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Purpose - The purpose of this exploratory study is to look at how the Internet was used by political parties and candidates during the Indian parliamentary elections of 2009.Design methodology approach - A total of 31 web sites belonging to political parties and their candidates in the Indian states of Maharashtra and Gujarat were examined for how they were used to mobilize volunteers and voters. An online questionnaire and in-depth interviews were administered to the web site coordinators designers and politicians.Findings - The study found that sites were not used to their maximum potential but instead, merely for publicity, online presence, and to explore the new medium. There was greater reliance by most candidates on traditional media such as rallies and face-to-face interaction. The reason may be due to the limited Internet penetration in India, which also means the Internet may have less influence on voters. Some candidates have shown the way to the potential use of the medium for fund raising and recruiting volunteers. But Indian politicians will likely continue to be cautious in using the Internet.Research limitations implications - This study was limited to the states of Maharashtra and Gujarat and did not consider the impact or the effectiveness of the Internet.Originality value - This is the first such study of the use of web sites for electioneering in India. It also documents the development in the use of the new medium for campaigning in 2009 as compared with the elections of 2004.

In this study, we describe the Facebook-use behaviour of a segment of college-going Indian youths. A survey of 455 students revealed that they started using the Internet in their teens and that they spend on average 1.6 hours a day on Facebook. Male and female students do not differ significantly in spending time on either the Internet or Facebook. The female respondents tend to be more connected than their male counterparts. Despite showing more connectedness, female students tend to be choosier while accepting friends' requests than their male counterparts. Factor analysis revealed five major gratifications the student-users seek while using Facebook: relationship maintenance, userfriendliness, relaxation, connecting with old friends and social interaction.

This study tries to explore relationships among knowledge of privacy, enactment of privacy and attitude toward privacy. Specifically, it investigates how users use privacy settings and what is their attitude towards privacy on Facebook. It further attempts to study whether knowledge of privacy settings and attitude towards privacy has any influence on users' privacy behavior on Facebook. It also looks at gender differences in terms of privacy behavior. An online survey was administered to 199 college going students. Knowledge was not found to be an influential factor for enactment of privacy. Attitude toward privacy shares relationship with the enactment of privacy. Gender approach reveals differences between males and females in terms of their concern toward privacy and enactment of privacy measures.

Pervious concrete is an environmentally sustainable material known for its ability to allow water to pass through, thereby reducing surface runoff and aiding groundwater recharge. However, its high porosity often results in lower compressive strength compared to conventional concrete. This study aims to enhance the performance of pervious concrete by partially replacing cement with Rice Husk Ash (RHA)and coarse aggregates with Perlite stone. RHA, an agro-industrial by- product with pozzolanic properties, and Perlite, a lightweight, porous volcanic material, were chosen for their environmental and structural benefits. The mix design followed a 1:4.5 cement- to-aggregate ratio with a constant water-cement ratio of 0.45. RHA was used in varying proportions (4%, 6%, 8%,10%) while Perlite stone was replaced as 10% by weight of the aggregate in all mixes. The concrete specimens were tested for compressive strength and permeability after a 28-day curing period. Results showed that permeability increased with 6 % RHA content and 10% Perlite, whereas compressive strength reduced as RHA content increased. The optimal balance was found at 6% RHA and 10% Perlite offering adequate strength and permeability. This study demonstrates that incorporating RHA and Perlite in pervious concrete enhances sustainability while maintaining functional performance, making it suitable for eco- friendly pavements and drainage systems.

Environmental monitoring in the city demands the solution which will enable to work with the huge volumes of data of different kinds and utilize the resources in the most effective way. The presented work proposes Big Data Analytics Platform to address the urban problems with the help of the improved algorithms. Preprocessing Data Aggregation and Preprocessing Algorithms Preprocessing Data Aggregation and Preprocessing Algorithms obtain and standardize different data to ensure that the data result of the analysis is standardized. The temporal and spatial relationships are determined using the Spatial Temporal Data Analysis Algorithms before explaining the urban dynamics. The learning based on the Machine Learning-Based Predictive Modelling Algorithms and offer the correct environmental conditions and trends within the urban regions to make timely decisions. Multi-Objective Optimization Algorithms are employed when there are resources to be controlled through the fulfillment of groups of competing goals including energy use and emission issues. These two algorithms provide the Real-Time Data Visualization and Reporting and expose the stakeholders to the relevant information in the format that will be understood by them. In general, one can refer to these algorithms as a strong foundation of the overall monitoring and management of sustainable environments in cities, and thus creation of sustainable cities. The fact that the control of emissions, traffic simulation, and waste disposal sustainability concepts are among the areas of discussion.

Living donor kidney transplantation plays a vital role in renal replacement therapy, particularly in India, where a substantial increase in kidney transplants has been observed. Thorough assessments of living kidney donors are crucial, focusing on parameters such as kidney size and glomerular filtration rate (GFR). Despite the importance of GFR in donor assessments, there is a noticeable lack of data on normal GFR ranges in the Indian population. This study aims to address the gap in knowledge by establishing a reference range for GFR in healthy kidney donors from the Marathwada region of Maharashtra. The research also explores the clinical profiles and ultrasonographic features of living kidney donors. A retrospective analysis was conducted at the Mahatma Gandhi Mission (MGM) Medical College and Hospital in Aurangabad, involving 134 living kidney donors. Inclusion criteria encompassed healthy donors with a BMI of less than 30 kg/m², while donors with uncontrolled hypertension, diabetes, microalbuminuria, or a measured GFR below 70 mL/min/1.73 m² were excluded. Comprehensive medical histories, demographic parameters, and ultrasonographic assessments were conducted, with GFR measured using 99M technetium diethylenetriamine pentaacetate scans. The study reveals that the majority of donors were females (80.6%), and the highest number fell within the 41-50 age group. Parents constituted the primary donor category (68.7%), reflecting a familial inclination toward organ donation. Ultrasonographic assessments indicated larger kidney sizes compared to other studies, suggesting regional or population-specific differences. The mean GFR for the right and left kidneys, as well as the total GFR, was within the expected range. The negative correlation between age and GFR emphasizes the need to consider age in donor assessments. The findings emphasize the unique features of this population, including a higher average age, female preponderance, and larger kidney sizes. The study contributes to the understanding of living kidney donors' profiles in the region and highlights the importance of individualized assessments in the donor selection process.

Laser welding stands out as one of the most precise and efficient manufacturing techniques, with its ability to generate minimal heat‐affected zones and limit material distortion. This study introduces a cutting‐edge neural network–based predictive model designed to estimate tensile strength and welding deformation in laser welding operations. By incorporating three critical input parameters, laser incident angle, laser velocity, and laser power, the model harnesses the power of a neural network to refine process optimization and elevate the quality of welded joints. Among the tested models, the Bayesian regularization (BR) model demonstrated superior accuracy, achieving a remarkably low mean absolute error (MAE) of just 0.0001982. In contrast, the Levenberg–Marquardt (LM) model yielded an MAE of 89.29, while the scaled conjugate gradient (SCG) model recorded an MAE of 41.67. These findings underscore the effectiveness of the BR model in enhancing predictive accuracy for laser welding applications.

This study proposes a novel technique for integration of off - shore wind generation system to conventional grid. The technique is used to control the output of voltage source inverter. Output voltages of inverter are synthesized by using space vector pulse width modulation (SVPWM) in stationary reference frame. In this control scheme, control voltages are obtained in polar form and hence SVPWM implementation is easier at constant switching frequency. The scheme can be used for the control of induction motor drives exploring furthermore, solar power based drive applications such as agricultural pump-sets, compressors, fans etc. The simulation study is carried out in MATLAB/SIMULINK on a 100KVA inverter.