Evaluating impact of demerit points system on speeding behavior of drivers

Objective The objective of this research was to evaluate the immediate impact of the demerit points system on speeding behavior of drivers in Al Ain. Al Ain is the fourth largest city in the United Arab Emirates, located about 120 Km from Dubai. Document type: Articl

Biliary stones: an atypical cause of abdominal pain in paediatric age group

OBJECTIVE: To identify Paediatric patients with biliary stone disease presenting to a tertiary care hospital in order to determine the etiology, presentation and management. METHODS: Retrospective study of all cases of ultrasonographically proven biliary stones under the age of 15 years from January 1988 to December 2008. Data included their risk factors, complications, management and outcome. RESULTS: Total 32 patients were identified with biliary stones, treated in the hospital. Mean age at presentation was 8.25 +/- 3.33 years. Sixteen patients underwent cholecystectomy. CONCLUSION: Paediatric cholelithiasis is an atypical and under-diagnosed cause of abdominal pain in childhood. True prevalence of the disease may be higher than reported. Appropriate surgical intervention is required in patients with symptomatic and complicated biliary lithiasis

Aplicación del modelo de gravedad aumentada de las TIC: análisis por sectores en la región de Asia Pacífico

The use of information and communication technologies (ICT) in commerce improves the commercial structure and economic capacity of a country. This study empirically assesses the impact of ICTs on international trade in 36 countries in Asia and the Pacific, at the sectoral level, between 2007 and 2018. The study evaluates whether ICTs improve international trade by hiring the gravity model of international trade and increasing it with the ICT variable. An ICT development indicator (IDI) is formed by joining seven different ICT variables that show ICT infrastructure, use, and skills. Using the Poisson pseudo-maximum likelihood (PPML) estimation technique, this study shows that ICTs improve trade by reducing transaction costs. The findings reveal that information and communication technology positively and significantly influence international trade in all sectors of the Asia-Pacific region, and that trade intensifies when both trading partners have a high endowment of information and communications technology. The study recommends that governments in developing countries upgrade their ICT infrastructure levels.El uso de las tecnologías de la información y las comunicaciones (TIC) en el comercio mejora la estructura comercial y la capacidad económica de un país. Este estudio evalúa empíricamente el impacto de las TIC en el comercio internacional en 36 países de Asia y el Pacífico a nivel sectorial entre 2007 y 2018. El estudio prueba si las TIC mejoran el comercio internacional mediante la contratación del modelo de gravedad del comercio internacional al aumentarlo con la variable TIC. Un indicador de desarrollo de las TIC (IDI) se forma uniendo siete variables TIC diferentes que muestran la infraestructura, el uso y las habilidades de las TIC. Al emplear la técnica de estimación de Poisson pseudo-máxima verosimilitud (PPML), este estudio muestra que las TIC mejoran el comercio al reducir los costos de transacción. Las conclusiones revelan que la tecnología de la información y las comunicaciones influye positiva y significativamente en el comercio internacional en todos los sectores de la región de Asia y el Pacífico y que el comercio se intensifica cuando ambos interlocutores comerciales tienen una alta dotación de tecnología de la información y las comunicaciones. El estudio recomienda que los gobiernos de los países en desarrollo actualicen sus niveles de infraestructura TIC

A Potential Approach to Enhance the Seebeck Coefficient of UHMWPE by Using the Graphene Oxide

Thermoelectric materials have been a competent source for the production of energy in the present decade. The most important and potential parameter required for the material to have better thermoelectric characteristics is the Seebeck coefficient. In this work, ultra high molecular weight polyethylene (UHMWPE) and graphene oxide (GO) nanocomposites were prepared by mechanical mixing by containing 10000ppm, 50000ppm, 70000ppm, 100000ppm, 150000ppm, and 200000ppm loadings of graphene oxide. Due to the intrinsic insulating nature of UHMWPE, the value of Seebeck for pristine UHMWPE and its nanocomposites with 10000ppm & 50000ppm of GO concentration was too low to be detected. However, the Seebeck coefficient for composites with 70000ppm, 100000ppm, 150000ppm, and 200000ppm loadings of GO was found to be 180, 206, 230, and 235 µV/ K, respectively. These higher values of Seebeck coefficients were attributed to the superior thermal insulating nature of UHMWPE and the conductive network induced by the GO within the UHMWPE insulating matrix. Although, the values of the figure of merit and power factor were negligibly small due to the lower concentration of charge carriers in UHMWPE/ GO nanocomposites but still reported, results are extremely hopeful for considering the composite as the potential candidate for thermoelectric applications

A Review of Conventional and Machine Learning Techniques for Malaria Parasite Detection Using a Thick Blood Smear

Life-threatening malaria is caused by parasites that are lethally effective and harmful and are transmitted through the bite of female Anopheles mosquitoes. In 2015, WHO reported more than 200 million deaths occurred because of this. This makes malaria one of the most vulnerable diseases. The Plasmodium parasite needs to be detected at the early stages for the patient’s survival. Microscopists over the years have been made such craftsmen that they through their expertise have been able to diagnose malaria, being followed by an area expansion support from computer-aided diagnosis. But the expertise required for feature extraction were questionable, which were later replaced by deep learning techniques through automatic feature extraction in CNN's. This paper provides a review of some such techniques and methods which were used for the said purposes

A Review of Conventional and Machine Learning Techniques for Malaria Parasite Detection Using a Thick Blood Smear

Life-threatening malaria is caused by parasites that are lethally effective and harmful and are transmitted through the bite of female Anopheles mosquitoes. In 2015, WHO reported more than 200 million deaths occurred because of this. This makes malaria one of the most vulnerable diseases. The Plasmodium parasite needs to be detected at the early stages for the patient’s survival. Microscopists over the years have been made such craftsmen that they through their expertise have been able to diagnose malaria, being followed by an area expansion support from computer-aided diagnosis. But the expertise required for feature extraction were questionable, which were later replaced by deep learning techniques through automatic feature extraction in CNN's. This paper provides a review of some such techniques and methods which were used for the said purposes