Effect of Circuit Training on Physical Fitness of the School Softball Players

The purpose of this study was to investigate the effect of circuit training on the physical fitness of the school\u27s softball players. Twenty (20) subjects were selected for the study; their ages were 18–19. They attended St. Joseph convent school in Namli Ratlam, M.P. Further, it is subdivided into two groups. Three variables were selected for this study to measure the physical fitness of schoolgirls’ softball fitness, i.e., standing broad jump, sit and reach, and 50-metre sprint. The data was examined by applying the analysis of paired t-tests. The level of significance to test the hypothesis was set at 0.05. Results of this study revealed that there was a significant effect of circuit training on the physical fitness of the school softball players and that it increased their performance at the school level

The Quantum Cryptography Approach: Unleashing the Potential of Quantum Key Reconciliation Protocol for Secure Communication

Quantum cryptography is the study of delivering secret communications across a quantum channel. Recently, Quantum Key Distribution (QKD) has been recognized as the most important breakthrough in quantum cryptography. This process facilitates two distant parties to share secure communications based on physical laws. The BB84 protocol was developed in 1984 and remains the most widely used among BB92, Ekert91, COW, and SARG04 protocols. However the practical security of QKD with imperfect devices have been widely discussed, and there are many ways to guarantee that generated key by QKD still provides unconditional security. This paper proposed a novel method that allows users to communicate while generating the secure keys as well as securing the transmission without any leakage of the data. In this approach sender will never reveal her basis, hence neither the receiver nor the intruder will get knowledge of the fundamental basis.Further to detect Eve, polynomial interpolation is also used as a key verification technique. In order to fully utilize the quantum computing capabilities provided by IBM quantum computers, the protocol is executed using the Qiskit backend for 45 qubits. This article discusses a plot of % error against alpha (strength of eavesdropping). As a result, different types of noise have been included, and the success probability of the desired key bits has been determined. Furthermore, the success probability under depolarizing noise is explained for different qubit counts.Last but not least, even when the applied noise is increased to maximum capacity, a 50% probability of successful key generation is still observed in an experiment

Profile of Selected Anthropometric Variables of Weight Category of -56 Kg to -60 Kg of Top-Class Indian Judokas

The purpose of the study was to prepare the profile of selected anthropometric variables of 20 athletes who had competed at the national level comprised only male players from Madhya Pradesh who were between the ages of 18 and 25 and who were residents of Madhya Pradesh. Each of these athletes was a native of the Indian state of Madhya Pradesh. All the anthropometric measurements were measured to the 1/10th of the centimeter. Non-stretchable measuring tape were used to measure the length. Results of the study showed that in anthropometric variables sitting height was lowest in -56 kg to -60 kg. Leg length was lowest in -56 kg to -60 kg. Shoulder width was lowest in -56 kg to -60 kg. Upper arm circumference was lowest in -56 kg to -60 kg. Forearm circumference was medium in -56 kg to -60 kg. Chest circumference was lowest in -56 kg to -60 kg. Thigh circumference lowest in -56 kg to -60 kg. Calf circumference lowest in -56 kg to -60 kg. Thus, height and skinfold thickness are mostly differentiated between heavier and lighter groups, but bone breadths and circumferences are the primary differentiators between weight categories

Development of Discriminant Model for Classifying Sprinters and Throwers on the Basis of Anthropometric and Physical Fitness Variables

In the present study, 100 athletes who competed at the state level was constitute up the sample; only male athletes from Madhya Pradesh in the senior category was included. During the morning and evening sessions, the necessary information on the subject was gathered using the random sampling method. before the National camps and practicing at TT Nagar Stadium Bhopal. The data were collected on anthropometric variables i.e. height, weight, sitting height, (leg, arm, hand) length, shoulder width, (chest, hip, thigh, calf) circumference, (back, shoulder, grip and leg explosive) strength. The anthropometric variables were measured by using selected standardized instruments and measuring tapes. Data collected was analysed at 0.05 level of significance, descriptive statistics and multiple discriminant analysis was applied to classify and predict group membership of the throwers into the sprinters and Throwers. The results showed that 100.0% of original grouped cases were correctly classified

Development of Discriminant Model for Classifying Throwers on the Basis of Anthropometric and Physical Fitness Variables

The purpose of this study was to develop a discriminate model to classify throwers (discus, javelin and hammer) on the basis of anthropometric variables and to predict their group membership based on the developed model. There were 25 discus throwers, 25 hammer throwers and 25 javelin throwers of age group 17-25 selected as subjects. The data were collected on anthropometric variables i.e. height, weight, sitting height, (leg, arm, hand) length, shoulder width, (chest, hip, thigh, calf) circumference, (back, shoulder, grip and leg explosive) strength. The anthropometric variables were measured by using selected standardized instruments and measuring tapes. Data collected was analyzed at 0.05 level of significance, descriptive statistics and multiple discriminant analysis was applied to classify and predict group membership of the throwers into the discus, hammer and javelin. The results showed that 68.0% of original grouped cases were correctly classified and 45.3% of cross-validated grouped cases correctly classified