A COMPARISON OF PACING STRATEGY BETWEEN INTERNATIONAL AND PAKISTANI 100-M SWIMMERS

The purpose of this study was to determine, based on the stroking time recorded in a 100-m swimming competition, whether elite international 100-m swimmers have the same pacing strategy as the Pakistani swimmers or not. Based on a video data, three different levels of performance were analyzed i.e. Finalists (G1, n=32) from the European Championship, 2012; Medalists (G2, n=12) and Non-medalists (G3, n=20) from the Pakistan National Swimming Championship, 2014. For the current analysis each stroking distance was divided into two sections (i.e. before and after the 25m mark). The average speed of each section (ST1, ST2, ST3 and ST4) was quantified in order to depict pacing strategy. Results showed even-positive pacing (1.75 ± 0.17 m/s) towards the end of the race in G1; whereas, variable speed pacing was observed in both G2 (1.42 ± 0.26 m/s) and G3 (1.21 ± 0.28 m/s). Based on these findings it is proposed to encourage an efficient pace strategy for 100-m swimmers

Role of heat stress in migration decisions : a case study of Faisalabad

This study explores the relation between migration decisions and heat stress, its impact on livelihoods and thermal comfort levels both at home and at work. Many developing countries face declining worker productivity due to heat stress. Migration provides an opportunity to reduce risk and diversify livelihoods. Extreme heat stress is associated with migration because it affects livelihood and reduces farm and non-farm income. The paper is based on a household survey using a structured questionnaire to compare two study sites 1) Rural areas of Faisalabad district 2) Peri-urban areas of Faisalabad city.UK Government’s Department for International Development (DfID)International Development Research Centre (IDRC

Variability of breast density assessment and the need for additional imaging: A comparison between computed mammography and digital mammography

Objective: To determine the variability of breast density assessment and the need for additional imaging using computed radiography (CR) mammography versus digital radiography (DR) mammography.Study design: Cohort study.Place and duration of study: Department of Radiology, The Aga Khan University Hospital, Karachi from March to June 2018.Methodology: Patients who underwent screening CR mammography, followed by DR mammography a year later, were selected. Only disease-free individuals were included in the study. Evaluation of breast density was done subjectively, using the breast imaging reporting and data system (BI-RADS) by two independent experienced radiologists. Statistical analysis was performed using the Wilcox Signed Rank-sum test to compare both modalities. Fisher Exact method was used to compare the need for ultrasound imaging.Results: A total of 295 patients were included in the study. The mean age of the patients was 52.76 ± 0.64 years. There was a significant difference in the change of breast density when comparing both modalities (Z= -11.839, p \u3c0.001). A statistically significant reduction in the need for further breast ultrasound was observed after DR mammography than with CR mammography (p \u3c0.001).Conclusion: Use of DR mammography, especially in patients with dense breast parenchyma, is a better screening tool overall. It translates to better feasibility for the radiologist and is more economical for the patient. DR mammography decreases unnecessary imaging and leads to better visualisation, thus providing a more accurate categorisation of breast density. Key Word: Computed radiography mammography, Breast density, Screening, Breast cancer, Digital mammography, Ultrasound

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients. It also attempts to automate the data analysis and represent the facts about a patient. The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system. The proposed IoT framework also benefits from machine learning based activity classification systems, with relatively high accuracy, which allow the communicated data to be translated into meaningful information

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Obesity is a critical health condition that severely affects an individual’s quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing AI-enabled machine learning on a real-life dataset. This dataset is in the form of electronic health records (EHR) containing data on several aspects of daily living, such as dietary habits, physical conditions, and lifestyle variables for various participants with different health conditions (underweight, normal, overweight, and obesity type I, II and III), expressed in terms of a variety of features or parameters, such as physical condition, food intake, lifestyle and mode of transportation. Three classifiers, i.e., eXtreme gradient boosting classifier (XGB), support vector machine (SVM), and artificial neural network (ANN), are implemented to detect the status of several conditions, including obesity types. The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods, achieving overall performance rates of 98.5% and 99.6% in the scenarios explored

Development of gliclazide matrix tablets from pure and blended mixture of glyceryl monostearate and stearic acid

The present study was undertaken to evaluate the effect of glyceryl monostearate (GMS) and stearic acid (SA) on the release profile of gliclazide from the matrix. Matrix tablets for the controlled delivery of gliclazide were prepared by hot melt method using pure and blended mixture of glyceryl monostearate and stearic acid in different drug to polymer and polymer to polymer ratios. In vitro release characteristics of gliclazide from these hydrophobic matrices were studied over 8 h in phosphate buffer media of pH 7.4. The release kinetics of drug was evaluated for zero order, first order, Higuchi and Peppas kinetic models. It was observed that the release of drug from the matrix was greatly retarded by GMS and retarding effect increased with increasing polymer to drug ratios. On the other hand SA appeared to channel the drug from the wax matrix and release was greatly increased with increasing polymer to drug ratios. The kinetic evaluation of release profile indicated that the Higuchi model was the most appropriate model for describing the release profile of gliclazide. The application of Peppas biexponential equation indicated that non-Fickian release was the predominant mechanism of drug release. The FTIR results showed no interaction between the drug and the polymers and DSC results indicated that both the drug and polymers are in amorphous state and no significant complexes were formed. The results indicated that proper selection of drug to polymer and polymer to polymer ratios were important in order to achieve the desired dissolution profile in these matrix tablets.Colegio de Farmacéuticos de la Provincia de Buenos Aire

IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient’s chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others

A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method

Investigation of vibration’s effect on driver in optimal motion cueing algorithm

The increased sensation error between the surroundings and the driver is a major problem in driving simulators, resulting in unrealistic motion cues. Intelligent control schemes have to be developed to provide realistic motion cues to the driver. The driver’s body model incorporates the effects of vibrations on the driver’s health, comfort, perception, and motion sickness, and most of the current research on motion cueing has not considered these factors. This article proposes a novel optimal motion cueing algorithm that utilizes the driver’s body model in conjunction with the driver’s perception model to minimize the sensation error. Moreover, this article employs H1 control in place of the linear quadratic regulator to optimize the quadratic cost function of sensation error. As compared to state of the art, we achieve decreased sensation error in terms of small root-mean-square difference (70%, 61%, and 84% decrease in case of longitudinal acceleration, lateral acceleration, and yaw velocity, respectively) and improved coefficient of cross-correlation (3% and 1% increase in case of longitudinal and lateral acceleration, respectively)