Actual Case Data Research Articles

Spatial-temporal distribution of neglected tropical diseases burdens in China from 2005 to 2020

BackgroundOut of the 21 neglected tropical diseases (NTDs) listed by the World Health Organization, 15 affect the People’s Republic of China. Despite significant achievements in controlling NTDs, comprehensive assessments of the disease burden based on actual case data and detailed information on spatial and temporal dynamics are still lacking. This study aims to assess the disease burden and spatial–temporal distribution of NTDs in China from 2005 to 2020, to provide a reference for the formulation of national health agendas in line with the global health agenda, and guide resource allocation.MethodsThe number of cases and deaths of major NTDs in China from 2005 to 2020 were downloaded from the China Public Health Science Data Center (https://www.phsciencedata.cn/Share/index.jsp) of the Chinese Center for Disease Control and Prevention and relevant literatures. Simplified formulas for disability-adjusted life years (DALYs) helped estimate the years of life lost (YLLs), years lived with disability (YLDs), and total DALYs. Spatial autocorrelation analysis of the average NTDs burden data for the years 2005 to 2020 was evaluated using Moran's I statistic.ResultsChina's overall NTDs burden decreased significantly, from 245,444.53 DALYs in 2005 to 18,984.34 DALYs in 2020, marking a reduction of 92.27%. In 2005, the DALYs caused by schistosomiasis and rabies represent a substantial proportion of the total disease burden, accounting for 65.37% and 34.43% respectively. In 2015, Hunan and Sichuan provinces had the highest diversity of NTDs, with 9 and 8 number of different NTDs reported respectively. And the highest disease burden was observed in Sichuan (242,683.46 DALYs), Xizang Zizhiqu (178,318.99 DALYs) and Guangdong (154,228.31 DALYs). The “high-high” clustering areas of NTDs were mainly in China's central and southern regions, as identified by spatial autocorrelation analysis.ConclusionsChina has made unremitting efforts in the prevention and control of NTDs, and the disease burden of major NTDs in China has decreased significantly. Using the One Health concept to guide disease prevention and control in the field to effectively save medical resources and achieve precise intervention.Graphical abstract

Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy

To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

Comparative Epidemiological Assessment of Monkeypox Infections on a Global and Continental Scale Using Logistic and Gompertz Mathematical Models.

The monkeypox virus (MPXV) has caused an unusual epidemiological scenario-an epidemic within a pandemic (COVID-19). Despite the inherent evolutionary and adaptive capacity of poxviruses, one of the potential triggers for the emergence of this epidemic was the change in the status of orthopoxvirus vaccination and eradication programs. This epidemic outbreak of HMPX spread worldwide, with a notable frequency in Europe, North America, and South America. Due to these particularities, the objective of the present study was to assess and compare cases of HMPX in these geographical regions through logistic and Gompertz mathematical modeling over one year since its inception. We estimated the highest contagion rates (people per day) of 690, 230, 278, and 206 for the world, Europe, North America, and South America, respectively, in the logistic model. The equivalent values for the Gompertz model were 696, 268, 308, and 202 for the highest contagion rates. The Kruskal-Wallis Test indicated different means among the geographical regions affected by HMPX regarding case velocity, and the Wilcoxon pairwise test indicated the absence of significant differences between the case velocity means between Europe and South America. The coefficient of determination (R2) values in the logistic model varied from 0.8720 to 0.9023, and in the Gompertz model, they ranged from 0.9881 to 0.9988, indicating a better fit to the actual data when using the Gompertz model. The estimated basic reproduction numbers (R0) were more consistent in the logistic model, varying from 1.71 to 1.94 in the graphical method and from 1.75 to 1.95 in the analytical method. The comparative assessment of these mathematical modeling approaches permitted the establishment of the Gompertz model as the better-fitting model for the data and the logistic model for the R0. However, both models successfully represented the actual HMPX case data. The present study estimated relevant epidemiological data to understand better the geographic similarities and differences in the dynamics of HMPX.

Research on the Group Innovation Information-Sharing Strategy of the Industry–University–Research Innovation Alliance Based on an Evolutionary Game

Based on various factors in information sharing between innovation alliance groups, this paper analyzes innovation information sharing under the influence of various factors and its evolutionary path to provide a reference for the decision-making of innovation information sharing between innovation alliance groups. Firstly, the paper analyzes the main factors influencing alliance group innovation information-sharing behavior. Secondly, based on the evolutionary game theory, an evolutionary game model of innovative information sharing under the cooperative trust degree of group decision-makers is constructed, and the related stable strategies are given. Finally, the sensitivity of innovative information-sharing strategies to key influencing factors is analyzed with the help of actual case data. The results show that a reasonable amount of innovation information sharing and fair profit distribution can improve the innovation information-sharing behavior among the industry–university–research alliance. Considering the trust degree of vertical partners, the alliance group pays more attention to the profit distribution ratio of collaborative innovation of the innovation alliance than the profit amount. When horizontal partner trust is considered, horizontal cooperation trust can promote information sharing among enterprises, universities, and research institutes in the short term but impedes information sharing among groups in the long term.

Multidimensional knowledge discovery of cultural relics resources in the Tang tomb mural category

PurposeCurrently, the Tang tomb mural cultural relic resources are presented in a multi-source and heterogeneous manner, with a lack of effective organization and sharing between resources. Therefore, this study aims to propose a multidimensional knowledge discovery solution for Tang tomb mural cultural relic resources.Design/methodology/approachTaking the Tang tomb murals collected by the Shaanxi History Museum as an example, based on clarifying the relevant concepts of Tang tomb mural resources and considering both dynamic and static dimensions, a top-down approach was adopted to first construct an ontology model of Tang tomb mural type cultural relics resources. Then, the actual case data was imported into the Neo4J graph database according to the defined pattern hierarchy to complete the static organization of knowledge, and presented in a multimodal form in knowledge reasoning and retrieval. In addition, geographic information system (GIS) technology is used to dynamically display the spatiotemporal distribution of Tang tomb mural resources, and the distribution trend is analysed from a digital humanistic perspective.FindingsThe multi-dimensional knowledge discovery of Tang tomb mural cultural relics resources can help establish the correlation and spatiotemporal relationship between resources, providing support for semantic retrieval and navigation, knowledge discovery and visualization and so on.Originality/valueThis study takes the murals in the collection of the Shaanxi History Museum as an example, revealing potential knowledge associations in a static and intelligent way, achieving knowledge discovery and management of Tang tomb murals, and dynamically presents the spatial distribution of Tang tomb murals through GIS technology, meeting the knowledge presentation needs of different users and opening up new ideas for the study of Tang tomb murals.

Nanophotonic reservoir computing for COVID-19 pandemic forecasting.

The coronavirus disease 2019 (COVID-19) has spread worldwide in unprecedented speed, and diverse negative impacts have seriously endangered human society. Accurately forecasting the number of COVID-19 cases can help governments and public health organizations develop the right prevention strategies in advance to contain outbreaks. In this work, a long-term 6-month COVID-19 pandemic forecast in second half of 2021 and a short-term 30-day daily ahead COVID-19 forecast in December 2021 are successfully implemented via a novel nanophotonic reservoir computing based on silicon optomechanical oscillators with photonic crystal cavities, benefitting from its simpler learning algorithm, abundant nonlinear characteristics, and some unique advantages such as CMOS compatibility, fabrication cost, and monolithic integration. In essence, the nonlinear time series related to COVID-19 are mapped to the high-dimensional nonlinear space by the optical nonlinear properties of nanophotonic reservoir computing. The testing-dataset forecast results of new cases, new deaths, cumulative cases, and cumulative deaths for six countries demonstrate that the forecasted blue curves are awfully close to the real red curves with exceedingly small forecast errors. Moreover, the forecast results commendably reflect the variations of the actual case data, revealing the different epidemic transmission laws in developed and developing countries. More importantly, the daily ahead forecast results during December 2021 of four kinds of cases for six countries illustrate that the daily forecasted values are highly coincident with the real values, while the relevant forecast errors are tiny enough to verify the good forecasting competence of COVID-19 pandemic dominated by Omicron strain. Therefore, the implemented nanophotonic reservoir computing can provide some foreknowledge on prevention strategy and healthcare management for COVID-19 pandemic.

Children’s participation in the child protection system: Are young people from poor families less likely to be heard?

Decisions in child protection affect children’s and young people’s lives substantially and sustainably. For young people to participate in these decisions is an ethical requirement, prominently coded in the UN Convention on the Rights of the Child. Although awareness of the importance of child participation and research thereon have grown, predictors of child participation have not been formalized in a conceptual model and studies on the topic have primarily relied on narrative accounts or fictional vignettes rather than actual case data. This article addresses the lack of conceptual modeling by presenting an approach that takes into account three domains of downstream predictors on the degree of child participation in the decision-making process of a child protection case: (a) External constraints; (b) professionals’ willingness and ability to facilitate the child’s participation; (c) the child’s willingness and ability to participate. It further addresses the lack of actual case data in child participation research, focusing on the understudied predictor of family poverty. Analyses are based on a sample of case files of n = 264 children in five Swiss CPS agencies. Outcomes and predictors were extracted from case files with a predefined coding system. Findings suggest that the raised awareness has so far not fully trickled down to an increase in real-life opportunities of participation for young people: The child’s subjective view was documented in the case worker’s report half of the time (48.9 %). Corroborating previous evidence, adolescents were much more likely to have their views included than younger children (OR = 3.715, p =.002). Case workers were less inclined to include the child’s views if the child came from a poor family (OR = 0.326, p =.003). We conclude by suggesting options for improving child participation, highlighting that protection of young people does not have to contradict participation.

Reactive–diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive–diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19.

A High-Precision Method for Evaluating Sector Capacity in Bad Weather Based on an Improved WITI Model

The rapid development of the civil aviation industry has increased the pressure on airspace resources in China. The traditional sector capacity assessment method does not take into account the impact of bad weather, resulting in flight plans often deviating markedly from the predicted plans, causing flight delays and affecting the punctuality rate of flights. To solve this issue, we propose a novel evaluation method based on an improved Weather-Impacted Traffic Index (WITI) model to calculate sector capacity. The WITI model is optimized in order to calculate the weather-influence coefficients under different types of bad weather. These coefficients were also considered in a controller workload model. Finally, the model was trained using a deep-neural-network algorithm, which is combined with a linear regression algorithm to calculate sector capacity under different bad weather conditions. The novel approach leads to the output results being within a specified error range, which greatly improves their accuracy. This method was applied to the actual case data of Yinchuan Hedong International Airport to consider different types of bad weather and quantify their severity, which more specifically assesses the sector capacity under the condition of bad weather.

Dengue risk zone mapping of Thiruvananthapuram district, India: a comparison of the AHP and F-AHP methods.

Dengue fever, which is spread by Aedes mosquitoes, has claimed many lives in Kerala, with the Thiruvananthapuram district bearing the brunt of the toll. This study aims to demarcate the dengue risk zones in Thiruvananthapuram district using the analytical hierarchy process (AHP) and the fuzzy-AHP (F-AHP) methods. For the risk modelling, geo-environmental factors (normalized difference vegetation index, land surface temperature, topographic wetness index, land use/land cover types, elevation, normalized difference built-up index) and demographic factors (household density, population density) have been utilized. The ArcGIS 10.8 and ERDAS Imagine 8.4 software tools have been used to derive the risk zone maps. The area of the risk maps is classified into five zones. The dengue risk zone maps were validated using dengue case data collected from the Integrated Disease Surveillance Programme portal. From the receiver operating characteristic (ROC) curve analysis and the area under the ROC curve (AUC) values, it is proved that the F-AHP method (AUC value of 0.971) has comparatively more prediction capability than the AHP method (AUC value of 0.954) in demarcating the dengue risk zones. Also, based on the comparison of the risk zone map with actual case data, it was confirmed that around 82.87% of the dengue cases occurred in the very high and high-risk zones, thus proving the efficacy of the model. According to the dengue risk map prepared using the F-AHP model, 9.09% of the area of Thiruvananthapuram district is categorized as very high risk. The prepared dengue risk maps will be helpful for decision-makers, staff with the health, and disaster management departments in adopting effective measures to prevent the risks of dengue spread and thereby minimize loss of life.

Localization Method and Finite Element Modelling of the Mid-Point Anchor of High-Speed Railway Distributed in Long Straight Line with Large Slope

In order to ensure the safe and reliable operation of a high-speed railway, the precise positioning of the mid-point anchor in the catenary is very important. In view of the two problems in the calculation of the mid-point anchor position of the catenary in a long ramp section, the calculation accuracy is low, and the calculation of the central anchor clamp position is lacking. In this study, the predetermined location of the mid-point anchor is chosen based on the mid-point anchor location principle and the line condition, and the range of the allowable error of the mid-point anchor setting is determined according to the predetermined position of the mid-point anchor. Secondly, by considering the impact of the line ramp and using the measured span length, the tension difference of the clue in the direction of the line is calculated. Then, the theoretical location of the mid-point anchor clamp is determined using the downhill component and the tension difference. Finally, the theoretical position of the clamp is corrected according to the setting of the dropper to obtain the corrected position of the clamp. An FEM (finite element method) of the catenary is established in ANSYS software to calculate the height difference between the messenger cable and the contact wire at the point of the mid-point anchor setting, and then the length of the mid-point anchor rope is obtained. Through the calculation of actual case data, the maximum value of the relative error of the location of the mid-point anchor obtained by this proposed method is very small compared with the actual position, which verifies the effectiveness of this method.

A Two-Stage Planning Optimization Study of an Integrated Energy System Considering Uncertainty

In the context of a rapidly evolving integrated energy system (IES), taking into account the uncertainty of the renewable energy output can make integrated energy system planning more realistic. In view of this, this paper proposes an integrated energy system planning approach that takes uncertainty into account. Firstly, in order to accurately describe the renewable energy output scenarios, this paper describes the IES model and introduces the scenario analysis methods used. Secondly, an integrated energy system equipment output model is constructed, the corresponding constraints and objective functions are set, an improved particle swarm-ant colony optimization algorithm is used for the solution, and a planning solution strategy considering uncertainty is proposed. Finally, the above conclusions are verified by the actual case data of a park, and the results show that the method proposed in this paper is economical and reasonable.

A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors.

We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios.This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.

Wastewater-based epidemiology: a new frontier for tracking environmental persistence and community transmission of COVID-19.

Recent research in many parts of the world has pointed towards evidence of SARS-CoV-2 RNA in both treated and raw municipal wastewater discharged by communities. Therefore, concerns regarding it being a possible enteric virus are abundant. Past history of SARS-CoV-1 outbreaks and viral survival information helps in establishing information regarding possible viral infectivity and survival of SARS-CoV-2. The paper examines the existing strategies and techniques including the efficacy of laboratory-based RT-qPCR technique for tracking environmental persistence and community transmission of COVID-19. Analysis of studies targeting untreated and treated wastewater as source of samples is carried out. The analysis shows that untreated samples were mostly positive for SARS-CoV-2 RNA in the target studies. Infectivity estimation from viral load data was found to be about two orders of magnitude higher than actual case data in one of the studies. Additionally, relevant research on environmental survivability of SARS-CoV-2 and possible gaps are examined. Biosensors and excretion metabolite tracking in viral detection are also examined, which hold tremendous importance for future research. Wastewater-based epidemiology (WBE) shows incredible promise in the near future for tracking environmental persistence and community transmission of highly infectious diseases such as SARS-CoV-2. With limited research available on SARS-CoV-2 with regard to WBE, it is imperative that focus be established on the evidence-based targeted studies.

Modelling network public opinion polarization based on SIR model considering dynamic network structure

Under the dispute between China and the United States, the international field of public opinion is dominated by increasing tensions, and the network rumors triggered by the COVID-19 epidemic are intensifying. In view of the above-mentioned context, this paper focuses on the development and the evolution process of public opinions. Since the evolution of public opinion is often accompanied by the spread and diffusion of information, this paper combines the process of information diffusion with the development process of polarization behavior, and brings in the dynamic network and the timeliness factor of public opinion dissemination, so as to better explore the polarization process of public opinion under the dynamic network. Then, this paper focuses on the analysis of the parameters of the model and through the dynamic adjustment of parameters, finding out the main factors that affect the trend and development of network public opinion. In addition, this paper introduces an actual case, and takes the actual case data as the support to demonstrate the reliability and practical application value of the model. Finally, based on the simulation results and analysis of actual cases, this paper puts forward the corresponding preventive measures to alleviate the polarization behavior of the group.

PREDICTION OF KARST GROUND COLLAPSE BASED ON FACTOR ANALYSIS-GA-ELM MODEL

Karst ground collapse is the result of many factors, and has the characteristics of concealment and abruptness. In order to predict karst ground collapse quickly and accurately, this paper presents a karst ground collapse prediction model which is based on factor analysis, genetic algorithm(GA) and extreme learning machine(ELM). Firstly, we identify eight typical influencing factors in the karst area. Then, we extract five common factors by factor analysis. Finally, we input the common factors into GA-ELM model and predict karst ground collapse. The sample data contains 20 groups of actual case data, of which 12 groups as training sets and the other 8 groups as test sets. The conclusions are as follows: Factor analysis can not only simplify the ELM model network structure, but also improve the prediction accuracy under the same number of hidden layer of neuron nodes. In the case of a small number of samples, the prediction accuracy of the ELM model can be improved by increasing the number of hidden layer neuron nodes. Compared with the ELM model, the prediction accuracy of GA-ELM model is improved significantly, with stronger learning and prediction ability. The prediction method of karst ground collapse based on Factor Analysis-GA-ELM model is simple, accurate and efficient.

Research on index system of evaluation of Prefabricated Building Industrialization based on BIM Technology

Firstly, this paper analyzes the functions of the BIM platform, comprehensiv ely considers and extracts the required evaluation index of industrial efficiency of pref abricated buildings, and calculates the weight value. Then, the actual case data extracti on of BIM platform was applied, and the fuzzy comprehensive evaluation method was used to evaluate the efficiency of the case. Finally the structure was calculated and ana lyzed, so as to obtain the impact results of the prefabricated building industrialization e fficiency after the application of BIM platform.

Analysis of Shortcomings Found in Most Appropriate Housing Delivery Method for Ameliorating Customer Satisfaction

Identify most appropriate housing delivery method is one of the critical emerging issues in the developing due to poor standard of living in cities. The scale and speed of urbanization and high population growth rate in India will pose an unprecedented managerial and policy level challenge on housing quality in residential areas. On the other side many stock of house were found vacant or non-usable. Even uses of an appropriate housing delivery method, some gaps /shortcoming are identifying. If these gaps can fulfill these gaps and can enhance customer satisfaction. This study was developing by using actual construction case data in quantitative data analysis methods such as Estimate track Estimate and Analytical Hierarchical process. This research focuses on the housing areas as understand primarily by available local housing delivery methods. This aim of the research is to develop a index for evaluating housing delivery performance of housing areas to improve the customer satisfaction of existing as well as upcoming housing areas.

Curve Fitting and Least Square Analysis to Extrapolate for the Case of COVID-19 Status in Ethiopia

On 30 January 2020 World Health Organization (WHO), declared the novel corona virus as a Public Health Emergency of International Concern (PHEIC), COVID-19 virus as an epidemic transmitted virus. It was on 31 December 2019, the WHO China Country office was informed the cases of pneumonia unknown etiology detected in Wuhan city, Hubei Province of China. Just after WHO’s declaration, Ethiopia has taken different measures to protect from this public health emergency problem. The disease is human to human transmitted virus. It comes from outside of the country, so that it opens check points in different entrance of the country. However, in 13 March 2020 the first positive case was reported from Japanese man. The virus is continuing the transmission in the public progressively more. While this research has been working, within 90 days from the first case, the country reported 2506 positive cases, 35 deaths. The research has done after collecting the first 90 days of data in Ethiopian case. Daily report announced by Ethiopian MoH is based on the test. And hence, the reported data as positive cases with COVID-19 is not actual positive case data in the country. There for, this paper has contribution for planning and taking further measure on the viruses by demonstrating the next 90 days predictive data. I use best curve fitting analysis using python function of the module polyfit algorithm to predict the trend of COVID-19 cases in Ethiopia.

An Optimization Method of Controller Scheduling at Small and Medium-sized Airports Based on Fatigue Analysis

In recent years, a number of aviation accidents have caused the social attention to aviation safety. The serious shortage of controllers at China's small and medium-sized airports has become an important hidden danger of aviation accidents. It is necessary to make a scientific and reasonable scheduling scheme in order to solve this problem. Based on civil aviation air traffic management rules to the controllers workload, this article proposes a linear programming method to analysis of China's small and medium-sized airport controllers scheduling model. At the same time, the fatigue factor is accounted into and the model is solved by 0-1 integer programming. Using an actual case data from Wanzhou Airport, the proposed mothed satisfies the needs of the airport and reduce controllers work fatigue to achieve a win-win results.