Miracle-dz/UniFAR-FTUs huggingface dataset:
HOW AUDIT FIRM SIZE MODERATE EFFECT OF TOE CONTEXT TOWARD AUDITOR ADOPTION OF MACHINE LEARNING

The era of industrial revolution 4.0 which is being intensively present today, business is increasingly moving towards digital. More big data companies have emerged, which require audit services. This also makes auditors have to develop, not only using manual systems but also using information technology assistance such as artificial intelligence and machine learning. The research conducted aims to determine the auditor's acceptance of the use of machine learning when dealing with Industry 4.0 which is intended to provide assistance to audit reports in customer finances. The instrument used in the research relates to the Technology Organizations Environment (TOE) approach as well as the size of an audit firm. included in quantitative research, the data used are primary data obtained through questionnaires distributed to the research object that has been determined, namely auditor workers at general accounting firms. In this study, hypothesis testing was conducted between research variables using path analysis, Structural Equation Modeling Partial Least Square (SEM PLS), in which the independent variables in the study were the technological context, organizational context and environmental context, while the moderating variables of the study were firm size and dependent variable is the auditor's adoption of machine learning. The research carried out gives results, namely an understanding of the adoption of auditors in the use of information technology systems used in the audit process, as well as a description given to auditors regarding the use of technology systems that can improve the quality of work that is more effective and efficient both in terms of time and energy deployed. In the research conducted, the researcher hopes to provide a summary of the description of technology adoption by financial auditors related to professionally managed audit firms.
Speci�c pathway abundances in the neonatal calf faecal microbiome are associated with susceptibility to Cryptosporidium parvum infection: A metagenomic analysis

Background Cryptosporidium parvum is the main cause of calf scour globally. With limited therapeutic options and research compared to other Apicomplexa, it is important to understand the parasites’ biology and interactions with the host and microbiome in order to develop novel strategies against this infection. The age-dependent nature of symptomatic cryptosporidiosis suggests a link to the undeveloped immune response, the immature intestinal epithelium, and its associated microbiota. This led us to hypothesise that speci�c features of the early life microbiome could predict calves’ susceptibility to C. parvum infection.
A ﬂow-curvature-based model for channel meandering in tidal marshes

Channel meandering is ubiquitous in tidal marshes, yet it is routinely omitted in morphodynamic models. Here we propose a novel numerical method to simulate channel meandering in tidal marshes on a Cartesian grid. The method calculates a ﬁrst-order ﬂow by considering the balance between pressure gradient and bed friction. To account for ﬂow momentum shift towards meander outer banks, the ﬂow is empirically modiﬁed. Unlike previous simpliﬁed methods that relied on the curvature of the bank, this modiﬁcation is based on the curvature of the ﬂow, making the model suitable for use in dendritic channel networks. The modiﬁed ﬂow intrinsically accounts for the topographic steering eﬀect, which tends to deﬂect the momentum toward the outer bank. As a result, the outer bank becomes steeper and erodes due to soil creep. Additionally, the outer bank experiences erosion proportional to the ﬂow curvature. This erosion mechanism parameterizes the direct erosion caused by ﬂow impacting the bank through a proportionality coeﬃcient, which modulates the rate of lateral channel migration. Deposition on the inner bank is automatically simulated by the model, triggered by reduced bed shear stress in that area. The model accurately reproduces meander sinuosity, migration rates, and associated processes such as cutoﬀs, channel piracies, and network reorganizations. The model provides an eﬃcient tool for predicting marsh landscape evolution from decades to millennia, which will enable exploring how lateral migration and meandering of tidal channels aﬀect marsh ecomorphodynamics, carbon and nutrient cycling, drainage eﬃciency, and pond dynamics.
' s personal copy Corporate governance and earnings management in the Chinese listed companies : A tunneling perspective ☆

This paper examines the relation between earnings management and corporate governance in China by introducing a tunneling perspective. We document systematic differences in earnings management across the universe of China's listed companies during 1999–2005, and empirically demonstrate that firms with higher corporate governance levels have lower levels of earnings management. We study two Chinaspecific situations, in which the listed firms have strong incentives to manage earnings in order to meet certain return on equity (ROE) thresholds, and earnings management has been shown to be the most conspicuous. We identify tunneling evidence for each. Our empirical findings, although not being able to completely exclude other explanations, strongly suggest that agency conflicts between controlling shareholders and minority investors account for a significant portion of earnings management in China's listed firms. © 2007 Elsevier B.V. All rights reserved. JEL classification: G32; G34; M41; M43
Biphasic Targeting And Cleavage Furrow Ingression Directed By Biphasic Targeting And Cleavage Furrow Ingression Directed By The Tail Of A Myosin II The Tail Of A Myosin II

localization depends on the IQGAP Iqg1. We also show that the Myo1 tail is sufficient for promoting the assembly of a “headless” AMR, which guides membrane deposition and extracellular matrix remodeling at the division site. Our study establishes a biphasic targeting mechanism for myosin II and highlights an underappreciated role of the AMR in cytokinesis beyond force generation
Water Scarcity and Degradation in the Rio San Juan Watershed of Northeastern Mexico

Freshwater has become a limited resource for development in the Rio San Juan watershed, the major tributary of the Lower Rio Bravo, in northeastern Mexico. The results of unsustainable practices include: the inter-basin transfer of freshwater, groundwater depletion, low or nonexistent river flows in several river segments, rising pollution levels, high per capita water use, low irrigation efficiency, the disturbance of riparian ecosystems, and social conflict between water uses and users, all exacerbated by recurrent drought episodes of several temporal scales. This report shows that improving sustainable management practices could make it possible to meet current conventional water demands for agriculture, population, industry, as well as for riparian environments and ease social tension but policies are required to increase water use efficiency in all economic sectors, together with new partnerships that cut across disciplinary and professional boundaries.
A GRID CONNECTED HYBRID RENEWABLE ENERGY SYSTEM FOR OPTIMAL ENERGY MANAGMENT BASED ON ANT-LION OPTIMIZATION ALGORITHM

Over the past several years, the need of hybrid renewable energy systems (HRES) integrated with the electrical grid system has significantly increased. This integration provides a better reliability, continuous supply, and improved system performance. The optimal size (rating) of a grid connected HRES is proposed in this paper. The Ant-Lion Optimization (ALO) algorithm is used for optimization. For the ALO algorithm, the decision variables are the number of PV panels (N PV ) and wind turbines (N WT ). The optimization considers a particular priority based on limitations to supply the load demand connected with the hybrid system from PV arrays, wind turbines, and ultimately the grid. Additionally, the impact of independently combining solar PV and wind with a single objective function is examined. The Total Net Present Cost (TNPC) and the Index of Reliability (IR), which serve as the objective function, are minimized using the ALO. Two different scenarios are proposed. After running the optimization algorithm, the two proposed scenarios are compared with each other in term of TNPC and IR. Thus, this determines the number of PVs and wind turbines to build HRES.
Dissection of Genotype x environment interaction and multi-trait selection for identi�cation of stable hybrids in okra [Abelmoschus esculentus (L.) Moench

Okra also known by lady's �nger or gumbo is the most signi�cant versatile vegetable in the world’s tropical and subtropical climates, well-renowned for its soft and �avorful pods. The complexity in yield improvement of any crop is escalated due to genotype x environment (G × E) interactions. Likewise success in okra improvement is limited by G × E interaction and needs to be determined for use in future decision-making process. Multivariate selection strategies like AMMI and GGE biplots were utilized in this study that graphically depict interrelationships between the tested environments and genotypes with simple visual evaluations of okra hybrids possessing high and stable yield. Whereas MTSI was used to identify okra hybrids with dynamic traits that executes better across varied environmental conditions. A set of 13 traits were accessed in okra 24 hybrids along with 10 parents evaluated under multi-environment trials. Among the selected genotypes, H23 and H10 were promising as they produced elevated and adaptable fruit yield altogether with traits that are favoured much by the farmers. MTSI also con�rmed high and desired selection gains in all traits among selected genotypes. Further, agronomic trials to determine best crop management practices of identi�ed superior genotypes can be done to introduce them as improved cultivars for diverse environments.
ENHANCING ARABIC FAKE NEWS DETECTION FOR TWITTERS SOCIAL MEDIA PLATFORM USING SHALLOW LEARNING TECHNIQUES

One of the most significant and severe issues facing social media nowadays is fake news, especially in social media applications - the intentional deception of users through the spread of propaganda, rumors, or misleading information about a variety of people or issues. A very common social media platform with the highest rapidly increasing number of users in the Middle East is Twitter. However, along with an increase in the number of Twitter users has come an increase in the amount fake news being spread across the platform. This, in turn, has caught the attention of researchers who seek nothing more than an online experience free of fake news. As are result, through utilizing both the transformer-based language and recurrent neural network models, this paper introduces an intelligent classification model that identifies fake news presented in Arabic tweets. Afterwards, this research presents a comparative study between deep learning and shallow learning. In Addition, to enhance the effectiveness of the proposed model, researchers also built an Arabic Twitter dataset that included 206,080 tweets. Results have shown that pre-trained deep learning models performed much better than shallow models when it came to identifying fake news in Arabic. That is, a 95.92% accuracy rate was reached when researchers applied shallow learning to the pre-processed data set; however, after applying the LSTM model to the same data, the accuracy rate increased to 96.71% while after apply the Bert model to that same data, the accuracy rate increased to a near perfect 99%.
Identifying barriers to environmental management accounting practices: a comparative study of Nigeria and South Africa

This study established the level of environmental management accounting practices among listed firms in Nigeria and South Africa, and identified the barriers limiting such practices. The study utilised primary data through the administration of structured questionnaire, and a total of 44 accountants (22 from each country) participated. Data collected were analysed using descriptive statistics involving mean scores, frequencies, tables and percentages. The study found that EMA practices are higher in South Africa than Nigeria given the number of EMA techniques applied by firms in South Africa (n=72) compared to Nigeria (n=41). Also, as indicated by the mean scores, the study identified institutional barrier (3.4148) as the major factor preventing EMA practices in Nigeria while EMA practices in South Africa are prevented mainly by financial barriers (3.5605). This paper concluded that the government and other stakeholders in Nigeria should play active roles in making and enforcing environmental laws and regulations so as to curb complacency in relation to environmental issues among firms. Also, given the long term benefits of EMA practices, the government of South Africa should introduce green tax incentives and other market-based environmental policy instruments as reward for environmental performance by firms. This will help ameliorate the short-term negative effect which EMA practices have on their financial performance.
University of Birmingham Location determinants of green technological entry:

In this paper, we explore the spatial distribution and the location determinants of new green technology-based firms across European regions. Integrating insights from evolutionary economic geography and the literature on knowledge spillovers, we study the importance of new knowledge creation and the conditioning role played by regional technological relatedness in fostering combinatorial opportunities underlying the process of green technological entry. The analysis is based on a dataset covering over 900 NUTS3 regions for 15 European countries obtained merging economic data from ESPON-Eurostat and patent information from the PATSTAT-CRIOS database for the period 1996 – 2006. Our results show that the geographical distribution of green technological entry across European regions is not evenly distributed, offering evidence of spatial path dependence. In line with this, we find evidence of a significant role played by the characteristics of the regional innovation system. New green innovators are more likely to develop in regions defined by higher levels of technological activity underlying knowledge spill-overs and more dynamism in technological entry. Moreover, our findings point to an inverted-U relationship between regional technological relatedness and green technological entry. Regions whose innovation activity is defined by cognitive proximity to environmental technologies support interactive learning and knowledge spillovers underlying entrepreneurship in this specific area. However, too much relatedness may cause technological lock-ins and reduce the set of combinatorial opportunities.
Multiple Equilibria of a Double-Gyre Ocean Model with Super-Slip Boundary Conditions

Abstract The steady-state solutions of a barotropic double-gyre ocean model in which the wind stress curl input of vorticity is balanced primarily by bottom friction are studied. The bifurcations away from a unique and stable steady state are mapped as a function of two nondimensional parameters (δI, δS), which can be thought of as measuring respectively the relative importance of the nonlinear advection and bottom damping of relative vorticity to the advection of planetary vorticity. A highly inertial branch characterized by a circulation with transports far in excess of those predicted by Sverdrup balance is present over a wide range of parameters including regions of parameter space where other solutions give more realistic flows. For the range of parameters investigated, in the limit of a large Reynolds number, δI/δS → ∞, the inertial branch is stable and appears to be unique. This branch is antisymmetric with respect to the midbasin latitude like the prescribed wind stress curl. For intermediate valu.
SOFTWARE EFFORT ESTIMATION USING HIERARCHICAL ATTENTION NEURAL NETWORK

In every software project, a software effort estimation process is not only vital but also extremely critical. Project success or failure depends massively on, the concise knowledge of effort and schedule estimates. The development of agile techniques in the field of software development has presented researchers and practitioners with many opportunities and challenges. An estimated effort for agile software development is one of the main challenges. Although traditional estimates of effort are used to estimate effort for agile software projects, most of them lead to inaccurate estimates. This paper focuses on the development of the agile effort estimation model. A machine learning classifier that uses information contained within an issue report is proposed to classify the difficulty or the weight of a given task according to a range of story point scales. The model has two levels of attention mechanisms implemented at the word and sentence levels, allowing it to pay distinguished attention to more and less relevant semantic features when constructing the document representation. The proposed model has achieved 87% classification accuracy. An empirical evaluation demonstrates that our approach has a greater or at least equivalent F score, Precision, and Recall when compared to classical classifiers.
The receptive vocabulary size of Moroccan learners of Modern Standard Arabic *

This study is an attempt to measure the receptive vocabulary size of Moroccan learners of Modern Standard Arabic (MSA). Although these learners, just like other Arab learners, are often assumed to be native speakers of MSA, not much is known about the development of their proficiency in the language and, consequently, it is not clear whether their vocabulary size should be compared to that of native or non-native speakers of other languages. To contribute to a better understanding of this issue, this study used a Yes/No test based on Buckwalter and Parkinson’s (2011) list of the 5000 most frequent words in the language. 121 participants were conveniently selected from the 5th, 7th, and 9th grades. The results show that they recognized a mean of around 3500 words and that the difference between levels was not always significant. This vocabulary size is relatively less than the vocabulary size of native speakers in non-diglossic societies, thus pointing to weak proficiency in language skills.
RECOGNITION OF SPEAKER’S EMOTION BY SQUEEZENET CONVOLUTIONAL NEURAL NETWORK

The article deals with the development of neural network means for analyzing a voice signal to recognize the speaker’s emotions. We have established the possibility of improving these means through the use of a convolutional neural network of the SqueezeNet type, which determines the necessity to assess the effectiveness of such use. We have also determined that it is possible to assess the efficiency of using the neural network model experimentally by means of indicators of recognition accuracy and duration of training. A software implementation of SqueezeNet has been developed, with a training sample formed, using the publicly available TESS database, consisting of samples of voice signals with 7 emotions for 2 users. Mel-frequency cepstral coefficients are used as the parameters characterizing a voice signal. Using computer experiments, we have found that after 80 periods of training on a fairly limited training sample, SqueezeNet enables using validation examples to achieve speaker recognition accuracy of about 0.95, which is proportionate to the results of the best modern systems of the similar purpose and confirms the possibility of effective use of this type of network for analyzing a voice signal. We have shown the necessity for further research related to the adjustment of neural network solutions to the recognition of the speaker’s emotions under a variety of noise interference. We have also determined the feasibility of developing a method for adjusting SqueezeNet architectural parameters to the conditions of the task to analyze a voice signal for simultaneous recognition of the speaker’s personality and emotions.
Optimization of yield and quality of agar polysaccharide isolated from the marine red macroalga Pyropia yezoensis

Title Optimization of yield and quality of agar polysaccharide isolated from the marine red macroalga Pyropia yezoensis Author(s) Sasuga, Keiji; Yamanashi, Tomoya; Nakayama, Sigeru; Ono, Syuetsu; Mikami, Koji Citation Algal Research, 26, 123-130 https://doi.org/10.1016/j.algal. 2017.07. 010 Issue Date 2017-09 Doc URL http://hdl.handle. net/2115/75361 Rights © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Rights(URL) http://creativecommons.org/licenses/by-nc-nd/4.0/ Type article (author version) File Information Sasuga Mikami.pdf
Seismicity and Seismotectonics Study in Southwestern Albania Using a Local Dense Seismic Network

Abstract The Albanides represent a complex orogen made up of a heterogeneous tectonic nappe pile of Paleozoic, Mesozoic, and Cenozoic domains. Albania is tectonically active, and moderate to strong earthquakes have occurred in the past. However, abundant microseismicity has not been monitored and studied by a dense seismic network. During this study, a seismic network of 40 stations was deployed in southern Albania for one year. A total of 2113 microearthquakes were well located. The most accurately located events and 810 focal mechanisms were used in order to define the seismotectonics and the stress pattern in the area. Results indicate that thrust and strike‐slip faulting both exist in southwestern Albania, suggesting a continuation of the complex tectonic setting of the neighboring northwestern Greece to the north.
FACTORS AFFECTING THE USE OF YOUTUBE AS A MEDIA SUPPORTING STUDENT LEARNING PERFORMANCE

The purpose of this study was to determine whether using YouTube as a medium to support student learning can improve learning performance by identifying the factors that encourage students to use YouTube as a medium to support learning. Therefore, this research was conducted by combining the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). This research was conducted using a survey method. This research was conducted on 906 respondents, with results showing that YouTube's perceived ease of use has a significant positive effect on perceived usefulness, YouTube's perceived ease of use has a significant positive effect on intent to learn, YouTube perceived ease of use has no effect on actual learning, perceived usefulness a significant positive effect on the intention to learn, perceived usefulness has a significant positive effect on actual learning, facilitating conditions have a significant positive effect on the intention to learn, intention to learn has a significant positive effect on actual learning, self-control has a significant positive effect on actual learning, and actual learning has a significant positive effect on significant positive on learning performance. The results of this study add to current knowledge about the importance of using YouTube as a medium to support student learning. Teachers can also use this to develop appropriate strategies to improve the quality of student learning with the support of learning media such as Youtube.