Efficacy of Kinesiotaping in Lower Trunk Flexion range of motion in Tennis Players

INTRODUCTION: Overuse or repetitive microtrauma to muscles, joints, ligaments and bones are common injuries seen in athletics. In athletes, low back strength is an important component of participating in many sports, including racquets sports, judo, weight lifting, baseball, martial arts and rock climbing. In Electromyographic (EMG) studies, performed on collegiate and professional tennis players, it was discovered that the back extensors, lumbar erector spinae, multifidus and DL facia showed marked activity during portions of the serves, forehand and backhand strokes.10,18 Therefore, a marked increase in activation of the lumbar muscles may lead to overuse injury resulting in reduced muscle strength or fatigue of low back muscles which may result in the muscle not being able to maintain force output and may also be a contributing factor in decreasing the range of motion of lumbar spine. This would result in decreasing the overall effectiveness of on athlete’s sports ability. NEED & SIGNIFICANCE OF THE STUDY: 1. Literature suggests that it’s not possible to gain effective performance without appropriate flexibility. So, if kinesiotaping proves to be an effective measure to increase flexibility of lower trunk then performance of tennis player can be enhanced using this measure. 2. This method if justified then it will also help the player by not only enhancing theflexibility but also support and protection in biomechanically using the extreme ranges of the body. METHODOLOGY: The purpose of the study is to find out the efficacy of kinesiotaping on lower trunk flexion range of motion in tennis player. Hypotheses: Experimental Hypothesis: There is significant effect of kinesiotaping on lower trunk flexion ROM in tennis player. Null Hypothesis: There is no significant effect of kinesiotaping on lower trunk flexion ROM in tennis player. Sample: a. Number of subjects- 30 b. Source of the subjects : Study was conducted in Life Spring, Tennis Academy, Coimbatore, Tamil Nadu. c. Method of Selection- Sample of Convenience. Study Design: Pre-test post-test single group experimental design. Study Setup And Duration: Total duration of the study 6 weeks. Each subject needs 2 days (Day 1 and 2). Each session of 1 hour for a day. Inclusion Criteria: a. Age group 18-24 years. b. Only male players were taken. Exclusion Criteria: a. Players having any low back injury with in 6 month. b. Players having any pathology of hip, knee, thigh, and back. c. Other factor affecting the flexibility was not calculated. Like temperature etc. CONCLUSION: Our research indicates that kinesiotaping when applied to tennis players, it enhance low back muscular flexibility (ROM) than that seen in a “no tape” condition. Also when KT using a Y flexion pattern was applied, it improve the active range of motion in lower trunk flexion. Although, future research must be done to test if Kinesiotaping has a therapeutic benefit for athletes with chronic back pain. Hence, null hypothesis that there is no significant effect of kinesiotaping on lower trunk flexion ROM in tennis player rejected and experimental hypothesis is accepted. However, since this study was of sample size, further studies can be done with large sample size which would support this conclusion more strongly. Despite limitation, this study provides evidence for the positive effect of K Tape in improving flexibility in Tennis players

Idiopathic Fascicular Ventricular Tachycardia

Idiopathic fascicular ventricular tachycardia is an important cardiac arrhythmia with specific electrocardiographic features and therapeutic options. It is characterized by relatively narrow QRS complex and right bundle branch block pattern. The QRS axis depends on which fascicle is involved in the re-entry. Left axis deviation is noted with left posterior fascicular tachycardia and right axis deviation with left anterior fascicular tachycardia. A left septal fascicular tachycardia with normal axis has also been described. Fascicular tachycardia is usually seen in individuals without structural heart disease. Response to verapamil is an important feature of fascicular tachycardia. Rare instances of termination with intravenous adenosine have also been noted. A presystolic or diastolic potential preceding the QRS, presumed to originate from the Purkinje fibers can be recorded during sinus rhythm and ventricular tachycardia in many patients with fascicular tachycardia. This potential (P potential) has been used as a guide to catheter ablation. Prompt recognition of fascicular tachycardia especially in the emergency department is very important. It is one of the eminently ablatable ventricular tachycardias. Primary ablation has been reported to have a higher success, lesser procedure time and fluoroscopy time

Predicting the dissolution kinetics of silicate glasses using machine learning

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models

Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Language models have demonstrated their capability to answer domain-specific questions and retrieve information from knowledge bases. However, there are no benchmark datasets in the materials domain that can evaluate the understanding of the key concepts by these language models. In this work, we curate a dataset of 650 challenging questions from the materials domain that require the knowledge and skills of a materials student who has cleared their undergraduate degree. We classify these questions based on their structure and the materials science domain-based subcategories. Further, we evaluate the performance of GPT-3.5 and GPT-4 models on solving these questions via zero-shot and chain of thought prompting. It is observed that GPT-4 gives the best performance (~62% accuracy) as compared to GPT-3.5. Interestingly, in contrast to the general observation, no significant improvement in accuracy is observed with the chain of thought prompting. To evaluate the limitations, we performed an error analysis, which revealed conceptual errors (~64%) as the major contributor compared to computational errors (~36%) towards the reduced performance of LLMs. We hope that the dataset and analysis performed in this work will promote further research in developing better materials science domain-specific LLMs and strategies for information extraction

Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors

Due to their disordered structure, glasses present a unique challenge in predicting the composition-property relationships. Recently, several attempts have been made to predict the glass properties using machine learning techniques. However, these techniques have the limitations, namely, (i) predictions are limited to the components that are present in the original dataset, and (ii) predictions towards the extreme values of the properties, important regions for new materials discovery, are not very reliable due to the sparse datapoints in this region. To address these challenges, here we present a low complexity neural network (LCNN) that provides improved performance in predicting the properties of oxide glasses. In addition, we combine the LCNN with physical and chemical descriptors that allow the development of universal models that can provide predictions for components beyond the training set. By training on a large dataset (~50000) of glass components, we show the LCNN outperforms state-of-the-art algorithms such as XGBoost. In addition, we interpret the LCNN models using Shapely additive explanations to gain insights into the role played by the descriptors in governing the property. Finally, we demonstrate the universality of the LCNN models by predicting the properties for glasses with new components that were not present in the original training set. Altogether, the present approach provides a promising direction towards accelerated discovery of novel glass compositions.Comment: 15 pages, 3 figure

A Peridynamics-Based Micromechanical Modeling Approach for Random Heterogeneous Structural Materials

This paper presents a peridynamics-based micromechanical analysis framework that can efficiently handle material failure for random heterogeneous structural materials. In contrast to conventional continuum-based approaches, this method can handle discontinuities such as fracture without requiring supplemental mathematical relations. The framework presented here generates representative unit cells based on microstructural information on the material and assigns distinct material behavior to the constituent phases in the random heterogenous microstructures. The framework incorporates spontaneous failure initiation/propagation based on the critical stretch criterion in peridynamics and predicts effective constitutive response of the material. The current framework is applied to a metallic particulate-reinforced cementitious composite. The simulated mechanical responses show excellent match with experimental observations signifying efficacy of the peridynamics-based micromechanical framework for heterogenous composites. Thus, the multiscale peridynamics-based framework can efficiently facilitate microstructure guided material design for a large class of inclusion-modified random heterogenous materials

Accelerated Design of Chalcogenide Glasses through Interpretable Machine Learning for Composition Property Relationships

Chalcogenide glasses possess several outstanding properties that enable several ground breaking applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite the ubiquitous usage of these glasses, the composition property relationships in these materials remain poorly understood. Here, we use a large experimental dataset comprising approx 24000 glass compositions made of 51 distinct elements from the periodic table to develop machine learning models for predicting 12 properties, namely, annealing point, bulk modulus, density, Vickers hardness, Littleton point, Youngs modulus, shear modulus, softening point, thermal expansion coefficient, glass transition temperature, liquidus temperature, and refractive index. These models, by far, are the largest for chalcogenide glasses. Further, we use SHAP, a game theory based algorithm, to interpret the output of machine learning algorithms by analyzing the contributions of each element towards the models prediction of a property. This provides a powerful tool for experimentalists to interpret the models prediction and hence design new glass compositions with targeted properties. Finally, using the models, we develop several glass selection charts that can potentially aid in the rational design of novel chalcogenide glasses for various applications.Comment: 17 pages, 8 figure