Gudlavalleru Engineering College

In this paper, the architectures of Nanosheet Field-Effect Transistor (NSFET) and Fin Field-Effect Transistor (FinFET) for biosensing applications are thoroughly compared. The study assesses their biosensing effectiveness and electrical performance of both devices. Both FinFET- and NSFET-based biosensor transfer characteristics (ID-VGS) are examined for situations containing charged and neutral biomolecules. The gate-all-around (GAA) design of NSFETs results in a greater Ion/Ioff ratio, ensuring better electrostatic control and less leakage current. The higher vertical fin structure of FinFETs, on the other hand, provides a greater gate-facing surface area and stronger field penetration into the sensing region, which is responsible for their increased sensitivity. Better dielectric modulation and increased interaction with target proteins are made possible by this shape, which leads to more noticeable current changes and enhanced detection power. To further improve sensitivity, spacer engineering with high-k designs is investigated. For performance adjustment, structural modifications like cavity thickness and length are assessed. In order to create realistic biosensing applications the effects of partially filled nanocavities are also investigated. This study examines several important parameters, such as sensitivity, Ion/Ioff ratio, transconductance, specificity, limit of detection (LOD), and time response.

This study investigates the performance of self-compacting geopolymer concrete (SCGC) incorporating fly ash (FA) and silica fume (SF) as aluminosilicate precursors, with emphasis on mechanical behaviour, durability, microstructural characteristics, environmental impact, and machine-learning-based performance prediction. Four geopolymer SCC mixes were prepared using SF to substitute FA at 0, 5, 10, and 15% by weight and an OPC-based SCC of the same class of strength was prepared to serve as a benchmark. Fresh properties were assessed as per EFNARC standards after which compressive, split tensile and flexure strength were done at intervals of 28, 90, and 180 days. Sorptivity, rapid chloride permeability (RCPT) and ultrasonic pulse velocity (UPV) were used to determine durability, whereas scanning electron microscopy (SEM) was used to measure the microstructural evolution. Findings reveal that SF is a considerable improvement to fresh and hardened geopolymer SCC, the best performance being found at a 10% replacement. The G10 mix recorded the best compressive strengths of 65.3 MPa at 180 days, which was 21% higher than the FA only geopolymer mix, and tensile and flexural strengths were 14-18% higher compared to FA only geopolymer mix. The performance of durability increased significantly, as sorptivity was reduced by about 21% and RCPT was lower than 1000 Coulombs (extremely low permeability) and the largest values of UPV were the highest, which shows the presence of a dense and uniform internal structure. Refined pore structure and well-developed the formation of aluminosilicate gels in the G10 mix had been confirmed by SEM observations. Parameters of mix, fresh properties, and curing age were used as inputs to develop machine learning models (KNN, SVM, Decision Tree, and Random Forest). Among them the predictive accuracy of the Random Forest model (R2 = 0.94) with the least error showed excellent performance forecasting and mix optimization. It was found by Life Cycle Assessment (LCA) that geopolymer SCC mixtures had less environmental impact through global warming potential (30-45% less than OPC-SCC) and energy (around 20-25% less than OPC-SCC) and the G10 mix had the lowest environmental impact. Overall, the study demonstrates that FA-SF geopolymer SCC with 10% SF replacement provides a good combination of workability, strength, durability, and sustainability that is proven in experimental, microstructural, data-driven modelling, and environmental analysis.

Rapid urban growth in Bengaluru has increased the need for well-integrated metro rail and feeder bus systems to improve accessibility and support sustainable urban mobility. This study aimed to assess the current level of integration and identify priority strategies for improvement using a combination of quantitative and perception-based methods. Data were collected through structured surveys at three major metro stations such as Baiyappanahalli, Majestic and Yeshwantpur covering commuter perceptions across 18 service indicators. The Sustainability Integration Index (SII) showed that Majestic station achieved the highest integration score (77.23%), followed by Baiyappanahalli (72.42%) and Yeshwantpur (65.04%). Policy analysis indicated that increasing bus frequency produced the greatest improvement in integration (+4.66%). Exploratory Factor Analysis (EFA) identified seven latent factors explaining 70.41% of the variance in perceptions, with frequency, reliability, and safety emerging as the most influential dimensions. Importance-Performance Analysis (IPA) revealed that while speed and reliability were perceived as strengths, waiting conditions had the largest negative performance gap (–0.814), highlighting a critical area for improvement. The Analytic Hierarchy Process (AHP) was applied to rank policy interventions, with experts assigning the highest priority to increasing bus frequency (49.06%) over implementing a single ticketing system and relocating bus stops. Overall, the findings confirm that enhancing service frequency, improving operational reliability, and simplifying fare systems are essential strategies to strengthen multimodal integration and encourage greater use of public transport in Bengaluru.

Cloud computing provides on-demand services with high performance and scalability over the Internet. The primary goals of task scheduling in cloud environments are to efficiently utilize available resources, minimize execution time, and ensure timely task completion. Load balancing is crucial to ensure that virtual machines (VMs) are evenly utilized. However, the key challenge in cloud computing lies in effectively balancing the load and scheduling tasks. In the proposed model, an optimal task-scheduling mechanism is designed using the State-Action-Reward-State-Action (SARSA) algorithm. The scheduling process is enhanced by the Osprey Optimization Algorithm (OOA) to select the best resources, minimizing execution time, cost, and resource utilization. Once the tasks are schelued in queue, the load balancing is carried out using the Walrus Optimization Algorithm (WaOA), which optimally balances the load across VMs based on task lifetime and response time. The performance of the proposed model is evaluated under various task conditions, and the results are compared to existing models for validation. The proposed approach demonstrates superior performance with a makespan time of 64.81 s, turnaround time of 7.05 s, waiting time of 203.48 s, response time of 70 s, scheduling time of 226 s, a success rate of 95.7

Abstract Approximate computing is a technique that has proven effective for enhancing the energy efficiency of image processing systems by relaxing the accuracy of computations in error-tolerant applications. In the typical conventional approach, however, the same approximate multiplier (AM) is used throughout the calculation, which is not well suited to adapting to the characteristics of different images or to application-specific requirements for image quality. This paper proposes an adaptive approximate computing framework based on machine learning techniques for blending an RGB image while satisfying a quality constraint. An AM library, with varying levels of approximation, is combined with a Gradient Boosting (GB) classifier to dynamically select the most suitable AM for the extracted image features and the given required output quality (ROQ). A dataset created with typical RGB image pairs under various ROQ constraints was used to test the proposed framework. The accuracy of the classifiers was compared by running a comparative study on five classifiers, namely Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, and GB with the maximum classification accuracy of 93.57% obtained by GB and a mean accuracy of 94.05% obtained by five-fold cross-validation. The application-level evaluation demonstrated the proposed framework’s ability to maintain the desired visual quality, with an average PSNR of 64.56 dB, an average SSIM of 0.99997, and an adaptive selection of AMs based on the specified ROQ. The results of the hardware synthesis further demonstrated the considerable savings in logic utilization, delay, and power consumption from the exact multiplier, further supporting the efficiency in the proposed multiplier library. The experimental results show that the proposed framework achieves a good balance between computational efficiency and image quality, offering a practical approach for quality-aware approximate computing in image processing applications.