Search Results (232)

Global refugee flows’ increasing scale and complexity pose significant challenges to national education systems. Turkey, hosting one of the largest populations of refugees and individuals under temporary protection, faces unique pressures in ensuring equitable educational access for refugee students. Addressing these challenges requires a shift from linear, fragmented interventions toward holistic, systemic approaches. This study applies a Community-Based System Dynamics (CBSD) methodology to explore the systemic barriers affecting refugee students’ participation in education. Through structured Group Model Building workshops involving teachers, administrators, and Non-Governmental Organization (NGO) representatives, a causal loop diagram (CLD) was collaboratively developed to capture the feedback mechanisms and interdependencies sustaining educational inequalities. Five thematic subsystems emerged: language and academic integration, economic and family dynamics, psychosocial health and trauma, institutional access and legal barriers, and social cohesion and discrimination. The analysis reveals how structural constraints, social dynamics, and individual behaviors interact to perpetuate exclusion or facilitate integration. This study identifies critical feedback loops and leverage points and provides actionable insights for policymakers and practitioners seeking to design sustainable, systems-informed interventions. Our findings emphasize the importance of participatory modeling in addressing complex societal challenges and contribute to advancing systems thinking in refugee education. Full article

Background: Heat stress challenges small ruminants in semi-arid regions, requiring integrative multi-modeling approaches to identify adaptive thermotolerance traits. This study aimed to identify phenotypic biomarkers and explore the relationships between thermoregulatory responses and hematological, behavioral, morphometric, carcass, and meat traits in lambs. Methods: Twenty 4-month-old non-castrated male lambs, with an average body weight of 19.0 ± 5.11 kg, were evaluated under natural heat stress. Results: Thermoregulatory variables were significantly associated with non-carcass components (p = 0.002), carcass performance (p = 0.027), commercial meat cuts (p = 0.032), and morphometric measures (p = 0.029), with a trend for behavioral responses (p = 0.078). The main phenotypic traits related to thermoregulation included idleness duration, cold carcass weight, blood, liver, spleen, shank, chest circumference, and body length. Exploratory factor analysis reduced the significant indicators to seven latent domains: carcass traits, commercial meat cuts, non-carcass components, idleness and feeding behavior, and morphometric and thermoregulatory responses. Bayesian network modeling revealed interdependencies, showing carcass traits influenced by morphometric and thermoregulatory responses and non-carcass traits linked to ingestive behavior. Thermoregulatory variables were not associated with meat quality or hematological traits. Conclusions: These findings highlight the complex biological relationships underlying heat adaptation and emphasize the potential of combining phenomic data with computational tools to support genomic selection for climate-resilient and welfare-oriented breeding programs. Full article

This article provides a concise overview of the existing tunnels located within the historic cultural heritage site of Gediminas Hill in Vilnius, with particular emphasis on the implications of a recently discovered tunnel. This newly identified tunnel is of particular interest due to its location beneath a retaining wall in close proximity to an adjacent structure. Long-term structural monitoring data indicate that the building has experienced displacement away from the retaining wall. Although the precise cause of this movement remains undetermined, the discovery of the tunnel adjacent to the structure has raised concerns regarding its potential role in the observed displacements. To investigate this hypothesis, a previously developed numerical model was employed to simulate the tunnel’s impact. The simulation results suggest that the tunnel’s construction was executed with careful consideration. During the excavation phase, the retaining wall exhibited displacements in a direction opposite to the expected ground pressure, indicating effective utilization of the wall’s gravitational mass. However, historical records indicate that no retaining structures were present in the area during the tunnel’s initial period of existence. Consequently, an additional simulation phase was introduced to model the behavior of the surrounding loose soil in the absence of retaining support. The results from this phase revealed that the deformations of the retaining wall and the adjacent building were elastically interdependent. The simulated deformation patterns closely matched the temporal trends observed in the monitoring data. These findings support the hypothesis that the tunnel’s construction may have contributed to the displacement of the nearby building. Full article

In increasingly competitive digital markets, software firms must strategically balance cybersecurity investments and pricing decisions to attract consumers while safeguarding their platforms. This study develops a game-theoretic model in which two competing firms choose among three cybersecurity strategies—no action, bug bounty programs, and in-house protection—before setting prices. We demonstrate that cybersecurity efforts and pricing are interdependent: investment choices significantly alter market outcomes by influencing consumer trust and competitive dynamics. Our analysis reveals that a bug bounty program is preferable when consumer sensitivity to security and the probability of ethical vulnerability disclosures are high, while in-house protection becomes optimal when firms must rebuild credibility from a weaker competitive position. Furthermore, initial service quality gaps between firms critically shape both investment intensity and pricing behavior. By jointly endogenizing security efforts and prices, this study offers new insights into strategic cybersecurity management and provides practical guidance for software firms seeking to integrate security initiatives with competitive pricing strategies. Full article

Polymer brushes (PBs) are transformative surface-modifying nanostructures, yet their synthesis via controlled methods like copper-mediated surface-initiated atom-transfer radical polymerization (Cu⁰-SI-ATRP) faces reproducibility challenges due to a lack of understanding of parameter interdependencies. This study systematically evaluates the Cu⁰-SI-ATRP process for polyacrylamide brushes (PAM-PBs), aiming to clarify key parameters that influence the synthesis process. This evaluation followed a step-by-step characterization that tracked molecular changes through infrared spectroscopy (IR) and surface development by contact angle (CA) through two different mixing methods: ultrasonic mixing and process simplification (Method A) and following literature-based parameters (Method B). Both methods, consisting of surface activation, 3-aminopropyltriethoxysilane (APTES) deposition, bromoisobutyryl bromide (BiBB) anchoring, and polymerization, were analyzed by varying parameters like concentration, temperature, and time. Results showed ultrasonication during surface activation enhanced siloxane (1139→1115 cm⁻¹) and amine (1531 cm⁻¹) group availability while reducing APTES concentration to 1 Vol% without drying sufficed for BiBB anchoring. BiBB exhibited insensitivity to concentration but benefited from premixing, evidenced by sharp C–Br (~1170 cm⁻¹) and methyl (3000–2800 cm⁻¹) bands. Additionally, it was observed that PAM-PBs improved with Method A, which had reduced variance in polymer fingerprint regions compared to Method B. Adding to the above, CA measurements gave complementary step-by-step information along the modifications of the surface, revealing distinct wettability behaviors between bulk PAM and synthesized PAM-PBs (from 51° to 37°). As such, this work identifies key parameter influence (e.g., mixing, BiBB concentration), simplifies steps (drying omission, lower APTES concentration), and demonstrates a step-by-step, systematic parameter decoupling that reduces variability. In essence, this detailed parameter analysis addresses the PAM-PBs synthesis process with better reproducibility than the previously reported synthesis method and achieves the identification of characteristic behaviors across the step-by-step process without the imperative need for higher-cost characterizations. Full article

The study of dynamical processes on complex networks constitutes a foundational domain bridging applied mathematics, statistical physics, systems theory, and data science. Temporal evolution, not static topology, determines the controllability, stability, and inference limits of real-world systems, from epidemics and neural circuits to power grids and social media. However, the methodological landscape remains fragmented, with distinct communities advancing separate formalisms for spreading, control, inference, and design. This review presents a unifying six-pillar framework for the analysis of network dynamics: (i) spectral and structural foundations; (ii) deterministic mean-field reductions; (iii) control and observability theory; (iv) adaptive and temporal networks; (v) probabilistic inference and belief propagation; (vi) multilayer and interdependent systems. Within each pillar, we delineate conceptual motivations, canonical models, analytical methodologies, and open challenges. Our corpus, selected via a PRISMA-guided screening of 134 mathematically substantive works (1997–2024), is organized to emphasize internal logic and cross-pillar connectivity. By mapping the field onto a coherent methodological spine, this survey aims to equip theorists and practitioners with a transferable toolkit for interpreting, designing, and controlling dynamic behavior on networks. Full article

As cyber systems increasingly converge with physical infrastructure and social processes, they give rise to Complex Cyber–Physical–Social Systems (C-CPSS), whose emergent behaviors pose unique risks to security and mission assurance. Traditional cyber–physical system models often fail to address the unpredictability arising from human and organizational dynamics, leaving critical gaps in how cyber risks are assessed and managed across interconnected domains. The challenge lies in building resilient systems that not only resist disruption, but also absorb, recover, and adapt—especially in the face of complex, nonlinear, and often unintentionally emergent threats. This paper introduces the concept of ‘responsible resilience’, defined as the capacity of systems to adapt to cyber risks using trustworthy, transparent agent-based models that operate within socio-technical contexts. We identify a fundamental research gap in the treatment of social complexity and emergence in existing the cyber–physical system literature. To address this, we propose the E3R modeling paradigm—a novel framework for conceptualizing Emergent, Risk-Relevant Resilience in C-CPSS. This paradigm synthesizes human-in-the-loop diagrams, agent-based Artificial Intelligence simulations, and ontology-driven representations to model the interdependencies and feedback loops driving unpredictable cyber risk propagation more effectively. Compared to conventional cyber–physical system models, E3R accounts for adaptive risks across social, cyber, and physical layers, enabling a more accurate and ethically grounded foundation for cyber defence and mission assurance. Our analysis of the literature review reveals the underrepresentation of socio-emergent risk modeling in the literature, and our results indicate that existing models—especially those in industrial and healthcare applications of cyber–physical systems—lack the generalizability and robustness necessary for complex, cross-domain environments. The E3R framework thus marks a significant step forward in understanding and mitigating emergent threats in future digital ecosystems. Full article

Compositional Data Analysis (CoDA) is a powerful statistical approach for analyzing 24 h time-use data, effectively addressing the interdependence of sleep, sedentary behavior, and physical activity. Unlike traditional methods that struggle with perfect multicollinearity, CoDA handles time use as proportions of a whole, providing biologically meaningful insights into how daily activity patterns relate to health. Applications in epidemiology have linked variations in time allocation between behaviors to key health outcomes, including adiposity, cardiometabolic health, cognitive function, fitness, quality of life, glycomics, clinical psychometrics, and mental well-being. Research consistently shows that reallocating time from sedentary behavior to sleep or moderate-to-vigorous physical activity (MVPA) improves health outcomes. Importantly, CoDA reveals that optimal activity patterns vary across populations, supporting the need for personalized, context-specific recommendations rather than one-size-fits-all guidelines. By overcoming challenges in implementation and interpretation, CoDA has the potential to transform healthcare analytics and deepen our understanding of lifestyle behaviors’ impact on health. Full article

Background: Effective decision-making in supply chain contexts requires understanding how criteria interact to shape rational and transparent decision structures. This study investigates how behavioral aspects influence the structuring of decision-making logic and the interdependencies between key criteria in supply chain contexts. Methods: Using Fuzzy DEMATEL, the research models the interactions between five core criteria —classification, definition, specification, decision, and action feedback—based on inputs from experienced professionals in a global chemical company. The approach enables mapping of causal influences while accounting for subjectivity and uncertainty in expert judgments. Results: The analysis identified specification, definition, and action feedback as causal criteria, with classification and decision being primarily influenced by them. The modeling process supported clearer prioritization and revealed how expert-based interactions can reduce decision biases. Conclusions: This study demonstrates how structuring decision-making logic through causal modeling enhances clarity and reduces subjectivity. The findings contribute to the development of decision support tools applicable across strategic supply chain contexts, offering practical implications for professionals seeking to improve decision transparency and effectiveness. Full article

Under low-temperature conditions, the load-bearing capacity of piezoelectric stacks arises from coupled thermo-electro-mechanical interactions, with temperature fluctuations, compressive prestress, and excitation voltage critically modulating performance. This study introduces an integrated measurement platform to systematically quantify these interdependencies, employing a cantilever-based sensing mechanism where bending strain serves as a direct metric of load-bearing capacity. A particle swarm-optimized theoretical framework guides the spatial configuration of actuators and sensors, maximizing strain signal fidelity while suppressing noise interference. Experimental characterization reveals three key findings: 1. Voltage-dependent linear enhancement of load-bearing capacity across all operational regimes, unaffected by thermal or mechanical variations; 2. Prestress-induced amplification (79–90% increase from 0 to 6 MPa) and thermally driven attenuation (15–30% reduction from 20 to −70 °C) of static performance; 3. Frequency-dependent degradation (1–6 Hz) in dynamic load-bearing capacity. The methodology establishes a robust foundation for designing multiphysics-compatible instrumentation systems, enabling precise evaluation of smart material behavior under extreme coupled-field conditions. Full article

Carbon capture and storage (CCS) plays a role in mitigating climate change, but effective implementation requires accurate prediction of CO₂ behavior in geological formations. This study introduces a novel machine learning framework for quantifying uncertainty across 2D and 3D carbon storage models. We develop a dimension-adaptive Bayesian neural network architecture that enables efficient knowledge transfer between dimensional representations while maintaining physical consistency. The framework incorporates aleatoric uncertainty from inherent geological variability and epistemic uncertainty from model limitations. Trained on over 5000 high-fidelity simulations across multiple geological scenarios, our approach demonstrates superior computational efficiency, reducing analysis time for 3D models by 87% while maintaining prediction accuracy within 5% of full simulations. The framework effectively captures complex uncertainty patterns in spatiotemporal CO₂ plume evolution. It identifies previously unrecognized parameter interdependencies, particularly between vertical permeability anisotropy and capillary entry pressure, which significantly impact plume migration in 3D models but are often overlooked in 2D representations. Compared with traditional Monte Carlo methods, our approach provides more accurate uncertainty bounds and enhanced identification of high-risk scenarios. This multidimensional framework enables rapid assessment of storage capacity and leakage risk under uncertainty, providing a practical tool for CCS site selection and operational decision-making across dimensional scales. Full article

This study explores the crystal structure, microstructure and magnetic phase evolution of the AlCoCrFeNi high-entropy alloy (HEA), highlighting its potential for applications requiring tailored magnetic properties across diverse temperatures. Electron microscopy and X-ray diffraction revealed that the as-cast alloy’s microstructure comprises equiaxed grains with branching dendrites, showing compositional variations between interdendritic regions enriched in Al and Ni. Temperature-induced phase transformations were observed above room temperature, transitioning from body centered cubic (BCC) phases (A2 and B2) to a predominant FCC phase at higher temperatures, followed by recrystallization of the A2 phase upon cooling. Magnetization measurements showed a drop near 380 K, suggesting the Curie temperature of BCC phases, a peak at 830 K attributed to optimal magnetic alignment in the FCC phase, and a sharp decline at 950 K marking the transition to a paramagnetic state. Magnetic moment calculations provided insights into magnetic alignment dynamics, while low-temperature analysis highlighted the alloy’s magnetically soft nature, dominated by ferromagnetic contributions from the A2 phase. These findings underscore the strong interdependence of microstructural features and magnetic behavior, offering a foundation for optimizing HEAs for temperature-sensitive scientific and industrial applications. Full article

Electrical transport in 2D materials exhibits unique behaviors due to reduced dimensionality, broken symmetries, and quantum confinement. It serves as both a sensitive probe for the emergence of coherent electronic phases and a tool to actively manipulate many-body correlated states. Exploring their interplay and interdependence is crucial but remains underexplored. This review integratively cross-examines the atomic and electronic structures and transport properties of van der Waals-layered crystals ZrTe₃, 2H-TaS₂, and Cr₂Si₂Te₆, providing a comprehensive understanding and uncovering new discoveries and insights. A common observation from these crystals is that modifying the atomic and electronic interface structures of 2D van der Waals interfaces using heteroatoms significantly influences the emergence and stability of coherent phases, as well as phase-sensitive transport responses. In ZrTe₃, substitution and intercalation with Se, Hf, Cu, or Ni at the 2D vdW interface alter phonon–electron coupling, valence states, and the quasi-1D interface Fermi band, affecting the onset of CDW and SC, manifested as resistance upturns and zero-resistance states. We conclude here that these phenomena originate from dopant-induced variations in the lattice spacing of the quasi-1D Te chains of the 2D vdW interface, and propose an unconventional superconducting mechanism driven by valence fluctuations at the van Hove singularity, arising from quasi-1D lattice vibrations. Short-range in-plane electronic heterostructures at the vdW interface of Cr₂Si₂Te₆ result in a narrowed band gap. The sharp increase in in-plane resistance is found to be linked to the emergence and development of out-of-plane ferromagnetism. The insertion of 2D magnetic layers such as Mn, Fe, and Co into the vdW gap of 2H-TaS₂ induces anisotropic magnetism and associated transport responses to magnetic transitions. Overall, 2D vdW interface modification offers control over collective electronic behavior, transport properties, and their interplays, advancing fundamental science and nanoelectronic devices. Full article

Cement bond quality is critical to ensuring the long-term safety and structural integrity of oil and gas wells. However, due to the complex interdependencies among geological conditions, operational parameters, and fluid properties, accurately predicting cement bond quality remains a considerable challenge. To improve the accuracy and practical applicability of cement bond prediction, this study develops an intelligent prediction model. A Wide and Deep neural network architecture is adopted, into which two key parameters of the cement slurry’s power-law rheological model—the consistency coefficient and the flow behavior index—are embedded. A temperature correction mechanism is incorporated by integrating the correction equations directly into the network structure, allowing for a more realistic representation of the cement slurry’s behavior under downhole conditions. The proposed model is designed to simultaneously predict the bonding quality at both the casing–cement sheath and cement sheath–formation interfaces. It is trained on a field dataset comprising 30,000 samples from eight wells in an oilfield in western China. On the test set, the model achieved prediction accuracies of 87.29% and 87.49% at the two interfaces, respectively. Furthermore, field testing conducted during a third-stage cementing operation of a well demonstrated a prediction accuracy of approximately 90%, indicating strong adaptability to real-world engineering conditions. The results demonstrate that the temperature-corrected neural network effectively captures the flow characteristics of the cement slurry. The proposed model meets engineering application requirements and serves as a reliable, data-driven tool for optimizing cementing operations and enhancing well integrity. Full article

The emergence of short video applications (“apps”) has facilitated the dissemination, inheritance, and protection of traditional sports and games (TSGs). However, the effectiveness of these apps in raising public awareness and responsibility toward the preservation and heritage of TSGs has received insufficient research attention. This study constructs a theoretical model based on value-belief-norm theory and the theory of planned behavior, employing both PLS-SEM and fsQCA methods to empirically analyze 417 questionnaires. The results indicate that the PLS-SEM method identifies key factors influencing users’ responsible behaviors toward TSGs on short video apps, along with the complex and interdependent relationships among these factors. The fsQCA method reveals the intricate interactions and nonlinear effects of the antecedents on users’ responsible behaviors and identifies six configurations that drive high-level TSG responsible behaviors among users. This paper extends research on public responsible behaviors concerning TSGs and provides important practical guidance for government managers, inheritors, and conservation entities in the protection and heritage of TSGs. Full article