咨询工程师如何做好工程前期沟通管理

一、咨询公司进行工程前期沟通的意义 在工程前期咨询过程中沟通的有效性往往能决定该阶段工作的成败,主要体现在以下几方面. （1）为项目的决策和计划提供信息.项目的相关各方通过工程项目内外部环境之间的有关信息的沟通,获得对自己有价值的信息,并依据这些信息进行项目的决策和计划.     

Preparation of BaTiO3-Based Nonreducible X7R Dielectric Materials Via Nanometer Powders Doping

Nonreducible X7R dielectric materials, which can be cofired with Ni electrode, were obtained by doping nanometer additives to BaTiO₃ powders synthesized by employing a sol-gel method. The crystallization process, structure and constituent of nanometer additives, as well as morphologies were studied via differential thermal and thermogravimetric analyses (DTA/TG), X-ray diffraction (XRD) and transmission electron microscopy (TEM). The effects of the calcining temperature of dopants on the dielectric properties were also discussed. The experiment results proved that higher-performance X7R dielectric materials could be obtained via nanometer doping compared with conventional doping. At room temperature, dielectric constant is more than 3000; dielectric loss lower than 1.0% and insulation resistance more than 10¹¹Ω ⋅cm.     

Informatics challenges of high-throughput microscopy

In this article, we discussed the emerging informatics issues of high-throughput screening (HTS) using automated fluorescence microscopy technology, otherwise known as high-content screening (HCS) in the pharmaceutical industry. Optimal methods of scoring biomarkers and identifying candidate hits have been actively studied in academia and industry, with the exception of data modeling topics. To find candidate hits, we need to score the images associated with different compound interventions. In the application example of RNAi genome-wide screening, we aim to find the candidate effectors or genes which correspond to the images acquired using the three channels. Scoring the effectors is equivalent to scoring the images based on the number of phenotypes existing in those images. Our ultimate objective of studying HTS is to model the relationship between gene networks and cellular phenotypes, investigate cellular communication via protein interaction, and study the disease mechanism beyond the prediction based on the molecular structure of the compound. Finally, computational image analysis has become a powerful tool in cellular and molecular biology studies. Signal processing and modeling for high-throughput image screening is an emerging filed that requires novel algorithms for dynamical system analysis, image processing, and statistical modeling. We hope that this article will motivate the signal processing communities to address challenging data modeling and other informatics issues of HTS.     

Clustering for Processing Rate Optimization

Clustering (or partitioning) is a crucial step between logic synthesis and physical design in the layout of a large scale design. A design verified at the logic synthesis level may have timing closure problems at post-layout stages due to the emergence of multiple-clock-period interconnects. Consequently, a tradeoff between clock frequency and throughput may be needed to meet the design requirements. In this paper, we find that the processing rate, defined as the product of frequency and throughput, of a sequential system is upper bounded by the reciprocal of its maximum cycle ratio, which is only dependent on the clustering. We formulate the problem of processing rate optimization as seeking an optimal clustering with the minimal maximum-cycle-ratio in a general graph, and present an iterative algorithm to solve it. Experimental results validate the efficiency of our algorithm     

宣传社会主义荣辱观加强大学生理想信念教育

社会主义荣辱观是培养社会主义“四有”新人的题中应有之义。我们应当把树立社会主义荣辱观渗透到大学生思想政治教育的各个环节中去。理想信念教育作为大学生思想政治教育的核心，更应宣传社会主义荣辱观，加强其世界观、人生观和价值观的教育。这对于加强大学生理想信念教育，推进高校思想政治教育改革，具有重大的现实意义。     

高等数学课程的教学改革

对学生进行数学课有关问题的调查．发现数学教学中存在教学大纲过时、教学内容陈旧、教学方法落后等问题。针对这些问题。对高职院校教学课程教学改革提出了新的要求。制定合适的教学大纲。理顺教学内容，推出具有创新意义的教材，结合计算机技术的应用改进教学方法，革新考试方法等，都是数学教学改革的重要组成部分。     

Automatic feature selection for unsupervised clustering of cycle-based signals in manufacturing processes

Recent developments in sensing and computer technology have resulted in most manufacturing processes becoming a data-rich environment. A cycle-based signal refers to an analog or digital signal that is obtained during each repetition of an operation cycle in a manufacturing process. It is a very important class of in-process sensing signals for manufacturing processes because it contains extensive information on the process condition and product quality (e.g., the forming force signal in forging processes). In contrast with currently available supervised classification approaches that heavily depend on the training dataset or engineering field knowledge, this paper aims to develop an automatic feature selection method for the unsupervised clustering of cycle-base signals. First, principal component analysis is applied to the raw signals. Then a new method is proposed to select information containing principal components to allow clustering to be performed. The dimension of the problem can be significantly reduced through the use of these two steps. Finally, a model-based clustering method is applied to the selected principal components to find the clusters in the cycle-based signals. A numerical example and a real-world example of a forging process are used to illustrate the effectiveness of the proposed method. The proposed technique is an important data pre-processing technique for the monitoring and diagnostic system development using cycle-based signals for manufacturing processes.     

The LabMatrix system

The LabMatrix^™ is a prototyping system designed to give the user a practical and versatile platform for testing microfluidic applications in the fields of health care and life sciences. The LabMatrix^™ system consists of a microfluidic breadboard and cover that align and secure a series of specially designed LabMatrix^™ microfluidic chips. Chips are easily arranged and rearranged into a user-defined fluidic network. The LabMatrix^™ system is designed with maximum flexibility in mind, providing the user with a means to prototype a wide range of microfluidic applications in a short period.     

Conjugate filters with spectral-like resolution for 2D incompressible flows

A conjugate filter oscillation reduction scheme originally developed for compressible flows and in general for hyperbolic conservation laws is applied to the solution of the incompressible Navier-Stokes equation with periodic boundary conditions. Conjugate low-pass and high-pass filters are constructed by using a local spectral method, the discrete singular convolution algorithm. A spectral-like resolution, i.e., near the machine precision obtained at a sampling rate close to the Nyquist limit (2 points per wavelength), is achieved in treating a smooth initial value problem which admits an exact solution. The spectral-like resolution is enhanced by the use of conjugate low-pass filters in treating the double shear layer and multi-shear layer problems, which exhibit extremely small flow features.