In this paper,we introduce the notion of G_(C)-X-injective modules,where X denotes a class of left S-modules and C represents a faithfully semidualizing bimodule.Under the condition that X satisfies certain hypotheses...In this paper,we introduce the notion of G_(C)-X-injective modules,where X denotes a class of left S-modules and C represents a faithfully semidualizing bimodule.Under the condition that X satisfies certain hypotheses,some properties and some equivalent characterizations of G_(C)-X-injective modules are investigated,and we also show that the triple(■,cores■,■)is a weak co-AB-context.As an application,two complete cotorsion pairs and a new model structure in Mod S are given.展开更多
Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the constru...Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the construction and optimization of a prediction model for deformation prediction,a multipoint ultrahigh arch dam deformation prediction model,namely,the CEEMDAN-KPCA-GSWOA-KELM,which is based on a clustering partition,is pro-posed.First,the monitoring data are preprocessed via variational mode decomposition(VMD)and wavelet denoising(WT),which effectively filters out noise and improves the signal-to-noise ratio of the data,providing high-quality input data for subsequent prediction models.Second,scientific cluster partitioning is performed via the K-means++algorithm to precisely capture the spatial distribution characteristics of extra-high arch dams and ensure the consistency of deformation trends at measurement points within each partition.Finally,CEEMDAN is used to separate monitoring data,predict and analyze each component,combine the KPCA(Kernel Principal Component Analysis)and the KELM(Kernel Extreme Learning Machine)optimized by the GSWOA(Global Search Whale Optimization Algorithm),integrate the predictions of each component via reconstruction methods,and precisely predict the overall trend of ultrahigh arch dam deformation.An extra high arch dam project is taken as an example and validated via a comparative analysis of multiple models.The results show that the multipoint deformation prediction model in this paper can combine data from different measurement points,achieve a comprehensive,precise prediction of the deformation situation of extra high arch dams,and provide strong technical support for safe operation.展开更多
The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on th...The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.展开更多
Fabrication of large-area perovskite solar modules under ambient air conditions remains a critical challenge due to air sensitivity of perovskite intermediate phases during crystallization.Here,we introduce 2-iodoimid...Fabrication of large-area perovskite solar modules under ambient air conditions remains a critical challenge due to air sensitivity of perovskite intermediate phases during crystallization.Here,we introduce 2-iodoimidazole(IIZ)into the perovskite precursor,enabling the formation of an air-stable pureδ-phase intermediate,which,upon annealing,fully transforms into a highly orientedα-phase perovskite film with reduced defects and variability.Leveraging this approach,we achieve a stabilized power conversion efficiency of 20.9%for 927.5 cm^(2)perovskite solar modules with high reproducibility.The encapsulated modules meet stringent international photovoltaic testing standards(IEC61215:2021),demonstrating excellent stability under continuous operation,thermal cycling(-40 to 85℃)and damp heat(85℃ and 85%relative humidity).展开更多
Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overb...Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overburden layer.The deep overburden layer causes uneven settlement between the overburden layer and the dam,which poses a serious threat to the safety of both the construction and operation of the dam.In this study,microseismic(MS)monitoring technology was employed for the firsttime in the fieldof dam fillingengineering,allowing for the real-time monitoring of microfracture in the bedrock during dam construction.The time-frequency analysis method was used to summarize the MS waveform characteristics induced by dam filling.The fracture mechanism of bedrock was revealed,and the relationships among slope deformation,dam settlement,and MS activity were analyzed.The following research results have been obtained.The MS signal induced by dam fillinghas low energy and amplitude,short duration,and high frequency.The fracture of the bedrock was mainly shear failure.MS monitoring can predict deformation during blasting excavation and capture the large settlement that may occur during dam fillingin advance.Research findingshave demonstrated the significantapplication value of MS monitoring technology in predicting the risk of dam settlement and provide a reference for similar projects.展开更多
In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this pape...In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this paper proposes an efficient detection framework based on an improved YOLOv11 architecture.First,a Re-parameterized Convolution(RepConv)module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency.Second,a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism(MSFF-CBAM)is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention.This mechanism effectively strengthens the specificity and robustness of feature representations.Third,a lightweight Dynamic Sampling Module(DySample)is employed to replace conventional upsampling operations,thereby improving the localization accuracy of small-scale defect targets.Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMDYOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision(mAP)@0.5,Precision,and Recall,achieving respective improvements of 4.70%,1.51%,and 5.50%.The model also exhibits notable advantages in inference speed and model compactness.Further validation on the ELPV dataset confirms the model’s generalization capability,showing respective performance gains of 1.99%,2.28%,and 1.45%across the same metrics.Overall,the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces,effectively reducing both false negatives and false positives.This advancement provides a robust and reliable technical foundation for automated PV module defect detection.展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
To researching the damage characteristics of typical logical chip modules in spacecraft due to plasma generated by hypervelocity impacts,we have established a triple Langmuir probe diagnostic system and a logical chip...To researching the damage characteristics of typical logical chip modules in spacecraft due to plasma generated by hypervelocity impacts,we have established a triple Langmuir probe diagnostic system and a logical chips measurement system,which were used to diagnose plasma characteristic parameters and the logical chip module＇s logical state changes due to the plasma created by a 7075 aluminum projectile hypervelocity impact on the 2A12 aluminum target.Three sets of experiments were performed with the collision speeds of 2.85 km/s,3.1 km/s and2.20 km/s,at the same incident angles of 30 degrees and logical chip module＇s positions by using a two-stage light gas gun loading system,a plasma characteristic parameters diagnostic system and a logical chip module＇s logical state measurement system,respectively.Electron temperature and density were measured at given position and azimuth,and damage estimation was performed for the logical chip module by using the data acquisition system.Experimental results showed that temporary damage could be induced on logical chip modules in spacecraft by plasma generated by hypervelocity impacts under the given experimental conditions and the sensors＇ position and azimuth.展开更多
Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this art...Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.展开更多
Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the ne...Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the next-generation commercial solar cells.However,critical challenges remain in preserving high efficiency practical large-scale commercialized PSCs:a)the long-term stability of the cell materials and devices,b)lead leakage,and c)methods to scale the cells for larger area applications.This paper summarizes the prior-art strategies to address the above challenges,including the latest studies on the traditional glass-glass and thin-film encapsulation methods to better improve the reliability of PSCs,new technologies for preventing lead leakage,and geometric improvement strategies to enhance the reliability,efficiency,and performance of perovskite solar modules(PSMs).Through these strategies,the device achieved enhanced performance in long-term stability tests.The encapsulation resulted in a high lead leakage inhibition rate of up to 99%,and the PSMs possessed a geometric fill factor of 99.6%and a power conversion efficiency(PCE)of 20.7%.The dramatic improvement of efficiency and reliability of perovskite solar cells and modules indicate the great potential for mass production and commer-cialization of perovskite solar applications in the near future.展开更多
Check dams are widely constructed on China's Loess Plateau,which had a total number of 58,776 by the end of 2019.Great achievements in check dam construction have been gained regarding the economic and environment...Check dams are widely constructed on China's Loess Plateau,which had a total number of 58,776 by the end of 2019.Great achievements in check dam construction have been gained regarding the economic and environmental impacts.This study reviews the remarkable benefits of check dams on the land reclamation and environmental improvement on the Loess Plateau,and sediment reduction entering the Yellow River.However,the flood incidents on check dams have been frequently reported for the past decades,which has attracted more attention in the context of climate change and extreme rainfall events recently.Advances in the flood migration techniques achieved by the research group led by the first author have been highlighted to migrate the breach risk of check dams due to floods.The“family tree method”has been proposed to determine the survival status and critical rainfall threshold of each check dam in the complicated dam system.An updated dam breach flood evaluation framework and the corresponding numerical algorithm(i.e.,DB-IWHR)have been developed.Moreover,innovative types of water-release facilities for check dams,including geobag stepped spillway and prestressed concrete cylinder pipe in the underlying conduit,have been proposed and developed.Finally,the perspectives concerning the check dam construction on the Loess Plateau have been put forward.展开更多
Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address ...Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address this challenge,a mushroom recognition method was proposed based on an erase module integrated into the EL-DenseNet model.EL-DenseNet,an extension of DenseNet,incorporated an erase attention module designed to enhance sensitivity to visible features.The erase module helped eliminate complex backgrounds and irrelevant information,allowing the mushroom body to be preserved and increasing recognition accuracy in cluttered environments.Considering the difficulty in distinguishing similar mushroom species,label smoothing regularization was employed to mitigate mislabeling errors that commonly arose from human observers.This strategy converted hard labels into soft labels during training,reducing the model’s overreliance on noisy labels and improving its generalization ability.Experimental results showed that the proposed EL-DenseNet,when combined with transfer learning,achieved a recognition accuracy of 96.7%for mushrooms in occluded and complex backgrounds.Compared with the original DenseNet and other classic models,this approach demonstrated superior accuracy and robustness,providing a promising solution for intelligent mushroom recognition.展开更多
文摘In this paper,we introduce the notion of G_(C)-X-injective modules,where X denotes a class of left S-modules and C represents a faithfully semidualizing bimodule.Under the condition that X satisfies certain hypotheses,some properties and some equivalent characterizations of G_(C)-X-injective modules are investigated,and we also show that the triple(■,cores■,■)is a weak co-AB-context.As an application,two complete cotorsion pairs and a new model structure in Mod S are given.
基金supported by the National Natural Science Foundation of China(Grant Nos.52069029,52369026)the Belt and Road Special Foundation of National Key Laboratory of Water Disaster Preven-tion(Grant No.2023490411)+2 种基金the Yunnan Agricultural Basic Research Joint Special General Project(Grant Nos.202501BD070001-060,202401BD070001-071)Construction Project of the Yunnan Key Laboratory of Water Security(No.20254916CE340051)the Youth Talent Project of“Xingdian Talent Support Plan”in Yunnan Province(Grant No.XDYC-QNRC-2023-0412).
文摘Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the construction and optimization of a prediction model for deformation prediction,a multipoint ultrahigh arch dam deformation prediction model,namely,the CEEMDAN-KPCA-GSWOA-KELM,which is based on a clustering partition,is pro-posed.First,the monitoring data are preprocessed via variational mode decomposition(VMD)and wavelet denoising(WT),which effectively filters out noise and improves the signal-to-noise ratio of the data,providing high-quality input data for subsequent prediction models.Second,scientific cluster partitioning is performed via the K-means++algorithm to precisely capture the spatial distribution characteristics of extra-high arch dams and ensure the consistency of deformation trends at measurement points within each partition.Finally,CEEMDAN is used to separate monitoring data,predict and analyze each component,combine the KPCA(Kernel Principal Component Analysis)and the KELM(Kernel Extreme Learning Machine)optimized by the GSWOA(Global Search Whale Optimization Algorithm),integrate the predictions of each component via reconstruction methods,and precisely predict the overall trend of ultrahigh arch dam deformation.An extra high arch dam project is taken as an example and validated via a comparative analysis of multiple models.The results show that the multipoint deformation prediction model in this paper can combine data from different measurement points,achieve a comprehensive,precise prediction of the deformation situation of extra high arch dams,and provide strong technical support for safe operation.
基金supported by the National Natural Science Foundation of China(Grant Nos.52379098 and 42122052)the Liaoning XingLiao Talent Program(Grant No.XLYC2203008).
文摘The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.
基金supported by the National Key R&D Program of China(2023YFB4204504)National Science Fund for Dis-tinguished Young Scholars(T2325016)+7 种基金National Natural Science Foundation of China(U21A2076)Natural Science Foundation of Jiangsu Province(BK20232022,BE2022021 and BE2022026)Fundamental Research Funds for the Central Universities(0213/14380206 and 0205/14380252)Frontiers Science Center for Critical Earth Material Cycling Fund(DLTD2109 and 2024ZD06)Program for Innovative Talents and Entrepreneur in JiangsuChina Postdoctoral Science Foundation(2023M731579)Jiangsu Funding Program for Excellent Postdoctoral Talent(2023ZB348)Postdoctoral Innovative Talents Support Project from the China Postdoctoral Science Foundation(BX20230157)。
文摘Fabrication of large-area perovskite solar modules under ambient air conditions remains a critical challenge due to air sensitivity of perovskite intermediate phases during crystallization.Here,we introduce 2-iodoimidazole(IIZ)into the perovskite precursor,enabling the formation of an air-stable pureδ-phase intermediate,which,upon annealing,fully transforms into a highly orientedα-phase perovskite film with reduced defects and variability.Leveraging this approach,we achieve a stabilized power conversion efficiency of 20.9%for 927.5 cm^(2)perovskite solar modules with high reproducibility.The encapsulated modules meet stringent international photovoltaic testing standards(IEC61215:2021),demonstrating excellent stability under continuous operation,thermal cycling(-40 to 85℃)and damp heat(85℃ and 85%relative humidity).
基金support from the Joint Funds of the National Natural Science Foundation of China(Grant No.42177143)the National Natural Science Foundation of China(Grant No.U23A2060).
文摘Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overburden layer.The deep overburden layer causes uneven settlement between the overburden layer and the dam,which poses a serious threat to the safety of both the construction and operation of the dam.In this study,microseismic(MS)monitoring technology was employed for the firsttime in the fieldof dam fillingengineering,allowing for the real-time monitoring of microfracture in the bedrock during dam construction.The time-frequency analysis method was used to summarize the MS waveform characteristics induced by dam filling.The fracture mechanism of bedrock was revealed,and the relationships among slope deformation,dam settlement,and MS activity were analyzed.The following research results have been obtained.The MS signal induced by dam fillinghas low energy and amplitude,short duration,and high frequency.The fracture of the bedrock was mainly shear failure.MS monitoring can predict deformation during blasting excavation and capture the large settlement that may occur during dam fillingin advance.Research findingshave demonstrated the significantapplication value of MS monitoring technology in predicting the risk of dam settlement and provide a reference for similar projects.
基金supported by the Gansu Provincial Department of Education Industry Support Plan Project(2025CYZC-018).
文摘In order to address the challenges posed by complex background interference,high miss-detection rates of micro-scale defects,and limited model deployment efficiency in photovoltaic(PV)module defect detection,this paper proposes an efficient detection framework based on an improved YOLOv11 architecture.First,a Re-parameterized Convolution(RepConv)module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency.Second,a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism(MSFF-CBAM)is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention.This mechanism effectively strengthens the specificity and robustness of feature representations.Third,a lightweight Dynamic Sampling Module(DySample)is employed to replace conventional upsampling operations,thereby improving the localization accuracy of small-scale defect targets.Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMDYOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision(mAP)@0.5,Precision,and Recall,achieving respective improvements of 4.70%,1.51%,and 5.50%.The model also exhibits notable advantages in inference speed and model compactness.Further validation on the ELPV dataset confirms the model’s generalization capability,showing respective performance gains of 1.99%,2.28%,and 1.45%across the same metrics.Overall,the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces,effectively reducing both false negatives and false positives.This advancement provides a robust and reliable technical foundation for automated PV module defect detection.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
基金supported by National Natural Science Foundation of China(Nos.10972145,11272218,11472178)Program for Liaoning Excellent Talents in University of China(No.LR2013008)Open Foundation of Key Laboratory of Liaoning Weapon Science and Technology,Liaoning Province Talents Engineering Projects of China(No.2012921044)
文摘To researching the damage characteristics of typical logical chip modules in spacecraft due to plasma generated by hypervelocity impacts,we have established a triple Langmuir probe diagnostic system and a logical chips measurement system,which were used to diagnose plasma characteristic parameters and the logical chip module＇s logical state changes due to the plasma created by a 7075 aluminum projectile hypervelocity impact on the 2A12 aluminum target.Three sets of experiments were performed with the collision speeds of 2.85 km/s,3.1 km/s and2.20 km/s,at the same incident angles of 30 degrees and logical chip module＇s positions by using a two-stage light gas gun loading system,a plasma characteristic parameters diagnostic system and a logical chip module＇s logical state measurement system,respectively.Electron temperature and density were measured at given position and azimuth,and damage estimation was performed for the logical chip module by using the data acquisition system.Experimental results showed that temporary damage could be induced on logical chip modules in spacecraft by plasma generated by hypervelocity impacts under the given experimental conditions and the sensors＇ position and azimuth.
文摘Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.
基金supported by the National Natural Science Foundation of China(No.62404041)the Natural Science Foundation of Jiangsu Province of China(No.BK20230830).
文摘Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the next-generation commercial solar cells.However,critical challenges remain in preserving high efficiency practical large-scale commercialized PSCs:a)the long-term stability of the cell materials and devices,b)lead leakage,and c)methods to scale the cells for larger area applications.This paper summarizes the prior-art strategies to address the above challenges,including the latest studies on the traditional glass-glass and thin-film encapsulation methods to better improve the reliability of PSCs,new technologies for preventing lead leakage,and geometric improvement strategies to enhance the reliability,efficiency,and performance of perovskite solar modules(PSMs).Through these strategies,the device achieved enhanced performance in long-term stability tests.The encapsulation resulted in a high lead leakage inhibition rate of up to 99%,and the PSMs possessed a geometric fill factor of 99.6%and a power conversion efficiency(PCE)of 20.7%.The dramatic improvement of efficiency and reliability of perovskite solar cells and modules indicate the great potential for mass production and commer-cialization of perovskite solar applications in the near future.
基金National Natural Science Foundation of China,Grant/Award Number:42330719National Natural Science Foundation of China,Grant/Award Number:U2443228+1 种基金Power Construction Corporation of China,Grant/Award Number:DJ-ZDXM-2021-51China Institute of Water Resources and Hydropower Research,Grant/Award Number:GE121003A0042022。
文摘Check dams are widely constructed on China's Loess Plateau,which had a total number of 58,776 by the end of 2019.Great achievements in check dam construction have been gained regarding the economic and environmental impacts.This study reviews the remarkable benefits of check dams on the land reclamation and environmental improvement on the Loess Plateau,and sediment reduction entering the Yellow River.However,the flood incidents on check dams have been frequently reported for the past decades,which has attracted more attention in the context of climate change and extreme rainfall events recently.Advances in the flood migration techniques achieved by the research group led by the first author have been highlighted to migrate the breach risk of check dams due to floods.The“family tree method”has been proposed to determine the survival status and critical rainfall threshold of each check dam in the complicated dam system.An updated dam breach flood evaluation framework and the corresponding numerical algorithm(i.e.,DB-IWHR)have been developed.Moreover,innovative types of water-release facilities for check dams,including geobag stepped spillway and prestressed concrete cylinder pipe in the underlying conduit,have been proposed and developed.Finally,the perspectives concerning the check dam construction on the Loess Plateau have been put forward.
文摘Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address this challenge,a mushroom recognition method was proposed based on an erase module integrated into the EL-DenseNet model.EL-DenseNet,an extension of DenseNet,incorporated an erase attention module designed to enhance sensitivity to visible features.The erase module helped eliminate complex backgrounds and irrelevant information,allowing the mushroom body to be preserved and increasing recognition accuracy in cluttered environments.Considering the difficulty in distinguishing similar mushroom species,label smoothing regularization was employed to mitigate mislabeling errors that commonly arose from human observers.This strategy converted hard labels into soft labels during training,reducing the model’s overreliance on noisy labels and improving its generalization ability.Experimental results showed that the proposed EL-DenseNet,when combined with transfer learning,achieved a recognition accuracy of 96.7%for mushrooms in occluded and complex backgrounds.Compared with the original DenseNet and other classic models,this approach demonstrated superior accuracy and robustness,providing a promising solution for intelligent mushroom recognition.
