Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety and efficacy of zinc chelate of methionine sulfate for all animal species

Abstract The additive ‘Zinc chelate of methionine sulfate’ is zinc chelated with methionine in a molar ratio 1:1, with a minimum zinc content of 19.1%. Owing to the limitations of the tolerance study, it could not be used for the assessment of safety for target animals; therefore, the EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP Panel) cannot conclude on the safety of zinc chelate of methionine sulfate for the target species. No concerns for consumer safety are expected from the use of the zinc chelate of methionine sulfate in animal nutrition when used up to the maximum EU authorised zinc levels in feed. Zinc chelate of methionine sulfate should be considered as a skin and eye irritant, and as a skin sensitiser; it is considered to pose a risk by inhalation to the users. The additive under assessment, zinc chelate of methionine sulfate, is intended to be a substitute for other authorised zinc additives and will not further increase the environmental burden of zinc; therefore, the FEEDAP Panel considers that the use of the additive in animal nutrition would not pose an additional risk for the environment. Based on literature studies and a specific study conducted with the additive under assessment, zinc chelate of methionine sulfate is an available source of zinc for all animal species.
Association between blood pressure dipping patterns and hypertension-mediated organ damage among Nigerians with newly- diagnosed hypertension

Introduction: Abnormal blood pressure (BP) dipping patterns are associated with increased cardiovascular risk among Africans. This study determined the association between BP dipping patterns and hypertension-mediated organ damage among patients with newly-diagnosed hypertension. Methods: Ambulatory BP monitoring and echocardiography were carried out on 120 participants. Participants were categorized based on the ratio of night-time to day-time systolic BP into 4 patterns: Normal dipper (≥ 10% but < 20%), non-dippers (≥ 0% but < 10%), reverse dippers (<0%) and extreme
Predicting Patient No-Show Using Machine Learning Techniques in the Healthcare Sector

Today, across the most critical problems faced by hospitals and health centers are those caused by the existence of patients who do not attend their appointments. Among others, this practice generates waste of resources and increases the patients’ waiting list. To handle these problems, hospitals are actively trying to implement methods to reduce the idle time caused by patient no-shows. Many scheduling systems developed require predicting whether a patient will show up for an appointment or not. Although, a challenging problem resides in obtaining these estimates precisely. The goal of this work is to analyze how objective factors influence a patient not to attending their appointment, to identify the main causes that contribute to a patient’s decision, and to be able to predict whether or not the patient will attend the scheduled appointment. As a result, the obtained model is tested on a real dataset collected in a health center linked to the University of Vale do Itajaí (UNIVALI), which includes 25 features and about 5000 samples. The algorithm that produced the best results for the available dataset is the Random Forest classifier. It reveals the best recall rate (0.91), since it measures the ability of a classifier to find all the positive instances and achieves a receiver operating characteristic curve rate of 0.969.
The Influence of Various Matrixes on the Strength Properties of Moulding Sands with Thermally Hardened Hydrated Sodium Silicate for the Ablation Casting Process

The essence of ablation casting technology consists in pouring castings into single-use moulds made from the mixture of sand and a water-soluble binder. After pouring the mould with liquid metal yet while the casting is still solidifying, the mould destruction (washing out, erosion) takes place using a stream of cooling medium, which in this case is water. This paper focuses on the selection of moulding sands with hydrated sodium silicate for moulds used in ablation casting. The research is based on the use of water glass 145 and 150 as binders. As part of the research, loose moulding mixtures based on two silica sands from different sand mines with different content of binders were prepared. The review of literature data and the results of own studies have shown that moulding sand with hydrated sodium silicate hardened by dehydration is characterized by sufficient strength properties to be used in the ablation casting process. Our own research also confirmed the possibility of using these sand mixtures in terms of both casting surface quality and sand reclamation. The results presented in this paper prove that both sand grains and types of binder tested may be used as components in moulding sands devoted to ablation casting.
Influence of simulated body fluid (normal and inflammatory) on corrosion resistance of anodized titanium

Titanium has been widely used as biomaterial, especially in implantables, in which osseointegration and corrosion resistance are needed. Studies have shown that the thickness and roughness of porous titanium oxides are related to the osseointegration. According to the literature, the best anodizing conditions for obtaining nanotubes in titanium oxide are the use of a voltage of 10V in an electrolyte containing 0.15% HF in H3PO4 (w/v). In this study, was to evaluate the corrosion capacity of simulated body fluid (SBF) over titanium samples anodized on 1 mol. L-1 H3PO4 and 0.15% HF (w/v) in 1 mol.L-1 H3PO4. To perform these evaluations samples of commercially pure titanium grade 2 were used. Samples were analyzed by scanning electron microscopy, atomic force microscopy and by electrochemical corrosion tests in healthy and simulating inflammatory conditions. The hydrophobicity of oxides was tested by sessile drop essay, also using SBF. Results show that oxides obtained in H3PO4 electrolyte, barrier type oxides, work better than the porous oxides obtained in H3PO4/HF electrolyte, suggesting that barrier oxide exhibit more biomaterial characteristics than the porous oxide. These results agree with previous studies, and stand out mainly in relation to the tests performed under inflammatory conditions, more aggressive to the biomaterial.
STREAMFLOW SIMULATION: COMPARISON BETWEEN SOIL WATER ASSESSMENT TOOL AND ARTIFICIAL NEURAL NETWORK MODELS

The present study compared the performance of two different models for streamflow simulation namely: Soil Water Assessment Tool (SWAT) and the Artificial Neural Network (ANN). During the calibration periods, the Nash-Sutcliff (NS) and Coefficient of Determination (R2) for SWAT was 0.74 and 0.81 respectively, whereas for ANN, it was 0.99 and 0.85 respectively. The ANN performs better during the validation period as the result revealed with NS and R2 having 0.98 and 0.89 respectively, while for the SWAT model it was 0.71 and 0.74 respectively. Based on the recommended comparison of graphical and statistical evaluation performances of both models, the ANN model performed better in estimating peak flow events than the SWAT model in the Upper Betwa Basin. Furthermore, the rigorous time required and expertise for calibration of the SWAT is much less as compared with the ANN. Moreover, the results obtained from both models demonstrate the performances of the
Integrating Design Patterns into Forward Engineering Processes

Design patterns are reusable solutions to recurring problems that occur during software development. Most UML CASE tools do not assist in the integration and code generation of design patterns, this task is still left to the programmer. This paper describes a forward engineering process of UML static models which supports working with design patterns. This approach is based on the integration of semi-formal notations in UML with algebraic specifications. Transformations are supported by a library of reusable schemes and by a system of transformation rules which allow translating UML to algebraic specifications and object oriented code step by step. All the proposed transformations could be automated; they allow traceability and could be integrated into iterative and incremental software development process. Eiffel was chosen to show the feasibility of our approach.
Business Meeting Summarization Using Natural Language Processing(NLP)

— Text summarization aims to condense a source text into a shorter version. Automatic data summarization is part of data mining. In order to build a corpus for this task, it is necessary to obtain the transcription of each meeting, and then to segment and align it with the corresponding manual report to produce training examples suitable for training. In this work, an MMS method combining the techniques of natural language processing (NLP), speech processing, computer vision and advanced encryption standard (AES) encryption is used to explore the rich information contained in multi-modal data, to improve the quality and security as well the key idea is to bridge and lessen the semantic gaps between multi-modal data. For audio, speech transcriptions are used. Finally, all the multi-modal aspects are considered to generate a textual summary by maximizing the salience, readability, non-redundancy. The summary so generated by text, audio is encrypted using AES encryption method (to make it secure) and sent to members of meeting on mail, from there the user can retrieve it whenever required by providing the decryption key.
Maximum Network Flow Algorithms

The aim of the maximum network flow problem is to push as much flow as possible between two special vertices, the source and the sink satisfying the capacity constraints. For the solution of the maximum flow problem, there exists a number of algorithms. The existing algorithms can be divided into two families. First, augmenting path algorithms that satisfy the conservation constraints at intermediate vertices and the second preflow push relabel algorithms that violates the conservation constraints at the intermediate vertices resulting incoming flow more than outgoing flow.In this paper, we study different algorithms that determine the maximum flow in the static and dynamic networks.
Multi-objective optimization of stepped spillway and stilling basin dimensions

In the present paper, an optimization procedure is proposed for stepped spillway design dimensions, which leads to maximum energy dissipation rate and minimum construction cost, considering independently the chute cost and stilling basin cost. Three independent objective functions are thus simultaneously satisfied. The procedure involves four main tools: the multi-objective particle swarm optimization method (MOPSO) to find Pareto solutions in one run, the K-means clustering algorithm to reduce the size of the obtained non-dominated solutions, the pseudo-weight vector approach (PWV) to facilitate the decision making and to select some adequate solutions, and finally, CFD simulations to analyze the retained optimal solutions. The suitability of the proposed procedure is tested through an example of application. As results, a set of 20 solutions with different satisfaction levels is found and compared to existing solutions. A multi-objective optimization problem may have many different solutions; the originality of the present work lies in the proposed procedure, which explores several possible solutions and reduces their number to give help to the decision making. Furthermore, an approximate expression of spillway total cost is also derived as a function of flow energy dissipation rate.
Age-dependent upregulation of Y RNAs in Caenorhabditis elegans

Y RNA is a conserved small non-coding RNA whose functions in aging remain unknown. Here, we sought to determine the role of C. elegans Y RNA homologs, CeY RNA (CeY) and stem-bulge RNAs (sbRNAs), in aging. We found that the levels of CeY and sbRNAs generally increased during aging. We showed that CeY was downregulated by oxidative and thermal stresses, whereas several sbRNAs were upregulated by oxidative stress. We did not observe lifespan phenotypes by mutations in CeY-coding yrn-1. Future research under various genetic and environmental conditions is required to further evaluate the role of Y RNA in C. elegans aging.
2. Tackling social challenges to avoid the middle-income trap

In 2018, the Chinese economy grew to equivalence to the economy of the euro area or two-thirds of the economy of the United States and accounted for 16 per cent of the global economy. With a growth rate of 6.1 per cent in 2019—higher than any other major economy—the Chinese economy further raised its importance in the world economy. At the same time, China’s gross domestic product (GDP) per capita exceeded US$10,000—significantly above the average level of the upper middleincome countries.1
Growth Pattern in Skeletal Class I Malocclusion: A Cephalometric Study

Introduction: Great emphasis has been given to the evaluation of sagittal apical base relationship in orthodontic diagnosis and treatment planning. The prediction of magnitude and direction of facial growth based on sagittal relationship will help in orthodontic treatment with growth modification. The objective of the study is to assess the growth pattern in skeletal Class I malocclusion. Materials and Method: 104 subjects (52 male and 52 female) with the age between 18-30 years with Class I skeletal relation was selected from lateral cephalograms of patients visiting the Department of Orthodontics, Kantipur Dental College. The ANB angle was measured to assess the sagittal jaw relationship and the Jarabak’s ratio to access the growth pattern. Descriptive statistics were calculated for each parameter. Pearson’s test was done to evaluate the correlation between the parameters. Independent t-test was done to compare Anterior Facial height (AFH), Posterior Facial Height (PFH) and Jarabak’s ratio between male and female subjects. Result: Among the total subjects with skeletal Class I malocclusion; hyperdivergent growth pattern was least (10.57%), followed by normodivergent (18.26%) and hypodivergent growth pattern (71.15%). Mean Jarabak’s ratio for hyperdivergent, normodivergent and hypodivergent growth pattern were 58.65±1.94, 63.98±0.85 and 69.98±4.13 respectively. Very strong correlation was found between AFH and PFH in hyperdivergent (r = 0.821) and normodivergent group (r =0.978). Strong correlation was found in hypodivergent group between AFH and PFH (r =0.743). Also, strong correlation was found in hypodivergent group between PFH and Jarabak’s ratio (r =0.643). Conclusion: Hypodivergent growth pattern was the dominant growth pattern in skeletal Class I malocclusion. PFH influenced the determination of Jarabak’s ratio more than the AFH in hypodivergent growth pattern. Hypodivergent growth pattern is correlated with large SNB angle.
Comprehensive Analysis of Various Deep Learning Based Facemask Detection Techniques

: Uniform spread of the coronavirus has caused a sustained increase in the number of victims in different countries of the world since the day it took off. Detecting criminals is extremely necessary to ensure that the spread of the virus is constantly reduced. This article shows the use of deep learning methods which deal with different person who does not wear a facemask. This mask detection dataset includes with and without mask images, we will perform face detection in real time from a live feed through our webcam. We will likely find out if the person is wearing a face mask in still images, as well as in computer video streaming and deep learning procedures. To think of a framework that will be updated broadly, multiple classifiers with multiple optimizers must be evaluated. This research article presents a review of classifiers such as SGD, ADAM, MobileNetV2, each with ADAGRAD, VGG16 and RESNET50 optimizers.
ADA for IBL: Lessons Learned in Aligning Learning Design and Analytics for Inquiry-Based Learning Orchestration

Orchestrating technology-enhanced learning is a difficult task, especially in demanding pedagogical approaches like inquiry-based learning (IBL). To foster effective teacher adoption, both the complexity of designing IBL activities and the uncertainty about the student learning path during enactment need to be addressed. Previous research suggests that aligning learning design and learning analytics can be an effective way to provide such orchestration support. This paper reports on a design-based research (DBR) project to explore teachers’ orchestration needs in Go-Lab (a technological ecosystem for IBL used by thousands of primary and secondary school teachers around the world), and on how solutions that align learning design and analytics can fulfill such needs. The analysis of data from multiple events (including surveys, case studies, workshops with teachers, and platform usage analyses) led to a catalogue of IBL orchestration needs that can be tackled by aligning learning design and analytics, as well as a list of guidelines for technology development aiming to support IBL orchestration. These two contributions can support the creation of future learning analytics–enhanced IBL environments that are both pedagogically grounded and usable by teachers in authentic settings.
Distribution Analysis of Heavy Metal Contaminants in Soil With Geostatistic Methods; Paper Review

Heavy metal contaminants in the soil will have a direct effect on human life. The spatial distribution of naturally occurring heavy metals is highly heterogeneous and significantly increased concentrations may be present in the soil at certain locations. Heavy metals in areas of high concentration can be distributed to other areas by surface runoff, groundwater flow, weathering and atmospheric cycles (eg wind, sea salt spray, volcanic eruptions, deposition by rivers). More and more people are now using a combination of geographic information science (GIS) with geostatistical statistical analysis techniques to examine the spatial distribution of heavy metals in soils on a regional scale. The most widely used geostatistical methods are the Inverse Distance Weighted, Kriging, and Spatial Autocorrelation methods as well as other methods. This review paper will explain clearly the source of the presence of heavy metals in soil, geostatistical methods that are often used, as well as case studies on the use of geostatistics for the distribution of heavy metals. The use of geostatistical models allows us to accurately assess the relationship between the spatial distribution of heavy metals and other parameters in a map.
Development Quality Evaluation of Henan Province and Zhengzhou City based on the Coupling and Coordination Relations

This paper constructs a quality evaluation indicator system of four systems: economy, society, culture and ecological civilization to measure their coupling relationship and coordination degree indicator, taking Henan province and Zhengzhou city as the research objects, and taking the panel data of 42 specific evaluation indicators from 2000 to 2019 as the evaluation sample. The results show that the biggest contributors to the development quality of economy, culture and ecological civilization of Henan province and Zhengzhou city are the total business volume of posts and telecommunications, the application number of patents, the proportion of investment in environmental pollution control in GDP respectively. During the period of 2000 to 2019, the comprehensive evaluation values of economy, society and culture development quality of Henan province and Zhengzhou city improved constantly, and the change trend were highly consistent, while the changes of that of ecological civilization development quality were far from each other. The coordination degree indicators of the four systems in Henan province and Zhengzhou city were very low, and they are in a state of low disorder at present.
Soil biological attributes in monoculture and integrated systems in the Cerrado region of Piauí State, Brazil

The implementation of integrated agricultural production systems is considered a promising strategy for sustainable agricultural intensification in Brazil. This study aimed to evaluate the effects of different monoculture and integrated production systems on the microbiological attributes and organic carbon of soil from the Cerrado region in Piauí, Brazil. Soil samples were collected from the 0.0-0.10-m layer in the following systems: no-tillage (PD), pasture (PAS), exclusive eucalyptus cultivation (CEE), integrated livestock-forest system (IPF) and a native Cerrado (CN) area, which was used as reference. Total organic carbon (TOC) and nitrogen (NT) contents, microbial biomass carbon (CMIC), microbial respiration (MR), microbial quotient (qMIC), metabolic quotient (qCO2), as well as the activities of the hydrolysis of fluorescein diacetate (FDA), acid phosphatase, β-glucosidase and urease enzymes were evaluated. High TOC contents were found in the CEE, IPF and PAS systems, and high CMIC and qMIC values were found in the CN and CEE systems. The variables MR, qCO2 and enzymatic activity varied as a function of the management systems. The IPF and CEE systems caused improvements in the soil attributes, with increases in organic carbon and microbial biomass. The IPF integrated production system promoted improvements in the microbiological indicators of soil quality and was considered an environmentally sustainable agricultural production system. The transition from CN to agricultural areas caused changes in the soil microbiological indicators, which were perceived several years after anthropogenic intervention, indicating that even with the adoption of conservation systems, it was not possible to reestablish the soil microbial biomass.
Bayesian Hetero-Elasticnet (A Gibbs Sampler Approach)

Combined heteroscedasticity and multicollinearity as dual non-spherical disturbances were experimented asymptotically. A Gibbs Sampler technique was used to investigate the asymptotic properties of hetero-elasticnet estimator with mean squares error (MSE) and bias as performance metrics. The seed was set to 12345; is set at ; Xs variables were generated as follow: the design matrix was generated from the multivariate normal distribution with mean > 0 and variance . and are truncated with Harvey (1976) heteroscedastic error structure; are collinear covariate with pairwise correlation between 0.6 and 0.9, the sample sizes were 25, 100 and 1000. The number of replications of the experiment was set at 10,000 with burn-in of 1000 which specified the draws that were discarded to remove the effects of the initial values. The thinning was set at 5 to ensure the removal of the effects of autocorrelation in the MCMC simulation. The study found that there is consistency of estimator asymptotically as the sample sizes increases from 25 to 50 so also to 1000, the larger sample size depicted least bias. The estimator exhibited efficiency asymptotically as larger sample sizes depicted least mean squares error. The study therefore recommended Bayesian hetero-elasticnet when data exhibit both heteroscedasticity and multicollinearity. Keywords: Elasticnet; Bayesian Inference and Gibbs sampler