Vasavi College of Engineering

Abstract Elastomeric isolators are widely recognized as effective solutions for mitigating the seismic and vibrational impacts in masonry structures. Nevertheless, the pursuit of cost-effective, viable, and reliable isolation strategies for masonry buildings remains an ongoing research priority. In response to this need, unreinforced elastomeric isolators (UEIs) seem to be promising alternatives among available isolator options. This study presents a comprehensive methodology for designing affordable UEI systems tailored to masonry construction, with a specific focus on enhancing seismic resilience in resource-constrained regions. By leveraging the Taguchi approach, regression analysis, and a weighted score optimization method, six critical parameters—load, excitation frequency, shear strain, hardness, thickness, and length—are systematically varied to optimize isolator performance. Experimental investigations, comprising cyclic compressive and shear tests on 27 UEI prototypes, reveal critical trends in vertical and effective lateral stiffness as well as damping characteristics. To address experimental limitations, numerical simulations in ABAQUS integrate hyperelastic and viscoelastic material models, and the results are validated against experimental data. Regression equations derived from these analyses facilitate predictive modeling across a broader design space, enabling practical extrapolations beyond the tested configurations. An example application for a masonry house demonstrates the step-by-step design methodology in a realistic context. Collectively, the findings underscore UEI’s potential as an accessible seismic isolation solution, effectively bridging the gap between advanced engineering methods and the needs of low-income regions.

Self-localization is the capability of a wireless sensor network (WSN) to estimate the location coordinates of a given target node (TN), using the location knowledge of few anchor node (AN)s. The ANs are the sensor nodes installed with GPS modules, and their locations are known in prior. Precision improvement of the TN’s location is an important issue for effective data transmission in WSNs. Localization draws attention in location aware applications like nuclear attacks, object tracking, healthcare, supply chain management, biological attacks, and traffic monitoring, etc. In this article, a weight based AN selection strategy in localization algorithm for WSNs is proposed, which uses only four ANs where each one is installed with GPS module, and deployed within the sensing area. The weights are assigned to ANs depend on the distance between ANs and TNs to mitigate the effect of noise due to spatial and temporal changes in the sensing area. The proposed method uses improved cuckoo search algorithm, which is a nature inspired optimization technique to address the anisotropic nature of WSNs, and compute global optimal location coordinates of TNs. It also enhances the localization accuracy compared to the nascent localization algorithms in WSNs. Rigorous simulations are conducted to prove the efficiency of the proposed method using the performance metrics as number of ANs, mean localization accuracy, and communication range. Based on the simulation results, the localization accuracy of the proposed CERBLA algorithm is increased to 99.24% and the range measurement error is minimized to 1.18 m. The localization accuracy using the CERBLA algorithm is enhanced by 25.32%, 25.19%, 21.47%, 128.21%, 84.48%, and 181.35% compared to DCK-GWO, WOA-QT, DECPSODV-Hop, ECS-NL, QABA, and IDE-NSL-AWSN algorithms respectively. The performance of the CERBLA is outperforming the existing algorithms particularly, when the number of ANs are less than ten, and this feature is attractive to build cost-effective localization algorithms for indoor applications of WSNs.

The widespread use of online learning has been facilitated by advancements in information technology. Less pertinent assessments and applications are available for students, nevertheless. All learning innovation efforts aim to improve education while simultaneously increasing student engagement in the classroom. By opting instructional resources that are suitable for each student’s learning preferences, teachers may increase the participation of their students in the teaching–learning process. This chapter aims to create and assess how well a learning style prediction based on AI model performs in a learning portal. In order to support the idea of personalized learning, an AI model was developed for the online learning site to provide educational resources that suit students’ learning preferences. Consequently, this project’s goal is to create a user interactive application that helps student understand his way of learning and improve himself in the way he understands and learns things.

Mortgage-backed securities (MBSs) have long been a staple in financial markets, offering investors exposure to the residential mortgage market. However, the prepayment risk associated with these securities presents a significant challenge for investors and issuers alike. Traditional models for predicting prepayment risk often fall short in capturing the complex dynamics of the mortgage market, leading to suboptimal risk management strategies. In this research paper, we propose a novel machine learning approach to predict MBS prepayment risk with the aim of enhancing risk mitigation strategies. Leveraging advanced techniques in data analytics and predictive modeling, our methodology integrates a diverse set of features, including borrower characteristics, economic indicators, and market trends, to generate accurate forecasts of prepayment behavior. Through comprehensive experimentation and validation using historical MBS data, we demonstrate the effectiveness of our approach in accurately predicting prepayment risk across various market conditions. Our results indicate significant improvements in prediction accuracy compared to traditional models, thereby empowering investors and issuers with valuable insights for making informed decisions and mitigating risk exposures. Our study contributes to the growing body of literature on MBS prepayment risk management by showcasing the potential of machine learning techniques to provide more accurate and reliable predictions, thereby enabling stakeholders to navigate the complexities of the mortgage market with greater confidence and efficiency.

A compact Ultra Wide Band (UWB) microstrip fractal antenna was developed on an octagonal patch in which Koch Snowflake features were involved by introducing two level fractals iterations on all the edges of the octagonal patch. The antenna is implemented on an FR4 substrate with ε _r = 4.4 and excited through a 50 microstrip feed line. A partial ground plane is employed, and parametric analysis is conducted by adjusting the feed width and ground plane length. The simulations were analyzed to arrive at wide impedance matching. This antenna with fractal iterations was simulated and the resulting bandwidth was 3.07– 11.54 GHz with the Return Loss ( S_11 ) plot below -10 dB exhibiting UWB frequency range. Antennas with fractal dimensions of 1.26, 1.32, 1.51 and 1.73 were analyzed with respect to the bandwidth performance in terms of impedance matching and the corresponding radiation behavior. The variation in the results was noticeable in E-plane and H-plane patterns due to the change in fractal dimension. To suppress the undesired frequency bands, a U shaped slot was introduced on the antenna structure optimized designed to introduce a notch at 5.5 GHz . The length of the slot was calculated using half wavelength resonance principle and it was further fine tuned using parametric analysis. The S_11 plot of the antenna was as expected, enabling rejection of the frequency band at the desired range, while the antenna performance remained intact over the other frequency ranges.