In this paper, we prove an extrapolation theorem of operators in martingale spaces with Ap weights, which shows that for operators T defined on martingale space Lu^I, if T is bounded on martingale space L^P0 （w） for...In this paper, we prove an extrapolation theorem of operators in martingale spaces with Ap weights, which shows that for operators T defined on martingale space Lu^I, if T is bounded on martingale space L^P0 （w） for some 1 〈 P0 〈 ∞ and every w ∈ A∞, so it is on L^P,S（w） for every 1〈p, s〈∞ , and w∈ A∞. We also get some properties of Ap weights and prove that if w ∈ AI, then the maximal operator M is bounded on L^p,q（w） with 1〈p〈∞, 1〈q≤∞.展开更多
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which ...In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which is a class of radial weights satisfying the two-sided doubling conditions.As an application,the bounded and compact positive Toeplitz operators T_(μ,ω)on the endpoint case weighted harmonic Bergman space L_(h,ω)^(1)(D)are characterized.展开更多
Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providin...Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providing a theoretical foundation for optimizing grain-filling processes.Two rice varieties with similar growth duration but different grain weights were selected:a large-grain variety,Lingliangyou 268(L268),and a small-grain variety,Ruiliangyou 1053(R1053).Differences in grain filling,grain photosynthetic rate,and grain chlorophyll content were systematically examined during the filling stage.Results showed significant differences in grain-filling,grain photosynthetic rate,and grain chlorophyll content between large-grain and small-grain rice varieties.The grain photosynthetic rate of L268 was a significantly higher than R1053.L268 also exhibited significantly higher initial grain filling rate,maximum grainfilling rate,and mean grain filling rate compared to R1053.Throughout the grain filling period,L268 showed higher grain chlorophyll content(including chlorophyll a,chlorophyll b,and total chlorophyll)than R1053.The increase in chlorophyll content,particularly total chlorophyll,enhanced the grain photosynthetic rate during the early and middle stages of grain filling significantly.These findings suggested that rice varieties with higher grain weights exhibited stronger panicle photosynthetic capacity due to their higher chlorophyll content.The enhanced grain photosynthetic rate contributed to improved grain filling and increased grain weight.展开更多
Machine learning has been widely applied in well logging formation evaluation studies.However,several challenges negatively impacted the generalization capabilities of machine learning models in practical imple-mentat...Machine learning has been widely applied in well logging formation evaluation studies.However,several challenges negatively impacted the generalization capabilities of machine learning models in practical imple-mentations,such as the mismatch of data domain between training and testing datasets,imbalances among sample categories,and inadequate representation of data model.These issues have led to substantial insufficient identification for reservoir and significant deviations in subsequent evaluations.To improve the transferability of machine learning models within limited sample sets,this study proposes a weight transfer learning framework based on the similarity of the labels.The similarity weighting method includes both hard weights and soft weights.By evaluating the similarity between test and training sets of logging data,the similarity results are used to estimate the weights of training samples,thereby optimizing the model learning process.We develop a double experts’network and a bidirectional gated neural network based on hierarchical attention and multi-head attention(BiGRU-MHSA)for well logs reconstruction and lithofacies classification tasks.Oil field data results for the shale strata in the Gulong area of the Songliao Basin of China indicate that the double experts’network model performs well in curve reconstruction tasks.However,it may not be effective in lithofacies classification tasks,while BiGRU-MHSA performs well in that area.In the study of constructing large-scale well logging processing and formation interpretation models,it is maybe more beneficial by employing different expert models for combined evaluations.In addition,although the improvement is limited,hard or soft weighting methods is better than unweighted(i.e.,average-weighted)in significantly different adjacent wells.The code and data are open and available for subsequent studies on other lithofacies layers.展开更多
The mode of delivery and gestational age for very-low-birth-weight (VLBW) preterm infants are not yet well established and are constant topics of debate. Objective: To analyze the impact of delivery mode on morbidity ...The mode of delivery and gestational age for very-low-birth-weight (VLBW) preterm infants are not yet well established and are constant topics of debate. Objective: To analyze the impact of delivery mode on morbidity in preterm infants weighing less than 1500 g. Results: Among 21,957 births, 81 were analyzed;53 were delivered vaginally, and 28 were delivered by cesarean section. The median maternal age, gestational age and body mass index among those delivered vaginally and by cesarean section were 20 years and 22.5 years, 27.6 weeks and 30.1 weeks, and 26.0 kg/m2 and 27.8 kg/m2, respectively. With respect to neonatal blood gas parameters, for those born vaginally and by cesarean section, the median pH was 7.32 and 7.24, the pCO2 was 41.5 mmHg and 51.1 mmHg, and the pO2 was 22.3 mmHg and 16 mmHg. The median fetal weight among those born by cesarean section and vaginally were 1180 g and 955 g, respectively. The median Apgar scores at the first and fifth minutes among those born by cesarean section and vaginally were 5.00 and 8.00 and 4.50 and 7.00, respectively. Conclusion: There was no significant difference between the results of vaginal and cesarean delivery for VLBW infants. Thus, further studies on this subject are needed.展开更多
基金Supported by the National Natural Science Foundation of China(10671147)
文摘In this paper, we prove an extrapolation theorem of operators in martingale spaces with Ap weights, which shows that for operators T defined on martingale space Lu^I, if T is bounded on martingale space L^P0 （w） for some 1 〈 P0 〈 ∞ and every w ∈ A∞, so it is on L^P,S（w） for every 1〈p, s〈∞ , and w∈ A∞. We also get some properties of Ap weights and prove that if w ∈ AI, then the maximal operator M is bounded on L^p,q（w） with 1〈p〈∞, 1〈q≤∞.
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
基金supported by the National Natural Science Foundation of China(12171075)the Science and Technology Research Project of Education Department of Jilin Province(JJKH20241406KJ)Zhan’s research was supported by the Doctoral Startup Fund of Liaoning University of Technology(XB2024029).
文摘In this paper,it is shown that the harmonic Bergman projection P_(ω)^(h),induced by a radial,induced by a radial weightω,is bounded and onto from L^(∞)(D)to the harmonic Bloch space B_(h)if and only ifω∈D,,which is a class of radial weights satisfying the two-sided doubling conditions.As an application,the bounded and compact positive Toeplitz operators T_(μ,ω)on the endpoint case weighted harmonic Bergman space L_(h,ω)^(1)(D)are characterized.
基金supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2023JJ40309)the Changsha Outstanding Innovative Youth Training Program(kq2306015).
文摘Grain filling is a critical determinant of yield and quality in rice.This study aims to clarify the association between grain photosynthesis and the filling rate of rice varieties with different grain weights,providing a theoretical foundation for optimizing grain-filling processes.Two rice varieties with similar growth duration but different grain weights were selected:a large-grain variety,Lingliangyou 268(L268),and a small-grain variety,Ruiliangyou 1053(R1053).Differences in grain filling,grain photosynthetic rate,and grain chlorophyll content were systematically examined during the filling stage.Results showed significant differences in grain-filling,grain photosynthetic rate,and grain chlorophyll content between large-grain and small-grain rice varieties.The grain photosynthetic rate of L268 was a significantly higher than R1053.L268 also exhibited significantly higher initial grain filling rate,maximum grainfilling rate,and mean grain filling rate compared to R1053.Throughout the grain filling period,L268 showed higher grain chlorophyll content(including chlorophyll a,chlorophyll b,and total chlorophyll)than R1053.The increase in chlorophyll content,particularly total chlorophyll,enhanced the grain photosynthetic rate during the early and middle stages of grain filling significantly.These findings suggested that rice varieties with higher grain weights exhibited stronger panicle photosynthetic capacity due to their higher chlorophyll content.The enhanced grain photosynthetic rate contributed to improved grain filling and increased grain weight.
基金supported by the Strategic Cooperation Technology Projects of China National Petroleum Corporation(CNPC)and China University of Petroleum(Beijing)(CUPB)(ZLZX2020-03)National Key Research and Development Program,China(2019YFA0708301)+1 种基金National Key Research and Development Program,China(2023YFF0714102)Science and Technology Innovation Fund of China National Petroleum Corporation(CNPC)(2021DQ02-0403).
文摘Machine learning has been widely applied in well logging formation evaluation studies.However,several challenges negatively impacted the generalization capabilities of machine learning models in practical imple-mentations,such as the mismatch of data domain between training and testing datasets,imbalances among sample categories,and inadequate representation of data model.These issues have led to substantial insufficient identification for reservoir and significant deviations in subsequent evaluations.To improve the transferability of machine learning models within limited sample sets,this study proposes a weight transfer learning framework based on the similarity of the labels.The similarity weighting method includes both hard weights and soft weights.By evaluating the similarity between test and training sets of logging data,the similarity results are used to estimate the weights of training samples,thereby optimizing the model learning process.We develop a double experts’network and a bidirectional gated neural network based on hierarchical attention and multi-head attention(BiGRU-MHSA)for well logs reconstruction and lithofacies classification tasks.Oil field data results for the shale strata in the Gulong area of the Songliao Basin of China indicate that the double experts’network model performs well in curve reconstruction tasks.However,it may not be effective in lithofacies classification tasks,while BiGRU-MHSA performs well in that area.In the study of constructing large-scale well logging processing and formation interpretation models,it is maybe more beneficial by employing different expert models for combined evaluations.In addition,although the improvement is limited,hard or soft weighting methods is better than unweighted(i.e.,average-weighted)in significantly different adjacent wells.The code and data are open and available for subsequent studies on other lithofacies layers.
文摘The mode of delivery and gestational age for very-low-birth-weight (VLBW) preterm infants are not yet well established and are constant topics of debate. Objective: To analyze the impact of delivery mode on morbidity in preterm infants weighing less than 1500 g. Results: Among 21,957 births, 81 were analyzed;53 were delivered vaginally, and 28 were delivered by cesarean section. The median maternal age, gestational age and body mass index among those delivered vaginally and by cesarean section were 20 years and 22.5 years, 27.6 weeks and 30.1 weeks, and 26.0 kg/m2 and 27.8 kg/m2, respectively. With respect to neonatal blood gas parameters, for those born vaginally and by cesarean section, the median pH was 7.32 and 7.24, the pCO2 was 41.5 mmHg and 51.1 mmHg, and the pO2 was 22.3 mmHg and 16 mmHg. The median fetal weight among those born by cesarean section and vaginally were 1180 g and 955 g, respectively. The median Apgar scores at the first and fifth minutes among those born by cesarean section and vaginally were 5.00 and 8.00 and 4.50 and 7.00, respectively. Conclusion: There was no significant difference between the results of vaginal and cesarean delivery for VLBW infants. Thus, further studies on this subject are needed.
