利用餐厨垃圾配制蚯蚓培养基的初步研究

分析了餐厨垃圾的基本成分.其中粗蛋白为16.83%、粗脂肪为29.04%、粗纤维为7.4%.在垃圾经过分拣、水洗、脱盐、脱脂处理后,通过测定,发现粗脂肪含量降到7.4%,含盐量降到0.6%.在一定条件下干燥,得到餐厨垃圾的干粉,作为饲养蚯蚓的培养基的主要组成部分.选用太平2号蚯蚓作为实验动物,以餐厨垃圾干粉为主要成分,...     

朱子和谐四重奏与当代社会

朱子是宋代新儒学的集大成者,是中华文化承先启后、继往开来的文化巨人。朱子和谐观包含四重含义：心和、人和、协和、中和,它们分别指向身心和谐、人际和谐、世界和谐和天人和谐,具有多维性和普遍性,是一个逐步放大的同心圆,一个逐步上升的理想境界。朱子的和谐四重奏,由低级到高级,由自我到他人,由人际到国别,由人类到宇宙,奏响了一曲...     

杂交中稻超多蘖壮秧超稀栽培关键技术问题探讨

以多年试验资料 ,对四川盆地两熟制杂交中稻“超多蘖壮秧超稀栽培模式”中几个关键技术问题进行了探讨 ,明确了两熟制杂交中稻超稀栽培必须以培育“超多蘖壮秧”为基础 ,而培育超多蘖壮秧的技术核心又在于保证秧苗在秧田阶段具有足够的单株营养面积 ;在培育超多蘖壮秧的基础上 ,大田栽培穴数可由常规栽培的 2 2 .5万～ 3 0 .0万穴 /hm2 降低至 1 0 .0万～ 1 8.0万穴 /hm2 ,且一般以每穴单株为好 ;超稀栽培合理的氮肥施用方法是在坚持重底早追的同时 ,适当增加中后期的用肥比例     

农村中小型超市连锁策略分析

当前,我国农村中小型超市发展连锁体系的过程中还存在管理体系不规范、网点布局盲目、商品定位不清及竞争手段单一等因素,为了更有效地建立连锁体系,农村中小超市应依据发展战略完善自身组织结构,加强组织系统、运营系统、支持系统和控制系统的建设与融合;依据长期发展目标,合理设计网点布局和连锁店选址;根据目标消费者需求与竞争态势做好商品定位;同时通过品牌管理决策从整体上提高自身竞争力。     

一种基于项目管理的车辆装备中修方法研究

介绍了一种基于项目管理的车辆装备中修设计.首先构建中修项目管理框架，确立车辆装备中修项目管理过程的4个阶段：启动阶段、计划阶段、实施阶段和收尾阶段.以某军区运用项目管理理论组织车辆中修为实例，按照项目启动、计划、实施、收尾的流程，制定车辆中修计划，综合运用多种项目管理方法对维修进度、维修资源、维修质量进行监控，确保中修任务按计划实施完成，最后构建车辆装备中修项目成效评价指标体系.通过和往年中修情况对比，对运用项目管理的成果进行分析，表明项目管理应用行之有效.     

Semiparametric regression estimation for longitudinal data in models with martingale difference error's structure

An inherent characteristic of longitudinal data is the dependence among the observations within the same subject. For exhibiting dependencies among the observations within the same subject, this paper considers a semiparametric partially linear regression model for longitudinal data based on martingale difference error's structure. We establish a strong consistency for the least squares estimator of a parametric component and the estimator of a non-parametric function under some mild conditions. A simulation study shows the performance of the proposed estimator in finite samples.     

Penalized regression models with autoregressive error terms

Penalized regression methods have recently gained enormous attention in statistics and the field of machine learning due to their ability of reducing the prediction error and identifying important variables at the same time. Numerous studies have been conducted for penalized regression, but most of them are limited to the case when the data are independently observed. In this paper, we study a variable selection problem in penalized regression models with autoregressive (AR) error terms. We consider three estimators, adaptive least absolute shrinkage and selection operator, bridge, and smoothly clipped absolute deviation, and propose a computational algorithm that enables us to select a relevant set of variables and also the order of AR error terms simultaneously. In addition, we provide their asymptotic properties such as consistency, selection consistency, and asymptotic normality. The performances of the three estimators are compared with one another using simulated and real examples.     

Masking and swamping effects on tests for multiple outliers in normal sample

A study of some commonly used multiple outlier tests in case of normal samples is presented. When the number of outliers in the sample is unknown, two phenomena, namely, the masking and the swamping effect can occur. The performance of the tests is studied using the measures of masking and swamping effects proposed by Bendre and Kale (1985) and Bendre (1985). The effects are illustrated in case of the Murphy test, Tietjen—Moore test and Dixon test. A small simulation study is carried out to indicate these effects.     

A Note on Estimation in Hilbertian Linear Models

We study estimation and prediction in linear models where the response and the regressor variable both take values in some Hilbert space. Our main objective is to obtain consistency of a principal component‐based estimator for the regression operator under minimal assumptions. In particular, we avoid some inconvenient technical restrictions that have been used throughout the literature. We develop our theory in a time‐dependent setup that comprises as important special case the autoregressive Hilbertian model.     

Consistency of a class of information criteria for model selection in non-linear regression

In this paper we prove the consistency in probability of a class of generalized BIC criteria for model selection in non-linear regression, by using asymptotic results of Gallant. This extends a result obtained by Nishii for model selection in linear regression.