5 emission fractions for combustion and industrial process sources. In addition, the meteorological influences favorable for transboundary pollutant transport have weakened during the period under COVID-19 social distancing.  https://www.selleckchem.com/products/Temsirolimus.html  Intra-city comparisons show that decreases in the intra-city variability of PM2.5 concentration were larger in coastal cities than in inland cities. Comparisons between the inter- and intra-city variabilities in the PM2.5 concentration changes under social distancing highlight the importance of taking into account intra-city variabilities in addition to inter-city variabilities.The COVID-19 pandemic had a substantial impact on the historical criminal trend around the world. This study explores the early impact of COVID-19 lockdown on selected crimes in Dhaka, Bangladesh. Based on open data of the total number of arrests reported by Dhaka Metropolitan Police (DMP), an uninterrupted historical time series analysis is applied to evaluate the immediate impact during and after the official stay-at-home order due to COVID-19. Auto-regressive moving average (ARIMA) modeling technique was used to compute 6-month-ahead forecasts of the expected frequency of the total number of arrests for illegal arms dealing, vehicle theft, and narcotics trafficking in the absence of the pandemic. These forecasts were compared with the observed data from April 2020 to September 2020. The results suggest that the observed numbers of total arrests for vehicle thefts and illegal arms dealing are not significantly different from their predicted values. However, the observed frequency of the total number of arrests for illegal drug trafficking shows a steep upward trend, which is 75% more than that of the expected frequencies. Estimated results are used to recognize scopes and suggestions for future research on the relationship between crimes and the pandemic.The spread of COVID-19 put prisons across the globe into an emergency state where extraordinary reactions and measures have been taken. Prison governance and management under such circumstances have facilitated the revelation of existing mechanisms of control. Focusing on the experience of frontline officers, this paper explores how the Chinese prison system contained the spread of COVID-19 inside its walls by demanding officers work on 'lockdown shifts', and what we can learn about its governing logic. Multi-sourced data is utilized, including government-issued policies and reports, media reports, blog posts written by prison officers and participant observation as well as semi-structured interviews with frontline prison officers. This study offers a diachronic analysis of pandemic control within the prison system, focusing on key turning points. By examining frontline prison officers' accounts through first- and second-hand data, the study explores the execution of control policies and how they affect individual lives. The study found that prison officers were ordered to fight at the forefront of pandemic control in prisons by working on shifts inside for an extended and indefinite period of time, which proved effective in terminating the spread of the virus, but placed a heavy burden on the personal lives of the officers. The findings also reveal new facets in the mobility and experience of frontline officers. While effective in terms of what the statistics have demonstrated, the Chinese measures have been less effective in adjusting to the needs of frontline staff and acknowledging the personal sacrifices demanded and made in this process.At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary.