National College

Brain tumor segmentation and classification (BTSC) from magnetic resonance imaging (MRI) is an important procedure in medical predictions, improving the precision and speed of treatment plans. However, the automatic detection of tumors is challenging because of similar intensity patterns, indistinct boundaries, and varying tumor shapes. This paper suggests a novel deep transfer learning (DTL) methodology to accurately segment and detect brain tumors (BTs) from MRI. Initially, the denoising is carried out using improved anisotropic diffusion filtering (IADF), and then dataset diversity is enhanced by applying data augmentation. Afterwards, a Swin Transformer U-shaped network with dense connections (STUNetDS) is utilized for segmenting the tumor, and finally, Kullback–Leibler loss with Optimized InceptionResNetv2 (KLOINRNetv2) is used for the classification of the tumor. The system uses the Multi-Strategy Improved Kookaburra Optimization (MSIKBO) algorithm for tuning the network parameters. The model achieves 98.67% accuracy for detecting and classifying BTs from the Kaggle BraTS2019 dataset, higher compared to the baseline techniques. The reliable and scalable solutions are offered by the proposed model, making it more suitable for automated tumor assessment in clinical applications.

Acute care surgery (ACS) was developed as a consultant led organizational framework designed to enhance the promptness and quality of emergency general surgical care. Appendectomy and cholecystectomy are considered standard ACS procedures; nevertheless, the reported rates of postoperative complications are varied, attributed to differences in patient demographics, illness severity, surgical care, and institutional policies. This meta-analysis, executed in alignment with PRISMA 2020 recommendations, assessed publications published from January 2010 to December 2025 that investigated postoperative outcomes following emergency appendectomy and cholecystectomy within the contexts of ACS or emergency general surgery. Eligible trials comprised adult or mixed-age groups with recorded perioperative outcomes. Independent reviewers did the data extraction and quality check. When it made sense, random-effects meta-analyses were done, and the I² statistic was used to measure heterogeneity. Evidence synthesis demonstrated that the application of ACS typically improves surgical speed, reduces hospital length of stay, and yields comparable or diminished postoperative complication rates in contrast to conventional on call systems. Infections at the surgical site, damage to the bile duct, bile leaks, and abscesses in the abdomen were all common problems. Comorbidity, illness severity, operating delay, and open surgical conversion were all factors that led to bad outcomes. On the other hand, early intervention and minimally intrusive methods were always linked to better results. In general, ACS-based care is a good and flexible way to handle emergency appendectomy and cholecystectomy. It follows the rules of quality, safety, and efficient use of resources. The ongoing diversity among studies highlights the necessity for defined care pathways and sustained high-quality research focused on ACS.

One of the major problems which the world faces today is water scarcity, as every life needs it for their day-to-day activity. Pollution and salinity are the two major causes of most waterbodies being unfit for use. Removing pollutants or salt from water became essential for overcoming the water shortage. Desalination technology is being used to achieve this, but the major disadvantage is its lower production. Applying solar energy and nanofluids may tackle the problem and increase the production. In this work, some physical and chemical parameters, in particular isoelectric point (pH), total dissolved solids (TDS), electric conductivity (EC), chloride (Cl–), fluoride (F–), iron(II) (Fe2+) content, were studied before and after desalination. Also, the influence of various water nanofluids (WNF) on increasing the output of glass-made double slope solar stills was studied. Physical and chemical parameter analysis revealed a reduction in values after desalination. Single and double-basin glass solar stills of 0.81 m2 basin area were fabricated, and the experiments were conducted at Kovilpatti. The experiments were carried out at a depth of 1 cm with borewell saline water and nanofluids under insulated conditions. A maximum productivity of 4350 mL per 0.81 m2/day (5405 mL/m2/day) was obtained while using Al2O3 nanofluid in a double basin glass solar still, which was higher than that obtained using water, SnO2 and ZnO WNF. This confirms the efficiency of Al2O3 aqueous nanofluid, which yielded more distillate than other two nanofluids used.

Adipose-derived stem cell (ADSC)-enhanced fat grafting, including enrichment with stromal vascular fraction (SVF) and adipose-derived regenerative cells (ADRCs), has been increasingly used to improve fat graft survival and regenerative outcomes in reconstructive and aesthetic plastic surgery, yet long-term safety, efficacy, and mechanistic understanding remain variably reported. This systematic review aimed to evaluate real-world clinical evidence on the long-term safety, volume retention, and functional outcomes of ADSC-, SVF-, and ADRC-enriched fat grafting across aesthetic and reconstructive indications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, comprehensive searches of PubMed, Scopus, Web of Science, and the Cochrane Library identified 3158 studies, of which 12 met the inclusion criteria, encompassing randomized controlled trials and prospective non-randomized clinical studies assessing ADSC-, SVF-, or ADRC-assisted fat grafting with reported outcomes related to graft survival, clinical function, aesthetic performance, or safety. Across studies, ADSC and SVF enrichment generally improved volume retention compared with conventional fat grafting, with several randomized trials demonstrating graft survival rates of 60-80% at three to six months, and ADSC-assisted grafts consistently enhanced dermal regeneration by improving collagen organization, skin texture, and elastic fiber architecture. In reconstructive applications, SVF therapy reduced digital ulcers and pain in patients with systemic sclerosis, while ADRC-assisted grafting alleviated symptoms of breast cancer-related lymphedema despite limited objective limb-volume reduction, and patient satisfaction was high across all enrichment techniques. Safety outcomes were favorable, with no increase in infection, cyst formation, fat necrosis, or oncologic recurrence across follow-up periods of up to four years. Overall, ADSC-, SVF-, and ADRC-enriched fat grafting appear to offer meaningful improvements in graft survival, skin quality, and functional recovery with a strong safety profile across diverse clinical indications, although methodological heterogeneity and limited long-term data support the need for standardized, regulated, and mechanistically informed clinical protocols, as well as larger, long-term randomized trials to optimize techniques and guide clinical use.

A delay-tolerant network is one that may temporarily hold packets at intermediate nodes while waiting for an end-to-end route to be rebuilt or restored. Due to the difficulty of establishing reliable routing in such a network, we use the Wavelength Routing Algorithm-based idea of hop-by-hop retransmission acknowledgement. It calculates the blocking probability for each connection request and decides whether the connection proceeds or not. This helps to reduce power consumption and the available resources. During retransmission, the intermediate nodes may have some duplicate messages; we utilize the concept of Cuckoo search to omit the duplicate messages. Our proposed mechanism is implemented in the ONE simulator, which states the performance of our reliable communication in comparison with other existing algorithms.

The rise of IIoT has had an exponential impact on sectors such as manufacturing, energy, and logistics, altering facilities in the areas of automation, process control, and real-time decision-making. But as a consequence of this digital convergence, new forms of cybersecurity threats like device cloning, unauthorized access, and tampering with data are now accessible to industrial infrastructures. In this research, a scalable and secure AI-based security architecture, particularly designed for industrial IoT environments is proposed. To monitor the device behavior and identify abnormal scenarios in real time, intelligent anomaly detection algorithms, such as Random Forest classifiers and Long Short-Term Memory (LSTM) networks are introduced into the system. It features a centralized threat intelligence module for unified decision-making and offers edge-level deployment for real-time response to minimize latency. The method aims to enhance the detection accuracy and offset the resilience to zero day attacks and false positives using publicly available IIoT data and simulated/synthetic production scenarios. This work paves the way for further developments in industrial cybersecurity by proposing a flexible and modular approach for securing IIoT devices.

The Institution Placement Data Management System is a comprehensive, web-based application designed to streamline and automate the entire campus recruitment process in academic institutions. The system acts as a centralized platform facilitating seamless interaction between students, placement officers, and recruiters. It empowers students to register, maintain detailed profiles, track eligibility, and apply for placement drives, while providing admins with robust tools to post job opportunities, manage student applications, filter candidates based on criteria, and schedule interviews or aptitude tests. The platform ensures secure and differentiated access using Role-Based Access Control (RBAC), enabling precise control over functionalities based on user roles such as student, admin, and recruiter. Students receive real-time notifications and alerts regarding new job postings, application status, and interview schedules, thus enhancing transparency and engagement. Developed using the MERN stack (MongoDB, Express.js, React.js, Node.js) with MySQL as the primary data storage solution for handling relational data, the system leverages modern web technologies to ensure a responsive, intuitive, and scalable user experience. The system incorporates data validation, search and filter functionalities, profile edit history, and analytics dashboards for placement statistics and drive performance. By automating manual processes and offering structured data management, the system significantly reduces administrative overhead, eliminates redundancies, and ensures efficient placement coordination.