Dynamic correlations of renewable-energy companies: Evidence from a multilayer network model

Against the background of seeking to achieve carbon neutrality, relationships among renewable-energy companies around the world have become multiple and complex. In this work, the Pearson, Kendall, tail, and partial correlation coefficients were applied to 51 global companies - including solar and wind firms, independent power plants, and utilities - to explore the linear, nonlinear, extreme-risk, and direct relations between them. Sample data from 7 August 2015 to 6 August 2021 were considered, and three sub-periods were extracted from these sample data by analysis of the evolution of multiple correlations combined with event analysis. A four-layer correlation network model was then constructed. The main results are as follows. (1) The multiple relations among the selected firms underwent dramatic changes during two external shocks (the China-US trade war and the COVID-19 pandemic). (2) The extreme-risk network layer verified that the trade war mainly affected the relationships among companies in the solar industries of China and the US. (3) During the COVID-19 pandemic period, the linear and direct relationships among wind firms from Canada, Spain, and Germany were significantly increased. In this sub-period, edge-weight distributions of the four different layers were heterogeneous and varied from power-law features to Gaussian distributions. (4) During all the sub-periods, most companies had similar numbers of neighbors, while the numbers of neighbors of a few companies varied greatly in the four different layers. These findings provide a useful reference for stakeholders and may help them understand the connectedness and evolution of global renewable-energy markets. © 2023 Author(s).

Risk of in-Hospital Mortality Due to Covid19 in Patients with Bronchial Asthma: Application of 4c Mortality Scale

Role of asthma as a risk factor in severity and mortality in COVID-19 varies in literature. In 2020, 4CMortality score was published, which through 8 parameters in the initial evaluation (age, sex, comorbidity index Charlson, respiratory rate, peripheral oxygen saturation, renal function, Glasgow scale and C-reactive protein) stratified risk of in-hospital mortality from COVID-19 into low(0-3 points), intermediate(4-8), high(9-14) and very high(from 15). Our objective is to assess usefulness of 4CMortality in asthmatic patients admitted for COVID-19 and to verify the degree of correlation between the score and the mortality data and hospital stay. Observational retrospective study of asthmatic patients admitted for COVID-19 between March 2020 and March 2021. Statistical analysis is performed using Fisher's exact test(risk scale-death), ANOVA(risk scale-days hospitalization), and Kaplan Meier curve, considering statistically significant those results with a p<0.05. Sample of 99 patients, 18 in low risk group, 35 intermediate risk, 44 high risk and 2 very high risk. In terms of mortality, 7 deaths(high risk, 15.9%) and 2(very high risk, 100%), statistically significant (Fisher 17.07, p<0.0001). In terms of hospitalization days, median 7 days(low risk), 10(intermediate risk), 17 (high risk) and 5 (very high risk);statistically significant(F 6.37, p 0.001). In the survival analysis, median survival of 7 days(low-risk), 10(intermediate risk) and 19(high risk)(Log Rank 32.887, p<0.0001)(Fig 1). In conclusion, 4CMortality score is a good tool to establish the probability of poor evolution in asthmatic patients admitted for COVID-19 due to increased mortality and hospital stay.

Generation of APN-chimeric gene-edited pigs by CRISPR/Cas9-mediated knock-in strategy

The prevalence of porcine enteric coronaviruses (PECs), including transmissible gastroenteritis virus (TGEV), swine acute diarrhea syndrome coronavirus (SADS-CoV), porcine delta coronavirus (PDCoV), and porcine epidemic diarrhea virus (PEDV), poses a serious threat to animal and public health. Here, we aimed to further optimize the porcine aminopeptidase N (pAPN) gene editing strategy to explore the balance between individual antiviral properties and the biological functions of pAPN in pigs. Finally, APN-chimeric gene-edited pigs were produced through a CRISPR/Cas9-mediated knock-in strategy. Further reproductive tests indicated that these gene-edited pigs exhibited normal pregnancy rates and viability. Notably, in vitro viral challenge assays further demonstrated that porcine kidney epithelial cells isolated from F1-generation gene-edited pigs could effectively inhibit TGEV infection. This study is the first to report the generation of APN-chimeric pigs, which may provide a natural host animal for characterizing PEC infection with APN and help in the development of better antiviral solutions. © 2022 Elsevier B.V.

Adversarial Training for Predicting the Trend of the COVID-19 Pandemic

It is significant to accurately predict the epidemic trend of COVID-19 due to its detrimental impact on the global health and economy. Although machine learning-based approaches have been applied to predict epidemic trend, standard models have shown low accuracy for long-term prediction due to a high level of uncertainty and lack of essential training data. This paper proposes an improved machine learning framework employing generative adversarial network (GAN) and long short-term memory (LSTM) for adversarial training to forecast the potential threat of COVID-19 in countries where COVID-19 is rapidly spreading. It also investigates the most updated COVID-19 epidemiological data before October 18, 2020 and models the epidemic trend as time series that can be fed into the proposed model for data augmentation and trend prediction of the epidemic. The model is trained to predict daily numbers of cumulative confirmed cases of COVID-19 in Italy, USA, China, Germany, UK, and across the world. The paper further analyzes and suggests which populations are at risk of contracting COVID-19.

Traditional Chinese medicine for promoting mental health of patients with COVID-19: a scoping review

This study aimed to systematically review and depict the current studies of traditional Chinese medicine for the mental health of patients with coronavirus disease 2019 (COVID-19).Methods:: A scoping review was conducted by searching PubMed, Web of Science, CNKI, Wanfang database, VIP database, and SinoMed, with the retrieval time being from the establishment of the database to April 18, 2022. The basic information of the included studies, objective, design, types of patients, interventions, outcomes, etc., was reviewed and summarized narratively. Methodological quality was assessed using the Cochrane Risk of Bias assessment tool, the methodological index for non-randomized studies or the Newcastle–Ottawa scale. Results:: We identified 30 traditional Chinese medicine (TCM) studies from six databases. Among them, finished randomized controlled trials (n = 16) accounted for most of the studies, followed by single-arm studies (n = 9). In terms of study theme, 20 studies defined the mental health of patients with COVID-19 as the research theme. Psychological assessment was included in the inclusion criteria (performed before participation) of nine studies, whereas the other studies only mentioned the mental outcomes. TCM interventions included TCM exercises (Yijinjing, Baduanjin, Liuzijue, Taichi), acupoint stimulation (auricular and body points), moxibustion, decoction, or granules based on TCM syndrome differentiation, decoction, or granules with fixed formulae (Baidu Jieduan granules, Xuanfei Baidu decoction, and Qingfei Paidu decoction), Chinese patent medicine (Jinhua Qinggan granules), TCM psychological therapy (TCM ideological therapy, TCM five-tone therapy, and TCM psychological sand table), and TCM nursing (dialectical care, dialectical diet, and psychological counseling). Anxiety and depression were the main outcomes evaluated in regard to mental health in patients with COVID-19. The limitations of methodological quality were predominantly from follow-up, blinding, and registration. Positive results were reported by 27 studies (90%, n = 30). Conclusion:: We summarized the existing literature about the impact of TCM on mental health in patients with COVID-19. The number of studies evaluating the impact of TCM on mental health is encouraging, but overall methodological quality was low. Several TCM interventions warrant further evaluation, particularly among populations outside of China, for the purpose of establishing supporting evidence. More importantly, research with stronger methodological quality needs to be developed. Graphical :: http://links.lww.com/AHM/A36.

Vaccination Progress Prediction in the U.S., India, and Brazil by Machine learning models

A worldwide pandemic began after a novel coronavirus SARS-CoV-2, also known as COVID-19, was identified in 2019. Until September 2021, COVID-19 caused 254 million cases and 5.11 million deaths. Fortunately, countries worldwide are actively creating and inventing vaccinations against the virus to prevent new infections and stop the spread. However, due to various reasons like commuting issues, limitation of medical technology, uncooperative individuals, etc., the amount of vaccinations is one of the problems for governmental organizations to solve. This essay uses machine learning methods to analyze vaccination progress, and the situation of COVID-19 spread over two years to predict the future vaccination amount. As a result, models that predict future vaccination in the U.S., India, and Brazil are generated. © 2022 IEEE.

Quantifying the evolutionary mechanism of COVID-19 impact on international construction multi-projects: a risk driver perspective

Purpose: The purpose of this paper is to quantify the evolutionary mechanism of coronavirus disease 2019 (COVID-19) impact on international construction multi-projects. Design/methodology/approach: From three concepts of complexity, stressor and detractor risks, a multi-project simulation model under COVID-19 outbreak is proposed to study the characteristics of contingency with three peaks of ordered and disordered states. Specially, COVID-19 brings forth mitigation fee, epidemic prevention fee, holdup fee and schedule delay fee of multi-projects. By integrating parametric model, Monte Carlo and chaos theory, a comparative analysis of its contingency with or without COVID-19 is conducted. Summarizing the simulated results, their total contingencies at certain risk tolerance are obtained at two status of static at one-time point and dynamic over time. Meanwhile, some major risks including detractors, complexities and stressors are screened out for mitigation, especially for epidemic prevention and control. Eventually, the real case is illustrated to demonstrate its validity. Findings: It provides a quantitative analysis framework for the impact of epidemic, a once-in-a-century black swan event with a long tail, on construction multi-projects. Practical implications: It conduct an effective model to quantify impacts of COVID-19 on international construction multi-projects for implementing effective counter-measures, which lay foundation for claims among different stakeholders. Originality/value: The term of detractor risk is applied to describe COVID-19 and quantify its impact upon international construction multi-projects. Further, a hybrid model by integrating parametric model and Monte Carlo in type I/II model is proposed to simulate their contingencies at disordered states. Finally, the simulated outcomes of these models are used to guide effective risk control to meet the requirements by the client. © 2022, Emerald Publishing Limited.

Highly sensitive and stretchable piezoelectric strain sensor enabled wearable devices for real-time monitoring of respiratory and heartbeat simultaneously

The World Health Organization has declared COVID-19 a pandemic. The demand for devices or systems to diagnose and track COVID-19 infections noninvasively not only in hospitals but also in home settings has led to increased interest in consumer-grade wearables. A common symptom of COVID-19 is dyspnea, which may manifest as an increase in respiratory and heart rates. In this paper, a novel piezoelectric strain sensor is presented for real-time monitoring of respiratory and heartbeat signals. A highly sensitive and stretchable piezoelectric strain sensor is fabricated using a piezoelectric film with a serpentine layout. The thickness of the patterned PVDF flexible piezoelectric strain sensor is only 168 μm, and the voltage sensitivity reaches 0.97 mV/μɛ. The effective modulus is 13.5 MPa, which allows the device to fit to the skin and detect the small strain exhibited by the human body. Chest vibrations are captured by the piezoelectric sensor, which produces an electrical output voltage signal conformally mapped with respiratory-cardiac activities. The separate heart activity and respiratory signals are extracted from the mixed respiratory-cardiac signal by an empirical mode decomposition data processing algorithm. By detecting vital signals such as respiratory and heart rates, the proposed device can aid early diagnosis and monitoring of respiratory diseases such as COVID-19. © 2022 Author(s).

A Project Based Learning Study through Student Design of a Low-Cost, Open-Source, Easy-to-Use, and Easy-to-Build Ventilator

This work presents a project-based learning (PBL) study, through student design and fabrication of a low-cost, open source, easy-to-build, and easy-to-use bag valve mask (BVM) ventilator, to potentially serve COVID-19 patients during the incubation period. A new learning outcomes framework, "Profiles of Learning for Undergraduate Success," was adopted as the pedagogical model, with a focus on problem solver and innovator. Using a reciprocating motion system, the ventilator is capable of providing an air supply with adjustable breath frequencies. 3D printing was used to fabricate customized components. In parallel to the mechanical assembly of the ventilator, a CAD model was developed to understand the motion mechanisms in the ventilator, which can further help optimize the system. The open access design files are available at GitHub. In actual usage, the project would serve as a backup to handle a surge of patients who may need breathing assistance in hospitals across the nation. The feedback from participating students is very positive. The success of this PBL-based project using the profiles of learning for undergraduate success shows promise, and it can be extended to other student learning experiences.

COVID-19 pandemic shifts in food-related parenting practices within an ethnically/racially and socioeconomically diverse sample of families of preschool-aged children.

This study aimed to evaluate the influence of the COVID-19 pandemic on food parenting practices used by parents of young children. Ecological Momentary Assessment (EMA) was used to evaluate parents' use of coercive, indulgent, structured, and autonomy supportive food parenting practices before and during the COVID-19 pandemic among a diverse racial/ethnic sample (n = 72) of parents of preschool-aged children. The impact of parent and child mood/behavior on use of specific food parenting practices was also evaluated during both time periods. Results revealed that most parents of preschoolers use a variety of food parenting practices, including coercive control, indulgence, structure, and autonomy support practices. The use of structured and autonomy supportive practices, however, decreased during the COVID-19 pandemic. Further, the types of practices used by parents were contextually associated with the mood of the parent as well as child mood. Parent negative mood during COVID-19 was associated with higher levels of coercive control and indulgence and lower levels of structure, whereas child positive child mood was associated with greater use of autonomy supportive practices. These findings suggest that effects of the COVID-19 pandemic on family dynamics around feeding young children include shifts away from theoretically supportive approaches to parenting and highlight the roles of parent and child mood/behavior as potentially important momentary influences on food parenting during this time. Public health practitioners and clinicians working with parents of young children during COVID-19, and in years to come, should consider the potential impact of parental mood and stress, as well as child mood and behaviors. Additional research is needed to better understand how to best help parents maintain supportive feeding practices in the face of challenging situations.

Risk factors of COVID-19. Role of chronic respiratory diseases

COVID-19 disease, caused by the SARS-CoV-2 coronavirus, has spread throughout the world. It can manifest from an asymptomatic form to the development of acute respiratory distress syndrome (ARDS). This article addresses aspects related to risk factors and comorbidities in patients with COVID-19, as well as the role of the main chronic respiratory diseases in their development and evolution, including chronic obstructive pulmonary disease (COPD), asthma and diffuse interstitial lung diseases (ILD). The most prevalent comorbidities in patients with COVID-19 are arterial hypertension (HTN), diabetes mellitus (DM) and cardiovascular diseases. In addition, it has been shown that both overweight and obesity predispose to the severity of the COVID-19 disease. With regard to chronic respiratory diseases, it seems that their prevalence in patients with COVID-19 is lower than expected.

Model Generalization on COVID-19 Fake News Detection

Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information. Considering the problematic consequences that the COVID-19 fake-news have brought, the scientific community has put effort to tackle it. To contribute to this fight against the infodemic, we aim to achieve a robust model for the COVID-19 fake-news detection task proposed at CONSTRAINT 2021 (FakeNews-19) by taking two separate approaches: 1) fine-tuning transformers based language models with robust loss functions and 2) removing harmful training instances through influence calculation. We further evaluate the robustness of our models by evaluating on different COVID-19 misinformation test set (Tweets-19) to understand model generalization ability. With the first approach, we achieve 98.13% for weighted F1 score (W-F1) for the shared task, whereas 38.18% W-F1 on the Tweets-19 highest. On the contrary, by performing influence data cleansing, our model with 99% cleansing percentage can achieve 54.33% W-F1 score on Tweets-19 with a trade-off. By evaluating our models on two COVID-19 fake-news test sets, we suggest the importance of model generalization ability in this task to step forward to tackle the COVID-19 fake-news problem in online social media platforms. © 2021, Springer Nature Switzerland AG.

Identification of Epidemiological Traits by Analysis of SARS-CoV-2 Sequences

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the ongoing global COVID-19 pandemic that began in late December 2019. The rapid spread of SARS-CoV-2 is primarily due to person-to-person transmission. To understand the epidemiological traits of SARS-CoV-2 transmission, we conducted phylogenetic analysis on genome sequences from >54K SARS-CoV-2 cases obtained from two public databases. Hierarchical clustering analysis on geographic patterns in the resulting phylogenetic trees revealed a co-expansion tendency of the virus among neighboring countries with diverse sources and transmission routes for SARS-CoV-2. Pairwise sequence similarity analysis demonstrated that SARS-CoV-2 is transmitted locally and evolves during transmission. However, no significant differences were seen among SARS-CoV-2 genomes grouped by host age or sex. Here, our identified epidemiological traits provide information to better prevent transmission of SARS-CoV-2 and to facilitate the development of effective vaccines and therapeutics against the virus.

All Roads Lead to Rome: Results of Non-Invasive Respiratory Therapies Applied in a Tertiary-Care Hospital Without an Intermediate Care Unit During the COVID-19 Pandemic

Introduction: Non-invasive respiratory therapies (NRT) were widely used in the first wave of the COVID-19 pandemic in different settings, depending on availability. The objective of our study was to present 90-day survival and associated factors in patients treated with NRT in a tertiary hospital without an Intermediate Respiratory Care Unit. The secondary objective was to compare the outcomes of the different therapies. Methods: Observational study of patients treated with NRT outside of an intensive care or intermediate respiratory care unit setting, diagnosed with COVID-19 and acute respiratory distress syndrome by radiological criteria and SpO2/FiO2 ratio. A multivariate logistic regression model was developed to determine independently associated variables, and the outcomes of high flow nasal cannula and continuous positive airway pressure were compared. Results: In total, 107 patients were treated and 85 (79.4%) survived at 90 days. Before starting NRT, the mean SpO2/FiO2 ratio was 119.8 ± 59.4. A higher SOFA score was significantly associated with mortality (OR 2,09;95% CI 1.34-3.27), while self-pronation was a protective factor (OR 0.23;95% CI 0.06-0.91). High flow nasal cannula was used in 63 subjects (58.9%), and continuous positive airway pressure in 41 (38.3%), with no differences between them. Conclusion: Approximately 4 out of 5 patients treated with NRT survived to 90 days, and no significant differences were found between high flow nasal cannula and continuous positive airway pressure.

Incidence of asymptomatic deep vein thrombosis in patients with COVID-19 pneumonia and elevated D-dimer levels.

AIM: An increased risk of venous thromboembolism (VTE) in patients with COVID-19 pneumonia admitted to intensive care unit (ICU) has been reported. Whether COVID-19 increases the risk of VTE in non-ICU wards remains unknown. We aimed to evaluate the burden of asymptomatic deep vein thrombosis (DVT) in COVID-19 patients with elevated D-dimer levels. METHOD: In this prospective study consecutive patients hospitalized in non-intensive care units with diagnosis of COVID-19 pneumonia and D-dimer > 1000 ng/ml were screened for asymptomatic DVT with complete compression doppler ultrasound (CCUS). The study was approved by the Institutional Ethics Committee. RESULTS: The study comprised 156 patients (65.4% male). All but three patients received standard doses of thromboprophylaxis. Median days of hospitalization until CCUS was 9 (IQR 5-17). CCUS was positive for DVT in 23 patients (14.7%), of whom only one was proximal DVT. Seven patients (4.5%) had bilateral distal DVT. Patients with DVT had higher median D-dimer levels: 4527 (IQR 1925-9144) ng/ml vs 2050 (IQR 1428-3235) ng/ml; p < 0.001. D-dimer levels > 1570 ng/ml were associated with asymptomatic DVT (OR 9.1; CI 95% 1.1-70.1). D-dimer showed an acceptable discriminative capacity (area under the ROC curve 0.72, 95% CI 0.61-0.84). CONCLUSION: In patients admitted with COVID-19 pneumonia and elevated D-dimer levels, the incidence of asymptomatic DVT is similar to that described in other series. Higher cut-off levels for D-dimer might be necessary for the diagnosis of DVT in COVID-19 patients.