Search Results (1,266)

Cold recycled mixtures with bitumen emulsion (CRME) serving in seasonally frozen regions are susceptible to mechanical deterioration under repeated freeze-thaw (F-T) cycles, which is primarily manifested as interfacial damage and crack propagation. However, the micro-mechanical processes associated with the transmission and dissipation of frost-heaving stresses induced by water-ice phase transition within the interfacial transition zone (ITZ) between reclaimed asphalt pavement (RAP) and asphalt mortar remain to be further characterized. In this study, a numerical simulation approach coupling frost heave effects with the phase transition of water-ice particles was developed based on X-ray computed tomography (CT) and the discrete element method (DEM), and the micro-mechanical parameters of the RAP-asphalt mortar ITZ were determined through laboratory experiments. Combined with acoustic emission (AE) monitoring, the damage evolution characteristics of cold recycled mixtures and the associated interfacial damage mechanisms under freeze-thaw action were systematically investigated. The results indicate that the optimal micro-parameters of the RAP-asphalt mortar ITZ can be taken as approximately 85% of those of virgin asphalt mortar. After 20 freeze-thaw cycles, the number of shear cracks and tensile cracks in ITZ on RAP surface reached 493 and 92, respectively, which were much higher than 11 and five on the surface of new aggregate. ITZ was the main control weak area of freeze-thaw damage. Compared with the unfrozen specimens, the minimum effective contact number of mortar decreased by 1.63%, 4.52% and 8.52% respectively after 5, 10 and 20 freeze-thaw cycles, and the total effective contact number decreased from 75,842 to 69,383. Freeze-thaw cycles significantly reduce the strain energy storage capacity of CRME: the maximum energy storage capacity of the adhesive spring decreased from 2.15 J in the non-freeze-thaw state to 1.28 J in 10 cycles (a decrease of 40.47%) and 1.16 J in 20 cycles (a decrease of 46.05%), and the damage mode changed from brittle fracture to interface-controlled energy dissipation. The proposed water-ice phase transition-based DEM framework provides a reliable numerical tool for investigating freeze-thaw damage mechanisms and supporting durability-oriented design of cold recycled pavement materials. Full article

Background: Bauhiniavariegata is a plant with considerable application potential owing to its combined ornamental, edible, aromatic, and medicinal values. However, research on this species remains limited and superficial both domestically and internationally, and systematic investigation of floral volatile organic compounds (VOCs) is still lacking. Methods: Through integrated metabolome and transcriptome analyses. Results: This study first comprehensively characterizes the VOC composition, floral scent profile, key aroma components, and the molecular mechanisms underlying VOC variation during anthesis in floral buds and flowers of B. variegata. A total of 1214 volatile compounds were identified across buds and flowers, including 239 odor-active compounds and 35 differential odor-active compounds. Flavor statistics revealed that the floral scent profile of B. variegata is dominated by fruity, sweet, floral, green, woody, herbal, citrus, phenol, fresh, and spicy notes. Compared to floral buds, most differential odor-active compounds were markedly upregulated in flowers, including key floral aroma constituents such as phenylacetaldehyde, rose oxide, (Z)-β-ocimene, 2-methylbenzaldehyde, and melon heptenal. Conversely, (R)-(+)-citronellal, which possesses defensive functions, and the bitter-tasting compound 1-methyl-4-nitro-benzene were significantly downregulated in flowers, reflecting a shift from a defense-oriented mode at the bud stage to an attraction-oriented mode at anthesis. Upregulation of phenylalanine/histidine ammonia-lyase, acyl-CoA synthetase, and squalene synthetase genes and downregulation of copper amine oxidase, O-methyltransferase, and aldo–keto reductase genes synergistically promoted accumulation of floral aroma compounds such as phenylacetaldehyde and facilitated the floral transition. Conclusions: This study provides important data support for understanding the ecological interactions between B. variegata floral scent and its pollinators, as well as the molecular mechanisms governing floral scent formation. Furthermore, it contributes to the application of B. variegata in landscaping, edible flower utilization, and fragrance development. Full article

Arid mountain–oasis–desert regions exhibit strong terrain and surface-cover heterogeneity that is difficult to represent using conventional land-cover classification schemes. This study developed an ecosystem-oriented hierarchical framework for the Kashi region by combining a terrain-constrained Mountain extraction method with Automatic Deep Forest Shrinkage model (ADeFS). Nine ecosystem elements were defined: Mountain, Water, Forest, Cropland, Lake, Grassland, Desert, Ice, and Human. Mountain was first delineated as an independent physiographic element using a locally derived baseline surface, relative relief, slope, and topographic position, thereby reducing semantic overlap between terrain units and spectrally similar surface-cover classes. ADeFS was then adapted to classify the seven non-mountain classes, and Lake was subsequently separated from the unified Water class through visual interpretation. The results show that ADeFS achieved the highest accuracy, with an overall accuracy of 88.7% and a Kappa coefficient of 0.868. Independent field validation of the 2026 map yielded an overall accuracy of 86.2% and a Kappa coefficient of 0.825. From 2015 to 2026, the mountain-oasis-desert structure remained broadly stable, while Desert and Ice decreased and Forest, Grassland, and Cropland expanded. Ecosystem-element transitions were concentrated before 2021 and weakened thereafter. Landscape metrics showed that Desert remained the dominant matrix, Grassland had the highest patch density and edge density, and Cropland became increasingly aggregated within oasis agricultural areas. The framework provides an ecologically interpretable approach for ecosystem-element mapping and long-term monitoring in arid heterogeneous regions. Full article

This study examines urban green infrastructure (UGI) branding as a governance practice shaped by institutional homogenization and strategic responses within China’s multi-level policy framework. Against the backdrop of global sustainability agendas, UGI branding is conceptualized as a policy-oriented, symbolically embedded instrument for communicating cities’ commitments to green development and climate resilience. Using an institutional “identity–label–image” framework, integrated with policy diffusion theory and multi-center regional development models, we systematically analyze historical brand-label data from 27 core cities in the Yangtze River Delta. Our findings reveal three key patterns: (1) brand diffusion follows dual pathways of mandatory and imitative homogenization, with evolutionary rhythms tied to five-year planning cycles, generating discernible “policy waves”; (2) within polycentric regional structures, core cities act as institutional “interpreters” and benchmarks, while peripheral cities pursue imitation-driven differentiation, reflecting complex dynamics of collaboration and competition; and (3) urban identities expressed through brand labels progressively align with substantive sustainability transitions, aiming to enhance regional competitiveness and upward recognition. We conclude that UGI branding constitutes a deep-seated mechanism of sustainable urban governance, offering a novel theoretical lens on China’s institutional logic of city branding and providing actionable insights for optimizing collaborative brand governance and integrated green infrastructure planning in urban agglomerations. Full article

Against the backdrop of high-density Asian cities shifting from production- to consumption-oriented spaces, commercial pedestrian streets are key to urban public life and vitality. This study selects six major commercial pedestrian streets in Chongqing and employs natural language processing (NLP) and importance–performance analysis (IPA) methods to construct a four-dimensional evaluation framework (spatial, commercial, cultural, location/facility). It analyzes public perception and experience based on user-generated content (UGC). Findings show that: (1) Significant differences across dimensions form three development types: cultural identity, functional hub, and distinctive growth, reflecting structural bottlenecks in transitioning from single- to multi-functional spaces. (2) IPA identifies “business formats,” “cultural activities,” and “consumption experience” as priorities for improvement, while “commercial atmosphere” and “transportation conditions” are current strengths to maintain. (3) Sentiment analysis reveals that negative perceptions focus on basic functions and sense of place, whereas positive sentiments relate to cultural expression and spatial esthetics, highlighting the role of cultural soft power and visual design in street appeal. This study reveals public perception patterns via big data analysis, offering empirical support for the refined renewal, cultural preservation, and sustainable management of commercial pedestrian streets in high-density Asian cities. Full article

The transition from early non-standalone 5G deployments to 5G Standalone and, more recently, 5G-Advanced has turned mobile networks into flexible, programmable infrastructures capable of supporting private, industrial, and research-oriented deployments for the development of beyond-5G applications and architectures. Evaluating these networks’ capabilities, however, remains challenging because commercial platforms often provide limited access to internal interfaces, radio parameters, and network measurements. This paper presents an open-source private 5G SA testbed for beyond-5G application validations built using Open5GS, srsRAN, Ettus USRP N310 software-defined radio, programmable SIM cards, and commercial 5G customer-premise equipment. The platform is deployed in a semi-anechoic chamber. End-to-end operation is validated through subscriber registration, authentication, PDU session establishment, and external data connectivity. The performance of the implemented 5G network is evaluated using throughput, block error rate, modulation and coding scheme, and gNB trace logs. Unlike previous open-source 5G testbeds that primarily use RF waveguides, individual network components, or a limited set of radio configurations, the proposed platform combines COTS SIM-based UE operation with a controlled over-the-air evaluation of FDD/TDD and multiple antenna configurations and correlates application-level throughput with internal gNB radio metrics. For FDD downlink operation, the average throughput increased by approximately 74% from 1 × 1 to 2 × 2 and by a further 57% from 2 × 2 to 4 × 4, although the additional peak-throughput gain from 2 × 2 to 4 × 4 remained limited. The platform provides a reproducible environment for validating beyond-5G mechanisms, comparing network configurations, and studying the behavior of future open-source 5G SA systems under controlled conditions. Full article

Global commodity chains reshape rural territories through interconnected environmental and socioeconomic processes; these dynamics are often examined through the lens of land-use change. Export-oriented agricultural frontiers illustrate how global market integration can generate landscape and societal transformations. In western Mexico, the expansion of the avocado industry exemplifies commodity-driven territorial change, linking land-use dynamics with transformations in rural economies and livelihoods. This study analyzes how export integration into the international avocado commodity chain was associated with transformations in land systems and socioeconomic dynamics across four municipalities in Michoacán, Mexico, between 2003 and 2023. Using high-resolution land-use maps alongside demographic and socioeconomic indicators, we apply a qualitative, comparative and inductive synthesis of spatial and census data to examine how commodity integration interacted with local conditions to shape landscape and territorial organization. Avocado expansion coincided with territorial transformation, including the conversion of pine-oak forest and traditional agricultural lands, as well as changes in labor arrangements, demographic dynamics, and rural livelihood systems. Land-use analysis shows continued orchard expansion and accelerated land conversion after 2015. Socioeconomic indicators reveal population growth, increased female employment, and persistent income inequality, highlighting the uneven incorporation of rural territories into global commodity chains. We identified four territorial pathways: agricultural specialization, agro-industrial urbanization, transitional diversification, and rural commodity integration. These findings contribute to debates on uneven geographical development and commodity frontier expansion by showing how global commodity integration intersects with ecological, economic, and social processes to generate distinct pathways. Full article

The sustainability of buildings and the built environment extends beyond energy and environmental performance to circular resource use, health and safety, digital infrastructure, and stakeholder collaboration. This exploratory integrative literature review examines how these established but insufficiently connected domains reshape business models in the built environment. Since the built environment is a major source of global carbon emissions and waste, a primary research stream concerns the transition toward circular economy principles beyond traditional profit-maximization logics. The literature also highlights the potential of health- and safety-oriented innovations to reduce risks and improve indoor environments. Other studies highlight the potential of digital innovations to improve life-cycle management, resource efficiency, and risk mitigation. However, their widespread adoption faces systemic barriers, including data interoperability and cybersecurity issues, implementation costs, skills shortages, organizational resistance, and regulatory and governance challenges. The literature often emphasizes technical potential while paying less attention to value-capture mechanisms and the allocation of costs, risks, benefits, and responsibilities among the actors involved. Integrating sustainable practices, digital infrastructures, circular-economy principles, and collaborative governance is therefore essential to develop economically viable, organizationally feasible, and ethically responsible business models for the built environment, across the building life cycle and among public and private stakeholders. Full article

This study presents a calibration-oriented multi-phase micromechanical homogenization framework for estimating the elastic modulus of sustainable concrete containing fly ash and recycled gypsum. In the absence of direct experimental measurements, a computational framework was developed to simulate the interactions among coarse aggregate, mortar, and the interfacial transition zone (ITZ). A two-phase homogenization model was first calibrated against the ACI 318-19 elastic modulus relationship through a sensitivity analysis, achieving relative errors below 3% with respect to the ACI-based empirical reference. Subsequently, a three-phase model incorporating a $30\mathsf{\mu }\mathrm{m}$ ITZ was developed to explicitly account for the influence of the interfacial transition zone on the effective elastic modulus within the adopted modelling assumptions. While the inclusion of the ITZ slightly increased the prediction error for the control mix, it consistently improved agreement with the ACI-based reference for all fly ash–gypsum blends. In addition, comparison with experimentally reported elastic modulus values for similar SCM-modified concretes demonstrated that the predicted values were physically reasonable and generally consistent with published data. Overall, the proposed framework reproduced stiffness trends predicted by the ACI empirical relationship and provides a physically informed computational baseline for future experimental validation and parametric investigations of sustainable concrete systems. Full article

Intelligent agents are emerging as an important system-level paradigm for smart agriculture. This review focuses on modern agricultural intelligent agents driven by large language models and related multimodal foundation models and examines how this emerging field is reshaping the organization of intelligent agricultural systems. It first clarifies the conceptual boundaries of agricultural intelligent agents and distinguishes them from traditional multi-agent systems, agent-based modeling, agricultural foundation models, and static retrieval-augmented question-answering systems. It then synthesizes their architectural foundations, key capabilities, application scenarios, deployment challenges, and future research directions. The reviewed literature indicates that agricultural intelligent agents are moving beyond isolated perception, prediction, and response generation toward the goal-oriented coordination of agricultural knowledge, dynamic data, external tools, and decision-making processes across agricultural task chains. They are beginning to support more integrated forms of knowledge services, crop monitoring and diagnosis, decision support, and farm-level collaborative management. Nevertheless, their transition from prototype systems to dependable and deployable agricultural systems remains constrained by context-aware knowledge grounding, heterogeneous data and tool integration, long-horizon reliability, the stability of multi-agent collaboration, and system security. This review further introduces an assessment perspective based on evidence reported in the original studies, comparing representative agricultural intelligent agents in terms of task decomposition, agronomic evidence applicability, tool-use validity, workflow reliability, multi-agent coordination, and deployment-related evidence. By distinguishing demonstrated capabilities from unevaluated dimensions, this review provides a structured framework for understanding the current status of agricultural intelligent agents and for guiding their future development toward reliable, deployable, and domain-oriented intelligent systems for smart agriculture. Full article

Sustainable poultry nutrition is increasingly challenged by feed variability, environmental constraints, resource competition, and the growing demand for precision feeding strategies. Conventional feed evaluation systems based on proximate analysis, static nutrient tables, and empirical formulation are often insufficient to predict the biological and functional value of modern sustainable feed resources. This critical integrative review examines emerging approaches for evaluating and optimizing sustainable feeds in poultry nutrition through the integration of advanced analytical technologies, biological validation systems, omics sciences, and artificial intelligence (AI). This review was developed as a structured narrative review following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-inspired workflow and organized around an integrated AI–omics–feed evaluation framework. Recent advances in spectroscopy-based analytical techniques, in vitro digestibility systems, microbiomics, metabolomics, nutrigenomics, machine learning, and predictive modeling are discussed in relation to feed characterization, nutrient utilization, host–microbiota interactions, and precision nutrition, with emphasis on the transition from static compositional assessment toward dynamic, system-oriented feed evaluation. Explainability, biological validation, and generalizability of AI-based models across heterogeneous production systems are highlighted as key challenges for practical implementation, alongside emerging frontiers in AI-driven nutritional decision-support. Integrating analytical, biological, molecular, and computational approaches may support adaptive precision nutrition systems capable of improving nutrient efficiency, reducing environmental emissions, and optimizing sustainable poultry production. Full article

Gender bias in generative artificial intelligence (GenAI) is both a technical and a social phenomenon: it emerges from historically patterned data, model design, and interactions in institutional use, and it cannot be understood by engineering or by social critique alone. This critical integrative review develops a more differentiated account. It connects feminist epistemology, Science and Technology Studies, critical AI scholarship, natural language processing, and governance research to examine five levels: historical knowledge production, technical representation and generation, benchmark evaluation, institutional deployment, and accountability. The review explains tokenization, next-token prediction, transformers, and the transition from static embeddings to contemporary language models before assessing evidence from standard fairness tests—coreference tests (WinoBias), sentence-pair tests (CrowS-Pairs), and stereotype tests (StereoSet)—as well as open-ended generation, multilingual testing, and text-to-image systems. It shows that measured bias varies with task, prompt, language, model version, and metric. What a test records and what that record means are therefore distinct questions: measurements are situated and depend on the instrument, and their interpretation draws on theory rather than following from the numbers alone. Evidence from employment, education, healthcare, and translation further indicates that the relevant unit of analysis is the model-in-context—the model together with the institution and workflow in which its outputs are used. Technical mitigation can reduce specific harms but does not repair unequal criteria, incomplete evidence bases, or weak institutional accountability. The review proposes a multilevel governance approach combining technical evaluation, documentation, professional and community oversight, appeals, remedies, and public-interest knowledge infrastructure. Its distinctive contribution is to connect three observations usually kept apart—how bias is measured, how generative systems concentrate epistemic authority, and how statistical learning is oriented toward past data—and to show why democratic and feminist governance can keep alternative technological futures open. Full article

This longitudinal study examined how pre-service teachers’ psychological need experiences are configured and how teacher professional identity is associated with profile membership and movement across these configurations. Drawing on self-determination theory (SDT), data were collected from Kuwaiti pre-service teachers across three waves over one academic year. Latent transition analysis was conducted with 317 complete-case participants using autonomy, competence, and relatedness satisfaction and frustration as indicators. A four-profile solution was retained: Flourishing, Moderately Thriving, Need-Frustrated, and Struggling. The label Moderately Thriving is comparative: this profile showed high need satisfaction in absolute terms but somewhat greater need frustration than Flourishing. The decoded profile assignments showed moderate descriptive stability across adjacent waves, with stability rates of 54.6% and 62.1%, though the Struggling profile showed markedly higher persistence than the others. Wave 1 teacher professional identity was associated with profile membership at both Wave 1 and Wave 3. In the dimension-level models, Teacher Self-Efficacy and Commitment to Teaching each showed adjusted associations across profiles and waves, whereas Professional Orientation was associated with profile membership only at Wave 1. Initial professional identity was not significantly associated with improvement or deterioration relative to stability. However, change in professional identity from Wave 1 to Wave 3 was associated with the direction of profile movement: increases were associated with lower odds of deterioration and higher odds of improvement. Because identity change and profile movement were assessed over the same period, the direction of this association cannot be established. These findings complicate a simple protective view of professional identity and suggest that supporting future teachers may require attention to both identity development and need-supportive programme conditions. Full article

Against China’s dual carbon peaking and carbon neutrality strategic goals, nationwide digital transformation brings both carbon abatement dividends and potential energy rebound risks, and its full low-carbon potential is constrained by local industrial foundations and regional economic development stages. Most existing studies merely treat industrial upgrading as an intermediate transmission channel, with little discussion of its moderating influence. Moreover, few threshold analyses take the comprehensive level of regional economic development as the core threshold variable to capture the boundary conditions of digital decarbonization effects. Based on balanced panel data covering 30 provincial-level regions of China from 2011 to 2023, this paper constructs a multi-dimensional digital economy index via the entropy weight method. Prior to formal regression, we conduct Pearson correlation analysis and mean-centered VIF multicollinearity diagnostics to avoid biased estimation. Two-way fixed-effects regression, moderation tests, Bootstrap-based regional heterogeneity comparison and Hansen’s single threshold model are adopted for empirical analysis. The results show that digital economy development significantly curbs carbon emission intensity; a one-standard-deviation increase in the digital economy composite index is associated with an approximately 9.7% decline in carbon emission intensity. The mean-centered interaction term DIG × UIS is significantly negative at the 1% level, proving that service-oriented industrial upgrading strengthens the carbon reduction effect of digitalization. The mitigation effect displays distinct spatial divergence: the estimated coefficient equals −2.638 for eastern provinces, −3.585 for central regions and −1.700 for western areas. Bootstrap inter-group coefficient tests confirm statistically significant gaps between east–west and central–western subgroups. Threshold regression identifies a single threshold of logarithmic per capita GDP at 11.94. After crossing this economic development threshold, the inhibitory coefficient of the digital economy rises markedly from −0.844 to −1.473. This study enriches the theoretical system of digital low-carbon transition by jointly uncovering the moderating role of industrial upgrading and the stage threshold constraint of economic development and offers differentiated digital low-carbon policy guidance for provincial governments. Full article

The spatial organization of public service facilities within transit-oriented development TOD blocks is closely related to residents’ daily travel conditions and travel-related carbon emissions. This study examined all 172 operating metro station areas in Qingdao using public service facility POIs, buildings, pedestrian road network and population data, field observations, and a resident travel survey. K-means clustering, spatial syntax analysis, Global Moran’s I, spatial regression, FDR-adjusted Pearson correlation analysis, and scenario simulation were jointly applied. Four facility layout types were identified: Spatially Balanced Type, Main-Road-Concentrated Type, Point-Concentrated Type, and Scattered-and-Disordered Type. The survey included 240 valid respondents distributed across all 41 station areas along Qingdao Metro Line 1. The Spatially Balanced Type had the lowest mean weekly per capita travel carbon emissions, followed by the Main-Road-Concentrated Type, whereas the Point-Concentrated and Scattered-and-Disordered types had similarly higher emission levels. The density and accessibility of Commercial and Entertainment facilities, Medical and Health facilities, and total facilities remained negatively associated with travel carbon emissions after FDR correction. Spatial syntax analysis showed that higher road network integration and connectivity were associated with stronger facility agglomeration. Facility density, road density, and population density exhibited significant positive network-based spatial autocorrelation, and the spatial error model provided the best fit, identifying positive associations of facility density with road density and population density. Based on these findings and field observations, three differentiated renewal pathways—node embedding, proximity coordination, and intensive integration—were proposed. Under the specified scenario, a 20% increase in facilities was associated with modeled reductions in aggregate weekly carbon emissions of 32.3% in Li Village, 28.1% in the University of Petroleum station area, and 33.0% in Jinggangshan Road. These results suggest that improving overall facility coverage may support lower-carbon travel across different facility layout contexts. This study connects facility layout typology, spatial structure, travel carbon emission associations, and differentiated renewal strategies at the TOD-block scale. Full article