Background and Objective:Refractive surgery has evolved significantly,with artificial intelligence(AI)offering new possibilities for enhancing patient selection,surgical planning,and postoperative outcome prediction.W...Background and Objective:Refractive surgery has evolved significantly,with artificial intelligence(AI)offering new possibilities for enhancing patient selection,surgical planning,and postoperative outcome prediction.While AI has demonstrated promising applications,its integration into clinical practice remains inconsistent due to challenges in data standardization,model interpretability,and regulatory concerns.This review examines the current applications,limitations,and future directions of AI in refractive surgery,with a focus on its role in laser vision correction(LVC)and phakic intraocular lens(IOL)implantation.Methods:A literature review was conducted using peer-reviewed studies published between January 2010 and October 2024,sourced from databases including Google Scholar,PubMed,Embase,and Web of Science.Studies were selected based on predefined inclusion criteria,covering AI applications in refractive surgery.A total of 33 key studies(16 on LVC and 17 on phakic IOLs)were analyzed,focusing on machine learning and deep learning techniques used for patient selection,surgical optimization,and complication prediction.Only English-language studies were included.Key Content and Findings:AI models utilizing structured tabular data,imaging,and multimodal inputs have demonstrated superior performance in predicting surgical outcomes and refining patient selection compared to traditional methods.Machine learning approaches such as random forests,extreme gradient boosting,and ensemble techniques,alongside deep learning architectures like convolutional neural networks and generative models,have improved risk assessment and surgical planning.In LVC,AI enhances ectasia risk assessment,keratoconus detection,and myopic regression prediction.In phakic IOL implantation,AI improves postoperative vault prediction,lens sizing,and refractive error estimation.Multimodal AI systems integrating imaging,textual data,and clinical parameters hold promise for more comprehensive patient evaluations.However,challenges such as data heterogeneity,limited external validation,and regulatory barriers hinder widespread clinical adoption.Conclusions:AI is transforming refractive surgery by enhancing precision,personalization,and patient safety.Its integration into clinical workflows has the potential to improve surgical outcomes and patient satisfaction.Future efforts should focus on advancing multimodal AI,improving model generalizability,and addressing ethical and regulatory challenges to fully harness AI’s potential in refractive surgery.展开更多
Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.展开更多
Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.展开更多
The Zenith Hydrostatic Delay(ZHD)is essential for high-precision Global Navigation Satellite System(GNSS)and Very Long Baseline Interferometry(VLBI)data processing.Accurate estimation of ZHD relies on in situ atmosphe...The Zenith Hydrostatic Delay(ZHD)is essential for high-precision Global Navigation Satellite System(GNSS)and Very Long Baseline Interferometry(VLBI)data processing.Accurate estimation of ZHD relies on in situ atmospheric pressure,which is primarily variable in the vertical direction.Current atmospheric pressure is either site-specific or has limited spatial coverage,necessitating vertical corrections for broader applicability.This study introduces a model that uses a Gaussian function for the vertical correction of atmospheric pressure when in situ meteorological observations are unavailable.Validation with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis(ERA5)reveals an average Bias and RMS for the new model of 0.31 h Pa and 2.96 h Pa,respectively.This corresponds to improvements of 37.5%and 80.3%in terms of RMS compared to two commonly used models(T0and Tvmodels)that require in situ meteorological observations,respectively.Additional validation with radiosonde data shows an average Bias and RMS of 1.85 h Pa and 4.87 h Pa,corresponding to the improvement of 42.8%and 71.1%in RMS compared with T0and Tv models,respectively.These accuracies are sufficient for calculating ZHD to an accuracy of 1 mm by performing atmospheric pressure vertical correction.The new model can correct atmospheric pressure from meteorological stations or numerical weather forecasts to different heights of the troposphere.展开更多
BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evalu...BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evaluate accurately using conventional two-dimensional imaging criteria due to the tumor’s diffuse and multifocal growth pattern.Volumetric imaging,especially enhanced tumor volume(ETV),offers a more comprehensive assessment.Nonetheless,bias field inhomogeneity in magnetic resonance imaging(MRI)poses challenges,potentially skewing volumetric measurements and undermining prognostic evaluation.AIM To investigate whether MRI bias field correction enhances the accuracy of volumetric assessment of infiltrative hepatocellular carcinoma treated with TACE,and to analyze how this improved measurement impacts prognostic prediction.METHODS We retrospectively collected data from 105 patients with invasive liver cancer who underwent TACE treatment at the Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2024.The improved N4 bias field correction algorithm was applied to process MRI images,and the ETV before and after treatment was calculated.The ETV measurements before and after correction were compared,and their relationship with patient prognosis was analyzed.A Cox proportional hazards model was used to evaluate prognostic factors,with Martingale residual analysis determining the optimal cutoff value,followed by survival analysis.RESULTS Bias field correction significantly affected ETV measurements,with the corrected baseline ETV mean(505.235 cm^(3))being significantly lower than before correction(825.632 cm^(3),P<0.001).Cox analysis showed that the hazard ratio(HR)for corrected baseline ETV(HR=1.165,95%CI:1.069-1.268)was higher than before correction(HR=1.063,95%CI:1.031-1.095).Using 412 cm^(3) as the cutoff,the group with baseline ETV<415 cm^(3) had a longer median survival time compared to the≥415 cm^(3) group(18.523 months vs 8.926 months,P<0.001).The group with an ETV reduction rate≥41%had better prognosis than the<41%group(17.862 months vs 9.235 months,P=0.006).Multivariate analysis confirmed that ETV reduction rate(HR=0.412,P<0.001),Child-Pugh classification(HR=0.298,P<0.001),and Barcelona Clinic Liver Cancer stage(HR=0.578,P=0.045)were independent prognostic factors.CONCLUSION Volume imaging based on MRI bias field correction can improve the accuracy of evaluating the efficacy of TACE treatment for invasive liver cancer.The corrected ETV and its reduction rate can serve as independent indicators for predicting patient prognosis,providing important reference for developing individualized treatment strategies.展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
The hot deformation behavior of as-extruded Ti-6554 alloy was investigated through isothermal compression at 700–950°C and 0.001–1 s^(−1).The temperature rise under different deformation conditions was calculat...The hot deformation behavior of as-extruded Ti-6554 alloy was investigated through isothermal compression at 700–950°C and 0.001–1 s^(−1).The temperature rise under different deformation conditions was calculated,and the curve was corrected.The strain compensation constitutive model of as-extruded Ti-6554 alloy based on temperature rise correction was established.The microstructure evolution under different conditions was analyzed,and the dynamic recrystallization(DRX)mechanism was revealed.The results show that the flow stress decreases with the increase in strain rate and the decrease in deformation temperature.The deformation temperature rise gradually increases with the increase in strain rate and the decrease in deformation temperature.At 700°C/1 s^(−1),the temperature rise reaches 100°C.The corrected curve value is higher than the measured value,and the strain compensation constitutive model has high prediction accuracy.The precipitation of theαphase occurs during deformation in the twophase region,which promotes DRX process of theβphase.At low strain rate,the volume fraction of dynamic recrystallization increases with the increase in deformation temperature.DRX mechanism includes continuous DRX and discontinuous DRX.展开更多
The forecast results of temperature based on the intelligent grids of the Central Meteorological Observatory and the meteorological bureau of the autonomous region and the numerical forecast model of the European Cent...The forecast results of temperature based on the intelligent grids of the Central Meteorological Observatory and the meteorological bureau of the autonomous region and the numerical forecast model of the European Center(EC model)from February to December in 2022 were used.Based on the data of the national intelligent grid forecast,the intelligent grid forecast of the regional bureau,EC model,etc.,temperature was predicted.According to the research of the grid point forecast synthesis algorithm with the highest accuracy rate in the recent three days,the temperature grid point correction was conducted in two forms of stations and grids.In order to reduce the deviation caused by the seasonal system temperature difference,a temperature prediction model was established by using the rolling forecast errors of 5,10,15,20,25 and 30 d as the basis data.The verification and evaluation of objective correction results show that the accuracy rate of temperature forecast by the intelligent grid of the regional bureau,the national intelligent grid,and EC model could be increased by 10%,8%,and 12%,respectively.展开更多
Correction to:Rare Met.https://doi.org/10.1007/s12598-021-01815-z In the original publication,Fig.5 was published with few mistakes.The correct version of Fig.5 is given in this correction.
Correction to:Nuclear Science and Techniques(2025)36:66 https://doi.org/10.1007/s41365-025-01662-y.In this article,the author’s name Hui-Ling Wei was incorrectly written as Hui-Ling We.The original article has been c...Correction to:Nuclear Science and Techniques(2025)36:66 https://doi.org/10.1007/s41365-025-01662-y.In this article,the author’s name Hui-Ling Wei was incorrectly written as Hui-Ling We.The original article has been corrected.展开更多
In this work,we apply tunneling formalism to analyze charged particles tunneling across a hairy black hole horizon.Such black hole solutions are essential for frameworks based on Horndeski's gravity theory.Applyin...In this work,we apply tunneling formalism to analyze charged particles tunneling across a hairy black hole horizon.Such black hole solutions are essential for frameworks based on Horndeski's gravity theory.Applying a semi-classical technique,we examine the tunneling of charged particles from a hairy black hole and derive the generic tunneling spectrum of released particles,ignoring self-gravitational and interaction.It is studied to ignore the back-reaction impact of the radiated particle on the hairy black hole.We analyze the properties of the black hole,such as temperature and entropy,under the influence of quantum gravity and also observe that the firstorder correction is present.We study tunneling radiation produced by a charged field equation in the presence of a generalized uncertainty effect.We modify the semi-classical technique by using the generalized uncertainty principle,the WKB approximation,and surface gravity.展开更多
Correction to:Nuclear Science and Techniques(2024)36:8 https://doi.org/10.1007/s41365-024-01570-7 In this article the affiliation details for Author Jian Shan were incorrectly given as‘College of Physics and Electron...Correction to:Nuclear Science and Techniques(2024)36:8 https://doi.org/10.1007/s41365-024-01570-7 In this article the affiliation details for Author Jian Shan were incorrectly given as‘College of Physics and Electronic Engi-neering,Hengyang Normal University,Hengyang 421008,China’but should have been‘School of Nuclear Science and Technology,University of South China,Hengyang 421001,China’.The original article has been corrected.展开更多
Correction to:Nuclear Science and Techniques(2024)35:162 https://doi.org/10.1007/s41365-024-01514-1 In this article,the citation information for Figures 6 and 7 was published incorrectly.The correct citations should r...Correction to:Nuclear Science and Techniques(2024)35:162 https://doi.org/10.1007/s41365-024-01514-1 In this article,the citation information for Figures 6 and 7 was published incorrectly.The correct citations should read as follows:In the sentence beginning‘the curve of Eq.17 is plotted…..’in this article,the text‘as shown in Fig.7a’should have read‘as shown in Fig.6a’.展开更多
Correction to:Nuclear Science and Techniques(2024)35:204 https://doi.org/10.1007/s41365-024-01538-7 In this article,the equations 3 and 4 were published incor-rectly.The incorrect version and the corrected version of ...Correction to:Nuclear Science and Techniques(2024)35:204 https://doi.org/10.1007/s41365-024-01538-7 In this article,the equations 3 and 4 were published incor-rectly.The incorrect version and the corrected version of the equations are given below.Incorrect equations.展开更多
Correction to:Journal of Biomedical Research https://doi.org/10.7555/JBR.26.20120003,published on June 8,2012.We sincerely apologize for the misuse of images in Fig.4 of our article.Specifically,the image in Fig.4D,de...Correction to:Journal of Biomedical Research https://doi.org/10.7555/JBR.26.20120003,published on June 8,2012.We sincerely apologize for the misuse of images in Fig.4 of our article.Specifically,the image in Fig.4D,depicting immunohistochemistry staining for Jagged1 expression,was partially incorrect because of our oversight.Upon reviewing the laboratory records,we have successfully located the original images from 12 years ago.We are now providing the corrected version of Fig.4D below.This correction does not alter the conclusions as originally reported.展开更多
文摘Background and Objective:Refractive surgery has evolved significantly,with artificial intelligence(AI)offering new possibilities for enhancing patient selection,surgical planning,and postoperative outcome prediction.While AI has demonstrated promising applications,its integration into clinical practice remains inconsistent due to challenges in data standardization,model interpretability,and regulatory concerns.This review examines the current applications,limitations,and future directions of AI in refractive surgery,with a focus on its role in laser vision correction(LVC)and phakic intraocular lens(IOL)implantation.Methods:A literature review was conducted using peer-reviewed studies published between January 2010 and October 2024,sourced from databases including Google Scholar,PubMed,Embase,and Web of Science.Studies were selected based on predefined inclusion criteria,covering AI applications in refractive surgery.A total of 33 key studies(16 on LVC and 17 on phakic IOLs)were analyzed,focusing on machine learning and deep learning techniques used for patient selection,surgical optimization,and complication prediction.Only English-language studies were included.Key Content and Findings:AI models utilizing structured tabular data,imaging,and multimodal inputs have demonstrated superior performance in predicting surgical outcomes and refining patient selection compared to traditional methods.Machine learning approaches such as random forests,extreme gradient boosting,and ensemble techniques,alongside deep learning architectures like convolutional neural networks and generative models,have improved risk assessment and surgical planning.In LVC,AI enhances ectasia risk assessment,keratoconus detection,and myopic regression prediction.In phakic IOL implantation,AI improves postoperative vault prediction,lens sizing,and refractive error estimation.Multimodal AI systems integrating imaging,textual data,and clinical parameters hold promise for more comprehensive patient evaluations.However,challenges such as data heterogeneity,limited external validation,and regulatory barriers hinder widespread clinical adoption.Conclusions:AI is transforming refractive surgery by enhancing precision,personalization,and patient safety.Its integration into clinical workflows has the potential to improve surgical outcomes and patient satisfaction.Future efforts should focus on advancing multimodal AI,improving model generalizability,and addressing ethical and regulatory challenges to fully harness AI’s potential in refractive surgery.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
基金the Research Grant of Kwangwoon University in 2024.
文摘Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
基金supported by the National Natural Science Foundation of China(42304018)the National Natural Science Foundation of China(42330105,42064002,42074035)+3 种基金the Guangxi Natural Science Foundation of China(Guike AD23026177,2020GXNSFBA297145)the Foundation of Guilin University of Technology(GUTQDJJ6616032)Guangxi Key Laboratory of Spatial Information and Geomatics(21238-21-05)the Innovation Project of Guangxi Graduate Education(YCSW2023341)。
文摘The Zenith Hydrostatic Delay(ZHD)is essential for high-precision Global Navigation Satellite System(GNSS)and Very Long Baseline Interferometry(VLBI)data processing.Accurate estimation of ZHD relies on in situ atmospheric pressure,which is primarily variable in the vertical direction.Current atmospheric pressure is either site-specific or has limited spatial coverage,necessitating vertical corrections for broader applicability.This study introduces a model that uses a Gaussian function for the vertical correction of atmospheric pressure when in situ meteorological observations are unavailable.Validation with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis(ERA5)reveals an average Bias and RMS for the new model of 0.31 h Pa and 2.96 h Pa,respectively.This corresponds to improvements of 37.5%and 80.3%in terms of RMS compared to two commonly used models(T0and Tvmodels)that require in situ meteorological observations,respectively.Additional validation with radiosonde data shows an average Bias and RMS of 1.85 h Pa and 4.87 h Pa,corresponding to the improvement of 42.8%and 71.1%in RMS compared with T0and Tv models,respectively.These accuracies are sufficient for calculating ZHD to an accuracy of 1 mm by performing atmospheric pressure vertical correction.The new model can correct atmospheric pressure from meteorological stations or numerical weather forecasts to different heights of the troposphere.
文摘BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evaluate accurately using conventional two-dimensional imaging criteria due to the tumor’s diffuse and multifocal growth pattern.Volumetric imaging,especially enhanced tumor volume(ETV),offers a more comprehensive assessment.Nonetheless,bias field inhomogeneity in magnetic resonance imaging(MRI)poses challenges,potentially skewing volumetric measurements and undermining prognostic evaluation.AIM To investigate whether MRI bias field correction enhances the accuracy of volumetric assessment of infiltrative hepatocellular carcinoma treated with TACE,and to analyze how this improved measurement impacts prognostic prediction.METHODS We retrospectively collected data from 105 patients with invasive liver cancer who underwent TACE treatment at the Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2024.The improved N4 bias field correction algorithm was applied to process MRI images,and the ETV before and after treatment was calculated.The ETV measurements before and after correction were compared,and their relationship with patient prognosis was analyzed.A Cox proportional hazards model was used to evaluate prognostic factors,with Martingale residual analysis determining the optimal cutoff value,followed by survival analysis.RESULTS Bias field correction significantly affected ETV measurements,with the corrected baseline ETV mean(505.235 cm^(3))being significantly lower than before correction(825.632 cm^(3),P<0.001).Cox analysis showed that the hazard ratio(HR)for corrected baseline ETV(HR=1.165,95%CI:1.069-1.268)was higher than before correction(HR=1.063,95%CI:1.031-1.095).Using 412 cm^(3) as the cutoff,the group with baseline ETV<415 cm^(3) had a longer median survival time compared to the≥415 cm^(3) group(18.523 months vs 8.926 months,P<0.001).The group with an ETV reduction rate≥41%had better prognosis than the<41%group(17.862 months vs 9.235 months,P=0.006).Multivariate analysis confirmed that ETV reduction rate(HR=0.412,P<0.001),Child-Pugh classification(HR=0.298,P<0.001),and Barcelona Clinic Liver Cancer stage(HR=0.578,P=0.045)were independent prognostic factors.CONCLUSION Volume imaging based on MRI bias field correction can improve the accuracy of evaluating the efficacy of TACE treatment for invasive liver cancer.The corrected ETV and its reduction rate can serve as independent indicators for predicting patient prognosis,providing important reference for developing individualized treatment strategies.
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
基金National Key R&D Program of China(2022YFB3706901)National Natural Science Foundation of China(52274382)Key Research and Development Program of Hubei Province(2022BAA024)。
文摘The hot deformation behavior of as-extruded Ti-6554 alloy was investigated through isothermal compression at 700–950°C and 0.001–1 s^(−1).The temperature rise under different deformation conditions was calculated,and the curve was corrected.The strain compensation constitutive model of as-extruded Ti-6554 alloy based on temperature rise correction was established.The microstructure evolution under different conditions was analyzed,and the dynamic recrystallization(DRX)mechanism was revealed.The results show that the flow stress decreases with the increase in strain rate and the decrease in deformation temperature.The deformation temperature rise gradually increases with the increase in strain rate and the decrease in deformation temperature.At 700°C/1 s^(−1),the temperature rise reaches 100°C.The corrected curve value is higher than the measured value,and the strain compensation constitutive model has high prediction accuracy.The precipitation of theαphase occurs during deformation in the twophase region,which promotes DRX process of theβphase.At low strain rate,the volume fraction of dynamic recrystallization increases with the increase in deformation temperature.DRX mechanism includes continuous DRX and discontinuous DRX.
文摘The forecast results of temperature based on the intelligent grids of the Central Meteorological Observatory and the meteorological bureau of the autonomous region and the numerical forecast model of the European Center(EC model)from February to December in 2022 were used.Based on the data of the national intelligent grid forecast,the intelligent grid forecast of the regional bureau,EC model,etc.,temperature was predicted.According to the research of the grid point forecast synthesis algorithm with the highest accuracy rate in the recent three days,the temperature grid point correction was conducted in two forms of stations and grids.In order to reduce the deviation caused by the seasonal system temperature difference,a temperature prediction model was established by using the rolling forecast errors of 5,10,15,20,25 and 30 d as the basis data.The verification and evaluation of objective correction results show that the accuracy rate of temperature forecast by the intelligent grid of the regional bureau,the national intelligent grid,and EC model could be increased by 10%,8%,and 12%,respectively.
文摘Correction to:Rare Met.https://doi.org/10.1007/s12598-021-01815-z In the original publication,Fig.5 was published with few mistakes.The correct version of Fig.5 is given in this correction.
文摘Correction to:Nuclear Science and Techniques(2025)36:66 https://doi.org/10.1007/s41365-025-01662-y.In this article,the author’s name Hui-Ling Wei was incorrectly written as Hui-Ling We.The original article has been corrected.
基金funded by the National Natural Science Foundation of China under Grant No.11975145。
文摘In this work,we apply tunneling formalism to analyze charged particles tunneling across a hairy black hole horizon.Such black hole solutions are essential for frameworks based on Horndeski's gravity theory.Applying a semi-classical technique,we examine the tunneling of charged particles from a hairy black hole and derive the generic tunneling spectrum of released particles,ignoring self-gravitational and interaction.It is studied to ignore the back-reaction impact of the radiated particle on the hairy black hole.We analyze the properties of the black hole,such as temperature and entropy,under the influence of quantum gravity and also observe that the firstorder correction is present.We study tunneling radiation produced by a charged field equation in the presence of a generalized uncertainty effect.We modify the semi-classical technique by using the generalized uncertainty principle,the WKB approximation,and surface gravity.
文摘Correction to:Nuclear Science and Techniques(2024)36:8 https://doi.org/10.1007/s41365-024-01570-7 In this article the affiliation details for Author Jian Shan were incorrectly given as‘College of Physics and Electronic Engi-neering,Hengyang Normal University,Hengyang 421008,China’but should have been‘School of Nuclear Science and Technology,University of South China,Hengyang 421001,China’.The original article has been corrected.
文摘Correction to:Nuclear Science and Techniques(2024)35:162 https://doi.org/10.1007/s41365-024-01514-1 In this article,the citation information for Figures 6 and 7 was published incorrectly.The correct citations should read as follows:In the sentence beginning‘the curve of Eq.17 is plotted…..’in this article,the text‘as shown in Fig.7a’should have read‘as shown in Fig.6a’.
文摘Correction to:Nuclear Science and Techniques(2024)35:204 https://doi.org/10.1007/s41365-024-01538-7 In this article,the equations 3 and 4 were published incor-rectly.The incorrect version and the corrected version of the equations are given below.Incorrect equations.
文摘Correction to:Journal of Biomedical Research https://doi.org/10.7555/JBR.26.20120003,published on June 8,2012.We sincerely apologize for the misuse of images in Fig.4 of our article.Specifically,the image in Fig.4D,depicting immunohistochemistry staining for Jagged1 expression,was partially incorrect because of our oversight.Upon reviewing the laboratory records,we have successfully located the original images from 12 years ago.We are now providing the corrected version of Fig.4D below.This correction does not alter the conclusions as originally reported.
