Search Results (18,682)

Livestock manure management contributes substantially to agricultural greenhouse gas emissions, making the adoption of low-carbon approaches urgent in ecologically sensitive regions. This study focuses on the County-wide Livestock Manure Resource Utilization Project in Danjiangkou City, the core water source area of China’s South-to-North Water Diversion Project. Based on field survey data, IPCC Guidelines, and a life cycle assessment framework, this study established a carbon accounting boundary covering excretion, collection, storage, treatment, and utilization stages. A scenario analysis was conducted to compare 2023 baseline emissions with 2026 project emissions and to quantify the carbon reduction potential. The research findings indicate that the overall carbon reduction rate following the project’s implementation reached 40.8%. However, the effectiveness varied considerably across the four management models. The Sedimentation–Crop Model and the Housing–Bedding Integrated Model, which employed integrated systemic interventions, achieved reductions of 61.50% and 60.09%, respectively. In contrast, the “124” Healthy Breeding Model and the Raised-Bedding Composting System, which relied primarily on single-stage upgrades, achieved reductions of only 32.04% and 27.70%. This disparity suggests that in decentralized livestock operations, isolated technological improvements fall short; meaningful decarbonization requires systemic interventions across the entire manure management chain. The findings provide a reference for low-carbon livestock manure management and regional development in ecologically sensitive areas. Full article

With the continuous development of drilling and reservoir stimulation technologies, the drilling depth of enhanced geothermal system projects is getting deeper and deeper, and the surrounding rock stress of dry hot rock reservoirs is also increasing. Therefore, it has become an inevitable demand for geothermal exploitation to study the evolution law of fracture seepage characteristics of granite under high temperature and ultra-high pressure. To reveal the evolutionary patterns of seepage characteristics in deep-seated hot dry rock fractures, an independently developed ultra-high pressure rock triaxial mechanical testing system was employed to investigate the seepage characteristics of fractured granite under varying temperatures (25–150 °C) and triaxial stresses (50–100 MPa). The study explores the influence of temperature on the seepage characteristics of granite fractures under ultra-high triaxial stress conditions. The results indicate that: (1) In the temperature range of 25–125 °C, as the rock temperature increases, the permeability of the Specimens showed a continuously decreasing trend due to the effect of thermal expansion. (2) In the temperature range of 125–150 °C, as the rock temperature increases, the permeability continues to decrease under low triaxial stress (50 MPa). However, under high triaxial stress (75 MPa) and extremely high triaxial stress (100 MPa), the permeability shows a slight increase instead. This phenomenon is attributed to free surface dissolution. (3) Quantitative analysis of the mesoscopic morphological data of the rock fracture surfaces after testing, combined with SEM images from scanning electron microscopy, confirms that within the high-temperature range of 125–150 °C, the differing levels of triaxial stress determine the variation in the dominant mechanism governing the evolution of the Specimen fracture surfaces, which in turn leads to the divergence in the trend of their permeability changes. Full article

Exploring public awareness, participation, and emotional inclination toward intangible cultural heritage (ICH) clarifies public attitudes and demands toward traditional culture, providing a crucial basis for targeted ICH protection and inheritance. Based on ICH text big data collected from China’s mainstream social media platform Weibo, this study improves the TF-IDF algorithm, integrates LDA topic analysis for semantic feature mining, and trains a new sentiment analysis model to explore public emotional attitudes and their formation mechanisms. The study is geographically limited to China and covers the entire year of 2023. The results show that: (1) Public ICH perception is multi-dimensional, with close attention to crafts like paper-cutting and traditional Chinese medicine; action-oriented terms reflect dynamic inheritance demands. Public discussions focus on three dimensions: ICH inheritance and development (39%), introduction and promotion (45%), and public experience and participation (16%), with the latter accounting for a low proportion. (2) Public sentiment toward ICH is predominantly positive, with all regions scoring above 0.730 (full score = 1), and Zhejiang (0.751) and Jiangsu (0.750) ranking significantly higher. (3) Spatial econometric analysis reveals marked regional differences in ICH sentiment distribution, mainly affected by three key factors—the number of ICH projects, the number of inheritors, and regional GDP—with regression coefficients of 0.699, 0.632, and 0.458 (p < 0.01). This finding provides a basis for formulating targeted ICH protection strategies. Full article

Artificial intelligence (AI) is increasingly being adopted for decision support in sustainable building risk management, yet the trustworthiness of AI-supported sustainability risk decisions depends as much on data quality as on analytical capability. Poor data conditions can amplify sustainability risks by producing unreliable decision support, yet existing studies provide limited insights into which data quality dimensions should be prioritized to enable trustworthy AI outcomes. This study identifies and prioritizes the critical data quality dimensions for trustworthy AI-supported decisions in sustainable building risk management. A questionnaire survey was conducted of accredited sustainable building professionals and their expert judgements were analyzed through an Analytic Hierarchy Process (AHP). The findings reveal that system-dependent dimensions, particularly traceability and interoperability, are prioritized over intrinsic dimensions like accuracy and consistency. The findings suggest that trustworthy AI-supported sustainability decisions depend strongly on a verifiable data provenance, cross-system integration and interpretable outputs rather than data correctness alone. This study reframes data quality from a general prerequisite to a prioritized, context-sensitive construct underpinning trustworthy AI applications, extending data-driven decision theory in the sustainable building domain. Ultimately, a phased data governance approach is recommended to prioritize traceability and interoperability as the foundational conditions for construction organizations implementing trustworthy AI in sustainable building risk management. Full article

Metal artifact reduction (MAR) remains a long-standing challenge in computed tomography (CT) reconstruction. Metallic implants introduce inconsistencies between the acquired projection data and the ideal Radon transform, resulting in severe streaking artifacts in images reconstructed using the conventional filtered back projection (FBP) algorithm. In this work, we propose a nonlinear weighted anisotropic total variation (NWATV) regularization method to mitigate metal artifacts and improve CT image quality. The effectiveness of the NWATV method is evaluated through three experiments, and the results demonstrate that it achieves superior reconstruction performance compared to the conventional linear interpolation method, the normalized metal artifact reduction method and the anisotropic total variation (TV) regularization method. Full article

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry. Full article

Narrow-range endemic plant species are highly sensitive to environmental variability due to their restricted distributions and narrow ecological niches, yet quantitative assessments of such species in Central Asian mountain ecosystem remain limited. This study applied an ensemble species distribution modeling (SDM) approach to assess the ecological constraints and conservation efforts of Lepidium olgae, a strict endemic species of the Nuratau Mountains in Uzbekistan. Species occurrence records from field surveys and herbarium data were integrated with remotely sensed climatic, vegetation, topographic, soil, and atmospheric variables. Parsimonious models (Generalized Linear Model (GLM), Maximum Entropy (MaxEnt), Multiple Adaptive Regression Splines (MARS), Surface Range Envelope (SRE)) were implemented in BIOMOD2 4.3.4, and ensemble predictions were used to reduce algorithmic uncertainty and identify core habitat patterns. Results showed that wet-season precipitation was the dominant driver of species distribution, followed by vegetation productivity (NDVI) and thermal stability, indicating a strong dependence on moisture availability and stable microhabitats. Ensemble projections revealed a highly fragmented potential distribution, with suitable habitats covering only 8% of the reserve area, closely matching the observed distribution of 6.5%. This strong spatial overlap confirms a narrowly constrained realized ecological niche. These findings highlight the critical role of microhabitat stability for the persistence of Lepidium olgae and provide a spatially explicit basis for prioritizing in situ conservation and guiding model informed translocation efforts. Full article

This work presents a mechanistic modeling approach for simulating methane emissions from triethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (<0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades. Full article

This study examines how firms’ financial heterogeneity shapes the transmission of monetary policy to investment in Latin American economies. It develops an extended theoretical model with heterogeneous firms, calibrated for Latin American economies, and validates it empirically through local projection models. These projections are applied to both a dataset of 72 of the most representative firms from the six analyzed Latin American economies and simulated data from the theoretical model, enabling direct comparison of the results. The research yields three main findings. First, it shows that financial heterogeneity is crucial and determines how firms respond to a monetary shock. Firms with fragile structures or high levels of indebtedness tend to restrict investment following monetary expansions, whereas firms with stronger financial positions or greater distance to default tend to increase it. The aggregate effect depends on the distribution of financial structures in the economy and which group dominates. Second, a transmission mechanism is identified via a financial channel based on a price–quantity sequence. The drop in the real rate compresses spreads and raises the price of capital; if financial constraints are active, the monetary relief is used to repair balance sheets rather than to invest; otherwise, the stimulus quickly translates into investment. Finally, the study shows that ignoring heterogeneity—as in representative–agent models—leads to a significant overestimation of both the magnitude and persistence of investment responses to monetary policy shocks. Full article

Lyme disease is the most common vector-borne disease in North America. Predicting Lyme disease incidence is a key component of public health preparedness. Previously, we demonstrated that the volume of data searches on Google Trends for terms related to Lyme disease, such as “Lyme” and “tick bite”, can be used as a tool to predict monthly human Lyme disease incidence at the state level. The objective of this project was to build upon our previous work by adding environmental and canine data to our predictive models for the prediction of state-level human and canine Lyme disease incidence. Human data were acquired from state health departments. Canine data were acquired from IDEXX Laboratories. We hypothesized that incorporating a One Health approach with human, animal, and environmental data would improve the predictive ability of the models. The One Health model performed significantly better (Mean Absolute Error [MAE] = 12.1) in predicting human disease incidence in 6 out of 16 states compared to the environmental data model (MAE = 16.5), human search terms model (MAE = 21.4), canine data (search terms + case count) model (MAE = 31.1), and the canine case data model (MAE = 32.0). For canine Lyme disease incidence, the One Health model performed worse (MAE = 330.5) compared to the canine search data model (MAE = 282.3), the human data (search terms + cases) model (MAE = 248.4), and the environmental data (MAE = 221.5) model. However, even the best-performing models had large prediction errors, which limit practical utility. Future studies should incorporate alternative data streams, such as electronic health records and insurance claims, to test predictive ability. Full article

Tropical forests are predicted to become carbon sources by mid-century under climate change. However, this trajectory may not be inevitable for forests under long-term protection. Using 12 years of eddy covariance flux data from a long-term protected tropical rainforest site in Xishuangbanna, China, we develop an explainable machine learning framework (SHAP + structural equation modeling) to disentangle the environmental drivers of net ecosystem exchange (NEE) and evapotranspiration (ET), and project their future trajectories under four CMIP6 climate scenarios. We find a fundamental divergence: while conventional climate models predict a sink-to-source transition by 2050–2066, our data-driven model—trained on conservation-era observations—projects a persistent carbon sink through 2100 across all the scenarios. This divergence suggests that long-term protection may buffer tropical forests against climate-driven decline, challenging the prevailing narrative of inevitable carbon loss. We further identify critical environmental thresholds—solar radiation (~200 W m⁻²) and air temperature (~25 °C)—beyond which carbon uptake efficiency declines. Our findings provide empirical support for nature-based climate solutions and highlight the need to integrate conservation legacies into Earth system models. Full article

Binary-cycle geothermal plants are inherently limited by thermodynamics, forcing operators to reinject fluids at temperatures that are still valuable for direct heating. This process results in substantial exergetic waste. While prior research has examined efficiency at the level of individual plants, this study introduces a regional-scale framework to convert these facilities into multi-purpose energy hubs. The research focuses on Türkiye’s Western Anatolia Graben, a region with high geothermal activity that, paradoxically, remains dependent on fossil fuels. By combining meteorological records with operational plant data, we evaluated the existing housing stock of 983,277 residences across 14 districts and modeled the heating requirements for a targeted capacity of 468,719 residences that the proposed system can serve. The results indicate that the currently wasted thermal load in 10 specific districts, including key centers such as Sarayköy and Alaşehir, is sufficient to cover peak winter heating demands without fossil fuel backup. Although the infrastructure requires a significant initial investment of $4.51 billion, the project demonstrates long-term viability with a Levelized Cost of Heat (LCOH) of 62.94 USD/MWh and a payback period of 10.43 years. Beyond economic considerations, the system serves as a major decarbonization tool, capable of cutting residential CO₂ emissions by 1.7 million tons annually (a 47.7% reduction). These findings suggest that policy incentives should move away from electricity-only models toward integrated reservoir management to maximize resource efficiency. Full article

Resource-constrained IoT systems face a fundamental conflict between cryptographic security and energy efficiency, particularly in critical infrastructure monitoring requiring long-term autonomous operation. This study presents a hybrid framework integrating signal-adaptive compression with hardware-accelerated authenticated encryption to resolve this trade-off. The Dynamic Payload Compression with Selective Encryption framework classifies sensor data into three SNR regimes and applies adaptive compression strategies: 24.15-fold compression for low-SNR backgrounds, 1.77-fold for transitional states, and no compression for high-SNR leak detection events. Experimental validation using 2714 acoustic sensor samples demonstrates 5.91-fold average payload reduction with 100% detection accuracy. The integration with STM32L5 hardware AES acceleration reduces power–data correlation from 0.820 to 0.041, increasing differential power analysis attack complexity from 500 to over 221,000 required traces. Compression-induced timing variance provides additional side-channel masking, burying cryptographic signals beneath a 0.00009 signal-to-noise ratio. Projected on 19,200 mAh lithium thionyl chloride batteries, the system achieves 14-year operational lifetime under realistic duty cycles, exceeding industrial requirements for critical infrastructure protection while maintaining robust security against physical attacks. Full article

While Digital Twins offer transformative potential, their efficacy for real-time control is constrained by the slow data acquisition and the high computational intensity required to process raw datasets like point clouds. This paper identifies these critical bottlenecks—specifically the latency between data capture and actionable insight—and proposes a refined theoretical framework for near-real-time automated progress monitoring and quality reporting. Building on the findings of the NORMENG project and informing the subsequent AutoGreenTraC project, this research synthesizes state-of-the-art advancements in reality capture, including LIDAR, SfM-MVS, and 360-degree vision. The study highlights a fundamental divergence in stakeholder requirements: the need for millimeter-level precision in quality control versus the demand for high-velocity documentation for progress monitoring. A key innovation presented is the shift toward neural rendering techniques to bypass the computational delays of traditional photogrammetry and enable immediate on-site visualization. By structuring a tiered processing hierarchy that combines lightweight edge analysis for immediate safety and progress monitoring with asynchronous high-fidelity Digital Twin updates, the framework aims to establish a single source of truth. Full article

Project-based organizations (PBOs) face persistent challenges in managing workload fluctuations that influence performance, competitiveness, and resource sustainability. Although previous research has explored bidding strategies and project inflows and outflows, few studies have systematically modeled workload-capacity dynamics or assessed policy responses to manage them effectively. To address this gap, this study develops a system dynamics (SD) model that integrates both pre-award and post-award project phases with internal and external organizational processes. Data for model development were drawn from the literature, industry reports, and expert interviews, resulting in the identification of 28 variables organized into subsystems covering demand, capacity planning, work execution, competitiveness, and financial performance. The model was validated through dimensional and structural tests, expert review, and further examined using social network analysis (SNA) and sensitivity analysis. The SNA results identified workload, production rate, and organizational capacity as the most influential variables. Sensitivity analysis conducted through Monte Carlo experiments, employing screening, regression, and ANOVA (analysis of variance) methods, revealed that capacity adjustment flexibility, minimum capacity, and demand level are critical factors influencing organizational stability. The validated model was then applied to evaluate policy alternatives under two distinct market conditions. Findings indicate that in lowest-price environments, a competitive, market-share-oriented policy enhances utilization and responsiveness, whereas in average-price markets, a stable capacity policy yields more sustainable outcomes. These results demonstrate how project-based organizations can strategically adjust bidding and capacity policies to stabilize workload dynamics and improve long-term operational resilience under different market conditions. The study contributes theoretically by extending the application of SD modeling to workload-capacity management in PBOs and contributes practically by offering a decision-support tool that helps managers assess capacity strategies, reduce risks, and align organizational policies with long-term sustainability objectives. Full article