Search Results (6,065)

With the transformation of resource-based cities and the restructuring of industrial sectors, the sustainable conservation of coal mining heritage has become a global focus. In China, coal mining heritage faces challenges such as degradation and inadequate management, highlighting the urgent need for more context-sensitive and systematic conservation approaches. This study develops an integrated, landscape-oriented analytical framework for stakeholder identification to address these challenges and to better understand stakeholder differentiation in coal mining heritage conservation. The research objectives are as follows: (1) to bring together a three-dimensional framework based on material-technical, socio-cultural, and experiential dimensions; (2) to analyse the roles and interactions of stakeholders; and (3) to explore how technical knowledge, socio-cultural memory, and daily experiences influence the protection and reuse of coal mining heritage sites. The study integrates the theoretical frameworks of landscape character assessment, historic urban landscape, and experiential landscape, using data from field observations and interviews analysed via ATLAS.ti. The findings show that the proposed framework offers a more systematic understanding of the dynamic relationships between stakeholders and heritage landscapes, thereby providing practical guidance for local governments and relevant institutions in developing inclusive and context-sensitive conservation strategies. Full article

The middle and lower reaches of the Yangtze River Basin occupy a strategically pivotal position in regional development; yet extreme drought-runoff events pose severe threats to water supply and ecological security. Despite this, systematic research gaps persist, including the lack of a unified definition, standardized identification criteria, and clear understanding of formation mechanisms for extreme drought-runoff. To address these limitations, this study focused on extreme drought-runoff in the basin, utilizing 1956–2024 discharge data from four mainstream hydrological stations and meteorological data from 171 stations. Quantitative discrimination criteria were established via Pearson-III frequency analysis; meteorological characteristics were analyzed using the Meteorological Drought Comprehensive Index; and formation mechanisms were explored through partial correlation analysis and multiple linear regression. This study innovatively proposed a basin-wide three-level quantitative discrimination criterion for drought-runoff based on the June–November flow frequency of key mainstream stations, which is distinguished from single-indicator drought identification methods (SPI/SPEI/SSI) by integrating basin-scale hydrological coherence and seasonal drought characteristics. The results revealed basin-wide extreme drought-runoff in 2006 and 2022, severe drought-runoff in 1972 and 2011, and relatively severe drought-runoff in 1959, 1992, and 2024. Typical extreme drought-runoff events were characterized by sustained low precipitation and high temperatures. Meteorological factors emerged as the primary driver during June–September, while reservoir operation and riverine water intake played secondary roles. Notably, the large-scale reservoir group in the Yangtze River Basin (53 key control reservoirs) helped alleviate drought-runoff impacts from December to May (non-flood season) via water supplementation. These findings provide a robust scientific basis for precise drought-runoff prediction and the development of targeted adaptation strategies in the Yangtze River Basin. Full article

The present study provides an exhaustive exploration of twelve macroalgal species from the Adriatic Sea, including seven brown algae (Ericaria amentacea, Fucus virsoides, Cutleria multifida, Cystoseira compressa, Cystoseira corniculata, Gongolaria barbata and Padina pavonica), three green algae (Codium adhaerens, Codium vermilara and Ulva lactuca), and two red algae (Scinaia furcellata and Asparagopsis taxiformis). Matrix solid-phase dispersion (MSPD) was applied as the extraction technique, using generally recognized as safe (GRAS) solvents. The bioactive profile of the extracts was assessed through the quantification of total phenolic content (TPC) and antioxidant activity. Among the three phyla, U. lactuca, F. virsoides and S. furcellata exhibited the highest TPC (0.8, 26 and 3.0 mgGAE·g⁻¹) and antioxidant activity (1.9, 38 and 7.5 mgTE·g⁻¹), respectively. Targeted HPLC-MS/MS analysis enabled the identification of nineteen phenolic compounds across all taxa. Chlorophyta showed a characteristic profile enriched in coumarins, benzaldehydes and flavanones, including the selective detection of 7-hydroxycoumarin in species with higher antioxidant potential. Additionally, compounds such as chlorogenic, rosmarinic and caffeic acids exhibited taxon-specific distributions that may explain differences in antioxidant activity. Complementary untargeted ultra-high performance liquid chromatography quadrupole time-of-flight (UHPLC-QToF) metabolomics analysis provided broader coverage, revealing eighty metabolites spanning phenolics, sugars, organic acids, lipids, amino acids and their derivatives. Notably, the proposed detection of fatty acid esters of hydroxy fatty acids (FAHFAs) represents the first report of these compounds in macroalgae, alongside a pronounced presence of sulphated phenolics. Overall, these findings provide a robust baseline on the bioactivity and chemical composition of Adriatic macroalgae, highlighting their value as a natural source of functional compounds. Full article

This study explored the continuous monitoring of hydrogen-induced cracking (HIC) in high-strength steel tension wires used in metal-based flexible pipes, exposed to a H₂S-saturated aqueous environment, using acoustic emission (AE) techniques. Armor wire samples were subjected to sour conditions under controlled environments for 24 and 96 h. To reinforce and validate the AE findings, a comprehensive characterization was performed, including X-ray microtomography, optical microscopy, and scanning electron microscopy. The experimental results demonstrated that AE techniques effectively monitored the evolution of HIC damage in the armor wire samples, enabling the identification of distinct damage stages and cracking phenomena. These findings confirm that AE can serve as a valuable complementary tool during HIC testing, optimizing test duration and providing insights into the kinetics of the cracking process. Full article

Composite materials are extensively used in the aeronautical and aerospace industries for their high strength-to-weight ratios but are vulnerable to barely visible impact damage (BVID), which can severely compromise structural integrity. Digital shearography (DS) provides a non-contact, full-field solution for subsurface inspection; however, low signal-to-noise ratios in raw phase maps often hinder precise damage identification. This study explores a post-processing methodology utilizing a band-pass filtering algorithm and temporal summation to isolate damage-related spatial frequencies. An in-house digital shearography system was used to inspect a carbon-fiber-reinforced polymer (CFRP) plate subjected to 13.5 J and 26.2 J impacts. Twelve phase maps, acquired during the thermal cooling stage, were processed using a multi-pass filters to systematically analyze different frequency ranges. Results demonstrate that summing multiple filtered phase maps significantly enhances the contrast of damage signatures compared to single phase maps or traditional unwrapping techniques. Furthermore, quantitative assessment using image quality metrics, such as the generalized contrast-to-noise ratio (gCNR), confirmed that optimal frequency selection is essential for an accurate damage delineation. This approach provides a robust framework for improving the reliability and sensitivity of non-destructive testing in composite structures. Full article

Real-world evidence for wearable noninvasive glucose monitoring (NIGM) remains limited. To evaluate the functional equivalence of a wearable NIGM device and explore its utility for T2DM and prediabetes screening. In this multicenter study, 12-h daytime glucose profiles obtained by a flexible reverse iontophoresis-based electrochemical sensor were compared with capillary glucose using functional equivalence. Subgroup analyses were conducted. Screening models of T2DM and prediabetes were developed using elastic net and Logistic regression. A total of 135 participants (mean age 35.3 years; 60.0% female) were included, and no serious device-related adverse events were reported. Compared to the capillary measurements, functional equivalence was confirmed (T = −6.537 < threshold = −2.081) in the general population but not in older adults or T2DM patients. The T2DM noninvasive screening model demonstrated discrimination and reclassification performance comparable to those of the capillary-based model (AUC: 0.906 vs. 0.850, NRI: 0.044, IDI: −0.078, p > 0.05). Functional principal component scores facilitated the identification of prediabetes (AUC = 0.760). The device demonstrated acceptable accuracy and functional equivalence with reference methods. Its capability to detect T2DM and early glycemic anomalies supports its feasibility as a wearable, interpretative adjunct tool for large-scale screening in free-living populations. Full article

There is now considerable effort in addressing complex problems through the application of systems science. This includes the use of methods to ‘co-inquire’ with community stakeholders and map systems to understand the nature of complex problems from multiple perspectives. However, moving from insight into the system to action has been more challenging. In this paper, we describe an approach to the identification of ‘relevant’ and ‘actionable’ local problems, where place-based systems change is the goal. The first phase includes an exploration of what is already known about the system, via an initial system scan, visual system mapping and community workshops. The next phase is an exploration of local problems and patterns, via community workshops and the development of a causal loop diagram. We argue for the identification of complex problems and their underlying causes that resonate with community stakeholders as immediately relevant and able to be acted upon locally. Our approach is neither rigid nor prescriptive. Through the configuration of systems methods (described above), it offers an inquiry pathway, enabling problem identification to emerge from engagement with the complexity of the system. We present both our approach and its application in one Australian community and invite researchers to apply, adapt, and refine this approach in diverse communities seeking place-based systems change. Full article

This study examines the role of artificial intelligence (AI) technologies in identifying and sustaining leadership talent within the educational sector in Oman, addressing the increasing demand for evidence-based and innovative approaches to leadership development. A qualitative phenomenological research design was employed to explore how AI experts and educational leaders perceive, evaluate, and conceptualize AI-driven tools for leadership talent identification and sustainability. In-depth semi-structured interviews were conducted with 25 participants from three major Omani educational institutions. Data were analyzed using thematic analysis, allowing systematic identification of recurring patterns, conceptual relationships, and shared professional insights. The findings indicate that AI applications—including big data analytics, behavioral assessment tools, competency identification platforms, and predictive analytics—provide effective mechanisms for early detection and assessment of leadership potential. Furthermore, integrating AI into personalized professional development programs and continuous performance evaluation contributes to the long-term sustainability and strategic utilization of leadership talent. This study underscores the potential of AI to enhance strategic leadership planning within educational institutions. The results expand our empirical understanding of AI-driven leadership development and offer practical insights for implementing AI-informed strategies in Oman and the broader Gulf region. Full article

Beekeeping is a profitable and mind-relaxing practice; however, monitoring beehives poses significant challenges, such as consuming time and potentially disturbing hive equilibrium, which may lead to colony collapse. Developing precision beekeeping (PB) systems is crucial to assist beekeepers in decision-making, automate redundant hive maintenance, and enhance the security and comfort of bee life. This review systematically explores research on PB systems, based on a keyword-driven search of Scopus and Web of Science databases, yielding 46 relevant publications. The analysis highlights a notable increase in research activity in the field since 2016. The integration of advanced technologies, including machine learning, cloud computing, IoT, and scenario-based communication methods, has proven instrumental in predicting hive states such as queen status, enemy attacks, readiness for harvest, swarming events, and population decline. Commonly measured parameters include hive weight, temperature, and relative humidity, with various sensors employed to ensure precision while minimizing bee disturbance. Additionally, bee traffic monitoring has emerged as a critical approach to assessing hive health. Most studies focus on honeybees rather than stingless bees and, in the context of enemy identification, Varroa destructor is the primary target. This review underscores the potential of novel technologies to revolutionize apiculture and enhance hive management practices. Full article

Chandipura virus (CHPV) is a neurotropic rhabdovirus associated with recurrent outbreaks of acute encephalitis in children and a high case fatality rate, particularly in India. Despite its public health relevance, the host molecular processes governing CHPV infection and disease progression remain poorly defined. To address this gap, we conducted a time-resolved transcriptomic analysis to characterize host responses to CHPV infection and to explore host-directed therapeutic opportunities. Human HEK293T cells were infected with CHPV, followed by RNA sequencing (RNA-seq) at 6, 12, 18, and 24 h post infection (hpi). Transcriptome profiling revealed a temporally ordered host response. At 6 hpi, CHPV infection was dominated by strong activation of innate immune and inflammatory pathways, including interferon-stimulated genes and cytokine signaling. Antiviral responses persisted at 12 hpi, accompanied by suppression of metabolic and translational processes, indicating a shift in host cellular priorities. By 18 hpi, metabolic reprogramming—particularly involving lipid and sphingolipid metabolism—was observed alongside altered immune signaling, consistent with viral exploitation of host cellular machinery. At 24 hpi, repression of genes involved in chromatin organization, RNA processing, spliceosome assembly, and ribosome biogenesis reflected a global transcriptional shutdown associated with cytopathic effects. Integration of temporal transcriptomic signatures enabled identification of host pathways amenable to pharmacological targeting. Selected host-directed compounds were evaluated in vitro and exhibited antiviral activity against CHPV in a neuronal cell line. Collectively, this study provides the first time-resolved transcriptomic landscape of CHPV infection in human cells and identifies host-targeted strategies relevant for antiviral development. Full article

Rare earth elements (REEs) play an important role in emerging renewable energy technology, the production of advanced materials, energy conservation, and high-end manufacturing industries, making them an irreplaceable strategic resource. The diagnostic spectral absorption features of REEs in the visible and near-infrared spectrum can be effectively used for identifying the occurrences of REEs on the Earth’s surface. This study systematically compared three airborne hyperspectral sensors—HyMap, CASI-1500h, and AisaFENIX 1K—for detecting REEs in the Bayan Obo area of Inner Mongolia, China. The CASI-1500h imagery performed most effectively in identifying the locations of REEs among the three sensors evaluated here. Additionally, this study proposed a hyperspectral workflow for REE identification, which enabled the detection of REE-bearing minerals regardless of the host rock types—including carbonatites and associated dikes, fenite-syenites, and metamorphic feldspar-quartz sandstone. Laboratory-based spectroscopy and mineral chemistry analyses indicated that the absorption features of the REE-bearing mineral monazite within the 400–1000 nm range can be ascribed to Nd³⁺. This study demonstrates the potential of airborne hyperspectral technology for efficient and large-scale exploration of REE deposits. Full article

Position-independent geometric errors crucially impact the accuracy of five-axis machine tools, yet their identification remains challenging due to computational complexities, inadequate measurement pose selection, and disturbances arising from thermal drift and residual uncompensated errors. Existing methods typically rely on linearized kinematic models, heuristic sampling of measurement poses, or computationally expensive global optimization procedures, which collectively limit their effectiveness in industrial environments. This study presents a unified identification framework that overcomes these limitations; it incorporates 3D offset parameters to enhance the decoupling of true geometric errors from non-PIGEs, an observability-driven measurement pose selection strategy to maximize the parameter sensitivity, and an ANN-surrogate model to accelerate high-dimensional global optimization. A genetic algorithm is used to optimize the measurement points based on the observability index of the machine tool. The ANN-surrogate model enhances the identification accuracy of error parameters (11 PIGEs + 3 offsets) through precise kinematic models, global exploration, and final refinement. Experimental validation on a five-axis machine tool demonstrates a volumetric error reduction of 88.615% after compensation, with RMSE decreasing to 0.4337 μm. Sensitivity analysis reveals that PIGEs contribute up to 75.26% of the total inaccuracy, while offset parameters capture 24.74% of the error from thermal and non-PIGE sources. The results confirm the method’s superiority over other techniques in terms of identification accuracy, efficiency, and robustness, providing a practical solution for high-precision applications in the manufacturing industries. Full article

This paper explores how supply chain risk management (SCRM) enhances organizational resilience (OR) in manufacturing enterprises through resource reconfiguration. In the context of the COVID-19 pandemic and rising political and economic uncertainties, manufacturing firms face severe supply chain disruptions. Based on dynamic capability theory, the study analyzes the relationship between SCRM, resource reconfiguration (RR), and OR, highlighting RR as a key mediator. Empirical analysis of 266 Chinese manufacturing firms reveals that risk identification, control, and mitigation promote RR, thus boosting resilience, while risk assessment is less effective. The study underscores that resource reconfiguration is crucial for dynamic adjustments and sustaining a competitive advantage. This research contributes to dynamic capability and supply chain resilience literature with both theoretical and practical implications. Full article

Anti-analysis techniques, also known as evasive techniques, enable Windows malware to detect and evade dynamic inspection environments, undermining the effectiveness of virtual-machine and sandbox-based inspection. Despite extensive prior research, no unified classification has been paired with a large-scale empirical evaluation of static detection capabilities for these behaviors. This paper addresses this gap by presenting a comprehensive classification and detection framework. We consolidate 94 anti-analysis techniques from academic, community, and threat-intelligence sources into nine mechanistic categories and derive corresponding YARA rules for static identification. In total, 82 YARA signatures were authored or refined and evaluated on 459,508 malware and 92,508 goodware samples. After iterative refinement using precision thresholds, 42 rules achieved high accuracy (≥75%), 16 showed moderate precision (50–75%), and 24 were discarded due to unreliability. The results indicate strong static detectability for firmware- and BIOS-based checks, but limited precision for timing-based evasions, which frequently overlap with benign behavior. Although YARA provides broad coverage of observable artifacts, its static nature limits detection under obfuscation or runtime mutation; our measurements therefore represent conservative estimates of technique prevalence. All validated rules are released in an open-source repository to support reproducibility, improve incident-response workflows, and strengthen prevention and mitigation against real-world threats. Future work will explore hybrid validation, container-evasion extensions, and forensic attribution based on signature co-occurrence patterns. Full article

Anthropogenic marine debris (AMD) in shallow coastal benthic areas poses serious threats to ecosystems, human health, and the economy. Addressing this issue is hindered by limited data on AMD distribution and classification. We explored the use of semantic segmentation, specifically Pyramid Scene Parsing Network (PSPNet) and Deep Convolutional Neural Network for Semantic Image Segmentation, Version 3, (DeepLabV3) models, for automated AMD detection and classification. The performance was evaluated using mean intersection over union (mIoU), pixel accuracy, and frames per second (FPS). PSPNet achieved a higher mIoU (77.03%) than DeepLabV3 (75.98%), indicating better object identification. However, DeepLabV3 outperformed PSPNet in pixel accuracy (92.24% vs. 92.01%) and FPS (8.83 vs. 6.92), making it more appropriate for real-time applications. To enable real-time identification and classification of AMD, the models are deployed in a minicomputer with adequate processing power, significantly enhancing the models’ frame rate during real-time image processing. While both models are effective, DeepLabV3 is recommended for real-time AMD segmentation. The study contributes to improving AMD monitoring and management in coastal environments through AI-driven solutions. Full article