Search Results (13,373)

The multifunctional role of forests in supplying renewable biomaterials and delivering ecosystem services (ESs) is often overlooked in standard life cycle assessment (LCA) methodologies, despite its relevance for sustainable construction. This study developed the BioCons Impact Compensation Model (ICM), which integrates ES into life cycle inventory (LCI) databases and quantifies proprietary BioCons Mitigation Indicators, capturing additional environmental information, ensuring transparency, and preventing greenwashing. Using structural Scots pine in Spain as a case study, the GWP_-luluc-roots indicator was found to be 226.84 kg CO_2-eq/FU, representing 36% of the biogenic carbon (616.45 kg CO_2-eq/FU), highlighting the contribution of root-derived carbon to long-term soil carbon storage. The BioCons Mitigation Indicators demonstrate that mitigation generally exceeds environmental impacts, except for HTP-nc-inorganics, with surplus ES available as biocredits to offset emissions in other life cycle stages. Integrating these indicators into environmental product declarations (EPDs) provides a transparent and accurate view of environmental performance. The results validate the hypothesis that forest bio-based construction products (FBCPs) act as carriers of ESs embedded in derived products, supporting more comprehensive and robust sustainability assessments. Full article

Accurate prediction of bridge deterioration is essential for optimizing inspection schedules, prioritizing maintenance activities, and ensuring infrastructure safety. This study developed machine learning-based predictive models to estimate the deterioration states of bridge decks, superstructures, and substructures using a comprehensive dataset from the Colorado National Highway System spanning 2014 to 2024. Structural, operational, and environmental parameters including freeze–thaw cycles, precipitation, condensation risk, and extreme temperatures were incorporated to capture both design-driven and climate-driven deterioration mechanisms. Decision Tree, Random Forest, and Gradient Boosting classifiers were trained and evaluated using Balanced Accuracy, Matthews Correlation Coefficient, Cohen’s Kappa, and macro-averaged F1-scores, with class imbalance addressed via SMOTETomek resampling. The Gradient Boosting classifier achieved the highest predictive performance, with balanced accuracy exceeding 97% across all components. Feature importance analysis revealed that sufficiency rating, year of construction, and environmental stressors were among the most influential predictors. Incorporating environmental variables improved predictive accuracy by up to 4.5 percentage points, underscoring their critical role in deterioration modeling. These findings demonstrate that integrating environmental factors into machine learning frameworks enhances the reliability of deterioration forecasts and supports the development of climate-aware asset management strategies, enabling transportation agencies to proactively plan maintenance interventions and improve infrastructure resilience. Full article

The optimal groundwater level is critical for maintaining the coordinated and healthy development of the ecological–agricultural production system in arid oasis regions. This study comprehensively considered factors such as ecological safety, soil salinization prevention and control, and ground subsidence constraints to determine the optimal groundwater level in a region. GIS technology and Visual MODFLOW Flex 6.1 software were used to construct a three-dimensional groundwater numerical model, and regional comprehensive evaluation values were applied to simulate and predict the spatiotemporal evolution of groundwater levels under different regulation schemes. Results indicated the following: (1) There were significant spatial differences in the study area. The optimal groundwater depths in agricultural and engineering/living areas were 2–4 and 3–4 m, respectively, as determined using methods such as capillary rise height and total sum of middle layers. (2) In long-term (≥10a) regulation, areas with a reduced regional comprehensive evaluation value > 0.20 exhibited the highest groundwater recharge rate (17.10%), while those with a reduced regional comprehensive evaluation value > 0.32 showed the best coverage of optimal groundwater levels. The opposite trend was observed in short-term regulation. (3) Considering both groundwater recharge and optimal groundwater level regulation, the Y₂ scheme demonstrated the best regulation effect. The findings of this study can provide theoretical references for the multi-objective optimization management of water resources in arid oasis regions. Full article

Traditional cotton field plastic film residue monitoring relies on manual sampling, with low efficiency and limited accuracy; therefore, large-scale nondestructive monitoring is difficult to achieve. A UAV-based prediction method for shallow plastic film residue pollution in cotton fields that uses RDT-Net and machine learning is proposed in this study. This study focuses on the weight of residual plastic film in shallow layers of cotton fields and UAV-captured surface film images, establishing a technical pathway for drone image segmentation and weight prediction. First, the images of residual plastic film in cotton fields captured by the UAV are processed via the RDT-Net semantic segmentation model. A comparative analysis of multiple classic semantic segmentation models reveals that RDT-Net achieves optimal performance. The local feature extraction process in ResNet50 is combined with the global context modeling advantages of the Transformer and the Dice-CE Loss function for precise residue segmentation. The mPa, F1 score, and mIoU of RDT-Net reached 95.88%, 88.33%, and 86.48%, respectively. Second, a correlation analysis was conducted between the coverage rate of superficial residual membranes and the weight of superficial residual membranes across 300 sample sets. The results revealed a significant positive correlation, with R² = 0.79635 and PCC = 0.89239. Last, multiple machine learning prediction models were constructed on the basis of plastic film coverage. The ridge regression model achieved optimal performance, with a prediction R² of 0.853 and an RMSE of 0.1009, increasing accuracy in both the segmentation stage and prediction stage. Compared with traditional manual sampling, this method substantially reduces the monitoring time per cotton field, significantly decreases monitoring costs, and prevents soil structure disruption. These findings address shortcomings in existing monitoring methods for assessing surface plastic film content, providing an effective technical solution for large-scale, high-precision, nondestructive monitoring of plastic film pollution on farmland surfaces and in the plow layer. It also offers data support for the precise management of plastic film pollution in cotton fields. Full article

Bankruptcy prediction in Slovakia’s industrial manufacturing sector is vital due to its significant role in the national economy. This study aims to develop a predictive model for forecasting corporate bankruptcy within the industrial manufacturing sector in Slovakia. The novelty of this study lies in developing a model tailored to crisis conditions, validated using COVID-19 data, and adapted to the Central European context for greater accuracy and relevance. The model is constructed using financial data extracted from the Orbis database, based on company financial statements from 2020 and 2021, and encompasses firms of various sizes. Employing backwards binary logistic regression, five statistically significant predictors were identified, enabling the model to forecast impending bankruptcy with a one-year lead time. The model was trained on a sample of 1305 companies and achieves an overall prediction accuracy of 83.78%, with an AUC (Area Under the Curve) value of 91.7%, indicating strong discriminative power. The resulting model demonstrates robust predictive capability and may serve as a practical decision-support tool for managers, investors, creditors, and other stakeholders assessing the financial health of firms. Full article

With the aging of China’s expressway network, the number of maintenance projects continues to increase, and issues such as construction safety, driving risk, and traffic efficiency have become increasingly prominent. This paper systematically reviews relevant research progress from four aspects: safety characteristics, traffic capacity, work-zone layout, and speed limit management. The review indicates that Western scholars have made extensive use of rich data resources—such as traffic parameters and accident records from expressway maintenance road sections—and have developed relatively systematic and well-established research frameworks in theoretical analysis, practical application, and evaluation methods. In contrast, Chinese studies have mainly relied on specific maintenance projects, commonly employing on-site investigations and traffic simulations to address particular problems, with limited systematization and generalization. Looking forward, it is essential to further strengthen the standardized collection and statistical analysis of traffic data (including accident data) for expressway maintenance road sections. Meanwhile, for complex scenarios such as multi-lane segments, special road sections, reconstruction and expansion sections, as well as extreme climatic conditions and nighttime operations, comprehensive research should be conducted by leveraging new-generation driving simulation, big data analytics, and artificial intelligence technologies, thereby providing scientific support and methodological foundations for building a systematic theoretical framework for traffic safety in expressway maintenance road sections. Full article

The Tarim River Basin, a core region for economic development and ecological security in China’s inland arid areas, faces the pressing challenge of synergistically improving land use efficiency to resolve human-land conflicts under water resource constraints and achieve sustainable development. Based on the “economic-social-ecological” benefit coordination theory, this study constructs a land use efficiency evaluation system with 16 indicators and integrates the coupling coordination degree model and gravity model to quantitatively analyze the spatiotemporal differentiation patterns and coupling mechanisms of land use efficiency in the basin from 1990 to 2020. Results show that economic and social benefits of land use increased during this period, exhibiting a “high-north, low-south” spatial pattern, while ecological benefits remained relatively high but declined gradually. The coupling coordination degree of subsystem benefits displayed significant spatial heterogeneity, with an overall upward trend, where composite factors emerged as the primary constraint. Spatially, land use efficiency coupling coordination evolved from “core polarization” to “axial expansion” and finally “networked synergy,” with stronger linkages concentrated in oasis irrigation districts. These findings provide theoretical support for ecological conservation, water management, and policy-making in southern Xinjiang, offering pathways to synergize the “economic-social-ecological” system and promote sustainable development in arid regions. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article

Montane shrubby grasslands, as one of the world’s important ecosystems, are highly sensitive to climate change and human activities, especially in the subtropical regions experiencing rapid economic development. However, little is known about how anthropogenic activities, such as firebreak construction, interact with elevation to influence plant diversity in these ecosystems. Shrub and herbaceous communities were surveyed in subtropical montane shrubby grassland within Baishanzu National Park, eastern China. Nine transects were established along firebreaks, each with two edge plots near firebreak and two interior plots away firebreak, and twelve additional control plots in adjacent undisturbed areas. Species diversity was assessed using the Hill index. Our results revealed distinct responses of shrubs and herbs to firebreak disturbance and elevation. Firebreaks reduced shrub diversity but enhanced herb diversity, and both groups exhibited contrasting elevational patterns. In control areas, shrub diversity decreased while herb diversity increased with elevation, whereas in firebreak zones, these relationships were altered, with edge plots showing a hump-shaped diversity pattern. Differences in shrub diversity but not herbs between interior and edge plots decreased with elevation. Species composition also differed significantly between firebreak and control areas, driven mainly by elevation in control areas and by soil properties near firebreaks. These findings demonstrate that firebreak construction reshapes the elevation–diversity relationships of both herbs and shrubs, highlighting the sensitivity of high-elevation montane shrubby grasslands to small-scale disturbances. Effective firebreak management should therefore account for both elevational context and disturbance intensity to maintain ecosystem biodiversity and stability. Full article

Fault diagnosis of battery energy storage systems (BESSs) in dynamic operating conditions presents significant challenges due to complex spatiotemporal patterns and measurement noise. This research proposes a novel thermal fault diagnosis framework for BESSs based on Bayesian inference and a Kalman filter. Firstly, PLS-based spatiotemporal feature extraction is designed to capture temporal dependencies. Based on Bayesian global exploration and Kalman real-time weight adaptation, a dual-stage optimization strategy is proposed to derive a multiscale detection index with the dominant statistic, the residual statistic, and the module voltage similarity. A time window-based cumulative contribution strategy is constructed for precise cell localization. Finally, the experimental validation on a Li-ion battery pack demonstrates the proposed method’s superior performance: 96.92–99.90% anomaly detection rate, false alarm rate ranging from 0.10% to 7.22%, detection delays of 1–27 s, and 100% accuracy in fault localization. The proposed framework provides a comprehensive solution for safety management of BESSs and is significant for battery life and energy sustainability. Full article

Accurate early warning of rainfall-induced landslides poses a critical challenge in geological disaster risk management. Conventional deterministic rainfall threshold models often overlook the heterogeneity of regional geological conditions, while landslide susceptibility assessment is plagued by uncertainties in selecting non-landslide samples. To address these issues, this paper took Zhushan County in Hubei Province as the study area, and the semi-supervised random forest (SRF) model was adopted to conduct landslide susceptibility assessment. The critical rainfall (Effective Rainfall-Duration, EE-D) threshold curves were constructed based on the antecedent effective rainfall (EE) and rainfall duration (D). Furthermore, EE-D threshold curves with different geological condition characteristics were established and analyzed according to the thickness, slope, and area of the landslides, respectively. By coupling the landslide susceptibility results with a classified multi-level rainfall threshold model, a spatiotemporally refined regional framework for tiered landslide early warning was developed. The results show that the SRF model solves the problem of non-landslide sample selection error in traditional supervised learning. The Area Under Curve (AUC) value reaches 0.91, which is better than the analytic hierarchy process, logistic regression, etc. Moreover, the models of landslide susceptibility and EE-D threshold can effectively achieve the hierarchical early warning of rainfall-induced landslide hazards. Full article

Evaluating the management effectiveness of protected areas (PAs) is critical for refining conservation strategies. One of the key components in the management of PA is the regulation of human disturbance. We evaluated the management effectiveness of the Qilian Mountain National Nature Reserve (QMNNR) in mitigating human disturbance for 2000–2022. Human footprint was used as a key indicator of human disturbance. It integrates eight human disturbance factors: built environments, population density, night-time lights, cropland, pastureland, roads, railways, and navigable waterways. Evaluations are conducted across dual spatial dimensions: (1) constructing an equal-area external buffer zone to compare human footprint dynamics inside versus outside the reserve; and (2) testing the hypothesis that “stricter zonation correlates with improved control of human disturbance” by analyzing management gradients across four functional zones (core, buffer, experimental, and peripheral protection zones). Key findings include the following: (1) The increase in human footprint within the reserve was markedly lower than in surrounding areas, with the internal–external human footprint disparity expanding from 2000 to 2022. (2) Spatial analysis reveals concentrated disturbance hotspots in northern buffer zones, whereas only marginal increases occurred in Sunan County within the reserve. (3) Human footprint growth across functional zones followed a clear ascending order: core zone < buffer zone < experimental zone < peripheral protection zone, validating the efficacy of zoned management. Collectively, these results demonstrate that the QMNNR has effectively curbed human disturbance expansion—particularly in its core area—though vigilance is warranted against emerging “ecological island” risks in the northern peripheral zone. The proposed dual-dimensional human footprint assessment framework further offers a standardized evaluation methodology for large-scale PA in mitigating human disturbance. Full article

Digital Twins (DTs) are transforming construction and energy management sectors by integrating 3D surveying, monitoring, Building Performance Simulation (BPS), and Building Energy Simulation (BES) from the earliest design or retrofit stages. Moreover, dynamic thermal simulations further support energy performance assessments by modeling indoor conditions to meet comfort and efficiency targets. However, their reliability depends on accurate, standards-compliant 3D building models, which are costly to create. This research introduces a complete framework for automatically generating energy-focused Digital Twins (EDTs) directly from unstructured point clouds. Combining Deep Learning-based instance detection, Scan-to-BIM techniques, and computational geometry, the method produces simulation-ready models without manual intervention. The resulting EDTs streamline early-stage performance evaluation, enable scenario testing, and enhance decision making for energy-efficient retrofits, advancing smart-building design through predictive simulation. Full article

In dealing with emergency events, it is very important when making scientific and correct decisions. As an important premise, the creation of information needs is quite essential. Taking earthquakes as a type of unexpected event, this paper constructs a large and model-driven system for automating the generating process of information requirements for earthquake response. This research explores how the different departments interact during an earthquake emergency response, how the information interacts with each other, and how the information requirement process operates. The system is designed from three points of view, building a knowledge base, designing and developing prompts, and designing the system structure. It talks about how computers automatically make info needs for sudden emergencies. During the experimental process, the backbone architectures used were four Large Language Models (LLMs): chatGLM (GLM-4.6), Spark (SparkX1.5), ERNIE Bot (4.5 Turbo), and DeepSeek (V3.2). According to the desired system process, information needs is generated by real-word cases and then they are compared to the gathered information needs by experts. In the comparison process, the “keyword weighted matching + text structure feature fusion” method was used to calculate the semantic similarity. Like true positives, false positives, and false negatives can be used to find differences and calculate metrics like precision and recal. And the F1-score is also computed. The experimental results show that all four LLMs achieved a precision and recall of over 90% in earthquake information extraction, with their F1-scores all exceeding 85%. This verifies the feasibility of the analytical method a chatGLM dopted in this research. Through comparative analysis, it was found that chatGLM exhibited the best performance, with an F1-score of 93.2%. Eventually, Python is used to script these aforementioned processes, which then create complete comparison charts for visual and test result checking. In the course of researching we also use Protege to create the knowledge requirements ontology, so it is easy for us to show and look at it. This research is particularly useful for emergency management departments, earthquake emergency response teams, and those working on intelligent emergency information systems or those focusing on the automated information requirement generation using technologies such as LLMs. It provides practical support for optimizing rapid decision-making in earthquake emergency response. Full article

While coastal zones support economic and social development, they also face prominent contradictions between shoreline utilization and ecological protection. This study proposed an innovative conflict-coordination framework for constructing living shorelines, aiming to identify and mitigate multi-dimensional conflicts in coastal engineering. The framework introduced a four-dimensional conflict analysis structure encompassing policy, social environment, ecological environment, and technical capacity, thereby extending beyond traditional single-dimensional or ecological-only assessments. Furthermore, it integrated the Comprehensive Conflict Index (CCI) with a multi-objective coordination model that couples three core indicators (e.g., whole-life-cycle carbon emissions, comprehensive impact intensity, and the living shoreline index) to achieve synergistic optimization among lower carbon emission, less human intervention, and higher ecological function objectives. Applied to an ecological restoration and seawall ecologization project in Zhenhai District, Ningbo, the results demonstrated that the framework helped constructing living shorelines by effectively reducing comprehensive conflict intensity with 21.2%, decreasing total carbon emissions with 60.2%, and significantly improving both the living shoreline index and multi-objective coordination level. Compared to traditional coastal zone assessment methods, these findings highlighted the differentiated advantages of the proposed framework in quantifying conflict sources, enhancing coordination among multi-objectives, and providing scientific support for living shoreline construction and sustainable coastal management. Full article