Search Results (831)

State-level health resource systems need precise and timely prediction models, but they are plagued by the ongoing problem of deteriorating model performance because of constantly shifting data distributions (data drift). Predicting uncommon but important events like sickle cell crisis is a classification task where this problem is most noticeable. This paper presents the Evolving AI-Driven Ensemble Learning Framework, which blends novelty detection using the F1-score with sophisticated ensemble approaches (stacking XGBoost, Deep Neural Network, and Random Forest with a meta-learner). Complex, high-dimensional health data are handled using sophisticated feature engineering techniques, such as automated feature selection via evolutionary algorithms and meta-learning (MAML). We empirically assessed a reactive retraining technique that was improved by ensemble stacking and simulated real-time data drift. After five retraining cycles, the improved ensemble and feature engineering showed significant performance improvements over the initial model, achieving substantial improvements: F1-score improved from 0.1250 to 0.9734 (an absolute increase of 0.8484, representing a 678.7% relative improvement), recall from 0.0714 to 0.9767 (an absolute increase of 0.9053), and precision from 0.5000 to 0.9702 (an absolute increase of 0.4702). The framework maintained high specificity (0.9700) and demonstrated outstanding discriminative performance with an AUC-ROC of 0.9909 (an 8.5% improvement). The model’s strong predictive capacity was confirmed by improvements in the Matthews Correlation Coefficient from 0.1000 to 0.9467 (846.7% improvement) and Cohen’s Kappa from 0.0800 to 0.9467 (1083.3% improvement). Model transparency in pipeline development is now made possible by a fixed runtime issued in the SHAP explainability layer. The efficiency of the framework is empirically validated by this study, showing that automated feature engineering and optimized ensemble learning greatly increase model stability and preserve remarkable accuracy for minority classes in complicated data contexts. Full article

Background: Non-communicable disease (NCD) emergencies, including hypoglycaemia, stroke, acute coronary syndrome, and hypertensive crisis, call for prompt action by households and communities. The psychosocial mechanisms associated with emergency management behavior in rural Thailand, however, are not well understood. Objectives: The study pursued three objectives. It described levels of NCD emergency management behavior and the characteristics of emergency events reported during the preceding six months. It examined associations linking knowledge, Health Belief Model (HBM) constructs, social support, participation, self-efficacy, and emergency management behavior. Then, it identified the constructs with the strongest relations to behavior, along with an indirect pathway operating through self-efficacy, via structural equation modelling. Methods: A cross-sectional study analyzed data from 1321 community adults in Sisaket Province, Thailand, drawn from an initial 1407 records after excluding 86 with age below 21 years, incomplete data on SEM variables, or invalid disease-status coding. A structured, interviewer-administered questionnaire measured knowledge of NCD emergencies, HBM constructs, social support, family and community participation, self-efficacy, and emergency management behavior. Covariance-based SEM with item parcels and maximum-likelihood estimation was conducted; knowledge was treated as an observed variable. Results: Mean age was 58.0 years (SD = 14.3); 15.5% reported a recent emergency event. Knowledge was moderate-to-low (mean = 5.79/10; KR-20 = 0.440). Model fit was acceptable (CFI = 0.980; RMSEA = 0.047; SRMR = 0.049). Discriminant validity was supported by the Fornell–Larcker criterion for all constructs. Participation was strongly associated with self-efficacy (β = 0.858) and with emergency management behavior (β = 0.433); self-efficacy was also associated with emergency management behavior (β = 0.354). Participation had the largest total effect on behavior (0.737), combining a direct effect (0.433) and an indirect, self-efficacy-related effect (0.304). All structural-path variance inflation factors (VIF) were below 5.0 except participation behavior (VIF = 6.19), which remained below the more permissive threshold of 10.0. Conclusions: NCD emergency management behavior in this rural community was most strongly associated with family and community participation and with self-efficacy. Because the design was cross-sectional, these findings indicate association rather than confirmed causal or mediating pathways. Programmes should combine scenario-based knowledge with participatory practice, family role preparation, rehearsal of emergency-service (1669) activation, and confidence-building activities. Full article

This study investigated the dynamic volatility connectedness among financial technology (FinTech), green indices, and global uncertainty metrics between June 2018 and May 2025. The research was conducted to understand how technological innovation and sustainability indices interact with systemic risk during periods of extreme global stress, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the market disruptions of early 2025. The analysis employed a time-varying parameter vector autoregression (TVP-VAR) framework to capture time-varying interdependencies and risk spillovers across multiple market regimes. Key findings indicated that total dynamic connectedness intensified significantly during crisis events, with major spikes occurring during the 2020 pandemic onset and the 2025 shocks possibly related to the “DeepSeek” AI disruption and the US tariff announcements. FinTech indices and green assets consistently functioned as net transmitters of shocks, while uncertainty indices, particularly the VIX, served as net recipients. Notably, the Alternative Finance Index (AFI) exhibited regime-dependent behaviour, transitioning from a transmitter to a recipient during the COVID-19 pandemic. These results imply that innovative and sustainable sectors have evolved into systemic drivers of global market sentiment rather than mere recipients of external shocks. The findings provide critical insights for stakeholders in financial markets, helping them to rethink their current approaches and prevent financial losses amid market upheaval. Full article

This paper examines the predictive power of credit default swap (CDS) spread correlations in the context of the 2023 Credit Suisse failure. Using a two-window design, we separate a 50-week pre-crisis period from the final two-week “jump” window and evaluate whether historical market-implied dependence anticipated cross-sectional crisis repricing across Global Systemically Important Banks (G-SIBs). We find that the magnitude of each bank’s crisis-period CDS jump is significantly related to its prior co-movement with Credit Suisse across the full sample ($r=0.80$, $p<0.001$, $n=15$), indicating that contagion followed a structured dependence pattern rather than an undifferentiated panic dynamic. The relationship holds across both regional cohorts, with the European G-SIB group displaying a considerably tighter fit ($r=0.96$, $p<0.001$, $n=8$) than the non-European group ($r=0.84$, $p=0.019$, $n=7$), consistent with geographic and institutional proximity to Credit Suisse amplifying the contagion channel. Additional empirical outputs, including stepwise-regression diagnostics and placebo/event-time checks, support the interpretation that the estimated relationship contains an economically meaningful signal while remaining partly event-driven in short horizons. Overall, the evidence suggests that rolling CDS dependence regimes may serve as a useful leading indicator for identifying institutions most likely to face disproportionate repricing pressure during a localized systemic shock. These findings are drawn from a single crisis episode and 15 peer institutions; they should be read as preliminary evidence of a potentially useful mechanism rather than as the basis for an operational early-warning system, and replication across additional crises and institutional settings is required before broader generalization. Full article

This article examines wildfire smoke as a critical yet disproportionately represented subject within ecocritical photography, arguing that its diffuse, persistent, and atmospheric qualities challenge dominant modes of landscape representation and environmental imagery. While photographic and artistic depictions of wildfire have frequently privileged the spectacle of flame—emphasizing destruction, immediacy, and sublime encounter—smoke operates through ambiguity, duration, and embodied experience. Drawing from my own photographic practice, archival research, and critical engagements with environmental history, this paper investigates how smoke disrupts aesthetic conventions rooted in clarity, distance, and visual mastery. Through a case study of the 2023 McDougall Creek wildfire in British Columbia’s Okanagan Valley, I consider how smoke functions simultaneously as material condition, photographic subject, and representational problem. The paper situates these questions within the broader context of the Pyrocene, examining how colonial fire suppression practices and contemporary climate change have shaped both wildfire regimes and their visual representation. By analyzing historical archives, contemporary photographic practices, and artistic responses to atmospheric crisis, I argue that smoke offers a critical alternative to spectacular images of burning landscapes. Rather than representing wildfire as a discrete event, attention to smoke reveals fire as an ongoing atmospheric condition—one that complicates the boundaries between landscape, image, and lived experience. In foregrounding smoke, this paper proposes new possibilities for environmentally engaged visual culture that move beyond spectacle toward more embodied and ethically attentive forms of representation. Full article

Public opinion event response requires not only timely decisions but also transparent and trustworthy collaboration among multiple stakeholders. To address delayed responses, fragmented collaboration, and information opacity, this study first proposes a collaborative-decision model based on blockchain for public opinion event response and then develops a blockchain-driven adaptive consensus method to improve consensus efficiency and decision quality. In the proposed model, public opinion information is mined to identify the attribute categories and weights of response alternatives, while collaborative-decision quality is evaluated by integrating decision reliability, opinion convergence, and individual comprehensive weights derived from social network influence. On this basis, smart contracts are designed to support transparent, traceable, and automated consensus processes. The adaptive consensus method dynamically terminates the consensus process by considering public opinion crisis levels and individual consensus differentiation. A utility-maximizing feedback mechanism is further introduced to improve consensus quality, and smart contracts are used to detect the adjustment willingness of inconsistent individuals and implement an elastic incentive mechanism. Case analysis and simulation experiments verify the effectiveness and robustness of the proposed model and method, showing their potential to support trustworthy collaborative decision-making in public opinion event response under uncertain and time-sensitive conditions. Full article

Extreme weather events increasingly threaten urban smart grids, where sudden power outages constitute not merely technical failures but socio-technical crises that test the very fabric of sustainable emergency governance. Drawing on framing theory, this study uses a 2 × 2 experiment with 1195 participants to examine how information content and timing affect public compliance. Results show action framing outperforms interpretive framing, with trust and safety mediating 46.9% and 34.7% of the effect. Timing moderates these pathways: action framing boosts compliance via trust and safety during crisis, while interpretive framing sustains compliance via safety and trust repair after restoration. This temporal asymmetry advances crisis communication theory and offers guidance for sustainable governance, turning crisis communication into a stage-matching process that bridges short-term recovery and long-term resilience. Full article

This study examines the determinants of conditional volatility in energy-sector equity returns and their implications for sustainable energy resilience, energy security, and investment stability. Using a multi-stage econometric framework, the analysis investigates how global financial, commodity, and macroeconomic shocks are transmitted to volatility dynamics across heterogeneous energy companies. The dataset includes domestic and international firms, enabling a comparative assessment of volatility behavior and risk-transmission mechanisms under different market and institutional conditions. The empirical framework combines ARMA models for return dynamics, EGARCH/GARCH specifications for conditional volatility estimation, and OLS regressions with HAC standard errors to identify key determinants of volatility, including market indices, commodity prices, exchange rates, and major geopolitical and economic events. The findings reveal strong volatility persistence across all assets and asymmetric responses to market shocks in most cases. Global market conditions, particularly lagged MSCI World returns, significantly affect volatility, whereas commodity effects related to oil, gas, and coal remain heterogeneous across firms. Event-based regressors show that systemic shocks, including the COVID-19 pandemic and the European energy crisis, increase volatility, although geopolitical effects depend on firm-specific exposure. The results contribute to the sustainability literature by linking energy-sector financial volatility with market resilience, energy security, and stable investment conditions for the energy transition. Full article

European energy markets have experienced significant instability as a result of consecutive global systemic shocks, particularly the COVID-19 pandemic and the geopolitical conflict in Ukraine. This paper analyses the development of crude oil and natural gas prices between 2019 and 2024 and discusses their implications for sustainable logistics, energy security and supply chain resilience in Europe. The study is based on secondary data from internationally recognized sources, including the International Energy Agency, OPEC, Eurostat, the European Council, the World Bank and the U.S. Energy Information Administration. An event-based comparative approach supported by descriptive price-change calculations was applied to distinguish between the pandemic-related demand shock and the geopolitical supply-side shock after 2022. The results show that crude oil prices declined from approximately 64 USD/barrel in 2019 to 41 USD/barrel in 2020, representing a decrease of about 3f5.9%, mainly in connection with reduced mobility, lower transport activity and industrial slowdown during the COVID-19 pandemic. In contrast, crude oil prices increased to approximately 100 USD/barrel in 2022, representing an increase of about 143.9% compared to 2020, coinciding with geopolitical uncertainty and supply-side pressures. The European natural gas market appeared particularly vulnerable to the 2022 crisis because of supplier dependence, pipeline infrastructure constraints and reduced Russian gas flows. EU natural gas demand declined by 55 billion m³, or 13%, in 2022, indicating the effect of high prices, energy savings and crisis adaptation. The findings suggest that crude oil shocks are mainly related to transport costs and freight rates, while natural gas shocks may influence energy-intensive production, warehousing, cold chains and broader supply chain stability. The study highlights the need for energy diversification, renewable and low-carbon energy development, energy efficiency and more resilient logistics strategies. Full article

Banking crises are a persistent threat to macroeconomic stability in emerging markets, where conventional econometric monitoring frameworks often fail to capture non-linear macro-financial relationships. This paper examines whether machine learning algorithms can improve the detection of banking crisis risk in Nigeria compared to standard logistic regression. We compare the performance of Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) against logistic regression using annual data from the African Financial Crises dataset (1954–2014). Resampling is only implemented on the training set to overcome the infrequency of crisis events. Performance on models is assessed based on accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) in a rigorous out-of-time validation setting. Our findings indicate that tree-based ensemble models outperform logistic regression on the test set: XGBoost achieves the best generalization performance (AUC = 1.0; F1 = 0.95 in non-crisis, 0.80 in crisis), whereas Random Forest has the highest cross-validated F1-score on the training set. The most important variables are exchange rate volatility, inflation, and indicators of systemic crisis. The most significant crisis indicators are, however, seen in crisis years, which means that the annual data do not provide much lead-time to detect the crisis. These results should be taken with caution because of the small sample size and the limited number of crisis observations during the test period. Altogether, machine learning models have potential as additional tools to monitor banking crises in Nigeria, though at the moment they are not fully operational as policy instruments. Full article

Quantifying the direction, strength and temporal clustering of cross-border financial risk contagion calls for methods that go beyond linear correlation. We suggest a two-layer framework that brings together transfer entropy and a multivariate Hawkes self-exciting point process on a time-varying, directed, weighted network. In the first layer, one-to-one transfer entropies of sovereign credit default swap spreads are estimated with a bias-corrected k nearest neighbour estimator, and this step detects nonlinear and directional information transfer between spreads. The second layer is a multivariate Hawkes process that models how extreme loss events arrive and mutually excite one another across countries, and it gives an excitation intensity matrix, encoding the way a tail event in one country raises the likelihood of an instantaneous hazard occurring in another. By merging these two layers, we obtain a composite, directed, weighted adjacency matrix in which the weights of the edges reflect both information flow and event clustering. We introduce a network-level contagion intensity index and split it into direct, indirect and feedback terms using the graph Laplacian spectrum. Von Neumann graph entropy together with the spectral gap ratio serve as entropy-based measures of the complexity and fragility of the evolving network. We validate the choice of Shannon-type entropy through a Tsallis q-sensitivity analysis, and we verify the nonlinear dependence structure of the data using BDS tests and maximal Lyapunov exponent estimates. Three empirical findings emerge from analysing 20 sovereign CDS markets from January 2015 to December 2025: (i) directional risk spillover signals derived based on transfer entropy are more timely than those derived from variance decomposition; (ii) the Hawkes excitation component amplifies measured contagion intensity by 35 to 58 percent during the COVID-19 shock and the 2022 European energy crisis relative to a transfer-entropy-only baseline; (iii) von Neumann graph entropy reaches historically extreme values 7 to 12 trading days before the peak drawdown in a Global Sovereign Bond Index. These results hold across rolling window lengths, significance thresholds, alternative entropy functionals and alternative Hawkes kernels. Full article

Global development frameworks, including the New Urban Agenda (NUA) and the Sustainable Development Goals (SDGs), advocate for safe, inclusive, and accessible public open spaces (POSs). However, their implementation in transitional post-socialist urban contexts requires balancing urban safety, technological innovation, and democratic use of public space. This paper examines how these global agendas are localized in Novi Sad, Serbia, investigating the relationship between international policy objectives and urban governance during times of crisis. A triangulated mixed-methods approach was employed, combining a structured survey of 1200 residents on perceptions of safety and smart-city surveillance, a media review of POS-related violence (2021–2026), and direct field observations of mass gatherings, including COVID-19-era events and subsequent major citizen protests. The findings indicate important differences between official approaches to spatial governance and citizens’ lived experience. While technological and spatial management measures supported urban safety objectives, surveys and field observation findings suggest that perceived safety remained closely associated with visible presence, public trust, and community organization. The study highlights the challenges of balancing urban safety, civil liberties, and democratic access to POS in transitional urban contexts, concluding that achieving the objectives of the NUA requires governance frameworks that integrate technological innovation with participatory and inclusive urban planning. Full article

A maritime chokepoint shock can spread beyond the countries directly connected to the affected route. We develop a monthly resilience screening model for a hypothetical Strait of Hormuz closure that separates direct disruption, scarcity-driven trade squeeze, household access pressure and delayed production risk. Hormuz severity variants and related Red Sea, Black Sea and fertilizer node scenarios test the stability of the modeled mechanism ordering. A three-event historical panel provides a limited external consistency check, not full model validation. In the baseline Hormuz stress test, mean modeled import availability pressure ranges from 0.0023 to 0.0076 on the normalized scale during March–August 2026. Trade squeeze accounts for 92.1–94.5% of that pressure, compared with 5.5–7.9% for direct disruption. Of 161 countries entering model-Alert, 39 later enter model-Crisis or a higher internal state. Crossings concentrate where reallocation pressure coincides with weak household access and delayed production risk. Historical estimates are directionally consistent for some events, although the Suez window exhibits non-flat pre-event coefficients and is not interpreted causally. The state labels are internal thresholds, not humanitarian classifications. The model identifies which propagation layer is binding and how quickly the associated intervention window closes. Full article

This study examines the relatively underexplored phenomenon of hostile takeovers within the insurance industry, a sector that played a significant role in the events surrounding the 2008 global financial crisis. Using a sample of 754 insurance merger and acquisition (M&A) transactions completed between 2008 and 2021, we investigate the determinants of hostile takeover activity. The dataset comprises acquiring and target firms from 46 countries and special administrative regions (SARs), providing a broad international perspective on post-crisis insurance-sector M&A dynamics. The findings reveal that several transaction- and firm-specific factors significantly affect the likelihood of hostile takeovers. In particular, a target firm’s prior M&A experience, increases in target book value, higher bidder research and development expenditures, bid revisions, and acquisition premia are positively associated with takeover hostility. While descriptive analyses document notable variation across jurisdictions, the primary empirical evidence is derived from pooled regression models incorporating country fixed effects. The results contribute to the literature on insurance-sector consolidation by identifying industry-specific factors associated with hostile acquisition activity and enhancing understanding of how information asymmetries, strategic considerations, and governance mechanisms shape takeover outcomes. These findings offer valuable implications for corporate managers, investors, and policymakers operating within a globally interconnected and highly regulated insurance industry. Full article

Humanity faces not isolated problems but a PolyCrisis, which is a set of 26 tightly interwoven existential crises spanning ecological collapse, planetary overheating, chronic disease epidemics, institutional fragility and social breakdown. Each crisis amplifies the others through cascading feedback loops, and 16 of these crises possess independently the capacity to cause human extinction or civilizational collapse. We are not entering an emergency, but we are already in a state of emergency. Multiple planetary boundaries have been transgressed, and climate tipping points are being crossed now. Extinction rates match historical great mass extinction events, while our food systems, primarily responsible for over half these crises, simultaneously drive hunger, obesity and chronic diseases. We argue that this PolyCrisis is not the result of isolated failures, but is best understood as the predictable, systemic outcome of Planet A, the prevailing Operating System of our mainstream civilization, characterized by economics of unbounded extraction and hoarding, violence-based and profit-based food systems, short-term thinking, and unlimited growth imperatives on a finite planet. Planet B is our proposed PolySolution framework, a complete alternative Operating System grounded in empirical reality and proven solutions. It integrates animal-free food systems releasing up to 5 billion hectares for rewilding, regenerative economics measuring non-violence and biocapacity, circular economy minimizing waste, technological restraint with democratic governance, seven-generation thinking, and PolyCommunity coordination, collaboration and co-creation of the PolySolution. It calls for the immediate emergency implementation of two planetary-scale MegaSolutions: (a) “Together Around Food”, implementing universal, free access to gourmet whole-food, plant-based vegan meals worldwide, eliminating hunger and accelerating food and health systems transformation, and (b) Cool, halting planetary overheating through agricultural emissions elimination, massive rewilding for carbon sequestration, and comprehensive stabilization of the life-support systems of our planet. Full article