Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Health Level on Agricultural Productivity in Cameroon

The objective of this study is to assess the effect of health on agricultural productivity in Cameroon. To achieve this objective, we use the techniques of Propensity Score Matching on data from the fourth Cameroonian household survey (ECAM 4). The results of the analysis show that healthy farmers produce on average than farmers in fragile health. Indeed, healthy farmers produce an average of FCFA 583,601 per hectare. This effect is statistically significant at the 5% threshold. Due to the fact that the level of health improves agricultural yields, we recommend to the public authorities, to facilitate farmers' access to health care through a reduction in health care costs, but also through a multiplication of health centers in rural areas.
A framework for analysis of real-time nucleic acid amplification data using novel multidimensional standard curves

Research into improving methods for absolute quantification of nucleic acids using standard curves has plateaued despite its positive, far-reaching impact on biomedical applications and clinical diagnostics. Currently, the mathematics involved in this mature area is restricted by the simplicity of conventional standard curves such as the gold standard cycle-threshold (Ct) method. Here, we propose a novel framework that expands current methods into multidimensional space and opens the door for more complex mathematical techniques, signal processing and machine learning to be implemented. The heart of this work revolves around two new concepts: the multidimensional standard curve and its home - the feature space. This work has been validated using phage lambda DNA and standard qPCR instruments. We show that the capabilities of standard curves can be extended in order to simultaneously: enhance absolute quantification, detect outliers and provide insights into the intersection between molecular biology and amplification data. This work and its vision aims to maximise the information extracted from amplification data using current instruments without increasing the cost or complexity of existing diagnostic settings.
A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts

To support Group Decision-Making processes when participants are dispersed is a complex task. The biggest challenges are related to communication limitations that impede decision-makers to take advantage of the benefits associated with face-to-face Group Decision-Making processes. Several approaches that intend to aid dispersed groups attaining decisions have been applied to Group Decision Support Systems. However, strategies to support decision-makers in reasoning, understanding the reasons behind the different recommendations, and promoting the decision quality are very limited. In this work, we propose a Multiple Criteria Decision Analysis Framework that intends to overcome those limitations through a set of functionalities that can be used to support decision-makers attaining more informed, consistent, and satisfactory decisions. These functionalities are exposed through a microservice, which is part of a Consensus-Based Group Decision Support System and is used by autonomous software agents to support decision-makers according to their specific needs/interests. We concluded that the proposed framework greatly facilitates the definition of important procedures, allowing decision-makers to take advantage of deciding as a group and to understand the reasons behind the different recommendations and proposals.
Revision of the palm-pollinating weevil genus Elaeidobius Kuschel, 1952 (Curculionidae, Curculioninae, Derelomini) with descriptions of two new species

The genus Elaeidobius Kuschel, 1952 (Curculionidae, Curculioniae, Derelomini) is an Afrotropical genus associated with the male inflorescences of the oil palm Elaeis guineensis Jacq. The activity of species in this genus is critical for pollen transportation and for the fruit set of this economically important palm. In this study, the genus Elaeidobius was revised using an integrative taxonomic approach, combining traditional taxonomic treatment of species and an analysis of sequences of mitochondrial genes (COI and COII). A total of eight species is now recognized: five now formally included within it [E. bilineatus Hustache, 1924) comb. nov., E. kamerunicus (Faust, 1898) comb. nov., E. plagiatus (Fahraeus, 1844) comb. nov., E. singularis (Faust, 1898) comb. nov., E. subvittatus Faust, 1898)], one transferred here from the genus Prosoestus to the genus Elaeidobius, E. spatulifer (Marshall, 1950) comb. nov., and two newly described species (E. pilimargo Haran & Kuschel sp. nov., E. piliventris Haran & Kuschel sp. nov.). The following new synonymies are proposed: Prosoestus armatus Voss, 1956 = E. bilineatus (Hustache, 1924) comb. nov. and Derelomus uelensis Hustache = E. singularis (Faust, 1898). An illustrated key to the species is provided with photographs of the adult habitus and male genitalia.
Development and validation of a research instrument for measuring the presence of democratic school leadership characteristics

The main purpose of this article is to describe the process of development and validation of a research instrument for measuring the presence of democratic school leadership characteristics. The instrument has been developed and validated in the city of Zagreb and Zagreb County in Croatia in four phases: theoretical construction; expert validation (N = 6 experts); pilot research (N = 77 teachers); and instrument structure validation through the main research (N = 651 teachers). The expert validation and pilot research took place in 2018. The main research was conducted in January and February 2019. According to Carmines and Zeller (1979), the results of the factor analysis, which show that the instrument is measuring a single phenomenon, indicate that all four hypotheses have been fulfilled. More specifically, several aspects of the extracted (i.e. unrotated) factor matrix support the following four hypotheses: the (unrotated) factor matrix of all 32 items explains a total of 66.793% of the variance; subsequent components demonstrate the fairly equal proportions of the remaining variance except for a gradual decrease; all items have high factor loadings on the first component (from .603 to .861); and all items have higher loadings on the first than on the second component.
An Ounce of Prevention

I look at prevention through an economic lens and make three main points. First, those advocating preventive measures are often asked how much money a given measure saves. This question is misguided. Rather, preventive measures can be thought of as insurance, with a certain cost in the present that may or may not pay off in the future. In fact, although most medical preventive measures improve expected health, they do not save money. Various lifestyle and early childhood interventions, however, may both save money and improve health. Second, preventive measures, including medical and life style measures, are heterogeneous in their value, both across measures and within measure, across individuals. As a result, generalizations in everyday discourse about the value of prevention can be overly broad. Third, health insurance coverage for medical preventive measures should generally be more extensive than coverage for the treatment of a medical condition, though full coverage of preventive services is not necessarily optimal.
MSS/1: Single‐Station and Single‐Event Marsquake Inversion

SEIS, the seismometer of the InSight mission, which landed on Mars on 26 November 2018, is monitoring the seismic activity of the planet. The goal of the Mars Structure Service (MSS) is to provide, as a mission product, the first average 1‐D velocity model of Mars from the recorded InSight data. Prior to the mission, methodologies have been developed and tested to allow the location of the seismic events and estimation of the radial structure, using surface waves and body waves arrival times, and receiver functions. The paper describes these validation tests and compares the performance of the different algorithms to constrain the velocity model below the InSight station and estimate the 1‐D average model over the great circle path between source and receiver. These tests were performed in the frame of a blind test, during which synthetic data were inverted. In order to propagate the data uncertainties on the output model distribution, Bayesian inversion techniques are mainly used. The limitations and strengths of the methods are assessed. The results show the potential of the MSS approach to retrieve the structure of the crust and underlying mantle. However, at this time, large quakes with clear surface waves have not yet been recorded by SEIS, which makes the estimation of the 1‐D average seismic velocity model challenging. Additional locatable events, especially at large epicentral distances, and development of new techniques to fully investigate the data, will ultimately provide more constraints on the crust and mantle of Mars.
Development of deep CNN architecture for anomalies in ECG signals

In this paper, we propose a deep learning convolutional neural network (CNN) approach to classify arrhythmia based on the time interval of the QRS complex of the ECG signal. The ECG signal was denoised using multiple filters based on the Pan Tompkins algorithm. QRS detection has been done using Pan Tompkins Algorithm. Then the QRS complex is identified using local peaks based technique inside the layers of the Convolutional Neural Network where the repeated application of the same filter to our input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input which in our case is the changes in the q-s time interval. Based on the R-R time interval, Heart rate variability (HRV) was computed, and Poincare plot was generated. Instead of using raw ECG signal to train the CNN, we used the feature extracted from ECG signal obtained from Physionet database to train the CNN and map the pattern changes for different classes of diseases. The classifier was then used to classify the test input as either or normal, tachyarrhythmia or intracardiac atrial fibrillation. Data acquisition, ECG data pre-processing and CNN classifier are the several methods that are involved for the classification of several arrhythmias.
META-ANALYSIS FOR EVALUATING THE EFFICIENCY OF GENOMIC SELECTION IN CEREALS

Genomic selection (GS) is used to predict the merit of a genotype with respect to a quantitative trait from molecular or genomic data. Statistically, GS requires fitting a regression model with multiple predictors associated with the molecular markers (MM) states. The model is calibrated in a population with phenotypic and genomic data. The abundance and correlation of MM information make model estimation challenging. For that reason there are diverse strategies to adjust the model: based on best linear unbiased predictors (BLUP), Bayesian regressions and machine learning methods. The correlation between the observed phenotype and the predicted genetic merit by the fitted model provides a measure of the efficiency (predictive ability) of the GS. The objective of this work was to perform a metaanalysis on the efficiency of GS in cereals. A systematic review of related GS studies and a meta-analysis, in wheat and maize, was carried out to obtain a global measure of GS efficiency under different scenarios (MM quantity and statistical models used in GS). The meta-analysis indicated an average correlation coefficient of 0.61 between observed and predicted genetic merits. There were no significant differences in the efficiency of the GS based on BLUP (RR-BLUP and GBLUP), the most common statistical approach. The increase of MM data, make GS efficiency do not vary widely.
A Ovsynch Produced Larger Follicles and Corpora Lutea of Lower Blood Flow associated Lower Ovarian and Uterine Blood Flows, Estradiol and Nitric Oxide in Cows : Ovsynch ovarian response and blood flow of cows

The use of ovsynch and its modifications for synchronizing estrous cycle and ovulation in ruminants is widely used with lowered conceptions rates. This study aimed to investigate the follicular (F1) and the corpus luteum (CL) hemodynamics, ovarian (OvA) and uterine arteries (UtA) blood flow volumes (BFV) and dynamics associated with the circulating estradiol (E2), progesterone (P4), glucose and nitric oxide (NO) in cows treated with the ovsynch protocol. Eight Friesian cows underwent trans-rectal Doppler scanning and blood sampling each other day throughout two successive non-treated (Spontaneous) and two treated successive estrous cycles (ovsynch). The results revealed that the existed dominant follicles (F1) area, antrum area, color area, and the CL area and the color area on the ovaries declined following the first dose of the gonadotropin-releasing hormone (GnRH, Day-11) till Day -5 then another dominant follicle started growth and reached ovulation (Day 0). Ovsynch pre-ovulatory phase (Day-5 to 0) had more (P<0.01) small, medium, and total follicles. The ovsynch F1 (P=0.001) and the CL (P=0.006) had higher areas but lower color areas percent. The ovsynch increased ipsi-lateral OvA pulsatility index (PI) that associated decreased diameter and time average mean velocity (TAMV) and blood flow volume except Day 6 (BFV, P<0.05). The ovsynch improved (P<0.01) the ipsilateral UtA PI but lowered its diameter, peak systolic velocity (PSV), TAMV, and BFV except Day 10. Ovsynch ovulation was characterized by low E2, NO, and high glucose but the late luteal phase had high (P<0.001) P4 and glucose with low E2. In conclusion, the decreased follicle and luteal vascularization with lowered uterine blood flow and estradiol may adversely affect the quality of the oocyte and the decreased progesterone from Day 7 till Day 10 and the ipsilateral uterine artery BFV may not support the implantation and disturb the maintenance of the embryo after timed insemination.
The effects of technology-integrated classroom instruction on K-12 English language learners’ literacy development: a meta-analysis

Abstract With the escalating growth of English language learners (ELLs) worldwide, educational technology has been identified as an important learning tool with significant potential to support second language literacy development. This review examined the effectiveness of technology-integrated literacy instruction in the classroom context for ELLs in Grades K-12. Thirty-six articles with 42 independent samples, based on a total of 4,354 participants, were included in this meta-analysis. Findings confirmed that technology-integrated instruction produced a positive, medium effect size (ES = 0.47) on literacy outcome as compared to traditional methods where no technology was involved. Moderator analyses were conducted with eight study features: grade level, learning context, study duration, intensity per week, total contact time, literacy outcome, pedagogical use, and teaching method. Learning context and literacy outcomes were found to be statistically significant to explain the variance of the effect sizes between the studies. These results advocate for the use of educational technology in literacy instruction for ELLs, regardless of literacy outcomes, pedagogical use, teaching methods, learning contexts, and grade levels. Theoretical and practical implications are discussed.
An Improved KNN-Based Slope Stability Prediction Model

An accurate slope prediction model is important for slope reinforcement before the disaster. The k-nearest neighbor (KNN) algorithm, as a simple and effective nonparametric machine learning method, is widely applied in classification recognition. In our study, the k-nearest neighbor (KNN) algorithm is improved to reduce its sample dependence and improve the robustness of the algorithm, and then the prediction model of the slope stability is proposed based on the improved k-nearest neighbor (KNN) algorithm. Extensive experimental results show that our proposed prediction model achieves high prediction performance in this regard. Moreover, a comparison between our proposed prediction model and the finite element method, which is the classical theoretical method of slope stability, was made, which will provide an important approach to predicting the slope stability for slope engineering. Finally, shaking table test of a slope model is conducted to evaluate whether the slope is stable or not, and the experimental results are in good agreement with the prediction results of our proposed prediction model, which further demonstrates its effectiveness.
Health and chemical burdens of fish species from polluted and hyper-eutrophic freshwater ecosystems in South Africa

Three aquatic ecosystems in South Africa, the Hartbeespoort, Klipvoor and Bospoort Dams, are classified as hyper-eutrophic, because of high nutrient loads and chemical pollution. Water and two fish species, Clarias gariepinus and Cyprinus carpio, were collected from these dams to assess the impact of eutrophication and chemical pollutants on their health status. Water and muscle samples were analysed for organic and inorganic chemicals. Condition factor was determined and a necropsy performed to note any macroscopic abnormalities. A histology-based fish health assessment was done on the liver, kidney, gills and gonads. A number of fish from the three dams exhibited livers with fatty change and focal discoloration, skin lesions and parasites within the visceral cavity. The prevalence and severity of histopathology in the liver resulted in higher liver index values than the index values for kidneys and gills. Aluminium, silicon and chromium were detected in the water and muscle tissue. The DDT metabolite p,p’-DDE was present in both species, as well as in fish from the reference site, Marico-Bosveld Dam. Only C. gariepinus from Hartbeespoort Dam had p,p’-DDD levels higher than 5 µg g−1 per edible portion. Water from hyper-eutrophic dams adversely affects the health of freshwater fish.
A REVIEW ON THE RECENT TRENDS IN FINANCIAL INCLUSION: THE RESEARCH JOURNEY SO FAR

Purpose of the study: This paper tries to review the existing literature on financial inclusion and to discuss the various definitions of financial inclusion, its diverse dimensions comprehensively - demand side and supply side - and multiple explanatory variables (economic, demographic, behavioural, and social factors) to explain the level of financial inclusion. Methodology: From various databases and e-journals, the papers were retrieved on the topic of financial inclusion during the time span of the last 15 years (2005-2019). After reading the abstract of totally collected 140 research papers, the researchers have chosen 84 articles for the present study based on the relevance of the content to the topic under study. The collected information was classified as per the major themes. Main Findings: Much research has been conducted on the measurement and impact assessment of financial inclusion, But the studies on the explanatory variables are comparatively fewer. Among the various factors influencing the financial inclusion level, no much emphasis has been laid on the usage dimension; the key role of behavioural, psychological, and sociological factors on the financial inclusion level still remains unexplored. Applications of this study: This paper will be useful for theoretical researchers who prefer to explore the factors that influence the usage dimension of financial inclusion. The policymakers and practitioners in the area of financial inclusion will be able to improve the effectiveness of their policies and implementation strategies of financial inclusion. Novelty/Originality of this study: This review paper explores the juncture at which the current research on financial inclusion has arrived. It would serve as a starting point for further in-depth explorations in the various segments of financial inclusion. Also, the present study points out and suggest potential areas for further research in the field of financial inclusion.
The Influence of Leadership Based on Emotional Intelligence Concerning the Climate of an Organisation

The article aims to define a behavioral matrix regarding the style of leadership applied to management in a public company. The dimensions include communication and persuasion, ethics, empathy, knowledge and information management, the desire to learn, the objective’s achievement, adaptability, innovation, and decision making. The behavioral competencies correlate with the four dimensions of emotional intelligence related to personal abilities such as self-knowledge and self-management, but also social competences, such as awareness and the management of relationships with others. The article analyses the extent to which the emotional intelligence among the personnel that makes up the middle management influences organizational climate at the company level.
Modification de la composition en molécules pharmaceutiques (furocoumarines) de la Rue officinale (Ruta graveolens) par transformation génétique

Les furocoumarines sont des metabolites secondaires vegetaux utilises dans le traitement de cancers. La Rue officinale (Ruta graveolens L. ) produit en grandes quantites ces molecules dont les syntheses chimiques sont onereuses. Nous avons decide d'approfondir nos connaissances sur la voie de biosynthese des furocoumarines, afin, a terme de pouvoir en ameliorer la production (demarche de type genie metabolique). Nous avons mis au point le premier protocole de transformation genetique de la Rue officinale (Ruta graveolens L. ) par Agrobacterium tumefaciens. Nous l'avons optimise afin de le rendre le plus simple et le plus efficace possible. Le taux de transformation obtenu est de pres de 10 %. Les transformants T0 peuvent etre cultives en sol 4 mois apres la transformation genetique. Les methodes de caracterisation moleculaire des transformants ont ete mis au point. Nous avons utilise ce protocole de transformation genetique pour ameliorer nos connaissances sur la voie de biosynthese des furocoumarines. L'introduction du gene CYP73A32 de la cinnamate-4-hydroxylase chez Ruta graveolens L. A permis une modification qualitative de la synthese des furocoumarines. Les transformants ont une diminution de leur teneur en psoralene au profit de ses derives methoxyles (bergaptene et xanthotoxine). Un transformant, nomme K38, presente une alteration dans son developpement (nain, tres ramifie) et a un profil en furocoumarines modifie quantitativement et qualitativement (teneur 75 fois plus faible et rapport bergaptene/xanthotoxine 3 fois superieurs a ceux des autres plantes). Ce travail demontre qu'il est possible de modifier la synthese des furocoumarines chez Ruta graveolens L. Par des outils de transformation genetique.