Background:Usually, surgical management cannot be completed without the use of antimicrobial and analgesic drugs. Irrational prescription may lead to severe postoperative complications.Aim:The objective of this study was to evaluate the prescription trend in the surgery department of a tribal district hospital so as to determine the extent of rational use of medicines.Materials and Methods:It was a retrospective study in which 50 cases were selected randomly. Case records were analyzed for prescription trend. Data was analyzed using Microsoft Office Excel 2007 and values were presented descriptively.Results:Most of the cases were between the age group of 21 and 40 years, 18 cases (36%). Commonest cause of hospitalization was renal calculi (10 (20%)) followed by acute abdomen and abscess (6, (12%)). Total of 255 numbers of drugs were used with an average of 5.1 drugs per patient. Most preferred route was intravenous route (174 drugs, 68.2%). Antimicrobial was the most common (97 (38.0%)) group of drugs followed by analgesic/antipyretics (50 (19.6%)). Among antimicrobials, ciprofloxacin (22 (22.7%)) was the most common drug followed by metronidazole (21 (18.5%)). All the cases were managed by empirical treatment. Two different antimicrobials were prescribed to 20 (40%) of cases. Dosage of 83 (32.6%) drugs was inappropriate while frequency was inappropriate in 26 (10.2%) cases.Conclusion:Urgent steps like specific guidelines, training, and monitoring of drugs use are needed to correct some irrational approaches.

Background:Suicide is an act of intentionally causing one's own death. Number of suicidal incidences is proportional to attempted suicide cases hence if attempt cases are reduced, number of suicidal death can also be decreased and for that purpose risk factors should be identified and reduced. Therefore, this study is planned to identify risk factors among lower socioeconomic rural population of surrounding areas of Hyderabad in India.Materials and Methods:This was a prospective study in which all the suicide attempt cases reported at Bhaskar Medical College and General Hospital were included. The study period was from January 2013 to July 2013. They were undergone a detailed psychiatric interview, including their demographic details, and complete suicide risk assessment was done using Beck's suicide intent scale.Results:It was found that females in the age group of 20-30 years, uneducated, married and daily laborers by occupation had higher incidence of suicidal attempts. Depressive disorder is the most common associated psychiatric disorder in both the genders, followed by alcohol use related problems. Family disputes are the other major risk factors. Most common mode for attempt was organophosphorous poisoning followed by ingestion of calotropis.Conclusion:Risk of suicide attempt is almost equal in terms of medium and high category of suicide assessment scale in both genders. We suggest that all individuals with alcohol related disorders must be screened for suicidal ideation so that appropriate methods can be adopted to reduce the risk.

Abstract-Cloud computing systems provide virtualized resources that can be provisioned on demand basis. Enormous number of cloud providers are offering diverse nu mber of services. The performance of these services is a critical factor for clients to determine the cloud provider that they will choose. However, determining a provider with efficient and effective services is a challenging task. There is a need for an efficient model that help clients to select the best provider based on the performance attributes and measurements. Cloud service ranking is a standard method used to perform this task. It is the process of arranging and classifying several cloud services within the cloud, then compute the relative ranking values of them based on the quality of service required by clients and the features of the cloud services. The objective of th is study is to propose an enhanced performance based ranking model to help users choose the best service they need. The proposed model co mbines the attributes and measurements fro m cloud computing field and the welldefined and established software engineering field. SMICloud Toolkit has been used to test the applicability of the proposed model. The experimentation results of the proposed model were promising.

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Distributed information retrieval methods are growing rapidly because of the rising need to access and search distributed digital documents. However, the content-based information retrieval (CBIR) is concentrated to extract and retrieve the information from massive digital libraries, which require a huge amount of computing and storage resources. The grid computing provides the reliable infrastructure for effective and efficient retrieval on these large collections. In order to build an effective and efficient CBIR technique, varieties of architectures were developed based on grid technologies. The goal of such architecture is to solve interoperability and heterogeneous resource issues, and increase the efficiency and effectiveness of information retrieval (IR) techniques by harnessing the grid computing capabilities. This paper reviews and analyzes latest research carried out in the domain of large-scale dataset IR based on a grid. The evaluation is based on scalability, response time, scope, data type, search technique, middleware, and query type. The contribution is to illustrate the features, capabilities, and shortages of current solutions that can guide the researchers in this evolving area.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.