Tectonic Implications of the Combined Use of Tectonomagmatic Geochemical Discrimination Diagrams and Indicators of Magma Flow Sense in Mafic Dykes

Geochemistry is a powerful tool to help characterize the tectonic setting of igneous rocks associations.However,when continental mafic dykes and flood basalts are the target most of the proposed geochemical discrimination diagrams fail to correctly classify them,i.e.many mafic

Assessing proprioception:A critical review of methods

To control movement,the brain has to integrate proprioceptive information from a variety of mechanoreceptors.The role of proprioception in daily activities,exercise,and sports has been extensively investigated,using different techniques,yet the proprioceptive mechanisms underlying human movement control are still unclear.In the current work we have reviewed understanding of proprioception and the three testing methods:threshold to detection of passive motion,joint position reproduction,and active movement extent discrimination,all of which have been used for assessing proprioception.The origin of the methods,the different testing apparatus,and the procedures and protocols used in each approach are compared and discussed.Recommendations are made for choosing an appropriate technique when assessing proprioceptive mechanisms in different contexts.

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

3D printed multicore fiber-tip discriminative sensor for magnetic field and temperature measurements

Miniaturized fiber-optic magnetic field sensors have attracted considerable interest owing to their superiorities in anti-electromagnetic interference and compactness.However,the intrinsic thermodynamic properties of the material make temperature cross-sensitivity a challenging problem in terms of sensing accuracy and reliability.In this study,an ultracompact multicore fiber(MCF)tip sensor was designed to discriminatively measure the magnetic field and temperature,which was subsequently evaluated experimentally.The novel 3D printed sensing component consists of a bowl-shaped microcantilever and a polymer microfluid-infiltrated microcavity on the end-facet of an MCF,acting as two miniaturized Fabry-Perot interferometers.The magnetic sensitivity of the microcantilever was implemented by incorporating an iron micro ball into the microcantilever,and the microfluid-infiltrated microcavity enhanced the capability of highly sensitive temperature sensing.Using this tiny fiber-facet device in the two channels of an MCF allows discriminative measurements of the magnetic field and temperature by determining the sensitivity coefficient matrix of two parameters.The device exhibited a high magnetic field intensity sensitivity,approximately 1805.6 pm/mT with a fast response time of~213 ms and a high temperature sensitivity of 160.3 pm/℃.Moreover,the sensor had a low condition number of 11.28,indicating high reliability in two-parameter measurements.The proposed 3D printed MCF-tip probes,which detect multiple signals through multiple channels within a single fiber,can provide an ultracompact,sensitive,and reliable scheme for discriminative measurements.The bowl-shaped microcantilever also provides a useful platform for incorporating microstructures with functional materials,extending multi-parameter sensing scenarios and promoting the application of MCFs.