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
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
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
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 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.
