Global Burden of Disease 2021 Health Financing Collaborator Network. Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

Utilizing waste compost to improve the atmospheric CO2 capturing in the rice-wheat cropping system and energy-cum‑carbon credit auditing for a circular economy

The study aimed to manage industrial wastes and create a module for using compost from waste for crops cultivation to conserve energy, reduce fertilizer use and Greenhouse gas (GHG) emissions, and improve the atmospheric CO2 capturing in agriculture for a green economy. In the main-plot, the experiment's results using NS3 found 50.1 and 41.8 % more grain yield and total carbon dioxide (CO2) sequestration in the wheat-rice cropping sequence, respectively, compared to the NS0. Moreover, the treatment CW + TV in the sub-plot observed 24.0 and 20.3 % higher grain yield and total CO2 sequestration than B + PS. Based on interaction, the NS3× CW + TV resulted in a maximum total CO2 sequestration and C credit of 47.5 Mg ha-1 and US$ 1899 ha-1, respectively. Further, it was 27.9 % lower in carbon footprints (CFs) than NS1 × B + PS. Regarding another parameter, the treatment NS3 observed a 42.4 % more total energy output in the main-plot than that of NS0. Further, in the sub-plot, the treatment CW + TV produced 21.3 % more total energy output than B + PS. Energy use efficiency (EUE) and net energy return in the interaction of NS3× CW + TV were 20.5 and 138.8 % greater than the NS0 × B + PS, respectively. In the main-plot, the treatment NS3 obtained a maximum of 585.0 MJ US$-1 and US$ 0.24 MJ-1 for energy intensity in economic terms (EIET) and eco-efficiency index in terms of energy (EEIe), respectively. While in the sub-plot, the CW + TV was observed at a maximum of 571.52 MJ US$-1 and US$ 0.23 MJ-1 EIET and EEIe, respectively. The correlation and regression study showed a perfect positive correlation between grain yield and total C output. Moreover, a high positive correlation (0.75 to 1) was found with all other energy parameters for grain energy use efficiency (GEUE). The variability in the wheat-rice cropping sequence's energy profitability (EPr) was 53.7 % for human energy profitability (HEP). Based on principal component analysis (PCA), the eigenvalues of the first two principal components (PCs) had been greater than two, explaining 78.4 and 13.7 % of the variability. The experiment hypothesis was to develop a reliable technology for safely using industrial waste compost, minimizing energy consumption and CO2 emissions by reducing chemical fertilizer input in agriculture soils.

E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review

E-learning is fast becoming an integral part of the teaching- learning process, particularly after the outbreak of Covid-19 pandemic. Educational institutions across the globe are striving to enhance their e-learning instructional mechanism in accordance with the aspirations of present-day students who are widely using numerous technological tools - computers, tablets, mobiles, and Internet for educational purposes. In the wake of the evident incorporation of e-learning into the educational process, research related to the application of Educational Data Mining (EDM) techniques for enhancing e-learning systems has gained significance in recent times. The various data mining techniques applied by researchers to study hidden trends or patterns in educational data can provide valuable insights for educational institutions in terms of making the learning process adaptive to student needs. The insights can help the institutions achieve their ultimate goal of improving student academic performance in technology-assisted learning systems of the modern world. This review paper aims to comprehend EDM's role in enhancing e-learning environments with reference to commonly-used techniques, along with student performance prediction, the impact of Covid-19 pandemic on e-learning and priority e-learning focus areas in the future.

Green and Sustainable Membranes: A review

Membranes are ubiquitous tools for modern water treatment technology that critically eliminate hazardous materials such as organic, inorganic, heavy metals, and biomedical pollutants. Nowadays, nano-membranes are of particular interest for myriad applications such as water treatment, desalination, ion exchange, ion concentration control, and several kinds of biomedical applications. However, this state-of-the-art technology suffers from some drawbacks, e.g., toxicity and fouling of contaminants, which makes the synthesis of green and sustainable membranes indeed safety-threatening. Typically, sustainability, non-toxicity, performance optimization, and commercialization are concerns centered on manufacturing green synthesized membranes. Thus, critical issues related to toxicity, biosafety, and mechanistic aspects of green-synthesized nano-membranes have to be systematically and comprehensively reviewed and discussed. Herein we evaluate various aspects of green nano-membranes in terms of their synthesis, characterization, recycling, and commercialization aspects. Nanomaterials intended for nano-membrane development are classified in view of their chemistry/synthesis, advantages, and limitations. Indeed, attaining prominent adsorption capacity and selectivity in green-synthesized nano-membranes requires multi-objective optimization of a number of materials and manufacturing parameters. In addition, the efficacy and removal performance of green nano-membranes are analyzed theoretically and experimentally to provide researchers and manufacturers with a comprehensive image of green nano-membrane efficiency under real environmental conditions.

Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining

The aim of this study was to compare impressions of COVID-19 vaccination and influenza vaccination in Japan by analyzing social media (Twitter®) using a text-mining method. We obtained 10,000 tweets using the keywords "corona vaccine" and "influenza vaccine" on 15 December 2022 and 19 February 2023. We then counted the number of times the words were used and listed frequency of these words by a text-mining method called KH Coder. We also investigated concepts in the data using groups of words that often appeared together or groups of documents that contained the same words using multi-dimensional scaling (MDS). "Death" in relation to corona vaccine and "severe disease" for influenza vaccine were frequently used on 15 December 2022. The number of times the word "death" was used decreased, "after effect" was newly recognized for corona vaccine, and "severe disease" was not used in relation to influenza vaccine. Through this comprehensive analysis of social media data, we observed distinct variations in public perceptions of corona vaccination and influenza vaccination in Japan. These findings provide valuable insights for public health authorities and policymakers to better understand public sentiment and tailor their communication strategies accordingly.

Secure transmission for IoT wireless energy-carrying communication systems

The wireless energy-carrying communication method for the Internet of Things (IoT) presents several difficulties for information security such as eavesdropping or data loss. To solve these issues, this paper presents a new secure transmission method for IoT wireless energy-carrying communication systems. In this method, first the secret message is turned into a word, delivered to the intended recipient and unlawful listener, respectively, and the received message is characterized as an entropy function. The message is iteratively solved using the block coordinate descent technique, and for each iteration, a digital baseband signal containing the receiver's secret message symbol and the matching beamforming vector is delivered. By concurrently optimizing the transmit beamforming vector, the noise covariance matrix, and the receiver power allocation factor based on a design that complies with the security rate and energy acquisition limitations for each receiver, the overall system transmit power is reduced. The Lagrangian method is used to solve the secure transmission problem of the communication system based on an iterative block coordinate descent algorithm, as well as to change the nonconvex problem into a convex problem and precisely derive the upper and lower bounds of the original transmission problem. In comparison to the conventional policy transmission scheme, the experimental results demonstrate that the DIPS (Digital Image Processing System) scheme can increase the STP (Signaling Transfer Point) by approximately 34.16 percent in the eavesdropper independent eavesdropping and joint eavesdropping scenarios. The usefulness of the secure transmission strategy for wireless energy-carrying communication systems is confirmed by this investigation.

Scientists' deficit perception of the public impedes their behavioral intentions to correct misinformation

This paper investigates the relationship between scientists' communication experience and attitudes towards misinformation and their intention to correct misinformation. Specifically, the study focuses on two correction strategies: source-based correction and relational approaches. Source-based approaches combatting misinformation prioritize sharing accurate information from trustworthy sources to encourage audiences to trust reliable information over false information. On the other hand, relational approaches give priority to developing relationships or promoting dialogue as a means of addressing misinformation. In this study, we surveyed 416 scientists from U.S. land-grant universities using a self-report questionnaire. We find that scientists' engagement in science communication activities is positively related to their intention to correct misinformation using both strategies. Moreover, the scientists' attitude towards misinformation mediates the relationship between engagement in communication activities and intention to correct misinformation. The study also finds that the deficit model perception-that is, the assumption that scientists only need to transmit scientific knowledge to an ignorant public in order to increase understanding and support for science-moderates the indirect effect of engagement in science communication activities on behavioral intention to correct misinformation using relational strategies through attitude towards misinformation. Thus, the deficit model perception is a barrier to engaging in relational strategies to correct misinformation. We suggest that addressing the deficit model perception and providing science communication training that promotes inclusive worldviews and relational approaches would increase scientists' behavioral intentions to address misinformation. The study concludes that scientists should recognize their dual positionality as scientists and members of their community and engage in respectful conversations with community members about science.

Can the development of renewable energy in China compensate for the damage caused by environmental pollution to residents' health?

China's rapid economic growth in recent decades has caused a growing problem of environmental pollution, which negatively impacts the physical and mental health of residents. In recent years, renewable energy has emerged as a promising solution to alleviate environmental pollution and improve residents' well-being. However, it is unknown whether renewable energy development can counterbalance the health impacts of environmental pollution. Therefore, we conducted a study using data from the China Family Panel Studies (CFPS) to examine the impact of environmental pollution and renewable energy on the health of 20,694 residents. Our analysis showed that renewable energy development can partially offset the negative health effects of environmental pollution. Specifically, we found that a 1% increase in environmental pollution is linked to an average decrease of 0.0911% in physical health (PHY) and 0.0566% in mental health (MEN), whereas each 1% rise in renewable energy corresponds to an average increase of 0.2585% in PHY and 0.1847% in MEN. These positive effects apply to male and female residents, urban and rural residents, young and middle-aged adults, and people with low, medium, and high levels of education. These findings are significant for decision-makers striving to improve Chinese residents' physical and mental health by considering the specific impact of renewable energy and comprehensive environmental pollution.

Moderating the role of social progress with greenhouse gases to determine the health vulnerability in developing countries

Human activities have compelled massive environmental degradation, which causes climate vulnerability and that has emerged as a significant health issue. The present study assesses the role of social progress with greenhouse gases to determine the health vulnerability in 77 developing countries from 2011 to 2020. The empirical results are estimated by using the panel ARDL econometric approach. The study found that greenhouse gas emission proposes a U-shaped relationship to determine health vulnerability. In this study, social progress is used as the moderator variable, which shifts the turning point of the U-shaped curve. For this purpose, the interaction term of social progress with greenhouse gases shifts the turning point to the left side of the U-shaped curve and further flattens it. Furthermore, this study explores that urbanization, export openness, and government education expenditure negatively impact health vulnerability while industrialization increases health vulnerability. The study recommends that government should pay special attention to declining greenhouse gases and rising social progress to improve health vulnerability.Graphical abstact.

Differences in Trends in Admissions and Outcomes among Patients from a Secondary Hospital in Madrid during the COVID-19 Pandemic: A Hospital-Based Epidemiological Analysis (2020-2022)

Spain had some of Europe's highest incidence and mortality rates for coronavirus disease 2019 (COVID-19). This study highlights the impact of the COVID-19 pandemic on daily health care in terms of incidence, critical patients, and mortality. We describe the characteristics and clinical outcomes of patients, comparing variables over the different waves. We performed a descriptive, retrospective study using the historical records of patients hospitalized with COVID-19. We describe demographic characteristics, admissions, and occupancy. Time series allowed us to visualize and analyze trends and patterns, and identify several waves during the 27-month period. A total of 3315 patients had been hospitalized with confirmed COVID-19. One-third of these patients were hospitalized during the first weeks of the pandemic. We observed that 4.6% of all hospitalizations had been admitted to the intensive care unit, and we identified a mortality rate of 9.4% among hospitalized patients. Arithmetic- and semi-logarithmic-scale charts showed how admissions and deaths rose sharply during the first weeks, increasing by 10 every few days. We described a single hospital's response and experiences during the pandemic. This research highlights certain demographic profiles in a population and emphasizes the importance of identifying waves when performing research on COVID-19. Our results can extend the analysis of the impact of COVID-19 and can be applied in other contexts, and can be considered when further analyzing the clinical, epidemiological, or demographic characteristics of populations with COVID-19. Our findings suggest that the pandemic should be analyzed not as a whole but rather in different waves.

Evaluation of the Global Health Security Index as a predictor of COVID-19 excess mortality standardised for under-reporting and age structure

BACKGROUND

Previous studies have observed that countries with the strongest levels of pandemic preparedness capacities experience the greatest levels of COVID-19 burden. However, these analyses have been limited by cross-country differentials in surveillance system quality and demographics. Here, we address limitations of previous comparisons by exploring country-level relationships between pandemic preparedness measures and comparative mortality ratios (CMRs), a form of indirect age standardisation, of excess COVID-19 mortality.

METHODS

We indirectly age standardised excess COVID-19 mortality, from the Institute for Health Metrics and Evaluation modelling database, by comparing observed total excess mortality to an expected age-specific COVID-19 mortality rate from a reference country to derive CMRs. We then linked CMRs with data on country-level measures of pandemic preparedness from the Global Health Security (GHS) Index. These data were used as input into multivariable linear regression analyses that included income as a covariate and adjusted for multiple comparisons. We conducted a sensitivity analysis using excess mortality estimates from WHO and The Economist.

RESULTS

The GHS Index was negatively associated with excess COVID-19 CMRs (table 2; β= -0.21, 95% CI= -0.35 to -0.08). Greater capacities related to prevention (β= -0.11, 95% CI= -0.22 to -0.00), detection (β= -0.09, 95% CI= -0.19 to -0.00), response (β = -0.19, 95% CI= -0.36 to -0.01), international commitments (β= -0.17, 95% CI= -0.33 to -0.01) and risk environments (β= -0.30, 95% CI= -0.46 to -0.15) were each associated with lower CMRs. Results were not replicated using excess mortality models that rely more heavily on reported COVID-19 deaths (eg, WHO and The Economist).

CONCLUSION

The first direct comparison of COVID-19 excess mortality rates across countries accounting for under-reporting and age structure confirms that greater levels of preparedness were associated with lower excess COVID-19 mortality. Additional research is needed to confirm these relationships as more robust national-level data on COVID-19 impact become available.

An Integrated Mediating and Moderating Model to Improve Service Quality through Job Involvement, Job Satisfaction, and Organizational Commitment

Employees’ perception of their job and organization is believed to influence service quality (SQ). Hence, this study aims to integrate a mediating and moderating model to improve SQ through job involvement (JI), job satisfaction (JS), and organizational commitment (OC), and investigate how that model incorporating JI, JS, and OC can improve SQ in higher education contexts in developing countries. This study applied a conceptual integration between employees who provide the service and customer-perceived service quality. Data collection from the respondents was performed using a two-sample research design and two sets of questionnaires. The academic staff (296 responses) and their students (1480 responses) formed the study’s sample size. Structural Equation Modelling (SEM) was used to analyze the collected data. The data analysis showed a significant impact of JS on OC and SQ (i.e., the quality of lectures they received in the classroom that shaped their learning experiences). OC significantly affected SQ and partially mediated the effect of JS and SQ. JI plays a moderator role in the JS-OC and SQ relationship. This study contributes to the literature by linking employees’ JI, JS, and OC to SQ. Employee JI, JS, and OC are crucial in promoting service quality. Practical implications for students, employees, institutions, and society were provided.

Postpartum post-traumatic stress symptoms during the COVID-19 period: exposure and fear as mediating factors

Background: Post-traumatic stress symptoms (PTSS) following childbirth are common within a stressful environment and are mitigated by social support. During the COVID-19 pandemic, an increase in such symptoms has been reported. The current study aims to longitudinally model the influence of general and pandemic-specific risk and protective factors on the temporal unfolding of symptoms among postpartum women.Methods: Participants were 226 women following a liveborn, term birth during the first lockdown in Israel. Participants completed questionnaires 10 weeks (T1) and 6 months (T2) after delivery. PATH analyses included predictors of symptoms in T1: demographics, exposure to traumatic events, medical complications during delivery or pregnancy, exposure to COVID-19-related events and their subjective impact, fear of COVID-19, and social support. Predictors of symptoms in T2 were: T1 predictors, both as direct effects and mediated by T1 PTSS, as well as predictors measured again in T2.Results: Results showed the suggested model fit the data. The effect of COVID-19-related fear and subjective impact at T1 on symptoms at T2 were fully mediated by PTSS in T1, as were the effects of marriage and high social support at T1. COVID-19-related fear at T2 positively predicted symptoms at T2, while social support at T2 had the opposite effect. Medical complications during pregnancy negatively predicted symptoms in T2 only.Discussion: Persistent fear appears to be a risk factor and supports a consistent buffer in postpartum PTSS during the COVID-19 pandemic. Medical complications during pregnancy served as a protective factor, possibly due to habituation to medical settings.