Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Research on the investment value of China's medical sector in the context of COVID-19

COVID-19 has slowed the growth of, the global economy, which has certain practical significance. Consequently, this study seeks to analyze the investment opportunities in the medical sector before and after the COVID-19 outbreak. In this study, the Markowitz mean–variance (MV) model, capital asset pricing model (CAPM), and correlation models are constructed based on the principle of Markowitz MV and correlation analysis. Simultaneously, statistical analysis is used to verify the analysis, and the MATLAB statistical tool is used to build the model. The results show that the actual expected yield of China's medical sector is significantly higher than that calculated by the CAPM before and after the pandemic, and that the investment value of the medical sector is undervalued by the market. From the perspective of risk, China's medical sector has a stable systemic risk premium. Based on the above analysis, when building investment portfolios in the post-pandemic era, investors should appropriately allocate stocks in the medical and pharmaceutical sectors to improve the portfolio income and diversify the investment risk. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Optimal low-carbon governance model of livestreaming supply chain based on multiple scenarios

Supply chain emissions reduction is an important way to promote the development of a low-carbon economy and address climate challenges. Although the scale of livestream shopping has demonstrated unprecedented growth globally, especially since the COVID-19 outbreak, livestreaming supply chains have also contributed significantly to carbon emissions. Currently, optimisation models for the low-carbon governance of livestreaming supply chains are relatively lacking. To address the issue of carbon emission reduction in livestreaming supply chains, this study paper proposes three low-carbon governance decision-making models based on environmental and operating costs to compare which governance model is optimal. The most suitable decision result for the policymaker and supply chain is both cost-effective and environmentally successful under the model considering carbon tax and carbon trade. The results show that 1) governance based only on carbon tax and collaborative operation will decrease the total cost of the livestreaming supply chain but increase the environmental cost. 2) Governance based only on carbon trading and collaborative operation will increase the total cost of the livestreaming supply chain, while the environmental cost will not change. 3) Under governance that combines carbon tax and carbon trading, collaborative operations can effectively reduce both the total cost and the environmental cost of livestreaming supply chains. Theoretically, our study enriches the research on the low-carbon governance of livestreaming supply chains. Moreover, the research results provide useful insights into the formulation of a low-carbon policy for livestreaming supply chains.

BioHD: An Efficient Genome Sequence Search Platform Using HyperDimensional Memorization

In this paper, we propose BioHD, a novel genomic sequence searching platform based on Hyper-Dimensional Computing (HDC) for hardware-friendly computation. BioHD transforms inherent sequential processes of genome matching to highly-parallelizable computation tasks. We exploit HDC memorization to encode and represent the genome sequences using high-dimensional vectors. Then, it combines the genome sequences to generate an HDC reference library. During the sequence searching, BioHD performs exact or approximate similarity check of an encoded query with the HDC reference library. Our framework simplifes the required sequence matching operations while introducing a statistical model to control the alignment quality. To get actual advantage from BioHD inherent robustness and parallelism, we design a processing in-memory (PIM) architecture with massive parallelism and compatible with the existing crossbar memory. Our PIM architecture supports all essential BioHD operations natively in memory with minimal modifcation on the array. We evaluate BioHD accuracy and efciency on a wide range of genomics data, including COVID-19 databases. Our results indicate that PIM provides 102.8× and 116.1× (9.3× and 13.2×) speedup and energy efciency compared to the state-of-theart pattern matching algorithm running on GeForce RTX 3060 Ti GPU (state-of-the-art PIM accelerator). © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Fujian Province highway motor vehicle pollutant emission inventory under the background of the COVID-19

Pollutants emitted from motor vehicles have become an important source of air pollution. Based on the traffic flow data of the expressways in Fujian Province, a high-resolution pollutant emission inventory of the expressways from January to July in 2020 was established by the bottom-up calculation method. The results show that under the influence of the COVID-19, the monthly average traffic flow and pollutant emissions of the expressways in Fujian province decreased firstly and then increased. Pollutant emissions reached the lowest in April, and quickly recovered to the pre-COVID-19 emission level in May. The pollutant emissions of the CO, HC, NOx, PM2.5 and PM10 in the middle stage of the COVID-19 decreased by 90.68%, 89.06%, 92.58%, 89.58% and 89.63%, respectively, compared with those in the post stage of the COVID-19. In the entire study period, different cities have different sharing rates of the pollutant emissions from motor vehicles, with Quanzhou, Fuzhou and Zhangzhou having higher motor vehicle emission sharing rates on the expressways. In terms of the vehicle types, the small passenger buses and the light trucks are the main contributors for CO and HC, and the heavy trucks and the light trucks are the main contributors for NOx and PM. In terms of the fuel types, the gasoline vehicle is the main source of CO and HC, and the diesel vehicle is the main source of NOx and PM. In terms of the emission standards, vehicles with China 3 and China 4 have the largest contribution rate to various pollutants. However, the spatial distribution of various pollutants is consistent, with the higher level emissions in the eastern coastal expressways, while the lower level emissions in the western inland expressways. From the spatial distribution of NOx emission intensity of Expressways in Fujian Province, the emission intensity of each section in March and April was at a very low level. Taking the main emission of the motor vehicle: NOx as a case, the Shenyang-Haikou Expressway section has the highest NOx emission intensity. Moreover, the Xiamen and Quanzhou sections of Shenyang-Haikou Expressway have relatively high exposure levels of motor vehicle pollution, while other sections have relatively low exposure levels. © 2022, Science Press. All right reserved.

Multiplex Digital Polymerase Chain Reaction Technology and Its Application

Digital polymerase chain reaction (PCR), as a nucleic acid detection technology with wide application prospect, has become one of the most accurate nucleic acid detection technology at present. Multiplex detection is an important direction for the development of digital PCR technique. With the development of microfluidic technology, multiplex digital PCR technique has become more and more mature. This paper reviewed the research progresses of multiplex digital PCR in recent years, especially summarized the implementation of multiplex digital PCR technique in the past five years, and introduced the application of multiplex digital PCR technique in hot areas such as liquid biopsy, transgenic detection, and SARS-Cov-2 detection. Finally, the issues and challenges faced by multiplex digital PCR technique were discussed and the future direction of the technology was foreseen.

Reintubation during COVID-19 pandemic: a simple self-made guiding device facilitates reintubation and minimizes transmission

Consensus guidelines to protect airway managers during COVID-19 were developed to encourage safe, accurate and swift performance in intubation and extubation, but reintubation was not considered. With the massive surge of patients requiring mechanical ventilation in this COVID-19 pandemic, great incidence of difficult airways may necessitate reintubation. Equipments could be used now in extubation and reintubation are either too expensive and time-consuming in decontamination, or have not gained wide acceptance. Here, we adapted an extubation device from an intubating stylet, which is provided as accessory of endotracheal tube. Such stylet could provide safe access for expediting reintubation both during and after the COVID-19 pandemic, which is inexpensive, single-use, readily available, straightforward to handle, and well-tolerated, thereby benefiting both the patients and healthcare providers.

Clinical characteristics and prediction analysis of the recovered COVID-19 patients with re-detectable positive RNA test

Background: Previous studies have unveiled the occurrence of re-detectable positive (RP) RNA test result after hospital discharge among recovered COVID-19 patients, but the clinical characteristics of RP patients (RP patients) and the potential features affecting RP RNA test outcome remain unclear. Methods: A total of 742 COVID-19 patients discharged between March 1st, 2020 and March 20th, 2020 were enrolled. All patients were followed-up for SARS-CoV-2 RNA test and RP patents were identified. The clinical characteristics between RP patients and NRP patients were compared, and the potential features affecting re-detectable RNA test outcome were further evaluated. Results: Up to April 9th, 2020, 60 recovered patients (8.09%) had been re-detected to be SARS-CoV-2 RNA positive. Among those 60 RP patients, the median RP time was 12 days from the last negative result of SARS-CoV-2 RNA test or 10 days from hospital discharge. RP patients were prone to be older, having mild/moderate conditions, unilateral lung involvement and fatigue, chills, stuffy or runny nose, with high lymphocyte count. Multivariate logistic analysis and COX regression analysis demonstrated that age, lymphocyte count, urea nitrogen, stuffy or runny nose as well as lung involvement were independently associated with RP RNA test (P<0.05). Conclusions: Older patients accompanied with stuffy or runny nose, low urea nitrogen as well as unilateral lung involvement were more likely to develop RP RNA test result after hospital discharge. Therefore, we strongly suggest using broncho-alveolar lavage fluid for RNA detection, extending quarantine time, and conducting continual follow-up medical examination for those discharged patients.

Associations between severe pulmonary function and residual CT abnormalities in rehabilitating COVID-19 patients

OBJECTIVE: Coronavirus disease 2019 (COVID-19) spread around the world in 2020. Abnormal pulmonary function and residual CT abnormalities were observed in COVID-19 patients during recovery. Appropriate rehabilitation training is around the corner. The correlation between spirometric impairment and residual CT abnormality remains largely unknown. PATIENTS AND METHODS: A cross-sectional study conducted on the pulmonary function of 101 convalescent COVID-19 patients before discharge. Multivariate analysis was used to establish a scoring system to evaluate the spirometric abnormality based on residual chest CT. RESULTS: Lung consolidation area >25% and severe-type COVID-19 were two independent risk factors for severe pulmonary dysfunction. Besides, a scoring system was established. People scoring more than 12 points have more chances (17 times) to get severe pulmonary function impairment before discharge. CONCLUSIONS: For the first time, a chest CT characteristics-based grading system was suggested to predict the pulmonary dysfunction of COVID-19 patients during convalescence in this study. This study may provide suggestions for pulmonary rehabilitation.

Spatial-temporal responses of atmospheric pollutants to the COVID-19 lockdown across the Yangtze River Delta region

From January 1 to February 29, 2020, using hourly monitoring data of criteria pollutants in Shanghai, Nanjing, Hefei and Hangzhou, combined with the vertical columns of SO2 and NO2 from satellite inversions, we explore the pollution levels and response characteristics of urban atmospheric pollutants in the Yangtze River Delta region to the COVID-19 lockdown. The results show that, except O3, the average concentrations of air pollutants during the lockdown (from January 24 to February 10) were lower than that before the lockdown but higher than that after the lockdown, indicating that emission reduction was not the only factor in terms of governing air quality. The ratio of PM2.5/PM10 during the lockdown period was higher than before and after the lockdown, indicating that the secondary generation of aerosols has an important contribution to the fine particle pollution that still occurs during the lockdown period. We observed a significant increase of Ox levels (p <0.01) during the lockdown, suggesting that atmospheric oxidation capacity may be substantially enhanced in the context of NO2 reduction. Spatially, the O3 typically centered in individual city as hotspots, indicating its nature of local emission and formation. However, other pollutants (including PM2.5, PM10, CO, SO2, and NO2) demonstrated a gradual decrease gradient from north to south, satellite observations pinpointed that North China Plain was the major source of regional transport, which can be further validated by back-trajectory analysis. © 2021, Science Press. All right reserved.

Clinical characteristics associated with long-term viral shedding in patients with coronavirus disease 2019

Background: To delineate the clinical characteristics associated with long-term viral shedding (>21 days) in patients with coronavirus disease 2019 (COVID-19). Methods: In this retrospective study, factors associated with long-term (>21 days) severe acute respiratory coronavirus 2 (SARS-CoV-2) RNA shedding were evaluated in a conhort of 609 patients from two hospitals in Wuhan. Results: The median duration of SARS-CoV-2 viral shedding was 19 days (interquartile range, 10-28 days) among all patients. There were 42% of patients having prolonged viral shedding time (>21 days), in which the longest viral shedding time was 58 days. When comparing patients with early (≤21 days) and late viral RNA clearance (>21 days), prolonged viral shedding was associated with age <65 (P=0.015), female sex (P=0.028), cough (P=0.025), fatigue (P=0.035), sore throat (P=0.013), aspartate aminotransferase (P=0.038), procalcitonin (P=0.010), albumin (P=0.003), D-dimer (P=0.011), lung involvement (P=0.014), reticular shadow (P<0.001) and lung consolidation (P=0.004). Age range (<65 years) (odds ratio [OR], 1.46 [95% CI, 1.05-2.03]) and female sex (odds ratio [OR], 1.40 [95% CI, 1.00-1.94]) were independent risk factors. Conclusions: Long-term viral shedding (>21 days) is not a rare phenomenon among COVID-19 infectious patients. Age range (<65) and female sex are independent risk factors for long-term viral shedding. Early antiviral treatment should be considered for COVID-19 patients with such risk factors. Further study should be conducted to know the infectivity of patients with long-term viral shedding in order to develop reasonable control measures.