Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Campus Configuration on Ninth-Grade Achievement

The purpose of this mixed-methods sequential explanatory research study was to determine whether there were significant differences between high schools having no freshman facility, a freshman wing, or a freshman campus on eight student performance measures. A second purpose was to examine principals’ perceptions of how their schools transition freshmen and how these transition strategies and arrangements worked. Of the eight ANOVAs conducted, only one analysis was statistically significant between the school configurations. In facilities using a freshman campus, high school students with disabilities earned significantly more core credit units than their counterparts in schools using no freshman facility. During individual interviews, all 15 principles emphasized using transition strategies to help students adjust to high school.
An Investigation into Factors Affecting the Performance of Public Construction Projects in Ondo State, Southwestern, Nigeria

This paper investigates the key performance indicators affecting Public Construction Projects in Akure, Ondo State Southwestern Nigeria. The study adopted a source technique with the administration of a well structured questionnaire to professionals in the construction industry. Descriptive statistical techniques such as frequency distribution, percentages and Relative Importance Index (RII) were used to analyze the data. The study revealed the top ten (10) most important factors influencing the performance of construction projects in Akure. These factors included: delay in progress payment to Contractors, Client’s interference during construction, Client’s inability to brief the project objectives and escalation of materials prices. Other factors were Client’s experience whether he is sophisticated or specialized; complexity of project; motivating skill of the project team leader; project team leader experience; Contactors commitment to ensure construction work is done according to specification as well as Client’s inability to make project decision. The paper concluded that the Contractors and Clients performance of the industry is below expectation. Keywords: Construction projects, Escalation, Performance, Public, Ranking
2-Amino-4-(4-methylphenyl)-5-oxo-5,6,7,8-tetrahydro-4H-chromene-3-carbonitrile

The 4H-pyran ring of the title compound, C17H16N2O2, is nearly planar [maximum deviation = 0.077 (2) Å] and the cyclohexene ring adopts a flattened chair conformation [puckering parameters: Q T = 0.435 (2) Å, θ = 122.0 (3)° and ϕ = 53.5 (3)°]. The 4H-pyran ring is almost perpendicular to the benzene ring [dihedral angle = 87.23 (8)°] and is almost coplanar with the mean plane of the cyclohexene ring [dihedral angle = 8.01 (8)°]. In the crystal, inversion-related molecules are linked by pairs of intermolecular N—H⋯N hydrogen bonds, forming inversion dimers with R 2 2(12) ring motifs. These dimers are further connected by N—H⋯O and C—H⋯N hydrogen bonds, forming a layer structure extending parallel to (0-12). Molecules within the layers interact with each other via C—H⋯π interactions.
Screening of Some Lactic Acid Bacteria Isolated from Selected Nigerian Fermented Foods for Vitamin Production

Human vitamin de? ciencies still occur in many countries although most vitamins are present in a variety of foods, mainly because of malnutrition not only as a result of insuf? cient food intake but also because of unbalanced diets; this work screens some Lactic acid bacteria isolated from selected Nigerian fermented foods for vitamin (thiamine, riboflavin and niacin) production. Five lactic acid bacteria were isolated from selected Nigerian fermented foods (yoghurt, ogi, ogiri, ugba). The isolated lactic acid bacteria were identified based on cultural and biochemical characteristics. All the isolates were screened for thiamine, riboflavin and niacin production using microbiological assay. Thiamine, riboflavin and niacin produced were quantified. The five lactic acid bacteria isolated were Lactobacillus plantarum, Lactobacillus brevis, Lactobacillus fermentum, Lactobacillus casei and Lactococcus lactis. The most frequent isolate was Lactobacillus plantarum (100%) followed by Lactobacillus caesi (75%). Lactobacillus brevis, Lactobacillus fermentum and Lactobacillus lactis were the least isolates having (50%) occurrence each. Lactobacillus plantarum produced the highest amount of thiamine (5.8833 μg/100ml) and riboflavin (5.0400 μg/100ml) while Lactobacillus lactis had the least thiamine (5.2267 μg/100ml) and Lactobacillus caesi had the least riboflavin (4.8300 μg/100ml). Lactobacillus casei produced the highest niacin (1.6433 μg/100ml) while Lactobacillus fermentum had the least (1.2900 μg/100ml). This study reveals Lactobacillus plantarum as the most efficient thiamine and riboflavin producer while Lactobacillus casei produced the most niacin.
Fault Prediction in Android Systems through AI

Software code defect prediction is important in improving code quality and the turnaround time of software products. In this thesis we investigate how to create and extract features, analyze existing work to create and realize a defect prediction technique that can be applied in an industrial setting. We conduct this investigation on version controlled source code from Git and Jira data. We identify and define metrics to be collected and build four Machine Learning (ML) models to predict if a file is clean or defective. We create a Cost Effectiveness (CE) evaluation technique to measure the performance of our ML models and achieve a score of 87% and an accuracy of 88 % on our best models.
Prevalence Visual Search: Optimal Performance and The Description-Experience Gap

Zhang, Hanshu. Ph.D., Department of Psychology, Wright State University, 2019. Prevalence visual search : optimal performance and the description-experience gap. Real-world visual search differs significantly from the laboratory task. One distinct feature is that most targets in real-world visual search are low prevalence. Considering the important practical connections between the laboratory study and applied research, there has been a resurgence in exploring prevalence effects on visual search performance, especially the effect that targets are more likely to be missed when they have low prevalence. Though there is a consensus that target misses are due to a liberal criterion, previous studies failed to consider the potentiality of optimal performance from the perspective of Signal Detection Theory, which also predicts a the liberal criterion shift. Moreover, previous decision making literature has demonstrated that observers subjectively weighted the probability based on the information communications they were given (i. e. the description-experience gap), motivates the current study to explore how target probability communications influence search performance. To explore the hypothesis of optimal performance and the influence of probability communications, the current research assessed observers’ performance from two aspects: behavioral performance and eye movements. The results indicated that with a high penalty on miss errors, observers’ criteria were more liberal toward “target-present” responses. However, the performance was not optimal as expected. The manipulation of information indicated visual search was affected by the way the target prevalence information was given to observers. Specifically, when target prevalence was low, learning prevalence from experience resulted in the belief in more targets and longer search time before quitting compared to the contexts in which observers had been explicitly informed about the target probability. The observed discrepancy narrowed with increased prevalence and reversed when target prevalence was high.
Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model

Abstract. The GEOS-Chem simulation of atmospheric CH4 was evaluated against observations from the Thermal and Near Infrared Sensor for Carbon Observations Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse Gases Observing Satellite (GOSAT), the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and the Total Carbon Column Observing Network (TCCON). We focused on the model simulations at the 4∘×5∘ and 2∘×2.5∘ horizontal resolutions for the period of February–May 2010. Compared to the GOSAT, TCCON, and ACE-FTS data, we found that the 2∘×2.5∘ model produced a better simulation of CH4, with smaller biases and a higher correlation to the independent data. We found large resolution-dependent differences such as a latitude-dependent XCH4 bias, with higher column abundances of CH4 at high latitudes and lower abundances at low latitudes at the 4∘×5∘ resolution than at 2∘×2.5∘. We also found large differences in CH4 column abundances between the two resolutions over major source regions such as China. These differences resulted in up to 30 % differences in inferred regional CH4 emission estimates from the two model resolutions. We performed several experiments using 222Rn, 7Be, and CH4 to determine the origins of the resolution-dependent errors. The results suggested that the major source of the latitude-dependent errors is excessive mixing in the upper troposphere and lower stratosphere, including mixing at the edge of the polar vortex, which is pronounced at the 4∘×5∘ resolution. At the coarser resolution, there is weakened vertical transport in the troposphere at midlatitudes to high latitudes due to the loss of sub-grid tracer eddy mass flux in the storm track regions. The vertical air mass fluxes are calculated in the model from the degraded coarse-resolution wind fields and the model does not conserve the air mass flux between model resolutions; as a result, the low resolution does not fully capture the vertical transport. This produces significant localized discrepancies, such as much greater CH4 abundances in the lower troposphere over China at 4∘×5∘ than at 2∘×2.5∘. Although we found that the CH4 simulation is significantly better at 2∘×2.5∘ than at 4∘×5∘, biases may still be present at 2∘×2.5∘ resolution. Their importance, particularly in regards to inverse modeling of CH4 emissions, should be evaluated in future studies using online transport in the native general circulation model as a benchmark simulation.
Should learners use their hands for learning? Results from an eye-tracking study

102 wileyonlinelibrary.com/journal/jcal Abstract Given the widespread use of touch screen devices, the effect of the users' fingers on information processing and learning is of growing interest. The present study drew on cognitive load theory and embodied cognition perspectives to investigate the effects of pointing and tracing gestures on the surface of a multimedia learning instruction. Learning performance, cognitive load and visual attention were examined in a one‐ factorial experimental design with the between‐subject factor pointing and tracing gestures. The pointing and tracing group were instructed to use their fingers during the learning phase to make connections between corresponding text and picture information, whereas the control group was instructed not to use their hands for learning. The results showed a beneficial effect of pointing and tracing gestures on learning performance, a significant shift in visual attention and deeper processing of information by the pointing and tracing group, but no effect on subjective ratings of cognitive load. Implications for future research and practice are discussed.
Testing covariance structure of spatial stream network data using Torgegram components and subsampling

Researchers analyzing data collected from a stream network need to determine the data’s second-order dependence structure before identifying an appropriate model and predicting the values of response variable at unsampled sites (via Kriging) or the average value of the variable over a stream interval (via Block Kriging). The Torgegram enables us to graphically detect the dependence structure of stream network data, but formal tests that resemble those in Euclidean space have not yet been developed on stream networks. The objective of this thesis is to construct nonparametric tests for pure tail-down and pure tail-up dependence on stream networks. These tests are based on the characteristics of some Torgegram components under specific types of dependence structure: the test for tail-down dependence relies on the fact that Type-0 and Type-1 subsemivariances at the same lag are equal when the covariance structure is tail-down, while the test for tail-up dependence takes advantage of a “flat” FUSD semivariogram under tail-up dependence. Several test statistics are proposed to test for pure tail-down and tail-up dependence. The general form of these test statistics is a fraction, whose numerator is the sample size multiplied by the squared difference of specific semivariances (or subsemivariances) and denominator is a consistent estimator of the variance of the square root of the numerator. The asymptotic behaviors of the vectors of semivariances or subsemivariances are proved on regular rooted binary trees with an increasing number of levels, so that these test statistics converge in distribution to a χ random variable with one degree of freedom. In order to obtain consistent estimators of the variance-covariance matrices of the vectors of semivariances or subsemivariances, two methods are introduced in this thesis: the plug-in method, which computes estimators as functions of linear combinations of FCSD and FUDJ semivariances, and the subsampling method, which computes estimators based on the semivariances or subsemivariances from overlapping subsamples. Then, those test statistics for tail-down and tail-up dependence are extended to irregular stream networks, based on Torgegram components and subsampling estimators. Simulation studies on regular rooted binary trees and real stream networks (from two real datasets) demonstrate the good performance of these tests: the larger the sample size or the stronger the spatial dependence, the higher the powers of the tests. However, the test for tail-down dependence requires Type-0 subsemivariances estimated from a sufficient number of pairs of sites on the same stream segments, which not every stream network dataset has.
Web Courseware Usability and Tools for the Enhancement of Teacher Education

Abstract This article presents the findings of a study designed to investigate the effectiveness of the usability and tools in on-line course management packages. It also describes the challenges that teacher educators may face with respect to student use of on-line courses. Five research questions were formulated to ·explore the usability issues and the use of teaching tools in the on-line courseware, WebCT. The findings indicated that there was a significant difference between students' acceptance of on-line courseware, WebCT, based on their genders: female students were more satisfied than male students. Significant variance among academic status (Freshman, Sophomore, Junior, Senior, and Graduate) was observed in students' acceptance ofWebCT, demonstrating that graduate students were more likely to prefer learning on-line than undergraduates. Two usability elements, perceptual limitation and learnability, were the most statistically significant predictors in the on-line courseware. No significant differences were observed among students' acceptance of WebCT in terms of their previous on-line experiences and computer skills. In addition, the qualitative results provided compelling evidence that learners did not use the Help tools as their primary aid when encountering difficulties. Some problems associated with the use of the discussion board, such as sending/ viewing attachments, were revealed in the discussion section.
The effectiveness of Geogebra-assisted direct instruction learning in improving students’ mathematical communication skill viewed from academic level

The aim of this study was to identify and analyze the effectiveness of utilizing Geogebra in improving students’ mathematical communication skill viewed from academic level. This study is a mixed method research which combines both quantitative and qualitative methods with sequential explanatory strategy. The population was the second semester students of Mathematics Education and used purposive sampling technique. The result of this study showed that there was effectiveness of using Geogebra in improving students’ mathematical communication skill viewed from their academic level. The students felt that their skill improved and developed through Geogebra-assisted Directed Instruction learning.
Design, Development and Analysis of Small Antennas

The thirst of human kind for compact and handy gadget are soaring up day by day. To fulfil his needs, to bring many sophisticated communication gadgets in to smaller space, all the departments should come to miniaturisation. One of the biggest challenge in this process is to make the wireless communication antennas very smaller to comply miniaturisation. In this scenario, novel design for smaller antennas are a big challenge. Three such small antenna are developed and analysed in this thesis. Out of the three, two such antennas are electrically small and the third one is a physically small antenna. Stepped Impedance Resonators (SIR) are used in two antennas to achieve compactness. The biggest challenge was to find a mechanism for exciting the SIR to radiate electromagnetic energy. Two different designs with different excitation technique for SIR are presented here. The thesis proposes design equations for designing with different substrate material and frequency. The third antenna is based on chip inductor loading technique for achieving compactness.
Human activity recognition with analysis of angles between skeletal joints using a RGB‐depth sensor

Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB‐depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method. Haar‐wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set. Finally, the K‐nearest neighbor (k‐NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms.
Unmanned cargo aircraft : the structured development of a deployment area assessment instrument

In this research a method is developed that helps its users assessing the attractiveness of an area regarding the deployment of Unmanned Cargo Aircraft (UCA). International market selection strategies used by companies as well as reports published by the Platform Unmanned Cargo Aircraft (PUCA) have been conducted. Research about UCA is still scarce. For this reason, existing knowledge has been supplemented with the knowledge from two experts within the field of UCA deployment. Literature combined with expert knowledge resulted in a list of factors that influence the attractiveness of an area. To keep the overview, a causal model has been developed with those factors that have a causal relationship with the main variable area attractiveness. The requirements for the existence of a causal relationship were provided by the book Geen Probleem (2012). One of the requirements for the instrument set by the Platform Unmanned Cargo Aircraft was that the method should be generally applicable. To do so, the analytical hierarchy process (AHP) has been used to reflect one’s indicator preference. Users must compare sets of two indicators after which the relative importance per factor is calculated according to the AHP approach. This relative importance per factor is called the eigenvector. In order to be able to assign a score for each area per factor, an adapted version of the GIS-based Landscape Appreciation Model (GLAM) has been used. This model uses positive and negative indicators that are assigned a score at the interval between 0 and 4. Scores can only be natural numbers. To ensure that the model is also applicable in this study, the model will be expanded with selection indicators that either can be assigned a score 0 or 1. The final score per area is calculated by multiplying the relative importance per factor with the area score for that factor. Positive scores are added up after which the scores for negative indicators will be subtracted from the this. The remaining score will be multiplied by the score for each selection indicator. This means that if an area does not meet a selection indicator and thus receives a score zero for this indicator, that the total score for that area also equals zero. For a given moment in time, the score of an area per factor is fixed. However, it is not likely to assume that every area has the same score on each criterion at a later point in time. For this reason, the source (often a database) from which the scores were derived, are described. The factors, the AHP and the adapted version of the GLAM model have been implemented in Microsoft Excel. Microsoft Excel is ideally suited because a user can perform the pair wise comparisons relatively easy, after which they are automatically calculated to a relative importance per factor. This relative importance together with the score of the areas on the factors are used to calculate the final score per area.
Neural Attention Frameworks for Explainable Recommendation

Neural attention, an emerging technique used to identify important inputs within neural networks, have become increasingly popular in the area of recommender systems. Not only allowing to better identify what defines users and items, attention-based recommender systems are further able to provide accompanying explanations. However, these representations usually capture only part of users’ preferences and items’ attributes, resulting in limited reasoning and accuracy. We therefore propose Dual Attention Recommender with Items and Attributes (DARIA), a novel approach able to combine two dependable neural attention mechanisms to better justify its suggestions. Utilizing the personalized history of users, DARIA identifies the most relevant past activities while considering the real-world features that contributed to the similarity. In addition, we adopt the novel approach of self-attention and introduce Self-Attention Recommender based on Attributes and History (SARAH). As a variation to DARIA, SARAH utilizes two self-attention components to describe users by their most characteristic past activities and items by their best depicting attributes. Various experiments establish the significant improvement of SARAH and DARIA over seven key baselines in diverse recommendation settings. By comparing our two proposed frameworks, we demonstrate the potential benefit of applying self-attention in different scenarios.