Mental Health, Loneliness, and Social Support Among Undergraduate Students: A Multinational Study in Asia.

In this study, we aimed to investigate the prevalence of poor mental health and its association with loneliness and social support among 3531 undergraduate students in nine Asian countries. Mental health was assessed using the Self-Reporting Questionnaire, which was developed by the World Health Organization. Across the entire sample, we detected that nearly half of the students reported poor mental health according to the Self-Reporting Questionnaire and nearly one out of seven students felt lonely. While feeling lonely increased the odds of experiencing poor mental health (odds ratio [OR]), moderate (OR: 0.35) and strong social support (OR: 0.18) decreases the odds of experiencing poor mental health. The high prevalence of poor mental health calls for further in-depth investigations and implementation of mental health support interventions.

Clergy's Viewpoint Change Toward Mental Health and Stigma on Mental Illness: A Short Course Training.

Background: As stigma is one of the main barriers in promoting the mental health, the present study was designed with the purpose of reviewing clergy's viewpoint regarding the effect of mental health workshops on these barriers. Methods: For this study, by order of Iran's Health Ministry, a questionnaire was designed to examine the clergy's viewpoint related to mental illnesses and the consequent stigma. Ten faculty members and psychiatrists confirmed the questionnaire's validity after some modifications. In this research, 30 members of the clergy from the main religious city in Iran's "Qom" Seminary attended the training workshops for 2 days. The data obtained from the clergy's responses were analyzed using the SPSS software (ver.16) and descriptive and analytical tests. Also, the significance level was considered p < 0.05 in all tests. The results exhibited that the mean and standard deviation (Mean ± SD) of the clergy's attitude domain and awareness before the workshop was 1.90 ± 26.30 and 8.31 ± 1.64, respectively. Also, average and standard deviation (Mean ± SD) of their attitude domain and awareness after the workshop was 1.95 ± 29.73 and 1.18 ± 10.70, respectively. Discussion: The present study, which was designed to examine the clergy's viewpoint toward mental illnesses and the consequent stigma in the most considerable religious base in the country, illustrated that one strategy for reducing mental illness stigma in religious communities can be by holding training sessions to promote the clergy's awareness of and attitude toward mental health. Conclusion: There was a significant statistical difference between their awareness and attitude scores before and after the workshop (p < 0.01). In the present research, the awareness and attitude of clergy toward mental health and stigma due to mental illness was relatively good and significantly increased by holding the workshop.

Learn to Steer through Deep Reinforcement Learning.

It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with unseen circumstances. Therefore, we propose an end-to-end deep reinforcement learning algorithm in this paper to improve the performance of autonomous steering in complex environments. By embedding a branching noisy dueling architecture, the proposed model is capable of deriving steering commands directly from raw depth images with high efficiency. Specifically, our learning-based approach extracts the feature representation from depth inputs through convolutional neural networks and maps it to both linear and angular velocity commands simultaneously through different streams of the network. Moreover, the training framework is also meticulously designed to improve the learning efficiency and effectiveness. It is worth noting that the developed system is readily transferable from virtual training scenarios to real-world deployment without any fine-tuning by utilizing depth images. The proposed method is evaluated and compared with a series of baseline methods in various virtual environments. Experimental results demonstrate the superiority of the proposed model in terms of average reward, learning efficiency, success rate as well as computational time. Moreover, a variety of real-world experiments are also conducted which reveal the high adaptability of our model to both static and dynamic obstacle-cluttered environments. A video of our experiments is available at https://youtu.be/yixnmFXIKf4 and http://v.youku.com/vshow/idXMzg1ODYwMzM5Ng.

Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.

Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system.

A new combined method in active filter design for power quality improvement in power systems.

The voltage & current harmonics produced by nonlinear loads in power systems cause a reduction in power quality. In order to improve the power quality, active power filters (APFs) can be used. In this paper, a new control system for designing active filters despite nonlinear loads of electric arc furnaces (EAFs) is presented. The system is composed of three main parts: computation of reference currents, regulation of DC capacitor voltage, and production of firing pulses. In the first part, the active filter control system is presented based on the combination of the synchronous detection method and instantaneous power theory. In the second part, the DC capacitor voltage regulator is applied, producing a reference current and a proper voltage regulator is developed. For the third part of the control system, we use a PI controller to provide some conditions that follow the reference current in a complete cycle, and generate firing pulses by the hysteresis method. The proposed control system not only reduces the voltage and current harmonics in power systems but can also improve the power quality indices. The above design was implemented in the EAF system of the Mobarakeh steel complex (Isfahan, Iran). The simulation results show the effectiveness of the APFs in improving the power quality indices.