399种高校学报开本及幅面尺寸调查

对2008年出版的399种公开发行的高校学报的开本及幅面尺寸进行调查.对高校学报开本及幅面尺寸存在的问题和开本及幅面尺寸不符合国家标准的原因进行了分析,并提出了实现标准化的建议.     

长江下游地区挖入式港池口门宽度的计算方法

受长江航道的自然地形水深和南京长江大桥通航净空的限制,港口物流服务的腹地纵向延伸和广域化辐射受到较大阻碍,因此,在长江下游河段新建的港口宜尽量兼备承接上游来船转运出海的功能和由海轮转驳江轮的功能。现行规范对于挖入式港池宽度已有较为详细的规定,但对于船舶进出于两码头之间的挖入式港池口门宽度还没有明确的规定和要求,从航道角度出发,引入航行漂角和偏航角概念,着力于阐述船舶进出于两码头之间的挖入式港池口门宽度的计算确定方法。     

CSTN-LCD驱动算法模型及硬件实现

针对多线寻址驱动方案,以驱动算法的数学理论为基础,建立了可由EDA工具综合的硬件驱动算法模型。该算法模型综合了FRC-PWM灰度技术的解决方案,通过FPGA验证,实现了在保证CSTN-LCD显示效果的前提下使电路设计达到最优化。     

各向异性指数和厚向异性指数对金属板料拉延成形的影响

通过金属板料的拉伸试验 ,分析了厚向异性指数和各向异性指数对金属板料拉延变形的影响     

Dedicated Transportation Subnetworks: Design,Analysis, and Insights

A dedicated subnetwork (DSN) refers to a subset of lanes, with associated loads, in a shipper's transportation network, for which resources—trucks, drivers, and other equipment—are exclusively assigned to accomplish shipping requirements. The resources assigned to a DSN are not shared with the rest of the shipper's network. Thus, a DSN is an autonomously operated subnetwork and, hence, can be subcontracted. We address a novel problem of extracting a DSN for outsourcing to one or more subcontractors, with the objective of maximizing the shipper's savings. In their pure form, the defining conditions of a DSN are often too restrictive to enable the extraction of a sizable subnetwork. We consider two notions—deadheading and lane‐sharing—that aid in improving the size of the DSN. We show that all the optimization problems involved are both strongly NP‐hard and APX‐hard, and demonstrate several polynomially solvable special cases arising from topological properties of the network and parametric relationships. Next, we develop a network‐flow‐based heuristic that provides near‐optimal solutions to practical instances in reasonable time. Finally, using a test bed based on data obtained from a national 3PL company, we demonstrate the substantial monetary impact of subcontracting a DSN and offer useful managerial insights.     

Simple Measures of Individual Cluster-Membership Certainty for Hard Partitional Clustering

We propose two probability-like measures of individual cluster-membership certainty that can be applied to a hard partition of the sample such as that obtained from the partitioning around medoids (PAM) algorithm, hierarchical clustering or k-means clustering. One measure extends the individual silhouette widths and the other is obtained directly from the pairwise dissimilarities in the sample. Unlike the classic silhouette, however, the measures behave like probabilities and can be used to investigate an individual’s tendency to belong to a cluster. We also suggest two possible ways to evaluate the hard partition using these measures. We evaluate the performance of both measures in individuals with ambiguous cluster membership, using simulated binary datasets that have been partitioned by the PAM algorithm or continuous datasets that have been partitioned by hierarchical clustering and k-means clustering. For comparison, we also present results from soft-clustering algorithms such as soft analysis clustering (FANNY) and two model-based clustering methods. Our proposed measures perform comparably to the posterior probability estimators from either FANNY or the model-based clustering methods. We also illustrate the proposed measures by applying them to Fisher’s classic dataset on irises.     

Order Statistics from the Generalized Exponential Distribution and Applications

With linear dispersion effects, the standard factorial designs are not optimal estimation of a mean model. A sequential two-stage experimental design procedure has been proposed that first estimates the variance structure, and then uses the variance estimates and the variance optimality criterion to develop a second stage design that efficiency estimates the mean model. This procedure has been compared to an equal replicate design analyzed by ordinary least squares, and found to be a superior procedure in many situations.

However with small first stage sample sizes the variance estiamtes are not reliable, and hence an alternative procedure could be more beneficial. For this reason a Bayesian modification to the two-stage procedure is proposed which will combine the first stage variance estiamtes with some prior variance information that will produce a more efficient procedure. This Bayesian procedure will be compared to the non-Bayesian twostage procedure and to the two one-stage alternative procedures listed above. Finally, a recommendation will be made as to which procedure is preferred in certain situations.     

Interpreting the Principal Component Analysis of Multivariate Density Functions

Functional principal component analysis (FPCA) as a reduction data technique of a finite number T of functions can be used to identify the dominant modes of variation of numeric three-way data.

We carry out the FPCA on multidimensional probability density functions, relate this method to other standard methods and define its centered or standardized versions. Grounded on the relationship between FPCA of densities, FPCA of their corresponding characteristic functions, PCA of the MacLaurin expansions of these characteristic functions and dual STATIS method applied to their variance matrices, we propose a method for interpreting the results of the FPCA of densities. This method is based on the investigations of the relationships between the scores of the FPCA and the moments associated to the densities.

The method is illustrated using known Gaussian densities. In practice, FPCA of densities deals with observations of multidimensional variables on T occasions. These observations can be used to estimate the T associated densities (i) by estimating the parameters of these densities, assuming that they are Gaussian, or (ii) by using the Gaussian kernel method and choosing the matrix bandwidth by the normal reference rule. Thereafter, FPCA estimate is derived from these estimates and the interpretation method is carried out to explore the dominant modes of variation of the types of three-way data encountered in sensory analysis and archaeology.     

The construction of optimal histograms

In this note, a stopping rule is derived that gives the optimal histogram bin width to approximate a probability density function, The procedure minimizes the mean integrated square error between the density function and the histogram, and requires no prior knowledge of the density function.     

A procedure for approximate negative binomial tolerance intervals

In this article, we present a procedure for approximate negative binomial tolerance intervals. We utilize an approach that has been well-studied to approximate tolerance intervals for the binomial and Poisson settings, which is based on the confidence interval for the parameter in the respective distribution. A simulation study is performed to assess the coverage probabilities and expected widths of the tolerance intervals. The simulation study also compares eight different confidence interval approaches for the negative binomial proportions. We recommend using those in practice that perform the best based on our simulation results. The method is also illustrated using two real data examples.