Miracle-dz/UniFAR-FTUs huggingface dataset:
Export Quality and Growth in Sub-Saharan Africa: The Role of Public Institutions

ABSTRACT:We examine the impact of improved quality of exports on per capita income convergence between the United States (U.S.) and Sub-Saharan Africa (SSA) countries. We repeat that analysis for East Asian Tiger economies and SSA. We use a two-step system GMM estimator for dynamic panel models to test two hypotheses. First, that improved quality of exports increases per capita income convergence between the U.S. and the SSA region. Second, that the magnitude of the convergence is augmented by observed improvements in the quality of institutions and policy frameworks in the SSA region. Our findings show that improvements in the quality of food and livestock exports from SSA countries does indeed increase the rate of convergence in per capita GDP between the U.S. and SSA. Also, the convergence effect between the U.S. and SSA is observed with increased quality of manufactured exports from SSA, and it is about twice the effect obtained from increases in the quality of food and livestock exports. Similar results are obtained for convergence between the East Asian Tiger countries and the SSA region. In addition, the rate of convergence between the U.S. and SSA is highly augmented by increases in; anti-corruption efforts, ease of doing business, economic freedom, public investment in health, and progress in the quality of country policies and institutions. These results show that both food and livestock products, and manufactured goods, that are produced for export remain highly relevant for economic growth in the SSA region. International data shows that, when compared with the rest of the world, the SSA region scores low on these social and economic indicators. This underscores the importance of improving the quality of SSA public institutions to achieve sustainable, export-led economic growth.
A Methodology for Measuring and Monitoring Congested Corridors: Applications in Manhattan Using Taxi GPS Data

ABSTRACT This paper describes how the Taxi Passenger Enhancement Program (TPEP) Trip Record data can be used to measure traffic congestion along selected corridors in Manhattan. Two corridors in the rapidly changing Far West Side, Ninth Avenue and Tenth Avenue, were selected for analysis in this case study. This paper will describe a methodology using GIS applications that can visualize and identify trends in traffic conditions over time along corridors selected for analysis. This methodology can be applied to evaluate and monitor congested corridors in response to changes in land use patterns, policy decisions, and various temporary conditions affecting street operations.
Binarized Eigenphases for Limited Memory Face Recognition Applications

Most of the algorithms proposed for face recognition involve considerable amount of calculations, and hence they can not be used on devices of limited memory constraints. In this paper, we propose a novel solution for efficient face recognition problem for the systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the images. The binarization step that is applied to the phases adds many interesting advantages to the system. It will be shown that the proposed technique maximizes the recognition rate while achieving substantial savings in computational time, when compared to other known systems.
Music Genre Classification Based on Deep Learning

Human music life can be traced back to ancient times. The music art of human society is rich and colorful, which makes the music classification unable to classify efficiently and accurately. Moreover, the classification has become a daunting task. On this basis, this paper studies the method of deep learning for processing music classification. Not only is the design structure of music signal channel classified, but also all connected neural networks associated with the music are investigated to design an appropriate network model. According to different music sequence measurements, the feature sequence mechanism of music design feedback optimization is also investigated. The type probabilities of different calculated orbits are measured by softmax activation function, and the function value of cross loss is obtained. Finally, an Adam optimization algorithm is used as the optimization algorithm of the proposed network model. Subsequently, an independent adaptive learning planning rate is designed. By adjusting the network parameters, the first- and second-order estimates of the calculated gradient are classified. The experimental outcomes prove that the anticipated method can meritoriously increase the correctness of music classification and is helpful for music channel classification. Moreover, we also observed that the number of neurons in the network has also a significant impact over the training and testing errors.
Design of manifold multiplexers in all‐inductive dual‐mode rectangular waveguide technology using the coupling matrix formalism

This paper presents for the first time the design of manifold multiplexers in waveguide technology using all‐inductive dual‐mode channel filters. It is shown that very complex transfer functions can be implemented for the channels, using simple structures that can be analyzed and manufactured with increased simplicity as compared to other commercial solutions. In this paper we adapt a standard design technique for manifold multiplexers to the new proposed technology. The paper is illustrated with the design of two triplexers, using H‐type and E‐type waveguide manifolds, with filters implementing two transmission zeros in the insertion loss response for maximum isolation between channels. Results show that the procedure is indeed effective and can be used for the design of practical multiplexer configurations.
Evaluation of color changes, wettability, and moisture sorption of heat-treated blue-stained radiata pine lumber

To improve the color and surface properties of blue-stained radiata pine, heat treatment was carried out at different temperatures and times. The color change, wettability, and dynamic moisture sorption of the wood were investigated. The results showed that, as the treatment temperature and duration time increased, the surface brightness of specimens gradually decreased, and the surface color became more reddish and blueish. In addition, the contact angle of distilled water at 0 s and 18 s gradually increased as the treatment temperature and time increased, which indicated a decrease in the surface wettability. The color of the blue-stained wood became more uniform with reduced surface wettability and hygroscopicity but increased sorption hysteresis after the treatment.
Spiral CT Image Characteristics and Differential Diagnosis Secondary Pulmonary Tuberculosis and Lung Cancer Based on Visual Sensors

Helical CT plain scan has high spatial and area resolution, which is beneficial to the extraction of CT features of pulmonary nodules, and is of great significance for the diagnosis and differential diagnosis of pulmonary diseases. In order to deeply study the role of visual sensor image algorithm in CT image, this paper adopts clinical simulation method, data fusion method, and image acquisition method to collect images, analyze CT image features, and simplify the algorithm and create a CT model that can better diagnose secondary tuberculosis and lung cancer. We selected 45 patients with lung disease in this group, with an average age of 38 years. At the same time, the consistency analysis results of the diameter and plain CT value data of the five groups of cases measured by two observers are between 0.82 and 0.88, which has a good consistency. We could find that the nodule diameters of the five groups of cases were different (F =16.99, P < 0.01), and the difference was statistically significant (P < 0.06), indicating that our data are not only accurate but also very reliable. ROC was used to analyze the precise value of CT values in the pulmonary tuberculosis group and lung cancer group, intrapulmonary lymph node group, and pulmonary hamartoma group to determine the cutoff value. The results showed that the AUC values of the pulmonary tuberculosis group and the lung cancer group were 0.788, and the middle was the largest, indicating that the values were guaranteed. The basic realization starts with visual sensor technology and designs a clinical model that can more accurately identify CT images and differential diagnosis.
An improved shock wave capturing method in high Mach numbers

This study describes an improved scheme that can suppress numerical instabilities caused by calculating strong shock waves at high Mach numbers. The current scheme combines a clear detection of shock waves based on a sonic point condition with a hybrid numerical flux used in a dissipative scheme and a low-precision interpolation only near shock waves. This scheme is easily obtained by combining existing schemes often used in hypersonic flows. Its performance in a two-dimensional system is compared to existing schemes using two inviscid flows at Mach numbers 15 and 20. The results show that the scheme outperforms existing schemes in terms of improving accuracy around shock waves. It is also discovered that this scheme can prevent non-physical numerical oscillations caused by a mismatch between grid and local-shock forms. The significance of the scheme is demonstrated in the interaction field between the normal shock wave and the boundary layer in competition between viscosity and numerical dissipations. Therefore, the current scheme is useful in a high-Mach-number problem.
“3D computational simulation and experimental validation of structured materials: Case studies of tissue papers

The development and optimization of structured materials, such as tissue paper materials, benefit from modeling strategies that take into consideration its structural hierarchy at the fiber and the paper levels. The use of an innovative three-dimensional voxel approach to model both the fiber and the 3D paper structure were validated by comparison of the computational structures with the laboratory-made structures. The main goal of this work was to model tissue structures and obtain a computational implementation adapted for tissue products. The fibers were modeled in 3D according to their dimensions, and the structures produced by them were characterized using the Representative Elementary Volume (REV) and image analysis computational tools. This methodology made it possible to model the fibers according to their morphology, flexibility, and collapse, resulting in a tissue structure with thickness, porosity, relative bonding area, coverage, among other properties. The experimental design plan included the production and characterization of isotropic laboratory structures with basis weights of 20, 40, and 60 g/m2 with different eucalyptus fibers and beating degrees. With the aid of these advanced computational tools, mathematic models with predictive capacity for tissue properties such as softness, strength, and absorption can be developed.
Short‐term probabilistic forecasting for regional wind power using distance‐weighted kernel density estimation

As the integration of wind power into the power grid increases rapidly, the total output of the regional wind farms has become the concern of the power system operators and market traders. This study proposes a short-term probabilistic forecast model for this regional application. The uncertainty information provided by the proposed model can help the users make better decisions in the power system. A new distance-weighted kernel density estimation (DWKDE) method is proposed to forecast the full distribution function of the wind power. Its distance kernel is able to assign different weights to the samples similar to the target point. The beta kernels are introduced to adapt to the double-bounded characteristic of the wind power density. To further improve the performance of the DWKDE model, a regime-switching strategy is applied based on the regional wind direction clustering, while a feature selection method of minimal-redundancy-maximal-relevance is provided to determine the proper feature set. A case study of 28 wind farms in the East China is provided to evaluate the performance with the quality measures of reliability, sharpness, and the pinball score. The proposed method is easy to use and performs well according to the results of the evaluation.
Nursing students' burnout, stress, and coping during the COVID-19 Pandemic

Nursing students are not immune to the stress that comes with being a student nurse. Despite the fact that there is a lot of literature on the issue, there has been little attention paid to stress in student nurses. This study examined a number of scientific papers on nursing student burnout, stress, and coping techniques. This was done through the use of a systematic review and the collection of publications. This systematic study relied on the databases CINAHL, PubMed, Medline, and Google Scholar. These databases were selected for bibliographic information for publications on the study topic: burnout syndrome, stress, and coping skills, all of which were considered. Searches in English, during the previous two years (2020 & 2021). This systematic review revealed that nursing students were experiencing moderate to severe stress as a result of a shift in the way they were taught, a lack of clinical skills, and a concern of getting COVID 19. Nursing students, on the other hand, dealt with stress in a variety of ways. The review findings might help nursing educators build stress management strategies that are both scientifically verified and culturally suitable for their students.
Pathomechanisms in Lysosomal Storage Disorders

Lysosomal diseases are inherited metabolic disorders caused by defects in a wide spectrum of lysosomal and a few non-lysosomal proteins. In most cases a single type of primary storage material is identified, which has been used to name and classify the disorders: hence the terms sphingolipidoses, gangliosidoses, mucopolysaccharidoses, glycoproteinoses, and so forth. In addition to this primary storage, however, a host of secondary storage products can also be identified, more often than not having no direct link to the primary protein defect. Lipids glycosphingolipids and phospholipids, as well as cholesterol - are the most ubiquitous and best studied of these secondary storage materials. While in the past typically considered nonspecific and nonconsequential features of these diseases, newer studies suggest direct links between secondary storage and disease pathogenesis and support the view that understanding all aspects of this sequestration process will provide important insights into the cell biology and treatment of lysosomal disease.
Human Immunodeficiency Virus Infection Is Associated With Preterm Delivery Independent of Vaginal Microbiota in Pregnant African Women

Background. During pregnancy, the vaginal microbiota is relatively stable. However, African women have more diverse vaginal microbiota than their European counterparts, in addition to high human immunodeficiency virus (HIV) prevalence and risk of adverse birth outcomes. Although HIV is associated with alterations in vaginal microbiota and inflammation in nonpregnant women, these relationships are underexplored in pregnant women. Methods. In this study, we characterize the vaginal microbiota and immune factors in pregnant African women who were HIV-uninfected (n = 314) versus HIV-infected (n = 42). Mucosal samples were collected once at the enrollment visit (between 15 and 35 weeks of gestation) and women were followed until delivery. Results. Vaginal microbial communities of pregnant women with HIV were significantly more diverse than women without HIV ( P = .004), with community structure also differing by HIV status ( P = .002, R 2 = 0.02). Human immunodeficiency virus infection was also associated with increased risk of preterm birth (PTB) (31% versus 15.3%; P = .066). In a multivariate analysis, HIV infection was independently associated with diverse vaginal community state type (CST)-IVA ( P = .005) and CST-IVB ( P = .018) as well as PTB ( P = .049). No association between HIV status and cytokine concentrations was found. Conclusions. Longitudinal studies with accurate gestational age assessment would be important to confirm these relationships.
A BERT’s Eye View: Identification of Irish Multiword Expressions Using Pre-trained Language Models

This paper reports on the investigation of using pre-trained language models for the identification of Irish verbal multiword expressions (vMWEs), comparing the results with the systems submitted for the PARSEME shared task edition 1.2. We compare the use of a monolingual BERT model for Irish (gaBERT) with multilingual BERT (mBERT), fine-tuned to perform MWE identification, presenting a series of experiments to explore the impact of hyperparameter tuning and dataset optimisation steps on these models. We compare the results of our optimised systems to those achieved by other systems submitted to the shared task, and present some best practices for minority languages addressing this task.
Utilization of Village Funds and Village Development Expenditures: The Role of Village Budget Stress as Moderator Variable

:- Inequality in development between regions in Indonesia is still a challenge for the government. The facts prove that there are still provinces, districts and villages that are categorized as underdeveloped. Therefore, the importance of using village funds in reducing development inequality is an important concern of the government. In addition, the inconsistency of prior research findings suggests the importance of this study. This study aims to examine the effect of the utilization of village funds on village development expenditures moderated by village budget stress. The research sample was all provincial governments using a purposive sampling method that reported village fund data, village fund allocations, village development expenditures, revenue, and expenditures for 2017-2019. This study uses Partial Least Square (PLS) to test the research hypothesis. The results suggests that the utilization of village funds has a positive and significant effect on village development expenditures, village budget stress has a negative and significant effect on village development expenditures. However, village budget stress cannot moderate the effect of the utilization of village funds on village development expenditures. The results of the sensitivity test support the main test results of this study. The implication of this research is important for the village government to utilize village funds and village fund allocations to reduce the village budget stress, so that it can increase village development expenditures in achieving the level of prosperity of all village residents.
CHARACTERIZATION OF PHYSICAL AND MECHANICAL PROPERTIES OF ROCKS FROM OTANMÄKI, FINLAND

Characterization of physical and mechanical properties of rocks from Otanmäki, Finland. Abubakar Osmanu University of Oulu, Mineral Resources and Sustainable Mining (Mining engineering and Mineral Processing) Master’s thesis 2020, 96 pages. Supervisor: Dr. Adeyemi Aladejare Physical and mechanical properties of rocks are important parameters for geological engineering and design of engineering structures, be it in the civil and/or mining sector. Rock physical properties include density, porosity, etc., and Young’s modulus, Poisson’s ratio and rock strength include some mechanical properties of rocks. These properties can be obtained by laboratory tests. This study aims at characterizing selected rock physical and mechanical properties to assist in predicting rock mass behavior when used in engineering structures, to discuss key rock petrographical features that affect strength and compare the prediction capacities of multiple linear regression and artificial neural network (ANN) models. The study investigates selected physical and mechanical properties from two igneous rock types, gabbro and granite, from the Otanmäki area, central Finland. The test results were used for the ANN and multiple regression models. In the analyses, a total of 25 cases from the two rocks were tested for uniaxial compression strength (UCS), Young’s modulus, Poisson’s ratio, Brazilian tensile strength (BTS), density, porosity and water content. Samples were also analyzed for petrographic and chemical compositions. Results from the analyses indicate the importance of adhering to testing standards because of inconsistencies and wide variations observed between nonstandardized as opposed to standardized specimens, and the need for large database for reliable predictive models. It presents ANN techniques as having a good generalization capacity for multi-variable nonlinear prediction. key words: Uniaxial compressive strength, Brazilian tensile strength, Young’s modulus, multiple regression, artificial neural network, Otanmäki, Finland
Stock Price Prediction Using Random Forest Method and Twitter Sentiment Analysis

: Stock price forecasting could be a vital and thriving topic in financial engineering especially since new techniques and approaches on this matter are gaining ground constantly. Within the contemporary era, the ceaseless use of social media has reached unprecedented levels, which has led to the belief that the expressed public sentiment could be correlated with the behaviour of stock prices. The concept is to acknowledge patterns which confirm this correlation and use them to predict the future behaviour of the assorted stock prices. With little doubt, though uninteresting individually, tweets can provide a satisfactory reflection of public sentiment when taken in aggregate. We develop a system which collects past tweets, processes them further, and examines the effectiveness of varied machine learning techniques like Naive Bayes Bernoulli classification and Support Vector Machine (SVM), for providing a positive or negative sentiment on the tweet corpus. Subsequently, we employ the identical machine learning algorithms to research how tweets correlate with exchange price behaviour. Finally, we examine our prediction's error by comparing our algorithm's outcome with next day's actual close price. Overall, the final word goal of this project is to forecast how the market will behave within the future via sentiment analysis on a collection of tweets over the past few days, also on examine if the idea of contrarian investing is applicable. the ultimate results seem to be promising as we found correlation between sentiment of tweets and stock prices.
FarsWikiKG: An Automatically Constructed Knowledge Graph for Persian

—We present FarsWikiKG, a Persian knowledge graph extracted from Wikipedia. Wikipedia infoboxes have been used as a valuable resource for building knowledge graphs in recent years. FarsWikiKG consists of more than 2 million entities, as well as 5.7 million facts about the entities. Using Wikidata, we constructed an ontology with more than 6000 classes representing entity types. As the second Persian knowledge graph, which has the ability of self-update, FarsWikiKG shows improvement on NLP tasks, especially question answering systems. Although FarsWikiKG is a dynamic knowledge graph, our evaluation shows a coverage of 90% on Persian Wikipedia pages. As Wikipedia information is constantly changing, a fixed knowledge graph can provide unstable data to the user. The proposed system, in addition to solving the problem of unstable data, reduces the need for experts to extract and construct knowledge graphs manually. Storing information in RDF as a standard method of storing knowledge graph information, FarsWikiKG allows NLP systems to run SPARQL queries on it.