Tamil Nadu Physical Education and Sports University

The purpose of this investigation was to find out how moderate intensity resistance training influences muscles growth in newbie college students living in urban areas. Forty men under graduate students were randomly selected as subjects and their aged ranged from 17 to 21 years. The selected subjects were randomly assigned into two equal groups namely experimental (n = 20) group (EG) which underwent moderate intensity resistance training for 8 weeks with 3 sessions per week and control (n = 20) group (CG) which did not undergo any special training. The thigh girth, calf girth and upper arm girth were selected as criterion variables for this study. The subjects were measured their thigh girth, calf girth and upper arm girth by a flexible tape. The obtained data were statistically assessed for any significant difference using analysis of co variance (ANCOVA). The result showed that there were significant improvement on thigh girth (56.86 ± 3.73 vs 53.02 ± 3.41, p≤ 0.05) and upper arm girth (33.86 ± 2.25 vs 29.85 ± 2.19, p ≤ 0.05) between the experimental group and control group. However there was no significant improvement in calf girth (36.17 ± 2.45 vs 34.93 ± 2.32, p ≥ 0.05) between the experimental group and control group. Eight weeks’ time is very short to increase the size of the muscles size in significant level, especially for beginners, but here it was increased significantly the thigh muscle size and upper arm muscles of the experimental groups when compare to control group. If the training duration and the intensity were increased, it would have increased the size of calf muscle as well.

Surface roughness classification is the most important measure in the pursuit of machining quality, but conventional offline measurements are not conducive to real-time monitoring. To address this issue, the paper presents an Attention-Guided Convolutional Neural Network (AG-CNN) for classifying levels of surface roughness from multivariate force signals (Fx, Fy, Fz). The model combines 1D convolutional layers with a temporal attention mechanism to concentrate on signal regions. The system is deployed with Python and TensorFlow, which allows effective preprocessing, training, and inference of the Models. To make use of the publicly released CNC Turning dataset, where force signals are normalized, segmented, and annotated as smooth, moderate, and rough classes in signals. The introduced AG-CNN achieves an accuracy of 85.3%, representing a 5.1% improvement over the baseline CNN. The findings verify that incorporating attention improves classification performance and provides a stable framework for real-time monitoring of surface quality in intelligent manufacturing systems.

In the previous chapter, we explored how Flutter enables deep integration with native platforms through platform channels, advanced plugin development, and native security best practices. These capabilities are critical for extending Flutter's core functionality and delivering powerful, low-level interactions across Android and iOS. However, the true strength of Flutter lies in its versatility across platforms—including not just mobile, but also web and desktop.

In the previous chapter, we explored the integration of powerful AI capabilities into Flutter apps. From setting up TensorFlow Lite and leveraging pre-trained models to building real-time image recognition systems and Natural Language Processing workflows, you gained insight into developing intelligent, responsive, and futuristic applications. These advanced ML features significantly elevate user experiences—but they also introduce new vectors for risk and exploitation.

The design and construction of the badminton stadium pose a unique wind engineering challenge due to the increase in complaints related to wind drift in badminton tournaments. It is caused by heating ventilation and air conditioning or natural/cross-ventilation systems in the stadium. This paper addresses cross-ventilation efforts on barrel roofs, which are widely used for indoor stadium construction. The computational fluid dynamics analysis is carried out on the flat roof before the barrel roof to validate the wind tunnel data from the literature, and the grid independence is studied. The simulation is performed with a shear stress transport k–ω model with a three-dimensional steady Reynolds-averaged Navier–Stokes (RANS) approach. The cross ventilation for the three different barrel roof configurations with two opening directions is studied in the numerical simulation to understand the influence over volume flow rate and velocity profile near the ground (h = 20 m). From the velocity contour, it is concluded that the longitudinal direction with a height less than 5% from the standard roof (h = 100 mm) performs better than other configurations.

Hand index, finger ratio, and grip strength are potential indicators of proficiency in court sports. The aim of this study is to explore hand dimensions, fingers length ratio, and grip strength in court sports players, a domain in which such characteristics can significantly influence performance. Measurements, such as hand length (HL), hand breadth (HB), hand index (HI), palm length (PL), hand span (HS), index finger length (2D), ring finger length (4D), 2D to 4D ratio (2D:4D), low digit ratio (LDR), and high digit ratio (HDR) were studied in the field of hand anthropometry, along with a handgrip strength (HGS) test. Data were obtained from 135 male court sports players, including basketball, handball, and volleyball, with 45 players from each discipline. Descriptive statistics, one-way ANOVA, and an independent t-test were used to compare variables, with statistical significance set at p < 0.05. The ANOVA results indicated that there were no significant differences in hand variables, namely HL, HB, PL, HS, 2D, 4D, 2D:4D, LDR, HDR, and HGS, except for the HI variable. Post-hoc test results showed HI differences in basketball versus handball and volleyball. Compared to standard HI, their ranges show that all-court sports often fall into the Dolichocheri type, characterized by long fingers and a narrow, small palm. A low digit ratio may have some effect on HI in basketball and volleyball. However, it does not appear to have a significant effect on HGS across the study. In summary, the results of our study show that court athletes are similar in hand characteristics, 2D:4D, and grip strength for the nature of the game.

Badminton is highly influenced by wind drift due to indoor ventilation, which changes the trajectory of the shuttlecock from the actual. To understand the influence of wind drift over different stadium configuration is studied using Computational Fluid Dynamics (CFD). CFD is an essential tool to assess and understand the impact of wind in the building environment for ventilation comforts. Many studies on natural or cross ventilation on the generic model and stadium infrastructure focus only on the ventilation comfort but limitedly on the wind influence like wind drift in badminton. Therefore, this paper presents a detailed study through computational analysis of the isolated generic model with three gable roof slopes ratio and two opening directions. The simulation is performed in a 3D steady Reynolds-Averaged Navier–Stokes (RANS) approach with the SST k–ω model. Validation is carried out with grid sensitivity study by comparing study of a published work with wind tunnel experiment data on a flat roof model. The results show that the gable roof in the longitudinal direction with a 6:12 and 4:12 sloped ratio performed better than a flat roof with a 6% and 13% volume flow rate higher with almost no wind drift near the ground (h=0.02m). The 2:12 sloped gable roof has a higher volume flow rate in both longitudinal and lateral wind flow than the flat roof, with 26% and 37 % higher but a more drifty environment.

The present investigation is essentially a descriptive study on Attitude and Awareness about Environment and Attitude towards Environmental Education of High and Higher School students in India. The study is conducted with the following purpose. To study the levels and to compare the Environmental Attitude, Environmental Awareness and Attitude towards Environmental Education of high and higher school students in India. To study the difference in Environmental Attitude and Environmental Awareness and Attitude towards Environmental Education among high and higher school students in India separately and together with respect to their background variables like, gender, academic qualification, length of experience and area of specialization. To study the main effect and interaction effect of Environmental Attitude and environmental Awareness on Attitude towards Environmental Education of the high and higher school students in India. In the present study, Environmental Attitude and Environmental Awareness and Attitude towards Environmental Education are considered as dependent variables and country, gender, academic qualification, length of experience and area of specialization are considered as independent variables. In the case of last objective, Attitude towards Environmental Education is considered as a dependent variable vi whereas environmental Attitude and Environmental Awareness are considered as independent variables.

The present study investigates gender-based differences in learning styles among IX standard students. Recognizing individual learning preferences, especially in relation to gender, is crucial for enhancing instructional effectiveness during adolescence a stage of cognitive and emotional development. Using a survey method, data were collected from 300 students across six schools using the standardized Learning Style Inventory by J.M. Reid. Statistical analysis, including descriptive statistics and the two-tailed t-test, revealed no significant difference in learning styles between boys and girls. These findings align with previous studies, indicating that while learning preferences may vary, gender alone does not significantly impact learning style. The study underscores the importance of adopting diverse, learner-centered teaching strategies that address varied learning needs, thereby promoting inclusive and effective classroom practices.

Diabetic Retinal Disease (DR) is a gradual obstruction in the functioning of the blood vessels in the retina of the eye caused by chronic hyperglycemia, which may be due to diabetes type 1 or type 2. LabVIEW software play as major role in predicting the diabetic retinopathy at different stages. The real time image of the affected eye is acquired using Vision Actuation tool in LabVIEW and then it is processed using Vision assistant tool. Lot technology is I for sending the output result to the corresponding end client. Image processing and lot. Both are implemented using LabVIEW. Additionally, the system extends its functionality beyond diagnostics by incorporating an innovative approach to monitor the temperature of the eye. By integrating LabVIEW's capabilities with lot, the proposed solution checks the temperature the eye in real-time. If the detected temperature is elevated, a Peltier crystal is employed to cool the eye, demonstrating an adaptive and responsive mechanism to address abnormal temperature conditions. Aims to check the patients whether they are suffering from diabetic retinopathy or not and if they are having the diabetic retinopathy in which stage it is. Because most diabetic people are suffering from this and may also lead to loss of vision. Due to this reason, it is highly required to classify the persons in a short time with high accuracy. Automation reduces human error and subjectivity. Can detect early stages of disease. Ability to process and analyze digital fundus images efficiently.