Search Results (11,135)

Traditional villages in the Jiangnan region have experienced significant spatial transformation under rural revitalization, yet thermal environment regulation in public spaces remains insufficiently addressed. This study examines how spatial morphology influences microclimate and outdoor thermal comfort during summer and proposes evidence-based climate-responsive strategies. Three representative provincial-level traditional villages in Wuxi—Yaogeli Village, Zhu Village, and Huangtutang Ancient Village Area—were selected as case studies. Public spaces were classified into open, semi-open, and semi-private types according to spatial openness. Field microclimate measurements and thermal comfort surveys were conducted, and Physiological Equivalent Temperature (PET) was calculated to evaluate thermal conditions. Results show that rural public spaces generally experience significant summer heat stress, with PET exceeding the neutral range during most daytime periods. Spatial openness is significantly positively correlated with PET, identifying solar radiation as the dominant thermal driver. Water bodies provide cooling benefits within limited spatial ranges, constrained by configuration and ventilation conditions. Ecological and composite surfaces reduce heat accumulation compared to single materials. These findings indicate that thermal comfort in rural public spaces is a multi-factor and interaction-driven process, providing empirical support for climate-adaptive rural renewal. Full article

Background: The aim of our study was to evaluate the association of angiopoietin 2 (ANGPT2) rs2442598 and vascular endothelial growth factor A (VEGFA) rs2010963 with diabetic nephropathy (DN) in Slovenian subjects with type 2 diabetes mellitus (T2DM). Angiopoietin–endothelial tyrosine kinase receptor (Ang-Tie2) and VEGF-A signaling regulate glomerular endothelial stability and permeability and may contribute to DN susceptibility. Methods: We conducted a case–control study including 897 unrelated Slovenian subjects with T2DM (344 DN cases; 553 long-standing T2DM controls without DN). ANGPT2 rs2442598 and VEGFA rs2010963 were genotyped using TaqMan assays. Genetic associations were analysed using co-dominant, additive, dominant, and recessive genetic models with logistic regression adjusted for waist circumference, systolic blood pressure, fasting glucose, and triglycerides. Results: ANGPT2 rs2442598 was significantly associated with DN, with increased risk in carriers of the C allele, including a significant additive per allele effect (OR 1.39, 95% CI 1.10–1.74) and a dominant model effect (OR 1.47, 95% CI 1.11–1.96). In contrast, VEGFA rs2010963 showed no evidence of association across genetic models. Conclusions: In Slovenian patients with T2DM, ANGPT2 rs2442598 is associated with DN, whereas VEGFA rs2010963 is not. This association suggests that ANGPT2 genetic variation may influence DN risk and supports further functional work to define the biological effects of rs2442598. Full article

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

Malaria, caused by Plasmodium parasites, remains a global health crisis, necessitating novel therapeutic strategies targeting host–parasite interactions. During liver-stage infection, parasites exploit host vesicular trafficking machinery, particularly SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) proteins that mediate membrane fusion. Using a CRISPR/Cas9 knockout system in HeLa cells combined with advanced microscopy of Plasmodium berghei-infected HeLa cells, we identified specific endolysosomal SNAREs including Vesicle-Associated Membrane Protein 7 (VAMP7), Vesicle-Associated Membrane Protein 8 (VAMP8), Vesicle Transport Through Interaction With T-SNAREs 1B (Vti1B), and Syntaxin 7 (Stx7) to be recruited to the parasitophorous vacuole membrane (PVM) with distinct temporal profiles. This demonstrates the parasite’s precise manipulation of host endolysosomal trafficking pathways. VAMP7 and Vti1B were localized to the PVM within 30 min post-infection, suggesting potential roles during invasion, while VAMP8 and Stx7 appeared later around 24 h post infection (hpi), coinciding with increased nutrient acquisition. Single gene deletions showed minimal impact, but combinatorial knockouts (KO) revealed critical redundancy. VAMP7-VAMP8 as well as VAMP7–Vti1B double KO significantly reduced parasite infection and growth, with Vti1B playing a dominant role. Triple KO phenotypes mirrored VAMP7-Vti1B disruption, underscoring Vti1B’s dominant role. SNARE depletion also impaired the lysosome–PVM association and LAMP1 positive vesicle recruitment. Our findings indicate Plasmodium hijacks a coordinated host SNARE network to fuse lysosomes with the PVM for nutrient uptake. Targeting Vti1B-containing complexes disrupts this pathway without host cell toxicity, offering a promising host-directed antimalarial approach. Full article

This study focuses on the reliability issue of magnetic fluid (MF) in the magnetic fluid sealing technology for the upper guide bearing (UGB) of hydro-generators and proposes selection schemes for MF suitable for different models of hydro-generators. By analyzing the performance indicators of five base fluids and MFs, including the acid value, flash point, oxidation stability, magnetorheological performance, breakdown voltage, dielectric loss factor and volume resistivity, the influencing factors of the insulating performance of MFs and their mechanism in sealing the UGBs of hydro-generators are investigated. The results show that, when the spindle speed is below 27 rpm, the viscosity of the MF is dominated by the magnetic field strength, while, when the speed exceeds 27 rpm, the viscosity of the MF is dominated by the shear rate. In addition, the addition of magnetic nanoparticles (MNPs) causes the breakdown voltage of the base carrier liquid to fluctuate in the range of 31.2–55.9 kV, the dielectric loss factor to fluctuate in the range of 2.5 × 10⁻⁴–6.7 × 10⁻³, and the volume resistivity to fluctuate in the range of 2.8 × 10¹¹–2.6 × 10¹² Ω·m. The research results provide a theoretical basis for the application of high-efficiency and stable magnetic fluid sealing technology. Full article

This study systematically quantified the carbon footprint generated by China’s consumption of eight major fossil energy sources (coal, coke, crude oil, petrol, kerosene, diesel, fuel oil, and natural gas), alongside the carbon carrying capacity of four vegetation ecosystems (forest, grassland, wetland, and crop), based on the IPCC inventory methodology. ArcGIS spatial analysis was employed to reveal the spatiotemporal distribution, while the STIRPAT model identified drivers of energy carbon footprint pressure (ECFP). Concurrently, the GM (1,1) model predicted evolution trends for both energy carbon footprint (ECF) and vegetation carbon carrying capacity. Results indicated that: (1) ECF increased from 12,039.89 million tons in 2015 to 13,896.41 million tons in 2022, representing a cumulative growth of 15.42%; (2) vegetation carbon carrying capacity increased from 4710.54 million tons in 2015 to 5300.76 million tons in 2022, representing a cumulative growth of 12.53%; (3) STIRPAT model analysis indicated that economic growth and technological progress were the dominant factors influencing ECFP; and (4) GM (1,1) predicted that the ECF would continue to grow at a slower pace by 2026, while vegetation carbon carrying capacity would steadily increase. It was concluded that optimizing the energy structure and strengthening vegetation conservation could effectively alleviate ECFP, providing crucial support for the carbon neutrality objectives of China. Full article

The 2022 Chongqing wildfires, occurring during an unprecedented heatwave, severely degraded subtropical forest ecosystems and disrupted hydrological cycling. We developed an integrated artificial intelligence framework combining Long Short-Term Memory and Transformer architectures to simulate post-fire evapotranspiration (ET) dynamics using 37 months of field observations (2022–2025) across 24 plots with four burn severities. The Penman–Monteith–Leuning model provided physically based benchmarks. Results revealed three distinct recovery phases: destruction/stagnation (0–7 months, ET at 6%–10% of pre-fire levels), rapid recovery (8–19 months), and stabilization (20–37 months, reaching 100% ET recovery). The coupled LSTM–Transformer ensemble achieved superior performance (RMSE = 0.10 mm·day⁻¹, NSE = 0.98), outperforming single models by 31% in uncertainty reduction. SHAP analysis identified phase-dependent factor shifts: soil water content dominated Stage I (42.5%), while leaf area index (LAI) controlled Stages II–III (>48%). A bimodal LAI time-lag effect emerged: 4–7 days (leaf water potential equilibrium, 27.7% contribution) and 8–14 days (root uptake compensation, 21.7%). Burn severity significantly extended time-lags (severe burns: 12/21 days vs. unburned: 5/12 days), indicating hydraulic system reconstruction requirements. Despite equivalent LAI recovery, severe burns maintained 12%–15% ET reduction, suggesting lasting hydraulic limitations. This study demonstrates that physics-constrained AI models effectively capture complex post-fire ecohydrological dynamics while providing mechanistic interpretability, advancing understanding of vegetation–water coupling reconstruction under increasing fire frequency. Full article

Wind erosion is a multidimensional, dynamic process driven by natural and anthropogenic factors, but existing statistical methods struggle to capture its complex nonlinear relationships, resulting in incomplete quantification of drivers and their spatial variability. To address this, we integrate the Revised Wind Erosion Equation (RWEQ)model with explainable artificial intelligence to disentangle the spatiotemporal positive and negative effects of dominant drivers and their synergistic interactions in Inner Mongolia. Results show that, from 2000–2022, wind erosion has been decreasing on average by 1.1 t·ha⁻¹·yr⁻¹, mainly in the western deserts and locally in Hulunbuir sandy land. Severe erosion is mostly due to nature (78.7%) rather than anthropogenic (21.3%). Normalized difference vegetation index (NDVI), clay content (CL), windy days (WD), precipitation (PRE), temperature (TEM), and sand content (SA) were found to be the most important drivers of wind erosion. Critical threshold conditions for severe wind erosion are NDVI < 0.14, CL < 12%, GD > 26, PRE < 73.15 mm, and SA > 66%. When there is a certain combination of variables, wind erosion risk is greatly increased, which mainly happens in the western part of Alxa, Bayannur, and the area near the desert edge. Wind erosion control should shift toward region-specific precision management, including engineering protection, optimized grazing management, and vegetation restoration. Full article

Kazakhstan is one of the world’s major wheat producers and exporters, playing an important role in regional and global food security. However, increasing quality requirements in domestic and export markets have exposed limitations in the country’s capacity to consistently supply high-quality spring bread wheat (Triticum aestivum L.). This review aims to assess the current status of spring wheat grain quality in Northern Kazakhstan, identify the main factors driving its variation, and outline pathways for quality improvement. The analysis is based on published literature, official statistics, national quality standards, and recent data on wheat production, grading, breeding systems, agronomic practices, and trade patterns. The review reveals that wheat production is dominated by medium-quality grain (primarily class 3), while high-quality classes suitable for premium and improver markets represent a small share. Compared with major exporters such as Canada, the United States, and Australia, Kazakh wheat is generally inferior across key quality parameters. Structural constraints include the limited integration of quality assessments within breeding programs, insufficient laboratory infrastructure, weak agroecological zoning by quality classes, and suboptimal agronomic management, particularly regarding nitrogen use. Environmental heterogeneity and climate change further influence the yield–quality balance. Overall, the findings suggest that improving wheat grain quality in Kazakhstan will require coordinated advances in breeding, agronomy, institutional capacity, and market alignment, enabling a gradual shift toward a more competitive, quality-oriented wheat production system. Full article

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article

Within the carbon capture, utilization and storage (CCUS) chain, buried CO₂ pipelines are an indispensable engineering solution under complex topographic conditions. Experimental investigations show that leakage from buried supercritical CO₂ (sCO₂) pipelines features a two-stage pressure decline: an initial rapid drop driven by high leaking medium mass flow, followed by a linear decrease governed by homogeneous liquid CO₂ vaporization. Notably, the choking flow effect homogenizes linear pressure drop rates across distinct experimental conditions. Leakage orifice diameter is a dominant factor for pipeline temperature distribution: small orifices yield consistent temperature drop rates at different vertical pipeline positions, while larger ones cause faster cooling at the pipeline bottom, forming significant vertical temperature gradients that intensify closer to the leakage orifice. Leakage direction and initial pipeline pressure are key regulators of leakage dynamics: vertical upward leakage (0°) leads to faster pressure drops due to the reduced soil resistance, and elevated initial pressure not only intensifies the pressure drop rate and amplifies CO₂’s endothermic effect but also modulates the phase transition pathway of sCO₂ during leakage. Full article

Maritime Autonomous Surface Ships (MASS) are progressing from proof-of-concept to engineering test and initial application phases due to advancements in intelligent sensing, automatic control, and communication technologies. However, numerous studies have shown that the improvement of automation level does not linearly reduce human factor risks. Instead, it exhibits more complex evolutionary characteristics at the medium automation level. In particular, MASS Level 2 (MASS L2) features a “system-dominated, human-supervised” operational mode, and its human factor risks have become one of the key factors restricting the safe operation, large-scale application and sustainable long-term deployment of autonomous ships. This study employs a systematic literature review to analyze 89 core articles (2020–2025) and summarizes the theoretical basis, risk characteristics, and evolutionary trends of human factor risk research in MASS L2. The review results indicate that the current research consensus has gradually shifted from the traditional “human error”-centered explanatory paradigm to a systematic understanding of “information mismatches, opacity, and coupling failures in the human-machine-shore collaborative system”. Typical human factor risks in MASS L2 are mainly manifested as the degradation of supervisory cognition and situation awareness, imbalance in trust in automation, vulnerability in mode switching and takeover, skill degradation, and structural risks in ship-shore collaboration. Based on these findings, this study constructs a classification system and a comprehensive analysis framework for human factor risks in MASS L2, reveals the interaction relationships and dynamic evolution mechanisms among different risk types from a system-level perspective, and further discusses the limitations of existing research in terms of methods, data, and engineering applicability. Finally, considering the development trends of autonomous ship technology, this study proposes future research directions in human factor theoretical modeling, dynamic risk assessment, system design, and operation management. This study aims to provide a systematic knowledge framework for human factor risk research in MASS L2 and offer references for the safety design, safety management, and development of higher-level automation of autonomous ships, while supporting the sustainable and safe advancement of the global intelligent shipping industry. Full article

The tumor suppressor protein p53, encoded by the TP53 gene, is known as the “Guardian of the Genome”, and alterations in TP53 are common to more than 50% of human cancers. p53 is a critical regulator of cellular responses to several stress conditions, such as DNA damage, oncogene activation, and nutrient starvation. p53 was traditionally described as a single transcription factor; however, now it is recognized as a complex family of isoforms generated through alternative promoter usage, alternative splicing, and alternative initiation of translation. These processes give rise to at least 12 distinct p53 isoforms in humans, including p53α (the canonical full-length isoform), p53β, p53γ, Δ40p53, Δ133p53, and Δ160p53, each with unique structural and functional properties. p53 isoforms differ in the presence or absence of specific and fundamental domains located both at N- and C-terminal ends, determining an altered DNA-binding potential, transcriptional activity, and protein–protein interactions. For instance, Δ133p53 isoforms lack part of the N-terminal domains and can exert dominant-negative effects over full-length p53α or modulate alternative transcriptional programs. Similarly, p53β and p53γ isoforms, which have a unique C-termini, influence cellular senescence. The expression patterns of p53 isoforms are tissue-specific and dynamically regulated under both physiological as well as pathological conditions. Alterations of isoform balance have been involved in tumor progression, metastasis, and therapy resistance. Importantly, specific isoforms can either enhance or limit canonical p53 tumor suppressor functions, thereby contributing to the functional diversity of the p53 network. Overall, the p53 isoform landscape adds a critical layer of complexity to p53 biology. In this review, we summarize the mechanisms underlying the production of p53 isoforms, their functions, and their expression in cancer, with the idea that a better understanding of the differential regulation and functional interplay of p53 isoforms may provide novel biomarkers and therapeutic targets in cancer. Full article

While existing research analyzes AI’s impact on platform enterprises’ competitive advantages from technological or organizational perspectives, it fails to adequately account for how multiple factors combined shape competitive advantages. From the perspective of situated AI theory, this study examines how the combinations among AI application characteristics, situated AI activities, platform enterprise attributes, and environmental characteristics collaboratively build platform enterprises’ competitive advantages. Drawing on panel data from Chinese listed platform enterprises and employing fuzzy-set Qualitative Comparative Analysis (fsQCA), this study reveals that (1) AI technology innovation and recasting AI are necessary conditions for platform enterprises to establish competitive advantages; (2) AI-enabled competitive advantages emerge from three types of configurations, the situated AI dominance type, the situated AI subsidiary type, or the collaborative drive type; (3) the AI-enabled combinations result in competitive advantages by three paths, AI internalization, AI leverage, and AI collaboration; and (4) the AI-enabled competitive advantages are more likely to be achieved by innovation platforms than by transaction platforms. These research findings fill the knowledge gap in AI-enabled competitive strategy, enrich the literature on situated AI theory, and offer practical guidance for platform enterprises’ AI applications. Full article

This study investigates the spatiotemporal variability of the near-surface air temperature lapse rate (NSATLR) in Calabria, a region representative of typical Mediterranean environmental and climatic conditions. Through the integration of observational datasets and model simulations, a global sensitivity analysis using the Sobol method, and Bayesian linear regression modelling across annual, seasonal, and monthly scales, the primary drivers of near-surface air temperature (NSAT) variability were identified. Results demonstrate that altitude is the dominant factor influencing temperature distribution, with minimal contributions from other geographical parameters such as latitude, longitude, and proximity to the sea. The Bayesian models yielded robust performance for mean and maximum temperatures, while minimum temperature proved more challenging to predict. Lapse rate analyses confirmed a consistent inverse relationship between temperature and elevation, with the steepest gradients observed for Tmin. In particular, a significant long-term decline in lapse rates over the past 70 years, especially during winter and autumn, points to accelerated warming at higher elevations, primarily driven by rising Tmin values. This trend suggests a gradual homogenization of temperature across altitudes, with important implications for ecosystem dynamics, snowpack stability, and climate-sensitive sectors such as agriculture and urban planning. Full article