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A design of measuring system with zero temperature drift is proposed for the tri-component array induction tool. The designed down-hole system is able to survive high temperature up to $175 ^{\circ}\mathrm{C}$. It deploys multiplexers before pre-amplifiers to switch 20 signals, including zero signals, calibration signals, and coil signals. A DSP chip and a FPGA chip are integrated for digital signal processing. An algorithm of digital phase sensitive detection is employed for calculating the amplitudes and phases of these three kinds of signals. In order to perform zero-temperature-drift measurements, real time calibrations are applied to the four analog conditioning channels, and the common-mode interference signals of the receiving coils are cancelled during the processing. Experimental data illustrate that, this high temperature system survives in the process of heating without obvious temperature drift.

This paper discusses the challenge of measuring signals for near-bit azimuthal resistivity tools. Current azimuthal resistivity tools present difficulties because of their length and complexity. These issues can be overcome by gathering azimuthal sensitive resistivity data near the bit. However, this still presents space difficulties. Single transmitters and receivers can simplify the structure, but encounter problems with temperature drift. Here, a design is proposed that uses a sensor structure with a single transceiver and two receivers. Real-time calibration and compensation for the analog circuits enables errors in the conditioning channels to be eliminated, with their temperature drift being suppressed across a wide range of temperatures. Experiments were undertaken to test the viability of the proposed measuring system and the results illustrate that its accuracy and reliability meet the requirements for improved azimuthal resistivity measurements.

To be higher the production efficiency of mechanical parts, after analyzing the shortcomings of conventional parts dimension measurement technology, a design scheme of automatic device which can realize on-line dimension measurement of parts by lathe is proposed. It integrates the work of lathe processing, dimension measurement and quality appraisal into one process innovatively. This scheme uses projection optical system to replace the traditional scanning scheme. The measuring device can not only complete the real-time and high-precision measurement of 15 parameters, but also ensure easy operation and maintenance. At the same time, the optical system design has a collimated beam expanding path, which makes the measuring device have a larger measurement range. Combining with the working environment of the measuring device, the design of the shock absorption mechanism is introduced in the structural design, so that the high stability of the measuring results can be maintained under the normal operation of the lathe. Finally, by extracting feature parameters and testing the matching interface, it is proved that the designed device fully meets the technical requirements proposed at the beginning of the study.

To realize the online multi-parameter measurement of the complex part with high precision, the method for measuring part shape based on integration technology of projection optics and image processing is proposed. Key techniques of this method including the projection optics system, the image feature extraction and the image feature matching are deeply studied. After a detailed analysis of the technical characteristics of object telecentric optical path adopted in the part shape measurement, an optical system for measuring instrument is designed. Through the in-depth study of algorithm in image feature extraction and matching, and after the actual test of shaft part, the feasibility of these researched techniques and the accuracy of the developed instrument are verified. The test results also show that this measurement technique of part shape has the characteristics of measurement speed ≤3 s, measurement accuracy ≤±2 μm, real-time 15 parameters measurement in a single. That fully meets the requirements of automated testing.

In this paper, a theoretical model of EM-MWDis set up based on the numerical mode matching method (NMM) and source equivalent principle. The model may consider radial and axial inhomogeneities, facilitate analysis of the influence of casing, mud, high conductivity layer, high resistivity layer on signal transmission. The correctness of this theoretical model is proved by calculation and field test. The effect of formation resistivity, working frequency, casing, drilling mud, pillar, high conductivity layer and high resistivity layer on the signal transmission is analyzed.

成熟森林的碳收支对陆地生态系统碳循环研究具有重要意义。目前，我国关于西南亚高山暗针叶林成熟林碳通量的研究还相对较少，尚不明确对碳循环的作用。以涡度相关技术为基础，对川西贡嘎山东坡峨眉冷杉成熟林生态系统尺度的CO2通量进行长期定位观测。利用2015年6月至2016年5月观测数据，分析了峨眉冷杉成熟林净生态系统CO2交换量（NEE）、生态系统呼吸（Re）和总生态系统生产力（GPP）的季节变异特征及其源汇状况，并结合环境因子，分析CO2通量的主要控制因子。结果表明：（1）峨眉冷杉成熟林NEE具有明显的日变化特征，呈现'U'形变化，白天为负值，夜间为正值，中午前后CO2通量达到最大；各月间日平均NEE变化差异显著，NEE峰值最大出现在2015年6月（-0.64 mg CO2 m-2 s-1），峰值最小出现在2016年1月（-0.08 mg CO2 m-2 s-1）；日平均NEE由正值变为负值的时间夏季最早，冬季最晚，NEE由负值变为正值的时间冬季最早，夏季最晚。（2）峨眉冷杉成熟林NEE、Re和GPP具有明显的月变化。2015年6月和12月NEE分别达到最大值（-46.02 gC m-2月-1）和最小值（-1.42 gC m-2月-1）；Re呈现单峰变化，最大和最小值分别出现在2015年6月（84.78 gC m-2 月-1）和2016年1月（12.82 gC m-2月-1）；GPP最大值和最小值分别出现在2015年6月（130.81 gC m-2月-1）与2016年1月（16.15 gC m-2月-1）。（3）空气温度（Ta）、5 cm土壤温度（Ts5）和光合有效辐射（PAR）是影响峨眉冷杉成熟林CO2通量的主要环境因子。Ta与CO2通量呈指数相关（R2=0.5283，P < 0.01）；白天CO2通量与PAR显著相关（R2=0.4373，P < 0.01）；夜晚CO2通量与Ts5显著相关（R2=0.4717，P < 0.01）。（4）全年NEE、Re和GPP分别为-241.87、564.81 gC m-2和806.68 gC m-2，表明川西贡嘎山峨眉冷杉成熟林具有较强的碳汇功能。

Terrestrial ecosystems are strongly influenced by climate change and soils are key compartments of the globalcarbon (C) cycle in terms of their potential to store or release significant amounts of C. This study is part ofthe interregional IAEA Technical Cooperation Project INT5153 “Assessing the Impact of Climate Change and itsEffects on Soil and Water Resources in Polar and Mountainous Regions” and aims to elucidate driving factors(climatic versus geochemical) of soil organic carbon (SOC) stabilization and greenhouse gas emissions alongaltitudinal transects in different mountain regions.We present novel data from altitudinal transects of four different mountain regions (Zongo, Cordillera Real, Bo-livia; Mount Kilimanjaro, Tanzania; Gongga, Hengduan Mountains, China; Rauris, Hohe Tauern, Austria). Allaltitudinal transects cover a wide range of natural ecosystems under different climatic (MAT, MAP) and soil geo-chemical parameters. Bulk soil samples (four field replicates per ecosystems) were subjected to a combination ofaggregate and particle-size fractionation followed by organic C, total nitrogen (N), stable isotope (13C,15N) andradiocarbon (14C) analyses of all fractions. Bulk soils were further characterized for their texture, geochemistry(Na, K, Ca, Mg, CECpot, Al, Fe, Mn, Si, pH), nutrient status (NH+4, NO−3, Ptot)and incubated for 63 days to assessgreenhouse gas emissions (CO2, CH4, NO, N2O). Moreover, stable C isotope signatures of CO2were determinedto estimate potential sources of soil respiration (using Keeling plots).Cumulative soil CO2emissions (in gCO2-Ckg−1SOC)were highest for the high altitude grassland and forest sitesof Rauris (25.5-25.8) and lowest for the Kilimanjaro forests (4.8-6.9) as well as Bolivian high altitude grasslands(2.5-4.3). Soil CO2emissions were negatively correlated with SOC content (Pearson correlation coefficient rp=-0.35, p=0.002), showing that soils with low SOC contents release the highest amount of CO2per soil C, possiblydue to large fractions of unprotected SOC and thus low SOC stabilization.Particulate organic matter (POM) and sand content were positively correlated with CO2emissions (rp=0.43,p, Geophysical Research Abstracts, 20, ISSN:1029-7006, ISSN:1607-7962

Precisely quantitative assessments of stream flow response to climatic change and permafrost thawing are highly challenging and urgent in cold regions. However, due to the notably harsh environmental conditions, there is little field monitoring data of runoff in permafrost regions, which has limited the development of physically based models in these regions. To identify the impacts of climate change in the runoff process in the Three-River Headwater Region (TRHR) on the Qinghai-Tibet Plateau, two artificial neural network (ANN) models, one with three input variables (previous runoff, air temperature, and precipitation) and another with two input variables (air temperature and precipitation only), were developed to simulate and predict the runoff variation in the TRHR. The results show that the three-input variable ANN model has a superior real-time prediction capability and performs well in the simulation and forecasting of the runoff variation in the TRHR. Under the different scenarios conditions, the forecasting results of ANN model indicated that climate change has a great effect on the runoff processes in the TRHR. The results of this study are of practical significance for water resources management and the evaluation of the impacts of climatic change on the hydrological regime in long-term considerations.