[Objectives]To reduce suicidal ideation and suicidal behavior through initial screening of patients with suicidal tendencies and implementing suicide prevention interventions during their hospitalization.[Methods]The ...[Objectives]To reduce suicidal ideation and suicidal behavior through initial screening of patients with suicidal tendencies and implementing suicide prevention interventions during their hospitalization.[Methods]The Taihe Emotion-distress Index(THEI)was used to conduct pre-admission and post-discharge tests to explore the effects of suicide prevention measures during hospitalization on the alleviation of the disease and the reduction of suicidal behaviors.The study selected patients who were diagnosed with depression in the psychological outpatient department of Taihe Hospital from April 2019 to September 2019 and had to be hospitalized,including patients with moderate depressive episodes,severe depressive episodes with or without psychotic symptoms,and patients with suicidal thoughts and self-harming behaviors.[Results]The pre-admission and post-discharge test data of hospitalized patients were analyzed,and the non-parametric paired sample T test was carried out,and the result was P<0.05,showing that there are significant differences between the pre-admission and post-discharge test data.[Conclusions]The measures of suicide prevention intervention are effective to a certain extent.展开更多
Purpose–Forecasting of stock indices is a challenging issue because stock data are dynamic,non-linear and uncertain in nature.Selection of an accurate forecasting model is very much essential to predict the next-day ...Purpose–Forecasting of stock indices is a challenging issue because stock data are dynamic,non-linear and uncertain in nature.Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices.The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.Design/methodology/approach–A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine(QSOS-ELM)is proposed to forecast the next-day closing prices effectively.Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases.This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.Findings–Simulation is carried out on seven stock indices,and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error,mean absolute percentage error,accuracy and paired sample t-test.Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.Originality/value–The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices.The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.展开更多
文摘[Objectives]To reduce suicidal ideation and suicidal behavior through initial screening of patients with suicidal tendencies and implementing suicide prevention interventions during their hospitalization.[Methods]The Taihe Emotion-distress Index(THEI)was used to conduct pre-admission and post-discharge tests to explore the effects of suicide prevention measures during hospitalization on the alleviation of the disease and the reduction of suicidal behaviors.The study selected patients who were diagnosed with depression in the psychological outpatient department of Taihe Hospital from April 2019 to September 2019 and had to be hospitalized,including patients with moderate depressive episodes,severe depressive episodes with or without psychotic symptoms,and patients with suicidal thoughts and self-harming behaviors.[Results]The pre-admission and post-discharge test data of hospitalized patients were analyzed,and the non-parametric paired sample T test was carried out,and the result was P<0.05,showing that there are significant differences between the pre-admission and post-discharge test data.[Conclusions]The measures of suicide prevention intervention are effective to a certain extent.
文摘Purpose–Forecasting of stock indices is a challenging issue because stock data are dynamic,non-linear and uncertain in nature.Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices.The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.Design/methodology/approach–A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine(QSOS-ELM)is proposed to forecast the next-day closing prices effectively.Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases.This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.Findings–Simulation is carried out on seven stock indices,and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error,mean absolute percentage error,accuracy and paired sample t-test.Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.Originality/value–The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices.The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.
