Construction of Early-warning Model for Plant Diseases and Pests Based on Improved Neural Network

By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.

Data Processing Model of Coalmine Gas Early-Warning System

The data processing mode is vital to the performance of an entire coalmine gas early-warning system, especially in real-time performance. Our objective was to present the structural features of coalmine gas data, so that the data could be processed at different priority levels in C language. Two different data processing models, one with priority and the other without priority, were built based on queuing theory. Their theoretical formulas were determined via a M/M/I model in order to calculate average occupation time of each measuring point in an early-warning program. We validated the model with the gas early-warning system of the Huaibei Coalmine Group Corp. The results indicate that the average occupation time for gas data processing by using the queuing system model with priority is nearly 1/30 of that of the model without priority.

Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-Cat Boost

As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.

Financial crisis early-warning model of listed companies based on predicted value

To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.

Early-warning signals for an outbreak of the influenza pandemic

Over the course of human history, influenza pandemics have been seen as major disasters, so studies on the influenza virus have become an important issue for many experts and scholars. Comprehensive research has been performed over the years on the biological properties, chemical characteristics, external environmental factors and other aspects of the virus, and some results have been achieved. Based on the chaos game representation walk model, this paper uses the time series analysis method to study the DNA sequences of the influenza virus from 1913 to 2010, and works out the early-warning signals indicator value for the outbreak of an influenza pandemic. The variances in the CCR wall〈 sequences for the pandemic years （or ＋ -1 to 2 years） are significantly higher than those for the adjacent years, while those in the non-pandemic years are usually smaller. In this way we can provide an influenza early-warning mechanism so that people can take precautions and be well prepared prior to a pandemic.

Development of Crisis Management Models Combined with Cloud Computing

Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and the combined early-warning model.Combined with the advantages of cloud computing,the prominent crisis management models are improved and more efficient,comprehensive and accurate in enterprise crisis management.Through the empirical study of the models,cloud computing makes the early warning structures of enterprise crisis tend to be more simple and efficient,cloud computing can effectively enhance the recognition ability and learning ability of the crisis management,and cloud computing can keep data information updating and realize the dynamic management of enterprise joint early-warning.At the same time,according to the comparative analysis and the experimental result,the crisis management models based on cloud computing also need some improvements.

European Tax Models

The research paper European Tax Models is a comparative member states, taking into account the main features in light analysis of the taxation systems in the European Union of the contribution of indirect and direct taxes and social contributions to the achievement of public revenues. Theme presents a topic of great interest, both theoretically and practically, given that how to place taxes has direct repercussions on the economic development of a country, and undoubtedly influence the rules of an economy, particularly in terms of investment, labor market, and social welfare. It was considered necessary in the first part of the paper to address the conceptual elements and present the most important features of tax systems and the principles that underlie them. It was studied from a theoretical perspective and it found the European tax models as follow： Nordic, continental, Anglo-Saxon, Mediterranean, and catching-up. Then, it analyzed each fiscal European model on each member country, starting from its economic indicators, based on Eurostat data. The objective of the research paper was to present a complete picture of the structure and trends of tax level of the member states of the European Union, sorted by European tax models and the impact of taxation on economic growth and social welfare. The research has concluded that, as long as the rules of the European Union, member states are free to choose their own tax system along with their fiscal policy for economic development and having in a view of their geographical, historical, and political situation.

失业补贴政策对促进共同富裕的作用机制分析

共同富裕是社会主义的本质要求,是中国式现代化的重要特征。本文将Huggett(1993)的异质性禀赋模型拓展为竞争性一般均衡模型,采用数值算法对模型进行了迭代求解,分析了失业补贴政策对我国居民平均财富提升和财富分布优化的作用效果和机制,并在进一步分析中将之与信贷政策进行了对比。第一,从一般均衡的角度来看,合理的失业补贴不仅不会降低经济效率,还能促进财富分布的长期平衡。第二,失业补贴政策优化财富分布的作用机制是收入效应和替代效应的权衡,对于贫困家庭收入效应占主导,储蓄增加使得财富积累,富裕家庭则与之相反。第三,虽然放松信贷约束能够缓解贫困家庭和失业家庭的资金压力,促进共同富裕,但总信贷需求上升导致的利率上升会损害负资产家庭的利益。第四,与宽松的信贷政策相比,适当的增加失业补贴是提高居民财富均值、优化财富分布的更有效手段。

青年就业与经济复苏——结构性与随机性视角

16~24岁青年失业率问题已成为当前经济复苏期的重要经济社会问题之一,是全面贯彻落实党的二十届三中全会精神和健全高质量充分就业促进机制必须面对的现实问题。本文以新冠肺炎疫情等引发的不确定性冲击为背景,通过构建随机可计算一般均衡模型,研究摩擦性、结构性以及周期性冲击对不同结构青年群体就业水平的影响,并模拟其就业的政策效果。本文发现:(1)摩擦性和结构性冲击是引起青年群体高失业率的重要原因,且青年群体对周期性尤其是投资需求冲击的抵抗能力较强;(2)不同受教育水平的青年群体面对疫情冲击时的就业变动存在差异,较高教育水平群体对结构性与投资需求冲击时表现出更强抵抗能力;(3)政策模拟显示,企业资助与扩岗补助政策均有助于提振就业,且低学历青年就业恢复能力更高。本文政策启示:(1)充分发挥政府的宏观调控作用,短期与长期工作并举,着力缓解青年结构性就业矛盾;(2)企业激励与青年个人提升并重,在推动经济复苏过程中,鼓励企业开发更多适合青年群体的就业岗位;(3)提振青年就业与保障青年民生并行,实施青年分类帮扶,兜牢青年民生底线。

Technological anxiety:Analysis of the impact of industrial intelligence on employment in China

Employment is the greatest livelihood.Whether the impact of industrial robotics technology materialized in machines on employment in the digital age is an“icing on the cake”or“adding fuel to the fire”needs further study.This study aims to analyze the impact of the installation and application of industrial robots on labor demand in the context of the Chinese economy.First,from the theoretical logic and the economic development law,this study gives the prior judgment and research hypothesis that industrial intelligence will increase jobs.Then,based on the panel data of 269 cities in China from 2006 to 2021,we use the two-way fixed effect model,dynamic threshold model,and two-stage intermediary effect model.The objective is to investigate the impact of industrial intelligence on enterprise labor demand and its path mechanism.Results show that the overall effect of industrial intelligence on the labor force with the installation density index of industrial robots as the proxy variable is the“creation effect”.In other words,advanced digital technology has created additional jobs,and the overall supply of employment in the labor market has increased.The conclusion is still valid after the endogeneity identification and robustness test.In addition,the positive effect has a nonlinear effect on the network scale.When the installation density of industrial robots exceeds a particular threshold value,the division of labor continues to deepen under the combined action of the production efficiency and compensation effects,which will cause enterprises to increase labor demand further.Further research showed that industrial intelligence can increase employment by promoting synergistic agglomeration and improving labor price distortions.This study concludes that in the digital China era,the introduction and installation of industrial robots by enterprises can affect the optimal allocation of the labor market.This phenomenon has essential experience and reference significance for guiding industrial digitalization and intelligent transformation and promoting the high-quality development of people’s livelihood.

失业保险给付期限差异下的失业持续时间研究

本文对享受不同失业保险给付期限的失业者的失业持续时间差异进行了实证研究。根据工作搜寻理论,应用存活分析方法建立了含有失业保险给付期限因素的失业持续时间模型,并对青岛市失业者登记数据进行了实证研究。结果表明:享受失业保险者的失业持续时间明显长于不享受失业保险者的失业持续时间,在失业保险给付额度相同的条件下,失业保险给付期限与失业持续时间成同向变动关系。

我国失业保险支出与城镇失业率关系研究——基于误差修正模型的分析

失业是市场经济条件下不可避免的社会风险。失业保险是解决失业所产生的不利因素的社会机制,对一国的社会保障和福利水平有重要的作用。本文通过建立误差修正模型,对我国失业保险支出与城镇失业率的相互关系进行研究。我国失业保险与城镇失业率之间存在着双向格兰杰因果关系,即失业保险支出的增加会导致城镇失业率的增加,城镇失业率的增加会引起失业保险支出的增加。另外,我国失业保险支出的变动对城镇失业率的影响是正激励与负激励并存。

延迟退休年龄对中国失业率的影响:理论与验证

"未富先老"与退休年龄偏低并行是当前中国经济社会发展中的一个突出问题,延迟退休年龄成为养老保险制度改革的必然选择,然而,延迟退休与就业之间的关系是政策制定者须慎重考虑的重要问题。通过理论与实证角度研究延迟退休年龄对失业率的影响,验证了工作搜寻理论在中国的有效性:即失业率与劳动年龄人口存在显著的负相关关系。延迟退休年龄会使劳动年龄人口增加同时老年抚养比下降,最终导致失业率下降。由于未来中国人口发展的特点决定了失业率的上升趋势,进一步模拟分析发现延迟退休能够降低中国失业率水平,延迟退休年龄的时机选择会对失业率造成不同影响,审慎地选择延迟退休开始的时机对于稳定就业具有重要意义。

基于对角Elman神经网络的失业预测模型

本文通过对角 Elman神经网络来挖掘我国失业规律 ,建立失业模型 ,并预测我国的失业形势 .结果表明 。

包含劳动力市场条件的劳资关系模型及实证分析

本文构建了包括城市失业人口和农村剩余劳动力的全社会角度的失业率指标,以工资和劳动生产率作为劳资之间经济利益关系的考察变量,运用自回归分布滞后模型,实证分析了我国特定劳动力市场条件下的第二产业和建筑业工资与劳动生产率之间的关系。发现工资增长促进劳动生产率提高的效应,在建筑业存在比第二产业更显著的揭示了通过提高工资改善劳资关系的经济意义。

我国高学历者失业的经济学解释

高学历者失业是近年来在我国社会中凸现的热点经济问题。高学历者失业对社会而言 ,不仅严重浪费整个社会人力资源 ,同时也严重遏制社会、企业与个人的人力资本投资积极性。本文立足于现代经济学的失业与人力资本理论 ,借助人力资本投资 -贡献模型对我国高学历者失业及其根源进行深刻剖析 ;并提出了相应的治理措施。本文研究对于促进我国人力资本投资、高学历者就业及其效率提高都具有非常重要理论与现实意义。

我国失业保险金待遇调整探索——以河北省为例

通过对我国失业保险金调整标准的理论分析,总结归纳了国内外失业保险待遇调整的经验和做法,并结合河北省失业保险待遇调整的实践,设计出河北省的失业保险金待遇调整模型和调整系数。调整模型主要考虑到河北省钉住最低工资标准调整的实际和现收现付制下缴费工资水平对保险金待遇水平起关键性作用。调整系数综合考虑国内外的实际做法,把当地经济发展水平、物价变动、工资增长水平、失业保险覆盖范围变化等调整因子作为主要影响因素。最后,结合河北省经验数据和秦皇岛市的实际执行标准,计算出模型的失业保险金理论金额,与实际发放水平进行对比验证。

搜寻理论、失业救济金与中国城镇人口失业持续时间

通过对享受失业救济金者和不享受者失业救济金者的再就业概率的分析,我们可以发现,与享受失业救济金者相比,人力资本特征、家庭特征和宏观经济环境对不享受失业救济金者的再就业概率有显著正向影响,转换模型也得出了失业救济金会降低再就业概率、延长失业持续时间的结论。

失业风险与城镇居民消费行为

该文集中讨论失业风险这一不确定性对城镇居民消费行为的影响。首先,建立一个具有微观基础的理论模型来研究失业风险对城镇居民消费的影响,接着在此基础上利用1984～2000年以来的数据对此进行实证分析,最后给出在失业风险存在的情况下,刺激城镇居民消费需求、扩大内需的政策建议。

中国劳动力市场均衡及失业问题研究

本文首先通过匹配方程研究各种因素对劳动力市场的影响，然后基于2004Q1—2014Q4中国季度数据，使用ARDL—UECM模型探究我国在长期和短期中就业人数、空岗数量、薪资水平、市场利率、政府失业补助对失业率的影响差异。研究发现无论从长期还是短期来看，就业人数、薪资水平和市场利率对我国失业率的弹性系数始终为负。说明这三个因素对失业率总是存在抑制作用；而失业补助和空岗数量对我国失业率的弹性系数长期为正、短期为负，说明这两个因素对失业率短期存在抑制作用，而长期存在促进作用。此外，本文结合理论与实证结果，在以不损失效率、不影响整体发展的情况下，提出相应的政策建议。