Search Results (970)

The settlements of the Great Wall are the product of the overlap of ancient Chinese agricultural civilization and non-agricultural civilization. The structure of the settlement system is of great value for understanding the law of defense engineering and social spatial organization. The Great Wall, built by a non-agricultural civilization, is an important part of the development history of the Chinese civilization. Its uniqueness reflects the relationship between institution and space. However, the archaeological remains and related research methods for non-agricultural Great Wall settlements are not perfect. This paper takes the typical case of the Great Wall built by a non-agricultural civilization (Linhuang Lu settlements of the Jin Great Wall) as the object and integrates spatial analysis and social network analysis. It aims to explore the structure of the settlement system. The settlements of Linhuang Lu show non-random distribution characteristics. They can be divided into four levels. The number ratio from high-level to low-level settlements is 70:30:10:1. Through the weighted Voronoi and social network analysis of human connection and geographical connection, this paper clarifies the structural characteristics of spatial association and social association of settlements. Combined with accessibility and geographical environment, the Linhuang Lu settlements were finally divided into 10 Meng’an defense units and 12 Mouke defense units. Quantitative analysis of the settlement system structure shows the hierarchical management of nature and military by non-agricultural civilization. This provides an empirical basis for the reconstruction of the military defense system of the Great Wall of the Jin Dynasty and further explores the applicability of the research paradigm. This paper has methodological innovation value for solving the problem of spatial cognition of settlement heritage. Full article

Pervious concrete is challenged by the inherent trade-off between permeability and mechanical strength. This study presents a systematic optimization of its mix design to achieve a balance between these properties. Single-factor experiments and an L₉(3³) orthogonal array test were employed to evaluate the effects of target porosity (14–26%), water–cement ratio (0.26–0.34), sand rate (0–10%), and VMA dosage (0–0.02%). Additionally, Spearman rank correlation analysis and nonlinear regression fitting were utilized to develop quantitative relationships correlating the measured porosity to material performance. The results revealed that increasing target porosity enhances permeability but reduces compressive and splitting tensile strengths. The optimal water-to-cement ratio (w/c) was found to be 0.32, balancing both permeability and strength. An appropriate sand content of 6% improved mechanical properties, while a VMA dosage of 0.01% effectively enhanced bonding strength and workability. The orthogonal experiment identified the optimal mix ratio as a w/c ratio of 0.3, VMA dosage of 0.12%, target porosity of 14%, and sand content of 7%, achieving a compressive strength at 28-days of 43.5 MPa and a permeability coefficient of 2.57 mm·s⁻¹. Empirical relationships for the permeability coefficient and mechanical properties as functions of the measured porosity were derived, demonstrating a positive exponential correlation between the measured porosity and the permeability coefficient, and a negative correlation with compressive and splitting tensile strengths. This research provides a systematic framework for designing high-performance pervious concrete with balanced permeability and mechanical properties, offering valuable insights for its development and application in green infrastructure projects. Full article

The research focuses on the National Rural Development Programme (NRDP) during the transition period, assessing the absorption level of sub-measure 4.1, “Investments in agricultural holdings”, which impacts rural development in the agricultural sector in western Romania. A quantitative and qualitative analysis of all selection reports associated with sub-measure 4.1 submitted during the transition period (2021–22) was conducted to investigate a potentially relevant link between the number of beneficiaries identified in the analysed region and their location. Fisher’s exact tests indicate that the null hypothesis, which postulates independence between county and measure in the observed dataset, cannot be rejected. Further empirical analysis was conducted using panel data analysis to identify any relevant regression traits. Tests indicate that funding allocation, the spatial dimension and the temporal dimension are all statistically and substantively significant. Larger budget allocations are associated with a higher volume of proposals. Two out of the four analysed counties systematically outperformed the predicted values in the model by submitting more proposals than would be expected given their budgets. Later application stages yielded a greater number of successful proposals, which is consistent with residual demand capture in sequential competitive calls. Full article

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article

Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. In this study, a Bayesian framework is used to construct the posterior distribution of target parameters based on acoustic travel-time data and prior information. A Hamiltonian Monte Carlo (HMC) approach is developed for SSP inversion, offering an effective solution to the computational issues associated with complex posterior distributions. The HMC algorithm has a strong physical basis in exploring distributions, allowing for accurate characterization of physical correlations among target parameters. It also achieves sufficient sampling of heavy-tailed probabilities, enabling a thorough analysis of the target distribution characteristics and overcoming the low efficiency often seen in traditional methods. The SSP dataset was created using temperature–salinity profile data from the Hybrid Coordinate Ocean Model (HYCOM) and empirical formulas for SSP. Experiments with acoustic propagation time data from the Kuroshio Extension System Study (KESS) confirmed the feasibility of the HMC method in SSP inversion. Full article

Macrophages are critical to the formation of infection- and non-infection-associated immune structures such as cancer spheroids, pathogen-, and non-pathogen-associated granulomas, contributing to the spatiotemporal and chemical immune response and eventual outcome of disease. While well established in cancer immunology, the prevalence of using three-dimensional (3D) cultures to characterize later-stage structural immune response in pathogen-associated granulomas continues to increase, generating valuable insights for empirical and computational analysis. To enable integration of data from 3D in vitro studies with the vast bibliome of standard two-dimensional (2D) tissue culture data, methods that determine concordance between 2D and 3D immune response need to be established. Focusing on macrophage migration and oxidative species production, we develop experimental and computational methods to enable concurrent spatiotemporal and biochemical characterization of 2D versus 3D macrophage–mycobacterium interaction. We integrate standard biological sampling methods, time-lapse confocal imaging, and 4D quantitative image analysis to develop a 3D ex vivo model of Mycobacterium smegmatis infection using bone-marrow-derived macrophages (BMDMs) embedded in reconstituted basement membrane (RBM). Comparing features of 2D to 3D macrophage response that contribute to control and resolution of bacteria infection, we determined that macrophages in 3D environments increased production of reactive species, motility, and differed in cellular volume. Results demonstrate a viable and extensible approach for comparison of 2D and 3D datasets and concurrent biochemical plus spatiotemporal characterization of initial macrophage structural response during infection. Full article

The transition towards sustainable agri-food systems requires understanding factors influencing innovation adoption across agri-food companies. This systematic literature review, following PRISMA methodology, examines innovation types, their intended purposes, and adoption determinants among worldwide stakeholders. Data were extracted from Scopus and Web of Science databases using rigorous selection criteria, covering publications from January 2014 to January 2025. From 775 initial records, 80 publications were selected for quantitative analysis, of these 74 empirical studies included in qualitative analysis. Innovations were categorized based on ecological, economic, social, and institutional purposes, revealing ecological purpose innovations predominated. Subsequently, adoption factors were classified using the tripartite framework based on extrinsic, intrinsic, and intervening variables. Findings reveal developing regions (Sub-Saharan Africa and Asia) representing 65% of studies. Agriculture sector dominated research attention, with cereals as the most investigated value chain, reflecting their fundamental role in global food security and nutrition. Analysis demonstrates that adoption decisions result from complex interactions between external structural conditions, individual psychological factors, and support mechanisms. Results underscore the context-dependent nature of innovation adoption and the need for context-sensitive, multi-stakeholder approaches facilitating sustainable and digital food system transformations. Full article

This study systematically investigates the gas-tightness evolution of three threaded casing connections (511 straight-thread, TPG2, and BGT2) under extreme downhole temperature–pressure conditions through multi-cycle experiments. A novel cyclic testing protocol was developed to simulate three critical scenarios: 50 °C/21 MPa (low-temperature high-pressure), 350 °C/21 MPa (high temperature and high pressure), and 450 °C/7 MPa (high temperature and low pressure). Quantitative leakage analysis using the ideal gas law revealed significant performance divergence: TPG2 demonstrated superior stability with leakage rates of 0.61% (350 °C/21 MPa) and 0.39% (450 °C/7 MPa), attributed to its barb-type threads and multi-stage sealing design. In contrast, conventional 511 connections showed 2.54% leakage under high-temperature, high-pressure conditions, while domestic BGT2 exhibited intermediate performance (2.46% at 350 °C/21 MPa). The results establish temperature–pressure synergy as the dominant degradation factor, with combined 350 °C/21 MPa conditions causing 300–400% higher leakage than individual extremes. These findings provide critical empirical evidence for optimizing premium connection designs in complex reservoirs, particularly for thermal recovery and ultra-deep applications where sealing integrity determines operational safety and efficiency. Full article

Food insecurity remains a persistent global challenge, despite significant advancements in agricultural production and technology. The main objective of this study is to identify and quantitatively assess some of the structural determinants influencing country-level food insecurity and provide an empirical background for policy-making aimed at achieving the Sustainable Development Goal of Zero Hunger (SDG 2). This study employs a beta regression model in order to study moderate or severe food insecurity across 153 countries, using a cross-sectional dataset that integrates economic, agricultural, political, and demographic independent variables. The analysis identifies low household per capita final consumption expenditure (β = −9 × 10⁻⁵, p < 0.001), high income inequality expressed as a high GINI coefficient (β = 0.047, p < 0.001), high long-term inflation (β = 0.0176, p = 0.003), and low economic globalization (β = −0.021, p = 0.001) as the most significant predictors of food insecurity. Agricultural variables such as land area (β = −1 × 10⁻⁵, p = 0.02) and productivity per hectare (β = −9 × 10⁻⁵, p = 0.09) showed limited but statistically significant inverse effects (lowering food insecurity), while factors like unemployment, political stability, and conflict were not significant in the model. The findings suggest that increased economic capacity, inequality reduction, inflation control, and global trade integration are critical pathways for reducing food insecurity. Future research could employ beta regression in time-series and panel analyses or spatial models like geographically weighted regression to capture geographic differences in food insecurity determinants. Full article

Stock price forecasting remains a central challenge in financial modeling due to the non-stationarity, noise, and high dimensionality of market dynamics, as well as the growing importance of unstructured textual information. In this work, we propose a multimodal prediction framework that combines FinBERT-based financial sentiment extraction with technical and statistical indicators to forecast short-term stock price movement. Contextual sentiment signals are derived from financial news headlines using FinBERT, a domain-specific transformer model fine-tuned on annotated financial text. These signals are aggregated and fused with price- and volatility-based features, forming the input to a gradient-boosted decision tree classifier (XGBoost). To ensure interpretability, we employ SHAP (SHapley Additive exPlanations), which decomposes each prediction into additive feature attributions while satisfying game-theoretic fairness axioms. In addition, we integrate differential privacy into the training pipeline to ensure robustness against membership inference attacks and protect proprietary or client-sensitive data. Empirical evaluations across multiple S&P 500 equities from 2018–2023 demonstrate that our FinBERT-enhanced model consistently outperforms both technical-only and lexicon-based sentiment baselines in terms of AUC, F1-score, and simulated trading profitability. SHAP analysis confirms that FinBERT-derived features rank among the most influential predictors. Our findings highlight the complementary value of domain-specific NLP and privacy-preserving machine learning in financial forecasting, offering a principled, interpretable, and deployable solution for real-world quantitative finance applications. Full article

As Saudi Arabia accelerates digital transformation under Vision 2030, the sustainable adoption of Health 4.0 technologies depends on equitable digital health literacy (DHL) and population-level readiness for eHealth engagement. Despite growing interest, empirical data on the behavioral, social, and contextual determinants of digital health adoption remain limited in Middle Eastern settings. This study investigates the readiness of Saudi adults for eHealth services, identifies key behavioral factors influencing digital tool adoption, and proposes an equity-centered, network-aware DHL framework to support inclusive and sustainable Health 4.0 implementation. A multi-phase, cross-sectional study was conducted among 430 Saudi adults using validated instruments including eHEALS, TRI 2.0, UTAUT, and EQ-5D. Quantitative analysis employed multiple linear regression (R² = 0.79), structural equation modeling (CFI = 0.96; RMSEA = 0.04), social network analysis (centrality scores), and network-based diffusion analysis (s = 0.17). Additionally, a three-round Delphi method (CI ≤ 0.25) ensured expert consensus on framework development. Significant predictors of digital health tool adoption included eHealth readiness (β = 0.18), perceived usability, and system trust. Social network metrics identified central actors who facilitated peer-driven behavioral diffusion, validated through NBDA modeling. Based on these findings, a comprehensive DHL Equity Framework was synthesized, integrating behavioral drivers, network diffusion pathways, and principles from the Triple Bottom Line (TBL) framework to mitigate structural disparities while addressing environmental, economic, and social dimensions of sustainable digital health access. The framework was also systematically mapped to relevant Sustainable Development Goals (SDGs), highlighting its alignment with global health and sustainability targets. This study presents a scalable and policy-relevant model to guide inclusive eHealth strategies in Saudi Arabia and similar developing contexts. The proposed framework advances national digital resilience, reduces inequities, and promotes sustainable Health 4.0 service delivery. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Slope stability monitoring in open-pit mining remains a critical challenge for geological hazard prevention, where conventional qualitative methods often fail to address dynamic risks. This study proposes an integrated framework combining empirical modeling (slope classification, hazard assessment, and safety ratings) with multi-source real-time monitoring (synthetic aperture radar, machine vision, and Global Navigation Satellite System) to achieve quantitative stability analysis. The method establishes an initial stability baseline through mechanical modeling (Bishop/Morgenstern–Price methods, safety factors: 1.35–1.75 across five mine zones) and dynamically refines it via 3D terrain displacement tracking (0.02 m to 0.16 m average cumulative displacement, 1 h sampling). Key innovations include the following: (1) a convex hull-displacement dual-criterion algorithm for automated sensitive zone identification, reducing computational costs by ~40%; (2) Ku-band synthetic aperture radar subsurface imaging coupled with a Global Navigation Satellite System and vision for centimeter-scale 3D modeling; and (3) a closed-loop feedback mechanism between empirical and real-time data. Field validation at a 140 m high phosphate mine slope demonstrated robust performance under extreme conditions. The framework advances slope risk management by enabling proactive, data-driven decision-making while maintaining compliance with safety standards. Full article

Glocalization plays a vital role in promoting regionally embedded sustainable development by enabling territories to adapt global economic impulses to local capacities, values, and institutional frameworks. This paper develops a framework for the quantitative assessment of economic glocalization at the regional level, focusing on the European Union. Drawing on the conceptual metaphor of “refraction”, glocalization is interpreted as a transformation of global economic impulses as they pass through and interact with localized socio-economic structures. The authors construct a Glocalization Index System comprising three sub-indices: (1) Index of Generation of Globalization Impulses, (2) Index of Resistance to Globalization Impulses, and (3) Index of Transformation of Globalization Impulses. Each sub-index integrates normalized indicators related to regional creativity—conceptualized through the four “I”s: Institutions, Intelligence, Inspiration, and Infrastructure—as well as trade and investment dynamics. The empirical analysis reveals substantial interregional variation in glocalization capacities, with regions of Germany, the Netherlands, Sweden, and Finland ranking among the most prominent generators and transformers of globalization impulses. Strong correlations are observed between the Resistance and Transformation indices, supporting the hypothesis that medium resistance levels contribute most effectively to transformation processes. By integrating both global (exogenous) and local (endogenous) dimensions, the proposed framework not only addresses a gap in economic literature but also offers a tool for guiding policies aimed at sustainable, adaptive, and innovation-driven regional development. Full article

This study investigates the psychological and emotional mechanisms underlying women’s reactions to breast cancer awareness advertisements through the dual lens of Protection Motivation Theory (PMT) and neuromarketing methods, addressing a gap in empirical research on the integration of biometric and cognitive approaches in health marketing. Utilizing a lab-based experiment with 78 women aged 40 and older, we integrated Facial Expression Analysis using Noldus FaceReader 9.0 with semi-structured post-exposure interviews. Six manipulated health messages were embedded within a 15 min audiovisual sequence, with each message displayed for 5 s. Quantitative analysis revealed that Ads 2 and 5 elicited the highest mean fear scores (0.45 and 0.42) and surprise scores (0.35 and 0.33), while Ad 4 generated the highest happiness score (0.31) linked to coping appraisal. Emotional expressions—including fear, sadness, surprise, and neutrality—were recorded in real time and analyzed quantitatively. The facial analysis data were triangulated with thematic insights from interviews, targeting perceptions of threat severity, vulnerability, response efficacy, and self-efficacy. The findings confirm that fear-based appeals are only effective when paired with actionable coping strategies, providing empirical support for PMT’s dual-process model. By applying mixed-methods analysis to the evaluation of health messages, this study makes three contributions: (1) it extends PMT by validating the emotional–cognitive integration framework through biometric–qualitative convergence; (2) it offers practical sequencing principles for combining threat and coping cues; and (3) it proposes cross-modal methodology guidelines for future health campaigns. Full article