Rashtreeya Vidyalaya College of Engineering (RVCE or RV College of Engineering, Rāshtrīya Vidyālaya Tāntrika Mahāvidyālaya) is an autonomous private technical co-educational college located in Bangalore, Karnataka, India. RVCE is recognized as center of excellence under Technical Education Quality Improvement Program by Government of India.Established in 1963, RVCE has 13 departments in engineering, one school in architecture, and a Master of Computer Applications department. It is affiliated to the Visvesvaraya Technological University, Belgaum. The undergraduate courses are granted academic autonomy by the university. RVCE is accredited by the All India Council for Technical Education and all its departments are accredited by the National Board of Accreditation. The college is managed by the Rashtreeya Shikshana Samiti Trust. Rashtreeya Shikshana Samiti Trust with its administrative offices located in Jayanagar, Bangalore. The Trust is chaired by Dr. Panduranga Setty.
This study addresses the pressing requirement to enhance the ergonomic quality of bus driver seats in India by combining traditional engineering evaluation methods with ergonomic assessment techniques to inform design improvements. A comprehensive methodology is adopted, integrating Rapid Upper Limb Assessment (RULA), finite element analysis (FEA), and a customized questionnaire to document the real-world experiences and expectations of drivers, many of whom report lower back discomfort and general dissatisfaction with existing seating systems. The findings reveal notable discrepancies between current seat configurations and recommended Indian anthropometric benchmarks. To overcome these limitations, the research introduces a comprehensive seat development strategy that balances structural integrity, ergonomic performance, and driver-centered input. The primary objective is to create seating solutions that meet safety requirements while simultaneously improving comfort and minimizing the risk of musculoskeletal issues. What distinguishes this work is its integrated analytical framework, which combines biomechanical evaluation with ergonomic insights to guide design refinement. This adaptable approach has the potential to be implemented across different regions, contributing to improved occupational health standards within public transportation environments.
For better exciton separation and high catalytic activity, the most trailblazing stratagem is to frame S-scheme heterojunction photocatalytic systems through a simple repeatable synthetic strategies. In context to the above, a solvothermal followed by thermal annealing method was developed to convert type-II g-C3N4/MIL-53 (Fe) (g-C3N4/ML) into nanostructured S-scheme g-C3N4/Fe2O3 (g-C3N4/FO) under N2 atmosphere at 500 degrees C. During the thermal annealing process of g-C3N4/ML, as revealed from the XRD, Raman, TEM, and XPS analysis, the MOF structures of MIL-53 (Fe) (ML) were destroyed. They were transformed to g-C3N4/FO with a thin layer of amorphous carbon around the developed alpha-Fe2O3 (FO) nanoparticles. The intercalated carbon between the g-C3N4 nanosheets and FO nanoparticles functions as an electron donor/acceptor, which converts g-C3N4/ML type-II heterojunction to g-C3N4/FO S-scheme heterojunction. When the photocatalytic activity of the developed nanocatalysts are scrutinized, it shows outstanding photocatalytic performance, which is 4.1, 2.9 and 4.5 folds times higher than g-C3N4/ML for bromoxynil degradation (97.3 %), As(III) oxidation (93.35 %), and H2 evolution (5336.53 mu mol g-1 h-1) reactions under visible light. It is also observed from the cytotoxicity studies that the obtained photocatalyst degraded toxic bromoxynil pesticide into non-toxic byproducts. This amplified photocatalytic activity can be attributable to the change in spatial charge carrier migrations from type-II to Sscheme followed by superior light absorption capacity and improved separation efficiency of the photogenerated charge carriers.
Interpreting Indian tax law is challenging due to frequent legislative amendments, complex statutory structures, and the requirement for precise citation. This paper presents TaxFlow, a domain-specific legal assistant that integrates a hybrid Retrieval-Augmented Generation (RAG) framework with the LLaMA-3 language model for statutory question answering. The system is trained on a curated corpus of Indian tax statutes, government gazettes, and case law, processed through structured extraction, segmentation, and dense-sparse indexing. TaxFlow incorporates temporal validity filtering to ensure that retrieved provisions reflect the legally effective version at query time. The architecture combines FAISS-based retrieval, legal-domain adapters, and critique-driven generation to reduce hallucinations and improve citation fidelity. Evaluation is conducted on a unified Indian tax law benchmark using automatic metrics. Experimental results show that TaxFlow achieves 96.5% accuracy and a 95.0% F1-score, demonstrating consistent and substantial improvements over representative baseline systems, while BLEU and ROUGE-L scores demonstrate significant improvements in linguistic quality. The findings confirm the effectiveness of domain-adapted hybrid RAG systems for reliable and scalable legal assistance.
ZnO thin films were deposited by the SILAR method onto glass substrates, and the influence of post-annealing on their structural, optical, and biological properties was systematically investigated. The as-deposited films exhibited a porous flower-like morphology with abundant oxygen vacancies, leading to high antibacterial activity with an 18 mm inhibition zone against Bacillus subtilis. Annealing converted the films into rod-like nanostructures with enhanced crystallinity (crystallite size: 18–22 nm) and optical transparency (> 82
Precision agriculture harnesses cutting-edge technologies to optimize crop management by addressing both spatial and temporal variability in fields. This paper introduces Agrova, an AI-powered autonomous rover purpose-built for intelligent crop monitoring in small-scale farms. Agrova features a rugged, custom-engineered six-wheel chassis—meticulously modeled in SolidWorks and structurally validated using ANSYS—to navigate diverse agricultural terrains. The rover seamlessly integrates advanced sensing systems with edge-deployed deep learning models for real-time weed detection and plant disease classification. Leveraging lightweight convolutional neural networks such as YOLOv5 Nano and MobileNetV2, Agrova achieves high-performance detection directly in the field, enabling precise interventions and minimizing manual labor. Field experiments demonstrate Agrova’s robust capabilities, with weed detection accuracy reaching approximately 89% and disease classification accuracy around 95%.
