Dynamic spillovers and portfolio risk management between defi and metals: Empirical evidence from the Covid-19

Using a novel TVP-VAR approach, we investigate the connectedness between precious metals, industrial metals, and decentralized finance (DeFi) assets during pre-pandemic and Covid sub-periods. We also calculate optimal portfolio weights, hedge ratios, and hedging effectiveness estimates for the portfolios of metals and DeFi assets. Results reveal that the association between DeFi-precious metal and DeFi-industrial metal pairs is weaker compared to the association between traditional precious and industrial metals. The interconnectedness of these markets increased during the Covid-19 period. All DeFi assets, as well as palladium, aluminum, zinc, and Nickel, are net importers of return spillover, while gold, silver, platinum, and copper are net exporters of return spillovers. The return transmission between these markets is rolling, with rapid fluctuations during the Covid-19 period. Finally, the optimal portfolio analysis reveals that adding DeFi assets to the metals-based portfolio is helpful in terms of diversification. These findings are insightful for portfolio managers and policymakers regarding portfolio construction, portfolio adjustment, hedging, and market stability.

Mean Temperature and Drought Projections in Central Africa: A Population-Based Study of Food Insecurity, Childhood Malnutrition and Mortality, and Infectious Disease.

The Central African Region is an agricultural and fishing-based economy, with 40% of the population living in rural communities. The negative impacts of climate change have caused economic/health-related adverse impacts and food insecurity. This original article aims to research four key themes: (i) acute food insecurity (AFI); (ii) childhood malnutrition and mortality; (iii) infectious disease burden; and (iv) drought and mean temperature projections throughout the twenty-first century. Food insecurity was mapped in Central Africa based on the Integrated Food Security Phase Classification (IPC) for AFI. The global hunger index (GHI) was presented along with the proportion of children with undernourishment, stunting, wasting, and mortality. Data for infectious disease burden was computed by assessing the adjusted rate of change (AROC) of mortality due to diarrhea among children and the burden of death rates due to pneumonia across all age groups. Finally, the mean drought index was computed through the year 2100. This population-based study identifies high levels of hunger across a majority of the countries, with the mean drought index suggesting extreme ends of wet and dry days and an overall rise of 1-3 °C. This study is a source of evidence for stakeholders, policymakers, and the population residing in Central Africa.

A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning.

This article proposes a novel method for detecting coronavirus disease 2019 (COVID-19) in an underground channel using visible light communication (VLC) and machine learning (ML). We present mathematical models of COVID-19 Deoxyribose Nucleic Acid (DNA) gene transfer in regular square constellations using a CSK/QAM-based VLC system. ML algorithms are used to classify the bands present in each electrophoresis sample according to whether the band corresponds to a positive, negative, or ladder sample during the search for the optimal model. Complexity studies reveal that the square constellation N=22i×22i,(i=3) yields a greater profit. Performance studies indicate that, for BER = 10-3, there are gains of -10 [dB], -3 [dB], 3 [dB], and 5 [dB] for N=22i×22i,(i=0,1,2,3), respectively. Based on a total of 630 COVID-19 samples, the best model is shown to be XGBoots, which demonstrated an accuracy of 96.03%, greater than that of the other models, and a recall of 99% for positive values.

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network.

The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc.

Statistical analysis of Covid-19 mortality rate via probability distributions.

Among other diseases, Covid 19 creates a critical situation around the world. Five layers have been recorded so far, resulting in the loss of millions of lives in different countries. The virus was thought to be contagious, so the government initially severely forced citizens to keep a distance from each other. Since then, several vaccines have been developed that play an important role in controlling mortality. In the case of Covid-19 mortality, the government should be forced to take significant steps in the form of lockdown, keeping you away or forcing citizens to vaccinate. In this paper, modeling of Covid-19 death rates is discussed via probability distributions. To delineate the performance of the best fitted model, the mortality rate of Pakistan and Afghanistan is considered. Numerical results conclude that the NFW model can be used to predict the mortality rate for Covid-19 patients more accurately than other probability models.

Statistical Analysis of the COVID-19 Mortality Rates with Probability Distributions: The Case of Pakistan and Afghanistan.

The COVID-19 pandemic has shocked nations due to its exponential death rates in various countries. According to the United Nations (UN), in Russia, there were 895, in Mexico 303, in Indonesia 77, in Ukraine 317, and in Romania 252, and in Pakistan, 54 new deaths were recorded on the 5th of October 2021 in the period of months. Hence, it is essential to study the future waves of this virus so that some preventive measures can be adopted. In statistics, under uncertainty, there is a possibility to use probability models that leads to defining future pattern of deaths caused by COVID-19. Based on probability models, many research studies have been conducted to model the future trend of a particular disease and explore the effect of possible treatments (as in the case of coronavirus, the effect of Pfizer, Sinopharm, CanSino, Sinovac, and Sputnik) towards a specific disease. In this paper, varieties of probability models have been applied to model the COVID-19 death rate more effectively than the other models. Among others, exponentiated flexible exponential Weibull (EFEW) distribution is pointed out as the best fitted model. Various statistical properties have been presented in addition to real-life applications by using the total deaths of the COVID-19 outbreak (in millions) in Pakistan and Afghanistan. It has been verified that EFEW leads to a better decision rather than other existing lifetime models, including FEW, W, EW, E, AIFW, and GAPW distributions.

A study on the role of aedes mosquitoes in arboviruses and SARS-CoV-2 infection: A new challenge.

Chikungunya, Zika, Dengue Viruses, and now Novel Coronavirus are global health challenges that cause human diseases ranging from febrile illnesses to death. Most of these viruses are mainly vectored by Aedes mosquitoes worldwide. Molecular detection of arboviruses was made in female Aedes mosquito pools caught from all the seven districts by using a reliable molecular technique, "RT-PCR." From 216 collections of Aedes species, arboviruses were detected in 27, including only Alphavirus genus to determine mosquito abundance and evaluate the potential role of Aedes aegypti and Ae. albopictus mosquitoes in arboviruses and nvel Coronavirus transmission. 5322 mosquitoes were collected using aspirators; 35.31% (n = 2049) were identified as female Aedes using morphological keys, pooled into 216 pools, and tested for arboviruses and coronaviruses by using RT-PCR with the help of specific primers. Novel Coronavirus was not detected in this study. Only the Flavivirus genus was detected in twenty-seven pools giving an infection rate of 62.96% (n = 17) for DENV2, while DENV3 was 37.03% (n = 10). Furthermore, our results indicated no role of mosquitoes in the spread of Covid-19. Results showed a higher infection rate in urban sites than in rural ones. The detection of arboviruses indicates possible human health risk due to active role of these mosquitoes in spreading of arbovirus in the study area.

A critical analysis of SARS-CoV-2 (COVID-19) complexities, emerging variants, and therapeutic interventions and vaccination strategies.

Coronavirus is a family of viruses that can cause diseases such as the common cold, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS). The universal outbreak of coronavirus disease 2019 (COVID-19) caused by SARS coronaviruses 2 (SARS-CoV-2) has become a global pandemic. The ß-Coronaviruses, which caused SARS-CoV-2 (COVID-19), have spread in more than 213 countries, infected over 81 million people, and caused more than 1.79 million deaths. COVID-19 symptoms vary from mild fever, flu to severe pneumonia in severely ill patients. Difficult breathing, acute respiratory distress syndrome (ARDS), acute kidney disease, liver damage, and multi-organ failure ultimately lead to death. Researchers are working on different pre-clinical and clinical trials to prevent this deadly pandemic by developing new vaccines. Along with vaccines, therapeutic intervention is an integral part of healthcare response to address the ongoing threat posed by COVID-19. Despite the global efforts to understand and fight against COVID-19, many challenges need to be addressed. This article summarizes the current pandemic, different strains of SARS-CoV-2, etiology, complexities, surviving medications of COVID-19, and so far, vaccination for the treatment of COVID-19.

Review of hospital plastic waste management strategies for Pakistan.

Healthcare waste management is considered one of the biggest challenges that the world is going to face in the future. This threat is becoming reality owing to the worldwide sharp rise in healthcare waste generation particularly during the current COVID-19 pandemic. Like many other environmental crises, hospital plastic waste management is an area that got very little attention despite being highlighted in the literature, local media, as well as in international electronic and print media. This mini-review was conducted to assess the overall prevailing situation regarding hospital plastic waste management in Pakistan. Several illegal and unethical activities have been observed regarding hospital plastic waste management in Pakistan which includes unhygienic recycling, repacking of used hospital plastic items, open dumping on land, and disposal of hospital plastic waste in the ocean. To improve these conditions, suggestions have been made regarding the better management of hospital plastic waste.

An innovative user-friendly platform for Covid-19 pandemic databases and resources.

BACKGROUND: The current coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global outbreak of a disease from a new coronavirus. Several databases have been published on this pandemic, but the research community still needs an easy way to get comprehensive information on COVID-19. OBJECTIVES: COVID-19 pandemic database (CO-19 PDB) aims to provide wonderful insights for COVID-19 researchers with the well-gathered of all the COVID-19 data to one platform, which is a global challenge for the research community these days. METHODS: We gathered 59 updated databases since December-2019 until May 2021 and divided them into six categories: digital image database, genomic database, literature database, visualization tools database, chemical structure database, and social science database. These categories focus on taking number of functions from the images, information from gene sequences, updates from relevant papers, essays, reports, articles, and books, the data or information in the form of maps, graphs, and charts, information of bonds between atoms, and updates about events of the physical and social environment, respectively. RESULTS: Users can search the information of interest in two ways including typing the name of the database in the search bar or by clicking the right category directly. Computer languages such as CSS, PHP, HTML, Java, etc. are utilized to construct CO-19 PDB. CONCLUSION: This article attempts to compile up-to-date appropriate COVID-19 datasets and resources that have not been compiled and given in such an accessible and user-friendly manner. As a result, the CO-19 PDB offers extensive open data sharing for both worldwide research communities and local people. Further, we have planned future development of new features, that will be awesome for future study.

A new approach for modeling covid-19 death data

The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW. [ABSTRACT FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press 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.)