Miracle-dz/UniFAR-FTUs huggingface dataset:
The Utilization of Educational Management Information System in Data Processing in Secondary Schools of Guji Zone, Ethiopia

In recent times, it is common to hear people saying” the world is becoming a small village”. This is definitely because of nothing but information. To get well organize information system at a national level; it is must that each authority at the grass root level is concerned with the compilation of reliable data at the local level. Likewise, schools need to have a good data compilation system to keep themselves up to date with the changing global situations and to feed genuine information for the data compiled at the national level. The purpose of this study is to examine the utilization of educational management information system for the betterment of data processing in secondary schools of Guji Zone, Ethiopia. To attain the objectives of the study, descriptive survey research design like frequency count, percentage, mean and inferential statistics like independent sample t-test were employed. The sources of data for the study were 55 teachers, 37 school management bodies and 7 Wereda education office (WEO) ICT service core process owners in the 7 randomly selected preparatory schools. Questionnaire was used to gather data from teachers and school management bodies while unstructured interview was employed to solicit qualitative data from WEO ICT service core process owners. The findings of the study revealed that school data collection practices are moderately exercised in the target secondary schools, though there is no uniformity of rating between school managers and teachers throughout all item. School managers viewed that there is moderate access to data management tools for EMIS in the secondary schools of Guji Zone, while teachers and wereda education office ICT service core process owners explained that there is a problem of access to data management tools for EMIS. Lack of database management, lack of data cleaning and lack of data analysis skills were rated by both school managers and teachers to be the major factors impeding the implementation of EMIS in the target secondary schools. To this end, it was recommended that provision of different skill trainings for teachers and even for the school managers by wereda education office in the target secondary schools is mandatory to promote the implementation of EMIS.
Nest predation in logged and unlogged sites in Kirindy Forest, Madagascar

Forest exploitation has been shown to increase bird nest predation rates mainly in temperate climates, but there is no such a clear support for this hypothesis in tropical forests. In this study we examined nest predation in logged and unlogged sites in the dry deciduous forest of Kirindy (Madagascar) at the end of dry season. An important effect on nest survival was found for the forest type and nest type. Nests in the canopy suffered from different proportions of predation and type of predator than ground nests. Both canopy and ground nests were preyed upon more often in logged forest, but the effect was more significant for terrestrial nests. The overall percentage of predation was almost 50% higher in the site with logging history. The most abundant predators in both types of forest were birds, rodents and a Narrow-striped Mongoose (Mungotictis decemlineata). The most significant effect for bird predation was found in ground nests, for rodent predation in canopy nests. We provide hypotheses about possible changes in predator composition and abundance linked to nest survival caused by human disturbance in unique dry seasonal forest habitat in Madagascar.
A novel approach for face annotation using multilabel correlation graph for web image search

Social media is growing up as a new way of interacting around the various media. As the digital images are increasing rapidly it is the biggest challenge to classify them. Also the large number of web and personal images requires an effective search and browsing mechanism in either a content or keyword based type. This paper is focused on different image mining applications i.e. human age estimator, web and personal image annotation, face annotation, hypergraph learning and biomedical and health informatics. To improve the image search result on large scale for user queries image annotation approach is implemented using, face annotation and the correlation graph is proposed. Image mining using face annotation graph can perform well in image mining result matching the query keyword exactly achieving the appropriate result. The proposed method can achieve satisfactory result.
CONTROLE DE SISTEMAS CHAVEADOS INCERTOS COM REALIMENTAÇÃO DA SAÍDA DA PLANTA

This paper investigates the design of static switched output feedback for continuous-time uncertain switched linear systems aiming to extend results avaliable in literature for open-loop systems. It is considered that the state variables are not available, however, the switching strategy depends on the controlled output. The formulation is based on Linear Matrices Inequalities (LMIs). Three theorems are proposed. One illustrative example shows that, when the conditions proposed in theorems are feasible, the static output feedback can increase the feasibility area and reduce the guaranteed cost, when compared with similar results available in literature. Keywords— Control Systems, Switched Systems, Static Output Feedback, Uncertain Linear Systems. Resumo— Este artigo investiga a realimentação estática de sáıda em sistemas lineares chaveados incertos e cont́ınuos no tempo visando generalizar resultados dispońıveis na literatura para sistemas em malha aberta. Considera-se que as variáveis de estado não estão totalmente dispońıveis para realimentação e, portanto, a estratégia de chaveamento depende da sáıda da planta. A formulação é baseada em Desigualdades Matriciais Lineares (LMIs). Três teoremas são propostos para sistemas lineares chaveados incertos. Um exemplo ilustrativo mostra que, quando as condições dos teoremas propostos são fact́ıveis, a realimentação estática de sáıda aumenta a região de factibilidade e também reduz o custo garantido, se comparado com resultados similares dispońıveis na literatura. Keywords— Controle de Processos, Sistemas Chaveados, Realimentação Estática de Sáıda, Sistemas Lineares Incertos.
Comparative Study of Pearson’s Chi-Square and Some Ordinal Contingency Tables Models

The aim of this article was to compare Pearson’s Chi-square to Uniform (U), Column (C), Row (R), and R+C ordinal contingency tables models. Data on gender, university attended for B.Sc., B.Sc. and M.Sc. grades of 116 M.Sc. graduates were collected from Department of Statistics, University of Ilorin, Nigeria. Model estimation was carried out by maximum likelihood method and goodness of fit was assessed by likelihood ratio statistic. Pearson’s chi-square rejected the null hypothesis of independence in all cases; the U model rejected in 2 of 6 cases while R rejected in 4 cases. The C model rejected in 3 cases while R+C rejected in 5 out of 6 cases. Pearson’s chi-square reached same conclusion with U model on 2 occasions, with C model on 3 occasions, with R on 4 and with R+C on 5 occasions. It reached same conclusion with U model on the only occasion the assumption of U was satisfied and reached same conclusion with R model on 3 out 5 occasions in which the assumption of the latter was satisfied. However, it reached a contrary conclusion with C and R+C models on the only occasion the assumptions of C and R+C models were met. While the four ordinal association models reached same conclusion of independence as did the Pearson’s chi-square on the only occasion the assumption of the latter was met, Pearson’s chi-square reached same conclusion only on 4 out of the 8 occasions on which the assumptions of ordinal association models were met, suggesting higher robustness of ordinal association models. It was found that Pearson’s chi-square agreed mostly with R+C, followed by R, then C and lastly, U model when each ordinal model underlying assumption was not taken into cognizance However, when taken into consideration, it agreed mostly with R and then, U model. The need to conduct larger scale study was recommended.
Optimization of Association Rule Mining for Mammogram Classification

Authors presents concept of image mining, an extension of data mining, for discovering image data relationship from a large collection of mammograms images. Association rule mining is the process of discovering useful and interesting rules from large datasets based on user specified minimum support and confidence values. The set of all possible item sets grows exponentially with the number of items in the database. These constraints lead to exponential search space and dataset dependent minimum support and confidence values. It generates a huge number of unnecessary rules from frequent item sets and results in weak mining performance. The authors propose two association rule optimization techniques for overcoming these problems. The first graph theory approach (OARGT) is based on objective function such that graph generated by the optimized rules is a simple graph with simple walk. The second approach is based on Multi-Objective Genetic algorithm (MOGA) with adaptive crossover and mutations (MOGAACM). Traditionally, input to ARM classifier is in binary format. The proposed system uses variable feature quantization and relevant feature selection. In MOAGAACM, ranks are assigned to rules as per fitness function. The rules with highest rank, low crossover and mutation rates are assigned and vice versa. Experimental results show that, MOGAACM generates more effective and strong association rules compared with objective function using graph theory and achieves 89.08% and 43.60 % reduction in association rules for benign and malignant class respectively for MIAS database and 80.13 % and 79.60 % reduction in association rules for benign and malignant class respectively for DDSM medical image database. Authors propose class identification using Strength of Classification Algorithm (CISCA) for classification of mammogram image into benign and malignant classes. The classification accuracy measures reported are 91.66 % for MIAS database using graph theory and 95.45 % and 92.5 % for MIAS and DDSM respectively using MOGAACM.
Population Structure and History of Steelhead Trout in California

Steelhead trout (Oncorhynchus mykiss) are the most widespread of the anadromous salmonids. In the western continental United States, 14 Evolutionarily Significant Units (ESUs) have been delineated on the basis of genetic, geographic and ecological variation. Of these 14 western ESUs, 11 have been listed as protected under the US Endangered Species Act (ESA). In California, there are six ESUs, 5 on the coast and 1 in the Central Valley. All but the Klamath Mountain Province ESU is listed, with the Southern California ESU classified as “Endangered” and the others “Threatened”. Here, we investigate genetic population structure and demographic history of steelhead trout in coastal California using multilocus genetic data. We use size variation at highly variable microsatellite loci from populations at 62 sites from 41 basins, covering almost the entire range of the species in coastal California. The list of sites is found in Table 1. Results of phylogeographic analyses and assignment tests are described and the partitioning of variation at the tributary, river basin and ESU level is examined.
UMA AVALIAÇÃO EMPÍRICA DE MODELOS DE PROCESSOS GAUSSIANOS PARA IDENTIFICAÇÃO ROBUSTA DE SISTEMAS DINÂMICOS

System identification comprises a number of linear and nonlinear tools for black-box modeling of dynamical systems, with applications in several areas of engineering, control, biology and economy. However, the usual Gaussian noise assumption is not always satisfied, specially if data is corrupted by impulsive noise or outliers. Bearing this in mind, the present paper aims at evaluating how Gaussian Process (GP) models perform in system identification tasks in the presence of outliers. More specifically, we compare the performances of two existing robust GP-based regression models in experiments involving five benchmarking datasets with controlled outlier inclusion. The results indicate that, although still sensitive in some degree to the presence of outliers, the robust models are indeed able to achieve lower prediction errors in corrupted scenarios when compared to conventional GP-based approach. Keywords— Robust system identification, Gaussian process, Approximate Bayesian inference, Adaptive and learning systems. Resumo— Identificação de sistemas consiste em diversas ferramentas lineares e não-lineares para modelagem do tipo“caixa-preta”de sistemas dinâmicos, com aplicações em diversas áreas, como engenharia, controle, biologia e economia. Entretanto, a suposição convencional de rúıdo Gaussiano não é sempre satisfeita, especialmente se os dados estão corrompidos por rúıdo impulsivo ou outliers. Nesse contexto, o presente trabalho visa avaliar o desempenho de modelos de Processos Gaussianos (PGs) em tarefas de identificação de sistemas na presença de outliers. Mais especificamente, o desempenho de dois modelos de regressão robusta baseados em PG já existentes são comparados em experimentos envolvendo cinco conjunto de dados artificiais com inclusão controlada de outliers. Os resultados indicam que, apesar de ainda senśıveis em certo ńıvel à presença de outliers, os modelos robustos são capazes de obter menores erros de predição nos cenários com contaminação quando comparados com a abordagem convencional de PG. Palavras-chave— Identificação robusta de sistemas, Processos Gaussianos, Inferência Bayesiana aproximada, Sistemas adaptativos e de aprendizagem.
Molecular Diversity Analysis of Some Ile-Ife Moss Species Expressed by Random Amplified Polymorphic DNA

Molecular studies using Random Amplified Polymorphic DNA profiling was carried out on some moss species from Ile-Ife, Nigeria in order to determine their genetic variation and species relationships. The result obtained revealed that the chloroplast DNA isolated using the SDS method produced higher yields though lesser quality, while those isolated using the Kit method produced lesser yield but higher quality. All the seven primers employed in this study showed RAPD fragments with varying bands ranging from 200 Bp to 3000 Bp. Out of a total of 55 individual fragments obtained from the seven primers used, 27 bands were polymorphic while 28 were species specific (unique). Monomorphic bands were absent. The values of the pair-wise comparison of the genetic distance (GDMR) and the percentage genetic variation of the moss species studied showed that they were genetically distinct from each other. The UPGMA phenogram of the RAPD data separated the moss species studied into seven main groups at 0.6 level of similarity. From the findings made, it was concluded that the moss species studied were quite distinct and distant genetically therefore there was unlikelihood of gene flow across them.
3D Human Hand Posture Reconstruction Using a Single 2D Image

Passive sensing of the 3D geometric posture of the human hand has been studied extensively over the past decade. However, these research efforts have been hampered by the computational complexity caused by inverse kinematics and 3D reconstruction. In this paper, our objective focuses on 3D hand posture estimation based on a single 2D image with aim of robotic applications. We introduce the human hand model with 27 degrees of freedom (DOFs) and analyze some of its constraints to reduce the DOFs without any significant degradation of performance. A novel algorithm to estimate the 3D hand posture from eight 2D projected feature points is proposed. Experimental results using real images confirm that our algorithm gives good estimates of the 3D hand pose.
Fatty acid composition of retail cuts from carcases from grass fed lambs

Cooperative Research Centre for Sheep Innovation, Armidale NSW 2350, Australia NSW Department of Primary Industries, Centre for Red Meat and Sheep Development, Cowra NSW 2794, Australia Graham Centre for Agricultural Innovation (NSW Department of Primary Industries and Charles Sturt University), Wagga Wagga NSW 2650, Australia Wollongbar Primary Industries Institute, NSW Department of Primary Industries, Wollongbar NSW 2477, Australia *Corresponding authors email: steph.fowler@dpi.nsw. gov.au
Predictors of Piano Student Dropouts

Dropping out of piano lessons before reaching a moderate mastery of the instrument is a frequent problem among students. Previous research finds that children often leave music lessons as the result of predictors such as expertise—measured by musical ability, academic achievement, and musical achievement—and environment—measured by social and educational status, gender differences, parental involvement, and home culture. Fifty-five former piano students and their parents filled out questionnaires with Likert-scale, multiple choice, and open-ended questions in a mixed-methods study. This group was compared to 153 students and parents who were still involved with lessons. The results between the two groups show that many of the attrition factors outlined in the literature predicted piano student dropouts as well.
The safety and efficacy of NEPA (netupitant and palonosetron) in the treatment of chemotherapy-induced nausea and vomiting

ABSTRACT Chemotherapy-induced nausea and vomiting (CINV) is associated with a significant deterioration in quality of life. The combination of a 5-hydroxytryptamine-3 (5-HT3) receptor antagonist, dexamethasone, and a neurokinin-1 (NK-1) receptor antagonist has significantly improved the control of CINV. Palonosetronis a second generation 5-HT3 receptor antagonist. Netupitant is a new NK-1 receptor antagonist with a high binding affinity and a long half-life of 90 hours. NEPA is an oral fixed-dose combination of netupitant and palonosetron (300 mg of netupitant plus 0.50 mg of palonosetron). Phase II and phase III clinical trials have demonstrated that NEPA significantly improved the prevention of CINV compared to the use of palonosetron alone in patients receiving either highly or moderately emetogenic chemotherapy. Adverse events were similar for the NEPA and the palonosetron groups. NEPA (Akynzeo) has recently been approved by the Food and Drug Administration (FDA) to treat nausea and vomiting in patients undergoing cancer chemotherapy.
On being a physics major: student perceptions of physics difficulties, rewards, and motivations

Undergraduate majors’ attitudes and perceptions about physics can strongly inﬂuence their development of a physics identity, persistence, and pursuit of physics-related careers. To explore students’ attitudes and perceptions, we surveyed 178 physics majors nationally. To analyze this data, we used descriptive statistics and emergent methods of qualitative analysis. While data collection is ongoing, preliminary results show themes of physics majors’ value for hard-work, broadly applicable problem-solving skills, and the rewarding quest for a meaningful application of their knowledge. Furthermore, we found that students position physics as a creative and communicative endeavor when provided with opportunities to do research and lab-work that values these aspects. This rich analysis of students’ attitudes and perceptions about physics can help educators better align learning opportunities with students’ needs and prepare students for a long-term and broadly applicable pursuit of physics.
REAL-TIME URBAN ROAD NETWORK TRAVEL TIME PREDICTION BASED ON PROBE DATA: FRAMEWORK AND STOCKHOLM CASE STUDY

1 The paper presents the architecture and methodology for an urban road network travel time predic2 tion framework based on low frequency probe vehicle data. The prediction framework is intended 3 to be used in a real-time traffic management and information provision tool. The design of the 4 framework has to satisfy three crucial aspects: computational efficiency of real-time prediction; 5 flexibility to changes in the network; and robustness against noisy and missing data. Correlation 6 patterns between links and time intervals are computed, updated and stored at infrequent inter7 vals based on historical data and a Probabilistic Principal Component Analysis (PPCA) model. 8 Prediction for future time intervals is performed in real-time using stored model parameters of 9 correlation patterns and recent current-day observations. A novel multivariate hybrid method of 10 PPCA and local smoothing, which considers local correlations among neighboring links, is pro11 posed. The methodology is applied to the road network of Stockholm, Sweden and probe data from 12 taxis. Computational experiments show that the proposed method provides high accuracy for both 13 the peak hours as well as off-peak traffic conditions. The effects of missing data and link speed 14 variation on the prediction accuracy are also analyzed. For links with large link speed variations 15 in particular, the PPCA based methods significantly outperform the historical mean, whereas for 16 links with high proportions of missing data the hybrid PPCA method is more reliable than pure 17 PPCA due to its ability to use local neighboring link correlations. 18 19
A Whiteness Test Based on the Spectral Measure of Large Non-Hermitian Random Matrices

In the context of multivariate time series, a whiteness test against an MA(1) correlation model is proposed. This test is built on the eigenvalue distribution (spectral measure) of the non-Hermitian one-lag sample autocovariance matrix, instead of its singular value distribution. The large dimensional limit spectral measure of this matrix is derived. To obtain this result, a control over the smallest singular value of a related random matrix is provided. Numerical simulations show the excellent performance of this test.
Label-free surface-enhanced Raman spectroscopic detection of HIV-1 infection in blood and plasma adsorbed on conductive silver pasted glass substrate

This study reports on application of conductive silver paste smeared glass slides as Raman spectroscopy sample substrates for label-free detection of HIV-1 p24 antigen in blood plasma. It showed that the same substrates can be applied in Raman spectroscopic screening of blood plasma for presence of HIV. The characteristic Raman spectrum of HIV-1 p24 antigen displayed prominent bands that were assigned to Ribonucleic acids (RNA) and proteins that constitute the antigen. These spectra can be used as reference during Raman spectroscopic screening for HIV in plasma within the first few days after exposure (<7 days). The Raman spectra obtained from HIV+ plasma displayed unique peaks centered at wavenumbers 1270 and1446 cm -1 attributed to the Raman active vibrations in the virion proteins. Other bands similar to those reported in literature were also seen and assigned to lipids and carbohydrates. The attachment of the HIV virions to silver nanoparticles via gp120 glycoprotein knobs were thought to be responsible for the enhanced Raman signals of proteins associated with the virus. The principal component analysis (PCA) applied on the combined spectral data showed that HIVand HIV+ spectra had differing spectral patterns. This indicated the great power of Raman spectroscopy in HIV detection when plasma samples are deposited onto silver paste smeared glass substrates. The Raman peaks responsible for the segregation of the spectral data in PCA were mainly those assigned to the viral proteins. Excellent results were also obtained from Artificial Neural Network (ANN) applied on the HIV+ data with R (coefficient of correlation) and R 2 (coefficient of determination) values of 0.9958 and 0.9895, respectively. The method has the potential of being used as quick blood screening for HIV before blood transfusion with the Raman peaks assigned to the virion proteins acting as a reference.