Search Results (386)

Electric vehicles equipped with in-wheel motors provide packaging, controllability, and drivetrain-simplification advantages; however, the increase in wheel-side unsprung mass can intensify vibration transmission to the chassis, seat, and occupant. This paper presents a coordinated active seat and active suspension control strategy for an integrated 8-DOF quarter-car model that includes an in-wheel motor, an active seat suspension, and a 4-DOF seated driver body model. The proposed controller combines a Harmony Search (HS)-optimized proportional–integral–derivative (PID) feedback baseline with a repeatable-disturbance feedforward compensation term. The HS-PID loop provides baseline transient attenuation, while the feedforward term compensates the repeatable component of the bump-induced disturbance transmitted through the coupled seat–vehicle system. The controller is evaluated against passive suspension, active-seat-only control, active-vehicle-suspension-only control, and an HS-PID baseline under repeated bump/shock excitation. The results show that coordinated actuation reduces occupant displacement and acceleration responses relative to the benchmark cases. The discussion explains the active-seat-only peak-acceleration amplification, the different magnitudes of displacement and acceleration improvements, and the practical implications of suspension stroke and actuator-force limits. The reported conclusions are therefore confined to the repeated bump/shock condition considered in this numerical study; broader ride-comfort generalization requires standardized whole-body vibration metrics, random-road validation, speed variation, parametric uncertainty analysis, and drivetrain energy evaluation. Full article

Artificial intelligence (AI) is increasingly regarded as a key instrument for sustainability governance, but its role may differ across risk domains. Rather than functioning solely as an anticipatory tool, AI may also develop reactively in response to risk shocks. Using monthly Chinese data from January 2013 to December 2023, this study examines the dual role of AI in sustainability governance by analyzing its time–frequency relationships with climate-related government response and infectious disease-related market attention. The continuous wavelet transform and partial wavelet coherence are employed, with the World Uncertainty Index controlled. The results show that AI-sector development is positively associated with both risk-related signals, but the lead–lag patterns differ substantially. In the climate-response domain, the relationship shifts from risk-signal precedence in 2015–2016 to AI-sector precedence in short-term bands after 2020, suggesting that AI-related capacity may have become increasingly embedded in anticipatory climate governance. In contrast, infectious disease-related attention mainly precedes AI-sector movements, especially during 2017–2018 and the COVID-19 period, indicating a more reactive role of AI in public health risk contexts. These findings do not provide causal evidence that AI directly reduces physical climate risks or epidemiological burdens. Instead, they reveal the dual role of AI as both a response to sustainability-related risk shocks and a potential contributor to forward-looking governance capacity. This study contributes to AI and sustainability governance research by clarifying the conditions and boundaries under which AI shifts from reactive crisis response toward anticipatory risk preparedness. Full article

Amid rising global economic uncertainty and frequent external shocks, strengthening marine economic resilience has become a core priority for coastal nations to stabilize industrial supply chains and achieve sustainable marine development. China’s traditional resource-driven marine economy faces persistent structural bottlenecks, including homogeneous industrial structure, low value addition and weak risk resistance. As a landmark national policy for sustainable marine economic growth, the Marine Economic Innovation and Development Policy (MEIDP) has been piloted in 15 coastal cities across two batches, yet its causal impact on marine economic resilience remains under systematic evaluation. Using panel data of 51 Chinese coastal cities from 2008 to 2023, this study employs a multi-period difference-in-differences approach with supporting analyses to systematically evaluate the MEIDP’s impact on marine economic resilience, as well as its moderating mechanisms and heterogeneous patterns. The key findings are threefold. First, the MEIDP significantly improves coastal cities’ marine economic resilience, and this positive effect remains stable after multiple robustness tests. Second, public health emergencies exert a significant positive moderating effect, where the industrial support capacity and risk-resilience foundations established through policy implementation function more effectively under shock conditions, thereby amplifying the enhancement of resilience. Third, the policy effect shows prominent heterogeneity, being more pronounced in high-tourism cities and the Northern Marine Economic Circle, while statistically insignificant in low-tourism cities and the Southern Marine Economic Circle. This study enriches the theoretical framework of marine economic policy evaluation and provides empirical evidence from a major developing country for global marine governance, confirming that marine policies that promote innovation are an effective path to strengthen economic risk resistance. In light of these findings, we propose targeted policy recommendations to steadily enhance overall marine economic resilience. Coastal regions should deepen marine policies that promote innovation to bolster industrial upgrading and technological empowerment, adopt differentiated schemes aligned with local industrial foundations and resource endowments, promote marine industrial diversification and chain extension to reduce structural vulnerability, and improve public risk response mechanisms to strengthen the counter-cyclical buffering capacity of the marine economy. Full article

Real-time estimation of the safety distance between power operation workers and energized equipment is essential for preventing electric shock, arc flash injury, and equipment accidents during live or near-energized operations. However, large-scale deployment remains difficult because conventional binocular ranging systems require accurate offline calibration, while monocular vision suffers from metric-scale ambiguity. This paper proposes a weakly calibrated multi-camera vision framework for safety distance estimation in power operation scenes. First, a cross-camera tracking strategy combining mask-guided ReID features, Kalman filtering, spatio-temporal constraints, and Hungarian matching is developed to maintain globally consistent worker identities. Second, a region-wise self-supervised depth estimation model is introduced: photometric consistency is imposed within worker regions, while cross-view feature reprojection is imposed within rigid equipment regions. Intrinsic parameter regularization and scene-inherent dimensional priors are then used to recover metric scale without placing additional calibration targets. Third, the YOLOv8-seg backbone is enhanced with a CNN-Transformer C2f_CT module to improve pixel-level segmentation of workers, energized equipment, and structural components under cluttered backgrounds. Experiments on three representative power operation scenarios show that the proposed method achieves 92.3% MOTA, 94.2% Rank-1 accuracy, 82.0% mAP, 8.7% relative distance error, and 22 FPS end-to-end speed. When using five-frame smoothing and uncertainty threshold, the proposed method can achieve 98.7% warning precision, 0.9% missed alarm rate and 1.3% false alarm rate. The results indicate that the proposed framework provides a practical balance between deployment cost, geometric accuracy, and real-time warning capability. Full article

Arctic shipping should not be understood as a sustainable transport outcome that will emerge automatically as sea ice declines. Rather, its long-term viability depends on whether stable operations can be achieved under persistent environmental and political uncertainties. Existing studies have examined Arctic shipping risks, governance arrangements, and route feasibility, but have paid limited attention to how cargo owners, shipping companies, and governments interact strategically under composite risk constraints. To address this gap, this paper develops a three-party evolutionary game model involving cargo owners, shipping companies, and the government under weather risk and political risk, and uses numerical simulation to examine system evolution, policy thresholds, and external shock responses. The results show that the system tends to converge toward a high-coordination equilibrium under low-risk conditions, whereas medium-risk conditions may lead to bistability and a low-coordination trap when government support remains below a critical threshold. Composite political shocks further amplify system vulnerability and weaken sustainable route operation. These findings suggest that the key challenge of Arctic shipping lies not in physical route accessibility alone, but in whether risk governance, market participation, and institutional support can jointly stabilize expectations and promote sustainable Arctic shipping operations. Full article

Introduction: Hepatic infarction is an uncommon but clinically important cause of focal ischemic liver injury. In internal medicine, it usually emerges in acutely ill or complex hospitalized adults with sepsis, shock, malignancy, thrombosis, vasculitis, transplantation, or recent hepatobiliary or interventional procedures who develop a new focal hepatic lesion and abnormal liver tests. Aims: This review aims to provide internists with a practical diagnostic and management pathway for recognizing hepatic infarction, distinguishing it from abscess and malignancy, selecting appropriate imaging, identifying vascular complications, and avoiding unnecessary biopsy or drainage when conservative management is safer. Methods: We performed a structured narrative review of adult hepatic infarction and related ischemic liver entities using targeted searches in PubMed/MEDLINE and Scopus. The search strategy, screening process, and evidence limitations are reported explicitly. Because the available evidence is dominated by case reports, small series, radiology reviews, and guidance on related vascular liver diseases, the synthesis is qualitative and the proposed pathway is conceptual rather than prospectively validated. Results: Hepatic infarction is most often recognized when systemic hypoperfusion, splanchnic vasoconstriction, microvascular dysfunction, or local macrovascular compromise coexist. Multiphasic computed tomography (CT) is usually the first-line acute-care modality, while MRI, contrast-enhanced ultrasound, Doppler ultrasound, CT angiography, or vascular imaging should be selected according to diagnostic uncertainty and suspected arterial or portal venous complications. The main diagnostic pitfalls are misclassification as abscess, malignancy, hematoma, or postoperative collection. Conclusions: In the appropriate clinical context, a wedge-shaped or geographic non-enhancing hepatic lesion should trigger vascular-ischemic reasoning before being labeled as abscess or tumor. The proposed internist-led pathway is intended as a pragmatic conceptual framework for diagnostic reasoning and multidisciplinary communication, not as a validated guideline or evidence-based algorithm. Full article

This study investigates and explores the volatility dynamics of the South African rand against the US dollar (ZAR/USD) using the Family GARCH (fGARCH) model and the First-Order Beta-Skew-T-Generalised Autoregressive Conditional Heteroskedasticity (Beta-Skew-T-EGARCH) model. Currency volatility across the globe, uncertainties, and instability in emerging markets have become increasingly consequential for trade flows, investment allocation, and macroeconomic management. The ZAR/USD serves as a benchmark of South Africa’s economic wealth and vulnerability to external shocks and is one of the most valued, significant, and heavily traded pairings of emerging market currencies. Simple standard GARCH (sGARCH) is one of the most useful models for exchange rate volatility; however, the sGARCH model has some limitations: it fails to accommodate or allow the long memory effects, skewness distribution, and leverage dynamics consistently observed in emerging-market currency returns. This study addresses these limitations by using the fGARCH model, which includes the most popular GARCH models and Beta-Skew-T-EGARCH for daily ZAR/USD returns ranging from 5 January 2000 to 1 October 2024. Five innovation distributions are used for evaluation and comparison under fGARCH and sGARCH, namely generalised hyperbolic (GH), generalised error (GED), skewed Student’s t (SSTD), skewed generalised error (SGED), and Student’s t (STD), with model fitness criteria assessed using the Shibata criterion (SIC), Hannan–Quinn criterion (HQ), Bayesian information criterion (BIC), and Akaike information criterion (AIC), choosing the specification with the lowest overall penalty. It is found that the fGARCH(1,1) model fitted to return-frequency data under the SSTD achieves the lowest AIC, outperforming sGARCH. The study also includes an analysis among covariates, which are day, month, trend, oil, and platinum; the trend variable is a statistically significant predictor, with p = 0.007, showing a positive influence on ZAR/USD volatility. The Beta-Skew-T-EGARCH model with two components divides volatility into long-run and short-run components, which is found to deliver a superior fit over the one-component variant, evidenced by a lower BIC (3.068435) and a higher log-likelihood (−748.464826). The two components confirm that the model captures declining conditional volatility, whereas the one-component model sustains persistence in the evaluated estimates. Full article

Renewable energy communities (RECs) are increasingly recognized as a strategic instrument for enhancing the sustainability and resilience of energy systems, promoting local renewable integration, and reducing consumer exposure to electricity market volatility. This study analyzes the Italian electricity market and assesses the economic performance of RECs relative to individual consumers using high-frequency hourly data from 2021 to 2023, covering both the 2022 European energy crisis and the subsequent Italian regulatory reform of incentive mechanisms. The optimization problem is formulated in physical terms, aiming to maximize locally utilized energy, defined as the sum of self-consumed and shared photovoltaic generation. This choice reflects the structure of the Italian regulatory framework, where incentives are directly linked to the amount of energy shared within the community. In this context, energy-based optimization is preferred to avoid embedding assumptions on discount rates, investment horizons, and financing conditions, which may vary significantly across users and introduce additional uncertainty. From a sustainability perspective, maximizing local energy utilization contributes to improving energy efficiency, reducing reliance on external energy sources, and enhancing the capacity of decentralized systems to absorb market shocks. For this reason, economic indicators such as Net Present Value (NPV) or payback period are not explicitly included in the optimization objective. This is justified by the focus of the analysis on short-term operational performance and exposure to electricity price volatility, rather than long-term investment evaluation. Moreover, given that the economic value of the REC is largely determined by shared energy volumes under the current Italian incentive scheme, maximizing local energy utilization provides a consistent proxy for economic performance. Nevertheless, the integration of financial metrics such as NPV or payback period represents a relevant extension for future research, particularly in the context of investment decision-making. Through panel econometric analysis, we estimate the sensitivity of economic value to electricity price fluctuations. Results show that RECs reduce price sensitivity by approximately 8–15% compared to individual users, as estimated by panel regression coefficients. Furthermore, the volatility of economic value decreases by around 1.95% under the community configuration, particularly during the 2022 price shock demonstrating that RECs exhibit significantly lower price dependence than standalone consumers. To assess the robustness of these findings, a machine learning framework is employed to relax linearity assumptions and capture potential non-linear effects. Results consistently show that while market prices remain an important determinant, RECs substantially attenuate their impact, particularly during periods of extreme price stress. A policy counterfactual comparison between pre- and post-reform incentive structures further indicates that the coefficient of variation decreases by approximately 4.4% under the post-reform incentive scheme, highlighting the role of policy design in supporting economically and operationally sustainable energy communities. Overall, this study develops a data-driven analysis based on a high-frequency synthetic dataset designed to reproduce realistic consumption and generation dynamics, providing robust evidence that RECs contribute not only to renewable energy deployment but also to the economic and systemic sustainability of electricity markets under conditions of high volatility. Full article

Geopolitical instability is increasingly affecting the development and deployment of artificial intelligence (AI) infrastructure by disrupting energy systems, semiconductor supply chains, and digital infrastructure networks. While existing research has explored geopolitical risk, digital sovereignty, and AI governance, little attention has been paid to understanding how geopolitical factors are integrated into the valuation and strategic allocation of AI infrastructure. This theory-building study introduces the concept of geopolitical repricing, defined as the process through which firms, investors, governments, and infrastructure operators revise their assessment of the economic value, risk profile, and strategic importance of AI infrastructure in response to geopolitical instability. Drawing on literature on geopolitical risk, AI infrastructure, digital sovereignty, geo-economics, and investment under uncertainty, the paper develops a four-stage analytical framework that links geopolitical shocks, transmission channels, revaluation, and strategic reallocation. The framework identifies three interconnected transmission channels: energy volatility, supply-chain disruption, and infrastructure vulnerability, and explains how their cumulative effects may influence valuation judgments, investment criteria, and infrastructure allocation decisions. The study further proposes a set of theoretically derived propositions and operational indicators to guide future empirical research. The paper contributes to the emerging political economy of AI by providing a conceptual explanation of how geopolitical instability may shape infrastructure valuation beyond the immediate effects of disruption. It lays the groundwork for future research on the connections between geopolitical factors, infrastructure strategies, and AI development. Full article

Traffic signal control increasingly faces disturbed operating conditions such as incidents, abrupt demand surges, sensing degradation, and abnormal driving patterns. Under these nonstationary regimes, classical fixed-time and actuated strategies may exhibit slow recovery, while purely data-driven controllers can be brittle when disturbance characteristics shift. This paper proposes an LLM-in-the-loop architecture for disturbed traffic signal control that integrates (i) deep learning for disturbance detection and short-horizon traffic forecasting, (ii) a disturbance-aware candidate generation and scoring layer (template/retrieval-based), and (iii) a constrained large language model (LLM) that selects or minimally repairs signal plans only within constraint-screened action templates. A deterministic validator enforces safety and operational constraints, including minimum/maximum greens, cycle feasibility, and clearance rules, by checking action feasibility before execution. The method is formulated as constrained decision making under uncertainty, where disturbance estimates and predictive confidence shape both retrieval/scoring and LLM supervision. The originally reported SUMO evaluation considered multiple disturbance categories, including capacity drops, demand shocks, and sensing dropouts as well as reported network delay, queue spillback, recovery time, and switching stability. Within the originally reported SUMO scenarios, descriptive results suggest that among the selected baselines, the proposed DL + LLM framework reported lower mean values of delay, spillback frequency, and recovery time than the fixed-time, actuated, and retrieval-only baselines. The reported validator-detected action-feasibility violations were zero; this result concerns timing-action feasibility rather than an absence of traffic-state risks such as spillback. Full article

We develop a stochastic Stackelberg model of a subscription market with cancellation costs. A representative consumer chooses when to subscribe to and cancel a service as the utility derived from the subscription evolves according to a diffusion process, while the firm selects the subscription fee and cancellation cost to maximize its expected payoff. The consumer’s problem is equivalent to the classical real-options model of entry and exit under uncertainty with adjustment costs and exhibits a two-threshold policy with an inaction band and hysteresis. Unlike the standard formulation, in which the optimal thresholds are characterized implicitly through a system of nonlinear equations, we derive an explicit parametric solution in closed form. This solution reduces the firm’s optimization problem to a two-dimensional unconstrained problem and yields a detailed characterization of the optimal pricing policy. We show that the firm’s strategy exhibits three qualitatively distinct regimes depending on the initial utility level. For small utility levels, the optimal cancellation cost is zero. In an intermediate regime, the firm’s optimal policy induces the consumer to set the entry threshold equal to the initial utility level, resulting in immediate subscription. For sufficiently large utility levels, the firm induces permanent lock-in by setting a high cancellation cost and a low subscription fee: the consumer subscribes immediately and never subsequently unsubscribes. The transition between the latter two regimes is discontinuous and results from competition between two local maxima of the firm’s payoff function. We then extend the model to a heterogeneous population of consumers. The superposition of individual two-threshold subscription strategies generates a Preisach hysteresis operator describing the aggregate dependence of the firm’s revenue on the utility dynamics. The discontinuous regime transition persists under heterogeneity, demonstrating the robustness of the underlying mechanism. The Preisach representation predicts complex history dependence and long-term effects of temporary utility shocks. For a gamma distribution of consumer preferences, the firm’s expected payoff is obtained in closed form in terms of incomplete gamma functions. Full article

Strategic competition in health service markets requires managers to make pricing, marketing, and investment decisions under uncertainty, yet educational programs in healthcare management and dietetics lack experiential tools for teaching these competencies within a game-theoretic framework. This study develops and computationally validates SY142-Game-Theory-1, a computational simulation framework that models strategic competition between two asymmetric healthy living centers as a 36-month repeated Prisoner’s Dilemma, integrating demand decomposition, net present value analysis, employee satisfaction dynamics with burnout thresholds, reputation feedback, and stochastic shock events. The simulation produces a valid and distinctly asymmetric Prisoner’s Dilemma structure in which the established provider faces the classical temptation to defect while the new entrant’s rational incentive aligns with cooperation; Axelrod-style tournaments across 22 strategies (96,800 simulations) identify Forgiving Tit-for-Tat as the top-performing strategy; Monte Carlo validation (n = 1000) confirms a statistically significant cooperation premium of 24.1% over Nash equilibrium; and sensitivity analyses across four parameters demonstrate robustness of all qualitative findings. The open-source framework bridges game theory, simulation-based learning, and health service management education, providing a computationally validated foundation for teaching strategic decision-making in competitive healthcare environments, with empirical evaluation of learning outcomes reserved for future work. Full article

Frequent introductions and revisions of climate policy instruments constitute a salient exogenous shock to firms’ strategic decisions. From a socio-technical systems perspective, climate policy uncertainty (CPU) represents an external institutional disturbance that reshapes the interaction between firms’ technological upgrading and organizational governance. Using panel data on 1783 Chinese listed firms from 2011–2024, we examined how CPU affects firms’ industrial intelligence. We employed fixed-effects models, mediation, and moderation analyses, supplemented by robustness tests. We found that, first, CPU significantly promotes firms’ industrial intelligence transformation. Second, board governance plays a key moderating role: higher board educational attainment, stronger innovation orientation, and an environmental or risk committee significantly strengthen CPU’s positive effect on industrial intelligence. Third, CPU promotes industrial intelligence mainly through two channels: an opportunity effect via increased R&D investment, and a pressure effect via reduced total factor productivity, pushing firms to adopt intelligent transformation to address productivity pressure. Moreover, this effect is stronger for firms in high-pollution industries, larger firms, and long-established firms. These findings suggest that corporate industrial intelligence is not merely a technological response, but a socio-technical adaptation process shaped by climate policy uncertainty, board governance, and resource reconfiguration. This study provides evidence on firms’ digital, intelligent, and green transformation under climate policy uncertainty and offers implications for board governance and sustainable adaptation. Full article

The COVID-19 pandemic generated unprecedented disruptions to the global tourism industry, severely affecting tourism-dependent economies and related employment. India, as one of the world’s major tourist destinations, experienced substantial declines in tourist arrivals during the pandemic period. This study investigates the effects of COVID-19-induced fear on tourism demand and economic performance in India using the COVID-19 Fear Index, which captures behavioural responses to pandemic-related uncertainty beyond conventional indicators such as infection rates, mortality, and lockdown restrictions. The COVID-19 Fear Index is constructed using reported COVID-19 cases and mortality data sourced from the European Centre for Disease Prevention and Control and the Johns Hopkins Coronavirus Resource Centre. Monthly data from January 2020 to October 2023 are analysed using autoregressive distributed lag (ARDL) and nonlinear autoregressive distributed lag (NARDL) models to examine both tourism demand dynamics and the asymmetric tourism–growth relationship. The results confirm a stable long-run cointegration relationship among tourism demand, the COVID-19 Fear Index, exchange rate, and ICT development. Pandemic-induced fear significantly reduces tourism demand in the long run ($-0.152$, $p=0.021$) and short run ($-0.084$, $p=0.000$), indicating that heightened uncertainty suppresses tourist arrivals. Exchange rate depreciation also negatively affects tourism demand ($-0.267$, $p=0.000$), whereas ICT development positively enhances tourism resilience ($0.463$, $p=0.000$). The error correction term ($-0.436$, $p=0.000$) confirms rapid adjustment toward long-run equilibrium. Furthermore, the nonlinear analysis reveals asymmetric effects, where positive tourism shocks increase economic growth by $0.088\%$ ($p=0.003$), while negative shocks exert a stronger contractionary effect ($0.409$, $p=0.000$). These findings highlight the vulnerability of tourism-dependent economies to uncertainty shocks and emphasise the importance of ICT-driven resilience strategies, adaptive tourism policies, and crisis-responsive economic planning. Full article

Understanding complex systems requires analytical tools capable of covering nonlinear dynamics, structural complexity, and informational uncertainty simultaneously. In this context, chaos theory, fractal analysis, and entropy measures provide complementary perspectives for examining any irregular behavior in natural and socio-economic systems. This paper examined the relation between the Geopolitical Risk Index and the World Uncertainty Index to the volatility of West Texas Intermediate crude oil, gold, and Bitcoin over the period October 2010–February 2026. The analysis was motivated by the recent intensification of geopolitical tensions, particularly conflicts involving Iran, the United States, and Israel, which have significantly heightened uncertainty in global energy and financial markets. The empirical analysis first investigated the underlying complexity of the variables using entropy, chaos, and fractionality measures. Results from the Shannon, R-T entropy, Kolmogorov–Sinai complexity, Hurst, H-M and Lo’s R/S statistics, Phillips, and GPH fractionality tests consistently indicate entropy, fractal persistence, and long-range dependence across the series. In addition, the largest Lyapunov exponents and Hurst coefficients confirmed the presence of chaotic dynamics. The results reveal strong regime heterogeneity with geopolitical shocks exerting significantly stronger effects during high-uncertainty periods. Forecast comparisons show that regime-switching models outperform linear specifications, highlighting the importance of fractal and nonlinear dynamics in understanding financial market responses to geopolitical risk. Full article