Periyar University

Proper use and replenishment of groundwater can help solve various problems, making it crucial to have a scientific understanding of its management. For effective management of groundwater systems and to preserve water quality, it is essential to identify the potential zones for groundwater recharge. Geographic Information System (GIS) methods and remote sensing technologies are utilized to identify areas with potential for groundwater. The integration of remote sensing (RS) and geographic information system (GIS) techniques is employed in this study to develop a standardized methodology for assessing groundwater potential. Groundwater potential map of the Gadilam river basin is generated using GIS technologies. The precise data is generated using satellite imagery and Survey of India (SOI) toposheets at a scale of 1:50,000. This data is utilized to identify parameters relevant to determining groundwater potential zones, including rainfall, geological structure, soil characteristics, slope, drainage density, geomorphic units, lineament density, and land use/land cover. It is then integrated with a weighted overlay in ArcGIS. Weight factors are established for the various geomorphic units based on their ability to store groundwater. This process is repeated for each subsequent level, and the resulting layers are reclassified. In weighted overlay analysis, different thematic layers are assigned a theme weight and class rank. The findings were validated through fieldwork. The resulting map was categorized into three levels: Poor, Moderate, and Excellent. The application of the suggested methodology to the Gadilam river basin is demonstrated. This study will be helpful for useful groundwater management for different area.

Mazkur maqolada Farg‘ona viloyati dala sharoitida karam (Brassica L.) ekinlarida keng tarqalgan ildiz bo‘rtma nematodalari - Meloidogyne hapla ga qarshi oksamil (oxamyl) nematitsid preparatining biologik samaradorligi o‘rganildi. Tadqiqotlar 2025 yil vegetatsiya davrida bo‘rtma nematodalar bilan zararlangan ochiq dala maydonlarida o‘tkazilib, tajribalar karamning “Burbon F1” navida olib borildi. Oksamil preparati 55, 65 va 75 ml/m² sarf-meyorlarida uch variantda qo‘llanildi. Preparatning samaradorligi Abbott formulasi asosida baholandi hamda tuproqdagi invazion lichinkalar soni va ildizlarning zararlanish darajasi aniqlandi. Tadqiqot natijalariga ko‘ra, preparatning 75 ml/m² sarf-meyorida qo‘llanilishi vegetatsiya oxirida tuproqdagi nematoda lichinkalari sonining sezilarli kamayishiga olib kelib, biologik samaradorlik 88,5 % ni tashkil etdi. Ushbu variantda ildizlarning zararlanish darajasi nazoratga nisbatan keskin kamaygani aniqlandi. Olingan natijalar oksamil preparatini bo‘rtma nematodalar bilan kuchli zararlangan tuproqlarda karam ekinini himoya qilishda samarali vosita sifatida tavsiya etish imkonini beradi

Accurate image segmentation is an essential process to detect tumors or lesions that make a reliable medical image analysis and diagnosis of diseases. Yet, the tiny size of lesions and high interclass similarity in terms of shape, color, and location make the segmentation a difficult task as traditional multilayer thresholding methods have high computational cost. To address these constraints, this work presents an optimal medical image segmentation model using the neural field ordinary differential equations (NF-ODE). It is used for segmentation to segregate edge representations and UNet-model is used for computational efficient region of interest extraction. To mitigate numerical instability and to resolve the convergence of NF-ODE leaping high-density data, a stochastic conjugate gradient optimization (SCGO) to have training stable toward global optimum. The results on three head-and-neck organs at risk (OAR) datasets show that the proposed NF-ODE+SCGO model achieved better performance than other alternatives, with average DSC of 95.98% (clean) and 96.9% (masked), which is better comparable to UNet, Conv-ODE and NM-ODE counterparts. The average accuracy and DSC of the proposed method is 98.39% and 98.12% on individual datasets, respectively, depicts efficacy in challenging lesion segmentation.

Understanding user sentiment has become more important for organizations and consumers due to the rapid growth of social media platforms such as marketplaces, platforms for connecting brands and consumers, and public discussion platforms. Emotions that are based on aspects, nuanced within context, and multifaceted often require complex sentiment analysis algorithms to interpret properly. Furthermore, these systems do not provide real-time information to help companies make better decisions and enhance consumer satisfaction. To tackle these challenges, a novel Interactive Chatbot artificial intelligence (IChat-AI) approach has been proposed in this paper for sentiment-aware chatbot interaction. The word to vector (W2V), term frequency-inverse document frequency (TF-IDF), and bag of words (BoW) are utilized to effectively extract essential features. The deep Kronecker neural network (DKNN) is utilized to predict and classify the emotions into five classes, such as sad, happy, neutral, angry, and fearful. Python has been used to simulate the suggested model. The efficacy of the suggested system is examined employing parameters including recall, execution time, F1-score, complexity, precision, scalability, accuracy, and response time. The developed IChat-AI strategy performs better regarding accuracy than the existing methods, including RoBERTa, TLSA, and multimodal transformers fusion for desire, emotion, and SA (MMTF-DES) approaches, by 5.33%, 4.73%, and 14.39%.

This study explores the competency skill expectations from management graduates in Bangalore, with a focus on both technical and soft skills. As the demand for job-ready graduates rises, a significant gap exists between the competencies taught in academic institutions and those required by employers. The research aims to evaluate the impact of technical skills and examine the influence of soft skills on the job readiness of graduates. A descriptive research design was used, with purposive sampling selecting 145 management graduates as respondents. Data was gathered through structured questionnaires and analyzed using tools like ANOVA, correlation analysis, and multiple regression. The results indicate that soft skills, particularly teamwork and time management, play a more crucial role in job readiness compared to technical skills. Additionally, educational qualifications and work experience were found to have a notable effect on the development of soft skills and job readiness. While technical competencies are important, they have a lesser impact on job preparedness. The study recommends that educational institutions integrate soft skill development alongside technical training through practical experiences such as internships. Enhanced collaboration between academia and industry is also suggested to better align graduate skills with employer needs. Overall, the study highlights the importance of a comprehensive approach in preparing management graduates, ensuring a balance between technical knowledge and essential soft skills for greater job readiness and career success.

ABSTRACT Textural characteristics of fluvial sediments are excellent proxies for deciphering: tectonic, geomorphic and climatic conditions at source‐sink and the depositional settings, prevalent environment and energy conditions and modes and medium of transport. In this paper, we have documented the textural characteristics and heavy mineral distribution in channel bed sediments ( n = 51) of the Thamirabarani River from its source to confluence, in order to understand current environmental characteristics, and to establish a baseline data for the future studies. The results show that the majority of the samples were coarse‐ to medium‐grained, and moderately sorted. The occurrences of monazite, garnet (almandine, spessartine), magnetite, zircon, tourmaline, hypersthene, kyanite, rutile, and ilmenite indicate the provenance and general lithological control over the heavy mineral distribution. Angular heavy mineral grains all through the stream, including the lower reaches of the channel, together with high fluctuations of heavy mineral (0%–30%) in addition to a perceptible decrease at the confluence, suggested ongoing exhumation of the source areas, lower sorting effect and recycling of sediments by natural and anthropogenic influences. The spatial distribution of the textural and morphological characteristics of all the sediments and the heavy mineral grains, togther with the inferences from discriminant functions and diagrams, revealed prevailing spatial heterogeneity in unroofing of catchment/provenance, dilution effects of sediment characteristics by tributaries, as well as anthropogenic pressures/land use changes. Collectively, the study implies corroboration with inferences drawn on sedimentary textural characteristics of adjacently located river basins, signifying the documented phenomena as a regional character rather than endemic to this basin.

ABSTRACT The green synthesis of titanium dioxide nanoparticles (TiO 2 NPs) using Endostemon viscosus (Roth) M. R. Ashby leaf extract, in which the plant metabolites acted as reducing and capping agents. The green synthesized TiO 2 NPs exhibited an anatase crystalline phase with spherical morphology, good colloidal stability (zeta potential −23.1 mV), and particle sizes ranging from 50 to 110 nm. The nanoparticles demonstrated notable biological activities, showing enhanced antibacterial efficacy against Gram‐positive bacteria, strong antioxidant potential (DPPH and ABTS assays), significant anti‐inflammatory activity, and potent anticancer activity with a low IC 50 value (10.38 ± 1.93 μg/mL). These findings highlight the effectiveness of E. viscosus ‐mediated TiO 2 NPs and their promise as eco‐friendly nanomaterials for pharmaceutical and anticancer applications.

The Triple Helix Model, conceptualized as a dynamic interaction between universities, industry, and government, plays a critical role in fostering innovation, particularly in the context of Artificial Intelligence (AI). This model emphasizes the importance of collaboration among these three sectors to promote knowledge exchange, technological advancements, and economic growth. As AI continues to revolutionize various industries, the integration of academic research, government policy, and industry expertise becomes essential for building a sustainable knowledge economy. This chapter explores the theoretical foundations of the Triple Helix Model, highlighting its relevance to AI development. It examines how university-government-industry partnerships can accelerate AI innovation by providing a structured approach to resource sharing, policy development, and commercial application.