Seeds of many hardwood trees are dispersed by scatter-hoarding rodents,and this process is often mediated by the traits of seeds.Although numerous studies have linked seed traits to seed preference by rodents,little i...Seeds of many hardwood trees are dispersed by scatter-hoarding rodents,and this process is often mediated by the traits of seeds.Although numerous studies have linked seed traits to seed preference by rodents,little is known about how rodents forage for seeds when multiple desirable and undesirable seed traits are available simultaneously.Here,we adopt a novel method of designing choice experiments to study how eastern gray squirrels(Sciurus carolinensis)select for 6 traits(caloric value,protein content,tannin concentration,kernel mass,dormancy period and toughness of shell)among seeds.From n=426 seed-pair presentations,we found that squirrels preferentially consumed seeds with short dormancy or tougher shells,and preferentially cached seeds with larger kernel mass,tougher shells and higher tannin concentrations.By incorporating random effects,we found that squirrels exhibited consistent preferences for seed traits,which is likely due to the fitness consequences associated with maintaining cached resources.Furthermore,we found that squirrels were willing to trade between multiple traits when caching seeds,which likely results in more seed species being cached in the fall.Ultimately,our approach allowed us to compute the relative values of different seed traits to squirrels,despite covariance among studied traits across seed species.In addition,by investigating how squirrels trade among different seed traits,important insights can be gleaned into behavioral mechanisms underlying seed caching(and,thus,seed survival)dynamics as well as evolutionary strategies adopted by plants to attract seed dispersers.We describe how discrete choice experiments can be used to study resource selection in other ecological systems.展开更多
An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effecti...An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.展开更多
Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent ...Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent secondary damage,thereby avoiding heavy conse-quences.In this study,fault diagnosis of tractor auxiliary gearbox is presented.Vibration signals of healthy and faulty pinions gear under three different operational conditions(Rotational speeds of 600 RPM,1350 RPM and 2000 RPM)were collected,and discrete wave-let transform(DWT)was used as signal processing.Useful statistical features were calcu-lated from collected signals.Correlation-based feature selection(CFS)method was used to find the best features.Random forest(RF)and multilayer perceptron(MLP)neural net-works were employed to classify the data.The overall accuracy of RF classifier without using feature selection were 86.25%,at 600 RPM.The corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%.The best results obtained at 1350 RPM,since the detection accuracy was 95%.The results of this study demonstrated the effectiveness and feasibility of the proposed method for fault diagnosis of tractor auxiliary gearbox.展开更多
Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods...Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.展开更多
With the advance of new computational technology,stochastic systems simulation and optimization has become increasingly a popular subject in both academic research and industrial applications.This paper presents some ...With the advance of new computational technology,stochastic systems simulation and optimization has become increasingly a popular subject in both academic research and industrial applications.This paper presents some of recent developments about the problem of optimizing a performance function from a simulation model.We begin by classifying different types of problems and then provide an overview of the major approaches,followed by a more in-depth presentation of two specific areas:optimal computing budget allocation and the nested partitions method.展开更多
This paper considers the problem of smoothing a non-stationary time series(having either deterministic and/or stochastic trends) using the discrete cosine transform(DCT).The DCT is a powerful tool which has found frui...This paper considers the problem of smoothing a non-stationary time series(having either deterministic and/or stochastic trends) using the discrete cosine transform(DCT).The DCT is a powerful tool which has found fruitful applications in filtering and smoothing as it can closely approximate the optimal Karhunen-Loeve transform(KLT).In fact,it is known that it almost corresponds to the KLT for first-order autoregressive processes with a root close to unity:This is the case with most economic and financial time series.A number of new results are derived in the paper:(a) The explicit form of the linear smoother based on the DCT,which is found to have time-varying weights and that uses all observations;(b) the extrapolation of the DCT-smoothed series;(c) the form of the average frequency response function,which is shown to approximate the frequency response of the ideal low pass filter;(d) the asymptotic distribution of the DCT coefficients under the assumptions of deterministic or stochastic trends;(e) two news method for selecting an appropriate degree of smoothing,in general and under the assumptions in(d).These findings are applied and illustrated using several real world economic and financial time series.The results indicate that the DCT-based smoother that is proposed can find many useful applications in economic and financial time series.展开更多
Efficiency of the autofocusing algorithm implementations based on various orthogonal transforms is examined. The algorithm uses the variance of an image acquired by a sensor as a focus function. To compute the estimat...Efficiency of the autofocusing algorithm implementations based on various orthogonal transforms is examined. The algorithm uses the variance of an image acquired by a sensor as a focus function. To compute the estimate of the variance we exploit the equivalence between that estimate and the image orthogonal expansion. Energy consumption of three implementations exploiting either of the following fast orthogonal transforms: the discrete cosine, the Walsh-Hadamard, and the Haar wavelet one, is evaluated and compared. Furthermore, it is conjectured that the computation precision can considerably be reduced if the image is heavily corrupted by the noise, and a simple problem of optimal word bit-length selection with respect to the signal variance is analyzed.展开更多
基金The Fred M.Van Eck Forest Foundation for Purdue University and the McIntire-Stennis program provided funding.
文摘Seeds of many hardwood trees are dispersed by scatter-hoarding rodents,and this process is often mediated by the traits of seeds.Although numerous studies have linked seed traits to seed preference by rodents,little is known about how rodents forage for seeds when multiple desirable and undesirable seed traits are available simultaneously.Here,we adopt a novel method of designing choice experiments to study how eastern gray squirrels(Sciurus carolinensis)select for 6 traits(caloric value,protein content,tannin concentration,kernel mass,dormancy period and toughness of shell)among seeds.From n=426 seed-pair presentations,we found that squirrels preferentially consumed seeds with short dormancy or tougher shells,and preferentially cached seeds with larger kernel mass,tougher shells and higher tannin concentrations.By incorporating random effects,we found that squirrels exhibited consistent preferences for seed traits,which is likely due to the fitness consequences associated with maintaining cached resources.Furthermore,we found that squirrels were willing to trade between multiple traits when caching seeds,which likely results in more seed species being cached in the fall.Ultimately,our approach allowed us to compute the relative values of different seed traits to squirrels,despite covariance among studied traits across seed species.In addition,by investigating how squirrels trade among different seed traits,important insights can be gleaned into behavioral mechanisms underlying seed caching(and,thus,seed survival)dynamics as well as evolutionary strategies adopted by plants to attract seed dispersers.We describe how discrete choice experiments can be used to study resource selection in other ecological systems.
基金Projects(61174040,61104178,61374136) supported by the National Natural Science Foundation of ChinaProject(12JC1403400) supported by Shanghai Commission of Science and Technology,ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.
文摘Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent secondary damage,thereby avoiding heavy conse-quences.In this study,fault diagnosis of tractor auxiliary gearbox is presented.Vibration signals of healthy and faulty pinions gear under three different operational conditions(Rotational speeds of 600 RPM,1350 RPM and 2000 RPM)were collected,and discrete wave-let transform(DWT)was used as signal processing.Useful statistical features were calcu-lated from collected signals.Correlation-based feature selection(CFS)method was used to find the best features.Random forest(RF)and multilayer perceptron(MLP)neural net-works were employed to classify the data.The overall accuracy of RF classifier without using feature selection were 86.25%,at 600 RPM.The corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%.The best results obtained at 1350 RPM,since the detection accuracy was 95%.The results of this study demonstrated the effectiveness and feasibility of the proposed method for fault diagnosis of tractor auxiliary gearbox.
文摘Objective: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most infor- mative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality re- duction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.
基金Some of this material was presented at the 2008 INFORMS Annual Meeting and 2008 Winter Simulation Conference[56,57]This work was supported in part by Department of Energy under Award DE-SC0002223NIH under Grant 1R21DK088368-01.
文摘With the advance of new computational technology,stochastic systems simulation and optimization has become increasingly a popular subject in both academic research and industrial applications.This paper presents some of recent developments about the problem of optimizing a performance function from a simulation model.We begin by classifying different types of problems and then provide an overview of the major approaches,followed by a more in-depth presentation of two specific areas:optimal computing budget allocation and the nested partitions method.
文摘This paper considers the problem of smoothing a non-stationary time series(having either deterministic and/or stochastic trends) using the discrete cosine transform(DCT).The DCT is a powerful tool which has found fruitful applications in filtering and smoothing as it can closely approximate the optimal Karhunen-Loeve transform(KLT).In fact,it is known that it almost corresponds to the KLT for first-order autoregressive processes with a root close to unity:This is the case with most economic and financial time series.A number of new results are derived in the paper:(a) The explicit form of the linear smoother based on the DCT,which is found to have time-varying weights and that uses all observations;(b) the extrapolation of the DCT-smoothed series;(c) the form of the average frequency response function,which is shown to approximate the frequency response of the ideal low pass filter;(d) the asymptotic distribution of the DCT coefficients under the assumptions of deterministic or stochastic trends;(e) two news method for selecting an appropriate degree of smoothing,in general and under the assumptions in(d).These findings are applied and illustrated using several real world economic and financial time series.The results indicate that the DCT-based smoother that is proposed can find many useful applications in economic and financial time series.
基金supported by the NCN grant UMO-2011/01/B/ST7/00666.
文摘Efficiency of the autofocusing algorithm implementations based on various orthogonal transforms is examined. The algorithm uses the variance of an image acquired by a sensor as a focus function. To compute the estimate of the variance we exploit the equivalence between that estimate and the image orthogonal expansion. Energy consumption of three implementations exploiting either of the following fast orthogonal transforms: the discrete cosine, the Walsh-Hadamard, and the Haar wavelet one, is evaluated and compared. Furthermore, it is conjectured that the computation precision can considerably be reduced if the image is heavily corrupted by the noise, and a simple problem of optimal word bit-length selection with respect to the signal variance is analyzed.
