Granular transport in a horizontally vibrated sawtooth channel

We present a new mode of transport of spherical particles in a horizontally vibrated channel with sawtooth shaped side walls. The underlying driving mechanism is based on an interplay of directional energy injection transformed by the sidewall collisions and density dependent interparticle collisions. Experiments and matching numerics show that the average particle velocity reaches a maximum at 60% of the maximal filling density. Introducing a spatial phase shift between the channel boundaries increases the transport velocity by an order of magnitude.Comment: 5 pages, 8 figure

The Effect of Change in Auditor’s Opinion on Timely Disclosure of Financial Information

The main purpose of this research is studying the effect of change in auditor’s opinion on timely disclosure of financial information. The statistical population of the current research is all firms listed in Tehran Stock Exchange during 2010 to 2012. The sample size is 101 firms after screening. Findings, using multiple regressions, show that change in auditor’s opinion contributes to firms’ increased timely disclosure of financial information

Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children

   Background: Shunt Infection is a common complication of shunt insertion in children which can lead to bad neuro-developmental conditions and impose a considerable economic burden for the health care system. So, identifying predictive factors of shunt infection could help us in the proper improvement of this deteriorating condition. Methods: In this study, related risk factors of 68 patients with history of shunt infection and 80 matched controls without any history of shunt infection, who were all operated in a single referral hospital were assessed. Three machine learning (ML)-based measures including sparsity, correlation, and redundancy along with specialist’s score were applied to select the most important predictive risk factors for shunt infection. ML was determined by summation of sparsity, correlation and redundancy measures, and the final total score was considered as normalization (ML-based score + specialist score). Results: According to the total score, prematurity, first ventriculoperitoneal shunting (VPS) age, intraventricular hemorrhage (IVH), myelomeningocele (MMC) and low birth weight had higher weights as shunt infection risk factors. icterus, trauma, co-infection and tumor had the lowest weights and history of meningitis and number of shunt revisions were defined as intermediate risk factors. Conclusion: The “ML-based clinical adjusted” method may be used as a complementary tool to help neurosurgeons in better patient selection and more accurate follow-up of children with higher risk of shunt infection

Enhancing the mechanical properties and formability of low carbon steel with dual-phase microstructures

In the present study, a special heat treatment cycle (step quenching) was used to produce a dual-phase (DP) microstructure in low carbon steel. By producing this DP microstructure, the mechanical properties of the investigated steel such as yield stress, tensile strength, and Vickers hardness were increased 14, 55, and 38%, respectively. In order to investigate the effect of heat treatment on formability of the steel, Nakazima forming test was applied and subsequently finite element base modeling was used to predict the outcome on forming limit diagrams. The results show that the DP microstructure also has a positive effect on formability. The results of finite element simulations are in a good agreement with those obtained by the experimental test

Sentiment Analysis of Corona-Related Tweets in Iran Using Deep Neural Network

With the spread of Covid-19 disease, quarantine, and social isolation, people are increasingly posting their opinions about the coronavirus on social networks such as Twitter. However, no study has yet been reported to analyze online opinions of individuals in order to understand their feelings about the Covid-19 epidemic in Iran. This study analyzes the emotions in the opinions of the Iranian people on the social network Twitter during the Corona crisis. For this purpose, a deep neural network model is presented. As there is no labeled dataset of Covid-19 tweets, the proposed model is first trained on the Stanford University Sentiment140 dataset, which contains 1.6 million tweets, and then used to classify the two classes of emotions contained in the collected corona-related tweets in Iran. The results show that the percentage of tweets with negative emotions is significantly higher than positive tweets. Also, the change in negative emotions of people in different months is proportional to the change in patient statistics

Evaluation of Musculoskeletal Disorders in Household Appliances Manufacturing Company

Work-related musculoskeletal disorders are the most prevalent work-related disorders and injuries and being the main cause of disability. This study was conducted to assessment of the prevalence of musculoskeletal disorders in worker company household appliances production. Posture analysis was evaluated by OWAS method and prevalence of musculoskeletal disorders by Nordic questionnaire. With evaluating musculoskeletal disorders among company household appliances production can intervention action to reduce musculoskeletal disorders was carried out. This cross-sectional study was performed on 100 workers of the appliance manufacturing industry. These Individuals were included 15 persons from foam injection workshop, 17 persons from molding workshop, 17 operators of presses, 17 persons from packaging, 17 person from cutting unit and 17 operators of rivet. The Nordic questionnaire was completed by Individuals for the organs of arm, back, leg and wrist and Posture analysis was performed by OWAS method. The data were analyzed using Spss software version 18 and descriptive statistics and Anova test. Nordic questionnaire results revealed that highest disorders were observed in the arm (25%), back (22%) and leg (21%). Also Anova test showed that was observed a significant correlation respectively between age and work experience with the prevalence of musculoskeletal disorders (

The Investigation Relationship between Mental Workload and Occupational Fatigue in the Administrative Staffs of a Communications Service Company

Mental workload reflects the level of attention resources required to meet both objective and subjective performance criteria, which may be affected by task demand, external support and past experience. Mental workload and occupational fatigue have been commonly cited as a major cause of workplace accidents. The aim of this study was to investigate the relationship between workload and occupational fatigue in the administrative staffs of a communications service company in Tehran. In this study, 94 employees of the administrative service (69 female and 25 male) were provided with a demographic characteristics questionnaire including age, body mass index (BMI), level of education and work experience. Then the Swedish occupational fatigue inventory questionnaire was used to determine the job fatigue. The NASA-TLX mental workload questionnaire used for assessing mental workload. Finally, Data were analyzed by SPSS Version 20, descriptive statistics, Pearson correlation test and ANOVA test. Results showed that NASA-TLX mental workload in female (59.14) is more than from male (54.56). Also result showed Swedish Occupational Fatigue Inventory (SOFI) in female (30.12) is more than from Male (28.12). Also, the Pearson correlation test showed that there is a significant correlation between NASA-TLX and SOFI (r = 0.76,

The Investigation Relationship between Mental Workload and Occupational Fatigue in the Administrative Staffs of a Communications Service Company

Mental workload reflects the level of attention resources required to meet both objective and subjective performance criteria, which may be affected by task demand, external support and past experience. Mental workload and occupational fatigue have been commonly cited as a major cause of workplace accidents. The aim of this study was to investigate the relationship between workload and occupational fatigue in the administrative staffs of a communications service company in Tehran. In this study, 94 employees of the administrative service (69 female and 25 male) were provided with a demographic characteristics questionnaire including age, body mass index (BMI), level of education and work experience. Then the Swedish occupational fatigue inventory questionnaire was used to determine the job fatigue. The NASA-TLX mental workload questionnaire used for assessing mental workload. Finally, Data were analyzed by SPSS Version 20, descriptive statistics, Pearson correlation test and ANOVA test. Results showed that NASA-TLX mental workload in female (59.14) is more than from male (54.56). Also result showed Swedish Occupational Fatigue Inventory (SOFI) in female (30.12) is more than from Male (28.12). Also, the Pearson correlation test showed that there is a significant correlation between NASA-TLX and SOFI (r = 0.76,

EIDA: An Energy-Intrusion aware Data Aggregation Technique for Wireless Sensor Networks

Energy consumption is considered as a critical issue in wireless sensor networks (WSNs). Batteries of sensor nodes have limited power supply which in turn limits services and applications that can be supported by them. An efcient solution to improve energy consumption and even trafc in WSNs is Data Aggregation (DA) that can reduce the number of transmissions. Two main challenges for DA are: (i) most DA techniques need network clustering. Clustering itself is a time and energy consuming procedure. (ii) DA techniques often do not have ability to detect intrusions. Studying to design a new DA technique without using clustering and with ability of nding intrusion is valuable. This paper proposes an energy-intrusion aware DA Technique (named EIDA) that does not need clustering. EIDA is designed to support on demand requests of mobile sinks in WSNs. It uses learning automata for aggregating data and a simple and effective algorithm for intrusion detection. Finally, we simulat