Search Results (4,679)

The vehicular energy consumption, primarily determined by the vehicle’s characteristics, exhibits significant variations influenced by driving behavior, traffic, and road attributes, with repercussions for emissions. This paper presents experimental results from real-traffic runs to characterize the relationship between fuel consumption and these factors. Data on consumption, performance, and kinematics of a light-duty vehicle were obtained using low-cost devices, including an On-Board Diagnostics (OBD) scanner, a unit integrating an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS) receiver. The data allowed distinguishing consumption patterns between two distinct scenarios: a collector road stretch with deteriorated pavement and an express road stretch with lower surface roughness. Relevant association was identified between fuel consumption and factors such as discrete pavement anomalies and variables related to driving and traffic. Moderate correlations were observed with slope, and weaker ones with pavement roughness. Regarding the regression analysis, results identified acceleration and engine speed as the primary operational determinants of fuel consumption, with road grade emerging as the dominant geometric constraint across all scenarios. The results reveal relevant associations between fuel consumption and road, driving, and traffic-related factors while simultaneously demonstrating a robust and replicable experimental methodology based on commercially available sensing devices for real-traffic energy and emission assessments. Full article

Müllerian anomalies are anatomical variations of the female reproductive tract resulting from the incomplete development of the embryonic Müllerian ducts. The molecular mechanisms driving Müllerian duct development are complex and poorly understood, resulting in the largely unexplained aetiology of these conditions. WNT5A is a critical regulator of key developmental processes, including patterning, cell proliferation, and migration. Mutations of WNT5A have been associated with Robinow syndrome, a congenital condition characterized by skeletal and genital anomalies. In the mouse, WNT5A is necessary for the posterior development of the Müllerian duct, and ablation of Wnt5a results in vaginal agenesis. However, Wnt5a-/- uterine horns are hypoplastic and over 60% shorter than the wild type, suggesting specific functions in anterior Müllerian duct development. To better understand the role of Wnt5a, we performed single-cell RNA sequencing of developing Müllerian ducts. We found that the non-canonical Wnt PCP pathway was dysregulated in Wnt5a-/- mice. In addition, Wnt5a-/- Müllerian ducts were enriched in oviductal mesenchymal cells due to the transformation of the anterior uterine horns into oviducts. Our results indicate additional roles for Wnt5a during Müllerian duct development, prompting further investigations into uterine functions and anatomy in complex clinical cases of Müllerian anomalies including Robinow syndrome. Full article

Cropland dynamics in ecologically fragile regions are central to balancing food security and ecological integrity in the Yellow River Basin. Ningxia Hui Autonomous Region is used as a case study. An integrated simulation framework is developed by coupling an improved grey prediction model (Improved GM(1,1)) with the CLUMondo spatial model. The analysis addresses four questions: how cropland changed during 2009–2024, which drivers explain cropland suitability and transitions, what spatial resolution is appropriate for implementation, and how cropland patterns differ under alternative development pathways for 2025–2040. Historical cropland change in Ningxia during 2009–2024 is quantified, and spatial patterns for 2025–2040 are projected under three scenarios: business-as-usual (BAU), ecological protection (EP), and rapid urbanization (URE). Cropland change during 2009–2024 shows pronounced phased fluctuations and a stable redistribution pattern described as “southern reduction and northern replenishment, urban decrease and rural increase”. Population growth, economic expansion, and policy regulation jointly drive this spatiotemporal reconfiguration. Land demand forecasting is improved by introducing a metabolism mechanism and residual correction into the grey model, which reduces mid- to long-term divergence. Multi-scale logistic regression tests show the highest AUC at 50 m, with AUC values exceeding 0.8 across land categories, and this resolution is used for model implementation. Model performance is evaluated using AUC, Kappa, and overall accuracy, supporting the applicability of the framework in arid, ecologically fragile regions. Scenario simulations reveal clear divergence in future spatial outcomes. BAU maintains sustained pressure on cropland protection and ecological security. URE increases the risk of encroachment on high-quality cropland in the central–northern irrigated areas due to urban expansion. EP constrains construction land growth and secures strategic ecological spaces, thereby slowing the loss of high-quality cropland while maintaining development capacity. These results provide a transparent basis for scenario-based territorial spatial planning in Ningxia and offer transferable evidence for managing cropland–ecology tradeoffs in arid and semi-arid regions. Full article

Vegetation cover characteristics underpin the understanding of regional ecosystem status and guide sustainable development. While extensive research has documented long-term vegetation dynamics in Qinghai Province, critical gaps remain in identifying driving factors, quantifying their thresholds, and uncovering nonlinear relationships governing vegetation cover. In view of this, based on the MOD13Q1V6 dataset from the Google Earth Engine (GEE) platform, this study constructed a kernel normalized difference vegetation index (kNDVI) dataset for Qinghai Province spanning the period 2001–2023. Furthermore, the spatiotemporal characteristics and future evolution trends of vegetation cover were revealed by employing methods including the Theil–Sen–Mann–Kendall (Theil–Sen–MK) trend test, Hurst exponent, and centroid migration model. At a grid scale of 5 km × 5 km, based on the combined model of Extreme Gradient Boosting and SHapley Additive exPlanations (XGBoost-SHAP), this study integrated 10 multi-source remote sensing variables related to natural conditions, socioeconomic factors, and geographical accessibility to reveal the nonlinear effects between driving factors and kNDVI and identify the key threshold inflection points. The results showed the following: (1) From 2001 to 2023, the kNDVI of Qinghai Province exhibited a fluctuating growth trend with an annual growth rate of 0.0016 per year, presenting a spatial pattern of being higher in the southeast and lower in the northwest. Specifically, the kNDVI of unused land achieved the highest growth rate (65.96%), which was significantly higher than that of other land use types. (2) The kNDVI in Qinghai Province was dominated by stable areas, accounting for 52.75%. Future trend analysis indicated that the region was primarily characterized by sustainable improvement zones (39.91%), while areas with uncertain future trends accounted for 39.70%. (3) The XGBoost-SHAP model revealed that the annual mean precipitation (AMP) (47.26%) and Digital Elevation Model (DEM) (20.40%) exerted substantial impacts on the kNDVI. Marginal effect curves identified distinct threshold inflection points for the major characteristic factors: AMP = 363.2 mm (95%CI: 361.2–365.2 mm), DEM = 4463.9 m (95%CI: 4446.0–4481.1 m), grazing intensity = 1.8 SU (Stocking Unit)·ha⁻¹ (95%CI: 1.8–1.9 SU·ha⁻¹), and slope = 2.8° (95%CI: 2.7–3.0°) and 19.0° (95%CI: 18.8–19.3°). The interaction combinations of AMP × DEM and DEM × distance to construction land exerted a strong positive effect on the kNDVI in the study area, which was conducive to enhancing vegetation cover. These findings verified the effectiveness of ecological projects implemented in Qinghai Province to a certain extent and provided data support for subsequent differentiated restoration and management. Full article

The successful integration of autonomous vehicle (AV) technologies into future mobility systems depends not only on technological maturity but also on user acceptance and perceived value. While existing research has identified several demographic determinants of AV acceptance, the role of educational background—particularly differences between humanities and STEM graduates—has received limited attention within the context of user-centred mobility research. This study examines how educational background and gender influence attitudes toward autonomous vehicle technologies using a large-scale survey conducted in Hungary (N = 8663). The analysis combines non-parametric statistical tests with effect size measures, exploratory factor analysis, and structural equation modelling (SEM) to capture both group differences and underlying attitudinal mechanisms. The results indicate no meaningful differences between humanities and STEM graduates in overall acceptance of autonomous vehicles or trust in the technology. Statistically significant differences are observed only in two dimensions: willingness to spend on autonomous driving features and expectations regarding improved travel speed. However, effect size analyses reveal that these differences are negligible in practical terms, indicating substantial overlap in user attitudes. SEM results show that educational background does not directly determine acceptance of autonomous vehicle technologies. Instead, its influence is mediated through three latent attitude dimensions relevant for electric and autonomous mobility adoption: willingness to invest, functional expectations (e.g., time savings and convenience), and safety orientation. Humanities graduates—especially men—exhibit slightly higher financial openness toward autonomous features, whereas STEM graduates place greater emphasis on functional performance. Safety-related attitudes play a central mediating role, with gender-specific patterns. By integrating large-sample effect size interpretation with SEM-based modelling, this study provides a nuanced understanding of user acceptance of autonomous vehicle technologies. The findings suggest that differences between educational groups reflect variations in attitudinal emphasis rather than fundamental divides, offering relevant insights for user-centred AV development, mobility policy design, and communication strategies in the transition toward automated and electric mobility systems. Full article

Despite the need to understand the spatial variation in human and biophysical drivers of fire spread, studies aimed at predicting remotely sensed fire kernel density at multiple scales are still relatively scarce. The current study aimed at the prediction of MODIS and VIIRS active fire kernel density (AFKD) using region-specific geographically weighted regression (GWR) in Mexico. GWR models overcame stationary models to predict AFKD at a selected 20 km optimum active fire kernel density bandwidth. Our observations confirmed, for the first time for remotely-sensed fire records, the regional variation and the non-stationarity of the human and fuel-related drivers of AFKD in Mexico. Aboveground biomass mainly showed positive relationships in low productivity areas and humped relationships in more productive areas. Contrary to previous observations for fire suppression records, road density mainly showed a negative relationship, and slope mainly showed a positive relationship with AFKD. This highlights the importance of monitoring fire spatial activity, not only from human-based suppression records, but also considering remotely-sensed AFKD, potentially improving fire prevention planning. The methodology shown in the current study can be replicated elsewhere for improving our understanding of the spatial drivers of remotely sensed fire activity. Full article

The Yellow River Basin is among the regions in China most severely affected by soil erosion. Elucidating the evolution trend of its ecological environment quality and identifying the key driving factors can provide a theoretical basis for the management and protection of the ecological environment in the Yellow River Basin. In this study, an improved remote sensing ecological index (ARSEI) was constructed by incorporating the soil erosion factor (A) into the original remote sensing ecological index (RSEI). Subsequently, the Theil–Sen slope estimator, Mann–Kendall trend test, coefficient of variation, Hurst index and Geodetector were employed to analyze the spatiotemporal evolution trend and driving factors of the ecological environment quality in the basin from 2002 to 2022. The results were as follows: (1) During the study period, the mean ARSEI of the basin increased from 0.518 to 0.568, representing an increase of 9.65%, with a spatial pattern of “poor in the north and excellent in the south.” (2) 62.12% of the basin exhibited improved ecological quality, 75.74% of the area showed medium or lower fluctuation levels, and 35.12% of the region is projected to be at risk of degradation in the future. (3) Annual precipitation was identified as the dominant factor influencing the spatial variation in ARSEI (q = 0.428), followed by land use type (q = 0.299). All interactions between factors exhibited either nonlinear enhancement or bi-factor enhancement. Specifically, the interaction between annual precipitation and land use type was the strongest, with a maximum q-value of 0.693. This study provides a novel approach for assessing the ecological environment quality in regions severely affected by soil erosion. Full article

Understanding the Holocene environmental history of desert landscapes in northern China contributes to elucidating the mechanisms driving desertification in the mid-latitudes of the Northern Hemisphere (NH). Based on a systematic and comparative analysis on integrated paleoclimatic data from both China and the international community, this paper reviews the environmental evolution history of the Alashan Plateau since the Holocene, drawing upon sedimentary and proxy records from three major sandy deserts on the plateau—the Badanjilin, Tenggeli, Wulanbuhe Deserts. The results indicate that the Alashan Plateau experienced generally humid conditions during the early and middle Holocene, characterized by the development of high-level lakes; in contrast, the late Holocene was marked by aridity and intensified aeolian activity. For the three deserts on the plateau, the environmental evolution of the Tenggeli Desert during the early Holocene diverges from that of the other two. Meanwhile, the mid-Holocene drought event in the Badanjilin Deserts remains debated, centering on whether its spatial scale was local or regional across the plateau. The driving mechanism of environmental evolution in the study area can be fundamentally understood through the atmospheric and oceanic circulation systems, combined with solar insolation in the middle latitudes of NH. This interplay is comprehensively reflected by the interactions between the westerlies and the East Asian summer monsoon (EASM) across different periods. Responses of the Alashan Plateau’s climate to global change involve the combined effects of multiple factors, including the Westerlies, the EASM, the Atlantic-Pacific-Ocean (APO) circulation anomalies, the ‘third polar’ environmental effect of the Qinghai–Tibet Plateau, and the hydrological influence of the Yellow River, etc. The Holocene environmental evolution history of the study area was primarily shaped by climate patterns characterized by cold-dry and cold–wet (or temperate-moist) regimes. Understanding these patterns may provide insights for forecasting future climate trends in the Alashan Plateau under current global warming. Full article

Against the backdrop of global digitalization and green development, digital innovation ecosystems have emerged as key drivers for advancing regional green innovation cooperation and achieving sustainable development goals. This study constructs a theoretical analytical framework encompassing “Actor-Resource-Environment.” Utilizing panel data from 30 Chinese provinces spanning 2012–2022, it employs machine learning and dynamic QCA methods to dissect the dynamic causal relationship between digital innovation ecosystems and regional green innovation cooperation. Key findings include: (1) Green innovation cooperation networks are evolving from a “core-periphery structure” toward new characteristics of multi-centered mutual coupling and coordination. (2) Different machine learning models yield varying effects on how digital innovation ecosystems influence regional green innovation cooperation, with the XGBoost model demonstrating the strongest performance. (3) No single element within the digital innovation ecosystem can serve as a necessary condition for driving regional green innovation cooperation. (4) Three configuration patterns emerge for achieving high-level regional green innovation cooperation, with digital innovation funding, digital talent resources, and digitally inclusive financial environments consistently serving as core prerequisites. These findings deepen our understanding of the complex causal mechanisms involving multi-factor matching and linkage that influence regional green innovation cooperation, offering valuable insights for advancing high-quality regional green innovation development. The research findings reveal the complex configuration pathways through which multidimensional elements of the digital innovation ecosystem collectively drive regional green innovation cooperation. This provides practical governance pathways for breaking down regional barriers and building highly resilient green innovation cooperation networks. Full article

Mining-resource-based cities, as distinctive human–environment systems, face urgent challenges from intensified urbanization and mining, leading to land imbalance and ecosystem service degradation. To enhance resilience, it is essential to identify the evolution and drivers of ecosystem services and construct targeted ecological compensation models. This study focuses on Xinzhou, a representative mining city in China, and systematically analyzes three aspects: (1) spatiotemporal dynamics of land use and ecosystem service value (ESV) from 2000 to 2023 using Markov chains, equivalent factor method, hotspot and sensitivity analyses; (2) identification of ESV driving mechanisms through an integrated “stepwise regression + geographical detector” framework; and (3) formulation of ecological compensation models via quantification of priority indices, demand intensity coefficients, and compensation standards. Key findings indicate that land conversion was concentrated in coalfield zones and surrounding built-up areas, involving 2,518,341.75 hm² (35.76% of total area), primarily characterized by a reduction in farmland and expansion of forest, grassland, and construction land. ESV showed a striped spatial pattern, with higher values in mountainous zones and lower values in valleys and basins with frequent human activity. The northwest coalfield region experienced an initial decline followed by a recovery in ESV. Annual mean temperature emerged as the dominant driver, while DEM influence increased annually. All factor interactions exhibited synergistic effects, with natural variables exerting greater influence than socio-economic ones. Ecological compensation demand was high overall, especially in Wutai, Kelan, and Pianguan counties, with high-value compensation areas mainly distributed in the eastern and central parts of Xinzhou. Looking ahead, a compensation framework prioritizing ecological–economic optimization should be developed, guided by zoned, typological, and dynamic configurations. By analyzing ecosystem governance from the perspective of a mining-resource-based city, this study enhances global ecosystem service evaluation frameworks and offers a replicable model to advance transnational ecological cooperation and green urban transformation. Full article

The overdose mortality landscape has shifted from predominantly opioid exposures to a polysubstance epidemic increasingly driven by illicit fentanyl and fentanyl analogs combined with other centrally active agents. Among the co-intoxicants, veterinary α₂-adrenoceptor (α₂AR) agonists such as xylazine have emerged as clinically confounding adulterants. Recent reports from forensic toxicology, medical examiners, and border/interdiction agencies indicate that medetomidine, a veterinary sedative racemate with the highly selective α₂AR agonist enantiomer dexmedetomidine, is increasingly being detected together with fentanyl and its analogs in seized materials and postmortem assays. Prior reviews have covered these aspects. The current review synthesizes current evidence and clinical experience relevant to fentanyl + medetomidine co-exposure-induced respiratory depression—a primary cause of death. We focus on convergent µ-opioid receptor (MOR) and α₂AR signaling within key physiological substrates, including respiratory rhythm-generating networks, ascending arousal pathways, chemosensory reflex control of ventilation, and autonomic cardiovascular regulation, integrating mechanistic pharmacology, respiratory and cardiovascular toxicology, emergency-room treatment, and emerging public-health implications. Available evidence supports a model in which combined MOR and α₂AR activation produces additive-to-synergistic suppression of ventilation and consciousness, attenuation of hypoxic ventilatory drive and CO₂ responsiveness, with marked sympatholysis manifested as bradycardia and hypotension, all of which can persist beyond presumptive opioid reversal with a MOR antagonist. We discuss the implications for prehospital and emergency care. In sum, the increasing detection of medetomidine in the illicit fentanyl supply represents an emerging and potentially high-risk co-exposure pattern that may be only partially naloxone-responsive. Lastly, we highlight potential future pharmacologic countermeasures for polysubstance overdose, such as the BK-channel antagonist ENA-001, which may address naloxone-insensitive ventilatory suppression in opioid-dominant polysubstance overdose. Full article

Marine fish communities in the Yangtze River Estuary and Adjacent East China Sea (YRE-ECS) are subject to complex environmental gradients; however, their multidimensional assembly mechanisms remain insufficiently resolved. Here, we integrated environmental DNA (eDNA) metabarcoding, co-occurrence network analysis, and environmental profiling to examine fish community structure across vertical layers, hydrographic zones, and seasons. Vertically, surface communities dominated by pelagic-associated Perciformes and Clupeiformes showed more variable assembly patterns, whereas bottom communities enriched in Gobiiformes and Pleuronectiformes were more strongly associated with temperature and dissolved oxygen. Horizontally, among three zones delineated by salinity and hydrographic characteristics, the Mixed Transitional Water (MTW) supported the most diverse and interactive assemblages and functioned as an ecological connector between estuarine (EHSW) and offshore (OWSW) waters. Seasonally, community structure shifted markedly: spring communities exhibited higher diversity and denser trophic networks supported by zooplankton-rich, phototrophic plankton (e.g., Arthropoda, Bacillariophyta), whereas autumn communities were simpler, dominated by Chlorophyta and microbial taxa, with fish assemblages showing increased modularity and reliance on fewer planktonic groups. This seasonal pattern suggests a transition from diversified energy pathways to more constrained trophic coupling. βNTI and Mantel analyses jointly revealed a stratified environment-response-feedback framework driving community differentiation through combined stochastic and deterministic mechanisms. These findings highlight the importance of integrated spatial-temporal monitoring and suggest that protecting transitional zones and spring food-web integrity is critical for ecosystem resilience in the YRE-ECS. Full article

Driving style recognition plays a crucial role in improving the operational safety, fuel efficiency, and intelligent control of commercial vehicles. Under real-world driving conditions, Controller Area Network (CAN) bus data from commercial vehicles simultaneously contain rapid transient variations induced by pedal and braking operations, as well as long-term behavioral trends reflecting driving habits, exhibiting pronounced multi-temporal characteristics. In addition, such data are typically affected by high noise levels, high dimensionality, and highly variable operating conditions, which makes it difficult for methods relying on single-scale features or handcrafted rules difficult to maintain robust and stable performance in complex scenarios. To address these challenges, this paper proposes a driving style classification network, termed the Multi-Scale Convolution and Efficient Channel Attention Network (MSCA-Net). By employing parallel convolutional branches with different temporal receptive fields, the proposed network is able to capture fast driver responses, local temporal dependencies, and long-term behavioral evolution, enabling unified modeling of cross-scale temporal patterns in driving behavior. Meanwhile, the Efficient Channel Attention mechanism adaptively emphasizes CAN signal channels that are highly relevant to driving style discrimination, thereby enhancing the discriminative capability and robustness of the learned feature representations. Experiments conducted on real-world multi-dimensional CAN time-series data collected from commercial vehicles demonstrate that the proposed MSCA-Net achieves improved classification performance in driving style recognition. Furthermore, the potential application of the recognized driving styles in adaptive Automated Manual Transmission shift strategy adjustment is discussed, providing a feasible engineering pathway toward behavior-aware intelligent control of commercial vehicle powertrains. Full article

Given the significant regulatory effect of stumping on carbon exchange processes in the vegetation–soil system, this study conducted a one-year continuous observation of soil respiration in Salix psammophila stands starting six months after stumping, aiming to reveal the changes in soil respiration characteristics and their main driving factors before and after stumping. In 2023, a stumping experiment was established in Dalate Banner, Ordos, and soil respiration in stumped Salix psammophila plantations was continuously monitored from 2023 to 2024. The relationships between soil respiration, soil temperature, and soil moisture were analyzed using fitting models, and the effects of stumping on soil physicochemical properties were further assessed. Results showed that soil respiration exhibited a unimodal diurnal pattern, reaching the annual minimum in winter (December 2023) and the maximum in midsummer (August 2024). Stumping significantly reduced soil respiration rates across diurnal, seasonal, and annual scales. This reduction was attributed both to direct effects, through decreased vegetation carbon input and altered root distribution, and to indirect effects, via changes in soil temperature and moisture. Model fitting indicated that the dual-factor model incorporating soil temperature and moisture explained variations in soil respiration more effectively than single-factor models, with the bivariate nonlinear model providing the best fit. In addition, stumping improved the vertical distribution of soil nutrients by enhancing the accumulation of organic matter and organic carbon in the middle soil layer and increasing total nitrogen content in the surface soil. Soil pH showed only slight variations across treatments and depths, remaining consistently within the strongly alkaline range (9.37–9.56). Full article

This study uses a globally coupled climate framework to examine how regional differences in sulfate emissions, through both direct and indirect aerosol effects, regulate interactions between clouds and radiation and drive nonlinear thermodynamic and hydrological responses in the East Asia and South Asia summer monsoon region. We employ the Community Earth System Model to compare the Shared Socioeconomic Pathways 1–2.6 and 5–8.5 against the historical scenario with perturbations of anthropogenic sulfate. The results reveal regional contrasts in sulfate concentration and aerosol optical depth: direct shortwave radiation increases in East Asia, while South Asia experiences radiation weakening due to higher aerosol optical depth. Indirect aerosol effects induce cloud adjustments, with East Asia developing more low clouds and higher cloud droplet number concentrations and liquid water paths, leading to greater attenuation of surface shortwave radiation and changes in precipitation and convection. Over the Tibetan Plateau, a higher fraction of high clouds and changes in cloud-top heights jointly drive warming, raising net radiation and strengthening both latent-heat and sensible-heat release. South Asia exhibits a north–south oriented precipitation pattern, with intensified warm advection but a distribution shaped by upper and mid-tropospheric circulations. Overall, the coupling of cloud macro-distribution and cloud microphysics emerges as the principal driver, with direct and indirect effects amplifying nonlinear regional responses. To improve predictability, we advocate multi-model comparisons, observational constraints, tighter bounds on cloud-droplet size distributions, liquid water paths, and cloud droplet number concentrations. Full article