Search Results (3,036)

Accurately predicting the reception area of the Automatic Identification System (AIS) is critical for ship tracking and anomaly detection, as errors in signal interpretation may lead to incorrect vessel localization and behavior analysis. However, traditional propagation models, whether they are deterministic, empirical, or semi-empirical, face limitations when applied to dynamic environments due to their reliance on detailed atmospheric and terrain inputs. Therefore, to address these challenges, we propose a data-driven approach based on graph neural networks (GNNs) to model AIS reception as a function of environmental and geographic variables. Specifically, inspired by attention mechanisms that power transformers in large language models, our framework employs the SAmple and aggreGatE (GraphSAGE) framework convolutions to aggregate neighborhood features, then combines layer outputs through Jumping Knowledge (JK) with Bidirectional Long Short-Term Memory (BiLSTM)-derived attention coefficients and integrates an attentional pooling module at the graph-level readout. Moreover, trained on real-world AIS data enriched with terrain and meteorological features, the model captures both local and long-range reception patterns. As a result, it outperforms classical baselines—including ITU-R P.2001 and XGBoost in F1-score and accuracy. Ultimately, this work illustrates the value of deep learning and AIS sensor networks for the detection of positioning anomalies in ship tracking and highlights the potential of data-driven approaches in modeling sensor reception. Full article

Once an online public opinion emerges, the interweaving of information distortion and public panic makes it difficult for enterprises to accurately grasp the emotional turning point and formulate sustainable marketing strategies. Based on the perspective of information asymmetry, in this paper, we construct a four-agent evolutionary game model involving the central government, local governments, enterprises and netizens. It analyzes the balance of strategies used by different actors in public opinion crises and examines how these strategies drive public panic from three perspectives: content, users and emotions. Finally, the findings are verified through simulation calculations. Our research reveals that when panic sentiment is in the medium range, the central government’s strengthened supervision coexists with enterprises’ deceptive marketing, and the impact of the event is magnified. When panic breaks through the threshold, local governments shift from full disclosure to partial disclosure, while consumers maintain their purchasing confidence and are less likely to be swayed by rumors. Research shows that after a public opinion crisis occurs, only by replacing deception with transparent and genuine content and jointly creating green solutions with consumers can enterprises transform panic into sustainable brand assets and provide a decision-making basis for the long-term development of the enterprise. Full article

Telemedicine has emerged as a promising tool in obstetric and reproductive healthcare, offering new possibilities for patient-centered care delivery. This literature review explores its impact across key areas, including abortion, assisted reproduction, childbirth, contraception, gestational diabetes, mental health, opioid and smoking cessation, and perinatal care during the COVID-19 pandemic. A structured narrative approach was applied, with studies identified through PubMed and Scopus databases for screening, with selection based on predefined inclusion and exclusion criteria, and synthesized narratively with attention to clinical outcomes, access, satisfaction, and barriers to implementation. Perspectives on the acceptance of telemedicine among healthcare providers, technological advancements enhancing reproductive outcomes, and telemedicine’s pivotal role in maintaining continuity of care during crises, such as the COVID-19 pandemic, are examined. The review also addresses challenges and barriers, including technological proficiency and patient acceptance, while emphasizing telemedicine’s potential to improve accessibility, patient satisfaction, and healthcare outcomes across diverse reproductive health services. Full article

Background: Online health information seeking emerged as a critical form of public health behavior during the COVID-19 pandemic, generating substantial research interest. However, empirical studies examining health information-seeking patterns among Korean populations and their behavioral outcomes during the pandemic remain limited. Grounded in the information–motivation–behavior skills model, this study investigates online health information-seeking behaviors, including information sources, search terms, and engagement patterns, while also exploring their association with actual health behaviors during the COVID-19 pandemic. Methods: A structured survey was developed based on 1014 adults aged 19 years or older using the 2021 Korean version of the Health Information National Trends Survey (K-HINTS) to obtain nationally representative data. We adopted a structural equation model and analyzed the data using SPSS 25.0 and the WordArt site. Results: Of the respondents, 74.2% sought health information online, with vaccine details being the most widely searched topic. Mobile phones were the most commonly used devices (75.8%), and 98% searched for health information online via mobile devices at least once a week. Information (β = 0.230, p < 0.001), motivation (β = 0.117, p < 0.01), and behavior skills (β = 0.117, p < 0.01) positively influenced consumers’ behavioral changes regarding health. Behavioral skills also mediated the influences that information seeking and motivation had on behavioral changes. Conclusions: This study examines four aspects of online health information seeking through nationally representative COVID-19 data in South Korea. Exploring the relationship between information-seeking and actual health behaviors provides crucial insights for predicting post-pandemic consumer behavior and developing effective public health communication strategies for future crises. Full article

Transportation systems are particularly vulnerable to disruptions such as pandemics, which create significant challenges for maintaining efficiency, safety, and service quality. This study focuses on rail passenger transport in the Slovak Republic and develops a simulation framework to evaluate system performance under crisis conditions. Weekly data from the national rail operator for the period 2019–2021 were combined with information on governmental restrictions, standardized into a five-level framework. A nonlinear autoregressive model with exogenous inputs (NARX), implemented and validated in MATLAB R2021b (MathWorks, Natick, MA, USA), was applied to simulate the impact of restrictive measures on passenger demand. The results revealed a strong relationship between the severity of measures and ridership levels, with the most significant effects observed in education, workplace access, movement limitations, and retail. For instance, during complete school closures, passenger volumes declined by up to 75% relative to the pre-pandemic baseline. Based on the simulation outcomes, recommendations were formulated for adapting railway operations, including dynamic adjustments of transport capacity (10–40%) according to restriction levels. The proposed modelling and simulation approach offers transport authorities a cost-effective tool for scenario testing, disruption management, and the design of resilient passenger rail systems capable of adapting to crises and uncertainties. Full article

Amid the increasing frequency and severity of major disasters, the rapid spread of online misinformation poses substantial risks to public safety, effective crisis management, and long-term societal sustainability. Current methods for managing disaster-related rumors rely on static, rule-based approaches that lack scalability, fail to capture nuanced misinformation, and are limited to reactive responses, hindering effective disaster management. To address this gap, this study proposes a novel framework that leverages large language models (LLMs) and event knowledge graphs (EKGs) to facilitate the sustainable agile identification and adaptive control of disaster-related online rumors. The framework follows a multi-stage process, which includes the collection and preprocessing of disaster-related online data, the application of Gaussian Mixture Wasserstein Autoencoders (GMWAEs) for sentiment and rumor analysis, and the development of EKGs to enrich the understanding and reasoning of disaster events. Additionally, an enhanced model for rumor identification and risk control is introduced, utilizing Graph Attention Networks (GATs) to extract node features for accurate rumor detection and prediction of rumor propagation paths. Extensive experimental validation confirms the efficacy of the proposed methodology in improving disaster response. This study contributes novel theoretical insights and presents practical, scalable solutions for rumor control and risk management during crises. Full article

The primary aim of this study is to develop an effective decision-support system for managing crises related to the release of hazardous airborne substances. Such incidents, which can arise from industrial accidents or intentional releases, necessitate the rapid identification of contaminant sources to enable timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection and employs the (1 + 1)-Evolution Strategy to solve the inverse problem of source localization. The initial section of this paper reviews existing methods for simulating atmospheric dispersion and reconstructing source locations. In the following sections, we describe the architecture of the proposed system, the modeling assumptions, and the experimental framework. A key feature of the method presented here is its reliance solely on concentration measurements obtained from a distributed network of sensors, eliminating the need for prior knowledge of the source location, release time, or emission strength. The system was validated through a two-stage process using synthetic data generated by a Gaussian dispersion model. Preliminary experiments were conducted to support model calibration and refinement, followed by formal tests to evaluate localization accuracy and robustness. Each test case was completed in under 20 min on a standard laptop, demonstrating the algorithm’s high computational efficiency. The results confirm that the proposed (1 + 1)-ES Sandpile model can effectively reconstruct source parameters, staying within the resolution limits of the sensor grid. The system’s speed, simplicity, and reliance exclusively on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications. Full article

Tourism is a key driver of Portugal’s economy, with the WTTC projecting it to contribute EUR 56.4 billion (21.1% of GDP) by 2033. However, the sector has proven highly vulnerable to external shocks, such as the 2008 financial crisis, Brexit, and the pandemic, which have disrupted demand patterns and exposed structural weaknesses. It is essential to understand these impacts at a regional level in order to design more resilient and sustainable tourism strategies. This study examines how major crises have shaped tourism in Portugal’s NUTS II regions, focusing particularly on overnight stays, and assesses the implications for sustainable development and regional policy. Quarterly data from the National Statistics Institute (INE) covering 2004/2024 are used. We apply ARIMA and SARIMA models to account for seasonality and autocorrelation, and evaluate the accuracy of our forecasts using Mean Absolute Percentage Error (MAPE) and Theil’s U statistics. Structural breaks are considered to capture the effects of crises. The findings show that crises have significantly altered tourism patterns, with a shift towards less crowded and more remote destinations. This reflects vulnerabilities and opportunities for sustainability-oriented tourism. The study offers policymakers actionable guidance by aligning its results with the United Nations Sustainable Development Goals (SDGs), particularly those related to economic resilience (SDG 8), innovation and infrastructure (SDG 9), and partnerships for sustainable governance (SDG 17). This work is original in combining long-term regional data with robust forecasting techniques to provide innovative insights for scientific research and practical policy planning. Full article

This study investigates the causal impact of board governance structures on firm profitability. We develop the Board Structure Influence (BSI) index, a composite metric that captures board independence, diversity, and role distribution—which we conceptualize as three structural pillars of Separation, Variety, and Disparity—to provide a comprehensive measure of governance effectiveness. Using a Difference-in-Differences (DiD) framework centered on the COVID-19 pandemic as an exogenous shock, we identify firms with strong governance and top BSI quartiles and compare their financial performance—measured by net profit margin—against firms with weaker board structures. Our results demonstrate that firms with higher BSI scores experience a statistically significant increase in profitability post-COVID-19. A Causal Forest analysis further reveals that this positive effect is heterogeneous, with the largest firms benefiting most significantly from strong board governance. Robustness checks—including placebo tests, parallel trends validation, and a SUTVA test—affirm the credibility of our findings. This research highlights the strategic importance of board structure for firm resilience during crises. It provides management insights for corporate leaders, investors, and policymakers aiming to align governance reform with financial profitability. Full article

Global Value Chains (GVCs) have driven the worldwide dispersion of manufacturing but remain highly vulnerable to macro-level shocks, including financial crises, geopolitical tensions, and the COVID-19 pandemic. These shocks expose manufacturing supply chains (MSCs) to systemic risks, but limited research has explored how GVC reconfiguration mediates their impact on manufacturing supply chain resilience (MSCR). To address this gap, this study conducts an enhanced bibliometric–systematic literature review (B-SLR) of 120 peer-reviewed articles. The findings reveal that macro-level shocks induce GVC reconfigurations along geographical, value, and governance dimensions, which in turn trigger MSCR through node- and link-level mechanisms. MSCR represents a manufacturer-centered capability that enables MSCs to preserve, realign, and enhance value amid shocks. Building on these insights, this research proposes a multi-tier strategy encompassing firm-level practices, inter-firm collaborations, and policy interventions. This study outlines three key contributions. First, at the theoretical level, it embeds MSCR within a GVC framework, clarifying how GVC reconfiguration mediates SCR under macro-level shocks. Second, at the methodological level, it ensures corpus completeness through snowballing and refines bibliometric mapping with multi-dimensional visualization. Third, at the managerial level, it provides actionable guidance for firms, industry alliances, and policymakers to align MSCR strategies with the dynamics of global production networks. Full article

Background: Numerous healthcare services have been affected by the COVID-19 pandemic worldwide. Specialized healthcare services were postponed or canceled, potentially compromising regular services for hemato-oncology patients. The current study aimed to analyze the impact of the COVID-19 pandemic on access to hemato-oncology services in Almaty, the largest city in Kazakhstan. Methods: We retrospectively analyzed the socio-demographic characteristics of patients admitted to two large tertiary centers rendering hemato-oncology services, the City Clinical Hospital 7 (H7) and the Kazakh Institute of Oncology and Radiology (KazIOR). All data were retrieved for the period spanning from 1 March 2019 to 28 February 2022. The retrieved variables included age, gender, type of residence, hospitalization rate, treatment outcomes (discharged/deceased), bed days, diagnoses according to International Classification of Diseases (ICD-10) (acute leukemia and hematopoietic depression, lymphoproliferative diseases, and myeloproliferative diseases), and referral sources (ambulance, another hospital, consultative and diagnostic assistance, primary healthcare, self-referral, and referrals from hematologists’ offices). Results: In the 2019–2022 period, 6763 hemato-oncology hospitalizations were registered: 3583 in H7 and 3180 in KazIOR. The mean age at hospitalization was 55.04 (SD = 16.07) for females and 51.2 (SD = 16.7) for males. The proportion of hospitalized urban and rural patients differed significantly: 6191 (92%) and 571 (8,4%), respectively (χ² = 13.8, p = 0.001). In the 2020–2021 period, fewer patients were discharged (n = 2047) compared to 2019–2020 (n = 2387) and 2021–2022 (n = 2081) (χ² = 20.09, p = 0.003). However, the proportion of deaths in the 2020–2021 period (3.5%) was higher than in the 2019–2020 (3.2%) and 2021–2022 periods (2.6%) (χ² = 20.09, p = 0.003). A total of 403 (19%) hospital admissions were carried out by ambulance (emergency cases) in the 2020–2021 period, 368 (14.8%) in 2019–2020, and 394 (18.3%) in 2021–2022 (χ² = 2231, p < 0.001). The number of patients transferred from other hospitals to H7 and KazIOR increased by 12.4% in the 2020–2021 period. Conclusions: Our findings indicate a negative impact of the COVID-19 pandemic on access to hemato-oncology services, leading to increased mortality. Further studies are warranted to explore factors underlying the trends in hospitalizations and mortality of hemato-oncology patients during healthcare crises. Full article

Sectoral vulnerability to distinct crisis types in small, open, and geopolitically exposed markets—such as Jordan—remains insufficiently quantified, constraining targeted policy design and portfolio allocation. This study’s primary purpose is to establish a transparent, comparable metric of sector-level market resilience that reveals how crisis typology reorders vulnerabilities and shapes recovery speed. Applying this framework, we assess Jordan’s equity market across three archetypal episodes—the Global Financial Crisis, the Arab Spring, and COVID-19—to clarify how shock channels reconfigure sectoral risk. Using daily Amman Stock Exchange sector indices (2001–2025), we estimate $GARCH\left(\mathrm{1,1}\right)$ models for each sector–crisis window and summarize volatility dynamics by persistence $(\alpha +\beta )$, interpreted as an inverse proxy for resilience; complementary diagnostics include maximum drawdown and days-to-recovery, with nonparametric (Kruskal–Wallis) and rank-based (Spearman, Friedman) tests to evaluate within-crisis differences and cross-crisis reordering. Results show pronounced heterogeneity in every crisis and shifting sectoral rankings: financials—especially banking—display the highest persistence during the GFC; tourism and transportation dominate during COVID-19; and tourism/electric-related industries are most persistent around the Arab Spring. Meanwhile, food & beverages, pharmaceuticals/medical, and education recurrently exhibit lower persistence. Higher persistence aligns with slower post-shock normalization. We conclude that resilience is sector-specific and contingent on crisis characteristics, implying targeted policy and portfolio responses; regulators should prioritize liquidity backstops, timely disclosure, and contingency planning for fragile sectors, while investors can mitigate crisis risk via dynamic sector allocation and volatility-aware risk management in emerging markets. Full article

Background/Objectives: As survival improves in congenital heart defects (CHD), psychosocial support—particularly during crises—has become increasingly important. We examined how concerns of CHD patients and their relatives evolved during the Coronavirus Disease 2019 (COVID-19) pandemic, focusing on the influence of role (patient vs. relative), gender, and CHD complexity. Methods: The German National Register for Congenital Heart Defects (NRCHD) conducted two nationwide online surveys in April 2020 (Survey 1) and April 2021 (Survey 2). Free-text responses were analyzed using Mayring’s summarizing content analysis. Categories were coded per respondent (present/absent) for exploratory comparisons by year, role, sex, and CHD complexity. Analyses were cross-sectional and descriptive (p-values unadjusted). Results: In survey 1, 15.9%, and in survey 2, 19.3% of respondents provided qualitative information. In 2020, dominant themes included general COVID-19 information (37.3%), lack of CHD-specific information (30.4%), worry (24.1%), fear (23.2%), isolation (21.4%), and uncertainty (21.2%). By 2021, concerns shifted toward vaccination (24.1%) and vaccination prioritization (23.4%), while information gaps (21.8%) and fear (21.0%) persisted. Significant year-to-year changes included decreases in general information needs, concern, isolation, and uncertainty, and increases in prioritization (all p < 0.01). Relatives consistently reported higher psychological burden than patients (p ≤ 0.01). Conclusions: Concerns moved from early fear/uncertainty to vaccination and prioritization one year later, with persistent information needs across subgroups. Clear CHD-specific communication, caregiver-inclusive psychosocial support, and crisis-resilient care pathways (including telemedicine) are essential for this vulnerable population. Full article

Tackling climate change in the public–private partnership (PPP) infrastructure sector requires radical transformation of projects to make them resilient against climate risks and free from excessive carbon emissions. Types of PPP infrastructure such as transport, power plants, hospitals, schools and residential buildings experience more than 30% of global climate change risks. Therefore, this study aims to examine the interrelationships between the climate risk management strategies in PPP infrastructure projects. The first step in conducting this research was to identify the strategies through a comprehensive literature review. The second step was data collection from 147 PPP stakeholders with a questionnaire. The third step was analysing the interrelationships between the strategies using a partial least square–structural equation model approach. The findings include green procurement, defined climate-resilient contract award criteria, the identification of climate-conscious projects and feasible contract management strategies. The results provide understanding of actionable measures to counter climate risks and they encourage PPP stakeholders to develop and promote climate-friendly strategies to mitigate climate crises in the PPP sector. The results also serve as foundational information for future studies to investigate climate change risk management strategies in PPP research. Full article

In increasingly turbulent environments, organizations must go beyond generic robustness and develop Requisite Resilience, the capacity to align internal variety with environmental variety to sustain core functions during crises. This study situates Requisite Resilience within organizational theory and strategic management, assessing how major theories of the firm contribute to its development. The analysis groups these perspectives into foundational/diagnostic theories, which clarify environmental, structural and institutional constraints and correspond to passive resilience frameworks, and enabling/capability-building theories, which emphasize managerial agency, resource orchestration and adaptive learning, corresponding to active resilience frameworks. Findings indicate that while foundational perspectives offer essential diagnostics, they are insufficient on their own to foster Requisite Resilience. A composite configuration provides the strongest fit: co-evolutionary views offer an integrative backbone, dynamic capabilities and organizational learning enhance sensing, seizing and acting, and resource dependence theory informs the design of permeable boundaries. Full article