The purpose of this study was to determine Correlation of hope theory on precision of users’ information retrieval from Databases of medical science. Research method was quasi experimental, and the population consisted of 45 final year medical students at medical Universities in Tehran. The data gathering instrument was the Schneider (1991) hope questionnaire that has 12 questions. Schneider questionnaire measures two compounds of hope (agency and pathway) in 8 degrees Likert scale. Cronbach’ s alpha for two compounds of hope theory includes agency and pathway was 92% and 82%. Two pages of the retrieved results (40 records) were examined by the users in terms of relevance to their information needs. Results showed a significant Correlation between the mean values of the hope of the users and the precision of the retrieved results. The users hope with the mean value of 54/11 was at the high level. There was significant relation between demographic characteristics of gender, age and employment status with hope of the users and precision of the retrieved results. Overall findings indicate that users hope, increases the precision of the retrieved results from medical Databases.

Proteins are sources of many peptides with diverse biological activity. Some of them are considered as valuable components of foods and drug targets with desired and designed biological activity. We are now entering an era rich in biological data in which the field of bioinformatics is poised to exploit this information in increasingly powerful ways. There are currently many Databases all over the world housing information that floods from the genome project. Nevertheless, as Databases become more comprehensive, as the volume of sequence data expands and search outputs become more complex, the different Databases play an increasingly major role in the post genomic era. However, the rate of sequence generation and overwhelmingly proliferation of Databases have made it difficult to keep up with developments. Researchers now need rapid, easy to use, reliable tools for their use in functional characterization of newly determined sequences. This paper aims to provide a comprehensive overview on the status of protein Databases available in the World Wide Web. As this is a fast moving area, a list of all the mentioned Databases along with their current URLs are presented ill a table at the end of the article.

Background and Aim: The main challenge in each organization, in particular healthcare organizations deal is effective utilization of information stored in their various information systems. Large amounts of data will have organizational value as they convert to a knowledge which help the organizations in cost controlling, profit increasing as well as maintaining the quality of patient care. The aim of this study was to determine initiatives and methods for effective utilization of data and information stored in Databases of healthcare organizations in developed countries. Materials and Methods: Through systematic review, the articles regarding KDD published since 1990 were reviewed. After studying books and journals related to data and Databases in the libraries of Iran University of medical sciences, central library of Amir Kabir University, library of Science and Industry University, articles published by Databases such as ScienceDirect, Pubmed, Proquest, Blakwell as well as the sites of international organizations working on knowledge discovery and data mining were also reviewed. Results: Necessity for effective utilization of large amount of data has been the subject of a field called Knowledge Discovery in Databases (KDD). Extensive initiatives have been done in developed countries and distinctive steps have been determined for knowledge discovery since 1989. Fraud detection, marketing and customer retention are some examples of KDD applications in several industries. Similar applications regarding patient-oriented and quality improvement in healthcare are examples of KDD applications in the healthcare industry. Using capabilities of various sciences, several approaches have been developed by which current soft wares are extracting hidden knowledge from large Databases. Quantitative approaches such as Probabilistic and statistical approaches, classification approaches such as Bayesian, decision tree and other methods including deviation and Trend Analysis, generic algorithms, neural networks and hybrid methods which use two or more techniques are used in KDD tools. Conclusion: Healthcare organizations can discover hidden knowledge in Databases by using software programs developed based on one or more KDD techniques, and along with cost reduction, they can use its interesting results for decision making in their activities.

The last three decades the use of Databases and registries in the field of medicine has created, along with the use of personal computers and the internet, a major revolution in the way of learning and practicing medicine and surgery.

1. Introduction The global investigation of disasters in recent years shows that their number has increased annually and the number of affected people and costs of disasters are increasing.Planning for preparation in disasters requires valid and systematic data of the disaster effects and awareness of policymakers of all stages of disaster. Also, with regard to increase in outcomes of the disaster effects, especially in developing countries, for prioritizing and allocating national and international budgets, we need precise information that can be compared in the entire country over time.