Multi-Attentional U-Net for Medical Image Segmentation

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.

Promoting Green Buildings and Low-Carbon Design Strategies of Green B&B Rooms for Sustainable Tourism after COVID-19

COVID-19 opened a window of opportunity to change the green development of the hospitality industry. For many years, Chinese tourists have been the world's largest source of outbound tourists. Therefore, this study attempted to improve built-environment strategies for green rooms at B&Bs using the empirical statistics of Chinese tourists after the end of COVID-19 control measures and different green B&B standards, combining IPA (importance-performance analysis). For the lack of a green built-environment study from a tourism perspective, this study can be used mainly for improving the green satisfaction of urban B&Bs as it attempted to fill the gaps in research on green B&B rooms. This study will significantly help improve the quality of green rooms for the B&B industry in the future, and it also provides an improved green B&B room sample for other countries and regions. Moreover, it is an optimistic attempt at hospitality and tourism recovery. © 2023 by the authors.

Modeling, equilibrium, and demand management for mobility and delivery services in Mobility-as-a-Service ecosystems

Mobility-as-a-Service (MaaS) is an emerging business model integrating various travel modes into a single mobility service accessible on demand. Besides the on-demand mobility services, instant delivery services have increased rapidly and particularly boomed during the coronavirus (COVID-19) pandemic, requiring online orders to be delivered timely. In this study, to deal with the redundant mobility resources and high costs of instant delivery services, we model an MaaS ecosystem that provides mobility and instant delivery services by sharing the same multimodal transport system. We derive a two-class bundle choice user equilibrium (BUE) for mobility and delivery users in the MaaS ecosystems. We propose a bilateral surcharge-reward scheme (BSRS) to manage the integrated mobility and delivery demand in different incentive scenarios. We further formulate a bilevel programming problem to optimize the proposed BSRS, where the upper level problem aims to minimize the total system equilibrium costs of mobility and delivery users, and the lower level problem is the derived two-class BUE with BSRS. We analyze the optimal operational strategies of the BSRS and develop a solution algorithm for the proposed bilevel programming problem based on the system performance under BSRS. Numerical studies conducted with real-world data validate the theoretical analysis, highlight the computational efficiency of the proposed algorithm, and indicate the benefits of the BSRS in managing the integrated mobility and delivery demand and reducing total system equilibrium costs of the MaaS ecosystems.

Public Emotional Diffusion over COVID-19 Related Tweets Posted by Major Public Health Agencies in the United States

Since the end of 2019, the COVID-19 outbreak worldwide has not only presented challenges for government agencies in addressing public health emergency, but also tested their capacity in dealing with public opinion on social media and responding to social emergencies. To understand the impact of COVID-19 related tweets posted by the major public health agencies in the United States on public emotion, this paper studied public emotional diffusion in the tweets network, including its process and characteristics, by taking Twitter users of four official public health systems in the United States as an example. We extracted the interactions between tweets in the COVID-19-TweetIds data set and drew the tweets diffusion network. We proposed a method to measure the characteristics of the emotional diffusion network, with which we analyzed the changes of the public emotional intensity and the proportion of emotional polarity, investigated the emotional influence of key nodes and users, and the emotional diffusion of tweets at different tweeting time, tweet topics and the tweet posting agencies. The results show that the emotional polarity of tweets has changed from negative to positive with the improvement of pandemic management measures. The public's emotional polarity on pandemic related topics tends to be negative, and the emotional intensity of management measures such as pandemic medical services turn from positive to negative to the greatest extent, while the emotional intensity of pandemic related knowledge changes the most. The tweets posted by the Centers for Disease Control and Prevention and the Food and Drug Administration of the United States have a broad impact on public emotions, and the emotional spread of tweets' polarity eventually forms a very close proportion of opposite emotions.

Comparative analysis of the impact of weather conditions and human activities on air quality in the Dongting and Poyang Lake Region during the COVID-19 pandemic

During the COVID-19 lockdown, only the most basic and necessary production activities were retained in China. Such strict measures have caused many inconveniences to the people and the economy, but also provided the research community with a rare opportunity to compare the effects of weather conditions and human activities on air quality in the region. Here, a comparative analysis of the impact of weather conditions and human activities on air quality in the Dongting and Poyang Lake Region (DPLR) is proposed during the COVID-19 pandemic based on a circulation-to-environment approach. T-mode objective circulation classification method was applied to explore the effects of weather conditions on the concentration of two typical pollutants (PM2.5 and ozone) affecting the DPLR. PM2.5 and ozone concentrations under the nine identified circulation patterns are discussed. Under the control of circulation type CT1, CT3 and CT6, the PM2.5 concentrations in this area are high, while under the control of CT2 and CT9, the ozone concentrations are high. By comparing the variation in PM2.5 and ozone concentrations in three important cities in the region (Wuhan, Changsha, and Nanchang) during the three stages (Before Controlling, During Controlling and After Controlling) of COVID-19 from January to April 2020 and the corresponding months of a reference period (2015–2019), it is found that after controlling the human activities, the PM2.5 concentration dropped by 37.45%, while the ozone concentration increased by 111.83%. Ozone concentration was mainly affected by the synoptic circulation pattern, while the PM2.5 concentration was more affected by human activities. © 2021 Turkish National Committee for Air Pollution Research and Control

Airway management in a critically ill patient with novel coronavirus pneumonia undergoing extracorporeal membrane oxygenation

This article summarizes the nursing experience in a critically ill patient with novel coronavirus pneumonia after extracorporeal membrane oxygenation (ECMO) treatment for lung function improvement. After oral intubation-assisted ventilation, anti-infection, and other symptomatic support treatments, the patient was still unable to breathe without the ventilator. For the sustained carbon dioxide retention and severe gas exchange impairment, he was treated with tracheotomy and ECMO. During the treatment, a series of nursing measures to improve lung function were adopted, such as sputum suction care, atomized inhalation therapy, bronchial irrigation, and lateral ventilation combined with postural drainage. After 7 days of ECMO treatment and nursing, the patient's lung function improved and then he was weaned from the machine.

Investigation of the discomforts of the medical staff in COVID-19 isolation room

Objective: To investigate the occurrence of medical staff leaving the COVID-19 isolation room due to discomforts and to provide reference for clinical prevention and treatment. Methods: Stratified sampling method was used to investigate the occurrence of medical staff from Shanghai medical team leaving isolation room earlier due to discomforts, as well as the main symptoms and signs of theirs. Logistic regression was used for risk factor analysis. Results: Among the 227 medical staff working in Leishenshan Hospital in Wuhan, Hubei Province, who were assisted by Shanghai, 69 (30.4%) staff left earlier due to discomforts while working in the isolation room. Two of them had syncope, and sixty-seven of them had symptoms and signs related to presyncope. Symptoms of presyncope include headache, nausea, sweating, dyspnea, and palpitations, etc. Univariate analysis revealed statistically significant differences in occupation (P=0.002), gender (P=0.006), and standing time (P=0.002). Logistic regression analysis showed that occupation (P=0.000), standing time (P=0.025), and hunger (P=0.029) were statistically significant. Conclusion: Different occupation, gender and standing time have different effects on the situation of medical staff leaving the isolation room due to discomforts. Occupation, standing time and feeling of hunger are the influencial factors for medical staff leaving the isolation room earlier.