The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul’s corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data was disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty four patent applications, eighty seven trademark applications, twenty three design patents and thirty two utility patents related to Juul Labs and its associates is presented spanning the years 2004 thought 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market, this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.

The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of April 17, 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world’s centers of active coronavirus cases. In this study, we predicted the spread of COVID-19 and its burden on hospital care under different conditions of social distancing in Lombardy and Emilia-Romagna, the two regions of Italy most affected by the epidemic. To do this, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model, which encompasses compartments relevant to public health interventions such as quarantine. A new compartment L was added to the model for isolated infected population, i.e., individuals tested positives that do not need hospital care. We found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations; on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate. Our results suggest that territory-specific forecasts under different scenarios are crucial to enhance or take new containment measures during the epidemic.

Referring to the concept of inclusive growth, the authors analyse the transition economies of the Central and Eastern European countries, which are the current EU members (Bulgaria, Croatia, the Czech Republic, Estonia, Latvia, Lithuania, Hungary, Poland, Romania, Slovakia and Slovenia). That region was selected as the CEE countries characterized by comparable historic and economic background but now they seem to reach diversified stages of development. The objective of the study is to identify the level of inclusive growth among the CEE countries, taking into account indicators assigned to its seven pillars. The thesis is that the CEE countries represent socio and economic heterogeneity as well as different levels of sustainable development. The research methods involved the application of the principal components analysis and the multivariate analysis. For literature review, the bibliometric analysis was conducted with the visualization prepared by the VOSviewer software. The main findings suggest that Estonia, Slovenia and the Czech Republic seem to be the ones with the highest inclusive growth. On the other hand, Bulgaria and Romania represent the lowest level of inclusive growth indicators.

Commons and food are experiencing a revival in recent years and yet the links between both are almost absent in academic and political discourses. Commons are often portrayed as historical and yet innovative governing mechanisms that can challenge the State-Market hegemony. On the other side, food is both a relevant agent of change and a major driver of planetary destruction, being thus cause and solution to multiple crises that affect humankind. Departing from the commodification of food as one root cause of the broken global food system, this text firstly situates and discusses the different schools of thought (or epistemologies) that have addressed the private/public, commodity/commons nature of goods in general, and then explores how those schools have considered food in particular. To do so, the author has defined five epistemologies, four academic (economic, legal, historical and political) and one non-academic (grassroots activists). The analysis highlights how those epistemologies have yielded incommensurable understandings and conflicting vocabularies, hence creating confusion in the socio-political realm and even rejection around the idea of food being considered as a commons. The economic epistemic regard has reigned over the others by applying an approach to commons, public and private goods that is theoretical, reductionist and ontological instead of phenomenological, therefore preventing or obscuring other scholarly or practical understanding of commons. When applied to food, the iron law of economics dictated that food, a private good based on rivalry and excludability, shall be better allocated through market mechanisms with absolute proprietary rights and valued as a pure commodity. This reductionist view collides with the plurality of meanings of food in different societies, civilisations and historical periods, as other schools of thought indicate. The author uses diverse epistemic tools to re-construct food as a commons, based on its essentiality to human beings and societies and the customary and contemporary praxis to produce, consume and govern food collectively through non-market mechanisms for more than 2000 centuries. As commoning has instituting power to create different political and legal frameworks, if food is valued differently the entire architecture of the global food system would change, as the grassroots activist school claims. Re-commoning food defies the legal and political scaffoldings that sustain the hegemony of market and state decision-makers over eaters and food producers and informs sustainable forms of food production (agro-ecology), new collective practices of governance (food democracies) and alternative policies to regain control over the food system (food sovereignty). Food as a commons is an agent of change with transformative power, no matter what economists say.

The war to technology and economic power have been the driver for industrialization in most developed countries. The first industrial revolution (industry 1.0) earned millions for textile mill owners while the second industrial revolution (industry 2.0) opened the way for tycoons and captains of industry like John D. Rockefeller, J.P. Morgan and Henry Ford. The third industrial revolution (industry 3.0) engendered technology giants like Apple and Microsoft, and made magnates of men like Steve Jobs and Bill Gates. Now, the race for the fourth industrial revolution (industry 4.0) is on and there is no option, every country whether developed or developing must participate. Many countries have positively responded to industry 4.0 by developing strategic initiatives to strengthen industry 4.0 implementation. Unlocking the country’s potential to industry 4.0 has been of interest to researchers in the recent past. However, the extent to which industry 4.0 initiatives being launched globally has never been revealed. Therefore, the present study aimed at exploring industry 4.0 initiatives through comprehensive electronic survey of literature to estimate the extend of its launching in different regions. Inferences were drawn from industry 4.0 initiatives in developed nations to be used as the recommendations for East Africa Community. Results of the survey revealed that 117 industry 4.0 initiatives have been launched in 56 countries worldwide consisting of five regions. The country’s percent of industry 4.0 initiatives as per region were: Latin America and the Caribbean (15%), North America (40%), Europe (53%), Asia and Oceania (25%), Middle East and Africa (11%). While the worldwide percent was estimated as 25%. This revealed that the big gap is existing between countries towards the race for industry 4.0.

This review focuses on the emerging evidence for the association between estimated cardiorespiratory fitness (eCRF) and metabolic risk factors. Given the challenges associated with directly measuring cardiorespiratory fitness (CRF) in large populations, eCRF presents a practical alternative for predicting metabolic health risks. A literature search identified six relevant cohort studies from 2021 to 2024 that investigated the association of eCRF with hypertension, hyperglycemia, dyslipidemia, and obesity. The review findings highlight a consistent inverse relationship between higher eCRF and lower incidence of metabolic risks in line with CRF cohort studies. This review highlights the importance of low eCRF as a primordial indicator for metabolic risks and underscores the need for broader application. Future research directions should include exploring eCRF's predictive accuracy across diverse populations and health outcomes and testing its real-world applicability in healthcare and public health settings.

The traditional manual recording of vaccination records in Germany faced challenges during the COVID-19 pandemic, prompting the introduction of a Covid smartphone app with QR codes. However, this solution brought new challenges, emphasizing the need for a centrally managed European digital vaccination record for efficiency and validity. Thus, this study assesses the feasibility of using the HL7 FHIR standard in the healthcare industry for implementing a digital vaccination pass management and monitoring system. The system aims to offer convenience and improved efficiency for both patients and healthcare providers while promoting interoperability with other healthcare systems. We developed a prototype using modern technologies, such as React, Quarkus, and Keycloak, evaluating the applicability of FHIR in promoting interoperability within the healthcare industry. Results indicate potential benefits for patients and healthcare providers, offering secure access to immunization records, personalized recommendations, and streamlined management. However, challenges in integrating nuanced vaccination processes into the standardized FHIR system, necessitating custom extensions, and issues with manual data entry and identity provider integration, pose obstacles. Despite these challenges, the study suggests that implementing HL7 FHIR can enhance efficiency, data accessibility, and accuracy in the vaccination process, supporting broader digitization efforts in the German healthcare system and beyond.

The paper proposes an approach to determine the optimal number of medical specialists in the region. According to the author's theoretical model, in order to maximize public welfare, the marginal contribution of the last physician recruited to the growth of the public utility function should be equal to the marginal cost of attracting him and providing conditions for his work. To empirically assess the contribution of physicians to the number of lives saved, the CVD mortality rate is modeled using the instrumental variable method. On average, a 1% increase in the number of cardiologists is associated with a 0.6% decrease in cardiovascular mortality. At the level of provision with cardiologists in the amount of 1 per 100 thousand people, their marginal contribution to the number of lives saved is not less than 129 per 100 thousand people, with a further decrease by 5 people with the increase in the level of provision by 1 unit. The use of the obtained results will increase the validity of managerial decisions and in determining the optimal level of doctors when choosing between alternative possibilities of spending money on hiring doctors of different profiles or other expenses, especially in the case of limited resources.

Through a partnership with three Federally Qualified Health Centers (FQHCs), the local health department in Los Angeles County implemented a produce prescription program (PPR) to increase fresh fruit and vegetable purchases among patients with diabetes or prediabetes. The PPR, which began in 2020, provided eligible patients with a $40 voucher every month for 6 months to promote the purchase of fresh produce at large chain grocery stores. This qualitative assessment describes staff experiences with the PPR, documenting the facilitators and barriers they encountered while implementing the program. Fifteen clinic staff (i.e., PPR implementers) were interviewed for this assessment. Thematic analysis and coding were conducted using the ATLAS.ti software; the coding was carried out by two separate coders. Interviewees discussed the importance of having preexisting partnerships and programs to support the PPR at their clinic site. Hidden costs related to implementing the program included a large and unexpected amount of staff time devoted to enrolling patients into the program. Collecting quality data and having limited expertise to rigorously evaluate the program were other challenges. Because patients often share their food with their family, the monthly $40 incentive was generally not enough to support their needs; interviewees suggested giving a higher inventive amount to those with a larger household. Future PPR efforts and similar food incentive programs should consider these and other facilitators and barriers to implementation and sustainability, especially when making adjustments to these programs to improve services and access to food resources.

During a sanitary crisis, excess mortality measures the number of all-cause deaths, beyond what we would have expected if that crisis had not occurred. The high number of COVID-19 deaths started a debate in Italy with two opposite positions: those convinced that COVID-19 deaths were not, by default, excess deaths, because many COVID-19 deaths were not correctly registered, with the most part attributable to other causes and to the overall crisis conditions, and those who presented the opposite hypothesis. We analyzed the curve of the all-cause excess mortality, during the period January 5, 2020 – August 28, 2022, compared to the curve of the daily confirmed COVID-19 deaths, investigating the association between excess mortality and the recurrence of COVID-19 waves in Italy. We compared the two curves looking for the corresponding highest peaks and we found that 5 out of the 6 highest peaks (83.3%) of the excess mortality curve have occurred, on average, just a week before the concomitant COVID-19 waves hit their highest peaks of daily deaths (Mean 6.4 days; SD 2.4 days). This temporal correspondence between the moments when the excess mortality peaked and the highest peaks of the COVID-19 deaths provides further evidence that the all-cause excess mortality wave has been mostly driven by COVID-19 deaths

This study aims to explore the link between public investment and private investment in South Africa, using time series data spanning 40 years (1980–2020). Private investment is subdivided into credit to private sector (CPS) and foreign direct investment (FDI). Several econometric methodologies were used in the study, including the unit root test, cointegration test, and Error Correction Method (ECM). The Phillips-Perron (PP) test results point out that all the variables are stationary at levels with the exception of public investment (PI) which is stationary at first difference. The co-integration test reveals that the variables have a long-run equilibrium relationship. According to the findings of the ECM, public investment has a negative relationship with private investment (as measured by credit to private sector and foreign direct investment). The conclusion implies that in South Africa, public investment crowds out private investment. Other results revealed that, RGDP crowds in credit to private sector while crowding out foreign direct investment. Finally, the ECM findings show that government consumption expenditure crowds out credit to private sector and foreign direct investment. The residuals are homoskedastic and show no serial correlation, indicating that the model is adequate, according to the test for adequacy.

Results from a behavioral economic laboratory experiment are used to enhance our understanding of public health decisions made during the COVID-19 pandemic. The identification of systematic biases from optimal decision theory found in controlled experiments could help inform public policy design for future public health crises. The laboratory and the shelter-in-place decisions made during COVID-19 included elements of risk, uncertainty and ambiguity. The lab findings found individuals adopt different decision rules depending on both personal attributes and on the context and environment in which the decision task is conducted. Key observations to consider in the context of the COVID-19 decision environment include the importance of past experience, the ability to understand and calculate the odds of each action, the size and differences in economic payoffs given the choice, the value of information received, and how past statistical independent outcomes influence future decisions. The academic space encompassing both public health and behavioral economics is small, yet important, particularly in the current crisis. The objective of continued research in this area would be to develop a more representative model of decision-making processes, particularly during crisis, that would serve to enhance future public health policy design.

There is a lack of knowledge concerning the effects of ambient heat exposure on morbidity in Northern Europe. Therefore, this study aimed to evaluate the relationships of daily summer-time temperature and heatwaves with cardiorespiratory hospital admissions in the Helsinki metropolitan area, Finland. Methods: Time-series models adjusted for potential confounders such as air pollution were used to investigate the associations of daily temperature and heatwaves with cause-specific cardiorespiratory hospital admissions, during summer months of 2001-2017. Daily number of hospitalizations was obtained from the national hospital discharge register, weather information from the Finnish Meteorological Institute. Results: Increased daily temperature was associated with decreased risk of total respiratory hospital admissions and asthma. Heatwave days were associated with 20.5% (95% CI: 6.9, 35.9) increased risk of pneumonia admissions and during long or intense heatwaves also with total respiratory admissions in the oldest age group (≥ 75 years). There were also suggestive positive associations between heatwave days and admissions due to myocardial infarction and cerebrovascular diseases. In contrast, risk of arrhythmia admissions was decreased 20.8% (95% CI: 8.0, 31.8) during heatwaves. Conclusions: Heatwaves, rather than single hot days, are a health threat affecting the morbidity even in a Northern climate.

Public goods are produced at all levels of the biological hierarchy, from the secretion of diffusible molecules by cells to social interactions in humans. However, the cooperation needed to produce public goods is vulnerable to exploitation by free-riders — the Tragedy of the Commons. The dominant solutions to this problem of collective action are that some form of positive assortment (due to kinship or spatial structure) or enforcement (reward and punishment) is necessary for public-goods cooperation to evolve and be maintained. However, these solutions are only needed when individual contributions to the public good accrue linearly, and the assumption of linearity is never true in biology. We explain how cooperation for nonlinear public goods is maintained endogenously and does not require positive-assortment or enforcement mechanisms, and we review the considerable empirical evidence for the existence and maintenance of nonlinear public goods in microbiology, cancer biology, and behavioral ecology. We argue that it is time to move beyond discussions about assortment and enforcement in the study of cooperation in biology.

(1) Background: Pedestrian injuries (PIs) represent a significant proportion of road traffic injuries. We aimed at investigating the incidence and mortality of PIs in different age groups and Socio-demographic Index (SDI) categories between 1990 and 2017. (2) Method: Estimates of age-standardized incidence and mortality along with trends of PIs by SDI levels were obtained from the Global Burden of Disease from 1990 to 2017. We also calculated forecasts until 2040. (3) Results: Globally, PIs incidence increased by 3.31% (–9.94 to 16.56) in 2017 compared to 1990. Men have a higher incidence of PIs than women. Forecasted incidence was 132.02 (127.37 to 136.66) per 100,000 population in 2020, 101.52 (65.99 to 137.05) in 2030 and reduced further to 71.02 (10.62 to 152.65) by 2040. Globally across all SDI categories, there was a decreasing trend in mortality due to PIs with the global estimated percentage reduction of 37.12% (–45.19 to –29.04). (4) Conclusions: The results show that PIs are still a burden for all SDI categories despite some variation. Although incidence and mortality are expected to decrease globally, some SDI categories may require particular attention in addition to specific vulnerable age groups. Further studies addressing incidence and mortality patterns in vulnerable SDI categories are needed.

With the increasingly online exposure of urban public spaces, the new concept of "Internet-celebrity cities" has emerged in China. The online internet-famous sites are the main characteristics of "Internet-celebrity cities". To fully understand online users' preferences in such kind of public spaces, this article took 27 typical riverfront internet-famous sites (RIFS) in Changsha city (China), as an example. Through the online images and review comments on social media platform "Xiaohongshu", the study analyzed users’ perception of RIFS specially on visual and emotional preferences. It revealed the following findings:(1) The popularity of RIFS had a significant head effect and there were far more positive emotions than neutral and negative emotions in review comments. (2) According to 5 different categories of RIFS: modern buildings with river views, restaurants with strong visual impact became the main attractions on commercial RIFS. The art exhibition RIFS attracted visitors to experience traditional culture and highlight their aesthetic pursuits. In the historical and cultural RIFS, people preferred to see the historical buildings with surrounding natural environment, as well as river and mountains views. Ecological Recreational RIFS showed the similar results with the uncultivated RIFS, for the main focus was on the ecological environment, a broader vision and the specific atmosphere. Moreover, the uncultivated RIFS showed more diversified attraction points. This study can provide a new perspective on the waterfront vitality and can offer a targeted and attractive manner for waterfront regeneration that are different from traditional methods.

Abstract Big datasets are becoming increasingly common and can be challenging to understand and apply in public health. One method for lowering the dimensionality of these datasets and improving interpretability while minimizing information loss is data reduction using principal component analysis (PCA). It achieves this by successively maximizing variance through the creation of new, uncorrelated variables. PCA is an adaptive data analysis technique because it simplifies the process of finding new variables, or principal components, by solving an eigenvalue or eigenvector problem. These new variables are determined by the dataset being used, rather than by the analyst starting from scratch. It is also adaptable in another way because varieties of the method have been designed to adjust to various data structures and types. However, there are serious problems in the theoretical understanding and practical application of PCA in public health researchers, whereas its application is becoming popular in developing countries. Therefore, this article, which concentrated on using PCA to reduce data, began by outlining the fundamental concepts of PCA and going over what it can and cannot do, as well as when and how to use it. We also discussed the fundamental assumptions, benefits, and drawbacks of PCA. Furthermore, this article demonstrated and resolved PCA practical application problems in public health that most scholars are unaware of, such as variable preparation, variable inclusion and exclusion criteria for PCA, iteration steps, wealth index analysis, interpretation, and ranking.

The challenge of campus design, like other aspects of contemporary environmental design, reveals a serious problem in education and practice. The foundational design theories of a century ago have been exposed as an obsolete way of thinking about cities, human nature, biological nature, and even the nature of mathematical and physical structures. Yet in practice, these discredited models persist, obscured by new theoretical language and extravagant “neoplastic” forms, but embodying persistent though untested ideologies and driven by systems inertia. This paper considers the campus design typology (including business campuses, commercial districts, hospitals, and schools) as a design paradigm for pedestrian public space with implications for human flourishing and well-being. We propose a specific human-oriented design method to encourage the well-being of occupants and improve the outcomes for creative development, education, and health.

Hispanics are disproportionately affected by low rates of physical activity and high rates of chronic diseases. Hispanics generally and Mexican Americans specifically are underrepresented in research on physical activity and its impact on mental well-being. Some community-based interventions have been effective in increasing physical activity among Hispanics. This study examined data from a sample of low-income Hispanic participants in free community exercise classes to characterize the association between self-reported frequency of exercise class attendance, intensity of physical activity, and participant well-being. As part of two cross-sectional samples recruited from a stratified random sample of community exercise classes, 302 participants completed a questionnaire consisting of a modified version of the Godin-Shephard Leisure-Time Exercise Questionnaire (LTEQ) and the Mental Health Continuum Short Form (MHC-SF). Adjusted binary logistic regression analyses indicated that those who achieve moderate and strenuous self-reported physical activity have 130% higher odds (p = 0.0422) of positive mental well-being after adjustment for age, attendance, and self-reported health. This study provides evidence that physical activity intensity and time are associated with flourishing mental health among Hispanics. The study provides insight into the planning and development of community-based physical activity programming tailored to low-income populations.

Virtual assistants (VAs) have gained widespread popularity across a wide range of applications, and the integration of Large Language Models (LLMs) such as ChatGPT has opened up new possibilities for developing even more sophisticated VAs. However, this integration poses new ethical issues and challenges that must be carefully considered, particularly as these systems are increasingly used in public services: transfer of personal data, decision-making transparency, potential biases, and privacy risks. This paper, an extension of the work presented at IberSPEECH 2022, analyzes the current regulatory framework for AI-based VAs in Europe and delves into ethical issues in depth, examining potential benefits and drawbacks of integrating LLMs with VAs. Based on the analysis, this paper argues that the development and use of VAs powered by LLMs should be guided by a set of ethical principles that prioritize transparency, fairness, and harm prevention. The paper presents specific guidelines for the ethical use and development of this technology, including recommendations for data privacy, bias mitigation, and user control. By implementing these guidelines, the potential benefits of VAs powered by LLMs can be fully realized while minimizing the risks of harm and ensuring that ethical considerations are at the forefront of the development process.

Extraversion, a Big Five personality trait, has been identified as a significant factor in COVID-19 positive coping—yet, relevant research is conflicting. Studies have not situated the influence of extraversion within a geographical and historical context. Thus, a sound base is lacking for assessing when opposing results regarding extraversion and COVID-19 positive coping are likely to result. Furthermore, extraversion with respect to COVID-19 coping has not been considered concerning other-directed learning in contrast to self-directed learning. To establish a sufficiently sound base, an examination of the range of high-ranking Google Scholar results on extraversion and COVID-19 coping from different countries during the pandemic’s various waves—pre and post vaccine introduction—is undertaken. The same are then considered for insights into public opinion. Following, extraversion is compared with other-directed learning and differentiated from self-directed learning. An understanding is thus presented for assessing when extraversion will necessarily be an effective personality trait for COVID-19 positive coping and when it will not. Extraversion’s effect will be found inherently inconsistent for identifying COVID-19 positive coping as a result, because of its dependence on other-directed learning. The conclusion: COVID-19 positive coping stability is contingent on personal values that guide self-directed learning, rather than other-directed extraversion.

Background: Heat waves are correlated with increased mortality in the aged population. Social isolation is known as a vulnerability factor. This study aims at evaluating the correlation between an intervention to reduce social isolation and the increase in mortality in the population over 80 during heat waves. Methods: The study adopts a retrospective ecologic design. We compared the excess mortality rate (EMR) in the over 80 population during heat waves in urban areas of Rome (Italy), where a program to reduce social isolation was implemented compared to others where it was not implemented. We measured mortality of the summer periods from 2015 to 2019 compared with 2014 (a year without heat waves). Winter mortality, cadastral income and proportion of over 90 were included in the multivariate Poisson regression. Results: The EMR in the intervention and controls was 2.70% and 3.81%, respectively. Rate ratio 0.70 (c.i. 0.54 - 0.92, p-value 0.01). The Incidence Rate Ratio (IRR) of the interventions with respect to the controls is 0.76 (c.i. 0.59 - 0.98). After adjusting for other variables, the IRR was 0.44 (c.i. 0.32 - 0.60). Conclusions: Reducing social isolation could limit the impact of heat waves on the mortality of the elderly population.

This research ventures into the critical public health challenge of childhood obesity by exploring the dynamic interplay between psychological well-being and Body Mass Index (BMI) throughout various developmental stages of childhood. It delves into how emotional regulation, attachment dynamics, and social relationships correlate with obesity from early childhood to adolescence. Highlighting key findings, such as the negative correlation between psychological resilience and higher BMI in young children, the impact of social relationships on obesity risk during pre-adolescence, and the link between adaptive emotional strategies and higher BMI in adolescents, this study brings to the fore the nuanced relationship between psychological factors and obesity. Utilizing the interdisciplinary process of bioinformatics, this research synergizes psychometric and biomedical data to unearth psychological markers critical for crafting targeted, age-appropriate interventions. This study advocates for a holistic healthcare approach, emphasizing the integration of psychological support within obesity prevention and management strategies, thereby underscoring the indispensable role of psychological factors in the fight against childhood obesity. The application of bioinformatics methods to analyze complex datasets demonstrates how collaboration across medical specialties can enrich our understanding and response to childhood obesity, contributing significantly to the development of comprehensive, bioinformatics-enhanced healthcare solutions.

This study examined the effect of the media usage environment, particularly TV and mobile devices, on advertisement acceptance, lifestyle, and psychological satisfaction. Hypothesis pathways and comparative structural mediating effects were analyzed. Media and Consumer Research public data were used for empirical analysis. A total of 6,640 people who participated in sports activities were included in the study. The results demonstrated that TV and mobile device usage environments had similar effects on advertisement acceptance. While TV did not influence the pursuit of personal life, mobile device use environments affected the pursuit of personal life. Mobile device use environments had a greater effect on individual psychological satisfaction than TV. Adoption had a mediating effect on the influence of media usage environments on the pursuit of personal life. The pursuit of a personal life had a significant mediating effect on the influence of media use environments on psychological stability. This study revealed no differences in the effects of new and traditional media on advertisement acceptance, while new media had a greater influence on lifestyle and psychological satisfaction.

Bacteria’s ability to withstand the detrimental effects of antimicrobials may take the form of resistance or tolerance with the minimum inhibitory concentration (MIC), the mutant prevention concentration (MPC), and the mutant selection window as salient concepts. Isolates exposed for 30 days to different concentrations of selected antibiotics, biocides, and heavy metals, singly and in combination, were additionally exposed to very high (15 × MIC) concentrations of ampicillin., and their tolerance was calculated the time to kill 99.9% of the isolate population (MDK99.9). These values were increased by 30 to 50% in test isolates compared to the untreated control. BAC 10-exposed isolates had the highest tolerance increase, and the tolerance appeared to originate from two sources, i.e., stochastic persistence and genetic-induced persistence, involving multiple genes using diverse mechanisms. The mutant selection window of the isolates to ampicillin, amoxicillin, and oxytetracycline also slightly increased compared to the control, indicating the amplification of persister cells during the 30-day exposure. These findings indicate that bacterial exposure to sub-inhibitory concentrations of environmental chemical stressors may not always result in the development of antimicrobial resistance but could initiate this process by selecting for persisters that could evolve into resistant isolates.

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. As a result, to monitor the health of individuals affected by these conditions, they must maintain up-to-date health records using digital health informatics apps for surveillance. In this review, we provide an overview of the existing literature on identifying long COVID manifestations through hierarchical classification and the characterization of long COVID by different hierarchical groups based on the Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) in artificial intelligence (AI) to identify long COVID. Knowledge exploration, using the concept map for the clinical pathways of long COVID presented in this paper, provides an overview of the data needed to explore tackling the long-term effect of COVID-19 by integrating innovative cohesive frameworks and designing health informatics-based applications. To the best of our knowledge, this is the first paper to explore the potential incorporation of long COVID as a variable risk factor within a digital health informatics application.

The paper presents the final results from the research project “Urban Abacus of Building Energy performances (Abaco Urbano Energeticodegli Edifci – AUREE)” aimed at supporting the renovation and energy efficiency enhancement of urban building heritage financed by the “Ricerca Sistema Elettrico (MISE) program”. The crux of AUREE project is a Web – GIS GeoBlog portal with customized semantic dashboards aimed at sharing information on urban built environment and promoting the participation of local stakeholders to its improvement. As the latest development of this research, a workflow integrating the AUREE portal with BIM authoring and an open source IoT platform has been implemented and applied to an experimental case study concerning a public building in Carbonia (Italy). The headquarters of the sustainable energy research center Sotacarbo was selected as case study. The presented results proved that it was possible to create an open system, which could be easily consulted by "non-expert" users as well, to make available real-time monitoring data obtained from IoT sensors measuring the energy consumption, the indoor environmental conditions and the usage profiles of public buildings. The proposed open system could also be suitable to be later used as an effective tool to support the rising “energy communities”.

In recent years, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns, policy decision support, etc. because the information on alighting stops of users is missing. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. Various studies to overcome this limitation have been conducted in South Korea and other countries to develop es-timation methodologies of alighting stops. Even existing studies introduce an advanced method, we found the margin for better accuracy by combining various estimation methodologies for estimating alighting stops. This study reviewed previously conducted studies to classify data with missing alighting stop information into trip types and then applied an appropriate alighting stop estimation methodology for the characteristics of each trip type by stage. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checked the accuracy for each standard of allowable error for sensitivity analysis. Furthermore, the number of trips, accuracy, and valid tag rate were checked for each type to examine the need for classifying the trip types. Finally, our evaluation also examines the impact of classifying trip types on estimation accuracy. The evaluation criteria are accuracy of the number of trips and valid tag rate. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can es-timate users’ destinations more accurately than previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

An evaluation of emerging issues in One Health (OH) in Sub-Saharan Africa was undertaken to map the existing OH initiatives in Sub-Saharan African (SSA) countries. Desk review, expert opinions survey, limited interviews and wider consultations with selected OH stakeholders were conducted. The strengths, weaknesses, opportunities and threats to OH initiatives were identified. OH influence, interest and impacts were evaluated. One Health is transiting from multidisciplinary to transdisciplinary concepts and OH viewpoint should move from ‘proxy for zoonoses’, to include issues of climate change, nutrition and food safety, social sciences, geography, policy and planning, economics, welfare and well-being, antimicrobial resistance (AMR), vector-borne diseases, toxicosis and pesticides issues. While the identified major strengths should be boosted, the weaknesses should be addressed.OH Networks in SSA were spatially and temporally spread across SSA and stakeholders were classified as key, latent, marginal and OH defenders. Imbalance in stakeholders’ representation led to hesitation in buying-in from stakeholders who are outside the main networks. Theory of change, monitoring and evaluation frameworks, and tools to standardized evaluation of OH policies is needed for sustained future of OH and the future OH engagement should be outputs and outcomes-driven and not activity-driven.National roadmap for OH implementation and institutionalization is necessary and proofs of concepts in OH should be verified and scaled-up. Dependence on external funding is unsustainable and must be addressed. Necessary policy and legal instrument to support OH nationally and sub-nationally should be implemented taking cognizance of contemporary issues like urbanization, endemic poverty and other emerging issues. Utilizing current technologies and OH approach to address ongoing pandemic of COVID-19 and other emerging diseases is desirable. Finally, OH implementation should be anticipatory and not reactive to significantly benefit budgeting and contain disease outbreaks in animal sources before the risk of spillover to human can be envisaged.

Industrial fires at facilities including waste management sites, warehouses, factories, chemical works and fuel storage depots are relatively frequent occurrences. Often, these fires occur adjacent to urban communities and result in ground-level airborne pollutant concentrations that are well above guideline values. Land, water, livestock and crops may also be contaminated by the emissions and by firefighting activities. Moreover, impacted communities tend to have a higher proportion of minority ethnic populations, individuals with underlying health vulnerabilities, as well as those of lower socio-economic status. Nevertheless, this is an aspect of air quality that is under-researched, and so this review aims to highlight the public health hazards associated with industrial fires and the need for an effective, coordinated, public health response. We also review the range of monitoring techniques that have been utilised in such fires and highlight the role of dispersion modelling in predicting plume trajectories and in estimating population exposure. We recommend establishing 1-h guideline values for particulate matter to facilitate timely public health interventions and we highlight the need to review regulatory and technical controls for sites prone to fires, particularly in the waste sector.

The healthcare sector in Kuwait has been nurtured for many decades by the government where the Ministry of Health (MoH) controls up to 80 percent of the country’s health services. General and specialized public hospitals represent a significant proportion of health expenditures in Kuwait; amidst dwindling health resources, a declining global oil market in an oil-dependent welfare state, and a heavy reliance on a non-national clinical workforce. Although healthcare services in public sector hospitals are at highly subsidized rates, causing private sector involvement in healthcare to be considerably low, the growing demands for private delivery of care burgeoned participation of private hospitals in Kuwait, and improving hospital efficiency and productivity is more critical and timelier than ever. This paper aims to analyze public health system efficiency and hospital performance in the State of Kuwait, where we begin by evaluating the input-oriented technical efficiency (TE) of MoH hospitals in 2015-2019 and identifying potential areas for efficiency improvement by exploring influencing institutional and environmental factors. Over the five years between 2015 and 2019, TE in MoH hospitals has decreased by an average of 2.98% solely based on technical regress, where the six MoH general hospitals reported a pooled mean efficiency of 86.58%, and the nine sampled MoH specialized hospitals had a five-year pooled average of 65.47% efficiency. MoH policymakers should focus on improving allocative efficiency in the public health system, and healthcare policy reforms should focus on strengthening management structures in Kuwait’s public hospitals to improve production efficiency and financial sustainability.

Vaccines decrease morbidity and mortality. Nevertheless, their benefits depend on public response. During COVID-19, vaccine hesitancy and refusal were rampant, threatening public health. A thorough understand-ing of opponents' arguments is required to address the diffusion of unreliable information on social media and prevent vaccine hesitancy from developing into vaccine refusal. Accordingly, the article examines vac-cine opponents' justifications on social media. Textual content analysis of reader comments on three health-related Israeli Facebook pages was conducted. Data collection encompassed the Israeli COVID-19 vaccination period from October 2020 to May 2022. The comments were analyzed according to the health beliefs model (HBM). We found that vaccine opponents were characterized by low perceptions of the severity of the disease combined with high perceptions of the damages of the vaccine; low perceived benefits of vac-cine compliance; vaccine hesitancy and fear along with public distrust as barriers to change; and call for ac-tion to resist the vaccine and spread related anti-establishment views on the web. As vaccination hesitancy was found to develop into public distrust in the state systems and escalate into conspiracy beliefs and an-ti-vaccination activism, its early detection may prevent future vaccine resistance.

Burden of diseases measured as disability-adjusted life years per 100,000 people can be mined from public domain data, when they are made available by population health surveillance systems. This can be analysed to allow insightful comparisons with the national average, and to understand differences in trends between the sexes, age groups, time periods, geographic regions and sub-regions. In this illustrative case study, we have analysed the Scottish burden of disease database to understand what ailed the population of the Grampian region before the COVID-19 pandemic. We have identified selected cancers, ischaemic heart disease, Alzheimer’s disease and other dementias amongst the highest contributors to the burden; that drug use disorders and colorectal cancer are showing worsening trends and require health promotion and disease prevention measures from ages 15 and 25 respectively, especially in Aberdeen City; and that males are more vulnerable to atrial fibrillation and flutter, diabetes mellitus and oesophageal cancer, while females are to cerebrovascular disease. We demonstrate the usefulness of our analysis and methodology for the wider health system, allowing targeted medical research investments and coordinated response from public health and health service delivery. We also show the need for up-to-date surveillance data, forecasts and evidence on the impact of interventions to be made available widely.

The West Pomeranian Anti-Crisis Package was an effective tool of the Voivodeship Executive Board of West Pomeranian self-government, which was used to combat and counteract and counteract the social and economic effects of the COVID-19 pandemic. The Anti-Crisis Package was prepared with a view to undertaking systemic, but also point-based measures in order to save and protect regional health care, entrepreneurs, the labour market, but also to use the existing social capital of non-governmental organizations involved in measures reducing the effects of the COVID-19 pandemic. The West Pomeranian Anti-Crisis Package, as evidenced not only by the financial outlays incurred, but also by the outcome of implementing measures achieved in 2020 and their continuation in 2021.

Background: Online survey-based observational cross-sectional study aimed at elucidating experience and attitudes of unstructured population regarding diagnostic imaging. Methods: Invitations to participate were distributed using mixed-mode design to deidentified residents aged 18 years and older. Main outcome measures included morbidity structure and incidence of diagnostic imaging administrations. Results: Respondents (n = 1069) aged 44.3 ± 14.4 years; 32.8% suffered from cardiovascular diseases (CVD); 9.5% had chronic respiratory pathology; 28.9% considered themselves healthy. Respondents with COVID-19 history (49.7%) reported higher rates of computed tomography (CT) (p < .0001), magnetic resonance imaging (MRI) (p < .001), and ultrasound (p < .05). COVID-19 history in CVD respondents shifted imaging administrations towards CT and MRI (p < .05). Every tenth respondent received MRI, CT, and ultrasound on paid basis; 29.0% could not pay for diagnostic procedures; 13.1% reported unavailable MRI. Professional status significantly affected the pattern of diagnostic modalities (p < .05). MRI and CT availability differed between respondents in urban and rural areas (p < .0001). History of technogenic events predisposed responders to overestimate diagnostic value of fluorography (p < .05). Conclusions: Preparedness to future pandemics requires development of community-based outreach programs focusing on people's awareness regarding medical imaging safety and diagnostic value.

The aim of study was to investigate epidemiology aspects of magnetic resonance imaging (MRI) during COVID-19 pandemic. The study comprised depersonalized residents of Tomsk and Tomsk Region (n = 1714). Invitations to take online survey were sent to 50,000 residents by target SMS with response rate of 1.2% (n = 727, Cohort 1). Cohort 2 comprised retrospective patients (n = 987) who underwent contrast-enhanced cardiac MRI (CMR) in 2019-2022. Referrals, clinical characteristics, diagnosis, gender, age, past COVID-19, MRI study protocols, and MRI data were analyzed. 29% of respondents in cohort 1 received MRI examination within past two years; 26% of respondents considered MRI the most informative imaging modality for detecting COVID-19 pneumonia; 12% of respondents reported MRI unavailable. Proportion of CMR among MRI studies increased during COVID-19 pandemic, and maximum incidence of cardiac diseases detected by MRI was in 2021. Incidence of myocardial fibrosis increased from ~67% in 2019 to ~84% in 2022. The rate of outpatient MRI studies significantly increased in 2020, but returned to pre-pandemic level in 2021. COVID-19 pandemic increased the need for MRI and CMR. Patients with history of COVID-19 had persistent and newly occurring symptoms of myocardial damage suggesting chronic cardiac involvement requiring continuous follow-up.

Low-cost personal exposure monitors (PEMs) to measure personal exposure to air pollution are potentially promising tools for health research. However, their adoption requires robust validation. This study evaluated the performance of twenty-one Plume Lab Flow2 (PLF) by comparing its air pollutant measurements, particulate matter with a diameter of 2.5 μm or less (PM2.5), 10 μm or less (PM10), and nitrogen dioxide (NO2), against several high-quality air pollution monitors under field conditions (at indoor, outdoor, and roadside locations). Correlation and regression analysis were used to evaluate measurements obtained by different PLFs against reference instrumentation. For all measured pollutants, the overall correlation coefficient between the PLFs and the reference instruments was often weak (r<0.4). Moderate correlation was observed for one PLF unit at indoor location and two units at roadside location, when measuring PM2.5, but not for PM10 and NO2 concentration. During periods of particularly higher pollution, 11 PLF tools showed stronger regression results (R2 values > 0.5) with one-hour and 9 PLF units with one-minute time interval. Results show that the PLF cannot be used robustly to determine high and low exposure to poor air. Therefore, the use of PLFs in research studies should be approached with caution if data quality is important to the research outputs.

Role identity theory describes the purpose and meaning in life that comes, in part, from occupying social roles. While robustly linked to health and well-being, this may become, however, when an individual is unable to fulfil the perceived requirements of an especially salient role in the way that they believe they should. Amid high rates of mental illness among public safety personnel, we interviewed a purposely selected sample of 21 paramedics from a single service in Ontario, Canada to explore incongruence between an espoused and able-to-enact paramedic role identity. Situated in an interpretivist epistemology, and using successive rounds of thematic analysis, we developed a framework for role identity dissonance wherein chronic, identity-relevant disruptive events cause emotional and psychological distress. While some participants were able to recalibrate their sense of self and understanding of the role, for others, this dissonance was irreconcilable, contributing to disability and lost time from work. In addition to contributing a novel perspective on paramedic mental health and well-being, our work also offers a modest contribution to the theory in using the paramedic context as an example to consider identity disruption through chronic workplace stress.

To predict how the COVID-19 pandemic progresses, we developed a systematic method for predicting disease outcomes. In the method, we evaluate how personal disease outcomes are mainly affected by viral concentration and exposure time and four defense mechanisms: human innate immunity/host response, acquired immune response, inflammation resolution and micro circulation, and the available space in the thorax cage. By considering how pandemic measures affect viral exposure and those mechanisms, we found many pandemic measures are misused or abused to deliver long-term adverse impacts. We noted that lifestyles have been changed as a result of movement restriction measures. By using the method, we found that altered lifestyles are predicted to raise infection rate, disability and death risks in the future. We show that a person can use personal, environmental, emotional factors to reduce infection rate and death risk. To prove the validity of this finding, we extensively examined medical research models, holistic and reductionist models, epidemiological models, disease risk factors, etc, and found that population methods are unfit for studying holistic health, statistical population does not exist in most clinical trials, mathematical models were misused for studying disease properties for a population, mathematical equations for modeling personal diseases are beyond human ability to solve, statistical models are misused, population-derived treatments are inherently dangerous to patients, vaccines have limited benefits due to unique lung structure and rapid RNA mutation, and immune system damage is caused by fast viral replication rate. We found that altering biological properties to improve the defense mechanisms could prevent a super majority of deaths and prevent the virus from reaching a point to damage the immune system. For vulnerable persons, such measure is a viable strategy for surviving from the pandemic. As a whole, holistic personalized medicine is more powerful than population-based reductionist treatment by one to several orders of magnitudes. We urge people do their parts to force the medical establishment to abandon population treatment models that are responsible for failure of medicine and dissemination of misleading and factually wrong information on the effectiveness of medical treatments.

Access to medicines is one of the essential problems in Public Health of low- and middle-income countries (LMICs). The World Health Organization (WHO) defines access to medicines as the possibility of "having continuously accessible and affordable medicines in public or private health facilities that are within a kilometer of the place of residence." Access to medicines, as defined by the WHO, is not fully guaranteed in many LMICs and even in many regions of high-resource countries. The WHO identifies several factors as determinants of limitations in the access to medicines: rational selection, affordable prices, sustainable financing, and reliable health services. The action on these factors makes it possible to improve universal access to medicines with consequent improvement in Public Health. Adequate access to antibiotics and vaccination will avoid a large part of the deaths caused by infectious diseases in the LMICs. However, the emergence of resistance and the difficulties in vaccination campaigns due to socio-political or cultural problems make it challenging to fight many easily treatable infectious diseases. The use and abuse of antibiotics are inevitably associated with the appearance of resistances that make them ineffective. Thus, whereas limited access to antibiotics raises mortality rates from infectious diseases, generalized open access to them ends up eliminating their clinical value. Moreover, the contraction of research in this field for many years has reduced the success in discovering new drugs. Additionally, local market regulations, inadequate selection, inaccessible prices, especially for those of second and third-generation, inefficient health systems, and difficulties of administration and control of prescription compliance, especially in the case of combined therapies, are additional obstacles to universal access to antibiotics. In order to simultaneously improve access to antibiotics and keep resistances under control, it is necessary to develop training and education activities at different social levels (from patients to various Health Care Providers) to complement the national or supranational strategic plans.

The COVID19 pandemic had a major impact on emergency medical communication centres (EMCC). A live video facility was made available to second-line physicians in an EMCC with a first-line paramedic to receive emergency calls. The objective of this study was to measure the contribution of live video to remote medical triage. The single-centre retrospective study included all telephone assessments of patients with suspected COVID19 symptoms from 01.04.2020 to 30.04.2021 in Geneva, Switzerland. The organisation of the EMCC and the characteristics of patients who called the two emergency lines (official emergency number and COVID19 number) with suspected COVID19 symptoms were described. A prospective web-based survey of physicians was conducted during the same period to measure the indications, limitations and impact of live video on their decisions. 8,957 patients were included. 2,157 (48.0%) of the 4,493 patients assessed on the official emergency number had dyspnoea. 4,045 (90.6%) of 4,464 patients assessed on the COVID19 number had flu-like symptoms. 1,798 (20.1%) patients were reassessed remotely by a physician, including 405 (22.5%) with live video, successfully in 315 (77.8%) attempts. The web-based survey (107 forms) showed that physicians used live video to assess mainly the breathing (81.3%) and general condition (78.5%) of patients. They felt that their decision was modified in 75.7% (n=81) of cases, and caught 7 (7.7%) patients in life-threatening emergency. Medical triage decisions for suspected COVID19 patients are strongly influenced by the use of live video.

There is growing recognition in research and policy of a mental health crisis among Canada’s paramedics but despite this, epidemiological surveillance of the problem is in its infancy. Just weeks before the emergence of the COVID-19 pandemic, we surveyed paramedics from a single, large, urban paramedic service in Ontario, Canada to assess for symptom clusters consistent with Post-Traumatic Stress Disorder (PTSD), major depressive disorder, and generalized anxiety disorder and to identify potential risk factors for each. In total, we received 589 completed surveys (a 97% completion rate) and found that 11% screened positive for PTSD, 15% for depression, and 15% for anxiety, with 1 in 4 active-duty paramedics screening positive for any of the three as recently as February 2020. In adjusted analyses, the risk of a positive screen varied as a function of employment classification, gender, self-reported resilience, and previous experience as a member of the service’s peer support team. Our findings support the position that paramedics screen positive for mental disorders at high rates – a problem likely to have worsened since the onset of the COVID-19 pandemic. We echo the calls of researchers and policymakers for urgent action to support paramedic mental health in Canada.

In this article, we estimate the value of Government Expenditure on Education-GEE in the context of Environmental, Social and Governance-ESG dataset of the World Bank. We use data from 193 countries in the period 2011-2020. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. Furthermore, we apply the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of the future value of GEE. We found that the Polynomial Regression is the best predictive algorithm. The Polynomial Regression predicts an increase in GEE of 7.09% on average for the analysed countries.

In this article, we estimate the role of Political Stability and Absence of Violence and Terrorism-PS in the context of Environmental, Social and Governance-ESG data at world level. We analyse data from 193 countries in the period 2011-2020. We apply Panel Data with Fixed Effects, Panel Data with Random Effects and Pooled Ordinary Least Square-OLS. We found that PS is positively associated, among others, to Population Density and Government Effectiveness, and negatively associated, among others, to Research and Development Expenditure and Maximum 5-day Rainfall. Furthermore, we apply the k-Means algorithm optimized with the application of the Elbow Method and we find the presence of four clusters. Finally, we propose a confrontation among eight different machine-learning algorithms for the prediction of PS and we find that the Polynomial Regression shows the higher performance. The Polynomial Regression predicts an increase in the level of PS of 0.25% on average for the analysed countries.

In this article, I estimate the value of “Individual Using Internet”-IUI in the context of Environmental, Social and Governance-ESG database of the World Bank. I use data from 193 countries for the period 2011-2020. I found that among others the value of IUI is positively associated to “Methane Emissions” and “People Using Safely Managed Sanitation Services” and negatively associated among others to “Fossil Fuel Energy Consumption” and “Renewable Energy Consumption”. I apply the k-Means algorithm for the clusterization optimized with the Elbow Method and we find the presence of three clusters. Finally, I confront eight machine-learning algorithms to predict the future value of IUI. I found that the best predictive algorithm is Linear Regression and that the value of IUI is expected to decrease on average of 0.30% for the analysed countries.

We analyze the question of GDP Growth-GDPG rate in the context of Environmental, Social and Governance-ESG framework. We use World Bank data for 193 countries in the period 2011-2020 using different econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS. We found that GDPG rate is positively associated, among others, to “Government Effectiveness” and “Prevalence of Undernourishment” and negatively associated among others to “Unemployment” and “Research and Development Expenditure”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow method and we found the presence of four clusters in the sense of GDPG rate. Finally, we confront eight machine learning algorithms to predict the value of GDPG rate and we found that the Polynomial Regression is the best predictor. The Polynomial Regression predicts an increase of GDPG rate equal to 2.88% on average for the analysed countries.

We estimate the determinants of Emissions-COE in the context of Environmental, Social and Governance-ESG model at world level. We use data of the World Bank for 193 countries in the period 2011-2020. We found that the level of COE is positively associated, among others to “Methane Emissions”, “Research and Development Expenditures”, and negatively associated among others to “Renewable Energy Consumption” and “Mean Drought Index”. Furthermore, we have applied a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four cluster. Finally, we apply eight machine-learning algorithms for the prediction of the future value of COE and we find that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predicts a reduction in the level of COE equal to 5.69% on average for the analysed countries.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

In this article, we analyse the determinants of Regulatory Quality-RQ for 193 countries in the period 2011-2020. We use a database from ESG-Environment Social Governance of the World Bank. We apply OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index”. We also found that the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method and we find the presence of five clusters. In adjunct, we confront eight machine learning algorithms to predict the value of RQ and we found that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of -1,29%. In the end, we present a network analysis with the Euclidean distance and we found the presence of a structure of seven networks using augmented data.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.

Air pollution, climate change and global warming show direct linkage. Introduction - Background: Research in the field has proposed and demonstrated their link to increased allergenicity in plants, leading to increased allergic diseases. Allergen patterns are changing in response to climate change. Across Europe, up to 12% of the population suffers from allergic diseases (bronchial asthma and allergic rhinitis) caused by the pollen grains. The medical costs of these types of allergies are enormous and are increasing with the spread of common ragweed - Ambrosia artemisiifolia* in Europe. In practice, more and more patients are turning to allergists. Methods: Based on these data, the research aims to identify safety measures and specific legislation to control and eradicate common ragweed in the countries most affected by the spread of this plant in Europe, increase in population exposure in countries affected by the presence of common ragweed and the socio-economic impact of allergic rhinitis and bronchial asthma at European level.[1][2] Our database search did not reveal any other research that has addressed this topic to date. Results: This research identifies specific legislation to the countries most affected by the spread of this plant pollen grains at European level, changes in allergen patterns and monitoring/safety eradication measures decided by local authorities, and data on the spread of common ragweed over the last two decades, costs per treatment, increased allergic rhinitis and bronchial asthma morbidity in the most affected areas. Conclusions: At European level, there are no universally valid models for eradicating this weed. Various methods can be used to limit the spread of common ragweed and its pollen production (e.g. mowing once or twice a year of overgrown land, ploughing, chemical control, public information, etc.). Local and regional cooperation should be considered essential to promote phytosanitary &amp; safety measures and exchange of information on methods to identify and manage the spread of common ragweed. Unified legislation at European level would help all affected countries; reduce morbidity from respiratory allergic diseases and the costs of treating these diseases.

In this article we estimate the level of Control of Corruption for 193 countries in the period 2011-2020 using data from the ESG World Bank Database. Various econometric techniques are applied i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS. Results show that “Control of Corruption” is positively associated, among others, to “Government Effectiveness” and “Political Stability and Absence of Violence/Terrorism”, while it is negatively associated among others to “Agriculture, Forestry, and Fishing Value Added as Percentage of GDP” and “GHG Net Emissions/Removals by LUCF”. A cluster analysis implemented with the k-Means algorithm optimized with the Elbow Method shows four clusters. A confrontation among eight Machine Learning algorithms is proposed for the prediction of Control of Corruption. Polynomial Regression is the best predictor for the training data. The level of Control of Corruption is expected to growth by 10.36% on average.

This paper intends to discuss SSLs, how they work, their advantages and drawbacks. It will end with the author’s thoughts on the efficacy of the system and whether it is a viable solution for the majority of network security problems or if those drawbacks prevent that.

This article explores the intersection of innovative leadership and climate change, aiming to provide outstanding contributions to the existing body of knowledge in this field. The article identifies the critical role of innovative leadership in driving sustainable solutions and strategies for addressing climate change. It examines the defining characteristics and behaviors of innovative leaders and explores how their transformative leadership approaches can create a positive impact on climate change mitigation and adaptation efforts. The article also highlights the importance of fostering innovation within organizations and societies to tackle the complex challenges posed by climate change. Through a comprehensive review of relevant literature and case studies, this article presents novel insights, theoretical frameworks, and practical implications for policymakers, organizations, and individuals involved in climate change leadership.

The Impact of Event Scale-Revised (IES-R) is the most popular measure of post-traumatic stress disorder (PTSD), which has been recently validated in Arabic. This instrumental study aimed to determine optimal cutoff scores of the IES-R and its subscales in Arab samples of psychiatric patients (N = 168, 70.8% females) and healthy adults (N = 992, 62.7% females) from Saudi Arabia during the COVID-19 pandemic as an ongoing collective traumatic event. Based on a cutoff score of 14 of the Depression Anxiety Stress Scale 8-items (DASS-8), receiver operator curve (ROC) analysis revealed two optimal points of 39.5 and 30.5 for the IES-R in the samples (area under the curve (AUC) = 0.86 &amp; 0.91, p values = 0.001, 95% CI: 0.80-0.92 &amp; 0.87 to 0.94, sensitivity = 0.85 &amp; 0.87, specificity = 0.73 &amp; 0.83, Youden index = 0.58 &amp; 0.70, respectively). Different cutoffs were detected for the six subscales of the IES-R, with numbing and avoidance expressing the lowest predictivity for distress. Meanwhile, hyperarousal followed by the irritability expressed stronger predictive capacity for distress than all subscales in both samples. In path analysis, pandemic-related irritability resulted from direct and indirect effects of key PTSD symptoms (intrusion, hyperarousal, and numbing). Irritability contributed to traumatic symptoms of sleep disturbance in both samples while the opposite was not true. The findings suggest usefulness of the IES-R at a score of 30.5 for detecting adults prone to trauma related distress, with higher scores needed for screening in psychiatric patients. Various PTSD symptoms may induce dysphoric mood, which represents a considerable burden that may induce circadian misalignment and more noxious psychiatric problems/ co-morbidities (sleep disturbance) in both healthy and diseased groups.