St. Xavier's Catholic College of Engineering is owned and managed by the Most. Rev. Dr. Jeromedhas Varuvel, as the chairperson. It was established in 1998 by the Roman Catholic bishop of Diocese of Kottar. It is located 5 km west of main town Nagercoil. The college is approved by the government of Tamil Nadu and is recognized by AICTE, New Delhi. The college is affiliated to Anna University.The college is situated in a hillock at Chunkankadai in Kanyakumari District, overlooking the highway, NH 47. In June 2002, the institution was awarded the ISO 9001:2000 certificate by STQC certification services. The institution has been accredited by the National Assessment and Accreditation Council (NAAC) with 'A' Grade.All UG Programs of the institution are accredited by National Board of Accreditation (NBA) and all courses are permanently affiliated by Anna University, Chennai.This is the first College in Kanyakumari District to get accredited by NAAC with A Grade and all UG Programs accredited by NBA..
This study investigates the Al6061-TiB2-Gr hybrid composites developed by high-energy stir casting with varying TiB2 (5, 10, 15, and 20 wt.
Rural electrification in developing regions remains a major challenge due to the absence of grid infrastructure and high costs of energy distribution. This study presents the design, simulation, and optimization of a hybrid photovoltaic (PV)–diesel–battery system to supply reliable electricity to an off-grid village in the Jawi region of Ethiopia. With an annual load demand of 153,396.36 kWh and average solar radiation of 1211.8 kWh/m2/year, a hybrid configuration comprising an 81 kW PV array, 25 kW diesel generator, 45 kW converter, and 200 batteries was modeled using PVsyst and HOMER software. Various system configurations were evaluated based on net present cost (NPC), cost of energy (COE), fuel consumption, and CO₂ emissions. The optimized hybrid system achieved a net present cost of 7,284,233 Birr, a cost of energy of 4.468 Birr/kWh, annual diesel consumption of 9,377 L, and CO₂ emissions of 24,693 kg, with a renewable contribution of 84
Accurate livestock localization in smart farming is challenging due to suboptimal anchor node utilization, collinearity problem and poor UAV-based path planning, leading to reduced localization accuracy and increased energy consumption. Balancing localization accuracy, energy efficiency, and full coverage in large-scale, resource-constrained deployments remains unresolved. To overcome these issues, we propose a novel livestock localization and path-planning framework (LivLocPath) that integrates collaborative adaptive weighted trilateration (CAWT), hybrid generalized learning perturbation equilibrium optimization (GLPEO), and modified human memory optimization (MHMO) algorithms for precise livestock monitoring, optimized anchor node deployment, and energy-efficient UAV-based path planning. This methodology introduces several key innovations: (1) dynamic adaptive weighting in trilateration to prioritize anchor nodes based on real-time signal strength, reliability, and environmental factors; (2) GLPEO-based anchor node optimization, which combines adaptive perturbation and equilibrium strategies to minimize localization errors and avoid local minima; and (3) memory-driven MHMO for refining anchor node deployment and UAV path planning using historical configurations to accelerate convergence and reduce redundant calculations. Furthermore, we introduce dynamic clustering-based UAV path planning, prioritizing high-activity clusters and optimizing energy consumption while ensuring complete area coverage. The proposed CAWT–GLPEO–MHMO LivLocPath framework was evaluated through extensive simulations under varying anchor node densities. The system achieved path efficiency values of 0.86, 0.88, 0.86, 0.87, and 0.85, corresponding to five different anchor node densities, demonstrating consistently near-optimal UAV trajectory performance. Furthermore, the framework attained a coverage efficiency of 98
This research investigates the development of strengthened 3D-printed composite materials using flexible PLA reinforced with biomass-extracted, silane-treated biocarbon via fused deposition modeling (FDM). Composite filaments were fabricated and tested according to ASTM standards. Results show that a 1 vol.
Character recognition is essential in so many aspects of modern life. Although there have been a lot of studies performed on handwritten character recognition, there is less work done on regional languages, particularly in the Tamil language. Analysing Tamil handwritten characters plays a major challenging task owing to the excessive diversity of writing patterns, differing sizes and orientation angles of the characters. A significant barrier still exists in the proper identification of complexly formed compound handwritten characters. Through the learning of discriminating qualities from enormous volumes of raw data, recent developments in neural networks have made significant progress in handwriting recognition. Therefore, this research work aims to develop a novel hybrid recognition model based on improved particle swarm optimization (IPSO) and feed forward back-propagation neural network (FFBNN) for classifying the Tamil handwritten characters. Primarily, the digitized text is pre-processed and segmented with the aid of the modified region growing algorithm. Afterward, the adaptive cuckoo search optimization algorithm can be carried out to extract the different influential features. These extracted features are then fed to the FFBNN model for identifying the Tamil handwritten characters where the IPSO is used to optimize the weights in the FFBNN model. The performance results expose that the proposed framework acquires a superior detection accuracy of 99.01
