Cyclic-vomiting syndrome (CVS) is a chronic functional gastrointestinal disorder characterized by recurrent episodes of nausea and vomiting. Although once thought to be a pediatric disorder, there has been a considerable increase in recognition of CVS in adults. The exact pathogenesis is unknown and several theories have been proposed. Migraine and CVS share a similar pathophysiology as suggested by several studies. Since there are no specific biomarkers available for this disorder, physicians should rely on Rome criteria for the diagnosis. Due to the lack of randomized control trials, the treatment of CVS is primarily empirical.

The 485-km-long coastline of Odisha, a state in the northeastern part of the Indian peninsula, is potentially vulnerable to several disaster events that take place frequently. In addition to threats due to natural hazards, these coastal regions also face immense population and developmental pressures. The increase in the intensity and frequency of cyclones and accelerated sea level rise related to increased sea surface temperature have led to flooding, coastal erosion and shoreline retreat causing damage to coastal ecosystems and resources in these regions. In recognition of these risks, the present work demonstrates a GIS-based approach to assess the vulnerability of the 187-km stretch from Puri to Konark out of the total 485-km coastline using analytical hierarchy process (AHP). The present study focuses on computation of integrated coastal vulnerability index which is an integration of physical vulnerability index, geotechnical vulnerability index and social vulnerability index using AHP taking nine risk variables into consideration. An attempt has been made to demonstrate the state-of-the-art microzonation of the coastal stretch between Puri and Konark based on the vulnerability indices using geographical information system.

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Worldwide landslide is a serious hazard that causes great damage not only to the economic and societal development but also to precious human lives. Therefore, there is an urgent need for a good landslide prediction system. Recently, Internet of Things (IoT) has emerged as a popular technology that has a quick response to rapid changes in data. Hence, it has been widely used for landslide monitoring and prediction. Present study focuses on a review of existing IoT based landslide prediction systems. Ten most widely referred systems developed by various researchers have been reviewed. A comparative analysis of their key features has been performed. Prioritization of these ten systems has been done using Analytical Hierarchy Process (AHP) based on three factors, i.e., Cost, number of parameters sensed, and technology. The most suitable IoT based landslide prediction system as obtained from AHP based prioritization is recommended for implementation.Landslide is a geological phenomenon caused due to perceptible downward and outward movement of soil, rock, and vegetation under the influence of gravity. Landslides can be classified according to geotechnical properties of rocks, movement of materials, etc. Landslide movement can be of various types viz., fall, slide, topple, spread or flow. The velocity can vary from very slow to rapid [1]. Mountainous regions are generally considered landslide prone areas. In low-relief areas like roadway and relief excavations, landslides occur as cut and fill failures, river bluff failures, lateral spreading landslides, collapse of mine-waste piles (especially