The ultraviolet(UV)light stability of silicon heterojunction(SHJ)solar cells should be addressed before large-scale production and applications.Introducing downshifting(DS)nanophosphors on top of solar cells that can ...The ultraviolet(UV)light stability of silicon heterojunction(SHJ)solar cells should be addressed before large-scale production and applications.Introducing downshifting(DS)nanophosphors on top of solar cells that can convert UV light to visible light may reduce UV-induced degradation(UVID)without sacrificing the power conversion efficiency(PCE).Herein,a novel composite DS nanomaterial composed of YVO_(4):Eu^(3+),Bi^(3+)nanoparticles(NPs)and AgNPs was synthesized and introduced onto the incident light side of industrial SHJ solar cells to achieve UV shielding.The YVO_(4):Eu^(3+),Bi^(3+)NPs and Ag NPs were synthesized via a sol-gel method and a wet chemical reduction method,respectively.Then,a composite structure of the YVO_(4):Eu^(3+),Bi^(3+)NPs decorated with Ag NPs was synthesized by an ultrasonic method.The emission intensities of the YVO_(4):Eu^(3+),Bi^(3+)nanophosphors were significantly enhanced upon decoration with an appropriate amount of~20 nm Ag NPs due to the localized surface plasmon resonance(LSPR)effect.Upon the introduction of LSPR-enhanced downshifting,the SHJ solar cells exhibited an~0.54%relative decrease in PCE degradation under UV irradiation with a cumulative dose of 45 k W h compared to their counterparts,suggesting excellent potential for application in UV-light stability enhancement of solar cells or modules.展开更多
We investigated the sebaceous gland metaplasia(SGM) of the esophagus and clarified the evidence of misdiagnosis and its diagnosis pitfall. Cases of pathologically proven SGM were enrolled in the clinical analysis and ...We investigated the sebaceous gland metaplasia(SGM) of the esophagus and clarified the evidence of misdiagnosis and its diagnosis pitfall. Cases of pathologically proven SGM were enrolled in the clinical analysis and reviewed description of endoscope. In the current study, we demonstrated that SGM is very rare esophageal condition with an incidence around 0.00465% and an occurrence rate of 0.41 per year. There were 57.1% of senior endoscopists identified 8 episodes of SGM. In contrast, 7.7% of junior endoscopists identified SGM in only 2 episodes. Moreover, we investigated the difference in endoscopic biopsy attempt rate between the senior and junior endoscopist(P = 0.0001). The senior endoscopists had more motivation to look for SGM than did junior endoscopists(P = 0.01). We concluded that SGM of the esophagus is rare condition that is easily and not recognized in endoscopy studies omitting pathological review.展开更多
Accurate prediction of pharmacological properties of small molecules is becoming increasingly important in drug discovery.Traditional feature-engineering approaches heavily rely on handcrafted descriptors and/or finge...Accurate prediction of pharmacological properties of small molecules is becoming increasingly important in drug discovery.Traditional feature-engineering approaches heavily rely on handcrafted descriptors and/or fingerprints,which need extensive human expert knowledge.With the rapid progress of artificial intelligence technology,data-driven deep learning methods have shown unparalleled advantages over feature-engineering-based methods.However,existing deep learning methods usually suffer from the scarcity of labeled data and the inability to share information between different tasks when applied to predicting molecular properties,thus resulting in poor generalization capability.Here,we proposed a novel multitask learning BERT(Bidirectional Encoder Representations from Transformer)framework,named MTL-BERT,which leverages large-scale pre-training,multitask learning,and SMILES(simplified molecular input line entry specification)enumeration to alleviate the data scarcity problem.MTL-BERT first exploits a large amount of unlabeled data through self-supervised pretraining to mine the rich contextual information in SMILES strings and then fine-tunes the pretrained model for multiple downstream tasks simultaneously by leveraging their shared information.Meanwhile,SMILES enumeration is used as a data enhancement strategy during the pretraining,fine-tuning,and test phases to substantially increase data diversity and help to learn the key relevant patterns from complex SMILES strings.The experimental results showed that the pretrained MTL-BERT model with few additional fine-tuning can achieve much better performance than the state-of-the-art methods on most of the 60 practical molecular datasets.Additionally,the MTL-BERT model leverages attention mechanisms to focus on SMILES character features essential to target properties for model interpretability.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.52202276 and 51821002)the China Postdoctoral Science Foundation (Grant No.2022M712300)+1 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No.22KJB480010)a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)。
文摘The ultraviolet(UV)light stability of silicon heterojunction(SHJ)solar cells should be addressed before large-scale production and applications.Introducing downshifting(DS)nanophosphors on top of solar cells that can convert UV light to visible light may reduce UV-induced degradation(UVID)without sacrificing the power conversion efficiency(PCE).Herein,a novel composite DS nanomaterial composed of YVO_(4):Eu^(3+),Bi^(3+)nanoparticles(NPs)and AgNPs was synthesized and introduced onto the incident light side of industrial SHJ solar cells to achieve UV shielding.The YVO_(4):Eu^(3+),Bi^(3+)NPs and Ag NPs were synthesized via a sol-gel method and a wet chemical reduction method,respectively.Then,a composite structure of the YVO_(4):Eu^(3+),Bi^(3+)NPs decorated with Ag NPs was synthesized by an ultrasonic method.The emission intensities of the YVO_(4):Eu^(3+),Bi^(3+)nanophosphors were significantly enhanced upon decoration with an appropriate amount of~20 nm Ag NPs due to the localized surface plasmon resonance(LSPR)effect.Upon the introduction of LSPR-enhanced downshifting,the SHJ solar cells exhibited an~0.54%relative decrease in PCE degradation under UV irradiation with a cumulative dose of 45 k W h compared to their counterparts,suggesting excellent potential for application in UV-light stability enhancement of solar cells or modules.
文摘We investigated the sebaceous gland metaplasia(SGM) of the esophagus and clarified the evidence of misdiagnosis and its diagnosis pitfall. Cases of pathologically proven SGM were enrolled in the clinical analysis and reviewed description of endoscope. In the current study, we demonstrated that SGM is very rare esophageal condition with an incidence around 0.00465% and an occurrence rate of 0.41 per year. There were 57.1% of senior endoscopists identified 8 episodes of SGM. In contrast, 7.7% of junior endoscopists identified SGM in only 2 episodes. Moreover, we investigated the difference in endoscopic biopsy attempt rate between the senior and junior endoscopist(P = 0.0001). The senior endoscopists had more motivation to look for SGM than did junior endoscopists(P = 0.01). We concluded that SGM of the esophagus is rare condition that is easily and not recognized in endoscopy studies omitting pathological review.
基金the National Key Research and Development Program of China(2021YFF1201400)the National Natural Science Foundation of China(U1811462 and 22173118)+5 种基金the Hunan Provincial Science Fund for Distinguished Young Scholars(2021J10068)the Science and Technology Innovation Program of Hunan Province(2021RC4011)the Project of Inteiligent Management Software for Multimodal Medical Big Data for New Generation Information Technology,Ministry of Industry and Information Technology of People's Republic of China(TC210804V)the Changsha Municipal Natural Science Foundation(kq2014144)the Changsha Science and Technology Bureau project(kq2001034)the HKBU Strategic Development Fund project(SDF19-0402-P02)。
文摘Accurate prediction of pharmacological properties of small molecules is becoming increasingly important in drug discovery.Traditional feature-engineering approaches heavily rely on handcrafted descriptors and/or fingerprints,which need extensive human expert knowledge.With the rapid progress of artificial intelligence technology,data-driven deep learning methods have shown unparalleled advantages over feature-engineering-based methods.However,existing deep learning methods usually suffer from the scarcity of labeled data and the inability to share information between different tasks when applied to predicting molecular properties,thus resulting in poor generalization capability.Here,we proposed a novel multitask learning BERT(Bidirectional Encoder Representations from Transformer)framework,named MTL-BERT,which leverages large-scale pre-training,multitask learning,and SMILES(simplified molecular input line entry specification)enumeration to alleviate the data scarcity problem.MTL-BERT first exploits a large amount of unlabeled data through self-supervised pretraining to mine the rich contextual information in SMILES strings and then fine-tunes the pretrained model for multiple downstream tasks simultaneously by leveraging their shared information.Meanwhile,SMILES enumeration is used as a data enhancement strategy during the pretraining,fine-tuning,and test phases to substantially increase data diversity and help to learn the key relevant patterns from complex SMILES strings.The experimental results showed that the pretrained MTL-BERT model with few additional fine-tuning can achieve much better performance than the state-of-the-art methods on most of the 60 practical molecular datasets.Additionally,the MTL-BERT model leverages attention mechanisms to focus on SMILES character features essential to target properties for model interpretability.
