Network-Based Discovery of Opioid Use Vulnerability in Rats Using the Bayesian Stochastic Block Model

Opioid use disorder is a psychological condition that affects over 200,000 people per year in the U.S., causing the Centers for Disease Control and Prevention to label the crisis as a rapidly spreading public health epidemic. The behavioral relationship between opioid exposure and development of opioid use disorder (OUD) varies greatly between individuals, implying existence of sup-populations with varying degrees of opioid vulnerability. However, effective pre-clinical identification of these sub-populations remains challenging due to the complex multivariate measurements employed in animal models of OUD. In this study, we propose a novel non-linear network-based data analysis workflow that employs seven behavioral traits to identify opioid use sub-populations and assesses contributions of behavioral variables to opioid vulnerability and resiliency. Through this analysis workflow we determined how behavioral variables across heroin taking, refraining and seeking interact with one another to identify potentially heroin resilient and vulnerable behavioral sub-populations. Data were collected from over 400 heterogeneous stock rats in two geographically distinct locations. Rats underwent heroin self-administration training, followed by a progressive ratio and heroin-primed reinstatement test. Next, rats underwent extinction training and a cue-induced reinstatement test. To enter the analysis workflow, we integrated data from different cohorts of rats and removed possible batch effects. We then constructed a rat-rat similarity network based on their behavioral patterns and implemented community detection on this similarity network using a Bayesian degree-corrected stochastic block model to uncover sub-populations of rats with differing levels of opioid vulnerability. We identified three statistically distinct clusters corresponding to distinct behavioral sub-populations, vulnerable, resilient and intermediate for heroin use, refraining and seeking. We implement this analysis workflow as an open source R package, named mlsbm.

Parameter Estimation of a dc Motor-Gear-ac Generator Mathematical Model

Mathematical models and there parameters are essential for designers to predict the close loop behaviors of the plant so that systems are stable. A block model is develop in the MATLAB/simulink for the DC Motor-Gear-AC-Generator mathematical model in this paper, the block built is used to estimate the parameters in the estimation node using the gradient descent, simplex search and nonlinear least square algorithm. Gradient descent curve match that of the experimental data and its values are used in the DC Motor-Gear-AC Generator mathematical model. Objective: To built block simulink Estimate the parameters of the DC Motor-Gear-Generator mathematical model.

A Dynamic Method to Predict the Earthquake-Triggered Sliding Displacement of Slopes

The earthquake-induced permanent displacement is an important index of the potential damage to a slope during an earthquake. The Newmark method assumes that a slope is a rigid-plastic body, and the seismic responses of sliding masses or seismic forces along the slide plane are ignored. The decoupled method considers no relative displacement across the sliding plane, so it overpredicts the seismic response of the sliding mass. Both dynamic and sliding analyses are performed in the coupled method, but when Ts/Tm is large, the results are unconservative. In this paper, a method is proposed to predict the earthquake-triggered sliding displacement of slopes. The proposed method is based on the Newmark rigid method, coupled method, and decoupled method considering both the forces at the sliding interface and the system dynamics under critical conditions. For the flexible system, the displacements are calculated with different stiffness values, and the results show that as the stiffness increases and tends to infinity, the critical acceleration and displacements of the proposed method are close to those of the Newmark method. The proposed method is also compared with the Newmark method with the period ratio Ts/Tm. At small values of Ts/Tm, the flexible system analysis results of the displacement are more conservative than those of the rigid block model; at larger values of Ts/Tm, the rigid block model is more conservative than the flexible system.

Comparative analysis of acoustic and electromagnetic emissions of rocks

Comparative analysis of acoustic and electromagnetic emissions recorded during the intact rock samples deformation and dynamic rupture of simulated crustal fault is presented. Specialized machines for uniaxial compression and shear tests of rock samples with identical data acquisition systems for both test cases were employed. Increase of acoustic emission was observed accompanied by significant rise of intensity and amplitude of electromagnetic signals at high stress of the rock samples under the uniaxial compression or dynamic failure in the spring-block model. Such correlation is consistent with the previous conclusions that an increase of electromagnetic emission may be considered as a rock failure precursor. Any specific characteristics of the detected electromagnetic signals to be used for prediction of impending rock failure or the earthquake fault rupture were not found. The similarity of electromagnetic signals and their spectra obtained at the press equipment and the spring-block model suggests that in both cases, the signals observed are generated by the crack formations and shear. The electromagnetic emission appeared only in dry samples. The samples saturated by water with the salinity of over 0.1% demonstrated no electromagnetic emission.