Plyometric Training for Young Male Field Hockey Players

Background: Field hockey is a team sport requiring a combination of skill set to enhance a player’s performance. Power, Fitness and Agility are few such basic parameters. Newer training protocols are constantly explored to achieve the desired effect and plyometrics is one such method. It is also important to know how long lasting the effects of training are. Methodology: Fifty (50) male field hockey players at interschool and zonal level participated in the study. These sports specific parameters - lower limb power, fitness level and agility - were tested using the vertical jump test, 40-metre sprint test, and shuttle cross pick up test respectively. Plyometric training was supervised for 6 weeks and the values were recorded at baseline, 3rd week, and 6th week. The training was stopped after six weeks and the post cessation values on the 8th week were recorded. Results: Plyometric training showed statistically significant improvement in all the test parameters throughout the 6 weeks. The effects of training post cessation of the plyometric regimen also were statistically significant after two weeks. Conclusion: Plyometric training was effective in improving the lower limb power, fitness level, and agility level. Long lasting effects of training were also noted. Coaches will find it a very effective protocol and one of the ideal methods to enhance player performance

Measurement Invariance of Early Development Instrument (EDI) Domain Scores Across Gender and ESL Status

The Early Development Instrument (EDI) is a widely used teacher rating tool to assess kindergartners’ developmental outcomes in Canada and a number of other countries. This paper examines the measurement invariance of EDI domains across ESL status and gender by means of multi-group confirmatory factor analysis. The results suggest evidence of measurement invariance for physical health and well-being, social competence, emotional maturity and language and cognitive development domains. Moreover, the communication skills and general knowledge domain did not show acceptable fit in terms of RMSEA. The results and potential explanations are discussed.L'instrument de mesure du développement de la petite enfance (IMDPE) est un outil d’évaluation largement utilisée pour mesurer le développement des élèves en maternelle au Canada et dans d’autres pays. Cet article porte sur l’équivalence de mesure des domaines de l’IMDPE entre le statut d’ALS et le sexe par une analyse factorielle confirmatoire multigroupe. Les résultats font ressortir des preuves d’équivalence de mesure pour les domaines de la santé physique et le bienêtre, la compétence sociale, la maturité affective, le développement langagier et cognitif. De plus, le domaine des compétences en communication et des connaissances générales n’a pas démontré une correspondance acceptable par rapport à l'erreur quadratique moyenne de l'approximation (RMSEA). Nous discutons des résultats et proposons des explications possibles

Comparison of haemoglobin assessment by HemoCue 301 and automated haematology analyser using flowcytometry among school going children: a one year study at a tertiary care hospital

Background: Anaemia defined as reduction in the concentration in Haemoglobin is one of the key health indicators of health care system of the country. Accurate screening methods are required to estimate the levels of haemoglobin for diagnosing the cause of anaemia. Objectives of the study was to analyze and compare the results of haemoglobin concentrations estimated with automated haematology analyzer and point of care device HemoCue Hb301.Methods: It is a prospective cross-sectional study was conducted for one year after ethical approval. Non fasting capillary and venous blood samples were collected from the selected cases of children and Haemoglobin concentrations were estimated by automated analyzer and HemoCue Hb301 system and the values were noted. Quality control checks were performed for both. Statistical analysis was done using IBM SPSS Version 24.0.Results: Mean Hb% concentration was estimated in 108 children with 44 female and 64 males. The mean value of Automated hematology analyzer (11.965±1.012) was significantly higher when compared with the mean value of HemoCue Hb301 (11.697±1.312) (p=0.002). There was a significantly strong correlation between HemoCue Hb301and Automated hematology analyzer (r-value = 0.732, p <0.0001).Conclusions: The HemoCue is useful in many different settings and remains a widely used method in field settings as it has several advantages and is relatively inexpensive compared with automated haematology analysers. Further studies are needed to better understand potential sources of error in the Hb assessment by HemoCue with the aim to better train phlebotomists and implement appropriate standardised procedures

Деякі аспекти процесу соціалізації старшокласників у загальноосвітній школі

(uk) У статті уточнюється структура процесу соціалізації; описуються рівні соціалізації старшокласників, їх змістовні характеристики; виділяються основні компоненти в структурі соціалізації.(ru) В статье уточняется структура процесса социализации; описываются уровни социализации старшеклассников, их содержательные характеристики; выделяются основные компоненты в структуре социализации

Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering

Alzheimer's disease is a kind of degenerative dementia that causes progressively worsening memory loss and other cognitive and physical impairments over time. Mini-Mental State Examinations and other screening tools are helpful for early detection, but diagnostic MRI brain analysis is required. When Alzheimer's disease (AD) is detected in its earliest stages, patients may begin protective treatments before permanent brain damage has occurred. The characteristics of the lesion sites in AD affected role, as identified by MRI, exhibit great variety and are dispersed across the image space, as demonstrated in cross-sectional imaging investigations of the disease. Optimized Adaptive Bilateral filtering using a deep learning model was suggested as part of this study's approach toward this end. Denoising the pictures with the help of the suggested adaptive bilateral filter is the first stage (ABF). The ABF improves denoising in edge, detail, and homogenous areas separately. After then, the ABF is given a weight, and the Adaptive Equilibrium Optimizer is used to determine the best possible value for that weight (AEO). LeNet, a CNN model, is then used to complete the AD organization. The first step in using the LeNet-5 network model to identify AD is to study the model's structure and parameters. The ADNI experimental dataset was used to verify the suggested technique and compare it to other models. The experimental findings prove that the suggested method can achieve a classification accuracy of 97.43%, 98.09% specificity, 97.12% sensitivity, and 89.67% Kappa index. When compared against competing algorithms, the suggested model emerges victorious

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Given the unpredictable nature of a MANET, routing has emerged as a major challenge in recent years. For effective routing in a MANET, it is necessary to establish both the route discovery and the best route selection from among many routes. The primary focus of this investigation is on finding the best path for data transmission in MANETs. In this research, we provide an efficient routing technique for minimising the time spent passing data between routers. Here, we employ a routing strategy based on Tom and Jerry Optimization (TJO) to find the best path via the MANET's routers, called Ad Hoc On-Demand Distance Vector (AODV). The AODV-TJO acronym stands for the suggested approach. This routing technique takes into account not just one but three goal functions: total number of hops. When a node or connection fails in a network, rerouting must be done. In order to prevent packet loss, the MANET employs this rerouting technique. Analyses of AODV-efficacy TJO's are conducted, and results are presented in terms of energy use, end-to-end latency, and bandwidth, as well as the proportion of living and dead nodes. Vortex Search Algorithm (VSO) and cuckoo search are compared to the AODV-TJO approach in terms of performance. Based on the findings, the AODV-TJO approach uses 580 J less energy than the Cuckoo search algorithm when used with 500 nodes

WORK IN PROCESS OPTIMISATION THROUGH LEAN MANUFACTRING

ABSTRACT Today the global economy has caused a stronger competitive manufacturing environment in all kinds of business. Manufacturing industries face continuous pressure to reduce the price to remain in the market. It eventually results in manufactures need to reduce the profit margins in order to keep a share of the market. The objective of this paper to find the work in process for the optimal size using lean Techniques in a multiproduct single conveyor assembly line of a leading Two Wheeler Manufactures in south India. It is useful to map the dynamics of the supply chain focusing on how the demand information is passed from the final customer, back to the material suppliers and manufactures inside the company. So in this paper attempt has been made to find work in process and reduction of value in terms of Rupees from the current process to the proposed process. A mathematical model developed using general inventory cost model to quantify the Optimal Work In Process for the entire product range in Engine assembly line. Also numerical example is done to demonstrate the mathematical model with the available data. The mathematical results are very much encouraging and it calculated as 40 % reduction in work in process over the current work in process. KEY WORDS: WIP, optimal WIP, inventory cost method, change over time, multi product single conveyor assembly line

Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection

Credit card fraud (CCF) has long been a major concern of institutions of financial groups and business partners, and it is also a global interest to researchers due to its growing popularity. In order to predict and detect the CCF, machine learning (ML) has proven to be one of the most promising techniques. But, class inequality is one of the main and recurring challenges when dealing with CCF tasks that hinder model performance. To overcome this challenges, a Deep Learning (DL) techniques are used by the researchers. In this research work, an efficient CCF detection (CCFD) system is developed by proposing a hybrid model called Convolutional Neural Network with Recurrent Neural Network (CNN-RNN). In this model, CNN acts as feature extraction for extracting the valuable information of CCF data and long-term dependency features are studied by RNN model. An imbalance problem is solved by Synthetic Minority Over Sampling Technique (SMOTE) technique. An experiment is conducted on European Dataset to validate the performance of CNN-RNN model with existing CNN and RNN model in terms of major parameters. The results proved that CNN-RNN model achieved 95.83% of precision, where CNN achieved 93.63% of precision and RNN achieved 88.50% of precision