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Custer randomized trials are often used to study large-scale public health interventions. In large trials, even small improvements in statistical efficiency can have profound impacts on the required sample size and cost. Pair matched randomization is one strategy with potential to increase trial efficiency, but to our knowledge there have been no empirical evaluations of pair-matching in large-scale, epidemiologic field trials. Location integrates many socio-demographic and environmental characteristics into a single feature. Here, we show that geographic pair-matching leads to substantial gains in statistical efficiency for 14 child health outcomes that span growth, development, and infectious disease through a re-analysis of two large-scale trials of nutritional and environmental interventions in Bangladesh and Kenya. We estimate relative efficiencies ≥ 1.1 for all outcomes assessed and relative efficiencies regularly exceed 2.0, meaning an unmatched trial would have needed to enroll at least twice as many clusters to achieve the same level of precision as the geographically pair-matched design. We also show that geographically pair-matched designs enable estimation of fine-scale, spatially varying effect heterogeneity under minimal assumptions. Our results demonstrate broad, substantial benefits of geographic pair-matching in large-scale, cluster randomized trials.

Ti-6Al-4 V-ELI alloy is acknowledged for its biomedical use. In non-traditional machining method like Electrical Discharge Machining, the material removal is through electro-thermal action with no direct contact between tool and work-piece. The emanation of spring-back effect apart from adherence of alloy results with poor surface finish. The same is nullified with the current EDM method by using effect of electro-thermal energy applications thereby achieving close tolerance. The effect of input parameters such as Voltage (V), Current (I) and Pulse on-Time ( T on ) on Material Removal Rate (MRR) and Surface Roughness ( R a ) was investigated. The T on was varied between (60&70) μ s , voltage of (50&55) V and peak current of (10&12) A. The analysis of result was carried out with 2 3 factorial degree of experiment and the response contributing highest effect was noted. The MRR, R a and electrode wear increase with each process parameters. It was found that factors like V, I and T on have significant effect to varying degrees on MRR and R a . MRR increased with increase in T on while R a increase with decreased in voltage.

BackgroundApparent treatment-resistant hypertension (aTRH) is defined as uncontrolled blood pressure (BP) in patients taking three or more antihypertensive medications. Some patients will have true treatment-resistant hypertension, some undiagnosed secondary hypertension, while others have pseudo-resistance. Pseudo-resistance occurs when non-adherence to medication, white-coat hypertension (WCH), lifestyle, and inadequate drug dosing are responsible for the poorly controlled BP.AimTo examine the feasibility of establishing non-adherence to medication, for the first time in primary care, using mass spectrometry urine analysis. Operationalisation would be established by at least 50% of patients participating and 95% of samples being suitable for analysis. Clinical importance would be confirmed by >10% of patients being non-adherent.Design and settingEligible patients with aTRH (n = 453) in 15 university research-affiliated Irish general practices were invited to participate.MethodParticipants underwent mass spectrometry urine analysis to test adherence and ambulatory BP monitoring (ABPM) to examine WCH.ResultsOf the eligible patients invited, 52% (n = 235) participated. All 235 urine samples (100%) were suitable for analysis: 174 (74%) patients were fully adherent, 56 (24%) partially adherent, and five (2%) fully non-adherent to therapy. A total of 206 patients also had ABPM, and in total 92 (45%) were categorised as pseudo-resistant. No significant associations were found between adherence status and patient characteristics or drug class.ConclusionIn patients with aTRH, the authors have established that it is feasible to examine non-adherence to medications using mass spectrometry urine analysis. One in four patients were found to be partially or fully non-adherent. Further research on how to incorporate this approach into individual patient consultations and its associated cost-effectiveness is now appropriate.

Computer vision has been proven a remarkable entity in modern computer science. Different applications of this field have been used on a regular basis. In this paper, we propose a new model for tracking real time object (i.e., car, human face, interior objects, arms, etc.) from video feed by providing training with HAAR features and also with the implementation of Lucas Kanade Optical Flow including Custom Masking technique. Object tracking has been considered to be very much useful in augmented reality, security, virtual reality, training with simulation, etc. In this research, we have trained our classifier of a specific object for detection purpose. Upon successful training of the classifier, a video footage has been passed into the classifier. Firstly, it detects region of interest (ROI) consisting of the object(s). Secondly, with necessary preprocessing techniques we have detected the contour area enclosing the object. Finding out the contour within the detected object(s) enabled us to create green color within the contour enclosing area. We only kept green contours and subtracted everything from the frames of the scene by Custom Masking technique. Finally, we tracked the object by trailing the green contours of the detected object(s) by Lucas Kanade Optical Flow. Our developed system is able to detect and track different object(s) from a video feed with the settings of less than or equal to 30 frames per second (FPS).

This paper is based on the theme of employee attrition where the reasoning behind employee turnover has predicted with the help of machine learning approach. As employee turnover has become a vital issue these days due to heavy work pressure, less salary, less work satisfaction, poor working environment; it’s high time to uphold a better solution on this term. Therefore, we have come up with a prediction model based on machine learning approach where we have used each feature’s respective Random Forest importance weights while threshold based correlated feature merging into each of the single combined variable. Again, we scale specific features to get the correlated matrix of features matrix by defining threshold. Certainly, this newly developed technique has achieved good result for some algorithms compared to Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for the same dataset.

Summary Objective To estimate the prevalence of β -thalassaemia in different subcastes of the Indian Sindhi population who, in general, have a high prevalence of this disease. Study design A two-phase, community-based survey. Methods Asymptomatic, Sindhi volunteers from Nagpur, central India, were recruited into the present study over a 7-year period. The first phase included the use of the Naked Eye Single Tube Red cell Osmotic Fragility Test (NESTROFT). Those positive for NESTROFT or those volunteering for haemoglobin A 2 (HbA 2 ) quantification entered the second phase of the survey. Appropriate statistical methods for estimating prevalence from two-phase surveys were used. Results The prevalence of β -thalassaemia carriers across the five major Sindhi subcastes varied substantially in the study population. Larkana Sindhis had the highest (17%) whereas Dadu Sindhis had the lowest (8%) frequency of the β -thalassaemia allele. As a corollary, the projected incidence of β -thalassaemia major in newborn babies greatly varied by the subcastes of the parents. Conclusion Ethnic subgroups within populations known to commonly carry the β -thalassaemia gene provide further information that is useful from epidemiological and public health perspectives.

It is being increasingly recognized that a majority of the countries in the thalassemia-belt need a cost-effective screening program as the first step towards control of thalassemia. Although the naked eye single tube red cell osmotic fragility test (NESTROFT) has been considered to be a very effective screening tool for beta-thalassemia trait, assessment of its diagnostic performance has been affected with the reference test- and verification-bias. Here, we set out to provide estimates of sensitivity and specificity of NESTROFT corrected for these potential biases. We conducted a cross-sectional diagnostic test evaluation study using data from 1563 subjects from Central India with a high prevalence of beta-thalassemia. We used latent class modelling after ensuring its validity to account for the reference test bias and global sensitivity analysis to control the verification bias. We also compared the results of latent class modelling with those of five discriminant indexes. We observed that across a range of cut-offs for the mean corpuscular volume (MCV) and the hemoglobin A2 (HbA2) concentration the average sensitivity and specificity of NESTROFT obtained from latent class modelling was 99.8 and 83.7%, respectively. These estimates were comparable to those characterizing the diagnostic performance of HbA2, which is considered by many as the reference test to detect beta-thalassemia. After correction for the verification bias these estimates were 93.4 and 97.2%, respectively. Combined with the inexpensive and quick disposition of NESTROFT, these results strongly support its candidature as a screening tool-especially in the resource-poor and high-prevalence settings.