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The global economy is moving into a new era characterized by digital and green development. To examine the impact of digital industrialization development on the energy supply chain, in relation to the sustainable development of China's energy security, we discuss the nonlinear impact and transmission mechanism of digital industrialization on the supply chain of the energy industry using a panel threshold regression model based on sample data on the development of the provincial natural gas industry in China from 2006 to 2020. We found that there are multiple threshold effects of digital industrialization level development on energy supply chain length, and the results are statistically significant, i.e., digital industrialization development positively contributes to natural gas supply chain length after digital industrialization is raised to or crosses the critical threshold. Meanwhile, the heterogeneity analysis results show that there are differences in the impact of digital industrialization on the energy supply chain from sub-sectors, regional development differences, and different development periods. Therefore, we provide some factual support and experience for achieving the construction goal of "Digital China" and accelerating the digital reform of the energy supply chain as well as transforming and upgrading the economic structure.
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With the global outbreak of COVID-19, hundreds of pneumonias caused by cold chain products occurred worldwide, which seriously threatened the safety of people's lives and properties. To effectively prevent product quality problems caused by cold chain logistics, it is urgent to establish a cold chain logistics traceability system with interoperability of heterogeneous systems, to record, share and track the temperature, location, time, and other specific information. The traditional cold chain logistics traceability systems have many problems, such as broken cold chains, untrustworthy data, and data tampering and sharing, which hinder the coordination and interaction efficiency of cold chain logistics traceability data. This paper creatively proposes a cold chain logistics traceability system framework based on the identification and resolution system for the Industrial Internet. It establishes a general cold chain logistics traceability identification data model. The system framework and data model can effectively solve the difficulties of multi-code identification and multi-source heterogeneous system interaction, to improve the efficiency of cold chain logistics traceability, and ensure the quality of cold chain logistics products. © 2022 ACM.
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Objective The time series analysis model was used to predict and warn the number of tuberculosis (TB) cases in Tangshan area in different time, which provided a reference for scientific prevention and control of TB epidemic in this area. Methods The number of monthly TB cases in Tangshan from January 2005 to December 2021 was collected, and the seasonal autoregressive integrated moving average (SARIMA) model was used to predict the number of TB cases in 2022. Meanwhile, the difference between the predicted number of TB cases and the actual observed number of TB cases in the area was explored during the period of COVID-19 in 2020 by this model and rank test. Results From January 2005 to December 2021, the ARIMA (1, 1, 0) (1, 1, 2)s model was fitted well with the actual observed number of TB cases (AR =-0. 530, ARs =-0.967, MAs = 0. 861, P0. 05;Stationary R2 = 0. 558, R2 = 0. 634, BIC = 7. 887;Ljung-Box Q = 25. 605, P 0. 05), with peaks TB incidence in March, April, and December every year, and the predicted number of TB cases in 2020 was 1 800. From 2005 to 2019, ARIMA (1, 1, 0) (0, 1, 2)s model was fitted well with the actual number of cases (AR =-0. 544, ARs =-0. 840, MAs = 0. 697, P 0. 05;Stationary R2 = 0. 582, R2 = 0. 621, BIC = 7. 939;Ljung-Box Q = 24. 211, P 0. 05), with peaks TB incidence in March, April, and December every year, and the predicted number of TB cases in 2020 was 1 985. The observed and predicted number of TB cases from January 2020 to May 2020 were statistically significant (Z =-2. 023, P0. 05). Conclusion It is necessary to increase the intensity of early warning of TB in March, April, and December every year in Tangshan to prevent the epidemic of TB. At the same time, the coordination of the staff of TB prevention institutions and the emergency system should be strengthened during the epidemic situation of COVID-19, and effectively ensure the registration and medical treatment of TB patients during the epidemic situation. © 2023, Editorial Department of Medical Pest Control. All rights reserved.
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For the purpose of capturing the semantic information accurately and clarifying the user's questioning intention, this paper proposes a novel, ensemble deep architecture BERT-MSBiLSTM-Attentions (BMA) which uses the Bidirectional Encoder Representations from Transformers (BERT), Multi-layer Siamese Bi-directional Long Short Term Memory (MSBiLSTM) and dual attention mechanism (Attentions) in order to solve the current question semantic similarity matching problem in medical automatic question answering system. In the preprocessing part, we first obtain token-level and sentence-level embedding vectors that contain rich semantic representations of complete sentences. The fusion of more accurate and adequate semantic features obtained through Siamese recurrent network and dual attention network can effectively eliminate the effect of poor matching results due to the presence of certain non-canonical texts or the diversity of their expression ambiguities. To evaluate our model, we splice the dataset of Ping An Healthkonnect disease QA transfer learning competition and "public AI star” challenge - COVID-19 similar sentence judgment competition. Experimental results with CC19 dataset show that BMA network achieves significant performance improvements compared to existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Objective To analyze the clinical characteristics and changes of serum IgG, IgM antibodies in patients infected with the SARS-CoV-2 B.1.1.529 (Omicron) variant. Methods The clinical data of 82 patients with SARS-CoV-2 B.1.1.529 variant was analyzed retrospectively. Based on the presence of pneumonia on chest CT, the patients were divided into pneumonia group and non-pneumonia group. Serum IgG, IgM antibodies were observed at 5 time points T1 (1~<4 d), T2 (4~<8 d), T3 (8~<15 d), T4 (15~<22 d) and T5 (22~<30 d) after admission. Results Among the 82 patients infected with the SARSCoV-2 B.1.1.529 variant strain, there were 62 cases of cough, 31 cases of fever, 33 cases of throat discomfort, 5 cases of muscle soreness and 3 cases of diarrhea. The serum IgG antibody levels at 5 time points were 50.22 (142.20) AU/mL, 326.50 (220.63) AU / mL, 368.23 (76.21) AU / mL, 368.65 (79) AU / mL, and 385.26 (113.10) AU / mL, respectively. The level of serum IgG antibody in the pneumonia group was lower than that of the non-pneumonia group at T1 and T4 time points, and the differences were statistically significant (P<0.05), the positive rate of serum IgG antibody in the pneumonia group was lower than that of the non-pneumonia group at the T1 time point, and the difference was statistically significant (P<0.05) . The serum IgM antibody levels at 5 time points were 0.41 (0.81) AU/mL, 0.95 (1.62) AU/mL, 1.09 (2.42) AU/mL, 0.74 (3) AU/mL, and 0.81 (3.10) AU / mL respectively, and there was no significant difference between the two groups. Conclusion The clinical symptoms of patients infected with the SARS-CoV-2 B.1.1.529 variant strain are mild. Serum IgG antibodies increased after infection, but there are some differences between the pneumonia group and the non-pneumonia group, whether serum IgG has a protective effect needs further research;the serum IgM antibodies do not increase highly after infection, there are some differences between individuals. © 2022 Editorial Office of Chinese Journal of Schistosomiasis Control. All rights reserved.
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The authors have been made the following changes for the above published article: 1. The second and third authors' affiliations should be read as follows. © 2022 World Scientific Publishing Co. Pte Ltd. All rights reserved.
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The COVID-19 Pandemic brought the whole society to a standstill, which has more significant psychological pressure on children and adolescents. Governments, companies, and social groups are trying to confront COVID-19 and social distancing in a gamified way. However, due to fear of the virus and uncertainty about the future, even after the Pandemic is well controlled in physical space, people are still reluctant to stop and play in public areas and are afraid to engage with others because of their internal sense of alienation. From the perspective of urban renewal and environmental design, creating a series of micro-scale design interventions in public spaces to relieve psychological pressure has urgency and relevant significance. This paper analyzes the symbiotic relationship between public art installations and communities. Then discovers the characteristics of public installations based on emotional healing. Furthermore, create two design prototypes to demonstrate more vividly how gamified interactive experience could relieve the mental pressure of the surrounding residents and help them gradually adapt to the new normal life. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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In recent years, with the frequent occurrence of viral diseases accompanied by high morbidity and mortality rates, there has been an increasing awareness of importance of antiviral drugs research. Traditional Chinese medicine contain biologically structurally diverse bioactive substances that provide important template structures for pharmaceutical research. Because of its novelty, multicomponent and multi-target characteristics, it is a valuable source for new drug development. The antiviral mechanisms of active components of traditional Chinese medicines include inhibition of viral replication, block binding of virus with receptor, directly killing virus, enhancement of the immune system and inhibition of cytokines/chemokines responses, etc. The active components of traditional Chinese medicine with antiviral active ingredients based on the mechanism of antiviral action were reviewed in this paper, in order to provide a basis for development of antiviral natural drugs to cope with the virus epidemic including the new variant of SARS-CoV-2 and other virus outbreaks that may occur in the future. © 2022 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.
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Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc. However, high-precision medical image segmentation remains a highly challenging task due to the existence of inherent magnification and distortion in medical images as well as the presence of lesions with similar density to normal tissues. In this paper, we propose TFCNs (Transformers for Fully Convolutional denseNets) to tackle the problem by introducing ResLinear-Transformer (RL-Transformer) and Convolutional Linear Attention Block (CLAB) to FC-DenseNet. TFCNs is not only able to utilize more latent information from the CT images for feature extraction, but also can capture and disseminate semantic features and filter non-semantic features more effectively through the CLAB module. Our experimental results show that TFCNs can achieve state-of-the-art performance with dice scores of 83.72% on the Synapse dataset. In addition, we evaluate the robustness of TFCNs for lesion area effects on the COVID-19 public datasets. The Python code will be made publicly available on https://github.com/HUANGLIZI/TFCNs. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The achievement of China's carbon dioxide (CO2) emission reduction target is of great significance in the face of global climate change. Accurate identification of key factors that affect CO2 emissions can provide theoretical support to policymakers when designing related policies. Compared to the traditional method, the generalized Divisia index method (GDIM) can capture the influence of multiple scale factors on carbon emissions, providing new tools for studying the decomposition of carbon emissions. The article proposed a GDIM-based decomposition method to analyze the drivers that influence CO2 emissions in China from 2000 to 2017. The results indicate that investment activity is the primary element in promoting China's carbon emissions, followed by energy use and economic activities. On the contrary, investment carbon intensity is the vital inhibitory factor, followed by GDP carbon intensity. Specifically, the positive driving force of investment and energy use is gradually weakening, while the contribution of economic activities is continuously strengthening. The effectiveness of carbon emission reduction in the Northeast, East, and Southwest is actively promoting China's carbon emission reduction, while the effectiveness of CO2 emission reduction in the Northwest is not performing well. The findings provide support and reference for carbon emission control in China. (C) 2022 Elsevier Ltd. All rights reserved.
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Objective To evaluate the evidence of the experience with medical sewage treatment procedures in medical institutions in China. Methods Databases including CNKI, WanFang Data, PubMed, Web of Science, and EBSCO were electronically searched to collect studies on the medical sewage treatment process, flow, and specifications in medical institutions in China. We used the quality evaluation system to classify and grade the experiences based on the principles and methods of evidence-based science and performed a descriptive analysis. Results After the SARS pandemic in 2003, China systematically established and standardized the technical criteria of medical sewage treatment and discharge. Moreover, a prevention system for the epidemic using medical sewage was constructed, which guaranteed that the quality of medical sewage treatment and discharge would meet the criteria and protect the citizens, and the technical specifications of medical sewage treatment would progress and increase strictly. At present, medical sewage treatment in medical institutions in China was based on mechanical and biological methods, and disinfection was mainly performed using chlorine and its compounds, ozone, and ultraviolet light. Conclusion The COVID-19 pandemic requires a higher quality of medical sewage treatment and discharge criteria for medical institutions in China. To meet these criteria, all medical institutions in China should check, replace, and update their old facilities;strengthen personnel training and effectively ensure the quality of medical sewage treatment.
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Introduction Due to propellants, metered dose inhalers (pMDIs) have a higher carbon footprint than low carbon footprint inhalers (LCFIs), such as dry powder or soft mist inhalers (1). Consequently, pMDIs contribute 3.5% of the NHS’s CO2 equivalent emissions (2). Local and national guidelines (NICE, British Thoracic Society) have attempted to increase use of LCFIs, but their effects and factors influencing success are unknown. Aim To investigate temporal and geographical variation in LCFI dispensing in England over five years. Methods Clinical commissioning group (CCG) dispensed items (March 2016-February 2021) were obtained from openprescribing.net for five classes of inhaler where a choice between pMDIs and LCFIs is available: short-acting beta-agonists (SABAs), long-acting beta-agonists (LABAs), inhaled corticosteroids (ICS), ICS plus LABA inhalers (ICS/LABA) and ICS/LABA plus long-acting muscarinic antagonist inhalers (ICS/LABA/LAMA). CCG population age profiles were obtained from the Office for National Statistics. CCG emergency hospital admission and mortality rates were obtained from Public Health England. CCG formularies and guidelines were reviewed to identify where guidance is available to prescribers. To control for total inhaler dispensing, the key measure used is the %LCFI: the number of LCFI items dispensed relative to the total number of pMDI and LCFI items. Multivariate regression models were used to investigate geographical variation. Results The total annual %LCFI increased from 19.5% to 26.3% over the study period. This was driven by the introduction of ICS/LABA/LAMA inhalers in 2018, as %LCFI decreased for SABA, ICS and ICS/LABA inhalers. %LCFI varied between classes. In the final year, it ranged from 6% for both SABA and ICS inhalers, to 41.2% and 43.9% for ICS/LABA and ICS/LABA/LAMA inhalers, respectively. Interestingly, the cost per item for ICS/LABA and ICS/LABA/LAMA inhalers was similar for both pMDIs and LCFIs, but for SABA and ICS inhalers LCFIs were more expensive. %LCFI in the final year varied between CCGs (10.7% to 30.9%). The North West, and Birmingham and London areas had consistently higher %LCFI for all classes. For SABA and ICS inhalers, both the presence of advice on climate change in CCG guidelines or formularies, and greater CCG asthma prevalence, were significantly associated with higher %LCFI (p<0.05). The proportion of CCG population <15 years had a significant negative association with %LCFI for ICS and ICS/LABA inhalers (p<0.05). There were no clinically significant associations between %LCFI and either emergency hospital admission or mortality rates. Conclusion Current initiatives have not been successful in increasing the use of LCFIs, indicating limited implementation of guidelines for unknown reasons. Further action is required to reduce the carbon footprint of inhaler prescribing. Actions to address the financial disincentives to LCFI prescribing, CCG leadership (e.g. guidelines) and the appropriate use of LCFI in young people should be considered. Research into facilitators and barriers to LCFI use would support this. An important limitation is the use of dispensed items data rather than the number of inhalers, although there is no evidence that the number of inhalers per item varies between pMDIs and LCFIs. In addition, the Covid-19 pandemic disrupted prescribing patterns and long-term NHS projects. References (1) Wilkinson AJK, Braggins R, Steinbach I, Smith K. Costs of switching to low global warming potential inhalers. An economic and carbon footprint analysis of NHS prescription data in England. BMJ Open. 2019;9:e028763. (2) Environmental Audit Committee. UK progress on reducing F-Gas emissions inquiry: Fifth report of session 2017-19. London (UK): House of Commons Environmental Audit Committee;25 April 2018. Available from https://publications.parliament.uk/pa/cm201719/cmselect/cmenvaud/469/469.pdf: [Accessed 27 September 2021].
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Objective: To analyze the mental health status and influencing factors of China medical team (CMT) members in Africa during COVID-19 pandemic. Methods: From July 2021 to August 2021, 72 members of the 8th CMT in Malawi, the 36th CMT in Sudan and the 22nd CMT in Zambia were tested by 12-item General Health Questionnaire (GHQ-12), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9(PHQ-9), general information form and influencing factors form. Results: The results of GHQ-12 were positive for 33.3% (24/72) of the CMT members. 51.4% (37/72) of the CMT members showed different levels of anxiety: 40.3% (29/72) of them had mild anxiety, 8.3% (6/72) of them had moderate anxiety, and 2.8% (2/72) of them had severe anxiety. 52.8% (38/72) of the CMT members had different degrees of depression: 34.7% (25/72) of them had mild depression, 11.1% (8/72) of them had moderate depression, 4.2% (3/72) of them had moderate-severe depression, and 2.8% (2/72) of them had severe depression. The CMT members who contacted with COVID-19 patients got significantly high scores of GHQ-12, GAD-7 and PHQ-9 (P<0.05) compared to those who did not have contact with COVID-19 patients. And CMT members who did not adapt to the local social life got significantly higher scores than those who adapted to the local social life (P<0.05). These factors were the main factors affecting the mental health of the CMT members. Conclusion: During COVID-19, the psychological pressure of CMT members was increased significantly, and both the incidence and severity of anxiety and depression were increased. Paying attention to and improving CMT members' mental health status can ensure the smooth development of medical aid to Africa.
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During this nearly two-years-long pandemic period, the COVID-19 impacts people's lives dramatically, many people were forced to stay at home by the government's lockdown policy, and they also need to work and study at home. Therefore, there is an equivalent impact on networks as people are more dependent on them. But there are only a limited number of research has been done in this intersection area between the pandemic and networks. So, we want to fill this gap. In this paper, we will study the mobile network data from U.S. Federal Communications Commission (FCC) and COVID-19 cases data from the U.S. centers for disease control and prevention (CDC), then use machine learning to investigate the relationship between mobile network data and COVID-19 cases. We will discuss other related works, which used other methods or investigated this topic in other regions, then we will introduce our machine learning methods, experiments and give the conclusion. © 2022 IEEE.
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By the natural parents are the essentials of educating young generations. In China during Covid-19, especially the lock-down period, parents companied children at home and observed their learning behavior. Parental acceptance of online teaming can offer a valuable referencefor thefurther adaptation of online k-12 education. This study used an integrated model of technology acceptance and expected confirmation, demonstrating Parental acceptance by three variables: acceptance, satisfaction, and continuance intention. Moreover, an influential factor model of Parental acceptance was built, which including teacher support, perceive autonomy and interactivity, technology preference and experience. Measurement and structural data models are verified by using partial least squares regression (PLS), which supports all the hypotheses empirically.
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In the beginning of 2020, COVID-19 pandemic emerged in many regions of China, and the spring semester of primary and middle schools was postponed At the call of "Suspension of Classes but not Learning" by MOE, all educational institutes adopted the online learning methods. However, the home-based online learning lacks teacher supervision, peer support, classroom environment constraints. These intensify students' attention difficulty when compared with face-to-face learning in the classroom, which makes students' learning engagement more important to ensure the learning effect. According to online focus group interviews with the education experts and K-12 teachers respectively, the researchers found out some possible influencing factors to K-12 students' online learning engagement: perceived teacher involvement, perceived parental involvement, students' self-discipline, and student emotion. Therefore, this study proposes a prediction model from the above four aspects. By using multivariate linear regression analysis and variance analysis, this study finds: (1) Perceived teacher involvement, perceived parent support, student selfdiscipline and student emotion all have significant positive effects on online learning engagement. (2) There are significant differences in students' online learning engagement for different learning stages and different network environments at home Students' online learning engagement has no significant difference between urban and rural areas.
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Background: The coronavirus disease 2019 (COVID-19) struck China from late 2019 before its rapid spread across the country. Tianjin, as one of the largest cites in the north of China, reported a number of confirmed COVID-19 cases shortly after its outbreak in Wuhan province. After the pandemic was brought under control in May, strict control measures were put in place as routine to prevent cross-infection, which contributed to the change in vascular access practice. Therefore, a retrospective study was conducted to evaluate the impact of COVID-19 on vascular access in non-hot-spot region, north China. Methods: In this multicenter cross-sectional study, vascular access data was collected from the hemodialysis patients treated at 52 hospitals in Tianjin from 1 January to 14 Decmeber 2020. The practice of vascular access was estimated during the outbreak of Covid-19 since late 2019. Results: Among the 6885 hemodialysis patients included, 4719 arteriovenous fistulas were identified as the main type of vascular access, accounting for 68.54%. While 2114 patients (30.7%) had tunneled cuffed catheter. The proportion of arteriovenous graft reached as low as 1%. Overall, 1819 vascular access sites were placed in the patients newly diagnosed with uremia, of whom 990 (54.5%) underwent catheter insertion, 811 (44.6%) underwent AVF creation, and only 18 AVGs were created. In addition, the proportion of vascular access sites performed in general hospitals was 88.6%. During the period, tempt catheter insertion was carried out for 1371 (75%) incident hemodialysis patients. Due to stenosis of AVF, percutaneous transluminal angioplasty was conducted for 83 patients. However, no patient got diagnosed with Covid-19. Conclusions: Catheter was the primary vascular access type during the pandemic and the rate of catheter use for incident patients was high. Most of vascular access creation was carried out in general hospitals while the numbers of AVG and PTA were relatively low.
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In recent years, online teaching has become a hot topic in K-12 education reform. Based on the expectation confirmation model of information systems continuance (ECM-IS), four individual characteristic factors of self-efficacy, innovation, perceived risk and information literacy as well as two external environmental factors of subjective norms and facilitating conditions were introduced to build a theoretical model of factors affecting teachers' continuance intention of online teaching from the perspective of technology-individual-environment. This study tested model encompassing nine variables through empirical research. Data were collected on a sample of 59156 K-12 teachers who have had an online teaching experience during COVID-19 using an online questionnaire. Data were modelled using the partial least squares-structural equation model (PLS-SEM) to test the hypotheses. Results indicated that perceived usefulness and satisfaction in the ECM-IS model have significant effect on teachers' continuance intention while self-efficacy, information literacy, innovation and subjective norms were found to significantly affect teachers' continuance intention. However, perceived risk and facilitating conditions have no effect on continuance intention. According to the results, there are some suggestions for better online teaching effects: improving hardware facilities and software resources, innovating teacher training and research methods, and optimizing online teaching service supply. © 2021 IEEE.
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College students are the online learning subjects in the Covid-19 pandemic and their preferences towards online learning have certain reference value for the subsequent improvement of online learning platforms and the optimization of online teaching and learning. The study combined information literacy with Uses and Gratifications Theory, characterized students' online learning preferences with usage, gratifications, and acceptance, and introduced online interaction to construct an influencing factor model for university students' preferences towards online learning. The partial least squares method was used to verify the measurement model and structural model. All the research hypotheses are supported by the empirical research. © 2021 IEEE.