Search Results (45,552)

As the development of the ocean extends to the deep and open seas, the application of multi-hull floating systems is becoming increasingly widespread, covering offshore oil and gas transfer and material replenishment operations. In multi-body floating systems, the hydrodynamic interactions between adjacent floating bodies significantly affect the overall motion response and load distribution. However, there is currently a lack of systematic experimental research on systems involving three or more units under the combined action of wind, waves, and currents. This study presents a 1:50 scale model experiment on a five-body offshore replenishment station, comprising a central transfer platform and four surrounding vessels. Absolute six-degree-of-freedom motions and relative displacements between the transfer platform and neighboring vessels were measured. The results indicate distinct differences among the units. The peripheral vessels have greater horizontal and yaw motions, while the central units are more restricted. The relative motions are substantially increased for beam and oblique wave conditions, implying increased interaction effects in the gaps between neighboring bodies. Moreover, the combined oblique environmental loading and asymmetric mooring stiffness result in increased global drift and yaw motions. These findings provide benchmark data for numerical validation and practical guidance for the design and operation of multi-body floating systems. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

The recognition of a clean, healthy, and sustainable environment as a human right has reshaped global human rights discourse, yet its operationalisation remains uneven. This article examines how the African human rights system which is uniquely grounded in collective rights, has reframed environmental protection as a constitutive element of development, sovereignty, and justice. Through doctrinal and case-law analysis, it traces the evolution from the African Commission’s foundational jurisprudence in SERAC, which extended state duties to the regulation of private and transnational corporate actors, to the African Court’s landmark judgment in LIDHO v. Côte d’Ivoire. The study demonstrates how the Court transforms the aspirational ‘greening’ of human rights into binding obligations by articulating a robust duty of vigilance and linking environmental harm to violations of the rights to life, health, and development. It further shows that LIDHO inaugurates a post-sovereign model of shared and polycentric responsibility, in which state accountability encompasses corporate conduct within their jurisdiction and, potentially, beyond it. The article concludes that the African Charter’s collective framework offers an implicit regional model of ecological justice, one capable of addressing extractive asymmetries and informing emerging climate-related obligations across the continent. Full article

Tamusu, the only identified super-large sandstone-hosted uranium deposit in the Bayingobi Basin, provides an important natural laboratory for evaluating ore-controlling factors and genetic models of sandstone-type uranium mineralization. Based on core descriptions from more than 200 boreholes, log facies analysis and geochemical environmental proxies, this study constrains the sedimentary–mineralization architecture and key controlling factors of the deposit. Uranium orebodies are mainly hosted in the upper member of the Lower Cretaceous Bayingobi Formation (Sq2) within a gravity flow-dominated fan-delta–lacustrine system. Braided distributary channel sands on the fan-delta plain and subaqueous distributary channel sands on the delta front constitute the principal uranium reservoirs, controlling both the migration pathways and storage space for U-bearing fluids. Mineralization is jointly governed by fan-delta architecture, interlayer oxidation zonation and reducing agents. The interlayer oxidation zone displays a north-thick–south-thin geometry, and uranium orebodies are concentrated at redox transition positions, with grades of 0.01–0.33 wt%. The metallogenic evolution can be summarized in three stages: syndepositional uranium pre-enrichment, interlayer oxidation mineralization, and a late hydrothermal/diagenetic overprint that mainly modified reservoir properties, favored ore preservation, and did not contribute to the primary uranium budget. Accordingly, a genetic model of “fan-delta architecture + interlayer oxidation control + late overprint and preservation” is proposed to guide exploration in the Bayingobi Basin and analogous sandstone-type uranium systems. Full article

Non-motorized mobility networks increasingly serve as critical infrastructure for sustainable regional development that integrates recreational, environmental, and transportation functions across diverse geographical contexts. To enhance the spatial planning efficiency and support evidence-based policy development, this study develops a Geographic Information Systems (GIS)-based analytical framework to evaluate the connectivity and accessibility of Korea’s integrated non-motorized mobility system. The model systematically maps 606 walking courses, 60 cycling routes, and 66 water activity sites nationwide, and examines their spatial relationships with major transportation hubs, including Korea Train e-Xpress (KTX) stations and airports within 20–30 km buffer zones. Using proximity analysis, connectivity mapping, and origin–destination (OD) cost matrix modeling, the framework identifies intermodal distance structures and spatial integration patterns. The analysis reveals a hybrid network configuration characterized by localized multimodal clustering alongside regional accessibility gaps, with urban–coastal regions demonstrating stronger connectivity than inland–rural areas. This study proposes a data-driven Korean mobility network framework that integrates walking, cycling, and water routes with the existing transportation infrastructure. These findings demonstrate how GIS-based tools can support evidence-based sustainable mobility policies and regional tourism planning on a national scale. Full article

The treatment of spent drilling fluids generated during the drilling of technological wells for uranium production represents an important engineering and environmental challenge associated with high energy consumption, significant waste generation, and the need for rational water use under arid regional conditions. Conventional phase separation methods based on gravitational settling and chemical–mechanical treatment are characterized by limited process controllability, long processing times, and increased consumption of reagents and energy. This study proposes an energy-efficient and reliable hydrodynamic technology for the treatment of spent drilling fluids based on the formation of controlled turbulent structures without the use of mechanical drives. The research object comprised spent drilling fluids (SDFs) generated during the drilling of technological wells for uranium production in the southern regions of the Republic of Kazakhstan and the Kyzylorda region. Experimental investigations were carried out using a laboratory–pilot hydrodynamic disperser with variations in velocity gradient, treatment time, flocculant dosage, and suspension flow rate. A mathematical model linking hydrodynamic process parameters with phase separation kinetics and energy characteristics was developed. Model calibration by weighted nonlinear least squares yielded a stable parameter set with 95% confidence intervals, and model validation demonstrated good agreement between calculated and experimental data (MAPE 8.4%; maximum relative error 11.8%). It was established that the use of a hydrodynamic disperser provides separation efficiency of up to 90–95% under optimal operating conditions while reducing specific energy consumption and maintaining stable repeated-cycle performance within the investigated operating window. Experimental results confirm that implementation of the hydrodynamic technology enables a reduction in sludge volume by 40–60%, recovery of up to 60–80% of process water, and a significant decrease in waste requiring transportation and disposal. The obtained results demonstrate the high environmental and resource-saving efficiency of the proposed technology and its suitability for scaling and industrial implementation at facilities drilling technological wells for uranium production. The developed hydrodynamic approach can be considered an effective engineering platform for creating energy-efficient and sustainable systems for drilling fluid treatment in regions with limited water resources and remote industrial infrastructure. Full article

Despite recent advances in artificial intelligence for agriculture, reliable crop recommendation remains constrained by limited access to soil diagnostics, insufficient integration of environmental context, and the absence of transparent, quantitative evaluation frameworks. This study addresses the research question: How can we integrate multiple indicators to generate accurate, explainable, and context-sensitive crop recommendations? To this end, we propose a multimodal decision-support framework that combines image-based soil texture classification with geospatial, and climatic information. A convolutional neural network was trained on a curated dataset of 3250 soil images aggregated from four publicly available sources, covering four primary soil texture classes, alongside tabular soil and nutrient data. The model was evaluated using 5-fold stratified cross-validation, achieving an average classification accuracy of 99.30% (standard deviation ≈ 0.66), and was further validated on an independent hold-out test set to assess generalization performance. To enhance practical applicability, the framework incorporates elevation, rainfall, temperature, and major soil nutrients, and employs a large language model to generate user-oriented, interpretable justifications for each recommendation. Crop recommendations were quantitatively evaluated using a novel Agronomic Suitability Score (ASS), which measures alignment across soil compatibility, climatic suitability, seasonal alignment, and elevation tolerance. Across six geographically diverse case studies, the framework achieved mean ASS values ranging from 3.76 to 4.96, with five regions exceeding 4.45, demonstrating strong agronomic validity, robustness, and scalability. A Streamlit-based application further illustrates the system’s ability to deliver accessible, location-aware, and explainable agronomic guidance. The results indicate that the proposed approach constitutes a scalable decision-support tool with significant potential for sustainable agriculture and food security initiatives. Full article

The modern high-pressure lifestyle has led to an increasing emphasis on the healing construction of residential spaces, while air, as an important environmental factor, has received little attention in terms of situational healing experiences within the context of residential culture. Employing grounded theory, this study develops a theoretical model to explain the mechanism through which indoor air environments influence the healing benefits of residential spaces. Guided by the dynamic interaction process of “physical attributes–embodied cognition–behavioral regulation–social context”, the analysis focuses on human embodied perception and emotional responses to indoor air environments as the foundation for healing effects. It highlights the joint role of behavioral regulation and social context, ultimately affecting four levels of healing benefits. Furthermore, it systematically elaborates a theoretical model for embodied interactive residential air experiences, expanding healing environment theory from a contextual air experience perspective, and providing new research paradigm and insights for promoting healing benefits in residential settings. Full article

Traditional one size fits all pharmacotherapy often yields suboptimal clinical outcomes, preventable adverse drug reactions (ADRs), and significant drug waste, imposing substantial economic and ecological burdens on healthcare systems. This review evaluates the transformative potential of pharmacogenomics (PGx) testing, particularly cytochrome P450 (CYP) gene variants, as a foundation for an ecosystem-centric accountability framework for green pharmacy and links human metabolic variability to specific environmental outcomes. Personalized CYP profiling is shown to minimize the environmental release of unused drugs and potentially ecotoxic metabolites into aquatic ecosystems, in contrast to standard uniform drug use approaches. The limitations of ethnicity-based dosing models, which rely on population genetic variation, are examined in the context of increasing global genetic admixture. It is argued that individual genetic profiling, conceptualized as a PGx-Green Passport, provides a reliable safety standard that accounts for individual differences, thereby enhancing efficiency and well-being in a globalized society. By integrating clinical data, including real-world evidence on hospital utilization, with sustainability frameworks, this review demonstrates that PGx-guided therapy is not only a tool for clinical efficiency but also a fundamental requirement for systematically achieving environmentally sustainable healthcare. Full article

Groundwater contamination caused by dense non-aqueous phase liquid (DNAPL) has long been recognized as a persistent environmental challenge, particularly in fractured porous media. DNAPL migration is highly uncertain due to the heterogeneity and complexity of fracture networks, which complicates risk assessment and remediation design. This paper begins with an overview of mathematical models for multiphase flow migration in fractured media, followed by a systematic analysis and classification of DNAPL migration mechanisms based on laboratory experiments and numerical simulations. Subsequently, key challenges in current DNAPL remediation practices are discussed, including difficulties in monitoring and characterizing fractured aquifers, limited delivery and utilization efficiency of remedial agents, and the back-diffusion of DNAPL from low-permeability zones. Based on this analysis, three primary DNAPL remediation approaches—physical, chemical, and biological methods—are reviewed and evaluated. Finally, future research directions for understanding DNAPL migration and improving remediation strategies in fractured media are proposed. Overall, this review bridges mechanistic knowledge, simulation research, and remediation practice, providing insights that contribute to future technological progress and management decision-making in DNAPL-contaminated fractured aquifers. Full article

Spatiotemporal heterogeneity of land-use multifunction (LUMF) is crucial for the preservation and management of large-scale national cultural parks in alleviating potential human-land conflicts. Using 28 multidimensional indicators across economic, social, and environmental dimensions, this study established an LUMF index system for the Long March National Cultural Park of China (CLMNCP). LUMF values for 77 prefecture-level cities were quantified from 2008 to 2023, and their spatiotemporal heterogeneity was examined using a spatial autocorrelation model. Subsequently, the Optimal Parameters-based GeoDetector (OPGD) model was applied to identify key driving factors. The main findings are as follows: (1) From 2008 to 2023, the total, economic (EF), social (SF), and environmental (EnF) functions in the CLMNCP exhibited a consistent upward trend. (2) Significant spatial heterogeneity characterized the trade-offs and synergies among these functions. The EF-EnF interaction displayed a concave synergistic relationship, while the EF-SF and SF-EnF interactions showed convex, fluctuating patterns during their transitions between trade-off and synergy. (3) The primary drivers varied across function pairs. The EF-SF synergy was predominantly influenced by agricultural production, resource supply, and cultural service factors. The EF-EnF interaction was mainly shaped by natural conditions and environmental improvement factors. In contrast, the SF-EnF interaction was primarily driven by economic development, cultural services, and resource supply. These findings support functional zoning and targeted management of large-scale national cultural park to balance development and conservation while reducing human-land conflicts. Full article

As the demand for polyethylene terephthalate (PET) continues to rise, significant environmental pollution caused by challenges in PET degradation has garnered global attention. Given the crucial role of esterases in depolymerizing PET into reusable monomers, such enzymes capable of degrading plastics have attracted considerable interest. In this study, we used the previously reported ultra-efficient mutant of the PET-degrading enzyme Ideonella sakaiensis PETase, known as FASTase, as a positive control. We investigated the PET-degrading activity of esterase E4, derived from Altererythrobacter indicus. The results demonstrated that E4 exhibits degradative activity toward the PET substrate bis(2-hydroxyethyl) terephthalate, the PET model substrate bis(benzyloxyethyl) terephthalate, and PET nanoparticles. Notably, E4 retains its degradation activity under high-temperature and high-salt conditions and can enhance the enzymatic activity of FASTase when acting synergistically. Given the low structural and sequence similarity between E4 and IsPETase, our research broadens the scope for screening PET-degrading enzymes. Full article

Data-centric approaches are increasingly shaping wireless communication research, where the availability and quality of datasets directly influence the reliability of learning-based and model-driven methods. In this context, unmanned aerial vehicle (UAV) communication poses unique challenges, as it requires datasets that jointly capture geometric information, propagation conditions, and diverse link configurations. This work introduces a geometry-aware UAV communication dataset designed to support research on controlled UAV communication link directions and propagation scenarios. The dataset is generated using standardized 3GPP and ITU-R channel models across multiple urban, suburban, and rural regions, accounting for variations in altitude, carrier frequency, and node distribution. The dataset provides spatially resolved channel parameters along with geometry-rich files containing environmental features, which can be used to extract relevant parameters for UAV communication studies. These data support reproducible research in geometry-aware channel modelling, path-loss prediction, LOS/NLOS analysis, delay-related modelling, and trajectory-conditioned link-quality analysis. Full article

The large-scale accumulation of steel slag from steelmaking and the over-exploitation of natural aggregates pose significant environmental and resource challenges. Focusing on the arid-cold region of Xinjiang, China, this study proposes the use of steel slag as a substitute for natural aggregates in pavement engineering. Through experimental performance evaluation and regionalized life cycle assessment (LCA), the technical feasibility and carbon reduction potential of this application were comprehensively evaluated. Results indicate that steel slag asphalt mixtures meet or exceed specification requirements in terms of high-temperature stability, water stability, and low-temperature crack resistance. However, volume stability decreases slightly with higher steel slag content and finer particle size, necessitating pretreatment for long-term durability. A local life cycle assessment model considering regional transportation factors was applied to the G30 Luhuo Expressway project. During the materialization stage, steel slag was used to replace 30% of the natural aggregates, reducing approximately 6718 kg of carbon dioxide equivalent emissions (31.4%). This, to some extent, reduced the extraction of natural resources, saved land resources, and alleviated the problems of resource shortage and price fluctuations. Sensitivity analysis reveals a positive correlation between carbon reduction and steel slag content, while transport distance strongly influences overall benefits, with a critical threshold of about 78 km defining the effective utilization range. Furthermore, a multi-objective optimization model balancing service life, cost, and carbon reduction was developed to identify an optimal steel slag content scheme, maximizing comprehensive benefits under constrained conditions. This work confirms the technical viability of steel slag pavement in extreme climates and provides a systematic framework integrating environmental benefits and logistical constraints, supporting regional industrial synergy and promoting circular economy practices in low-carbon infrastructure. Full article

Sustainable development has become an important global goal for environmental protection and economic growth. Promoting environmental sustainability and green development has become an inevitable trend for global economic transformation. The carbon emission trading market (carbon market) is a crucial market-based mechanism for pricing greenhouse gas emissions, where carbon trading prices signal the costs of emission reduction and drive firms to engage in green technology innovation for a low-carbon transition. Using a sample of A-share listed companies in China’s eight carbon pilot regions from 2013 to 2024, this study employs a two-way fixed effects model to examine how carbon prices affect both the quantity and quality of corporate green technological innovation. Baseline regressions show that a one-unit increase in carbon prices significantly boosts green patent quantity (GreNum) by 0.018 and quality (GreQua) by 0.361, both at the 1% significance level. Mechanism analysis reveals that financing constraints act as a partial mediator, while environmental regulation and media attention further amplify the positive impact of carbon prices on corporate green technological innovation. Heterogeneity analysis further indicates that this positive effect is more pronounced in non-high-tech enterprises, China’s northern and eastern regions, and state-owned enterprises. This study provides empirical evidence for optimizing carbon market allocation, supporting corporate low-carbon transition, and promoting global environmental sustainability and green development. Full article