Search Results (23,982)

The rapid increase in the number of cars in large cities has made efficient parking management one of the major challenges of urban transportation systems. The present study aims to develop a smart framework for sustainable allocation of parking spaces in urban environments, and presents an integrated approach based on digital twin and multi-objective optimization. In this framework, a digital model of the urban parking system is created that is able to analyze real and simulated data related to parking demand, space occupancy status, and traffic flow and support optimal allocation decisions. The results of the analysis show that using the proposed framework can reduce parking search time by an average of 28%, make the distribution of parking use more balanced, and consequently reduce the amount of pollutant emissions from vehicle movement by about 17%. Also, sensitivity and scalability analyses show that the proposed model also has stable and reliable performance in large urban networks. These results indicate that the proposed framework can be used as an effective tool for developing sustainable parking management systems in smart cities. Full article

We construct and analyze a family of support-defined Brauer-type configurations canonically associated with the Boolean geometry underlying the Grassmann algebra. The construction is governed by an x-support map on monomial labels, which identifies the vertex set with the Boolean lattice $\mathcal{P}\left(\right[n\left]\right)$. This identification yields a Boolean support quiver isomorphic to the directed Hasse diagram of $\mathcal{P}\left(\right[n\left]\right)$, equivalently, to an oriented hypercube. We then equip the family with a canonical cyclic ordering at each vertex and obtain a genuine connected reduced Brauer configuration in the standard sense, together with its associated Brauer configuration algebra and its standard Brauer quiver. A ghost-variable mechanism is introduced to obtain a connected realization without altering any support-controlled invariants. We prove that polygon membership, valencies, multiplicities, Boolean stratification, and the support quiver are invariant under support-preserving ghost relabelings. We also give an explicit description of the standard Brauer quiver and show that it is different from the Boolean support quiver. On the algebraic side, we derive closed formulas for the center dimension, the algebra dimension, and the normalization constant of the induced weighted distribution. On the probabilistic side, we distinguish the vertex entropy from the layer entropy, establish an exact decomposition of the former by Hamming layers, and show that the layer distribution is asymptotically concentrated on the middle layers, while extremal vertices and any fixed maximal path contribute a negligible fraction of the total weight. As a consequence, the layer entropy satisfies a logarithmic asymptotic law. We also investigate geometric consequences of the Boolean model transported through the support identification. Coordinate projections produce a rigidity phenomenon for antipodal pairs, providing a combinatorial analogue of Greenberger–Horne–Zeilinger (GHZ)-type fragility, whereas the first Boolean layer exhibits a persistence property analogous to W-type robustness. Together, these results exhibit a concrete bridge between Grassmann combinatorics, Brauer configuration theory, hypercube geometry, and entropy asymptotics. Full article

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

In this study, a multiphysics finite-element model was developed for the deposition of W–C–B coatings in a hot-wall tubular CVD reactor from a gas mixture of tungsten hexafluoride (WF₆), hydrogen (H₂), and trimethylamine borane ((CH₃)₃N:BH₃) at 550 °C and 5 Torr. The aim of this work is to deepen the understanding of reactant transport mechanisms and to optimize the process parameters for obtaining targeted tungsten carbide or boride phases. The simulations were performed in COMSOL Multiphysics (ver. 6.1) using a 2D axisymmetric formulation that couples laminar flow, heat transfer, and multicomponent diffusion, accounting for heterogeneous chemical reactions at the reactor walls. The obtained spatial distributions of reactant concentrations demonstrate precursor depletion along the reactor length. A comparison of the calculated B/W and C/W stoichiometric ratios for 13 operating conditions with experimental data confirms a transition from W and W–B phases at low trimethylamine borane (TMAB) flow rates to tungsten carbide-based coatings at higher flow rates. Furthermore, a parametric sweep was utilized to determine the optimal parameter range for the synthesis of tungsten borides. Full article

Grassland lake basins are mostly located in arid and semi-arid regions and represent typical ecologically fragile zones. As a representative inland lake in the cold and arid region of northern China, Hulun Lake serves as a crucial node for maintaining the ecological balance of the Hulunbuir grassland. Studying its ecological carrying capacity is particularly key to implementing the philosophy of a holistic approach to the management of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts. Based on data from 2018 to 2024 across four cities (banners, districts) in the Hulun Lake basin, this study constructs an evaluation system to measure ecological carrying capacity across three dimensions—ecosystem support, human activity pressure, and socio-economic response—using the Pressure–State–Response (PSR) model. Spatial analysis and geodetector methods are employed to explore its spatiotemporal differentiation and influencing factors. The findings are as follows: (1) The ecological carrying capacity in the Hulun Lake basin exhibits a significant spatial differentiation pattern, characterized by a gradient of “high in the east, low in the west; high in pastoral areas, low in urban areas.” (2) The overall trend in ecological carrying capacity shows a slow increase amid fluctuations, but the carrying capacity level remains relatively low. (3) The core driving forces of ecological carrying capacity primarily stem from the dimensions of population quality and infrastructure, while the direct influence of agricultural production is relatively limited. (4) Transportation infrastructure plays a strongly influential role as a driving mechanism of ecological carrying capacity in the Hulun Lake basin. Its synergy with factors such as education, information, and industry significantly affects both the ecosystem support capacity and the socio-economic responses of the basin. This study provides a reference for ensuring the ecological security of the Hulun Lake basin. Full article

Electric vehicles represent a promising path toward reducing transportation-related greenhouse gas emissions, but partisan polarization presents a significant barrier to their widespread adoption in the United States. This study provides a detailed look at the auto industry’s strategies for reframing electric vehicles (EVs) to resonate with mainstream American consumers, and it contributes to scholarly understanding of how sustainable products are framed to politically diverse audiences. Through a comprehensive content analysis, we analyze the persuasive strategies in all available EV video advertisements run in the U.S. from 2018 to 2023. Spanning 263 unique advertisements and 62 vehicle models, our analyses reveal the ways that nature and the environment are used in EV ads. Our data show that 90% of EV ads do not make any reference to sustainability, and 71% do not employ nature in any way. Instead, EV ads tend to emphasize vehicle features and performance, and they portray EVs as a futuristic transportation revolution. We situate these findings within the broader literature on partisan polarization of environmental issues, identity signaling in green consumer behavior, and green marketing strategy. We argue that the near-total absence of sustainability messaging in EV advertising reflects an industry-wide strategy to decouple electric vehicles from environmental identity and reframe them as mainstream consumer products. Full article

This study used Computational Fluid Dynamics (CFD) with the Reynolds-Averaged Navier–Stokes (RANS) k-ω Shear Stress Transport (SST) model to evaluate how crop presence and evapotranspiration affect airflow and thermal stratification in a naturally ventilated tropical tomato greenhouse. Three configurations were simulated: SP-SC-R (No Plants—No crop thermal load—Radiation), CP-SC-R (Crop Present—No crop thermal load—Radiation), and CP-CC-R (Crop Present—Crop thermal load (233.68 W·m⁻²)—Radiation). Mesh independence analysis yielded numerical uncertainties of 1.58% (velocity) and 1 × 10⁻⁶ (temperature). Vegetation reduced canopy air velocity by 55% (from 4 m·s⁻¹ to values below 2 m·s⁻¹). Evapotranspiration enhanced buoyancy-driven mixing, decreasing temperature gradients by up to 1.5 °C, but thermal stratification persisted above 4.5 m in all cases (vertical gradients 0.31–0.42 °C·m⁻¹; maximum roof temperature 37.95 °C). Extreme wind speeds (greater than 20 m·s⁻¹) occurred in the leeward span but above the main foliage. Natural ventilation alone is insufficient for tomato cultivation under tropical conditions. Practical recommendations include increasing roof vent area, installing windbreak baffles, and adopting hybrid ventilation. Future work should use unsteady, RANS/large-eddy simulation (LES), porous media models based on leaf area density (LAI), and field validation. This study demonstrates that coupling crop geometry and evapotranspiration is essential for realistic greenhouse CFD modelling in warm climates. Full article

Background: Gliomas are among the most common and heterogeneous primary brain tumors, exhibiting substantial molecular and transcriptomic diversity that complicates diagnosis, grading, and treatment planning. Advances in artificial intelligence (AI), particularly machine learning (ML), offer powerful opportunities to analyze high-dimensional gene expression data and support precision oncology. Methods: This study proposes an interpretable ML framework to classify brain tumor subtypes—glioblastoma, astrocytoma, and oligodendroglioma—and to predict tumor grades (2, 3, and 4) using microarray-based gene expression data. The analysis was conducted on the REMBRANDT dataset, comprising 464 labeled samples (221 glioblastoma, 148 astrocytoma, 67 oligodendroglioma, and 28 controls) and 314 tumor samples for grade classification. Results: The ML models achieved high performance for disease classification, with accuracies of 99.6% (AUC 99.89%) for glioblastoma, 98.3% (AUC 99.83%) for astrocytoma, and 98.95% (AUC 100%) for oligodendroglioma. Tumor grade predictions also performed strongly, achieving 83.7% accuracy (AUC 88.2%) for grade II vs. III, 91.3% (AUC 94.8%) for grade II vs. IV, and 84.2% (AUC 90.8%) for grade III vs. IV. SHAP analysis identified key genes contributing to the model predictions (e.g., WIF1, STX6, RGS5, and ACTR2), and KEGG enrichment identified the candidate pathways involved in vesicular transport, metabolism, and immune signaling. Conclusion: Overall, our findings demonstrate that interpretable ML models can accurately differentiate glioma subtypes and grades, and SHAP analysis can help identify the strongest predictors of our models. These findings provide additional insights into the heterogeneous genetic and molecular landscape of brain gliomas and are intended to complement, not replace, conventional histopathological diagnosis. Full article

This paper provides a systematic review of recent applications of NLP methods for analyzing traffic crash reports, with a focus on estimating crash severity, crash duration, and crash causation. The review covers prior research using probabilistic topic modeling methods such as LDA, STM, and hierarchical Dirichlet processes in addition to research using transformer-based language models, which include encoder-based models like BERT and PubMedBERT as well as decoder-based models like GPT, GPT2, ChatGPT, GPT-3, and LLaMA. The review starts with a systematic literature selection process with predefined inclusion criteria. We categorize the reviewed studies into the following application areas: crash severity prediction, risk factor identification in crashes, and road safety analysis. The results show several complementary advantages of using different NLP techniques to achieve different analytical goals. Topic models allow for interpretable and exploratory pattern discovery, while encoder models are well-suited for structured prediction problems. Decoder models have the additional flexibility to perform zero-shot and few-shot reasoning, which makes them useful for reasoning about under-sampled or under-reported data. Across the literature, hybrid methods that combine text and structured data outperform individual methods in terms of prediction accuracy and broad applicability. Challenges across the literature include class imbalance, lack of standardization in preprocessing and evaluation methods, and the tradeoff between prediction accuracy and interpretability of prediction models. These findings highlight the importance of aligning model selection with data availability and operational constraints, pointing toward future research directions in hybrid modeling frameworks, standardized evaluation protocols, and real-world deployment of NLP-driven traffic safety systems. Full article

Direct methanol fuel cells (DMFCs) offer significant advantages including high energy density and rapid refueling, making them promising power sources for portable electronic products. However, their practical application, particularly in passive systems, is hindered by critical mass transport limitations: water flooding in the cathode and CO₂ bubble blockage in the anode. Herein, a novel dual-gradient patterned wettability current collector (CC) was designed to alleviate this mass transport impedance. The design uniquely integrates wedge-shaped gradients with surface energy gradients to create a unified, self-driven mechanism for efficient water and CO₂ bubble transport at both electrodes. A mathematical model was developed to quantitatively evaluate the effects of the dual-gradient structure. The results confirm that water removal is enhanced when the cathode current collector features a hydrophobic periphery with a dual-gradient patterned wettability interior on the gas-diffusion-layer side and a fully hydrophilic air-side surface, whereas an inverted pattern facilitates anode CO₂ removal. Optimal fabrication parameters on 316 L stainless steel were established by investigating laser scanning conditions and low-surface-energy agent concentrations. The experimental results show that the passive DMFCs incorporating the optimized current collectors delivered marked performance improvements. At 1 mol·L⁻¹ methanol, the novel anode and cathode current collectors increased peak power density by 15.6% and 14.5%, respectively. Electrochemical impedance spectroscopy revealed a 31.4% and 31.9% reduction in mass transfer resistance of the cell with novel anode and cathode current collectors, respectively, confirming improved gas–liquid self-driven efficiency. Furthermore, the new cells exhibited substantially enhanced long-term stability over 18 h of continuous discharge, attributed to the robust wettability achieved via laser–silane modification. Overall, these findings suggest that the proposed dual-gradient wettability design is a promising method for improving internal mass transport, potentially supporting the development of more robust passive DMFCs. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

Urban agglomerations have become the dominant spatial platform of urbanization, regional coordination, and economic transformation in China. Yet whether the expansion of agglomeration scale at the urban-agglomeration level alleviates or intensifies energy use remains insufficiently understood. Extending the scale of analysis from individual cities to integrated urban agglomerations, this study investigates 64 cities in four major Chinese urban agglomerations, including Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu–Chongqing, over the period 2006–2023. Using panel data models, this study examines the impact of the scale agglomeration within urban agglomeration on urban energy intensity. The results show that the overall agglomeration scale generated by urban agglomeration formation significantly suppresses energy intensity while indicating a robust energy-saving effect: every 10% increase in agglomeration scale is associated with a decline of approximately 0.0893 million tons of standard coal per CNY 100 million of GDP. This finding remains stable after addressing endogeneity concerns and performing a series of robustness checks. Mechanism analyses further suggest that this effect operates primarily through talent agglomeration, technological progress, and public transportation expansion. In addition, the energy-saving effect is more pronounced in smaller cities, cities with lower administrative rank, cities with weaker factor mobility, and cities characterized by poorer air quality but stronger public environmental attention. These findings contribute to the literature on urban agglomeration and green development by showing that the agglomeration scale within urban agglomerations can generate inclusive energy-efficiency gains, especially for relatively disadvantaged cities, thereby offering important implications for spatial governance and low-carbon transition in rapidly urbanizing economies. Full article

Chemotherapy-induced mucositis (CIM) is a dose-limiting toxicity of cancer therapy that is mainly associated with mitochondrial dysfunction in epithelial cells. We investigated whether GV1001, a mitochondrial protective peptide from human telomerase reverse transcriptase (hTERT), attenuates 5-fluorouracil (5-FU)-induced mucositis in a murine model. 5-FU induced notable mortality, leukopenia, and mucositis in the gastrointestinal (GI) tract, including tongue, esophagus and small intestine. It promoted epithelial–mesenchymal transition (EMT), nuclear factor kappa-B (NF-κB) activation, systemic and mucosal inflammation, DNA damage, impaired cell proliferation, and apoptosis throughout the GI tract. GV1001 blocked 5-FU–associated mortality, significantly attenuated leukopenia, and notably prevented mucositis. GV1001 also suppressed 5-FU-induced DNA damage, EMT, loss of proliferative capacity, apoptosis, and NF-κB activation in mucosal epithelium. In normal human keratinocytes, 5-FU inhibited the cell proliferation, disrupted mitochondrial function, as evidenced by reduced mitochondrial membrane potential, increased reactive oxygen species (ROS) production, impaired electron transport chain (ETC) complex integrity, decreased ATP synthesis, and cytochrome c release into the cytosol. GV1001 markedly mitigated these 5-FU-induced mitochondrial defects. Taken together, GV1001 mitigates CIM by most likely preserving mitochondrial integrity and function, supporting its potential as a strategy to prevent cancer chemotherapy-associated mucosal injury in patients. Full article

Uranium is a resource with exceptionally high energy density, releasing substantially more energy per unit mass than conventional fossil fuels. In uranium mining, in situ leaching offers significant advantages over open-pit and underground mining, including reduced environmental impact, lower operational costs, enhanced safety, and improved controllability. Within the in situ leaching framework, acid leaching faces limitations in high-carbonate ore bodies, while alkaline leaching is unsuitable for deposits rich in pyrite and other sulfide minerals due to side reactions and precipitate formation that hinder leaching efficiency. In contrast, CO₂-O₂ leaching, as a neutral leaching approach, exhibits broader applicability across diverse ore types and geological settings. Incorporating CO₂ into the leaching process also enables carbon utilization, offering a potential pathway to cleaner uranium extraction aligned with carbon reduction and sustainable energy goals. This review systematically examines the geochemical principles, as well as hydrological and transport phenomena governing CO₂-O₂ in situ leaching. Recent technological advances are summarized, including progress in reaction kinetics and leaching efficiency, leaching solution design and control, and reservoir modification. Furthermore, the techno-economic implications of CO₂-O₂ in situ leaching are critically assessed, with particular emphasis on operational cost structures and the evolution of techno-economic analysis methodologies. On this basis, key challenges and future directions are identified. This work aims to support the future large-scale and economically efficient deployment of CO₂-O₂ in situ leaching for uranium resource development. Full article

Sandstone-hosted uranium deposits in the western Qaidam Basin are spatially associated with hydrocarbon-bearing structures, yet the specific roles of different sulfur sources in uranium mineralization remain poorly constrained. This study aims to distinguish the contributions of bacterial sulfate reduction and hydrocarbon-associated sulfate reduction to uranium precipitation by integrating detailed petrography, in situ trace element analyses, and sulfur isotope measurements of chalcopyrite from the Yuejin Ⅱ deposit. Chalcopyrite is restricted to high-grade uranium ores and occurs intergrown with uranium minerals, pyrite, baryte, and carbonate cements. Trace element patterns indicate that oxidizing brines acted as the main transport medium for both uranium and copper, as evidenced by positive correlations between U and brine-related elements (Ba, Sr, Na, K). Positive U-Th correlations with relatively constant Th/U ratios (0.027–0.225) reflect a combination of source composition, fluid transport capacity, and limited thorium remobilization in this near-source, hydrocarbon-rich environment. Correlations between U and high field strength elements (Sn, W) point to a highly evolved granitic origin, with Altyn granitoids likely supplying the copper. Sulfur isotopes show a clear bimodal distribution: one group exhibits heavy δ³⁴S values (+6.9‰ to +18.5‰), while the other shows extremely light values (–36.0‰ to –44.6‰). The light group reflects bacterial sulfate reduction in shallow strata, supported by framboidal pyrite textures, whereas the heavy group corresponds to surface-derived sulfate reduced at hydrocarbon-associated redox fronts, rather than direct incorporation of deep H₂S. The lack of intermediate δ³⁴S values indicates that two discrete sulfur reduction mechanisms coexisted within the same deposit, refining genetic models for uranium mineralization in petroliferous basins and challenging frameworks that invoke a single dominant sulfur source. Full article