Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Governance on Financial Development in West African Countries

The study explores the impacts of governance on financial development in West African countries. Data for the study were obtained from the World Bank Database covering the period from 2006-2017. The variables for governance comprise the six world governance indicators while financial development was represented by the ratio of bank deposit to GDP and domestic credit to the private sector. The control variables included in the model are interest rate, inflation, GDP per capita and trade openness. The findings from the study revealed that political stability and absence of violence and regulatory quality have significant effects on both proxies of financial development. Also, voice and accountability, trade openness and interest rate show significance on the ratio bank deposit to GDP while government effectiveness (negative), rule of law, and control of corruption show significance on domestic credit to the private sector. Thus, improving the quality of governance through strengthening legal and institutional frameworks, enforcement of standards and empowering of supervisory agencies, introducing an efficient regulatory environment to facilitate financial inclusion will play significant roles in the pace of financial development in the West Africa region.
Identified of Potential Therapeutic Targets for Salivary Gland Adenoid Cystic Carcinoma by Bioinformatic Analysis

Salivary Gland Adenoid Cystic Carcinoma (SACC) is the most common type of salivary gland malignancy associated with inferior chemoradiotherapy, which behaves unfavorable prognosis and poor overall survival. There is a growing body of literature that focuses on the biomarker research. However, only a limited number of pharmaceutical targets have been identified. The objectives of this research are to screen the potential biomarker from microarrays and determine the putative drug target by bioinformatics analysis. To identify hub genes, microarrays data was downloading from Gene Expression Omnibus (GEO) in order to screen 1282 genes. Functional analysis and pathway enrichment were addressed using DAVID. After 47 hub genes felted by STRING and Cytoscape, 18 candidates drug targets were performed using Oncomine analyses. The network of these potential biomarkers was provided with cBio Portal online analysis. In conclusion, this study offers some insights into understanding the link between the effective bioinformatics approach and potential biomarker, which makes a contribution to therapeutic targets research.
Comparative Study of the Test-Re-Test Reliability of Four Instruments to Measure Grip Strength in a Healthy Population

Outcome measures need to have an acceptable level of reliability for sound use in clinical and research practice. This study compared the test-re-test reliability of four hand dynamometers measuring grip strength. Using a counterbalanced, repeated measures design, 30 healthy individuals performed three consecutive grip measurements on the MIE digital grip analyser, Baseline Bulb, Jamar® and Lafayette hand dynamometers in a random order on two separate days at the same time. Results indicated that all tools showed a similar and acceptable level of reliability, with a minimum of 90% of the repeat grip data falling within 95% of agreed levels of agreement. The dominant hand showed less variation in grip strength than the non-dominant. Hand pressure was seen to be no less reliable than hand force in a healthy population. Clinicians and researchers can be reassured that the test-re-test reliability of four commonly available dynamometers is acceptable for clinical and research work.
Challenges and Promises in Mitigating Transportation Noise

Ian A. Waitz is director of PARTNER (Partnership for AiR Transportation Noise & Emissions Reduction) and the Jerome C. Hunsaker Professor of Aeronautics and Astronautics, Massachusetts Institute of Technology. Robert J. Bernhard is vice president for research, Notre Dame University. Carl E. Hanson is supervisory consultant, Harris Miller Miller & Hanson Inc. There are only a few places in the United States where transportation noise is not noticeable.1 Figure 1 shows the results of an analysis estimating the percentage of each county in the United States where highway, rail, and aircraft noise are noticeable during the day (Miller, 2003). In 1981, the Environmental Protection Agency (EPA) estimated that 19.3 million people in the United States were exposed to a day-night average sound level (Ldn) greater than 65 dB from highway traffic. The corresponding numbers
Covariate Regularized Community Detection in Sparse Graphs

In this document we collect technical details and accompanying lemmas which are necessary for the main results in the paper Covariate Regularized Community Detection in Sparse Graphs. When we make references to equations or theorems etc. in the main document, we follow the numbering scheme of the main document, and the references do not have any alphabets in them. In Sec. A we introduce the background for sub-gaussian random vectors. In Sec. B, we present the proof of Lemma 1. Proofs for graph only and covariates only SDP (Propositions 1 and 2) are shown in Sec. C and Sec. D respectively, and the proof of the main theorem is in Sec. E. Sec. F shows the results and proofs related to dimensionality reduction, and Sec. G establishes an upper bound of clustering error in terms of the Frobenius deviation of SDP solution to ground truth clustering matrix. Finally, we present additional experimental results in Sec. H.
Agronomic potential value of household urban solid wastes by composting and composts quality assessment

Using composting technology, the biodegradable fraction of solid wastes collected from Agbalepedogan district in Lomé (Togo) was transformed into composts with no phytotoxicity suitable for use in agriculture. The household urban solid wastes were mixed with poultry manure and Mucuna pruriens leaves in appropriate percentages and transformed into composts. The composting process was monitored by temperature, pH and C/N ratio controlling. The composts quality was appreciated by phytotoxicity test, particle size distribution, contents of nitrogen, phosphorus, potassium, organic matter and trace elements. The results showed that composts particle size distribution presented four fractions (>10 mm, 5-10 mm, 2-5 mm and ≤2 mm) whose average percentages are respectively 9.04±0.2%; 10.71±0.2%; 12.68±0.4% and 67.53±0.6%, respectively. Composts produced contained 1.02±0.1% 2.72±0.2% of nitrogen, 1.35±0.3% 2.70±0.4% of phosphorus, 1.31±0.2% 1.72±0.3% of potassium and 23.50±0.4% 37.75±0.5% of organic matter. The pH in final composts were between 8.50±0.2 and 9.00±0.3 while C/N ratio varying from 8.06±0.2 to 13.13±0.2. The concentrations of some heavy metals in final products were 79.8±1.7 140.2±1.6 mg/kg.dm of zinc, 27.1±1.01 76.6±1.03 mg/kg.dm of copper, 2.07±0.33 9.19±0.22 mg/kg.dm of lead, 4.13±0.16 11.05±0.36 mg/kg.dm of nickel and 0.79±0.02 2.15±0.03 mg/kg.dm of cadmium. These concentrations were lower than the limited value for heavy metals prescribed by French and US EPA regulation for organic and organic-mineral fertilizers.
CHARLES UNIVERSITY IN PRAGUE

Since its birth in the 1980s, pairs trading has become a widely used strategy for making profits among hedge funds and institutional investors. This technique identifies pairs of securities whose historical prices show long-run relationship, and takes advantage of their short-term relative mispricing. Profit is generated due to correcting behavior of security prices as they converge towards equilibrium value of their spread. The aim of this thesis is to compare two traditional approaches to pairs trading: cointegration and sum of squared deviations between normalized historical returns, known as distance criterion, within the Prague Stock Exchange equity market. We further investigate whether the two methods, so commonly employed in the US equity market, can be applied with similar success in the PSE. Our results reveal that the strategy using distance criterion outperforms the method of cointegration in nearly every aspect considered. Nevertheless, its returns are not statistically different from zero, and in other measures the return distribution lags behind the one found in the US equity market.We conclude that with the form of trading we present here the PSE is not a suitable stock market for pairs trading technique. JEL classification: G10, G11
The Relationships between Technology and User Satisfaction in Online Tax Filing: The Mediating Role of Confirmation of Expectation

—This study investigates the relationship between the quality of the e-file system and user satisfaction. Building on the success of information systems (IS) and user satisfaction literature, this study adds confirmation of expectation as a mediating variable. This study collected the research data through a web-based survey. Structural Equation Modeling-Partial Least Square (SEM-PLS) was used to analyze the data from an online questionnaire on 220 e-tax service users in Indonesia. The results confirm that the quality dimensions of e-files consisting of information, systems, and service quality are factors that influence user satisfaction. The study also found that there were indirect effects of information system success factors on user satisfaction through the confirmation of expectations. The high quality of the e-file system can meet user expectations so that it can lead to their satisfaction. The study concludes with useful implications for academics, policymakers, and tax authorities in the economic context that appears in the future.
Coupling Nicotiana tabaccum Transgenic Plants with Rhizophagus irregularis for Phytoremediation of Heavy Metal Polluted Areas

AURORA NEAGOE1,2*, GABRIELA TENEA3, NATALIA CUCU3, STELIAN ION4, VIRGIL IORDACHE1 1 University of Bucharest, Research Center for Ecological Services (CESEC), 13 Portocalelor Alley, 060101 Bucharest, Romania. 2 Valahia University of Targoviste, Faculty of Environmental Engineering and Food Science, Campus, 13 Sinaia Alley, 1300014, Targoviste, Romania 3 University of Bucharest, Faculty of Biology, Department of Genetics, Transgenesis and Epigenetics Laboratory, 1-3 Portocalelor Alley, 060101, Bucharest, Romania 4 Gheorghe Mihoc Caius Iacob Institute of Statistical Mathematics and Applied Mathematics, Romanian Academy, 13 Calea 13 Septembrie, 050711, Bucharest, Romania
Why did deep convection persist over four consecutive winters (2015–2018) southeast of Cape Farewell?

Abstract. After more than a decade of shallow convection, deep convection returned to the Irminger Sea in 2008 and occurred several times since then to reach exceptional convection depths (> 1500 m) in 2015 and 2016. Additionally, deep mixed layers deeper than 1600 m were also reported southeast of Cape Farewell in 2015. In this context, we used Argo data to show that deep convection occurred southeast of Cape Farewell (SECF) in 2016 and persisted during two additional years in 2017 and 2018 with a maximum convection depth deeper than 1300 m. In this article, we investigate the respective roles of air–sea buoyancy flux and preconditioning of the water column (ocean interior buoyancy content) to explain this 4-year persistence of deep convection SECF. We analyzed the respective contributions of the heat and freshwater components. Contrary to the very negative air–sea buoyancy flux that was observed during winter 2015, the buoyancy fluxes over the SECF region during the winters of 2016, 2017 and 2018 were close to the climatological average. We estimated the preconditioning of the water column as the buoyancy that needs to be removed (B) from the end-of-summer water column to homogenize it down to a given depth. B was lower for the winters of 2016–2018 than for the 2008–2015 winter mean, especially due to a vanishing stratification from 600 down to ∼1300 m. This means that less air–sea buoyancy loss was necessary to reach a given convection depth than in the mean, and once convection reached 600 m little additional buoyancy loss was needed to homogenize the water column down to 1300 m. We show that the decrease in B was due to the combined effects of the local cooling of the intermediate water (200–800 m) and the advection of a negative S anomaly in the 1200–1400 m layer. This favorable preconditioning permitted the very deep convection observed in 2016–2018 despite the atmospheric forcing being close to the climatological average.
Improving accuracy and efficiency of CFD predictions of propeller open water performance

This research proposes mesh and domain optimization strategies for a popular Computational Fluid Dynamics (CFD) technique to estimate the open water propulsive characteristics of fixed pitch propellers accurately and time-efficiently based on examining the effect of various mesh and computation domain parameters. It used a Reynolds-Averaged Navier-Stokes (RANS) solver to predict the propulsive performance of a fixed pitch propeller with varied meshing, simulation domain and setup parameters. The optimized mesh and domain size parameters were selected using Design of Experiments (DoE) methods enabling simulations in a limited memory and in a timely manner without compromising the accuracy of results. The predicted thrust and torque for the propeller were compared to the corresponding measurements for determining the prediction accuracy. The authors found that the optimized meshing and setup arrangements reduced the propeller opens simulation time by at least a factor of six as compared to the generally popular CFD parameter setup. In addition, the accuracy of propulsive characteristics was improved by up to 50% as compared to published simulation results. The methodologies presented in this paper can be similarly applied to other simulations such as calm water ship resistance, ship propulsion etc. to systematically derive the optimized meshing arrangement for simulations with minimal simulation time and maximum accuracy. This investigation was carried out using a commercial CFD package; however, the findings can be applied to any RANS solver.
House finches learn canary trills

ABSTRACT We analyse data from a cross-fostering experiment in which house finches were fostered by canary parents. Some individuals received canary song input, while others received no input, after a period of masking noise. We compare audio recordings of songs by these individuals to each other and to species-typical house finch and canary songs. Canary-tutored house finches learn to trill as part of their song. Since trills are not present in typical house finch song, naturally occurring song patterns underestimate what a species is capable of learning and producing. These results highlight a potential avenue for the origin of novel syntax in songbirds.
1DCNN Fault Diagnosis Based on Cubic Spline Interpolation Pooling

The conventional pooling method for processing one-dimensional vibration signals may lead to certain issues, such as weakening and loss of feature information. The present study proposes the cubic spline interpolation pooling method. The method is appropriate for processing one-dimensional signals. The proposed method can transform the pooling problem into a linear fitting problem, use the cubic spline interpolation method with outstanding fitting effects, and calculate the fitting function of the input signals. Moreover, the values of the interpolation points are sequentially taken as the feature value output. Furthermore, the network using the conventional pooling method and the pooling network model proposed in the present study are compared, tested, and analyzed on the constructed simulation signals and the measured bearing dataset. It is concluded that the proposed pooling method can reduce the data dimension while improving the network feature extraction capability and is more appropriate for pooling one-dimensional signals.
Environmental Applications and Bio-Profiling
of Tribulus Terrestris: an Ecofriendly Approach

The antibacterial and antioxidant activity of different parts of Tribulus terrestris, such as leaves and roots, was carried out using methanol and n-hexane as solvents. The leaves of plant have greater extraction yield than roots. The antibacterial activity was checked against both gram positive bacteria (Staphylococcus aureus) and gram negative bacteria (Escherichia coli). Antibacterial activity was performed by disk inhibition method. Leaves show greater antibacterial activity (29 mm and 30 mm) against both S. aureus and E. coli, whereas roots show (16 mm and 21 mm) against both species respectively. Antioxidant activity was carried out by 1, 1-diphenyl-2-picrylhydrazyl (DPPH) scavenging free-radical assay. Leaf extract showed greater antioxidant activity (73%) than that of roots (52%). Total phenolic content (TPC) and total flavonoid content (TFC) were determined by spectrophotometer analysis. The results indicate that leaf extract contains higher phenolic and flavonoid contents (723 mg Gallic acid equivalents (GAE)/g and 476 mg Quercetin equivalents (QE)/g as compared to roots extract (235mg GAE/g and 93 mg QE/g).
The Importance of Physiological, Structural and Trait

The capacity of Amazon forests to sequester carbon is threatened by climate change-induced shifts in precipitation patterns. However, the relative importance of plant physiology, ecosystem structure, and trait composition responses in determining variation in gross primary productivity (GPP), remain 35 largely unquantified, and vary among models. We evaluate the relative importance of key climate constraints to gross primary productivity (GPP), comparing direct plant physiological responses to water availability and indirect structural and trait responses (via changes to leaf area index (LAI), roots and photosynthetic capacity). To separate these factors we combined the Soil-Plant-Atmosphere model with forcing and observational data from seven intensively studied forest plots along an Amazon 40 drought stress gradient. We also used machine learning to evaluate the relative importance of individual climate factors across sites. Our model experiments showed that variation in LAI was the principal driver of differences in GPP across the gradient, accounting for 33% of observed variation. Differences in photosynthetic capacity (Vcmax and Jmax) accounted for 21% of variance, and climate (which included physiological responses) accounted for 16%. Sensitivity to differences in climate was highest where 45 shallow rooting depth was coupled with high LAI. On sub-annual timescales, the relative importance of LAI in driving GPP increased with drought stress (R=0.72), coincident with decreased whilst the importance of solar radiation decreased (R=0.90). Given the role of LAI in driving GPP across Amazon forests, improved mapping of canopy dynamics is critical, opportunities for which are offered by new satellite-based remote sensing missions such as GEDI, Sentinel and FLEX. 50
VarFish - Collaborative and Comprehensive Variant Analysis for Diagnosis and Research

VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of panel and exome variant data for rare disease genetics. It is capable of processing variant call files with single or multiple samples. The variants are automatically annotated with population frequencies, molecular impact, and presence in databases such as ClinVar. Further, it provides support for pathogenicity scores including CADD, MutationTaster, and phenotypic similarity scores. Users can filter variants based on these annotations and presumed inheritance pattern and sort the results by these scores. Filtered variants are listed with their annotations and many useful link-outs to genome browsers, other gene/variant data portals, and external tools for variant assessment. VarFish allows user to create their own annotations including support for variant assessment following ACMG-AMP guidelines. In close collaboration with medical practitioners, VarFish was designed for variant analysis and prioritization in diagnostic and research settings as described in the software’s extensive manual. The user interface has been optimized for supporting these protocols. Users can install VarFish on their own in-house servers where it provides additional lab notebook features for collaborative analysis and allows re-analysis of cases, e.g., after update of genotype or phenotype databases.
The role of cell free DNA and liquid biopsies in haematological conditions

Cell free nucleic acids (CFNAs) are nucleic acids released from cells that circulate within bodily fluids. Recent advances in molecular techniques have led the ability to interrogate CFNAs in a clinically meaningful way, for example the identification and assessment of foetal CFNAs in maternal blood, allowing minimally invasive testing for foetal genetic abnormalities. The majority of CFNAs arise from haemopoietic cells, making it a particularly rich source of genetic information in haematological conditions. Furthermore, the innate genetic heterogeneity of haematological malignancies, as epitomised by multiple myeloma, lend itself well to “liquid biopsies”. This approach promises to provide a more wholistic assessment of whole disease genetics, especially when contrasted against the current gold-standard of single site tissue biopsies. This review briefly summarises the definitions and physiology of CFNAs, both cell free DNA (cfDNA) and extracellular RNA (exRNA), before exploring the literature surrounding the current and future roles of cfDNA in the haematological malignancies and patient care.
Enhanced Approaches In Decision Support System Using Ai For Achieving Precision Medicine

: The deep learning based healthcare technology is evolving recently to identify, classify and give precision medicine to patients. Recent advancement in internet of things, cyber physical systems brings healthcare technology into doorstep. This paper reviews the various diagnosis methods of healthcare industries dealing with cancer, diabetics, heart failure which seeks computational intelligence techniques to identify and treat patients with better care. The various research articles collected from popular journals like science direct, PubMed, ACM, IEE, Clinical Oncology have been taken to analyze its experimental methods and their outcomes. This review mainly focused on dealing electronic health records (EHR), cancer prediction model using recent deep learning techniques and some of framework based mechanisms which automate healthcare process. This article deals with major recent algorithms like support structured vector machine, auto encoder, convolution neural network etc., in the clinical setting of cancer and major diseases and diagnostic setting like genomic sequence based mechanisms. The healthcare industry has their own processing techniques to deal with various predictions and treatment like gene based techniques, clinical laboratory testing, observation model, diagnostic model. It also requires many statistical reference model and medical reference to acquire quick prediction along with patient’s information. As a result decision support system using AI for realizing precision medicine can be delivered by CNN positively.