Search Results (3,085)

Accommodation establishments constitute a core component of tourism infrastructure, and their location choices directly affect water resource utilization, land pressure, and the spatial equilibrium of tourism development—issues that are particularly acute in vast arid regions. Yet the spatial organization of accommodation supply across extensive drylands characterized by fragmented oasis distribution, and the reasons why standard and non-standard accommodation follow divergent location logics, remain poorly understood. This study addresses three questions: (1) How are nine accommodation categories, differentiated by type and quality, distributed across Xinjiang? (2) Do directional spatial associations exist among categories that are consistent with hierarchical, path-dependent development? (3) Which factors drive these patterns, and do their effects exhibit the nonlinearity and threshold behavior predicted by location theory? Drawing on 12,073 accommodation establishments from the Ctrip platform, we construct a staged analytical framework in which each technique answers a specific question: the nearest-neighbor index and standard deviational ellipse characterize global patterns; kernel density estimation and OPTICS clustering identify local agglomerations; directional local co-location quotients measure asymmetric spatial associations; and XGBoost–SHAP isolates nonlinear drivers and threshold effects. Results reveal a highly concentrated “single-core, multi-center” structure anchored by Urumqi, Yining, and Kashgar, with rapid expansion toward the Ili Valley, Kashgar, and Altay since 2019. Standard accommodation tracks urban centrality and transport nodes, while non-standard accommodation tracks tourism resource endowments, consistent with location-theoretic expectations. Directional co-location analysis reveals hierarchical spatial associations among categories, and driving factors exhibit pronounced nonlinear threshold effects. From a sustainability perspective, the identified thresholds—elevation (1360 m), water-body proximity, and distance to rural tourism demonstration sites (3 km)—constitute quantifiable, spatially explicit sustainability indicators that can be incorporated into planning tools to monitor and steer accommodation development away from ecologically sensitive zones. Global Moran’s I diagnostics of model residuals (reduction of 83–99.7%) suggest that these findings are unlikely to be artifacts of spatial autocorrelation; this diagnostic, however, complements rather than replaces spatially blocked validation. The study contributes category-differentiated, spatially directed evidence for policies balancing tourism expansion against water security and ecosystem integrity, serving sustainable tourism development in arid-region destinations. Full article

At every level of resolution, over many orders of magnitude in time, size, and space, every aspect of the universe is constantly evolving under the pressure of the major forces of nature to resolve gradients of disparity in mass and energy. This imperative for change is channeled by two fundamental constraints: the bias of the Second Law of Thermodynamics (SLT) toward increasing entropy, and the mandate by the Principle of Least Action (PLA) that change must occur by the most direct and efficient path possible. While the SLT would seem to predict that the world would unwind rather than complicate itself, the opposite often occurs at the local level. While the evolutionary imperative drives the universe as a whole toward an ever higher level of entropy, it promotes increased local granularity and complexity to effect change in the net direction required by the SLT over the optimal path prescribed by the PLA. This provides a unifying perspective for all the complexity that astronomical and geophysical forces have created in the physical world, and that random variation and natural selection have induced in the living world―a consequence of nature’s imperative to dissipate energy as thoroughly and efficiently as possible. Full article

In the context of the global green development wave and the rapid iteration of digital technology, digital empowerment has become the core driving force for high-end manufacturing enterprises to achieve green transformation. At present, China’s manufacturing industry is facing the dual pressures of tightening resource and environmental constraints and industrial upgrading. How to break the bottleneck of green development through digital technology innovation has become a key issue to be solved urgently. Based on the techno-economic paradigm, green development theory and value creation theory, this study constructs a theoretical analysis framework for the green development of a digital-enabling manufacturing industry and deeply analyzes the mechanisms of digital technology (such as big data, Internet of Things, artificial intelligence, etc.) in optimizing energy allocation, improving production efficiency and reducing environmental emissions. By selecting 303 manufacturing enterprises of different scales in China as samples, the structural equation model is used for empirical tests. The results show that (1) digital empowerment has a significant positive impact on the green value performance of manufacturing enterprises, and (2) green development plays an intermediary role between digital empowerment and the green value performance of enterprises; that is, digital technology indirectly promotes green development by improving energy conservation and emission reduction, green innovation and green upgrading of enterprises. The research reveals the internal logic of digitally enabling the green development of Chinese manufacturing enterprises and provides a theoretical basis and implementation path for enterprises to formulate the innovation mechanism of digital–green development. Full article

Population immune pressure from vaccination and prior infection continues to drive the evolution of SARS-CoV-2. Systematic characterization of RBD mutations under complex immune backgrounds is essential for understanding viral adaptation and evolutionary trajectories. Here, we applied a deep mutational scanning (DMS) to comprehensively map the neutralization escape landscape of the Omicron variant JN.1 and its descendant lineage XEC, under immune pressure from individuals who experienced Omicron breakthrough infections following three doses of inactivated vaccines. A neutralization escape map for the single amino acid substitutions in the RBD of JN.1 or XEC was generated, and the escape efficiency of each mutation was determined. The results show that RBD escape mutations are hierarchically organized: low-intensity signals are widespread, whereas high-intensity escape is confined to a few key sites. These escape mutations are not confined solely to the receptor-binding motif (RBM) but are broadly distributed across the entire RBD. Many escape sites could accommodate multiple amino acid substitutions. Integration of DMS data with genomic surveillance of circulating variants from 2024 to 2025 revealed significant overlap between experimentally identified escape sites and mutations observed in natural isolates. This overlap increased substantially in 2025, with site concordance rising from 27.17% and 26.81% to 45.09% and 47.10% for JN.1 and XEC, respectively. The natural prevalence of these escape mutations is further shaped by factors such as receptor-binding affinity, protein stability, and epistatic interactions. Overall, our findings suggest that SARS-CoV-2 antigenic evolution follows the pattern of multiple pathways within a constrained space, providing new insights into the adaptive mechanisms of Omicron-derived variants under hybrid immune pressure. Full article

In this study, an adhesive wear model for a gear drive in mixed elastohydrodynamic lubrication (EHL) is proposed. The mixed-EHL model combines the average Reynolds equation with the ZMC rough-surface contact model to determine the asperity contact pressure. By incorporating the fractional film defect into the Archard wear equation, a wear rate model under mixed EHL is developed and verified against published experimental data. This wear-rate model is subsequently coupled with a transient line-contact mixed-EHL model for gears to establish a tooth-surface wear prediction model that iteratively updates the tooth geometry and contact pressure. The evolution of tooth-surface wear under mixed EHL is investigated, and the resulting wear characteristics are compared with those under dry-contact conditions. The influence of tooth-surface roughness is also systematically evaluated. The results indicate that tooth-surface wear is substantially reduced under mixed EHL and that the maximum wear occurs between the lowest point of single-tooth contact and the pitch point. Increasing surface roughness intensifies wear and shifts the maximum-wear location toward the tooth root. These findings demonstrate that appropriate lubricant selection and effective control of tooth-surface roughness are important for improving the wear resistance of gear drives. Full article

Cutterhead driving torque is the primary load indicator of earth-pressure-balance shield machines, yet its dependence on strongly coupled multi-source operating parameters limits the reliability of empirical formulations. This study proposes IALA-edRVFL-FIS-Reg, a fuzzy ensemble deep random vector functional link regression model optimized by an improved artificial lemming algorithm (IALA). The base learner maps continuous operating parameters into fuzzy-state features through a Gaussian-membership Sugeno inference layer, propagates the concatenated raw and fuzzified inputs through stacked randomized hidden layers with direct input links, and obtains layer-wise output weights by regularized closed-form least squares before ensembling, thereby combining fuzzy-state representation with deep random feature mapping without gradient back-propagation. Distinct from the standard ALA, IALA introduces three explicitly defined mechanisms: an error-feedback exploration–exploitation transition factor normalized by the initial-population loss, which replaces the fixed energy factor; an adaptive step size coupling sigmoid error-gating with cosine annealing to preserve jumping capability while refining local search; and a stagnation-counter-triggered directional-disturbance jump for escaping local optima. Using 48,646 valid tunnelling records from 301 rings of Beijing Metro Line 22 and 65 raw and mechanism-based engineered features, the model attains R² = 0.9555, RMSE = 382.52 kN·m, MAE = 302.95 kN·m and MAPE = 9.18%, outperforming eleven benchmarks on a ring-disjoint holdout, previously unseen rings of the same section, IALA yields an R² gain of 0.0104 over ALA. Full article

Background/Objectives: Mechanical power (MP) quantifies the energy delivered to the respiratory system during ventilation and serves as a promising marker for ventilator-induced lung injury (VILI). According to its original definition by Gattinoni, MP reflects the energy transferred from the ventilator to the respiratory system under conditions of deep sedation, passive breathing, neuromuscular blockade, and volume-controlled ventilation. Its role in coronavirus disease 2019 (COVID-19)-associated acute respiratory distress syndrome (ARDS) remains under investigation. This systematic review aimed to synthesize the available evidence on the association between MP and VILI, complications related to mechanical ventilation (MV), and mortality in adult patients with COVID-19 undergoing invasive mechanical ventilation (IMV). Methods: A systematic review was conducted using PubMed-MEDLINE (Medical Literature Analysis and Retrieval System Online) for studies published in recent years, focusing on adult COVID-19 patients undergoing IMV. Inclusion criteria centered on studies reporting MP and its association with VILI, complications, or mortality. Ten studies met eligibility criteria after screening 356 retrieved articles. Results: Most included studies were retrospective and observational, encompassing critically ill COVID-19 patients. Elevated MP was correlated with more severe outcomes, including increased 28-day mortality, prolonged MV, and weaning failure. Franck et al. demonstrated strong correlations between MP and driving pressure, elastance, and positive end-expiratory pressure, emphasizing the importance of calculation methods. González-Castro et al. identified a threshold of 17 J/min, above which mortality risk increased. Stalla et al. highlighted that dynamic MP reductions during prone positioning were associated with survival. Registry-based analyses confirmed that both magnitude and cumulative exposure above 18 J/min increased intensive care unit mortality. Novel indices combining MP with oxygenation parameters improved prognostic accuracy. While absolute MP at initiation provided limited predictive value, temporal trends and individual components were strongly linked to VILI. Conclusions: Higher MP has been associated with adverse clinical outcomes in patients with COVID-19 receiving invasive mechanical ventilation, supporting its potential role as a prognostic indicator. Its dynamic assessment, thresholds, and integration with ventilatory strategies such as prone positioning enhance risk stratification and may guide individualized, lung-protective ventilation. Continuous monitoring and standardized calculation are recommended to optimize clinical decision-making. Full article

Mobility as a service (MaaS) has emerged as a means to promote multimodal public transport and shared mobility trips, with the “Metro+” integrated mobility approach being its primary form. However, it has recently witnessed major global providers’ bankruptcies. Therefore, this study aims to identify factors driving users’ substitution of ride-hailing by “Metro+” MaaS intermodal trips. Specifically, we took ride-hailing trips as the baseline and constructed a multinomial logit (MNL) model incorporating travel-scenario factors, including trip purpose, weather, and urgency. The results show that travel-scenario factors significantly affected “Metro+” MaaS intermodal trip adoption for urban medium–long-distance trips. Users preferred MaaS intermodal trips for long-distance trips amid clear weather and no time pressure, and they favored ride-hailing in extreme weather conditions or for time-sensitive trips. Therefore, MaaS providers should emphasize travel scenarios in their marketing messaging. Finally, marginal rate of substitution (MRS) and elasticity analyses were conducted, and several strategies were proposed: (1) increasing ride-hailing availability; (2) improving transfer facilities and conditions; and (3) implementing dynamic demand matching. The study facilitates the identification of market opportunities for MaaS instead of car usage, contributes to MaaS marketing and service strategy optimization, and promotes sustainable urban transportation system development. Full article

Existing studies often reduce private firms’ environmental behaviors to mere greenwashing, rarely unpacking the underlying mechanisms that drive polluting enterprises to develop and sustain substantive compliance. This paper constructs a “Relational Self-Governing” analytical framework grounded in Self-Governance Theory’s core premise of stakeholder interdependence, identifying local social relationality as a critical mechanism that addresses the theory’s inherent limitations of narrow actor scope and interest-bound action logic. This mechanism drives polluting firms to maintain stable, substantive environmental compliance that exceeds minimum legal requirements. This finding expands Self-Governance Theory’s interest-centered practical strategy spectrum and enriches the analytical connotation of corporate agency embedded in localized environmental governance practices. Hierarchical environmental supervision pressures push firms to align operational targets with governmental environmental objectives, while embedded local social relations reinforce compliance incentives to sustain long-term substantive environmental practices. Three core relational paths underpin this process: goal alignment with local governments, peer recognition among firm owners, and shared environmental responsibility with local residents. Leveraging these paths, firms build stable interactive systems spanning government information sharing, inter-firm mutual supervision, and resident collaborative action, collectively constructing organizational legitimacy. This process gradually institutionalizes substantive environmental compliance and fosters public interest-aligned compliance behaviors. This study counters the one-sided greenwashing narrative in prior literature, extending Self-Governance Theory’s application by unpacking the unique operational logic of corporate substantive compliance in environmental governance. Full article

In the development of carbonate reservoirs via water flooding huff-n-puff, the osmotic pressure effect is frequently overlooked, and existing models inadequately quantify the matrix imbibition and oil expulsion driven by salinity gradients. To address this issue, this study establishes a coupled oil–water two-phase huff-n-puff flow model for carbonate reservoirs that incorporates the interplay between salt concentration and osmotic pressure, which, for the first time, fully couples the van ’t Hoff osmotic pressure equation with solute transport equations for fractured-vuggy carbonate huff-n-puff, filling the gap that prior tight/shale reservoir low-salinity flow models fail to adapt to cyclic injection-soaking production regimes of carbonates. Based on the IMPES (implicit pressure–explicit saturation) numerical simulation method, an equivalent single-nucleus model is adopted to characterize the fractured-vuggy reservoir architecture. The model integrates the osmotic pressure formula, solute transport equation, and two-phase seepage governing equations, enabling a systematic analysis of the mechanisms by which osmotic pressure affects the multi-stage seepage process and the influence of key parameters on development performance. Quantitative simulation reveals three core laws controlled by salinity-induced osmosis: first, osmotic pressure drives water molecules to spontaneously migrate from the high-permeability fracture inner core toward the tight matrix pores, thereby modifying the water saturation distribution, expanding the water sweep region, and smoothing the saturation gradient between the inner and outer cores, which effectively mitigates water channeling in fractured reservoirs. Under the base case (injected water salinity = 1000 mg/L, inner-core permeability = 1000 mD, shut-in time = 80 d), the oil recovery factor with osmotic pressure considered reaches 13.46%, representing a 3.50% increment over the case without osmotic pressure. The recovery factor decreases monotonically with increasing injected water salinity, while it increases with longer shut-in time and higher inner-core permeability, both exhibiting pronounced diminishing marginal returns; the optimal shut-in time is approximately 80 d under the simulated conditions. This work delivers a fully coupled numerical tool and quantitative evaluation standard for osmotic imbibition mechanisms in fractured-vuggy carbonates. The quantified recovery increment and optimal soaking window established herein can directly guide field parameter optimization of injection water salinity, shut-in cycle and fracture reconstruction scale, balancing oil increment revenue and water treatment/well shutdown operation costs for on-site low-salinity huff-n-puff design. Full article

Bovine coronavirus (BCoV) is a key pathogen causing calf diarrhea and bovine respiratory diseases, bringing sustained economic losses to the cattle industry. As an RNA virus, BCoV possesses high mutation and recombination capacities, leading to prominent genomic genetic diversity. The genome contains hypervariable and conserved regions, with the S (especially S1), HE and Open Reading Frame (ORF4) genes serving as major variation hotspots linked to viral antigenicity, tissue tropism shift and immune evasion. Host immune pressure drives strong positive selection on S protein antigenic variation. This review discusses existing research limitations and proposes future directions including genomic surveillance, reverse genetics verification and broad-spectrum vaccine development to support BCoV prevention and control. Full article

Under the guidance of China’s “dual carbon” goals, the importance of carbon information disclosure in textile and apparel enterprises has become increasingly prominent, and its mechanism for enhancing corporate innovation behavior requires further clarification. Based on regression analysis, this study examines listed textile and apparel companies in China’s Shanghai and Shenzhen A-share markets from 2012 to 2024 using a fixed-effects model to empirically test the impact of carbon information disclosure intensity on corporate innovation behavior and its underlying mechanisms. The results demonstrate that increased carbon information disclosure intensity significantly promotes growth in corporate innovation behavior, a core conclusion that remains valid even after conducting a series of robustness tests addressing endogeneity issues. Mechanistic analysis reveals that the positive driving effect of carbon information disclosure intensity on innovation behavior is weakened by investor attention, with investor focus playing a negative moderating role in this relationship: high-quality carbon information disclosure should enhance innovation by reducing information asymmetry; however, under heightened investor scrutiny, short-term investment orientation and management pressure for immediate performance may distort this transmission pathway, thereby inhibiting innovation promotion. Heterogeneity analysis further shows significant differences in the impact of carbon information disclosure intensity on innovation behavior across textile and apparel firms with varying ownership structures and industry categories. This study provides theoretical foundations and practical guidance for advancing carbon information disclosure practices in the textile and apparel sector, guiding investor focus appropriately, and fostering corporate innovation development. Full article

Pressure ulcers are a major clinical problem in patients with severe immobility; however, their pathophysiology extends beyond mechanical pressure and localized ischemia. Repetitive ischemia–reperfusion promotes sustained reactive oxygen species (ROS) production, leading to persistent inflammation, mitochondrial dysfunction, metabolic stress, and chronic wound refractoriness. In this review, we propose the ROS Gatekeeper Hypothesis, in which a ROS-dominant redox microenvironment functions as the central determinant of signaling permissiveness for gaseous signaling molecules. Within this framework, nitric oxide (NO) serves as the principal redox-responsive signaling axis, whereas hydrogen sulfide (H2S) and carbon monoxide (CO) function as complementary redox-responsive modulators whose biological effects depend on the surrounding redox environment. Under ROS-dominant conditions, NO bioavailability is reduced through superoxide scavenging and endothelial nitric oxide synthase (eNOS) uncoupling, while progressive oxidative stress is proposed to drive a transition toward a redox-constrained state in which responsiveness to all three gasotransmitters becomes increasingly limited. Accordingly, therapeutic efficacy is proposed to depend on preservation or restoration of signaling permissiveness rather than gasotransmitter abundance alone. The ROS Gatekeeper Hypothesis provides a unified conceptual framework for interpreting heterogeneous therapeutic responses and guiding future stage-specific, redox-oriented therapeutic strategies for chronic wounds. Full article

Air-operated double-diaphragm (AODD) pumps in industrial sludge transfer suffer from gradual performance degradation due to rheological variations and component wear, yet conventional monitoring relies on scarce labeled fault data. This paper presents an unsupervised multi-sensor framework that requires no fault labels, integrating physics-informed dual health indices, HI-P (sludge load) and HI-V (mechanical stress), with a Gaussian Mixture Model anomaly detector and a physics residual attribution module. Governing equations motivate the use of these indices from five sensors: inlet and outlet flow meters (100 Hz), an air pressure transducer (100 Hz), and inlet and outlet accelerometers (1652 Hz). Trained on one healthy baseline day (86,218 one-second windows), the Gaussian Mixture Model achieves 100% day-level classification performance on the evaluated dataset (F1 = 1.00) across 455,201 test windows from nine operating days, with window-level receiver operating characteristic area under the curve (ROC-AUC) = 0.8580 and precision–recall AUC (PR-AUC) = 0.9082. Residual attribution analytically confirms that pressure residuals drive Episode 1 (HI-P peak 3.63 times baseline, Cohen’s d = 1.70) and vibration residuals drive Episode 2 (HI-V peak 5.44 times the baseline, d = 4.10), providing empirical support for the proposed physics-informed formulation without requiring fault labels. Comparisons with four unsupervised benchmarks confirm that this is the only approach that simultaneously enables label-free operation, physics-driven features, exact attribution, real-world deployment, and perfect day-level F1. Full article

Pregnancy-related acute kidney injury (PR-AKI) increases the risk of chronic kidney disease (CKD) in the postpartum period, yet mechanisms driving this transition remain unclear. Uremic toxins, including indoxyl sulfate (IS), are implicated in AKI-to-CKD progression. Using a rat model of PR-AKI induced by ischemia–reperfusion on gestational day (GD) 18, we assessed IS contributions to renal injury in the postpartum. A subset of rats received the oral adsorbent AST-120 in the postpartum period to reduce IS. Additional groups received IS during pregnancy with or without AST-120 treatment in the postpartum period. Renal function, blood pressure, circulating and urinary IS concentrations, and renal histopathology were evaluated. PR-AKI resulted in sustained postpartum elevations in circulating (p = 0.03) and urinary (p < 0.03) IS, reduced urine output (p = 0.03), increased proteinuria (p < 0.0001), increased serum albumin, and increased renal fibrosis (p = 0.008) compared to normal pregnant control rats. Absorption of indole, a precursor for IS, significantly reduced urinary IS (p = 0.03), reduced serum creatinine (p = 0.006), and attenuated renal fibrosis (p = 0.002) in treated PR-AKI rats. While not significant, indole absorption improved urine output (p = 0.06) and reduced proteinuria (p = 0.07) in treated PR-AKI rats. IS administration during pregnancy recapitulated key features of postpartum CKD. Elevated IS contributes to persistent renal injury following PR-AKI. Postpartum reduction in IS with AST-120 dampens the progression of renal injury. These findings highlight uremic toxins as mechanistic drivers and potential therapeutic targets in post partum CKD following PR-AKI. Full article