煤炭资源勘查误差理论研究

对煤炭资源勘查资源量与客观物质量之间差值的误差理论探讨,提出误差由真值误差、系统误差和离差3个基本部分组成,其中真值误差是不可消除的观测误差、系统误差是控制网度所决定的准确度标准、而离差是由地质构造及矿床形态的多解性产生的资源量波动。在此基础上,构筑了一个关于误差的数学模型,最后应用误差理论对现行勘查工作中常见的错误进行分析,并就如何避免错误及减少误差提出了见解。     

CPⅢ测量数据处理系统开发若干关键技术研究

本文首先介绍了无砟轨道的相关内容,指出CPⅢ测量数据处理是整个建设中的关键,提出系统开发的总体目标与要求且设计了系统总体结构,详细阐述了系统开发中涉及的关键技术与基本理论,文中最后结合具体工程实例验证系统开发成果。     

富宁洞哈钛铁矿

印支期暗色、致密块状辉绿岩中含较高的含铁矿物,地磁异常较高,为矿源层。辉绿岩与灰岩接触形成矽卡岩带为有利的含矿带。矿体沿大断裂或次级断层形成的通道侵入穹窿状构造中,于二叠系砂岩、板岩隔层的下部形成工业钛铁矿体。     

A coupling of mixed and discontinuous Galerkin finite-element methods for poroelasticity

In this paper, we formulate a finite-element procedure for approximating the coupled fluid and mechanics in Biot’s consolidation model of poroelasticity. We approximate the flow variables by a mixed finite-element space and the displacement by a family of discontinuous Galerkin methods. Theoretical convergence error estimates are derived and, in particular, are shown to be independent of the constrained specific storage coefficient, c _o . This suggests that our proposed algorithm is a potentially effective way to combat locking, or the nonphysical pressure oscillations, which sometimes arise in numerical algorithms for poroelasticity.     

Managing sustainable farmed landscape through 'alternative' food networks: a case study from Italy

This paper focuses on a case study of an 'alternative' food network based in the Abruzzo National Park, Italy, to explore how ideas of sustainable farmland management can be expressed through broader understandings of developing networks of care concerned with local economies and societies, high-quality specialist food products, particular 'traditional' farming practices and livestock breeds, as well as the ecology of a farmed landscape. The scheme allows customers, internationally as well as in Italy, to 'adopt' a milking sheep on a large mountain farm. In return, adopters are sent food products from the farm. The adoption scheme is inter-twined with an agri-tourism project which provides accommodation, runs a restaurant and engages in educational activities. The scheme is the result of the individual initiative of its founder, and is associated with a strongly expressed ethical position concerning the value of sustaining valued local rural landscapes and lifestyles, and the importance of 'reconnecting' urban dwellers with rural areas, farming and 'quality' food production. Yet the localness of the scheme is sustained through wider national and international networks: volunteer and paid workers are drawn from several European countries, funding has been acquired from the EU LEADER programme, and internet and transport technologies are essential in connecting with and supplying an international customer base. The broader economy of care instanced in this case study draws attention to a need to develop strategies for sustainable farmland management constructed around wider programmes of social, economic and cultural, as well as environmental, concern.     

Verification of the motion fields over Southeast Asia by the Florida State University (FSU) superensemble forecast

The Florida State University (FSU) multimodel superensemble forecast is evaluated against several other operational weather models for the Southeast Asia region. The superensemble technique has demonstrated its exceptional skills in forecasting precipitation, motion and mass fields compared to either individual global operational or ensemble mean forecasts. The motion field investigation for the season of 2001 reveals that the superensemble forecasts are closer to the observed data compared to the other global member operational models through its low systematic errors at the 850 hPa level. The FSU multimodel superensemble forecasts exhibit the lowest root mean square errors (RSMEs), the highest correlation against the best observed data and the lowest systematic errors compared to the other operational model members. These forecasts have the potential to provide better daily weather predictions over the Southeast Asia region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asia region.     

推进煤矸石资源化利用的对策建议

煤矸石是我国排放量和堆存量最大的工业固体废弃物之一，但又是可以利用的资源，只要加以资源化利用，就能会变废为宝。在研究我国煤矸石综合利用现状的基础上，指出了煤矸石综合利用中存在的主要问题，从制定规划、完善经济政策、加强法制建设等方面提出了加快推进煤矸石资源化利用的建议。     

Impact of Southern Oscillation on the Frequency of Monsoon Depressions in the Bay of Bengal

The impact of Southern Oscillation on thecyclogenesis over the Bay of Bengal duringthe summer monsoon has been investigated.The analysis of correlation coefficients(CCs) between the frequency of monsoondepressions and the Southern OscillationIndex (SOI) reveals that more depressionsform during July and August of El Niñoyears. Due to this, the seasonal frequencyof monsoon depressions remains little higherduring El Niño epochs even though thecorrelations for June and September are notsignificant. The CCs for July and August aresignificant at the 99% level.The El Niño-Southern Oscillation (ENSO)is known to affect Indian MonsoonRainfall (IMR) adversely. The enhancedcyclogenesis over the Bay of Bengal duringJuly and August is an impact of ENSO whichneeds to be examined closely. Increasedcyclogenesis over the Bay of Bengal may bereducing the deficiency in IMR duringEl Niño years by producing more rainfallover the eastern parts of India duringJuly and August. Thus there is a considerablespatial variation in the impact of ENSOon the monsoon rainfall over India and El Niñoneed not necessarily imply a monsoonfailure everywhere in India.The area of formation of monsoon depressionsshifts eastward during El Niño years.Warmer sea surface temperature (SST) anomaliesprevail over northwest and adjoiningwestcentral Bay of Bengal during premonsoon andmonsoon seasons of El Niño years.May minus March SOI can provide useful predictionsof monsoon depression frequencyduring July and August.     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.