Search Results (640)

Risk assessment is an effective management tool for mitigating waterlogging disasters. In this study, a novel airport waterlogging risk assessment framework based on the analytic network process with triangular fuzzy numbers (TFN-ANP) was developed to evaluate hazard, exposure, vulnerability, and comprehensive risk under different return periods. The proposed framework was compared with the triangular fuzzy analytic hierarchy process (TFN-AHP). The results indicated that water depth, land cover type, and maintenance cost exerted dominant influences on hazard, exposure, and vulnerability, respectively. Compared with TFN-AHP, TFN-ANP produced different global weight distributions and a broader spatial extent of high-risk areas. Under the 50-year return period, TFN-ANP classified 31.65% of the study area as highest-risk, whereas TFN-AHP did not delineate any highest-risk zones and classified 40.05% of the study area as higher risk. A similar pattern was observed under the 100-year return period. TFN-ANP delineated 35.41% of the study area as being at the highest risk under the 100-year return period. By explicitly accounting for interdependencies among risk factors, TFN-ANP generated more differentiated spatial risk patterns. The proposed framework provides an effective decision-support tool for waterlogging risk management in data-scarce airport environments. Full article

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

Against the backdrop of intensified global talent competition, the return of overseas talents has become a key engine driving the enhancement of core competitiveness in developing countries. Accurately identifying its critical driving factors is essential for China to address the challenges of talent introduction. This study constructs a hybrid multiple-criteria decision-making framework to systematically explore the influence mechanism of overseas talent return: first, a 15-criterion decision structure covering economic, policy, educational, technological, and social aspects is established via systematic literature review and two-round Delphi expert surveys; second, the grey DEMATEL-ANP technique is adopted to quantify the inter-relationships and relative weights of the criteria and screen and rank the critical driving factors accurately. Empirical results show that the six core driving factors ranked by importance are talent policy support, economic development level, scientific and technological development strength, public service quality, educational resource supply, and attention to science and technology, with significant synergistic interaction effects among these factors. This research provides a scientific decision-making framework and empirical support for developing countries to formulate targeted talent introduction policies and optimize the talent development ecosystem. Full article

Background/Objectives: Serum neurofilament light chain (sNfL) is a biomarker for peripheral neuropathy as sNfL correlates with polyneuropathy severity in hereditary transthyretin (ATTRv) amyloidosis. It is unclear whether sNfL also correlates with autonomic neuropathy (ANP). In this exploratory study, we aimed to evaluate the value of sNfL as marker for ANP in patients with ATTRv amyloidosis. Methods: sNfL was measured retrospectively in 10 pathogenic transthyretin gene variant (TTRv) carriers and 28 patients with ATTRv amyloidosis. All 38 individuals underwent a comprehensive evaluation for ANP. Results: Individuals with ANP had a higher median sNfL level compared to those without ANP (p < 0.001). In univariable logistic regression analysis, age-adjusted sNfL status (normal versus abnormal for age) was associated with sex, ANP, and peripheral neuropathy. In multivariable logistic regression analysis, only peripheral neuropathy significantly predicted age-adjusted sNfL status (normal versus abnormal for age), and no signal was detected for ANP. Receiver operating characteristic analysis showed a considerable area under the curve for ANP. However, the confidence interval was wide for both analyses and only four cases with isolated ANP were included. Conclusions: Therefore, in this exploratory cohort, sNfL could not be identified as a marker for ANP, and larger studies are needed to clarify its value. Full article

Safety monitoring of scaffolding operations is essential for preventing accidents in high-altitude construction. This study proposes an integrated computer vision and multi-criterion decision-making (MCDM) framework that combines object detection, pose estimation, Analytic Network Process (ANP) and ELECTRE III methods to evaluate safety risks of construction workers. Specifically, computer vision techniques are employed to extract objective visual evidence related to workers’ behaviors, protective equipment (PPE) usage, and working environments, which serve as the basis for subsequent safety risk quantification. A four-criterion system, including action risk, PPE compliance, working height, and structural integrity, is established. Weights are determined via the ANP, and risk ranking is conducted using ELECTRE III. Experiments on a self-built dataset achieved an mAP@0.5 of 92.3%, a segmentation IoU of 67.2%, and a pose OKS@0.5 of 89.6%. The evaluation results correlate strongly with expert assessments (Kendall’s τ = 0.79). The proposed framework effectively identifies unsafe behaviors and quantifies safety risks, providing reliable decision support for intelligent construction safety management. Full article

Background: Natriuretic peptides are endocrine factors that regulate various physiological processes via natriuretic peptide receptors (NPRs). Regulation of the atrial natriuretic peptide ANP during weight loss remains widely unknown. The present study investigated serum quantities of the circulating ANP precursor NT-proANP in obesity and during therapy-induced weight loss. Methods: The study enrolled 284 severely obese individuals. A total of 163 patients underwent metabolic surgery (either Roux-en-Y gastric bypass or vertical sleeve gastrectomy) and 121 patients participated in a non-invasive obesity therapy applying low-calorie formula diet. Anthropometric and physiological data were assessed, and blood serum was prepared at study baseline and at follow-up visits (3 and 12 months after start of intervention). Subcutaneous and visceral adipose tissue specimen were obtained from metabolic surgery patients. Circulating NT-proANP levels were determined by ELISA and gene expression levels of the receptor NPRA in adipose tissue were quantified by real-time RT-PCR. Results: Comparative analysis revealed significantly higher NPRA expression in visceral than in subcutaneous adipose tissue. NT-proANP levels significantly increased during weight loss over 12 months upon diet and metabolic surgery. NT-proANP serum concentrations were positively correlated with fibroblast growth factors 19 and 21 quantities at study baseline and considerably increased during weight loss in both cohorts after 12 months. We conclude that weight loss is a positive regulator of circulating NT-proANP quantities, regardless of the applied therapy. Full article

Construction safety has garnered extensive attention, among which the hoisting construction safety of prefabricated buildings constitutes a distinct concern warranting further focus, as it fundamentally differs from traditional cast-in situ construction. However, relevant studies remain relatively scarce, and there is a lack of research frameworks that enable the multi-dimensional comprehensive assessment of the significance of influencing factors. This study aims to comprehensively account for both the mechanisms of influence and the inherent importance of factors, thereby determining the significance of the influencing factors for hoisting construction safety in prefabricated buildings. Fifteen influencing factors were identified, and the fuzzy-DEMATEL and ANP methods were adopted, respectively to investigate the inter-factor mechanisms of influence and the systemic importance of these factors. This study finds that: at the level of the influence mechanism, factors such as workers’ behavior and construction process control play a core hub role in the system; management factors and external environments are the primary factors affecting workers’ behavior, and workers’ behavior tends to influence physical factors and construction site coordination; at the level of system importance, factor weights show a stepped distribution, among which management personnel competence is the most important factor; factors such as policies and regulations, as well as safety assurance plans, are also relatively significant. A comprehensive analysis of the two calculation results reveals that construction process control is the most critical factor, followed by workers’ behavior, the competence of management personnel, and construction operation coordination. Drawing on the functions of these factors, a series of recommendations was put forward, covering the aspects of safety resource allocation, safety training, and safety supervision. The present study facilitates a more comprehensive evaluation of the importance levels of each influencing factor and delivers practically accessible guidance for safety management in the hoisting operation of prefabricated buildings. Full article

This study proposes an indicator system for evaluating AI-assisted learning in higher education, combining evidence-based indicator development with expert-validated weighting. First, we review recent studies to extract candidate indicators and organize them into coherent dimensions. Next, a Delphi session with domain experts refines the second-order indicators and produces a measurable, non-redundant, implementation-ready index system. To capture interdependencies among indicators, we apply a hybrid Decision-Making Trial and Evaluation Laboratory–Analytic Network Process (DEMATEL–ANP, DANP) approach to derive global indicator weights. The framework is empirically illustrated through a course-level application to examine its decision usefulness, interpretability, and face validity based on expert evaluations and structured feedback from academic staff. The results indicate that pedagogical content quality, adaptivity (especially difficulty adjustment), formative feedback quality, and learner engagement act as key drivers in the evaluation network, while ethics-related indicators operate primarily as enabling constraints. The proposed framework provides a transparent and scalable tool for quality assurance in AI-assisted higher education, supporting instructional design, accreditation reporting, and continuous improvement. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

Against the backdrop of global climate change and China’s “dual-carbon” goals, carbon emissions from the construction phase of transportation infrastructure, particularly the rapidly expanding railway network, have garnered significant attention. However, systematic research and general evaluation models targeting the factors influencing carbon emissions during the railway bridge construction phase remain insufficient. To address this gap, this study presents a novel low-carbon evaluation model that integrates the analytic network process (ANP) and the fuzzy comprehensive evaluation (FCE) method. First, a carbon accounting model covering four stages—material production, transportation, construction, and maintenance—is established based on life cycle assessment (LCA) theory, providing a data foundation. Second, an innovative low-carbon evaluation index system for railway bridges, comprising 5 criterion layers and 23 indicator layers, is constructed. The ANP method is employed to calculate weights, effectively capturing the interdependencies among indicators, while the FCE method handles assessment ambiguities, forming a comprehensive evaluation framework. A case study of the bridge demonstrates the model’s effectiveness, yielding an evaluation score of 82.38 (“excellent” grade), which is consistent with expert judgement. The ranking of indicator weights from the model is highly consistent with the actual carbon emission inventory ranking (Spearman coefficient of 0.714). Key indicators—C21 (use of high-performance materials), C22 (concrete consumption), and C25 (transportation energy consumption)—collectively account for approximately 60% of the total impact, accurately identifying the major emission sources. This research not only verifies the model’s efficacy in pinpointing critical carbon sources but also provides a scientific theoretical basis and practical tool for low-carbon decision-making and optimization in the planning and design stages of railway bridge projects. Full article

The widening digital divide within the Global Critical Mineral Resource Supply Chain (GCMRS) 4.0 creates significant barriers to cross-border governance and operational efficiency. To quantify and address this disparity, this study identifies 20 Critical Success Factors (CSFs) through expert interviews with 15 industry specialists in South Korea. A hybrid multi-criteria decision-making framework integrating Fuzzy DEMATEL, Analytic Network Process (ANP), and the Choquet integral is developed to map causal relationships and determine factor weights. The empirical results reveal a distinct ‘technology-first’ dependency. Specifically, Scalable Technical Solutions and Cloud Computing Access emerge as the primary driving forces with the highest global weights, while Digital Investment Subsidies serve as the central hub for resource allocation. Unlike generic governance models, this study provides a quantifiable decision-making basis for policymakers. It demonstrates that bridging the hard infrastructure gap is a prerequisite for the effectiveness of soft collaborative mechanisms in the critical mineral sector. Full article

The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance. Full article

Polar compounds can be separated on polar stationary phases attached on the surface of silica hydride (Type C silica). Although aqueous normal phase (ANP) chromatography has been used to denote this mode of separation, there have been no detailed studies on the retention mechanisms. We have applied the quantitative assessment methodology to investigate the retention mechanisms of polar compounds on the silica-hydride-based polar phases using a widely used hybrid silica-based amide phase for comparison. The study results indicate that the silica-hydride-based polar phases are not fundamentally different from the hybrid silica-based phase in terms of the adsorbed water layer and the retention mechanisms for polar compounds. Similar forces governing the retention in HILIC (i.e., partitioning, adsorption, and electrostatic interactions) are sufficient to describe the retention mechanisms of polar compounds on the silica-hydride-based polar phases. However, some small differences in selectivity are observed between the silica-hydride-based and hybrid silica-based phases. Full article

Transient receptor potential melastatin 2 (TRPM2) channel is a Ca²⁺-permeable, redox-activated cardiac ion channel protective in ischemia–reperfusion, but whether it regulates atrial endocrine output under stress is unclear. Here, we investigated whether TRPM2 contributes to the atrial natriuretic peptide (ANP) response during β-adrenergic stimulation. We compared how male C57BL/6J wild-type (WT) and TRPM2 knockout (TRPM2^−/−) mice (8–12 weeks old) respond to β-adrenergic stress induced by isoproterenol (ISO) using echocardiography, histology, RT-PCR, electrophysiology, Ca²⁺ imaging, ELISA, and atrial RNA-seq. We detected abundant Trpm2 transcripts in WT atria and measured ADP-ribose (ADPr)-evoked currents and hydrogen peroxide (H₂O₂)-induced Ca²⁺ influx characteristic of TRPM2; these were absent in TRPM2^−/− cells. Under the ISO-induced hypertrophic model, TRPM2^−/− mice developed greater cardiac hypertrophy, fibrosis, and systolic dysfunction compared with WT mice. Atrial bulk RNA-seq showed significant induction of Nppa (ANP precursor gene) in WT + ISO, accompanied by higher circulating ANP; TRPM2^−/− + ISO showed blunted Nppa and ANP responses. ISO-treated TRPM2^−/− mice exhibited more blunt responses, in both Nppa transcripts and circulating ANP levels. Exogenous ANP attenuated ISO-induced dysfunction, hypertrophy, and fibrosis in TRPM2^−/− mice, suggesting that TRPM2 is needed for the cardioprotective endocrine response via ANP to control stress-induced β-adrenergic remodeling. Full article

This study proposes the Urban Landscape Emotion Analysis Framework (ULEAF) based on images of urban parks shared on social media. This framework integrates an emotion recognition module driven by a convolutional neural network (ConvNeXt Tiny) with a semantic extraction module supported by multimodal semantic matching models (CLIP and DeepSentiBank ANP lexicon). It constructs a systematic analysis pathway from semantic understanding to emotional perception, effectively overcoming the limitations of traditional research methods. Results indicate that positive emotion images predominantly correlate with nature, health, and openness, while negative emotion images are closely associated with the characteristics of decay, abandonment, and oppression, as well as loneliness and calmness, estrangement and disharmony, and gloom and bleakness. Findings reveal trends consistent with prior research, further validating the stable association between urban landscape visual features and emotional perception. The analytical framework developed in this study facilitates the systematic revelation of semantic characteristics and affective perception mechanisms in large-scale urban park imagery, providing scientific reference for optimizing urban park landscapes and implementing emotion-oriented design. Full article