Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of High Gain Modified SEPIC Converter for Photovoltaic Applications

In designing photovoltaic systems connected to the grid system usually DC-DC converter is required to increase the output voltage of the photovoltaic. The most commonly design of DC-DC converters are typically used a converters with high gain static in order to increase the output voltage of the photovoltaic and obtain a high conversion efficiency. One type of converter used is a SEPIC converter topology. However, the conventional DC-DC converter topologies such as SEPIC converters can only increase by 5 times of the input voltage when the Duty Cycle is set to 0.82. Meanwhile, to meet the dc input voltage of the inverter, the input voltage of the converters has to increase above than 10 times. Therefore, to overcome these problems, this paper proposes the design of DC-DC modified SEPIC converter topologies for photovoltaic applications. Modifications to the conventional SEPIC converter are done by adding capacitors and diodes. From the experimental results shows that this converter can increase the output voltage about 10 times and has the efficiency about 91.5%. Furthermore, topology of the this converter can be effectively applied for photovoltaic system.
Targeting ACLY efficiently inhibits SARS-CoV-2 replication

The Coronavirus Disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the biggest public health challenge the world has witnessed in the past decades. SARS-CoV-2 undergoes constant mutations and new variants of concerns (VOCs) with altered transmissibility, virulence, and/or susceptibility to vaccines and therapeutics continue to emerge. Detailed analysis of host factors involved in virus replication may help to identify novel treatment targets. In this study, we dissected the metabolome derived from COVID-19 patients to identify key host factors that are required for efficient SARS-CoV-2 replication. Through a series of metabolomic analyses, in vitro, and in vivo investigations, we identified ATP citrate lyase (ACLY) as a novel host factor required for efficient replication of SARS-CoV-2 wild-type and variants, including Omicron. ACLY should be further explored as a novel intervention target for COVID-19.
Managerial discretions and loan loss provisions in Nigerian banks: empirical IFRS and risk evidence

Aim: The high level of non-performing exposures and the existing crisis in the Nigerian banking sector is a source of concern. To create a basis for solving the troubles caused by the loan loss crisis, this study investigated the managerial discretionary use of loan loss provisions (LLPs) by Nigerian deposit money banks (DMBs). This is considered in the context of solvency risk and reforms embedded in the adoption of International Financial Reporting Standards (IFRSs). Design/research methods: Datasets related to the variables of the study were hand-collected from annual reports of a sample of 16 Nigerian deposit money banks over the period of 2007-2017. The analyses were performed using principal components analysis to derive the managerial discretions index (MDI), Prais-Winsten ordinary least square regression to segregate LLP into reported LLPs (TLLP) and discretionary LLPs (DLLP) and appropriate panel data regression models to test the study’s hypotheses subsequent to series of diagnostic te sts. Conclusions/findings: The results revealed that managerial discretions negatively influence TLLP and DLLP represented by absolute value of DLLP (ADLLP). This represents an increase in profitability without manipulatingloan loss provisions. However, the reforms embedded in IFRSs revealed the use of LLPs for managerial discretions despite reduction in provisioning level noticeable during IFRS. The situation of Nigerian banks threatened by solvency risk use of LLPs for managerial discretions while attempting to increase profit was exemplified in the increase in ADLLP rather than TLLP. However, improvement was noticeable for risky Nigerian banks during IFRS. The managerial discretionary use of LLPs especially during IFRS was engendered by use of LLPs for capital management and earnings smoothing rather than earnings signalling as further revealed. This shows that adoption of International Financial Reporting Standards reduces reporting quality of Nigerian banks in their loan loss decisions. Originality/value of the article: The derivation of MDI from measures of earnings smoothing, capital management and earnings signalling is the study’s contribution to accounting for loan losses literature. The adjustments to LLPs to reduce variability of earnings, meet up with minimum regulatory bank capital adequacy ratio and signal strength to absorb future losses encapsulate earnings smoothing, capital management and earnings signalling respectively. Implications of the research: The discretionary use of LLPs found in this study beckons an increased level of surveillance, oversights and reforms on the part of the regulators for compliance level devoid of managerial opportunistic behaviour to be identifiablewith Nigerian banks.
DEALING WITH GLOBAL, LOCAL AND INTERCULTURAL ISSUES FOR GLOBAL COMPETENCE DEVELOPMENT IN TEACHER TRAINING: A PILOT STUDY ON THE VIEWS OF UNIVERSITY TUTORS IN HUNGARY

In today’s divisive world, education needs to empower students to become active global citizens who are prepared for the challenges of the 21st century and who can solve local and global problems. The urgency of the implementation of global education is stressed by the fact that in many countries, OECD PISA has already started to measure students’ global competence. As teachers are often seen as educational gatekeepers, who decide on the content and the quality of the learning experience, it is worth examining their views on the global dimension of education. This pilot study has a dual aim. First, it proposes an interview guide specially designed to explore teachers’ views on global competence development (GCD). The other aim of the study, involving four participants working in an EFL teacher training programme in Budapest, is to reveal their views on GCD and their perceptions of their role in developing their students’ global competence. Findings suggest that participants have a reasonable understanding of GCD, but they lack some knowledge about its dimensions. Also, they think that they play an important role in developing their students’ GCD, mostly by selecting appropriate topics to raise their awareness of certain issues and by acting as role models. As the results indicate, it might be advisable to incorporate GCD more clearly, systematically and with well-defined goals in the EFL teacher training programme.
Ultra-Wideband Homogenization of Glide-Symmetric Holey Parallel-Plate Waveguides

—Glide-symmetric waveguides made of metallic meta- surfaces are a wideband, low-loss, low-cost, and conformable alternative to dielectric materials for the design of antenna lenses at millimeter waves. However, computing the effective refractive index of glide-symmetric waveguides with existing full-wave analysis techniques results in cumbersome parametric studies for each new design. This paper presents a new analytic homogenization technique for glide-symmetric holey parallel-plate waveguides. The dispersion equation of these structures, found by way of mode-matching, is simpliﬁed at low frequency using the properties of the modes resonating within the holes. This simpliﬁed equation yields a closed-form expression of the effective refractive index. This index characterizes propagation over an ultra-wide band, due to the low dispersive properties of glide symmetric structures. The formula avoids solving a full- wave problem and is applicable to any hole shape. It is a function of the angle of propagation within the waveguide, and can thus be used to study anisotropic properties. Its efﬁciency is demonstrated with the example of glide-symmetric parallel-plate waveguides with rectangular and circular holes.
Detection and phylogenetic analysis of Porcine epidemic diarrhea virus based on the ORF3 gene in pigs in the north of Vietnam

To identify further the infectivity, genetic diversity and molecular characteristics of the porcine epidemic diarrhea virus (PEDV). 286 samples of the intestinal tissue of pigs with severe diarrhea were assessed from 5 provinces in the north of Vietnam. The full-length ORF3 gene of 20 representative field strains from 36 farms in 5 provinces was sequenced and analyzed. The prevalence of PEDV was 90.56% and was detected in 259 of 286 samples, with 94.44% (34 of 36) of pig farms harboring the disease. Based on the phylogenetic analysis of the ORF3 genes, our isolates all fell into group G2 (variants) and showed a close relationship to isolates from Chinese (GH1, QH2, and SX1), Korean (DR13 and DR13 attenuated) sources, and these isolates differed genetically from other Chinese (CH and CV777), Korean (DBI865) and French (CV777) strains. Our isolates also differed from attenuated vaccine strains, CV777 (used in China) and DR13 (used in Korea). According to our derived amino acid sequence analysis, we detected one novel variant PEDV, viz: BF, with 6-nt insertion, 4-nt difference, 68-nt deletion and 17-aa deletion at position 63-79 compared to the CV777 attenuated strain. Our ORF3 gene analyses showed that the prevalent PEDV isolates were variants and the isolated strains differed genetically from the vaccine strains. These results demonstrated the existence of genetic diversity among geographically distinct PEDV strains and our study has contributed to conducting further researches into PEDV and on an efficacious vaccine design in the future.
DATA-DRIVEN NON-LOCAL WAVELET FRAME CONSTRUCTION AND IMAGE RESTORATION

By assuming that the interest image can be sparsely approximated in some system, the sparsity-based regularization has been one promising approach emerging in recent years to solving many image restoration problems. One popular system used for sparsely approximating images is the wavelet tight frame which is very eﬃcient for exploiting the sparse nature of local variations of image intensity. However, another powerful image prior often seen in natural images is missing when using existing wavelet frame systems, i.e., the self-recursions of image structures over the whole image. Such a global self-recursive prior of image structures has led to many powerful non-local image restoration schemes with impressive performance. This paper aims at developing a scheme for constructing a non-local wavelet frame or wavelet tight frame that is adaptive to the given image. The constructed non-local wavelet frame, concatenated by one non-local frame and one wavelet tight frame, allows the resulting regularization simultaneously exploits both the sparse prior of local variations of image intensity and the global self-recursive prior of image structures over the image. Based on the proposed construction scheme, a powerful regularization method is developed for solving general image restoration problems. The experiments showed that the results from the proposed regularization method are compared favorably against that from several popular image restoration methods.
The Impact of Extracurricular Activities and High School Students

Extracurricular activities have become very common in the day-to-day life of high school students, however, the reason for participating as well as the impacts may be different for each student. The purpose of this literature review is to understand the impacts of extracurricular activities on high school students and how participation in extracurricular activities impacts high school students’ academic performance and social-emotional learning. In addition, the literature review examines if the breadth and intensity of extracurricular activity participation can change those impacts for high school students. The research explores five main categories of activities and the impacts participating in the particular activities has on high school students. The examination of the research indicates that students who participate in extracurricular activities find positive attributes, academically and in social-emotional development, that help them both in high school and in their future.
Automated defect detection in CFRP using flash infrared thermography and deep learning method

Carbon fiber reinforced polymer (CFRP) composites have become an important material for many industry applications due to their high specific stiffness and specific strength. Inevitably, defects generated during manufacturing and/or in-service process could compromise the structural health of the CFRP components. Flash infrared thermography(IRT) is a promising NDT technique, which has already been successfully applied for the detection of various defects in a range of materials. In order to bring this technique to the next level, this study aims to accomplish automatic detection and localization of delaminations in CFRP using flash IRT experiments and deep learning-based object detection method. A virtual dataset has been generated by means of a custom developed fast numerical simulator (programmed in Fortran) of flash IRT. Then, a pre-trained object detection framework from literature, i.e. Faster-RCNN, is applied to the virtual dataset using the concept of transfer learning. A comparison with classical post-processing methodologies, e.g. thermographic signal reconstruction, and principal component thermography, is presented. Finally, a CFRP slab including twelve artificial rectangular delaminations was inspected using flash IRT and evaluated through trained Faster-RCNN.
Corpora for Bilingual Terminology Extraction in Cybersecurity Domain

The paper aims at presenting English-Lithuanian corpora for bilingual term extraction (BiTE) in the cybersecurity domain within the framework of the project DVITAS. It is argued that a system of parallel, comparable, and training corpora for BiTE is particularly useful for less resourced languages, as it allows to efﬁciently use strengths and avoid weaknesses of comparable and parallel resources. A special focus is given to the open nature of the data, which is achieved by publishing the data in CLARIN-LT repository.
Conservative Retrieval of Fractured Abutment Screw – A Case Report

Failure of implant components can occur due to biological or mechanical factors. Biological factors comprise ineffective osseointegration and occurrence of peri-implantitis. Mechanical failures include crown breakage, framework fracture, screw loosening, and screw fracture. Dental implants can be rendered ineffective due to such complications related to implant components. One of the most serious mechanical consequences of implant treatment is screw loosening and fracture, which is now becoming increasingly common. Removal of fractured fragments is deemed necessary for the sake of prosthetic restoration. Various techniques have been defined for the removal of the fractured segment from the screw hole. This case report presents a rare incident of implant abutment screw fracture and the utility of a piezoelectric ultrasonic scaler in conservatively retrieving the fractured abutment screw fragment.
Crude Oil Prediction Using LSTM

:- Crude oil has another name called black gold which has an essential role in evolution of global wealth and financial market. Therefore, dynamic information of future expected price will lead to enhancement of decision making at different levels. Specifically, this is an attempt made to forecast price prediction using long short-term memory neural network rather than using convolutional neural network. We have come across testing different versions of model using various lookback and alternative tuning methods. The conclusion derived from this study are promising and represent a more precise prediction for the crude oil price in coming days.
Particle concentration and stokes number effects in multi-phase turbulent channel flows

This investigation examines the effect that particle concentration has on the dynamics of two-phase turbulent channel flows at low and high density ratios. In the literature, little explanation is offered for the existence of high particle turbulence intensities in the buffer layer and viscous sublayer for particles with high Stokes number. The present study aims to explore particle dynamics in those regions. The spectral element method DNS solver, Nek5000, is used to model the fluid phase at a shear Reynolds number, . Particles are tracked using a Lagrangian approach with inter-phase momentum exchange (two-way coupling). Mean fluid and particle velocity statistics are gathered and analysed to determine the effect of increasing both Stokes number and concentration. Results indicate that the system with the greater Stokes number (air) has a much larger impact on the mean streamwise velocity and turbulence intensity profiles. As the concentration is increased, the mean flow velocity and turbulence intensity are reduced in the bulk and increased very close to the wall. For the low Stokes system, there is negligible effect on the flow statistics at low concentration. One-way coupled solid-phase statistics indicate that particles in water follow the flow very closely. At the higher densityratio, particles lag behind the flow in the bulk, but overtake the flow in the near-wall region, where the existence of increased streamwise turbulence intensities is also observed. To elucidate the dynamics, concentrations and fluxes are analysed. Particles are observed to be distributed more densely close to the wall in air, compared to a reasonably uniform distribution in water. Finally, contour plots indicate that particles in air tend to congregate in regions of low streamwise fluid velocity, and the extent to which this differs between the two systems is then quantitatively measured.
Organizational Factors Associated with Evidence-Based Practice Knowledge, Attitudes, and Implementation among Nurses in Saudi Arabia

Evidence-based practice (EBP) is crucial in keeping nurses aware of the current knowledge and improving clinical decision-making. The integration of nurses’ EBP competencies and organizational support has been suggested to create an effective arena in implementing EBP. The purpose of the study was to examine organizational factors influencing nurses’ EBP knowledge, attitudes, and implementation and identify staff nurses’ perceptions of EBP nursing leadership and hospital supports in Saudi Arabia. Data were collected from a convenience sample of staff nurses (N = 227) working in four hospitals using a cross-sectional, correlational descriptive design. Level of education (p < 0.05), EBP training (p < 0.05), unit type (ICU (p < 0.001) and ER (p < 0.01)), perceived nursing leadership (p < 0.001), and work environment (p < 0.05) supports were found significantly associated with nurses’ knowledge. Magnet recognition (p < 0.01) and knowledge (p < 0.001) had significant influence on nurses’ attitudes. Unit type (ER) (p < 0.05), knowledge (p < 0.001), and attitudes (p < 0.001) were associated with implementation. Encouragement to attend EBP trainings from nursing leadership was perceived by most nurses (51.1%). Nurses reported their hospitals support EBP through training (68.2%). Findings support the need for healthcare systems to create a culture that facilitates EBP implementation to enhance nurses’ EBP competencies and improve patients’ outcomes. Nursing managers may consider preparing nurses through education.
Transient upregulation of EGR1 signaling enhances kidney repair by activating SOX9+ renal tubular cells

Background: Acute kidney injury (AKI) is associated with damage to the nephrons and tubular epithelial cells (TECs), which can lead to chronic kidney disease and end-stage renal disease. Identifying new biomarkers before kidney dysfunction will offer crucial insight into preventive and therapeutic options for the treatment of AKI. Early growth response 1 (EGR1) has been found to be a pioneer transcription factor that can sequentially turn on/off key downstream genes to regulate whole-body regeneration processes in the leopard worm. Whether EGR1 modulates renal regeneration processes in AKI remains to be elucidated. Methods: AKI models of ischemia-reperfusion injury (IRI) and folic acid (FA) were developed to investigate the roles of EGR1 in kidney injury and regeneration. To further determine the function of EGR1, Egr1-/- mice were applied. Furthermore, RNA sequencing of renal TECs, Chromatin Immunoprecipitation (ChIP) assay, and Dual-luciferase reporter assay were carried out to investigate whether EGR1 affects the expression of SOX9. Results: EGR1 is highly expressed in the kidney after AKI both in humans and mice through analysis of the Gene Expression Omnibus (GEO) database. Furthermore, we verified that EGR1 rapidly up-regulates in the very early stage of IRI and nephrotoxic models of AKI, and validation studies confirmed the essential roles of EGR1 in renal tubular cell regeneration. Further experiments affirmed that genetic inhibition of Egr1 aggravates the severity of AKI in mouse models. Furthermore, our results revealed that EGR1 could increase SOX9 expression in renal TECs by directly binding to the promoter of the Sox9 gene, thus promoting SOX9+ cell proliferation by activating the Wnt/β-catenin pathway. Conclusions: Together, our results demonstrated that rapid and transient induction of EGR1 plays a renoprotective role in AKI, which highlights the prospects of using EGR1 as a potential therapeutic target for the treatment of AKI.
2D and Stereo Digital Image Correlation Written in MATLAB

Although there are many publications documenting the theory of Digital Image Correlation (DIC), there is a lack of resources bridging the gap between the theory of DIC and its practical implementation as code. Two papers have been published, for 2D and Stereo-DIC respectively, which discuss the theory of a subset based DIC framework that is predominantly consistent with current state-of-the-art techniques, and explain how it is implemented as modular 117 line and 202 line MATLAB codes respectively. This aims to provide newcomers to the concept of DIC with the resources required to gain a deep understanding of DIC which is necessary to contribute to the field of DIC.
A Comparative Study of Image Classification Models using NN and Similarity Distance

— The following paper presents a comparative analysis based on two algorithms for Covid-19 image classification. Both of them use wavelet coefficients forming image features vector but they differ in the way the vectors are treated for the purpose of classification. The first algorithm uses the mechanism of similarity distance computation using Euclidean distance. The second model uses neural networks for the process of classification. The comparative analysis on the base of the two models is performed on accuracy, precision and recall performance measures. The obtained results show the advantages and disadvantages of both models for the analyzed parameters.