Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach

This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.

Prognostic model for esophagogastric variceal rebleeding after endoscopic treatment in liver cirrhosis: A Chinese multicenter study

BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

LoRa Sense:Sensing and Optimization of LoRa Link Behavior Using Path-Loss Models in Open-Cast Mines

The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.

Reduced mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor contributes to neurodegeneration in a model of spinal and bulbar muscular atrophy pathology

Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.

Physiology and health assessment,risk balance,and model for endstage liver disease scores:Postoperative outcome of liver transplantation

BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.

Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.

RCEP框架下关税减让对中国机电产品出口的影响研究——基于GTAP模拟分析

机电产品是中国与RCEP成员国贸易往来中的重要出口产品,运用显示性比较优势指数、贸易竞争优势指数和贸易互补性指数,探讨2012—2022年中国与其他RCEP成员国机电产品的贸易竞争性和互补性。在此基础上,运用GTAP软件分析关税减让对中国机电产品出口的影响。研究发现:RCEP框架下关税减让有利于中国机电产品出口规模的扩大,且长期带来的效果比短期更显著;关税减让产生的贸易创造效应和贸易转移效应十分明显,具体表现为从RCEP域外国家转移至域内;关税减让对中国不同种类机电产品出口的影响有所不同,提出了加强汽车领域交流与合作,加快机电产业结构优化升级,制定差异化出口战略等对策建议。

印度视角的RCEP贸易损益模拟研究——基于GTAP模型的实证分析

《区域全面经济伙伴关系协定》(RCEP)已于2022年1月1日正式生效,印度是否永久性退出RCEP尚不可知,若在RCEP效果凸显后再度重返,则其国内贸易损益计算依据以及将对成员带来何种宏观经济层面的影响无疑是个值得关注的问题。本文利用GTAP模型对印度是否加入RCEP不同情形之贸易损益进行了模拟验证,结果表明,RCEP生效后各成员的国家总福利水平及总产出水平都将高于生效前,与印度的成员身份并无必然关联,但印度的RCEP局外人身份将对其进出口总额的增长产生负面影响;若印度重返RCEP则不仅对本国进出口贸易额增长发挥正向拉动作用,还会为其他成员带来更大的贸易增长效应;印度加入RCEP也的确存在一定程度的对外贸易逆差增大风险。为此,展望印度加入RCEP的前景,可以做出以下预判和启示:印度可能适时选择重新加入RCEP而非永久退出;印度重返RCEP可为中国经济发展带来新动力;中国应积极运用外交和经济渠道促使印度尽早重返RCEP,为中印双方未来的经济增长创造更好条件。

中国与厄瓜多尔自由贸易协定(FTA)的贸易效应研究——基于GTAP动态递归的模拟分析

2023年5月,中国与厄瓜多尔签署自由贸易协定,中厄FTA将对两国双边贸易产生重要影响。通过分析中国与厄瓜多尔的贸易现状及竞争与互补关系,运用动态递归GTAP模型模拟分析中厄FTA的贸易效应。研究发现,中国与厄瓜多尔双边贸易规模逐年上升,两国贸易互补性较强。FTA的关税减让将促进中国与厄瓜多尔双边贸易增长,降低技术性贸易壁垒、提升贸易便利化水平对两国贸易的影响更显著,且FTA更有利于中方扩大对厄方的出口。中厄双方需加快推进FTA各项条款的生效及实施,中方应增加进口厄方的资源型产品,扩大对其出口和直接投资。

RCEP对中日汽车及零部件产出和贸易的影响——基于动态递归GTAP的模拟分析

《区域全面经济伙伴关系协定》(RCEP)首次达成了中、日两国之间双边关税的减让安排协议。中国是全球最大的汽车生产和消费市场,日本是中国汽车的第一大进口来源国。本文通过分析中、日两国汽车及零部件产品贸易的现状及特征,总结RCEP协定下中日汽车及零部件的具体降税安排,并采用GTAP模型模拟分析了RCEP对中日汽车及零部件产出和贸易的影响。研究发现:近二十年来,中、日两国汽车及零部件双边贸易大幅增长;日本的汽车产品具有较强的国际竞争力,且对中国贸易互补性较强。RCEP关税减免和消除技术性贸易壁垒等措施将显著促进中、日两国汽车及零部件产出和贸易的增长。中国企业要充分利用RCEP原产地累积规则,增加对日本汽车零部件产品的出口额,并积极扩大新能源汽车出口。

基于GTAP模型的中缅印度洋新通道贸易效应研究

中国-缅甸-印度洋海公铁联运新通道,是从中国西南地区打通印度洋出海通道的必经之路,其发展与“一带一路”孟中印缅经济走廊建设和中国西部沿边开放紧密关联。从贸易网络、贸易依赖度、竞争性和互补性等方面探究通道开通后沿线国家和地区间贸易规模、贸易结构和贸易壁垒的变动,揭示了沿线国家和地区贸易关系的特征和动态变化。根据运输成本对贸易的传导机制,采用GTAP模型,模拟预测中缅印度洋新通道在部分联通、全线贯通和路网形成三种不同情景下产生的贸易效应。研究发现:中缅印度洋新通道的开通将直接刺激沿线地区贸易规模增长,贸易条件显著改善,中国、缅甸和印度等国实际GDP和社会福利水平显著提升;随着物流成本下降和贸易网络深化,新通道促进贸易从高成本的域外地区向低成本的域内地区转移,重构区内贸易格局;新通道的开通将有利于推动中国与周边国家和地区形成产业链互补,促进区域经济一体化发展。

RCEP对中国机电产品产出及贸易的影响--基于动态递归GTAP的模拟分析

RCEP的实施将对中国机电产品的产出及贸易产生重要影响。采用RRCA和TCI指数分析了中国与RCEP成员国机电产品的贸易竞争与互补关系,运用动态递归GTAP模拟了RCEP对中国机电产品产出及贸易的影响。研究发现:近20年来,中国对各成员国机电产品进出口额增长较快,且对各国机电产品的贸易互补性较强。降低关税与技术性贸易壁垒、提升互联互通水平、增加投资及劳动力等措施都将促进中国机电产品产出及贸易增长,其中对日本、韩国、东盟的进出口增加额较大。中国企业要充分利用RCEP关税减让表,扩大机电产品出口,加强与各成员国的机电产业合作,积极面对贸易竞争加剧带来的挑战。

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

Empirical analysis of carbon tariff's effect on the export structure of China's manufacturing industry and social welfare based on the GTAP model

In recent years,carbon emissions have gradually evolved from an environment issue into a political and economic one.Carbon tariff has brought about new trade barriers of developed countries,and in order to enhance the industrial competitiveness of developed countries,it will produce unfavorable impact on developing countries.Concentrated on the manufacturing industry,which is the most intensive high-carbon industry in China's export structure,this article studies the relationship between carbon tariff policy and industry structure of export trade and builds up a relation between climate change and international trade.First,by means of establishing a partial equilibrium model,it applies geometric analysis and mathematical analysis to compute the impact on China's manufacturing export trade and the consequences of the introduction of the US carbon tariff to China's manufacturing industry that has already imposed a domestic shipping carbon tax.Furthermore,with the application of the GTAP model,it estimates the overall economic and welfare effects on China's manufacturing industry if the US and Europe introduce carbon tariff by means of four ways,and then analyzes the influence on China's manufacturing industry export structure and social welfare as well.The result shows that the introduction of the US carbon import tariff lowers China's export price and export volume,and the implementation of a domestic carbon tax justifies a higher export price and a lower export volume for China.However,the degree of export reduction is smaller than that under the effect of the US carbon tariff.In the case of developed countries imposing carbon tariff on China's energy-intensive industries,such as chemical rubber products,oil and coal-processing industry and paper industry,whose export would be reduced,the negative impact on the paper industry is the severest,which will decrease the paper industry's export ranging from 1.79%to 6.05%,whereas the other industries' export will increase.Anyhow,it will promote China's manufacturing industry to adjust the export structure to a certain extent.In addition,it will lead to a decrease in China's welfare,with a decrease between $2,134 billion and $8,347 billion.Finally,this paper provides information on international coordination,export structure adjustment and green manufacturing adjustment as a reference for the development of China's manufacturing industry.

基于GTAP模型的RCEP关税协调贸易效应研究

文章运用GTAP模型,模拟关税降税的三种情景,预测RCEP生效后关税协调的贸易效应。通过模拟分析得出:一是成员国之间的进出口变动率呈增长态势,且成员国中的发达国家的受益程度普遍高于发展中国家,而非RCEP成员国的进出口变动率受到负面冲击。二是贸易条件出现了两极分化的情况,大多数成员国呈正向增长,成员国中的发达国家增长幅度明显;但部分成员国和非RCEP成员国的贸易条件却受到了负面影响。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

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