南方丘陵区新增耕地质量空间特征研究*——以江西省宜春市为例

［目的］粮食安全的根本是在耕地，关键在于耕地质量。目前新增耕地主要源于土地开发项目、城乡建设用地增减挂钩土地复垦项目等，这些新增耕地需要对其开展耕地质量等别评定工作，了解新增耕地的质量状况，分析其空间特征，这对落实国家耕地保护制度、开展农村土地整治工作等具有重要作用。［方法］文章以南方典型丘陵区域——江西省宜春市作为研究区域，在新增耕地质量评价的基础上，运用地统计学、景观生态学、Arcgis空间分析技术和典型相关分析方法，研究宜春市新增耕地质量的空间特征，掌握耕地质量等别的分布规律，了解耕地质量的影响因素，为制定合理的耕地资源利用与保护政策提供依据。［结果］（1）Moran′s I值结果表明宜春市2014年新增耕地利用质量在空间分布上的空间自相关性最强，而耕地自然质量空间自相关性最弱。（2）景观破碎度指数结果表明优、高等别的新增耕地质量破碎度大于中、低等别的； 多样性指数结果表明新增耕地质量多样性指数比较大； 优势度指数结果表明新增耕地质量等别优势度指数比较小，与多样性指数结果相呼应； 均匀度指数结果表明，新增耕地质量等别均匀度指数相差不大，新增耕地质量等别比较均匀。（3）质心结果表明2013—2014年间宜春市新增耕地质量的格局整体上是向西南方向变化。［结论］宜春市新增耕地质量不高； 新增耕地的利用水平差异性较小，自然本底状况差异较大； 新增耕地质量等别类型多，各质量等别类型耕地均有增加，耕地质量参差不齐，较高等地的新增耕地相对低等地而言更破碎化； 新增耕地的开发格局向着西南方向移动； 该研究为南方丘陵区的新增耕地保护提供了一定的参考依据。     

品牌认同视角下“生产商-经销商-终端商”价值共创的逻辑再构

随着产业链与供应链的深入与完善,以品牌认同为引领的价值创造再一次引起关注,基于"生产商-经销商-终端商"三位一体的价值共创思维是重塑品牌形象强化品牌认同的关键.研究认为:经销商层面中人口基数、人均可支配收入、配送能力等是实现价值增值的因素,生产商层面中市场费用支持、分销政策支持、品牌战略提升是促进价值提升的关键,终端商层面中客情维护、货龄管理、品牌维护是保障价值实现的途径.     

Fuzzy modelling of static system optimum traffic assignment problem having multi origin-destination pair

Traffic congestion is an unpreventable problem to avoid in a transportation network and it has negative effects on traffic accident, time wasting, traffic delay and safety problem. Besides, in transportation networks, drivers do not want to deal with traffic jam while traversing between specified origin-destination pair. Therefore, traffic assignment (TA) is imperative to improve traffic management, transportation safety, time, and cost savings. System Optimum Traffic Assignment Problem (SOTAP) is a kind of TA model which aims to minimize the total system travel time on the network, and satisfies the flow conservation constraints. To model the SOTAP more realistically, the imprecise parameters can be taken as fuzzy. Therefore, in this paper, we focus on converting the conventional SOTAP to a fuzzy quadratic programming problem (QPP) which is named System Optimum Fuzzy Traffic Assignment Problem (SOFTAP). Here, link travel time is expressed with BPR function as generally used in the literature by converting to fuzzy except link-dependent parameters. Thus, the nonlinear objective function of SOFTAP is expressed in terms of fuzzy link flows and fuzzy link travel times. A solution approach from the literature is modified to the reconstructed SOFTAP.     

一种级联两箱通用数字预失真器及其辨识算法

提出了一种由单形规范线性分段(SCPWL)函数与记忆多项式级联的数字预失真器，并给出了复数域两步最小二乘参数辨识算法。不同于以往一种预失真器适用一种功放模型的情况，所提的预失真算法利用SCPWL函数的分段特性以及记忆多项式的非线性记忆特性，在完成参数辨识的同时自动地调整结构，可适用于传统以及强非线性新型功放模型的线性化补偿。将所提预失真器分别应用于传统记忆多项式、两箱模型以及新型包络跟踪功放。经过计算机仿真，功放输出的幅频特性和频谱曲线表明所提预失真器能够有效地补偿多种功放的非线性特性。算法仿真比较结果也表明，针对包络跟踪功放，所提复数两步最小二乘算法的邻道泄漏比(ACLR)可改善约35 dB，性能优于最小均方(LMS)类算法约30 dB。     

互联网下高校外卖物流配送的现状与发展

随着互联网的快速发展,我国的手机愈发成为一部掌上电脑,通过手机进行外卖订餐已经成为现代高校大学生的日常状态,外卖的物流配送大大改变了学生的生活饮食方式。但是高校周围的外卖物流配送也容易导致很多问题,影响大学生的生活。针对高校外卖的特殊性,文章以互联网下逐渐形成的O2O模式来研究高校外卖物流配送的现状与发展,并且针对存在的问题提出相关建议。     

Personal Income Tax Reform in China in 2018 and Its Impact on Income Distribution

In the context of personal income tax (PIT) reform in China in 2018, this paper examines some of the major issues of concern regarding the reform and income distribution. Using the China Personal Income Tax Micro‐simulation model, the paper compares the differences between the 2011 and 2018 PIT systems, and finds that residents relying on different income sources may face a large degree of real tax rate change. Once the tax system is altered to PIT 2018, the coverage of PIT for wage earners will decrease from 46.9 to 23.4 percent, the income redistributive effect will drop from 1.95 to 1.22 percent and the PIT's role in fiscal revenue will also be negatively affected. Nevertheless, if individual income continues to grow, the share of PIT in fiscal revenue is expected to return to the 2018 level in 2022, but its income redistribution function is difficult to recover in the short term. The paper finds that the effect of PIT on income distribution depends on the tax structure. Gradual transition to an “entirely comprehensive” tax system when conditions are appropriate will achieve better income redistribution results at a lower average tax rate.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

LT码新型度分布及其在电力线通信中的应用

Luby变换(Luby Transform,LT)码作为信道编码应用于电力线通信(Power Line Communication,PLC)，可实现电信号的可靠传输。度分布对LT码编译码性能的影响至关重要。为了得到更优的度分布，首先调整二进制指数分布(Binary Exponential Distribution,BED)中的度数比例，获得一种译码性能更优的改进的二进制指数分布(Improved BED,IBED)。然后，根据IBED在冗余度较小时译码成功率高，而冗余度增大后鲁棒孤子分布(Robust Soliton Distribution,RSD)的译码性能表现更佳的特点，通过求和归一化的方式将IBED与RSD两种度分布的优势进行有机结合，提出一种新型二进制鲁棒孤子分布(Binary RSD,BRSD)。仿真结果表明，与其他方法及传统的RSD相比，采用新度分布进行LT编码，可明显降低译码开销，并节约编译码耗时。将新型度分布应用于基于LT码的PLC系统中，能有效地抑制PLC信道中各种噪声对电信号的干扰，并提高通信效率。