Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlates of Engagement Within an Online HIV Prevention Intervention for Single Young Men Who Have Sex With Men: Randomized Controlled Trial

Background Digital HIV interventions (DHI) have been efficacious in reducing sexual risk behaviors among sexual minority populations, yet challenges in promoting and sustaining users’ engagement in DHI persist. Understanding the correlates of DHI engagement and their impact on HIV-related outcomes remains a priority. This study used data from a DHI (myDEx) designed to promote HIV prevention behaviors among single young men who have sex with men (YMSM; ages 18-24 years) seeking partners online. Objective The goal of this study is to conduct a secondary analysis of the myDex project data to examine whether YMSM’s online behaviors (eg, online partner-seeking behaviors and motivations) are linked to participants’ engagement (ie, the number of log-ins and the number of sessions viewed). Methods We recruited 180 YMSM who were randomized into either myDEx arm or attention-control arm using a stratified 2:1 block randomization. In the myDEx arm, we had 120 YMSM who had access to the 6-session intervention content over a 3-month period. We used Poisson regressions to assess the association between YMSM’s baseline characteristics on their DHI engagement. We then examined the association between the participants’ engagement and their self-reported changes in HIV-related outcomes at the 3-month follow-up. Results The mean number of log-ins was 5.44 (range 2-14), and the number of sessions viewed was 6.93 (range 0-22) across the 3-month trial period. In multivariable models, the number of log-ins was positively associated with high education attainment (estimated Poisson regression coefficient [β]=.22; P=.045). The number of sessions viewed was associated with several baseline characteristics, including the greater number of sessions viewed among non-Hispanic YMSM (β=.27; P=.002), higher education attainment (β=.22; P=.003), higher perceived usefulness of online dating for hookups (β=.13; P=.002) and perceived loneliness (β=.06; P=.004), as well as lower experienced online discrimination (β=–.01; P=.007) and limerence (β=–.02; P=.004). The number of sessions viewed was negatively associated with changes in internalized homophobia (β=–.06; P<.001) and with changes in perceived usefulness of online dating for hookups (β=–.20; P<.001). There were no significant associations between the number of log-ins and changes in the participants’ behaviors at the 90-day follow-up. Conclusions DHI engagement is linked to participants’ sociodemographic and online behaviors. Given the importance of intervention engagement in the intervention’s effectiveness, DHIs with personalized intervention components that consider the individuals’ differences could increase the overall engagement and efficacy of DHIs. Trial Registration ClinicalTrials.gov NCT02842060; https://clinicaltrials.gov/ct2/show/NCT02842060.
A Performance Analysis of Classifiers on Imbalanced Data

: In the machine learning ﬁeld, there are many classiﬁcation algorithms. Each algorithm performs better in certain scenarios, which are very difﬁcult to deﬁne. There is also the concept of grouping multiple classiﬁers, known as ensembles, which aim to increase the model generalization capacity. Comparing multiple models is costly, as, for certain cases, training classiﬁers can take a long time. In the literature, many aspects of the data have already been studied to help in the task of classiﬁer selection, such as measures of diversity among classiﬁers that form an ensemble, data complexity measures, among others. In this context, the main objective of this work is to analyze class imbalance and how this measure can be used to guide the selection of classiﬁers. We also compare the model’s performances when using class balancing techniques such as oversampling and undersampling.
Niacin Alleviates the Acidosis of Rumen Epithelial Cells Induced by Low pH Resulting from Volatile Fatty Acids

To investigate the effects of different niacin treatments on low pH-induced goat rumen epithelial cells acidosis, cells were exposed to solutions with a pH of 7.4, 5.8, 5.6, 5.2, and 5.0 for 3, 6, 12, and 24 h. The precise pH was adjusted using volatile fatty acids (acetate, propionate, and butyrate). All the cell viability in low pH treatment declined more than 50% for 12 h stimulation, and cells treated with low pH for 12 h was selected to be optimal ruminal acidosis model. Cells were then cultured with niacin (0, 20, 40, 80, 160, or 320 mM) for 12 h before, concurrent, and after 12 h treatment with low pH. Results showed that, 1) Pre-treatment protection of niacin: pre-treatment with 20 or 40 mM niacin increased the cell viability under pH 5.8, while 80-320 mM niacin decreased the cell viability compared with the control group ( P < 0.01). Under pH 5.5, results were similar to those under pH 5.8, except for pre-treatment with 40 mM niacin with no significant effect on the cell viability. Under pH 5.2 or 5.0, pre-treatment with niacin reduced the cell viability compared with the control group ( P < 0.01). 2) Concurrent treatment protection of niacin: under all pH value, all concurrent treatment with niacin reduced the cell viability compared with the control group ( P < 0.01). 3) Post-treatment protection of niacin: under pH 5.8 or 5.5, post-treatment with 20 or 40 mM niacin increased the cell viability compared with the control group, while 80-320 mM niacin decreased the cell viability ( P < 0.01). Under pH 5.2 or 5.0, post-treatment with niacin reduced the cell viability compared with the control group ( P < 0.01). These results may provide a reference for ruminal acidosis mitigation. Low-concentration niacin could relieve subacute ruminal acidosis, and it could be added after symptoms appear. However, niacin had a negative role in the protection of acute ruminal acidosis.
Savonius Wind Turbine Performances on Wind Concentrator

The air streams from the outlet of an air compressor can be used to generate electricity. For instance, if a micro-sized Vertical-Axis Wind-Turbine (VAWT) is installed towards the airflow, some amount of electricity can be generated before being stored in a battery bank. The research’s objectives are to design, fabricate and analyze the performance of Helical Savonius VAWT blade rotors, which is tested with and without using a wind concentrator. The Helical Savonius VAWT is tested at 0 cm without the concentrator, whereas the blade rotor is tested at concave-blade position when using the concentrator. The blade and the wind concentrator designs were based on the dimensions and the constant airflow of the air compressor. The findings suggested that the blade produced its best performance when tested using wind concentrator at concave-blade position in terms of angular speed ( ω ), tip speed ratio ( TSR ) and the generated electrical power ( P E ). The findings concluded that the addition of wind concentrator increases the airflow which then provided better performances on the blades. Full Text: PDF DOI: http://dx.doi. org/10.11591/ijpeds.v8. i1.pp376-383
Adapting Strategies for Water Supply Management to Climate Change in Nzoia River Basin, Kenya

Climate change is influencing the hydrological cycle on a global scale, and enhancing communities' ability to adapt to these changes is critical for long-term growth and survival. Climate change is expected to have a negative influence on water resources and supply. Other water stresses, such as population expansion, land-use change, growing per capita water demand, and regulatory changes, frequently exacerbate the consequences of climate change. Mitigation will not be enough to minimise the significant repercussions on the water industry, especially in poor nations. The study adopted a cross-sectional survey design. Three counties were randomly selected from Nzoia River Basin for study with Busia representing the lower catchment, Kakamega middle catchment and Trans Nzoia upper catchment. The study was carried out from May, 2017 to September, 2017.A carefully selected team of water and climate change experts consisting of decision makers, practitioners, managers, scientists, technology adopters and other stakeholders were used to identify the five main climate change challenges affecting water supply management in Nzoia River Basin (water scarcity and drought, flooding, water quality effects, unknown climate change risks and disaster preparedness) and the accompanying strategies and technologies for adapting water supply management to climate change through questionnaires, in-depth expert-interviews, brainstorming sessions, field observations and literature review. The adaptation strategies and technologies identification exercise drew from multiple sources of information including the national context where national development plans were used; adaptation technologies proposed in previous documents; technologies currently in use and supported by national water policy; initiatives in the pipeline under the larger water sector; appropriateness of technologies in the local context and the social acceptability among other sources. Stakeholder engagement helps to foster local ownership and buy-in, which is often necessary for long-term project viability. The study has also assessed the level of implementation for the adaptation strategies and the agencies involved. The adaptation strategies were identified and ranked using the total weighted scores as follows: Water storage, Water efficiency and demand management, Water augmentation, Alternative water sources, Water allocation, Urban storm water management, Riverine flood protection, Hazard and risk assessment, Vulnerability assessment, Disaster response, and Early warning. The Total weighted scores under ranking are very close to each other, implying that all the strategies/technologies listed for adapting water supply management to climate change are good. The results of this study provide valuable information that can be used by national and county governments in adapting water supplies to climate change.
A Study on the Tolerance Band of Voltage Drop during Motor Startup for Refineries and Chemical Plants with Isolated Power Systems

Refineries and chemical plants with isolated power systems that have a limited power supply are more susceptible to voltage changes from disturbances compared to power systems connected with a power company. Furthermore, most loads in such cases are induction motor loads, and therefore, transient voltage characteristics when starting a high-capacity motor must be examined. In general, high-capacity motors are customized appropriately to the load performance curve by the manufacturer during the construction of an industrial plant. Subsequently, when complying with the voltage drop permitted by international standards during the design process, power supply equipment such as transformers and generators is overdesigned. Therefore, a novel analysis is necessary on standards for startup and constraint voltage drops, as well as on identifying the voltage drop limitations for starting high-capacity motors in refineries and chemical plants with isolated power systems. In this study, field tests on an industrial plant were conducted,, and simulations modeled under conditions identical to those of the field test system were performed using the general-purpose program ETAP in order to compare the results.
Pollution Detection in a Low-Cost Electronic Nose, a Machine Learning Approach

In this study, machine learning on a microcontroller-based electronic nose is proposed to identify danger in gas mixtures of nitrogen dioxide, carbon monoxide, and formaldehyde. This electronic nose is low-powered and low-cost, making new areas for pollution detection possible. This study researches the use of machine learning in combination with low-cost chemical sensors. The machine learning can filter the noise in the sensors and to some extent compensate for the influence of disturbances such as a change in humidity, temperature, and cross-sensitivity. With reliable, cheap sensors it is possible to produce low-cost electronic noses that may detect environments which represents a health risk. By using a classification approach, it is possible to identify the compound of a threat and separate safe environments from dangerous. A Feed Forward Neural Network (FFNN) and a Random Forest Classifier (RFC) demonstrates a 96 % accuracy to identify harmful concentrations of nitrogen dioxide, carbon monoxide, and formaldehyde using the datasets collected during this study.
Merging Experimental Design and Nanotechnology for the Development of Optimized Simvastatin Spanlastics: A Promising Combined Strategy for Augmenting the Suppression of Various Human Cancer Cells

Simvastatin (SMV) is an antihyperlipidemic agent that has been investigated as a possible anti-cancer agent. An obstacle to malignant tumor therapy using drugs is the delivery of adequate levels to the cancer cells while minimizing side effects following their systemic administration. To circumvent this challenge, the researchers directed towards the field of nanotechnology to benefit from the nano-size of the formulation in passively targeting the tumor cells. Thus, our study aimed at investigating the potential of a combined mixture–process variable design for optimization of SMV spanlastics (SMV-SPNs) with minimized particle size and maximized zeta potential to enhance the anticancer activity of the drug. The study investigated the effects of Span® 20 and Tween® 80 as mixture components and sonication time as a process variable on particle size, polydispersity index, and zeta potential as responses. SPNs were prepared using an ethanol injection method. Combining the predicted optimized variables’ levels is supposed to achieve the set goals with a desirability of 0.821. The optimized spanlastics exhibited a measured globule size of 128.50 nm, PDI of 0.329, and ZP of −29.11 mV. The percentage relative error between predicted responses and the observed ones were less than 5% for the three responses, indicating the optimization technique credibility. A significant improvement in the cytotoxicity of the optimized formulation against three different cancerous cell lines was observed in comparison with SMV. The inhibitory concentration (IC50) values of MCF-7, HCT-116, and HEPG2 were found to be 0.89, 0.39, and 0.06 μM at 24 h incubation. The enhanced cytotoxicity could be assigned to the possible improved permeation and preferential build-up within the cancerous cells by virtue of the minimized size. These findings imply that SMV-SPNs could be an ideal strategy to combat cancer.
Chemical and the Acceptability of Chicken Nuggets as Functional Food with Utilization Rice Bran to Substitute Wheat Flour

The purpose of this research was produce a product with the chemical properties and acceptability as well as having health benefits. The research was conducted from July to September 20 12. The variables in this research were insoluble dietary fiber, antioxidant activity, and sensory test. Dietary fiber was measured using the total multienzyme method, antioxidant activity was measured using DPPH method and the acceptability for the sensory test. Completely Randomized Design (CRD) with 4 treatments and 5 replications were used in this research. Treatment in this research was the total substitution of rice bran (w / w), consisting of : T0 = 0%, T1 = 25%, T 2 = 50%, T 3 = 75%, T4 = 100. The data obtained were further processed by analysis of variance to determine the effect of treatment. If there was any significant effect of treatment then it was followed by Duncan's Multiple Range Test to determine the differences among the treatments. Based on the results of the study showed that the use of rice bran increase insoluble dietary fiber. Similarly, the antioxidant activity, the higher utilization of rice bran, significantly (P <0.05) increased the antioxidant activity of rice bran chicken nuggets. Overall, the use of rice bran as a substitute for wheat flour can improve the chemical properties but lower the acceptability of chicken nuggets as functional food.
The occurrence of multidrug-resistant Mycobacterium tuberculosis from patients of pulmonary tuberculosis.

INTRODUCTION Multidrug-resistant Mycobacterium tuberculosis (MDR-TB) is one of the leading causes of death in the world. The resource constraints make it difficult to diagnose and monitor the cases of MDR-TB. GeneXpert is a recognized tool used to diagnose the patients of pulmonary tuberculosis in clinical settings across the globe. METHODOLOGY The present one-year cross-sectional study was conducted to estimate the occurrence of MDR-TB in patients with pulmonary TB. A total of 1000 patients suspected of pulmonary tuberculosis were included in this study. A random convenient sampling technique was done to collect the sputum samples (twice) from the patients. Samples were processed for the detection of Mycobacterium tuberculosis using conventional detection methods like the Ziehl Nelson staining method and fluorescent microscopy. Additionally, Cepheid GeneXpert was used for molecular detection of MDR-TB in smear-positive samples of pulmonary tuberculosis by amplifying the rifampicin resistance determining region (RRDR; rpoB gene). All the tests were performed in the biosafety level III lab of District Headquarters Hospital Nankana Sahib. RESULTS It was observed that 103 (10.3%) individuals were diagnosed as positive for tuberculosis among 1000 patients. Among these 103 TB positive cases, there were 11 (10.7%) patients diagnosed with rifampicin resistance gene (RR-Gene) of Mycobacterium tuberculosis. CONCLUSIONS Overall findings of the study showed that MDR-TB is prevalent in pulmonary TB patients and GeneXpert is the most sensitive technique for early diagnosis of the disease, which may be very helpful in the treatment and control of this public health menace in low and middle-income countries.
The Effect of Chemical Modification on the Rheological Properties and Structure of Food Grade Modified Starches

Starch in its pure form can be used as a functional component of numerous food products; however, much better results both in terms of technological and economical aspects are obtained with the use of modified starches. The aim of the work was to establish how chemical modification affects the molecular structure of potato starch and, as a consequence, its rheological properties as well as texture forming ability. Commercial food-grade potato starch preparations oxidized starch, acetylated starch, distarch phosphate, acetylated distarch phosphate and acetylated distarch adipate were the investigated material. The experimental methods included: viscographic analysis of pasting properties, flow rheometry, texture profile analysis, size exclusion chromatography with triple detection. The obtained data were further analyzed employing principal component and hierarchical cluster analysis. It was found that chemical modification leads to substantial changes in the molecular and functional properties of starch products. Oxidation process leads to depolymerization, which causes a substantial decrease in viscosity. Acetylation results mostly in stabilization of rheological properties during thermal processing. Crosslinking of starch leads to an improvement in thickening capabilities, while the type of crosslinking agent used for modification has a secondary effect. Sterilization of all types of modified potato starch pastes leads only to minor changes in their texture and rheological properties.
Controller tuning with Bayesian optimization and its acceleration: Concept and experimental validation

This work discusses a data‐driven approach to controller parameter tuning based on Bayesian optimization. In particular, we propose to design the prior mean function based on a model of the plant. By encoding the information on the model, the optimization needs a much fewer iterations than standard approaches. The effectiveness of the proposed method is demonstrated with a practical experiment.
Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features

: Today, the amount of data is growing rapidly, which makes it nearly impossible for human analysts to comprehend the data or to extract any knowledge from it. To cope with this, as part of the knowledge discovery process, many different data mining and machine learning techniques were developed in the past. A famous representative of such techniques is clustering, which allows the identiﬁcation of different groups of data (the clusters) based on data characteristics. These algorithms need no prior knowledge or conﬁguration, which makes them easy to use, but interpreting and explaining the results can become very difﬁcult for domain experts. Even though different kinds of visualizations for clustering results exist, they do not offer enough details for explaining how the algorithms reached their results. In this paper, we propose a new approach to increase explainability for clustering algorithms. Our approach identiﬁes and selects features that are most meaningful for the clustering result. We conducted a comprehensive evaluation in which, based on 216 synthetic datasets, we ﬁrst examined various dispersion metrics regarding their suitability to identify meaningful features and we evaluated the achieved precision with respect to different data characteristics. This evaluation shows, that our approach outperforms existing algorithms in 93 percent of the examined datasets.
Azadirachta indica-Derived Silver Nanoparticle Synthesis and Its Antimicrobial Applications

Advancement of biologically driven experimental procedure for the generation of metal nanoparticles is a major aspect in the domain of nanotechnology. Herein, we synthesized one pot silver nanoparticles (AgNPs) using Azadirachta indica (Neem plant) via green synthesis method. The alkaloids and flavonoids in the plant extract served as capping and reducing agent to develop silver nanoparticles. The characterization of synthesized AgNPs was performed using analytical techniques such as UV-visible spectroscopy, FTIR, and SEM. The synthesized AgNPs were further impregnated onto a cotton cloth and the antimicrobial studies were performed. The AgNPs embedded in cotton fabric were found to show zone of inhibition against the fungi Candida albicans, gram-negative bacteria Escherichia coli, and gram-positive bacteria Staphylococcus aureus. This experimental study suggests that the AgNPs impregnated on cotton cloth could have great potential for application in smart nanobandages for wound dressing, medical textiles, sports clothing, etc.
Study and Analysis of the Implementation of 4.0 Technologies in the Agri-Food Supply Chain: A State of the Art

: Industry 4.0 is changing the industrial environment. Particularly, the emerging Industry 4.0 technologies can improve the agri-food supply chain throughout all its stages. This study aims to highlight the beneﬁts of implementing Industry 4.0 in the agri-food supply chain. First, it presents how technologies enhance the agri-food supply chain development. Then, it identiﬁes and highlights the most common challenges that Industry 4.0 implementation faces in agri-food’s environment. After that, it proposes key performance indicators to measure the advantages of this implementation. To achieve this, a systematic literature review was conducted. It combined conceptual and bibliometric analyses of 78 papers. As a result, the most suitable technologies were identiﬁed, e.g., Internet of Things, Big Data, blockchain and cyber physical systems. The most used indicators are proposed and the challenges of implementation were detected and classiﬁed in three groups, i.e., technical, educational and governmental. This paper highlights and exempliﬁes the beneﬁts of implementing Industry 4.0 facing the lack of knowledge that exists nowadays. Moreover, it fulﬁls the gaps in literature, i.e., the lack of information about the implementation of technologies 4.0 or the description of the most relevant indicators for Industry 4.0 implementation.