Hydration Practices of Long-Distance Trail Runners Competing in Connecticut Events during COVID-19

Overhydration is the primary cause of exercise-associated hyponatremia (EAH). If intended hydration practices are different than actual practices, this may lead to overhydration and will increase the risk of EAH. The purpose of this research was to assess and compare the self-reported hydration practices of long-distance trail runners during an intended long training run, an intended event, and during an actual event. The researchers hypothesized hydration volumes and strategies intended would be different than actual. In a cross-sectional pre and post-online survey design, the participants completed a pre-race survey (n = 26) and a post-race survey (n = 17) in two different Connecticut trail races. A moderate correlation (R = 0.52, P = 0.05) was found when comparing volumes during the actual event to the intended event. In addition, moderate agreement (64-85%) was found to be significant by the Kappa statistic when comparing the hydration strategies in the intended and the actual events. Finally, this study found that 50% of participants reported knowledge and awareness of EAH, while 62% felt they had a solid understanding of an effective hydration plan. These findings indicate the need for ongoing and further education in the trail running community. © 2022, Journal of Exercise Physiology Online. All Rights Reserved.

A Cyber-Physical-Social Perspective on Future Smart Distribution Systems

An increasing number of distributed energy resources (DERs), such as rooftop photovoltaic (PV), electric vehicles (EVs), and distributed energy storage, are being integrated into the distribution systems. The rise of DERs has come hand-in-hand with large amounts of data generated and explosive growth in data collection, communication, and control devices. In addition, a massive number of consumers are involved in the interaction with the power grid to provide flexibility. Electricity consumers, power networks, and communication networks are three main parts of the distribution systems, which are deeply coupled. In this sense, smart distribution systems can be essentially viewed as cyber-physical-social systems. So far, extensive works have been conducted on the intersection of cyber, physical, and social aspects in distribution systems. These works involve two or three of the cyber, physical, and social aspects. Having a better understanding of how the three aspects are coupled can help to better model, monitor, control, and operate future smart distribution systems. In this regard, this article provides a comprehensive review of the coupling relationships among the cyber, physical, and social aspects of distribution systems. Remarkably, several emerging topics that challenge future cyber-physical-social distribution systems, including applications of 5G communication, the impact of COVID-19, and data privacy issues, are discussed. This article also envisions several future research directions or challenges regarding cyber-physical-social distribution systems.

Privacy Preservation of COVID-19 Contact Tracing Data

Big data are everywhere. Examples of big data include contact tracing data of patients who contracted coronavirus disease 2019 (COVID-19). On the one hand, mining these contact tracing data can be for social good. For instance, it helps slow down the spread of COVID-19. It also helps people diagnosed with COVID-19 get referrals for services and resources they may need to isolate safely. On the other hand, it is also important to protect the privacy of these COVID-19 patients. Hence, we present in this paper a solution for privacy preservation of COVID-19 contact tracing data. Specifically, our solution preserves the privacy of individuals by publishing only their spatio-temporal representative locations. Evaluation results on real-life COVID-19 contact tracing data from South Korea demonstrate the effectiveness and practicality of our solution in preserving the privacy of COVID-19 contact tracing data.

Big Data Mining on Health Informatics Data for Cities

Advancements in modern technologies has generated and collected very large volumes of data at a rapid rate. Embedded in these big data is implicit, previously unknown and potentially useful information and knowledge. This explains why big data are often considered as a new oil. Discovered knowledge may help cities to enhance performance and well-being, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens. To elaborate, discovered knowledge from digital technologies may support urban and transportation analytics for smart cities. Discovered knowledge from healthcare data and disease reports may support and enhance decision or policy making for the well-being of citizens within a city. For example, analyzing and mining health informatics data - such as COVID-19 epidemiological data - for cities help decision markers get a better understanding of the disease and come up with ways to detect, control and combat the disease. It also help them prepare for the needs of their citizens (e.g., needs for hospital beds in regular wards or ICU, needs of patients of different age groups). Hence, in this paper, we present a solution for big data mining on health informatics data for cities. Specifically, we mine COVID-19 epidemiological data with spatial and demographic hierarchies capturing characteristics of COVID-19 patients. Evaluation on real-life COVID-19 data demonstrates the practicality of our solution. © 2021 IEEE.

THE FIRST HYBRID SURGERY IN CHINA: REPAIR OF TRACHEAL LACERATION WITH EXTRACORPOREAL MEMBRANE OXYGENATION

Objective: In this example, the patient accidentally fell from 8 meters high, causing trauma to the patient’s chest with tracheal laceration and ‘white lung’ in both lungs. The patient lost respiratory function and was using a breathing machine with 100% pure oxygen while still maintaining 80% oxygen saturation. Routine tracheal intubation under general anaesthesia could potentially cause patient death during the operation. The objective was to assess the use of extracorporeal membrane oxygenation (ECMO) in surgery to repair the patient’s tracheal laceration. Methods: The thoracic surgery department applied hybrid surgery combined with ECMO to rescue the patient. With the support of ECMO, the patient’s intraoperative vital signs were stable, blood oxygen saturation was 100% and the surgery for repairing the laceration with fibreoptic bronchoscopy was successfully completed. Results: The patient recovered and was discharged from hospital. Conclusion: ECMO has successfully treated many critically ill COVID-19 patients during the pandemic, but this is the first time in China that ECMO has been applied to patients suffering from multiple critical injuries such as chest trauma and tracheal laceration.

Analyzing COVID-19 Epidemiological Data

Technological advancements have made it easy and quick to generate and collect huge volumes of varieties of data from wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by big data science and analysis for social good. In this paper, we propose a solution to analyze coronavirus disease 2019 (COVID-19) epidemiological data. In particular, the solution focuses on analyzing valuable information and useful knowledge (e.g., distribution, frequency, patterns) of health-related states and characteristics in populations. Discovered information and knowledge helps users (e.g., researcher, civilian) to understand the disease better, and thus take an active role in fighting, controlling, and/or combating the disease. Evaluation of our solution on real-life data demonstrates its practicality in analyzing COVID-19 epidemiological data and revealing demographic relationships among COVID-19 cases. © 2021 IEEE.

Forward privacy multikeyword ranked search over encrypted database

Dynamic searchable encryption (SE) aims at achieving varied search function over encrypted database in dynamic setting, which is a trade-off in efficiency, security, and functionality. Recent work proposes a file-injection attack which can successfully attack by utilizing some information leaked in the update process. To mitigate this attack, some SE schemes with forward privacy are proposed. However, these schemes are designed to achieve single keyword or conjunctive keyword search, which cannot support multikeyword search. Moreover, these schemes do not consider the function of results ranking. In this paper, we propose a forward privacy multikeyword ranked search scheme over encrypted database. We design a forward privacy multikeyword search scheme based on the classic MRSE scheme. Our scheme makes the cloud cannot obtain the actual match results of the past query with the newly updated files by adding the well-chosen dummy elements to the original index and query vectors. We rank the search results based on the matched keyword number and the (Formula presented.) rule in the dynamic setting. Our scheme uses only the symmetric encryption primitive. We implement our scheme for COVID-19 data set and the experimental evaluation results show that the proposed scheme is secure and efficient. © 2022 Wiley Periodicals LLC.

Independent public video conference network

Triggered by the necessity of social distancing due to the current pandemic situation, people increasingly need video conference technology for various activities such as study and work. Currently, there are several public video conference services, both free and paid, that can be utilized without having to set up complex devices and infrastructure. However, in addition to the problems caused by dependence on certain service providers, the public services are mostly run from several centralized places, while the users are from various regions. That causes increased network latency and bandwidth costs between regions. We propose a video conference network that can be openly participated by various service providers that can be optimally utilized based on the closest location and network quality. © 2020 IEEE.

Privacy-Preserving Healthcare Analytics of Trajectory Data

Technological advancements have led to generation and collection of big data from various data sources including mobile devices. For instance, to prevent, combat and detect COVID-19, citizens of many countries were encouraged to use contact tracing apps on their mobile devices. Collection of their trajectories can be analyzed and mined for social goods. At the same time, their privacy also needs to be preserved. In other words, the advent of COVID-19 has made releasing of patient records become imperative and yet privacy of individuals must be protected. Releasing spatio-temporal COVID-19 data plays a significant role in contact tracing and may help in reducing the spread of the disease due to likelihood of increasing adherence to social distancing and other health related guidelines by the people around the cluster of the released data. In this paper, we examine the problem of preserving privacy of spatio-temporal trajectory data and introduce a hierarchical temporal representative point (HTRP) differential privacy model. We evaluate our framework using a South Korean COVID-19 patient route dataset. Empirical results show a balance of utility and privacy provided by our framework with our HTRP for privacy-preserving healthcare data analytics. © 2021, Springer Nature Switzerland AG.

Explainable Data Analytics for Disease and Healthcare Informatics

With advancements in technology, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. Examples of these valuable data include healthcare and disease data such as privacy-preserving statistics on patients who suffered from diseases like the coronavirus disease 2019 (COVID-19). Analyzing these data can be for social good. For instance, data analytics on the healthcare and disease data often leads to the discovery of useful information and knowledge about the disease. Explainable artificial intelligence (XAI) further enhances the interpretability of the discovered knowledge. Consequently, the explainable data analytics helps people to get a better understanding of the disease, which may inspire them to take part in preventing, detecting, controlling and combating the disease. In this paper, we present an explainable data analytics system for disease and healthcare informatics. Our system consists of two key components. The predictor component analyzes and mines historical disease and healthcare data for making predictions on future data. Although huge volumes of disease and healthcare data have been generated, volumes of available data may vary partially due to privacy concerns. So, the predictor makes predictions with different methods. It uses random forest With sufficient data and neural network-based few-shot learning (FSL) with limited data. The explainer component provides the general model reasoning and a meaningful explanation for specific predictions. As a database engineering application, we evaluate our system by applying it to real-life COVID-19 data. Evaluation results show the practicality of our system in explainable data analytics for disease and healthcare informatics. © 2021 ACM.

Temporal Data Analytics on COVID-19 Data with Ubiquitous Computing

With technological advancements in computing and communications, huge amounts of big data are generated and collected at a very rapid rate from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge. An example is healthcare and epidemiological data such as data related to patients who suffered from viral diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data via data science helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a temporal data science algorithm for analyzing big COVID-19 epidemiological data, with focus on the temporal data analytics with ubiquitous computing. The algorithm helps users to get a better understanding of information about the confirmed cases of COVID-19. Evaluation results show the benefits of our system in temporal data analytics of big COVID-19 data with ubiquitous computing. Although the algorithm is designed for temporal data analytics of big epidemiological data, it would be applicable to other temporal data analytics of big data in many real-life applications and services.

Optimizing broad learning system hyper-parameters through particle swarm optimization for predicting COVID-19 in 184 Countries

The Coronavirus Disease 2019 (COVID-19) began to outbreak since December 2019 and widely spread over the world. How to accurately predict the spread of COVID-19 is one of the essential issues for controlling the pandemic. This study establishes a general model that can predict the trend of COVID-19 in a country based on historical COVID-19 data in 184 countries. First, Savitzky-Golay (S-G) filter is utilized to detect multiple waves of COVID-19 in a country. Then, a PSO-SIR (particle swarm optimization susceptible-infected-recovery) model is provided for data augmentation. Finally, a novel PSO-BLS (particle swarm optimization broad learning system) is proposed for predicting the trend of COVID-19. Experimental results show that compared with the deep learning models (ANN, CNN, LSTM, and GRU), the PSO-BLS algorithm has higher accuracy and stability in predicting the number of active infected cases and removed cases. © 2021 IEEE.