The Development of an Empirical Model for Estimation of the Sensitivity to Heat Stress in the Outdoor Workers at Risk

Background: Workers who work in hot environments may be at risk for heat stress. Exposure to heat can result in occupational illnesses, including heat stroke, heat cramps, and heat exhaustion. The risk of exposure to heat depends on individual, environmental, and occupational risk factors. Individual risk factors may decrease the individual’s tolerance to heat stress. Sensitivity as an intrinsic factor may predispose a person to heat stress. Aim: This study was aimed to determine the criteria for sensitivity parameter, specify their weights using the fuzzy Delphi-analytical hierarchy, and finally providing a model to estimate sensitivity. The significant of the study is presenting a model to estimate the sensitivity to heat stress. Materials and Methods: The expert’s opinions were used to extract the criteria in Delphi method. After determining the weight of each criterion, Fuzzy analytic hierarchy Process (FAHP), by mathematical principles matrix and triangular fuzzy numbers, was applied for the prioritization of criteria. Results: According to experts’ viewpoints and considering some exclusion, 10 of 36 criteria were selected. Among 10 selected criteria, age had the highest percentage of responses (90% (27/30)) and its relative weight was 0.063. After age, the highest percentages of response were assigned to the factors of preexisting disease (66.6% (20/30)), body mass index (56.6% (17/30)), work experience (53.3% (16/30)), and clothing (40% (16/30)), respectively. Other effective criteria on sensitivity were metabolic rate, daily water consumption, smoking habits, drugs that interfere with the thermoregulatory processes, and exposure to other harmful agents. Conclusions: Eventually, based on the criteria, a model for estimation of the workers’ sensitivity to heat stress was presented for the first time, by which the sensitivity is estimated in percent.Keywords: Heat stress, Sensitivity, Personal factors, Fuzzy AH

Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective: This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods: This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results: The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion: Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications

Quantifying cortical bone free water using short echo time (STE-MRI) at 1.5 T

Purpose: The purpose of our study was to use Dual-TR STE-MR protocol as a clinical tool for cortical bone free water quantification at 1.5 T and validate it by comparing the obtained results (MR-derived results) with dehydration results. Methods: Human studies were compliant with HIPPA and were approved by the institutional review board. Short Echo Time (STE) MR imaging with different Repetition Times (TRs) was used for quantification of cortical bone free water T1 (T1free) and concentration (�free). The proposed strategy was compared with the dehydration technique in seven bovine cortical bone samples. The agreement between the two methods was quantified by using Bland and Altman analysis. Then we applied the technique on a cross-sectional population of thirty healthy volunteers (18F/12M) and examined the association of the biomarkers with age. Results: The mean values of �free for bovine cortical bone specimens were quantified as 4.37 and 5.34 by using STE-MR and dehydration techniques, respectively. The Bland and Altman analysis showed good agreement between the two methods along with the suggestion of 0.99 bias between them. Strong correlations were also reported between �free (r2 = 0.62) and T1free and age (r2 = 0.8). The reproducibility of the method, evaluated in eight subjects, yielded an intra-class correlation of 0.95. Conclusion: STE-MR imaging with dual-TR strategy is a clinical solution for quantifying cortical bone �free and T1free. © 2020 Elsevier Inc

Evaluation of RGP contact lens fitting in keratoconus patients using hierarchical fuzzy model and genetic algorithms

Keratoconus is a progressive non-inflammatory disease of the cornea. Rigid gas permeable contact lenses (RGPs) are prescribed when the disease progresses. Contact lens fitting and assessment is very difficult in these patients and is a concern of ophthalmologists and optometrists. In this study, a hierarchical fuzzy system is used to capture the expertise of experienced ophthalmologists during the lens evaluation phase of prescription. The system is fine-tuned using genetic algorithms. Sensitivity, specificity and accuracy of the final system are 88.9, 94.4 and 92.6 respectively. © 2016 The authors and IOS Press. All rights reserved

Development and validation of an individual job performance questionnaire (IJPQ)

BACKGROUND: Developing reliable tools to tap into all the behavioral dimensions of individual job performance and identifying the right sub-dimensions is necessary for both research and practice. OBJECTIVE: This study aimed at developing and validating an IJPQ that addresses shortcomings of existing questionnaires. METHODS: After a comprehensive systematic literature review, a framework consisting of four dimensions, including task performance (TP), contextual performance (CP), counterproductive work behavior (CWB), and adaptive performance (AP) was structured for measuring IJP. As well, 45 sub-dimensions were identified for measuring IJP's dimensions. Content and face validity were evaluated, and item impact score (IS), content validity index (CVI), Kappa, and content validity ratio (CVR) were calculated. For reliability and confirmatory factor analysis (CFA), 525 workers completed the validated questionnaire and Cronbach alpha and goodness of fit indexes were determined, respectively. RESULTS: Of the 62 items generated to measure dimensions, 53 were approved. Based on item-level CVI, of the 53 items, only 45 items were accepted. Finally, the results of item level CVR led to the extraction of 27 questions to evaluate IJP. The obtained scale level CVI and scale level CVR were 0.91 and 0.68, respectively. Based on the results obtained from 525 Iranian workers, values of Cronbach's Alpha, X2/df, RMSEA, and P-value were in the acceptable range. CONCLUSIONS: Conclusively, a questionnaire containing 20 items was developed and validated for measuring IJP of Iranian worker's culture. The four dimensions of TP, CO, CWB, and AP consisted of 6, 5, 5, and 4 items each, respectively. Overall, IJPQ is a theory-based, reliable, and valid instrument for assessing job performance

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response