Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical relevance of expanded quantitative urine culture in health and disease

“Expanded quantitative urine culture (EQUC)” is an enhanced culture protocol for the detection of viable microbes in urine specimens. Using a large volume of urine and different sets of cultural conditions, EQUC is able to uncover a wide range of bacteria and fungi (yeasts) that were otherwise undetected by the standard urinary culture. In addition to common urinary pathogens, EQUC has been shown to detect emerging and new pathogens, and commensal microbiota. Although the usefulness of EQUC protocol in clinical set up has not yet been fully established, recent studies have demonstrated that EQUC can provide valuable information regarding symptom resolution, treatment responses and diagnosis of major urinary disorders including urinary tract infections, urinary incontinence and other lower urinary tract symptoms. EQUC may also help in evaluating the utility of beneficial microbiota as biotherapeutics. This narrative minireview describes the current research findings regarding the clinical utility of EQUC in characterizing the role of urinary microbiome and uropathogens in health and disease. The literature which are written in English, available on “PubMed” and contain any of the terms- “expanded quantitative urine culture”, “enhanced quantitative urine culture” and “EQUC” in the abstracts were used as the source articles to prepare this minireview.
Combined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification

Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases that target the liver and have a wide spectrum of presentation. A global overview of quantitative variations on the salivary proteome in presence of these two pathologies is investigated in this study. The acid-insoluble salivary fraction of AIH and PBC patients, and healthy controls (HCs), was analyzed using a gel-based bottom-up proteomic approach combined with a robust machine learning statistical analysis of the dataset. The abundance of Arginase, Junction plakoglobin, Desmoplakin, Hexokinase-3 and Desmocollin-1 decreased, while that of BPI fold-containing family A member 2 increased in AIHp compared to HCs; the abundance of Gelsolin, CD14, Tumor-associated calcium signal transducer 2, Clusterin, Heterogeneous nuclear ribonucleoproteins A2/B1, Cofilin-1 and BPI fold-containing family B member 2 increased in PBCp compared to HCs. The abundance of Hornerin decreased in both AIHp and PBCp with respect to HCs and provided an area under the ROC curve of 0.939. Machine learning analysis confirmed the feasibility of the salivary proteome to discriminate groups of subjects based on AIH or PBC occurrence as previously suggested by our group. The topology-based functional enrichment analysis performed on these potential salivary biomarkers highlights an enrichment of terms mostly related to the immune system, but also with a strong involvement in liver fibrosis process and with antimicrobial activity.
Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children’s handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children’s handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.
Magnesium Ingot Stacking Segmentation Algorithm for Industrial Robot Based on the Correction of Image Overexposure Area

This paper proposes an adaptive threshold segmentation algorithm for the magnesium ingot stack based on image overexposure area correction (ATSIOAC), which solves the problem of mirror reflection on the surface of magnesium alloy ingots caused by external ambient light and auxiliary light sources. Firstly, considering the brightness and chromaticity information of the mapped image, we divide the exposure probability threshold into weak exposure and strong exposure regions. Secondly, the saturation difference between the magnesium ingot region and the background region is used to obtain a mask for the magnesium ingot region to eliminate interference from the image background. Then, the RGB average of adjacent pixels in the overexposed area is used as a reference to correct the colors of the strongly exposed and weakly exposed areas, respectively. Furthermore, in order to smoothly fuse the two corrected images, pixel weighted average (WA) is applied. Finally, the magnesium ingot sorting experimental device was constructed and the corrected top surface image of the ingot pile was segmented through ATSIOAC. The experimental results show that the overexposed area detection and correction algorithm proposed in this paper can effectively correct the color information in the overexposed area, and when segmenting ingot images, complete segmentation results of the top surface of the ingot pile can be obtained, effectively improving the accuracy of magnesium alloy ingot segmentation. The segmentation algorithm achieves a segmentation accuracy of 94.38%.
Analyzing Common Errors in Differentiating and Integrating Functions as Basis for Supplemental Calculus Learning Tool

Calculus is one of the most interesting fields of Mathematics yet difficult to pass. In this study, the common errors committed by the students were analyzed and categorized based on Newman’s error analysis guide. Using the quantitative research method, 10 Bachelor of Secondary Education major in Mathematics students enrolled in Calculus I and II were purposively chosen. The study hypothesized that no significant effects of the errors committed when classified according to types of errors and math fields to the overall performances in Calculus I and II of the students. Data collected were analyzed and interpreted using Mean Percentage Scores and Structural Equation Modelling. Based on the results, students commit errors mostly in differentiating functions involving logarithms. In terms of integration, students commit errors more on algebraic functions. Based on Newman’s error analysis, errors committed by mathematics major students are categorized into comprehension in both differentiation and integration processes. The result reveals that while students are equipped with the necessary skills needed for the transformation, process, and encoding, comprehension of the pre-requisite concepts should be strengthened and given attention specifically in algebraic and logarithmic functions. Finally, an instructional material addressing the identified common errors was developed.
A NEW FILLED FUNCTION FOR FINDING GLOBAL MINIMIZER OF THE UNCONSTRAINED OPTIMIZATION PROBLEM

In this paper, a new filled function which is a continuously differentiable function with only one parameter is proposed. The new filled function eliminates some drawbacks of the traditional filled functions. Based on the new filled function, an algorithm for solving unconstrained global optimization problems is developed. The algorithm are implemented on some test problems, and the results show the effectiveness of the proposed filled function method.
Sensorless Rotor Position Estimation For Brushless DC Motors

Brushless DC motor speed is controlled by synchronizing the stator coil current with rotor position in order to acquire an accurate alignment of stator rotating field with rotor permanentmagnet field for efficient transfer of energy. In order to accomplish this goal, a motor shaft is instantly tracked by using rotating rotor position sensors such as Hall effect sensors, optical encoders and resolvers etc. Adding sensors to detect rotor position affects the overall reliabiltity and mechanical robustness of the system. Therefore, a whole new trend of replacing position sensors with sensorless rotor position estimation techiques has a promising demand. Among the sensorless approaches, Back-EMF measurement and high frequency signal injection are the most common. Back-EMF is an electromotive force, directly proportional to the speed of rotor revolutions per second, the greater the speed motor acquires the greater the Back-EMF amplitude appears against the motion of rotation. However, the detected BackEMF is zero at start-up and does not provide motor speed information at this instant. Therefore, Back-EMF based techniques are highly unfavourable for low speed application specially near zero. On the other hand, signal injection techniques are comparatively developed for low or near zero motor speed applications and etimate on-line motor parameters exploiting identification theory on phase voltages and currents signals. The signal injection approach requires expensive additional hardware to inject high frequency signal. Since, motors are typically driven with pulse width modulation techniques, high frequency signals are naturally already present which can be used to detect position. This thesis presents rotor position estimation by measuring the voltage and current signals and also proposes an equivalent permanent-magnet synchronous motor model by fitting the data to a position dependent circuit model.
A Modular Deep Learning Architecture for Voice Pathology Classification

The development of methods that combine different sources of information for medical diagnosis is an essential challenge in the field of medical informatics. In this context, we introduce a machine-learning framework for automatic voice pathology classification and, in particular, a modular deep learning architecture that classifies voice signals stemming from four types of voice disorders. To this end, we design a multimodal deep learning architecture that fuses medical metadata with voice signals. Our classifier is a combination of fully convolutional and feed-forward sub-networks that simultaneously process low-level and mid-level features which are extracted from acoustic signals of varying duration and medical records, respectively. A key objective of our study is to develop an architecture that is capable of processing voice samples of varying duration, to enhance the system’s learning and inference capabilities. Our research also focuses on overcoming performance limitations of neural networks that stem from the lack of extensive volumes of training data. We therefore, investigate problem-specific augmentation techniques based on the feature sequence segmentation and coloured noise injection and we show that the proposed method gives state-of-the-art results, achieving 64.4% classification accuracy, compared to the 63.5% classification score of the best performing method of the 2019 FEMH data challenge.
Effective medium theory for graphene-covered metallic gratings

We propose an effective medium theory for a one-dimensional periodic array of rectangular grooves covered by a graphene sheet. Parameters of the effective medium model are given by explicit analytical expressions for both major polarizations TM and TE, and for all incident angles. In extraction of this model, we assumed single mode approximation inside the grooves. The effect of non-specular diffraction orders outside the grating, as well as the plasmonic response of the graphene sheet in the far-infrared spectrum, is addressed by introducing an effective surface conductivity at the interface of the metallic grating and the ambient environment. It is shown that surface plasmons in graphene effectively capture diffracted waves in the metallic grating leading to near total absorption. Results of this work may pave the way for designing wide-band absorbers for terahertz applications.
COMBINING AGGREGATE DATA AND INDIVIDUAL PATIENT DATA IN META-ANALYSIS: AN ALTERNATIVE METHOD

It has been shown that, in cases where both the AD and IPD studies are available, combining these two levels of data could improve the overall meta-analysis estimates, compared to utilizing AD studies alone. However, the coverage probability of estimates based on combined studies are relatively low compared to the AD-only meta-analysis, when the existing standard method was used to combine these studies. The aim of this paper is to introduce some modifications to the existing two-stage method for combining the aggregate data (AD) and individual patient data (IPD) studies in meta-analysis. We evaluated the effects of these modifications on the estimates of the overall treatment effect, and compared them with those from the standard method. The influence of the number of studies included in a meta-analysis, N, and the ratio of AD: IPD on these estimates were also examined. We used percentage relative bias (PRB), root mean-square-error (RMSE), and coverage probability to assess the overall efficiency of these estimates. The results revealed that the proposed method had been able to improve the coverage probability while maintaining the level of bias and RMSE at par to their existing counterpart. These findings demonstrated that the technique for combining different levels of studies influenced the efficacy of the overall estimates, which in turn is crucial for drawing reliable and valid conclusions. Article DOI: https://dx.doi. org/10.20319/mijst.2016. s11.144158 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
A Near Real-Time Big Data Provenance Generation Method Based on the Conjoint Analysis of Heterogeneous Logs

Data provenance is an effective approach for data security supervision. In the distributed, multi-user, and multi-layer big data system, only the provenance generation method, which leverages the information logged at both application and operating system level, has the capacity to completely obtain the provenance information required for data usage supervision. However, the current research on the conjoint analysis of multiple logs is inadequate, and it is difficult for them to effectively integrate the provenance information extracted from different logs, especially in the big data scenario. For the near real-time provenance generation based on the analysis of multiple heterogeneous logs, this paper employs a Hadoop-based big data system as the research object, and proposes a parallel log analysis method based on auxiliary data structures and multi-threading. For the efficient conjoint analysis of multiple logs, 5 auxiliary data structures are constructed as the medium for the correlation and fusion of log information, and a multi-threading method is presented to parallelize the lookup of provenance information. In order to cope with the complex log record generation rules, log analysis methods for nondeterministic records, non-instantaneous operations, and instantaneous batch operations are proposed to generate provenance information correctly. In addition, a provenance generation framework is established to implement the proposed log analysis method. The experimental results show that the log collection time overhead caused by processing files above MB level is less than 0.1%. The proposed method can analyze logs in near real time and generate provenance information correctly.
A lifting of graphs to 3-uniform hypergraphs, its generalization, and further investigation of hypergraph ramsey numbers

Ramsey theory has posed many interesting questions for graph theorists that have yet to besolved. Many different methods have been used to find Ramsey numbers, though very feware actually known. Because of this, more mathematical tools are needed to prove exactvalues of Ramsey numbers and their generalizations. Budden, Hiller, Lambert, and Sanfordhave created a lifting from graphs to 3-uniform hypergraphs that has shown promise. Theybelieve that many results may come of this lifting, and have discovered some themselves.This thesis will build upon their work by considering other important properties of theirlifting and analogous liftings for higher-uniform hypergraphs. We also consider ways inwhich one may extend many known results in Ramsey Theory for graphs to the r-uniformhypergraph setting.
Phylogenetic diversity is a weak proxy for functional diversity but they are complementary in explaining community assembly patterns in temperate vegetation

Ecological differences between coexisting species within plant communities can be assessed by considering functional and phylogenetic dissimilarities either separately or in a complementary way. Here, we studied (a) the potential overlap between functional (FD) and phylogenetic diversities (PD) and (b) their combined and unique roles in explaining community assembly patterns across different temperate vegetation types and across functional traits representing multiple dimensions of plant strategy (plant size, leaf, floral and reproductive, clonal and bud bank traits). We tested the strength of the PD–FD relationship within and across vegetation types and functional traits (Pearson correlations) and tested whether it depended on the strength of the phylogenetic signal (Pagel's lambda and Blomberg's K). We tested deviations from random expectations in FD and ‘decoupled FD’ (i.e. functional dissimilarity after accounting for the effect of phylogenetic distances between species) to reveal the importance of ecological differences for community assembly. PD–FD correlations were predominantly significant but rarely strong, and largely depended on the studied functional trait and vegetation type. Phylogenetic signals were partially but inconsistently related to the overlap between FD and PD. Community assembly patterns tended to shift from under‐dispersion (FD lower than random expectations) towards over‐dispersion (FD higher than random expectations) when functional distances were decoupled from phylogenetic distances indicating that species within the same clade were dissimilar to each other regarding their traits. However, we found the opposite pattern as well, mainly for floral and below‐ground traits, which indicated functional differentiation across clades. Synthesis. Decoupling functional and phylogenetic differences between species might provide further information on plant community assembly: showing cases where the strongest ecological differentiation between coexisting species occurs between phylogenetically related species rather than between phylogenetically unrelated ones.
Flows with fractional quantum circulation in Bose-Einstein condensates induced by nontopological phase defects

It is a common view that rotational motion in a superfluid can exist only in the presence of quantized vortices. However, in our numerical studies on the merging of two concentric Bose-Einstein condensates with axial symmetry in two-dimensional space, we observe the emergence of a spiral dark soliton when one condensate has a non-zero initial angular momentum. This spiral dark soliton enables the transfer of angular momentum between the condensates and allows the merged condensate to rotate even in the absence of quantized vortices. We examine the flow field around the soliton and reveal that its sharp endpoint can induce flow like a vortex point but with a fraction of a quantized circulation. This interesting nontopological "phase defect" may generate broad interests since rotational motion is essential in many quantum transport processes.
A new species of bannerwing damselfly, Polythore albistriata sp. nov. (Odonata: Polythoridae)

The traditional method of classifying the twenty-one species within the South American genus Polythore has been relying on wing color patterns and male genital ligula shape. However, recent molecular research has shown that wing color patterns can vary significantly within some species, making it an insufficient means of species diagnosis by itself in some cases. In this study, we employ a combined approach of morphological and molecular data to describe a new Polythore species found in the southeastern Colombian Andes. Our analysis includes detailed illustrations and pictures of diagnostic features, haplotype networks for two barcode genes, COX1 and NADH I, a distribution map, and a brief discussion of the conservation status of the species. We found wing color pattern, male genital ligula morphology, and NADH I barcode sequences to be sufficiently diagnostic to identify the species as new. Our results highlight the importance of integrating multiple data sources for accurate species identifications and descriptions in this genus.
Family Engagement in Early Childhood Education: A Phenomenological Study

Family engagement contributes greatly to families, children, teachers, and classrooms succeed. The purpose of this research is to examine in detail the views and activities of preschool teachers about family engagement activities. To this end, phenomenology was employed as one of the qualitative research methods. There are 35 preschool teachers in the study group of the research. These teachers were identified using the maximum diversity sampling method. As a result of the research, two main themes emerged: family engagement perception and family engagement experience. The theme family engagement perception includes the meaning of family engagement and the ideal family engagement qualities. Family engagement theme involves planning, preparation, activities, evaluation, engagement issues, strategies to increase engagement and their effect. The findings of the study revealed the necessity and importance of family engagement in early childhood.