Organized Crime

The face of organized crime is continually changing, yet its foundation has remained the same over many years. One of the distinguishing features of organized crime is in the challenge to clearly define it, which impacts on the implementation of transnational prevention and control policies. Although organized crime is as much disorganized as it is organized, it does feature specific characteristics. In order to understand organized crime, it must be viewed as a social process and a community social institution. Organized crime is, in its nature, highly adaptable to opportunities that major incidents, such as the Covid-19 pandemic, bring. © 2022 Elsevier Inc. All rights reserved.

MobApp4InfectiousDisease: Classify COVID-19, Pneumonia, and Tuberculosis

Illness due to infectious diseases has been always a global threat. Millions of people die per year due to COVID-19, pneumonia, and Tuberculosis (TB) as all of them infect the lungs. For all cases, early screening/diagnosis can help provide opportunities for better care. To handle this, we develop an application, which we call MobApp4InfectiousDisease that can identify abnormalities due to COVID-19, pneumonia, and TB using Chest X-ray image. In our MobApp4InfectiousDisease, we implemented a customized deep network with a single transfer learning technique. For validation, we offered in-depth experimental study and we achieved, for COVID-19-pneumonia-TB cases, accuracy of 97.72%196.62%199.75%, precision of 92.72%1100.0%199.29%, recall of 98.89%188.54%199.65%, and F1-score of 95.00%194.00%199.00%. Our results are compared with state-of-the-art techniques. To the best of our knowl-edge, this is the first time we deployed our proof-of-the-concept MobApp4InfectiousDisease for a multi-class infec-tious disease classification. © 2022 IEEE.

Dominican Republic: The Response to the Pandemic in 2021

This article presents the case of the Dominican Republic in relation to its emergency preparedness, environmental and social vulnerability framework, its response to the COVID-19 pandemic, including the policies implemented to manage it, and its prospects for the future. The Dominican Republic, being highly vulnerable to climate change and environmental risks, needs to be prepared for national emergencies, including the current pandemic. At the time of the pandemic, the country had a weak health system and weak public funding and, in this context, experienced a significant number of confirmed cases. The country’s trajectory in terms of the number of cases, mortality, and availability of beds and intensive care units for the disease is analyzed, and the experience is compared with other countries in the Americas. As of November 2021, the Dominican Republic has been able to respond adequately, maintaining one of the lowest case-fatality rates in the region and substantially controlling its number of cases in the last year, especially after the vaccination process was initiated. The control measures implemented in the country, consisting of restrictive and timely distancing policies, are noteworthy. Likewise, the country’s successful vaccination program is being followed up as part of these measures. Regardless of the good management of the pandemic by the Dominican Republic and the positive outlook for the future in economic and social areas, the need to improve the preparedness of the country’s health system, such as increasing public spending on health and investment in the first level of care, is emphasized. © 2022 Academia Nacional de Medicina. All rights reserved.

Sentiment Analysis of COVID-19 Vaccination Responses From Twitter Using Ensemble Learning

Despite the evidence that shows the benefits and safety of immunizations, the widespread vaccine-related misinformation and conspiracy theories online have fueled a general vaccine hesitancy, and coronavirus disease (COVID-19) vaccinations are no exception. COVID-19 vaccine hesitancy is considered a global threat to public health that undermines the efforts to control the COVID-19 pandemic. Twitter and other social media platforms allow people to exchange information and express concerns and emotions on COVID-19-related issues. This research aims to understand people's sentiment on COVID-19 vaccines from data collected from Twitter. Analyzing the public attitude toward the vaccines helps the authorities to make better decisions and reach the intended herd immunity. In this paper, we utilize the state-of-the-art transformer-based classification models, RoBERTa and BERT, along with multiple task-specific versions, to classify people's opinions about COVID-19 vaccinations into positive, negative, and neutral. A Twitter dataset that consists of people's opinions about vaccines is used to train and evaluate the presented models. Two ensemble learning techniques that aggregate the individual classifiers are presented for further performance improvement: majority voting and stacking with Support Vector Machine (SVM) as meta-learner. The results also show that applying ensemble learning significantly outperforms the individual classifiers using all evaluation measures. We also found that ensembling with stacking has an advantage over simple majority voting. © 2022 IEEE.

Priority tasks and marine aquaculture development strategy

Aquaculture (artificial cultivation, processing and sale of aquatic biological resources: fish and seafood) plays the role of a powerful locomotive for the development of the food sector of the 21st century and can become one of the catalysts for the deep economic, social and environmental changes in food systems around the world. COVID-19 significantly halted globalization processes, strengthening social autonomy, closing many markets, which affected the volume of trade in fish and seafood and its subsequent processing for the catering industry. Global expectations imply further closure of borders and localization of the business, reduction in the number of employees and distance from large urbanization sites, which suggests the need for a new model for the development of the fish resource business in the context of sustainable development, environmental and social significance of the safest food. The current state of the functioning of the aquaculture and seafood market is investigated and proposals for the development of the industry are formulated. An analysis of international approaches revealed the need for structural transformation of the industry in accordance with the global concept of sustainable development of the agro-food complex and food security. The application of the proposed measures will lead to the achievement of performance indicators in line with international standards, improve the livelihoods of the adjacent urbanization areas and the overall economic progress of the country, and strengthen the system of organization, control and management of the aquatic biological resources sector. © 2022 Author(s).

Analysis and Prediction of COVID-19 datasets using Machine Learning Algorithms

The devastating spread caused by Severe Acute Respiratory Disorder - Coronavirus (SARS-CoV-2) which is also known as COVID-2019 has brought global threat to our society. Every country is making immense efforts to stop the spread of the deadly disease through the use of finance, infrastructure and data sources, as well as protective devices, life-risk treatments, as well as other sources. Researchers studying artificial intelligence focus their skills to create mathematical models for studying the scourge of this disease using and shared data. In order to improve the wellbeing of our society. This article proposes using model of deep and machine-learning to understand its daily exponential behavior, as well as the prediction of the future impact of the COVID-2019 across nations using the live data of the Johns Hopkins dashboard © 2022 IEEE.

A Simulation Study of COVID-19 Out Break as an Epidemic Disease

The COVID-19 Pandemic is regarded as the worst public health disaster in the history of the world. As of September 2021, around 219 million cases of the coronavirus have been confirmed globally. The pandemic caused by the disease is considered a global threat. It has caused 4.5 million deaths globally. The coronavirus pandemic caused by the COVID-19 disease is disrupting various sectors of the economy. It has caused havoc in the aviation, retail, and financial markets. This study aims to investigate the effects of the Coronavirus to answer concerns about the number of people affected during influenza outbreaks. we propose to use the MATLAB tool to study the spread of infectious diseases across a population. This paper presents a spatially explicit simulation model of infectious illness to study the spatial dispersion of diseases in humans. © 2022 IEEE.

Analysing the Twitter Sentiments in COVID-19 Using Machine Learning Algorithms

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores. © 2022 IEEE.

Regression Analysis and Prediction of the COVID-19

The corona virus disease is recognized as a global threat to the health industry and is a new challenge to the research area. To deal with this corona virus disease (COVID-19), which is currently sparked, all over the globe, machine learning (ML) plays a major role in variety of ways. This paper presents the analysis of the deadly COVID-19 outbreak to fight against this pandemic. This study is based on the dataset of confirmed cases, deaths, and recoveries worldwide as provided by the Johns Hopkins University. At first, we analyzed the pattern and characteristics of the growth of the pandemic by publicly available data. Secondly, we presented a comparative study and, finally, developed a future forecast model by taking three machine learning algorithms are support vector machine, linear regression, and Bayesian ridge regression. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Developing an Assessment Framework of the Recovery Calendar for COVID-19 Calamity: Based on the Data from the June 2021 Survey

The COVID-19 pandemic continues to pose a global threat. It is considered a CBRNE (chemical, biological, radiological, nuclear, explosive) disaster that has caused not only a public health crisis but also psychological, social, and economic problems. The recovery of social and economic activities remains an urgent issue. This study developed an assessment framework of the “recovery calendar” to visualize the process of people’s recognition of recovery from the COVID-19 calamity. Data on this recovery calendar were collected from an online questionnaire survey administered on a total of 449 respondents from 10 groups divided by gender (male or female) and age (20s, 30s, 40s, 50s, 60s, and above). The results showed that the recovery process took place in the following order: Recognition of COVID-19’s impact on society and of the imposition of a constrained lifestyle, recognition of returning to work or the resumption of local schools, and finally, recognition of the recovery of the household and local economies, although these remained at a low level of activity. Importantly, the recovery progressed slowly. The results also indicated that measures such as the declaration or lifting of the state of emergency, or the “Go To” travel campaign, affected people’s recognition of recovery. Moreover, the recognition of recovery depended on social demographics. Men, younger people, and those with a stable life base were more likely to perceive recovery from the disaster. This study discussed the applicability of the assessment framework of the recovery calendar to visualize people’s recovery process from the COVID-19 calamity. © Fuji Technology Press Ltd.

Covid-19 Analysis using Deep Learning Methods and Computed Tomography Scans

Covid-19 has quickly emerged as a global threat, tipping the world into a new phase. The delay in medical care because of the quickly rising Covid-19 cases makes it necessary to overcome the manual and time taking technique such as RTPCR. This paper implements different pre-trained CNN feature extraction models using various Machine Learning (ML) classifiers on chest CT scans to analyze Covid-19 infected patients. It may be observed from the obtained results that accuracy of 96.4% was obtained using the VGG16 model and neural network classifier. The implementation of pre-trained models and classifiers reduce the time taken for manual detection of disease and helps doctors to prevent life of a patient. © 2021 IEEE.

Russian Regions and the Federal Center Against Global Threats: a Year Of Fighting COVID-19

—: The article examines the preliminary results and lessons of interaction between the federal center and Russian regions in countering global threats. The specifics of the Russian Federation are revealed that distinguish it from a number of other states in fighting COVID-19, both negative and favorable factors and conditions are considered. It is shown that the Russian Federation, despite how grave the situation was, demonstrated fairly high resistance to unraveling coronavirus crisis during the first wave of COVID-19. In 2020, in federal relations between the center and regions, there were tendencies towards decentralization;however, the redistribution of powers to regions often ran up against the low quality of regional governance. In the context of joint confrontation with global threats, it is shown that in Russia, it is necessary to find a compromise between competitive and cooperative federalism. The pandemic-related growth of the digital economy, expansion of teleworking practices, use of telemedicine, etc., will lead to an increase in digital inequality, asymmetry, and competition among Russian regions. This will require special forms of government regulation and appropriate resources. The article concludes that only a sharp breakthrough in the knowledge economy is capable of ensuring Russia’s independence in the future in combating similar global threats (as well as consolidating and strengthening the practice of proactive and effective public administration at all levels: federal, regional and local). © 2021, Pleiades Publishing, Ltd.

The Global Pandemic Has Shown We Need an Action Plan for the Ocean

The urgency of the challenge requires an internationally coordinated effort that draws on existing global research capacity and networks;a key opportunity presented by the UN Decade of Ocean Science for Sustainable Development 2021-2030 (UNESCO-IOC, 2021) that must not be missed if we are to minimize change in ocean systems and impacts on the services they provide to society. Without mitigation and adaptation measures, sea level rise scenarios project annual losses of 0.3–9.3% of global GDP by 2100 (IPCC, 2019;equivalent to ~US$0.25 to US$7.88 trillion per year based on 2020 GDP, World Bank, 2021), while losses from declines in ocean health and services by 2050 are projected to be US$428 billion per year, and by 2100 US$1.979 trillion per year. Over the last decade, a number of programmes and projects have driven international efforts to develop the integration of human systems in global ocean ecosystem science, including the Integrated Marine Biosphere Research (IMBeR) project (Hofmann et al., 2015) to which the authors contribute. To help meet this challenge, the UN Decade of Ocean Science for Sustainable Development 2021-2030 (UNESCO-IOC, 2021) provides an opportunity to build global support systems for informing decision making on the critical time scales of the coming years and decades.