Search Results (7,973)

This study develops an integrated decision-support framework to advance green supply chain management (GSCM) by systematically linking Environmental, Social, and Governance (ESG) practices, environmental product innovation, corporate performance, and strategic alternatives. Employing the Analytic Network Process (ANP), the proposed model captures complex interdependencies and feedback relationships across life-cycle value chain stages, enabling a holistic evaluation of sustainability-oriented strategies. A Delphi panel comprising 15 experts from academia, industry, and government is used to validate the evaluation criteria and network structure. The empirical results indicate that eco-friendly design, energy and resource efficiency, and carbon–climate management are the most influential drivers shaping green supply chain performance. Moreover, operational and sustainability performance are found to exert greater strategic importance than short-term financial performance, highlighting GSCM as a long-term capability-building approach rather than a cost-centered initiative. To enhance analytical adaptability, this study proposes a conceptual extension integrating neural feature extraction (NFE) signals with ANP-based expert weights. The NFE module is not empirically trained or validated; rather, it illustrates a theoretically consistent mechanism for incorporating data-driven feature signals into structured multi-criteria decision frameworks. Empirical validation of the NFE component is proposed as a future research direction. Full article

This study evaluated Apis mellifera brood fat extracts as a sustainable alternative to beeswax for anti-inflammatory topical delivery, including their formulation into nanostructured lipid carriers (NLCs). Brood fat was extracted using acetone, ethyl acetate (EA), and hexane, and the resulting extracts were characterized for fatty acid composition and physicochemical properties. Safety was assessed using the hen’s egg chorioallantoic membrane test and cytotoxicity testing in RAW 264.7 macrophages. Anti-inflammatory activity was assessed by inhibition of lipopolysaccharide-induced interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) production. The most suitable extract was formulated into NLCs using sugar squalane as liquid lipid, and the effects of lipid ratio and preparation method were investigated. The results showed that the ethyl acetate extract had the highest yield. Compared with beeswax, all fat extracts exhibited a favorable oleic acid–rich fatty acid profile with comparable crystallinity and thermal behavior, while showing significantly enhanced anti-inflammatory activity (p < 0.05). All extracts and their NLCs were non-irritating and non-cytotoxic. Ethyl acetate extract-based NLCs exhibited favorable particle sizes (72.1 ± 0.3 nm) and narrow polydispersity (0.14 ± 0.00), with high-pressure homogenization producing smaller particles compared to probe sonication without affecting IL-6 or TNF-α inhibition. Therefore, A. mellifera brood fat extract is a sustainable anti-inflammatory lipid source with strong potential as an alternative to beeswax in topical nano-formulations. Full article

This study investigates the thermo-hydro-mechanical (THM) coupling impacts on seepage and heat transfer characteristics to enhance the efficient utilization of hot dry rock resources in the Guanzhong Basin. A computational model of thermo-hydro-mechanical three-field coupling for an enhanced geothermal system is developed based on the geological context and rock thermophysical properties of the Huazhou-Huayin target area in the Guanzhong Basin. The effects of differential pressure during injection and production, injection temperature, and well configuration on the reservoir stress field, permeability variations, temperature distribution, and heat recovery efficiency of the system are carefully simulated and analyzed. Simulations indicate that increasing the injection–production pressure differential from ±1 MPa to ±7 MPa dramatically enhances heat recovery, yielding a fivefold increase in the extraction rate and an 11.54-fold rise in cumulative heat production. Conversely, this aggressive approach severely impacts long-term sustainability, accelerating thermal breakthrough and drastically cutting the operational lifespan by 93.30%. Lowering the injection temperature from 60 °C to 20 °C yields a 24.14% enhancement in heat output over the same duration, together with a 24.14% increase in the geothermal extraction rate. Increasing the number of injection–production wells from one to two broadens the heat extraction range and improves system heat production by 35.82%, concurrently diminishing lifespan by 39.50%. This work possesses theoretical importance for the progression of hot dry rock initiatives similar to those in the Guanzhong Basin and other geological settings. Full article

Komagataeibacter strains are important bacterial cellulose producers, yet closely related isolates can differ in cellulose yield, pellicle properties, and genetic stability during propagation. Such variability suggests that lineage structure and mobile genetic elements both contribute to strain-level genomic divergence. Here, complete genome comparisons were used to integrate vertical relatedness, gene-content structure, cellulose-associated signatures, and mobilome heterogeneity across 22 closed Komagataeibacter assemblies. A maximum likelihood phylogeny inferred from 642 single copy core genes provided the lineage scaffold. An anvi’o pangenome analysis defined a constant core gene cluster component across genomes and a noncore fraction that accounted for most of the genome differences in gene content. Targeted features linked to cellulose biosynthesis and local c-di-GMP-associated context were extracted from each genome. These features captured differences in bcs neighborhood composition and the presence of nearby GGDEF and EAL domain signals. The resulting feature matrix was projected by principal component analysis to summarize between-genome variation. Mobilome profiles were strongly strain dependent. Plasmid homology clustering identified 12 clusters comprising 36 plasmids from 13 genomes, including two dominant clusters of seven and six plasmids. Mash-based distance summaries further distinguished clusters consistent with conserved backbones from clusters consistent with heterogeneous, module-driven relationships. Prophage sequences, assessed as VIBRANT-predicted regions, were widespread but sparse per genome and dominated by medium length fragments. Insertion sequence burden ranged from 50 to 181 elements per genome, indicating substantial differences in transposition-associated sequence content. Pairwise association tests did not support robust cross module covariation beyond expected relationships among pangenome composition metrics at the current sampling depth. Overall, these results provide a complete genome reference framework linking lineage structure and mobilome heterogeneity, and they define reusable resources for comparative studies in bacterial cellulose biotechnology. Full article

With the continuous elevation of aviation safety standards, accurate monitoring of ground support vehicles in aircraft stand areas has become a critical task for enhancing overall aircraft stand operational safety. Given the limitations of existing surface movement radar and multi-camera surveillance systems in terms of cost, deployment complexity, and coverage, this paper proposes a lightweight vision-based framework for vehicle state perception and spatiotemporal behavior analysis oriented toward aircraft stand safety. Leveraging existing fixed monocular monitoring resources in the stand area, the framework first establishes a precise mapping from image pixel coordinates to the physical plane through self-calibration and homography transformation utilizing scene line features, thereby achieving unified spatial measurement of vehicle targets. Subsequently, it integrates an improved lightweight YOLO detector (incorporating Ghost modules and CBAM for noise suppression) with the ByteTrack tracking algorithm to enable stable extraction of vehicle trajectories under complex occlusion conditions. Finally, by combining functional zone division within the stand, a semantic map is constructed, and a behavior analysis method based on a spatiotemporal finite state machine is proposed. This method performs joint reasoning by fusing multi-dimensional constraints including position, zone, and time, enabling automatic detection of abnormal behaviors such as “intrusion into restricted areas” and “abnormal stop.” Quantitative evaluations demonstrate the framework’s efficacy: it achieves an average physical localization error (RMSE) of 0.32 m, and the improved detection model reaches an accuracy (mAP@50) of 90.4% for ground support vehicles. In tests simulating typical violation scenarios, the system achieved high recall (96.0%) and precision (95.8%) rates in detecting ‘area intrusion’ and ‘abnormal stop’ violations, respectively. These results, achieved using only existing surveillance cameras, validate its potential as a cost-effective and easily deployable tool to augment existing safety monitoring systems for airport ground operations. Full article

The failure of top-down approaches to education policy in England draws attention to the importance of context and foregrounds the need to help teachers to see themselves as practitioner–researchers, scholars and researchers capable of conducting systematic and trustworthy research into the improvement of their educational practice from the ground up and on the inside. This empirical, small-scale, qualitative study presents accounts of the lived experiences of 12 practitioner–researchers as they engage in the national practitioner research programme (PRP). The PRP offers intensive MPhil/PhD research training in which the evocative powers of aesthetic experience, culture and the arts are purposefully introduced to support practitioner–researcher–scholar identity formation and to encourage teachers to heighten the vitality of pedagogy and curriculum content by putting the cultural resources of society to work to make key ideas and concepts in education and educational research more accessible to all learners. Methods include 12 semi-structured interviews of 45–60 min, observation, field notes, case studies and extracts from MPhil/PhD theses. An objective of PRP research is to contribute to understanding how educational change and improvement might be done differently, including how persistent divisions, and barriers to teachers’ successful engagement in educational research and improvement, might be dismantled and dissolved through the strategic development of system-wide, HE-supported practitioner research. This article examines and calls into question the commonly held view that the arts are basically only instrumentally useful for their impact upon something else, such as the development of critical thinking and creativity. Main findings suggest that the use of aesthetic experience and the arts create epistemic-shortcuts which can not only help practitioners to overcome “imposter syndrome” but also enable them to access key ideas theories and concepts, theories and ideas in education and educational research more easily from the ground up, in context-attuned ways. Full article

Artificial intelligence (AI) is at the vanguard of transforming radiology in several ways, including augmenting diagnoses, improving workflows, and increasing operational efficiency. Several integration challenges, including concerns over privacy, clinical usability, and workflow compatibility, still remain. This review discusses the foundations and current trends of clinical AI in radiology to provide essential context for ongoing developments. To illustrate translational potential, we describe representative applications, including: (1) local deployment of large language models (LLMs) for restructuring and streamlining radiology reports, improving clarity and consistency without relying on external resources; (2) multimodal AI frameworks combining CT images, clinical data, laboratory biomarkers, and LLM-extracted features from clinical notes for early detection of cachexia in pancreatic cancer; (3) privacy-preserving federated learning (FL) infrastructure enabling collaborative AI model development across institutions without sharing raw patient data; and (4) an uncertainty-aware de-identification pipeline for removing Protected Health Information (PHI) from radiology images and clinical reports to support secure data analysis and sharing. We further discuss emerging opportunities for tumor board decision support, clinical trial matching, radiology report quality assurance, and the development of an imaging complexity index. Collectively, these applications highlight the importance of local deployment, multimodal reasoning, privacy preservation, and human-in-the-loop oversight in translating AI models from research to oncology radiology practice. Full article

The growing interest in bio-based and biodegradable polymer composites reinforced with agricultural waste reflects global efforts to reduce dependence on fossil resources and improve the sustainability of materials. However, biocomposites are not necessarily more sustainable, and their environmental performance requires careful life cycle assessment (LCA). This review critically analyses recent LCA studies of biodegradable biocomposites reinforced with agricultural waste, focusing on methodological choices, data quality, results and limitations. A systematic literature review was conducted using the Scopus database, focusing on studies from the last five years. Selected studies were examined using a structure consistent with ISO 14040, with defined data extraction categories and key questions. The analysis shows that although biocomposites often demonstrate advantages in terms of climate change and fossil resource depletion compared to traditional materials, the results vary significantly depending on the definition of the functional unit, geographical context, processing pathways, and data assumptions. Limitations include reliance on laboratory data, uncertainties, incomplete system boundaries, inconsistent allocation methods, and limited end-of-life (EoL) modelling. Overall, the review highlights the need for improved data quality, performance-based functional units, geographically representative inventories, and more standardised LCA practices to ensure meaningful comparisons and support the sustainable development of biocomposites. Full article

Geothermal energy is a clean, renewable, and baseload-stable resource of strategic importance for carbon neutrality. Hot dry rock (HDR) reservoirs are characterized by high temperatures, great depths, and abundant reserves. However, their extremely low natural permeability requires artificial fracturing to establish effective heat exchange networks. Conventional hydraulic fracturing in enhanced geothermal systems (EGS) faces major challenges under HDR conditions, including excessive water consumption, strong water–rock interactions, and elevated induced seismicity risks, limiting its engineering applicability. Waterless or low-water fracturing technologies offer alternative stimulation pathways due to their distinctive physicochemical properties. Existing reviews have mainly addressed individual aspects, such as specific fracturing media or proppant transport, without systematically integrating recent advances in supercritical CO₂ fracturing, foam fracturing, liquid nitrogen fracturing, and hybrid-fluid fracturing technologies, or comprehensively evaluating their engineering implications. This review systematically analyzed the fracturing mechanisms, heat exchange performance, environmental risks, and HDR-specific engineering challenges of these technologies. Results indicate that waterless/low-water fracturing technologies enhance heat extraction efficiency by generating complex fracture networks while mitigating seismic and reservoir damage risks. However, large-scale application requires further advances in the high-temperature stability of fracturing media, material durability, multiphase flow control, and field validation. Full article

The efficient exploration and development of mineral resources rely on deep mining and correlation analysis of massive, multi-source, and unstructured geological data. Knowledge graph technology provides a structured solution for integrating fragmented knowledge in the field of mineral resources. This study takes the iron ore resources in Linyi City, Shandong Province as a typical case and proposes a method framework for automatically constructing a regional mineral resource knowledge graph from unstructured text. Firstly, seven types of mineral entities (location, ore body, scale, type, attitude, alteration, development degree) and five semantic relationships (type, scale, location, inclusion, development) were defined, and a high-quality Chinese annotation corpus containing 10,434 entities and 6660 relationships was constructed through domain ontology design. Secondly, BiLSTM-CRF, BiGRU-CRF, and various BERT based models were compared in the named entity recognition task, and it was found that the optimized BERT-CRF model achieved the best performance (F1 score: 82.8%). The BERT based model significantly outperforms traditional PCNN and BiGRU models, achieving an F1 score of 98.14%, which was found in relation extraction tasks. Finally, based on the extracted triples, a visual knowledge graph of iron ore resources in Linyi City was constructed using the Neo4j graph database, in order to achieve knowledge association queries and visual navigation. Full article

Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text extraction, yet their relative strengths compared with supervised methods remain unclear. This study presents a controlled evaluation of three dominant strategies for structured clinical information extraction from electronic health records: prompting-based extraction using LLMs, retrieval-augmented generation for terminology canonicalization, and supervised fine-tuning of domain-specific transformer models. Using discharge summaries from the MIMIC-IV dataset, we compare zero-shot, few-shot, and verification-based prompting across closed-source and open-source LLMs, evaluate retrieval-augmented canonicalization as a post-processing mechanism, and benchmark these methods against a fine-tuned BioClinicalBERT model. Performance is assessed using a multi-level evaluation framework that combines exact matching, fuzzy lexical matching, and semantic assessment via an LLM-based judge. The results reveal clear tradeoffs across approaches: prompting achieves strong semantic correctness with minimal supervision, retrieval augmentation improves terminology consistency without expanding extraction coverage, and supervised fine-tuning yields the highest overall accuracy when labeled data are available. Across all methods, we observe a consistent $40-50\%$ gap between exact-match and semantic correctness, highlighting the limitations of string-based metrics for clinical Natural Language Processing (NLP). These findings provide practical guidance for selecting extraction strategies under varying resource constraints and emphasize the importance of evaluation methodologies that reflect clinical equivalence rather than surface-form similarity. Full article

The evaluation of offshore wind energy resources is important to the construction of offshore wind power facilities. In this paper, using four models from CMIP6 and the ERA5 reanalysis dataset, a deep learning model termed SwinWind was developed and proposed to evaluate future offshore wind energy resources in Southeastern China for the periods 2020–2050 and 2070–2100. The feature extraction capability of the Swin Transformer was utilized to construct a bias correction and downscaling framework. This approach achieves performance comparable to existing high-cost models while significantly reducing computational costs and complexity. The SwinWind model corrected most of the biases and effectively learned spatial relationships, successfully performing the downscaling task. Based on future wind speed projections derived from the SwinWind model, this study presents a comprehensive evaluation of offshore wind resources, examining five critical dimensions: resource abundance, efficiency, stability, the impact of extreme winds, and economic feasibility. It is projected that offshore wind resources around Shanghai, Jiangsu and Zhejiang will experience a decline in the 21st century, while offshore wind resources around the Guangdong, Fujian and the Beibu Gulf show an increasing trend. The evaluation index shows that the coastal areas of Guangdong and the southern coastline of Taiwan are the most suitable locations for wind power exploitation. The Taiwan Strait, which has the highest wind energy density, is not the best spot due to its extreme wind speed and unstable wind resources. This study provides an important reference for the location of wind farms with practical application value. Full article

Pueraria lobata (Willd.) Ohwi root is a traditional medicinal resource rich in bioactive isoflavonoids with known antioxidant and anti-inflammatory properties. However, the chemical composition and biological activities of P. lobata root extracts can vary depending on the extraction solvent. In this study, we systematically compared P. lobata root extracts prepared using water, ethanol (30%, 70%, and 100%), and methanol to evaluate the effects of solvent selection on extraction yield, HPLC-based chemical profiles of major isoflavonoids, antioxidant capacity, and cellular responses in vitro. Chemical characterization by HPLC revealed distinct solvent-dependent differences in the relative abundance of key isoflavonoids, including puerarin, daidzin, and daidzein, defining characteristic chemical profiles for each extract. Antioxidant activity was evaluated using DPPH and ABTS radical scavenging assays, along with measurements of total polyphenol and flavonoid content. Cell viability was examined in HeLa cells using an MTT assay to define non-cytotoxic concentration ranges. The anti-inflammatory potential of the extracts was further assessed by measuring TNF-α-induced secretion of pro-inflammatory cytokines in HeLa cells. The results revealed marked solvent-dependent differences in extraction yield, chemical composition, and functional activity. Notably, methanol and ethanol extracts exhibited enriched isoflavonoid profiles associated with enhanced antioxidant and anti-inflammatory responses. Overall, this integrated chemical and functional evaluation demonstrates that solvent selection plays a critical role in determining the chemical characteristics and bioactivity of P. lobata root extracts. These findings provide a basis for rational solvent selection in the preparation of plant-derived extracts and support the potential use of P. lobata root as a functional source of antioxidant and anti-inflammatory compounds. Full article

Hurricanes have intensified and become more persistent under a changing climate, increasing the risk of infrastructure damage and property loss in coastal regions, threatening their sustainability. This study examines how hurricane intensity and persistence influence infrastructure loss, contributing to a more comprehensive understanding of climate-related risks. Using data from the National Oceanic and Atmospheric Administration (NOAA) Storm Events Database from 1996 to 2024, we develop a series of machine learning models to predict property losses based on storm characteristics and contextual vulnerability factors. Narrative-based text analysis and time-series feature engineering were applied to extract meteorological and temporal attributes, while regression and ensemble models were used for predictive evaluation. Results show that storm intensity alone explains only a small portion of loss variance, with persistence influencing damage primarily through rainfall and hydrological effects. The findings highlight that vulnerability, exposure, and cumulative risk dynamics are essential for accurate long-term prediction and for assessing infrastructure sustainability. Overall, the study demonstrates that combining machine learning techniques with climate and vulnerability data can inform future research on infrastructure sustainability. The quantified vulnerability-versus-intensity breakdown presented here can support post-disaster resource allocation, insurance risk modeling, and the prioritization of infrastructure maintenance in hurricane-prone regions. Full article

The NdFeB magnetic material is widely used in modern industry and electronics. Recycling spent magnets containing 30–40 wt.% Rare Earth Elements (REEs) and 60–70 wt.% iron is essential for resource recovery. This study focuses on treating wastewater from hydrometallurgical REE extraction to enable water reuse and iron recovery. Experiments investigated the influence of temperature (25–60 °C) and oxalic acid concentration (50–120% stoichiometric amount) on iron precipitation as the main focus. Optimal iron recovery of 85.3% and 86.8% was achieved at 25 °C using 80% and 100% stoichiometric oxalic acid, respectively. Simultaneously, praseodymium removal exceeded 94%. The Differential Scanning Calorimetry (DSC) analysis identified a peak between 168.8 °C and 245 °C, with the peak temperature recorded at 214.5 °C, indicating the optimal calcination temperature for iron oxalate. This was confirmed through oven testing and X-ray Diffraction (XRD) analysis. The treated effluent exhibited a Chemical Oxygen Demand (COD) of 418 mg/L, with residual Fe and Pr concentrations significantly reduced to 5.7 mg/L and 4.3 mg/L, respectively. This approach demonstrates an efficient method for wastewater treatment and resource valorization, promoting sustainability in the recycling sector. Full article