提高节目质量视评与收视调查缺一不可

众所周知,电视节目的收视率和收视份额是电视台的生存之本。而收视率或收视份额的高低则取决于电视节目质量的好坏。面对当今媒体竞争异常激烈的形势,地市级电视台该如何提高节目质量,稳住乃至提高节目的收视率和收视份额是一个值得探讨的问题。梧州电视台经过十年时间的摸索实践,逐步形成了观众、专家视评与专业机构收视调查报告相结合的节目评价模式。

Debris Flow in Metropolitan Area - 2011 Seoul Debris Flow

A large number of debris flows occurred simultaneously at around 8:30 to 8:50 a.m.on July 27,2011,at the center of Seoul,Korea.This area is located in the southern part of Seoul and is a densely populated district.As a result of the debris flow event,16 people were killed,30 houses were buried,and 116 houses were damaged around Umyeon Mountain,a relatively small mountain with a height of 312.6 m.Since the debris flow event,field investigations on the initiation and transportation zones of debris flows have been carried out.Rainfall data were collected from the automatic weather stations(AWSs) which are operated by the Korea Meteorological Administration(KMA).Video files recorded by residents were also acquired and used to analyze the flow characteristics of the debris flow.Field investigation shows that about 40 debris flows occurred around Umyeon Mountain and most of the debris flows were initiated by small slope failures.The effects of the precipitation that triggered the debris flows were analyzed as well.A landslide hazard map which considers slope gradient and aspect,strength of soil,hazard record,rainfall conditions,and vegetation,was constructed and compared with the initiation zones of debris flows.

Improved nonconvex optimization model for low-rank matrix recovery

Low-rank matrix recovery is an important problem extensively studied in machine learning, data mining and computer vision communities. A novel method is proposed for low-rank matrix recovery, targeting at higher recovery accuracy and stronger theoretical guarantee. Specifically, the proposed method is based on a nonconvex optimization model, by solving the low-rank matrix which can be recovered from the noisy observation. To solve the model, an effective algorithm is derived by minimizing over the variables alternately. It is proved theoretically that this algorithm has stronger theoretical guarantee than the existing work. In natural image denoising experiments, the proposed method achieves lower recovery error than the two compared methods. The proposed low-rank matrix recovery method is also applied to solve two real-world problems, i.e., removing noise from verification code and removing watermark from images, in which the images recovered by the proposed method are less noisy than those of the two compared methods.

Effects of thermal transport properties on temperature distribution within silicon wafer

A combined conduction and radiation heat transfer model was used to simulate the heat transfer within wafer and investigate the effect of thermal transport properties on temperature non-uniformity within wafer surface. It is found that the increased conductivities in both doped and undoped regions help reduce the temperature difference across the wafer surface. However, the doped layer conductivity has little effect on the overall temperature distribution and difference. The temperature level and difference on the top surface drop suddenly when absorption coefficient changes from 104 to 103 m-1. When the absorption coefficient is less or equal to 103 m-1, the temperature level and difference do not change much. The emissivity has the dominant effect on the top surface temperature level and difference. Higher surface emissivity can easily increase the temperature level of the wafer surface. After using the improved property data, the overall temperature level reduces by about 200 K from the basis case. The results will help improve the current understanding of the energy transport in the rapid thermal processing and the wafer temperature monitor and control level.

WATERiD＇s Novel Methodology for Condition Assessment Cost Data Collection and Visualization

A profound understanding of the costs to perform condition assessment on buried drinking water pipeline infrastructure is required for enhanced asset management. Toward this end, an automated and uniform method of collecting cost data can provide water utilities a means for viewing, understanding, interpreting and visualizing complex geographically referenced cost information to reveal data relationships, patterns and trends. However, there has been no standard data model that allows automated data collection and interoperability across platforms. The primary objective of this research is to develop a standard cost data model for drinking water pipeline condition assessment projects and to conflate disparate datasets from differing utilities. The capabilities of this model will be further demonstrated through performing trend analyses. Field mapping files will be generated from the standard data model and demonstrated in an interactive web map created using Google Maps API （application programming interface） for JavaScript that allows the user to toggle project examples and to perform regional comparisons. The aggregation of standardized data and further use in mapping applications will help in providing timely access to condition assessment cost information and resources that will lead to enhanced asset management and resource allocation for drinking water utilities.