University of Mumbai

Objective To design and develop hyaluronic acid (HA) conjugated Albendazole (ABZ) loaded chitosan nanoparticles (HA-ABZ-CSNP) loaded hydrogel for the treatment of psoriasis. Significance Albendazole (ABZ), a commonly used anti-parasitic drug, has garnered a lot of attention lately including anticancer and anti-psoriasis properties. Chitosan nanoparticle followed by conjugation with HA was formulated in hydrogel base making it an excellent strategy for targeting overexpressed CD44 receptor on psoriatic skin as well as alleviating the problems, such as dryness, itchiness associated with psoriasis. Methods ABZ-CSNP was formulated by ionic gelation technique followed by conjugation with hyaluronic acid (HA) and was evaluated for particle size, zeta potential, entrapment efficiency, respectively. Developed HA-ABZ-CSNP were incorporated into hydrogel base and were evaluated for in-vitro drug release. Ex-vivo studies were performed. In-vivo studies were performed on Balb/c mice and tests, such as Psoriasis Area Severity Index (PASI), Spleen weight assessment, and histopathological studies were conducted. Results Optimized HA-ABZ-CSNP showed a particle size of 170.1 ± 76.38 nm, zeta potential of 31.6 mV, and entrapment efficiency of 98.89%, respectively. Developed HA-ABZ-CSNP hydrogel showed a Korsemeyer and Peppas release model and an in-vitro release of 93.90 ± 32.50 % in around 24 h. Ex-vivo studies were conducted which showed 30.74 ± 13.65% in 24 h. In-vivo studies conducted on Balb/c mice showed reduced PASI, Spleen weight as well as reduced keratinocyte proliferation in histopathological studies. Conclusions In conclusion, developed novel formulation of ABZ reduced keratinocyte proliferation making it a possible effective strategy in the management of psoriasis.

The maritime hospitality sector, a critical component of the Merchant Navy, is currently navigating a digital transformation driven by the need for operational efficiency and enhanced crew welfare. This paper explores the AI-Integrated Flow of Functional Management, examining how Artificial Intelligence restructures traditional administrative and service frameworks at sea. By synthesizing data-driven automation with core functional areas—such as supply chain logistics, victualling management, human resources, and waste optimization—the study illustrates a shift from reactive to predictive management. The key areas include Predictive Inventory Control: Utilizing machine learning to forecast consumption patterns and reduce spoilage in global supply chains. Automated Crew Services: Implementing AI for personalized meal planning and digital concierge services to improve life-at-sea standards. Operational Sustainability: Leveraging smart systems to monitor and reduce the environmental footprint of hospitality operations in compliance with international maritime regulations. The findings suggest that integrating AI into the functional flow does not merely replace manual tasks but acts as a force multiplier, allowing hospitality managers to focus on high-value leadership and safety protocols. Ultimately, this integration establishes a new benchmark for operational resilience and cost-effectiveness in the modern maritime navy

Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder in women, and it is difficult to diagnose at an early stage due to varying symptoms and limitations of traditional diagnostic methods. Early detection of PCOS is important to prevent long-term health complications. In this study, a hybrid machine learning model is proposed for PCOS detection using clinical and hormonal data. A dataset containing 541 patient records was used for analysis. Missing values were imputed using K-Nearest Neighbour (KNN), and the most relevant features were selected using Mutual Information. To address class imbalance, SMOTE was applied to the training data. Individual machine learning models were first evaluated, and based on their performance, a hybrid model was developed using a weighted soft-voting approach that combines Gaussian Naïve Bayes, Logistic Regression, and Random Forest. The experimental results suggest that the hybrid model strikes a better balance between accuracy, precision, and recall than any single model on its own. This makes it a more trustworthy approach for predicting PCOS. This project marks just the first phase of research, laying the groundwork for future studies that will combine these findings with ultrasound image analysis.

The increasing complexity of constrained engineering design problems has intensified the demand for metaheuristic optimization algorithms that are both computationally efficient and robust against premature convergence and search stagnation. This paper presents the Signal Lock Optimization Algorithm (SLOA), a novel population-based metaheuristic founded on the dual mechanisms of confidence reinforcement and noise suppression. The core principle of SLOA lies in identifying high-confidence solution components-referred to as signal locks-and reinforcing them during the search process while dynamically filtering stochastic perturbations that may mislead exploration in multimodal and highly constrained landscapes. The proposed algorithm incorporates adaptive parameter updating and an effective constraint-handling strategy to maintain a balanced exploration–exploitation trade-off. SLOA is extensively evaluated on a suite of widely adopted engineering benchmark problems, including Welded Beam Design, Pressure Vessel Design, Tension/Compression Spring Design, and Car Side-Impact Design. Comparative experimental results and statistical analyses demonstrate that SLOA consistently achieves superior or highly competitive solution quality, faster convergence rates, and high feasibility compared to several state-of-the-art metaheuristic algorithms. The findings confirm that the signal lock mechanism provides a reliable and scalable optimization framework for solving complex real-world engineering design problems.

Electoral voter lists form the backbone of democratic processes, ensuring that every eligible citizen is granted a fair opportunity to vote. However, large-scale voter databases often suffer from data quality issues such as duplicate or nearduplicate entries caused by spelling variations, data entry errors, migration, and inconsistent demographic updates. These redundancies can compromise the integrity of elections, increase administrative costs, and reduce public trust. This research proposes an intelligent, analytics-driven framework for detecting duplicate entries in electoral voter lists using a combination of data pre processing techniques, rule-based matching, and machine learning models. The proposed system integrates phonetic similarity, demographic attribute comparison, and supervised classification models to identify potential duplicates with high accuracy. Experimental analysis demonstrates that the hybrid approach significantly outperforms traditional exact-matching techniques, offering a scalable and reliable solution for election management bodies. The study emphasizes transparency, accuracy, and scalability while maintaining compliance with ethical and data privacy considerations.

Little is known about Undetectable=Untransmittable (U = U) awareness among MSM in India. We analyzed baseline survey data from MSM (N = 1004) in an mHealth HIV testing trial in Mumbai and Thane, India. We assessed factors associated with U = U awareness and agreement (HIV treatment optimism) using multivariable Poisson regression. U = U awareness (29%) was associated with English survey completion, searching for sexual health information online, awareness of PrEP/PEP, and participation in MSM-focused HIV prevention programs (all P < 0.05). Overall, 50% of the participants agreed with HIV treatment optimism, which was associated with searching for sexual health information online, PrEP knowledge, and being out about their sexual orientation. Focused, culturally tailored U = U education is needed to improve awareness and treatment optimism among diverse Indian MSM.

ABSTRACT Social multimedia (SM) platforms such as Instagram, LinkedIn, YouTube, Facebook, and Twitter enable users to communicate, share ideas, and exchange information within virtual networks. The widespread use of multimedia‐based applications has led to a substantial increase in social media traffic, often containing sensitive personal and interaction data. These data are vulnerable to security threats, including identity theft and information misuse, highlighting the need for effective anomaly detection mechanisms. This research proposes an enhanced binary spotted hyena optimized dependent linear regression (EBSHO‐DLR) method to detect anomalies in SM networks, ensuring secure data transmission. The framework begins with collecting a Twitter dataset from India, followed by pre‐processing using Gaussian blur (GB) to filter anomalies and histogram of oriented gradients (HOG) for feature extraction, removing irrelevant information. The proposed method integrates software‐defined networking (SDN) to provide dynamic, programmatically efficient network management, improving performance and monitoring compared to conventional approaches. Experimental evaluation demonstrates the effectiveness of the method in detecting fraudulent activities such as identity theft, profile cloning, and unauthorized data collection. Key performance metrics include precision (96.7%) and accuracy (97.8%), confirming the robustness and reliability of the EBSHO‐DLR approach for securing social multimedia communication systems.