Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of HIV Self-Testing on PrEP Adherence Among Gender-Diverse Sex Workers in Uganda: A Randomized Trial

Supplemental Digital Content is Available in the Text. Background: HIV self-testing (HIVST) and pre-exposure prophylaxis (PrEP) are complementary tools that could empower sex workers to control their HIV protection, but few studies have jointly evaluated PrEP and HIVST in any setting. Methods: The Empower Study was an open-label randomized trial in Uganda. Sex workers were offered F/tenofovir disoproxil fumarate and randomized 1:1 to monthly HIVST and quarterly in-clinic testing (intervention) or quarterly in-clinic HIV testing alone (standard of care) and followed up for 12 months. PrEP adherence was measured using electronic adherence monitoring and tenofovir diphosphate (TFV-DP) levels in dried blood spots. Adherence outcomes and sexual behaviors were compared by arm using generalized estimating equation models. Results: We enrolled 110 sex workers: 84 cisgender women, 14 transgender women, 10 men who have sex with men, and 2 transgender men. The median age was 23 years. The 12-month retention was 75%. Nearly all (99.4%) used ≥1 HIVST kit. The proportion with TFV-DP levels ≥700 fmol/punch in the HIVST and standard of care arms at the 3-, 6-, 9-, and 12-month visits was 2.4%, 2.3%, 0%, and 0% and 7.9%, 0%, 0%, and 0%, respectively, with no differences by randomization arm (P > 0.2). Self-reported condomless sex acts with paying partners was similar by arm [adjusted incidence rate ratio 0.70; 95% confidence interval (CI): 0.42 to 1.17; P = 0.18]. One seroconversion occurred (HIV incidence, 0.9/100 person-years); TFV-DP was not detected at any visit. Conclusions: A gender-diverse sample of sex workers in Uganda used HIVST but not daily oral PrEP for HIV protection. Alternate approaches to promote PrEP use, including long-acting formulations, should be considered in this population.
Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

ABSTRACT Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.
A Predictive Model Implemented in KNIME Based on Learning Analytics for Timely Decision Making in Virtual Learning Environments

The present research aims to implement a predictive model in the KNIME platform to analyze and compare the prediction of academic performance using data from a Learning Management System (LMS), identifying students at academic risk in order to generate timely and timely interventions. The CRISP-DM methodology was used, structured in six phases: Problem analysis, data analysis, data understanding, data preparation, modeling, evaluation and implementation. Based on the analysis of online learning behavior through 22 behavioral indicators observed in the LMS of the Faculty of Educational Sciences of the National University of San Agustin. These indicators are distributed in five dimensions: Academic Performance, Access, Homework, Social Aspects and Quizzes. The model has been implemented in the KNIME platform using the Simple Regression Tree Learner training algorithm. The total population consists of 30,000 student records from which a sample of 1,000 records has been taken by simple random sampling. The accuracy of the model for early prediction of students' academic performance is evaluated, the 22 observed behavioral indicators are compared with the means of academic performance in three courses. The prediction results of the implemented model are satisfactory where the mean absolute error compared to the mean of the first course was 3. 813 and with an accuracy of 89.7%, the mean absolute error compared to the mean of the second course was 2.809 with an accuracy of 94.2% and the mean absolute error compared to the mean of the third course was 2.779 with an accuracy of 93.8%. These results demonstrate that the proposed model can be used to predict students' future academic performance from an LMS data set.
Workshop scheduling using practical (inaccurate) data Part 1: The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach

This paper is a report on a simulation study to investigate the performance of a number of scheduling rules on the basis of a rolling time horizon approach for a dynamic job shop environment. The performance measure considered is an economic objective which includes the main costs involved in a scheduling decision. The first purpose of the study was to find the best scheduling rule and the second to investigate the effects of the rescheduling interval on performance and examine whether there is a policy that can always improve performance. The simulation study, which is part of a larger project on practical workshop scheduling, has been carried out under widely varying conditions in terms of due date tightness, shop load level, and shop load balance level. The results show that a recently developed scheduling rule, SPT-C/R, is the most appropriate scheduling rule in minimizing overall cost and that the relationship between performance and rescheduling interval can be shown.
OIL & GAS INDUSTRY IN BRAZIL: A BRIEF HISTORY AND LEGAL FRAMEWORK

The recently announced discovery of potential large-scale reserves in the Brazilian so called pre-salt layer has resulted in a new legal framework for the country. In this new architecture, old and new regulation share the legal arena. Exploring this context, this paper provides an overview of the emergence and evolution of the oil and gas market in Brazil, and discusses the new legal configuration where the prevailing Concession System co-exists with the Production Sharing System and the Onerous Assignment. The conclusion pinpoints the challenges that the country faces in dealing with two energy sources –oil and gas— that will play an increasing role in Brazil’s future. It also indicates that the introduction of competition also has brought new features and improvements to oil and gas industry in Brazil. Structuring a robust legal framework that will foster the necessary investments is not only a challenge for the Brazilian economy, but also one that has to be tackled by many emergent economies with newly hydrocarbon discoveries.
Explicit Solutions for Simple Models of Wind Turbine Interference

Wind turbine interference - the reduction in output power of a turbine downwind of any others - is a major problem for wind farm optimization and control. With interference, it is well-known for specific cases that co-operative optimization of power output yields more power than does the selfish optimization of individual turbines. This paper develops explicit solutions for simple models of interference for the general case of any number of turbines in line with the wind. For the simplest case of no wake recovery, analytical solutions for co-operative and selfish optimization are derived. They show that co-operative optimization nearly always yields more power and never yields less. Adding a simple form of wake recovery for equally spaced turbines precludes analytical solutions, but numerical solutions are developed to find the power to any required level of accuracy. Again, co-operative optimization is superior in nearly all cases. These simple solutions should be useful for demonstrating the importance of interference and for testing methods for optimizing wind farm layout and operation. It is shown that the maximum benefit from co-operative operation occurs at turbine spacing comparable to that commonly used in wind farms. The analysis is then extended to include topographical effects modeled as changes in wind speed along the line. Explicit solutions are obtained for two turbines in line. Co-operative optimization remains the best strategy. In particular, when the wind speed increases, it quickly becomes optimal to shut down the first turbine.
Determinants of Millennial behaviour towards current and future use of video streaming services

Purpose This study aims to investigate the evolution of factors that influence the current and future use of video streaming applications by Millennial consumers. Design/methodology/approach Combining technology acceptance, perceived values and user identity theory this study used factor analysis and multiple regression to examine data from a survey of 292 university undergraduates. Findings Millennial’s current and future use of video streaming services remains driven more by social and emotional values and their effect on identity salience with their choice of content. Ease of use, convenience and monetary value remains less of an influence currently but may become more important in the future with the continued maturity of the industry. Practical implications The results of this study suggest that video streaming service providers should be developing business models that recognize the increasing importance of emotional appeal and self-identity of their service offerings as the industry matures and competition increases. Originality/value The research is novel in addressing future video streaming service provision by examining changes in young consumer behaviour over time within a similar sample population and considering the growth and technological advancement of video streaming services. The results are significant in addressing the gap that exists in understanding whether perceived values for technology adoption of the same product or service by millennials change over time and the implications that have for product and service providers.
An Efficient Approach to Select Instances in Self-Training and Co-Training Semi-supervised Methods

Semi-supervised learning is a machine learning approach that integrates supervised and unsupervised learning mechanisms. In this learning, most of labels in the training set are unknown, while there is a small part of data that has known labels. The semi-supervised learning is attractive due to its potential to use labeled and unlabeled data to perform better than supervised learning. This paper consists of a study in the field of semi-supervised learning and implements changes on two well-known semi-supervised learning algorithms: self-training and co-training. In the literature, it is common to develop researches that change the structure of these algorithms, however, none of them proposes automating the labeling process of unlabeled instances, which is the main purpose of this work. In order to achieve this goal, three methods are proposed: FlexCon-G, FlexCon and FlexCon-C. The main difference among these methods is the way in which the confidence rate is calculated and the strategy used to select a label in each iteration. In order to evaluate the proposed methods’ performance, an empirical analysis is conducted, in which the performance of these methods has been evaluated on 30 datasets with different characteristics. The obtained results indicate that all three proposed methods perform better than the original self-training and co-training methods, in most analysed cases.
Different City's Short-Term Housing Prices Affect the Relationship from Empirical Analysis in China

According to the city development level and the regional difference, selecting 10 main cities of China and using the monthly average date of house price about three years, the effect of relationship between cities prices of different levels of development and the region in short-term was studed through the unit root test by using the method of VAR analysis model and the pulse and variance. The results show that, the rate of increase in housing prices in China between different city in short-term are clearly linked, the changes of overall level of first-tier cities affect the more rational second-tiers housing prices next period, which has a significant influence on the second-tier city to the same regions three-tier city next period. The impact of China's city structured relationship in housing prices is figured out.
3-db Couplers Constructed from Two Tandem Connected 8.34-dB Asymmetric Couplers (Correspondence)

The technique of tandeming two symmetric 8.34-dB directional couplers to produce a 3-dB coupler was described in 1965 by J.P. Shelton, et al. This technique is useful because broadband 3-dB couplers are difficult to synthesize directly, due to the impedance levels involved. This correspondence shows that it is possible to tandem connect two asymmetric 8.34-dB couplers to yield a broadband 3-dB device. Design tables for 8.34-dB asym. metric couplers which give the normalized even-mode impedances required for a given ripple, number of sections, and bandwidth are also presented.
3D Double-Vision Inspection Based on Structured Light

3D double-vision inspection is very necessary. It has a larger field of view, and can solve the problem of blind area for 3D measurement, as proposed by 3D single-vision inspection. At the beginning of this paper, the principle of structured-light based 3D vision inspection is introduced. Then, a method of gaining calibration points for 3D double-vision inspection system is proposed in detail. In order to gain calibration points with high precision, a double-directional photoelectric aiming device is designed as well, and a method for compensating the position-setting error of the aiming device is described. The coordinates of all calibration points are precisely unified in a world coordinate system. The application of RBF (radial basis function) neural network in establishing the inspection model of structured-light based 3D vision is described in detail. Finally, with the use of the calibration points, the inspection model of 3D double-vision based on RBF neural network is successfully established. The model's training accuracy is 0.078 mm, and the testing accuracy is 0.084 mm.
A Survey on Fault Detection and Recovery techniques in Wireless Sensor networks

From last few years Wireless Sensor Networks have gained attention of lots of researchers in various applications like environmental monitoring, battle field and health care monitoring etc. Sensor nodes in WSN are prone to failure due to hardware failure, depletion of energy, malicious attacks etc. This paper presents the survey on various fault detection and recovery technique.
Reviewing Existing and Proposed Emissions Trading Systems

Putting a price on greenhouse gas emissions is a cornerstone policy in climate change mitigation. To this end, many countries have implemented or are developing domestic emissions trading systems. This paper reviews key design features of mandatory emissions trading systems that had been established or were under consideration in 2010, with a particular focus on implications for the energy sector.
A critical review on green corrosion inhibitors based on plant extracts: Advances and potential presence in the market

Corrosion occurs in all sectors including oil pipelines, drinking water and sewerage in the majority of cases linked to corrosion of steel. Good corrosion management includes optimising corrosion control actions and minimising lifecycle corrosion costs whilst meeting environmental goals. The toxicity of commonly used synthetic inhibitors are the subject of recent legislations (REACH and PARCOM) have led to search on more eco-friendly corrosion inhibitors. Extensive research is conducted to assess the corrosion inhibition rate of diverse green inhibitors. However, it was not adequately recommended for widespread use furthermore, presenting at the business level. This paper critically reviews green plant extracts corrosion inhibitors with a particular emphasis on their efficiency. In addition, a comprehensive list of potentially effective plant extracts has been compiled. Also, the successful use of naturally occurring substances to inhibit the corrosion of metals in acidic and alkaline environment has been investigated. The results showed that the corrosion rate of mild steel in 1 M HCl and 0.5 M H 2 SO 4 decreases with the increase in concentration of the banana extracts. In conclusion, corrosion inhibitors based on plant extracts represent a reliable economic eco-friendly alternative for corrosion inhibition based on their availability and efficiency. The challenges toward the commercial application of green inhibitors have been drawn from the literature.
Using hiPSC‐CMs to Examine Mechanisms of Catecholaminergic Polymorphic Ventricular Tachycardia

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a potentially lethal inherited cardiac arrhythmia condition, triggered by physical or acute emotional stress, that predominantly expresses early in life. Gain‐of‐function mutations in the cardiac ryanodine receptor gene (RYR2) account for the majority of CPVT cases, causing substantial disruption of intracellular calcium (Ca2+) homeostasis particularly during the periods of β‐adrenergic receptor stimulation. However, the highly variable penetrance, patient outcomes, and drug responses observed in clinical practice remain unexplained, even for patients with well‐established founder RyR2 mutations. Therefore, investigation of the electrophysiological consequences of CPVT‐causing RyR2 mutations is crucial to better understand the pathophysiology of the disease. The development of strategies for reprogramming human somatic cells to human induced pluripotent stem cells (hiPSCs) has provided a unique opportunity to study inherited arrhythmias, due to the ability of hiPSCs to differentiate down a cardiac lineage. Employment of genome editing enables generation of disease‐specific cell lines from healthy and diseased patient‐derived hiPSCs, which subsequently can be differentiated into cardiomyocytes. This paper describes the means for establishing an hiPSC‐based model of CPVT in order to recapitulate the disease phenotype in vitro and investigate underlying pathophysiological mechanisms. The framework of this approach has the potential to contribute to disease modeling and personalized medicine using hiPSC‐derived cardiomyocytes. © 2021 Wiley Periodicals LLC.
Limitations of Surface Current Model of Magnetic Field and a Remedy

It is well known that any realistic static magnetic field in a source-free volume can be generated by electric currents on a surface enclosing the volume. The magnetic field generated by surface currents is equivalent to that from magnetic dipoles distributed on the surface and along the surface normal directions. We demonstrate the limitations of the surface current model for instrumentation, and show vertical current loops corresponding to magnetic dipoles along the surface tangential directions may be employed to mitigate these limitations. We consider loop coil elements distributed on a cylindrical surface. A closed-surface array has loops on the entire surface, and an open-surface array does not have loops on the surface of both ends. A coil array may contain only flat elements (each loop is inside the cylindrical surface) or composite elements consisting of three orthogonal loops (one flat loop and two vertical loops) instead of just one flat loop at the location of each element. With computer simulations, the array coils were tested for generating the spatial field pattern of a vertical loop or a linear field gradient. The simulations showed that (1) an open-surface composite loop array produces less errors than a similar flat loop array in generating linear field patterns; the percentage root mean square error (RMSE) of the composite coil loops was 8.46% compared to 21.5% for the similar flat loops; (2) A composite loop array consumes less power than a corresponding flat array. For the open-surface array with a similar number of loops, the power consumption of the composite loop array was 73.9 versus 184 (arbitrary power units) for the flat loop array.
Inclusion Complexes of Rifampicin with Native and Derivatized Cyclodextrins: In Silico Modeling, Formulation, and Characterization

Inclusion complexation of rifampicin (RIF) with several types of cyclodextrins (βCD, hydroxypropyl-βCD, γCD, hydroxypropyl-γCD) in aqueous solutions at different pH values was investigated to assess the interactions between RIF and cyclodextrins (CDs). Molecular modeling was performed to determine the possible interactions between RIF and CDs at several pH values. The inclusion complexes were characterized by differential scanning calorimetry, Fourier transform infrared spectroscopy, powder X-ray diffractometry, and scanning electron microscopy. Moreover, this study evaluated the dissolution profile and antibacterial activity of the formed complexes. Phase solubility analysis suggested the formation of RIF-CD affirmed 1:1 stoichiometry at all pH values (except RIF-βCD at pH 4.0 and both βCD and γCD at pH 9.0). The inclusion complexation of RIF with CD successfully increased the percentage of RIF released in in vitro studies. The inclusion complexes of RIF exhibited more than 60% of RIF released in 2 h which was significantly higher (p < 0.05) than release of pure RIF, which was only less than 10%. Antibacterial activity of RIF-CD complexes (measured by the minimum inhibitory concentration of RIF against Staphylococcus aureus and methicillin-resistant Staphylococcus aureus) was lower for both RIF-βCD and RIF-HPγCD at pH 7.0 to pure RIF suspension. In conclusion, this work reports that both βCD and γCD can be used to enhance the solubility of RIF and thus, improve the effectivity of RIF by decreasing the required daily dose of RIF for the treatment of bacterial infections.