The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides ide...The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides identification algorithm for portable gamma spectrometers.First,the gamma spectra of 12 target nuclides(including the background case)were measured to create training datasets.The characteristic energies,obtained through energy calibration and full-energy peak addresses,are utilized as input features for a neural network.A large number of single-and multiple-nuclide training datasets are generated using random combinations and small-range drifting.Subsequently,a multi-label classification neural network based on a binary cross-entropy loss function is applied to export the existence probability of certain nuclides.The designed algorithm effectively reduces the computation time and storage space required by the neural network and has been successfully implemented in a portable gamma spectrometer with a running time of t_(r)<2 s.Results show that,in both validation and actual tests,the identification accuracy of the designed algorithm reaches 94.8%,for gamma spectra with a dose rate of d≈0.5μSv∕h and a measurement time t_(m)=60 s.This improves the ability to perform rapid on-site nuclide identification at important sites.展开更多
<span style="font-family:Verdana;">A simple </span><span style="font-family:Verdana;">portable X-Ray Fluorescence (</span><span style="font-family:;" "=&qu...<span style="font-family:Verdana;">A simple </span><span style="font-family:Verdana;">portable X-Ray Fluorescence (</span><span style="font-family:;" "=""><span style="font-family:Verdana;">XRF) spectrometer was successfully used for </span><i><span style="font-family:Verdana;">in-situ</span></i><span style="font-family:Verdana;"> and nondestructive identification of the painting materials in two 15</span><sup><span style="font-family:Verdana;">th</span></sup><span style="font-family:Verdana;"> century icons from the Onufri Museum in Beart, Albania. </span></span><span style="font-family:Verdana;">The spectrometer is based on a low power X-ray tube, a thermoelectrically cooled Si PIN detector and the spectrum acquisition system. It was assembled and adjusted at our laboratory for the investigation of the icons. </span><span style="font-family:Verdana;">A small number of pigments were clearly identified by </span><span style="font-family:Verdana;">X-Ray Fluorescence (</span><span style="font-family:Verdana;">XRF) measurements in both icons. This include</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> Lead white for the white color, gold and yellow ochre for the yellow color, red lead, cinnabar and red ochre for the red color, as well as cooper based pigments for the green color. At the same time, the investigation raised some new questions that need further investigations by </span><span style="font-family:Verdana;">the use of additional analytical techniques. The results show that in both</span><span style="font-family:Verdana;"> icons are used similar pigments, which are in accordance with the Byzantine icon painting tradition.</span></span>展开更多
Soil is the basis of agricultural and forestry production,and it is of great significance to obtain soil information efficiently and comprehensively for soil management.Due to the complexity of soil organic components...Soil is the basis of agricultural and forestry production,and it is of great significance to obtain soil information efficiently and comprehensively for soil management.Due to the complexity of soil organic components,it is difficult to obtain the information of soil organic components comprehensively by traditional chemical analysis method.As a non-destructive,real-time and high-throughput analysis method,mid infrared spectroscopy(MIR)has the ability to obtain soil organic environmental information efficiently and accurately.It can provide a large number of basic data for soil environmental monitoring,digital mapping,agricultural and forestry production,and help to realize the real-time monitoring of soil environment and the informatization of agriculture and forestry.In this paper,the detection process of MIR obtaining soil environmental spectral information and processing methods of spectral data were briefly introduced,and the research progress on extraction and influencing factors of mid infrared spectrum characteristics of soil in recent years was reviewed.Moreover,the significance and future development direction of soil science for the technology were discussed.展开更多
Iodine-131 is a highly toxic and volatile artificial radionuclide that is easily inhaled or ingested by the human body and selectively accumulates in thyroid tissue.With the development of nuclear medicine and nuclear...Iodine-131 is a highly toxic and volatile artificial radionuclide that is easily inhaled or ingested by the human body and selectively accumulates in thyroid tissue.With the development of nuclear medicine and nuclear power plants,the unintended release of ^(131)I has been widely studied,and the in vivo measurement of ^(131)I in the thyroid has become a research hotspot in the field of radiation protection.In recent decades,several methods and devices have been developed for in vivo measurements with respect to different measurement purposes and requirements.In this work,for more accurate determinations of individual ^(131)I in the thyroid in the field,the uncertainties of measurements by using portable gamma spectrometers were reviewed and analyzed,and monitoring strategies for improving the accuracy were proposed and prospected.展开更多
Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging ...Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest(RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models(DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO_(2) contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.展开更多
文摘The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides identification algorithm for portable gamma spectrometers.First,the gamma spectra of 12 target nuclides(including the background case)were measured to create training datasets.The characteristic energies,obtained through energy calibration and full-energy peak addresses,are utilized as input features for a neural network.A large number of single-and multiple-nuclide training datasets are generated using random combinations and small-range drifting.Subsequently,a multi-label classification neural network based on a binary cross-entropy loss function is applied to export the existence probability of certain nuclides.The designed algorithm effectively reduces the computation time and storage space required by the neural network and has been successfully implemented in a portable gamma spectrometer with a running time of t_(r)<2 s.Results show that,in both validation and actual tests,the identification accuracy of the designed algorithm reaches 94.8%,for gamma spectra with a dose rate of d≈0.5μSv∕h and a measurement time t_(m)=60 s.This improves the ability to perform rapid on-site nuclide identification at important sites.
文摘<span style="font-family:Verdana;">A simple </span><span style="font-family:Verdana;">portable X-Ray Fluorescence (</span><span style="font-family:;" "=""><span style="font-family:Verdana;">XRF) spectrometer was successfully used for </span><i><span style="font-family:Verdana;">in-situ</span></i><span style="font-family:Verdana;"> and nondestructive identification of the painting materials in two 15</span><sup><span style="font-family:Verdana;">th</span></sup><span style="font-family:Verdana;"> century icons from the Onufri Museum in Beart, Albania. </span></span><span style="font-family:Verdana;">The spectrometer is based on a low power X-ray tube, a thermoelectrically cooled Si PIN detector and the spectrum acquisition system. It was assembled and adjusted at our laboratory for the investigation of the icons. </span><span style="font-family:Verdana;">A small number of pigments were clearly identified by </span><span style="font-family:Verdana;">X-Ray Fluorescence (</span><span style="font-family:Verdana;">XRF) measurements in both icons. This include</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> Lead white for the white color, gold and yellow ochre for the yellow color, red lead, cinnabar and red ochre for the red color, as well as cooper based pigments for the green color. At the same time, the investigation raised some new questions that need further investigations by </span><span style="font-family:Verdana;">the use of additional analytical techniques. The results show that in both</span><span style="font-family:Verdana;"> icons are used similar pigments, which are in accordance with the Byzantine icon painting tradition.</span></span>
基金Supported by Independent Subject of Guangxi Key Laboratory of Superior Timber Trees Resource Cultivation(2020-A-04-01)the Central Finance Demonstration Project for Promoting Forestry Science and Technology([2021]TG18)Big Data Mining Technology and Industrialization Application of Plantation Soil Environment in Guangxi(Guilin Chanye[2020]01).
文摘Soil is the basis of agricultural and forestry production,and it is of great significance to obtain soil information efficiently and comprehensively for soil management.Due to the complexity of soil organic components,it is difficult to obtain the information of soil organic components comprehensively by traditional chemical analysis method.As a non-destructive,real-time and high-throughput analysis method,mid infrared spectroscopy(MIR)has the ability to obtain soil organic environmental information efficiently and accurately.It can provide a large number of basic data for soil environmental monitoring,digital mapping,agricultural and forestry production,and help to realize the real-time monitoring of soil environment and the informatization of agriculture and forestry.In this paper,the detection process of MIR obtaining soil environmental spectral information and processing methods of spectral data were briefly introduced,and the research progress on extraction and influencing factors of mid infrared spectrum characteristics of soil in recent years was reviewed.Moreover,the significance and future development direction of soil science for the technology were discussed.
基金partially supported by the National Natural Science Foundation of China(No.11775053).
文摘Iodine-131 is a highly toxic and volatile artificial radionuclide that is easily inhaled or ingested by the human body and selectively accumulates in thyroid tissue.With the development of nuclear medicine and nuclear power plants,the unintended release of ^(131)I has been widely studied,and the in vivo measurement of ^(131)I in the thyroid has become a research hotspot in the field of radiation protection.In recent decades,several methods and devices have been developed for in vivo measurements with respect to different measurement purposes and requirements.In this work,for more accurate determinations of individual ^(131)I in the thyroid in the field,the uncertainties of measurements by using portable gamma spectrometers were reviewed and analyzed,and monitoring strategies for improving the accuracy were proposed and prospected.
基金BL Allen Endowment in Pedology at Texas Tech University,USAthe Brazilian funding agencies National Council for Scientific and Technological Development (CNPq) (Nos.301930/2019-8 and 306389/2019-7)+1 种基金the Coordination for the Improvement of Higher Education Personnel (CAPES),Brazil (No.590-2014)Research Support Foundation of the State of Minas Gerais (FAPEMIG),Brazil (No.PPM 00305-17) for the financial support provided。
文摘Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest(RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models(DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO_(2) contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.
