With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study...With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.展开更多
The China Infectious Disease Automated-alert and Response System（CIDARS） was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the...The China Infectious Disease Automated-alert and Response System（CIDARS） was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control（CDC） at all levels in China. In the CIDARS, thresholds are determined using the ？Mean＋2SD？ in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the ？Mean ＋2SD？ method to the performance of 5 novel algorithms to select optimal ？Outbreak Gold Standard（OGS）？ and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The ？Mean＋2 SD？, C1, C2, moving average（MA）, seasonal model（SM）, and cumulative sum（CUSUM） algorithms were applied. Outbreak signals for the predicted value（Px） were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A（chickenpox and mumps）, TYPE B（influenza and rubella） and TYPE C [hand foot and mouth disease（HFMD） and scarlet fever]. Optimized thresholds for chickenpox（P_（55））, mumps（P_（50））, influenza（P_（40）, P_（55）, and P_（75））, rubella（P_（45） and P_（75））, HFMD（P_（65） and P_（70））, and scarlet fever（P_（75） and P_（80）） were identified. The C1, C2, CUSUM, SM, and MA algorithms were appropriate for TYPE A. All 6 algorithms were appropriate for TYPE B. C1 and CUSUM algorithms were appropriate for TYPE C. It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.展开更多
Heterogeneous catalysis is of tremendous importance to modern industries. Exposed atoms of heterogeneous catalysts are heavily involved in surface processes such as the adsorption, activation, diffusion and reaction o...Heterogeneous catalysis is of tremendous importance to modern industries. Exposed atoms of heterogeneous catalysts are heavily involved in surface processes such as the adsorption, activation, diffusion and reaction of substrate molecules. Surfaces of metal or metal oxide based catalysts are usually taken as hard templates that only undergo limited relaxation during catalytic reactions, especially in theoretical simulations. In this work, by using genetic algorithm (GA) aided density functional theory (DFT) calculations, we studied the surface processes involved in CO oxidation on the Au(100) surface. The use of GA greatly improves the capacity of DFT calculations in locating the potential energy surface (PES) of the surface reactions, and surprisingly, it has been found that the Au(100) surface can undergo drastic reconstruction under the influence of O adsorption and the adapted partially oxidized Au surface exhibits unique activities for subsequent adsorptions and reactions. This work depicts the kinetic nature of the Au (100) surface in its catalyzed reactions and also significantly expands our understanding of how surface atoms act in heterogeneous catalysis.展开更多
Gold price is affected by a variety of factors and has highly nonlinear and random features. Some traditional forecast methods emphasize linear relations excessively and some ignore the price randomness. The predictiv...Gold price is affected by a variety of factors and has highly nonlinear and random features. Some traditional forecast methods emphasize linear relations excessively and some ignore the price randomness. The predictive error is relatively large. Therefore, a BP neural network model based on principal component analysis (PCA) and genetic algorithm (GA) was proposed for the short-term prediction of gold price. BP could establish the gold price forecasting model. The weights and thresholds of BP neural network are optimized by GA, which overcome the shortcoming that BP algorithm falls into local minimum easily. PCA can effectively simplify the network input variables and speed up the convergence. The results showed that, compared with GA-BP and BP, the convergence rate of PCA-GA-BP neural network model was faster and the prediction accuracy was higher in the prediction of gold price.展开更多
This paper analyzes an open pit gold mine project based on the O'Hara cost model. Hypothetical data is proposed based on different authors that have studied open pit gold projects, and variations are proposed acco...This paper analyzes an open pit gold mine project based on the O'Hara cost model. Hypothetical data is proposed based on different authors that have studied open pit gold projects, and variations are proposed according to the probability distributions associated to key variables affecting the NPV, like production level, ore grade, price of ore, and others, so as to see what if, in a gold open pit mine project of 3000 metric tons per day of ore. Two case scenarios were analyzed to simulate the NPV, one where there is low certainty data available, and the other where the information available is of high certainty. Results based on genetic algorithm metaheuristic simulations, which combine basically Montecarlo simulations provided by the Palisade Risk software, the O'Hara cost model, net smelter return and financial analysis tools offered by Excel are reported, in order to determine to which variables of the project is more sensitive the NPV.展开更多
基金supported by the National Natural Science Foundation of China(Nos.41374130 and 41604154)
文摘With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.
基金supported by the Key Laboratory of Public Health Safety of the Ministry of Education,Fudan University,China(No.GW2015-1)
文摘The China Infectious Disease Automated-alert and Response System（CIDARS） was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control（CDC） at all levels in China. In the CIDARS, thresholds are determined using the ？Mean＋2SD？ in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the ？Mean ＋2SD？ method to the performance of 5 novel algorithms to select optimal ？Outbreak Gold Standard（OGS）？ and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The ？Mean＋2 SD？, C1, C2, moving average（MA）, seasonal model（SM）, and cumulative sum（CUSUM） algorithms were applied. Outbreak signals for the predicted value（Px） were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A（chickenpox and mumps）, TYPE B（influenza and rubella） and TYPE C [hand foot and mouth disease（HFMD） and scarlet fever]. Optimized thresholds for chickenpox（P_（55））, mumps（P_（50））, influenza（P_（40）, P_（55）, and P_（75））, rubella（P_（45） and P_（75））, HFMD（P_（65） and P_（70））, and scarlet fever（P_（75） and P_（80）） were identified. The C1, C2, CUSUM, SM, and MA algorithms were appropriate for TYPE A. All 6 algorithms were appropriate for TYPE B. C1 and CUSUM algorithms were appropriate for TYPE C. It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.
基金supported by National Key R&D Program of China(No. 2018YFA0208602)National Natural Science Foundation of China(Nos. 21421004, 21825301, 21573067, 91545103)Program of Shanghai Academic Research Leader (No. 17XD1401400)
文摘Heterogeneous catalysis is of tremendous importance to modern industries. Exposed atoms of heterogeneous catalysts are heavily involved in surface processes such as the adsorption, activation, diffusion and reaction of substrate molecules. Surfaces of metal or metal oxide based catalysts are usually taken as hard templates that only undergo limited relaxation during catalytic reactions, especially in theoretical simulations. In this work, by using genetic algorithm (GA) aided density functional theory (DFT) calculations, we studied the surface processes involved in CO oxidation on the Au(100) surface. The use of GA greatly improves the capacity of DFT calculations in locating the potential energy surface (PES) of the surface reactions, and surprisingly, it has been found that the Au(100) surface can undergo drastic reconstruction under the influence of O adsorption and the adapted partially oxidized Au surface exhibits unique activities for subsequent adsorptions and reactions. This work depicts the kinetic nature of the Au (100) surface in its catalyzed reactions and also significantly expands our understanding of how surface atoms act in heterogeneous catalysis.
文摘Gold price is affected by a variety of factors and has highly nonlinear and random features. Some traditional forecast methods emphasize linear relations excessively and some ignore the price randomness. The predictive error is relatively large. Therefore, a BP neural network model based on principal component analysis (PCA) and genetic algorithm (GA) was proposed for the short-term prediction of gold price. BP could establish the gold price forecasting model. The weights and thresholds of BP neural network are optimized by GA, which overcome the shortcoming that BP algorithm falls into local minimum easily. PCA can effectively simplify the network input variables and speed up the convergence. The results showed that, compared with GA-BP and BP, the convergence rate of PCA-GA-BP neural network model was faster and the prediction accuracy was higher in the prediction of gold price.
基金the Mine Planning Research Group–GIPLAMIN-of the Mines Faculty,National University of Colombia
文摘This paper analyzes an open pit gold mine project based on the O'Hara cost model. Hypothetical data is proposed based on different authors that have studied open pit gold projects, and variations are proposed according to the probability distributions associated to key variables affecting the NPV, like production level, ore grade, price of ore, and others, so as to see what if, in a gold open pit mine project of 3000 metric tons per day of ore. Two case scenarios were analyzed to simulate the NPV, one where there is low certainty data available, and the other where the information available is of high certainty. Results based on genetic algorithm metaheuristic simulations, which combine basically Montecarlo simulations provided by the Palisade Risk software, the O'Hara cost model, net smelter return and financial analysis tools offered by Excel are reported, in order to determine to which variables of the project is more sensitive the NPV.
