Non-compliance to social distancing during COVID-19 pandemic: A comparative cross-sectional study between the developed and developing countries

Background: Despite mass vaccination campaigns, the world has seen a steady rise in the number of SARS-CoV-2 cases, with 178,765,626 cases and 3,869,994 COVID-19 related deaths by June 19th, 2021. Therefore, it is important to enforce social distancing to control its spread. With the variation observed in the severity of the pandemic in different countries, it is also imperative to study the social distancing behaviors amongst the population in developed and developing countries. Design and Methods: In this cross-sectional study, a total of 384 participants from 14 different countries were surveyed via an online REDCap form. Results: In this study, it was highlighted that despite adequate knowledge, the overall compliance to COVID-19 related preventive measures remains poor, the lowest being in the senior age group (≥ 65 years), and the highest being in adults aged between 25-64 years (p-value =0.003). Population from the developing countries were more compliant to all preventative measures against COVID-19 spread, except for handwashing, where the difference between the two populations remained insignificant (p-value = 0.038, \u3c0.001, 0.016). Socioeconomic status, prior history of COVID-19 infection, or presence of comorbidities did not significantly affect compliance rates, however, participants with no prior history of this infection were found to be more compliant to donning a mask in public as compared to those with a positive history (p-value = 0.044). Conclusions: Since compliance remains subpar in both the developing and the developed countries, mass campaigns about COVID-19 related preventive measures remain essential in controlling the disease spread

Computer Aided Design of a Low-Cost Painting Robot

The application of robots or robotic systems for painting parts is becoming increasingly conventional; to improve reliability, productivity, consistency and to decrease waste. However, in Pakistan only highend Industries are able to afford the luxury of a robotic system for various purposes. In this study we propose an economical Painting Robot that a small-scale industry can install in their plant with ease. The importance of this robot is that being cost effective, it can easily be replaced in small manufacturing industries and therefore, eliminate health problems occurring to the individual in charge of painting parts on an everyday basis. To achieve this aim, the robot is made with local parts with only few exceptions, to cut costs; and the programming language is kept at a mediocre level. Image processing is used to establish object recognition and it can be programmed to paint various simple geometries. The robot is placed on a conveyer belt to maximize productivity. A four DoF (Degree of Freedom) arm increases the working envelope and accessibility of painting different shaped parts with ease. This robot is capable of painting up, front, back, left and right sides of the part with a single colour. Initially CAD (Computer Aided Design) models of the robot were developed which were analyzed, modified and improved to withstand loading condition and perform its task efficiently. After design selection, appropriate motors and materials were selected and the robot was developed. Throughout the development phase, minor problems and errors were fixed accordingly as they arose. Lastly the robot was integrated with the computer and image processing for autonomous control. The final results demonstrated that the robot is economical and reduces paint wastage

Assessment of weather research and forecasting (WRF) physical schemes parameterization to predict moderate to extreme rainfall in poorly gauged basin

Incomplete hydro-meteorological data and insufficient rainfall gauges have caused difficulties in establishing a reliable flood forecasting system. This study attempted to adopt the remotely sensed hydro-meteorological data as an alternative to the incomplete observed rainfall data in the poorly gauged Kuantan River Basin (KRB), the main city at the east coast of Peninsula Malaysia. Performance of Weather Research and Forecasting (WRF) schemes’ combinations, including eight microphysics (MP) and six cumulus, were evaluated to determine the most suitable combination of WRF MPCU in simulating rainfall over KRB. All the obtained results were validated against observed moderate to extreme rainfall events. Among all, the combination scheme Stony Brook University and Betts–Miller–Janjic (SBUBMJ) was found to be the most suitable to capture both spatial and temporal rainfall, with average percentage error of about ±17.5% to ±25.2% for heavy and moderate rainfall. However, the estimated PE ranges of −58.1% to 68.2% resulted in uncertainty while simulating extreme rainfall events, requiring more simulation tests for the schemes’ combinations using different boundary layer conditions and domain configurations. Findings also indicate that for the region where hydro-meteorological data are limited, WRF, as an alternative approach, can be used to achieve more sustainable water resource management and reliable hydrological forecasting

Global, regional, and national burden of chronic kidney disease, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Urban Expansion Analysis Using Semi- Supervised Classification (SSIC) of Landsat-5 Image: A Case Study in Kuantan, Malaysia

The Information of Land use/Land cover (LULC) modification is an important component for the sustainable environmental planning and management. It is required to monitor the changes of LULC under the diverse demographic conditions. Kuantan the city of East Coast part peninsular Malaysia, has been going to experience changes in LULC since the majority of population started migrating from rural to urban areas for economic and social cause. Therefore, changes in land use accompanying with deplantantation, removal of soil, and degrading land surface fluctuating the rainfall runoff relationship follow-on peak discharge and increased flood frequencies. This study has attempted to evaluate and investigate the dynamics of urban growth in Kuantan, based on the technique of Semi- supervised classification using Landsat-5. The methodology is purposed for SSIC by incorporating high resolution of Google Earth images, offers high resolution satellite imageries in different date and times for many places. Land-5 (TM) colour composite Images of 1993, 1999 and 2010 were employed for calibration and correction for atmospheric noise to ensure accuracy of LULC analysis. The images were then categorized into distinguished feature classes for locating training samples areas for SSIC classification. The Integrated technique of Remote Sensing (RS) and Geographic Information System (GIS) helped to analyse the changes in LULC in Kuantan using well accepted method of maximum likelihood classification (MLC). The accuracy assessment of each classification was done from reference data consist of per feature class. The level of accuracy between the referenced values and classified values of the same image were described by fundamental of error metrics producer accuracy (PA), user accuracy (UA) and Kappa statistic (Kc) . The overall accuracy of each classification were estimated to be 82% in 1993, 1999 and 80% in the year 2010, which reveal SSIC provide a good level of agreement with the kappa coefficient values ranged from 0.789 to 0.761.However, obtained result also revealed the notable increased urban pattern in the study area. The growth trend over the last decades has produced over all 15.96 % north-easterly during the 17 years. The proposed method of assessing Urban expansion in Kuantan based on semi- supervised method were validate with freely online source i-e Google Earth. The acquired results show the virtuous accuracy agreement. Therefore, it consider to be a rise tool for reliable approach of data verification as an alternative of some other assessment techniques such as field survey, topographic maps. The study found, SSIC approach as the reliable, cost effective and time saving techniques. It also enhances the online sources as an alternative for verification of images classification when there is the lack of financial assistance to arrange field survey or and sparse availability of referenced maps

Flood forecasting using semi-distributed hydrological model coupled with weather research and forecasting model

Kuantan River Basin (KRB), is an important watershed of Kuantan District which has been experiencing floods since decades. The incomplete information of hydro-meteorological data, and insufficient rainfall and streamflow gauging stations remain the key factors influenced on flood forecasting accuracy. This study aimed to cope with the problem by bridging the gap of missing hydro-meteorological data using Weather Research and Forecasting (WRF) model coupled with Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model. Three rainfall event categories (extreme, heavy, and moderate) were used to evaluate the model’s capability in simulating flood events. The research was covered 4 objectives; (i) to evaluate the performance of microphysics (MP) and cumulus (CU) schemes parameterization for WRF model,(ii) to identify the best physical schemes combination of WRF for precipitation forecasting at KRB iii) to determine GIS-based hydrological model parameters for KRB, and (iv) to quantify the ability and accuracy of proposed flood forecasting framework based on a coupled semi-distributed hydro-meteorological model. Performance of 48 combinations of WRF schemes including 8 MP and 6 CU schemes were first evaluated to simulate single rainfall event. Then selected top 5 best WRF schemes combinations were further investigated to determine the highest performance scheme to simulate events for all categories in KRB. All the obtained results were validated against the observed rainfall data. HEC-HMS model integrated with ArcGIS was used estimate flood hydrographs. Statistical indices include Percentage Error (PE), Nash-Sutcliff Efficiency (NSE), Root Mean Square Error (RMSE), Hit Rate (HR), False Alarm Ratio (FAR), Proportion of Correction (PC), Threat Score (TS) and Bias (B) were applied to evaluate the model performances. The results of the 48 schemes simulations revealed that all the parametrized schemes were found less sensitive to HR and FAR. an average range of PC (0.61 to 0.67), TS (0.55 to 0.67), and RMSE (41.8 to 54.4) indicated the parametrization of WSM6GF, SBUBMJ, LinGF, MDMBMJ, and MDMGF performed relatively better to simulate the event Comparison results of objective (ii) identified SBUBMJ as the most suitable schemes to capture spatial and temporal rainfall in KRB with mean average PE of ±5.1%, ±20.2%, ±23.7% for extreme, heavy, and moderate rainfall, respectively. In HEC-HMS streamflow calibration and validation processes showed that the parameters Soil Conservation Service- Curve Number (SCS-CN) and Storage Coefficient (R) were found to be sensitive to the model performance. Validation results of the coupled WRF and HEC-HMS simulation revealed satisfactory performance in simulating heavy rainfall events with NSE ranges from 0.59 to 0.65 and 0.73 to 0.83, PE for peak discharge ranges from -23.30% to -36.37%, and peak-volume ranges from -20.8% to -28.9%. Good agreement between the models was identified in moderate rainfall events with NSE ranges 0.73 to 0.83, PE for peak discharge ranges from -6.89% to 14.48%, and peak volume range from 4.7% to 4.9%. For the extreme events, the models indicated low performance with NSE ranges from 0.40 to 0.06, PE of peak discharge from -15.74% to 17.23%, and peak volume from -14.65% to -26.06%. From the overall analysis, the study has determined that WRF model can be applied as the best alternative meteorological input to be used for sparse rainfall gauge areas or areas where rainfall observation stations fail to function. Hence the model framework is significant in providing reliable information on flood forecasting by considering about average percentage error of about ±16% to ±25% flow discharge values

A Critical Review of Floods History in Kuantan River Basin: Challenges and Potential Solutions

Kuantan River basin (KRB) is important watershed passing through Kuantan city of state Pahang. Usually, it receives massive precipitation during north east monsoon season start from November to March. Since past years, it is experiencing severe floods with increased frequency by perceiving heavy rainfall, which produces surpass flow rate than river capacity resulting inundation of low- laying or flood plain areas hampered the human social and economic life. KRB has experienced the worst flood events during recent years that caused massive destruction of land use infrastructure, agriculture and irrigation system, loss of lives and properties, which ultimately affect the revenue loss suffered by Government in recovery of survival and loss. The change in climatic condition and anthropogenic activities following change in nature of flood seems occurrence getting more frequent in urbanized areas. The rapid urbanization leads land degradation and deforestation, which result high flow of surface runoff.However, insufficient drainage system and river evolution and its inadequate capacity also irrefutable factors for flood. The determination of this article is to review the ascertaining KRB flooding factors and discuss the existing challenges that are being faced in order to avert the loss from flood catastrophe

Evaluation of Weather Research and Forecasting (WRF) Microphysics single moment class-3 and class-6 in Precipitation Forecast

In this study, the performance of two different Microphysics Scheme options in Weather Research and Forecasting (WRF) model were evaluated for the estimating the precipitation forecast. The schemes WRF single moment class-3 (WSM-3) and single moment class-6 (WSM-6) were employed to produce the minimum, medium and maximum precipitation for the selected events over the Kuantan River Basin (KRB). The obtained simulated results were compared with the observed data from eight different rainfall gauging stations. The results comparison indicate that WRF model provides better forecasting at some rainfall stations for minimum and medium rainfall events but did not produce good result during maximum rainfall overall. The WSM-6 scheme is found to produce better result compared to WSM-3. The study also found that to acquire accurate precipitation results, it is also required to test some other physics scheme parameterization to enhance the model performance

Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques

Rapid urbanization and the risk of climatic variations, including a rise in temperature and increased rainfall, have urged research in the development of methods and techniques to monitor the modification of land use/land cover (LULC). This study employed the normalized differencing vegetative index (NDVI) and semi-supervised image classification (SSIC) integrated with high-resolution Google Earth images of the Kuantan River Basin (KRB) in Malaysia. The Landsat-5 (TM) images for the years 1993, 1999, and 2010 were selected. The results from both classifications provided a consistent accuracy of assessment with a reasonable level of agreement. However, SSIC was found to be more precise than NDVI. Overall accuracy was 82% for 1993 and 1999, and 80% for 2010, with the kappa values ranging from 0.789 to 0.761. Meanwhile, NDVI accuracy was attained at 64% with kappa value at 0.527 for 1999. In addition, 70% and 72% accuracy were obtained for 1993 and 2010 with estimated kappa values of 0.651 and 0.672, respectively. The study is anticipated to assist decision makers for better emergency response and sustainable land development action plans, thus mitigating the challenges of rapid urban growt