Online grocery shopping has quickly become more popular because of how convenient it is and easy it is to access. E-commerce platforms face rising competition, so businesses use website personalization as their strategic weapon to deliver better UX and boost sales results. The article investigates how website personalization affects consumer purchase decisions within online grocery shopping domains. The integration of suitable content with product suggestions and intuitive interfaces shapes customer happiness levels as well as trust relations and purchasing choices. The rapid growth of online shopping during the COVID-19 pandemic brought about major changes in how people buy food items. Traditional shopping behavior moves to digital channels causing retailers to embrace cutting-edge technologies which aim to fulfill changing consumer needs. Website personalization which provides tailored encounters according to user information and preference data represents today's crucial distinguishing feature. The research investigates the impact of customized website functions on customer buying actions together with customer retention results and purchase conversion rates in the online grocery market.

Compressed Natural Gas (CNG) has developed as a viable alternative fuel in the transportation industry, providing environmental and economic benefits compared to traditional fossil fuels.  Notwithstanding its advantages, the rate of consumer acceptance and implementation differs markedly among areas and demographic groupings.  This study seeks to examine the principal factors affecting consumer adoption of CNG as a favoured transportation fuel.  The research is motivated by the growing global demand for a transition to sustainable and low-emission energy sources, especially in metropolitan areas where vehicle emissions significantly contribute to air pollution.  Initial studies indicate that environmental concern, cost-effectiveness, fuel availability, and perceived performance are essential factors influencing adoption, whereas obstacles comprise inadequate refuelling infrastructure, insufficient awareness, and apprehensions about vehicle performance.  Moreover, governmental activities including subsidies, tax incentives, and awareness initiatives significantly influence consumer perception.  This study enhances the existing information on sustainable transportation by elucidating the many aspects influencing consumer choices related to alternative fuels.  The results can aid politicians, automotive manufacturers, and energy firms in formulating specific strategies to expedite the adoption of CNG.

The present era of information technology has opened new vistas for transfer of methodology and technology between the producers and the users of information in least possible time. Information technology has become a medium for communication of ideas and a resource necessary for the sustenance and promotion of the progress in agriculture and thus in GDP of country. Agriculture is the main occupation of the majority of population in Salem district. The farmers of the district rely heavily on agriculture for earning their livelihood. The development of agriculture depends on effectiveness of utilization of agriculture equipment’s. The impact of these aspects of agriculture varies in different areas of the district. Especially now a days agricultural machineries and equipment’s plays important role in maximise the yield for the farmers. When there is maximum yield utilizing by the agricultural equipment’s, the farmers get satisfaction. The satisfaction of the farmers turns to continue the agriculture and endorse to their next generation. Keeping these points in view, the Salem district has been selected as the study area because there has been significant development in agriculture in the district in the post independence era. This research paper entitled “A study on the farmers satisfaction towards utilization of agricultural equipment in Salem district” has been studied . The study was undertaken with the objective of analysis of the farmers satisfaction and their attitude towards modern agriculture equipment’s. The objective was achieved by using the instrument by questionnaire. The findings have been summarized and the suggestions have been enumerated. The study analyzed the overall farmers satisfaction towards the agricultural equipment’s in Salem district.

Banking is defined as the business of accepting and securing money owned by other people and companies, and then lending it out to execute economic activities such as creating a profit or merely covering operating expenditures. The Indian Commercial banks face a vivid competition from private sector banks. Private Banks, having more incentive and risk, are right now one step ahead, but public sector banks have been doing well too. Banks play a key role in India's financial system and underpin economic growth. Although Indian authorities have taken a number of steps to strengthen the banking system, progress has been difficult and has been further curtailed by the COVID-19 pandemic. In the banking field, there has been an unprecedented growth and diversification of banking industry and banks are now utilizing the latest technologies like internet and mobile devices to carry out transactions and communicate with the masses. The performances of various banks have been measured using some crucial ratios in the current study to determine the working efficiency of the banks. This study intends to cover the crucial aspects and dynamics that has affected or will further affect the banking sector throughout India. The current study is naïve and will contribute a little to existing literature on banking sector and will be useful for bankers, strategist, policy makers and researchers.

This study presents a modular and scalable Autonomous Valet Parking (AVP) system, tailored for structured, single-floor parking facilities. The proposed framework facilitates fully automated vehicle parking and retrieval, eliminating human involvement while optimizing spatial efficiency, user accessibility, and traffic flow within urban environments. The operational sequence is initiated when the user leaves the vehicle at Ground 0 Level, activating Vehicle-to-Infrastructure (V2I) communication with a cloud-integrated parking management system. This system identifies and allocates an available parking slot in real time, subsequently generating and transmitting a collision-free trajectory that accounts for both static architecture and dynamic obstacles. The autonomous vehicle executes the assigned trajectory using a sensor fusion suite comprising LiDAR, 3D depth camera, ultrasonic sensors, wheel encoders, ESP module, and real-time localization modules. (Liu et al., 2019; Kuutti et al., 2018). Navigation is governed through a hybrid control strategy combining Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC), integrated with Simultaneous Localization and Mapping (SLAM) to ensure precise maneuvering in GPS-denied environments. (Cadena et al., 2016; Labbé & Michaud, 2019). Vehicle retrieval is initiated via a mobile application, prompting autonomous traversal along the reverse trajectory to the designated pickup zone. The system was prototyped and validated on a scaled model, with experimental results confirming its robustness in trajectory tracking, obstacle avoidance, and retrieval accuracy. The outcomes affirm the AVP system’s viability as a foundational element in future smart mobility ecosystems and intelligent urban infrastructure.

The rapid advancement of autonomous vehicle technology has paved the way for autonomous racing, where the high-speed, competitive nature of motorsports fosters accelerated innovation. One of the primary challenges in this domain is determining the optimal path commonly referred to as the racing line for autonomous vehicles. Traditional methods for identifying such trajectories often either fall short in terms of time optimization or demand high computational resources, making them impractical for real-time execution on embedded hardware.

This paper presents a machine learning-based solution that enables real-time prediction of racing lines using desktop-level computing resources. The method utilizes a feedforward neural network trained on a dataset of racing lines derived from conventional optimal control-based lap time simulations across numerous circuits. The model achieves a mean absolute prediction error of ±0.27 meters, with an impressive ±0.11 meters accuracy at corner apexes comparable to both professional drivers and existing autonomous driving control systems. Notably, the system generates trajectory predictions in just 33 ms. These results highlight the potential of data-driven models to deliver near-optimal racing line predictions more efficiently than conventional computational methods, especially in time-sensitive applications.

This paper presents a novel approach to addressing power quality issues that arise from the unpredictable nature of renewable energy sources such as solar and wind. In standalone hybrid systems combining solar photovoltaic (PV) arrays and wind turbine generators (WTGs), fluctuations in power output can result in voltage instability, frequency variation, and harmonic distortion, all of which affect the reliability of power delivery. Conventional power conditioning methods, including passive filters and voltage regulators, often fail to respond effectively to rapid and irregular changes in energy input. To overcome these limitations, this study proposes the integration of a DVR managed by an Artificial Neural Network (ANN). The ANN is trained using historical performance data to identify patterns associated with various power disturbances. Once deployed, it monitors the system in real time, detecting anomalies and activating the DVR to inject appropriate compensating voltages. This dynamic correction restores load voltage to acceptable limits, ensuring system stability. Simulation tests conducted on a hybrid PV-wind model validate the effectiveness of the proposed system, showing significant improvements in voltage regulation and overall power quality. This method provides a smart, adaptive solution for enhancing the performance of standalone renewable energy systems.

Modern electrical systems necessitate precise Short-Term Load Forecasting (STLF) to enhance power generation, distribution, and pricing strategies. This research employs machine learning techniques, specifically the Support Vector Machine (SVM) model, to predict electrical load demand with precision. The SVM model was developed to analyze complex energy consumption patterns and fluctuations by utilizing historical load data and temporal variables. The model's performance was evaluated through statistical metrics including MAE, MSE, RMSE, and MAPE, while the R² score represented the proportion of variance accounted for by the model's predictions. The results indicated that the SVM model effectively predicted load demand, achieving a low MAE of 1887.41 and a R² score of 91.95%, thereby accounting for the majority of data variation. The research indicated that prediction errors increased during periods of high load variability. The findings indicate that incorporating meteorological data or enhancing model hyperparameters may enhance model accuracy. This study demonstrates the efficacy of the SVM model in short-term load forecasting, providing insights into energy management through machine learning techniques. The findings confirm the potential of SVM in this domain and highlight the necessity for ongoing model refinement to achieve optimal forecasting accuracy. Hybrid models and advanced methodologies warrant investigation to enhance load forecasting and facilitate more efficient and sustainable operations within energy systems.