Search Results (696)

Urban resilience has gained significant further attention since the COVID-19 pandemic, resulting in various assessments comparing cities’ ability to respond to, and recover from, diverse shocks. This paper responds to the call for grounding urban resilience in context by examining a case study of the city regions on the island of Hainan Province, China, following the onset of the COVID-19 outbreak. After content analysis to trace the lineage of urban resilience in the Chinese context, an exploratory study, including analysis and mapping of statistical data, was conducted to examine the city’s economic and social performance from 2018 to 2021 and beyond. Our study suggests a largely positive trend in the bouncing back and forward of city regions shortly after the pandemic began, as well as a rural–urban gap and growing regional disparities that need to be addressed to enhance resilience for all. This study provides a contextualized understanding of Hainan as it navigates pandemic stresses and builds capacities during state-supported structural transformations in its development as a free trade port. Furthermore, this study suggests a valuable city region analytical lens and a geographical perspective for implementing the urban resilience concept and building urban resilience efforts in China and elsewhere. Full article

The recent COVID-19 pandemic outbreak has demonstrated all the limitations of modern healthcare information systems in preventing and controlling pandemics, especially following an unexpected event. Existing approaches often fail to integrate real-time data and adaptive learning mechanisms, leading to inefficient response strategies and resource allocation challenges. To address this gap, in this paper, we propose PROTECTION, an innovative data-centric process-modeling-managing-and-mining framework for pandemic control and prevention that is based on the new paradigm that we name Knowledge-, Decision- and Data-Intensive (KDDI) processes. PROTECTION adopts Business Process Model and Notation (BPMN) as a standardized approach to model and manage complex healthcare workflows, enhancing interoperability and formal process representation. PROTECTION introduces a structured methodology that integrates Big Data Analytics, Process Mining and Adaptive Learning Mechanisms to dynamically update healthcare processes in response to evolving pandemic conditions. The framework enables real-time process optimization, predictive analytics for outbreak detection, and automated decision support for healthcare. Through case studies and experimental validation, we demonstrate how PROTECTION can effectively deal with the complex domain of pandemic control and prevention. Full article

This research provides valuable insights into the application of mathematical modeling to real-world scenarios, as exemplified by the COVID-19 pandemic. After data collection, the preparation stage included exploratory analysis, standardization and normalization, computation, and validation. A mathematical model initially developed for COVID-19 dynamics in Romania was subsequently applied to data from Italy and Switzerland during the same time interval. The model is structured as a multiple-input single-output (MISO) system, where the inputs underwent a neural network-based training stage to address inconsistencies in the acquired data. In parallel, an ARMAX model was employed to capture the stochastic nature of the epidemic process. Results demonstrate that the Romanian-based model generalized effectively across the three countries, achieving a strong predictive accuracy (forecast accuracy > 98.59%). Importantly, the model maintained robust performance despite significant cross-country differences in testing strategies, policy measures, timing of initial cases, and imported infections. This work contributes a novel perspective by showing that a unified data-driven modeling framework can be transferable across heterogeneous contexts. More broadly, it underscores the potential of integrating mathematical modeling with predictive analytics to support evidence-based decision-making and strengthen preparedness for future global health crises. Full article

After the COVID-19 pandemic, online and blended learning (BL) have been very popular worldwide. They have become as important as face-to-face (F2F) learning. Previous meta-analyses examined the effects of BL and online learning (OL) compared to F2F learning. However, there is no meta-analytic evidence on the effects of BL vs. OL. Which is more effective: BL or OL? So, this study compares the impact of BL and OL based on 37 empirical articles (2000–2024) via meta-analysis. The results suggest that BL has a positive upper-medium effect on student learning outcomes (SMD = 0.611, p < 0.001), especially on cognitive outcomes (SMD = 0.698, p < 0.001) and affective outcomes (SMD = 0.533, p < 0.001). Moreover, moderator analysis finds that BL’s effects are better than OL (1) for a class size of 0~50 students (2) for K-12 and university students (3) within 3 months of intervention (4) on non-STEM subjects (5) with different teachers (6) with 30%~69% proportion of OL (7) using mixed interaction (8) with mixed and group learning (9) on Asian students. Moreover, the results provide valuable suggestions for educators and researchers to improve BL’s practices. Full article

Air pollution represents a critical environmental challenge in stressed riverine cities, particularly in regions experiencing rapid urbanization and inadequate emission management infrastructure. This study investigates the spatio-temporal dynamics of atmospheric pollution in Baghdad, Iraq, during 2012–2023, analyzing seven key pollutants (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD) using NASA’s Giovanni platform coupled with Google Earth Engine analytics. Monthly time-series data were processed through advanced statistical techniques, including Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling and correlation analysis with meteorological parameters, to identify temporal trends, seasonal variations, and driving mechanisms. The analysis revealed three distinct pollutant trajectory categories reflecting complex emission–atmosphere interactions. Carbon monoxide exhibited dramatic decline (60–70% reduction from 2021), attributed to COVID-19 pandemic restrictions and demonstrating rapid responsiveness to activity modifications. Conversely, greenhouse gases showed persistent accumulation, with CO₂ increasing from 400.5 to 417.5 ppm and CH₄ rising 5.9% over the study period, indicating insufficient mitigation efforts. Sulfur compounds and ozone displayed stable concentrations with pronounced seasonal oscillations (winter peaks 2–3 times summer levels), while aerosol optical depth showed high temporal variability linked to dust storm events. Spatial analysis identified pronounced urban–rural concentration gradients, with central Baghdad CO levels exceeding 0.40 ppm compared to peripheral regions below 0.20 ppm. Linear concentration patterns along transportation corridors and industrial zones confirmed anthropogenic source dominance. Correlation analysis revealed strong relationships between meteorological factors and pollutant concentrations (atmospheric pressure: r = 0.62–0.70 with NO₂), providing insights for integrated climate–air quality management strategies. The study demonstrates substantial contributions to Sustainable Development Goals across four dimensions (Environmental Health 30%, Sustainable Cities and Climate Action 25%, Economic Development 25%, and Institutional Development 20%) while providing transferable methodological frameworks for evidence-based policy interventions and environmental monitoring in similar stressed urban environments globally. Full article

Accelerated digital transformations and the evolution of consumer behavior in recent years underscore the need for a systemic perspective in marketing analytics to better comprehend the complex interplay between technology, data, and the profound changes triggered by global events, such as the COVID-19 pandemic. The COVID-19 pandemic has catalyzed a massive shift toward digitalization and transformed e-commerce from an option to a necessity for both businesses and consumers. This paper analyzes the total store and non-store sales, as well as total e-commerce sales, of the US retail trade across six main business categories and nine subcategories from the first quarter of 2018 to the first quarter of 2024. The data was divided into three time spans, corresponding to pre-, during, and post-COVID-19 pandemic periods, to examine the changing behavior of US consumers over time for different business categories. The statistical and econometric methods employed are the partial autocorrelation function (PACF), autocorrelation function, autoregressive integrated moving average model, inferential statistics, and regression model. The results indicate that the pandemic significantly increased non-store retailer sales compared to the pre-pandemic period, underscoring the importance of e-commerce. When physical stores reopened, e-commerce sales did not decline to pre-pandemic levels. The PACF analysis showed seasonality and lagged correlations. Thus, the pandemic-induced buying behaviors of US consumers continue to influence current sales patterns. The pandemic was more than just a temporary disruption, which permanently changed the retail sector. Retailers that quickly adapted to online models gained a competitive edge, whereas US consumers became accustomed to the convenience and flexibility of e-commerce. The behavior of US consumers adapted not only in response to immediate needs during the pandemic but also led to longer-term shifts in spending patterns, with each category reacting uniquely based on product type and perceived necessity. The analysis of how the COVID-19 pandemic transformed consumer behavior in the US reveals several important implications for both consumers and trade policymakers. First, the long-lasting and structural shift toward e-commerce is confirmed, representing a fundamental change in the dynamics of demand and supply. For consumers, the convenience, flexibility, and accessibility of digital channels have moved beyond mere situational advantages to become a behavioral norm. This shift has empowered consumers by giving them greater access to price comparisons, more diverse options, and increased informational transparency. Additionally, the data shows the emergence of hybrid consumption models: essential goods are mainly purchased online, while purchases of branded clothing, electronics, furniture, luxury items, and similar products continue to favor the traditional retail experience. Full article

The COVID-19 pandemic created unprecedented conditions for examining how vaccination willingness evolves during prolonged health crises. This longitudinal mixed-methods study examines temporal dynamics in COVID-19 vaccination willingness across three phases of Germany’s vaccination campaign (N = 1063 survey respondents; n = 40 interview participants). Using mixed-effects models and thematic analysis, we tested whether institutional trust and personal risk perception predict vaccination willingness and how their relative importance changes over time. Results reveal that trust in scientific institutions emerges as the strongest predictor, outperforming political trust and becoming more influential over time, while risk perceptions become less predictive with time. Qualitative analysis identified a multitude of different argumentative themes for and against COVID-19 vaccination (as well as conditional acceptance), with 30% of participants expressing both. The themes complement the quantitative analysis by demonstrating a shift from analytical, risk-focused decision-making to heuristic, trust-based processing as vaccination campaigns progress, with important implications for adaptive public health communication strategies. Full article

The COVID-19 pandemic brought significant challenges to university students in China, including the Hong Kong Special Administrative Region. To understand the stress and coping strategies of university students during the pandemic, we conducted focus groups with 56 Hong Kong university students from late December 2022 to mid-January 2023. Thematic analysis using a deductive data analytic approach based on the Transactional Theory of Stress and Coping was applied to form concepts on coping strategies. The findings revealed four major challenges faced by Hong Kong university students, which were the accumulation of negative emotions, health-related anxiety and frequent change in pandemic-related policies, conflict with family members, and challenges in online learning and academic and career development. When coping with these challenges, students used the following coping strategies: (1) seeking social support, emphasizing the positive and tension reduction to manage their negative emotions; (2) problem-focused coping and emphasizing the positive to deal with health-related anxiety and stress arising from the frequent change in pandemic-related policies; (3) seeking social support, tension reduction, distancing/detachment and self-isolation/keeping to themselves to handle conflict with family members; (4) seeking social support, problem-focused coping, emphasizing the positive and using distancing/detachment to cope with challenges in online learning and academic and career development. Moreover, comparing students with different backgrounds, the findings showed that more students with a high level of self-perceived resilience employed the emphasizing the positive coping strategy, while more students with financial difficulties applied tension-reduction coping strategies. This study contributes to the stress and coping literature by illustrating Hong Kong young people’s stress and coping strategies during the COVID-19 pandemic. This study also supports the Transactional Theory of Stress and Coping and extends the discussion to various coping theories. Full article

Background: The perfusion of viscera, kidney, and spinal cord represents one of the main concerns during open repair (OR) of Thoraco-Abdominal Aortic Aneurisms (TAAAs). Passive shunting (PS) has been historically used for intraoperative distal aortic perfusion but has been progressively replaced almost entirely by partial left-sided heart or total cardiopulmonary bypass with extra-corporeal circulation (ECC). Despite several advantages of these methods, PS still has potential in mitigating some drawbacks of long extracorporeal circuits connected with centrifugal or roller pumps, such as the need for cardiac and great vessels cannulation, priming and large intravascular fluid volume shifts, high heparin dose, immunosuppressive effects, and systemic inflammatory response syndrome. Methods: This study prospectively analyzed data of a cohort of patients who underwent TAAA OR using a PS in a single institution. Outcomes of interest were mortality, rate of mesenteric, renal and spinal cord ischemia, cardiac complications, and intraoperative hemodynamic stability achieved in this setting. Our institutional bundle and a comprehensive literature review about the different configurations and applicability of PS for TAAA OR is also reported. The search was performed based on three databases (PubMed, EMBASE, and Cochrane Library) by two independent reviewers (LS and AA) from inception to 31 December 2023, and the reported clinical results (visceral, renal, and spinal cord complications and mortality) using PS during TAAAs OR were analyzed. Results: Between March 2021 and December 2023, 51 TAAA repairs were performed and eleven patients (n = 8, 73% male; mean age 67 years, range 63–79) were operated using a PS for a total of one (9%) type I, one (9%) type II, two (18%) type III, five (45%) type IV, and two (18%) type V TAAA. In our early experience, PS was indicated for limited staff resources during the COVID-19 pandemic to treat five non-deferable cases. The sixth and seventh patients were selected for PS as they already had a functioning axillo-bifemoral bypass that was used for this purpose. For the most recent cases, PS was chosen as the primary perfusion method according to a score based on clinical and anatomical factors with ECC as a bailout strategy. Selective renal perfusion with cold (4 °C) Custodiol solution was the method of choice for renal protection in all cases while antegrade perfusion of the coeliac trunk and the superior mesenteric artery was assured by PS through a loop graft (8–10mm) proximally anastomosed to the axillary artery (10 patients, 90.9%) or the descending thoracic aorta (one patient, 9%) and distally anastomosed to the infrarenal aorta (3), common iliac (3), or femoral vessels (5). In-hospital mortality was 9% as one patient died on the 10th postoperative day from mesenteric ischemia following hemodynamic instability; permanent spinal cord ischemia rate was 0% and the rate of AKI stage 3 was 9% (one patient). Bailout shifting to ECC was never required. No cardiac complications, nor a significant increase in serum CK-MB were reported in any patient. No prolonged severe intraoperative hypotension episodes (Mean Arterial Pressure < 50 mmHg) were assessed using the Software Acumen Analytics (Edwards LifeSciences, Irvine CA, USA). No peri-operative coagulopathy nor major bleeding was reported. Conclusions: Our experience showed satisfactory outcomes with the use of PS in specifically selected cases. Current data indicate that PS may represent an alternative to ECC techniques during TAAAs OR in high volume centers where assisted extracorporeal circulation could eventually be applied as a bailout strategy. However, due to the small sample size of this and previously published series, more data are needed to clearly define the potential role of such approach during TAAA OR. Full article

This study evaluates the eco-efficiency and eco-productivity of 141 countries using data-driven analytical frameworks over the period 2018–2023, covering the pre-COVID, COVID, and post-COVID phases. We employ an input-oriented Slack-Based Measure Data Envelopment Analysis (SBM-DEA) under variable returns to scale (VRS), combined with the Malmquist Productivity Index (MPI), to assess both static and dynamic performance. The analysis incorporates three inputs—labor force, gross fixed capital formation, and energy consumption—one desirable output (gross domestic product, GDP), and one undesirable output (CO₂ emissions). Eco-efficiency (the joint performance of energy and carbon efficiency) and eco-productivity (labor and capital efficiency) are evaluated to capture complementary dimensions of sustainable performance. The results reveal significant but temporary gains in eco-efficiency during the peak pandemic years (2020–2021), followed by widespread post-crisis reversals, particularly in labor productivity, energy efficiency, and CO₂ emission efficiency. These reversals were often linked to institutional and structural barriers, such as rigid labor markets and outdated infrastructure, which limited the translation of technological progress into operational efficiency. The MPI decomposition indicates that, while technological change improved in many countries, efficiency change declined, leading to overall stagnation or regression in eco-productivity for most economies. Regression analysis shows that targeted policy stringency in 2022 was positively associated with eco-productivity, whereas broader restrictions in 2020–2021 were less effective. We conclude with differentiated policy recommendations, emphasizing green technology transfer and institutional capacity building for lower-income countries, and the integration of carbon pricing and innovation incentives for high-income economies. Full article

The impacts of the COVID-19 pandemic and post-pandemic recovery in urban communities in the Arctic have been substantial, but their gendered aspects remain largely unknown. The goal of this study was to enhance the understanding of the gender-based impact on women in the urban areas of Alaska by exploring strengths and constraints to resilience in the social and economic domains of gender equality during and in the aftermath of the COVID-19 pandemic. Drawing on grounded theory methodology, this study is based on 29 in-depth, semi-structured interviews. The study methodology utilized a conceptual framework that integrated deficit-based and strength-based analytical perspectives. The paper implemented a voice-centered approach that drew on thematic interviews conducted with women in Anchorage and Nome. Alaska’s urban women demonstrated resilience rooted in self-empowerment and community caregiving. This was reflected in their critical re-evaluation of social and economic gendered structures, a reassessment of priorities in family and social relationships, and the mobilization of support networks. These acts of reflection and care transformed into processes of constructing new meanings of life during dramatic events and became a source of personal strength. The crisis also enabled a re-evaluation of entrenched gender dynamics and women’s ability to challenge gendered divisions in both the workplace and at home. Despite signs of resilience, the pandemic signified a setback for gender equality. It exacerbated pre-existing gender disparities within households, disrupted established pre-pandemic social support networks, increased unpaid domestic labor and a motherhood penalty, and deepened unemployment and income gaps. To further adapt to post-pandemic conditions, women need empowerment and greater representation in decision-making roles, which are critical to strengthening resilience in both the social and economic domains of gender equality. Full article

Background: Healthcare systems are increasingly strained by limited operating room resources and rising demand, a situation intensified by the COVID-19 pandemic. These pressures have resulted in overcrowded surgical departments, prolonged waiting times for elective procedures, worsened patient health outcomes, and increased hospital expenditure costs. Methods: To address these challenges, this study proposes a multi-objective mathematical optimization model as the analytical core of a decision support approach for OR resource allocation. The model considers multiple constrained resources, including OR time, intensive care units, medium care units, and nursing staff, and aims to minimize both elective patients’ waiting times and total incurred costs over a one-week planning horizon. Developed using real hospital data from a large facility in Thailand, the model was implemented in LINGO version 16.0, and a sensitivity analysis was conducted to assess the impact of surgical department priorities and overtime allowances. Results: Compared to current practices, the optimized OR schedule reduced average waiting times by approximately 7% and total costs by 5%, while balancing resource utilization. Conclusions: This study provides a data-driven tool to support hospital resource planning, improve OR efficiency, and respond effectively to future healthcare crises. Full article

Introduction: Respiratory infections cause serious complications responsible for a significant number of deaths in Brazil. In addition, the causes of death can be influenced by social and economic inequalities in Brazilian regions. Objective: To analyze the epidemiological profile and the influence of demographic and socioeconomic factors on deaths from respiratory infections in the adult population between 2014 and 2023 in Brazil. Methods: This was an analytical ecological study using data from the Death Information System. Death incidences were calculated. Multinomial logistic regressions and correlation tests were used to analyze the influence of socioeconomic factors on deaths. Results: There were high incidences of deaths from unspecified pneumonia, unconfirmed tuberculosis and complicated influenza. Deaths from pneumonia and the Gini index were positively correlated, considering the variables black ethnicity (R = 0.894), age over 90 (R = 0.869) and no schooling (R = 0.818) before the pandemic. The odds ratio of death from tuberculosis and influenza in the 70–79 age group (OR = 3.97) and black ethnicity (OR = 1.24), respectively, were higher in the pandemic and post-pandemic periods compared to the previous period. Conclusions: Deaths from respiratory infections were mainly influenced by demographic variables and socioeconomic inequalities in Brazil. Full article

Achieving sustainable development goals remains a core issue in global development. In response, China has proposed the development of new quality productive forces (NQPFs) through innovative thinking, emphasizing that fostering NQPFs is both an intrinsic requirement and a pivotal focus for advancing high-quality development. Concurrently, the intelligent transformation of the manufacturing sector serves as a critical direction for China’s economic restructuring and upgrading. This paper places “new quality productive forces” and “intelligent transformation of manufacturing” within the same analytical framework. Starting from the logical chain of “new quality productive forces—three major mechanisms—intelligent transformation of manufacturing,” it concretizes the value implications of new quality productive forces into a systematic conceptual framework driven by the synergistic interaction of three major mechanisms: the mechanism of revolutionary technological breakthroughs, the mechanism of innovative allocation of production factors, and the mechanism of deep industrial transformation and upgrading. This study constructs a “3322” evaluation index system for NQPFs, based on three formative processes, three driving forces, two supporting systems, and two-dimensional characteristics. Simultaneously, it builds an evaluation index system for the intelligent transformation of manufacturing, encompassing intelligent technology, intelligent applications, and intelligent benefits. Using national time-series data from 2012 to 2023, this study assesses the development levels of both NQPFs and the intelligent transformation of manufacturing during this period. The study further analyzes the impact of NQPFs on the intelligent transformation of the manufacturing sector. The research results indicate the following: (1) NQPFs drive the intelligent transformation of the manufacturing industry through the three mechanisms of innovative allocation of production factors, revolutionary breakthroughs in technology, and deep transformation and upgrading of industries. (2) The development of NQPFs exhibits a slow upward trend; however, the outbreak of the pandemic and Sino-US trade frictions have caused significant disruptions to the development of new-type productive forces. (3) The level of intelligent manufacturing continues to improve; however, from 2020 to 2023, due to the impact of the COVID-19 pandemic and Sino-US trade conflicts, the level of intelligent benefits has slightly declined. (4) NQPFs exert a powerful driving force on the intelligent transformation of manufacturing, exerting a significant positive impact on intelligent technology, intelligent applications, and intelligent efficiency levels. Full article

Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era. Full article