Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Committee Audit Characteristics and Reputation of Audit Firm on the Integrity of Financial Statement With Company Size as Moderating Variable

The purpose of this study is to examine the effect of committee audit characteristics and audit firm reputation on the integrity of financial statement considering company size as moderating variable. This study uses panel data analysis and Gretl Program to test the direct effect and moderating effect of the variables. Data were collected from Consumer Goods sector companies that are listed on Indonesia Stock Exchange from year 2017-2019. The result shows that number of audit committee member and proportion of female audit committee are significantly related to integrity of financial statement. The moderating effect of company size on the integrity of financial statement is also confirmed. This research paper can give several recommendations for company in terms of having more relevant and reliable financial statements, there are: (1) we suggest company to have a sufficient amount of audit committee member; (2) we suggest company to have at least one female on the audit committee structure.
A Survey Study of Adoption of Mobile Payment in the U.S Market

Mobile payment obtained extensive attention in recent decades as a new means of commerce with various advantages. This paper aims to empirically investigate key adoption determinants that contribute to the relative slow uptake of mobile payment service in the U.S. market. The study aims to examine the key facilitators and hindrances in the mobile payment adoption process from the consumer perspective. We also take the varied demographic condition into consideration, that serves as the moderating factors in the adoption process of mobile payment, A survey is developed to understand people’s intention and self-reported usage of mobile payment based on previous literature. The survey includes demographic information (such as age, gender, income, and education), and six constructs - performance expectancy, effort expectancy, social influence, facilitating condition, perceived risk, and individual innovativeness. All six constructs except effort expectancy showed significant direct effect to behavior intention.
Pressurized Oxygen Blown Entrained-Flow Gasification of Wood Powder

In the present study, an oxygen blown pilot scale pressurized entrained-flow biomass gasification plant (PEBG, 1 MWth) was designed, constructed, and operated. This Article provides a detailed description of the pilot plant and results from gasification experiments with stem wood biomass made from pine and spruce. The focus was to evaluate the performance of the gasifier with respect to syngas quality and mass and energy balance. The gasifier was operated at an elevated pressure of 2 bar(a) and at an oxygen equivalence ratio (λ) between 0.43 and 0.50. The resulting process temperatures in the hot part of the gasifier were in the range of 1100–1300 °C during the experiments. As expected, a higher λ results in a higher process temperature. The syngas concentrations (dry and N2 free) during the experiments were 25–28 mol % for H2, 47–49 mol % for CO, 20–24 mol % for CO2, and 1–2 mol % for CH4. The dry syngas N2 content was varied between 18 and 25 mol % depending on the operating conditions of the gasifier.
Numerical study of soot formation / oxidation mechanisms and radiative heat transfer in closed-and open - tip laminar diffusion flames

Microgravite ethylene laminaires couche limite flammes de diffusion generes par un bruleur poreux plat et caracterisees par les vitesses d'injection de carburant de 3 et 4 mm / s et une vitesse d'oxydation de 250 mm / s ont ete simulees en utilisant un modele de rayonnement precis, un mecanisme cinetique complete et un modele de suie constitue de lancement par suite de la collision de deux molecules de pyrene, l'evolution de la surface heterogene et oxydation suivant l'abstraction d'un atome d'hydrogene addition d'acetylene (HACA) mecanisme, la coagulation a particules de suie, et la condensation de la surface de l'HTAP. La distance d'ecartement et la production de suie sont ameliorees lorsque la vitesse du carburant augmente. H et des radicaux OH, responsables de la de-hydrogenation des sites dans le processus HACA, et le pyrene, de l'espece pour la creation de la suie et des processus de condensation HAP, se trouvent a etre situe dans une region qui suit la distance stand-off. La suie est ensuite produite dans cette region et est transporte a l'interieur de la couche limite par convection et thermophorese. Perte radiatif est sensiblement plus eleve dans ces flammes que dans flammes de diffusion de gravite normales dues a beaucoup plus longues durees de sejour. Calculs effectues par negliger le rayonnement de la suie et en utilisant l'approximation optiquement mince (OTA) a revele que la suie domine le transfert de chaleur par rayonnement dans ces flammes et que l'utilisation de l'OTA donne lieu a des ecarts significatifs dans la fraction du volume temperature et la suie.
Sterile water; a novel and promising human experimental muscle pain model

Background Intramuscular injection of hypertonic saline evokes pain with similar characteristics as clinical myalgia and is thus, considered a valid human experimental model. The aim of this study was to investigate if intramuscular injection of sterile water can be used as a human experimental pain model by comparing it with hypertonic and isotonic saline, and to analyze if the effects differ between men and women. Methods This randomized double blind and placebo-controlled study included 15 healthy women and 15 healthy age-matched men (mean (SD) age of 23.6 (2.4) years). The study comprised of three separate sessions, with at least one week of wash out between each session. Sterile water (i.e. the test-substance), hypertonic saline (active control), and isotonic saline (passive control) were injected intramuscularly into one of the masseter muscles in a randomized order. Pain intensity (VAS) was assessed every 15 th s after the injection and pain duration (s) as well as pain drawing area (au) were recorded. Pressure pain thresholds (kPa) were assessed every 5 min after injection during 30 min. Results All substances evoked pain ( P < 0001), but sterile water and hypertonic saline induced pain with higher intensity ( P < 0.001), longer duration ( P < 0.001), and larger pain drawing area ( P < 0.001) than isotonic saline. The pain intensity was higher for hypertonic saline than sterile water 45-165 s after injection ( P < 0.015), but there were no significant differences between them regarding maximum pain intensity, pain duration or pain drawing area. There was no significant difference in PPT values with time or between substances. The pain duration was longer in the men compared to the women for all substances ( P < 0.006), while the pain drawing area was larger in women after injection of hypertonic saline ( P < 0.003), but not after injection of the other substances. No other sex differences were found. Conclusion Injection of sterile water had similar effects as hypertonic saline on pain variables, but no effect on PPT. Hence, the model mimics clinical myalgia and may offer a novel and simpler alternative to hypertonic saline injections.
Phase-Induced Gabor-Based Multiview Active Learning for Hyperspectral Image Classification

In this letter, we propose a new phase-induced Gabor-based multiview active learning (MVAL) (PGMVAL) approach for hyperspectral image (HSI) classification. Our main contribution is to explore the potential of the phase offset term <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> in hand-crafted Gabor feature extraction, which is rarely exploited in previous works. The Gabor filters with <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> added, named as the phase-induced Gabor filters, are able to adjust their frequency response characteristics through <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>. Specifically, we utilize the phase-induced Gabor filtering for view generation purposes under a MVAL framework. As a result, PGMVAL is capable to exploit the complementary information residing in the phase-induced Gabor features corresponding to different <inline-formula> <tex-math notation="LaTeX">$P\text{s}$ </tex-math></inline-formula> and simultaneously avoids high memory consumption and a large number of training samples required caused by introducing a new parameter. The experimental results obtained on two benchmark HSI data sets show that the proposed PGMVAL approach using phase-induced Gabor filtering could achieve better classification results with limited training samples.
IMPROVED MICROPLATE FLUOROMETRIC SOIL ENZYME ASSAY FOR β-GLUCOSIDASE DETECTION

The critical role of soil enzymes as catalysts in soil nutrient cycling and soil organic matter decomposition are consider expressions of soil microbial community diversity, metabolic requirements and nutrients available in the soil environment. Soil enzymes are sensitive to a variety of environmental factors such as temperature, pH, nutrients and water content. Understanding the presence and activity of soil enzymes in soil nutrient cycles may have important implications the microbial community structure and reactions to ecosystem disturbances. This review provides a brief introduction to seven soil enzymes involved in important nutrient cycle in the soil: β-glucosidase, βglucosaminidase, arylsulfatase, amidase, urease, alkaline phosphatase and acid phosphatase. In addition, an overview of two common assay enzyme analysis, spectrophotometric and fluorometric, is discussed with an emphasis on what the measurements mean and the supportive role of soil enzyme activity in research. The importance of enzyme research as an indicator of soil health is pivot for future understanding of microbial community composition and diversity in disturbed and natural ecosystems to maintain and support a sustainable, healthy soil ecosystem.
Liouville Correspondence Between the Short-Pulse Hierarchy and the Sine-Gordon Hierarchy

Abstract The Liouville correspondence between the short-pulse integrable hierarchy and the sine-Gordon integrable hierarchy is studied. It is shown that the transformation relating the short-pulse equation with the sine-Gordon equation also establishes the correspondence between their flows and Hamiltonian conservation laws in respective hierarchy. This proposes an alternative approach to derive the Hamiltonian conservation laws of the short-pulse equation from the known ones of the classical sine-Gordon equation.
Operation of Meshed Hybrid Microgrid During Adverse Grid Conditions With Storage Integrated Smart Transformer

Microgrid faces various challenges such as reverse power flow, peak power demand, voltage limit violations, etc., due to significant installations of renewable energy sources (RESs) and electric vehicles (EVs). For continuous and effective operation of such a system, enhanced and complex control methodologies are needed. Smart transformer (ST) has emerged as a promising solution for overcoming such challenging issues. This paper proposes the operation of battery energy storage system (BESS) integrated ST in a meshed hybrid microgrid. Instead of connecting through ac interconnections with normally open (NO) circuit breakers (CBs), the same line is connected through the medium voltage (MV) dc links of ST. This introduces several paths for controlled power flow in the system. As compared to ac interconnections, this configuration improves the performance of the overall system during various adverse operating conditions such as reverse power flow, peak power demand, and voltage sag. Moreover, the MVDC line voltage is controlled by one ST, resulting in reduced control complexity of other power converters. Detailed simulation and experimental results are provided to show the merits of the proposed system.
MOTOR-COGNITIVE DUAL-TASK PERFORMANCE: A NEURO-COGNITIVE APPROACH INVESTIGATING AGE-RELATED DIFFERENCES BASED ON THE ‘THEORY OF VISUAL ATTENTION’ Dissertation zur Erlangung des akademischen Grades

Dual tasking, or the simultaneous execution of two continuous tasks, is frequently associated with a performance decline that can be explained within a capacity sharing framework. In this study, we assessed the effects of a concurrent motor task on the efficiency of visual information uptake based on the ‘theory of visual attention’ (TVA). TVA provides parameter estimates reflecting distinct components of visual processing capacity: perceptual threshold, visual processing speed, and visual short-term memory (VSTM) storage capacity. Moreover, goodness-of-fit values and bootstrapping estimates were derived to test whether the TVA-model is validly applicable also under dual task conditions, and whether the robustness of parameter estimates is comparable in singleand dual-task conditions. 24 subjects of middle to higher age performed a continuous tapping task, and a visual processing task (whole report of briefly presented letter arrays) under both singleand dual-task conditions. Results suggest a decline of both visual processing capacity and VSTM storage capacity under dualtask conditions, while the perceptual threshold remained unaffected by a concurrent motor task. In addition, goodness-of-fit values and bootstrapping estimates support the notion that participants processed the visual task in a qualitatively comparable, although quantitatively less efficient way under dual-task conditions. The results support a capacity sharing account of motor-cognitive dual tasking and suggest that even performing a relatively simple motor task relies on central attentional capacity that is necessary for efficient visual information uptake.
Challenges and Strategies using Application in Online Teaching during Pandemic

This study aims to explain (1) the teacher’s challenges using the application in online teaching classroom during the COVID-19 pandemic, and (2) the strategies used by teachers in teaching using the application. The participants of the research were the English teachers at SMA/SMK/MA in Yogyakarta, Gunungkidul, Magelang, and Kebumen. A qualitative method was employed in this research and using interviews via WhatsApp as a data collection technique. The findings showed that teachers had the challenges such as (1) operating the application during online learning, (2) developing the material, assessment, and evaluation in teaching, (3) developing the teaching method, (4) combining the material and application, and (5) motivating the students and parents. The strategies used by the teacher when using the application in teaching such as (1) watching the video tutorial, (2) choosing a suitable application for teaching, (3) arranging the new learning schedule, (4) developing the creativity in designing the material, and (5) giving the simple material and task for the students.
An application of matrix eQTL to billions hypothesis testing to identify expression quantitative trait loci in genome wide association studies of inflammatory bowel disease

Introduction: Genome wide association studies (GWAS) have been widely used in recent years to identify new information on genetic variants which are associated with complex trait in many diseases. Advances in identifying the Single Nucleotide Polymorphisms (SNPs) facilitate the study of etiologies of common disorders including cancers, inflammatory bowel diseases (IBD) and colorectal cancer. Variations in gene expression demonstrate that transcript levels of many RNAs behave as heritable quantitative traits. Studying the genetics of gene expression can provide additional power to the roles of GWAS variants. Expression quantitative trait loci (eQTL) mapping links the genome-wide SNPs with RNA expression. Methods: In this study, we performed expression quantitative trait loci (eQTL) analysis using the Matrix eQTL R package. This technique implements matrix covariance calculation and efficiently runs ANOVA and linear regression analysis for eQTL studies. The statistical test determines the association between SNP and gene expression, where the null hypothesis is no association between genotype and phenotypes. False Discovery Rate (FDR) is used to identify significant cis and trans eQTL and adjust for multiple hypothesis testing. Results: We applied matrix eQTL to a real data set consisting of 730,256 SNP and 33,298 RNA for 173 samples. SNPs with minor allele frequency (MAF) less than 0.05 and those violating the Hardy_Weinberg equilibrium (HWE), were excluded from the study. In this study, 15,408 cis eQTL and 27,562 trans eQTL are identified at a FDR less than 0.05, corresponding to p value thresholds of 8e-5 and 1e-8, respectively. Conclusion: We found out that matrix eQTL is a computationally efficient and user friendly method for analysis of eQTL studies. Our application provides insight into the genomic architecture of gene regulation in inflammatory bowel disease (IBD).
ST_OS: An Effective Semisupervised Learning Method for Course-Level Early Predictions

A course-level early final study status prediction task is to predict as soon as possible the final success of each student after studying a course. It is significant because each successful course accomplishment is required for a degree. Further, early predictions provide enough time to make necessary changes for ultimate success. This article aims at an effective solution to this task. Different from the existing works, we resolve the task in a more practical context. First, the temporal aspects of the task and its data are considered. For the task, historical datasets are used to support the task on current ones. For the data, both labeled and unlabeled data before the midterm break are used. Second, our solution examines assessment data for the task, and thus, requires less data collection cost and effort over time. Third, we propose a semisupervised learning method, ST_OS, to obtain a better prediction model because ST_OS handles data insufficiency when exploiting all the labeled and unlabeled data. Moreover, ST_OS combines Self-Training and Tri-Training to create a resulting ensemble model in an effective semisupervised learning process with local learning for each selected unlabeled instance. Above all, the task is addressed in a general manner for different course types. As a result, our solution outperforms several existing supervised and semisupervised learning ones with higher Accuracy and F-measure. Therefore, it can be used as a forecasting tool before their courses end. More activities can be then improved to help the students complete the courses successfully.
Aggression and Peer Violence Manifestation in Youth Sport – The Case Study

This study was designed to detect aggression and peer violence manifestation forms in youth sports in Subotica. The sample consisted of athletes (N=255), aged between 11 and 18 years. Besides presenting descriptive statistics, the Mann-Whitney U test was used to assess the differences in variables (gender, school age, training years, sport type) among the forms of violence that can occur and among the ones that do occur. The results indicate the presence of aggression and violence. The most frequent place is in the dressing room, and the more common period is after or during training and while traveling to competitions, sports camps, etc. The study indicates significant differences in sexual violence between primary and secondary school-age children. Moreover, physical and psychological violence is significantly dominant in collective sports compared to the individual ones. Recognizing the presence of these phenomena is the first step toward reducing their potential negative outcomes.
Large-Scale Quantitative Structure–Property Relationship (QSPR) Analysis of Methane Storage in Metal–Organic Frameworks

Metal–organic frameworks (MOFs) present a combinatorial design challenge. The structural building blocks of MOFs can be combined to synthesize a nearly infinite number of materials. This suggests that computational tools, rather than experimental trial and error, can be used for high-throughput screening. Here, in the context of methane storage, we report the first large-scale, quantitative structure–property relationship (QSPR) analysis of MOFs. We investigated the effect of geometrical features, such as pore size and void fraction, on the simulated methane storage capacities of ∼130 000 hypothetical MOFs at 1, 35, and 100 bar at 298 K. From these data we developed models that can predict methane storage with high accuracy, based only on knowledge of the geometric features. Several models were developed: multilinear regression (MLR) models, decision trees (DTs), and nonlinear support vector machines (SVMs). In each case, 10 000 MOF structures were used to “train” the QSPR regression models, and the accur.
Analysis of convergence and divergence in chat conversations

A discussion between several participants is often accompanied by an exchange of information between speakers and, in the case of collaborative learning, a polyphonic interanimation is desired. They have, according to the polyphonic model, different points of view that are convergent or divergent by which one of the participants approves or disapproves of another person that is participating in the discussion. This paper presents an approach for identifying convergent or divergent utterances using neural networks and other machine learning methods in order to help people analyze a dialogue conducted in an online environment. Especially in collaborative chats used in education, this solution allows professors to identify how the participants in the discussion exchange information and how the debate evolved in time.
Patterning of Conductive Nano–Layers on Garnet

Synthetic crystalline materials of the garnet group are used as scintillators in scanning electron microscopy. If a thick conductive layer is applied on the garnet surface, slower electrons don’t have enough energy to pass through this relatively thick conductive layer on the scintillator surface. Therefore, either thinner conductive layer or appropriate patterning of the thicker layer has to be used. Within this contribution we study the patterning process of such conductive nano-layer. Resolution of the patterning process is of high interest. Two approaches are compared: direct writing electron beam lithography and mask projection UV lithography.
X-NEST: A Scalable, Flexible, and High-Performance Network Architecture for Distributed Machine Learning

In a large-scale distributed machine learning system, the interconnection network between computing devices has an important impact on performance in the training of neural network models. The current expansion of training data and model size has led to a rapid increase in the number of computing devices used in distributed machine learning systems, which places higher demands on network scalability. In addition, the synchronization algorithms used for data exchange between computing devices have different communication topologies, and traditional electrical networks have difficulty matching them due to their fixed network topology. Neural network models and model partitioning methods can also affect the amount of communication between devices, but the overprovisioned bandwidth of traditional electric networks incurs unnecessary costs. To address these issues, we propose a scalable, flexible, and high-performance network architecture called X-NEST. The flexibility of optical switching devices allows X-NEST to dynamically change its topology and the number of links between devices according to traffic pattern variations, thereby improving network performance and resource utilization. Although changes in the connection relationships between devices depend on the controller, the simple and flexible control plane of X-NEST can quickly respond to network communication requirements. Extensive analytical simulations using different traffic patterns demonstrate that X-NEST copes well with the communication characteristics of various synchronization algorithms.