Comparison of Antiretroviral Therapy Adherence Among HIV-Infected Older Adults with Younger Adults in Africa: Systematic Review and Meta-analysis.

As access to antiretroviral treatment in low- and middle-income countries improves, the number of older adults (aged ≥ 50 years) living with HIV is increasing. This study compares the adherence to antiretroviral treatment among older adults to that of younger adults living in Africa. We searched PubMed, Medline, Cochrane CENTRAL, CINAHL, Google Scholar and EMBASE for keywords (HIV, ART, compliance, adherence, age, Africa) on publications from 1st Jan 2000 to 1st March 2016. Eligible studies were pooled for meta-analysis using a random-effects model, with the odds ratio as the primary outcome. Twenty studies were included, among them were five randomised trials and five cohort studies. Overall, we pooled data for 148,819 individuals in two groups (older and younger adults) and found no significant difference in adherence between them [odds ratio (OR) 1.01; 95% CI 0.94-1.09]. Subgroup analyses of studies using medication possession ratio and clinician counts to measure adherence revealed higher proportions of older adults were adherent to medication regimens compared with younger adults (OR 1.06; 95% CI 1.02-1.11). Antiretroviral treatment adherence levels among older and younger adults in Africa are comparable. Further research is required to identify specific barriers to adherence in the aging HIV affected population in Africa which will help in development of interventions to improve their clinical outcomes and quality of life

Injury rate and patterns of Sydney grade cricketers : A prospective study of injuries in 408 cricketers

Background The grade cricket competition, also known as premier cricke

Trends, Surveillance, Epidemiology and Injury Prevention Strategies for Junior and Amateur Cricketers

Background Cricket participation carries an inherent risk of injury, with injury incidence ranging between 20-50% per season among junior and amateur cricketers (community cricketers). Despite the significant risk of injury, there is no structured cricket specific injury prevention program (IPP). The aim of this thesis was to establish an information base for development of a cricket specific IPP. Methods The thesis is written in the form of six publications. The first two papers are systematic reviews with meta-analyses on cricket injuries and efficacy of IPPs. Followed by a survey and a cohort study on injury epidemiology, along with a protocol and evaluation of surveillance app. The final paper synthesizes the evidence for a protocol of a cricket specific IPP. Results Meta-analysis of 1.12 million hours of cricket play showed an injury rate of 53.16 (95% CI 51.84 – 54.52) per 10,000 hours of play. The high injury rate justifies need of a cricket specific IPP. In adolescent team sports IPPs can reduce injuries by 32% (IRR = 0.68; 95% CI = 0.54-0.84) and include components of resistance training (RT). However, only 23% of accredited community cricket coaches engage fast bowlers in RT. Sydney Grade Cricket has an injury incidence rate of 35.54 injuries/10,000 hours with lower-back injuries (20%) being the most common. There are also no injury surveillance tools available at the community level. TeamDoc mobile app bridges this gap and assists in the on-field recording of injuries and workload with satisfactory user rating 3.5 out of 5. The novel cricket IPP (CIPP) for community cricketers is a 15 minute, full-body, cricket specific IPP that can be performed on the field without the need of additional equipment apart from the regular cricket gear. CIPP has 14 exercises which target body regions most commonly injured in cricket. Conclusions The thesis offers a valuable insight to researchers, coaches, cricket boards, medical staff to prevent cricket related injuries

Work Health and Safety in Cotton Ginning Industry: A Survey of Practices in Australia

This survey focuses on the WH&S (Work Health and Safety) practices in Australia and relates them with those in Pakistan. It also highlights the planned areas of work required on WH&S in cotton ginning industry of Pakistan. This article is one a series of research studies that will inform a broader approach development. The aim of the survey was to design a standardized health and safety Act for cotton ginning industry of Pakistan and to help ginners meet their due industry obligations under the model WH&S Act. The first component of the research study survey was to review the relevant Australian work and safety model as this provides a framework to protect the health, safety and welfare of all employees at work and of other people who might be affected by the job. The second aspect of the research study survey concerned site visits to cotton gins with the support of Australian Centre for Agricultural Health & Safety, Moree, NSW. During these visits the existing ginning process in terms of WH&S were reviewed. Informal interviews were also conducted with Gin Managers and Ginning Experts regarding WH&S in the Australian cotton ginning industry

Adding a post-training FIFA 11+ exercise program to the pre-training FIFA 11+ injury prevention program reduces injury rates among male amateur soccer players: a cluster-randomised trial

Question: Does adding a post-training Fédération Internationale de Football Association (FIFA) 11+ exercise program to the pre-training FIFA 11+ injury prevention program reduce injury rates among male amateur soccer players? Design: Cluster-randomised, controlled trial with concealed allocation. Participants: Twenty-one teams of male amateur soccer players aged 14 to 35 years were randomly assigned to the experimental group (n = 10 teams, 160 players) or the control group (n = 11 teams, 184 players). Intervention: Both groups performed pre-training FIFA 11+ exercises for 20 minutes. The experimental group also performed post-training FIFA 11+ exercises for 10 minutes. Outcome measures: The primary outcomes measures were incidence of overall injury, incidence of initial and recurrent injury, and injury severity. The secondary outcome measure was compliance to the experimental intervention (pre and post FIFA 11+ program) and the control intervention (pre FIFA 11+ program). Results: During one season, 26 injuries (team mean = 0.081 injuries/1000 exposure hours, SD = 0.064) were reported in the experimental group, and 82 injuries were reported in the control group (team mean = 0.324 injuries/1000 hours, SD = 0.084). Generalised Estimating Equations were applied with an intention-to-treat analysis. The pre and post FIFA 11+ program reduced the total number of injuries (χ2 (1) = 11.549, p = 0.001) and the incidence of initial injury (χ2 (2) = 8.987, p = 0.003) significantly more than the pre FIFA 11+ program alone. However, the odds of suffering a recurrent injury were not different between the two groups (χ2 (1) = 2.350, p = 0.125). Moreover, the severity level of injuries was not dependent upon whether or not the pre and post FIFA 11+ program was implemented (χ2 (1) = 0.016, p = 0.898). Conclusion: Implementation of the FIFA 11+ program pre-training and post-training reduced overall injury rates in male amateur soccer players more than the pre FIFA 11+ program alone. Trial registration: ACTRN12615001206516. [Al Attar WSA, Soomro N, Pappas E, Sinclair PJ, Sanders RH (2017) Adding a post-training FIFA 11+ exercise program to the pre-training FIFA 11+ injury prevention program reduces injury rates among male amateur soccer players: a cluster-randomised trial. Journal of Physiotherapy 63: 235–242

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others

A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion

Funding Information: The authors would like to thank Universiti Teknologi PETRONAS (UTP) Malaysia for giving the opportunity to conduct research under grant number 015LC0-381 for the project “Failure Prediction Model for Stress Corrosion Cracking Using Deep Learning Approach." Publisher Copyright: © 2022 Elsevier LtdTo forecast safety and security measures, it is vital to evaluate the integrity of a pipeline used to carry oil and gas that has been subjected to corrosion. Corrosion is unavoidable, yet neglecting it might have serious personal, economic, and environmental repercussions. To predict the unanticipated behavior of corrosion, most of the research relies on probabilistic models (petri net, markov chain, monte carlo simulation, fault tree, and bowtie), even though such models have significant drawbacks, such as spatial state explosion, dependence on unrealistic assumptions, and static nature. For deteriorating oil and gas pipelines, machine learning-based models such as supervised learning models are preferred. Nevertheless, these models are incapable of simulating corrosion parameter uncertainties and the dynamic nature of the process. In this case, Bayesian network approaches proved to be a preferable choice for evaluating the integrity of oil and gas pipeline models that have been corroded. The literature has no compilations of Bayesian modeling approaches for evaluating the integrity of hydrocarbon pipelines subjected to corrosion. Therefore, the objective of this study is to evaluate the current state of the Bayesian network approach, which includes methodology, influential parameters, and datasets for risk analysis, and to provide industry experts and academics with suggestions for future enhancements using content analysis. Although the study focuses on corroded oil and gas pipelines, the acquired knowledge may be applied to several other sectors.Peer reviewe

Insights into modern machine learning approaches for bearing fault classification: A systematic literature review

Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete system failure, and it accounts for approximately 45 %–50 % of failures in rotating machinery. Hence, it is imperative to establish a thorough and accurate predictive maintenance program that can efficiently foresee and prevent mishaps or malfunctions. The literature has employed a variety of techniques and approaches, from conventional methods to contemporary machine learning (ML) and ML-integrated IoT-based solutions, to categorise bearing faults. This article provides an overview of the most recent research and models used in the classification of bearing faults. The literature summary highlights various significant challenges in current models, such as issues with the classification function, complexities in the neural network structure, unrealistic datasets, dynamic working conditions of rotating machines, noise in the dataset, limited data availability, and imbalanced datasets. In order to tackle the problems, researchers have endeavored to improve and apply different methods, such as convolutional neural networks, deep belief neural networks, and LiNet, among others. Researchers have primarily developed these approaches using datasets from publicly accessible sources. This study also identified research gaps and deficiencies, including limited data availability, data imbalance, and difficulties in data integration. The nascent technologies in the field of problem diagnosis and predictive maintenance are acknowledged as Internet of Things-based ML and vision-based deep learning techniques, which are currently in their initial phases of advancement. Ultimately, the study puts forth several prospective suggestions and recommendations

Train High Eat Low for Osteoarthritis study (THE LO study) : protocol for a randomized controlled trial

Introduction: Osteoarthritis (OA) is one of the most prevalent chronic conditions among older adults, with the medial tibio-femoral joint being most frequently affected. The knee adduction moment is recognized as a surrogate measure of the medial tibio-femoral compartment joint load and therefore represents a valid intervention target.This article provides the rationale and methodology for THE LO study (Train High, Eat Low for Osteoarthritis), which is a randomized controlled trial that is investigating the effects of a unique, targeted lifestyle intervention in overweight/obese adults with symptomatic medial knee OA. Research question: Compared to a control group given only lifestyle advice, do the effects of the following interventions result in significant reductions in the knee adduction moment: (1) gait retraining; and (2) combined intervention (which involves a combination of three interventions: (a) gait retraining, (b) high-intensity progressive resistance training, and (c) high-protein/low-glycaemic-index energy-restricted diet)? It is hypothesized that the combined intervention group will be superior to the isolated interventions of the high-protein/low-glycaemic-index diet group and the progressive resistance training group. Finally, it is hypothesized that the combined intervention will result in a greater range of improvements in secondary outcomes, including: muscle strength, functional status, body composition, metabolic profile, and psychological wellbeing, compared to any of the isolated interventions or control group. Design: Single-blinded, randomized controlled trial adhering to the CONSORT guidelines on conduct and reporting of non-pharmacological clinical trials. Participants: One hundred and twenty-five community-dwelling people are being recruited. Inclusion criteria include: medial knee OA, low physical activity levels, no current resistance training, body mass index ≥ 25kg/m2 and age ≥ 40 years. Intervention and control: The participants are stratified by sex and body mass index, and randomized into one of five groups: (1) gait retraining; (2) progressive resistance training; (3) high-protein/low-glycaemic-index energy-restricted diet (25 to 30% of energy from protein, 45% of energy from carbohydrates, < 30% of energy from fat, and glycaemic index diet value < 50); (4) a combination of these three active interventions; or (5) a lifestyle-advice control group. All participants receive weekly telephone checks for health status, adverse events and optimisation of compliance. Measurements: Outcomes are measured at baseline, 6 and 12 months. The primary outcome is the peak knee adduction moment during the early stance phase of gait. The secondary outcome measures are both structural (radiological), with longitudinal reduction in medial minimal joint space width at 12 months, and clinical, including: change in body mass index; joint pain, stiffness and function; body composition; muscle strength; physical performance/mobility; nutritional intake; habitual physical activity and sedentary behaviour; sleep quality; psychological wellbeing and quality of life. Discussion: THE LO study will provide the first direct comparison of the long-term benefits of gait retraining, progressive resistance training and a high-protein/low-glycaemic-index energy-restricted diet, separately and in combination, on joint load, radiographic progression, symptoms, and associated co-morbidities in overweight/obese adults with OA of the knee