What can we learn from the 2008 financial crisis for global power decarbonization after COVID-19?

Since the outbreak of the COVID-19 pandemic, power generation and the associated CO2 emissions in major countries have experienced a decline and rebound. Knowledge on how an economic crisis affects the emission dynamics of the power sector would help alleviate the emission rebound in the post-COVID-19 era. In this study, we investigate the mechanism by which the 2008 global financial crisis sways the dynamics of power decarbonization. The method couples the logarithmic mean Divisia index (LMDI) and environmentally extended input-output analysis. Results show that, from 2009 to 2011, global power generation increased rapidly at a rate higher than that of GDP, and the related CO2 emissions and the emission intensity of global electricity supply also rebounded;the rapid economic growth in fossil power-dominated countries (e.g., China, the United States, and India) was the main reason for the growth of electricity related CO2 emissions;and the fixed capital formation was identified as the major driver of the rebound in global electricity consumption. Lessons from the 2008 financial crisis can provide insights for achieving a low-carbon recovery after the COVID-19 crisis, and specific measures have been proposed, for example, setting electricity consumption standards for infrastructure construction projects to reduce electricity consumption induced by the fixed capital formation, and attaching energy efficiency labels and carbon footprint labels to metal products (e.g., iron and steel, aluminum, and fabricated metal products), large quantities of which are used for fixed capital formation. © 2023 The Authors

Are radiators ready for the challenges of the future: A review of advancements in radiators

Radiators play an important role in providing a comfortable and safe indoor environment while maintaining high-energy efficiency. In the perspective of future climate change with expected larger temperature fluctuations and the rapidly changing heat supply and demand, it is required that the current radiator technology is adaptable. The heat supply is changing towards a lower supply temperature to enable an increase in energy efficiency and an increase in the share of renewable energy. Simultaneously, both the heat supply and demand are expected to have more variations in the future. An additional concern that has come into more focus after the experience with the COVID 19 pandemic is the prevention of the spread of infection in indoor environments. Researchers have extensively studied several innovations in radiator technologies and their deployment that addresses these challenges. Some of the solutions available in the literature include floor heating, ceiling heating, ventilation radiator, stratum ventilation. Researchers have used advanced modeling and experimental techniques to understand how to deploy different types of radiator technologies. This review summarizes solutions in the literature that address these challenges and identifies knowledge gaps that need to be addressed. In particular, this study explores the gaps in knowledge of practical issues, such as the position of furniture and the position of people, which have received less attention in the literature. Research that addresses the effect of radiators on ventilation and a healthy indoor environment is also of particular interest in this review. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)

Influencing Factors of Online Learning Satisfaction of University Students in the Post-COVID-19 Era in China

Since the post-COVID-19 era, online teaching has become the normalization of universities in China. However, it is still unclear whether students are satisfied with online teaching and what factors affect online learning satisfaction of university students. Therefore, based on the establishment of basic hypothesis, through the construction of path model and questionnaire survey, this paper found the online learning satisfaction of university students in the post-COVID-19 era in China and revealed the main factors affecting online learning satisfaction. The results show that university students are satisfied with the overall evaluation and sub-item evaluation of online learning, and students have a positive attitude toward online learning. From the perspective of the influencing path, curriculum design and learning environment have a positive direct impact on the online learning satisfaction, while teacher characteristics have an indirect positive impact on the online learning satisfaction mainly through curriculum design and learning environment. From the perspective of the degree of influencing factors, although teachers' characteristics have indirect influence, the degree of influence is the largest. According to the results, this paper provides some suggestions on the improvement of teachers, curriculum design, and learning environment, to improve the online learning satisfaction of university students. © 2022 ICCREM 2022: Carbon Peak and Neutrality Strategies of the Construction Industry - Proceedings of the International Conference on Construction and Real Estate Management 2022. All rights reserved.

COVID-19 Pandemic Prediction using Machine Learning Methods

In this paper, we aim to predict the cases of covid-19 pandemic according to linear regression model and random forest model. We decide to try to predict the virus using the daily high and low temperatures because it is one of the biggest factors that can affect the spread and death of the virus.we decide to use days_num, vaccine_days, and ma_temp_high as features.Cases and deaths as labels. We find that that the virus surely has some relationship with temperature. If the theory turns out to be true, in the future, adjusting control efforts based on temperature could greatly improve efficiency and save money. Reduce ineffective expenditures and improve the quality of prevention and control. © 2022 SPIE.

Investigating How University Students in the United States Encounter and Deal with Misinformation in Private WhatsApp Chats during COVID-19

Misinformation can spread easily in end-to-end encrypted messaging platforms such as WhatsApp where many groups of people are communicating with each other. Approaches to combat misinformation may also differ amongst younger and older adults. In this paper, we investigate how young adults encountered and dealt with misinformation on WhatsApp in private group chats during the first year of the COVID-19 pandemic. To do so, we conducted a qualitative interview study with 16 WhatsApp users who were university students based in the United States. We uncovered three main findings. First, all participants encountered misinformation multiple times a week in group chats, often attributing the source of misinformation to be well-intentioned family members. Second, although participants were able to identify misinformation and fact-check using diverse methods, they often remained passive to avoid negatively impacting family relations. Third, participants agreed that WhatsApp bears a responsibility to curb misinformation on the platform but expressed concerns about its ability to do so given the platform's steadfast commitment to content privacy. Our findings suggest that conventional content moderation techniques used by open platforms such as Twitter and Facebook are unfit to tackle misinformation on WhatsApp. We offer alternative design suggestions that take into consideration the social nuances and privacy commitments of end-to-end encrypted group chats. Our paper also contributes to discussions between platform designers, researchers, and end users on misinformation in privacypreserving environments more broadly. © 2022 by The USENIX Association. All Rights Reserved.

Deep-learning Based Approach to Identify Covid-19

COVID-19 is a large-scale contagious respiratory disease that has spread across the world in 2020. Therefore, a low-cost, fast, and easily available solution is needed to provide a COVID-19 diagnosis to curb the outbreak. According to recent studies, one of the main symptoms of COVID-19 is coughing. The goal of this research effort is to develop a method for the automatic diagnosis of COVID-19 by detecting cough during recorded conversations. The method is composed of five main modules: sound extraction, sound feature extraction, cough detection, cough classification, and COVID-19 diagnosis. The method extracts relevant features from the audio signal and then uses machine learning and deep learning models, like SVM, KNN, and RNN, to classify them, which can successfully diagnose COVID-19 from audio recordings. Our method has relatively high accuracy when dealing with completely unfamiliar cough samples. When the training set and the test set are from two different databases, it still achieves an accuracy of 81.25% (AUC of 0.79). As more data sets are collected, the model can be further developed and improved to create a machine learning solution based on cough analysis for COVID-19 detection, which may be promoted as a non-clinical self-inspection solution.

[Analysis of CT features of 15 children with 2019 novel coronavirus infection].

Objective: To explore imaging characteristics of children with 2019 novel coronavirus (2019-nCoV) infection. Methods: A retrospective analysis was performed on clinical data and chest CT images of 15 children diagnosed with 2019-nCoV infection. They were admitted to the Third People's Hospital of Shenzhen from January 16 to February 6, 2020. The distribution and morphology of pulmonary lesions on chest CT images were analyzed. Results: Among the 15 children, 5 were males and 10 females, aged from 4 to 14 years. Five of the 15 children were febrile and 10 were asymptomatic on the first visit. The first nasal or pharyngeal swab samples in all the 15 cases were positive for 2019-nCoV nucleic acid. For their first chest CT images, 6 patients had no lesions, while 9 patients had pulmonary inflammatory lesions. Seven cases had small nodular ground glass opacities and 2 cases had speckled ground glass opacities. After 3 to 5 days of treatment, 2019-nCoV nucleic acid in a second respiratory sample turned negative in 6 cases. Among them, chest CT images showed less lesions in 2 cases, no lesion in 3 cases, and no improvement in 1 case. The remaining 9 cases were still positive in a second nucleic acid test. Six patients showed similar chest CT inflammation, while 3 patients had new lesions, which were all small nodular ground glass opacities. Conclusions: The early chest CT images of children with 2019-nCoV infection are mostly small nodular ground glass opacities. The clinical symptoms of children with 2019-nCoV infection are nonspecific. Dynamic reexamination of chest CT and nucleic acid are important.

Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs

The COVID-19 pandemic caused by the novel SARS-Coronavirus-2 (n-SARS-CoV-2) has impacted people's lives in unprecedented ways. During the time of the pandemic, social vloggers have used social media to actively share their opinions or experiences in quarantine. This paper collected videos from YouTube to track emotional responses in conversational vlogs and their potential associations with events related to the pandemic. In particular, vlogs uploaded from locations in New York City were analyzed given that this was one of the first epicenters of the pandemic in the United States. We observed some common patterns in vloggers' acoustic and linguistic features across the time span of the quarantine, which is indicative of changes in emotional reactivity. Additionally, we investigated fluctuations of acoustic and linguistic patterns in relation to COVID-19 events in the New York area (e.g.The number of daily new cases, number of deaths, and extension of stay-At-home order and state of emergency). Our results indicate that acoustic features, such as zero-crossing-rate, jitter, and shimmer, can be valuable for analyzing emotional reactivity in social media videos. Our findings further indicate that some of the peaks of the acoustic and linguistic indices align with COVID-19 events, such as the peak in the number of deaths and emergency declaration. © 2020 ACM.

CHOCSLAT: Chinese Healthcare-Oriented Computerised Speech & Language Assessment Tools

This paper describes an on-going project to develop diagnostic profiling tools for healthcare professionals to identify potential speech and language developmental problems of Chinese-speaking children. The tools aim to provide a technical advance in helping children who may have speech impairment or language delay. The project is currently being carried out as a collaboration with Chinese healthcare professionals, and a multidisciplinary team including applied linguists, speech and language pathologists, and computer scientists (staff and students) at the first Sino-foreign higher education institution in China, University of Nottingham Ningbo China (UNNC). The paper presents the background, development, and current state of the project. Challenges to project completion, including difficulties encountered due to the Covid-19 outbreak and subsequent world-wide emergency and lock-down are also discussed. © 2020 IEEE.

[Analysis of CT features of 15 Children with 2019 novel coronavirus infection].

Objective: To explore imaging characteristics of children with 2019 novel coronavirus (2019-nCoV) infection. Methods: A retrospective analysis was performed on clinical data and chest CT images of 15 children diagnosed with 2019-nCoV. They were admitted to the third people's Hospital of Shenzhen from January 16 to February 6, 2020. The distribution and morphology of pulmonary lesions on chest CT images were analyzed. Results: Among the 15 children, there were 5 males and 10 females, aged from 4 to 14 years old. Five of the 15 children were febrile and 10 were asymptomatic on first visit. The first nasal or pharyngeal swab samples in all the 15 cases were positive for 2019-nCoV nucleic acid. For their first chest CT images, 6 patients had no lesions, while 9 patients had pulmonary inflammation lesions. Seven cases of small nodular ground glass opacities and 2 cases of speckled ground glass opacities were found. After 3 to 5 days of treatment, 2019-nCoV nucleic acid in a second respiratory sample turned negative in 6 cases. Among them, chest CT images showed less lesions in 2 cases, no lesion in 3 cases, and no improvement in 1 case. Other 9 cases were still positive in a second nucleic acid test. Six patients showed similar chest CT inflammation, while 3 patients had new lesions, which were all small nodular ground glass opacities. Conclusions: The early chest CT images of children with 2019-nCoV infection are mostly small nodular ground glass opacities. The clinical symptoms of children with 2019-nCoV infection are nonspecific. Dynamic reexamination of chest CT and nucleic acid are important.