Ergonomics factors influencing school education during the COVID-19 pandemic: A literature review.

BACKGROUND: The school is one of the most critical social, educational, and training institutions and the main pillar of education in society. Education and, consequently, educational environments have the highest effect on the mentality, development, growth, welfare, concentration, performance, and learning efficiency of students. OBJECTIVES: The present study aimed to examine the effects of environmental ergonomics on the learning and cognition of pre-school students during the COVID-19 pandemic. METHODS: The study was carried out as a review article using some keywords, namely "children", "learning", "pre-school", "COVID-19", "ergonomics", and "environmental factors". Scopus, PubMed, Science Direct and Web of Science were searched to find related articles. RESULTS: Factors like color, form, and layout of classrooms, lighting and ventilation, interior decoration, and educational equipment are effective in creating interest and motivation for students to learn. CONCLUSIONS: A review of these articles showed that the presence of ergonomics in educational spaces for children increases the quality of learning and reduces stress and anxiety, and by observing health protocols, a healthy and safe environment can be provided for students.

The prevalence of metabolic syndrome in drivers: A meta-analysis and systematic review.

BACKGROUND: Metabolic syndrome is an increasing disorder, especially in night workers. Drivers are considered to work during 24 hours a day. Because of job characteristics such as stress, low mobility and long working hours, they are at risk of a metabolic syndrome disorder. OBJECTIVES: The purpose of this study is a meta-analysis and systematic review of the prevalence of metabolic syndrome in drivers. METHODS: In this systematic review, articles were extracted from national and international databases: Scientific Information Database (SID), Iran Medex, Mag Iran, Google Scholar, Science Direct, PubMed, ProQuest, and Scopus. Data analysis was performed using meta-analysis and systematic review (random effect model). The calculation of heterogeneity was carried out using the I2 index and Cochran's Q test. All statistical analyses were performed using STATA software version 11. RESULTS: A total of nine articles related to the prevalence of metabolic syndrome in drivers in different regions of the world from 2008 to 2016 were obtained. The total sample size studied was 26156 with an average of 2906 samples per study. The prevalence of metabolic syndrome in drivers was 34% (95% CI: 30-37)CONCLUSIONS:According to the results of this study, the prevalence of metabolic syndrome in drivers is high. Occupational stress, unhealthy diet and physical inactivity cannot be cited as causes of metabolic syndrome prevalence in drivers. Therefore, to maintain and to improve the health of this group, the implementation of preventive, therapeutic and rehabilitation measures for these people as well as training should be considered.

Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks.

We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on encoderdecoders. While previous work in encoder-decoders implies the use of multiple resolutions simply by downsampling and upsampling images, we make this process explicit, with branches of the network tasked separately with obtaining local high-resolution segmentation, and wider low-resolution contextual information. The complete network is a memory efficient implementation that is still able to resolve small root detail in large volumetric images. We compare against a number of different encoder-decoder based architectures from the literature, as well as a popular existing image analysis tool designed for root CT segmentation. We show qualitatively and quantitatively that a multi-resolution approach offers substantial accuracy improvements over a both a small receptive field size in a deep network, or a larger receptive field in a shallower network. We then further improve performance using an incremental learning approach, in which failures in the original network are used to generate harder negative training examples. Our proposed method requires no user interaction, is fully automatic, and identifies large and fine root material throughout the whole volume.

Shift working disorders among nurses of Tehran hospital and its related factors in 2016.

INTRODUCTION: Many adverse effects occur among the nurses due to shift work Hence, the present study aimed to determine the prevalence of shift work-related disorders and its related factor among the nurses at Tehran University Subsidiary Hospital, Iran, and to find solutions for managing the relevant health problems. METHODS: In this cross-sectional study, the Survey of Shift workers (SOS) questionnaire and the Personal Information Form were used to collect data related to demographics and working conditions of 1259 randomly selected nurses working at Tehran University Subsidiary Hospital as statistical population. RESULTS: According to the results, psychological disorders (95%), digestive problems (85%) and social problems (80%) were the most frequent problems among the subjects. Additionally, the satisfaction rate was higher among the volunteer nurses compared to nurses who were forced to do shift work (P < 0.05). CONCLUSION: The nurses volunteered for shift work had higher satisfaction rate compared to nurses forced to shift work system; moreover, they had more job satisfaction and less shift work-related complaints. Therefore, it is important to select the nurses who are volunteer for shift work system. In addition, the shift work schedule in hospitals should be set based on workload and requirements because the shift schedule can adversely influence the social and family issues of the nurses, as well as their sleep quality and body biological process.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PURPOSE: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). METHODS: The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. RESULTS: The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. CONCLUSIONS: This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.