Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrodynamic study of multi-stage plate column scrubber for removal of particulate

Air pollution, which is a threat to human health and to the natural environment, is growing worldwide day by day and the regulations for air pollutants made us to investigate an alternative method to control the emissions from industries, which is of low cost, simple design and of high performance. Plate scrubbers are medium level energy wet scrubbers which have enormous advantages in various chemical process industries for simultaneous removal of particulate matter and gas pollutants from effluent gas streams. A sieve plate scrubber has been modelled and installed in the lab scale. This paper presents the hydrodynamics of the three stage plate column wet scrubber which will be used to clean the synthesis gas by removing particulate matter and gaseous pollutants simultaneously. Experimental study has been done for the pressure drop (P) measurement at different liquid-gas ratios. The highest plate scrubber pressure drop is 1456 Pa, which is obtained at gas flow rate (QG) of 15.134x10-3 Nm3/s and liquid flow rate (QL) of 48.183x10-6 m3/s with 7.5x10-3 kg/m3 inlet fly ash loading (Si) and it is a medium value when compared to other type of scrubbers. The effect of different variables such as inlet flow rates of air and liquid, and inlet fly ash loading on the hydrodynamics was keenly observed. A model has been suggested for the prediction of pressure drop for which the values from the experimental study and predicted values matched with attainable standard deviation and percentage error.
How to Make Youth Participate In Farming? A Literature Review

Agriculture, worldwide, is facing a dilemma in the last decade. Modernization, which contributes to agricultural productivity, is blamed for being one of the causes of the decrease of agricultural, human resource, particularly the young intention and their participation in agriculture-related activities. Many young farmers leaving the village abandon their farming activities for changing life orientation in urban. This paper will employ the literature review method to explore factors by collecting and analyzing international and national journals/ scientific sources, published in the range ten years, from 2010 to 2019. The reviewing process results in factors contributing to the young's participation in agriculture-related activities, such as personal factors, information supporting factors, learning process factors, and environmental influence, including government policy.
Behavioral Self-Regulation as a Protective Factor for Children at Risk of Reading Failure: Predicting First-Grade Reading from Kindergarten Entry Assessment (KEA) Data

ABSTRACT Research Findings: This study used data from a large-scale kindergarten entry assessment (KEA) to understand how well two state screening measures, administered at school entry, predicted the first-grade reading outcomes of a large sample of first-time kindergarteners (N=5,480) at high risk for future reading failure. We examined young children’s emergent literacy and behavioral self-regulation skills in the fall of kindergarten in relation to decoding skills in the spring of first grade. We also explored whether behavioral self-regulation moderated the effect of emergent literacy on first-grade reading outcomes. Consistent with prior research, results of multilevel regression models revealed that scalable measures of emergent literacy and behavioral self-regulation, assessed via a multidimensional KEA, positively predicted literacy outcomes in first grade, with a stronger association for emergent literacy. Additionally, school-entry behavioral self-regulation moderated the effect of children’s initial emergent literacy skills on first-grade decoding, such that stronger than average behavioral self-regulation partially compensated for weak emergent literacy at school entry. Practice or Policy: Findings underscore the importance of screening young children on both academic and non-academic domains of school readiness and of seeking to understand children’s risk of reading failure from multiple perspectives.
Prediction of NOx emissions from a simplified biodiesel surrogate by applying stochastic simulation algorithms (SSA)

A stochastic simulation algorithm (SSA) approach is implemented with the components of a simplified biodiesel surrogate to predict NOx (NO and NO2) emission concentrations from the combustion of biodiesel. The main reaction pathways were obtained by simplifying the previously derived skeletal mechanisms, including saturated methyl decenoate (MD), unsaturated methyl 5-decanoate (MD5D), and n-decane (ND). ND was added to match the energy content and the C/H/O ratio of actual biodiesel fuel. The MD/MD5D/ND surrogate model was also equipped with H2/CO/C1 formation mechanisms and a simplified NOx formation mechanism. The predicted model results are in good agreement with a limited number of experimental data at low-temperature combustion (LTC) conditions for three different biodiesel fuels consisting of various ratios of unsaturated and saturated methyl esters. The root mean square errors (RMSEs) of predicted values are 0.0020, 0.0018, and 0.0025 for soybean methyl ester (SME), waste cooking oil (WCO), and tallow oil (TO), respectively. The SSA model showed the potential to predict NOx emission concentrations, when the peak combustion temperature increased through the addition of ultra-low sulphur diesel (ULSD) to biodiesel. The SSA method used in this study demonstrates the possibility of reducing the computational complexity in biodiesel emissions modelling.
Improved Adsorption Capacity of EDTA-type Chitosan for Separation of Rare Earths

By optimizing the raw material ratio for the preparation of ethylenediaminetetraacetic acid-type chitosan (EDTA-type chitosan), the extraction efficiency of rare earth elements was improved. The synthesis with a lower concentration ratio of chitosan to acetic acid than that in the previously reported preparation method was confirmed by IR spectra that the peak assigned as the carboxyl group of EDTA was drastically decreased and the amide bond reaction between chitosan and EDTA was more facilitated. In the preparation method of this work, the amide formation was preferable to the competitive acetylation of chitosan. By the optimizing preparation conditions, the adsorption capacities for Nd, Sm, Tb, and Dy were improved about twice. On the contrary, the separation factor of rare earths was not changed, and it is suggested that other compounds having adsorption ability were not produced. EDTA-type chitosan was applied to chromatographic separation of La, Nd, Sm, Tb, and Dy. In the column method, it was confirmed that lighter rare earth sequentially eluted from the column, therefore it has been proved that adsorption of lighter rare earth was substituted by heavier rare earth.
The Prediction Factors of Anabolic-Androgenic Steroid Use among Bodybuilders: Application of Prototype Willingness Model

Background: One of the types of doping commonly used by bodybuilders is androgenic-anabolic steroids (AAS). The use of AAS in addition to violating sporting ethics, has serious consequences on physical and mental health. The purpose of this study was to determining the most important factors of AAS use among bodybuilders using prototype willingness model (PWM). Methods: In this analytical cross-sectional study, 280 male bodybuilders were selected using multistage sampling from the bodybuilding clubs in Hamadan city in 2016.A self-administered questionnaire consisting of demographic information and constructs of the PWM was used to collect data and random forest model was used to analyses the gathered data.Results: behavioral willingness, Attitude, previous AAS use were the most important factors in determining the behavioral intention. Subjective norms, attitude, BMI and prototypes were the factors that have the greatest impact on predicting of behavioral willingness of AAS use. Also behavioral intention was more important rather than behavioral willingness for predicting of AAS use.Discussion: The results based on PWM and random forest showed that the reasoned action path has a greater impact than social reaction path to predict AAS use among bodybuilders.
Prenatal Methamphetamine Exposure could Cause Chronic Abnormalities in Transcriptomic Pattern; an Extended Follow-up Cohort Study

BackgroundThe high rate of methamphetamine abuse among young adults and women of childbearing age makes it imperative to clarify the long-term effects of Methamphetamine exposure on the offspring. Behavioral and cognitive problems had reported in children with parental Methamphetamine exposure (PME). The present study aimed to assess the acute and chronic effects of PME in molecular regulations and gene expression profiles of children during their first years of life.ResultsAll subjects were recruited before birth, and sampling was conducted from the first ten days of birth, twelve months, twenty months and thirty-six months of age. Finally, 2658 children with PME and 3573 normal children had been finished the follow-up. RNA extraction was operated from blood samples and gene expression profiling was conducted by using the Affymetrix GeneChip Human Genome U133 plus 2.0 Array Platform. Gene expression data were confirmed by Real-time PCR. Gene expression profiling during thirty-six months showed several constant mRNA level alterations in children with PME compared with normal. These genes are involved in several gene ontology and pathways involved with the immune system, neuronal functions and bioenergetic metabolism. It seems that Methamphetamine abuse before and during the pregnancy period may affect the expression profile of children, and these changes could be remain years after birth. Affected genes have some similarities to the gene expression patterns of addiction, psychiatric disorders, neurodevelopmental disabilities and immune deficiencies. ConclusionFindings may shed light on the molecular effects of prenatal methamphetamine exposure and may lead to new psychological and somatic caring protocols for these children based on their potential abnormalities.
MEASURING THE THERMODYNAMIC PROPERTIES OF ADENINE; TOWARDS A MODEL FOR THE ORIGINS OF LIFE.

MEASURING THE THERMODYNAMIC PROPERTIES OF ADENINE; TOWARDS A MODEL FOR THE ORIGINS OF LIFE. Alexander Rowland Lowe Advisor: University of Guelph, 2013 Professor P. R. Tremaine Models for the origin of life in hydrothermal environments require the thermodynamic data for aqueous adenine solutions. Apparent molar volumes were measured using a Anton Paar DMA 5000 densitometer and apparent molar heat capacities were measured using the cylinder cell Nano-DSC III 6300 nano-calorimeter from 10 to 90 °C. This calorimeter was compared to the capillary cell version using standard potassium chloride solutions and both were shown to yield equally accurate results. NMR spectroscopy was used to measure the self-association constants and enthalpies of self association up to 55 °C. The standard partial molar volumes and standard partial molar heat capacities for an adenine monomer dimer equilibrium mixture and adenine hydrochloride were measured from 15 to 90 °C, and for sodium adeninate from 10 to 40 °C. The New experimental data for the adenine species AdH2, AdH, Ad, and (AdH)2 used to derive parameter for the density model that can be used to calculate adenine equilibrium constants under hydrothermal conditions.
Metalcone Chemistry: In pursuit of improved mechanical properties in thin film deposition

Atomic Layer Deposition (ALD) and its subset, Molecular layer deposition (MLD), is a thin film deposition technique using alternating self-limiting precursors to grow thin films on a substrates. Recent research has expanded MLD to use reactive metal alkyls/halides and organic fragments containing alcohol groups such as ethylene glycol for use in protective barriers. Unfortunately using ethylene glycol as a precursor causes film stability issues and does not provide adequate mechanical properties for projected uses. The current research looks into the investigation of using different metal alkyls and metal halides with the trifunctional precursor glycerol. The three main films investigated were the AlGL using trimethylaluminum and glycerol, ZnGL using diethyl zinc and glycerol and TIGL using titanium (IV) chloride and glycerol. The films were investigated using quartz crystal microbalance, X-ray reflectivity, and spectroscopic ellipsometry for characterization of their film growth. All films exhibited linear growth at a range of temperatures above 130° C with AlGL, ZnGL and TiGL displaying a growth rate at 150° C of ~ 2.34 A/cycle, ~ 1.29 A/cycle, and ~ 2.2 A/cycle respectfully. Initially film growth was investigated in situ using quartz crystal microbalance and then the growth rates confirmed ex situ using X-ray reflectivity. Once the film growth and mechanism were investigated, the mechanical properties were investigated to determine if there was an improvement in cross-linking in the films. The mechanical properties of these MLD films were also investigated using a mechanical testing system and nanoindenter. The mechanical properties for AlGL and ZnGL showed a large improvement from 0.69% to 1.2% for their critical tensile strain compared to previous MLD films. The films also showed an increase in mechanical properties from their nanoindentation results consistent with an increase in cross-linking.
Epidemiological and Histopathological Aspects of New Cancer Cases Diagnosed In South of Tunisia during 2015-2016

Introduction: Cancer is a national and global public health problem. Updated epidemiological data are needed to develop cancer control plans. Material and methods: This is a retrospective, observational study of new cancer cases reported in the governorate of Medenine during the years 2015 and 2016. Results: A total of 1061 cases of cancer were collected. A slight female predominance was noted. The Age Standardized Incidence Rate (ASIR) was 104.55/100000. The patients’ mean age was 60.5 years. The five most common cancers in men were bladder (19.1%), lung (14.4%), colorectal (13.6%), prostate (11.8%), and stomach (5.1%). However, the five most common cancers diagnosed in women were breast (35.6%), colorectal (12.3%),stomach (5.2%), uterine corpus(4.5%) and bladder (4%). Conclusion: This work would be the draft of a cancer register in the governorate of Medenine. This register is essential for the development of a health policy adapted to the specificities of the region.
Genetic and morphological analyses uncover a new record and a cryptic species in Allonais (Clitellata: Naididae)

Allonais inaequalis (Stephenson, 1911) is a well-known “morpho-species” of the subfamily Naidinae with a wide distribution in the world. In this study we describe it, as a new record, from China, and we compare molecular data of A. inaequalis from two geographic populations in China and one in Peru, and we re-evaluate the phylogenetic status of Allonais. Sequence data of both mitochondrial (COI, 12S rDNA and 16S rDNA) and nuclear (18S rDNA and 28S rDNA) from 26 species of naidids (representing 14 of the 22 genera currently recognized) and 7 outgroup taxa were used. The combined data set were analyzed with Bayesian inference and Maximum Likelihood. The different phylogenetic analyses gave largely congruent trees, and they corroborate monophyly of Allonais. The results show that the genetic distances among the three geographic populations of A. inaequalis for COI are 0.0%-7.8% and for 28S vary between 0.0% and 0.8%. Allonais inaequalis falls into two groups, one from China, and one from Peru. We suggest that the Chinese morphotype more resembles A. inaequalis from India, where the species was first described, whereas the species in Peru is likely to be another species.
Short-Term Multi-Step Ahead Wind Power Predictions Based On A Novel Deep Convolutional Recurrent Network Method

In this paper, a novel deep convolutional recurrent network method, the K-shape and K-means guided Convolutional Neural Network integrating Gated Recurrent Units (KK-CNN-GRU), for short-term multi-step ahead predictions of wind turbine power generations is proposed. The KK-CNN-GRU is composed of three modules, an input tensor construction module, a two-layer clustering module, and a prediction module. In the input tensor construction module, data of spatial, temporal, and physical meaning features are fused and organized as a tensor. Meanwhile, the two-layer clustering module is used to recognize patterns of a wind power time series and offer a cluster membership to the associated input tensor. Finally, predictions are generated by feeding the input tensor into the prediction module, which is composed of a feature weighting unit, a convolutional neural network, and a set of gated recurrent unit cells with fully connected layers switched according to the cluster membership of the input tensor. In experimental studies, two sets of six-step ahead prediction experiments are conducted with considered prediction horizons from 7.1 seconds to 42.6 seconds and from 10 minutes to 1 hour, respectively. The high prediction accuracy of the proposed KK-CNN-GRU is validated by comparing with state-of-the-art benchmarking methods.
Modeling and Analysis of Novel CSRRs-Loaded Dual-Band Bandpass SIW Filters

Dual-band substrate integrated waveguide (SIW) bandpass filter (BPF) operating at 6 GHz and 12 GHz is presented. The two identical complementary split-ring resonators (CSRRs) make up the resonant unit. And the two CSRRs are strongly coupled. For the first time, two resonant units are horizontally etched on the upper metal of SIW. The equivalent circuit model of the novel dual-band SIW BPF is established and analyzed in detail. To realize the miniaturization design of the filter, a dual-band half-mode SIW BPF is proposed. The center frequencies of the passbands are located at 4.56 GHz and 9.36 GHz. The filter exhibits the characteristics of higher slope selectivity, lower insertion loss, wider 3 dB fractional bandwidths and compact size.
Effect of emoji prime on the understanding of emotional words – evidence from ERPs

ABSTRACT Emojis play an increasingly important role as nonverbal symbols in online communication, and this study investigates their effect on the processing of subsequent words. We suppose that emojis, as an aid to emotional expression and semantic comprehension, prime the cognitive processing of the textual stimuli that follow. An affective priming experiment using ERPs was conducted, in which Semantic Relationship between the emoji and the word (Congruent, Incongruent, Non-related) was manipulated to assess their impact in an affective word categorization task. Behavioral results revealed no significant difference, while electrophysiological data showed otherwise. N1 component elicited by semantically congruent and incongruent emoji-word pairs showed greater amplitudes than those by non-related pairs. N400 elicited by incongruent pairs showed greater amplitudes than the N400 by congruent pairs. These data indicate that while emojis do not influence the outcomes of linguistic processing, emoji primes enhance attentional level, and semantically congruent contents demand fewer cognitive resources and facilitate better processing in comparison to incongruent ones. The findings point to emojis’ role not only as affective primes, but also as paralanguage that is semantically processed. Implications for computer-mediated communication are discussed.
Low-rank sparse subspace clustering with a clean dictionary

Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l 1 -norm of the data matrix while LRR promotes a low-rank structure through minimizing the nuclear norm. In this paper, considering the problem of fitting a union of subspace to a collection of data points drawn from one more subspaces and corrupted by noise, we pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise. We propose a new algorithm, named Low-Rank and Sparse Subspace Clustering with a Clean dictionary (LRS2C2), by combining SSC and LRR, as the representation is often both sparse and low-rank. The effectiveness of the proposed algorithm is demonstrated through experiments on motion segmentation and image clustering.
Exploration of Molecular Mechanism of Traditional Chinese Tuina Therapy for Insomnia by Proteomics Analysis

Insomnia, one of the most common mental disorder, not only affects the quality of life, but also damages physical and mental health. Therefore, it is very necessary to explore the molecular mechanism of insomnia and find some suitable treatments. At present, methods of using drugs to treat insomnia is not satisfactory due to lack of evidences and side effects. Hence, development of non-drug treatments is particularly important. Tuina, a Chinese massage method, has achieved certain therapeutic effects on injuries, rheumatism, neurological diseases and other types of diseases. We have treated patient with insomnia by Tuina, and obtained therapeutic effects indeed. In the current study, we used iTRAQ (isobaric Tags for Relative and Absolute Quantitation) quantitative proteomics to analyze plasma samples taken from the healthy control (HC) group, insomnia patients group (before Tuina treatment, BTT) and insomnia-therapy group (after Tuina treatment, ATT) to identify the molecular correlation of insomnia. In BTT vs HC, we found that the expression of many immune-related and stress-related proteins were out of control, and it revealed that Tuina had the capacities to regulate expression of immune-related and stress-related proteins in ATT vs BTT, suggesting that Tuina may improve insomnia by regulating immune-related and stress-related proteins. The proteomics verification results had been verified by commercial ELISA(Enzyme linked immunosorbent assay. All in all,Our study not only found a good way to treat insomnia, but also provided a research foundation for improving insomnia.
Formation of an Export-Oriented Agricultural Economy and Regional Open Innovations

The aim of the work is to find relevant indicators for assessing the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports using tools for modeling the impact of innovation and investment development on increasing production and export potential in the context of the formation of an export-oriented agricultural economy. The modeling methodology and the proposed estimating and forecasting tools for diagnosing and monitoring the state of sectoral and regional innovative agricultural systems are used to analyze the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports based on the construction of the classification of Russian regions by factors that aggregate these features to diagnose incongruence problems and to improve institutional management in regional innovative export-oriented agrosystems. Based on the results of the factor analysis application, an underestimated role of indicators of investment in agriculture, the intensity and efficiency of agricultural production, were established. Based on the results of the cluster analysis, the established five groups of regions were identified, with significant differences in the level of investment in agriculture, the volume of production of the main types of agricultural products, and the export and exported food. The research results are of practical value for use in improving institutional management when planning reforms and transformations of regional innovative agrosystems.