Search Results (1,311)

The current green building assessment system suffers from issues such as insufficient coverage of smart indicators, significant biases in subjective weighting, and weak dynamic adaptability, which restrict the scientific promotion of green buildings. This study focuses on the gaps in the quantitative assessment of smart technologies in China’s green building evaluation standards (such as the current Green Building Evaluation Standard). While domestic standards are relatively well-established in traditional dimensions like energy conservation and environmental protection, there are fragmentation issues in the assessment of smart technologies such as the Internet of Things (IoT) and BIM. Moreover, the coverage of smart indicators in non-civilian building fields is significantly lower than that of international systems such as LEED and BREEAM. This study determined the basic framework of the evaluation indicator system through the Delphi method. Drawing on international experience and contextualized within China’s (GB/T 50378-2019) standards, it systematically integrated secondary indicators including “smart security,” “smart energy,” “smart design,” and “smart services,” and constructed dual-drive evaluation dimensions of “greenization + smartization.” This elevated the proportion of the smartization dimension to 35%, filling the gap in domestic standards regarding the quantitative assessment of smart technologies. In terms of research methods, combined weighting using the Analytic Hierarchy Process (AHP) and entropy weight method was adopted to balance subjective and objective weights and reduce biases (the resource conservation dimension accounted for 39.14% of the combined weights, the highest proportion). By integrating the grey clustering model with the whitening weight function to handle fuzzy information, evaluations were categorized into four grey levels (D/C/B/A), enhancing the dynamic adaptability of the system. Case verification showed that Project A achieved a comprehensive evaluation score of 5.223, with a grade of B. Among its indicators, smart-related ones such as “smart energy” (37.17%) and “smart design” (37.93%) scored significantly higher than traditional indicators, verifying that the system successfully captured the project’s high performance in smart indicators. The research results indicate that the efficient utilization of resources is the core goal of green buildings. Especially under pressures of energy shortages and carbon emissions, energy conservation and resource recycling have become key priorities. The evaluation system constructed in this study can provide theoretical guidance and technical support for the promotion, industrial upgrading, and sustainable development of green buildings (including non-civilian buildings) under the dual-carbon goals. Its characteristic of “dynamic monitoring + smart integration” forms differentiated complementarity with international standards, making it more aligned with the needs of China’s intelligent transformation of buildings. Full article

Climate change is expected to have significant effects on crop yield in the Canadian Prairies. The objective of this study was to investigate these possible effects on spring wheat, barley and canola production using the Decision Support System for Agrotechnology Transfer (DSSAT) modelling platform. We applied 21 climate change scenarios from high-resolution (0.22°) regional simulations to three modules, DSSAT-CERES-Wheat, DSSAT-CERES-Barley and CSM-CROPGRO-Canola, using a historical baseline period (1985–2014) and three future periods: near (2015–2040), middle (2041–2070), and far (2071–2100). These simulations are part of CMIP6 (Coupled Model Intercomparison Project Phase 6) and have been processed using statistical downscaling and bias correction by the NASA Earth Exchange 26 Global Daily Downscaled Projections project, referred to as NEX-GDDP-CMIP6. The calibration and validation results surpassed the thresholds for a high level of accuracy. Simulated yield changes indicate that climate change has a positive effect on spring wheat and barley yields with median model increases of 7% and 11.6% in the near future, and 5.5% and 9.2% in the middle future, respectively. However, in the far future, barley production shows a modest increase of 4.4%, while spring wheat yields decline significantly by 17%. Conversely, simulated canola yields demonstrate a substantial decrease over time, with reductions of 25.9%, 46.3%, and 62.8% from the near to the far future, respectively. Agroclimatic indices, such as Number of Frost-Free Days (NFFD), Heating Degree-Days (HDD), Length of Growing Season (GSL), Crop Heat Units (CHU), and Effective Growing Degree Days (EGDD), exhibit significant correlations with spring wheat. Conversely, precipitation indices, such as very wet days and annual 5- and 10-day maximum precipitation, have a stronger correlation with canola yield changes when compared with temperature indices. The results provide key guidance for policymakers to design adaptation strategies and sustain regional food security and economic resilience, particularly for canola production, which is at significant risk under projected climate change scenarios across the Canadian Prairies. Full article

The operation range of the adaptive cycle engine (ACE) compression system is constrained by both the compression components and the bypass ducts, resulting in intricate matching mechanisms. Conventional analysis methods struggle to adequately evaluate the feasible operating range or the coupled constraints between components. This study employs an integrated hybrid-dimensional approach, combining zero-dimensional bypass analysis with one-dimensional/quasi-two-dimensional component analysis, to systematically investigate the matching effects of a triple-bypass compression system. The influence of key matching parameters, including the compression component operating points, high-pressure (HP) and low-pressure (LP) shaft speeds, and the core-driven fan stage (CDFS) variable inlet guide vane (VIGV) angles, is investigated. Results indicate that compression component matching primarily influences adjacent downstream bypass ratios, while HP/LP shaft speeds and the CDFS VIGV angle predominantly regulate the first and second bypass ratios. The feasible operating envelope is determined by the superimposed effects of these control parameters. To maximize the total bypass ratio, optimal operation requires increasing the front fan stall margin, elevating LP shaft speed, reducing HP shaft speed, and implementing partial CDFS VIGV closure to enhance pre-swirl. These findings provide critical guidance for control logic refinement and design optimization in advanced variable-cycle compression systems. Full article

Cannabis sativa is a crop which has been cultivated since ancient times, with important cultural and industrial value. Due to its substantial economic impact, cannabis has attracted widespread scientific attention. A pan-genome is a significant tool for breeding, because it provides a comprehensive representation of genetic diversity. To provide a valuable tool for Cannabis breeding, we constructed a Cannabis pan-genome based on 113 accessions. A total of 24,679,380 bp of non-reference-genome sequences were assembled, identifying 1313 protein-coding genes. Using pan-genome analyses, a total of 32,428 gene presence/absence variations (PAVs) were obtained, and gene loss was recovered during the domestication of Cannabis. By partitioning the pan-genome using PAVs, a total of 23,309 core genes were identified, accounting for 71.88% of all genes in the pan-genome. In particular, there were 7148 flexible genes, making up 22.05% of the pan-genome. The flexible genes were associated with adaptive traits, including stress resistance and disease resistance in Cannabis. Population genetic analysis presented gene distribution, gene flow, and gene specificity on a pan-genome level. These results provide important genetic basis, functional genes, and guidance for Cannabis breeding. Full article

Global climate change has intensified urban heat exposure risks due to extreme heat events, posing significant health threats, particularly to socially vulnerable groups such as the elderly and children. However, the spatial allocation of urban public cooling resources exhibits heterogeneity, leading to insufficient or mismatched provision of cooling facilities in high heat exposure areas. Taking the central urban area of Guangzhou, China as an example, we employ the hazard–exposure–vulnerability (HEV) framework to evaluate a composite heat risk index (HRI). Using a coupling coordination degree and development coordination coefficient, we identify the matching status and temporal dynamic between heat risk and facility supply across 2010 and 2020. The results indicate that (1) HRI generally exhibits high-value clustering in the core areas of the old city, while peripheral areas show relatively lower levels; (2) the coupling coordination degree (CCD) exhibits clear spatial clustering characteristics, and highly coordinated streets are mostly concentrated in old city areas, whereas newly developed and peripheral districts generally show low coordination; and (3) from 2010 to 2020, cooling facility development in old city districts was generally proactive, while newly developed and peripheral areas exhibited slower progress relative to increasing heat risk. This study highlights the issue of adaptive imbalance in the allocation of cooling resources concerning vulnerable populations, providing guidance for future urban planning. Full article

This case study examines the integration of artificial intelligence (AI) into undergraduate architectural education through a 2024–25 core studio teaching experiment at Zhejiang University. A dual-module framework was implemented, comprising a 20 h AI skills training module and in-class ethics discussions, without altering the existing studio structure. The AI skills module introduced deep learning models, LLMs, AIGC image models, LoRA fine-tuning, and ComfyUI, supported by a dedicated technical instructor. Student feedback indicated phase-dependent and tool-sensitive engagement, and students expressed a preference for embedded ethical discussion within the design studio rather than separate formal instruction. The experiment demonstrated that modular AI education is both scalable and practical, highlighting the importance of phase-sensitive guidance, balanced technical and ethical framing, and institutional support such as cloud platforms and research-based AI tools. The integration enhanced students’ digital adaptability and strategic thinking while prompting reflection on issues such as authorship, algorithmic bias, and accountability in human–AI collaboration. These findings offer a replicable model for AI-integrated design pedagogy that balances technical training with critical awareness. Full article

In the current context where China is continuously emphasizing the construction and supply of indemnificatory housing, and actively promoting the construction of “Better Housing” for such housing, the development experiences of Japan and South Korea in the field of public housing reveal that the construction and supply of public housing cannot be separated from the interaction and coordinated development of the policy system, spatial composition, and cultural factors. Based on this, this study takes the public rental housing in China, Japan, and South Korea as the research objects, through comparative analysis of their policy systems, cultural backgrounds, and spatial composition characteristics of unit plans, to explore the implications for the development of China’s indemnificatory housing, and provides theoretical basis and practical references for optimizing the supply system and space design of China’s indemnificatory housing. The study selects typical cases of public rental housing from the three countries, and conducts comparisons from dimensions such as unit plane shape, L.D.K. layout, bedroom configuration, transitional space, balcony design, and bathroom composition. Findings indicate that Japan’s UR rental housing focuses on refined and diversified design, South Korea’s public housing emphasizes spatial flexibility, while China’s indemnificatory housing, while pursuing standardized construction, faces challenges of area limitations and insufficient functional adaptability. Based on the experiences of the three countries, this study proposes a tripartite guidance suggestion of “Policy–Space–Culture” to advance the realization of “Better Housing” objectives and ensure that China’s indemnificatory housing meets both international advanced experience and local social and cultural specific needs: (1) policy systems—strengthening legalization and long-term sustainability in governance; (2) spatial composition—incorporating flexible layouts and human-centric detailing; (3) cultural adaptability—balancing traditional living habits with contemporary needs. Full article

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article

Underwater debris detection plays a crucial role in marine environmental protection. However, existing object detection algorithms generally suffer from excessive model complexity and insufficient detection accuracy, making it difficult to meet the real-time detection requirements in resource-constrained underwater environments. To address this challenge, this paper proposes a novel lightweight object detection network named the Shared Pyramid Lightweight Network (SPyramidLightNet). The network adopts an improved architecture based on YOLOv11 and achieves an optimal balance between detection performance and computational efficiency by integrating three core innovative modules. First, the Split–Merge Attention Block (SMAB) employs a dynamic kernel selection mechanism and split–merge strategy, significantly enhancing feature representation capability through adaptive multi-scale feature fusion. Second, the C3 GroupNorm Detection Head (C3GNHead) introduces a shared convolution mechanism and GroupNorm normalization strategy, substantially reducing the computational complexity of the detection head while maintaining detection accuracy. Finally, the Shared Pyramid Convolution (SPyramidConv) replaces traditional pooling operations with a parameter-sharing multi-dilation-rate convolution architecture, achieving more refined and efficient multi-scale feature aggregation. Extensive experiments on underwater debris datasets demonstrate that SPyramidLightNet achieves 0.416 on the mAP@0.5:0.95 metric, significantly outperforming mainstream algorithms including Faster-RCNN, SSD, RT-DETR, and the YOLO series. Meanwhile, compared to the baseline YOLOv11, the proposed algorithm achieves an 11.8% parameter compression and a 17.5% computational complexity reduction, with an inference speed reaching 384 FPS, meeting the stringent requirements for real-time detection. Ablation experiments and visualization analyses further validate the effectiveness and synergistic effects of each core module. This research provides important theoretical guidance for the design of lightweight object detection algorithms and lays a solid foundation for the development of automated underwater debris recognition and removal technologies. Full article

Underwater fish image segmentation is the key technology to realizing intelligent fisheries and ecological monitoring. However, the problems of light attenuation, blurred boundaries, and low contrast caused by complex underwater environments seriously restrict the segmentation accuracy. In this paper, RetinalConet, an underwater fish segmentation network based on bionic retina dual-channel and multi-module cooperation, is proposed. Firstly, the bionic retina dual-channel module is embedded in the encoder to simulate the separation and processing mechanism of light and dark signals by biological vision systems and enhance the feature extraction ability of fuzzy target contours and translucent tissues. Secondly, the dynamic prompt module is introduced, and the response of key features is enhanced by inputting adaptive prompt templates to suppress the noise interference of water bodies. Finally, the edge prior guidance mechanism is integrated into the decoder, and low-contrast boundary features are dynamically enhanced by conditional normalization. The experimental results show that RetinalCoNet is superior to other mainstream segmentation models in the key indicators of mDice, reaching 82.3%, and mIou, reaching 89.2%, and it is outstanding in boundary segmentation in many different scenes. This study achieves accurate fish segmentation in complex underwater environments and contributes to underwater ecological monitoring. Full article

In the context of the energy crisis and the urgency of passive design in contemporary architecture, this study focuses on the Tujia-style Sanheyuan in southeastern Chongqing, China, which is highly adaptable to local climatic conditions. Using field surveys, architectural mapping, computational fluid dynamics numerical simulations, and multi-parameter comparative analysis, this study systematically explores the relationship between the geometric form of the Sanheyuan and its courtyard ventilation performance. Based on the Tujia construction scale modulus, this study summarizes the basic prototype of the Sanheyuan, analyzes the selection paths of its three sets of construction parameters, and constructs 48 typical courtyard models for wind environment simulation. By introducing five evaluation indicators—wind speed uniformity coefficient, proportion of strong wind zone area, proportion of calm wind zone area, and unit area wind rate—this study comprehensively assesses the impact of Sanheyuan design parameters on courtyard wind environment adaptability. This study concludes that specific spatial design parameters of the Tujia-style Sanheyuan significantly influence wind environment adaptability, offering quantitative guidance for climate-responsive and culturally informed architectural design. This study found that the optimal side room width-to-depth ratio is [1.00, 0.86, 0.83]; the optimal ridge height-to-stilt height ratio is [4.29, 8.00, 2.96]; and the optimal building footprint-to-side room area ratio is [3.01, 5.06, 4.75]. Full article

In increasingly complex Engineer-to-Order (EtO) supply chains, accurately predicting supplier delivery delays is essential for ensuring operational resilience. This study proposes an intelligent Internet of Things (IoT)-enhanced probabilistic framework for early warning and dynamic prediction of supplier lead times in smart manufacturing contexts. Within this framework, three novel Early Warning Systems (EWS) are introduced: the Baseline Probabilistic Alert System (BPAS) based on fixed thresholds, the Smart IoT-Calibrated Alert System (SIoT-CAS) leveraging IoT-driven calibration, and the Adaptive IoT-Driven Risk Alert System (AID-RAS) featuring real-time threshold adaptation. Supplier lead times are modeled using statistical distributions and dynamically adjusted with IoT data to capture evolving disruptions. A comprehensive Monte Carlo simulation was conducted across varying levels of lead time uncertainty (σ), alert sensitivity ($P_{threshold}$), and delivery constraints ($L_{max}$), generating over 1000 synthetic scenarios per configuration. The results highlight distinct trade-offs between predictive accuracy, sensitivity, and robustness: BPAS minimizes false alarms in stable environments, SIoT-CAS improves forecasting precision through IoT calibration, and AID-RAS maximizes detection capability and resilience under high-risk conditions. Overall, the findings advance theoretical understanding of adaptive, data-driven risk modeling in EtO supply chains and provide practical guidance for selecting appropriate EWS mechanisms based on operational priorities. Furthermore, they offer actionable insights for integrating predictive EWS into MES (Manufacturing Execution System) and digital control tower platforms, thereby contributing to both academic research and industrial best practices. Full article

Background: Chinese Red Steppe cattle (CRS) combine indigenous environmental resilience with moderate dairy performance, whereas Holstein cattle (HOL), despite their high milk yield, suffer reduced genetic diversity and compromised adaptation. A comparative analysis of their population genetic architecture and selection signatures can reveal valuable targets for CRS dairy improvement. Methods: We genotyped 61 CRS and 392 HOL individuals using the Illumina GGP Bovine 100K SNP array and performed stringent quality control. Population structure was assessed via principal component analysis, neighbor-joining trees, and sparse nonnegative matrix factorization. Historical effective population size (Ne) and divergence time were inferred with SMC++. Genome-wide selection scans combined Fixation Index (FST) and Cross-Population Composite Likelihood Ratio test (XP-CLR); overlapping high-confidence regions were annotated and subjected to GO and KEGG enrichment analyses. Results: CRS and HOL were clearly separated along PC1 (explaining 57.48% of variance), with CRS exhibiting high internal homogeneity and weak substructure, versus greater diversity and complex substructure in HOL. SMC++ indicated a split approximately 3500 years ago (700 generations) and a pronounced recent decline in Ne for both breeds. Joint selection mapping identified 767 candidate genes; notably, the ACSM1/2B/3/4 cluster on chromosome 25—key to butanoate metabolism—showed the strongest signal. Enrichment analyses highlighted roles for proteasome function, endoplasmic reticulum stress response, ion homeostasis, and RNA processing in regulating milk fat synthesis and protein secretion. Conclusion: This study delineates the genetic divergence and demographic history of CRS and HOL, and pinpoints core genes and pathways—particularly those governing butanoate metabolism and protein quality control—underlying dairy traits. These findings furnish molecular markers and theoretical guidance for precision breeding and sustainable utilization of Chinese Red Steppe cattle. Full article

The content and stoichiometric ratios of plant biogenic elements are key indicators for understanding plants’ ecological traits and their responses to environmental changes. However, it remains unclear how wetland herbaceous plants allocate these biogenic elements and how they relate to soil conditions. This study examines the variations in carbon (C), nitrogen (N), and phosphorus (P) stoichiometry across different organs and life forms, and their response to soil factors in Yellow River Delta wetlands. We analyzed the stoichiometric characteristics of 44 herbaceous species (17 annuals and 27 perennials) and their organs (leaves and stems). The results showed that annual plants show higher N and P but lower C content compared to perennials, indicating distinct life history strategies. In plant organs, leaves exhibited higher C, N, and P concentrations than stems, reflecting functional adaptation. Notably, random forest analysis identified stem C content as a key indicator for life history strategy differentiation. Furthermore, soil factors directly influenced organ-level stoichiometry but showed limited effects across life forms. The plants demonstrated P limitation with high sensitivity to soil P availability. This study provides new insights into organ-specific nutrient allocation strategies in wetland plants and offers valuable guidance for coastal wetland conservation. Full article

The long-term sustainability of conservation efforts in critical reforestation regions requires timely, spatiotemporal assessments of ecological quality. In alignment with China’s environmental initiatives, this study integrates Google Earth Engine (GEE) and MODIS data to construct an enhanced Remote Sensing Ecological Index (RSEI) for two decades of ecological monitoring. Hotspot analysis (Getis-Ord Gi*) revealed concentrated high-quality zones, particularly in Xinjiang’s Altay Prefecture, with ‘Good’ and ‘Excellent’ areas increasing from 21.64% in 2000 to 31.30% in 2020. To uncover driving forces, partial correlation and geographic detector analyses identified a transition in the Three-North Shelter Forest Program (TNSFP) from climate–topography constraints to land use–climate synergy, with land use emerging as the dominant factor. Socioeconomic influences, shaped by policy interventions, also played an important but fluctuating role. This progression—from natural constraints to active human regulation—underscores the need for climate-adaptive land use, balanced ecological–economic development, and region-specific governance. These findings validate the effectiveness of current conservation strategies and provide guidance for sustaining ecological progress and optimizing future development in the TNSFP. Full article