Assessment of risk factors for suicidal behavior: results from the Tehran University of Medical Sciences Employees' Cohort study

IntroductionSuicide is a major issue of concern for public health. It is estimated that suicide accounts for 700,000 deaths every year. A personal history of one or more suicide attempts is the most important determinant of suicide among the general population. This study aimed to assess the major risk factors associated with suicidal behaviors among Iranian employees in a medical setting.MethodsIn this study, 3,913 employees of Tehran University of Medical Sciences who participated in the employees' cohort study conducted by the university were recruited. Suicidal behaviors (SBs) and their associated risk factors were evaluated using the World Mental Health Composite International Diagnostic Interview (CIDI) Version 3.0. Univariate and multivariate logistic regressions were performed to identify the determinants of SBs among the participants, and crude and adjusted odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were calculated.ResultsOverall, 49.6% of respondents (n = 1,939) reported that they were tired of life and thinking about death. The lifetime prevalence rate of suicidal ideation (SI) was 8.1% (n = 317), that of suicide planning (SP) was 7.3% (n = 287), and that of suicide attempts (SA) was 3.1% (n = 122). Being female (OR: 1.87, CI: 1.64–2.12), being divorced (OR: 3.13, CI: 1.88–5.22), having a low level of education (OR: 1.57, CI: 1.15–2.14), and working in clinical and medical services (OR: 1.25, CI: 1.09–1.43) were associated with being tired of life and thinking about death. These factors were also associated with SI, SP, and SA.DiscussionThese findings highlight the need to prioritize mental health for suicide prevention, especially for high-risk groups, in workplace mental health promotion programs and policies

Osdes_net: oil spill detection based on efficient_shuffle network using synthetic aperture radar imagery

Synthetic Aperture Radar (SAR) imagery can be beneficial for segmenting oil spills, which are a common environmental hazard. Oil spill detection in SAR imagery faces several challenges, including speckle noise, heterogeneous backgrounds, blurred edges, and a lack of comprehensive datasets with multiple images. ShuffleNet is one of the deep networks, which has never been used for oil spill segmentation. In this article, ShuffleNet blocks are used to detect oil spills in SAR images, which is more effective than other methods. Besides, the main network design, six other blocks were evaluated, and the most valuable one was selected. We use group convolutions, shuffle channels, and atrous convolutions in this model with a minimum number of layers of ReLU. The methods are evaluated based on the Intersection Over Union (IoU) parameter so that the proposed method improved the mIoU by 7.1% over the best results of some previous methods