Local travel behaviour under continuing COVID-19 waves- A proxy for pandemic fatigue?

COVID-19 continues to threaten the world. Relaxing local travel behaviours on preventing the spread of COVID-19, may increase the infection risk in subsequent waves of SARS-CoV-2 transmission. In this study, we analysed changes in the travel behaviour of different population groups (adult, child, student, elderly) during four pandemic waves in Hong Kong before January 2021, by 4-billion second-by-second smartcard records of subway. A significant continuous relaxation in human travel behaviour was observed during the four waves of SARS-CoV-2 transmission. Residents sharply reduced their local travel by 51.9%, 50.1%, 27.6%, and 20.5% from the first to fourth pandemic waves, respectively. The population flow in residential areas, workplaces, schools, shopping areas, amusement areas and border areas, decreased on average by 30.3%, 33.5%, 41.9%, 58.1%, 85.4% and 99.6%, respectively, during the pandemic weeks. We also found that many other cities around the world experienced a similar relaxation trend in local travel behaviour, by comparing traffic congestion data during the pandemic with data from the same period in 2019. The quantitative pandemic fatigue in local travel behaviour could help governments partially predicting personal protective behaviours, and thus to suggest more accurate interventions during subsequent waves, especially for highly infectious virus variants such as Omicron.

COVID-19 and Cardiovascular Diseases: From Cellular Mechanisms to Clinical Manifestations.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), quickly spread worldwide and led to over 581 million confirmed cases and over 6 million deaths as 1 August 2022. The binding of the viral surface spike protein to the human angiotensin-converting enzyme 2 (ACE2) receptor is the primary mechanism of SARS-CoV-2 infection. Not only highly expressed in the lung, ACE2 is also widely distributed in the heart, mainly in cardiomyocytes and pericytes. The strong association between COVID-19 and cardiovascular disease (CVD) has been demonstrated by increased clinical evidence. Preexisting CVD risk factors, including obesity, hypertension, and diabetes etc., increase susceptibility to COVID-19. In turn, COVID-19 exacerbates the progression of CVD, including myocardial damage, arrhythmia, acute myocarditis, heart failure, and thromboembolism. Moreover, cardiovascular risks post recovery and the vaccination-associated cardiovascular problems have become increasingly evident. To demonstrate the association between COVID-19 and CVD, this review detailly illustrated the impact of COVID-19 on different cells (cardiomyocytes, pericytes, endothelial cells, and fibroblasts) in myocardial tissue and provides an overview of the clinical manifestations of cardiovascular involvements in the pandemic. Finally, the issues related to myocardial injury post recovery, as well as vaccination-induced CVD, has also been emphasized.

Highly sensitive electrochemical aptasensor for SARS-CoV-2 antigen detection based on aptamer-binding induced multiple hairpin assembly signal amplification.

In this work, a brief electrochemical aptasensor was developed for highly sensitive detection of SARS-CoV-2 antigen utilizing an aptamer-binding induced multiple hairpin assembly strategy for signal amplification. In the presence of SARS-CoV-2, a pair of aptamers was brought in a close proximity according to the aptamer-protein antigen binding, which initiated strand displacement reaction thereby triggering a multiple hairpin assembly to obtain long linear DNA concatemers on the electrode surface. As the fabricated hairpin probes were labeled with biotin, massive streptavidin-alkaline phosphatases (ST-ALP) could be further introduced on the electrode interface via biotin-streptavidin interaction thus generating strong electrochemical signal in electrolyte solution containing 1-naphthol phosphate. Benefiting from the non-enzymatic multiple hairpin assembly signal amplification strategy, the designed aptasensor for SARS-CoV-2 spike protein detection exhibited the wide linear range from 50 fg·mL-1 to 50 ng·mL-1 and low detection limit of 9.79 fg·mL-1. Meaningfully, this proposed electrochemical assay provided a potential application for the point of care analysis of viral diseases under ambient temperature.

Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine.

With the national goal of "carbon peak by 2030 and carbon neutral by 2060 in China", studies on carbon prices of China's Emissions Trading System (ETS) pilots have shown growing interest in the related fields. Carbon price fluctuations reflect the scarcity of carbon resources, and accurate prediction can improve carbon asset management capabilities. Therefore, in order to clarify the dynamics of carbon markets and assign carbon emissions allocation rationally, we propose a hybrid feature-driven forecasting model with the framework of decomposition-reconstruction-prediction-ensemble. In this paper, the non-stationary, nonlinear and chaotic characteristics of carbon prices in China's ETS pilots have been verified, and then the prediction model is built based on the tested features. Firstly, the original carbon price series are decomposed by Variational Mode Decomposition (VMD), and then reconstructed by Sample Entropy (SE). Next, Extreme Learning Machine (ELM) optimized by Particle Swarm Optimization (PSO) is conducted to predict the subsequences. Lastly, the forecasting series of every subseries are summed to obtain the final results. The empirical results based on carbon prices of China's ETS pilots proved that the proposed model performs more efficiently than the current benchmark models. As carbon prices are expected to increase across all ETS during the post-COVID-19 recovery stage, the new prediction model will be useful for improving the guiding principles of the existing government policies including the likely introductions of Border Carbon Adjustment (BCA) in the EU and the US, and governing the large global public companies to deliver their "net zero" commitments.

Artificial Intelligence of Things (AIoT) Enabled Virtual Shop Applications Using Self-Powered Sensor Enhanced Soft Robotic Manipulator.

Rapid advancements of artificial intelligence of things (AIoT) technology pave the way for developing a digital-twin-based remote interactive system for advanced robotic-enabled industrial automation and virtual shopping. The embedded multifunctional perception system is urged for better interaction and user experience. To realize such a system, a smart soft robotic manipulator is presented that consists of a triboelectric nanogenerator tactile (T-TENG) and length (L-TENG) sensor, as well as a poly(vinylidene fluoride) (PVDF) pyroelectric temperature sensor. With the aid of machine learning (ML) for data processing, the fusion of the T-TENG and L-TENG sensors can realize the automatic recognition of the grasped objects with the accuracy of 97.143% for 28 different shapes of objects, while the temperature distribution can also be obtained through the pyroelectric sensor. By leveraging the IoT and artificial intelligence (AI) analytics, a digital-twin-based virtual shop is successfully implemented to provide the users with real-time feedback about the details of the product. In general, by offering a more immersive experience in human-machine interactions, the proposed remote interactive system shows the great potential of being the advanced human-machine interface for the applications of the unmanned working space.