ASSESSING THE INCLINATION OF UNDERGRADUATE’S JHANG STUDENT TO THE PHYSICAL ACTIVITIES AND SPORTS

Objectives: Assessing the Inclination of undergraduate’s Jhang student to thePhysical Activities and sports. In instructive organizations requirement toinspire the scholars to join in physical events and sports. Physical education,health and sports science is an important part of schooling. Persons are fullystrain, depressive and nervousness during lifecycle. So, people need to regulartake part in physical events and sports. Method: A simple questionnairesurvey method had applied and used a random sampling technique to collectthe study information of 200 subjects from selected four institutesundergraduate’s Jhang student. The variables of the study were physicalactivities as esthetic practices, societal skills and healthiness & wellbeing hasrich perception in the inclination of undergraduate’s student study. Results:The results of consistency were found for healthiness & wellbeing 0.828,social knowledge 0.817 and an Esthetic experience 0.712. The total overall200 subject Cronbach’s alpha significance was 0.831 of both genders.Conclusion: The overall inclination to the selected variables was important.Undergraduate’s student was cognizant of the profits of physical activities andsports, healthiness and wellbeing for their esthetic practice to remainwellbeing in life

Causes of Carpal Tunnel syndrome (CTS)

Objective: To know about the pathological causes of carpal tunnel syndrome.Materials and Methods: This is retrospective cross sectional study on the patients operated by the senior author at his private clinic between January 2008 and March 2014. All the patients operated for carpal tunnel syndrome during the study period were included while those managed conservatively were excluded from the study. Pre-operatively all patients had undergone Nerve conduction studies and the procedure was performed under local anaesthesia. The pathology responsible for causing CTS was noted.Results: A total of 73 patients were operated for CTS during the study period by the senior author. There were 18 males and 55 females with a male to female ratio approaching 3.0:1. The age range was from 24 – 58 years with a mean age of 43.6 ± 4 years. The pathology was hypertrophied transverse carpal ligament in 66 (90%) cases, abnormal vessels in 1 (1%), neuroma in 1 (1%), fracture distal end of the radius in 2 (5%), ganglion com-pressing the nerve in 1 (1%), post cellulitis in 1 (1%) and direct trauma to the nerve in 1 (1%).Conclusion: Thickened ligamentum flavum is the most common cause of carpal tunnel syndrome and space occupying lesion constitute a considerable percent of pathology

Assessment of Young Drivers' Driving Behaviour and Driving Speed Along Horizontal and Vertical Alignments

Young drivers are more likely to experience car crashes as they tend to have risky driving behaviours. This study aims to assess young drivers' driving behaviour and driving speed along the horizontal and vertical alignments of roads. The 20 young drivers who participated in this study were asked to complete a self-reported assessment (Driver Behaviour Questionnaire) and then invited for an on-road driving assessment during daytime and night-time, along horizontal and vertical road alignments at a selected route in Skudai, Johor. The results from the Driver Behaviour Questionnaire revealed that distractions during driving was the most frequently reported behaviour that caused car crashes amongst young drivers, followed by error and violation. Speed profile was found to be higher during daytime when compared to night-time. A significant difference in speed between male and female drivers was noted at horizontal curves during daytime and vertical curves during night-time. The study concluded that such aberrant driving behaviours would have an impact on the driving performance, particularly on horizontal and vertical curves

Mechanical Performance of Polymeric ARGF-Based Fly Ash-Concrete Composites: A Study for Eco-Friendly Circular Economy Application

At present, low tensile mechanical properties and a high carbon footprint are considered the chief drawbacks of plain cement concrete (PCC). At the same time, the combination of supplementary cementitious material (SCM) and reinforcement of fiber filaments is an innovative and eco-friendly approach to overcome the tensile and environmental drawbacks of plain cement concrete (PCC). The combined and individual effect of fly ash (FA) and Alkali resistance glass fiber (ARGF) with several contents on the mechanical characteristics of M20 grade plain cement concrete was investigated in this study. A total of 20 concrete mix proportions were prepared with numerous contents of FA (i.e., 0, 10, 20, 30 and 40%) and ARGF (i.e., 0, 0.5, 1 and 1.5%). The curing of these concrete specimens was carried out for 7 and 28 days. For the analysis of concrete mechanical characteristics, the following flexural, split tensile, and compressive strength tests were applied to these casted specimens. The outcomes reveal that the mechanical properties increase with the addition of fibers and decrease at 30 and 40% replacement of cement with fly ash. Replacement of cement at higher percentages (i.e., 30 and 40) negatively affects the mechanical properties of concrete. On the other hand, the addition of fibers positively enhanced the flexural and tensile strength of concrete mixes with and without FA in contrast to compressive strength. In the end, it was concluded that the combined addition of these two materials enhances the strength and toughness of plain cement concrete, supportive of the application of an eco-friendly circular economy. The relationship among the mechanical properties of fiber-reinforced concrete was successfully generated at each percentage of fly ash. The R-square for general relationships varied from (0.48–0.90) to (0.68–0.96) for each percentage of FA fiber reinforced concrete. Additionally, the accumulation of fibers effectively boosts the mechanical properties of all concrete mixes.publishedVersio

Design a framework for IoT- Identification, Authentication and Anomaly detection using Deep Learning: A Review

The Internet of Things (IoT) connects billions of smart gadgets so that they may communicate with one another without the need for human intervention. With an expected 50 billion devices by the end of 2020, it is one of the fastest-growing industries in computer history. On the one hand, IoT technologies are critical in increasing a variety of real-world smart applications that can help people live better lives. The cross-cutting nature of IoT systems, on the other hand, has presented new security concerns due to the diverse components involved in their deployment. For IoT devices and their inherent weaknesses, security techniques such as encryption, authentication, permissions, network monitoring, \& application security are ineffective. To properly protect the IoT ecosystem, existing security solutions need to be strengthened. Machine learning and deep learning (ML/DL) have come a long way in recent years, and machine intelligence has gone from being a laboratory curiosity to being used in a variety of significant applications. The ability to intelligently monitor IoT devices is an important defense against new or negligible assaults. ML/DL are effective data exploration techniques for learning about 'normal' and 'bad' behavior in IoT devices and systems. Following a comprehensive literature analysis on Machine Learning methods as well as the importance of IoT security within the framework of different sorts of potential attacks, multiple DL algorithms have been evaluated in terms of detecting attacks as well as anomaly detection in this work. We propose a taxonomy of authorization and authentication systems in the Internet of Things based on the review, with a focus on DL-based schemes. The authentication security threats and problems for IoT are thoroughly examined using the taxonomy supplied. This article provides an overview of projects that involve the use of deep learning to efficiently and automatically provide IoT applications

An efficient machine learning model based on improved features selections for early and accurate heart disease predication

Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example, take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset's features as well as a subset of them. The reduction of features has an impact on the performance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation times of 4.4, 7.3, and 8seconds respectively

Secure and efficient data storage operations by using intelligent classification technique and RSA algorithm in IoT-based cloud computing

In mobile cloud services, smartphones may depend on IoT-based cloud infrastructure and information storage tools to conduct technical errands, such as quest, information processing, and combined networks. In addition to traditional finding institutions, the smart IoT-cloud often upgrades the normal impromptu structure by treating mobile devices as corporate hubs, e.g., by identifying institutions. This has many benefits from the start, with several significant problems to be overcome in order to enhance the unwavering consistency of the cloud environment while Internet of things connects and improves decision support system of the entire network. In fact, similar issues apply to monitor loading, resistance, and other security risks in the cloud state. Right now, we are looking at changed arrangement procedures in MATLAB utilizing cardiovascular failure information and afterward protecting that information with the assistance of RSA calculation in mobile cloud. The calculations tried are SVM, RF, DT, NB, and KNN. In the outcome, the order strategies that have the best exactness result to test respiratory failure information will be recommended for use for enormous scope information. Instead, the collected data will be transferred to the mobile cloud for preservation using the RSA encryption algorithm

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation: Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries