Search Results (22,282)

This study investigates the interaction between a manufacturer’s blockchain adoption strategy and a retailer’s demand information sharing strategy in a green supply chain. For four strategy combinations, we establish a multi-stage game-theoretical model of a green supply chain consisting of a single manufacturer and a single retailer. We first derive the optimal pricing, greenness, service level, and profits, followed by sensitivity and comparative analyses. Next, by examining how consumer price sensitivity and the unit adoption cost of blockchain technology interact, we identify equilibrium strategy combinations. Finally, we validate the relevant findings through numerical analysis. The results demonstrate that adopting blockchain can mitigate the double marginalization effect when consumer price sensitivity is moderate, and can enhance product greenness and service level when the adoption cost remains low. Interestingly, the manufacturer is inclined to adopt blockchain irrespective of the degree of consumer skepticism. Meanwhile, the implementation of blockchain may motivate the retailer to share information when price sensitivity falls within a moderate range. These findings present actionable guidance for green supply chains regarding blockchain and information-sharing strategies. Full article

The SAE J2841 utility factor (UF) estimates the fraction of driving expected to occur in charge-depleting (CD) mode for plug-in hybrid electric vehicles. Emerging in-use data suggest that real-world electric usage is lower than assumed, motivating a reassessment of how charging behavior and related factors should be incorporated into the UF curve. Using trip-level data from approximately 1000 PHEVs observed over one year, we develop a charging model that captures both population-level heterogeneity in charging frequency and day-to-day characteristic temporal patterns in individual charging. The charging behavior modeling is applied to NHTS driving data to generate UF curves spanning 5 to 200 miles (8 to 322 km) of CD range. When key behavioral features are included, the resulting CD driving fractions align closely with industry-provided data. Sensitivity analysis indicates that the assumed share of habitual non-chargers is among the most influential parameters affecting the gap between the original UF and in-use data. Multiple modeling approaches were used to explore the problem and compare results, including machine learning, logistic regression, and parametric methods. Additional factors such as blended CD operation and temperature effects are discussed within a modular framework for refining J2841. These findings inform ongoing discussions on PHEV utility representation in analytical and regulatory contexts. Full article

Environmental pollution constitutes an increasingly complex global challenge, largely driven by industrial expansion and the consequent release of toxic species such as Cd²⁺, Pb²⁺, Cu²⁺, Hg²⁺, Fe³⁺, As³⁺, and Rh³⁺ into natural ecosystems. These contaminants pose significant risks to environmental integrity and public health, motivating the development of analytical technologies capable of sensitive, selective, and reliable detection. In this context, graphene-based electrochemical sensors have emerged as versatile platforms for monitoring a broad range of analytes, particularly in environmental applications involving heavy-metal detection. The intrinsic physicochemical properties of graphene derivatives have enabled low detection limits, rapid response times, and tunable selectivity. Despite analytical advances, critical challenges persist regarding operational stability in complex matrices, inter-batch reproducibility, and robustness to interfering species, which continue to hinder large-scale deployment and real-world applicability. However, challenges remain regarding stability and performance in complex arrays, reproducibility, and resistance to interference, necessitating innovative strategies for functionalization and molecular recognition. This review article establishes a comparative framework based on functionalization strategies (covalent, non-covalent, and hybrid), the chemical nature of graphene (GO, rGO, and doping), and various types of polymers (conductors and insulators), using statistical metrics such as the limit of detection (LOD), linear range, working potential, stability, and interferences, employing a bibliometric analysis using the PRISMA 2020 methodology. This comparative framework enables analysis and explanation of performance trends, and the generation of design and functionalization recommendations for versatile applications, including criteria for reproducibility and sustainability. Full article

Background/Objectives: Nowadays, social media is increasingly utilized in the field of orthodontics for information sharing and promotion, yet its influence on patients’ willingness to initiate orthodontic treatment remains insufficiently defined. This systematic review aims to synthesize the available evidence on the impact of orthodontic-related social media content on patients’ willingness to seek orthodontic treatment. Methods: An extensive literature search was performed across five electronic databases up to August 2025, complemented by manual screening of reference lists. Randomized and non-randomized studies evaluating orthodontic-related social media exposure and reported treatment-related willingness or motivation outcomes were considered for inclusion. Results: A total of 1243 records were identified, and eight studies met the inclusion criteria, including six cross-sectional studies, one randomized controlled trial, and one qualitative study. Given the diversity of study designs and assessment methods, the results were synthesized narratively. Visually oriented orthodontic-related social media posts, particularly outcome-focused imagery such as before–after photographs, were more frequently associated with increased willingness to seek orthodontic treatment compared with technical content. Gender-related differences were reported, with female participants appearing more responsive to orthodontic-related social media exposure. Across the included studies, Instagram was identified as the platform exerting the strongest influence. Conclusions: The findings of this systematic review indicate that visually oriented orthodontic-related social media content, particularly outcome-focused imagery such as before–after photographs, shows more consistent associations with willingness to seek orthodontic treatment, alongside gender-related differences and platform-specific effects. Full article

The cathodic oxygen reduction reaction (ORR) remains a major kinetic barrier to high-efficiency proton exchange membrane fuel cells (PEMFCs), motivating the search for electrocatalysts that combine high activity, low metal usage, and long-term durability. This review examines single-atom catalysts (SACs) as an emerging platform for fuel-cell cathodes with particular emphasis on how atomic-level design, ORR mechanism, and practical deployment barriers are interrelated. The review discusses the key ORR pathways, intermediate binding principles, and scaling constraints that govern cathodic performance, and examines how metal-center selection, coordination-environment engineering, support regulation, synergistic multi-site construction, and morphology-controlled synthesis can be used to tune intrinsic activity and stabilize isolated active sites. It further highlights mechanistic insights from theoretical and operando studies, with emphasis on structure–activity relationships, dynamic active-site evolution, and approaches to mitigate scaling limitations. Major barriers to practical deployment, including carbon corrosion, demetalization, agglomeration, peroxide/reactive oxygen species attack, and the persistent gap between half-cell metrics and membrane electrode assembly performance, are also critically assessed. Rather than treating these topics separately, this review discusses them as connected factors that together determine the viability of SAC-based fuel-cell cathodes. Full article

Learning is fundamentally human, even as Artificial Intelligence (AI) challenges human exclusivity. AI, along with Virtual Reality (VR), emerges as a powerful tool that is set to transform higher education, the institutional embodiment of this pursuit at its highest level. These technologies offer the potential not to replace the human factor, but to enhance our ability to create more adaptive, immersive, and truly human-centric learning experiences, aligning powerfully with the emerging vision of Education 5.0, which emphasizes ethical, collaborative learning ecosystems. This research maps how AI and VR tools act as a disruptive force, examining additionally their capabilities and limitations. Moreover, it explores how AI and VR interact to overcome traditional pedagogy’s constraints, fostering environments where technology serves human learning goals. Employing a comprehensive two-month audit of over 60 AI, VR, and AI-VR hybrid tools, the study assesses their functionalities and properties such as technical complexity, cost structures, integration capabilities, and compliance with ethical standards. Findings reveal that AI and VR systems provide significant opportunities for the future of education by providing personalized and captivating environments that encourage experiential learning and improve student motivation across disciplines. Nonetheless, numerous challenges limit widespread adoption, such as advanced infrastructure requirements and strategic planning. By articulating a structured evaluative framework and highlighting emerging trends, this paper provides practical guidance for educational stakeholders seeking to select and implement AI and VR tools in higher education. Full article

Human motivation is governed by a long-memory cognitive process in which the depth of temporal integration—how far into the past the system draws upon accumulated experience—is not fixed, but dynamically compressed under cognitive stress. Despite extensive empirical evidence that acute stress impairs working memory and narrows temporal integration in decision-making, no existing mathematical framework has formally coupled the memory depth of the governing operator to a physiologically grounded stress indicator. To address this gap, we propose a stress-adaptive variable-order fractional model for motivational intensity $M\left(t\right)$, in which the Caputo fractional order $\alpha \left(t\right)$ varies inversely with an aggregated stress indicator $\sigma \left(t\right)$ through the Hill-type coupling $\alpha \left(t\right)={\alpha }_{min}+({\alpha }_{max}-{\alpha }_{min})C/(C+\sigma \left(t\right))$, thereby encoding the empirically documented shift from deep integrative to shallow heuristic processing as cognitive load increases. Rather than deriving the model by algebraic manipulation of a differential equation, we formulate it directly as a causally consistent type-III Volterra integral equation, in which the memory kernel is evaluated at the history time s, ensuring that the weight assigned to each past state reflects the memory depth that was physiologically active when that state was experienced. Well-posedness is established rigorously via the Banach fixed-point theorem with explicit contraction constants, uniform boundedness and non-negativity of solutions are derived through the fractional Gronwall inequality, and numerical solutions are computed using an Adams–Bashforth–Moulton predictor–corrector scheme adapted to the variable-order kernel. Five numerical experiments demonstrate that stress-induced variation in $\alpha \left(t\right)$ produces qualitatively richer dynamics compared with the tested constant-order baselines: the proposed model achieves a steeper peak decline rate (0.48 versus 0.19–0.45), a larger burnout gap (3.15 versus 1.92–2.81), and faster recovery to ninety percent of peak motivation (4.2 versus 3.9–7.3 time units), while the empirically observed numerical convergence approaches $O\left(h^2\right)$ for sufficiently small step sizes. The framework offers a principled phenomenological substrate for memory-adaptive cognitive modelling, with direct implications for stress-aware intelligent tutoring systems that are capable of inferring $\alpha \left(t\right)$ in real time from biometric signals such as heart rate variability or galvanic skin response, and adjusting instructional complexity accordingly. Empirical calibration against learning-analytics and psychophysiological datasets, together with stochastic extensions for probabilistic burnout-risk prediction, are identified as immediate priorities for future research. Full article

Trauma-induced heterotopic ossification (tHO) is characterized by aberrant ectopic bone formation in soft tissue following high-energy trauma, affecting >60% of combat-related amputees and >50% of major burn patients. Current prophylactic strategies (including NSAIDs, bisphosphonates, and low-dose radiation) lack mechanistic specificity, carry significant side effects, and surgical excision carries a 27% recurrence rate. This review reframes tHO pathogenesis through the neural–immune axis, arguing that ectopic bone formation is a downstream consequence of dysregulated neuroimmune signaling rather than a primary osteogenic event. Following trauma, nociceptor activation drives nociception-induced neural inflammation (NINI), releasing substance P (SP) and calcitonin gene-related peptide (CGRP), which disrupts the blood–nerve barrier, mobilizes neural crest-derived progenitor cells, and, alongside BMP-2/SMAD1/5/8 signaling and M1-polarized macrophage activation, establishes a permissive osteogenic microenvironment. A BMP-2/CGRP positive feedback loop sustains aberrant osteogenesis, converging on osteogenic transcription factors Runx2, SOX5/6/9, and Osterix. Dysregulated noncoding RNAs represent promising pre-radiographic biomarkers. This neural–immune framework motivates mechanism-based therapeutic strategies targeting CGRP (fremanezumab, erenumab), SP/NK1 signaling (aprepitant), and macrophage polarization (metformin, palovarotene, rapamycin), with multi-node combination approaches tailored to the temporal stages of tHO offering the most promise for precision prophylaxis. Full article

Motivated by the original idea of defending the Roman Empire, all these domination concepts can be interpreted as vertex-labeling schemes that model the allocation of resources to protect a graph against attacks. A Roman dominating function (RDF) is a labeling of the vertices of a graph with labels in $\{0,1,2\}$ such that every vertex labeled 0 is adjacent to at least one vertex labeled 2. The weight of an RDF is the sum of all vertex labels. Vertices labeled 2 are intended to protect their neighbors labeled 0. The Roman domination number is the minimum weight of an RDF on the graph. In 2017, Álvarez et al. introduced strong Roman domination as a variant of Roman domination designed to protect the vertices of a graph against multiple simultaneous attacks. In 2021, Ahangar et al. defined $\left[k\right]$-Roman domination, another model intended to defend a graph against individual attacks on vertices. In this paper, we investigate the computational complexity of the associated decision problems for $\left[k\right]$-Roman domination and strong Roman domination. Furthermore, we determine exact values of these parameters for several graph families under both variants. Full article

The Landauer principle motivates the definition of economic temperature as the monetary price of processing a bit irreversibly. No empirical test of this definition exists in transparent fee markets. This paper fills that gap using daily Bitcoin and Ethereum data, constructing canonical thermodynamic state variables and evaluating five diagnostic layers: state variable behavior, Maxwell-type integrability, Carnot-style efficiency bounds, nonlinear regime separation, and structural break sensitivity to protocol events. Bitcoin’s log-temperature behaves as a persistent mean-reverting process with an AR(1) coefficient of 0.97 and a half-life of 21 days; Ethereum is highly persistent, with weaker formal evidence of stationarity than Bitcoin. Maxwell integrability is frequency-dependent: Bitcoin passes all four relations at monthly frequency, whereas Ethereum passes two of four. Carnot-style evidence is the strongest: realized fee extraction efficiency stays well below the implied bound, with daily compliance exceeding 97% on both chains. Structural breaks around Bitcoin ordinals, EIP-1559, the merge, and Shanghai confirm that protocol changes reorganize the temperature relation. The thermodynamic framework provides structure that standard fee market analysis does not, including a first principles efficiency bound and a state space coherence test. The findings provide partial, frequency-dependent, and chain-specific empirical support for a Landauer-based thermodynamic description of blockspace markets. Full article

Climate change is increasingly recognized as a source of psychological distress, yet the prevalence, predictors, and behavioral implications of climate anxiety remain unevenly understood. This study examines climate anxiety, its key drivers, and associated behavioral responses in a multi-country sample of adults. A cross-sectional online survey was conducted across 21 countries using the Climate Change Anxiety Scale (CCAS), alongside measures of awareness, coping strategies, social support, and food-related behaviors, including food waste reduction, increased plant-based food consumption, and home or community gardening. Analyses included descriptive statistics, exploratory factor analysis (EFA), and multivariable regression. Given the uneven country-level representation, results are reported as pooled patterns with a few exploratory cross-country comparisons. Climate anxiety was widely reported, with over 60% of participants indicating that climate challenges were emotionally overwhelming. Regression analyses showed that climate awareness and frequency of climate-related thinking were positively associated with higher anxiety, although the effect sizes were small and explanatory power was limited (R² = 0.055). EFA identified two related dimensions: cognitive concern about future impacts and affective distress. Climate anxiety across countries showed modest variation (2.44–3.23) and no statistically significant differences, despite variation in awareness. A gap between concern and climate action was evident: only 39.1% reported environmentally motivated dietary changes. Cost, limited availability, and lack of information were the main barriers to climate action, and only 24.4% reported frequent social support. These findings indicate that climate anxiety is shaped by both psychological and structural factors, and that reducing it requires not only increasing awareness but also enabling conditions that support meaningful climate action. Full article

Dense infrared small target detection (ISTD) in long-range remote sensing is critical for multi-target surveillance, yet existing benchmarks mostly contain only sparsely distributed targets and rarely reflect dense scenes. To address this limitation, we construct a new dense satellite ISTD dataset, IR-SatDense, by compositing small targets onto real satellite infrared backgrounds and partitioning it into subsets using the Average Minimum Inter-Target Distance (AMID) to explicitly control target density. By visualizing multi-stage backbone features, we observe that in dense scenes the deepest stage naturally forms compact, high-response target clusters in the semantic feature maps, while low- and middle-level features remain heavily cluttered. This motivates us to treat high-level semantic density as a global prior to guide low-level feature enhancement. Therefore, we propose Semantic Density-Guided ResNet (SDG-ResNet), a plug-in backbone that attaches a lightweight semantic density head to the deepest stage and injects the predicted density map into intermediate layers through Semantic Density-Guided Refine (SDGR) blocks with residual spatial gating. Integrated into representative transformer-based detectors, including Deformable DETR, DETA, and DINO, SDG-ResNet consistently improves the probability of detection (PD) at comparable false alarm (FA) levels on IR-SatDense while maintaining competitive performance on the sparse dataset IRSTD-1K. Full article

This study uses the Motivation–Opportunity–Ability (MOA) framework to examine the drivers of food waste behavior among Generation Z, a demographic that contributes disproportionately to household food waste. Using structural equation modeling on survey data from 349 undergraduate students, the influence of morals and attitudes toward food waste (motivation), cooking and grocery shopping skills (ability), food purchase triggers, and frequencies of grocery shopping and cooking (opportunity) was investigated. The results indicate that strong moral convictions and creative cooking skills significantly reduce waste. Conversely, susceptibility to marketing-driven purchase triggers increases wasteful behavior. Notably, other factors such as shopping frequency and attitudes toward food waste showed no significant impact. These findings highlight the need for targeted interventions for young consumers that reinforce moral motivations, enhance practical culinary abilities, and mitigate the impact of predatory purchase triggers to effectively curb food waste. Full article

Women’s advancement into leadership roles remains uneven in many post-socialist labor markets despite high levels of female education and workforce participation. While prior research has emphasized structural barriers and national institutional conditions, less is known about how firm-level organizational environments shape women’s evaluations of leadership as a viable career pathway. This study aims to examine how firm type shapes women’s managerial aspirations across career stages in post-socialist Mongolia. Using cross-sectional survey data from 191 employed women in Ulaanbaatar, aspiration patterns were compared across three organizational contexts: foreign-owned firms, domestic private firms, and public-sector organizations. Career aspirations were operationalized as three states—high aspiration, constrained aspiration, and low aspiration—to capture differences between leadership motivation and perceived feasibility. Pearson’s chi-square tests and Cramér’s V were used to assess associations between firm type, career stage, and aspiration categories. The results show that women in foreign-owned firms are more likely to sustain leadership aspirations, whereas constrained and low aspirations are more prevalent in domestic private firms and the public sector, particularly at mid-career stages. These findings suggest that leadership aspirations reflect organizationally shaped feasibility assessments rather than individual motivation alone, and that firm type operates as a critical meso-level opportunity structure within shared post-socialist institutional conditions. Full article

This paper addresses active-mode detection for switched discrete-time Takagi–Sugeno systems from noisy input–output data under candidate-dependent input correction and uncertainty in data-driven observability subspaces. A lifted input–output formulation is developed in which each candidate mode is associated with a mode-dependent forced-response correction and a nominal observability subspace identified offline from representative data. Based on this construction, a practical residual criterion is introduced together with an ideal residual criterion defined by the exact residual projector. An online verifiable sufficient condition is then derived to guarantee consistency between the practical and ideal residual orderings, yielding a conservative but theorem-consistent certification mechanism. To quantify the effect of measurement uncertainty, a component-wise noise-to-signal ratio (NSR) analysis is established, leading to explicit conservative NSR bounds when signal-floor conditions are available offline. These results motivate an adaptive observation-window strategy driven by an explicit online NSR estimate. In addition, an uncertainty-corrected discernibility index based on principal angles between estimated observability subspaces is introduced to assess offline mode separability. Simulations on a switched T–S benchmark show high practical detection accuracy, sound but conservative certification, informative NSR bounds, and stable adaptive-window regulation, including under reviewer-motivated switching stress tests and baseline comparison experiments. Full article