Search Results (378)

Supply chains operate in increasingly volatile environments, making it essential to understand the mechanisms through which partner characteristics shape risk-response capability. This study examines how compatibility between supply chain partners promotes collaboration and, in turn, strengthens robustness and resilience. Using survey data from 219 managers in South Korea, the study develops a conceptual model grounded in congruence theory and the dynamic capability view, and tests it through partial least squares path modeling. The results show that compatibility enhances collaboration, which subsequently improves risk-response capability and mediates the effect of compatibility on robustness and resilience. These findings provide empirical support for a capability-building mechanism in which inter-organizational compatibility enables more effective collaborative practices that enhance a supply chain’s ability to withstand and recover from disruptions. The study extends prior research by shifting the discussion of compatibility from interpersonal or person–organization settings to the inter-organizational domain and by demonstrating its critical role in cultivating dynamic capabilities in supply chain risk management. Full article

This study presents a sociotechnical framework for mitigating ethnic violent conflicts by integrating information and communication technologies (ICTs) with community-based social capital. Drawing on longitudinal case studies from three conflict-prone regions in Kenya, Mt. Elgon, Muhoroni, and the Turkana–West Pokot borderlands, the research examines how ICT-enabled peace networks, particularly the Early Warning and Early Response System (EWERS), mobilize bonding, bridging, and linking social capital to reduce violence. The study employs a multi-phase qualitative design, combining retrospective analysis, key informant interviews, focus group discussions, action participation, and thematic coding of EWERS data collected between 2009 and 2021. This approach enabled the reconstruction of system evolution, stakeholder dynamics, and community responses across diverse socio-political contexts. Findings demonstrate that embedding ICTs within trusted social structures fosters inter-ethnic collaboration, inclusive decision-making, and trust-building. EWERS facilitated confidential reporting, timely alerts, and coordinated interventions, leading to reductions in livestock theft, improved leadership accountability, emergence of inter-ethnic business networks, and enhanced visibility and response to gender-based violence. The system’s effectiveness was amplified by faith-based legitimacy, local governance integration, and adaptive training strategies. The study argues that ICTs can become effective enablers of peace when sensitively contextualized within local norms, relationships, and community trust. Operationalizing social capital through digital infrastructure strengthens community resilience and supports inclusive, sustainale peacebuilding. These insights offer a scalable model for ICT-integrated violence mitigation in low- and middle-income countries. This is among the first studies to operationalize bonding, bridging, and linking social capital within ICT-enabled peace networks in rural African contexts. By embedding digital infrastructure into trusted community relationships, the framework offers an analytical approach that can inform inclusive violence mitigation strategies across low- and middle-income settings. While the framework demonstrates potential for scalability, its outcomes depend on contextual adaptation and cannot be assumed to replicate uniformly across all environments. Full article

Despite the growing importance of green building projects, limited research has explored the factors influencing intergenerational knowledge transfer (IGKT) in this context. As green building projects are increasingly characterized by high environmental standards, technological complexity, and interdisciplinary collaboration, effective knowledge transfer from older to younger employees becomes crucial for ensuring the success and sustainability of these projects. This study addresses this gap by systematically examining the key factors influencing IGKT in green building projects, applying an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methodology. Firstly, twelve factors were identified across five dimensions: transfer subjects, inter-subject relationships, transfer objects, transfer channels, and transfer context. Based on expert input, a direct influence matrix was constructed, and centrality and cause degrees were calculated to distinguish causal and result factors. Subsequently, the ISM method was employed to classify the key factors hierarchically and develop a multi-level structural model of their interaction paths. Results show that organizational support climate ranked highest in both centrality and influence, while digital transformation capacity emerged as a key driver in green project environments. Surface-level factors (e.g., knowledge absorption and transmission capability) were highly susceptible; intermediate factors (e.g., motivation, knowledge distance) acted as bridges; and deep-level factors (e.g., knowledge complexity and embeddedness), though lower in centrality, posed long-term structural constraints. This study provides valuable insights for enhancing IGKT and fostering effective cross-generational collaboration, which is essential for advancing sustainable practices in the green building sector. Full article

The Upper Permian marine shale of the inter-platform basin in the Nanpanjiang Basin are rich in organic matter, widely distributed, and relatively thick, indicating abundant resource potential for hydrocarbon exploration. To clarify the sedimentary condition and the variability of reservoir properties, the paleo-environment was reconstructed by using geochemical, mineralogical, rock-property, and pore-structure data. Building on a lithofacies classification, the development patterns of different shale lithofacies were revealed. Reservoir characteristics among lithofacies were compared using scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), and low-temperature Nuclear Magnetic Resonance Cryoporometry (NMRC) experiments. A fractal analysis was performed based on NMR and NMRC data to quantify pore-scale heterogeneity, calculate fractal dimensions (D₁, D₂, and D_c), and evaluate the complexity of pore systems across lithofacies. Correlation analysis and redundancy analysis were applied to further explore the controlling factors of reservoir heterogeneity. The results showed that organic-rich shale in the Permian Linghao Formation occurred mainly in the 1st Member, with average total organic carbon (TOC) content of 2.57%, and the lower part of the 3rd Member (average TOC content 2.88%). In the 1st Member, high-carbon shale was deposited under humid conditions with intense weathering, abundant fine-grained clastic input from basin margins, strongly reducing (anoxic) bottom waters, vigorous phosphorus recycling, and moderate to low primary productivity. Using TOC and mineral composition, seven shale lithofacies were identified in the Linghao Formation, and their development patterns were established based on depositional paleo-environment characteristics and evolution. In the 1st Member, organic-rich shale was dominated by mixed lithofacies with moderate to high TOC. The paleo-environment exerted a primary control on reservoir properties, gas content, pore structure, and heterogeneity. The high-carbon lithofacies had the most favorable rock properties—higher porosity, greater pore volume, and higher gas content—and contained a larger proportion of well-developed organic pores. Fractal analysis revealed that seepage pores exhibited greater structural complexity than adsorption-related pores, with the high-carbon lithofacies showing the highest overall fractal dimensions and thus the strongest heterogeneity. Across the formation, higher clay content and TOC were the primary drivers of increased pore-scale heterogeneity, whereas greater feldspar and quartz contents tended to diminish it. Carbonates exerted a minor effect. Heterogeneity in adsorption pores exerted the strongest influence on differences among lithofacies. These results highlighted the utility of fractal analysis in quantitatively linking shale mineralogy and organic content to multiscale heterogeneity in inter-platform basin settings. Full article

This study supports the preservation and sustainable development of traditional villages by examining their spatial distribution patterns and mechanisms underlying those patterns in Henan Province. The study utilizes data from six batches of Chinese traditional villages in the province, which are studied using kernel density estimation (KDE), spatial autocorrelation, optimal GeoDetector, and the geographically weighted regression (GWR) model, to explore the spatial differentiation pattern in depth and its mechanisms of influencing traditional villages in Henan Province. This study reveals that traditional villages in the province exhibit a “multi-core” clustering pattern, influenced by the natural environment, historical culture, location and transportation, and economic development. The Optimal Parameter GeoDetector indicates that 15 factors, including the average altitude, academy density, road density, and annual GDP, vary significantly in their impact. Furthermore, these factors exhibit a notable interactive, synergistic effect. Meanwhile, the GWR model indicates spatial heterogeneity in the influences of factors like the average rainfall, river density, road density, academy density, and GDP on the distribution of traditional villages. This study suggests developing tailored protection and development strategies for different clusters, enhancing inter-administrative joint protection, and building a radiation network centered on core areas to promote sustainable preservation and coordinated rural revitalization of traditional villages. Full article

Amid growing threats of image data leakage and misuse, image encryption has become a critical safeguard for protecting visual information. However, many recent image encryption algorithms remain constrained by trade-offs between security, efficiency, and practicability. To address these challenges, this paper first proposes a novel two-dimensional variable fractional-order coupled quadratic hyperchaotic map (2D-VFCQHM), which incorporates a state-dependent dynamic memory effect, wherein the fractional-order is adaptively determined at each iteration by the mean of the system’s current state. This mechanism substantially enhances the complexity and unpredictability of the underlying chaotic dynamics. Building upon the superior hyperchaotic properties of the 2D-VFCQHM, we further develop a high-performance image encryption algorithm that integrates a novel fusion strategy within a dynamic vector-level diffusion-scrambling framework (IEA-VMFD). Comprehensive security analyses and experimental results demonstrate that the proposed algorithm achieves robust cryptographic performance, including a key space of $2^{298}$, inter-pixel correlation coefficients below 0.0018, ciphertext entropy greater than 7.999, and near-ideal plaintext sensitivity. Crucially, the algorithm attains an encryption speed of up to 126.2963 Mbps. The exceptional balance between security strength and computational efficiency underscores the practical viability of our algorithm, rendering it well-suited for modern applications such as telemedicine, instant messaging, and cloud computing. Full article

This study investigates the transferability of measured road-damage data between distinct geographic domains using the YOLOv8s deep-learning framework. A comparative evaluation was performed on two datasets: the locally developed RDD2024_SA (South Africa) and the publicly available RDD2022_India (India). Five training–testing scenarios were designed to analyze intra- and inter-dataset generalization, emphasizing the influence of dataset scale, annotation consistency, and class structure on detection performance. When trained and tested within the same domain, YOLOv8s achieved high accuracy (mAP@0.5 > 0.95), confirming the strength of localized feature learning. However, performance degraded substantially under cross-domain testing, revealing a sensitivity to differences in road texture, illumination, and labeling style. Reducing the number of classes from six to four dominant types improved stability (mAP@0.5 ≈ 0.78) by mitigating annotation noise and class imbalance. Furthermore, a transfer-learning configuration, in which the India-trained model was fine-tuned on 20% of the South-African dataset, achieved mAP@0.5 = 0.86, demonstrating effective recovery of cross-domain detection performance. These findings highlight the importance of domain-aligned data preparation, targeted fine-tuning, and balanced class representation in building robust and transferable AI systems for sustainable, data-driven road maintenance. Full article

This study introduces a Fuzzy–AHP–based analytical model for the quantitative assessment of authenticity loss in adaptive reuse practices, addressing a persistent gap in heritage research—the lack of reproducible mathematical frameworks capable of linking authenticity evaluation with sustainability indicators. Unlike previous studies that approach authenticity conceptually or qualitatively, this research develops a hybrid decision-support system that translates both intangible and tangible heritage attributes into measurable linguistic variables, enabling systematic and comparable authenticity assessments. The model was applied to ten traditional houses in Alanya, Türkiye, representing different adaptive reuse types (residential, cultural, commercial, and touristic). A total of 17 experts contributed to the Analytic Hierarchy Process (AHP) weighting stage, producing a Consistency Ratio of 0.0156 (<0.10), and 8 experts provided scoring inputs for the fuzzy system. The fuzzy inference system was implemented in MATLAB R2023a, incorporating seven main criteria and three subcriteria, nine input variables, five linguistic categories, and a rule base of 3400 fuzzy rules. Membership functions were defined within the 0–100 numerical range, and the centroid defuzzification method was used to compute final authenticity values. Model reliability was confirmed through Kendall’s W = 0.87, demonstrating strong inter-rater agreement. Results show that buildings retaining their original residential function achieved the highest authenticity scores (Final Score ≈ 86), while structures converted into boutique hotels or restaurants exhibited substantial authenticity losses (Final Score range: 25–45), especially within Group 2 criteria (environment, function, spirit, and intangible cultural heritage). This divergence illustrates a sustainability paradox: although adaptive reuse prolongs building life cycles and reduces embodied carbon, it may simultaneously undermine cultural sustainability when authenticity is significantly compromised. The proposed Fuzzy–AHP authenticity model provides a replicable, transparent, and empirically validated tool for evaluating the effects of functional transformation within a sustainability framework. By quantifying the relationship between adaptive reuse types and authenticity retention, the study contributes to sustainable heritage management research and supports the implementation of SDG 11—Sustainable Cities and Communities. Full article

This research examined the phenomenon of pounding between neighboring steel structures, both with and without tension-only braces, utilizing an extensive nonlinear time-history analysis. The evaluation of the structural response was conducted using essential engineering demand parameters such as inter-story drift ratio (IDR), residual inter-story drift ratio (RIDR), peak floor acceleration (PFA), base shear, and base moment. The findings indicated that the addition of tension-only braces plays a crucial role in diminishing the structural response during seismic activities. The tension-only brace effectively alleviates the negative impacts of structural pounding by offering supplementary restraint and energy dissipation mechanisms, which enhances the overall seismic performance of the steel frame structures. These results highlight the potential advantages of adopting tension-only braces as a passive control method to mitigate pounding-related structural damage in closely situated buildings. Full article

Nowadays, driven by the exponential growth of parameters and training data of AI applications and Large Language Models, a single GPU is no longer sufficient in terms of computing power and storage capacity. Building high-performance multi-GPU systems or a GPU cluster via vertical scaling (scale-up) has thus become an effective approach to break the bottleneck and has further emerged as a key research focus. Given that traditional inter-GPU communication technologies fail to meet the requirement of GPU interconnection in vertical scaling, a variety of high-performance inter-GPU communication protocols tailored for the scale-up domain have been proposed recently. Notably, due to the emerging nature of these demands and technologies, academic research in this field remains scarce, with limited deep participation from the academic community. Inspired by this trend, this article identifies the challenges and requirements of a scale-up network, analyzes the bottlenecks of traditional technologies like PCIe in a scale-up network, and surveys the emerging scale-up targeted technologies, including NVLink, OISA, UALink, SUE, and other X-Links. Then, an in-depth comparison and discussion is conducted, and we express our insights in protocol design and related technologies. We also highlight that existing emerging protocols and technologies still face limitations, with certain technical mechanisms requiring further exploration. Finally, this article presents future research directions and opportunities. As the first review article fully focusing on intra-node GPU interconnection in a scale-up network, this article aims to provide valuable insights and guidance for future research in this emerging field, and we hope to establish a foundation that will inspire and direct subsequent studies. Full article

Multi-modal object tracking (MMOT) has received widespread attention for the ability to overcome single-sensor perception limitations. However, existing methods encounter several critical challenges. Representation learning and generalization capabilities of models are constrained by the inherent heterogeneity of cross-task multi-modal data and inter-modal synergy imbalance. Particularly, in dynamically changing complex scenarios, the reliability and stability of data significantly degrade, further exacerbating the difficulty in multi-modal consistent perception and aggregation. To tackle the above issues, we propose SMUTrack, a unified framework with global shared parameters integrating three downstream MMOT tasks. SMUTrack implements a batch merging-and-splitting alternating strategy, coupled with multi-task joint training, to establish latent correlations across inter- and intra-task modalities, effectively avoiding over-reliance on certain modalities. Concurrently, we design a hierarchical modality synergy and reinforcement (HMSR) module, and a gated fusion and context awareness (GFCA) module to enable progressive multi-modal information exchange and integration, yielding the more discriminative and robust multi-modal representation. More importantly, we introduce a spatial–temporal information propagation (SIP) mechanism, which synchronously learns object trajectory cues and appearance variations to effectively build contextual relationships in long-term video tracking. Experimental results definitively validate the outstanding performance of SMUTrack on mainstream MMOT datasets, exhibiting its powerful adaptability to various MMOT tasks. Full article

Modular lightweight shear walls can not only facilitate easy installation, thereby improving construction efficiency, but also demonstrate potential to enhance the lateral stiffness when applied in frame structures. The aim of this paper is to investigate the effectiveness of a novel modular cold-formed steel (CFS) shear wall connected with rectangular steel tubes on improving the lateral performance of existing frame structures. Based on the test results of the lateral resistance of four full-scale specimens of modular CFS shear walls connected with rectangular steel tubes, the fine model and simplified model of test specimens were respectively established by the SAP2000v26.0.0 software. The performance indices of the yield load, yield displacement, peak load, peak displacement, and ductility factor were compared, and the maximum error of performance indices was satisfactory. The numerical results show that both the fine and simplified models can well simulate the deformation of walls under lateral cyclic loading, while the simplified models substantially simplify the calculation, which is more adaptable to the subsequent analysis of the multi-story building structure. Then, seismic response analyses of a frame with infilled modular walls and another frame without infilled modular walls were performed. The results indicate that, under the same seismic condition, the lateral displacements of the top floor of the six-story frame with infilled modular walls were reduced by 11–71%, and the maximum inter-story displacement angles were reduced by 15–67% compared to the frame without infilled walls. Therefore, it is demonstrated that the infilled modular CFS shear walls can significantly improve the lateral stiffness and the seismic performance of the steel frame structures. Full article

This study investigates the influence of stratification—the vertical layering of particles with different sizes—on porosity in granular sediment packings. Conventional porosity models are typically formulated for homogeneous, well-mixed grain assemblies; however, natural riverbed sediments often exhibit stratification, leading to deviations from these idealized conditions. Part I established empirical relationships describing transition layer geometry and porosity in systems composed of low-friction glass beads. Building on this foundation, Part II extends the analysis by incorporating the higher inter-particle friction characteristic of natural sediments, using discrete element method (DEM) simulations to quantify its effect on packing structure and porosity. A refined method is used to extract porosity and density distributions from simulated packings, enabling accurate identification of transition layers. Empirical formulas are developed to predict key transition-layer parameters (thickness, average porosity, and minimum porosity) as functions of the grain-size ratio. A density-based porosity prediction model is introduced and coupled with an existing model for well-mixed sediments, allowing for a quantitative comparison between stratified and homogeneous packing scenarios. Results show that stratification can increase porosity by 44–57% relative to well-mixed samples of an identical grain-size composition. These findings highlight the importance of considering sediment stratification when modeling riverbed porosity and pave the way for improved sediment transport and hydraulic predictions. Full article

Accurate prediction of urban grid-scale population inflow is crucial for smart city management and emergency response. However, existing methods struggle to model spatial heterogeneity and quantify prediction uncertainty, limiting their accuracy and decision-support capabilities. This paper proposes PDCDM (POI-enhanced Dual-Dimensional Conditional Diffusion Model), which integrates urban functional semantic awareness with conditional diffusion modeling. The model captures urban functional attributes through multi-scale Point of Interest (POI) feature representations and incorporates them into the diffusion generation process. A dual-dimensional Transformer architecture is employed to decouple the modeling of temporal dependencies and inter-grid interactions, enabling adaptive fusion of grid-level features with dynamic temporal patterns. Building upon this dual-dimensional modeling framework, the conditional diffusion mechanism generates probabilistic predictions with explicit uncertainty quantification. Real-world urban dataset validation demonstrates that PDCDM significantly outperforms existing state-of-the-art methods in prediction accuracy and uncertainty quantification. Comprehensive ablation studies validate the effectiveness of each component and confirm the model’s practicality in complex urban scenarios. Full article

This study extends research on sustainability transformation by investigating how power dynamics operate across organizational boundaries to enable or constrain substantive change. Using a mixed-methods approach combining survey data (n = 127) from sustainability professionals across multiple sectors and in-depth interviews (n = 18) with transformation leaders, this research identifies how organizations address power asymmetries in supply chains and multi-stakeholder partnerships. Exploratory statistical analysis indicates that inter-organizational power mobilization is associated with the effectiveness of integration mechanisms (β = 0.36, p < 0.01), with digital transparency tools showing the strongest association with integration across boundaries (β = 0.41, p < 0.01). Qualitative findings reveal three critical pathways for addressing power dynamics: technological transparency mechanisms, collaborative governance structures, and capability building networks. The research contributes to sustainability science by advancing understanding of how organizations can move beyond internal transformation to address systemic sustainability challenges through approaches that consider power relationships across organizational boundaries. The findings offer preliminary guidance for practitioners seeking to enhance sustainability impact through strategic management of inter-organizational power dynamics. Full article