Miracle-dz/UniFAR-FTUs huggingface dataset:
Visco-Elastoplastic Constitutive Fatigue Model for Rocks

The study on the constitutive fatigue model for rocks under cyclic loading has an important significance in rock engineering. In order to study the fatigue properties of rocks under cyclic loading, according to the theory of rheological mechanics and the existing three basic one-dimensional fatigue elements, i.e., elastic, viscous, and plastic fatigue elements, the three-dimensional elastic, viscous, and plastic fatigue elements were constructed. Meanwhile, a fatigue yield criterion for rocks under cyclic loading was proposed, and a three-dimensional nonlinear visco-elastoplastic fatigue constitutive model (NVPFM) for rocks was established by using the flow criterion related to the proposed fatigue yield criterion. Compared with the test results of rocks under cyclic loading, the three-dimensional NVPFM could be used to describe the transient, steady-state, and tertiary creep phases of rocks under cyclic loading.
A Comparison of Students’ Academic Achievement in English in Single-Sex and Co-Educational Schools

In this ex post facto research, the difference in students’ academic achievement in English has been examined in single-sex and co-educational schools on the bases of data obtained from 576 students from 03 boys’ only, 04 girls’ only, and 07 co-educational secondary schools from Multan. Students’ score in achievement test and gain in grades obtained in the subject of English from 2001 to 2005 were analysed using t-test of independent samples against grouping variable type of school students attended. Statistical control was also applied in the analyses of co-variance over confounding variables associated with students’ family and school. Both boys and girls from single-sex schools obtained better score in achievement test and gained better grades than students from co-educational schools and this difference remained statistically significant even after applying statistical control.
Reducing Wildfire Ignitions from Open Burning in the Black Hills of South Dakota

The research purpose of this study was to identify and analyze the causal factors of wildfire ignitions from open burning activities in the Black Hills of South Dakota. A descriptive methodology that incorporated a causal-comparative experimental design was used to answer the research questions of the description of the factors and their relationships to wildfire ignitions during the winter burning season. The Mann-Whitney test and classification and regression tree analysis were used as procedures to identify and describe the relationships for the factors of ignition. Results showed that temperature and wind factors were most significant in causing wildfire ignitions. Recommendations are to apply the results within a comprehensive community risk reduction strategy framework in the fire department’s service area. Wildfire Ignitions 4
Vision Mediated Story Generation

Story generation is the task of automatically generating a complete story.For this task, the dataset is especially important. Looking back to our childhood, after we could understand each word’s meaning, it still took a long time for us to achieve the ability to write a good story. According to this observation, a large dataset is undoubtedly necessary. However, unfortunately, the story data is hard to be obtained. So in this work, we propose a unsupervised method to train the story generation model without any story data. On the other hand, different from the novel, the storybooks for children contain many pictures and we call them the picture-book. It means that the image information would help children to understand the context of the story. The case of picture-books tells us that visual information might play a significant role in a story. For another example, when people start writing a story, they will imagine the pictures of the next event in their minds and then write down what happened in the image. So in this view, we state that the image information is indispensable in the open story generation task. To summary our contribution:
Magnetogenesis by Wave-driven Momentum Exchange

When multiple species interact with an electrostatic ion acoustic wave, they can exchange momentum, despite the lack of momentum in the field itself. The resulting force on the electrons can have a curl, and thus give rise to compensating electric fields with curl on magnetohydrodynamic timescales. As a result, a magnetic field can be generated. Surprisingly, in some astrophysical settings, this mechanism can seed magnetic fields with growth rates even larger than through the traditional Biermann battery.
Nonlinear Multivariable Analysis of SOI, Precipitation, and Temperature in Fukuoka, Japan

Global climate variations are expected to affect local hydro-meteorological variables like precipitation and temperature. The Southern Oscillation (SO) is one of the major driving forces that give impact on regional and local climatic variation. The relationships between SO and local climate variation are, however, characterized by strong nonlinear variation patterns. In this paper, the nonlinear dynamic relationship between the Southern Oscillation Index (SOI), precipitation, and temperature in Fukuoka, Japan, is investigated using by a nonlinear multivariable approach. This approach is based on the joint variation of these variables in the phase space. The joint phase-space variation of SOI, precipitation, and temperature is studied with the primary objective to obtain a better understanding of the dynamical evolution of local hydro-meteorological variables affected by global atmosphericoceanic phenomena.
Effect of ankle joint fixation on tibialis anterior muscle activity during split-belt treadmill walking in healthy subjects: A pilot study

Objectives This study aims to examine the characteristics of muscle activity change of the tibialis anterior (TA) muscle in healthy adults while they walked on a split-belt treadmill with one fixed ankle. Patients and methods This randomized controlled trial was conducted between November 2017 and July 2018. Fourteen healthy male individuals (mean age 31.4 years; range, 23 to 50 years) were divided into two groups: right ankle joint fixed by ankle-foot orthosis (fixation group) and no orthosis (control group). Both groups were asked to walk on a treadmill with the same belt speed. After familiarizing with walking on both belts at 5.0 km/h, they walked for 6 min with the right belt slower (2.5 km/h) and the left faster (5.0 km/h). For analysis, the 6 min were divided equally among three time periods. The TA muscle activity was calculated at first and last time periods. We compared muscle activities in time periods (early and late phase) and in groups (fixation and control) using two-way mixed analysis of variance. Results The TA muscle activity decreased in the late phase regardless of ankle joint fixation, and also decreased in the fixation group regardless of the time periods. There was an interaction between these factors. Conclusion These data show that changes in the TA muscle activity were smaller in the fixation group, suggesting that the ankle joint fixation reduces the adaptation.
Aircraft Detection on Panchromatic Imagery Based on Densely Connected Convolutional Network

This paper presents an aircraft detection on panchromatic image using densely connected convolutional network. This algorithm connects all preceding feature-maps to all subsequent layers. It is encouraged to reuse feature-maps and enhance feature-maps representation. This algorithm is driven to learn aircraft feature to detect aircraft objects on panchromatic imagery. Based on the experimental result, it can yield accuracy of 92%.
Interplay of Organizational Commitment and Turnover Intention in Academic Sector

Organizational commitment is considered as an important variable in the reduction of turnover of employees. It is well documented that those employees who are more committed with their respective organizations are less likely to quit those organizations. This study is aimed to examine the relationship of organizational commitment with turnover intention of academic staff as it has been less focused in academic sector of Pakistan. We collected data from 320 respondents of seven public universities located at Peshawar division, Pakistan. We used structural equation modeling (SEM) in order to analyze the collected data. Findings of our study also reported significant negative relationship between the two variables. Such results further revealed that if academic staffs are psychologically, emotionally and morally attached with their respective universities, they are less intended to quit those universities. Results also validate social exchange theory. Theoretical and practical implications are discussed in the study.
3D Shape Reconstruction of Human Foot using Distance Sensors

: We proposed a simple, easy-to-use system to reconstruct an individual's foot shape in 3D by using an array of distance sensors and data of known foot shape collected in a database. In our system, values from the sensors will be used to generate a silhouette image, where its information will then be used as the base to search for the most similar model from the database to reconstruct a 3D foot shape. In order to verify the effectiveness of our method, we conducted experiments to evaluate the accuracy of the reconstructed foot shape
User-Assisted Tabular Extraction in Japanese Invoice

We present a user-driven approach to extract tabular information from Japanese business document. Following the previous researches Bart and Sarkar (2010)[1] of the same task, we further extend the approach to cover Japanese business document. The method utilizes a set of (cid:12)elds (on the (cid:12)rst row of a table) provided by the user to extract the remaining rows that match the structure of the provided (cid:12)elds set. Firstly, a set of potential (cid:12)eld candidates is generated and evaluated against the user-selected (cid:12)elds based on an extended set of spatial features. Another level evaluation is performed to verify the structure similarity based on inter-(cid:12)eld spatial relationship. Both evaluation models were trained through Automated Machine Learning (AutoML). On top of that, we added a simple re-evaluation scheme which proven to reduce the false-negatives case. Pairing it up with the evaluations, we achieved an accuracy improvement of 4.56%. We demonstrate that this method is able to handle a wide variety of tabular structure regardless of the language of the documents.
Automobile Maintenance Modelling Using gcForest

Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on automobile historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost.
HeMI ++: A Genetic Algorithm based Clustering Technique for Sensible Clusters

We propose a new clustering technique called HeMI++. It uses cleansing and cloning operations that help to produce sensible clusters. HeMI++ learns necessary properties of a good clustering solution for a dataset from a high-quality initial population, without requiring any user input. It then disqualifies the chromosomes that do not satisfy the properties through its cleansing operation. In the cloning operation, HeMI++ replaces the chromosomes by high-quality chromosomes already found in the initial population. We compare HeMI++ with six (6) existing techniques on twenty (20) publicly available datasets using the Tree Index metric. Our experimental results indicate a clear superiority of HeMI++ over existing methods. We also apply HeMI++ on a brain dataset and demonstrate its ability to produce sensible clusters.
Large Dimensional Data Reduction by Various Feature Selection Techniques: A Short Review

In a data dependent era, dimensions contain a huge number of variables both in rows and columns forming more complex data matrices and these dimensional expansions generate a large dimensional data (LDD). The dimensionality problem of LDD is a massive challenge for analytics purpose and it somehow burdens the machine learning approaches. Due to the faster growing rate in innovative Internet of Things and web-based technologies, static data becomes noisy and nonstochastic that results in data loss and instability. Therefore, the demand for complex data dimension reduction technique (DDR) is growing immensely to improve data prediction, analysis and visualization. Several computational techniques have implemented for DDR which is further segregated into two categories such as feature extraction techniques (FET) and feature selection techniques (FST). But, most of the existing FET methods focus on transforming the higher dimensional data into a lower dimensional space and unable to tackle with the dimensionality reduction problem. Hence, this paper focuses on various dynamic FST that not only reduces the dimensionality load but also catalyze the data analysis process. General Terms Machine Learning, Feature Extraction, Feature Selection, Curse of Dimensionality, and Data dimension reduction.
Comparative e¡ects of exopolysaccharides from lactic acid bacteria and fructo-oligosaccharideson infantgutmicrobiota tested inan invitro colonicmodelwith immobilized cells

The aim of this study was to compare the effects of purified exopolysaccharides from Lactobacillus rhamnosus RW-9595M with those of a well-known prebiotic (short-chain fructo-oligosaccharides) on infant colonic microbiota using a new three-stage chemostat model with immobilized infant faecal microbiota. Two continuous cultures with different faecal inocula were tested with different compositions of carbohydrate media. During the first fermentation (F1), fructooligosaccharides tested at a concentration of 9.8 g L 1 increased the number of lactobacilli and decreased coliforms both in gel beads and in effluent from all three reactors, in agreement with data from the literature. During the second fermentation (F2), the effect of fructo-oligosaccharides tested at a lower concentration (7.5 g L ) was reduced compared with F1. Fructo-oligosaccharides also increased total organic acid concentration and decreased ammonia production. Results obtained for exopolysaccharide tested at 1.5 g L 1 indicate that exopolysaccharides from L. rhamnosus RW-9595M was not metabolized by infant microbiota and lacked any prebiotic effect.
One or Two Frequencies? The Scattering Transform Answers

With the aim of constructing a biologically plausible model of machine listening, we study the representation of a multicomponent stationary signal by a wavelet scattering network. First, we show that renormalizing second-order nodes by their first-order parents gives a simple numerical criterion to assess whether two neighboring components will interfere psychoacoustically. Secondly, we run a manifold learning algorithm (Isomap) on scattering coefficients to visualize the similarity space underlying parametric additive synthesis. Thirdly, we generalize the “one or two components” framework to three sine waves or more, and prove that the effective scattering depth of a Fourier series grows in logarithmic proportion to its bandwidth.
Variance of particle location in the stochastic model of PSO with inertia weight

In the Particle Swarm Optimization (PSO) method, the behavior of particles depends on movement parameters. Effective application of the PSO method in real-world problems requires stable behavior of particles in the swarm. In the stochastic particle stability analysis, recurrent formulas of expected value and variance of particle location are used. An explicit formula for the variance can also be obtained, however, it cannot be applied in practice due to its complexity. In our research, we propose assumptions guaranteeing a simple explicit formula for location variance. For the formula and given assumptions, we show stability areas in the particle configuration space, which guarantee order-2* stability of particles also for probability distributions of movement parameters other than uniform. The areas are verified in simulations.
The Relationship Between Genetric Polymorphism of Dopamine Receptor-D2 Gene and Parkinson's Disease in Chinese

Objective To detect the polymorphism of dopamine receptor-D2(DRD2) gene TaqI site and susceptility of Parkinson’s disease(PD). Methods One hundred and sixty-six patients with PD and 170 normal controls were selected as the experimental groups.PCR-RFLP was used to detect the distri-bution of TaqI alleles A1/A1,A1/A2,A2/A2;B1/B1,B1/B2,B2/B2 genotypes in the selected PD pa-tients and control groups.Results There was no significant difference between the PD patients group and control group in allelic and genotypic distributions of the DRD2 gene TaqI loci.There was no clear different in the frequencies of alleles and genotypes both in the patient and control groups regardless of whether early or late outbreak PD and gender(P0.05). Conclusion The results in present study don’t support the hypothesis that susceptibility to PD is associated with the DRD2 gene polymorphisms exam-ined in our population.
Pre-service teachers’ experiences in selecting and proposing challenging tasks in secondary classrooms

This study aims to know if pre-service secondary school mathematics teachers are able to select, adapt or design appropriate challenging tasks and what are the main challenges that they face when working with challenging tasks in the classroom. The study is qualitative, based on observation and interviews. The participants are two preservice teachers at the 2 (and final) year of their master of teaching degree. The results show that the preservice teachers, albeit facing several constrains, were able to find and adapt interesting tasks. They also identified several challenges in this activity, in the planning phase, during students’ autonomous work and, most especially, during the whole class discussions.
Nonuniformly Spaced Array with the Direct Data Domain Method for 2D Angle-of-Arrival Measurement in Electronic Support Measures Application from 6 to 18 GHz

This paper introduces a 2D angle-of-arrival (AoA) estimator, which has a 6–18 GHz 7-element nonuniformly spaced array (NSA) and a Direct Data Domain- (D3-) based AoA algorithm for a 2D isotropic-element planar array (IEPA). A 2D calibration and data-transformation method is developed to convert the NSA data to the output of the IEPA, so that the NSA-measured data can be used in the D3 algorithm. Using the steering vector (SV) of the IEPA and the results derived from the D3 method, a new 2D AoA searching method is also developed, which offers frequency-independent performance defined by the probability of AoA estimation, when the required estimation accuracy and signal-to-noise ratio (SNR) are given. For the applications of electronic support measures, this paper also presents the use of precalculated SV and data-transformation matrix databases built on preselected frequency points and a 2D-angle grid that is close to uniformly distributed directions. The simulation results show that with good SNR (≥15 dB), the estimator can have 50% probability of AoA estimation with root mean square error (RMSE) less than or equal to 1° using just a few samples from the NSA. Moreover, the study focuses on the applications with low SNR by using more samples from the NSA. Results show that the estimator has 52% and 80% probabilities of AoA estimation with RMSE ≤1° and 5°, respectively, for phase- or frequency-modulated radar pulses, when the SNR is within [−10, 0] dB. The study also shows that the estimator prefers a circular-shaped planar array with a triangular interelement pattern, since it presents more symmetrical characteristics from different azimuth angles.
Does Gender, Age and Usage Matter in Big Data’s Perception Applied in Online Tourism?

It is often asserted that big data can potentially help firms make data-oriented decisions. The rise of big data completely revolutionized the tourism industry, explaining partly why this research intends to explore the relationship between big data applied in online tourism and online tourists’ behavior. This study will tackle the question in relation to big data’s perception based on gender, age and usage’s interaction, factors identified throughout the literature as important. It is found that both advantages and disadvantages of big data positively impact online consumer behavior. Although surprising, this result is justified in the fact that consumers always consider the trade-off between advantages and disadvantages which explains their willingness to still use online services. It is also found that usage is a determinant factor influencing big data’s perception. One major take away is that disadvantages of big data do not necessarily translate into a negative behavior. Moreover, online tourism website designers can tailor their products in a way to appeal to light users of online tourism services, considering heavy users are likely to buy in no matter what.
A Long Short Term Memory Deep Learning Network for the Classification of Negative Emotions Using EEG Signals

In cognitive science and human-computer interaction, automatic human emotion recognition using physiological stimuli is a key technology. This research considers classification of negative emotions using EEG signals in response to emotional clips. This paper introduces a long short term memory deep learning (LSTM) network to recognize emotions using EEG signals. The primary goal of this approach is to assess the classification performance of the LSTM model for classifying emotions. The secondary goal is to assess the human behavior of different age groups and genders. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), Deep Belief Network based SVM (DBN-SVM), and LSTM based deep learning model for classification of negative emotions using brain signals. The analysis shows that for four class of negative emotion recognition LSTM based deep learning model provides classification accuracy as 81.63%, 84.64%, 89.73%, and 92.84% for 50-50, 60-40, 70-30, and 10-fold cross-validation. Generalizability and reliability of this approach is evaluated by applying our approach to publicly available EEG dataset DEAP and SEED. In compliance with the self-reported feelings, brain signals of 26-35 years of age group provided the highest emotional identification. Among genders, females are more emotionally active as compared to males.