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1.
International Transactions in Operational Research ; 2022.
Article in English | Scopus | ID: covidwho-1874435

ABSTRACT

The spread of COVID-19 outbreak has promoted truck-drone delivery from trials to commercial applications in end-to-end contactless solutions. To fully integrate truck-drone delivery in contactless solutions, we introduce the robust traveling salesman problem with a drone, in which a drone makes deliveries and returns to the truck that is moving on its route under uncertainty. The challenge is to find, for each customer location in truck-drone routing, an assignment to minimize the expected makespan. Apart from the complexity of this problem, the risk of synchronization failure associated with uncertain travel time should be also considered. The problem is first formulated as a robust model, and a novel efficient frontier heuristic is proposed to solve this model. By coupling the implicit adaptive weighting with epsilon-constraint methods, the heuristic generates a series of scalarized single-objective problems, where the goal is to minimize expected makespan under the constraint of synchronization risk. The experiment results show that the robust (near-)optimal solutions offer a considerable reduction in risk, yet only hint at a small increase in makespan. The heuristic in the present study is effective to construct approximations of Pareto frontier and allows for assignment decisions in a priori or a posteriori manner. © 2022 The Authors. International Transactions in Operational Research © 2022 International Federation of Operational Research Societies.

2.
Anesthesia and Analgesia ; 134(4 SUPPL):12-14, 2022.
Article in English | EMBASE | ID: covidwho-1820600

ABSTRACT

Background/Introduction: Amidst the COVID-19 pandemic, the sudden demand for virtual medical visits drove the drastic expansion of telemedicine across all medical specialties. Current literature demonstrates limited knowledge on the impact of telehealth on appointment adherence particularly in preoperative anesthesia evaluations. We hypothesized that there would be increased completion of preoperative anesthesia appointments in patients who received telemedicine visits. Methods: We performed a retrospective cohort study of adult patients at UCLA who received preoperative anesthesia evaluations by telemedicine or in-person within the Department of Anesthesiology and Perioperative Medicine from January to September 2021 and assessed appointment adherence. The primary outcome was incidence of appointment completion. The secondary outcomes included appointment no show and cancellations. Patient demographic characteristics including sex, age, ASA physical status class, race, ethnicity, primary language, interpreter service requested, patient travel distance to clinic, and insurance payor were also evaluated. Demographic characteristics, notably race and ethnicity, are presented as captured in the electronic health record and we recognize its limitations and inaccuracies in illustrating how people identify. Patient reported reasons for cancellations were also reviewed and categorized into thematic groups by two physicians. Statistical comparison was performed using independent samples t test, Pearson's chi-square, and Fischer's exact test. Results: Of 1332 patients included in this study, 956 patients received telehealth visits while 376 patients received in-person preoperative anesthesia evaluations. Compared to the in-person group, the telemedicine group had more appointment completions (81.38% vs 76.60%, p = 0.0493). There were fewer cancellations (12.55% vs 19.41%, p = 0.0029) and no statistical difference in appointment no-shows (6.07% vs 3.99%, p = 0.1337) in the telemedicine group (Figure 1). Compared to the in-person group, patients who received telemedicine evaluations were younger (55.81 ± 18.38 vs 65.97 ± 15.19, p < 0.001), less likely American Indian and Alaska Native (0.31% vs 1.60%, p = 0.0102), more likely of Hispanic or Latino ethnicity (16.63% vs 12.23%, p = 0.0453), required less interpreter services (4.18% vs 9.31%, p = 0.0003), had more private insurance coverage (53.45% vs 37.50%, p < 0.0001) and less Medicare coverage (37.03% vs 50.53%, p < 0.0001). Main reasons for cancellation included patient request, surgery rescheduled/cancelled/already completed, and change in method of appointment. Conclusions: In 2021, preoperative anesthesia evaluation completion was greater in patients who received telemedicine appointments compared to those who received in-person evaluations at UCLA. We also demonstrate potential shortcomings of telemedicine in serving patients who are older, require interpreter services, or are non-privately insured. Knowledge of these factors can provide feedback to improve access and equity to telehealth for patients from all backgrounds, particularly during the COVID pandemic as virtual evaluations increase. (Table Presented).

3.
INFORMS International Conference on Service Science, ICSS 2020 ; : 443-452, 2022.
Article in English | Scopus | ID: covidwho-1750472

ABSTRACT

Misinformation is rampant in the modern information age and understanding how social media misinformation diffuses can provide vital insight on how to combat it. With social media becoming a major information source, it is increasingly important to address this concern. Social media misinformation has negatively impacted healthcare response in the past and may have played a major role in how to respond to COVID-19. Understanding how misinformation diffuses through online social networks can provide help healthcare and government entities information on how to mitigate the associated negative impact. This paper proposes a data set as criterion for identifying pandemic specific misinformation and develops a Convolution Neural Network model and. A case study is then conducted to illustrate how diffusion can be explored using labelled misinformation. The work shows a decrease of COVID-19 misinformation over time and a pattern that does not depend on regional geographic location. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

4.
IEEE Access ; 2022.
Article in English | Scopus | ID: covidwho-1699194

ABSTRACT

The smart revolution has penetrated in a wide range of applications. Smart campus, as the high-end form of education systems, deploys cutting-edge information and communication technologies to enhance the effectiveness and efficiency of campus services. Under the pandemic of COVID-19, smart campus has shown unprecedented importance owing to its remote, personalized, and ubiquitous features. All these factors have made smart campus an ongoing intense research topic in recent years, whereas existing reviews on smart campus were conducted in earlier years and thus an update is imperatively needed to investigate and summarize the emerging knowledge, technologies, and applications in this context. This paper conducts a systematic review on smart campus technologies and applications, and then strategically classifying them into different domains to investigate the current research pattern. Moreover, adhering to the human-centered principle of smart campus development, a human-centered case study has been carried out and presented in this paper to evaluate the consistency and adherence of current research trend to the stakeholders needs and interests. Author

5.
Acta Medica Mediterranea ; 36(6):3747-3752, 2020.
Article in English | Web of Science | ID: covidwho-1579547

ABSTRACT

Background and Purpose: Corona Virus Disease 2019 (COVID-19) is a highly contagious disease which continuously and rapidly circulating around the world now. The patients with severe COVID-19 have relatively high mortality. Therefore, there is an urgent need for methods to assess mortality risk in patients with COVID-19 accurately. Materials and methods: We conducted a retrospective study focusing on the clinical characteristics of 194 confirmed cases of severe COVID-19. Personal information, clinical data and laboratory information of patients with COVID-19 were collected by consulting case records so as to investigate the risk of death related to COVID-19. Results: In the 194 patients with COVID-19, there was no difference in prevalence between men and women. Comorbidities (such as hypertension, cerebral infarction) associated with severe clinical features and mortality are prevalent in non-survivors. 86.1% of patients with severe COVID-19 had fever and 46.9% coughed, and the proportion of chest tightness, airlessness and dyspnea in non-survivors was significantly higher than that in survivors. There were multiple laboratory indicator differences between survivors and non-survivors. Non-survivors had significantly lower lymphocyte count (including T lymphocyte). Changes in liver (aspartate aminotransferase, AST), kidney [Urea, creatinine (Cr)], and heart [lactate dehydrogenase (LDH), creatine kinase (CK), B-type natriuretic peptide (BNP)] damage markers, coagulation, and inflammation indicators in severe patients were related to their risk of death. Multivariable logistic regression model revealed that age (OR 1.082, 95% CI 1.024-1.357), interleukin-6 (IL-6). (OR 1.568, 95% CI 1.149-2.138), D-dimer (OR 1.327, 95% CI 1.087-1.621) were associated significantly with risk of death, whereas CD4 count was associated with a lower risk (OR 0.972, 95% CI 0.953-0.992). Conclusion: This study found that age, IL-6, D-dimer and CD4 counts are closely related to mortality risk in patients with severe COVID-19, and they are useful in assessing the prognosis of patients.

6.
23rd International Conference on Human-Computer Interaction, HCII 2021 ; 13095 LNCS:445-454, 2021.
Article in English | Scopus | ID: covidwho-1549380

ABSTRACT

This paper explores recent roles of artificial intelligence and extended reality development during the coronavirus pandemic and then predicts their significant roles in the post-COVID-19 era in an interdisciplinary manner. To begin with, we investigate roles of artificial intelligence in tackling coronavirus during the outbreak since 2020 until today. It has been effectively used for many ways, such as forecasting the spread of COVID-19 on multimodal data using data analytics, preliminary diagnosis the virus disease from specific symptoms using machine learning, and analyzing big data from social media platforms to accurately prevent the spread of virus. At the same time, due to rapid advancement in recent immersive technology and extended reality is a very popular research topic in computer science, we discuss roles of extended reality which has been extensively used during the virus outbreak for various purposes, such as supporting for businesses and education and helping the medical and health care workers. For instance, it can used for supporting psychological recovery from medical treatment for virus patients, reducing the face-to-face interactivity of physicians with the symptomatic patients, and helping people with the use of telemedicine. Next, we present a new summary of integrated roles of artificial intelligence and extended reality development in the post-COVID-19 era in an interdisciplinary perspective. Moreover, we suggest possible directions of artificial intelligence in extended reality which can be used to guide the design of the next-generation human-computer interaction applications in the future. © 2021, Springer Nature Switzerland AG.

7.
2021 International Conference on Big Data Analysis and Computer Science, BDACS 2021 ; : 13-16, 2021.
Article in English | Scopus | ID: covidwho-1437906

ABSTRACT

The novel coronavirus pneumonia is a major public health emergency with fast transmission rate, wide infection range and great difficulty in prevention and control, which poses challenges to the urban governance system and governance capacity. At this moment, it is particularly significant to get the track of people's movements. And at the same time, trajectory, as a typical spatio-temporal data, has been more and more used in subject researches such as road change detection, travel pattern exploration and urban hotspot analysis in recent years. In this paper, based on Spark and GeoSpark technology, real-time monitoring of the whereabouts of the community, schools and other personnel is carried out, in order to generate action tracks. At the same time, the deep learning algorithm is used to classify and warn the danger level of the trajectory of the people who are about to go in or go out of the residential district, schools, etc. It provides strong support for the public security, health and epidemic command and other government departments to achieve scientific prevention and control, intelligent prevention and control. The results show that spark can achieve high throughput and fault-tolerant real-time stream data processing. Geospark processes large-scale spatial data on the basis of spark, and can create point, line, surface and other spatial data structures based on longitude and latitude information. At the same time, the semi supervised learning model based on recurrent neural network is used to classify and early warn the danger level of personnel trajectories. The experiment randomly selected 2000 users from districts and schools in Chengdu, and divided the experimental data set into training set and verification set in the proportion of 8:2. The best performance of the trained model is 96.2%. © 2021 IEEE.

8.
Environmental Research Letters ; 16(3):8, 2021.
Article in English | Web of Science | ID: covidwho-1125262

ABSTRACT

More and more studies have evaluated the associations between ambient temperature and coronavirus disease 2019 (COVID-19). However, most of these studies were rushed to completion, rendering the quality of their findings questionable. We systematically evaluated 70 relevant peer-reviewed studies published on or before 21 September 2020 that had been implemented from community to global level. Approximately 35 of these reports indicated that temperature was significantly and negatively associated with COVID-19 spread, whereas 12 reports demonstrated a significantly positive association. The remaining studies found no association or merely a piecewise association. Correlation and regression analyses were the most commonly utilized statistical models. The main shortcomings of these studies included uncertainties in COVID-19 infection rate, problems with data processing for temperature, inappropriate controlling for confounding parameters, weaknesses in evaluation of effect modification, inadequate statistical models, short research periods, and the choices of research areal units. It is our viewpoint that most studies of the identified 70 publications have had significant flaws that have prevented them from providing a robust scientific basis for the association between temperature and COVID-19.

9.
Medical Journal of Chinese People's Liberation Army ; 45(9):947-956, 2020.
Article in Chinese | Scopus | ID: covidwho-934649

ABSTRACT

Objective To analyze and predict hematopoietic injury caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and potential therapeutic drugs, and to provide theoretical basis for clinical treatment of the hematopoietic injury. Methods The gene expression omnibus (GEO) database was used to screen the whole genome expression data related to SARS-CoV-2 infection. The R language package was used for differential expression analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. The core genes were screened by protein-protein interaction (PPI) network analysis using STRING online analysis website. Then the self-developed apparent precision therapy prediction platform (EpiMed) was used to analyze diseases, drugs and related target genes. Results A total of 222 differential genes were screened, including 172 up-regulated and 50 down-regulated. GO enrichment analysis suggested that gene is mainly related to type I interferon response, cell cycle regulation, inflammatory cell migration, innate immune response, secretion of blood particles and vesicles, chemokines and their receptors. KEGG enrichment analysis suggested that gene is mainly related to viral infection, myocardial injury, complement and coagulation cascade, cell chemotaxis, platelet activation, acute inflammation, immune response, cellular signal transduction and so on. Ten core genes such as STAT1, IL-6, IRF7, TNF, MX1, ISG15, IFIH1, IRF9, DDX58 and GBP1were screened by PPI network analysis. EpiMed screened 10 drugs with potential intervention effects, including Rabdosia rubescens, sirolimus, glucocorticoid, Houttuynia cordata, Polygonum multiflorum, Red peony, tretinoin, Glycyrrhiza, cyclosporine A, fluvastatin and so on. Conclusions SARS-CoV-2 infection can damage the hematopoietic system by changing the expression of a series of genes. The potential intervention drugs screened from this have certain reference significance for the basic and clinical research of coronavirus disease 2019 (COVID-19). © 2020 People's Military Medical Press. All rights reserved.

10.
Chinese Journal of Laboratory Medicine ; 43(4):386-390, 2020.
Article in English | EMBASE | ID: covidwho-842302

ABSTRACT

The prevention and control of novel coronavirus pneumonia caused by 2019 novel coronavirus is at a critical period. Nucleic acid detection, as the definite diagnosis tool, plays an important role in rapid diagnosis, therapeutic efficacy, epidemic prevention and control. However, the disease is outbreak, and the time of nucleic acid detection in clinical application is short. So the insufficient method verification and clinical evaluation has been made. "False negative" is observed in clinical practice, and the result of nucleic acid detection is not matched with the clinical diagnosis. Therefore, it is urgent to optimize the current methods and improve detection sensitivity. Based on latest studies of 2019 novel coronavirus, this article reviews the current status and application prospects of nucleic acid detection. Also, this article provides references for clinicians and researchers.

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