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This review delves into the processing of tannery solid waste, emphasizing fleshings and chromium-tanned leather waste. This paper centers on fat recovery, chromium elimination, and protein preservation, aiming to make them apt for animal consumption. This paper also assesses the potential of introducing such recycled products to the global market. Drawing on the literature from the past two decades, sourced comprehensively from Scopus and Web of Science, 36 articles were selected because of their significant contributions from leather production powerhouses such as India and Brazil. Fleshings have shown immense potential as animal feed, and the extraction of tallow and collagen from rawhide trimmings yields up to 98% and 93%, respectively. Fermented tannery fleshings, notably with Enterococcus faecium HAB01, also demonstrate strong antioxidant capabilities. The overarching consensus emphasizes the need for rigorous purification when dealing with chromium-containing wastes, addressing concerns tied to Cr (III) and Cr (VI). Furthermore, raw tannery fleshings stand out as a sustainable, cost-effective, and globally marketable solution for animal feed production. [ABSTRACT FROM AUTHOR]

Data breaches result in data loss, including personal, health, and financial information that are crucial, sensitive, and private. The breach is a security incident in which personal and sensitive data are exposed to unauthorized individuals, with the potential to incur several privacy concerns. As an example, the French newspaper Le Figaro breached approximately 7.4 billion records that included full names, passwords, and e-mail and physical addresses. To reduce the likelihood and impact of such breaches, it is fundamental to strengthen the security efforts against this type of incident and, for that, it is first necessary to identify patterns of its occurrence, primarily related to the number of data records leaked, the affected geographical region, and its regulatory aspects. To advance the discussion in this regard, we study a dataset comprising 428 worldwide data breaches between 2018 and 2019, providing a visualization of the related statistics, such as the most affected countries, the predominant economic sector targeted in different countries, and the median number of records leaked per incident in different countries, regions, and sectors. We then discuss the data protection regulation in effect in each country comprised in the dataset, correlating key elements of the legislation with the statistical findings. As a result, we have identified an extensive disclosure of medical records in India and government data in Brazil in the time range. Based on the analysis and visualization, we find some interesting insights that researchers seldom focus on before, and it is apparent that the real dangers of data leaks are beyond the ordinary imagination. Finally, this paper contributes to the discussion regarding data protection laws and compliance regarding data breaches, supporting, for example, the decision process of data storage location in the cloud. [ABSTRACT FROM AUTHOR]

Inequality refers to the extent to which income is evenly distributed across a population (IMF, 2022). The World Inequality Report (WIR, 2022) reveals the latest trends in global income inequality. It shows that the richest 10% of the global population currently earns 52% of global income, while the poorest half of the population takes 8.5% of it. In emerging market economies, indications are that income inequality is rising; sometimes accompanied by accelerating economic growth. Reports reveal that Brazil's six richest individuals command the same wealth associated with the poorest 50% of the population, or about 100 million people. The World Inequality Report (2021) reveals India as a poor and unequal country, with the bottom half of the population earning only 13% of the nation's income, while the top 10% controls 57% by 2021. In South Africa, the IMF (2020) acknowledges that the nation's inequality has remained at high levels, perhaps the highest in the world. This is characterized by a highly skewed income distribution pattern, with the top 20% of the population controlling 68% of national income. These developments hold grave implications for emerging markets, particularly their ability to meet the 2030 Sustainable Development Goals. The main objective of this paper is to explore income inequality in emerging market economies, with a multi-country study of Brazil, India and South Africa. The methodological approach to the study involves both qualitative and quantitative analytical techniques. It relies on secondary data in publications from various sources, complemented by interviews of stakeholders in the study areas. Findings reveal the income inequality is undermining economic growth and development in emerging markets, fueling conflicts and triggering migration in some countries. The paper presents recommendations, underpinned by progressive taxation, social safety nets, gender equality, development of social services, and leveraging economic and financial incentives for labour-intensive industries. [ABSTRACT FROM AUTHOR]

This paper aimed to analyze how Brazil and India faced the challenge of a large amount of personal information being exchanged, stored, and analyzed. The relevance lies in the fact that data protection and privacy were concepts discussed almost everywhere in the world since the era of Big Data highlighted the challenge of protecting these fundamental rights. Therefore, the research problem was to analyze to what extent these countries effectively faced the challenge presented. The methodology used was exploratory and hypothetical-deductive. As a result, it was identified that the rights to privacy and personal data were not absolute and had to be balanced with other social interests, such as public security, law enforcement, and freedom of expression. It was concluded that inspired by international standards on the subject, such as the Universal Declaration of Human Rights (UDHR), the International Covenant on Civil and Political Rights (ICCPR), and the General Data Protection Regulation (GDPR) of the European Union, both countries had legislative protection over these rights and a framework to address them. The legislation provided fundamental principles, but only time will show their effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi is the property of Journal of Economic Policy Researches / Iktisat Politikasi Arastirmalari Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The higher education efficiency evaluation model using the data envelopment analysis method has interested many researchers. This paper uses bibliometric analysis on publications extracted from the Scopus database to provide a comprehensive overview of research publications on the measurement of higher education efficiency based on data envelopment analysis: its growth rate, major collaboration networks, the most important and popular research topic. A total of 169 related publications were collected and analyzed from 1988 to 2021. The analysis results show that: Publications published every year have increased sharply in the last six years; The quality of publications is relatively high as publications tend to be published in journals with high-ranking indexes; Countries with the most influence in studies on this topic are: Italy, China, Spain, the USA, and the United Kingdom; Authors with the most influence in this research direction are Agasisti T., Abbott M., Doucouliagos C., Avkiran N.K., and Johnes J.; The research cooperation among countries and among affiliations is not strong. Finally, the paper has provided recommendations for future studies based on the findings.

The link between school feeding programmes (SFP) and the promotion of healthy eating and health is being explored in studies performed in different countries. The coronavirus disease-19 pandemic has revealed flaws and weaknesses in contemporary food systems, with many school-age children experiencing food insecurity and hunger. There is intense debate among policymakers regarding whether government SFP should be universal or targeted. Countries such as Brazil and India, which have two of the most comprehensive universal free-of-charge programmes, have shown the benefits of SFP, including improved nutritional status, support for more sustainable food systems, attendance and academic performance. Evidence shows and supports actions advocating that it is time to offer healthy and free school meals for all students. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected the health, economy, and all aspects of human lives around the world. Accurate prediction of the daily new cases of COVID-19 is critical for precise programming and taking needed measures to prevent the outbreak of it. Hence, in the present study, a new hybrid intelligent model is developed by hybridizing the artificial neural network (ANN) with the honey badger algorithm (HBA) to accurately predict the daily new cases of COVID-19 in Brazil, India, Russia, and the US. The performance of the hybrid model, namely HBA-ANN, was compared with the stand-alone ANN and gene expression programming (GEP) model using statistical criteria, such as correlation coefficient (R), root mean square error, scatter index (SI), and Nash–Sutcliffe efficiency (NSE), and graphical criteria, such as Taylor diagram, scatter plot, and box plot. According to the Taylor diagram, in each data series, the predicted values with the HBA-ANN model have the lowest distance from the observation points, indicating the high accuracy of the HBA-ANN model compared to the ANN and GEP models. Furthermore, the HBA-ANN model has high values for R (0.999, 0.899, 0.853, and 0.993 for Brazil, Russia, India, and the US, respectively) and could reduce the errors of the ANN model by 97.22, 43.68, 85.41, and 93.83% for Brazil, Russia, India, and the US, respectively. For each data series, the SI for the HBA-ANN model is less than 0.1, and the NSE is between 0 and 1, indicating a very good performance in predicting the COVID-19 cases with the developed model. Based on the superior performance of the HBA-ANN model, it is recommended to implement the HBA algorithm to increase the prediction accuracy of the models in medicine and other scientific fields. Moreover, it is suggested to investigate the accuracy of the hybridized HBA algorithm with other artificial intelligence (AI) models. [ABSTRACT FROM AUTHOR]

In this work, a bibliometric analysis of the investigations of the last 54 years focused on the teaching of topology and its applications in the learning of other areas of knowledge was carried out. The articles that appear in the SCOPUS database were taken into account under the search criteria of the words topology and teaching, connected with the Boolean expression AND in the search field ABS. As a result, 329 articles were obtained which, based on the PRISMA methodology, were reduced to 74 papers. In them publication trends, impact of publications, citation frequencies, among others, were compared. In addition, its use was identified for learning topology at different levels of training, areas of knowledge where this discipline is most applied and strategies used to teach these applications.

Despite the orthopedics markets in the US and the EU reaching a plateau, the market size in countries such as Brazil, Russia, India, and China is steadily growing. As a result, major orthopedic companies are shifting their focus towards these markets and developing products tailored to their needs. However, a significant challenge associated with this new opportunity is the requirement for the development of more affordable prostheses compared to those sold in the US and Europe. With the introduction of these lower-cost models into the market, this article aims to assess their performance in comparison to traditional models. A literature review was conducted, analyzing four parameters—the Hospital for Special Surgery Score, Knee Society Score, Range of Motion, and Western Ontario and McMaster Universities Arthritis Index—to evaluate different models. The findings indicated that low-cost models perform either equally well or, in some cases, slightly worse than traditional ones. It is worth to mention that the existing literature on this topic is limited, resulting in a relatively small number of models and studies included in this specific study. Nevertheless, this latter serves as a valuable foundation for future in-depth analyses and investigations. [ABSTRACT FROM AUTHOR]

The IBSA forum is an important collaboration of India-Brazil-South Africa to address the social developmental challenges of developing countries through South-South Cooperation. All three countries share same colonial history and at present have developing economies and struggling to provide best public services to their citizens through the appropriate policy planning and introduction of welfare schemes. In the field of education, all these countries are facing problem in the expansion of access of education for all due to the challenges such as high number of out of school children, high drop-out rate, high repetition rates etc. To tackle these problems, appropriate education policies on free and compulsory education have been implemented in these countries. Besides these education policies, these countries have started many compensatory programmes to address the major challenges in the goal of universal elementary education. One of the most important compensatory programmes in these countries is school meal programme. In all the three countries, school meal programmes have been started with dual purpose of keeping the children in the school and improving their nutritional status. This paper is an attempt to review the school meal programmes of IBSA countries to highlight their impact on the health and educational attainment of children especially children from the disadvantaged sections of the society.

Escherichia coli O157:H7 is a foodborne pathogen, which causes various health conditions in humans, including fatigue, nausea, bloody diarrhoea and in some cases, even death. In 2017, 15.71% of the total imported food products in Saudi Arabia (SA) were meat-based. India and Brazil are two of the top five countries from where SA imports meat. According to the Saudi Food and Drug Authority, in 2017, at least 562, 280, and 50 samples of imported beef, chicken and sheep meat, respectively, were tested for the presence of E. coli O157:H7. Amongst these, E. coli O157:H7 was detected in respectively 6.80% and 2.20% of the tested beef meat samples imported from India and Brazil as well as in respectively 6.96% and 3.57% of the tested chicken samples imported from Brazil and Ukraine. Moreover, the pathogen was detected in 2.13% of the tested sheep meat samples imported from India. The present report provides evidence that imported meat can serve as the carrier of E. coli O157:H7, which may lead to epidemics within the Kingdom of Saudi Arabia. [ABSTRACT FROM AUTHOR]

India is one of the leading citrus−producing countries, next to China and Brazil. Despite the sizeable production, especially of lemons and limes, India contributes meagerly to the world's citrus market. Of the various factors responsible for the restricted quantum of citrus export, citrus canker (Xanthomonas citri pv. citri) is one of the leading serious causes and directly impacts the appearance of the fruits. Considering the extent of damage, the present study aimed to understand the impact of Xanthomonas citri pv. citri (Xcc) on the physio−biochemical responses in contrasting Citrus spp. Two genotypes, each of three citrus species, namely Citrus aurantifolia (Pusa Udit and ALC−35), C. limon (Kagzi Kalan and Konkan Seedless), and C. paradisi (Redblush and Marsh Seedless), were artificially inoculated with Xcc (108 to 109 cfu/ mL) by the pinprick method. The physio−biochemical changes in the host were evaluated after 48 h post inoculation (hpi). The chlorophyll content (total, a, and b) degradation and reduction in leaf gas exchange parameters, such as photosynthetic rate (A), transpiration rate (E), stomatal conductance (gs), and intrinsic water−use efficiency (iWUE), were measured to a greater extent in susceptible than resistant genotypes. The microscopic observations also evidenced higher stomatal density with larger stomatal areas in susceptible genotypes, favoring the easier penetration of Xcc in host tissues than resistant species or genotypes. The higher activities of various antioxidant enzymes, viz., superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and glutathione reductase (GR), the contents of soluble protein, and phenolics were measured in resistant genotypes in contrast to susceptible genotypes. The activities of phenyl ammonia lyase (PAL) and polyphenol oxidase (PPO) were also higher in resistant genotypes, whilst the levels of ROS (superoxide radical and hydrogen peroxide) production were enhanced in susceptible genotypes. Based on the host–pathogen interaction, the order of resistance in a descending manner was found as Kagzi Kalan, Marsh Seedless, Redblush, Konkan Seedless, Pusa Udit, and ALC−35. Further, the results will enhance the understanding of the pathogen mechanism during pathogenesis in resistant and susceptible Citrus species. [ABSTRACT FROM AUTHOR]

The present work elucidates miofloral records from the Ganmachidam Formation (late Carboniferous–earliest Permian) of Spiti Valley, Tethyan Himalaya. The Ganmachidam Formation was hitherto considered as unfossiliferous, both for the faunal as well as floral records, and has been subjected to contentious age assessment. The present study has been carried out to find out the microfloral remains, assess the palynostratigraphic status, and the age of the Ganmachidam Formation. From the splintery shale sequence, a rich assemblage of palynomorphs has been recovered, having a dominance of monosaccate pollen grains, namely Parasaccites (Cannanoropollis), Crucisaccites, Divarisaccus, Densipollenites, Maculatasporites, followed by diverse bisaccate pollen grains, namely Scheuringipollenites, Crescentipollenites, Faunipollenites (Protohaploxypinus), Lunatisporites, Striatopodocarpites, Striatites, Rhizomaspora, Primuspollenites, and the spores Callumispora (Punctatisporites) and Calamospora in addition to woody fragments and fungal spores. Three palynoassemblages are identified. The recovered palynocomposition is corroborated with known records of palynomorphs from the Tethyan realm of India and from the late Carboniferous–Early Permian records of contemporaneous sequences across the globe. The composition of these palynoassemblages shows close correlation with the Crucisaccites monoletus Zone and Vittatina costabilis Interval Zone in the northeastern area of the Paraná Basin, Brazil, and lower Barakar palynozone (Scheuringipollenites barakarensis Assemblage Zone) of Peninsular India which indicates the studied section of the Ganmachidam Formation is late Pennsylvanian to early Permian in age. Moreover, occurrences of palynomorphs from the Carboniferous sequences having a glossopterid affinity further strengthen the idea that glossopterids might have originated in Carboniferous time. [ABSTRACT FROM AUTHOR]

Background: COVID-19 poses a severe threat to global human health, especially the USA, Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on people's health, social activities, and the local economic situation.Methods: The study proposed the ARIMA, SARIMA and Prophet models to predict daily new cases and cumulative confirmed cases in the USA, Brazil and India over the next 30 days based on the COVID-19 new confirmed cases and cumulative confirmed cases data set(May 1, 2020, and November 30, 2021) published by the official WHO, Three models were implemented in the R 4.1.1 software with forecast and prophet package. The performance of different models was evaluated by using root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).Results: Through the fitting and prediction of daily new case data, we reveal that the Prophet model has more advantages in the prediction of the COVID-19 of the USA, which could compose data components and capture periodic characteristics when the data changes significantly, while SARIMA is more likely to appear over-fitting in the USA. And the SARIMA model captured a seven-day period hidden in daily COVID-19 new cases from 3 countries. While in the prediction of new cumulative cases, the ARIMA model has a better ability to fit and predict the data with a positive growth trend in different countries(Brazil and India).Conclusions: This study can shed light on understanding the outbreak trends and give an insight into the epidemiological control of these regions. Further, the prediction of the Prophet model showed sufficient accuracy in the daily COVID-19 new cases of the USA. The ARIMA model is suitable for predicting Brazil and India, which can help take precautions and policy formulation for this epidemic in other countries. [ABSTRACT FROM AUTHOR]

This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread. [ABSTRACT FROM AUTHOR]

Objective: To provide experience for formulating prevention and control policies, this study analyzed the effectiveness of the Coronavirus disease 2019(COVID-19) prevention and control policies, and evaluated health equity and epidemic cooperation among BRICS countries. Methods: This study summarized the pandemic prevention and control policies in BRICS countries and evaluated the effectiveness of those policies by extracting COVID-19 related data from official websites. Result: As of May 4, 2021, responding to COVID-19. China adopted containment strategies. China's total confirmed cases (102,560) were stable, without a second pandemic peak, and the total deaths per million (3.37) were much lower than others. India and South Africa who adopted intermediate strategies have similar pandemic curves, total confirmed cases in India (20,664,979) surpassed South Africa (1,586,148) as the highest in five countries, but total deaths per million (163.90) lower than South Africa (919.11). Brazil and Russia adopted mitigation strategies. Total confirmed cases in Brazil (14,856,888) and Russia (4,784,497) continued to increase, and Brazil's total deaths per million (1,936.34) is higher than Russia (751.50) and other countries. Conclusion: This study shows BRICS countries implemented different epidemic interventions. Containment strategy is more effective than intermediate strategy and mitigation strategy in limiting the spread of COVID-19. Especially when a strict containment strategy is implemented in an early stage, but premature relaxation of restrictions may lead to rebounding. It is a good choice to combat COVID-19 by improving the inclusiveness of intervention policies, deepening BRICS epidemic cooperation, and increasing health equities. [ABSTRACT FROM AUTHOR]

Cashew (Anacardium occidentale. L, family Anacardiaceae) a native of Brazil was presented in India during sixteenth century. The present investigation "A Study on level of knowledge of cashew growers in Ariyalur district of Tamil nadu" was designed to study the level of knowledge possessed by the cashew growers on recommended cashew cultivation practices. This study also aimed to find out the relationship between the knowledge level and profile characteristics of cashew growers. A sample size of 300 respondents was selected by using proportionate random sampling technique. The collected data were tabulated and analyzed using appropriate statistical tools. The result of this study revealed that majority of the cashew growers had medium level of knowledge on cashew cultivation practices. As regards to relationship between the knowledge level and profile characteristics of cashew growers, out of eighteen variables considered for the study, six variables viz., educational status, experience in cashew cultivation, extension agency contact, mass media exposure, scientific orientation and innovativeness were found to have positive and significant relationship with the knowledge level. [ABSTRACT FROM AUTHOR]

Since late 2019, COVID-19 has significantly impacted the world. Understanding the evolution of SARS-CoV-2 is crucial for protecting against future infectious pathogens. In this study, we conducted a comprehensive chronological analysis of SARS-CoV-2 evolution by examining mutation prevalence from the source countries of VOCs: United Kingdom, India, Brazil, South Africa, plus two countries: United States, Russia, utilizing genomic sequences from GISAID. Our methodological approach involved large-scale genomic sequence alignment using MAFFT, Python-based data processing on a high-performance computing platform, and advanced statistical methods the Maximal Information Coefficient (MIC), and also Long Short-Term Memory (LSTM) models for correlation analysis. Our findings elucidate the dynamics of SARS-CoV-2 evolution, highlighting the virus's changing behaviour over various pandemic stages. Key results include the discovery of three temporal mutation patterns-lineage distinct, long-span, and competitive mutations-with varying levels of impact on the virus. Notably, we observed a convergence of advantageous mutations in the spike protein, especially in the later stages of the pandemic, indicating a substantial evolutionary pressure on the virus. One of the most significant revelations is the predominant role of natural immunity over vaccination-induced immunity in driving these evolutionary changes. This emphasizes the critical need for regular vaccine updates to maintain efficacy against evolving strains. In conclusion, our study not only sheds light on the evolutionary trajectory of SARS-CoV-2 but also underscores the urgency for robust, continuous global data collection and sharing. It highlights the necessity for rapid adaptations in medical countermeasures, including vaccine development, to stay ahead of pathogen evolution. This research provides valuable insights for future pandemic preparedness and response strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Nitrous oxide (N 2 O) is the third most potent greenhouse gas (GHG) and the most important ozone depleting substance. But how global N 2 O emissions are connected through the interwoven trade network remains unclear. This paper attempts to specifically trace anthropogenic N 2 O emissions via global trade networks using a multi-regional input-output model and a complex network model. Nearly one quarter of global N 2 O emissions can be linked to products traded internationally in 2014. The top 20 economies contribute to about 70% of the total embodied N 2 O emission flows. In terms of the trade embodied emissions classified by sources, cropland-, livestock-, chemistry-, and other industries-related embodied N 2 O emissions account for 41.9%, 31.2%, 19.9%, and 7.0%, respectively. Clustering structure of the global N 2 O flow network is revealed by the regional integration of 5 trading communities. Hub economies such as mainland China and the USA are collectors and distributors, and some emerging countries, such as Mexico, Brazil, India, and Russia, also exhibit dominance in different kinds of networks. This study selects the cattle sector to further verify that low production-side emission intensities and trade cooperation can lead to N 2 O emission reduction. In view of the impact of trade networks on global N 2 O emissions, achieving N 2 O emission reduction calls for vigorous international cooperation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic has led governments worldwide to impose extensive restrictions on citizens, some of which may have long-term impact after their removal. Education is arguably the policy domain where closure policies are anticipated to lead to greatest lasting loss, in this case learning loss. Currently, limited data exists from which researchers and practitioners can draw insightful conclusions about how to remedy the problem. In this paper, we outline the global pattern in pandemic school-closure periods and illustrate data needs through the examples drawn from Brazil and India, two large countries which experienced prolonged periods of school closures during the pandemic. We conclude with a series of recommendations for building an improved data environment at government, school and household levels, to serve the building back agenda in education, and to provide better opportunities for evidence-based policymaking thereafter., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

The COVID-19 pandemic has highlighted a need for better understanding of countries' vulnerability and resilience to not only pandemics but also disasters, climate change, and other systemic shocks. A comprehensive characterization of vulnerability can inform efforts to improve infrastructure and guide disaster response in the future. In this paper, we propose a data-driven framework for studying countries' vulnerability and resilience to incident disasters across multiple dimensions of society. To illustrate this methodology, we leverage the rich data landscape surrounding the COVID-19 pandemic to characterize observed resilience for several countries (USA, Brazil, India, Sweden, New Zealand, and Israel) as measured by pandemic impacts across a variety of social, economic, and political domains. We also assess how observed responses and outcomes (i.e., resilience) of the COVID-19 pandemic are associated with pre-pandemic characteristics or vulnerabilities, including (1) prior risk for adverse pandemic outcomes due to population density and age and (2) the systems in place prior to the pandemic that may impact the ability to respond to the crisis, including health infrastructure and economic capacity. Our work demonstrates the importance of viewing vulnerability and resilience in a multi-dimensional way, where a country's resources and outcomes related to vulnerability and resilience can differ dramatically across economic, political, and social domains. This work also highlights key gaps in our current understanding about vulnerability and resilience and a need for data-driven, context-specific assessments of disaster vulnerability in the future., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Beesley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Scientometrics is a discipline that allows the analysis of the characteristics of publications in each field of knowledge using different indicators. The aim of this research was to analyse world scientific publication as to COVID-19 related to dentistry., Methods: A specialised search strategy was used to obtain all the documents published in journals indexed in the Scopus database between December 2019 and February 2022. The study variables were exported and analysed in SciVal (Elsevier)., Results: In all, 2071 documents were retrieved; of those, Oral Diseases has the highest impact with an average of 12.3 citations per publication, and the British Medical Journal was identified as the journal with the highest scientific output (107). India (292) and the UK (287) were the countries with the most published papers. The Universidade de São Paulo in Brazil was the institution with the highest number of published papers (61) and with the highest impact., Conclusions: During the pandemic, scientific publications on dentistry and COVID-19 have been increasing considerably. International collaboration has the highest percentage of publications whilst India and the UK are the countries with the highest scientific production., Competing Interests: Conflict of interest None disclosed., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Aim: To analyze the characteristics and visibility of the scientific production of monkeypox in dentistry., Materials and Methods: A bibliometric study was carried out with the publications indexed in the Scopus database up to 22 September 2022. A search strategy was developed using Boolean operators "AND" and "OR," in addition to the MeSH term "monkeypox virus" (MPXV) in the subarea of dentistry. The bibliometric indicators were measured objectively with the SciVal program., Results: Of the publications identified, 40% were indexed in first-quartile journals. India and Brazil are the countries with two published papers, while India has more views than the rest. Banaras Hindu University, Varanasi, Uttar Pradesh, India and Dr. Ram Manohar Lohia Hospital, New Delhi, India are the institutions with the highest number of citations with respect to the world average (FWCI: 2.74). The International Dental Journal has a publication on monkeypox in the field of dentistry. India is the country with the most authors (06) who have published about the study. Samaranayake Lakshman Perera is the most productive and high-impact author., Conclusion: In the area of dentistry, scientific production on monkeypox is still scarce; however, the publications identified are mainly in high-impact indexed journals (Q1 and Q2). It is necessary to prioritize this disease as a line of research, in addition to articulating efforts between dental teams from different institutions., Clinical Significance: It is necessary to demonstrate the characteristics of scientific publications on monkeypox in dentistry worldwide in order to have a panoramic view of the dynamics of scientific articles in this area.

While the Global North is historically responsible for the majority of greenhouse gas emissions in the atmosphere, Newly Industrializing Countries (NICs) are expected to overtake developed country emissions in the coming years. At the same time, NICs are climbing the ladder of the global economy, increasing their competitiveness on the global stage and catching up with technological competencies of developed economies. Against this background, this paper explores innovation and collaboration in Climate Change Mitigation Technologies (CCMTs) in NICs. The research question is whether the propensity to innovate and diffuse CCMTs is impacted by technological collaboration with two highly developed countries, Germany and The United States. The sample of NICs includes the BRICS (Brazil, Russia, India, China, South Africa) plus Israel, Mexico, and Turkey, in a panel from 1995 to 2015. The empirical results suggest that collaboration with both Germany and the U.S. is highly significant for domestic CCMT innovation in NICs. These findings are important because, stepping beyond the literature on the merits and drawbacks of global climate governance tools such as the Clean Development Mechanism (CDM) and related UNFCCC processes, they show that collaboration for climate and environmental technologies could become a key tool to significantly improve the chances to stay in line with the Paris Climate Agreement. Finally, the policy advice for NICs and developing countries is to, above all else, focus on incubating strong technological innovation systems, including strengthening domestic Intellectual Property Rights (IPR), as well as to enhance technological collaboration with developed countries., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

In this paper, Transfer Learning is used in LSTM networks to forecast new COVID cases and deaths. Models trained in data from early COVID infected countries like Italy and the United States are used to forecast the spread in other countries. Single and multistep forecasting is performed from these models. The results from these models are tested with data from Germany, France, Brazil, India, and Nepal to check the validity of the method. The obtained forecasts are promising and can be helpful for policymakers coping with the threats of COVID-19., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

The global impact of coronavirus disease 2019 (COVID-19) is unprecedented, and many control and prevention measures have been implemented to test for and trace COVID-19. However, invisible-spreaders, who are associated with nucleic acid detection and asymptomatic infections, have received insufficient attention in the current COVID-19 control efforts. In this paper, we analyze the time series infection data for Italy, Germany, Brazil, India and Sweden since the first wave outbreak to address the following issues through a series of experiments. We conclude that: 1) As of June 1, 2020, the proportion of invisible-spreaders is close to 0.4% in Sweden, 0.8% in early Italy and Germany, and 0.4% in the middle and late stages. However, in Brazil and India, the proportion still shows a gradual upward trend; 2) During the spread of this pandemic, even a slight increase in the proportion of invisible-spreaders could have large implications for the health of the community; and 3) On resuming work, the pandemic intervention measures will be relaxed, and invisible-spreaders will cause a new round of outbreaks., Competing Interests: The authors have declared that no competing interests exist.

Identifying agricultural disaster risk regions before the occurrence of climate-related disasters is critical for early mitigation planning. This paper aims to identify these regions based on data from the Food and Agriculture Organization of the United Nations (FAO), the bilateral and multilateral trade network data of the World Integrated Trade Solution(WITS) and the agent-based economic model Acclimate. By applying a uniform forcing across agricultural sectors of some breadbasket regions (US, EU and China), when single and simultaneous extreme weather events occur, such as the 2018 European heatwave, production and consumption value losses and gains are calculated at regional and global levels. Comparing the FAO data sets, WITS, and Acclimate's production value losses, the results show a strong dependence of agricultural production losses on a region's output and connectivity level in the global supply and trade network. While India, Brazil, Russia, Canada, Australia, and Iran are highly vulnerable, the imposition of export restrictions to compensate for demand shortfalls makes Sub-Saharan Africa the most vulnerable region, as it is heavily dependent on agricultural imports. In addition, simultaneous extreme weather events can exacerbate the loss of value of agricultural production relative to single extreme weather events. Agricultural practices to increase production such as smart farming, increased investment in plantation agriculture, and diversification of trading partners can help mitigate future food security risks in Sub-Saharan Africa and other agricultural import-dependent regions., Competing Interests: The authors have declared that no competing interests exist.