Gaussian Process-aided Transfer Learning for Probabilistic Load Forecasting against Anomalous Events

A probabilistic load forecasting method that can deal with sudden load pattern changes caused by abnormal events such as COVID-19 is proposed in this paper. The deep residual network (ResNet) is first applied to extract the load pattern for the normal period from historical data. When an abnormal event occurs, a Gaussian Process (GP) with a composite kernel is utilized to adapt to the changes on load pattern by estimating the forecasting residual of the ResNet. The designed kernel enables the proposed method to adapt rapidly to changes in the load pattern and effectively quantify the uncertainties caused by the abnormal event using a few training samples. Comparative tests with state-of-the-art point and probabilistic forecasting methods demonstrate the effectiveness of the proposed method. IEEE

A comparison of the early impact of government restriction and risk perception on tourist attraction demand during the COVID-19 pandemic

By collecting the daily visit data of each 5A scenic spot in China from January 1 to March 31, 2020, this paper adopted a two-way fixed-effects model to calibrate the effects of government restriction and risk perception during the pandemic. Results show that a 1% increase in government restriction level led to a 0.806% decrease in daily tourist attraction demand, while a 1% rise in individuals' risk perception resulted in a 0.084% decline. The extent of these declines moderated by factors such as GDP, population density, urbanization rate, and attraction type. The implications of these findings are discussed. © 2023 Asia Pacific Tourism Association.

An ultra-sensitive luteolin sensor based on Co-doped nitrogen-containing carbon framework/MoS2−MWCNTs composite for natural sample detection

As a common antioxidant and antimicrobial agent in plants, luteolin has a variety of pharmacological activities and biological effects, the ability to specifically bind proteins and thus inhibit novel coronaviruses and treat asthma. Here, Co doped nitrogen-containing carbon frameworks/MoS2−MWCNTs (Co@NCF/MoS2−MWCNTs) nanocomposites have been synthesized and successfully applied to electrochemical sensors. X-ray photoelectron spectroscopy, scanning electron microscopy and X-ray diffraction were used to examine the morphology and structure of the samples. Meanwhile, the electrochemical behavior of Co@NCF/MoS2−MWCNTs was investigated. Due to its excellent electrical conductivity, electrocatalytic activity and adsorption, it is used for the detection of luteolin. The Co@NCF/MoS2−MWCNTs/GCE sensor can detect luteolin in a linear range from 0.1 nM to 1.3 μM with a limit of detection of 0.071 nM. Satisfactory results were obtained for the detection of luteolin in natural samples. In addition, the redox mechanism and electrochemical reaction sites of luteolin were investigated by the scan rate of CV curves and density functional theory. This work demonstrates for the first time the combination of ZIF-67-derived Co@NCF and MoS2−MWCNTs as electrochemical sensors for the detection of luteolin, which opens a new window for the sensitive detection of luteolin. © 2022 Elsevier Ltd

Ensuring Utility Workers' Health and Safety: Keeping the Lights On and Protecting the Workforce

The sudden emergence and spread of COVID-19 brought many challenges affecting everyday life. Many companies promptly shifted to remote work to reduce the potential for transmission and maintain an effective workforce. However, essential workers-those in industries that are essential to ensure the continuity of critical functions-were not able to switch to working remotely. Energy sector workers who are unable to work remotely are considered essential workers along with workers in health care, transportation and logistics, and government operations. Some essential utility functions, such as control room operations and in-home visits, need to continue to be conducted in person. The pandemic underscored the importance of a reliable utility workforce and thus the importance of adequate pandemic preparedness. © 2003-2012 IEEE.

A functionalized microwave sensor in a microfluidic platform for rapid detection of SARS-CoV-2

The global COVID-19 pandemic caused by the SARS-CoV-2 has claimed >3.5 million lives and resulted in detrimental social-economic impact. If reliable and rapid test systems were available at home or community level such as drive-by stations, the scope and impact of this tragedy could be largely reduced. Although the vaccine roll out has helped control the pandemic, it is important to develop rapid and accurate testing methods for detection of the SARS-CoV-2 which can be tuned to respond to its variants or similar corona viruses in response to potential outbreaks. In this work, we present a novel method for detection of the SARS-CoV-2 virus based on an antibody functionalized microwave sensor integrated with a microfluidic platform. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

SpiroFi: Contactless Pulmonary Function Monitoring using WiFi Signal

Human pulmonary function declines with age. Elders, especially those with lung or cardiovascular diseases, yearn for daily lung function tests for timely diagnosis and treatment. However, current clinical spirometers are cumbersome and ex-pensive while home-use portable ones' accuracy is questionable. Moreover, both kinds require contact measurements and could cause cross infection, especially hazardous for contagious diseases like COVID-19. To this end, we propose SpiroFi, a contactless system that leverages WiFi Channel State Information (CSI) for convenient yet accurate Pulmonary Function Testing (PFT) out of clinic. The key enabler underlying SpiroFi is a set of algorithms that can extract chest wall movement from WiFi signal variations and interpret such information into lung function indices. We have realized SpiroFi on low-cost commodity WiFi devices and tested it in a home-like site where it achieves 2.55% monitoring error over healthy youths. Then, with the Ethics Committee (EC) approval, we conducted a 2-month clinic study in a city hospital over elders with basic diseases. SprioFi still yields 6.05% monitoring error despite elders' degenerated pulmonary function and body control. Also, the correlation between lung function and age as well as chronic diseases has been revealed, highlighting the importance of daily PFT for the elderly. © 2022 IEEE.

Genera of phytopathogenic fungi: GOPHY 4.

This paper is the fourth contribution in the Genera of Phytopathogenic Fungi (GOPHY) series. The series provides morphological descriptions and information about the pathology, distribution, hosts and disease symptoms, as well as DNA barcodes for the taxa covered. Moreover, 12 whole-genome sequences for the type or new species in the treated genera are provided. The fourth paper in the GOPHY series covers 19 genera of phytopathogenic fungi and their relatives, including Ascochyta, Cadophora, Celoporthe, Cercospora, Coleophoma, Cytospora, Dendrostoma, Didymella, Endothia, Heterophaeomoniella, Leptosphaerulina, Melampsora, Nigrospora, Pezicula, Phaeomoniella, Pseudocercospora, Pteridopassalora, Zymoseptoria, and one genus of oomycetes, Phytophthora. This study includes two new genera, 30 new species, five new combinations, and 43 typifications of older names. Taxonomic novelties: New genera: Heterophaeomoniella L. Mostert, C.F.J. Spies, Halleen & Gramaje, Pteridopassalora C. Nakash. & Crous; New species: Ascochyta flava Qian Chen & L. Cai, Cadophora domestica L. Mostert, R. van der Merwe, Halleen & Gramaje, Cadophora rotunda L. Mostert, R. van der Merwe, Halleen & Gramaje, Cadophora vinacea J.R. Úrbez-Torres, D.T. O'Gorman & Gramaje, Cadophora vivarii L. Mostert, Havenga, Halleen & Gramaje, Celoporthe foliorum H. Suzuki, Marinc. & M.J. Wingf., Cercospora alyssopsidis M. Bakhshi, Zare & Crous, Dendrostoma elaeocarpi C.M. Tian & Q. Yang, Didymella chlamydospora Qian Chen & L. Cai, Didymella gei Qian Chen & L. Cai, Didymella ligulariae Qian Chen & L. Cai, Didymella qilianensis Qian Chen & L. Cai, Didymella uniseptata Qian Chen & L. Cai, Endothia cerciana W. Wang. & S.F. Chen, Leptosphaerulina miscanthi Qian Chen & L. Cai, Nigrospora covidalis M. Raza, Qian Chen & L. Cai, Nigrospora globospora M. Raza, Qian Chen & L. Cai, Nigrospora philosophiae-doctoris M. Raza, Qian Chen & L. Cai, Phytophthora transitoria I. Milenkovic, T. Májek & T. Jung, Phytophthora panamensis T. Jung, Y. Balci, K. Broders & I. Milenkovic, Phytophthora variabilis T. Jung, M. Horta Jung & I. Milenkovic, Pseudocercospora delonicicola C. Nakash., L. Suhaizan & I. Nurul Faziha, Pseudocercospora farfugii C. Nakash., I. Araki, & Ai Ito, Pseudocercospora hardenbergiae Crous & C. Nakash., Pseudocercospora kenyirana C. Nakash., L. Suhaizan & I. Nurul Faziha, Pseudocercospora perrottetiae Crous, C. Nakash. & C.Y. Chen, Pseudocercospora platyceriicola C. Nakash., Y. Hatt, L. Suhaizan & I. Nurul Faziha, Pseudocercospora stemonicola C. Nakash., Y. Hatt., L. Suhaizan & I. Nurul Faziha, Pseudocercospora terengganuensis C. Nakash., Y. Hatt., L. Suhaizan & I. Nurul Faziha, Pseudocercospora xenopunicae Crous & C. Nakash.; New combinations: Heterophaeomoniella pinifoliorum (Hyang B. Lee et al.) L. Mostert, C.F.J. Spies, Halleen & Gramaje, Pseudocercospora pruni-grayanae (Sawada) C. Nakash. & Motohashi., Pseudocercospora togashiana (K. Ito & Tak. Kobay.) C. Nakash. & Tak. Kobay., Pteridopassalora nephrolepidicola (Crous & R.G. Shivas) C. Nakash. & Crous, Pteridopassalora lygodii (Goh & W.H. Hsieh) C. Nakash. & Crous; Typification: Epitypification: Botrytis infestans Mont., Cercospora abeliae Katsuki, Cercospora ceratoniae Pat. & Trab., Cercospora cladrastidis Jacz., Cercospora cryptomeriicola Sawada, Cercospora dalbergiae S.H. Sun, Cercospora ebulicola W. Yamam., Cercospora formosana W. Yamam., Cercospora fukuii W. Yamam., Cercospora glochidionis Sawada, Cercospora ixorana J.M. Yen & Lim, Cercospora liquidambaricola J.M. Yen, Cercospora pancratii Ellis & Everh., Cercospora pini-densiflorae Hori & Nambu, Cercospora profusa Syd. & P. Syd., Cercospora pyracanthae Katsuki, Cercospora horiana Togashi & Katsuki, Cercospora tabernaemontanae Syd. & P. Syd., Cercospora trinidadensis F. Stevens & Solheim, Melampsora laricis-urbanianae Tak. Matsumoto, Melampsora salicis-cupularis Wang, Phaeoisariopsis pruni-grayanae Sawada, Pseudocercospora angiopteridis Goh & W.H. Hsieh, Pseudocercospora basitruncata Crous, Pseudocercospora boehmeriigena U. Braun, Pseudocercospora coprosmae U. Braun & C.F. Hill, Pseudocercospora cratevicola C. Nakash. & U. Braun, Pseudocercospora cymbidiicola U. Braun & C.F. Hill, Pseudocercospora dodonaeae Boesew., Pseudocercospora euphorbiacearum U. Braun, Pseudocercospora lygodii Goh & W.H. Hsieh, Pseudocercospora metrosideri U. Braun, Pseudocercospora paraexosporioides C. Nakash. & U. Braun, Pseudocercospora symploci Katsuki & Tak. Kobay. ex U. Braun & Crous, Septogloeum punctatum Wakef.; Neotypification: Cercospora aleuritis I. Miyake; Lectotypification: Cercospora dalbergiae S.H. Sun, Cercospora formosana W. Yamam., Cercospora fukuii W. Yamam., Cercospora glochidionis Sawada, Cercospora profusa Syd. & P. Syd., Melampsora laricis-urbanianae Tak. Matsumoto, Phaeoisariopsis pruni-grayanae Sawada, Pseudocercospora symploci Katsuki & Tak. Kobay. ex U. Braun & Crous. Citation: Chen Q, Bakhshi M, Balci Y, Broders KD, Cheewangkoon R, Chen SF, Fan XL, Gramaje D, Halleen F, Horta Jung M, Jiang N, Jung T, Májek T, Marincowitz S, Milenkovic T, Mostert L, Nakashima C, Nurul Faziha I, Pan M, Raza M, Scanu B, Spies CFJ, Suhaizan L, Suzuki H, Tian CM, Tomsovský M, Úrbez-Torres JR, Wang W, Wingfield BD, Wingfield MJ, Yang Q, Yang X, Zare R, Zhao P, Groenewald JZ, Cai L, Crous PW (2022). Genera of phytopathogenic fungi: GOPHY 4. Studies in Mycology 101: 417-564. doi: 10.3114/sim.2022.101.06.

Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science. Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. Here, we introduce a novel framework that can extract the COVID-19 public health evidence knowledge graph (CPHE-KG) from papers relating to a modelling study. We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process. We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset (CPHIE). We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++ based on the dataset. Leveraging the model on the new corpus, we construct CPHE-KG containing 60,967 entities and 51,140 relations. Finally, we seek to apply our KG to support evidence querying and evidence mapping visualization. Our SS-DYGIE++(SpanBERT) model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks. It has also shown high performance in the relation identification task. With evidence querying, our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions. The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic. Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health. © 2021

Comprehensive comparison of e-scooter sharing mobility: Evidence from 30 European cities

Although e-scooter sharing has become increasingly attractive, little attention has been paid to a comprehensive comparison of e-scooter sharing mobility in multiple cities. To fill this gap, we conduct a comparative study to reveal the similarity and difference of e-scooter sharing mobility by collecting and analyzing vehicle availability data from 30 European cities during post COVID-19 pandemic. The comparisons are implemented from four perspectives, including temporal trip patterns, statistical characteristics (i.e., trip distance and duration), utilization efficiency, and wasted electricity during idle time. Results suggest that the similarity and difference co-exist between e-scooter sharing services in the cities, and utilization efficiency is significantly related with the number of e-scooters per person and per unit area. Surprisingly, on average nearly 33% of electricity are wasted during idle time in these cities. These research findings can be beneficial to further optimizing e-scooter sharing mobility services for transportation planners and micro-mobility operators. © 2022 The Authors

Relationship between Anti-Spike Protein Antibody Titers and SARS-CoV-2 In Vitro Virus Neutralization in Convalescent Plasma

Newly emerged pathogens such as SARS-CoV-2 highlight the urgent need for assays that detect levels of neutralizing antibodies that may be protective. We studied the relationship between anti-spike ectodomain (ECD) and anti-receptor binding domain (RBD) IgG titers, andSARS-CoV-2 virus neutralization (VN) titers generated by two different in vitro assays using convalescent plasma samples obtained from 68 COVID-19 patients, including 13 who donated plasma multiple times. Only 23% (16/68) of donors had been hospitalized. We also studied 16samples from subjects found to have anti-spike protein IgG during surveillance screening of asymptomatic individuals. We report a strong positive correlation between both plasma anti-RBD and anti-ECD IgG titers, and in vitro VN titer. Anti-RBD plasma IgG correlated slightly better than anti-ECD IgG titer with VN titer. The probability of a VN titer 160 was 80% or greater with anti-RBD or anti-ECD titers of 1:1350. Thirty-seven percent (25/68) of convalescent plasma donors lacked VN titers 160, the FDA-recommended level for convalescent plasma used for COVID-19 treatment. Dyspnea, hospitalization, and disease severity were significantly associated with higher VN titer. Frequent donation of convalescent plasma did not significantly decrease either VN or IgG titers. Analysis of 2,814 asymptomatic adults found 27 individuals with anti-RBD or anti-ECD IgG titers of 1:1350, and evidence of VN1:160. Taken together, we conclude that anti-RBD or anti-ECD IgG titers can serve as a surrogate for VN titers to identify suitable plasma donors. Plasma anti-RBD or anti-ECD titer of1:1350 may provide critical information about protection against COVID-19 disease.

A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media

The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the early stage of COVID-19. Our method performs a semantic-based quantitative analysis to unveil essential spatial-temporal characteristics of COVID-19 stigmatization for timely alerts and risk mitigation. Empirical evaluations are carried out to examine our method's predictive utilities. The visualization results of the co-occurrence network using Gephi indicate two distinct groups of stigmatized words that pertain to people in Wuhan and their dietary behaviors, respectively. Netizens' participations and stigmatizations in the Hubei region, where the COVID-19 broke out, are twice (p < 0.05) and four (p <0.01) times more frequent and intense than those in other parts of China, respectively. Also, the number of COVID-19 patients is correlated with COVID-19-related stigma significantly (correlation coefficient = 0.838, p <0.01). The responses to individual users' posts have the power law distribution, while posts by official media appear to attract more responses (e.g., likes, replies, and forward). Our method can help platforms and government agencies manage public health disasters through effective identification and detailed analyses of social stigma on social media.

Evaluating the Impact of Vaccination on COVID-19 Pandemic Used a Hierarchical Weighted Contact Network Model

The 2019 Novel Coronavirus Disease (COVID-19) vaccines have been placed significant expectation to end the COVID-19 pandemic sooner. However, issues related to vaccines still need to be resolved urgently, including the vaccination number and range. In this paper, we proposed an epidemic spread model based on the hierarchical weighted network. This model fully considers the heterogeneity of the community social contact network and the epidemiological characteristics of COVID-19 in China, which enables to evaluate the potential impact of vaccine efficacy, vaccination schemes, and mixed interventions on the epidemic. The results show that a mass vaccination can effectively control the epidemic but cannot completely eliminate it. In the case of limited resources, giving vaccination priority to the individuals with high contact intensity in the community is necessary. Joint implementation with non-pharmacological interventions strengthening the control of virus transmission. The results provide insights for decision-makers with effective vaccination plans and prevention and control programs. © 2021 IEEE.

Extracting Impacts of Non-pharmacological Interventions for COVID-19 from Modelling Study

COVID-19 pandemic continues to rampage in the world. Before the achievement of global herd immunity, non-pharmacological interventions(NPIs) are crucial to mitigate the pandemic. Although various NPIs have been put into practice, there are many concerns about the impacts and effectiveness of these NPIs. COVID-19 modelling study (CMS) in epidemiology can provide evidence to solve the aforementioned concerns. It is time-consuming to collect evidence manually when dealing with the vast amount of CMS papers. Accordingly, we seek to accelerate evidence collection by developing an information extraction model to automatically identify evidence from CMS papers. This work presents a novel COVID-19 Non-pharmacological Interventions Evidence (CNPIE) Corpus, which contains 597 s of COVID-19 modelling study with richly annotated entities and relations of the impacts of NPIs. We design a semi-supervised document-level information extraction model (SS-DYGIE++) which can jointly extract entities and relations. Our model outperforms previous baselines in both entity recognition and relation extraction tasks by a large margin. The proposed work can be applied towards automatic evidence extraction in the public health domain for assisting the public health decision-making of the government. © 2021 IEEE.

Developing mental health nursing strategies for the inbound quarantined population in China during the COVID-19 global pandemic.

OBJECTIVE: The aim of the study was to develop mental health nursing strategies for the inbound quarantined population based on the results of a survey study and frontline nursing experiences. SUBJECTS AND METHODS: A mixed research method was selected, we collected data by questionnaires from 128 quarantined people, and by semi-structured interviews from 5 registered nurses. Generalized anxiety disorder-7 (GAD-7), the patient health questionnaire-9 (PHQ-9), the Pittsburgh Sleep Quality Index (PSQI), Social Support Rating Scale (SSRS) were used in the quantitative research to identify the prevalence of psychological issues and risk factors. Semi-structured interviews were conducted in the qualitative study to conclude nursing experiences from RNs. RESULTS: The overall prevalence of anxiety, depression, and insomnia were 34%, 41%, and 18% respectively. Binary logistic regression analysis showed that social support, urban residence, and chronic disease were associated with mental health problems in certain aspects. Three themes were emerged from the analysis of RNs interviews: personality, chronic diseases, and social support. CONCLUSIONS: The prevalence of mental health issues in the inbound quarantined population was the same as the general population in the initial stage of COVID-19 outbreak, and significantly lower than people who lived in high-risk areas. Living in urban areas, with chronic diseases, and obtaining less social support are the risk factors. Finally, four nursing strategies were proposed by the research team for mental health well-being.