Hongzhi Hu

Bio: Hongzhi Hu is an academic researcher from Fudan University. The author has contributed to research in topics: Social influence & Information Dissemination. The author has an hindex of 2, co-authored 2 publications receiving 29 citations.

Information dissemination of public health emergency on social networks and intelligent computation

Abstract: Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

CyberPsychological computation on social community of ubiquitous learning

Abstract: Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns.

Vicarious traumatization in the general public, members, and non-members of medical teams aiding in COVID-19 control

Abstract: Since December 2019, more than 79,000 people have been diagnosed with infection of the Corona Virus Disease 2019 (COVID-19). A large number of medical staff was sent to Wuhan city and Hubei province to aid COVID-19 control. Psychological stress, especially vicarious traumatization caused by the COVID-19 pandemic, should not be ignored. To address this concern, the study employed a total of 214 general public and 526 nurses (i.e., 234 front-line nurses and 292 non-front-line nurses) to evaluate vicarious traumatization scores via a mobile app-based questionnaire. Front-line nurses are engaged in the process of providing care for patients with COVID-19. The results showed that the vicarious traumatization scores for front-line nurses including scores for physiological and psychological responses, were significantly lower than those of non-front-line nurses (P 0.05). Therefore, increased attention should be paid to the psychological problems of the medical staff, especially non-front-line nurses, and general public under the situation of the spread and control of COVID-19. Early strategies that aim to prevent and treat vicarious traumatization in medical staff and general public are extremely necessary.

Vicarious traumatization in the general public, members, and non-members of medical teams aiding in COVID-19 control

Abstract: Since December 2019, more than 79,000 people have been diagnosed with infection of the Corona Virus Disease 2019 (COVID-19). A large number of medical staff were dispersed for Wuhan city and Hubei province to aid COVID-19 control. Psychological stress, especially vicarious traumatization (VT) caused by the COVID-19 pandemic, should not be ignored. To address this concern, the study employed a total of 214 general public (GP) and 526 nurses to evaluate VT scores via a mobile app-based questionnaire. Results showed that the VT scores slightly increased across periods of aiding COVID-19 control, although no statistical difference was noted (P = 0.083). However, the study found lower scores for VT in nurses [median = 69; interquartile range (IQR) = 56–85] than those of the GP (median = 75.5; IQR = 62–88.3) (P = 0.017). In addition, the VT scores for front-line nurses (FLNs; median = 64; IQR = 52–75), including scores for physiological and psychological responses, were significantly lower than those of non-front-line nurses (nFLNs; median = 75.5; IQR = 63–92) (P 0.05). Importantly, nFLNs are more likely to suffer from VT, which might be related to two factors, namely, gender [odds ratio (OR) = 3.1717; 95% confidence interval (CI) = 4.247–18.808; P = 0.002] and fertility [OR = 2.072; 95%CI = 0.626–24.533; P = 0.039]. Therefore, increased attention should be paid to the psychological problems of the medical staff, especially nFLNs, and GP under the situation of the spread and control of COVID-19. Early strategies that aim to prevent and treat VT in medical staff and GP are extremely necessary.

Image contrast enhancement using an artificial bee colony algorithm

Abstract: The objective of image contrast enhancement is to improve the contrast level of images, which are degraded during image acquisition. Image contrast enhancement is considered as an optimization problem in this paper and the artificial bee colony (ABC) algorithm is utilized to find the optimal solution for this optimization problem. The contribution of the proposed approach is two-fold. First, in view of that the fitness function is indispensable to evaluate the quality of the enhanced image, a new objective fitness function is proposed in this paper. Second, the image transformation function is critical to generate new pixel intensities for the enhanced image from the original input image; more importantly, it guides the searching movements of the artificial bees. For that, a parametric image transformation function is utilized in this paper so that only the optimal parameters used in the transformation function need to be searched by the ABC algorithm. This is in contrast to that the whole space of image intensity levels is used in the conventional ABC-based image enhancement approaches. Extensive experiments are conducted to demonstrate that the proposed approach outperforms conventional image contrast enhancement approaches to achieve both better visual image quality and higher objective performance measures.

Mental health status among family members of health care workers in Ningbo, China, during the coronavirus disease 2019 (COVID-19) outbreak: a cross-sectional study.

Abstract: To date, the psychological impact of COVID-19 epidemic among family members of health care workers (HCWs) in China has been neglected. This cross-sectional study investigates the mental health status and related factors in families of HCWs employed in designated hospitals in Ningbo, China. Family members of HCWs in five designated hospitals in Ningbo, China, were recruited in February, 2020 for this study. Demographic variables, COVID-19-related events in the lives of the participants, knowledge of COVID-19, and the working status of family members (that is, HCWs) were collected using online self-administered questionnaires. Mental health status was assessed using the Chinese versions of the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). Multivariable logistic regression analyses were performed to identify the main factors associated with the mental health conditions. In total, 845 participants completed the questionnaires correctly (95.80% response rate). The prevalence of anxiety and depression symptoms were respectively 33.73% (95% CI: 30.53–36.92%) and 29.35% (95% CI: 26.27–32.43%) when a cut-off score of 5 was used for GAD-7 and PHQ-9. Risk factors for anxiety symptoms included more time (hours) spent thinking about the COVID-19, and whether or not family members (that is, HCWs) had direct contact with confirmed or suspected COVID-19 patients while high participants’ self-reported safety scores for HCW’s protective equipment was a protective factor. More time (hours) spent thinking about COVID-19, longer average working time per week worked by family members (that is, HCWs), and being parents and other next of kin of HCWs were risk factors for depressive symptoms. Compared to participants who were HCWs, participants who were private sector workers were more likely to develop depressive symptoms, while government or institutional employees were less likely to suffer from depressive symptoms. Psychological responses to COVID-19 have been dramatic among family members of HCWs during the rising phase of the outbreak. Our findings provide strong evidence to examine and attend to the mental health of this population during the COVID-19 epidemic.