Background: Open fractures are orthopedic emergencies with a high risk of infection due to direct exposure of bone and soft tissue to environmental contaminants. Factors such as time of arrival, preoperative hospital stay, and duration of surgery may significantly influence the bacteriological profile and infection rates in these cases.
Material and Method: This study is a retrospective analysis of the bacteriological profile and antimicrobial susceptibility pattern of open fracture wound isolates in upper and lower limb organisms over a period of 1 year, i.e., from April 2023 to March 2024. All samples were processed as per standard Microbiological guidelines. Antimicrobial susceptibility was done by the Kirby-Bauer disk diffusion method as per CLSI guidelines.
Result: Most patients were males (87%) with a mean age of 42.23 years; road traffic accidents (61%) and lower limb fractures (71%) were common. Infection rates rose with delayed hospital arrival (12.1% at <4 hrs vs. 76.9% at 20–24 hrs), prolonged surgeries (22% to 50%), and longer preoperative stays. Early isolates were mainly MRSA and E. coli, while delays led to more resistant organisms like Acinetobacter and Pseudomonas.
Conclusion: This study highlights that delayed presentation, prolonged preoperative stay, and extended operative time significantly contribute to increased infection rates and microbial diversity in open fracture wounds.

Introduction: Infections in open fracture wounds are influenced by host factors such as gender and comorbidities. This study investigates the role of comorbidity burden and gender in determining infection rates.
Material and Method: This study is a retrospective analysis of the bacteriological profile and antimicrobial susceptibility pattern of open fracture wound isolates in upper and lower limb organisms over a period of 1year, i.e., from April 2023 to March 2024. All samples were processed as per standard Microbiological guidelines. Antimicrobial susceptibility was done by the Kirby-Bauer disk diffusion method as per CLSI guidelines.
Results: Out of 40 patients, 34 were male and 6 female. Pathogenic isolates were found in 20 males and 2 females. Among 25 patients with multiple comorbidities, 17 (68%) had infections. In contrast, only 5 (33%) of 15 patients with a single comorbidity showed pathogenic growth. Males and patients with multiple comorbidities had higher infection rates.
Conclusion: Male gender and multiple comorbidities are associated with increased infection risk in open fracture wounds, likely due to immunosuppressive, behavioral, and physiological factors. Enhanced care is needed for these high-risk groups.

Biometric authentication systems play a crucial role in ensuring secure and reliable identity verification. Traditional methods such as fingerprint and facial recognition face challenges related to forgery, environmental conditions, and privacy concerns. This project proposes an Anatomical Finger Vein Recognition System that leverages vein pattern analysis for enhanced security and accuracy. The system captures finger vein images using near-infrared (NIR) imaging, processes them through image enhancement and feature extraction techniques, and classifies them using machine learning and deep learning models. Vein patterns are unique, difficult to forge, and remain stable over time, making them an ideal biometric trait for authentication. The proposed system enhances security in applications such as banking, access control, and forensic identification, offering a non-invasive, robust, and efficient biometric verification solution.

This project focuses on predicting students' academic performance by analyzing detailed behavioral data collected throughout their educational journey. By examining various factors such as class attendance, participation in discussions, homework completion, and engagement with learning resources, we aim to build predictive models that forecast future academic success or challenges. The project leverages machine learning algorithms to analyze these data points and identify trends or patterns that can indicate potential academic outcomes. The ultimate goal is to provide educators with timely insights and early warnings, allowing for targeted interventions and personalized support to enhance student performance and overall educational achievement. This project aims to predict students' academic performance by analyzing comprehensive behavioral data. By examining factors such as attendance, participation, study habits, and engagement levels, the project uses advanced data analytics to identify patterns and predict future academic outcomes. The goal is to provide educators and administrators with actionable insights to enhance student support and intervention strategies, ultimately improving educational outcomes and student success.

Recommendation systems are designed to help online platform users manage large volumes of information and provide them with a personalized experience. This is achieved by suggesting items of interest to users based on their explicit and implicit preferences. There have been growing interests in the area of recommendation systems using machine learning algorithms. Because there is a large number of explicit and implicit characteristics. Which can be used to estimate user preferences, requires scalable and precise algorithms with a system with High availability and scalability.  Apache Spark is an open source distributed platform. To process big data, obtaining good speed and scalability and Suitable for iterative self-learning algorithms.

Background: Hand hygiene (HH) is a fundamental and effective measure in preventing healthcare-associated infections (HAIs). Despite well-established guidelines, compliance among healthcare providers often remains suboptimal, particularly in high-risk clinical settings.
Objective: To evaluate and improve hand hygiene compliance among doctors, nurses, and other healthcare workers (HCWs) in the AICU, Labour Room, and PICU of DRPGMC Hospital, Kangra at Tanda (H.P).
Methods: This observational study was conducted over one year, from January to December 2022. An Infection Control Nurse (ICN) observed 20 hand hygiene opportunities per day in each ward during 6-hour sessions. Compliance was recorded based on WHO’s “Five Moments for Hand Hygiene” criteria. Quarterly trends were analyzed across different categories of healthcare workers.
Results: Initial compliance in the January–March quarter was 50% for doctors, 51% for nurses, and 40.6% for HCWs. A gradual improvement was seen throughout the year, peaking in October–December at 55%, 57%, and 47% for doctors, nurses, and HCWs, respectively. Nurses consistently showed the highest compliance across all quarters.
Conclusion: The study demonstrated a positive trend in hand hygiene compliance across all healthcare worker categories. Regular monitoring, targeted interventions, and continuous training are essential for sustaining high compliance and reducing the risk of HAIs.