Air Quality Assessment over Sudan using NASA Remote Sensing Satellites Data and MERRA-2 Model

Satellite remote detecting instruments have been to a great extent used to evaluate air pollutants on the ground and their impacts on human wellbeing. These instruments play an essential job by assessing emanations and air quality models yield. The study concentrated on the analysis of monthly data for the period January 2003 -December 2016 using remote sensing technology and via satellite data products for NASA's Earth navigation satellite. The tools used are Medium Resolution Imaging Spectrophotometer (MODIS), Multi-angle Imaging Spectrophotometer (MISR), the Ozone Monitoring Instrument (OMI), and the Retrospective Analysis of Modern Times for Research and Applications, Version 2 (MERRA-2). Sudan is influenced by airborne particles because of its diverse climate systems, which differ from the desert in the north to poor savanna in the center and to rich savanna in the south. The impact of air pollution is obvious during these years in Sudan. Likewise, OMI trace gas vertical column observations of nitrogen dioxide (NO2) watched higher convergences of tropospheric column NO2 in 2016 than in 2005 over Khartoum that recommends NOx emissions have increased in the city over this time period. The most elevated grouping of dust, a particulate matter (PM2.5), observed in March 2012 over Khartoum state. The highest concentration of sulfur dioxide (SO2) saw by MERRA-2 over Kuwait and South Sudan during December 2015. Noteworthy centralization concentration of black carbon observed over Iraq, Egypt, Central Africa, and South Sudan in December 2015. The most contamination from carbon monoxide watched by MERRA-2 over Iraq and north of Uganda during December 2014

Factors Influencing Blockchain-based Mobile Banking Adoption: Evidence from a Developing Country

This study attempts to explain the factors influencing blockchain-based mobile banking acceptance in Bangladesh. Based on a technology acceptance framework termed UTAUT2 (unified theory of acceptance and use of technology 2), an enhanced model with a mediating variable is built for this research. Data were collected from the first-ever blockchain-based mobile banking stakeholders in Bangladesh called 'UPAY' by applying a structured questionnaire. Structural equation modeling was then processed using Smart-PLS. There are eight direct hypotheses and one mediating hypothesis in this research. The findings reveal that all of the direct hypotheses except the impact of social influence on the behavioural intention (BI) to use blockchain are statistically significant. The mediating role of BI in the connection between facilitating conditions (FC) and actual blockchain use is also supported. The combination of FC and BI contributes to 88.8% of the variation in blockchain usage behaviour for mobile banking adoption. The findings of this study can help banking regulators devise a strategy for engaging a significant number of banks to create a blockchain-based mobile banking platform Keywords: Blockchain use behaviour; Mobile banking, PLS-SEM; UPAY; UTAUT

Performance Evaluation of Ad Hoc Network over Moving Vehicles in a City

A mobile ad hoc network (MANET) is a collection of wireless mobile nodes that can dynamically form a temporary network without the aid of any existing network infrastructure. Wireless connectivity on vehicles is an important mode of communication. It is more challenging to provide high-bandwidth networking over fast moving vehicles. Ad Hoc network can be formed on fast moving vehicles where the interior node acts as rely node. A dynamic routing protocol is needed for a node to exchange data with another. In this research work, we consider the traffic density of a typical district town where traffic density much lower than a metropolitan city and vehicle speed is regulated according to traffic law. We have studied two routing protocols AODV and DSR in city traffic. According to our study, AODV shows performance than DSR on city roa

Host candidate gene polymorphisms and clearance of drug-resistant Plasmodium falciparum parasites

Resistance to anti-malarial drugs is a widespread problem for control programmes for this devastating disease. Molecular tests are available for many anti-malarial drugs and are useful tools for the surveillance of drug resistance. However, the correlation of treatment outcome and molecular tests with particular parasite markers is not perfect, due in part to individuals who are able to clear genotypically drug-resistant parasites. This study aimed to identify molecular markers in the human genome that correlate with the clearance of malaria parasites after drug treatment, despite the drug resistance profile of the protozoan as predicted by molecular approaches

Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network

BACKGROUND: The current burden of >5 million deaths yearly is the focus of the Sustainable Development Goal (SDG) to end preventable deaths of newborns and children under 5 years old by 2030. To accelerate progression toward this goal, data are needed that accurately quantify the leading causes of death, so that interventions can target the common causes. By adding postmortem pathology and microbiology studies to other available data, the Child Health and Mortality Prevention Surveillance (CHAMPS) network provides comprehensive evaluations of conditions leading to death, in contrast to standard methods that rely on data from medical records and verbal autopsy and report only a single underlying condition. We analyzed CHAMPS data to characterize the value of considering multiple causes of death. METHODS AND FINDINGS: We examined deaths identified from December 2016 through November 2020 from 7 CHAMPS sites (in Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa), including 741 neonatal, 278 infant, and 241 child <5 years deaths for which results from Determination of Cause of Death (DeCoDe) panels were complete. DeCoDe panelists included all conditions in the causal chain according to the ICD-10 guidelines and assessed if prevention or effective management of the condition would have prevented the death. We analyzed the distribution of all conditions listed as causal, including underlying, antecedent, and immediate causes of death. Among 1,232 deaths with an underlying condition determined, we found a range of 0 to 6 (mean 1.5, IQR 0 to 2) additional conditions in the causal chain leading to death. While pathology provides very helpful clues, we cannot always be certain that conditions identified led to death or occurred in an agonal stage of death. For neonates, preterm birth complications (most commonly respiratory distress syndrome) were the most common underlying condition (n = 282, 38%); among those with preterm birth complications, 256 (91%) had additional conditions in causal chains, including 184 (65%) with a different preterm birth complication, 128 (45%) with neonatal sepsis, 69 (24%) with lower respiratory infection (LRI), 60 (21%) with meningitis, and 25 (9%) with perinatal asphyxia/hypoxia. Of the 278 infant deaths, 212 (79%) had ≥1 additional cause of death (CoD) beyond the underlying cause. The 2 most common underlying conditions in infants were malnutrition and congenital birth defects; LRI and sepsis were the most common additional conditions in causal chains, each accounting for approximately half of deaths with either underlying condition. Of the 241 child deaths, 178 (75%) had ≥1 additional condition. Among 46 child deaths with malnutrition as the underlying condition, all had ≥1 other condition in the causal chain, most commonly sepsis, followed by LRI, malaria, and diarrheal disease. Including all positions in the causal chain for neonatal deaths resulted in 19-fold and 11-fold increases in attributable roles for meningitis and LRI, respectively. For infant deaths, the proportion caused by meningitis and sepsis increased by 16-fold and 11-fold, respectively; for child deaths, sepsis and LRI are increased 12-fold and 10-fold, respectively. While comprehensive CoD determinations were done for a substantial number of deaths, there is potential for bias regarding which deaths in surveillance areas underwent minimally invasive tissue sampling (MITS), potentially reducing representativeness of findings. CONCLUSIONS: Including conditions that appear anywhere in the causal chain, rather than considering underlying condition alone, markedly changed the proportion of deaths attributed to various diagnoses, especially LRI, sepsis, and meningitis. While CHAMPS methods cannot determine when 2 conditions cause death independently or may be synergistic, our findings suggest that considering the chain of events leading to death can better guide research and prevention priorities aimed at reducing child deaths

Reversible ionic surfactants for the preperation of supported nanoparticle catalysts

Developing sustainable catalyst preparation techniques has received considerable attention recently. One approach to improving sustainability is by improving catalyst efficiency, minimizing waste, and minimizing the energy requirements for preparing and using catalysts. In this work we present the synthesis, deposition, and catalytic evaluation of gold nanoparticles synthesized using reversible ionic liquids. The nanoparticles are supported on silica gel, and their activity is evaluated for the hydrogenation of 4-nitrophenol. We show that leveraging the switchable nature of the ionic liquids produces catalytically active supported nanoparticles that do not require calcination. We compare the supported gold nanoparticles synthesized in this manner to supported gold nanoparticles stabilized with citrate, and then subsequently calcined. In this work we present a promising sustainable synthesis technique that utilizes a recyclable solvent (thereby minimizing waste) to produce catalytically active nanoparticles that do not require calcination. The particles show similar activity to gold nanoparticles synthesized using traditional techniques. The nanoparticles we synthesize using reversible ionic did not need to undergo the calcination step. Calcining these particles actually inhibited their activity significantly

Kamus Arab Indonesia

239 hlm. : - ; 20 cm

Molecular Characterization of Sudanese and Southern Sudanese Chicken Breeds Using mtDNA D-Loop

The objective of this study was to assess the genetic relationships and diversity and to estimate the amount of gene flow among the five chicken populations from Sudan and South Sudan and commercial strain of egg line White Leghorn chickens. The chicken populations were genotyped using mtDNA D-loop as a molecular marker. PCR product of the mtDNA D-loop segment was 600 bp and 14 haplotypes were identified. The neighbor-joining phylogenetic tree indicated that the indigenous Sudanese chickens can be grouped into two clades, IV and IIIa only. Median joining networks analysis showed that haplotype LBB49 has the highest frequency. The hierarchal analysis of molecular variance (AMOVA) showed that genetic variation within the population was 88.6% and the differentiation among the population was 11.4%. When the populations was redefined into two geographical zones, rich and poor Savanna, the results were fractioned into three genetic variations: between individuals within population 95.5%, between populations within the group 0.75%, and genetic variation between groups 3.75%. The pair wise Fst showed high genetic difference between Betwil populations and the rest with Fst ranging from 0.1492 to 0.2447. We found that there is large number of gene exchanges within the Sudanese indigenous chicken (Nm=4.622)

A comparative study of machine learning-based load balancing in high-speed

With the rapid developments of fifth generation (5G) mobile communication networks in recent years, different use cases can now significantly benefit from 5G networks. One such example is high-speed trains found in several countries across the world. Due to the dense deployment of 5G millimetre wave (mmWave) base stations (BSs) and the high speed of moving trains, frequent handovers (HOs) occur which adversely affect the Quality-of-Service (QoS) of mobile users. User association for load balancing is also a key issue in 5G networks. Therefore, HO optimisation and resource allocation are major challenges in the mobility management of high-speed train systems. Handover Margin (HOM) and Time-to-Trigger (TTT) parameters are crucial for the HO process since they affect the key performance indicators (KPIs) of high-speed train systems in 5G networks. To manage system performance from the aspect of predictive analytics, we have modelled system performance of mobility management through machine learning (ML). First, the HO management process of a high-speed train scenario is framed as a supervised ML problem. The inputs for the problem are regression task, HOM and TTT and the outputs are key performance indicators (KPIs). Second, data processing is accomplished after generating a simulation dataset. Several methods are employed for the dataset, such as Adaptive Boosting (AdaBoost), Gradient Boosting Regression (GBR), CatBoost Regression (CBR), Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Kernel Ridge Regression (KRR) and K-Nearest Neighbour Regression (KNNR). Tenfold cross validation is then applied for choosing the best hyperparameters. Finally, the deployed methods are compared in terms of the Mean Absolute Error (MAE), Mean Square Error (MSE), Maximum Error (Max E), and R2 score metrics. From the MAE results, CBR achieves the best outcomes for load level and throughput KPIs with 0.003 and 0.0144, respectively. On the other hand, GBR achieves the best results for call dropping ratio (CDR), radio link failure (RLF) and spectral efficiency KPIs with 0.354, 0.082 and 0.354, respectively. CBR also follows GBR for the three KPIs with 0.356, 0.082 and 0.357, respectively. Only a slight difference in estimations is present. MLP achieves the best results for HO ping-pong (HOPP) and HO probability (HOP) KPIs with 0.0045 and 0.011, respectively. This is followed by GBR and CBR. From the MSE outcomes, CBR and GBR exhibit the best results for load level and throughput KPIs with 2e-5 and 3e-5, respectively. GBR attains the best results for CDR, RLF and spectral efficiency KPIs with 0.25, 0.011 and 0.025, respectively. Accordingly, CBR follows GBR with slightly different errors for the three KPI estimations. MLP achieves the best results for HOPP and HOP KPIs with 5e-5 and 3.6e-5, respectively. Again, this is followed by GBR and CBR for the estimation of these results. This indicates that CBR and GBR can capture relationships between inputs and KPIs for the dataset used in this study, outperforming all other methods generally used for solving this problem