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This paper provides a novel and effective compensation method by improving the hardware design and software algorithm to achieve optimization of piezoresistive pressure sensors and corresponding measurement systems in order to measure pressure more accurately and stably, as well as to meet the application requirements of the meteorological industry. Specifically, GE NovaSensor MEMS piezoresistive pressure sensors within a thousandth of accuracy are selected to constitute an array. In the versatile compensation method, the hardware utilizes the array of MEMS pressure sensors to reduce random error caused by sensor creep, and the software adopts the data fusion technique based on the wavelet neural network (WNN) which is improved by genetic algorithm (GA) to analyze the data of sensors for the sake of obtaining accurate and complete information over the wide temperature and pressure ranges. The GA-WNN model is implemented in OPEN ACCESSMicromachines 2015, 6 555 hardware by using the 32-bit STMicroelectronics (STM32) microcontroller combined with an embedded real-time operating system µC/OS-II to make the output of the array of MEMS sensors be a direct digital readout. The results of calibration and test experiments clearly show that the GA-WNN technique can be effectively applied to minimize the sensor errors due to the temperature drift, the hysteresis effect and the long-term drift because of aging and environmental changes. The maximum error of the low cost piezoresistive MEMS-array pressure transmitter proposed by us is within 0.04% of its full-scale value, and it can satisfy the meteorological pressure measurement.

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Due to the solar radiation effect, current air temperature sensors inside a thermometer screen or radiation shield may produce measurement errors that are 0.8 °C or higher. To improve the observation accuracy, an aspirated temperature measurement platform is designed. A computational fluid dynamics (CFD) method is implemented to analyze and calculate the radiation error of the aspirated temperature measurement platform under various environmental conditions. Then, a radiation error correction equation is obtained by fitting the CFD results using a genetic algorithm (GA) method. In order to verify the performance of the temperature sensor, the aspirated temperature measurement platform, temperature sensors with a naturally ventilated radiation shield, and a thermometer screen are characterized in the same environment to conduct the intercomparison. The average radiation errors of the sensors in the naturally ventilated radiation shield and the thermometer screen are 0.44 °C and 0.25 °C, respectively. In contrast, the radiation error of the aspirated temperature measurement platform is as low as 0.05 °C. This aspirated temperature sensor allows the radiation error to be reduced by approximately 88.6% compared to the naturally ventilated radiation shield, and allows the error to be reduced by a percentage of approximately 80% compared to the thermometer screen. The mean absolute error and root mean square error between the correction equation and experimental results are 0.032 °C and 0.036 °C, respectively, which demonstrates the accuracy of the CFD and GA methods proposed in this research.

Mechanical properties of silicon nanobeams are of prime importance in nanoelectromechanical system applications. A numerical experimental method of determining resonant frequencies and Young's modulus of nanobeams by combining finite element analysis and frequency response tests based on an electrostatic excitation and visual detection by using a laser Doppler vibrometer is presented in this paper. Silicon nanobeam test structures are fabricated from silicon-on-insulator wafers by using a standard lithography and anisotropic wet etching release process, which inevitably generates the undercut of the nanobeam clamping. In conjunction with three-dimensional finite element numerical simulations incorporating the geometric undercut, dynamic resonance tests reveal that the undercut significantly reduces resonant frequencies of nanobeams due to the fact that it effectively increases the nanobeam length by a correct value ΔL, which is a key parameter that is correlated with deviations in the resonant frequencies predicted from the ideal Euler—Bernoulli beam theory and experimentally measured data. By using a least-square fit expression including ΔL, we finally extract Young's modulus from the measured resonance frequency versus effective length dependency and find that Young's modulus of a silicon nanobeam with 200-nm thickness is close to that of bulk silicon. This result supports that the finite size effect due to the surface effect does not play a role in the mechanical elastic behaviour of silicon nanobeams with thickness larger than 200 nm.

The Xiajinbao gold deposit is a medium-sized gold deposit in the Jidong gold province. Ore bodies mainly occur within the Xiajinbao granite porphyry and along the contact zone between the intrusion and Archean plagioclase hornblende gneiss. The zircon LA-ICP-MS age of the Xiajinbao granite porphyry yields 157.8 ± 3.4 Ma, which reflects the metallogenic age of the gold mineralization. Its petrographic features, major and trace element contents, zircon Hf isotopic model ages and compositional features all demonstrate that the Xiajinbao granitic magma is derived from partial melting of the Changcheng unit. The results of H–O isotopic analyses of auriferous quartz veins indicate that the ore-forming fluids are derived from magmatic waters that gradually mixed with meteoric waters during the evolution of the ore-forming fluids. S–Pb isotopic data indicate that the ore-forming fluids were mainly provided by the magma and by plagioclase hornblende gneisses. The gold metallogeny of the Xiajinbao gold deposit is temporally, spatially, and genetically associated with the high-K calc-alkaline-shoshonitic granitic magma emplaced during the Yanshanian orogeny and intruding the Archean plagioclase hornblende gneisses. These magmatic events mainly occurred during the period of 223–153 Ma and comprise three peak periods in the late Triassic (225–205 Ma), the early Jurassic (200–185 Ma) and the middle–late Jurassic (175–160 Ma), respectively. The metallogenic events in this area mainly occurred during the period of 223–155 Ma with the peak periods during the late Triassic (223–210 Ma) and the middle–late Jurassic (175–155 Ma), respectively. Both mineralization and magmatism occurred in a post-collisional tectonic setting related to the collision between the Mongolian plate and the North China plate at the end of the Permian. The magmatism of the early Jurassic occurred during the collision between the Siberian plate and the Mongolian plate, which caused the thickening and melting of the northern margin of the North China plate. The middle and late Jurassic magmatism and metallogenic activities are products of crustal thickening and partial melting during the Yanshanian intra-continental orogeny at the northern margin of the North China plate.