Miracle-dz/UniFAR-FTUs huggingface dataset:
Does trade openness contribute to driving financing flows for development

Trade has been recognized in the 2030 development Agenda as well as in the Addis Ababa Agenda for Action as an important means for the implementation of the Sustainable Development Goals (SDGs). This paper questions whether trade openness could be an important driver of financing for development flows, notably development aid (ODA), Foreign Direct Investment (FDI) inflows, and government public revenue, the latter being the ultimate financing source for development for any country. The paper also takes advantage of the framework of analysis to investigate the interplay between these three types of financial flows. Relying on an unbalanced panel dataset comprising 125 countries, of which 37 are Least Developed Countries (LDCs), the empirical analysis uses the three stage least squares econometric approach and provides two important pieces of evidence: first, international trade openness is an important tool for driving financial flows for development, including government public revenue, development aid and FDI. As a result, it could contribute to mobilizing the substantial financial resources needed for development, including for the implementation of the SDGs. Second, there are strong interactions (complementarity and substitutability) between these three types of financing for development flows.
Effectiveness of an accounting videogame in terms of attributes , motivation and learning outcomes

The objective of this study is to assess the effectiveness of an accounting videogame with regard to attributes, motivation, and learning outcomes. Results show that, regarding attributes, most of them were favourably assessed, being the most valued those related to fun, engagement and the provision of clear information about the goals while the less valued were those regarding feedback. For motivation, results show that also participants perceive the learning experience as motivating, with a particular focus on enjoyment and sense of success. And regarding learning outcomes, results show the highest values meaning that students perceive that they are learning with the experience and achieving their objectives.
DAYLIGHT PERFORMANCE OF SINGLE PEDENTIVE DOME MOSQUE DESIGN DURING WINTER SOLSTICE

In this study, the analysis is to measure lighting performance of single pendentive dome type and pyramid roof type in mosque design built during the Ottoman Empire in Istanbul, Turkey and Mostar, BosniaHerzegovina. The selected case studies are the Firu zaga and Orhan Gazi Mosques as single pendentive dome styl and Neziraga Mosque as case study for the pyramid roof type. This analysis aims to have a research finding to show that Turkish style’s peden tive dome mosque design provides efficient indoor daylighting in the Orhan Gazi Mosque in comparison with the Firuzaga Mosque and Neziraga. This assessment is simulated during winter solstice occu rred when the sun is perimetering at its most south ern position along the Tropic of Capricorn. This study applies simulation analysis using Autodesk software known as 3D Studio Max Design 2011 programme. The simulations were done by the program using weather data file to provide the weather informatio n and climate changes of the study area. The analys is shows that both mosques have mostly an evenly distr ibuted illuminance level with Scales 1 to 5. The Or han Gazi Mosque has slightly higher illuminance levels compared to those of the Firuzaga Mosque and bothe of the pendentive dome mosques have butter illuminance level than the pyramid roof mosque. The study concludes that the pedentive dome mosque design has an effect on the mosque indoor daylighting. Having excellent illuminance level distributed at all the locations is one of the crucial reasons why the mos ques with pedentive dome roof cover are built by Ottoman master builders.
Robust weighted scan matching with quadtrees

This paper presents the improvement of the robustness and accuracy of the weighted scan matching algorithm matching against the union of earlier acquired scans. The approach allows to reduce the correspondence error, which is explicitly modeled in the weighted scan matching algorithm, by providing a more complete and denser frame of reference to match new scans. By making use of the efficient quadtree data structure, earlier acquired scans can be stored with millimeter accuracy for environments with dimensions larger than 100x100 meter. This can be realized with the preservation of real-time performance. In our experiments we illustrate the significant gains in robustness and accuracy that can be the result with this approach.
When and why do initially high achieving poor children fall behind ? 1

Copyright and reuse: The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.
Charged rotating dilaton black holes with Kaluza-Klein asymptotics

We construct a class of stationary and axisymmetric solutions to the 5D Einstein-Maxwell-dilaton gravity, which describe configurations of charged rotating black objects with Kaluza-Klein asymptotics. The solutions are constructed by uplifting a vacuum seed solution to six dimensions, performing a boost, and a subsequent circle reduction. We investigate the physical properties of the charged solutions, and obtain their general relations to the properties of the vacuum seed. We also derive the gyromagnetic ratio and the Smarr-like relations. As particular cases we study three solutions, which describe a charged rotating black string, a charged rotating black ring on Kaluza-Klein bubbles, and a superposition of two black holes and a Kaluza-Klein bubble.
The Role of Topography on the Shape of Unit Hydrographs in Small and Medium Sized Watersheds through a Physical Model

This study intends to establish the main relations between topographic characteristics of the watershed and the main parameters of the unit hydrograph measured at the outlet. It looks to remove the subjectivity found in traditional synthetic methods and the trial and error setting of the main parameters of the hydrograph. The work was developed through physical experimentation of the rainfall-runoff process using the observed information of different watersheds of Chiapas, Mexico, as the reference. The experiments were carried out on a state-of-the-art semi-automatic runoff simulator, which was designed and built specifically for this study. Polynomial regression and fuzzy logic models were obtained to confirm the hypothesis of hydrological parameters being obtained from topographic data only by assuming uniform precipitation. Empirical relations were found for the peak flow, time to peak, base time and volume of the unit hydrograph and the watershed area, the main stream average slope, and the length of the stream of highest order. The main finding is that a unit hydrograph can be described based only on the watershed area when fuzzy logic models are applied.
Machine Translation Optimization using Hybrid Architectures

Humans learn their culture through language. Its inquisitive nature of human and passion to travel across the world, warrants different cultures interact with each other, the means to achieve this is through human language, often interacting cultures communicate through different languages. To add to this, off-late, the increased use of social networking and the flood of information in foreign languages through web, the use of machine translation technology became inevitably significant. Researchers have predominantly employed two approaches in machine translations, based either on rule-based or statistical approaches. Both approaches have their strengths and weaknesses. Researchers working on both Rule-based and Statistical approaches have shown a keen interest in the area of hybrid machine translation. These systems try to profit from other respective approaches, combining data-driven and knowledge-driven elements. The interest is to investigate different hybrid arrangements required for optimizing the machine translations which will contribute to an overall increase in MT quality.
Effector Detection Problem in Social Networks

Nowadays, different innovations spread rapidly in online social networks. An activation state can indicate whether each user adopts the target information. The effector detection problem aims to find a way to generate an activation state as close to an observed one as possible. In this article, based on the influence spread, the unconstrained and constrained effector detection problems are proposed. To tackle them, we design two approximation algorithms since the problem is NP-hard, and the objective function is nonsubmodular. For the unconstrained case, our objective function can be best provided with the difference of two submodular functions. Thus, we address this problem through the modular–modular algorithm. For the constrained case, we devise the solutions for the original function, submodular upper bound, and lower bound according to an idea of reverse influence sampling. Then, there is a data-dependent approximate solution using the sandwich approximation algorithm. Finally, we show the correctness and superiority of our methods through massive experiments in three real-world networks.
Students’ self-regulation and motivation in learning science

The student's self-regulation and motivation are essential for students to study. Especially with 21st-century learning students are required to be more active in learning than teachers. This Mixed Methods research aimed to determine the influence between the students' self-regulation with the motivation of students learning. The sample size was 534 students from Madrasah Tsanawiyah in Jambi city, Indonesia determined by total sampling technique. Data were then analyzed with the SPSS 21 application to find descriptive statistics as well as inferential statistics using simple regression and for comparison using the independent sample T-Test. The results of this research are dominant in both self-regulation and student motivation in learning, which is strengthened by the relationship and influence between self-regulation and the motivation of students in learning. It is seen from the value of sig = 0.019 and has a contribution of 70.3%. According to the results, it is recommended that students need to be given the opportunity to conduct learning directly and teachers must perform innovative learning.
Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction

Data-driven public security networking and computer systems are always under threat from malicious codes known as malware; therefore, a large amount of research and development is taking place to find effective countermeasures. These countermeasures are mainly based on dynamic and statistical analysis. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. Based on this understanding, we first propose a method to de-obfuscate and unpack the malware samples. Additional, cross-method-based big data analysis to dynamically and statistically extract features from malware has been proposed. The Application Programming Interface (API) call sequences that reflect the malware behavior of its code have been used to detect behavior such as network traffic, modifying a file, writing to stderr or stdout, modifying a registry value, creating a process. Furthermore, we include a similarity analysis and machine learning algorithms to profile and classify malware behaviors. The experimental results of the proposed method show that malware detection accuracy is very useful to discover potential threats and can help the decision-maker to deploy appropriate countermeasures.
Soil Fungal Community Responses to the Silver Nanoparticles Contamination as Assessed by Illumina Next Generation Sequencing (NGS)

The increasing use of silver nanoparticles (AgNPs) due to its excellent antimicrobial activity in commercial products prompting concerns about their fate in the environment. The toxicity of AgNPs is mainly the result of Ag + ions. In this study, soil was experimentally contaminated with 100 mg kg -1 of AgNO 3 to investigate its effect on fungal soil community. Deoxyribonucleic acid (DNA) from the soil was extracted at the 6 th , 12 th , and 24 th month of observation and assessed by Illumina Next Generation Sequencing (NGS). The results show that, the pollutant change fungal community in soil. After 12 months incubated the number of fungal species in the soil reduced significantly and 40% of the community was dominated by one species.
Semiotic Approach to Evaluating the Quality and Veracity of CSR Reporting

This study employs the use of a linguistic based theory and analytical tool – Semiotics – to investigate the quality and veracity of Corporate Social Responsibility (CSR) as disclosed in annual reports. To do this, the texts of Corporate Community Involvement (CCI) narratives in the annual reports of sampled companies were analysed in order to reveal the reality of the disclosures. The study revealed that signification of reality is either doubtful or unreal for most sampled companies. Consequently, CCI disclosure could be perceived as just another management process which enables companies to signal CSR compliance. As well as the novelty of introducing semiotics into the CSR disclosure literature, this paper presents a unique CSR Semiotic Reality Model capable of guiding corporations in their CSR activities and reporting.
The Role of Noninvasive Imaging for Detection High Risk Patients with Subclinical Atherosclerosis

Citation: Hristova K (2016) The Role of Noninvasive Imaging for Detection High Risk Patients with Subclinical Atherosclerosis. Arch Clin Hypertens 2(1): 004-012. DOI: http://dx.doi. org/10.17352/ach.000005 004 identification of new early risk factors as well as early diagnosis of subclinical CVD in asymptomatic individuals who are at The sudden death is the primary consequence of coronary artery disease in 50% of men and 64% of women. Historically an early detection of atherosclerosis has become eligible due to the noninvasive imaging techniques for patients with risk factors, allowing assessing and confirming plaque presence and estimating the extent of the flow limiting stenosis. This remodeling of the arterial wall is accelerated in diseases mentioned as inflammatory-related abnormalities and autoimmune mechanisms may work synergistically with traditional risk factors, making them particularly harmful and therefore contribute to endothelial dysfunction, which is considered the first step in atherogenesis.
Amplified Flow Imaging (aFlow): A Novel MRI-Based Tool to Unravel the Coupled Dynamics Between the Human Brain and Cerebrovasculature

With each heartbeat, periodic variations in arterial blood pressure are transmitted along the vasculature, resulting in localized deformations of the arterial wall and its surrounding tissue. Quantification of such motions may help understand various cerebrovascular conditions, yet it has proven technically challenging thus far. We introduce a new image processing algorithm called amplified Flow (aFlow) which allows to study the coupled brain-blood flow motion by combining the amplification of cine and 4D flow MRI. By incorporating a modal analysis technique known as dynamic mode decomposition into the algorithm, aFlow is able to capture the characteristics of transient events present in the brain and arterial wall deformation. Validating aFlow, we tested it on phantom simulations mimicking arterial walls motion and observed that aFlow displays almost twice higher SNR than its predecessor amplified MRI (aMRI). We then applied aFlow to 4D flow and cine MRI datasets of 5 healthy subjects, finding high correlations between blood flow velocity and tissue deformation in selected brain regions, with correlation values ${r}= {0.61}$ , 0.59, 0.52 for the pons, frontal and occipital lobe ( ${p} < {0.001}$ ). Finally, we explored the potential diagnostic applicability of aFlow by studying intracranial aneurysm dynamics, which seems to be indicative of rupture risk. In two patients, aFlow successfully visualized the imperceptible aneurysm wall motion, additionally quantifying the increase in the high frequency wall displacement after a one-year follow-up period (20%, 76%). These preliminary data suggest that aFlow may provide a novel imaging biomarker for the assessment of aneurysms evolution, with important potential diagnostic implications.
Optimizing Broodstock Conditioning for the Tropical Clam Anomalocardia brasiliana

ABSTRACT The tropical clam Anomalocardia brasiliana is a commercially important bivalve living along the south Atlantic coast of Latin America. Harvest of natural stocks is the principal method for supplying market demand—the market average price ranging from 5 to 10 US$ per kilogram of meat. As a first step in the development of hatchery techniques for A. brasiliana seed production, this study examined the effects of temperature and salinity on conditioning broodstock for spawning. Two treatments tested conditioning at 25°C at salinities of either 30 or 35. A third treatment examined the effects of an initial 10-day conditioning at 16°C followed by a gradual temperature increase to 25°C at a salinity of 35. As a control, clams were sampled from the wild over the same experimental period. Tissue samples were taken at the initiation of the experiment and at 15, 30, 45, and 55 days of conditioning and examined histologically to determine changes in the sexual development of the clams. Four reproductive stages were identified during the experimental period: gametogenesis, mature, spawned, and absorption. The treatment with the initial 10-day conditioning period at 16°C demonstrated that it is possible to synchronize gamete development in both males and females to result in the maximum proportion of mature broodstock at 55 days of conditioning.
From the discrete to the continuous brachistochrone: a tale of two proofs

In a previous paper (2019 Eur. J. Phys. 40 035005) we showed how to design a discrete brachistochrone with an arbitrary number of segments. We have proved, numerically and graphically, that in the limit of a large number of segments, N ≫ 1, the discrete brachistochrone converges into the continuous brachistochrone, i.e. into a cycloid. Here we show this convergence analytically, in two different ways, based upon the results we obtained from investigating the characteristics of the discrete brachistochrone. We prove that at any arbitrary point, the sliding bead has the same velocity on both the continuous and discrete paths, and the radius of the curvature of both paths is the same at corresponding points. The proofs are based on the well-known fact that the curve of a cycloid is generated by a point attached to the circumference of a rolling wheel. We also show that the total acceleration magnitude of the bead along the cycloid is constant and equal to g, whereas the acceleration vector is directed toward the center of the wheel, and it rotates with a constant angular velocity.
Error-Bounded Graph Construction for Semi-supervised Manifold Learning

Graphs are commonly used in semi-supervised learning to represent a manifold on which the data reside in a high-dimensional ambient space. The graph can then be utilized in different ways, typically via the Laplacian of the graph, in order to leverage associations among the unlabeled data to improve learning. One common way to leverage the graph Laplacian is as a regularization term, where models that would disagree with the graph are penalized. More often the spectrum of the graph Laplacian is used to find a lower dimensional embedding in which neighboring relations encoded via the graph are preserved. Most manifold-based methods of semi-supervised learning depend upon geometric structure in the ambient feature space in order to construct a graph whose edges encode similarity that should be useful in selecting a model. A critical assumption is that some standard measure of similarity applied to the ambient space can be used to construct a graph that is error-free or of low error, meaning that examples (i.e., vertices) from distinct classes are not connected. However, this assumption often precludes the use of such methods in noisy or complex feature spaces, even though such spaces often arise in problems that can most benefit from structure that might be uncovered within the unlabeled data. This paper presents a method of graph construction for manifold-based semi-supervised learning that respects the manifold assumptions underlying these methods and bounds the error on the graph itself, which then permits bounds on the overall generalization error of the learning algorithms without relying on assumptions that do not hold in many modern problem domains.
UvA-DARE ( Digital Academic Repository ) From stress to engagement

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COMBINED USE OF MATLAB / SIMULINK AND MULTIBODY SIMULATION SOFTWARE BASED ON C + +

Simulation of complex mechatronic systems like an automobile, involving multibody components as well as active electronic control devices, can be accomplished by combining tools that deal with the simulation of the different subsystems. In this sense, it is desirable to couple a multibody simulation software (for the mechanical simulation) with a block diagram simulation software (for the simulation of electronical components), and to employ a co-simulation scheme to synchronize both tools. During the last years, the Laboratory of Mechanical Engineering of the University of A Coruña has been developing an open and modular software architecture for the simulation of multibody systems. Support for the analysis of non-mechanical components is not originally provided in this package and it has been added through the coupling with an external simulation tool. In this research, a communication interface between the multibody simulation software and a platform for numerical computation has been developed and implemented. Several communication techniques have been tested, and optimal application fields have been delimited for each of them. The numerical performance of each technique has been studied, in order to assess its suitability for high demanding applications, such as real-time simulations. Comparisons between monolithic and co-simulation implementations of the models have been carried out, and results have been analyzed to quantify their numerical efficiency. Francisco J. González, Manuel González and Aki Mikkola 2