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The Hijras are commonly ridiculed in most parts of the world due to certain misconceptions including the question of their sexual deformity. Pakistan is the home of a million Hijras mostly living below the poverty line and now commonly seeing begging at different traffic signals. The present study was designed to study the issue faced by the transgender community in accessing public services in Khyber Pakhtunkhwa. The study covers four districts of Khyber Pakhtunkhwa (Swabi, Mardan, Haripur, and Mansehra) with a total sample size of 20. This qualitative study was based on interviews with transgender gurus this research covers six themes transgender population identification, public sector employment literacy, and schooling, public health, and protection. The semi-structured interview was conducted through structured questioners to gather the data from the target samples. Data were analyzed through thematic analysis by using NVIVO Software. The result show population in all four districts is mostly found uneducated presently engaged in such a profession which are not socially considered honorable. The finding also shows that they face a lot of problems while accessing public services including services in hospitals, education, less cooperation of police, totally ignored from society and also avoid by government. This research study will highlight the issues which are faced by transgender in the community.

Aims And Objectives:1: To evaluate the role of laparoscopy in the diagnosis and management of ectopic pregnancy. 2: To record the operative findings. Materials And Methods: This prospective observational study was conducted in the Department of Obstetrics and Gynecology, LD Hospital, an associated hospital of Government Medical College Srinagar in patients of ectopic pregnancy between January 2016 to August 2017. 100 consecutive cases of suspected Ectopic pregnancy that were admitted in our hospital as out-patient or emergency basis were recruited for this study. Hemodynamically stable patients who underwent Laparoscopy were included. Results: Out of 100 cases of suspected ectopic pregnancy patients 95 were actually ectopic diagnosed on laparoscopy and 5 patients who were suspected to be ectopic by TVS and beta hCG were not ectopic on laparoscopy .32.6% were ruptured, 58.9% were un ruptured and 8.4% tubal abortion. In 72.6% site of ectopic pregnancy was ampullary ,17.9% were infundibular,8.4% were isthmic and interstitial 1.1%. Salpingectomy was done in 42.1% salpingostomy in 46.3%,and milking in 11.6%. All the hemodynamically stable patients whether ruptured or un ruptured were managed by laparoscopically while as hemodynamically unstable patients were managed by laparotomy. Conclusion: Laparoscopy permits diagnosis and treatment to be combined in the same procedure and ectopic pregnancies can be diagnosed and treated at an early stage. In fact, laparoscopy is not only suitable for early ectopic pregnancies but it is also safe and feasible in instances where there is tubal rupture and hemoperitonium, provided the patient is not severely compromised hemodynamically. The laparoscopic approach is now the gold standard for the treatment of ectopic pregnancy and safer approach than laparotomy.

This study aims to enhance business sustainability in the context of Higher Education Institutions (HEIs) by utilizing AI and forecasting techniques. It explores the development and comparison of prediction models, including the use of dashboard development, to support decision-making processes within HEIs. The study covers various aspects, including the background of forecasting and prediction models, the use of specific models such as the Prophet Model, Long Short-Term Memory (LSTM) Model, and Polynomial Regression Model, as well as the importance of dashboards for HEIs. The methodology section outlines the data collection and preparation process, model selection, approach, diagrams, functional and non-functional requirements, justification of tools, and libraries and models used. The implementation section delves into the system design and development of the dashboard, including the login page, homepage, forecast page, and insert data page. As for the findings, the LSTM Model has proven to be the most accurate and suitable model to be implemented for forecasting student enrolment data in this study. The dashboard's future enhancements involve adding more faculties, predictive features for resource allocation, refining the visual identity, improving user registration on the login page, and exploring better models for student enrolment predictions. Overall, the study provides valuable insights into the application of AI and forecasting techniques in HEIs, aiming to enhance business sustainability and decision-making processes. It contributes to the growing body of knowledge on the use of technology-enabled AI in higher education institutions, with a focus on forecasting student enrolment data and developing prediction models.