CT诊断胸部平片隐匿部位肿瘤的检出价值

本文对２２７例胸部肿瘤中的３４例Ｘ线不易发现的肿瘤进行了Ｘ线与ＣＴ诊断的对照分析,此组病例共占胸部肿瘤的１４．９％,胸部隐匿部位的肿瘤在传统Ｘ光正位胸片上受组织重叠的影响是不易看到的,容易漏诊,须通过ＣＴ扫描检查方可观察到,ＣＴ扫描是诊断胸部隐匿部位肿瘤的最好,最有效的检查方法之一。     

TSP超前地质预报方法在隧道工程中的应用研究

在隧道施工期地质超前预报中采用综合的预报方法对隧道开挖工作面前方地质条件（情况）进行及时准确的预报,TSP超前地质预报是目前应用最为广泛的长距离预报手段,本文主要从原理,数据采集和数据处理等方面进行了介绍,并结合工程实例分析说明其应用效果。     

The Formation Mechanism and Developmental Characteristics of Tight Oil

<正>1 Introduction Unconventional oil and gas,a kind of resources of great exploration potential,has a wide application future.Tight oil,an important part of unconventional oil and gas,attracts much more attention.With the continuous improvement of technology exploration and the continuous exploitation of oil and gas,tight oil is widedly concerned in recent years.In order to exploite tight oil effectively,many researchers using different materials and methods to discuss the     

2017年11月7—8日南昌市一次重度污染天气过程分析

利用地面常规观测资料、探空资料和大气污染物监测数据等资料,对2017年11月7—8日南昌市一次重度污染天气过程的大气污染物特征、天气形势及气象要素特点等进行了分析,并运用拉格朗日混合单粒子轨道模型（HYSPLIT）分析了大气污染物的后向轨迹。结果表明：1） 此次过程的首要污染物为PM2.5,空气质量呈东部劣于西部,市内劣于市郊的特点。AQI有明显的日变化特征,凌晨和上午10时各有一个AQI高峰,傍晚有一个AQI低谷,中午前后为AQI明显下降时段。2） 地面气压梯度小、风速小,不利于污染物的扩散。对流层中层有低槽东移,低层有弱切变线位于江西省北部,低层弱辐合导致周边污染物向中心辐合堆积。地面能见度与相对湿度变化呈反相关,在重度污染天气发生时多为雾和霾的混合物。南昌上空表现为“中层湿、低层干”的特点,在较干的低层有等温层和弱的逆温层,能抑制大气污染物的垂直扩散。3） 此次重度污染天气过程的大气污染物来源主要有三个,即南昌市周边环境污染、本地污染源的排放和大气污染物的外来输入,外来输入源主要为广东、广西及湖南上空的大气污染物。     

Eco-environmental spatial characteristics of Fildes Peninsula based on TuPu models

This study applies a TuPu analysis to investigate ecological and environmental aspects of an Antarctic ice-free area, using Fildes Peninsula as an example. The TuPu unit was determined using a vector-grid mixed data model. Information from the eco-environment elements was effectively extracted, and was generalized into different classes by means of data mining technol- ogy. A series of single-factor thematic information TuPu models, such as topography, soil, animal and vegetation, and human activities for Fildes Peninsula were built in this study. The topography TuPu model contained information on elevation and slope. The soil TuPu model involved soil development stages and soil thickness information. The animal and vegetation TuPu model contained the distribution of animals, plant types, lichen cover and lichen height. The human activities TuPu model included popu- lation density and human disturbance index information. The landscape comprehensive information TuPu model of Fildes Penin- sula also was established, and contains twenty-nine landscape units and twelve types of combined environments. The study quan- titatively revealed the spatial morphology and correlation of the regional eco-environment based on the analysis of these TuPu models. From these models, we can draw the conclusion that there is a regular differentiation of eco-environment from the coastal bands to the central hills in Fildes Peninsula, and that the eco-environment condition of the eastern coasts is different from that of the western coasts. The eco-environmental spatial variation also differs greatly from north to south. Based on analysis of spatial correlation, the vegetation in Fildes Peninsula has the greatest correlation with human activity, and has a certain correlation with topography and soil. This research may provide a new technical approach and scientific basis for the in-depth study of Antarctic eco-environments.     

Spectral analysis of vehicle pollutants at traffic intersection in Hong Kong

This paper reports an investigation on the periodic variation of pollutant levels at a typical traffic intersection of Hong Kong. Carbon monoxide, carbon dioxide and particulate matters (PM _x ) were measured respectively and the measured data show periodic variations with the traffic signal intervals. The power spectral density (PSD) approach was used to inspect the trends and periodic oscillations of measured pollutants. Singular spectrum analysis was applied to decompose the measured data into statistically significant non-linear trends and oscillations in the process. From the results, most of the trends tend to increase due to the upcoming rush hour during the experiment. In addition, all the oscillations changed regularly with a period of 136?s, which is coincident with the traffic signal period and the frequency calculated by using PSD. The trends, together with the oscillations, collectively explain the most percentage of the variability of the data in the time series and provide the principal components of the data in understanding the periodic variation of the pollutant concentration. It can be deduced that vehicle emission is the major contributor to the air pollution in downtown area and pedestrians should be more alerted when crossing the busy traffic intersections.     

Instructions for Authors

Instructions for Authors

Instructions for Authors