Search Results (6,558)

High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R² of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_SocR, 1.5 for CSR_S, and 5 for CSR_CV. SOE status, EPU, ownership concentration, firm size, and housing prices emerge as the most influential predictors. Notable shifts in factor importance occur during the COVID-19 pandemic period (2020–2022). This work contributes a scalable, interpretable machine learning architecture for high-dimensional financial prediction problems, with applications in risk assessment, portfolio optimization, and regulatory monitoring systems. Full article

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results. Full article

The work presents a comprehensive analysis and costing of sugar beet cultivation in 2020–2022, for individual farms of the Lublin region. About 120 farms were analyzed. Based on this analysis, the criteria for a model farm were determined and adopted for the calculation of sugar beet production costs. ARIMA process modeling was performed, based on which forecasts were determined for several selected parameters. Customs tariffs introduced by the USA have a drastic impact on the economy. The effects of the COVID19 pandemic may also have a significant impact on the current market situation. Forecasting in the current geopolitical situation is very difficult because of the lack of stationarity of parameters. The financial result obtained by growers is mainly influenced by indirect costs absorbing 61.31% of total costs in 2020. In 2021 and 2022, indirect costs were 61.16% and 59.61% of production income, respectively. Among this group of costs, the largest share is accounted for by the costs of sowing services, sugar beet harvesting, and soil liming amounting from 14.27% to 15.92%. During the analyzed period, sugar beet cultivation remained profitable, with a production profitability index of 1.31 in 2020 and 2021, and 1.10 in 2022. The unit cost of production increased every year. In 2020, it was 14.27% and in 2021, it increased to 15.19%. The unit cost of production in 2022 was the highest, at 23.41%. Sugar beet cultivation is one of the profitable activities in agricultural production, but it is characterized by high production costs, which increased during the years analyzed (2020 to 2022), topping out at 90.87% of total revenue. The information and data presented in this study will be used in the development of a farmer-oriented application and will support the creation of an expert system for sugar beet growers. Cost forecasting will enable farmers to plan their production more effectively. Full article

The COVID-19 pandemic precipitated unprecedented social isolation measures, profoundly disrupting daily life, educational routines, and mental health worldwide. University students, already susceptible to psychological distress, encountered intensified challenges under remote learning and prolonged confinement. This longitudinal study examined fluctuations in anxiety and mood among 102 Brazilian university students during the pandemic, distinguishing between those solely engaged in academic pursuits and those simultaneously balancing work and study. Data collected via the Brunel Mood Scale and State-Trait Anxiety Inventory in April and July 2021 revealed that students exclusively focused on studies exhibited significant increases in depressive symptoms, anger, confusion, and anxiety, alongside diminished vigor. Conversely, participants who combined work and study reported reduced tension, fatigue, confusion, and overall mood disturbance, coupled with heightened vigor across the same period. Notably, women demonstrated greater vulnerability to anxiety and mood fluctuations, with socioeconomic disparities particularly pronounced among females managing dual roles, who reported lower family income. These findings suggest that occupational engagement may serve as a protective factor against psychological distress during crises, underscoring the urgent need for tailored mental health interventions and institutional support to mitigate the enduring impacts of pandemic-related adversities on the student population. Full article

This study investigates the seasonal effectiveness of high-efficiency particulate air (HEPA) purifiers and window-opening behaviors in three London nurseries, using continuous indoor and outdoor PM_2.5 monitoring, window state and air purifier use, and occupant questionnaire data collected from March 2021 to February 2022. Of the approximately 40–50 nurseries contacted, only three agreed to participate. Results show that HEPA purifiers substantially reduced indoor particulate matter (PM_2.5), with the greatest effect observed during the heating season when windows remained closed for longer periods. Seasonal and behavioral analysis indicated more frequent and longer window opening in the non-heating season (windows were open 41.5% of the time on average, compared to 34.2% during the heating season) driven by both ventilation needs and heightened COVID-19 concerns. Predictive modeling identified indoor temperature as the main driver of window opening, while carbon dioxide (CO₂) had a limited effect. In addition, window opening often increased indoor PM_2.5 under prevailing outdoor air quality conditions, with mean concentrations rising from 2.73 µg/m³ (closed) to 3.45 µg/m³ (open), thus reducing the apparent benefit of air purifiers. These findings underscore the complex interplay between mechanical purification and occupant-controlled ventilation, highlighting the need to adapt indoor air quality (IAQ) strategies to both seasonal and behavioral factors in educational settings. Full article

Early-career teachers (ECTs) entered the profession during the COVID-19 pandemic, a period that introduced unique stressors to an already-demanding career phase. This systematic review examines empirical studies published between 2020 and February 2025 to explore how the pandemic influenced ECT wellbeing, with particular attention to stressors and protective factors impacting long-term retention and professional sustainability. Guided by PRISMA protocols, databases including Web of Science, ERIC, Google Scholar, and Scopus were searched, screening 470 records and identifying 30 studies that met inclusion criteria: peer-reviewed, empirical, focused on early-career teachers (within the first five years), and situated in or explicitly addressing the pandemic and its ongoing impacts. The results of Braun and Clarke’s thematic analysis (2006) revealed that pandemic-related challenges such as increased workload, professional isolation, disrupted induction processes, and emotional strain have persisted into the post-pandemic era, contributing to sustained risks of burnout and attrition. Regardless, protective factors identified during the pandemic—including high-quality mentoring, structured induction programmes, collegial support, professional autonomy, and effective individual coping strategies—continue to offer essential support, enhancing resilience and professional wellbeing. These findings underscore the necessity of institutionalising targeted supports to address the enduring effects of pandemic-related stressors on ECT wellbeing. By prioritising sustained mental health initiatives and structural supports, education systems can effectively mitigate long-term impacts and improve retention outcomes for early-career teachers in a post-pandemic educational landscape. Full article

Background/Objectives: Due to the significant overlap in symptoms between COVID-19 and other respiratory infections, a multiplex PCR-based platform was developed to simultaneously detect 22 respiratory pathogens. Target sequences were retrieved from the GenBank database and aligned using Clustal Omega 2.1 to identify conserved regions prioritized for primer design. Primers were designed using Primer Express^® 3.0.1 and evaluated in Primer Explorer to ensure specificity and minimize secondary structures. A multiplex strategy organized primers into three groups, each labeled with distinct fluorophores (FAM, VIC, or NED), allowing for detection by conventional PCR or capillary electrophoresis (CE). Methods: After reverse transcription for RNA targets, amplification was performed in a single-tube reaction. A total of 340 clinical samples—nasopharyngeal and saliva swabs—were collected from patients, during the COVID-19 pandemic period. The automated analysis of electropherograms enabled precise pathogen identification. Results: Of the samples analyzed, 57.1% tested negative for all pathogens. SARS-CoV-2 was the most frequently detected pathogen (29%), followed by enterovirus (6.5%). Positive results were detected in both nasopharyngeal and saliva swabs, with SARS-CoV-2 predominating in saliva samples. Conclusion: This single-tube multiplex PCR-CE assay represents a cost-effective and robust approach for comprehensive respiratory pathogen detection. It enables rapid and simultaneous diagnosis, facilitating targeted treatment strategies and improved patient outcomes. Full article

Background/Objective: The COVID-19 pandemic profoundly transformed social life worldwide, indiscriminately affecting individuals across all age groups. Children have not been exempted from the risk of severe illness and death caused by COVID-19. Objective: This paper sought to describe the clinical findings, laboratory and imaging results, and hospital care provided for severe and critical cases of COVID-19 in unvaccinated children, with or without severe asthma, hospitalized in a public referral service for COVID-19 treatment in the Brazilian state of Pernambuco. Methods: This was a case series study of severe and critical COVID-19 in hospitalized, unvaccinated children, with or without severe asthma, conducted in a public referral hospital between March 2020 and June 2021. Results: The case series included 80 children, aged from 1 month to 11 years, with the highest frequency among those under 2 years old (58.8%) and a predominance of males (65%). Respiratory diseases, including severe asthma, were present in 73.8% of the cases. Pediatric multisystem inflammatory syndrome occurred in 15% of the children, some of whom presented with cardiac involvement. Oxygen therapy was required in 65% of the cases, mechanical ventilation in 15%, and 33.7% of the children required intensive care in a pediatric intensive care unit. Pulmonary infiltrates and ground-glass opacities were common findings on chest X-rays and CT scans; inflammatory markers were elevated, and the most commonly used medications were antibiotics, bronchodilators, and corticosteroids. Conclusions: This case series has identified key characteristics of children with severe and critical COVID-19 during a period when vaccines were not yet available in Brazil for the study age group. However, the persistence of low vaccination coverage, largely due to parental vaccine hesitancy, continues to leave children vulnerable to potentially severe illness from COVID-19. These findings may inform the development of public health emergency contingency plans, as well as clinical protocols and care pathways, which can guide decision-making in pediatric care and ensure appropriate clinical management, ultimately improving the quality of care provided. Full article

This study presents an extended approach to compartmental modeling of infectious disease spread, focusing on regional heterogeneity within affected areas. Using classical SIS, SIR, and SEIR frameworks, we simulate the dynamics of COVID-19 across two major regions of Ukraine—Dnipropetrovsk and Kharkiv—during the period 2020–2024. The proposed mathematical model incorporates regionally distributed subpopulations and applies a system of differential equations solved using the classical fourth-order Runge–Kutta method. The simulations are validated against real-world epidemiological data from national and international sources. The SEIR model demonstrated superior performance, achieving maximum relative errors of 4.81% and 5.60% in the Kharkiv and Dnipropetrovsk regions, respectively, outperforming the SIS and SIR models. Despite limited mobility and social contact data, the regionally adapted models achieved acceptable accuracy for medium-term forecasting. This validates the practical applicability of extended compartmental models in public health planning, particularly in settings with constrained data availability. The results further support the use of these models for estimating critical epidemiological indicators such as infection peaks and hospital resource demands. The proposed framework offers a scalable and computationally efficient tool for regional epidemic forecasting, with potential applications to future outbreaks in geographically heterogeneous environments. Full article

The COVID-19 pandemic triggered unprecedented economic disruptions, raising critical questions about the resilience and adaptability of macroeconomic productivity across countries. This study examines the impact of COVID-19 on macroeconomic efficiency and productivity dynamics in 37 OECD countries using quarterly data from 2018Q1 to 2024Q4. By employing a Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist Productivity Index (MPI), we decompose total factor productivity (TFP) into efficiency change (EC) and technological change (TC) across three periods: pre-pandemic, during-pandemic, and post-pandemic. Our framework incorporates both desirable (GDP) and undesirable outputs (inflation, unemployment, housing price inflation, and interest rate distortions), offering a multidimensional view of macroeconomic efficiency. Results show broad but uneven productivity gains, with technological progress proving more resilient than efficiency during the pandemic. Post-COVID recovery trajectories diverged, reflecting differences in structural adaptability and innovation capacity. Regression analysis reveals that stringent lockdowns in 2020 were associated with lower productivity in 2023–2024, while more adaptive policies in 2021 supported long-term technological gains. These findings highlight the importance of aligning crisis response with forward-looking economic strategies and demonstrate the value of DEA-based methods for evaluating macroeconomic performance beyond GDP. Full article

Background: The surge in COVID-19 cases during the pandemic created a disease burden. An epidemiological study on COVID-19 is required as not much is known about the impact of containment and mitigation on health. We aimed to compare the epidemiological features during the years of the COVID-19 pandemic in the Vidarbha region in Maharashtra, India, to understand the epidemiology changes throughout the pandemic’s progression. Method: All of the cases reported at our testing centers in Nagpur and its periphery during the three years of the pandemic (i.e., from February 2020 to December 2022) were included. Descriptive analyses of variables of interest and statistical measures were compared. Results: There were 537,320 tests recorded during the study period. Of these, 13,035 (13.29%), 42,909 (13.70%), and 19,936 (15.91%) tested positive in 2020, 2021, and 2022, respectively. Hospitalization decreased from 2020 to 2022. An age group shift from 45 to 16–30 years over the pandemic was noticed. Seasonally, positivity peaked in September (27.04%) in 2020, in April (43.4%) in 2021, and in January in 2022 (35.30%). The estimated case fatality ratio was highest in 2021 (36.68%) over the three years in the hospital setting. Conclusion: Understanding the changing epidemiology of SARS-CoV-2 strengthens our perceptive of this disease, which will aid in improving the healthcare system in terms of both controlling and preventing the spread of COVID-19. Full article

A multilayer linear response model (MLRM) is proposed to generate time-series data based on linear response theory. The proposed MLRM is designed to generate data for anomalous dynamics by extending the conventional single-layer linear response model (SLRM) into multiple layers. While the SLRM is a linear equation with respect to external forces, the MLRM introduces nonlinear interactions, enabling the generation of a wider range of dynamics. The MLRM is applicable to various fields, such as finance, as it does not rely on machine learning techniques and maintains interpretability. We investigated whether the MLRM could generate anomalous dynamics, such as those observed during the coronavirus disease 2019 (COVID-19) pandemic, using pre-pandemic data. Furthermore, an analysis of the log returns and realized volatility derived from the MLRM-generated data demonstrated that both exhibited heavy-tailed characteristics, consistent with empirical observations. These results indicate that the MLRM can effectively reproduce the extreme fluctuations and tail behavior seen during high-volatility periods. 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

Background and Objectives: Preterm birth and stillbirth are primary adverse pregnancy outcomes. Research during the COVID-19 pandemic revealed reductions in preterm birth in some countries, while stillbirth rates increased or remained unchanged. These findings suggest the presence of preventable risk factors associated with changes in physical activity and lower exposure to community-acquired infections due to lockdown measures, altered social interaction patterns or reduced access to antenatal care. Assessing seasonal variation may offer insights into whether lifestyle changes during the COVID-19 lockdown period influenced preterm birth rates. Materials and Methods: This retrospective cohort study used data from the electronic medical records of Bihor and Sibiu counties. Preterm deliveries (<37 weeks) and stillbirths during the COVID-19 pandemic (2020 and 2021) were compared with the corresponding pre-pandemic (2018 and 2019) and post-pandemic (2022 and 2023) period. Preterm birth rates during summer and winter in the pre-pandemic, pandemic, and post-pandemic years were analyzed. A comparison with rates during strict lockdown was made. Results: Out of 52,021 newborn infants, 4473 were born preterm. Preterm birth rates remained stable across all three periods (p = 0.13), and no significant seasonal pattern was identified (p = 0.65). In contrast, stillbirth rates increased notably during the strict lockdown period, with the median incidence almost doubling compared to other periods (0.87%, p = 0.05), while remaining unchanged during the rest of the pandemic (p = 0.52). Conclusions: Our study found that preterm birth rates remained unaffected by the pandemic and lockdown periods, while stillbirths increased significantly during the strict lockdown. These findings highlight the importance of maintaining access to timely antenatal care during public health emergencies to prevent adverse perinatal outcomes. Full article

With the development of information and communication technologies, analysing new risks of moral harassment at work is becoming increasingly pertinent, especially with the expansion of teleworking and hybrid working (a mix of remote and face-to-face work per week) in the wake of the COVID-19 pandemic. In an attempt to respond to the new issues of labour regulation, this study places special emphasis on new risks of moral harassment in hybrid work and teleworking contexts, considering both the international and European framework and the legal regime in Portugal, identifying its specificities. With the rise in teleworking in the post-pandemic period, the online monitoring of workers has accentuated the difficulty in drawing the line between managerial power and harassment. Moral harassment at work is a persistent challenge and organisations must recognise, prevent and respond to inappropriate behaviour in the organisation. The results of this study—based on the results of an online survey completed by employees (with employment contracts)—show that teleworking employees recognise that they have been pressured, above all, both to respond to messages quickly and pressure to work beyond hours and suggest possible gender differences in the way harassment in hybrid work and teleworking contexts is reported. Full article