Cluster system for binary data frame

Author(s):  
Yang Yue ◽  
Fanzhi Meng ◽  
Chunrui Zhang ◽  
Yuan Liu
2016 ◽  
Vol 32 (2) ◽  
pp. 111-118 ◽  
Author(s):  
Marianna Szabó ◽  
Veronika Mészáros ◽  
Judit Sallay ◽  
Gyöngyi Ajtay ◽  
Viktor Boross ◽  
...  

Abstract. The aim of the present study was to examine the construct and cross-cultural validity of the Beck Hopelessness Scale (BHS; Beck, Weissman, Lester, & Trexler, 1974 ). Beck et al. applied exploratory Principal Components Analysis and argued that the scale measured three specific components (affective, motivational, and cognitive). Subsequent studies identified one, two, three, or more factors, highlighting a lack of clarity regarding the scale’s construct validity. In a large clinical sample, we tested the original three-factor model and explored alternative models using both confirmatory and exploratory factor analytical techniques appropriate for analyzing binary data. In doing so, we investigated whether method variance needs to be taken into account in understanding the structure of the BHS. Our findings supported a bifactor model that explicitly included method effects. We concluded that the BHS measures a single underlying construct of hopelessness, and that an incorporation of method effects consolidates previous findings where positively and negatively worded items loaded on separate factors. Our study further contributes to establishing the cross-cultural validity of this instrument by showing that BHS scores differentiate between depressed, anxious, and nonclinical groups in a Hungarian population.


2018 ◽  
Vol 55 (4) ◽  
pp. 652-657 ◽  
Author(s):  
Gabriel Murariu ◽  
Razvan Adrian Mahu ◽  
Adrian Gabriel Murariu ◽  
Mihai Daniel Dragu ◽  
Lucian P. Georgescu ◽  
...  

This article presents the design of a specific unmanned aerial vehicle UAV prototype own building. Our UAV is a flying wing type and is able to take off with a little boost. This system happily combines some major advantages taken from planes namely the ability to fly horizontal, at a constant altitude and of course, the great advantage of a long flight-time. The aerodynamic models presented in this paper are optimized to improve the operational performance of this aerial vehicle, especially in terms of stability and the possibility of a long gliding flight-time. Both aspects are very important for the increasing of the goals� efficiency and for the getting work jobs. The presented simulations were obtained using ANSYS 13 installed on our university� cluster system. In a next step the numerical results will be compared with those during experimental flights. This paper presents the main results obtained from numerical simulations and the obtained magnitudes of the main flight coefficients.


Author(s):  
Andreas Beger ◽  
Jacqueline H.R. DeMeritt ◽  
Wonjae Hwang ◽  
Will H. Moore
Keyword(s):  

1999 ◽  
Vol 513 (2) ◽  
pp. 733-751 ◽  
Author(s):  
Arunav Kundu ◽  
Bradley C. Whitmore ◽  
William B. Sparks ◽  
F. Duccio Macchetto ◽  
Stephen E. Zepf ◽  
...  

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jill de Ron ◽  
Eiko I. Fried ◽  
Sacha Epskamp

Abstract Background In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. Methods In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. Results The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. Conclusion Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.


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