ABSTRACT
This paper presented a simple and easy-To-use intelligent mirror with the activated function by face recognition. Firstly, the function of face recognition was realized by the OpenMV platform, and the recognition information was transmitted to the main controller, i.e., Loongson 1C Zhilong development board. The main controller connected to the Django server through the distant communication function of ESP8266 module. The user's schedules were acquisitioned by such a communication pathway and analyzed by the main controller. Finally, the recognized user's business or traveling schedule was shown on a screen located in the rear of a semitransparent mirror. For strangers of this smart mirror, the successful rate of strangers was 100%. For the user, the successful rate of strangers was 90% and accuracy of user's recognition was 100% in 120 times of tests. Furthermore, Adaptive Neuro Fuzzy Inference System supports a nice performance for Automatic classification in computer simulation. The COVID-19 pandemic is still threatening human beings. A smart mirror with the function of face recognition activation is a non-Touching solution for avoiding the infections to support an idea for elevating human health. © 2023 Global IT Research Institute (GiRI).
ABSTRACT
The emergence of the Covid-19 pandemic has greatly impact transportation, and unmanned transportation has been widely used in medical. The average precision of object detection as an important part in unmanned medical transportation. Object detection mainly relies on sensors of vehicles to obtain information about the surrounding obstacles like camera and LIDAR. In this paper, we introduce a new fusion way to fuse data from different modalities, as 2D and 3D object detection encouraging performance, they are typically based on a single modality and are unable to leverage information from other modalities. We leverage the geometric semantic consistency of 2D and 3D detection to obtain more accurate fusion results, and address the weaknesses of IoU in fusion network by using a generalized version as both a new loss and a new metric. The experimental evaluation on the challenging KITTI object detection benchmark, shows significant improvements in average precision, especially at bird's eye view metrics, which shows the feasibility and applicability of the network. © 2022 IEEE.
ABSTRACT
The outbreak of the Covid-19 pandemic in recent years and the epidemics of infectious diseases that have occurred around the world over the years, there are problems of lack of medical supplies and difficulties in personnel scheduling. Intelligent medical transportation through modern technology is an effective means to solve this problem. AGV(Automated Guided Vehicle) transportation and UAV(Unmanned Aerial Vehicle) transportation are important ways for intelligent transportation of medical materials. This paper investigates semantic segmentation as a key technology for AGV transport and UAV transport. This paper compares other traditional semantic segmentation networks, and at the same time considers the characteristics of all-weather, all-terrain, and complex transportation of materials in medical transportation, and proposes SSMMTNet(Semantic segmentation of medical material transportation Net). Among them, we propose a Scaling Transformer Block that can extract depth features of point clouds to enrich contextual information. At the same time, the network is validated on the benchmark Semantic3D dataset, obtaining 71.5% mIoU and 90.6% OA. © 2022 IEEE.
ABSTRACT
Rapid growth of emissions from Chinese civil aviation in recent years has largely accompanied with its fast expansion，and accurate emission inventory estimation is necessary for assessing the environmental impacts of the domestic civil aviation industry. To address the shortcomings of current studies in terms of accuracy and coverage， we proposed a bottom-up approach to estimate fuel consumptions and emissions， including HC，CO，NOx，PM，SO2，and CO2 from each individual flight with finer-scaled quick access recorder（QAR）data，collected from the domestic flights from January 2018 to November 2021. In this study，the durations of each flight phase were concerned with respect to various aircraft types，take-off and landing airports. Results show that the larger the airport is and the higher the emissions from this single flight is，and emissions during the CCD phase are the most important part and positively related to the flight duration. Emissions from 2018 to 2019 shew increasements，where the total emissions of CO2 were 73.17 ×106 t and 76.85 ×106 t，respectively. However，unexpected fluctuations occurred since the end of 2019 due to the outbreak of Covid19 epidemic，where the emission of CO2 were 61.51 ×106 t and 60.08 ×106 t in 2020 and 2021（January to November）. To explore the impacts of the Covid19 on flight emissions，an Autoregressive Integrated Moving Average（ARIMA）model was used to estimate emissions after December 2019 with assuming the absence of Covid19，and results show that total emissions decreased by about 20% up to November 2021. © 2022 Science Press. All rights reserved.
ABSTRACT
Campylobacter is one of the most common foodborne pathogens worldwide. A new smartphone-assisted high-throughput integrated color-sensing platform, called the HICS platform, was developed for the rapid detection of Campylobacter coli. This platform was based on the visual loop-mediated isothermal amplification method. Using this system, as many as 64 samples could be assessed in less than an hour after enrichment. 60 meat samples were tested to compare the performance of the HICS platform and other methods. Having the consistent detection result with TaqMan qPCR (Quantitative Real-time Polymerase Chain Reaction), the HICS platform was able to reliably detect C. coli in meat samples, and its limit of detection is 550 CFU/mL and 120 copies/mu L, which was 10-fold higher than that of the PCR (Polymerase Chain Reaction) method. To conclude, considering that our platform showed robust performance and does not require any expensive equipment, it can also be reliably applied for the high-throughput detection of other pathogens.
ABSTRACT
Coronavirus pandemic (COVID-19), caused by the SARS-CoV-2 virus, has spread expeditiously around the world since early 2020 and led to a tremendous number of deaths, severely impacting overall human well-being. The pandemic largely affected economic and social activities. The beneficial way to slow down or prevent the transmission is to be well informed about the disease and how the virus spreads. Therefore, analyzing factors that affect the COVID-19 transmission was of great importance in disease control and policy decisions. Socio-demographic factors show considerable impacts on the rate of COVID-19 infection, but the correlations would vary both temporally and spatially. Generally, the global correlation coefficients of all variables rocketed at the beginning of the COVID-19 outbreak and plateaued at a high level eventually. Then localized correlations were also calculated to map the spatial distribution of correlation coefficients. Results show that in the north of England, all socio-demographic factors are highly related to the COVID-19 cases with figures above 0.75, arising from the climatic, cultural and economic differences. As time flowed for both 55+ age structure and GDP, the southern part experienced sustainable increases in correlation values, which eventually rose above 0.5 at most locations. This finding confirmed our expectation that the higher GDP was, the more COVID-19 cases were, since high GDP always accompanies by more entertainment activities and more chances for face-To-face human contact. However, the interesting point was that around London, the GDP maintained uncorrelated and even negatively correlated with the cumulative cases as time went by. As for the number of pubs, the overall spatial distribution of correlation coefficients experienced unremarkable changes at three-Time points. The variable was significantly correlated with COVID-19 cases in the north. In contrast, in the south values kept below 0.5. Overall, this study provides an interesting view on investigating the relative factors of the COVID-19 pandemic. © 2022 IEEE.
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OBJECTIVES: We aimed to (1) assess parental hesitancy about category A (Expanded Program on Immunization (EPI)) and B (non-EPI) vaccines, (2) assess parental willingness for COVID-19 and influenza vaccinations, and (3) explore the association of vaccination hesitancy of parents and healthcare workers (HCWs). METHODS: The study was performed in Wuxi, eastern China between 21 September 2020 and 17 October 2020. Parents of children aged <18 years and HCWs were recruited from the selected immunization clinics. Vaccine hesitancy was assessed using the Strategic Advisory Group of Experts (SAGE) vaccine hesitancy survey (VHS) by summing the total score for 10 items (maximum 50 points). RESULTS: A total of 3009 parents and 86 HCWs were included in the analysis. The category A VHS scores were significantly higher than the category B VHS scores (p = 0.000). Overall, 59.3% and 52.4% of parents reported willingness to avail COVID-19 and influenza vaccination for their children, respectively;51.2% of the HCWs wanted to be vaccinated against COVID-19. Parental category B VHS scores were associated with HCW category B VHS scores (r = 0.928, p = 0.008). CONCLUSIONS: In China, parents are more hesitant about category B vaccines than category A vaccines. More than 40% of parents showed hesitancy and a refusal to use COVID-19 and influenza vaccines.
ABSTRACT
Considering the deficiency of existing propagation dynamics models, as well as the transmission characteristics of 2019 novel coronavirus (2019-nCoV) and prevention-control measurements, a new infectious disease model is established, which included eight compartments, i.e. susceptible, uninsulated, isolated, diagnosed, asymptomatic, cured after diagnosis, cured after asymptomatic and died of disease. In order to simulate the pressure of a large number of isolated patients on the operation of isolation points and the pressure of a large number of confirmed patients on the operation of hospitals, the saturation characteristics of state transfer parameters are considered in the model. In the aspect of parameter configuration, the time-varying characteristics of state transition parameters in different stages of epidemic development are analyzed. A parameter identification model is established and solved by Markov chain Monte Carlo algorithm. In addition, the risk indicators of epidemic transmission are established from multiple dimensions to comprehensively assess the risk of epidemic transmission. Through the case studies of coronavirus disease 2019 (COVID-19) in Wuhan and foreign countries (America and Spain), the results show that the calculated results are in better agreement with the official data and reveal the mechanism of epidemic transmission compared with other classical dynamics models. © 2021, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.