Abstract: The coronavirus pandemic of 2019 has resulted in extensive social regulations and affected many aspects of life. It has led to significant stress and adversely impacted mental health across the globe. The virus has been found to directly increase neuropsychiatric sequelae in those affected. Various psychosocial factors have also increased the incidence and prevalence of mental health problems worldwide. There was a need for a ramp-up of psychiatric services to support individuals in such a situation. Even after the pandemic, there is a need for improving access to mental health services for the mentally ill as well as those affected by the regulations brought about to tackle the pandemic. Telepsychiatric services are in place throughout the world in different forms and are the answer to bridging the mental health gap during and in the aftermath of the pandemic. Hence, it is important to continue developing and enhancing tele psychiatric services in different countries for supporting and treating individuals affected by the pandemic.

Volatility is a measure of the instantaneous variability of a financial asset. High-volatility assets is often associated with high risk, highlighting the importance of precisely estimating the volatility. This paper proposes an autoregressive support vector regression (SVR) model integrating the lagged range-based Parkinson volatility measure and four lagged logarithmic prices ( SVR LagPK _ LagPrices ) jointly as predictor variables to capture the dynamics of volatility of asset returns. An empirical analysis based on the Standard and Poor’s 500 was adopted. We performed extensive comparisons among SVR models to determine the significance of integrating the predictor variables encompassing the lagged range-based Parkinson volatility measure and four lagged logarithmic prices, both jointly and singly in the autoregressive SVR models with different kernel settings. Additionally, the conditional autoregressive range (CARR) models were also evaluated. The in-sample results based on the two realised volatility measures that act as proxies for the unobserved true volatility, revealed two important findings: (i) Although the volatility estimates based on CARR models outperformed other SVR models in terms of root mean squared error (RMSE) and mean absolute error (MAE), the goodness-of-fit analysis results show that these models did not fulfil the underlying model assumptions, (ii) The SVR LagPK _ LagPrices model generally predominates other SVR models for the in-sample model fit based on the RMSE and MAE. An examination of the SVR LagPK _ LagPrices model with linear kernel yielded the best out-of-sample forecasts, characterised by the smallest RMSE and MAE which were tested based on the mean squared error loss function using Hansen’s model confidence set.

Purpose of the study: The purpose of this study is to find out how the influence of attitudes on motivation to learn about the problems that occur in learning physics.
 Methodology: The method used in this study is a quantitative method using survey assessment stages. The sampling technique used simple random sampling with a total sample of 72 high school students. The data collection technique uses a questionnaire on learning motivation and student attitudes. Data analysis used a simple linear regression hypothesis test assisted by SPSS software.
 Main Findings: The results of the research and testing of the hypothesis can be concluded that there is a significant positive influence on students' learning attitudes towards physics learning motivation. Each increase in one unit of learning attitude will increase learning motivation by 0.91 units significantly.
 Novelty/Originality of this study: The novelty of this research is the discovery of a positive influence of students' learning attitudes on their physics learning motivation. Where every increase of one unit of students' learning attitudes will increase 0.91 students' motivation to learn physics.

The Coronavirus disease 2019 (COVID-19) pandemic has led to massive social and economic disruptions around the world, impacting the mental health. Health inequality refers to health disparities that may be unfair and unjust. Inequity in mental health can exist in access to care, utilization, and efficiency of healthcare (e.g., morbidity and fatality), and can be influenced by geographical area (rural/urban), gender, socioeconomic level, racial or ethnic origin, and sexual orientation, among other factors. The consequences of mental health inequalities include continued unnecessary suffering and premature deaths, increased stigma and marginalization, lack of investment in mental health workforce and infrastructure, and limited or lack of treatment for people suffering from these conditions. Because of the underlying nature of disorders and treatment approaches, ethical and legal frameworks are critical components in mental health care. These protect patients’ rights and mental health professionals’ rights and privileges. This paper aimed to highlight the issues of mental health inequity and the psychological burden in this situation.

<p>This study aims to examine and analyze the effect of the transformational leadership of school principals, work environment, and school culture on the performance of teachers in Nisam Antara District. This research was conducted in the Nisam Antara sub-district in November 2022. The population in this study were junior high school teachers in the Nisam Antara sub-district. The number of samples in this study amounted to 68 people from three junior high schools in Nisam Antara District. The sample in this study used a saturated sampling technique. Data was collected using multiple linear regression. The results of the analysis show that the principal's transformational leadership, the work environment and school culture together influence teacher performance. While the work environment has no effect on teacher performance. </p><p> </p><table border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="161"><p align="right"><strong>Keywords:</strong></p></td><td valign="top" width="369"><p>Principal transformational leadership, work environment, school culture and teacher performance.</p></td></tr></tbody></table>

Abstract 
 This study focuses on the application and comparison of the epoch, time, performance/MSE training, and performance/MSE testing of variations of the Backpropagation algorithm. The main problem in this study is that the Backpropagation algorithm tends to be slow to reach convergence in obtaining optimum accuracy, requires extensive training data, and the optimization used is less efficient and has performance/MSE which can still be improved to produce better performance/MSE in this research—data prediction process. Determination of the best model for data prediction is seen from the performance/MSE test. This data prediction uses five variations of the Backpropagation algorithm: standard Backpropagation, Resistant Backpropagation, Conjugate Gradient, Fletcher Reeves, and Powell Beale. The research stage begins with processing the avocado production dataset in Indonesia by province from 2016 to 2021. The dataset is first normalized to a value between 0 to 1. The test in this study was carried out using Matlab 2011a. The dataset is divided into two, namely training data and test data. This research's benefit is producing the best model of the Backpropagation algorithm in predicting data with five methods in the Backpropagation algorithm. The test results show that the Resilient Backpropagation method is the best model with a test performance of 0.00543829, training epochs of 1000, training time of 12 seconds, and training performance of 0.00012667.

This article aims to determine the perception of urban communities living in Bukit Antarabangsa regarding the issue of social vulnerability (SV) to landslide and landslide-related disasters. Bukit Antarabangsa is widely known as one of the highly urbanized areas near the metropolitan of Kuala Lumpur which are highly susceptible to landslides. Few communities which currently under the SeDAR project (i.e., a join-community-based disaster risk reduction/CBDRR program between Selangor State Disaster Management Centre – JICA – Local NGO: SlopeWatch Bukit Antarabangsa) were selected as a case study. Focus Group Discussion (FGD) and household surveys were conducted during Conditional Movement Order (CMCO) on March 27th 2021 and April 11th 2021 to gather relevant information on the researched topic. The questionnaire for the survey was formulated based on the proposed detailed list of SV components, indicators, sub-indicators and weightage which was derived from a review of the literature and internal consultation among disaster experts. The data analysis process in this study served to achieve the objective of social and economic vulnerability assessment in general based on the usage of sub-indicators to point out the score value and level of performance of each sub-indicator based on the survey of local stakeholders. Results from data analysis were translated into spatial context through the production of an SV map of the study area. In summary, the integrated approach to the assessment of SV involving data analysis and mapping/spatial representation has offered some valuable insights towards strengthening local community resilient to disaster and should be considered for inclusion into the establishment of a long-term community-based disaster risk reduction (CBDRR) planning and assessment, and in formulating DRR strategies at local and/or municipal context.

BackgroundCannabis is the most commonly used illicit substance globally, in India particularly. In recent times, younger people started abusing cannabis, resulting in academic decline and psychological disorders. Research from developed countries had shown that abstinence from cannabis reverses cognitive decline in the young population to a certain extent. Studies on this topic have been very few in India. We intended to assess the effects of cannabis use and abstinence from cannabis on the cognitive functioning of young adults.The study was an observational study including 50 consecutive young male patients, who got admitted to our rehabilitation centre with cannabis use disorder (group A). The Montreal Cognitive Assessment (MoCA) test was used to assess the baseline cognitive functioning of these patients initially after 1 week of abstinence and compared with 50 graduate students with no history of cannabis use (group B). The MoCA test was re-administered to group A subjects after 3 months of strict abstinence from cannabis use.ResultsThere was a statistically significant difference between the baseline MoCA score of cannabis users and the controls (P < 0.001). Both the duration (r = −0.296, P = 0.036) and the quantity (r= −0.491, P < 0.001) of cannabis use had a negative correlation with the MoCA score. When the MoCA test was re-administered after 3 months of abstinence, we found a statistically significant improvement in cognitive functioning in cannabis users (P < 0.001), but the mean score was still less than the mean score of the control group (24.08 ± 2.66 vs 28.62 ± 0.85, P < 0.001) showing only partial improvement.ConclusionThis study showed that cognitive deficits were seen in cannabis users as compared to nonusers. It also had shown that abstinence from cannabis had partially reversed the impairment, but still some deficits were remaining. There is an urgent need for primary prevention strategies at community level to decrease the prevalence of cannabis use.