Disease X Testing: The results of an international external quality assessment exercise

The United Nations Secretary-General Mechanism (UNSGM) for investigation of the alleged use of chemical and biological weapons is the only established international mechanism of this type under the UN. The UNGSM may launch an international investigation, relying on a roster of expert consultants, qualified experts, and analytical laboratories nominated by the member states. Under the framework of the UNSGM, we organized an external quality assurance exercise for nominated laboratories, named the Disease X Test, to improve the ability to discover and identify new pathogens that may cause possible epidemics and to determine their animal origin. The "what-if" scenario was to identify the etiological agent responsible for an outbreak that has tested negative for many known pathogens, including viruses and bacteria. Three microbes were added to the samples, Dabie bandavirus, Mammarenavirus, and Gemella spp., of which the last two have not been taxonomically named or published. The animal samples were from Rattus norvegicus, Marmota himalayana, New Zealand white rabbit, and the tick Haemaphysalis longicornis. Of the 11 international laboratories that participated in this activity, six accurately identified pathogen X as a new Mammarenavirus, and five correctly identified the animal origin as R. norvegicus. These results showed that many laboratories under the UNSGM have the capacity and ability to identify a new virus during a possible international investigation of a suspected biological event. The technical details are discussed in this report.Copyright © 2022

The covid-19 pandemic: A double threat to Chinese americans' mental health

Objective: To explore the double psychosocial threats of the COVID-19 pandemic, targeted behavior toward Chinese Americans, and the correlates to their mental health. Methods: A quantitative, cross-sectional, and descriptive design was utilized by using a purposive convenience sample of 301 Chinese Americans over the age of 18 residing in the United States. Online data collection was conducted through the social media platform WeChat from April 8–21, 2021. Descriptive statistical analysis was used for the participants' demographic characteristics, Multidimensional Scale of Perceived Social Support (MSPSS), Double Threat Situations, COVID-19 Racial Discrimination, and General Anxiety Disorder-7 (GAD-7). Stepwise logistic regression was conducted to verify predictors for anxiety levels by GAD-7. Results: In this sample (N = 301), 127 (42.19%) were male and 174 (57.81%) were female. The average age was 41.67 (SD = 5.89). Among MSPSS subscales, social support from family (MSPSS-Fam, 79.73%, n = 240) and social support from significant others (MSPSS-SO, 73.75%, n = 222) were high. 231 (76.74%) reported threats due to their Chinese ethnic background during the COVID-19 outbreak. Predictors for the high anxiety level by GAD-7 were COVID-19 racial discrimination from the local community (OR = 0.47, 95% CI = 0.39–0.71, p < 0.001), media/online (OR = 0.36, 95% CI = 0.26–0.53, p < 0.001), the perceived threat from the COVID-19 virus (OR = 0.33, 95% CI = 0.23–0.51, p < 0.001) and Perceived racism threat from Chinese background related to COVID-19 (OR = 0.31, 95% CI = 0.21–0.49, p < 0.001). Conclusions: COVID-19 double-threats (The virus and racial discrimination) situations are significantly related to the high level of anxiety among Chinese Americans. The sense of belonging and social perceptions of Chinese immigrants is closely related to public health problems in Western societies and needs to be addressed at all levels. Our findings call for the attention of healthcare workers to specific racism double-threatened situations and high mental health risks, as well as direct and indirect ethnic discrimination that Chinese Americans are experiencing during this pandemic, the long-term influences and effective coping ways related to this issue should be explored in further research. © 2023, Tech Science Press. All rights reserved.

The Influencing Factors of Learning Satisfaction in Blended Learning during Covid-19 in Chinese medical universities

By using a technology acceptance model (TAM) on survey results collected from medical universities of China, this paper aims to uncover the major factors that affect medical students' acceptance of blended learning during the Covid-19 period. 1238 medical students completed the study survey after experiencing 3 months of blended learning. The results show that the social influence (SIN) and perceived ease of use (PEOU) have positive impacts on perceived usefulness (PU);PU has positive impacts on learning motivation (LM);LM can moderately affect learning satisfaction (LS). There is also a positive relationship between SIN and LS. It is worth noting that SIN as an intermediary factor directly affects PEOU, PU and LS. In fact, SIN is considered the key factor that affect student satisfaction of blended learning during Covid-19. © 2022 IEEE.

OTN-over-WDM optimization in 5G networks: key challenges and innovation opportunities

The continued growth of both mobile broadband and fixed broadband subscriptions as well as the added deployment of Internet of Things devices has led to making 5G networks a reality. More specifically, 5G networks are expected to support a diverse set of new applications/services in addition to existing applications/services from previous generations (2G/3G/4G). The COVID-19 pandemic has further increased the demand for such services which has resulted in a further surge in the Internet usage. Thus, 5G networks are expected to have a highly flexible architecture at all levels including at the radio, core, and transport levels. Optical Transport Networks (OTN) have been proposed as one potential and promising supporting technology for 5G networks at the transport level, particularly for next generation transport networks featuring large-granule broadband service transmissions. This is because it allows for more flexible, efficient, and dynamic networks. However, adopting and deploying OTNs in 5G networks comes with its own set of challenges including control, management, and orchestration of such networks as well as their security. Accordingly, this paper overviews 5G networks along with their requirements and provides a brief summary of OTNs and the corresponding optimization mechanisms. Additionally, this work discusses the challenges facing OTNs and their optimization within the context of 5G. Moreover, it outlines some of the key research areas and opportunities for innovation stemming from the data-driven intelligent networking paradigm using Machine Learning techniques.

A Remote Rehabilitation and Evaluation System Based on Azure Kinect

In response to the shortage, uneven distribution, and high cost of rehabilitation resources in the context of the COVID-19 pandemic, we developed a low-cost, easy-to-use remote rehabilitation system that allows patients to perform rehabilitation training and receive real-time guidance from doctors at home. The proposed system uses Azure Kinect to capture motions with an error of just 3% compared to professional motion capture systems. In addition, the system pro-vides an automatic evaluation function of rehabilitation training, including evaluation of motion angles and trajectories. After acquiring the user's 3D mo-tions, the system synchronizes the 3D motions to the virtual human body model in Unity with an average error of less than 1%, which gives the user a more intuitive and interactive experience. After a series of evaluation experiments, we verified the usability, con-venience, and high accuracy of the system, finally con-cluding that the system can be used in practical rehabilitation applications. © Fuji Technology Press Ltd.

Kinect-based 3D Human Motion Acquisition and Evaluation System for Remote Rehabilitation and Exercise

With the increasingly serious aging situation, more and more elderly people are physically disabled. In addition, the current rehabilitation resources have the problems of shortage and uneven distribution, coupled with the impact of COVID-19 in early 2019, most patients have been greatly restricted from going to the rehabilitation center for training. To solve these problems, we propose a "Kinect-based 3D Human Motion Acquisition and Evaluation System for Remote Rehabilitation and Exercise"which uses the Kinect3 camera to obtain human motion with an error rate of only 3% when the body is in front of the camera. Then we use Unity to create a humanoid virtual model and interactive scene and synchronize the real body motion to the virtual model with an average error less than 1%. At the same time, our system provides reliable and highly accurate methods for evaluating actions based on angles and trajectories. What's more, users don't need to wear any wearable devices when using the system. It is a mark-less motion acquisition system, which reduces the cost and improves the usability and scalability of the system. And the interactive virtual scenes also increase the training motivation of users. © 2022 IEEE.

Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers

Online symptom checkers (OSC) are widely used intelligent systems in health contexts such as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as machine learning to facilitate self-diagnosis and triage based on symptoms input by healthcare consumers. However, intelligent systems' lack of transparency and comprehensibility could lead to unintended consequences such as misleading users, especially in high-stakes areas such as healthcare. In this paper, we attempt to enhance diagnostic transparency by augmenting OSCs with explanations. We first conducted an interview study (N=25) to specify user needs for explanations from users of existing OSCs. Then, we designed a COVID-19 OSC that was enhanced with three types of explanations. Our lab-controlled user study (N=20) found that explanations can significantly improve user experience in multiple aspects. We discuss how explanations are interwoven into conversation flow and present implications for future OSC designs.

Mask Aware Network for Masked Face Recognition in the Wild

Face recognition is one of the most important research topics for intelligence security system, especially in the COVID-19 era. Medical research has proven that wearing a mask is the most efficient way to avoid the risk of COVID-19. Nevertheless, classic face recognition systems often fail when dealing with masked faces. So it is essential to design a method that is robust to Masked Face Recognition (MFR). In this paper, to relieve the degraded performance of MFR, we propose Mask Aware Network (MAN) including a mask generation module and a loss function searching module. The mask generation module utilizes the face landmarks to obtain more realistic and reliable masked faces for training. The loss function searching module tries to match the most suitable loss for face recognition. On ICCV MFR challenge, our team victor-2021 achieves 5 first places (including 3 champions in standard face recognition and 2 champions in masked face recognition) and 1 third place by 3rd August 2021. These results demonstrate the robustness and generalization of our method in both standard or masked face recognition task.

Service-Oriented Architecture-Guided Information Service System for Design and Implementation of Rural Tourism

Rural tourism has become a way for people to pursue a simple life and relax, thanks to the variety of tourism forms available. Rural tourism, on the other hand, lacks a unified information platform for providing services to users and is unable to provide humanized services based on tourism experience. Rural tourism around the city has become the main force of tourism recovery because outbound tourism has yet to begin and domestic interprovincial tourism has only recently recovered. The development of rural tourism can be aided by an information service system that can effectively improve the utilization efficiency of tourism resources and explore new and reasonable paths for the development of rural tourism. This paper examines the opportunities and challenges that rural tourism faces in the face of the COVID-19 epidemic, as well as actionable strategies for the rapid growth of rural tourism. This paper introduces the Service-Oriented Architecture (SOA) technology required in the construction of the tourism service information sharing platform as the research object. In addition, the rural tourism information service system was established. So that rural tourism can be at the forefront of tourism's rapid recovery and revitalization, it should be promoted to become more vibrant. © 2022 Yukun You.

"You can't always get what you want ": economic thinking, constrained optimization and health professions education.

Every choice we make in health professions education has a cost, whether it be financial or otherwise; by choosing one action (e.g., integrating more simulation, studying more for a summative examination) we lose the opportunity to take an alternative action (e.g., freeing up time for other teaching, leisure time). Economics significantly shapes the way we behave and think as educators and learners and so there is increasing interest in using economic ways of thinking and approaches to examine and understand how choices are made, the influence of constraints and boundaries in educational decision making, and how costs are felt. Thus, in this article, we provide a brief historical overview of modern economics, to illustrate how the core concepts of economics-scarcity (and desirability), rationality, and optimization-developed over time. We explain the important concept of bounded rationality, which explains how individual, meso-factors and contextual factors influence decision making. We then consider the opportunities that these concepts afford for health professions education and research. We conclude by proposing that embracing economic thinking opens up new questions and new ways of approaching old questions which can add knowledge about how choice is enacted in contemporary health professions education.

Discovery of Travelling Companions from Trajectories with Different Sampling Rates

Discovery of travelling companions from trajectories can provide empirical support for various applications, such as COVID-19 contact tracing, suspects tracking and detection, tourist behavior analysis, etc. One challenge is trajectories of travelling companions are from different data sets with different sampling rates and granularities. Most current researches for discovery of travelling companions focus on using snapshot-based clustering methods to identify travelling groups, or using trajectory similarity algorithms to mine companion relationships. However, the constantly changing sampling rate limits the application of clustering methods in the companion relationship mining. Although some similarity algorithms can mitigate this negative impact, they usually focus on the spatial distribution of trajectories and the time complexity is very high. In this paper, we designed a Spatio-Temporal Trajectory Companion DEtection Framework (STCDEF) to detect travelling companions from trajectories with different sampling rates, which can effectively reduce the time consumption caused by the matching mechanism. Within the STCDEF, an approximate trajectory similarity algorithm, Fast Spatio-Temporal Similarity (FSTS) measure, is presented. Moreover, the concept of Mutual Following Degree (MFD) is introduced into STCDEF to detect travelling companions with FSTS, so as to further improve the efficiency when dealing with trajectories of varying sampling rates. © 2020 ACM.