G. H. Raisoni College of Engineering and Management

Freshwater resources are rapidly diminishing due to extensive industrialization and population growth creating an urgent need for desalination of brackish water as a reliable alternative for drinking, agriculture, and industrial use. The solar stills have been utilized for a considerable duration to desalinate brackish water by harnessing solar energy. Among the diverse solar still designs, the tubular solar still (TSS) has emerged as a particularly promising option, however, enhancement of its efficiency and thereby productivity remains the scope for research. The present study focuses on augmenting the productivity of tubular solar still by integrating it with side wall reflectors and an additional heating source utilizing solar PV modules and heaters to elevate basin temperatures and enhance evaporation rates. Experimental investigations were conducted at Nagpur, India, comparing the energy and economic analyses of the modified tubular solar still with a reference to conventional design. The results indicate a notable improvement in the productivity of the solar still, it shows increment from 4.32 L/m2 to 6.86 L/m2 in the modified set up. Furthermore, the cost of water production for the modified solar still with aluminum reflector was found to be 0.0137 $ per liter.

The growing complexity of pet healthcare demands a unified digital framework to streamline services for pet owners, veterinarians, trainers, and welfare organizations. Traditional platforms often face fragmentation, limited accessibility, and weak data security, hindering efficient, transparent care. Zoodo addresses these challenges through a web-based solution that combines advanced Artificial Intelligence (AI) and Blockchain technology. The multi-modal AI assistant analyzes pet symptoms, recommends suitable providers and trainers based on breed, medical condition, geography, and budget, and orchestrates personalized care routines and dietary advice. Blockchain-driven digital health records (DHRs) ensure tamper-proof storage, secure sharing, and traceability of sensitive medical data. The platform further supports a wide spectrum of consultations, including telemedicine, home visits, and in-clinic appointments, and directs users to community resources such as adoption events and wellness initiatives. Empirical evaluation across $9,000+$ veterinary records demonstrates Zoodo's ability to enhance clinical triage, decision support, and health record integrity. By merging AI-powered intelligence with blockchain-backed security in an intuitive interface, Zoodo establishes a new paradigm for transparent, efficient, and patient-centric pet healthcare.

Abstract Parabolic Trough Solar Collectors (PTCs) are among the most mature and widely implemented Concentrating Solar Power technologies for electricity generation and industrial thermal applications. These systems use parabolic reflectors to focus solar radiation onto a receiver tube, where a heat transfer fluid (HTF) absorbs and transports thermal energy. The thermo-physical properties, thermal stability, and operating temperature range of the HTF significantly influence system efficiency. Various HTFs, including synthetic oils, molten salts, water/steam, and nanofluids, have been investigated to enhance thermal performance and reduce losses. This review presents the working principles of PTCs, selection criteria for HTFs, heat transfer enhancement techniques, and recent technological advancements. Thermal loss mechanisms, receiver design improvements, and integration of thermal energy storage systems are also discussed. Furthermore, emerging modeling approaches such as Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems are highlighted for performance prediction and optimization. The study identifies key challenges including experimental validation, advanced fluid development, and heat loss characterization. The findings contribute to the development of efficient, reliable, and sustainable solar thermal systems. Keywords Concentrating solar power, Heat transfer fluids, Nanofluids, Parabolic trough collectors, Thermal energy storage, Solar thermal systems

In order to reduce the rate of turnover and maintain valuable talent, research has been emphasized on employee retention strategies. High turnover could increase the cost of recruiting new staff members, loss of organizational knowledge, and interruption of team dynamics. Therefore, making accurate predictions for the potential turnover of an employee will be possible with promising predictive analytics by machine learning models. This study uses the most advanced machine learning models, such as artificial neural network (ANN), XGBoost, recurrent neural network (RNN), and random forest (RF), to predict employee turnover from a rich dataset that includes employee demographics, tenure, job satisfaction, performance ratings, compensation details, work environment factors, and feedback. The data was sourced from HR analytics repositories, industry surveys, and Kaggle datasets for training the models. The results indicate that ANN produces the highest accuracy in predicting employee turnover, that is 96.76

Mental health disorders such as anxiety, stress, and depression have become significant global health concerns due to their growing prevalence and impact on individuals and society. Early identification of these disorders is essential for providing timely intervention and improving overall well-being. Recent advancements in artificial intelligence, particularly machine learning and deep learning, have shown promising potential in analyzing large-scale health data for early diagnosis. A hybrid deep learning framework was implemented which consist of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). The purpose of this framework is to predict mental health conditions at the early stage. The experiment was conducted on a dataset of 9879 records gathered from individuals of different age groups through a questionnaire in both online and offline modes. It contains a total of 36 attributes responses based on standardized mental health assessment scales. The CNN-BiLSTM model captures both local feature patterns and long-term contextual dependencies within the dataset. The performance was evaluated and compared with CNN model across multiple classification scenarios. Experimental results demonstrate that the CNN-BiLSTM model significantly outperforms the standalone CNN model, achieving accuracy values ranging from 96.2% in binary classification to 89.4% in multi-class classification. The findings suggest that the hybrid model is effective in improving predictive accuracy and supporting early detection of mental health disorders.