2022 Multiple-country Monkeypox Outbreak and Its Importation Risk into China:An Assessment Based on the Risk Matrix Method

Objective To analyze the 2022 multiple-country monkeypox outbreak and assess its importation risk into China.Methods Data was from United States Centers for Disease Control and Prevention.We described the global epidemic and calculated concentration index to measure economic-related inequality.Importation risk into China was evaluated and ranked by using risk matrix method and Borda count method,respectively.Results As of July 29,2022,of 79 countries or territories,39(49.37%,39/79),17(21.52%,17/79),6(7.59%,6/79),12(15.19%,12/79),and 5(6.33%,5/79)country or territories identified cases<10,10-,51-,101-,and>1,000.There were economic-related health disparities exist in the distribution of cases(the concentration index=0.42,P=0.027),and the inequality disadvantageous to the rich(pro-poor).There were 12(15.38%,12/78),15(19.23%,15/78),6(7.69%,6/78),and 45(57.69%,45/78)countries or territories with extremely high,high,moderate,and low importation risk.United States and France ranked first with the highest Borda points of 156,and counts of zero.Conclusion Of 78 countries or territories,the key attention need be paid to the United States and France,relatively.As the epidemic progresses,preparing prevention and control measures to further reduce importation risk was crucial.

Global Epidemic of Ebola Virus Disease and the Importation Risk into China: An Assessment Based on the Risk Matrix Method

Objective To analyze the global epidemic status of the Ebola virus disease(EVD) and assess the importation risk into China.Methods Data from World Health Organization reports were used. We described the global epidemic status of EVD from 1976–2021, and assessed and ranked the importation risk of EVD from the diseaseoutbreaking countries into China using the risk matrix and Borda count methods, respectively.Results From 1976–2021, EVD mainly occurred in western and central Africa, with the highest cumulative number of cases(14,124 cases) in Sierra Leone, and the highest cumulative fatality rate(85%) in the Congo. Outbreaks of EVD have occurred in the Democratic Republic of the Congo and Guinea since 2018. The importation risk into China varies across countries with outbreaks of disease.The Democratic Republic of the Congo had an extremely high risk(23 Borda points), followed by Guinea and Liberia. Countries with a moderate importation risk were Nigeria, Uganda, Congo, Sierra Leone,Mali, and Gabon, while countries with a low importation risk included Sudan, Senegal, and Co te d’Ivoire.Conclusion China is under the risk of EVD importation with the globalization and severe epidemic status of EVD. Key attention need to be paid to the Democratic Republic of the Congo, Guinea, and Liberia. Therefore, it is necessary to prevent and prepare in advance for importation risk in China.

Technology Importation and Domestic Technology Acquisition in China from the Perspective of Innovation

Foreign and domestic technologies play different roles in a country's innovation. In recent years, Chinese firms have spent less on importing foreign technology and more on acquiring domestic technology. Based on the panel data model, state space model(SSM) and the Bayesian vector autoregressive model, this paper finds that the purchase of domestic technology plays a critical role in the innovation of high-tech firms with an elasticity coefficient higher than that of technology importation before 2010. China's high-tech firms can effectively absorb purchased domestic technology, which also brings about an increase in their independent R&D input and contributes more to innovation output than imported technology. Performance of technology importation is lackluster but shows an improving trend. Technology importation is not correlated with innovation output and has a substitutive effect with independent R&D input. China is yet to enhance its absorption of imported technology. Elasticity coefficient of technology importation turned positive in 2007 and increased year by year. China should promote synergy between technology importation and domestic technology acquisition.

Univariate Time-Series Analysis of Second-Hand Car Importation in Zambia

Zambia largely depends on the international second-hand car (SHC) market for their motor vehicle supply. The importation of Second hand Cars in Zambia presents a time series problem. The data used in this paper is monthly data on SHC importation from 1st January, 2014 to 31st December, 2016. Data was analyzed using Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA) models. The results showed that ARIMA (2, 1, 2) was the best fit for the SHC importation since its errors were smaller than those of the SES, DES and TES. The four error measures used were Root-mean-square error (RMSE), Mean absolute error (MAE), Mean percentage error (MPE) and Mean absolute percentage error (MAPE). The forecasts were also produced using the ARIMA (2, 1, 2) model for the next 18 months from January 2017. Although there is percentage increase of 90.6% from November 2015 to December 2016, the SHC importation generally is on the decrease in Zambia with percentage change of 59.5% from January 2014 to December 2016. The forecasts also show a gradual percentage decrease of 1.12% by June 2018. These results are more useful to policy and decision makers of Government departments such as Zambia Revenue Authority (ZRA) and Road Development Agency (RDA) in a bid to plan and execute their duties effectively.

Dengue outbreaks in Taiwan, 1998-2017: Importation, serotype and temporal pattern

Objective: To ascertain the role of imported cases and serotypes on dengue outbreaks in Taiwan which have been sporadic yet highly volatile during the past two decades, exhibiting record-breaking magnitude in recent years. Methods: Confirmed case and serotype data from Taiwan Centers for Disease Control during 1998-2017 were fully examined, with fitting of weekly and daily case data of each city/county to a mathematical model to pinpoint the waves of cases and their locations. Moreover, we quantify the timing of turning point and transmission potential of each wave and determine its circulating serotype, to ascertain any pattern or connection between the variations in circulating serotypes and the magnitude/transmissibility of outbreak. Results: While the number of imported case increased steadily during past two decades, the yearly number of indigenous cases fluctuated wildly. Moreover, while yearly percentages of serotypes for imported cases remains steady, that of indigenous cases does not exhibit any clear pattern. There was at least one wave of reported cases somewhere in Taiwan every year from 1998 to 2015, except in 2016-2017. The effective reproduction number R for all waves in all locations ranged from 1.14 to 2.87, with the exception of two Tainan waves, in 2010(3.95) and 2015(6.84). Four major outbreaks of over 2000 cases reveal circulation of one dominant serotype. Conclusions: Correlation between imported cases and indigenous outbreak prove to be difficult to ascertain, even with the availability of serotype data. However, although there had been occasional co-circulation of serotypes in one location, and for some years with different serotypes circulating in different locations, all major outbreaks of over 2 000 cases during the past two decades are due to circulation of mainly a single serotype, perhaps indicating greater transmission potential with one dominating serotype.

The risk of mpox importation and subsequent outbreak potential in Chinese mainland: a retrospective statistical modelling study

Background The 2022-2023 mpox(monkeypox)outbreak has spread rapidly across multiple countries in the non-endemic region,mainly among men who have sex with men(MSM).In this study,we aimed to evaluate mpox's importation risk,border screening effectiveness and the risk of local outbreak in Chinese mainland.Methods We estimated the risk of mpox importation in Chinese mainland from April 14 to September 11,2022 using the number of reported mpox cases during this multi-country outbreak from Global.health and the international air-travel data from Official Aviation Guide.We constructed a probabilistic model to simulate the effectiveness of a border screening scenario during the mpox outbreak and a hypothetical scenario with less stringent quarantine requirement.And we further evaluated the mpox outbreak potential given that undetected mpox infections were introduced into men who have sex with men,considering different transmissibility,population immunity and population activity.Results We found that the reduced international air-travel volume and stringent border entry policy decreased about 94% and 69% mpox importations respectively.Under the quarantine policy,15-19% of imported infections would remain undetected.Once a case of mpox is introduced into active MSM population with almost no population immunity,the risk of triggering local transmission is estimated at 42%,and would rise to>95% with over six cases.Conclusions Our study demonstrates that the reduced international air-travel volume and stringent border entry policy during the COvID-19 pandemic reduced mpox importations prominently.However,the risk could be sub-stantially higher with the recovery of air-travel volume to pre-pandemic level.Mpox could emerge as a public health threat for Chinese mainland given its large MSM community.

Risk of COVID-19 variant importation-How useful are travel control measures?

We consider models for the importation of a new variant COVID-19 strain in a location already seeing propagation of a resident variant.By distinguishing contaminations generated by imported cases from those originating in the community,we are able to evaluate the contribution of importations to the dynamics of the disease in a community.We find that after an initial seeding,the role of importations becomes marginal compared to that of community-based propagation.We also evaluate the role of two travel control measures,quarantine and travel interruptions.We conclude that quarantine is an efficacious way of lowering importation rates,while travel interruptions have the potential to delay the consequences of importations but need to be applied within a very tight time window following the initial emergence of the variant.

Beyond statistical significance:Embracing minimal clinically important difference for better patient care

The minimal clinically important difference(MCID)represents a pivotal metric in bridging the gap between statistical significance and clinical relevance,addressing the direct impact of medical interventions from the patient's perspective.This comprehensive review analyzes the evolution,applications,and challenges of MCID across medical specialties,emphasizing its necessity in ensuring that clinical outcomes not only demonstrate statistical significance but also offer genuine clinical utility that aligns with patient expectations and needs.We discuss the evolution of MCID since its inception in the 1980s,its current applications across various medical specialties,and the methodologies used in its calculation,highlighting both anchor-based and distribution-based approaches.Furthermore,the paper delves into the challenges associated with the application of MCID,such as methodological variability and the interpretation difficulties that arise in clinical settings.Recommendations for the future include standardizing MCID calculation methods,enhancing patient involvement in setting MCID thresholds,and extending research to incorporate diverse global perspectives.These steps are critical to refining the role of MCID in patient-centered healthcare,addressing existing gaps in methodology and interpretation,and ensuring that medical interventions lead to significant,patient-perceived improvements.

Variations in quantifying patient reported outcome measures to estimate treatment effect

In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.

Surveillance and Analysis of SARS-CoV-2 Variant Importation—China,January–June 2022

Introduction:The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)Omicron variant is the dominant circulating strain worldwide.To assess the importation of SARS-CoV-2 variants in the mainland of China during the Omicron epidemic,the genomic surveillance data of SARS-CoV-2 from imported coronavirus disease 2019(COVID-19)cases in the mainland of China during the first half of 2022 were analyzed.Methods:Sequences submitted from January to July 2022,with a collection date before June 30,2022,were incorporated.The proportions of SARS-CoV-2 variants as well as the relationships between the origin and destination of each Omicron imported case were analyzed.Results:4,946 sequences of imported cases were submitted from 27 provincial-level administrative divisions(PLADs),and the median submission interval was within 1 month after collection.In 3,851 Omicron sequences with good quality,1 recombinant(XU)and 4 subvariants under monitoring(BA.4,BA.5,BA.2.12.1,and BA.2.13)were recorded,and 3 of them(BA.4,BA.5,and BA.2.12.1)caused local transmissions in the mainland of China later than that recorded in the surveillance.Omicron subvariants dominated in the first half of 2022 and shifted from BA.1 to BA.2 then to BA.4 and BA.5.The percentage of BA.2 in the imported SARS-CoV-2 surveillance data was far higher than that in the Global Initiative on Sharing All Influenza Data(GISAID).The imported cases from Hong Kong Special Administrative Region,China,accounted for 32.30%of Omicron cases sampled,and 98.71%of them were BA.2.Conclusions:The Omicron variant showed the intra-Omicron evolution in the first half of 2022,and all of the Omicron subvariants were introduced into the mainland of China multiple times from multiple different locations.

中国古代槟榔文化的接受与传播

作为舶来品的槟榔,在饮食、医药、赋税等方面扮演过重要的角色,具有丰厚的历史文化。梳理文献发现,两汉三国时期,槟榔视作“奇草异木”记载在各种《异物志》中,开始被接受;南北朝时期,槟榔是贵族士宦的“奢侈品”,唐代受佛教平等思想影响日渐向大众传播,宋代逐渐成为百姓家的“寻常物”。槟榔之所以能被接受与传播,并在海南用于婚俗,是因其本身的物质文化之外,还与其因贵异而用于重要的人际礼仪、文人墨客极力推崇渲染和赋予了槟榔君子品格等精神文化有着莫大的关系。

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

基于Null Importance和Stacking模型的知识追踪研究

为应对基于游戏的学习平台在知识追踪应用方面的不足,本研究利用Field Day Lab提供的教育游戏用户日志进行深入分析。采用方差法和Null Importance方法对数据集进行降维处理,并结合K折交叉验证与LightGBM算法,建立了一个高效的预测模型。此外,通过集成Logistic模型,构建起Stacking模型。研究表明,该模型在验证集上的Macro-F1值显著提升至0.699,同时也在测试集上显示出优异的泛化能力。本研究为教育游戏领域的知识追踪提供了创新方法,并为游戏开发与教育实践提供了宝贵参考,支持教育游戏的开发者为学生创造更有效的学习体验。

Identification of Important FPGA Modules Based on Complex Network

The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing hardware.However,existing defense or detection approaches often require additional circuitry to perform security verification,and are thus constrained by time and resource limitations.Considering the scale of actual engineering tasks and tight project schedules,it is usually difficult to implement designs for all modules in field programmable gate array(FPGA)circuits.Some studies have pointed out that the failure of key modules tends to cause greater damage to the network.Therefore,under limited conditions,priority protection designs need to be made on key modules to improve protection efficiency.We have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems,to identify key modules in FPGA systems.For the single FPGA designs,considering the topological structure,network characteristics,and directionality of FPGA designs,we propose a node importance evaluationmethod based on the technique for order preference by similarity to an ideal solution(TOPSIS)method.Then,for the multi-FPGA designs,considering the influence of nodes in intra-layer and inter-layers,they are constructed into the interdependent network,and we propose a method based on connection strength to identify the important modules.Finally,we conduct empirical research using actual FPGA designs as examples.The results indicate that compared to other traditional indexes,node importance indexes proposed for different designs can better characterize the importance of nodes.

Development of endemicity and conservation importance indices for tropical forests and the floral diversity assessment of Mt.Natoo in Benguet,Philippines

The scientific community faces the challenge of measuring progress toward biodiversity targets and indices have been traditionally used.However,recent inventories in secondary tropical mountain forests using traditional biodiversity indices have yielded results that are indistinct with primary ones.This shows the need to develop complementary indices that goes beyond species count but integrates the distribution and conservation status of the species.This study developed endemicity and conservation importance index for tropical forest that incorporated the distribution and conservation status of the species.These indices were applied to Mt.Natoo,a remnant primary mossy forest in Buguias,Benguet,Philippines,that resulted to endemicity index of 81.07 and conservation importance index of 42.90.Comparing these with secondary forest sites with comparable Shannon-Wiener,Simpson,Evenness and Margalef’s indices,our endemicity and conservation indices clearly differentiates primary forest(our study site)with higher values from secondary forests with much lower values.Thus,we are proposing these indices for a direct but scientifically-informed identification of specific sites for conservation and protection in tropical forests.Additionally,our study documented a total of 168 vascular plant species(79 endemic and 12 locally threatened species)in Mt.Nato-o.Majority are of tropical elements for both generic and species levels with some temperate elements that could be attributed to the site's high elevation and semi-temperate climate.These are important baseline information for conservation plans and monitoring of tropical mossy forests.

Prognostic prediction models for postoperative patients with stageⅠtoⅢcolorectal cancer based on machine learning

BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.

Treatment of a patient with severe cerebral malaria during the COVID-19 pandemic in China:A case report

BACKGROUND On June 30,2021,China received certification from the World Health Organization for malaria elimination.However,this certification does not signify the absence of malaria within China.Due to the increasing frequency of international exchanges and collaborations,the threat of imported malaria persists in China.Consequently,the prevention and control of imported malaria have become a primary focus for our country to maintain its malaria elimination status.CASE SUMMARY Herein,we present a case report of a 53-year-old Chinese man who worked in Africa for nearly two years.He was diagnosed with malaria in the Democratic Republic of the Congo between November 19 and November 23,2022.After receiving effective treatment with oral antimalarial drugs,his condition improved,allowing him to return to China.He was later admitted to our hospital on January 12,2023,during the coronavirus disease 2019 pandemic in Huangshi,China.Through a thorough evaluation of the patient's symptoms,clinical signs,imaging and laboratory test results,and epidemiological data,he was rapidly diagnosed with severe cerebral malaria.The patient underwent successful treatment through a series of intensive care unit interventions.CONCLUSION The successful treatment of this imported case of severe cerebral malaria provides a valuable reference for managing patients with similar malaria infections and has significant clinical implications.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.