Vidya Prathishthan

This paper proposes an intelligent system for verifying the authenticity of academic documents and detecting tampering using artificial intelligence methods. The YOLOv8n deep learning model is used for detecting key areas on an image of an academic document, based on an image of the document being verified (e.g., name, roll number, marks, percentage, institutional seal). The text created by Optical Character Recognition (OCR) from the identified areas is checked against two different ways of determining whether there has been any tampering or forgery of the document using logical validation of the data in question. For example, whether the percentage computed matches the total amount of marks received. If any inconsistency exists between the extracted data from the document being verified, as well as from the logical validation, the areas of the document will be marked on the webpage, which shows both visually the document and the results of the verification. A variety of experiments with test cases show that the current system classifies academic documents into three categories (i.e., legitimate, forged and needing to be confirmed) based on the verification results. Thus, the proposed technique has reduced the time and effort required to manually verify a document and can also assist in detecting academic documents that have possibly been tampered with..

This study explores the flow dynamics of chemically reacting Maxwell fluids over porous stretching sheets, focusing on the interplay between fluid rheology, chemical kinetics, and porous media effects. It analyzes the influence of the viscoelastic properties of a fluid on momentum and concentration of boundary layers, considering chemical reactions and the permeability of the stretching sheet. The mathematical model accounts for the non-Newtonian nature of the Maxwell fluid, reaction kinetics, and porous medium resistance. Numerical solutions explore the effects of key parameters on velocity, concentration profiles, heat, and mass transfer rates. The findings offer insights into coupled transport phenomena in engineering processes involving non-Newtonian fluids, chemical reactions, and porous media. Dimensionless problems are numerically solved using the Runge-Kutta approach. The study focuses on important engineering parameters that affect velocity, concentration, and temperature profiles. Quantitative values of engineering factors such as skin-friction, Sherwood number, and Nusselt number coefficients are presented, and the results are analyzed using graphics and tabular representations. For program code validation, a numerical comparison with previously released data is provided.

Robotic joints are essential for defining mobility and functionality in robotics, particularly in industrial, medical, and autonomous systems. These joints experience complex loads, including bending, torsion, and axial forces, which cause stress concentrations and fatigue over time. In high-precision tasks or high-load environments, such stress can lead to premature wear or catastrophic failure. Understanding stress distribution is critical for ensuring the structural integrity, efficiency, and longevity of robotic joints. This paper proposes a computational framework that integrates mathematical modeling, computational techniques, and sensor placement strategies to optimize robotic joint design and performance. The framework combines analytical, numerical, and experimental methods to enhance stress predictions, reduce computational costs, and ensure sensor reliability. The approach will be validated using case studies to demonstrate its versatility and accuracy in various robotic applications.

In wireless power transfer application, Inductive power techniques are commonly used for Battery Charging for electric vehicle. Wireless Power Transfer offers a promising avenue for automating power delivery to consumers, reducing the necessity for manmade interface. On other side, appropriate vehicle to grid linking and appropriate control and compensation network are needed for efficient wireless power transfer method. Compensation topologies are essential for ensuring optimal power delivery between primary and secondary to enable effective and reliable power transfer. This paper presents WPT used in Series-Series compensation topologies.

Abstract Present study gives an information on the effects of ultraviolet-C (UV-C, 254 nm) radiation on embryonic development and protein secretion in Gallus gallus embryos. For this purpose, fertilized eggs of Gallus gallus were divided into control and UV-exposed groups. The eggs were subjected to the continuous exposure for seven days. The protein secretion was stable in control group with an average concentration of 85.89 µg/ml, whereas UV-exposed group demonstrated an initial increase in protein concentration, up to 180 µg/ml on third day of treatment followed by gradual decrease to 87.97 µg/ml, Thus UV exposure triggers protein synthesis as a response to the stress, while prolonged exposure leads to oxidative stress with decreased protein secretion.

Urbanization and climate change have negative effects on the changes of natural precipitation and runoff regime, which results with more frequent floods and landslides. Besides of the increased quantity of the stormwater that drainage channels have to collect, neg- ative consequences of urbanization are also evident through increased pollution of runoff stormwater. Stormwater has immense impacts on urban flooding and water quality, leaving the marginalized and the impoverished disproportionately impacted by and vulnerable to stormwater hazards. However, the environmental health concerns of socially and econom- ically marginalized individuals are largely underestimated. Therefore stormwater manage- ment should be adapted to these changes so that the negative effects of new hydrological conditions of precipitation and runoff and stormwater pollution in the urban areas are mitigated. By 2050 the world population will grow to about 9 billion contributing to deep changes in urban areas structure. This would increase the effect of water deficiency and along with projected climate changes the impact of urban flooding, urban heat islands or drought. Stormwater threats can be mitigated with the application of stormwater management meth- ods named differently such as ‘Sustainable (Urban) Drainage Systems’ (SUDS), ‘Low Impact Development’ (LID) or ‘Best Management Practices’ (BMPs). This method includes differ- ent techniques such as Green roof, Rain barrels and cisterns, Infiltration basin, Detention Ponds, Retention Ponds, Green walls, Porous pavements, Bioretention basins, Bioswales. The aim of the research includes a study of the effectiveness of urban stormwater manage- ment techniques for the case study area. The concept of urban stormwater management techniques refers to the ability to provide and manage this primary resource in quantitative and qualitative terms in order to satisfy the future needs of population. The storm wa- ter management techniques provides several benefits: mitigation of the urban heat island effects, reduction of air pollution, additionally, It induces important hydraulic advantages acting as an effective tool for reducing flood risk in urban area with runoff reduction, attenuation and delay of the peak flow. The results confirm that bioretention basin, Permeable pavement, Grassed swale, Infiltration basin are effective techniques for the reduction of total runoff volume of rainwater falling on their area.

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS’s ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.