A Deep Learning based Feature Entity Relationship Extraction Method for Telemedicine Sensing Big Data

To solve the problem of inaccurate entity extraction caused by low application efficiency and big data noise in telemedicine sensing data, a deep learning-based method for entity relationship extraction in telemedicine big data is proposed. By analyzing the distribution structure of the medical sensing big data, the fuzzy function of the distribution shape is calculated and the seed relationship set is transformed by the inverse Shearlet transform. Combined with the deep learning technology, the GMM-GAN data enhancement model is built, the interactive medical sensing big data features are obtained, the association rules are matched one by one, the noiseless medical sensing data are extracted in time sequence, the feature items with the highest similarity are obtained and used as the constraint to complete the feature entity relationship extraction of the medical sensing data. The experimental results show that the extracted similarity of entity relations is more than 70%, which can handle overly long and complex sentences in telemedicine information text;the extraction time is the shortest and the volatility is low.

An undergraduate course for FOSS and with FOSS

Many universities have incorporated free and open-source software (FOSS) into undergraduate courses, though few research efforts have specifically investigated the interactions between undergraduates and real-world FOSS projects. In this paper, we present our experience of participating in the development of real-world FOSS projects, dealing with the challenges caused by COVID-19, and the feasibility of remote education with FOSS only. Our experience shows that undergraduates and FOSS projects can help each other well. Moreover, we summarize the strengths and weaknesses of the existing FOSS communities in attracting undergraduates and provide some suggestions. © 2021 Association for Computing Machinery.

Recent advances and future directions in macro-finance: macroeconomic conditions and corporate decisions

A decade after the 2007-2009 financial crisis, the covid-19 pandemic causes disruptive economic conditions and its uncertain nature makes the formulation of macroeconomic policy response challenging. Meanwhile, extreme weather and natural disasters linked to climate change become more frequent and adversely affect the economy. These challenges highlight the importance of better understanding the effects of macroeconomic conditions on corporate decisions. This article reviews the related macro-finance literature and introduces articles included in this special issue on this theme. In the end, we suggest possible future research directions.

Asymmetric Risk Spillover Networks and Risk Contagion Driver in Chinese Financial Markets: The Perspective of Economic Policy Uncertainty

The global financial market shocks have intensified due to the COVID-19 epidemic and other impacts, and the impacts of economic policy uncertainty on the financial system cannot be ignored. In this paper, we construct asymmetric risk spillover networks of Chinese financial markets based on five sectors: bank, securities, insurance, diversified finance, and real estate. We investigate the complexity of the risk spillover effect of Chinese financial markets and the impact of economic policy uncertainty on the level of network contagion of financial risk. The study yields three findings. First, the cross-sectoral risk spillover effects of Chinese financial markets are asymmetric in intensity. The bank sector is systemically important in the risk spillover network. Second, the level of risk stress in the real estate sector has increased in recent years, and it plays an important role in the path of financial risk contagion. Third, Economic policy uncertainty has a significant positive impact on the level of network contagion of financial risk of Chinese financial markets.

Efficacy and safety of ivermectin for the treatment of COVID-19: a systematic review and meta-analysis.

BACKGROUND: Ivermectin became a popular choice for COVID-19 treatment among clinicians and the public following encouraging results from pre-print trials and in vitro studies. Early reviews recommended the use of ivermectin based largely on non-peer-reviewed evidence, which may not be robust. This systematic review and meta-analysis assessed the efficacy and safety of ivermectin for treating COVID-19 based on peer-reviewed randomized controlled trials (RCTs) and observational studies (OSs). METHODS: MEDLINE, EMBASE and PubMed were searched from 1 January 2020 to 1 September 2021 for relevant studies. Outcomes included time to viral clearance, duration of hospitalization, mortality, incidence of mechanical ventilation and incidence of adverse events. RoB2 and ROBINS-I were used to assess risk of bias. Random-effects meta-analyses were conducted. GRADE was used to evaluate quality of evidence. RESULTS: Three OSs and 14 RCTs were included in the review. Most RCTs were rated as having some concerns in regards to risk of bias, while OSs were mainly rated as having a moderate risk of bias. Based on meta-analysis of RCTs, the use of ivermectin was not associated with reduction in time to viral clearance, duration of hospitalization, incidence of mortality and incidence of mechanical ventilation. Ivermectin did not significantly increase incidence of adverse events. Meta-analysis of OSs agrees with findings from RCT studies. CONCLUSIONS: Based on very low to moderate quality of evidence, ivermectin was not efficacious at managing COVID-19. Its safety profile permits its use in trial settings to further clarify its role in COVID-19 treatment. PROTOCOL REGISTRATION: The review was prospectively registered in PROSPERO (CRD42021275302).

Environmental effects of stratospheric ozone depletion, UV radiation, and interactions with climate change: UNEP Environmental Effects Assessment Panel, Update 2020.

This assessment by the Environmental Effects Assessment Panel (EEAP) of the United Nations Environment Programme (UNEP) provides the latest scientific update since our most recent comprehensive assessment (Photochemical and Photobiological Sciences, 2019, 18, 595-828). The interactive effects between the stratospheric ozone layer, solar ultraviolet (UV) radiation, and climate change are presented within the framework of the Montreal Protocol and the United Nations Sustainable Development Goals. We address how these global environmental changes affect the atmosphere and air quality; human health; terrestrial and aquatic ecosystems; biogeochemical cycles; and materials used in outdoor construction, solar energy technologies, and fabrics. In many cases, there is a growing influence from changes in seasonality and extreme events due to climate change. Additionally, we assess the transmission and environmental effects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for the COVID-19 pandemic, in the context of linkages with solar UV radiation and the Montreal Protocol.

Initial chest radiograph scores inform COVID-19 status, intensive care unit admission and need for mechanical ventilation.

AIM: To evaluate whether portable chest radiography (CXR) scores are associated with coronavirus disease 2019 (COVID-19) status and various clinical outcomes. MATERIALS AND METHODS: This retrospective study included 500 initial CXR from COVID-19-suspected patients. Each CXR was scored based on geographic extent and degree of opacity as indicators of disease severity. COVID-19 status and clinical outcomes including intensive care unit (ICU) admission, mechanical ventilation, mortality, length of hospitalisation, and duration on ventilator were collected. Multivariable logistic regression analysis was performed to evaluate the relationship between CXR scores and COVID-19 status, CXR scores and clinical outcomes, adjusted for code status, age, gender and co-morbidities. RESULTS: The interclass correlation coefficients amongst raters were 0.94 and 0.90 for the extent score and opacity score, respectively. CXR scores were significantly (p < 0.01) associated with COVID-19 positivity (odd ratio [OR] = 1.49; 95% confidence interval [CI]: 1.27 - 1.75 for extent score and OR = 1.75; 95% CI: 1.42 - 2.15 for opacity score), ICU admission (OR = 1.19; 95% CI: 1.09 - 1.31 for extent score and OR = 1.26; 95% CI: 1.10 - 1.44 for opacity score), and invasive mechanical ventilation (OR = 1.22; 95% CI: 1.11 - 1.35 for geographic score and OR = 1.21; 95% CI: 1.05 - 1.38 for opacity score). CXR scores were not significantly different between survivors and non-survivors after adjusting for code status (p>0.05). CXR scores were not associated with length of hospitalisation or duration on ventilation (p>0.05). CONCLUSIONS: Initial CXR scores have prognostic value and are associated with COVID-19 positivity, ICU admission, and mechanical ventilation.

Engineering Education in Intelligent Manufacturing

The age of digitization is changing the way universities need to educate their engineering graduates. Thus, universities must train their graduates with the skills required in the era of digitization. In this regard universities face significant pressure to overhaul their established sometimes traditional curriculums and expand them by focusing on methodologies and technologies required as skills in the 21st century. In this regard technology enhanced learning methods are essential while flexible to provide engineering students with the respective skills in complex system design in the manufacturing domain. This technology enhanced learning approach also helps to overcome education lags caused by the COVID 19 pandemic. The digital transformative impact on intelligent manufacturing also requires dealing with risk management with regard to vulnerability through cyber threat attacks. Thus, this paper describes an educational approach which refers to the essential skills required in engineering education in intelligent manufacturing- © 2020 IEEE.

Analysis of medication characteristics of 66 preventive formulae for COVID-19 in China

Coronavirus disease 2019 (COVID-19) is a serious threat to the lives and health of our people, but there is currently no clear target for therapeutic drugs. Therefore, prevention is of great significance for the prevention and control of COVID-19. The Health Management Administration of the different provinces and cities have successively released the “Traditional Chinese Medicine Diagnosis and Treatment Programs for New Coronavirus Pneumonia” in response to the epidemic situation, among them including preventive prescriptions, which showed the theoretical characteristics of “treating disease before its onset” of traditional Chinese medicine (TCM), and also showed the characteristics of medication in different provinces. Therefore, we collected 66 formulas for treating COVID-19 in China and studied the rules of TCM prevention in various provinces and autonomous regions, and unified the thinking of medication, which could provide a reference for the prevention of COVID-19a.