Neural network structure identification in inflation forecasting

Neural networks (NNs) are appropriate to use in time series analysis under conditions of unfulfilled assumptions, i.e., non‐normality and nonlinearity. The aim of this paper is to propose means of addressing identified shortcomings with the objective of identifying the NN structure for inflation forecasting. The research is based on a theoretical model that includes the characteristics of demand‐pull and cost‐push inflation; i.e., it uses the labor market, financial and external factors, and lagged inflation variables. It is conducted at the aggregate level of euro area countries from January 1999 to January 2017. Based on the estimated 90 feedforward NNs (FNNs) and 450 Jordan NNs (JNNs), which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, and weight value of the context unit), the mean square error (MSE), and the Akaike Information Criterion (AIC) are calculated for two periods: in‐the‐sample and out‐of‐sample. Ranking NNs simultaneously on both periods according to either MSE or AIC does not lead to the selection of the ‘best’ NN because the optimal NN in‐the‐sample, based on MSE and/or AIC criteria, often has high out‐of‐sample values of both indicators. To achieve the best compromise solution, i.e., to select an optimal NN, the preference ranking organization method for enrichment of evaluations (PROMETHEE) is used. Comparing the optimal FNN and JNN, i.e., FNN(4,5,1) and JNN(4,3,1), it is concluded that under approximately equal conditions, fewer hidden layer neurons are required in JNN than in FNN, confirming that JNN is parsimonious compared to FNN. Moreover, JNN has a better forecasting performance than FNN.     

Neuroleptic-induced prolactin rise: Influence of pharmacological alterations of different neurotransmitter systems

Summary Oral administration of 2 neuroleptic drugs, haloperidol and LR511 induced in male rats a marked, dose-dependent and sustained rise of plasma prolactin.     

热输入对深海X70粗晶区组织和韧性的影响

为解决深海X70管线钢在实际焊接中粗晶区(CGHAZ)的脆化问题,在不同热循环工艺下对X70管线钢进行了热模拟研究。采用Gleeble-3800热模拟机模拟X70管线钢CGHAZ,研究CGHAZ在10～60 kJ/cm不同热输入(HI)条件下组织和韧性的变化规律,并通过光学显微镜(OM)、扫描电镜(SEM)和夏比冲击试验等手段表征CGHAZ的组织和韧性。结果表明,不同热输入下试验钢的组织主要由粒状贝氏体(GB)、贝氏体铁素体(BF)和马-奥组元(M-A组元)组成;当HI不断增大时,BF比例减少,GB比例增加,M-A组元粗化,冲击吸收能先升高再降低;当HI为20 kJ/cm时,BF和GB可获得优异组合,断口为韧性断裂,冲击吸收能达到173.8 J;当HI大于20 kJ/cm时,断口解离断裂,冲击吸收能下降明显,最低为18.8 J。因此,较低的热输入可提高CGHAZ的韧性,使X70管线钢具有高强度、高韧性和良好的焊接性。研究结果可为优化焊接工艺提供理论依据。     

临近空间等离子体鞘套对太赫兹波传播特性分析

针对高超声速飞行器在临近空间巡航时出现的通信"黑障"问题,根据RAM C提供的飞行试验数据,建立一维等离子体鞘套模型,通过数值计算分析了等离子体与太赫兹波的相互作用机理,并从等离子体厚度、等离子体电子密度、等离子体碰撞频率和太赫兹波入射角等条件得到了太赫兹波在等离子体鞘套中的传输特性曲线。仿真结果表明:把太赫兹波段作为临近空间平台通信,有利于解决"黑障"问题,其中在大气窗口0.22THz处的衰减均在30dB以下。此论证结果可为临近空间平台设计的高超声速飞行器选用通信频段时提供参考。     

Postcranial evidence from early Homo from Dmanisi, Georgia

The Plio-Pleistocene site of Dmanisi, Georgia, has yielded a rich fossil and archaeological record documenting an early presence of the genus Homo outside Africa. Although the craniomandibular morphology of early Homo is well known as a result of finds from Dmanisi and African localities, data about its postcranial morphology are still relatively scarce. Here we describe newly excavated postcranial material from Dmanisi comprising a partial skeleton of an adolescent individual, associated with skull D2700/D2735, and the remains from three adult individuals. This material shows that the postcranial anatomy of the Dmanisi hominins has a surprising mosaic of primitive and derived features. The primitive features include a small body size, a low encephalization quotient and absence of humeral torsion; the derived features include modern-human-like body proportions and lower limb morphology indicative of the capability for long-distance travel. Thus, the earliest known hominins to have lived outside of Africa in the temperate zones of Eurasia did not yet display the full set of derived skeletal features.     

基于社区森林模型的分布式重叠社区发现算法

重叠社区发现是复杂网络挖掘中的重要基础工作,可以应用于社交网络、通讯网络、蛋白质相互作用网络、代谢路径网络、交通网络等多种网络的数据分析,从而服务智慧交通、传染病防治、舆情分析、新药研制和人力资源管理等领域.传统的单机运算架构已经难以满足各类大规模复杂网络的分析和计算要求.人工智能领域的研究人员提出将社区发现应用到网络...     

解体好氧颗粒污泥修复

考察了加入新的活性污泥使解体好氧颗粒污泥完成修复的可行性.解体好氧颗粒污泥对新加入的活性污泥进行吸附,在各种选择压力特别是水力剪切力作用下和原有颗粒污泥形成一个有机整体.大约3周时间,解体颗粒污泥被完全修复.扫描电镜观察发现,被修复后的颗粒污泥呈现非常规则的结构,微生物相十分致密.在解体颗粒污泥逐渐被修复的过程中,颗粒平均粒径仅从最初的2.8mm增至2.9mm,说明活性污泥在颗粒污泥上的附着主要发生在颗粒的空穴.而颗粒污泥的沉降性能和强度都得到了极大的改善,颗粒沉降速率和完整性系数分别由最初的72m/h和56.8%提高到110m/h和65.8%.新加入的活性污泥除了部分用于修复解体颗粒污泥,其余在选择压力等的作用下形成了新的好氧颗粒污泥.