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Stress is a state to which the natural body equilibrium i.e. Homeostasis is disturbed caused by any threat to organism. Only some stresses are universal. Stress relates to the force applied to a system that invariably brings about some change or modification. The present study was conducted in university of Kashmir. For the Present study, few departments were selected which are M.C.A, Botany, chemistry, Home Science, Education and Psychology department. Six hundred (600) women students were serving as subject in the study. The sample was consists of 100 M.C.A, 100 Botany, 100 chemistry, 100 Home Science, 100 Education and 100 Psychology women students of Kashmir University. The stress of various departments’ women college students of Kashmir University was measured by the standard questionnaire of stress inventory designed by Arun Singh, K. Singh and Arpana Singh. All the statement was scored on the five point (3) scale. All the items were given a score of 3 for frequently, 2 for Sometimes, 1 for Seldom. The sum of these scores gives the attitude score for the subject. The total score varies from showing positive/ negative stress facing by various departments’ women students of Kashmir University.

The main purpose and aim of the study was to find out and compare the Social Behaviour in the Union Territory of Jammu and Kashmir school going students. The subjects for the study were taken 500 in which (250) were from Kashmir Division and (250) were from Jammu Division. The subjects were selected by using simple random sampling method. Social Behaviour was considered as variable for the present study. The Social Behaviour of two divisions was measured through Standard Questionnaire constructed by M.C. Joshi and Jagdish Pandey which consists of (34 Items). No motivation technique was used while filling the Questionnaire of Social Behaviour by the students of both divisions. It was hypothesized that there will be significant difference in Social Behaviour between Kashmir Division and Jammu Division students. After the collection of data from the subjects through the Questionnaire, the data was analyzed by using statistical techniques ‘t’ test in order to test the hypothesis. The level of significance was set at 0.05 levels. The analysis of data has been presented and applied in the following order i.e. Means, Standard Deviations. The range of scores was calculated and shown in tabular form and graphical form respectively.

Background: The academic communication is being attacked by paper mills, altered peer review, fake data, plagiarised content, hijacked journals, citation engineering, and undisclosed machine-generated content. However, the retractation data should be taken with a pinch of salt, as it contains both misconduct and honest error. As a result, it does not measure the prevalence of fake science. The study’s objective was to analyse recent patterns of retraction, and develop a layered preventive framework for detecting unreliable publications while preserving due process and legitimate correction. According to Retraction Watch Database, methods used are retrospective cross-sectional bibliometric analysis done. The retraction records was filtered for 2020-2025, duplicate revisions were removed, and records were de-duplicated based on valid original-paper DOI or record identifier. Different reasons were explained, and a unique family hierarchy was useful for testing associations. We used Chi-square tests, Mann–Whitney U testing, and multivariable logistic regression to study whether the retraction patterns within 2 years of publication. Results: The final dataset included 37,344 records. The top reason for core family publication-manipulation, while peer-review concerns, paper-mill signals, computer-aided or generated-content tags increased sharply in some years. The time to retraction (TTR) was 593 days median. Although the statistical associations were significant, many effect sizes were small, suggesting that statistical significance should not be treated as practical significance. To produce authentic research, coordinating author, journal, institution, database, funder and regulator verification systems. While automated screening play a role in triage, transparent human adjudication, metadata validation, data availability, reviewer authentication and proportionate correction are still essential.