Degradation of methylene blue dye as wastewater pollutant by atmospheric pressure plasma: A comparative study

The industrial application of non-thermal plasma has been a research field in the last few years. One of the potential applications of non-thermal plasma is in treating dye effluents of textiles industries which are considered as one of the environmental pollutants. Before scaling up plasma technology at the industrial level, it is required to understand the interaction of non-thermal plasma with a synthetic dye-containing solution in laboratory experiments. A detailed comparative study of MB dye degradation using an atmospheric pressure air plasma (corona discharge) source is carried out in this report. The results are qualitatively discussed in line with the available theoretical and experimental background of plasma-water interaction

Gestational Diabetes Mellitus in a Multi-Ethnic, High-Risk Population: Adequacy of Screening for Diabetes Mellitus 6 Weeks after Delivery

Gestational diabetes mellitus (GDM) during pregnancy is a marker for future type 2 diabetes mellitus (T2DM); therefore, a meticulous follow-up after delivery can help identify women at risk for T2DM. In a cohort of 5504 pregnant women, the postpartum follow-up of all 1043 women with GDM for hyperglycemia in a multi-ethnic, high-risk Arab population was investigated. The prevalence of GDM was 18.9%. A total of 265 (25.4%) women returned for an oral glucose tolerance test (OGTT) 4–6 weeks after delivery, with more South Asian than Arab women (p < 0.01). The other factors associated with return were (a) family history of T2DM, (b) lower basic metabolic index, (c) higher abortions and (d) lower gravida (p < 0.05), all with minimal effect. An abnormal postpartum OGTT was statistically associated with previous GDM history and hypoglycemic drug treatment, although these effects were small. Overall, the follow-up of women with GDM postpartum was dismal, ethnicity being the major factor influencing return. Urgent public measures are needed to educate women with GDM about follow-up highlighting (a) risk awareness for T2DM and (b) a healthy lifestyle after childbirth—if we are to turn the tide on the epidemic of T2DM plaguing the Arab world

A Futuristic Survey on Learning Techniques for Internet of Things (IoT) Security : Developments, Applications, and Challenges

In today's era, internet-connected things provide immense opportunities to the world for enhancing the quality of lives through better data processing and intelligent decision making. Since the last decade, IoT brought numerous changes in people's personal as well as professional lives. With the enhancement in quality of lives, IoT also comes up with challenges such as security and privacy of data and devices. Every day, the attacker generates new zero-day attacks for IoT devices and data, and it's important to detect and protect the IoT eco-system from this type of attacks. Numerous researchers have proposed security schemes and methods to protect the IoT eco-system through either cryptography way or learning technique based way. AI and ML learning techniques have got immense popularity in handling the IoT security challenges as they are automatic in nature and can outperform provided the sufficient quality and quantity of data. Moreover, the AI techniques, including ML, DL and FL helps in intelligent decision-making and can also generate knowledge through its learning techniques. AI needs data to process, and IoT supplies the necessary data to process. In this paper, we provide a state-of-the-art survey for IoT security solutions proposed based on learning techniques. We provide an in-depth review of available learning techniques to solve critical security challenges such as IoT authentication, access control, anomaly detection and malware analysis. At the end, we also highlighted various futuristic technologies that can invigorate IoT research and help in the design of full proof IoT eco-system.</jats:p

A Futuristic Survey on Learning Techniques for Internet of Things (IoT) Security : Developments, Applications, and Challenges

In today's era, internet-connected things provide immense opportunities to the world for enhancing the quality of lives through better data processing and intelligent decision making. Since the last decade, IoT brought numerous changes in people's personal as well as professional lives. With the enhancement in quality of lives, IoT also comes up with challenges such as security and privacy of data and devices. Every day, the attacker generates new zero-day attacks for IoT devices and data, and it's important to detect and protect the IoT eco-system from this type of attacks. Numerous researchers have proposed security schemes and methods to protect the IoT eco-system through either cryptography way or learning technique based way. AI and ML learning techniques have got immense popularity in handling the IoT security challenges as they are automatic in nature and can outperform provided the sufficient quality and quantity of data. Moreover, the AI techniques, including ML, DL and FL helps in intelligent decision-making and can also generate knowledge through its learning techniques. AI needs data to process, and IoT supplies the necessary data to process. In this paper, we provide a state-of-the-art survey for IoT security solutions proposed based on learning techniques. We provide an in-depth review of available learning techniques to solve critical security challenges such as IoT authentication, access control, anomaly detection and malware analysis. At the end, we also highlighted various futuristic technologies that can invigorate IoT research and help in the design of full proof IoT eco-system.</jats:p

A RARE CASE OF CRIMEAN-CONGO HEMORRAGIC FEVER

Crimean-Congo hemorrhagic (CCHF) fever is a viral hemorrhagic fever caused by the Nairovirus of Bunyaviridae family. The course of illness is often acute and rapidly progressive with symptoms such as fever, Headache, Bodyache, Back ache. As the disease progresses large areas of bruising, uncontrolled bleeding nose and injection sites can occur. In the worst case scenarios complications such as disseminated intravascular coagulation, Shock and Acute respiratory distress syndrome can occur. The fatality of CCHF ranges from 9-40%. The long term effects of CCHF are yet to be studied. The majority of deaths have been reported in duration of 5-14 days of illness.</jats:p

Evaluation of the OCTA Transit Probe System

The OCTA (Orange County Transit Authority) Transit Probe Project is a field operational test of an automatic-vehicle-location (AVL) system operating in Orange County, California. This report presents the final evaluation results for the project, concentrating on the operational period from June 1, 1998 to May 30, 1999. The report provides a detailed description of the system and the motivation for its design. It provides analyses of data reliability and accuracy, and analysis of the usefulness of transit probe data for predicting automobile travel times. Institutional issues are evaluated, based on interviews with involved personnel, direct observation of meetings and review of project documents. Customer surveys with bus riders and kiosk users are also documented. Bus tracking systems provide many potential benefits, helping: (1) drivers stay on schedule, (2) dispatchers respond to problems, (3) schedulers determine how much time to allocate between schedule check points, and (4) general public know when buses will arrive. Capturing these benefits requires careful planning for operational procedures, data maintenance, and system interfaces and ensuring the equipment itself it reliable. Bus tracking implementations need to involve many parties within the transportation agency, and include task assignments, data transfer methods, and strategies for using information. Because of the small size of the Transit Probe project, along with a competing project within the agency, these factors received only limited attention. As a consequence, the system was not used to a significant degree in the agency