An analysis of agarwood trade patterns,historical perspectives,and species identification challenges:repercussions for importing nations

Background:Agarwood,primarily derived from the Aquilaria and Gyrinops genera,holds significant economic importance.However,there is a lack of comprehensive investigations providing guidance to importing nations regarding cultivation quantities and expected yields of Agarwood from distinct species.This study aims to address this gap by exploring the historical context and trade evolution of Agarwood,highlighting its global importance,and the challenges associated with securing accurate species information.Method:On-site visits to Agarwood cultivation sites were conducted to gain a nuanced understanding of Aquilaria species and their cultivation requirements.Additionally,a thorough analysis of global export and import data for Agarwood products over the last decade was undertaken.Results:China Mainland emerged as the leading exporter of Agarwood,averaging an annual export value of USD 1 million.India’s substantial exports challenge the prevailing notion of limited Agarwood production within its borders.Hong Kong and Singapore are pivotal distribution hubs,while Hong Kong and Taipei feature prominently as import destinations.Our analysis uncovers anomalies in the representation of Agarwood producers from 2001 to 2008,suggesting potential misclassification of Aquilaria Agarwood as Gyrinops in global export information.These findings underscore the urgency of investigating classification and reporting practices in the Agarwood trade.Furthermore,A.filaria emerges as a notable source,while A.malaccensis is decline in prominence.Conclusion:This study provides crucial insights for policymakers,stakeholders,and industry players seeking to make informed decisions in the Agarwood trade landscape.The results highlight the need for accurate species identification,classification,and reporting practices to ensure sustainable cultivation and trade of Agarwood.

Resilience based importance measure analysis for SoS

In a system of systems(SoS),resilience is an important factor in maintaining the functionality,stability,and enhancing the operation effectiveness.From the perspective of resilience,this paper studies the importance of the SoS,and a resilience-based importance measure analysis is conducted to provide suggestions in the design and optimization of the structure of the SoS.In this paper,the components of the SoS are simplified as four kinds of network nodes:sensor,decision point,influencer,and target.In this networked SoS,the number of operation loops is used as the performance indicator,and an approximate algorithm,which is based on eigenvalue of the adjacency matrix,is proposed to calculate the number of operation loops.In order to understand the performance change of the SoS during the attack and defense process in the operations,an integral resilience model is proposed to depict the resilience of the SoS.From different perspectives of enhancing the resilience,different measures,parameters and the corresponding algorithms for the resilience importance of components are proposed.Finally,a case study on an SoS is conducted to verify the validity of the network modelling and the resiliencebased importance analysis method.

Subinterval Decomposition-Based Interval Importance Analysis Method

The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty.When an input variable is described by a specific interval rather than a certain probability distribution,the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods.Generally,the non-probabilistic importance analysis methods involve the Monte Carlo simulation(MCS)and the optimization-based methods,which both have high computational cost.In order to overcome this problem,this study proposes an interval important analytical method avoids the time-consuming optimization process.First,the original performance function is decomposed into a combination of a series of one-dimensional subsystems.Next,the interval of each variable is divided into several subintervals,and the response value of each one-dimensional subsystem at a specific input point is calculated.Then,the obtained responses are taken as specific values of the new input variable,and the interval importance is calculated by the approximated performance function.Compared with the traditional non-probabilistic importance analysis method,the proposed method significantly reduces the computational cost caused by the MCS and optimization process.In the proposed method,the number of function evaluations is equal to one plus the sum of the subintervals of all of the variables.The efficiency and accuracy of the proposed method are verified by five examples.The results show that the proposed method is not only efficient but also accurate.

Importance Analysis of a Multi-state System Based on Direct Partial Logic Derivatives and Multi-valued Decision Diagrams

Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.

Importance Measure Analysis for Use in Dynamic Performance Design of a Co-cured Composite Damping Instrument Panel

For the best dynamic performance of a co-cured composite damping instrument panel with light weight and high strength,a multilayer sandwich structure with polymethacrylimide( PMI) foam combined with embedded and co-cured composite damping structure is proposed. The structue can maintain the excellent mechanical properties of composite materials,and achieve the damping and light effect at the same time. Input variables which may affect the dynamic performance of the instrument panel were selected and variance based importance measure was analyzed through multifinite element method( FEM) analysis. Using the results of the importance measure analysis,with other design requirements,the important design variable was optimized and an instrument panel with the best dynamic performance under the requirements of light weight and high strength was obtained. The structure of the instrument panel can provide reference for the design of precision,high speed,and dynamic composite component. The importance measure analysis of dynamic performance of the instrument panel can provide a reference for relative design.

Measuring Global Supply Chain Vulnerabilities Using Trade Network Analysis Method

With the trade network analysis method and bilateral country-product level trade data of 2017-2020,this paper reveals the overall characteristics and intrinsic vulnerabilities of China’s global supply chains.Our research finds that first,most global supply-chain-vulnerable products are from technology-intensive sectors.For advanced economies,their supply chain vulnerabilities are primarily exposed to political and economic alliances.In comparison,developing economies are more dependent on regional communities.Second,China has a significant export advantage with over 80%of highly vulnerable intermediate inputs relying on imports of high-end electrical,mechanical and chemical products from advanced economies or their multinational companies.China also relies on developing economies for the import of some resource products.Third,during the trade frictions from 2018 to 2019 and the subsequent COVID-19 pandemic,there was a significant reduction in the supply chain vulnerabilities of China and the US for critical products compared with other products,which reflects a shift in the layout of critical product supply chains to ensure not just efficiency but security.China should address supply chain vulnerabilities by bolstering supply-side weaknesses,diversifying import sources,and promoting international coordination and cooperation.

2007 China Rare Earths Import & Export Analysis and Suggestions(continued)

4. Increasing import of rare earth resources products As a big producer and an important export country of rare earth products for years, rare earths import is in an auxiliary position in China. Import volume is rather small. However, since the strengthened macro control measures

China Import & Export Analysis in 2006 and Outlook in 2007 (Continued)

Problems existed in the import & export of Chinese rare earths in 2006 Although it was holistically stable on the export of Chinese rare earth products, there were some problems that are reflected in the following aspects:

Sensitivity of Sample for Simulation-Based Reliability Analysis Methods

In structural reliability analysis,simulation methods are widely used.The statistical characteristics of failure probability estimate of these methods have been well investigated.In this study,the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample,called‘contribution indexes’,are proposed to measure the contribution of sample.The contribution indexes in four widely simulation methods,i.e.,Monte Carlo simulation(MCS),importance sampling(IS),line sampling(LS)and subset simulation(SS)are derived and analyzed.The proposed contribution indexes of sample can provide valuable information understanding the methods deeply,and enlighten potential improvement of methods.It is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples,which are the main factors to the efficiency of the methods.Moreover,numerical examples are used to validate these findings.

Availability Importance Measures for Virtualized System with Live Migration

This paper presents component importance analysis for virtualized system with live migration. The component importance analysis is significant to determine the system design of virtualized system from availability and cost points of view. This paper discusses the importance of components with respect to system availability. Specifically, we introduce two different component importance analyses for hybrid model (fault trees and continuous-time Markov chains) and continuous-time Markov chains, and show the analysis for existing probabilistic models for virtualized system. In numerical examples, we illustrate the quantitative component importance analysis for virtualized system with live migration.

Time-Dependent Reliability Analysis of Offshore Structures

During the life of an offshore structure, its structural strength declines due to various kinds of damages related to the time factor. In this paper, four major kinds of damages, including damages caused by fatigue, dent, corrosion and marine life, are discussed. Based on these analyses, formulas for the evaluation of the damaged structure reliability are derived. Furthermore the computer program ISM for the analysis of structural reliability is developed by the use of Advanced First Order Second Moment method and Monte-Carlo Importance Sampling method. The reliability of a turbular joint and a beam are studied as numerical examples. The results show that the theory and the analysis method given in this paper are reasonable and effective.

Variable Importance Measure System Based on Advanced Random Forest

The variable importance measure(VIM)can be implemented to rank or select important variables,which can effectively reduce the variable dimension and shorten the computational time.Random forest(RF)is an ensemble learning method by constructing multiple decision trees.In order to improve the prediction accuracy of random forest,advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees.Referring to the Mean Decrease Accuracy(MDA)index based on Out-of-Bag(OOB)data,the single variable,group variables and correlated variables importance measures are proposed to establish a complete VIM system on the basis of advanced random forest.The link of MDA and variance-based sensitivity total index is explored,and then the corresponding relationship of proposed VIM indices and variance-based global sensitivity indices are constructed,which gives a novel way to solve variance-based global sensitivity.Finally,several numerical and engineering examples are given to verify the effectiveness of proposed VIM system and the validity of the established relationship.

A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory

Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes.Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions.In this paper,a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes,which contains data preprocessing,dimensionality reduction,multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method.In the established model,dextrose equivalent value is selected as the output,and 654 sites from the DCS system are selected as the inputs.LASSO analysis is first applied to reduce the data dimension to 155,then the inputs are dimensionalized to 50 by means of genetic algorithm optimization.Ultimately,variable importance analysis is carried out by the extended weight connection method,and 20 of the most important sites are selected for each neural network.The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%,which have a better prediction result than other models,and the 20 most important sites selected have better explicable performance.The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.

Maculopapular Rash with Fever: Reporting of a Case with Imported Measles and Diagnostic Difficulties in Non-Epidemic Times

Background: Measles is a highly contagious infection caused by the measles virus with a worldwide distribution. Measles is one of the diseases that have been reported in our country since 1945. It is known that international travelers are an important source of infectious pathologies. Our goal is to document a case of imported Measles and the difficulty of diagnosing it, especially in non-epidemic times. Cases Presentation: We presently report a 20-year-old woman who was hospitalized at the Infectious Disease Service for fever and maculopapular rash. She had traveled outside of Albania. Measles ELISA IgM (blood) resulted positive while other serological examinations resulted negative. Our case was treated with antibiotics, multivitamins and intravenous fluids. She was subsequently discharged home in good clinical condition. Conclusions: Measles should be included in the differential diagnosis of patients with symptoms of fever and rash, in particular when they have traveled abroad. Patients who have received the Measles vaccine should not be excluded from clinical suspicion and further diagnostic tests for this disease as it can affect this group of patients as well.

由规模化到精准化:国家中小学智慧教育平台教师研修的差异化需求及优化策略研究

立足常态化的新发展阶段,国家智慧教育平台教师寒暑假研修如何从规模化走向差异化、精准化,“按需施训”尤为关键。研究以基础教育阶段参训教师的9.06万条、149万余字反馈文本为研究对象,综合运用结构主题模型(STM)及重要性-绩效分析法(IPA),构建了国家中小学智慧教育平台教师研修需求框架,并深入分析其差异、明确基于需求要素的优化方向。研究发现:参训教师需求包含个体发展和系统环境两个层面的五类需求,且均存在显著的区域及城乡差异,针对时间安排及学时认定等研修机制需求仍须优化改进。研究为教师寒暑假研修精准化、差异化发展提供实证依据与决策支持,为优化研修服务提供策略建议。

2020-2023年进口化学药品注册检验工作情况分析及思考

目的:为进一步提高进口化学药品注册检验的工作效率及工作质量提供参考。方法:对2020-2023年药品注册检验工作程序的发展变化、进口化学药品注册检验的工作概况及存在问题等多个方面进行总结分析。结果:随着注册检验工作程序的修订完善,进口化学药品注册检验申报数量呈明显上升趋势,检验技术难度也逐步增加,同时存在申报单位的资料、样品等准备不充分,信息化建设不足等问题。结论:通过总结分析,探讨进口化学药品注册检验工作面临的问题及挑战,建议加强口岸药品检验机构的能力建设及信息化建设,加强申报人员的指导培训工作等,为进一步提高进口化学药品注册检验的工作效率及工作质量提供参考。

基于重要度的航空器系统定量相似性评估方法

目前民航飞机机型的更新换代越来越快,新型飞机设备清单的制定也成为了关键。将已有成熟机型的设备清单进行进一步的修改优化后使用,可以加快研发进展,如何界定是否适合优化使用较为困难,因此提出一种基于重要度与余弦相似性理论的航空器系统相似性评估方法。分析波音和空客相似机型的液压系统功能与结构,进行功能危险性评估;建立故障树,根据部件故障发生概率,计算部件概率重要度与关键重要度。根据重要度数据进行余弦相似性计算,结果近似于1,表明相似机型液压系统之间具有一定的相似相关性。方法可以对航空器系统是否相似相关进行判断,提供了依赖已有相似机型进行修改完善新型飞机设备清单的支撑。

国产金刚石与进口金刚石对比的试验研究

对三种品级的进口金刚石与国产金刚石样品的性能进行了全面的对比,以了解目前国产金刚石与国外高品级金刚石性能的差异。系统地分析了进口金刚石的性能指标,包括单颗粒静压强度、热冲击强度TTI、冷冲击强度TI、磁化率以及晶型构成等,提出了国产金刚石对标进口金刚石的思路。最后将这些金刚石制成Φ350 mm锯片做切割试验,对比国产与国外金刚石的实际使用效果。试验结果表明:湿切C45混凝土时,国产金刚石与进口金刚石切割速度基本相当,干切C25时,国产金刚石比进口金刚石慢15%左右,进口金刚石的切割手感要好于国产金刚石。从切割后顶刃金刚石状态可以看出进口金刚石切削刃会出现微破碎,能够持续稳定干切。在建筑工程湿切的应用中,国产金刚石的性能接近于国外中档品级产品的水平,干切时和进口金刚石还有一定的差距。