非法经营罪探析

《中华人民共和国刑法》关于非法经营罪的规定是空白罪状,是一"口袋罪",其刑罚的设置不科学,因此,该罪应予分解,对已经分解出来的罪,不论刑罚轻重均不应再按非法经营罪追诉。     

湘语的确认标准及湘乡方言的归属

湘语的确认经历了从单一标准到综合标准的发展过程。无论从共时的语音现状还是从历史的角度来看,湖南湘乡方言均应视为典型的湘方言。     

基于多标准的ABC分类库存模型

ABC分类库存方法是企业应用最广泛的库存分类方法之一。国内外学者在研究ABC库存分析法时通常只针对一种分类标准,然而按照多种标准进行分类的库存模型则研究的相对较少。提出了一种新型的基于多标准的ABC分类库存模型,此模型概括了线性和非线性的情况,重点研究库存物品权重在分类中的影响作用。通过对比本模型与AHP方法所得出的结果,然后进行数据整理、对比分析,得出了库存物品的权重在分类决策中占有非常重要的地位。最后,通过数据证明得到此研究方法在库存决策方面更优,能够更好地为库存管理者提供决策依据。     

A closer look at de-aliasing effects using an efficient foldover technique

Foldover techniques are used to reduce the confounding when some important effects (usually lower order effects) cannot be estimated independently. This article develops an efficient foldover mechanism for symmetric or asymmetric designs, whether regular or nonregular. In this paper, we take the uniformity criteria (UC) as the optimality measures to construct the optimal combined designs (initial design plus its corresponding foldover design) which have better capability of estimating lower order effects. The relationship between any initial design and its combined design is studied. A comparison study between the combined designs via different UC is provided. Equivalence between any combined design and its complementary combined design is investigated, which is a very useful constraint that reduce the search space. Using our results as benchmarks, we can implement a powerful algorithm for constructing optimal combined designs. Our work covers as well as gives results better than recent works of about 20 articles in the last few years as special cases. So this article is a good reference for constructing effective designs.     

Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.     

Relevance of Risk Predictions Derived from a Chronic Species Sensitivity Distribution with Cadmium to Aquatic Populations and Ecosystems

Criteria to protect aquatic life are intended to protect diverse ecosystems, but in practice are usually developed from compilations of single‐species toxicity tests using standard test organisms that were tested in laboratory environments. Species sensitivity distributions (SSDs) developed from these compilations are extrapolated to set aquatic ecosystem criteria. The protectiveness of the approach was critically reviewed with a chronic SSD for cadmium comprising 27 species within 21 genera. Within the data set, one genus had lower cadmium effects concentrations than the SSD fifth percentile‐based criterion, so in theory this genus, the amphipod Hyalella, could be lost or at least allowed some level of harm by this criteria approach. However, population matrix modeling projected only slightly increased extinction risks for a temperate Hyalella population under scenarios similar to the SSD fifth percentile criterion. The criterion value was further compared to cadmium effects concentrations in ecosystem experiments and field studies. Generally, few adverse effects were inferred from ecosystem experiments at concentrations less than the SSD fifth percentile criterion. Exceptions were behavioral impairments in simplified food web studies. No adverse effects were apparent in field studies under conditions that seldom exceeded the criterion. At concentrations greater than the SSD fifth percentile, the magnitudes of adverse effects in the field studies were roughly proportional to the laboratory‐based fraction of species with adverse effects in the SSD. Overall, the modeling and field validation comparisons of the chronic criterion values generally supported the relevance and protectiveness of the SSD fifth percentile approach with cadmium.     

基于协调发展度的重大工程团队免疫系统健康评价研究

为评价重大工程团队免疫系统的健康和协调发展状况,本文提出了一种基于协调发展度的评价模型。首先通过文献查询和深度访谈,构建能够体现重大工程团队免疫系统特征的健康评价指标体系;其次利用改进的DEMATEL确定指标权重,然后采用多层次模糊综合评价将相关指标定量化,最终得出重大工程团队免疫系统的健康指数和协调发展度;再次确立重大工程团队免疫系统健康等级和协调发展度划分标准,同时提出免疫系统健康的跟踪评价策略;最后,利用本文模型对某跨国输气管道工程团队的免疫系统进行实证研究。结果表明,协调发展度模型既能反映重大工程团队免疫系统的整体健康程度,又能衡量固有性免疫和适应性免疫的协调发展状况,同时对免疫系统健康的跟踪评价也取得了良好效果。     

Multicriteria decision support in local energy planning: An evaluation of alternative scenarios for the Sustainable Energy Action Plan

One of the major challenges for the implementation of local energy planning is the successful development of a Sustainable Energy Action Plan (SEAP) by the local authorities (especially within the framework of their participation to the Covenant of Mayors’ initiative). This aspect constitutes a decision making problem, since the local authorities have to identify the best fields of actions and opportunities for reaching their long-term CO₂ reduction target. However, the already available methods and tools do not offer an integrated framework for the SEAPs’ development and especially the selection of sustainable Renewable Energy Sources (RES) and Rational Use of Energy (RUE) technologies. In this context, the aim of this paper is to present a participatory supportive framework for the implementation of local energy planning. At the first level, the proposed approach incorporates the development of alternative Scenarios of Actions (using knowledge-based process, participatory approach and aspiration level). At the second level, a direct and transparent multicriteria decision support is introduced, in order to evaluate the feasible Scenarios. It includes the application of a multicriteria ordinal regression approach and an extreme ranking analysis method for the estimation of the best and worst possible ranking position of each Scenario. The results from the pilot appraisal of the methodological approach to a “real” problem are presented and discussed. The adopted approach contributes to the selection of the most appropriate combination of RES/RUE actions, supporting in this way the local authorities to the development of their SEAP.     

A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem

In this paper, multiple criteria sorting methods based on data envelopment analysis (DEA) are developed to evaluate research and development (R&D) projects. The weight intervals of the criteria are obtained from Interval Analytic Hierarchy Process and employed as the assurance region constraints of models. Based on data envelopment analysis, two threshold estimation models, and five assignment models are developed for sorting. In addition to sorting, these models also provide ranking of the projects. The developed approach and the well-known sorting method UTADIS are applied to a real case study to analyze the R&D projects proposed to a grant program executed by a government funding agency in 2009. A five level R&D project selection criteria hierarchy and an assisting point allocation guide are defined to measure and quantify the performance of the projects. In the case study, the developed methods are observed to be more stable than UTADIS.