The application of support vector machine in classifying potential archers using bio-mechanical indicators

This study classifies potential archers from a set of bio-mechanical indicators trained via different Support Vector Machine (SVM) models. 50 youth archers drawn from a number of archery programmes completed a one end archery shooting score test. Bio-mechanical evaluation of postural sway, bow movement, muscles activation of flexor and extensor as well as static balance were recorded. k-means clustering technique was used to cluster the archers based on the indicators tested. Fine, medium and coarse radial basis function kernel-based SVM models were trained based on the measured indicators. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the employment of SVM is able to assist coaches in identifying potential athletes in the sport of archery

Impact of a 10-Year Eye Care Program in Sokoto, Nigeria: Changing Pattern of Prevalence and Causes of Blindness and Visual Impairment.

BACKGROUND: This study was undertaken to ascertain the current magnitude and causes of blindness and visual impairment in persons aged 50 years and over and to assess the impact of a 10-year eye care program in Sokoto State, Nigeria. METHODS: A rapid assessment of avoidable blindness (RAAB) survey (in persons 50 years and over) was conducted in 2016. Participants were selected in Wurno health zone using a two-stage cluster randomized sampling with probability proportional to size. Operational definitions were based on RAAB and World Health Organization eye examination record definitions. Eye care program documents were reviewed and data from a baseline survey undertaken in 2005 were reanalyzed. RESULTS: A response of 89.1% (2405 of 2700 participants) was obtained in the 2016 survey. With available correction, the unadjusted prevalence of blindness was 7.7% (95% confidence interval [CI]: 6.4, 8.9). The odds of blindness were 1.8 times higher in females than males (95% CI: 1.3, 2.4; P < 0.001). Major causes of blindness were cataract (48.9%) corneal disease (20.1%), glaucoma (10.3%), and uncorrected refractive error/aphakia (8.7%). The age- and sex-adjusted prevalence of blindness has declined from 11.6% (95% CI: 7.4, 17.0) in 2005 to 6.8% (95% CI: 5.6, 8.0%) in 2016. CONCLUSION: The blindness prevalence is high, and the major causes are avoidable in the health zone. The findings suggest that investments in the program over the last 10 years might have led to almost a halving in the prevalence of blindness in th e population. However, the small sample size of persons 50+ years from Wurno zone in the 2005 survey necessitate caution when comparing the 2005 and the 2016 surveys

Impact Survey Results after SAFE Strategy Implementation in 15 Local Government Areas of Kebbi, Sokoto and Zamfara States, Nigeria.

PURPOSE: To determine prevalence of trachoma after interventions in 15 local government areas (LGAs) of Kebbi, Sokoto and Zamfara States, Nigeria. METHODS: A population-based impact survey was conducted in each LGA using Global Trachoma Mapping Project (GTMP) protocols. In each LGA, 25 villages were selected, except in Arewa LGA, where we selected 25 villages from each of four subunits to obtain finer-resolution prevalence information. Villages were selected with probability proportional to size. In each village, 25 households were enrolled and all consenting residents aged ≥1 year were examined by GTMP-certified graders for trachomatous inflammation-follicular (TF) and trachomatous trichiasis (TT). Information on sources of household water and types of sanitation facilities used was collected through questioning and direct observation. RESULTS: The number of households enrolled per LGA ranged from 623 (Kware and Tangaza) to 2488 (Arewa). There have been marked reductions in the prevalence of TF and TT since baseline surveys were conducted in all 15 LGAs. Eight of the 15 LGAs have attained TF prevalences <5% in children, while 10 LGAs have attained TT prevalences <0.2% in persons aged ≥15 years. Between 49% and 96% of households had access to water for hygiene purposes within 1 km of the household, while only 10-59% had access to improved sanitation facilities. CONCLUSION: Progress towards elimination of trachoma has been made in these 15 LGAs. Collaboration with water and sanitation agencies and community-based trichiasis surgery are still needed in order to eliminate trachoma by the year 2020

Grand Challenges in global eye health: a global prioritisation process using Delphi method

Background We undertook a Grand Challenges in Global Eye Health prioritisation exercise to identify the key issues that must be addressed to improve eye health in the context of an ageing population, to eliminate persistent inequities in health-care access, and to mitigate widespread resource limitations. Methods Drawing on methods used in previous Grand Challenges studies, we used a multi-step recruitment strategy to assemble a diverse panel of individuals from a range of disciplines relevant to global eye health from all regions globally to participate in a three-round, online, Delphi-like, prioritisation process to nominate and rank challenges in global eye health. Through this process, we developed both global and regional priority lists. Findings Between Sept 1 and Dec 12, 2019, 470 individuals complete round 1 of the process, of whom 336 completed all three rounds (round 2 between Feb 26 and March 18, 2020, and round 3 between April 2 and April 25, 2020) 156 (46%) of 336 were women, 180 (54%) were men. The proportion of participants who worked in each region ranged from 104 (31%) in sub-Saharan Africa to 21 (6%) in central Europe, eastern Europe, and in central Asia. Of 85 unique challenges identified after round 1, 16 challenges were prioritised at the global level; six focused on detection and treatment of conditions (cataract, refractive error, glaucoma, diabetic retinopathy, services for children and screening for early detection), two focused on addressing shortages in human resource capacity, five on other health service and policy factors (including strengthening policies, integration, health information systems, and budget allocation), and three on improving access to care and promoting equity. Interpretation This list of Grand Challenges serves as a starting point for immediate action by funders to guide investment in research and innovation in eye health. It challenges researchers, clinicians, and policy makers to build collaborations to address specific challenge

The classification of skateboarding trick manoeuvres through the integration of image processing techniques and machine learning

More often than not, the evaluation of skateboarding tricks executions are carried out subjectively based on the judges’ experience and hence are susceptible to biasness in not inaccurate judgement. Therefore, an objective and means of evaluating skateboarding tricks particularly in big competitions are non-trivial. This study aims at classifying skateboarding flat ground tricks namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180 through camera vision and machine learning models. An amateur skateboarder (23 years of age ± 5.0 years’ experience) executed five tricks for each type of trick repeatedly on an ORY skateboard from camera distance at 1.26m on a cemented ground. From the images captures, a number of features were engineered via the Inception-V3 image embedder. A number of classification models were evaluated, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Logistic Regression (LR), Random Forest (RF) and Naïve Bayes (NB) on their ability in classifying the tricks based on the engineered features. It was observed from the preliminary investigation that the SVM model attained the highest classification accuracy with a value of 99.5% followed by LR, k-NN, RF and NB with 98.6%, 95.8%, 82.4% and 78.7% respectively. It could be concluded that the proposed method is able to classify the skateboard tricks well and would eventually assist the judges in providing more objective based judgement

The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables

The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. The bio-physiological variables namely resting heart rate, resting respiratory rate, resting diastolic blood pressure, resting systolic blood pressure, as well as calories intake, were measured prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models i.e. linear, quadratic and cubic kernel functions, were trained on the aforementioned variables. The k-means clustered the archers into high (HPA) and low potential archers (LPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy with a classification accuracy of 94% in comparison the other tested models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected bio-physiological variables examined

Thermal conductivity and specific heat capacity of different compositions of Yttria stabilized zirconia-nickel mixtures

Ceramic-metal composites also known as functionally gradient materials (FGM) are composite materials which are fabricated in order to have a gradual variation of constituent materials’ thermal and mechanical properties so as to have a smooth variation of the material properties in order to improve the overall performance and reduce the thermal expansion mismatch between ceramic and metal. The objective of the study is to determine the thermal properties of various percentage composition of Yttria stabilized zirconia-Nickel mixtures for application as thermal barrier coating materials in automotive turbocharger turbine volute casing. Specific heat capacity of different percentage composition of ceramic-metal powder composite were determined using DSC822 differential scanning calorimeter (Mettle Tolodo, Switzerland) at temperature ranges between 303K to 873K. While the thermal conductivity of the different percentage composition of ceramic-metal composite structures were determined using P5687 Cussons thermal conductivity apparatus (Manchester, UK) which uses one-dimensional steady-state heat conduction principle. The results have indicated that the specific heat capacity of the FGM increases sharply with an increase in temperature while the thermal conductivity of the FGM decreases with an increase in temperature. These results strongly agree with the theoretical and experimental values as well as the rule of mixtures obtainable in literature, which indicated the suitability of these FGM materials for thermal barrier coating applications

Wurno zone Sokoto Nigeria_2005

This data was part of an all-age blindness prevalence survey conducted in Sokoto state Nigeria in 2005. The data is for persons aged 50 years and over who were examined in Wurno health of Sokoto state. The sample were selected using a stratified sampling strategy. The operational definitions were based on WHO/PBL coding instructions for eye examination record