Data visualization of item-total correlation by median smoothing

This paper aims to illustrate how data visualization could be utilized to identify errors prior to modeling, using an example with multi-dimensional item response theory (MIRT). MIRT combines item response theory and factor analysis to identify a psychometric model that investigates two or more latent traits. While it may seem convenient to accomplish two tasks by employing one procedure, users should be cautious of problematic items that affect both factor analysis and IRT. When sample sizes are extremely large, reliability analyses can misidentify even random numbers as meaningful patterns. Data visualization, such as median smoothing, can be used to identify problematic items in preliminary data cleaning. Accessed 4,139 times on https://pareonline.net from February 01, 2016 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

A Parametric Study of Piled Raft Foundation in Clay Subjected to Concentrated Loading

The use of piled raft foundation in building and infrastructure constructions is increasingly popular because of its effectiveness in reducing overall and differential settlements. Parameters influencing the performance of the piled raft foundation need to be comprehended in order to optimize the design of the piled raft system. Most of the current available literature focused on the piled raft foundation subjected to a uniform distributed load in sandy material.  This parametric study aims to provide insights into the performance of the piled raft foundations subjected to concentrated loading in clay. A series of 2D finite element analyses were performed to investigate the influencing parameters affecting the load distribution and settlement behaviour of the piled raft. The results suggested that increases in both pile length and raft thickness, as well as a decrease in pile spacing would reduce the differential settlement of the piled raft. Comparatively, raft thickness was the most significant controlling parameter affecting the differential settlement. The study also revealed the importance of placing the pile nearer to the location of concentrated load as it would yield a more uniform load distribution, and hence a lower differential settlement

Quantifying Desiccation Cracks for Expansive Soil Using Machine Learning Technique in Image Processing

The formation of desiccation cracks has detrimental effects on the hydraulic conductivity that affects the overall mechanical strength of expansive soil. Qualitative analysis on the desiccation cracking behaviour of expansive soil provided understanding of the subject based on various concepts and theories, while quantitative analysis aided these studies through numerical supports. In this study, a machine learning technique in image processing is developed to evaluate the surface crack ratio of expansive soil. The desiccation cracking tests were conducted on highly plastic kaolinite slurry samples with plasticity index of 29.1%. Slurry-saturated specimens with thickness of 10 mm were prepared. The specimens were subjected to cyclic drying-wetting conditions. The images are acquired through a digital camera (12 MP) at constant distance to monitor the desiccation cracks. The images are then pre-processed using OpenCV before crack feature extraction. In this study, a total of 54 desiccation crack images were processed, along with 8 images from trial test to train the model. The processed images are used to quantify the desiccation cracks by evaluating surface crack ratio and average crack width. It was identified that the accuracy of the model for the quantification of surface crack ratio and average crack width were 97.24% and 93.85% respectively with average processing time of 1.51s per image. The results show that the model was able to achieve high accuracy with sufficient efficiency in determining important parameters used for crack characterization

novoBreak: local assembly for breakpoint detection in cancer genomes.

We present novoBreak, a genome-wide local assembly algorithm that discovers somatic and germline structural variation breakpoints in whole-genome sequencing data. novoBreak consistently outperformed existing algorithms on real cancer genome data and on synthetic tumors in the ICGC-TCGA DREAM 8.5 Somatic Mutation Calling Challenge primarily because it more effectively utilized reads spanning breakpoints. novoBreak also demonstrated great sensitivity in identifying short insertions and deletions

[5-Hydroxy-3-phenyl-1-(pyridin-2-yl)pyrazol-5-olato]diphenylboron

In the title compound, C26H20BN3O, the B atom has tetra­hedral geometry and is linked to two phenyl rings, the O atom of the hy­droxy­pyrazole ring and the N atom of the pyridinyl ring. A six-membered BOCNCN ring forms by coordination of the B atom and the pyridinyl N atom. The BOCNCN ring has an envelope conformation [dihedral angle = 36.7 (1)° between the planar ring atoms and the flap] with the B atom out of the plane. In the 1-(2-pyridin­yl)-3-phenyl-5-hy­droxy­pyrazole group, the pyridinyl ring, the phenyl ring and the pyrazole ring are almost coplanar: the pyrazole ring makes a dihedral angle of 9.56 (8)° with the pyridinyl ring and 17.68 (7)° with the phenyl ring. The crystal structure is stabilized by π–π stacking inter­actions involving the pyridinyl and pyrazole rings of centrosymmetrically related mol­ecules, with ring centroid separations of 3.54 (5) Å

The role of high-frequency data in volatility forecasting: evidence from the China stock market

This research investigates the role of high-frequency data in volatility forecasting of the China stock market by particularly feeding different frequency return series directly into a large number of GARCH versions. The contributions of this research are as follows. 1) We provide clear evidence to support that the superiority of traditional time series models in volatility forecasting remains by taking advantage of high-frequency data. 2) We incorporate different distribution assumptions in GARCH models to capture the stylized facts of high-frequency data. The result shows that: 1) data frequency in GARCH application substantially influence the accuracy of volatility forecasting, as the higher the frequency is of the return series, the better are the forecasts provided; 2) non-normal distributions such as skewed student-t and generalized error distribution are more capable at reproducing the stylized facts of both intraday and daily return series than normal distribution; and 3) GARCH estimated by 5-min returns not only outperforms other GARCH alternatives, but also considerably beats RV-based models such as HAR and ARFIMA at volatility forecasting