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Much of the existing research on banking systemic risk focuses on static single-risk exposures, and there is a lack of research on multiple-risk exposures. The reality is that the banking system is facing an increasingly complex environment, and dynamic measures of multiple-risk integration are essential. To reveal the risk accumulation process under the multi-risk exposures of the banking system, this article constructs a dynamic banking system as the research object and combines geometric Brownian motion, the BSM model, and the maximum likelihood estimate method. This article also aims to incorporate three types of exposures (interbank lending market risk exposures, entity industry credit risk exposures, and market risk exposures) within the same framework for the first time and builds a model of the dynamic evolution of banking systemic risk under multiple exposures. This study included the collection of a large amount of real data on banks, entity industries, and market risk factors, and used the Δ C o V a R model to evaluate the systemic risk of the China banking system from the point of view of the accumulation of risk from different exposures, revealing the dynamic process of risk accumulation under the integration of multiple risks within the banking system, as well as the contribution of different exposures to banking systemic risk. The results showed that the banking systemic risk of China first increased and then decreased with time, and the rate of risk accumulation is gradually slowing down. In terms of the impact of different kinds of exposures on system losses, the credit risk exposure of the entity industry had the greatest impact on the banking systemic risk among the three kinds of exposures. In terms of the contribution of the interbank lending market risk to the systemic risk, the Bank of Communications, China Everbright Bank, and Bank of Beijing contributed the most. In terms of the contribution of the bank–entity industry credit risk to the systemic risk, the financial industry, accommodation and catering industry, and manufacturing industry contributed the most. Considering the contribution of market risk to the systemic risk, the Shanghai Composite Index, the Hang Seng Composite Index, and the Dow Jones Index contributed the most. The research in this paper enriches the existing banking systemic risk research perspective and provides a reference for the regulatory decisions of central banks. [ABSTRACT FROM AUTHOR]

Background: At present, there is no established biomarker for the diagnosis of depression. Meanwhile, studies show that acoustic features convey emotional information. Therefore, this study explored differences in acoustic characteristics between depressed patients and healthy individuals to investigate whether these characteristics can identify depression. Methods: Participants included 71 patients diagnosed with depression from a regional hospital in Beijing, China, and 62 normal controls from within the greater community. We assessed the clinical symptoms of depression of all participants using the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Patient Health Questionnaire (PHQ-9), and recorded the voice of each participant as they read positive, neutral, and negative texts. OpenSMILE was used to analyze their voice acoustics and extract acoustic characteristics from the recordings. Results: There were significant differences between the depression and control groups in all acoustic characteristics (p < 0.05). Several mel-frequency cepstral coefficients (MFCCs), including MFCC2, MFCC3, MFCC8, and MFCC9, differed significantly between different emotion tasks; MFCC4 and MFCC7 correlated positively with PHQ-9 scores, and correlations were stable in all emotion tasks. The zero-crossing rate in positive emotion correlated positively with HAMA total score and HAMA somatic anxiety score (r = 0.31, r = 0.34, respectively), and MFCC9 of neutral emotion correlated negatively with HAMD anxiety/somatization scores (r = −0.34). Linear regression showed that the MFCC7-negative was predictive on the PHQ-9 score (β = 0.90, p = 0.01) and MFCC9-neutral was predictive on HAMD anxiety/somatization score (β = −0.45, p = 0.049). Logistic regression showed a superior discriminant effect, with a discrimination accuracy of 89.66%. Conclusion: The acoustic expression of emotion among patients with depression differs from that of normal controls. Some acoustic characteristics are related to the severity of depressive symptoms and may be objective biomarkers of depression. A systematic method of assessing vocal acoustic characteristics could provide an accurate and discreet means of screening for depression; this method may be used instead of—or in conjunction with—traditional screening methods, as it is not subject to the limitations associated with self-reported assessments wherein subjects may be inclined to provide socially acceptable responses rather than being truthful. [ABSTRACT FROM AUTHOR]

Background: There are currently no objective biomarkers that allow the quantification of negative symptoms of schizophrenia. This study therefore explored the use of acoustic features in identifying the severity of negative symptoms in patients with schizophrenia.Methods: We recruited 79 inpatients who were diagnosed with schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (the schizophrenia group) at the Huilongguan Hospital in Beijing, China, and 79 healthy controls from the surrounding community (the control group). We assessed the clinical symptoms of the patients with schizophrenia using the Positive and Negative Syndrome Scale (PANSS) and the Brief Negative Symptom Scale (BNSS) and recorded the voice of each participant as they read emotionally positive, neutral, and negative texts. The Praat software was used to analyse and extract acoustic characteristics from the recordings, such as jitter, shimmer, and pitch. The acoustic differences between the two groups of participants and the relationship between acoustic characteristics and clinical symptoms in the patient group were analysed.Results: There were significant differences between the schizophrenia and control groups in pitch, voice breaks, jitter, shimmer, and the mean harmonics-to-noise ratio (p < 0.05). Jitter was negatively correlated with the blunted affect and alogia subscale scores of the BNSS, both in the positive and neutral emotion conditions, but the correlation disappeared in the negative emotion condition. However, shimmer exhibited a stable negative correlation with the blunted affect and alogia subscale scores of the BNSS in all three emotion conditions. A linear regression analysis showed that pitch, jitter, shimmer, and age were statistically significant predictors of BNSS subscale scores.Conclusions: Acoustic emotional expression differs between patients with schizophrenia and healthy controls. Some acoustic characteristics are related to the severity of negative symptoms, regardless of semantic emotions, and may therefore be objective biomarkers of negative symptoms. A systematic method for assessing vocal acoustic characteristics could provide an accurate and feasible means of assessing negative symptoms in schizophrenia.Tweet: Acoustic emotional expression differs between patients with schizophrenia and healthy controls. A systematic method for assessing vocal acoustics could provide an accurate and feasible means of assessing negative symptoms in schizophrenia. [ABSTRACT FROM AUTHOR]