How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

Modelling interaction forces at a curved physical human-exoskeleton interface

In virtual modelling of exoskeletons, the human-exoskeleton interface is often simplified by modelling the interface forces at a single point instead of contact forces due to the straps or cuffs. In the past, force-generating elements (FGEs) have been used to predict ground reaction forces. However, unlike the ground, which is a planar surface, the human-exoskeleton interface presents curved surfaces. This work discusses the modifications required for using the FGEs for predicting the curved human-exoskeleton interface forces of a passive lower-limb exoskeleton, the Chairless Chair. A pressure mat was positioned at the human-exoskeleton interface to measure the area of contact and the centre of pressure (CoP) in three different sitting conditions. The strength of the FGEs was analysed in detail and its optimization based on the model outputs is discussed. The strength affects the model assistance and the CoP, and these outputs could be used to identify the optimal value of the strength. The strength of the FGEs affects the biomechanical outputs from the model also. Therefore, it is crucial to select the correct value of the strength. The results of this work would be useful for the detailed modelling of the human-exoskeleton interface

A summary of computational experience at GE Aircraft Engines for complex turbulent flows in gas turbines

This viewgraph presentation summarizes some CFD experience at GE Aircraft Engines for flows in the primary gaspath of a gas turbine engine and in turbine blade cooling passages. It is concluded that application of the standard k-epsilon turbulence model with wall functions is not adequate for accurate CFD simulation of aerodynamic performance and heat transfer in the primary gas path of a gas turbine engine. New models are required in the near-wall region which include more physics than wall functions. The two-layer modeling approach appears attractive because of its computational complexity. In addition, improved CFD simulation of film cooling and turbine blade internal cooling passages will require anisotropic turbulence models. New turbulence models must be practical in order to have a significant impact on the engine design process. A coordinated turbulence modeling effort between NASA centers would be beneficial to the gas turbine industry

Applying systems thinking to identify enablers and challenges to scale-up interventions for hypertension and diabetes in low-income and middle-income countries: Protocol for a longitudinal mixed-methods study

INTRODUCTION: There is an urgent need to reduce the burden of non-communicable diseases (NCDs), particularly in low-and middle-income countries, where the greatest burden lies. Yet, there is little research concerning the specific issues involved in scaling up NCD interventions targeting low-resource settings. We propose to examine this gap in up to 27 collaborative projects, which were funded by the Global Alliance for Chronic Diseases (GACD) 2019 Scale Up Call, reflecting a total funding investment of approximately US$50 million. These projects represent diverse countries, contexts and adopt varied approaches and study designs to scale-up complex, evidence-based interventions to improve hypertension and diabetes outcomes. A systematic inquiry of these projects will provide necessary scientific insights into the enablers and challenges in the scale up of complex NCD interventions. METHODS AND ANALYSIS: We will apply systems thinking (a holistic approach to analyse the inter-relationship between constituent parts of scaleup interventions and the context in which the interventions are implemented) and adopt a longitudinal mixed-methods study design to explore the planning and early implementation phases of scale up projects. Data will be gathered at three time periods, namely, at planning (T ETHICS AND DISSEMINATION: The current protocol was approved by the Monash University Human Research Ethics Committee (HREC number 23482). Informed consent will be obtained from all participants. The study findings will be disseminated through peer-reviewed publications and more broadly through the GACD network

Off-label use of antibiotics in hospitalised children in a tertiary care teaching hospital

Background: Children constitute one-third of the population and they suffer from a variety of infectious diseases and are commonly prescribed antibiotics. Most of the antibiotics lack sufficient information on safety and efficacy in children and are thus prescribed off-label. This study was envisaged to assess the off-label use of antibiotics in hospitalized children.Methods: Total 120 patients were included in the study. Off-label use was determined on the basis of product literature and National Formulary of India. Descriptive statistics was used to present the data i.e. percentage; proportions, frequency, mean and standard deviation using Microsoft excel worksheet.Results: A total of 791 drugs from different classes were prescribed to 120 patients with a mean of 6.6±2.68 drugs described per patient during their stay in the hospital. 100 out of 120 (83%) patients were prescribed at least one antibiotic during their stay in the hospital. Out of the 204 antibiotics prescribed, 43(21%) were prescribed off-label. Antibiotic dose was the most common reason followed by age (1month-1 year more than 2-6 years of age) and frequency in off-label use.Conclusions: Antibiotics are commonly prescribed to children with substantial off-label use. The same must be seriously addressed by the policy makers and stakeholders in order to promote their rational use

Identifying individuals with virologic failure after initiating effective antiretroviral therapy: The surprising value of mean corpuscular hemoglobin in a cross-sectional study

<p>Abstract</p> <p>Objective</p> <p>Recent studies have shown that the current guidelines suggesting immunologic monitoring to determine response to highly active antiretroviral therapy (HAART) are inadequate. We assessed whether routinely collected clinical markers could improve prediction of concurrent HIV RNA levels.</p> <p>Methods</p> <p>We included individuals followed within the Johns Hopkins HIV Clinical Cohort who initiated antiretroviral therapy and had concurrent HIV RNA and biomarker measurements ≥4 months after HAART. A two tiered approach to determine whether clinical markers could improve prediction included: 1) identification of predictors of HIV RNA levels >500 copies/ml and 2) construction and validation of a prediction model.</p> <p>Results</p> <p>Three markers (mean corpuscular hemoglobin [MCH], CD4, and change in percent CD4 from pre-HAART levels) in addition to the change in MCH from pre-HAART levels contained the most predictive information for identifying an HIV RNA >500 copies/ml. However, MCH and change in MCH were the two most predictive followed by CD4 and change in percent CD4. The logistic prediction model in the validation data had an area under the receiver operating characteristic curve of 0.85, and a sensitivity and specificity of 0.74 (95% CI: 0.69-0.79) and 0.89 (95% CI: 0.86-0.91), respectively.</p> <p>Conclusions</p> <p>Immunologic criteria have been shown to be a poor guideline for identifying individuals with high HIV RNA levels. MCH and change in MCH were the strongest predictors of HIV RNA levels >500. When combined with CD4 and percent CD4 as covariates in a model, a high level of discrimination between those with and without HIV RNA levels >500 was obtained. These data suggest an unexplored relationship between HIV RNA and MCH.</p

Self heating Effects in GaN High Electron Mobility Transistor for Different Passivation Material

In this paper effect of self-heating has been studied of AlGaN/GaN high electron mobility transistor (HEMT) for different passivation layers which is promising device for high power at high frequencies. The different passivation layers used are aluminium oxide (Al2O3), silicon nitride (SiN) and silicon dioxide (SiO2). The device GaN HEMT has been simulated and characterised for its thermal behaviour by the distribution of lattice temperature inside the device using device simulation tool ATLAS from SILVACO. The transfer and output characteristics with and without self-heating has been studied for electrical characterisation. The channel temperature for different passivation observed is 448 K, 456 K and 471 K forAl2O3, SiN and SiO2 respectively. The observed different temperatures are due to difference in their thermal conductivity. This channel temperature information is critical to study the reliability of the device at high power levels

Heterosis in single cross inter and intra-specific hybrids of Desi cotton (Gossipium arboreum and G. herbaceaum) for their seed cotton yield, fibre quality and seed oil content

The present investigation was carried out to assess the expression of per se performance and heterotic effect for fibre quality and seed oil content besides seed cotton yield, studied involving ten desi cotton (Gossipium arboreum and G. herbaceaum) genotypes and their 45 cross combinations in half diallel analysis. F-1 hybrids GBhv-282 x G 27 (67.36%), GBhv- 287 x 824 (58.14%), GBhv- 282 x GAM- 173 (35.00%), GBhv- 286 x G 27 (20.50%), and GBhv- 283 x 824 (18.75%) recorded highest per se performance and significant positive standard heterosis while the maximum heterobeltiosis for seed cotton yield per plant was exhibited by the hybrid GBhv- 287 x 824(155.60 %) followed by GBhv- 282 x G 27 (151.29%) and GBhv- 282 x GAM- 173 (130.30%). Similar trend of heterosis for numbers of boll per plant were observed in above hybrids. For fibre quality traits none of the cross showed consistent high performance for all the characters. Cross GBhv- 283 x 824 was exhibited high standard heterosis for 2.5 % span length, fibre strength, fibre elongation percentage as well as for short fibre index (SFI) while cross GBhv- 286 x 824 were promising for 2.5 per cent span length, fibre strength and fibre fineness. In case of oil content intraarboreum crosses resulted as better crosses and among them cross combination 824 x GAM- 173 was best. Desi cotton hybrids are having lower fibre quality and yield. So, improvement for yield and fibre quality of diploid native varieties through heterosis breeding provided better hybrids for rainfed farming

NeuroWrite: Predictive Handwritten Digit Classification using Deep Neural Networks

The rapid evolution of deep neural networks has revolutionized the field of machine learning, enabling remarkable advancements in various domains. In this article, we introduce NeuroWrite, a unique method for predicting the categorization of handwritten digits using deep neural networks. Our model exhibits outstanding accuracy in identifying and categorising handwritten digits by utilising the strength of convolutional neural networks (CNNs) and recurrent neural networks (RNNs).In this article, we give a thorough examination of the data preparation methods, network design, and training methods used in NeuroWrite. By implementing state-of-the-art techniques, we showcase how NeuroWrite can achieve high classification accuracy and robust generalization on handwritten digit datasets, such as MNIST. Furthermore, we explore the model's potential for real-world applications, including digit recognition in digitized documents, signature verification, and automated postal code recognition. NeuroWrite is a useful tool for computer vision and pattern recognition because of its performance and adaptability.The architecture, training procedure, and evaluation metrics of NeuroWrite are covered in detail in this study, illustrating how it can improve a number of applications that call for handwritten digit classification. The outcomes show that NeuroWrite is a promising method for raising the bar for deep neural network-based handwritten digit recognition.Comment: 6 pages, 10 figure

DSTATCOM with Improved LMS based IRP theory

In this paper, grid connected DSTATCOM with three phase three wire has been proposed for harmonics elimination, reactive power compensation and active power injection into the grid. An improved instantaneous reactive power theory based on the least mean square is computed. The least mean square (LMS) algorithm is combined with instantaneous reactive power theory (IRPT) to improve its dynamics performance and eliminates the use of low pass filter requirement for computation of reference current. The harmonics component of the active power is estimated from LMS algorithm generated load harmonics currents. The performance characteristic of DSTATCOM is estimated with control scheme using computer simulation. The technique is based on the instantaneous transformation of instantaneous signals and taking advantage of the subsequent calculation of power from supply side to the loads. The neural network based method to estimate the harmonic online, DSTATCOM can compensate the harmonics. The computer simulation is carried out with MATLAB Simulink power system block sets