺ transport through tall stems, salt sequestration in senescent bark and wood parenchyma, and deep-root ion exclusion strategies. Comparative insights from other woody genera are incorporated to highlight convergent and divergent mechanisms. On this basis, we propose an integrated multi-level regulatory model in which Na⁺ compartmentalization/efflux serves as the core, ROS homeostasis as the key regulatory axis, and osmotic adjustment as the auxiliary strategy. Outstanding challenges—including unresolved primary salt-signal perception, insufficient pathway integration, and limited in planta gene-function verification—are critically assessed, and future research priorities encompassing multi-omics integration, CRISPR-based gene editing, and natural-population genomics are outlined. Full article

Distributed-drive all-wheel steering (AWS) six-axle vehicles possess distinct advantages in power performance, maneuverability, and environmental adaptability. However, when navigating tight curves under sudden low-friction road conditions, their inherent long wheelbase and strong inter-axle coupling typically lead to compromised spatial maneuverability, trajectory decoupling between the vehicle nose and tail, and lateral dynamic instability. To resolve these critical issues, this paper proposes a geometry–dynamics coupled lateral control scheme with adaptive speed planning for six-axle vehicles under confined spatial and low-friction conditions by seamlessly fusing a dual-point preview mechanism with multi-mode steering mappings. First, a three-degree-of-freedom nonlinear vehicle dynamic model incorporating longitudinal, lateral, and yaw motions is constructed, alongside the formulation of extended Ackermann kinematic steering manifolds for three distinct modes: rear-axle steering, center steering, and crab steering. To rectify the kinematic under-constrained deficiency inherent in conventional single-point preview path-tracking architectures, a joint front-and-rear dual-point preview constraint mechanism is established. This framework permits the quantitative derivation of a spatial geometric reconstruction method for the instantaneous center of rotation (ICR), which algebraically maps the ideal ICR trajectory requirements onto the physical constraints of the selected steering modes. Consequently, complete geometric constraints on both the front and rear trajectories are achieved, enabling active compression of the vehicle’s turning radius. Furthermore, to handle sudden low-friction disturbances, road adhesion limits and vehicle lateral stability boundaries are explicitly incorporated to design a multi-scale adaptive preview distance dynamic scaling mechanism driven by dynamic safety margin corrections. By adaptively scaling the spatial constraint at the geometric layer, this mechanism proactively mitigates nonlinear tire sideslip force saturation via feedforward action, thereby preventing tracking divergence and catastrophic sideslip instability under physical adhesion limits. Co-simulations based on the high-fidelity TruckSim-Simulink platform demonstrate that, in standard curves, the proposed dual-point preview manifold fusion strategy reduces the minimum turning radius by 9.6–10.1% and shortens the cornering transit time by 7.5% compared with the traditional single-point preview mechanism. By actively constraining the front and rear trajectories, the trajectory decoupling between the vehicle nose and tail is effectively resolved. Under narrow-lane scenarios, the maximum lateral error is restricted within 0.78 m, representing a 37.6% reduction relative to the single-point preview, while the maximum steering angle of the front axle is compressed by approximately 18%, thereby significantly improving spatial passability and preventing intermediate body interference. Most notably, under low-friction surface disturbances, the dynamic-margin-corrected adaptive preview adjustment mechanism exhibits remarkable robustness, constraining the maximum lateral tracking error to within 0.68 m. The proposed geometry–dynamics coupled lateral control strategy successfully elevates the tight-curve maneuverability of heavy transport vehicles while concurrently reinforcing their lateral dynamic stability under limit combined spatial and adhesion constraints. Full article

Road transport is an important source of carbon emissions worldwide, yet the factors driving these emissions may differ under varying geographical conditions. Plateau regions are characterized by high altitude and strong spatial constraints, but their transport carbon emission mechanism remains insufficiently understood. Taking Qinghai Province on the Qinghai–Tibetan Plateau as a case, this study estimates road transport CO₂ emissions from 2003 to 2022 using annual statistical data. It constructs a multidimensional indicator system covering economic development, industrial structure, energy use, and road transport activity, and applies correlation analysis, PCA, and LASSO regression to diagnose variable relationships and identify key drivers. The results show that GDPI, VATSP, CVO, and TPT have stable positive effects on road transport CO₂ emissions, indicating that economic expansion, transport services, vehicle ownership, and passenger mobility are the dominant drivers. Industrial and energy-related variables have more indirect and stage-dependent effects: NMI and NFMO are negatively associated with emissions, whereas NMEC has a weak positive effect. These findings suggest that, under the dispersed spatial development and long-distance transport dependence of plateau regions, emissions are more directly shaped by economic and transport activity than by short-term changes in energy structure. Low-carbon transport policy in Qinghai should therefore combine transport-demand management, more efficient transport organization, public-transport improvement, and gradual transport electrification. The results provide evidence for emission-reduction strategies in high-altitude and ecologically fragile regions. Full article

The objective of this study was to identify the factors that most strongly influence the postharvest quality of ‘Gala Schniga^® SchniCo Red(s)’ apples under conditions of simulated transport and simulated trading at elevated temperature following long-term storage. The study was conducted over two storage seasons (2022/2023 and 2023/2024) on fruit originating from the experimental orchard of the Warsaw University of Life Sciences (SGGW-WULS) in Warsaw. The effects of harvest date (optimal—OHD and delayed by 14 days—DH), four variants of 1-MCP (1-methylcyclopropene) application: (control, Harvista™—preharvest, SmartFresh™—postharvest, and Harvista™ + SmartFresh™), controlled-atmosphere storage technology (ULO 1: 1.2% CO₂ and 1.2% O₂; ULO 2: 0.6% CO₂ and 0.6% O₂), storage period (5, 7, and 9 months), duration of simulated transport (4 or 6 weeks at 1 °C in normal atmosphere), and shelf life (0, 7, and 14 days at 25 °C) were analyzed. Five quality parameters were evaluated: firmness (F), soluble solids content (SSC), titratable acidity (TA), SSC/TA ratio, and 1-aminocyclopropane-1-carboxylic acid (ACC) content. Stepwise regression with backward elimination was applied to identify significant predictors, and partial eta squared (η²) was calculated to compare the relative strength of effects. Postharvest 1-MCP application had the greatest impact on maintaining firmness and TA (F: η² = 75.8%; TA: η² = 56.3%), whereas shelf life was the key factor in the deterioration of quality parameters after removal from storage (F: η² = 55.5%; TA: η² = 30.1) and in increasing the SSC/TA ratio (η² = 29.6%). Harvest date strongly differentiated firmness (η² = 51.3) and significantly affected TA (η² = 14.4), while storage period had the greatest effect on ACC content (η² = 14.2) and TA decline (η² = 15.6). Preharvest 1-MCP application had a smaller effect on F and TA but significantly reduced SSC (η² = 24.9), highlighting the importance of the timing of ethylene inhibitor application. The effects of simulated transport and preharvest weather indicators were statistically significant but relatively small compared with the effects of postharvest technological decisions and exposure time under retail conditions. The results indicate that maintaining target quality parameters throughout an extended supply chain requires precise determination of the harvest date, prioritizing postharvest 1-MCP application, and limiting shelf life under elevated-temperature conditions. Full article

Sequential resource allocation has long been a central problem in operations research, yet ongoing technological developments, particularly in cloud and high-performance computing and in multi-channel marketing, are giving rise to new structural constraints that classical methods were not designed to handle. Semi-Bandit Feedback (SBF) has emerged as the dominant framework for these modern settings. This paper introduces an alternative that recasts the allocation problem within the Bayesian Optimization (BO) paradigm. All three proposed BO algorithms consistently outperform SBF, with BORAw_SE showing a particularly clear advantage under time-varying budget settings, while CBO achieves comparable rewards under constant budget conditions. The core methodological contribution is a reformulation in which each candidate allocation is represented as a discrete probability distribution over the available options, making the probability simplex the natural search domain. Grounding the search in this space calls for a geometry that respects the structure of distributions: we adopt the optimal transport (Wasserstein) distance, which allows both the Gaussian process surrogate and the acquisition function to be extended as functionals over the simplex. A further practical advantage of the proposed method is its applicability to problem instances where SBF cannot be used without modification. The approach is evaluated on two case studies: the benchmark computing-resource allocation scenario from the original SBF paper, and a budget allocation problem across marketing channels. Full article

Dynamic Wireless Power Transfer (DWPT) is attracting increasing interest as a promising solution to extend the operating range of battery electric vehicles while reducing stationary charging needs. In this study, a DWPT system for Electric Vehicle charging is investigated through a comparative simulation-based case study focused on the Italian A4 highway, a strategic transport corridor characterized by high traffic intensity and long-distance mobility demand. The proposed system is based on a segmented magnetic coupling architecture with planar circular coils installed along the roadway and a vehicle-side pickup coil. Under common roadway, vehicle, and magnetic coupling assumptions, a benchmark Tesla Model 3 Long Range traveling at a constant speed of 90 km/h and characterized by an estimated energy consumption of 0.129 kWh/km is considered. Two compensation solutions are comparatively assessed, namely the Series–Series (SS) topology and the Inductor-Capacitor-Capacitor (LCC) topology. The methodology evaluates the two topologies under the same benchmark conditions in terms of peak power, average transferred power, transferred energy per kilometer, and effect on vehicle State Of Charge (SOC). The SS topology provides a peak power of 22.52 kW, an average power of 12.30 kW, and an energy transfer of 0.14 kWh/km, whereas the LCC topology reaches a peak power of 20.44 kW, an average power of 13.47 kW, and an energy transfer of 0.15 kWh/km. Starting from an initial SOC of 30%, the final SOC after traveling through the usable electrified highway section reaches 37.48% with SS compensation and 44.28% with LCC compensation. The results show that both topologies enable effective dynamic charging, with the LCC solution exhibiting better energy transfer capability and higher operational stability, while the SS topology delivers higher instantaneous power peaks. From a comparative simulation perspective, the study supports the technical feasibility of DWPT deployment in highway environments and provides useful design insights for selecting compensation topologies in dynamic electric vehicle charging applications. Full article

The electrification of long-haul freight transport requires a comprehensive public charging infrastructure along motorways. This study presents a framework combining multi-agent transport simulation (MATSim) with evolutionary bi-objective optimization (NSGA-II) to determine the number and spatial distribution of high-power charging (HPC) points for battery-electric trucks (BETs) on the German motorway network. Beyond infrastructure sizing, the approach also quantifies the impact of BET charging on the duration and distance of long-haul truck trips. The optimization simultaneously addresses the perspectives of two key stakeholders: charge point operators (CPOs), who seek to maximize charger utilization, and logistics operators, who aim to minimize waiting times. The results yield a range of Pareto-optimal configurations balancing the two objectives. A multi-iteration replanning step further lets trucks adapt their routes to experienced waiting times for a more realistic performance assessment, reducing mean waiting times by up to 92%. We evaluate five electrification levels from 1% to 20% across two charging network scenarios with 347 and 779 potential locations, respectively. For the balanced solutions—the knee-point configurations that best reconcile both objectives—at a 10% electrification level, the optimized network reaches a temporal charger utilization of 23% to 32% at mean waiting times of about 1.4 to 1.9 min per charging process. Compared with an internal combustion engine truck (ICET) reference, BET trip durations increase by only 0.9% to 1.3% due to charging detours. Overall, the fast-charging network planned by the German federal government appears sufficient for the HPC demand at electrification levels up to 10% to 15%, whereas additional low-power charging (LPC) infrastructure beyond the planned locations will be needed to cover overnight charging requirements. Full article

Selenium (Se) is a beneficial element involved in plant growth, metabolism, stress adaptation, and crop quality improvement, but its effects are strongly influenced by chemical form, application dose, plant species, growth stage, and environmental conditions. To integrate mechanistic understanding with global research trends, this study combines a concise mini-review with a bibliometric analysis of Se research in plants from 2000 to 2025. The mini-review summarizes Se speciation and bioavailability in the soil–plant–microbe system, root uptake and long-distance transport, metabolic assimilation and detoxification, physiological regulation, stress tolerance, biofortification, and nano-Se applications. Bibliographic data were retrieved from the Web of Science Core Collection and analyzed using CiteSpace, VOSviewer, and Scimago Graphica. A total of 3451 valid publications were identified, showing a sustained increase in annual output, especially after 2018. The field has expanded from early studies on Se speciation, uptake, assimilation, and antioxidant responses toward broader themes involving crop biofortification, molecular regulation, stress physiology, foliar application, nano-Se applications, green synthesis, and phytoremediation. Overall, plant Se research has evolved into an interdisciplinary field linking mechanistic studies with safe agricultural application. Future work should emphasize standardized experimental frameworks, causal mechanism validation, precise biofortification, field-based evaluation, and safety assessment of emerging Se-based technologies. Full article

Saudi Arabia currently lacks a nationally representative, multi-day National Household Travel Survey comparable to the US, UK, or New Zealand programmes; existing official data products focus on aggregate road-transport indicators or general household statistics rather than detailed day-to-day travel diaries. This study develops a benchmark-driven framework for NHTS–KSA by comparing Saudi demographic, geographic, infrastructure, climate, and mobility indicators with those of the United States, United Kingdom, and New Zealand, and by systematically assessing 15 survey-design indicators across their national household travel surveys. Context benchmarking identifies the United States as the closest for highway-oriented interurban structure and motorisation level, New Zealand for geography and demographic structure (in particular, near-identical physiological density on limited arable land), and the United Kingdom as the most aspirationally aligned benchmark for the multimodal mobility patterns Saudi Arabia aims to develop under Vision 2030. Design benchmarking shows that the three surveys are closely matched in aggregate similarity but lead on distinct elements: New Zealand on diary length and integrated passive tracking, the US on digital tools and emerging-behaviour modules, and the UK on interviewer-led recruitment and multimodal analysis, a pattern that proves robust to plausible variation in individual scores. The resulting NHTS–KSA blueprint specifies a statistically justified, stratified multistage annual household sample, a two-day diary with rolling 12-month fieldwork, interviewer-assisted recruitment, a digital-first diary with optional GPS tracking, and modules on long-distance travel, telework, e-commerce, gendered mobility, accessibility, safety, and environmental attitudes. While preserving international comparability, the framework provides the data foundation required to steer public-transport investment, demand-management measures, and land-use policies in line with Saudi Arabia’s Vision 2030 objectives for sustainable, inclusive, and smart mobility. Full article

With the expansion of ammonia energy applications, long-distance liquid ammonia pipelines are expected to support large-scale cross-regional ammonia transport. In the liquid ammonia pipeline commissioning process, gaseous ammonia purging involves ammonia–nitrogen mixing and possible liquefaction, while liquid ammonia injection may induce flashing and severe local cooling, all of which can affect commissioning safety. To characterize these thermodynamic phenomena, a transient gas–liquid two-phase flow model was established and validated using OLGA 2022.1.0 software for simulating the long-distance liquid ammonia pipeline commissioning. The model adopts the cross-sectionally averaged one-dimensional approach. A volume-corrected Soave–Redlich–Kwong (SRK) equation of state for ammonia was adapted, validated, and used to generate OLGA-compatible thermodynamic property tables. The results show that, during gaseous ammonia purging, a higher flowrate shortens the displacement time by accelerating nitrogen removal, and this effect is more pronounced at higher ambient temperatures due to enhanced molecular diffusion. Along the pipeline, pressure gradually decreases from frictional resistance, with a steeper drop near the outlet caused by gas acceleration, and temperature gradually approaches ambient through heat exchange with the pipe wall and surrounding soil. A high gaseous ammonia flowrate can cause partial liquefaction, regasification, and temperature fluctuations. During liquid ammonia injection, local condensation and slight liquid accumulation occur before the liquid front arrives, and the low-temperature region moves with the liquid front. The liquid ammonia mass flowrate has the strongest influence on the injection process, as it reduces the completion time but increases the outlet temperature, outlet pressure, and the low-temperature risk downstream of the valve. Therefore, it should be controlled within an appropriate range to balance efficiency and low-temperature safety risks. This work provides a rapid and efficient prediction model for key thermo-hydraulic parameters during liquid ammonia pipeline commissioning, and the overall analyses offer insights for on-site process design and safety control. Full article

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

The article presents the development of a multi-zone temperature control model in an Internet of Things (IoT) environment, designed for the cold chain of pharmaceutical products transported by sea. The model is based on the Elephant Herding Optimization (EHO) algorithm, which is used to regulate cooling modes in three independent temperature zones. The study is designed as a simulation-based proof-of-concept rather than as a full-scale experimental validation on an industrial refrigerated container. The proposed framework evaluates whether an EHO-based controller can generate spatially differentiated cooling decisions under synthetic but controlled disturbance scenarios. The variability of sensor readings reflects conditions typical of long-distance maritime transport. These include transitions across different climate zones, changes in solar exposure, and local differences in thermal load. The simulation results indicate that EHO maintains the temperature within the target range required for pharmaceutical cargo, i.e., 0–8 °C. The algorithm responds effectively to local disturbances and to asymmetry between zones. The proposed model provides a basis for further research on autonomous monitoring and control methods in IoT-based cold chain systems; however, validation using measurements from real refrigerated containers, physical heat-transfer modelling, refrigeration-unit response delays, and IoT communication disturbances remains necessary before operational deployment. Full article

This study proposes a multi-criteria decision-making framework for determining optimal locations for shared micromobility stations in Kars, Türkiye. The approach integrates spatial data with structured expert evaluation and applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank candidate locations. Eight representative locations were evaluated based on five criteria: points of interest (POId), public transport distance, activity level, accessibility, and installation suitability. Spatial indicators were obtained through map-based measurements, while qualitative criteria were assessed using expert-based scoring by 11 experts. The results indicate that locations with high activity density, strong accessibility, and a high concentration of POIs achieve the highest suitability scores. The city center (L2) and Kafkas University (L1) were identified as the most suitable locations, with closeness coefficients of 0.862 and 0.783, respectively. In contrast, the train station (L5) showed the lowest suitability, with a closeness coefficient of 0.326. A sensitivity analysis confirmed that the ranking structure remained unchanged under moderate variations in criteria weights, indicating the robustness of the proposed model. The findings suggest that micromobility systems are primarily driven by intra-urban mobility demand rather than by long-distance transportation nodes. From a sustainability perspective, the proposed framework supports evidence-based planning of shared micromobility infrastructure, which can contribute to reducing dependence on private automobiles, improving urban accessibility, and promoting low-carbon transportation. The findings provide practical guidance for municipalities seeking to develop environmentally sustainable, socially accessible, and resource-efficient urban mobility systems in medium-sized cities. The framework can also support broader sustainable urban development strategies and contribute to the achievement of sustainable mobility objectives. Full article

Gas blending with hydrogen represents a core research direction for present and future energy transport systems. The throttling of natural gas and hydrogen mixtures through pressure-regulating valves inevitably induces thermodynamic temperature variations. Theoretical analyses and simulated thermal profiles demonstrate that hydrogen blending effectively counteracts the extreme expansion temperature drop post-throttling. This thermodynamic shift alleviates the localized microclimatic thermal conditions favorable to ice-plugging, validating the feasibility of hydrogen injection as a systematic thermal mitigation strategy for high-pressure pipeline networks. This study utilizes computational fluid dynamics software to model the flow field variations in pure hydrogen and gas–hydrogen mixtures under the influence of pressure-regulating valves. Employing a real gas equation of state across varying operational temperatures and pressure conditions, this research calculates and analyzes the flow field variations driven by the Joule–Thomson effect for pure hydrogen and mixtures with varying hydrogen blending ratios. The objective is to inform temperature regulation strategies for long-distance hydrogen–natural gas pipeline networks and to establish an empirical temperature fitting relationship for pure hydrogen. The numerical evaluation indicates a maximum relative error of 6.02% and a maximum absolute error of 0.06877 K. Furthermore, guided by the localized temperature variation patterns, the temperature rise results from 75 pure hydrogen simulation cases were extracted. A Multilayer Perceptron artificial intelligence algorithm was utilized to perform inverse calculation iterations on the thermal data and regulation results. Through the stochastic selection of initial parameters and repeated training iterations referencing the fitting formula, an optimized regulation sequence was obtained. This process drives the fluid temperature to approach the practical regulation target. Following the network training phase, the maximum absolute error between the calculated temperature regulation result and the target regulation temperature is recorded at 0.0556 K, providing a methodological reference for subsequent high-pressure hydrogen applications. Full article

The integrated usage of metro and bus services plays a key role in long-distance trips in big cities. Revealing the nonlinear relationship between the intermodal transfer demand and the built environment is significant for building a sustainable public transport system. This paper proposes a stacking ensemble explainable machine learning framework, which uses meta-learner to learn the prediction results of diverse base learners to improve performance, to detect how the impact factors impact the intermodal demand, including metro-to-bus and bus-to-metro directions. In this framework, the ensemble model is the stacking model; the ridge regression model is the second model. The base learners contain tree-based models (e.g., Random Forest, XGBoost and CatBoost) and non-tree-based models (e.g., SVR and KNN). The framework is applied to the case study of Beijing, China, based on one weekday (13 May 2019) and one weekend day (18 May 2019) of smart card data covering the main urban districts within the Sixth Ring Road. The results indicate that the stacking ensemble learning model outperforms the base learning models. For the metro-to-bus direction, transfer time, bus station count, and degree centrality are the top three influential factors; for the bus-to-metro direction, transfer time, bus station count, and shopping POI count are the top three, with lower predictive performance due to greater variability in this direction. However, the interaction effect of transfer time and bus station count is negative. This study could provide new insights into public transport planning and management. Full article