Prediction of the Stability of the Loaded Rock Based on the Acoustic Emission Characteristics of the Loaded Rock Based on Data Mining

The rock masses that occur in nature are damaged and unstable due to the impact of rock burst, coal and gas outbursts, and other human mining activities, posing a major threat to human life and safety. In the light of the early warning of the danger of the loaded rock mass, this paper adopts acoustic emission (AE) device to analyze the AE signal characteristics and damage laws of the loaded rock under different stress levels. Then, based on the AE signal characteristics of the loaded rock, data mining technology is used to construct a model to predict the failure and instability of the loaded rock mass and, finally, verify the reliability of the prediction model based on data mining. The results show that the AE signal characteristics of red sandstone under uniaxial load are related to the magnitude of the bearing stress. Before the plastic deformation stage, the AE energy and the cumulative count per second are both small. After the loaded rock enters the plastic deformation stage, the AE energy and the cumulative count per second both increase sharply. After the AE energy is greater than 500 mV ∗ ms and the cumulative count per second is greater than 150, the loaded rock mass will issue an early warning signal. The research results can provide a reference value for the safe production of the project site and the dangerous early warning of the loaded rock mass.

Predicting Managers' Mental Health Across Countries Using Country-Level COVID-19 Statistics

Background: There is limited research focusing on publicly available statistics on the Coronavirus disease 2019 (COVID-19) pandemic as predictors of mental health across countries. Managers are at risk of suffering from mental disorders during the pandemic because they face particular hardship. Objective: We aim to predict mental disorder (anxiety and depression) symptoms of managers across countries using country-level COVID-19 statistics. Methods: A two-wave online survey of 406 managers from 26 countries was finished in May and July 2020. We used logistic panel regression models for our main analyses and performed robustness checks using ordinary least squares regressions. In the sample of 406 managers from 26 countries, 26.5% of managers reached the cut-off levels for anxiety (General Anxiety Disorder-7; GAD-7) and 43.5% did so for depression (Patient Health Questionnaire-9; PHQ- 9) symptoms. Findings: We found that cumulative COVID-19 statistics (e.g., cumulative cases, cumulative cases per million, cumulative deaths, and cumulative deaths per million) predicted managers' anxiety and depression symptoms positively, whereas daily COVID-19 statistics (daily new cases, smoothed daily new cases, daily new deaths, smoothed daily new deaths, daily new cases per million, and smoothed daily new cases per million) predicted anxiety and depression symptoms negatively. In addition, the reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor. Individually, we found that the cumulative count of deaths is the best single predictor of both anxiety and depression symptoms. Conclusions: Cumulative COVID-19 statistics predicted managers' anxiety and depression symptoms positively, while non-cumulative daily COVID-19 statistics predicted anxiety and depression symptoms negatively. Cumulative count of deaths is the best single predictor of both anxiety and depression symptoms. Reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor.

Evaluation of the mechanical properties of Inconel 718 to SCM 440 dissimilar friction welding through real-time monitoring of the acoustic emission system

Friction welding was chosen for its versatility in the joining of dissimilar materials with high quality. The aim of this study is to determine the optimal welding conditions for attaining quality joints by using online monitoring of acoustic emission system signals. During friction welding, the formation of cracks, defects, or any abnormalities in the joining process which have a detrimental effect on the joints quality was identified. The most widely used materials in the aerospace industry—Inconel 718 and molybdenum steel—were joined by friction welding. The precision of the joints, internal defects, and quality are major concerns for aerospace parts. The results of the present research determined the optimal welding conditions for high tensile strength by nondestructively inducing acoustic emission signals. During friction time and upset time periods, the typical waveforms and frequency spectrum of the acoustic emission signals were recorded, and their energy level, average frequency, cumulative count, and amplitude were analyzed. Both cumulative count and amplitude were found to be useful parameters for deriving the optimal welding conditions. In the initial stage of friction welding, a very high voltage of continuous form was generated with frequency characteristics of 0.44 MHz and 0.54 MHz. The signals generated during the upset stage had a low voltage, but a very high frequency of 1.56 MHz and 1.74 MHz with a burst-type signal. The amplitude of the signal generated for the optimally welded joints was about 100 dB at the friction time and about 45 dB at the upset time.

Variational formulation incorporating spatial capacity changes to reconstruct trajectory for heterogeneous traffic conditions

This study deals with the reconstruction of vehicle trajectories incorporating a data fusion framework that combines video and probe sensor data in heterogeneous traffic conditions. The framework is based on the application of variational formulation (VF) of kinematic waves for multiple lane conditions. The VF requires cumulative count and reference trajectory as boundary conditions. The VF also requires generation of lopsided network using fundamental diagram (FD) parameters. In this regard, cumulative count and FD parameters are obtained from the video sensor, while reference vehicle trajectory is obtained from the probe sensor. The analysis shows that the framework can provide an accuracy of 83% in trajectory estimation from the nearest reference trajectory. However, the accuracy decreases as the reference trajectory gets farther away from the estimated one. Additionally, an extension of the VF to accommodate roadway side friction is presented. The FD as well as lopsided network reform when the roadway capacity varies due to side friction. Consequently, the vehicle trajectory bends to accommodate the capacity fluctuation.

Late complications among adult survivors of neuroblastoma in the St. Jude Lifetime Cohort Study (SJLIFE).

10561 Background: Assessment of late outcomes in neuroblastoma survivors has generally consisted of self-reported health events within retrospective cohorts. We aimed to characterize the health outcomes of a clinically-assessed cohort of long-term survivors of neuroblastoma diagnosed between 1963-2003. Methods: In a cohort of 239 ten-year survivors of neuroblastoma, of whom 137 (57%) underwent comprehensive clinical assessments, chronic conditions were graded using a modified version of the Common Terminology Criteria of Adverse Events, version 4.03. Comparisons were made using 272 clinically assessed community controls. Log-binomial regression was used to compare the prevalence of chronic conditions (grade 1-5) between survivors and controls and to calculate prevalence ratio (PR) and 95% confidence intervals (CI). Mean cumulative count (treating death as a competing risk) of chronic conditions by age was used to estimate cumulative burden with imputation of outcomes for non-clinically assessed survivors. Results: The median age at diagnosis was 0.9 (range: 0.0-14.4) and the median age at follow-up was 31.9 (range: 20.2-54.6) years for clinically assessed survivors. Median age of controls was 34.7 (range: 18.3-70.2). Treatment consisted of chemotherapy (75%), radiation (23%) and surgery (91%). Survivors were more likely than controls to have hearing loss (31.4% vs. 2.9%, PR = 10.7, 95% CI = 5.2-22.0), cardiomyopathy (8.8% vs. 0.7%, PR = 11.9, 95% CI = 2.7-52.5), hypothyroidism (10.9% vs. 5.2%, PR = 2.1, 95% CI = 1.1-4.3) or neurological disorders (56.9% vs. 32.4%, PR = 1.8, 95% CI = 1.4-2.2). At 35 years of age, the cumulative incidence of survivors experiencing at least one grade 3-5 condition was 67.3% (95% CI = 58.3-76.0%). By age 35 survivors experienced, on average, 8.5 (95% CI = 7.6-9.3) grade 1-5 and 2.4 (95% CI = 2.0-2.8) grade 3-5 conditions per 100 survivors, which was higher than the burden of grade 1-5 (3.3 [95%CI = 2.9-3.7]) and grade 3-5 (0.9 [95%CI = 0.7-1.0]) conditions identified among controls. Conclusions: Two-thirds of survivors are affected by severe or life-threatening health conditions. Continued follow-up, screening and intervention provide opportunities to optimize health.

A Study of Cumulative Quantity Control Chart for a Mixture of Rayleigh Model under a Bayesian Framework

<p>This study deals with the cumulative charting technique based on a simple and a mixture of Rayleigh models. The respective charting schemes are referred as the SRCQC-chart and the MRCQC-chart. These are stimulated from existing statistical control charts in this direction i.e. the cumulative quantity control (CQC) chart, based on exponential and Weibull models, and the cumulative count control (CCC) chart, based on the simple geometricmodel. Another motivation for this study is the mixture cumulative count control (MCCC) chart based on the two component geometric model. The use of mixture cumulative quantity is an attractive approach for process monitoring. The design structure of the proposed control chart is derived by using the cumulative distribution function of simple, and two components of mixture distribution(s). We observed that the proposed charting structure is efficient in detecting the changes in process parameters. The application of the proposed scheme is illustrated using a real dataset.</p>

Highway travel time information system based on cumulative count curves and new tracking technologies

Travel time is probably the most important indicator of the level of service of a highway, and it is also the most appreciated information for its users. Administrations and private companies make increasing efforts to improve its real time estimation. The appearance of new technologies makes the precise measurement of travel times easier than never before. However, direct measurements of travel time are, by nature, outdated in real time, and lack of the desired forecasting capabilities. This paper introduces a new methodology to improve the real time estimation of travel times by using the equipment usually present in most highways, i.e., loop detectors, in combination with Automatic Vehicle Identification or Tracking Technologies. One of the most important features of the method is the usage of cumulative counts at detectors as an input, avoiding the drawbacks of common spot-speed methodologies. Cumulative count curves have great potential for freeway travel time information systems, as they provide spatial measurements and thus allow the calculation of instantaneous travel times. In addition, they exhibit predictive capabilities. Nevertheless, they have not been used extensively mainly because of the error introduced by the accumulation of the detector drift. The proposed methodology solves this problem by correcting the deviations using direct travel time measurements. The method results highly beneficial for its accuracy as well as for its low implementation cost.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3209

Glass is Heavy, Too: Testing the Field-Processing Model at the Modena Obsidian Quarry, Lincoln County, Southeastern Nevada

The field-processing model (FPM) relates degree of resource processing at procurement site to round-trip travel distance. Typically applied to food getting, its extension to stone tools is part of a larger trend to broaden the scope and strengthen the theoretical foundation of inference from lithic data. This trend guided the FPM's application at the Modena obsidian quarry in the Great Basin, which follows earlier analyses by comparing the proportion by reduction stage of biface preforms between quarry and outlying sites and the technological characteristics of debris assemblages. It also uses two ratio-scale methods, slope of the regression of preform weight upon the first principal-component of linear dimensions, which declines with reduction, and slope of cumulative-count of flakes upon flake size, which increases with reduction. Results support the FPM and previous studies that document early-stage reduction at quarries vs. later-stage reduction across the landscape. The essential next step in FPM testing requires data on pattern and extent of biface reduction as a continuous function of distance from source. As useful as are existing measures and approaches, newly defined ratio-scale measures can particularly expedite testing of the FPM in its continuous terms.