Thanthai Hans Roever College, is a general degree college located in Perambalur, Tamil Nadu. It was established in the year 1985. The college is affiliated with Bharathidasan University. This college offers different courses in arts, commerce and science.Thanthai Hans Roever College of Arts and Science is an educational institution run by the St. John Sangam Trust. It was established in 1985, as a religious minority institution in memory of Rev. Fr. Hans Roever, a missionary from Germany, with the primary objective of providing higher education to the weaker and backward sections of the society in general and the Christian minority in particular. The trust is now run under the efficient and benevolent leadership of the founder – chairman, Dr. K. Varadharaajen who is hailed not only as an inspiring Father of Education but also as the Foundation-Head of wisdom and un paralleled service to humanity. His Vision and mission include a relentless pursuit to provide qualitative education, to empower the youth through updated knowledge and adequate skills, to faster socio-economic and culture changes leading to sustainable growth and evolution, especially to the youth from backward and rural areas. The College is a self-financing and co-educational institution, affiliated to Bharathidasan Universityand approved by the University Grants Commission, New Delhi..
Abstract: Consumers across different segments express good feelings about organic food. Buying organic food online makes the items better, saves time, and makes customers happier. People have been eating organic foods for a long time, but after the COVID-19 outbreak, people are more aware of them, mostly due of social media. The levels of customer satisfaction with online organic products depend on the time, how urgent the purchase is, and if the client may place it through a mobile app or a website. These are all important factors in getting new customers and keeping current ones. This study analyzes customer satisfaction about the online acquisition of organic food goods, utilizing primary and secondary data from previously published academic papers, journals, and theses. The conclusion is that people are happier when they buy organic food online, which shows that companies who make organic food are doing a good job.
This study examined the foraging behaviour of buffaloes (Bubalus bubalis) on a private dairy farm in southern India from April 2016 to March 2017. Foraging behaviour plays a major role in improving production, well-being, and sustainable buffalo farming practices. Using direct observation and focal animal sampling, data were collected daily across four seasons: summer, pre-monsoon, monsoon, and post-monsoon. Seasonal forage availability and the distances travelled for foraging were analysed to understand behavioural patterns and habitat preferences. Mathematical models based on Optimal Foraging Theory (OFT), the Marginal Value Theorem (MVT), and partial differential equations (PDEs) for resource dynamics were applied to quantify and validate the observed seasonal foraging strategies. The results indicated that buffaloes travelled varying distances depending on seasonal forage availability. The distances recorded were highest in summer (124.66 ± 47.82 km) and numerically lower during pre-monsoon (82.67 ± 15.50 km), monsoon (79.00 ± 17.32 km), and post-monsoon (82.00 ± 8.72 km), although this difference did not reach statistical significance. This directional pattern is consistent with predictions based on forage availability, which was comparatively lower during the wet seasons. The findings supported the application of optimal foraging theory, which highlights the dynamic dietary shifts buffaloes undertake to meet their nutritional needs according to forage composition. Additionally, the 90-day observational study on buffalo olfactory communication suggested that faeces and urine marking support group identification and social cohesion. Quantified markings (0–5/day) and repeated observations showed that when a buffalo temporarily separated, others followed using olfactory cues present in urine and dung, demonstrating the functional role of chemical signals in coordinating herd behaviour. Observing these natural herd behaviours is essential for animal comfort and welfare, which provides the rationale for establishing foraging areas in farm design; in intensive farming systems, where such behaviours are restricted, animal welfare is reduced. The study concluded that creating artificial forage habitats could enhance forage accessibility and improve buffalo productivity, welfare, and economic sustainability.
Undoped and cobalt-doped ZnO thin films were prepared on glass substrates using the sol–gel dip-coating method, followed by annealing at 450°C. The cobalt concentration was varied between 2 and 10 at% in steps of 2 at%. X-ray diffraction analysis confirmed the formation of a hexagonal wurtzite structure, with the preferred orientation changing from (100) in the undoped films to (002) in the doped samples. UV–Visible spectroscopy indicated that the films were highly transparent (over 89%), and the optical band gap decreased from 3.327 eV to 3.211 eV as the cobalt content increased. Photoluminescence studies showed near-band-edge, blue, and green emissions, with both the absorption edge and near-band-edge emission shifting to longer wavelengths after doping. Magnetic characterization revealed that while undoped ZnO was diamagnetic, the cobalt-doped films exhibited ferromagnetic behavior at room temperature.
Sustainable cities and people's well-being depend on efficient and reliable public transportation networks. To improve the reliability of bus arrival forecasts, we offer a new method that uses Internet of Things (IoT) data and advanced Gradient Boosting Machine learning algorithms to combine the two. Using real-time information from IoT sensors installed on buses and at strategic nodes along their routes, it constructs a comprehensive dataset that accounts for many variables contributing to bus transit delays, such as traffic, weather, and passenger load. Then, it creates superior prediction models than conventional approaches. It uses Gradient Boosting, an effective ensemble learning methodology. The findings will lead to a more dependable and efficient public transportation system by improving the accuracy of predicted bus arrival times. It helps in the continuous struggle over urban problems with mobility and sets the path for more data-driven, efficient improvement of public transportation.
