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International audience; Traditional healthcare systems are confronted with new problems as the number of patients continues to rise. To solve this problem and improve the healthcare domain's accuracy, reliability, performance, and effectiveness. Since its introduction, the Medical Internet of Things, also known as (MIoT), has become increasingly important in improving the health, protection, and treatment of billions of people. The Medical Internet of Things (MIoT) is a network of linked healthcare software and hardware devices that forms the medical IT systems. MIoT devices are linked to cloud systems, which store and analyze the data they collect. MIoT tools are rapidly transforming healthcare delivery due to their ability to capture, interpret, and distribute health data. By linking patients to their physicians and allowing the transfer of medical data over a secure network, it can reduce unnecessary hospital visits and the strain on healthcare systems. The MIoT has the potential to decrease device interruption through remote provision from the provider healthcare perspective. The MIoT is also expected to provide real-time healthcare services via the internet and smart devices like Smartphone's, wearable medical devices, and embedded medical devices. Moreover, the MIoT can properly detect optimum times for replacing supplies for several devices for their continuous and smooth operation. In this paper, we present a comprehensive literature review of recent contributions focused on the Medical Internet of Things (MIoT). Medical Internet of Things-based healthcare services are expected to improve the user's quality of life, lower prices, and expand their expertise.

If we talk about data earlier, it was not much larger and complicated because earlier the use of internet and technologies were not in use as it is in current days. Nowadays large amount of data is generated in every second, that’s why Cloud environment became very challenging because of its popularity with the communication, networking and storage theorizes [5]. Today our modern era is significantly dependent on Internet and Mobile applications. Today’s generation prefer to communicate with each other virtually rather than meeting with each other face to face so they use many mobile application or online application for communication using internet and technologies. Survey statics shows that mobile advertisement is rapidly growing that majorly focuses on marketing. In this paper we have surveyed different scenarios of mobile advertisement and marketing that how different methods, algorithm and approaches is being used for mobile advertisement and marketing., {"references":["Jerry Gao and ChandrasekarVuppalapati \"Building a Big Data Analytics Service Framework for Mobile Advertising and Marketing,\" IEEE First International Conference, 2015","Assist. Prof. Dr. Selda Ene and Assist. Prof. Dr. BetülÖzkaya, \"The Role of Mobile Advertising on Consumers' Purchase Decisions: A Research on Consumer Attitudes towards MobileAdvertising\" International Journal ofHumanities and Social Science Vol. 5, No. 1, 2015","Dhruv Grewal a, & Yakov Bart b & Martin Spann c & Peter Pal Zubcsekd \"Mobile Advertising: A Framework and Research Agenda,\" Journal of InteractiveMarketing 34, pp 3–14, 2016","Shalini N Tripathi and Masood HSiddiqui \"Effectiveness of Mobile Advertising: The Indian Scenario,\"VIKALPA, Vol 33, No 4, 2008","Mr. Ganapati V Ourasang, Mr. Prakash V Parande \"Big Data Analytics ServiceFramework for Advertising and Marketing,\" IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 3, 2016","Antoine Lamarre1, Simon Galarneau1 and Harold Boeck1, 2 \"Mobile Marketing and Consumer Behaviour Current Research Trend,\" International Journal ofLatest Trends in Computing, Vol-3 No 12012","Rakesh Roushan, Mita Mehta and ArtiChandani \"Study of Mobile Marketing Communication in India,\" Indian Journal of Science and Technology, Vol 8, No 6, 2015"]}

Background Reject analysis can be used as a quality indicator, and is an important tool in localizing areas where optimization is required. Reducing number of rejects is important yielding reduced patient exposure and increased cost-effectiveness. Purpose To determine rejection rates and causes in direct digital radiography. Material and Methods Data were collected during a three-month period in spring 2010 at two direct digital laboratories in Norway. All X-ray examinations, types, numbers, and reasons for rejections were obtained using automatic reject analysis software. Thirteen causes for rejection could be selected. Results Out of the 27,284 acquired images, 3206 were rejected, yielding an overall rejection rate of 12%. Highest rejection rates were found for examination of knees, shoulders, and wrist. In all, 77% of the rejected images arose from positioning errors. Conclusion An overall rejection rate of 12% indicates a need for optimizing radiographic practice in the department.

Angiotensin converting enzyme (ACE)-inhibitors are effective in patients with mild to moderately severe hypertension, collagen vascular and cardiovascular disease. They are also used in the prevention and treatment of myocardial infarction and in the management of cardiac arrhythmias. Patients with cardiovascular diseases are generally on multiple medicines that’s why it is imperative to study drug–drug interactions of medicines which are commonly taken together in any given case, as combined administration of different medicines can significantly influence the availability of drugs. In the present study we investigated the “in vitro” interactions of ACE inhibitors (enalapril, captopril and lisinopril) with frequently prescribed and co-administered drugs in simulated human body environments. These interactions were monitored by means of UV spectrophotometry and separation technique as RP-HPLC. Prior to start of actual drug interactions, the method of analysis of each drug was established and its various parameters validated for considering its use in testing of drug in vitro as well as in human serum. For this purpose, an attempt was made to develop a number of new HPLC methods for determination of ACE inhibitors (enalapril, captopril and lisinopril) and simultaneously with interacting drugs. These methods were optimized, validated and then successfully employed for the quantitation of enalapril, captopril and lisinopril and selected drugs in interactions studies. As a result, new methods for the quantitation of individual as well as multiple drugs were developed. The interacting drugs selected were antidiabetic drugs (metformin, glibenclamide, glimepride and pioglitazone. Interaction consequences revealed that the availability of enalapril was not affected in presence of antidiabetic drugss whereas the availability of captopril and lisinopril were altered in presence of NIDDMs.