Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

政府投资工程总承包项目设计优化认定与收益归属研究

工程总承包模式以设计与施工的高度融合,日益在建筑业受到青睐。但由于政府投资项目中存在有关结余资金上缴国库等规定,容易导致政府投资工程总承包项目下设计优化产生的成果是进行分成或作为结余资金上缴界定不清。鉴于此,本文通过政策文件,并结合政府投资项目特点,分析政府投资工程总承包项目设计优化的情形认定以及在不同结算依据和合同计价方式下设计优化收益归属的界定,为有效激励总承包单位积极进行设计优化奠定基础。     

航班计划优化研究概述

从航班计划优化的不同时间阶段分析,可以将航班计划优化分为航班计划静态编排优化、基于航班延误预测的航班计划动态反馈优化和基于机场协同决策(A-CDM)的航班计划动态调整角度三类;进而从航班时刻、机型指派、航班频率等编制环节分析了航班计划静态编排优化;随后利用延误波及预测与数据挖掘预测的优化方法分析了基于航班延误预测的航班计划动态反馈优化的相关研究。最后,根据航班计划优化复杂性分析,给出了航班计划优化的发展趋势和未来研究方向。     

Factor extraction using Kalman filter and smoothing: This is not just another survey

Dynamic factor models have been the main “big data” tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance.     

基于模糊多属性群决策的维护方式选择研究

维护方式选择是维护管理中的重要工作之一,合理的维护方式既能达到保障设备的稳定运行,又能同时兼顾其它各个方面的要求。由于对维护方式的评价涉及多个部门、人员和属性,有些指标只是一个模糊的概念,因而采用模糊多属性群决策的方法对维护方式进行优先抉择。本文结合A公司的设备维护方式选择问题,尝试使用模糊多属性群决策折衷算法求解最佳的维护方式。     

Optimal distribution of the injured in a multi-type transportation network with damage-dependent travel times: Two metaheuristic approaches

We study a location-allocation-routing problem for distribution of the injured in a disaster response scenario, considering a three-type transportation network with separate links. A circle-based approach to estimate the impacts of the disaster is presented. After formulating relations for computing the percentage of the injured, the destruction percentage and the damage-dependent travel times, the problem is formulated as an integer nonlinear program. We utilize a genetic algorithm and a discrete version of the imperialist competitive algorithm for solving large problems. An empirical study focused on earthquakes in Tabriz, Iran, illustrates applicability of the proposed model and performance of the proposed algorithms.     

Linear programing models for portfolio optimization using a benchmark

We consider the problem of constructing a perturbed portfolio by utilizing a benchmark portfolio. We propose two computationally efficient portfolio optimization models, the mean-absolute deviation risk and the Dantzig-type, which can be solved using linear programing. These portfolio models push the existing benchmark toward the efficient frontier through sparse and stable asset selection. We implement these models on two benchmarks, a market index and the equally-weighted portfolio. We carry out an extensive out-of-sample analysis with 11 empirical datasets and simulated data. The proposed portfolios outperform the benchmark portfolio in various performance measures, including the mean return and Sharpe ratio.     

海南岛休闲农业布局现状的实证分析

[目的]全面分析海南岛休闲农业布局现状,为海南岛休闲农业科学合理规划和均衡协调发展提供决策参考。[方法]文章利用海南岛259家休闲农业企业的地理数据,采用比较、缓冲、密度分析工具,从数量、类型、密度、距离衰减方面实证分析海南岛休闲农业的布局特征,并提出相应的优化对策。[结果](1)海南岛东部、中部和西部休闲农业数量分布比重分别为51%、29%和20%,其中国家级示范点数量分布比重分别为83%、17%和0%;(2)休闲农业主要类型有休闲农庄、观光农园、市民农园和农家乐,除休闲农庄在东部、中部、西部分布较均匀外,其他类型分布较不均衡;(3)距离市县中心越远,休闲农业分布密度越小,95%的休闲农业分布在距离市县中心30km以内,距离市县中心10~15km是休闲农业的高峰集聚区;(4)海南岛休闲农业已在海口、保亭、三亚、澄迈、琼中、琼海、定安、儋州形成8个高度集聚带。[结论]海南岛休闲农业无论在数量、类型和密度上都是东部地区最多,中部次之,西部最少。应积极推进具有农业发展优势的地区,因地制宜拓展产业休闲功能;休闲农业产品类型单一的地区,深度挖掘特色资源和文化内涵;加强休闲农业点与旅游景区、生活服务中心之间的跨区域联系,形成和谐发展的有机旅游整体,使休闲农业真正成为农业转型、农民增收的引擎。     

一种动态加权的LDPC译码方法

为了加快低密度奇偶校验（LDPC）码的译码速度，有效改善LDPC码的译码性能，针对校验节点更新过程中的对数似然比（LLR）值的大小，设计了一种LDPC码的动态加权译码方法。以IEEE 802.16e标准的奇偶校验矩阵为例，根据LLR值的变化规律，利用增长因子和抑制因子对和积译码算法和最小和译码算法进行动态加权。仿真结果显示，基于动态加权的译码方法相对于传统译码方法误码率都有明显改进，译码复杂度也有所降低。     

Exploring the development trend of internet finance in China: Perspective from club convergence

We first employ β-conditional convergence and log t regression tests based on nonlinear time-varying factor model and club clustering algorithm to analyze the convergence characteristics of the development level of Internet finance in 335 prefecture-level cities in China. The result of log t regression test illustrates that there is no convergence as a whole in the development level of China's Internet finance. However, seven convergence clubs and a divergent group have been formed, and the development level and growth rate of Internet finance among these convergence clubs have shown obvious differences. Moreover, we also employ the Ordered Probit to explore the formation mechanism of the convergence clubs. The results reveal that the regions with a higher level of economic development, traditional financial development, economic openness and Internet development are more inclined to converge in a club with a higher Internet finance development level. Alternatively, the regions that are interfered with more by the government or that have a lower degree of marketization, tend to converge in a club with a lower level. Finally, according to the conclusions, we propose corresponding policy recommendations for promoting the regional coordinated development of China's Internet finance.