Research on two-dimensional code scanning and automatic recording device for human body temperature measurement based on stm32

With the outbreak and normal development of COVID-19, the effective detection and recording of body temperature has become a new focus of our attention. At present, there is no complete system to measure temperature, automatic record and specific information at home and abroad. To this end, combined with professional knowledge, our team designed a two-dimensional code scanning and human body temperature automatic recording device with STM32F1 as the core. The device STM32F1 development board is the main control chip. By connecting the WIFI module through the serial port, STM32F1 uses the function of wireless communication. Through the communication protocol, the link between the router and the ESC cloud server of Ali Cloud is utilized. The router or mobile data is transmitted to the user side (APP, applets) according to the specified communication protocol. Inside the development board, the code of each part is written to complete the device integrating code scanning and temperature measurement, which can be displayed and alarm through the node (OLED display screen). This will play a good role in preventing the spread of COVID-19. The system can be used in hospitals, communities, railway stations, shopping malls and many other public places. © 2022 IEEE.

A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem

Air transportation is eminent for its fast speed and low cargo damage rate among other ways. However, it is greatly limited by emergent factors like bad weather and current COVID-19 epidemic, where irregular flights may occur. Confronted with the negative impact caused by irregular flight, it is vital to rearrange the preceding schedule to reduce the cost. To solve this problem, first, we established a multi-objective model considering cost and crew satisfaction simultaneously. Secondly, due to the complexity of irregular flight recovery problem, we proposed a tabu-based multi-objective particle swarm optimization introducing the idea of tabu search. Thirdly, we devised an encoding scheme focusing on the characteristic of the problem. Finally, we verified the superiority of the tabu-based multi-objective particle swarm optimization through the comparison against MOPSO by the experiment based on real-world data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Fate of Microplastics Released by Discarded Disposable Masks

The pandemic of COVID-19 has led to a surge increase in the production of masks. Due to the rapid propagation of COVID-19 and the long survival time of plastic surfaces, a large number of masks are discharged into the environment without treatment. In this paper, the release of microplastics (MPs) in nature was simulated by using mask samples irradiated by ultraviolet (UV) light. After 28 days of ultraviolet radiation, part of the main chain of the mask was broken and a large number of transparent MPs fell off. The longer the UV irradiation time, the larger the proportion of small particle MPs. The middle layer of surgical mask is the most difficult to release MPs due to charge treatment, and N95 mask is the most difficult to degrade the inner material. © 2022, HARD Publishing Company. All rights reserved.

An Improved Fireworks Algorithm for Integrated Flight Timetable and Crew Schedule Recovery Problem

Abnormal flights, which deviate from their scheduled plans, incurred huge costs for airlines and serious inconvenience for passengers. This phenomenon occurs frequently, especially under the influence of COVID-19 and requires high-quality solution within short time limits. To mitigate these negative effects, first, an integrated flight timetable and crew schedule recovery model with the aim of minimizing total cost is constructed in this paper. Second, an improved fireworks algorithm is proposed to effectively solve the model. Finally, an unscheduled temporary aircraft maintenance scenario is obtained to illustrate the superiority of the proposed algorithm in terms of computing time and solution quality. © 2022, Springer Nature Switzerland AG.

An Improved Particle Swarm Optimization Algorithm for Irregular Flight Recovery Problem

As with the rapid development of air transportation and potential uncertainties caused by abnormal weather and other emergencies, such as Covid-19, irregular flights may occur. Under this situation, how to reduce the negative impact on airlines, especially how to rearrange the crew for each aircraft, becomes an important problem. To solve this problem, firstly, we established the model by minimizing the cost of crew recovery with time-space constraints. Secondly, in view of the fact that crew recovery belongs to an NP-hard problem, we proposed an improved particle swarm optimization (PSO) with mutation and crossover mechanisms to avoid prematurity and local optima. Thirdly, we designed an encoding scheme based on the characteristics of the problem. Finally, to verify the effectiveness of the improved PSO, the variant and the original PSO are used for comparison. And the experimental results show that the performance of the improved PSO algorithm is significantly better than the comparison algorithms in the irregular flight recovery problem covered in this paper. © 2022, Springer Nature Switzerland AG.

Study on the construction workforce management based on lean construction in the context of COVID-19

Purpose: The construction industry is facing challenges not only for workers' mobility in the pandemic situation but also for Lean Construction (LC) practise in responding to the high-quality development during the post-pandemic. As such, this paper presents a construction workforce management framework based on LC to manage both the emergency goal in migrant worker management and the long-term goal in labour productivity improvement in China. Design/methodology/approach: The framework is created based on the integrated culture and technology strategies of LC. A combination of qualitative and quantitative methods is taken to explore factors influencing the mobility of construction workers and to measure labour productivity improvement. The case study approach is adopted to demonstrate the framework application. Findings: For method application, a time-and-motion study and Percent Plan Complete indicator are proposed to offer labour productivity measurements of “resources efficiency” and “flow efficiency”. Moreover, the case study provides an industry level solution for construction workforce management and Lean Construction culture shaping, as well as witnesses the LC culture and technology strategies alignment contributing to LC practise innovation. Originality/value: Compared with previous studies which emphasised solely LC techniques rather than socio-technical system thinking, the proposed integration framework as well as implementation of “Worker's Home” and “Lean Work Package” management models in the COVID-19 pandemic contribute to new extensions of both the fundamental of knowledge and practise in LC. © 2022, Emerald Publishing Limited.

Global patterns of daily CO2 emissions reductions in the first year of COVID-19

Observed daily changes in CO2 emissions from across the globe reveal the sectors and countries where pandemic-related emissions declines were most pronounced in 2020. Day-to-day changes in CO2 emissions from human activities, in particular fossil-fuel combustion and cement production, reflect a complex balance of influences from seasonality, working days, weather and, most recently, the COVID-19 pandemic. Here, we provide a daily CO2 emissions dataset for the whole year of 2020, calculated from inventory and near-real-time activity data. We find a global reduction of 6.3% (2,232 MtCO(2)) in CO2 emissions compared with 2019. The drop in daily emissions during the first part of the year resulted from reduced global economic activity due to the pandemic lockdowns, including a large decrease in emissions from the transportation sector. However, daily CO2 emissions gradually recovered towards 2019 levels from late April with the partial reopening of economic activity. Subsequent waves of lockdowns in late 2020 continued to cause smaller CO2 reductions, primarily in western countries. The extraordinary fall in emissions during 2020 is similar in magnitude to the sustained annual emissions reductions necessary to limit global warming at 1.5 degrees C. This underscores the magnitude and speed at which the energy transition needs to advance.

[Course of disease and related epidemiological parameters of COVID-19: a prospective study based on contact tracing cohort].

Objective: To analyze the course of disease and epidemiological parameters of COVID-19 and provide evidence for making prevention and control strategies. Methods: To display the distribution of course of disease of the infectors who had close contacts with COVID-19 cases from January 1 to March 15, 2020 in Guangdong Provincial, the models of Lognormal, Weibull and gamma distribution were applied. A descriptive analysis was conducted on the basic characteristics and epidemiological parameters of course of disease. Results: In total, 515 of 11 580 close contacts were infected, with an attack rate about 4.4%, including 449 confirmed cases and 66 asymptomatic cases. Lognormal distribution was fitting best for latent period, incubation period, pre-symptomatic infection period of confirmed cases and infection period of asymptomatic cases; Gamma distribution was fitting best for infectious period and clinical symptom period of confirmed cases; Weibull distribution was fitting best for latent period of asymptomatic cases. The latent period, incubation period, pre-symptomatic infection period, infectious period and clinical symptoms period of confirmed cases were 4.50 (95%CI:3.86-5.13) days, 5.12 (95%CI:4.63-5.62) days, 0.87 (95%CI:0.67-1.07) days, 11.89 (95%CI:9.81-13.98) days and 22.00 (95%CI:21.24-22.77) days, respectively. The latent period and infectious period of asymptomatic cases were 8.88 (95%CI:6.89-10.86) days and 6.18 (95%CI:1.89-10.47) days, respectively. Conclusion: The estimated course of COVID-19 and related epidemiological parameters are similar to the existing data.

[Epidemiological characteristics of local COVID-19 epidemics and control experience in routine prevention and control phase in China].

The COVID-19 pandemic is still ongoing in the world, the risk of COVID-19 spread from other countries or in the country will exist for a long term in China. In the routine prevention and control phase, a number of local COVID-19 epidemics have occurred in China, most COVID-19 cases were sporadic ones, but a few case clusters or outbreaks were reported. Winter and spring were the seasons with high incidences of the epidemics; border and port cities had higher risk for outbreaks. Active surveillance in key populations was an effective way for the early detection of the epidemics. Through a series of comprehensive prevention and control measures, including mass nucleic acid screening, close contact tracing and isolation, classified management of areas and groups at risk, wider social distancing and strict travel management, the local COVID-19 epidemics have been quickly and effectively controlled. The experiences obtained in the control of the local epidemics would benefit the routine prevention and control of COVID-19 in China. The occurrence of a series of COVID-19 case clusters or outbreaks has revealed the weakness or deficiencies in the COVID-19 prevention and control in China, so this paper suggests some measures for the improvement of the future prevention and control of COVID-19.

Dynamic Survival Analysis with Individualized Truncated Parametric Distributions

Dynamic survival analysis is a variant of traditional survival analysis where time-to-event predictions are updated as new information arrives about an individual over time. In this paper we propose a new approach to dynamic survival analysis based on learning a global parametric distribution, followed by individualization via truncating and renormalizing that distribution at different locations over time. We combine this approach with a likelihood-based loss that includes predictions at every time step within an individual's history, rather than just including one term per individual. The combination of this loss and model results in an interpretable approach to dynamic survival, requiring less fine tuning than existing methods, while still achieving good predictive performance. We evaluate the approach on the problem of predicting hospital mortality for a dataset with over 6900 COVID-19 patients.

Determinants of turnaround time in a rapid genome sequencing program

Previous studies of genome sequencing (GS) in critically ill childrenhave made use of either modified hardware or working procedureswhich would be difficult, if not impossible, to integrate into existingclinical workflows1. Our lab’s transition from exome sequencing (ES) to GS offered an opportunity to implement in-house rapid genomesequencing (rGS) in critically ill children in a manner which couldintegrate with existing clinical workflows. We conducted a feasibilityand implementation pilot by offering rGS to child-parent triosconcurrently undergoing clinical rapid ES (rES) via a reference lab.The purpose of this study was to identify and address operationalbarriers to implementation of a rGS program capable of communicatinga preliminary result within 7 days of consent. We consideredthis time span to be more reflective of clinical realities than lab-quotedturnaround times (TAT) which typically start at sample receipt andthus do not account for challenges in sample acquisition and pre-testcounseling in a critical care setting, nor the impact of shipping times.Here we present data on TAT and lessons learned from the first 27subjects enrolled.Using rapid cycle improvement methodologies, we identified fourdistinct but inter-related workflows requiring optimization:1. Pre-analytic: patient identification through acquisition ofsamples2. Wet-lab: extraction through sequencing3. Bioinformatics: secondary and tertiary analysis as well as rapididentification of causal variants4. Return of resultsFigure 1 summarizes TAT across cases, demonstrating the markedimprovements in TAT with our programmatic approach to improvement.We used our first 9 cases to determine a baseline TAT for theentire process and to delineate the 4 main workflows (above). Atbaseline, excluding cases delayed by COVID-19 restrictions, mean TATwas 17.12 days (3 sequential deviant range: 7.05–27.19 days).Following deployment of our programmatic approach to rGS, meanTAT fell to 6.19 days (3 sequential deviant range: 0.51–11.87 days).Table 1 summarizes the observations and insights, by workflow, whichimpacted upon TAT and/or implementation. The single biggest impacton TAT was optimization of bioinformatics by removing all manualsteps between starting sequencing and producing human interpretable,filtered, annotated output of high-priority variants for interpretation.The second biggest source of improvement was optimization ofthe sequencing itself as well as prioritizing sample processing for andaccess to sequencing runs. While variant ranking is helpful in identifying causal variants, in 9/10 cases with a diagnostic findingthe causal variant(s)were obvious to the study teamwithin minutes ofviewing the annotated variant list, regardless of variant rank. (Figure Presented) As time required for sequencing and analytic workflows fell, therelative contribution of other workflows to overall TAT shifted and itbecame more obvious that early identification and utilization of thisapproach is very important in lowering overall time to diagnosis(Figure 2). In 6/10 cases with a diagnostic finding, the initial approachof the clinical team was NOT rES (and thus patients were not eligiblefor rGS on a research basis). Had rGS been the initial diagnosticmodality chosen, a diagnosis could have been reached in a median 12days sooner (range 2–28 days). There were also several cases wheresequencing was delayed when one or both parents did not present tothe lab to provide a blood sample in a timely manner. Optimization ofsequencing or analytic workflows cannot meaningfully improveoutcomes either of these situations.Our findings suggest some important considerations for institutionsdeveloping or seeking to improve rapid sequencing programs for acuteand critically ill children: (Table Presented) • Optimization of computational resource utilization and phenotypecuration saves more time than improved variant filtering orprioritization.• Obtaining samples from parents is non-trivial.• Even trained geneticists may fail to recognize appropriatecandidates for rGS.

The effect of microplastics on earthworm-assisted sludge treatment wetlands

Due to the characteristics of rapid transmission of COVID-19 and extended survival time on the plastic surface, resulting in a sharp increase in the number of microplastics (MPs) in sludge. In this paper, different amounts of terephthalate (PET) were added to four earthworm-assisted sludge treatment wetland (ESTW) reactors to investigate the effect of MPs on the operation process of ESTW. The results show that the addition of a small number of MPs had little effect on the ESTW reactor, and the negative impact was evident when a large number of MPs. The ESTW4 reactor with 200000 particles/kg the worst organic matter removal and sludge stability. The plant height, root length and chlorophyll growth of the ESTW4 reactor were significantly inhibited, and the yield of catalase (CAT) increased significantly, which proved that a large number of MPs had noticeable side effects on plants. The number of MPs (35.21particles/kg) and small size MPs (62.6%) in earthworm casts reached maximum, CAT (38.1U/mg prot) and acetylcholine esterase (AChE) (14.3U/mg prot) have the same trend in ESTW4. A large number of MPs during the ESTW reactor lead to an obvious oxidative stress reactions and lasting neurotoxicity of earthworm. Additionally, the type and quantity of bacteria in the ESTW reactor could reduce when the MPs adding too much. © 2021 Elsevier Ltd

A novel Nongfu Spring's best selling strategy based on big data

In order to overcome the threat of traditional drinking water in marketing, such as fierce market competition, threats of new entrants, and achieving leading enterprise, this paper proposes a novel Nongfu Spring's best selling strategy based on big data. Except the pricing solutions such as the discount on bundles or boxes, sales signs, 8-endings, and premium price, the tactics are different from traditional ones in that they focus on reinforcement of a 'natural and healthy' brand image, improvement of diversity and creativity in product design, and higher investment in direct and social marketing. The research results show that those tactics could assist increase health awareness after the COVID-19 pandemic, build a better brand image, satisfy a variety of customer needs, increase the scope of the promotion campaign, and more importantly, maintain dominant market share of Nongfu Spring. © 2020 IEEE.

Research on graph-based SLAM for UVC disinfection robot

With the emergence of the COVID-19 pandemic, more and more non-contact mobile disinfection robots have appeared in the medical field, which have made great contributions to the fight against the epidemic. Aiming at the problems of single disinfection method, single application scenario, low degree of intelligence and lack of autonomous mobile disinfection in existing disinfection robots, this paper proposes and designs a disinfection and epidemic prevention intelligent robot called Aimi-Robot UVC, which is based on graph-optimized slam algorithm to complete the localization and map creation functions of the robot in the unknown environment. After testing in the isolated single ward of the hospital, the realtime localization accuracy reaches 0.04m, which provides high-precision and high-reliability localization for the disinfection robot in the hospital scene and has great practical significance for the application of intelligent disinfection robots in epidemic prevention and control. © 2021 IEEE.

Nitrogen Oxides Spatial Distribution and Emissions with Mobile Multi-Axis Differential Optical Absorption Spectroscopy in Wuhan City

For NOx, pollutions in Wuhan during the outbreak of COVID-19, we applied a mobile MAX-DOAS and a portable ultraviolet DOAS to cooperatively measure NOx, concentration in the third ring road of Wuhan from Feb. 29th to Mar. 14th, 2020. The mobile MAX-DOAS acquired the vertical column concentration (VCD) distribution of NO2 along its course and the portable ultraviolet DOAS measured the NO and NO2 concentrations to calculate [NOx]/[NO2]. Then, the NOx emission flux and its error of the third ring road of Wuhan were calculated in conjunction with the data of the wind field. The results show that the NO, emission flux during the measurements in the third ring of Wuhan ranges from 7.78 mol/s to 15.71 mol/s, about 10.78 mol/s on average. Compared with the average [NOx]/[NO2], the real-time [NOx]/[NO2] along the route of the mobile MAX-DOAS derived from the portable ultraviolet DOAS could effectively reduce the error of NOx emission flux caused by the [NOx,]/[NO2] error. However, this method is not recommended in scenarios with substantial near-surface NOx emission sources.

Outcomes for hospitalized cancer patients with COVID-19 during the height of pandemic in New York City

Background: Several reports have suggested that cancer patients are at increased risk for contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and suffering worse coronavirus disease 2019 (COVID-19) outcomes. However, little is known about the impact of cancer status on presentation and outcome. Here, we report on the association between cancer status and survival in hospitalized patients who tested positive for SARSCoV- 2 during the height of pandemic in New York City. Methods: Of the 6,724 patients who were hospitalized at NYU Langone Health (3/16/20 -7/31/20) and tested positive for SARS-CoV-2, 580 had either active cancer (n = 221) or a history of cancer (n = 359). Patients were classified as having active malignancy if they either received treatment within six months of their COVID-19 diagnosis or they had measurable disease documented at the time of their hospitalization. Patients were categorized as having a history of cancer if there was no evidence of measurable disease or there were no treatments administered within six months of their COVID-19 diagnosis. We compared the baseline clinicodemographic characteristics and hospital courses of the two groups, and the relationship between cancer status and the rate of admission to the intensive care unit (ICU), use of invasive mechanical ventilation (IMV), and all-cause mortality. Results: There was no differences between the two groups in their baseline laboratory results associated with COVID- 19 infection, incidence of venous thromboembolism, or incidence of severe COVID- 19. Active cancer status was not associated with the rate of ICU admission (P =0.307) or use of IMV (P = 0.236), but was significantly associated with worse all-cause mortality in both univariate and multivariate analysis with ORs of 1.48 (95% CI: 1.04-2.09;P = 0.028) and 1.71 (95% CI: 1.12- 2.63;P = 0.014), respectively. Conclusions: Active cancer patients had worse survival outcomes compared to patients with a history of cancer despite similar COVID-19 disease characteristics in the two groups. Our data suggest that cancer care should continue with minimal interruptions during the pandemic to bring about response and remission as soon as possible. Additionally, these findings support the growing body of evidence that malignancy portends worse COVID-19 prognosis, and demonstrate that the risk may even apply to those without active disease.

Impact of COVID-19 on the clinical status of patients with Wilson disease

The coronavirus disease 2019 (COVID-19) pandemic has greatly impacted health systems. Many guidelines on chronic liver diseases have been released to optimize the use of medical resources and patient management. However, most of these guidelines have been established through expert consensus because the existing data do not provide strong evidence for developing effective recommendations. As Wilson disease (WD) is a rare chronic liver disease, the impact of COVID-19 on the clinical status of patients with WD is unclear. The present study showed a marked shortage of medical resources for clinically managing patients with WD during the pandemic. Although patients with WD who consistently took anticopper therapy showed no significant differences in hepatic and extrahepatic markers before and after the pandemic, their complication incidences, especially the infection incidence, were significantly increased during the study period. Therefore, patients with WD should be encouraged to adhere to anticopper therapy and be closely monitored to prevent infections and other complications. The present study provides a clinical basis for further managing WD during the pandemic.

Impact on China's CO2 emissions from COVID-19 pandemic

Assessing the impact of the COVID-19 pandemic on China's carbon emissions is crucial for China to lead global climate change mitigation. However, thorough quantitative analyses of the pandemic's effects on energy use and emissions are still lacking. This is largely because well-known published datasets of greenhouse gas emissions are based on annual statistics that commonly become available one or more years after they are gathered. A high temporal resolution emission dataset is critical to capture the immediate effects of an event such as the COVID-19 pandemic. Such an improved resolution might also allow policy makers to more quickly observe the effects of policies aimed at decreasing CO2 emissions, facilitating rapid adjustments. Here, based on a newly developed, near real-time global emission dataset, Carbon Monitor, we estimate China's daily energy consumption and associated CO2 emissions and use this dataset to estimate the impact of COVID-19 on CO2 emission trends. Such a near-real-time CO2 emission database based on activity data quantifies both anthropogenic CO2 emissions from fossil fuel combustion and cement production. We show that the higher-resolution emission dataset can be used to assess the impact of COVID-19 (and similar future disruptions) within a reasonable range of uncertainty. This study finds that China's CO2 emissions in the first four months of 2020 decreased by 6.9% compared to the same period in 2019, with a total emission reduction of 234.5 million tons of CO2. The provinces of Jiangsu, Hubei, and Zhejiang were most severely affected by COVID-19, accounting for 19.4%, 17.0% and 12.5% of the total reduction in CO2 emissions, respectively. The reduction of CO2 emissions from Shandong, Hebei, Anhui, Henan and Chongqing provinces is more than 10 Mt CO2, and the sum of the reduction from these five provinces accounts for 28.8% of the national total reductions. CO2 emissions in Yunnan Province, Gansu Province, Guangxi Zhuang Autonomous Region, Ningxia Hui Autonomous Region, Shaanxi Province and Xinjiang Uygur Autonomous Region increased slightly compared to the same period in 2019. In addition, COVID-19 has a little effect on CO2 emissions from Qinghai Province and Tibet Autonomous Region. This decrease in atmospheric pollutants is the largest decrease recorded and is consistent with the decrease in air pollutants observed by ground observation stations. This study finds that China's national economy has recovered rapidly. In April 2020, China's carbon emissions returned to the same level observed last year. This study predicts that China's full-year CO2 in 2020 will be 2% lower than in 2019. This will be the first time China has experienced a decline since 1997. China's carbon emissions have not rebound significantly, and the dynamic changes in China's CO2 indicate a rapid recovery of the Chinese economy. Regarding long-term trends, it is still unclear how much China's CO2 emissions will change at the end of this year and how fast the economy and industry will return to normal. With policy support, the economy will be stimulated as the pandemic fades. The IMF predicts that the global annual economic output will decrease sharply by -3.0% in 2020, which is worse than the financial crisis in 2008, based on the assumption that COVID-19 will fade globally in the second half of this year. Based on current emission dynamics, China's emission decline is estimated to be less than 5%, and the future trend will be affected by whether there will be another pandemic in the future. Current statistics are still not capable of comprehensively capturing the dynamics of CO2 emissions during the COVID-19 pandemic, and further monitoring, observation and data collection are urgently needed. © 2021, Science Press. All right reserved.

Depression status and influencing factors of front-line nurses during prevention and control of COVID-19

Objective: To explore the depression status and its influencing factors of front-line nurses in a COVID-19 hospital in Wuhan during the period of prevention and control. Methods: 521 front-line nurses in a hospital in Wuhan during the epidemic of COVID-19 were selected for the cross-sectional survey study. The general data, the PHQ-9 depression screening scale, and the perceived stress scale were used to assess their mental status. Results: The average depression score of the front-line nurses was 6.49±5.40, among which 57.94% of them were more than 5 point, the average score was 10.12±4.17. Multiple stepwise regression analysis indicated that gender, average sleep time per night, frequency of regular meals per week, negative events experience, fear of epidemic, and perceived stress level were independent factors for depressive symptoms of nurses. Conclusion: The depression symptoms of front-line nurses are common in the work against COVID-19. The government and the medical departments should formulate and adopt effective strategies to prevent psychological stress, and to promote the mental health of front-line nurses. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.