Search Results (930)

The use of multimodal data can effectively compensate for the lack of temporal resolution in streetscape imagery-based studies and achieve hourly refinement in the study of street walkability dynamics. Exploring the 24 h dynamic pattern of urban street walkability and its diurnal variation characteristics is a crucial step in understanding and responding to the accelerated urban metabolism. Aiming at the shortcomings of existing studies, which are mostly limited to static assessment or only at coarse time scales, this study integrates multimodal data such as streetscape images, remote sensing images of nighttime lights, and text-described crowd activity information and introduces a novel approach to enhance the simulation of pedestrian perception through a visual–textual multimodal deep learning model. A baseline model for dynamic assessment of walkability with street as a spatial unit and hour as a time granularity is generated. In order to deeply explore the dynamic regulation mechanism of street walkability under the influence of diurnal shift, the 24 h dynamic score of walkability is calculated, and the quantification system of walkability diurnal change characteristics is further proposed. The results of spatio-temporal cluster analysis and quantitative calculations show that the intensity of economic activities and pedestrian experience significantly shape the diurnal pattern of walkability, e.g., urban high-energy areas (e.g., along the riverside) show unique nocturnal activity characteristics and abnormal recovery speeds during the dawn transition. This study fills the gap in the study of hourly street dynamics at the micro-scale, and its multimodal assessment framework and dynamic quantitative index system provide important references for future urban spatial dynamics planning. Full article

To address issues commonly observed during the inference phase of large language models—such as inconsistent labels, formatting errors, or semantic deviations—a series of targeted strategies has been proposed. First, a relation label refinement strategy based on semantic similarity and syntactic structure has been designed to calibrate the model’s outputs, thereby improving the accuracy and consistency of label prediction. Second, to meet the contextual modeling needs of different types of instance bags, a multi-level contextual augmentation strategy has been constructed. For multi-sentence instance bags, a graph-based retrieval enhancement mechanism is introduced, which integrates intra-bag entity co-occurrence networks with document-level sentence association graphs to strengthen the model’s understanding of cross-sentence semantic relations. For single-sentence instance bags, a semantic expansion strategy based on term frequency-inverse document frequency is employed to retrieve similar sentences. This enriches the training context under the premise of semantic consistency, alleviating the problem of insufficient contextual information. Notably, the proposed multi-granularity framework captures semantic symmetry between entities and relations across different levels of context, which is crucial for accurate and balanced relation understanding. The proposed methodology offers practical advancements for semantic analysis applications, particularly in knowledge graph development. Full article

Multiple-resolution modeling (MRM) has emerged as a foundational paradigm in modern simulation, enabling the integration of models with varying levels of granularity to address complex and evolving operational demands. By supporting seamless transitions between high-resolution and low-resolution representations, MRM facilitates scalability and interoperability, particularly within distributed simulation environments such as military command and control systems. This paper provides a structured review and comparative analysis of prominent MRM methodologies, including multi-resolution entities (MRE), agent-based modeling (from a federation viewpoint), hybrid frameworks, and the novel MR mode, synchronizing resolution transitions with time advancement and interaction management. Each approach is evaluated across critical dimensions such as consistency, computational efficiency, flexibility, and integration with legacy systems. Emphasis is placed on the applicability of MRM in distributed military simulations, where it enables dynamic interplay between strategic-level planning and tactical-level execution, supporting real-time decision-making, mission rehearsal, and scenario-based training. The paper also explores emerging trends involving artificial intelligence (AI) and large language models (LLMs) as enablers for adaptive resolution management and automated model interoperability. Full article

Enhanced Geothermal Systems (EGS) require thermochemically stable proppant materials capable of sustaining fracture conductivity under harsh subsurface conditions. This study systematically investigates the response of commercial proppants to coupled thermo-hydro-chemical (THC) effects, focusing on chemical stability and microstructural evolution. Four proppant types were evaluated: an ultra-low-density ceramic (ULD), a resin-coated sand (RCS), and two quartz-based silica sands. Experiments were conducted under simulated EGS conditions at 130 °C with daily thermal cycling over a 25-day period, using diluted site-specific Utah FORGE geothermal fluids. Static batch reactions were followed by comprehensive multi-modal characterization, including scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), and micro-computed tomography (micro-CT). Proppants were tested in both granular and powdered forms to evaluate surface area effects and potential long-term reactivity. Results indicate that ULD proppants experienced notable resin degradation and secondary mineral precipitation within internal pore networks, evidenced by a 30.4% reduction in intragranular porosity (from CT analysis) and diminished amorphous peaks in the XRD spectra. RCS proppants exhibited a significant loss of surface carbon content from 72.98% to 53.05%, consistent with resin breakdown observed via SEM imaging. While the quartz-based sand proppants remained morphologically intact at the macro-scale, SEM-EDS revealed localized surface alteration and mineral precipitation. The brown sand proppant, in particular, showed the most extensive surface precipitation, with a 15.2% increase in newly detected mineral phases. These findings advance understanding of proppant–fluid interactions under low-temperature EGS conditions and underscore the importance of selecting proppants based on thermo-chemical compatibility. The results also highlight the need for continued development of chemically resilient proppant formulations tailored for long-term geothermal applications. Full article

As BIM projects grow in volume and complexity, automated Information Compliance Checking (ICC) is becoming essential to meet demanding regulatory and contractual requirements. This study presents novel controlled vocabularies and processes for the management of information requirements, along with a structured evaluation of the Information Delivery Specification (IDS) and its associated tools. The controlled vocabularies are important as they provide support to standardization, information retrieval, data-driven workflows, and AI integration. Information requirements are classified by input type and project interaction context (phase, origin, project role, and communication), as well as by applicability (data management function, model granularity, BIM usage, and checkability). The ontology comprises seven categories: identity, geometry, design/performance, fabrication/construction, operation/maintenance, cost, and regulatory category, each linked to verification principles such as uniqueness and consistency. This enables systematic implementation of validation checks aligned with company and project needs. We introduce three ICC workflows in relation to the BIM authoring tools (inside, outside, and hybrid) and suggest key criteria for the functional and non-functional evaluation of IDS tools. Empirical results from a real project using five IDS tools reveal implementation issues with the classification facet, regular expressions, and issue reporting. The proposed ontology and framework lay the foundation for a scalable, transparent ICC within openBIM. The results also provide ICC process guidance for practitioners, a SWOT analysis that can inform enhancements to the existing IDS schema, identify possible inputs for certification of IDS tools, and generate innovative ideas for research and development. Full article

The Diamond Sutra (Vajracchedikā-Prajñāpāramitā-Sūtra) translated by the Sui Dynasty monk Dharmagupta is the fourth Chinese rendition of the Diamond Sutra. Characterized by unprecedented linguistic opacity and syntactic complexity within the history of Buddhist textual transmission, this translation’s distinctive features have attracted significant scholarly attention. This study synthesizes existing academic perspectives and employs Sanskrit–Chinese textual criticism and comparative analysis of parallel translations to conduct a granular examination of Dharmagupta’s retranslation. Our findings reveal that this text fundamentally deviates from conventional sutras designed for religious dissemination or liturgical recitation. Its defining traits, including morphological calquing of Sanskrit structures, simplified pronominal systems, and etymologically prioritized equivalence, collectively reflect a pedagogical focus characteristic of language instructional texts. Dharmagupta’s approach epitomizes a translation-as-pedagogy paradigm, with the text’s deviations from conventional norms resulting from the interplay of religious development, historical context, and translator agency. We argue that the Diamond Sutra retranslation constitutes a radical experimental paradigm in translation history, warranting re-evaluation of its significance within the broader trajectory of Buddhist textual practice. Full article

Losses from floods and the wide range of impacts have been at the forefront of hazard-triggered disasters in China. Affected by large-scale human activities and the environmental evolution, China’s defense flood situation is undergoing significant changes. This paper constructs a comprehensive flood disaster risk assessment model through systematic analysis of four key factors—hazard (H), exposure (E), susceptibility/sensitivity (S), and disaster prevention capabilities (C)—and establishes an evaluation index system. Using the Analytic Hierarchy Process (AHP), we determined indicator weights and quantified flood risk via the following formula R = H × E × V × C. After we applied this model to 16 towns in coastal Zhejiang Province, the results reveal three distinct risk tiers: low (R < 0.04), medium (0.04 ≤ R ≤ 0.1), and high (R > 0.1). High-risk areas (e.g., Longxi and Shitang towns) are primarily constrained by natural hazards and socioeconomic vulnerability, while low-risk towns benefit from a robust disaster mitigation capacity. Risk typology analysis further classifies towns into natural, social–structural, capacity-driven, or mixed profiles, providing granular insights for targeted flood management. The spatial risk distribution offers a scientific basis for optimizing flood control planning and resource allocation in the district. Full article

Wastewater treatment plants (WWTPs) face increasing pressure to handle higher volumes of water due to climate change causing storm surges, which current infrastructure cannot handle. Aerobic granular sludge (AGS) is a promising alternative to activated sludge systems due to their improved settleability property, lowering the land footprint and improving efficiency. This research investigates the optimisation of a lab-scale sequencing batch reactor (SBR) into a continuous flow reactor through mathematical modelling, sensitivity analysis, and a computational fluid dynamic model. This is all applied for the future goal of scaling up the model designed to a full-scale continuous flow reactor. The mathematical model developed analyses microbial kinetics, COD degradation, and mixing flows using Reynolds and Froude numbers. To perform a sensitivity analysis, a Python code was developed to investigate the stability when influent concentrations and flow rates vary. Finally, CFD simulations on ANSYS Fluent evaluated the mixing within the reactor. An 82% COD removal efficiency was derived from the model and validated against the SBR data and other configurations. The sensitivity analysis highlighted the reactor’s inefficiency in handling high-concentration influents and fast flow rates. CFD simulations revealed good mixing within the reactor; however, they did show issues where biomass washout would be highly likely if applied in continuous flow operation. All of these results were taken under deep consideration to provide a new reactor configuration to be studied that may resolve all these downfalls. Full article

Against the backdrop of China’s “dual-carbon” initiative, this study innovatively applies a process-based life cycle assessment (PLCA) methodology, meticulously tracking energy and carbon flows across material production, transportation, and maintenance processes. By comparing six asphalt pavement maintenance technologies in Xinjiang, the research reveals that milling and resurfacing (MR) exhibits the highest energy consumption 250,809 MJ/10³ m²) and carbon emissions (15,095.67 kg CO₂/10³ m²), while preventive techniques like hot asphalt grouting reduce emissions by up to 87%. The PLCA approach uncovers a critical insight: 40–60% of total emissions originate from the raw material production phase, with cement and asphalt identified as primary contributors. This granular analysis, unique in regional road maintenance research, challenges traditional assumptions and emphasizes the necessity of upstream intervention. By contrasting reactive and preventive strategies, the study validates that early-stage maintenance aligns seamlessly with circular economy principles. Tailored to a local arid climate and vast transportation network, the study concludes that prioritizing preventive maintenance, adopting low-carbon materials, and optimizing logistics can significantly decarbonize road infrastructure. These region-specific strategies, underpinned by the novel application of PLCA, not only provide actionable guidance for local policymakers but also offer a replicable framework for sustainable road development worldwide, bridging the gap between scientific research and practical decarbonization efforts. Full article

Against the backdrop of the profound restructuring in global climate governance, China’s energy management system is undergoing a comprehensive transition from dual energy consumption control to dual carbon emissions control. This policy shift fundamentally alters the underlying logic of energy-focused regulation and inevitably impacts the economy–energy–environment (3E) system. This study innovatively constructs a “Triangular Trinity” theoretical framework integrating internal, intermediate, and external triangular couplings, as well as providing a granular analysis of their transmission relationships and feedback mechanisms. Using Guangdong Province as a case study, this study takes the dual control emissions policy within the external triangle as an entry point to research the restructuring logic of dual carbon emissions control for the coupling coordination mechanisms of the 3E system. The key findings are as follows: (1) Policy efficacy evolution: During 2005–2016, dual energy consumption control significantly improved energy conservation and emissions reduction, elevating Guangdong’s 3E coupling coordination. Post 2017, however, its singular focus on total energy consumption revealed limitations, causing a decline in 3E coordination. Dual carbon emissions control demonstrably enhances 3E systemic synergy. (2) Decoupling dynamics: Dual carbon emissions control accelerates economic–carbon emission decoupling, while slowing economic–energy consumption decoupling. This created an elasticity space of 5.092 million tons of standard coal equivalent (sce) and reduced carbon emissions by 26.43 million tons, enabling high-quality economic development. (3) Mechanism reconstruction: By leveraging external triangular elements (energy-saving technologies and market mechanisms) to act on the energy subsystem, dual carbon emissions control leads to optimal solutions to the “Energy Trilemma”. This drives the systematic restructuring of the sustainability triangle, achieving high-order 3E coupling coordination. The Triangular Trinity framework constructed by us in the paper is an innovative attempt in relation to the theory of energy transition, providing a referenceable methodology for resolving the contradictions of the 3E system. The research results can provide theoretical support and practical reference for the low-carbon energy transition of provinces and cities with similar energy structures. Full article

This study compares two risk tolerance scales used in the Survey of Consumer Finances (SCF), namely the long-standing 4-point scale and the newer 11-point scale, to determine which better captures an individual’s investment risk preferences. The analysis includes exploring how each scale relates to household demographics, socioeconomic factors, and ownership of risky versus conservative investments. By utilizing prospect theory, the findings reveal that while both scales effectively measure risk tolerance, the 11-point scale provides a more detailed understanding of differences in asset ownership across risk levels. For financial professionals, these results highlight the value of using a more granular risk assessment tool to better align investment strategies with client preferences, leading to improved client relationships and outcomes. Full article

Technology roadmapping is conducted by systematic mapping of technological evolution through patent analytics to inform innovation strategies. This study proposes an integrated framework combining hierarchical Latent Dirichlet Allocation (LDA) modeling with multiphase technology lifecycle theory, analyzing 113,449 Derwent patent abstracts (2008–2022) across three dimensions: technological novelty, functional applications, and competitive advantages. By segmenting innovation stages via logistic growth curve modeling and optimizing topic extraction through perplexity validation, we constructed dynamic technology roadmaps to decode latent evolutionary patterns in AI-powered programmable manipulators (B25J classification) within an innovation trajectory. Key findings revealed: (1) a progressive transition from electromechanical actuation to sensor-integrated architectures, evidenced by 58% compound annual growth in embedded sensing patents; (2) application expansion from industrial automation (72% early stage patents) to precision medical operations, with surgical robotics growing 34% annually since 2018; and (3) continuous advancements in adaptive control algorithms, showing 2.7× growth in reinforcement learning implementations. The methodology integrates quantitative topic modeling (via pyLDAvis visualization and cosine similarity analysis) with qualitative lifecycle theory, addressing the limitations of conventional technology analysis methods by reconciling semantic granularity with temporal dynamics. The results identify core innovation trajectories—precision control, intelligent detection, and medical robotics—while highlighting emerging opportunities in autonomous navigation and human–robot collaboration. This framework provides empirically grounded strategic intelligence for R&D prioritization, cross-industry investment, and policy formulation in Industry 4.0. Full article

This study systematically compares the environmental and economic performance of three wastewater treatment systems: constructed wetlands (CWs), microbial fuel cells (MFCs), and their integration (CW–MFC). Lab-scale units of each system were constructed using a multi-media matrix (gravel, zeolite, and granular activated carbon), composite native wetland species (Juncus effusus, Iris sp., and Typha angustifolia), carbon-based electrodes (graphite), and standard inoculum for CW and CW–MFC. The MFC system employed carbon-based electrodes and proton-exchange membrane. The experimental design included a parallel operation of all systems treating domestic wastewater under identical hydraulic and organic loading rates. Environmental impacts were quantified across construction and operational phases using life cycle assessment (LCA) with GaBi software 9.2, employing TRACI 2021 and ReCiPe 2016 methods, while techno-economic analysis (TEA) evaluated capital and operational costs. The key results indicate that CW demonstrates the lowest global warming potential (142.26 kg CO₂-eq) due to its reliance on natural biological processes. The integrated CW–MFC system achieved enhanced pollutant removal (82.8%, 87.13%, 78.13%, and 90.3% for COD, NO₃, TN, and TP) and bioenergy generation of 2.68 kWh, balancing environmental benefits with superior treatment efficiency. In contrast, the stand-alone MFC shows higher environmental burdens, primarily due to energy-intensive material requirements and fabrication processes. TEA results highlight CW as the most cost-effective solution (USD 627/m³), with CW–MFC emerging as a competitive alternative when considering environmental benefits and operational efficiencies (USD 718/m³). This study highlights the potential of hybrid systems, such as CW–MFC, to advance sustainable wastewater treatment technologies by minimizing environmental impacts and enhancing resource recovery, supporting their broader adoption in future water management strategies. Future research should focus on optimizing materials and energy use to improve scalability and feasibility. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

The widely utilized TiO₂ nanoparticles (NPs) tend to accumulate in wastewater and affect microbial growth. This work investigated the impacts of prolonged TiO₂ NP addition to filamentous aerobic granular sludge (AGS) using two identical sequencing batch reactors (SBRs, R1 and R2). R1 (the control) had no TiO₂ NP addition. In this reactor, filamentous bacteria from large AGS grew rapidly and extended outward, the sludge volume index (SVI₃₀) quickly increased from 41.2 to 236.8 mL/g, mixed liquid suspended solids (MLSS) decreased from 4.72 to 0.9 g/L, and AGS disintegrated on day 40. Meanwhile, the removal rates of COD and NH₄⁺-N both exhibited significant declines. In contrast, 5–30 mg/L TiO₂ NPs was added to R2 from day 21 to 100, and the extended filamentous bacteria were effectively controlled on day 90 under a 30 mg/L NP dosage, leading to significant reductions in COD and NH₄⁺-N capabilities, particularly the latter. Therefore, NP addition was stopped on day 101, and AGS became dominant in R2, with an SVI₃₀ and MLSS of 48.5 mL/g and 5.67 g/L on day 130. COD and NH₄⁺-N capabilities both increased to 100%. Microbial analysis suggested that the dominant filamentous bacteria—Proteobacteria, Bacteroidetes, and Acidobacteria—were effectively controlled by adding 30 mg/L TiO₂ NPs. XRF analysis indicated that 11.7% TiO₂ NP accumulation made the filamentous bacteria a framework for AGS recovery and operation without NPs. Functional analysis revealed that TiO₂ NPs had stronger inhibitory effects on nitrogen metabolism compared to carbon metabolism, and both metabolic pathways recovered when NP addition was discontinued in a timely manner. These findings offer critical operational guidance for maintaining the stable performance of filamentous AGS systems treating TiO₂ NP wastewater in the future. Full article