Psychotherapy and Follow-Up in Health Care Workers After the COVID-19 Epidemic: A Single Center's Experience.

Objective: The present study aims to analysis the mental health of high-risk health care workers (HHCWs) and low-risk HCWs (LHCWs) who were respectively exposed to COVID-19 wards and non-COVID-19 wards by following up on mental disorders in HCWs in China for 6 months. Methods: A multi-psychological assessment questionnaire was used to follow up on the psychological status of HCWs in the Affiliated Hospital of Xuzhou Medical University in Xuzhou City (a non-core epidemic area) at 6 months after the first evaluation conducted during the COVID-19 epidemic. Based on the risk of exposure to COVID-19 patients, the HCWs were divided into two groups: high-risk HCWs, who worked in COVID-19 wards, and low-risk HCWs, who worked in non-COVID-19 wards. Results: A total of 198 HCWs participated in the study, and 168 questionnaires were selected for evaluation. Among them, 93 (55.4%) were in the HHCW group and 75 (44.5%) were in the LHCW group. Significant differences were observed in salary, profession, and altruistic behavior between the two groups (P < 0.05). There were no significant differences in the anxiety, depression, insomnia, or posttraumatic stress disorder (PTSD) scores between the two groups. Logistic regression revealed that work stress was a major joint risk factor for mental disorders in HCWs. Among all the HCWs, a total of 58 voluntarily participated in psychotherapy; the analysis showed a significant decrease in anxiety, depression, PTSD, work stress, and work risk after attending psychotherapy. There were also significant differences in positive and negative coping styles before and after psychotherapy. Conclusion: In the present follow-up, work stress was the major contributing factor to mental disorders in HCWs. Psychotherapy is helpful in terms of stress management and should be provided to first-line COVID-19 HCWs.

Deep Learning-Based Methods for Forecasting Brent Crude Oil Return Considering COVID-19 Pandemic Effect

Forecasting return and profit is a primary challenge for financial practitioners and an even more critical issue when it comes to forecasting energy market returns. This research attempts to propose an effective method to predict the Brent Crude Oil return, which results in remarkable performance compared with the well-known models in the return prediction. The proposed hybrid model is based on long short-term memory (LSTM) and convolutional neural network (CNN) networks where the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) outputs are used as features, along with return lags, price, and macroeconomic variables to train the models, resulting in significant improvement in the model's performance. According to the obtained results, our proposed model performs better than other models, including artificial neural network (ANN), principal component analysis (PCA)-ANN, LSTM, and CNN. We show the efficiency of our proposed model by testing it with a simple trading strategy, indicating that the cumulative profit obtained from trading with the prediction results of the proposed 2D CNN-LSTM model is higher than those of the other models presented in this research. In the second part of this study, we consider the spread of COVID-19 and its impact on the financial markets to present a precise LSTM model that can reflect the impact of this disease on the Brent Crude Oil return. This paper uses the significance test and correlation measures to show the similarity between the series of Brent Crude Oil during the SARS and the COVID-19 pandemics, after which the data during the SARS period are used along with the data during COVID-19 to train the LSTM. The results demonstrate that the proposed LSTM model, tuned by the SARS data, can better predict the Brent Crude Oil return during the COVID-19 pandemic.

Designing an optimization model for the vaccine supply chain during the COVID-19 pandemic.

The COVID-19 pandemic has affected people's lives worldwide. Among various strategies being applied to addressing such a global crisis, public vaccination has been arguably the most appropriate approach to control a pandemic. However, vaccine supply chain and management have become a new challenge for governments. In this study, a solution for the vaccine supply chain is presented to address the hurdles in the public vaccination program according to the concerns of the government and the organizations involved. For this purpose, a robust bi-level optimization model is proposed. At the upper level, the risk of mortality due to the untimely supply of the vaccine and the risk of inequality in the distribution of the vaccine is considered. All costs related to the vaccine supply chain are considered at the lower level, including the vaccine supply, allocation of candidate centers for vaccine injection, cost of maintenance and injection, transportation cost, and penalty cost due to the vaccine shortage. In addition, the uncertainty of demand for vaccines is considered with multiple scenarios of different demand levels. Numerical experiments are conducted based on the vaccine supply chain in Kermanshah, Iran, and the results show that the proposed model significantly reduces the risk of mortality and inequality in the distribution of vaccines as well as the total cost, which leads to managerial insights for better coordination of the vaccination network during the COVID-19 pandemic.

Density Map-based vehicle counting in remote sensing images with limited resolution

Observing traffic flow is of great significance to contemporary urban management. Overhead images, as represented by remote sensing images, provide a major source of information about traffic flow. However, the spatial resolutions of most common high-resolution remote sensing images are often limited to 0.5 m and even below, which makes it unrealistic to count vehicles by means of widely used object detection methods. Therefore, to explore the potential of remote sensing data for studying global urban development and management, this paper introduces a density map-based vehicle counting method for remote sensing imagery with limited resolution. Density map-based models regard the vehicle counting task as estimating the density of vehicle targets in terms of pixel values. We propose an improved CNN-based network, called Congested Scene Recognition Network Minus (CSRNet—), that generates a density map of vehicles from the input remote sensing imagery. A new dataset, RSVC2021, which was generated from the public DOTA and ITCVD datasets, is also introduced for network training and testing. A benchmark on the RSVC2021 dataset is accordingly established and CSRNet— is selected as the baseline model for subsequent experiments. A set of GF-2 time series images with a resolution of 1 m taken before, during and after the COVID-19 epidemic lockdown covering Wuhan city are applied for real-world application testing. The testing results on both the RSVC2021 dataset and real satellite images confirm that, in terms of both the counting values and the visualized density maps, the proposed method achieves good performance and exhibits considerable application potential in this task. The generating codes of RSVC2021 dataset will be publicly available at https://github.com/YinongGuo/RSVC2021-Dataset.

Integrating GIS in reorganizing blood supply network in a robust-stochastic approach by combating disruption damages.

As supplying adequate blood in multiple countries has failed due to the Covid-19 pandemic, the importance of redesigning a sensible protective-resilience blood supply chain is underscored. The outbreak-as an extensive disruption-has caused a delay in ordering and delivering blood and its by-products, which leads to severe social and financial loss to healthcare organizations. This paper presents a robust multi-phase optimization approach to model a blood supply network ensuring blood is collected efficiently. We evaluate the effectiveness of the model using real-world data from two mechanisms. Firstly, a Geographic Information System (GIS)-based method is presented to find potential alternative locations for blood donation centers to maximize availability, accessibility, and proximity to blood donors. Then, a protective mathematical model is developed with the incorporation of (a) blood perishability, (b) efficient collation centers, (c) multiple-source of suppliers, (d) back-up centers, (e) capacity limitation, and (f) uncertain demand. Emergency back-up for laboratory centers to supplement and offset the processing plants against the possible disorders is applied in a two-stage stochastic robust optimization model to maximize the level of hospitals' coverage. The results highlight the fraction cost of considering back-up facilities in the total costs and provide more resilient decisions with lower risks by examining resource limitations.

An investigation of traffic density changes inside Wuhan during the COVID-19 epidemic with GF-2 time-series images.

In order to mitigate the spread of COVID-19, Wuhan was the first city to implement strict lockdown policy in 2020. Even though numerous researches have discussed the travel restriction between cities and provinces, few studies focus on the effect of transportation control inside the city due to the lack of the measurement and available data in Wuhan. Since the public transports have been shut down in the beginning of city lockdown, the change of traffic density is a good indicator to reflect the intracity population flow. Therefore, in this paper, we collected time-series high-resolution remote sensing images with the resolution of 1 m acquired before, during and after Wuhan lockdown by GF-2 satellite. Vehicles on the road were extracted and counted for the statistics of traffic density to reflect the changes of human transmissions in the whole period of Wuhan lockdown. Open Street Map was used to obtain observation road surfaces, and a vehicle detection method combing morphology filter and deep learning was utilized to extract vehicles with the accuracy of 62.56%. According to the experimental results, the traffic density of Wuhan dropped with the percentage higher than 80%, and even higher than 90% on main roads during city lockdown; after lockdown lift, the traffic density recovered to the normal rate. Traffic density distributions also show the obvious reduction and increase throughout the whole study area. The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.

Contractual and relational enforcement in the aftermath of contract violations: the role of contracts and trust

PurposeThe purpose of this paper is to establish an integrated framework of the antecedents of enforcement after contract violations in construction projects and to examine whether contract provisions (control and coordination provisions) and trust (goodwill and competence trust) affect enforcement mechanisms (contractual enforcement and relational enforcement).Design/methodology/approachA survey method was employed to test the hypotheses. The authors collected data from the Chinese construction industry, and general contractor respondents were asked to answer a questionnaire about a contract violation by one of their subcontractors.FindingsControl provisions and competence trust are positively related to contractual enforcement, but goodwill trust is negatively related to contractual enforcement. Relational enforcement is influenced by goodwill trust and competence trust.Research limitations/implicationsThis study treats contract violations as a given variable, and it focuses on contract violations by subcontractors. The cross-sectional design makes it difficult to confirm the causality of the relationships.Practical implicationsOverly strict contractual enforcement can generate disputes and a vicious cycle of retaliation, and overly severe relational enforcement can damage a potentially profitable long-term relationship. In construction projects, the violating party will benefit from this study to avoid excessively contractual enforcement and relational enforcement, thus developing a more collaborative atmosphere on the current project and even establishing a solid long-term relationship.Originality/valueThis study extends the project management literature by investigating the antecedents of enforcement after contract violations, an area not yet fully researched.