Miracle-dz/UniFAR-FTUs huggingface dataset:
Young Drivers’ Preferences for Gamification Schemes Toward Safer Driving Behaviors: A Pilot Study

Young drivers are over-represented in motor vehicle crashes, and how to improve the risky behaviors of young drivers has been a critical issue around the world. In contrast to penalty programs to improve safety, incentive programs are usually less controversial and more politically feasible. This study used a stated preference survey to investigate the preferences of young drivers for four incentive programs, including two gamified schemes, an insurance fee discount scheme, and a license fee discount scheme, and how these programs may change driving behaviors. A pilot study was conducted on 500 young drivers in Queensland, Australia. The estimation results using a latent class logit model showed the sample of young drivers could be naturally categorized into three groups with more or less equal preference for each scheme. Less than 5% of young drivers were not interested in joining any one of these schemes, suggesting that most of the sample of young drivers could be motivated in the future to have better driving behavior via one of these incentive schemes.
High Performance Integration Pipeline for Viral and Epitope Sequences

With the spread of COVID-19, sequencing laboratories started to share hundreds of sequences daily. However, the lack of a commonly agreed standard across deposition databases hindered the exploration and study of all the viral sequences collected worldwide in a practical and homogeneous way. During the first months of the pandemic, we developed an automatic procedure to collect, transform, and integrate viral sequences of SARS-CoV-2, MERS, SARS-CoV, Ebola, and Dengue from four major database institutions (NCBI, COG-UK, GISAID, and NMDC). This data pipeline allowed the creation of the data exploration interfaces VirusViz and EpiSurf, as well as ViruSurf, one of the largest databases of integrated viral sequences. Almost two years after the first release of the repository, the original pipeline underwent a thorough refinement process and became more efficient, scalable, and general (currently, it also includes epitopes from the IEDB). Thanks to these improvements, we constantly update and expand our integrated repository, encompassing about 9.1 million SARS-CoV-2 sequences at present (March 2022). This pipeline made it possible to design and develop fundamental resources for any researcher interested in understanding the biological mechanisms behind the viral infection. In addition, it plays a crucial role in many analytic and visualization tools, such as ViruSurf, EpiSurf, VirusViz, and VirusLab.
Preliminary Investigation of Mixed Orchard Hays on the Meat Quality, Fatty Acid Profile, and Gastrointestinal Microbiota in Goat Kids

Simple Summary Goat meat constitutes one of the main animal protein sources in the human diet in developing countries. Consumers realize that there are multiple links between diet and happiness. Livestock feeding strategies can increase the concentration of a number of healthy fatty acids. Forages play an important role in maintaining rumen function. This preliminary investigation evaluated the effects of feeding different mixed hays grown in the orchard on meat quality, fatty acids, amino acids, and the gastrointestinal tract microbial ecosystem. The results indicated that goats fed alfalfa + oats mixed hay displayed a more favorable fatty acid profile and microbiota for human health. Feeding higher nutritional value forage is a good strategy for providing high-quality goat meat and is beneficial to the ruminant production system. Abstract This preliminary investigation was designed to study the effects of different mixed orchard hays on meat quality, fatty acids, amino acids, rumen intestinal microflora, and the relationship between rumen bacteria and fatty acids in the longissimus dorsi muscle of Saanen dairy goats. In this preliminary investigation, goats were separately fed crop straws (corn and wheat straws) and alfalfa (Medicago sativa L.) (CK group), alfalfa + oats (Avena sativa L.) (group I), alfalfa + perennial ryegrass (Lolium perenne L.) (group II), and hairy vetch (Vicia villosa Roth.) + perennial ryegrass (group III). There were differences in shear force and cooking loss between treatments. The contents of saturated fatty acids (SFAs) C14:0, C16:0, and C18:0 in the CK group were significantly higher than those in other three groups (p < 0.001). The 16S rDNA sequencing results showed that the relative abundance of Proteobacteria in group II were higher than those in other three groups (p < 0.05). Association analysis showed that Prevotella_1 was negatively correlated with C18:0 and significantly positively correlated with C16:1, while Clostridium and Romboutsia showed a positive correlation with monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs). Therefore, feeding mixed hays can increase beneficial fatty acids and the percentages of associated bacteria in rumen and intestines.
Dynamic analysis and performance assessment of a vibratory feeder for different motor positions on trough

Abstract Material feeding through vibratory feeders is a major part of various industries. Optimum material flow rate depends on proper operation of the feeder. The change in position of the feeder drive system affects the dynamics as well as material conveying velocity on the feeder trough. This article presents an experimental investigation to determine the changes in dynamic parameters like vibration amplitude and force transmissibility at varying operating frequencies for different positions of drive motor in a linear electromechanical feeder using signal processing techniques. Variation in dynamic parameters as well as particle conveying velocity were investigated for three motor positions. Values of dynamic parameters obtained analytically were verified using experimental analysis. The optimum motor position from the cases under consideration was identified on the basis of lowest force transmissibility and highest particle conveying velocity on the trough.
Towards Predicting the Sequential Appearance of Zeolitic Imidazolate Frameworks Synthesized by Mechanochemistry

We use computational materials methods to study the sequential appearance of zinc-based zeolitic imidazolate frameworks (ZIFs) generated in the mechanochemical conversion process. We consider nine ZIF topologies, namely RHO, ANA, QTZ, SOD, KAT, DIA, NEB, CAG and GIS, combined with the two ligands 2-methylimidazolate and 2-ethylimidazolate. Of the 18 combinations obtained, only six (three for each ligand) were actually observed during the mechanosynthesis process. Energy and porosity calculations based on density functional theory, in combination with the Ostwald rule of stages, were found to be insufficient to distinguish the experimentally observed ZIFs. We then show, using classical molecular dynamics, that only ZIFs withstanding quasi-hydrostatic pressure P ≥ 0.3 GPa without being destroyed were observed in the laboratory. This finding, along with the requirement that successive ZIFs be generated with decreasing porosity and/or energy, provides heuristic rules for predicting the sequences of mechanically generated ZIFs for the two ligands considered.
Prediction of Harvest Time of Apple Trees: An RNN-Based Approach

In the field of agricultural research, Machine Learning (ML) has been used to increase agricultural productivity and minimize its environmental impact, proving to be an essential technique to support decision making. Accurate harvest time prediction is a challenge for fruit production in a sustainable manner, which could eventually reduce food waste. Linear models have been used to estimate period duration; however, they present variability when used to estimate the chronological time of apple tree stages. This study proposes the PredHarv model, which is a machine learning model that uses Recurrent Neural Networks (RNN) to predict the start date of the apple harvest, given the weather conditions related to the temperature expected for the period. Predictions are made from the phenological phase of the beginning of flowering, using a multivariate approach, based on the time series of phenology and meteorological data. The computational model contributes to anticipating information about the harvest date, enabling the grower to better plan activities, avoiding costs, and consequently improving productivity. We developed a prototype of the model and performed experiments with real datasets from agricultural institutions. We evaluated the metrics, and the results obtained in evaluation scenarios demonstrate that the model is efficient, has good generalizability, and is capable of improving the accuracy of the prediction results.
Electropolishing and Shaping of Micro-Scale Metallic Features

Electropolishing (EP) is most widely used as a metal finishing process. It is a non-contact electrochemical process that can clean, passivate, deburr, brighten, and improve the biocompatibility of surfaces. However, there is clear potential for it to be used to shape and form the topology of micro-scale surface features, such as those found on the micro-applications of additively manufactured (AM) parts, transmission electron microscopy (TEM) samples, micro-electromechanical systems (MEMs), biomedical stents, and artificial implants. This review focuses on the fundamental principles of electrochemical polishing, the associated process parameters (voltage, current density, electrolytes, electrode gap, and time), and the increasing demand for using environmentally sustainable electrolytes and micro-scale applications. A summary of other micro-fabrication processes, including micro-milling, micro-electric discharge machining (EDM), laser polishing/ablation, lithography (LIGA), electrochemical etching (MacEtch), and reactive ion etching (RIE), are discussed and compared with EP. However, those processes have tool size, stress, wear, and structural integrity limitations for micro-structures. Hence, electropolishing offers two-fold benefits of material removal from the metal, resulting in a smooth and bright surface, along with the ability to shape/form micro-scale features, which makes the process particularly attractive for precision engineering applications.zx3.
Effects of Perchlorate and Other Groundwater Inorganic Co-Contaminants on Aerobic RDX Degradation

Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) pollution is accompanied by other co-contaminants, such as perchlorate and chlorates, which can retard biodegradation. The effects of perchlorate and chlorate on aerobic RDX degradation remain unclear. We hypothesized that they have a negative or no impact on aerobic RDX-degrading bacteria. We used three aerobic RDX-degrading strains—Rhodococcus strains YH1 and T7 and Gordonia YY1—to examine this hypothesis. The strains were exposed to perchlorate, chlorate, and nitrate as single components or in a mixture. Their growth, degradation activity, and gene expression were monitored. Strain-specific responses to the co-contaminants were observed: enhanced growth of strain YH1 and inhibition of strain T7. Vmax and Km of cytochrome P450 (XplA) in the presence of the co-contaminants were not significantly different from the control, suggesting no direct influence on cytochrome P450. Surprisingly, xplA expression increased fourfold in cultures pre-grown on RDX and, after washing, transferred to a medium containing only perchlorate. This culture did not grow, but xplA was translated and active, albeit at lower levels than in the control. We explained this observation as being due to nitrogen limitation in the culture and not due to perchlorate induction. Our results suggest that the aerobic strain YH1 is effective for aerobic remediation of RDX in groundwater.
Constructing an Online Sustainable Educational Model in COVID-19 Pandemic Environments

The outbreak of COVID-19 forced billions of learners to stay at home in order to receive online education. The aim of the study is to construct an online sustainable educational model to facilitate this learning approach. This study included 53 peer-reviewed articles for the review study following the selection process of the Preferred Reporting Items for Systematic Review and Meta-analysis Protocol (PRISMA-P) and the clustering techniques of VOSviewer. It is concluded that the model consists of online educational platforms, online assessment systems, social media, and digital literacy. With these features, online education could be sustained. However, challenges should also be addressed to sustain online education during the pandemic. Designers, scientists, and teachers should make every effort to increase learning engagement, enhance learning supervision, formulate adequate emergency programs, minimize educational inequalities, solve technical issues, and formulate systematic learning management and organization. The sustainable online educational model may be updated and perfected by including more practical features in the future.
Phosphatidic Acid in Plant Hormonal Signaling: From Target Proteins to Membrane Conformations

Cells sense a variety of extracellular signals balancing their metabolism and physiology according to changing growth conditions. Plasma membranes are the outermost informational barriers that render cells sensitive to regulatory inputs. Membranes are composed of different types of lipids that play not only structural but also informational roles. Hormones and other regulators are sensed by specific receptors leading to the activation of lipid metabolizing enzymes. These enzymes generate lipid second messengers. Among them, phosphatidic acid (PA) is a well-known intracellular messenger that regulates various cellular processes. This lipid affects the functional properties of cell membranes and binds to specific target proteins leading to either genomic (affecting transcriptome) or non-genomic responses. The subsequent biochemical, cellular and physiological reactions regulate plant growth, development and stress tolerance. In the present review, we focus on primary (genome-independent) signaling events triggered by rapid PA accumulation in plant cells and describe the functional role of PA in mediating response to hormones and hormone-like regulators. The contributions of individual lipid signaling enzymes to the formation of PA by specific stimuli are also discussed. We provide an overview of the current state of knowledge and future perspectives needed to decipher the mode of action of PA in the regulation of cell functions.
Development and Application of a High-Resolution Melting Analysis with Unlabeled Probes for the Screening of Short-Tailed Sheep TBXT Heterozygotes

Simple Summary TBXT (c.333G > C; c.334G > T) has been identified as a molecular genetic marker in short-tailed sheep. This paper describes a high-resolution melting (HRM) analysis using unlabeled probes and asymmetric PCR for the detection of genetic variants of TBXT in short-tailed sheep populations. The detection results of this method are consistent with those of Sanger sequencing and can help farmers with marker-assisted breeding. Abstract The short-tailed phenotype has long been considered one of the best traits for population genetic improvement in sheep breeding. In short-tailed sheep, not only is tail fat eliminated but also the pubic area is exposed due to the lack of a tail covering, giving them an advantage in reproduction. Recent studies have shown that two linked mutations in sheep TBXT at nucleotides 333 and 334 are associated with the short-tailed phenotype. In the population of short-tailed sheep, several heterozygous mutants of this gene are found. In our research, we used high-resolution melting (HRM) to identify homozygous and heterozygous genotypes in a flock of short-tailed sheep and compared the results with those of Sanger sequencing, which were identical. This demonstrates that our established HRM method, a rapid and inexpensive genotyping method, can be used to identify homozygous and heterozygous individuals in short-tailed sheep flocks.
Synthesis of Zinc Oxide Nanoparticles by Precipitation and Sol Gel Methods from Different Precursors and their Comparison Impact on Seedling Attributes of Wheat

Zinc plays an important role as a micronutrient in both animals and plants. The application of zinc oxide nanoparticles as a potential solution to alleviate zinc deficiency and its effect on the growth of seedling attributes of wheat. These particles were prepared by precipitation and sol-gel method using zinc sulfate and zinc chloride as precursors. The prepared samples were characterized by Scanning electron microscopy and Fourier-transform spectroscopy. Zinc oxide nanoparticles were found to be elliptical in shape and their diameter ranges were observed 10.6 to 10.7nm by Scanning electron microscopy. Fourier-transform spectroscopy analyses showed the presence of the various functional group in the zinc oxide nanoparticles like N-H, CH2, CH3, RCH=CHR, C=O, -COOH, -OH and CH2-Cl. Solutions of different concentrations of zinc oxide nanoparticles (0.1%, 0.2%, 0.3%, 0.4%, 0.5%) were prepared and seeds were soaked in distilled water (hydro priming). After the 6 days observations, it was found that nanoparticles (0.3%) prepared from the zinc sulfate by sol-gel method showed significant variation for germination and seedling attributes of wheat as compared to control. The decreasing order for the zinc chemicals was zinc sulfate nanoparticles > zinc chloride for methods sol-gel method > precipitation method > standard (original salt) and for the levels was 0.3% > 0.2% > 0.4% > 0.5% > 0.1% > hydro priming > control. In crux, nanoparticles prepared from zinc sulfate enhanced the germination of wheat as compared to control.
Comparison of Anterior Corneal Aberrometry, Keratometry and Pupil Size with Scheimpflug Tomography and Ray Tracing Aberrometer

This study aimed to assess the anterior corneal wavefront aberrations, keratometry, astigmatism vectors and pupil size between Pentacam HR® (Oculus Optikgeraete GmbH, Wetzlar, Germany) and iTrace® (Tracey Technologies Corp., Houston, TX, USA). In this observational study, 100 eyes (50 healthy volunteers) were scanned in mesopic light condition with a Pentacam HR® and iTrace®. Anterior corneal aberrations (spherical aberration (Z40), vertical coma (Z3 − 1), horizontal coma (Z3 + 1)), keratometry in the flattest (K1) and steepest meridian (K2), mean astigmatism, astigmatic vectors (J0 and J45), and pupil size were measured. We found a significant difference in Z40 (Pentacam®: +0.30 ± 0.11 µm and iTrace®: −0.03 µm ± 0.05 µm; p < 0.01) with no correlation between the devices (r = −0.12, p = 0.22). The devices were in complete agreement for Z3 − 1 (p = 0.78) and Z3 + 1 (p = 0.39), with significant correlation between the machines (r = −0.38, p < 0.01 and r = −0.6, p < 0.01). There was no difference in K1, K2 and mean astigmatism. J0 was negative with both devices (against-the-rule astigmatism), but there was no correlation. J45 was negative with the Pentacam HR® (more myopic oblique astigmatism) but significantly correlated between the devices. Pupil size was smaller with Pentacam HR® (p < 0.01). In summary, these devices cannot be used interchangeably. Corneal Z40 was significantly different with more negative Z40 with iTrace® compared to Pentacam HR®. iTrace® operates with lower illumination, giving larger pupil size than Pentacam HR®, which uses intense blue light during measurement. No correlation was found for J0. Pentacam HR® had a trend to record more negative J45 (myopic oblique astigmatism).
Physicochemical and Microbiological Analysis of Ikogosi Warm Water Spring

Introduction: Water is one of the indispensable natural resources for the continued existence of all living things including man. Aims: This study investigated the bacterial diversity in Ikogosi warm spring in Ekiti State, Nigeria. Methodology: Water samples were taken for analyses from Ikogosi warm spring and analyses were made of the hot stream, cold stream, and meeting point region. Twenty isolates were characterized by morphological, biochemical, physiological characteristics and thermophilic screening was carried out. Results: The results revealed that electrical conductivity was 215.050 μmS/cm, (cold); 320.500 μmS/cm (warm), and 305 μmS/cm (meeting point), temperature was close (24.5oC, cold to 37oC, warm). pH, total dissolved solids and chlorine were all far below the WHO guideline values. Average ranges of the heavy metals in water samples were: 1.080, 0.424 and 0.343 ppm iron (Fe) in hot, cold and meeting point samples respectively, 0.010, 0.006 and 0.004 ppm lead (Pb) in hot, cold and meeting point samples respectively; and 0.006, 0.010 and 0.019 ppm chromium (Cr) in hot, cold and meeting point samples respectively. Nickel (Ni), Arsenic (As) and Cadmium (Cd) were not detected. Bacteria majorly found in water samples were Bacillus spp. , Escherichia coli, Klebsiella spp. and Staphylococcus spp. Out of the twenty (20) isolates screened at high temperature range of 45oC to 65oC, all grew at 45oC and 50oC while nine (9) grew at 55oC. Only two isolates were able to survive at 60oC, while none grew at 65oC. Conclusion: These isolates can be in the production of thermostable enzymes which can be used in biotechnology.
Trajectory Generation of a Quadrotor Transporting a Bulky Payload in the Cluttered Environments

When an aerial robot carries a bulky and heavy payload, it suffers from a wavering effect because of the large inertia of the heavy mass of a payload, which can cause a serious crash or fall to the ground.We propose the path-planning algorithm for an aerial robot to pass a narrow space while transporting a bulky payload to resolve the issue. Unlike the existing conventional path-planning method that treats the payload as a point-mass, this paper generates the trajectory considering the shape of a payload and passage size of an Unmanned Aerial Vehicle (UAV). First, we design a map-generation algorithm with the Voronoi diagram by exploiting the fact that the UAV has an ellipsoidal shape in transport operation with a bulky object. The modified map data can be exploited for the global path generation such as A* path-planner. For the rotational motion of the UAV, the heading angle was determined by considering the lengths of the major and minor axes of the ellipse. The global path was optimized based on the elastic band optimization algorithm to solve the jerky motion on the trajectory. Through the simulations and experiments, we validate the efficiency of our algorithm in the environment with the narrow space. Experimental results showed that the proposed path-planning algorithm could be used for safe aerial transportation in realistic environments.
COVID-19 Fake News Detection System

This article deals with the problem of the rapidly increasing COVID-19 infodemic in the world. Thus, there is a need for an effective framework of detecting fake information or misleading news related to COVID-19 virus/disease. To resolve this, we have used a dataset obtained from ConstraintAI'21. The dataset consists of 10,700 tweets and online posts of fake and real news concerning COVID-19. Machine Learning (ML) algorithms compared in this paper to classify the given news or tweet into real or fake are Logistic Regression (LR), K-Nearest Neighbor (KNN), Linear Support Vector Machine (LSVM), Random Forest Classifier (RFC), Decision Tree (DT), Naive Bayes (NB) and Stochastic Gradient Descent (SGD) algorithm. Two feature extraction techniques were used count vectorization and TF-IDF. Deep Learning (DL) algorithms implemented using Adam optimizer are Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The best testing accuracy was achieved with the LSVM model using TF-IDF feature extraction method followed by Stochastic Gradient Descent classifier with TF-IDF feature extraction technique. LR, DT, and RFC performed better with the Count vectorization feature extraction technique, whereas LSVM, KNN, NB and SGD had better accuracy with TF-IDF feature extraction technique. The LSTM model performed slightly better among the DL algorithms.