Search

Our study is the first to provide statistics on opioid use among Social Security Disability Insurance (SSDI) applicants. We use an innovative machine-learning method to identify opioids in open-ended text fields in SSDI administrative data. We find that more than 30% of applicants between 2007 and 2017 reported using one or more opioids, a rate that is about 50% higher than in the general population. Rates of reported opioid use varied over time, peaking at 32% in 2012. Reported opioid use also varied by age, gender, education, receipt of SSI, and across states. There was a positive and statistically significant association between (a) reported opioid use and SSDI awards and (b) reported opioid use SSDI award and death; these are associations and do not demonstrate a causal relationship. [ABSTRACT FROM AUTHOR]

We extend the theory of polynomial moments by proving their spectral behavior with respect to Gaussian noise. This opens the door to doing computations on the signal-to-noise ratios of polynomial filters and with this, the comparability to classical filters is made possible. The compactness of the information in the polynomial and Fourier spectra can be compared to determine which solution will give the best performance and numerical efficiency. A general formalism for filtering with orthogonal basis functions is proposed. The frequency response of the polynomials is determined by analyzing the projection onto the basis functions. This reveals the tendency of polynomials to oscillate at the boundaries of the support; the resonant frequency of this oscillation can be determined. The new theory is applied to the extraction of 3-D embossed digits from cluttered surfaces. A three-component surface model is used consisting of a global component, corresponding to the surface; a Gaussian noise component, and local anomalies corresponding to the digits. The extraction of the geometric information associated with the digits is a preprocessing step for digit recognition. It is shown that the discrete polynomial basis functions are better suited than Fourier basis functions to fulfill this task. [ABSTRACT FROM AUTHOR]

In the case of thermographic inspection, the workpiece is heated in a particular manner followed by the observation of the resulting temperature increase at the material surface by means of an infrared camera. Inhomogeneities such as surface cracks cause a nonuniform distribution of the temperature; consequently, they can be localized in the infrared images. For metallic pieces, the most efficient way is inductive heating, whereby the induced eddy current generates heat directly in the surface skin of the sample. Experiments have been carried out on how steel workpieces, especially castings, can be thermographically inspected to detect cracks. The testing is a nondestructive and contact-free method. The goal is to develop a fully automated testing equipment with high throughput, where the flawed pieces are identified by evaluation and classification of the infrared images. The classification task is to distinguish between temperature increase around a crack and additional heating at the edges of the workpieces. Neural network has been used to train and to classify about 750 images, and good results have been achieved. [ABSTRACT FROM AUTHOR]

This article presents a new approach of quality control to vibro ground improvement techniques based on hybrid machine learning (ML), i.e., a combination of classical analysis and ML techniques. The process is monitored with an instrumented rig equipped with multiple sensors. Key performance indicators (KPIs) are used to identify anomalous foundation columns. As the foundation columns are sub‐surface, there is no direct access to ground truth; consequently, unsupervised ML is applied to the recorded time‐series data. The risk of not detecting defective elements is reduced by the combination of two independent methods for anomaly detection, KPI‐ and ML‐based classification. The ML is used to gain a deeper process understanding and to detect anomalies which were not considered in the design phase of the KPI. New pre‐processing techniques were derived from the insights gained from the ML classifier; this led to a more robust classifier. It is shown how unsupervised ML, using a multi‐channel variational autoencoder (VAE) with long short‐term memory (LSTM) layers, can be utilized in a knowledge discovery process (KDP). [ABSTRACT FROM AUTHOR]

Abstract: The infrared image of a microbolometer camera can show significant blurring effects if the object is moving. The blurring mechanism of a microbolometer is different to that encountered with classical CCD and CMOS cameras. The electrical signal in the pixel of a microbolometer detector decays exponentially with a time constant of 10–15ms; therefore, the moving object is mapped to more pixels, resulting in a blurred image. Because of this blurring effect, the recorded infrared radiation of the object is dispersed and the contrast of the object corrupted. In this paper it is shown how the image can be restored and the blurring eliminated. The point spread function of the microbolometer camera is determined and the impact of the blurring from objects of different sizes is investigated. In order to suppress the noise in the restoration, a Wiener filter is used and it is demonstrated how objects of different sizes can be restored. Examples are presented for objects moving linearly and/or rotating; furthermore, an application is described where this technique has been used for automated thermographical testing. [Copyright &y& Elsevier]

The major advantage of products made from composite materials is given by their superior weight-specific mechanical properties. These can be weakened by defects induced in the manufacturing process. Therefore, online detection and analysis of the processed fiber bundle geometry is a key factor for the quality assurance of the final part. In this article, the instrumentation and data evaluation for determining the surface geometry of fiber bundles by means of light sectioning was examined. Bundles of glass and carbon fibers were measured continuously on an inspection test rig. Different background materials have been used in order to validate the applicability of the approach. By utilization of a polynomial fitting algorithm, data segmentation of object and baseline was robustly achieved. By means of cross correlation, the data alignment could be evaluated faster and more reliable compared to a method previously presented by us. The information could then be used for the determination of the fiber bundle width, centerline, spatial changes, and oscillations. In addition, unwanted defects as well as lateral movement of the fiber bundles were reliably detected. The information revealed by the proposed algorithm provides the basis for robust online monitoring of fiber bundle geometry in highly automated composite manufacturing processes. [ABSTRACT FROM AUTHOR]

Recent studies have shown increased all-cause mortality among workers following disabling workplace injury. These studies did not account for 2 potentially important confounders, smoking and obesity. We estimated injury-related mortality accounting for these factors. We followed workers receiving New Mexico workers' compensation benefits (1994–2000) through 2013. Using data from the Panel Study of Income Dynamics, we derived the joint distribution of smoking status and obesity for workers with and without lost-time injuries. We conducted a quantitative bias analysis (QBA) to determine the adjusted relationship of injury and mortality. We observed hazard ratios after adjusting for smoking and obesity of 1.13 for women (95% simulation interval (SI) 0.97 to 1.31) and 1.12 for men (95% SI 1.00 to 1.27). The estimated fully adjusted excess hazard was about half the estimates not adjusted for these factors. Using QBA to adjust for smoking and obesity reduced the estimated mortality hazard from lost-time injuries and widened the simulation interval. The adjusted estimate still showed more than a 10 percent increase for both women and men. The change in estimates reveals the importance of accounting for these confounders. Of course, the results depend on the methods and assumptions used. [ABSTRACT FROM AUTHOR]