Empirical evidence describing the psychosocial consequences of occupational injury is still limited. The effect of occupational injury on depression might pose unique challenges in workers compared ...with other kinds of injury. This study aimed to assess the differential impact of workplace injury compared with non-workplace injury on depression over time, and to identify the potential risk factors associated with post-injury depression in the US working population.
Using pooled panel data from the Medical Expenditure Panel Survey 2000-2006, a total of 35,155 workers aged 18-64 years who had been followed for about 18 months in each panel were analyzed. Injuries in the 4-5 months before baseline, and subsequent depression incidence during follow-up, were identified using ICD-9 codes for the medical conditions captured in personal interviews. A discrete time-proportional odds model was used.
A total of 5.5% of workers with occupational injury at baseline reported depression at follow-up, compared with 4.7% of workers with non-occupational injury and 3.1% of workers without injuries. Those with occupational injuries had more severe injuries and required longer treatment, compared with those with non-occupational injuries. Only 39% of workers with workplace injuries were paid Workers' Compensation (WC). The association between injury and depression appeared to be stronger for workplace injury, and the adjusted odds ratio for depression was 1.72 for those with occupational injury (95% CI: 1.27-2.32), and 1.36 for those with non-occupational injury (95% CI: 1.07-1.65) compared with the no-injury group, after controlling for relevant covariates. Occupational injury was associated with higher odds of developing depression over time. WC as a source of medical payment was associated with 33% higher odds of developing depression (95% CI: 1.01-1.74). Part-time work, shorter job tenure, and long working hours were independently associated with post-injury depression risk.
Workers with occupational injury were more likely to become depressed than those with non-occupational injury. The psychosocial consequences of occupational injury, including depression, deserve further exploration to adequately support those injured at work. This finding also emphasizes a need for early intervention to reduce the burden of depression associated with occupational injury.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To advance current Li rechargeable batteries further, tremendous emphasis has been made on the development of anode materials with higher capacities than the widely commercialized graphite. Some of ...these anode materials exhibit capacities above the theoretical value predicted based on conventional mechanisms of Li storage, namely insertion, alloying, and conversion. In addition, in contrast to conventional observations of loss upon cycling, the capacity has been found to increase during repeated cycling in a significant number of cases. As the internal environment in the battery is very complicated and continuously changing, these abnormal charge storage behaviors are caused by diverse reactions. In this review, we will introduce our current understanding of reported reactions accounting for the extra capacity. It includes formation/decomposition of electrolyte-derived surface layer, the possibility of additional charge storage at sharp interfaces between electronic and ionic sinks, redox reactions of Li-containing species, unconventional activity of structural defects, and metallic-cluster like Li storage. We will also discuss how the changes in the anode can induce capacity increase upon cycling. With this knowledge, new insights into possible strategies to effectively and sustainably utilize these abnormal charge storage mechanisms to produce vertical leaps in performance of anode materials will be laid out.
•The growth pattern of algal groups can be different and vary depending on the region.•The single algae simulation method has limitations in reproducing algal blooms accurately in the study area.•The ...maximum growth rate of algae in this study is assumed to reflect various environmental factors.•Algal growth scenarios have proven that the growth of phytoplankton is a complicated process and requires further studies.
The lower part of the Han River, which flows through Seoul, Korea, experienced excessive toxic cyanobacterial growth in 2015. Modeling of algal bloom occurrence patterns in the lower part of this river was performed using the Environmental Fluid Dynamics Code (EFDC) to understand algal dynamics and thus better develop management alternatives. For a 71km long river section, 1175 horizontal 2-D grid elements were developed. This grid system was determined adequate, as the maximum values of the Courant–Friedrichs–Lewy condition and orthogonality deviation were 0.5 and 20.1, respectively. Chlorophyll-a (Chl-a) was chosen as the primary indicator for the likelihood of algal blooms. Calibration and verification of EFDC were performed by comparing the model results to three years of field data collected from 2013 to 2015. Calibration accuracy was verified not only for physical variables, including the mean water level and temperature, but also for other water quality variables in various locations of the study area. To improve the prediction accuracy of Chl-a, three dominant groups of algae were considered: diatoms, green algae, and cyanobacteria. The optimum growth temperature ranges were selected based on field data for the study area. It was found necessary to apply different maximum growth rates for algal groups for the upstream and downstream regions of the study area to appropriately reflect field observations. This result indicates that more than three algal groups need to be included to improve Chl-a calibration accuracy for the study area, yet the current EFDC model can consider only up to three phytoplankton groups. Although this problem could be overcome by assigning different maximum growth rates for different regions, it may be necessary to improve EFDC so that it can include more phytoplankton groups.
Apoptosis, a type of programmed cell death that plays a key role in both healthy and pathological conditions, releases extracellular vesicles such as apoptotic bodies and microvesicles, but exosome ...release due to apoptosis is not yet commonly accepted. Here, the reports demonstrating the presence of apoptotic exosomes and their roles in inflammation and immune responses are summarized, together with a general summary of apoptosis and extracellular vesicles. In conclusion, apoptosis is not just a 'silent' type of cell death but an active form of communication from dying cells to live cells through exosomes.
In this study, a deep learning-based method for developing an automated diagnostic support system that detects periodontal bone loss in the panoramic dental radiographs is proposed. The presented ...method called DeNTNet not only detects lesions but also provides the corresponding teeth numbers of the lesion according to dental federation notation. DeNTNet applies deep convolutional neural networks(CNNs) using transfer learning and clinical prior knowledge to overcome the morphological variation of the lesions and imbalanced training dataset. With 12,179 panoramic dental radiographs annotated by experienced dental clinicians, DeNTNet was trained, validated, and tested using 11,189, 190, and 800 panoramic dental radiographs, respectively. Each experimental model was subjected to comparative study to demonstrate the validity of each phase of the proposed method. When compared to the dental clinicians, DeNTNet achieved the F1 score of 0.75 on the test set, whereas the average performance of dental clinicians was 0.69.
This paper presents a reliable fault diagnosis methodology for various single and multiple combined defects of low-speed rolling element bearings. This method temporally partitions an acoustic ...emission (AE) signal and selects a portion of the signal, which contains intrinsic information about the bearing failures. This paper then performs frequency analysis for the selected time-domain AE signal by using multilevel finite-impulse response filter banks to obtain the most informative subband signals involving abnormal symptoms of the bearing defects. It does this by using a 2-D visualization tool that represents the percentage of the Gaussian-mixture-model-based residual component-to-defect component ratios via time-varying and multiresolution envelope analysis (TVMREA). Then, fault signatures in the time and frequency domains are extracted in the informative subband signals. Since all the extracted fault features may not be equally useful for diagnosis, the proposed genetic algorithm (GA)-based discriminative feature analysis (GADFA) selects the most discriminative subset of fault signatures. In experiments, single and multiple combined bearing defects under various conditions are used to validate the effectiveness of this fault diagnosis scheme using TVMREA and GADFA. Experimental results indicate that this reliable fault diagnosis methodology accurately identifies bearing failure type across a variety of conditions. In addition, GADFA outperforms other state-of-the-art feature analysis techniques, yielding 7.3%-46.6% performance improvements in average classification accuracy.
In practice, outliers, defined as data points that are distant from the other agglomerated data points in the same class, can seriously degrade diagnostic performance. To reduce diagnostic ...performance deterioration caused by outliers in data-driven diagnostics, an outlier-insensitive hybrid feature selection (OIHFS) methodology is developed to assess feature subset quality. In addition, a new feature evaluation metric is created as the ratio of the intraclass compactness to the interclass separability estimated by understanding the relationship between data points and outliers. The efficacy of the developed methodology is verified with a fault diagnosis application by identifying defect-free and defective rolling element bearings under various conditions.
The demand for online fault diagnosis has recently increased in order to prevent severe unexpected failures in machinery. To address this issue, this paper first proposes a comprehensive bearing ...fault diagnosis algorithm, which consists of fault signature extraction through time-frequency analysis and one-against-all multiclass support vector machines in order to make reliable decisions. In addition, acoustic emission (AE) signals sampled at 1 MHz are used for the early identification of bearing failures. Despite the fact that the proposed fault diagnosis methodology shows satisfactory classification accuracy, its computation complexity limits its use in real-time applications. Therefore, this paper also presents a high-performance multicore architecture, including 64 processing elements operating at 50 MHz in a Xilinx Virtex-7 field-programmable gate array device to support online fault diagnosis. The experimental results indicate that the multicore approach executes 1339.3x and 1293.1x faster than the high-performance Texas Instrument (TI) TMS320C6713 and TMS320C6748 digital signal processors (DSPs), respectively, by exploiting the massive parallelism inherent in the bearing fault diagnosis algorithm. In addition, the multicore approach outperforms the equivalent sequential approach that runs on the TI DSPs by substantially reducing the energy consumption.
This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal ...Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing's speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds.
This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple ...low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposed GA-based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.
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