Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Bayesian Compressive Multipolarization Indoor Radar Imaging

This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced.
Confirmatory Factor Analysis of Electronic Word of Mouth in Private College Students in Medan

Electronic word of mouth (e-WOM) is a word-of-mouth promotion using internet-connected electronic devices. However, e-WOM among students has not been effective as it has not created any impacts on universities concerned. The purpose of this research is to evaluate e-WOM among students and direct the occurrence of positive e-WOM which will ultimately impact universities, especially private universities in Medan. This study used the confirmatory factor analysis (CFA) method to test how well measured variables represent constructs or the preformed factors. This research is quantitative involving 210 students of the fourth semester above. The data collected were processed with AMOS Program. The confirmatory factor analysis tests showed as many as 21 indicators remained in the model. The goodness-of-fit value of all models for each variable is received after the modification process.
Accurate 3D reconstruction of complex defects based on combined method of MFL and MFDs

The two-step strategy for 3D reconstruction of complex defects was updated by combining the magnetic field disturbance (MFD) with magnetic ﬂux leakage (MFL) measurement methods. Firstly, the plane-scanned MFD imaging was employed to achieve high-accuracy reconstruction of the opening outlines of complex defects. Secondly, the finite element simulation model of the defect with the recognized opening outline was established as the forward model for depth profile inversion with the particle swarm optimization algorithm. The performances of the conventional MFL-based and updated strategies were carefully compared through finite element simulation. The results proved that the updated method combining MFD signals with MFL signals had the better performance in accurate 3D reconstruction of complex defects than the conventional MFL-based inversion strategy, displaying smaller errors in opening outline, peak depth and the profile. Experimental results confirmed that the updated strategy could accurately reconstruct the 3D shape of 8-shaped defect and rectangular defect with rounded corners.
The pH-triggered drug release and simultaneous carrier decomposition of effervescent SiO2–drug–Na2CO3 composite nanoparticles: to improve the antitumor activity of hydrophobic drugs

To achieve a better release effect of hydrophobic drugs and spontaneous nanocarrier disintegration by dissolution as well as the CO2 production of Na2CO3 further, improving the therapeutic effect of hydrophobic drugs, and thereby avoiding the accumulation of the nanocarrier in vivo to produce organ toxicity, effervescent SiO2–drug–Na2CO3 composite nanoparticles (ESNs) were prepared in this study using a tetraethyl orthosilicate hydrolysis method. Sodium carbonate was used as the effervescent disintegrant to respond to the acidic microenvironment of the tumor. The properties of ESNs were assessed and TEM images were taken to verify the self-disintegration characteristics of nanocarrier materials. The in vitro anticancer efficacy of ESNs was evaluated in human breast cancer MCF-7 cells. ESNs loaded with hydrophobic drugs were successfully constructed, and showed high entrapment efficiency and drug loading. The nanocarrier successfully achieved self-disintegration in a PBS environment of pH value at 5.0, and showed excellent antitumor effect in vitro. ESNs can effectively load hydrophobic drugs and achieve self-disintegration, while avoiding toxicity from the accumulation of the nanocarrier. These results suggest that ESNs are a promising drug delivery system capable of maximizing the anticancer therapeutic efficacy and minimizing the systemic toxicity.
EELS investigations of stoichiometric niobium oxides and niobium-based capacitors

A comprehensive electron energy-loss spectroscopy (EELS) study of stoichiometric niobium oxides and niobium was performed in a transmission electron microscope. Numerous EELS features were identified allowing the distinction of different Nb-oxidation states. Optimized sensitivity factors were determined for accurate quantification of the Nb-O system which were applied to nanoscale analysis of solid-electrolyte capacitors with Nb anodes and anodically grown niobium-oxide layers as dielectric.
The Influence of Using Computers in Mathematics Learning to Students Mathematical Ability

The use of technology has entered all aspects of human activities, belonging education. The use of computer-based media in the learning process is very useful. This study aims to describe the results of various studies on the use of computers in the learning of mathematics. The method used is descriptive research. The aspects seen are the various types of software used and their effects on mathematics learning. Based on the results of the research, it can be concluded that the use of computers can help students visualize and understand mathematics material.
Journeys from discomfort to comfort: how do university students experience being taught and assessed by adults with intellectual disabilities?

Abstract From 2016-17, academics at a leading Irish university collaborated with a group of self- advocates with intellectual disabilities to co-design, co-deliver, and co-assess an entire disability module for third-year undergraduate social work students. There are few, if any, reports of university modules of this type, involving adults with intellectual disabilities, in the existing literature. Alongside and after the module, our group of academics and self-advocates conducted inclusive research regarding relevant stakeholders’ experiences of the initiative. This paper relates to university students’ experiences of being taught and assessed by adults with intellectual disabilities. These experiences were overwhelmingly positive with reported benefits including increased comfort around disability and greater empathy with people with people with disabilities. This said, some aspects of being taught and assessed by self-advocates with intellectual disabilities also provoked considerable anxiety among students. Points of interest From 2016 to 2017, academics at a leading Irish university collaborated with a group of self-advocates with intellectual disabilities to design, teach and mark a disability module for social work students. The group also conducted inclusive research regarding people’s experiences of the module This paper explores how university students experienced being taught and marked by adults with intellectual disabilities. Students’ experiences were generally very positive. They said the module led to more empathy with disabled people and reduced their discomfort around disability At the same time, some aspects of the module, particularly the idea of being marked by adults with intellectual disabilities, made them very nervous. We hope that our research might encourage others to employ adults with intellectual disabilities as university lecturers and markers.
Intelligent IoT systems for traffic management: A practical application

The incorporation of Artiﬁcial Intelligence algorithms in Intelligent Transportation Systems gives rise to new opportunities for a more sustainable urban mobility. However, one of the main challenges is to decide when and where these techniques should be applied; several options appear, such as cloud computing, fog computing, edge computing, or even edge devices. In this paper, an Internet of Things-based solution for smart trafﬁc management is presented. Using the lightweight Random Early Detection for Vehicles Dynamic mechanism as a basis, we optimize using evolutionary algorithms. Random Early Detection for Vehicles Dynamic can be applied in signaled intersections to proactively detect incipient congestion and set the best cycle and phases of trafﬁc lights. Then, the authors demonstrate that once Random Early Detection for Vehicles Dynamic has been appropriately optimised ofﬂine, it can be later used in unknown trafﬁc scenarios without the burden of applying Artiﬁcial Intelligence in constrained Internet of Things devices. The performance of this mechanism is widely tested with the SUMO simulation tool. Results show that this improved version, called iREDVD, notably reduces the vehicles’ waiting time, average trip time, fuel consumption, and emission of particles and gaseous pollutants compared with other proposals.
Intelligent Clothing Interaction Design and Evaluation System Based on DCGAN Image Generation Module and Kansei Engineering

The deep convolutional generative adversarial network (DCGAN) model can be applied to personalized fashion design. Determining the best design from fashion sketches that is of interest to customers is an important research field. In this study, we propose a rapid evaluation method based on Kansei engineering to evaluate personalized fashion images generated by DCGAN. The degree of customer interest in the generated fashion images is determined by tracking the eye movements and facial expressions of customers. Images with high number of fixations are added to the training data for iterative training; this aims to improving customer satisfaction. The method was verified in an interaction design of personalized black dresses. After several iterations, the customers’ satisfaction rating of the generated images by DCGAN improved. This method can be applied to other styles of fashion design.
The organization technology of Ukrainian logistic operators’ work on an e-commerce basis

The purpose of the article is to develop a comparative description of order fulfillment models by logistics operators, analysis of the estimated average transit time in the Asia Europe direction. The main approach for studying this problem is a systematic approach that allows to identify technology elements of Ukrainian logistics operators based on electronic commerce and the use of rail transport in the Ukraine Asia direction, and consider each element as a subsystem and identify bottlenecks. The article presents the technology to organize the work of Ukrainian logistics operators in the needs of the current market, which includes components of automation, artificial intelligence, warehousing and shipping directly to customers by 3PL operators. The materials in the article are of practical value for the professional and productive training of logistics operators, employees of transport companies, for research and teaching staff, in order to improve their professional skills.
Variability of soil physical and hydraulic properties along a toposequence in the coastal lowlands of Rio de Janeiro

Soil physical and hydraulic properties are interdependent, and soil-use planning and management are complicated by the high variability of these attributes. The current study assessed the variability of physical and hydraulic properties of soils along a toposequence located in a forest fragment under spontaneous regeneration in the coastal lowlands of Rio de Janeiro. Four soil profiles were selected, and samples were collected from surface and subsurface horizons for determination of moisture content, bulk density, macroporosity, microporosity, hydraulic conductivity, texture, and soil resistance to penetration (measured using benchtop and field penetrometers). Exploratory and principal component analyses were performed, and descriptive parameters and Pearson’s correlation coefficients were calculated. In A horizons of profiles 3 (Planossolo) and 4 (Gleissolo), clay contents ranged from 59.0 to 577.0 g kg-1, moisture contents from 1.5 to 16.4%, and microporosity from 10.4 to 46.2%, respectively. Field- and laboratory-measured penetration resistances showed a stronger association with profile 1 (Argissolo); however, values were not sufficiently high to limit root development. The variability in soil physical and hydraulic properties in short slopes is high, mainly in terms of hydraulic conductivity, moisture, microporosity, and clay content. The greater variability of soil physical and hydraulic properties in lowland is attributed to clay and silt fractions, microporosity, and moisture content. The increment in clay content with depth increases bulk density and resistance to root penetration.
Combining self-organizing maps and hierarchical clustering for protein–ligand interaction analysis in post-fragment molecular orbital calculation

Fragment molecular orbital (FMO) calculation is a useful ab initio method for analyzing protein–ligand interactions in the current structure-based drug design. When multiple ligands exist for one receptor, a post-FMO calculation tool is required because of large numbers of interaction energy decomposition terms calculated using this method. In this study, a method that combines self-organizing maps (SOM) and hierarchical clustering analysis (HCA) was proposed to analyze the results of the FMO energy components. This method could effectively compress the high-dimensional energy terms and is expected to be useful to analyze the interaction between protein and ligands. A case study of antitype 2 diabetes mellitus target DPP-IV and its inhibitors was analyzed to verify the feasibility of the proposed method. After performing dimensional compression using SOM and further grouping using HCA, we obtained superclasses of the inhibitors based on the dispersion energy (DI), which showed consistency with structural information, indicating that further analyses of detailed energies per superclass can be an effective approach for obtaining important ligand–protein interactions.
Substitutions anioniques et cationiques de films minces d'orthoferrite de lanthane LaFeO3 élaborés par pulvérisation cathodique magnétron

Les travaux de these ont porte sur la substitution cationique et anionique du compose LaFeO3, elabore en couche mince par pulverisation cathodique magnetron reactive (deux cibles metalliques et melange gazeux Ar+O2). La premiere partie de la these est consacree a la substitution du lanthane par d'autres terres rares plus petites : Praseodyme, Neodyme et Samarium. Les quatre perovskites LaFeO3, PrFeO3, NdFeO3 et SmFeO3 ont donc ete synthetisees et caracterisees (caracterisations optiques, electriques et structurales), et l'influence de la substitution sur les proprietes physiques est discutee. Les travaux ont montre que la substitution du lanthane par d’autres terres rares plus petites entraine une modification des proprietes, que nous pouvons directement relier a la distorsion de la maille. Dans une deuxieme partie, le dopage a l’azote du compose LaFeO3 a ete realise, en utilisant de l'azote en tant que gaz reactif (deux cibles + melange gazeux Ar+O2+N2). Les conditions de depot ont ete etudiees dans le but d'optimiser la synthese. A partir de mesures in situ des parametres de la cible en fonction des debits de gaz, associees aux mesures post depot (composition, epaisseur, conductivite electrique), des conditions particulieres de synthese ont ete retenues, afin d’optimiser le dopage a l’azote. L’utilisation de ces conditions de syntheses specifiques ont permis la realisation de plusieurs depots LaFeO3-xNx qui ont ensuite ete caracterises (caracterisations chimiques, optiques, electriques et structurales). Il en resulte que le dopage a l’azote du compose LaFeO3 entraine une modification du gap de la perovskite, ainsi que des proprietes electrique, et optique, malgre une faible teneur en azote.
In Vitro Evaluation of The Inhibitory Power of Trichoderma harzianum Against Pathogens that Cause Anthracnose in Chili

This study aims to determine the inhibition of Trichoderma harzianum Tc-Jjr-02 isolates against two Colletotrichum isolates that cause anthracnose in chili in vitro. The first stage of the test was carried out by a double culture method on the agar dextrose-chloramphenicol medium with five replications. Inhibition of biocontrol agents was determined by comparing the difference between growth of pathogenic colonies in multiple cultures and monoculture with growth of monoculture pathogenic colonies measured every 24 hours for six days. At two weeks after the end of the Observation Test the second stage of research was carried out. Mycelium biocontrol agents that overlap with pathogenic mycelium isolates are sampled and observed under a microscope with 400 times magnification. In vitro test results showed that T. harzianum Tc-Jjr-02 isolates were able to inhibit the growth of C. capsici and C. gloeosporioides colonies with an average of 64.2 ± 3.54 and 65.0 ± 3.93%, respectively. On microscopic observations of overlapping mycelium it appears that T. harzianum Tc-Jjr-02 damages the C. capsici and C. gloeosporioides hyphae cell walls. T. harzianum has the potential to be an effective biocontrol agent against pathogens that cause chili anthracnose.
A General Algorithm of Association Rule-Based Machine Learning Dedicated for Text Classification

Many data mining techniques and machine learning algorithms have been developed to classify textual data involving decision tree, support vector machine, K-Nearest neighbour, in addition to machine learning-based algorithms. Association rules based machine learning is accomplished in two phases; training phase and testing phase that may be reinforced to enhance the classification accuracy according to new minimum support and confidence. Association rules mining/processing, in its various applications, passes through two massive computation steps; frequent itemsets mining and association rules extraction. This paper presents a general algorithm for association rules-based machine learning dedicated to text classification. To verify the efficiency of the algorithm, different text datasets were used such as tweets dataset for sentiment classification, pdf documents and HTML documents. Experiments of sentiment classification showed that the classifier constructed according to minsup threshold =%700 and minconf threshold =50% gives the best performance with F1 = 0.9861811 while the experiments of HTML and PDF appeared accurate classification equal to (94%).
Epidemiology of Psychotic Disorders Based on Demographic Variables in Iranian Children and Adolescents

Objective: Psychosis is still among the most debilitating and severe mental disorders. The main objective of the present study was to investigate the estimated prevalence of psychotic disorders and finding the main predictors of psychotic disorders among Iranian children and adolescents. Method : Our total sample consisted of 30 553 individuals (49% males and 51%females) from 30 provinces of Iran, aged between 6 and 18 years, who were selected via cluster sampling method from rural and urban areas of all provinces. The data were analyzed using descriptive statistical analysis and multiple logistic regression method. Results: The results of multiple regression analysis showed that prevalence estimate of psychotic disorders was 0.25%. It was 0.3% and 0.2% in males and females, respectively. The age of 10-14 (OR = 2.24; 95% CI, 1.11-4.55) and the age of 15-18 (OR = 3.42; 95% CI, 1.74-6.75) were significant positive predictors, whereas none of the demographic variables were predictors for psychotic symptoms. Conclusion: This research highlights the main predictors of psychosis in children and adolescents. The study design also allowed a better understanding of predictors of psychotic disorders. The assessment of the prevalence of psychiatric disorders, particularly their comorbidities, may help to prevent mental illnesses in children and adolescents.
Object Recognition of Urban Buildings in High Spatial Resolution Remote Sensing Imagery

High-resolution remote sensing images can finely express rich surface information. Using the macroscopic and spatial-temporal full coverage advantages of high-resolution remote sensing images for urban building objectification recognition is a current research hot spot in the field of remote sensing image analysis and application. However, the current research still lacks effective technical means to convert surface elements quickly and accurately from remote sensing image space to geographic information space. In this paper, the complementary advantages between image processing and deep learning are combined to research target-level extraction of high-resolution remote sensing urban buildings based on the building element information data. The experimental results achieved the precision of 0.9481 and the recall of 0.9733, indicating that the method proposed in this paper can be applied to the effective extraction of urban-level buildings, which expands the theoretical basis of the object recognition method of remote sensing thematic features based on the idea of object image analysis.
Variation of Green Synthesis Techniques in Fabrication of Zinc Oxide Nanoparticles – A Mini Review

The field of nanotechnology has been one of the major focus of research for scientists across the world. This field deals with the production and usage of nanoscale materials. The popularity of nanotechnology is due to its unique properties that cannot be found in its large counterpart. In these recent years, zinc oxide nanoparticles (ZnO NPs) emerged as an important ceramic material that can be utilized across various fields such as medicine, cosmetics, textiles, wastewater treatment and many others. The fabrication of ZnO NPs can proceed through three major pathways which are physical, chemical and green synthesis. Among these synthesis method, green synthesis is preferable as it is more environmentally friendly. In this review, we summarize the various techniques of green synthesis in fabrication of ZnO NPs.
Modification of carbon fiber by magnetite particles

In order to expand the application range of carbon reinforced composite materials in shielding field, this paper aimed to modify the chopped carbon fiber (CF) by magnetite particles with ultrasonic treatment. The modification was carried out in three stages and the increasing amount of the cover was proved by TGA analyze. SEM analyze is showed the most part of CF surface was coated by magnetite particles Fe3O4. Also it was noticed some agglomeration on each stages what is possibly connected with high surface energy of these particles. The chemical compound of the coating corresponds to ferrous oxide (II and III) what is confirmed by Raman spectroscopy.