A two-year field experiment was conducted to evaluate the effects of plant density on tassel and ear differentiation, anthesissilking interval(ASI), and grain yield formation of two types of modern maize hybrids(Zhong...A two-year field experiment was conducted to evaluate the effects of plant density on tassel and ear differentiation, anthesissilking interval(ASI), and grain yield formation of two types of modern maize hybrids(Zhongdan 909(ZD909) as tolerant hybrid to crowding stress, Jidan 209(JD209) and Neidan 4(ND4) as intolerant hybrids to crowding stress) in Northeast China. Plant densities of 4.50×104(D1), 6.75×104(D2), 9.00×104(D3), 11.25×104(D4), and 13.50×104(D5) plants ha-1had no significant effects on initial time of tassel and ear differentiation of maize. Instead, higher plant density delayed the tassel and ear development during floret differentiation and sexual organ formation stage, subsequently resulting in ASI increments at the rate of 1.2–2.9 days on average for ZD909 in 2013–2014, 0.7–4.2 days for JD209 in 2013, and 0.5–3.7 days for ND4 in 2014, respectively, under the treatments of D2, D3, D4, and D5 compared to that under the D1 treatment. Total florets, silking florets, and silking rates of ear showed slightly decrease trends with the plant density increasing, whereas the normal kernels seriously decreased at the rate of 11.0–44.9% on average for ZD909 in 2013–2014, 2.0–32.6% for JD209 in 2013, and 9.7–28.3% for ND4 in 2014 with the plant density increased compared to that under the D1 treatment due to increased florets abortive rates. It was also observed that 100-kernel weight of ZD909 showed less decrease trend compared that of JD209 and ND4 along with the plant densities increase. As a consequence, ZD909 gained its highest grain yield by 13.7 t ha-1on average at the plant density of 9.00×104 plants ha-1, whereas JD209 and ND4 reached their highest grain yields by 11.7 and 10.2 t ha-1at the plant density of 6.75×104 plants ha-1, respectively. Our experiment demonstrated that hybrids with lower ASI, higher kernel number potential per ear, and relative constant 100-kernel weight(e.g., ZD909) could achieve higher yield under dense planting in high latitude area(e.g., Northeast China展开更多
Abiotic stress such as high temperature at flowering is one of many conditions reducing yield of corn(Zea mays L.).Mixing corn cultivars with diverse functional traits increases within-crop diversity and provides a po...Abiotic stress such as high temperature at flowering is one of many conditions reducing yield of corn(Zea mays L.).Mixing corn cultivars with diverse functional traits increases within-crop diversity and provides a potential means of mitigating yield losses under stress conditions.We conducted a three-year field study to investigate the effects of cultivar mixtures on kernel setting rate,pollen sources,and yield.This study consisted of six treatments,including two high temperature-tolerant(HTT)monocrops of WK702 and DH701,two high temperature-sensitive(HTS)monocrops of DH605 and DH662,and two HTT–HTS mixtures of WK702-DH605 and DH701-DH662.The anthesis–silking interval(ASI)was 0.9–1.6 days shorter in mixtures than in monocrops.Kernel setting rate was increased in mixtures(86.4%–88.7%)compared with those in monocrops(74.7%–84.1%)as a result of synchrony and complementarity of pollination.Grain yields of the HTT–HTS mixtures increased by 13.3%–18.7%,equivalent to 1169 to1605 kg ha^(-1),in comparison with HTS corn monocrops.The results of SSR markers showed that crossfertilization percentage in corn cultivar mixtures ranged from 29.3%to 47.8%,partially explaining yield improvement.Land equivalent ratio(LER)was 1.12 for corn mixtures and the partial land equivalent ratio(e.g.,>0.5)showed the complementary benefits in corn mixtures.The results indicated that mixing corn cultivars with diverse flowering and drought-tolerance traits increased yields via pollination synchrony.展开更多
This paper presents a method using support vector machine with polyspectral kernels for classification of individual transmitters.Then,the neighborhood-roughset-based weighted feature set is proposed.The experiments o...This paper presents a method using support vector machine with polyspectral kernels for classification of individual transmitters.Then,the neighborhood-roughset-based weighted feature set is proposed.The experiments of the algorithms mentioned above indicate that they have consistency,which raises a new weighted kernel.The experiment shows that better classification rate can be achieved.展开更多
A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the r...A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the redundant attribute for forecasting from condition attribute by rough set method; then use the minimum condition attribute set obtained after the reduction and the corresponding initial data, reform a new training sample set which only retain the important attributes influencing the forecasting accuracy; study and train the support vector machine with the training sample obtained after reduction, and then input the reformed testing sample set according to the minimum condition attribute and corresponding initial data. The model was tested and the mapping relation was got between the condition attribute and forecasting variable. Eventually, power supply and demand were forecasted in this model. The average absolute error rates of power consumption of the whole society and yearly maximum load are respectively 14.21% and 13.23%. It shows that RS-SVM time series forecasting model has high forecasting accuracy.展开更多
Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.Design/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive ...Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.Design/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better segmentation.Then,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.Findings-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost.The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and accuracy.The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing algorithms.Practical implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm.However,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.Originality/value-The image preprocessing is carried out using CLAHE algorithm.The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm.In this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.展开更多
基金supported by the National Basic Research Program of China (2015CB150404)the National Natural Science Foundation of China (31671642)+1 种基金the Key Program of Science and Technology Department of Jilin Province, China (LFGC14205)the Innovation Project of Chinese Academy of Agricultural Sciences (CAAS-XTCX2016008)
文摘A two-year field experiment was conducted to evaluate the effects of plant density on tassel and ear differentiation, anthesissilking interval(ASI), and grain yield formation of two types of modern maize hybrids(Zhongdan 909(ZD909) as tolerant hybrid to crowding stress, Jidan 209(JD209) and Neidan 4(ND4) as intolerant hybrids to crowding stress) in Northeast China. Plant densities of 4.50×104(D1), 6.75×104(D2), 9.00×104(D3), 11.25×104(D4), and 13.50×104(D5) plants ha-1had no significant effects on initial time of tassel and ear differentiation of maize. Instead, higher plant density delayed the tassel and ear development during floret differentiation and sexual organ formation stage, subsequently resulting in ASI increments at the rate of 1.2–2.9 days on average for ZD909 in 2013–2014, 0.7–4.2 days for JD209 in 2013, and 0.5–3.7 days for ND4 in 2014, respectively, under the treatments of D2, D3, D4, and D5 compared to that under the D1 treatment. Total florets, silking florets, and silking rates of ear showed slightly decrease trends with the plant density increasing, whereas the normal kernels seriously decreased at the rate of 11.0–44.9% on average for ZD909 in 2013–2014, 2.0–32.6% for JD209 in 2013, and 9.7–28.3% for ND4 in 2014 with the plant density increased compared to that under the D1 treatment due to increased florets abortive rates. It was also observed that 100-kernel weight of ZD909 showed less decrease trend compared that of JD209 and ND4 along with the plant densities increase. As a consequence, ZD909 gained its highest grain yield by 13.7 t ha-1on average at the plant density of 9.00×104 plants ha-1, whereas JD209 and ND4 reached their highest grain yields by 11.7 and 10.2 t ha-1at the plant density of 6.75×104 plants ha-1, respectively. Our experiment demonstrated that hybrids with lower ASI, higher kernel number potential per ear, and relative constant 100-kernel weight(e.g., ZD909) could achieve higher yield under dense planting in high latitude area(e.g., Northeast China
基金supported by National Natural Science Foundation of China(31801308)Henan Provincial Higher Education Key Research Project(21A210024)CMA·Henan Key Laboratory of Agrometeorological Support and Applied Technique(AMF202109)。
文摘Abiotic stress such as high temperature at flowering is one of many conditions reducing yield of corn(Zea mays L.).Mixing corn cultivars with diverse functional traits increases within-crop diversity and provides a potential means of mitigating yield losses under stress conditions.We conducted a three-year field study to investigate the effects of cultivar mixtures on kernel setting rate,pollen sources,and yield.This study consisted of six treatments,including two high temperature-tolerant(HTT)monocrops of WK702 and DH701,two high temperature-sensitive(HTS)monocrops of DH605 and DH662,and two HTT–HTS mixtures of WK702-DH605 and DH701-DH662.The anthesis–silking interval(ASI)was 0.9–1.6 days shorter in mixtures than in monocrops.Kernel setting rate was increased in mixtures(86.4%–88.7%)compared with those in monocrops(74.7%–84.1%)as a result of synchrony and complementarity of pollination.Grain yields of the HTT–HTS mixtures increased by 13.3%–18.7%,equivalent to 1169 to1605 kg ha^(-1),in comparison with HTS corn monocrops.The results of SSR markers showed that crossfertilization percentage in corn cultivar mixtures ranged from 29.3%to 47.8%,partially explaining yield improvement.Land equivalent ratio(LER)was 1.12 for corn mixtures and the partial land equivalent ratio(e.g.,>0.5)showed the complementary benefits in corn mixtures.The results indicated that mixing corn cultivars with diverse flowering and drought-tolerance traits increased yields via pollination synchrony.
基金Supported by National Natural Science Foundation of China(60675039)National High Technology Research and Development Program of China(863 Program)(2006AA04Z217)Hundred Talents Program of Chinese Academy of Sciences
基金This work was supported by the National High Technology Research and Development Program of China(Grant No.2009AA01Z430)the Natural Science Foundation of Beijing(No.9092009)the National Science and Technology Major Program(2009ZX03004-003-03).
文摘This paper presents a method using support vector machine with polyspectral kernels for classification of individual transmitters.Then,the neighborhood-roughset-based weighted feature set is proposed.The experiments of the algorithms mentioned above indicate that they have consistency,which raises a new weighted kernel.The experiment shows that better classification rate can be achieved.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the redundant attribute for forecasting from condition attribute by rough set method; then use the minimum condition attribute set obtained after the reduction and the corresponding initial data, reform a new training sample set which only retain the important attributes influencing the forecasting accuracy; study and train the support vector machine with the training sample obtained after reduction, and then input the reformed testing sample set according to the minimum condition attribute and corresponding initial data. The model was tested and the mapping relation was got between the condition attribute and forecasting variable. Eventually, power supply and demand were forecasted in this model. The average absolute error rates of power consumption of the whole society and yearly maximum load are respectively 14.21% and 13.23%. It shows that RS-SVM time series forecasting model has high forecasting accuracy.
文摘Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.Design/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better segmentation.Then,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.Findings-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost.The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and accuracy.The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing algorithms.Practical implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm.However,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.Originality/value-The image preprocessing is carried out using CLAHE algorithm.The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm.In this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.
