Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the denitration mechanism of fly ash catalysts modified by low-temperature plasma technology

There are three different fly ashes mixed with bentonite respectively as raw material to preparation of denitration catalyst. Then the catalyst combined with the low temperature plasma for denitration. The different mixing ratio, drying temperature and drying time of catalyst preparation were studied. The denitration mechanism of fly ash catalyst modified with different gases (O2, N2, Ar, and hydrocarbon gas) by low-temperature plasma technology was studied. The compositions of fly ash were detected by element analysis, ICP analysis, Boehm analysis, and Infrared spectral analysis which affected the denitration performance of fly ash catalyst. And we discussed the effect of denitration performance with different types of fly ash and plasma power. The results shown that: fly ash mixed with bentonite for 2:1, drying temperature is 100°C and drying time is 30 min are the optimal preparation conditions; The denitration performance is best of the catalyst which produced by circulating fluidized bed when the pla.
Awe in Advertising: The Mediating Role of an Abstract Mindset

Abstract Awe represents a powerful positive emotion that has received limited examination in the advertising context. The present research draws on construal-level theory to shed light on when and how awe increases persuasion. Across five experimental studies, we show that the effect of awe elicits an abstract mindset and, therefore, increases advertising persuasiveness when the advertised product emphasizes desirability features and is perceived as temporally distant. The findings offer insights about how awe influences advertising effectiveness that was heretofore lacking. Theoretical contributions and practical implications for leveraging emotional appeals in advertising are discussed.
The Effect of Some Vegetable Oils Added to Dairy Calf Rations on In Vitro Feed Value and Enteric Methane Production

The aim of this study was to determine the effects of the addition of Safflower, Sunflower and Corn vegetable oils to dairy cattle rations on in vitro gas and methane production, true dry matter (TDMD), organic matter (TOMD) and NDF (TNDFD) digestibilities values and microbial protein (MP) production. Dairy cattle TMR ration consisting of milk feed, corn silage, alfalfa hay and meadow hay was prepared as the control group, and the experimental groups were prepared with the addition of safflower, sunflower and corn vegetable oils at the level of 3% in each of the control groups, respectively. Vegetable oils added to the diet significantly affected in vitro gas production and organic matter digestibility (OMD). Methane (ml) production values in the experimental groups varied between 10.00 and 10.71 ml. The Metabolic energy (ME) and OMD values of the control and experimental groups varied between 7.00 and 7.29 MJ/kg DM and between 53.78 and 51.20. TDMD values of the rations were determined between 48.49 and 52.63%. While the control group had the highest TDMD value, the ration containing safflower oil had the lowest TDMD value. TNDFS values of the rations varied between 67.26 and 68.80%. As a result; Since the vegetable oils added to the ration increase the net energy lactation (NE L ) content of the ration, it can be said that it used to meet the energy needs of high milk yielding cattle in the lactation period, provided that they do not exceed the upper limits specified in the literature.
Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X- and Ku-Bands

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient <inline-formula> <tex-math notation="LaTeX">$R \simeq 0.77$ </tex-math></inline-formula> between estimated and target SD, a root-mean-square error (RMSE) <inline-formula> <tex-math notation="LaTeX">$\simeq 13$ </tex-math></inline-formula> cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with <inline-formula> <tex-math notation="LaTeX">$R =0.74$ </tex-math></inline-formula>, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount of <italic>in situ</italic> data required for training the algorithm and of extending the algorithm exportability to other test sites.
Novel Ensemble-Based Machine Learning Models Based on The Bagging, Boosting and Random Subspace Methods for Landslide Susceptibility Mapping

Indivisual machine learning models show different limitations such as low generalization power for modeling nonlinear phenomena with complex behavior. In recent years, one of the best approaches to this issue is to use ensemble models. The purpose of this paper is to investigate the predictive power and modeling of three novel ensemble models constructed with four machine learning models: Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB) models based on three approaches of Bagging, boosting and Random Subspace (RS) in landslide susceptibility mapping (LSM) in the Province of Ajloun in Jordan. A total number of 91 landslide locations along with 16 conditioning factors in LSM were identified and used. Also, before modeling, the selection of effective conditioning factors in LSM was done using genetic algorithm and four single models including DT, KNN, NB and SVM. The selected factors were used in modeling with individual and ensemble models. The results show that the area under the receiver operating characteristic curve (AUROC) for ensemble models is significantly higher than the individual models and the AUC for ensemble models was on average 14% higher than individual models. Based on the results, the most accurate models were RS ensemble model (AUROC = 0.850), Boosting (AUROC = 0.848) and Bagging (AUROC = 0.814), respectively. This study showed that by combining the results of simple machine learning models and making ensemble models, models with the desired accuracy can be achieved.
Modern Analytical Techniques for Detection of Bacteria in Surface and Wastewaters

Contamination of surface waters with pathogens as well as all diseases associated with such events are a significant concern worldwide. In recent decades, there has been a growing interest in developing analytical methods with good performance for the detection of this category of contaminants. The most important analytical methods applied for the determination of bacteria in waters are traditional ones (such as bacterial culturing methods, enzyme-linked immunoassay, polymerase chain reaction, and loop-mediated isothermal amplification) and advanced alternative methods (such as spectrometry, chromatography, capillary electrophoresis, surface-enhanced Raman scattering, and magnetic field-assisted and hyphenated techniques). In addition, optical and electrochemical sensors have gained much attention as essential alternatives for the conventional detection of bacteria. The large number of available methods have been materialized by many publications in this field aimed to ensure the control of water quality in water resources. This study represents a critical synthesis of the literature regarding the latest analytical methods covering comparative aspects of pathogen contamination of water resources. All these aspects are presented as representative examples, focusing on two important bacteria with essential implications on the health of the population, namely Pseudomonas aeruginosa and Escherichia coli.
Automatic body part and pose detection in medical infrared thermal images

ABSTRACT Automatisation and standardisation of the diagnosis process in medical infrared thermal imaging (MITI) is crucial because the number of medical experts in this area is highly limited.The current studies generally need manual intervention. One of the manual operations requires physician’s determination of the body part and orientation. In this study automatic pose and body part detection on medical thermal images is investigated. The database (957 thermal images - 59 patients) was divided into four classes upper-lower body parts with back-front views. First, histogram equalization (HE) method was applied on the pixels only within the body determined using Otsu’sthresholding approach. Secondly, DarkNet-19 architecture was used for feature extraction, and principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) approaches for feature selection. Finally, the performances of various machine learning based classification methods were examined. Upper vs. lower body parts and back vs. front of upper body were classified with 100% accuracy, and back vs. front classification of lower body part success rate was 93.38%. This approach will improve the automatisation process of thermal images to group them for comparing one image with the others and to perform queries on the labeled images in a more user-friendly manner.
A comparative study of metabolic profile in pre-eclampsia and normotensive preterm pregnancy

Background: Hypertensive disorders of pregnancy are important and common cause of severe acute morbidity, long-term disability and death among mothers and babies. The metabolic characteristics (hypertriglyceridemia, hyperinsulinemia, hyperuricemia, low HDL2 cholesterol) in pregnancy-induced hypertension resemble the main features of the “insulin resistance syndrome”. This may result in endothelial cell dysfunction as evidenced by PGI2 suppression. We have designed this study with an objective to compare the metabolic parameters like fasting and post prandial blood glucose, fasting plasma insulin, HOMA-IR and lipid profile between pre-eclampsia and normotensive pregnant women at late preterm (32-36 weeks). Methods: Based on enrolment criteria and statistical analysis 224 patients were enrolled in study during study period and divided in to two groups, group 1- normotensive subjects, group2- pre-eclampsia subjects. Various biochemical parameters and obstetric profile was measured and compared with standard normotensive subjects.Results: The mean fasting plasma insulin was significant higher in group 2 than group 1 (18.36±6.48 mIU/dl versus 3.42±1.68 mIU/dl) (p=0.0001). The mean value of HOMA-IR was significant higher in group 2 than group 1 (3.82±1.68 versus 1.98±1.68) (p=0.0001). The mean value of HDL was significant higher in group 1 than group 2 (42.56±3.28 mg/dl versus 34.26±3.63 mg/dl) (p=0.0001). The mean concentration of LDL was significant higher in group 2 than group 1 (142.23±24.36 mg/dl vrsus 84.32±18.24 mg/dl) (p=0.0001). Conclusions: Systolic and diastolic blood pressure was significantly higher in preeclampsia patients and plasma fasting glucose was higher in normotensive subjects. In present study fasting plasma insulin and HOMA-IR are significantly higher in cases then control. Dyslipidemia is significantly evident in preeclampsia patients in our study.
Investigation of the characteristics of concentrated leachate from six municipal solid waste incineration power plants in China

The characteristics of concentrated leachate in municipal solid waste (MSW) incineration power plants were analyzed for CODCr, BOD5, the concentration of heavy metals, and spectral characteristics of dissolved organic matter (DOM). We performed correlation-principal component analysis to analyze the data. The samples were collected from leachate treatment plants of six MSW incineration power plants in China, all of which have a typical treatment process. Our study showed that the samples have high content of organic matter with extremely low biodegradability, various heavy metals, and a large amount of negative ions such as Cl−, SO42− and PO43− in the concentrated leachate. The intense fluorescent absorption peaks in the visible and ultraviolet regions of the ultraviolet-visible spectra and three-dimensional fluorescence spectra suggest that the concentrated leachate contains a large amount of refractory organic matter mainly consisting of fulvic acid. The humification indexes (HIX) of samples ranged from 1.26 to 14.24 when the biodegradability indexes (BIX) ranged from 0.10 to 2.25. Correlation analysis revealed that the concentration of Cl−, PO43−, and NO3− is significantly correlated with the property of the DOM, and the concentration of Ca, Cr, and SO42− almost has no correlation with other indicators in the concentrated leachate. The characteristics of the concentrated leachate are reflected by three principal components: PC1 is mainly related to the DOM, which is relatively stable and hardly degrades, this component may reflect the degradability and humification degree of the concentrated leachate. PC2 reflects the degradability of the concentrated leachate; a higher score in its positive direction indicates greater degradability of the concentrated leachate. PC3 has little influence on the characteristics of the concentrated leachate. This research can provide a theoretical basis for the effective treatment of concentrated leachate of MSW incineration power plants.
Smart Matching Platforms and Heterogeneous Beliefs in Centralized School Choice

Many school districts with centralized school choice adopt strategyproof assignment mechanisms to relieve applicants of the need to strategize on the basis of beliefs about their own admissions chances. This paper shows that beliefs about admissions chances shape choice outcomes even when the assignment mechanism is strategyproof by influencing the way applicants search for schools, and that “smart matching platforms” that provide live feedback on admissions chances help applicants search more effectively. Motivated by a model in which applicants engage in costly search for schools and over-optimism can lead to under-search, we use data from a large-scale survey of choice participants in Chile to show that learning about schools is hard, that beliefs about admissions chances guide the decision to stop searching, and that applicants systematically underestimate non-placement risk. We then use RCT and RD research designs to evaluate live feedback policies in the Chilean and New Haven choice systems. 22% of applicants submitting applications where risks of non-placement are high respond to warnings by adding schools to their lists, reducing non-placement risk by 58%. These results replicate across settings and over time. Reducing the strategic burden of school choice requires not just strategyproofness inside the centralized system, but also choice supports for the strategic decisions that inevitably remain outside of it.
Honey: Determination of volatile compounds, antioxidant and antibacterial activities

Honey is an important functional food for human health and nutrition that is collected by honey bees and stored in the honeycombs. In this study, total phenolic content, antioxidant activity and volatile compounds of 13 different honey samples collected from various districts of Malatya province were investigated. As a result of this study, it was determined that the total phenolic content varied between 8.50 mg GAE 100 g–1 and 73.90 mg GAE 100 g–1 and it was observed that the honey samples were rich in aldehydes, aliphatic acid and esters, alcohols, hydrocarbons, carboxylic acid esters, ketones, terpenes, fatty acids and esters. In addition, the antibacterial effects of honey samples were determined against 18 different pathogenic bacteria using agar well diffusion (AWD) method, minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). In AWD assay, it was recorded that inhibition zone diameters varied between 9 mm and 14 mm and honey samples were found to have a partial inhibitory effect against selected target pathogens.
Book Review: Evaluating Second Language Vocabulary and Grammar Instruction: A Synthesis of the Research on Teaching Words, Phrases, and Patterns

language, culture and educational policy. Despite the weaknesses mentioned above, this is an informative and thoughtprovoking publication worth adding to the library of anyone who is interested in understanding how young migrants, as well as their parents, maintain their identity linguistically and culturally in community language schools. This book is also recommended to researchers who want to research complex issues on language, culture and identity in a multilingual context since it provides a comprehensive picture of different stakeholders, insightful dimensions and effective approaches. Aside from these theoretical contributions, the author’s focus on the two Chinese community schools may draw more people’s attention to the conditions of other migrant communities. All these strengths make the book a welcome and timely addition to the academic literature on these issues.
Relationship between precarious work and racism for migrants in Brazil

Abstract Amid the growing global flow of goods, workers migrating in search of work face a major challenge of integration in destination countries. Issues of racism and discrimination emerge in the workplace, causing inequality of opportunity. This research aims to describe the relationship between precarious work, discrimination at work, and perception of racism by migrant workers. The preliminary analysis of scientific production on the subject in Brazil suggests that the racist social structure is a condition for the insertion of migrant workers in precarious working conditions, compromising their social insertion. A quantitative cross-sectional study was conducted through a survey of four ethnic groups from different Brazilian regions. The results confirm the influence of precarious work on the perception of racism, with discrimination at work being a moderating variable in this relationship.
The Effect of Seed Size on Germination and Seedling Growth in Sweet Chestnut (Castanea sativa Mill.)

The quantity and quality of seedlings in the nursery has an impact on the success of re-establishment and later growth. High germination rates enable a sufficient number of seedlings, and their quality is assessed using a number of parameters, including seedling height and root collar diameter. These parameters are influenced in some species by seed size, but the correlation between them is species-specific. The model species in this research was sweet chestnut (Castaneasativa Mill.), and seeds from 12 populations from two distinct biogeographical regions of Croatia were collected. We examined the influence of seed size on four parameters: germination rate, seedling height, root collar diameter and sturdiness quotient. Seed size has been shown to have a positive influence on both seedling height and root collar diameter, whereas no such correlation was noted for germination rate and sturdiness quotient. Significant differences in nut size and seedling growth parameters were found between the Mediterranean and continental populations, with higher values observed in the coastal Mediterranean populations. We concluded that seed origin and seed size have a significant impact on seedling growth and are important factors to consider when choosing seed material. Further nursery operations should consider seed origin and local environmental conditions when choosing seedlings for reforestation efforts and general forest operations.
Computational Studies on Drug Likeness and Pharmacokinetic Profile of Selected Phytoconstituents from Liquorice

Liquorice consists of dried, unpeeled, roots and stolons of Glycyrrhiza glabra Linn. , traditionally used as an expectorant, demulcent, antispasmodic, flavoring agent and also used in the treatment of rheumatoid arthritis inflammation and addision’s disease. It has reported for the presence of wide variety of simple and complex primary and secondary phytoconstituents. In the present investigation an attempt has been made to study the drug likeness properties and pharmacokinetic profile of selected phytoconstituents from Liquorice using computer and server based technology. We have selected Liqhiritin, Isoliqiritin, Shinflavanone, Licopyranocoumarin, Methoxyficifolinol, Licoriphenon, Isoliquiritigenin, Ligquritigenin, Prenyllicoflavone A, and Glisoflavone. PubChem database server is used for the find out the canonical smiles. Molsoft server is used for the drug likeness and molecular property prediction. The using the admetSAR server was used for the determination of pharmacokinetic profile along with some toxicity investigations. The results of investigation yield the drug likeness score of selected phytoconstituents along with its absorption, distribution, metabolism, and excretion profile along with some toxicity behaviors of selected phytoconstituents. The present investigation concludes that computer and server based screening play important role for identification of drug like candidates from Liquorice and also data obtained from admetSAR is valuable for research on Liquorice.
Analysis of Narrow Slot Loading on a Half Guided Wavelength Folded Substrate Integrated Waveguide

In this paper, effects due to variation in positioning, width and length of a narrow slot loaded on central metal septum of a half guided wavelength Folded Substrate Integrated Waveguide (FSIW) segment is presented. The study shows that the most significant effect is due to variation in slot length. It was observed that the slot loading can be used both for slow wave structure and filter depending on the length of the slot. The smaller lengths of the slot provide the slow wave effect, whereas the longer lengths result in filtering effect. Both the phenomena are explained with the help of field diagrams for different propagating modes and extracted equivalent circuit for the Slot Loaded Folded Substrate Integrated Waveguide (SFSIW) segment. This study will help in deciding the dimensions of the slot as per application. The measured scattering parameters of the fabricated structure are compared with the simulated results obtained from the HFSS and the circuit simulator in ADS and are in good agreement.