As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security ...As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion det...With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.展开更多
Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accu...Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.展开更多
The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can serio...The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore,early orthodontic treatment for Class Ⅲ malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class Ⅲ malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class Ⅲ malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class Ⅲ malocclusion through early orthodontic treatment.展开更多
Olfactory ensheathing glia promote axonal regeneration in the mammalian central nervous system,including retinal ganglion cell axonal growth through the injured optic nerve.Still,it is unknown whether olfactory enshea...Olfactory ensheathing glia promote axonal regeneration in the mammalian central nervous system,including retinal ganglion cell axonal growth through the injured optic nerve.Still,it is unknown whether olfactory ensheathing glia also have neuroprotective properties.Olfactory ensheathing glia express brain-derived neurotrophic factor,one of the best neuroprotectants for axotomized retinal ganglion cells.Therefore,we aimed to investigate the neuroprotective capacity of olfactory ensheating glia after optic nerve crush.Olfactory ensheathing glia cells from an established rat immortalized clonal cell line,TEG3,were intravitreally injected in intact and axotomized retinas in syngeneic and allogeneic mode with or without microglial inhibition or immunosuppressive treatments.Anatomical and gene expression analyses were performed.Olfactory bulb-derived primary olfactory ensheathing glia and TEG3 express major histocompatibility complex classⅡmolecules.Allogeneically and syngenically transplanted TEG3 cells survived in the vitreous for up to 21 days,forming an epimembrane.In axotomized retinas,only the allogeneic TEG3 transplant rescued retinal ganglion cells at 7 days but not at 21 days.In these retinas,microglial anatomical activation was higher than after optic nerve crush alone.In intact retinas,both transplants activated microglial cells and caused retinal ganglion cell death at 21 days,a loss that was higher after allotransplantation,triggered by pyroptosis and partially rescued by microglial inhibition or immunosuppression.However,neuroprotection of axotomized retinal ganglion cells did not improve with these treatments.The different neuroprotective properties,different toxic effects,and different responses to microglial inhibitory treatments of olfactory ensheathing glia in the retina depending on the type of transplant highlight the importance of thorough preclinical studies to explore these variables.展开更多
Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in ...Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.展开更多
China’s growing number of co#ee consumers helps to boost Uganda’s coffee production In recent years,China has witnessed a significant surge in co!ee consumption,driven by a growing middle class and increasing demand...China’s growing number of co#ee consumers helps to boost Uganda’s coffee production In recent years,China has witnessed a significant surge in co!ee consumption,driven by a growing middle class and increasing demand for specialty brews.This trend has had a profound impact on co!ee-producing countries around the world,including Uganda.展开更多
My school life is very wonderful. I go to school at seven in the morning. The campus is always filled of(1) vitality.I have many interesting classes everyday(2). In English class, we learn new words and practice speak...My school life is very wonderful. I go to school at seven in the morning. The campus is always filled of(1) vitality.I have many interesting classes everyday(2). In English class, we learn new words and practice speaking. Math class challenges my mind and art class is my favorite.展开更多
This paper addresses a fundamental question in rock mechanics:Are there Class II rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing ...This paper addresses a fundamental question in rock mechanics:Are there Class II rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing machines.The pioneering work of some researchers is reviewed,and the misconception of classifying rocks into Class I and Class II is discussed.The mechanism of post-peak Class II behavior is discussed based on some recent test results.When a brittle hard rock is tested using a soft testing machine under axial-strain-controlled loading,violent failure can occur when the peak strength is reached,and the post-peak stress-strain curve cannot be obtained.However,a Class II post-peak stress-strain curve can be obtained when the rock is tested under lateral-strain-controlled loading.If a stiff testing machine is used,Class I and Class II post-peak stress-strain curves will be obtained under axial-and lateral-strain-controlled loadings,respectively.It is therefore not appropriate to classify rocks into Class I or Class II rocks.The influences of other conditions,such as rock type,confinement,and specimen height-to-diameter ratio,on the type(Class I or Class II)of post-peak stress-strain curves are also discussed.Finally,some misconceptions in the rock mechanics community,stemming from the concept of“Class II rock”,are discussed.By clarifying these concepts related to Class I and Class II behaviors,this paper seeks to clarify misunderstandings and misapplications related to post-peak strength and deformation properties in the field.展开更多
Hello,everyone.My English name is Molly.I am a girl.I am 7 years old.I'm a primary school student.I'm in Class 4,Grade 2 at Beijing Jianhua ExperimentalSchooll.
Jiang Shaohong is my best friend.He is 14years old and he is in Class 1,Grade 8.He is clever and can learn things quickly.He works hard at all his subjects.He has many hobbies,such as reading,singing,dancing,and playi...Jiang Shaohong is my best friend.He is 14years old and he is in Class 1,Grade 8.He is clever and can learn things quickly.He works hard at all his subjects.He has many hobbies,such as reading,singing,dancing,and playing sports.He loves animals,so he keeps a small dog as a pet and takes good care of it at home.He is kind and helpful.He often helps his classmates with their studies.展开更多
Objectives:To identify the subgroups of self-reported outcomes and associated factors among breast cancer patients undergoing surgery and chemotherapy.Methods:A cross-sectional study was conducted between January and ...Objectives:To identify the subgroups of self-reported outcomes and associated factors among breast cancer patients undergoing surgery and chemotherapy.Methods:A cross-sectional study was conducted between January and November 2021.We recruited patients from two tertiary hospitals in Shanghai,China,using convenience sampling during their hospitalization.Patients were assessed using a questionnaire that included sociodemographic and clinical characteristics,the Patient Reported Outcomes Measurement Information System profile-29(PROMIS-29),and the PROMIS-cognitive function short form 4a.Latent class analysis was performed to examine possible classes regarding self-reported outcomes.Multiple logistic regression analysis was used to determine the associated factors.Analysis of variance(ANOVA)was conducted for symptoms across the different classes.Results:A total of 640 patients participated in this study.The findings revealed three subgroups in terms of self-reported outcomes among breast cancer patients undergoing surgery and chemotherapy:low physical-social-cognitive function,high physical-low cognitive function,and high physical-socialcognitive function.Multivariable logistic regression analysis showed that age(≥60 years old),menopause,the third chemotherapy cycle,undergoing simple mastectomy and breast reconstruction,duration of disease 3-12 months,stageⅢ/Ⅳcancer,and severe pain were associated factors of the functional decline groups.Besides,significant differences in depression and sleep disorders were observed among the three groups.Conclusions:Breast cancer patients receiving surgery and chemotherapy can be divided into three subgroups.Aging,menopause,chemotherapy cycle,surgery type,duration and stage of disease,and severe pain affected the functional decline groups.Consequently,healthcare professionals should make tailored interventions to address the specific functional rehabilitation and symptom relief needs.展开更多
Calvin Wee first set foot in China at the age of 14—not for a diplomatic forum or a youth initiative,but on a week-long school trip to Guangzhou.Yet even then,the experience left a lasting impression.“We visited a l...Calvin Wee first set foot in China at the age of 14—not for a diplomatic forum or a youth initiative,but on a week-long school trip to Guangzhou.Yet even then,the experience left a lasting impression.“We visited a local school and sat in on several classes,”he recalled.“The math lessons stood out—the students’intensity and focus were striking.In Singapore,we study hard,but this was on another level.”That early glimpse into China’s education system stayed with him,and years later,it resurfaced as he began to engage more deeply with the Chinese language and culture.A pocket-sized wuxia xiaoshuo(martial arts novel),picked up on a whim in a Guangzhou bookstore,opened the door to martial arts fiction—and sparked a lasting interest in Chinese literature and,eventually,China Studies.展开更多
When I caught myself deliberately or unintentionally showing off my knowledge of luxury brands,I began to feel wary of myself.As a small-town girl raised in a working-class family,Tve always harbored a kind of intelle...When I caught myself deliberately or unintentionally showing off my knowledge of luxury brands,I began to feel wary of myself.As a small-town girl raised in a working-class family,Tve always harbored a kind of intellectual pride in being poor.High consumption is wrong,and spending extravagantly on luxury goods is even worse.My expectation for myself was never to be like a movie character who could rattle off details about luxury brands.Tve always felt that the ideal version of me should have more depth.展开更多
基金supported by the Key Project of Joint Fund of the National Natural Science Foundation of China“Research on Key Technologies and Demonstration Applications for Trusted and Secure Data Circulation and Trading”(U24A20241)the National Natural Science Foundation of China“Research on Trusted Theories and Key Technologies of Data Security Trading Based on Blockchain”(62202118)+4 种基金the Major Scientific and Technological Special Project of Guizhou Province([2024]014)Scientific and Technological Research Projects from the Guizhou Education Department(Qian jiao ji[2023]003)the Hundred-Level Innovative Talent Project of the Guizhou Provincial Science and Technology Department(Qiankehe Platform Talent-GCC[2023]018)the Major Project of Guizhou Province“Research and Application of Key Technologies for Trusted Large Models Oriented to Public Big Data”(Qiankehe Major Project[2024]003)the Guizhou Province Computational Power Network Security Protection Science and Technology Innovation Talent Team(Qiankehe Talent CXTD[2025]029).
文摘As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by the High-Level Talent Foundation of Jinling Institute of Technology(grant number.JIT-B-202413).
文摘With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.
基金funded by the Youth Fund of the National Natural Science Foundation of China(Grant No.42261070).
文摘Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.
文摘The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore,early orthodontic treatment for Class Ⅲ malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class Ⅲ malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class Ⅲ malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class Ⅲ malocclusion through early orthodontic treatment.
基金supported by the Spanish Ministry of Economy and Competitiveness,No.PID2019-106498GB-I00(to MVS)the Instituto de Salud CarlosⅢ,Fondo Europeo de Desarrollo Regional“Una manera de hacer Europa”,No.PI19/00071(to MAB)+1 种基金Ministerio de Ciencia e Innovación Project,No.SAF2017-82736-C2-1-R(to MTMF)in Universidad Autónoma de MadridFundación Universidad Francisco de Vitoria(to JS)。
文摘Olfactory ensheathing glia promote axonal regeneration in the mammalian central nervous system,including retinal ganglion cell axonal growth through the injured optic nerve.Still,it is unknown whether olfactory ensheathing glia also have neuroprotective properties.Olfactory ensheathing glia express brain-derived neurotrophic factor,one of the best neuroprotectants for axotomized retinal ganglion cells.Therefore,we aimed to investigate the neuroprotective capacity of olfactory ensheating glia after optic nerve crush.Olfactory ensheathing glia cells from an established rat immortalized clonal cell line,TEG3,were intravitreally injected in intact and axotomized retinas in syngeneic and allogeneic mode with or without microglial inhibition or immunosuppressive treatments.Anatomical and gene expression analyses were performed.Olfactory bulb-derived primary olfactory ensheathing glia and TEG3 express major histocompatibility complex classⅡmolecules.Allogeneically and syngenically transplanted TEG3 cells survived in the vitreous for up to 21 days,forming an epimembrane.In axotomized retinas,only the allogeneic TEG3 transplant rescued retinal ganglion cells at 7 days but not at 21 days.In these retinas,microglial anatomical activation was higher than after optic nerve crush alone.In intact retinas,both transplants activated microglial cells and caused retinal ganglion cell death at 21 days,a loss that was higher after allotransplantation,triggered by pyroptosis and partially rescued by microglial inhibition or immunosuppression.However,neuroprotection of axotomized retinal ganglion cells did not improve with these treatments.The different neuroprotective properties,different toxic effects,and different responses to microglial inhibitory treatments of olfactory ensheathing glia in the retina depending on the type of transplant highlight the importance of thorough preclinical studies to explore these variables.
基金supported by National Key R&D Program of China(2022YFA1008000)the National Natural Science Foundation of China(12571297,12101585)+1 种基金the CAS Talent Introduction Program(Category B)the Young Elite Scientist Sponsorship Program by CAST(YESS20220125).
文摘Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.
文摘China’s growing number of co#ee consumers helps to boost Uganda’s coffee production In recent years,China has witnessed a significant surge in co!ee consumption,driven by a growing middle class and increasing demand for specialty brews.This trend has had a profound impact on co!ee-producing countries around the world,including Uganda.
文摘My school life is very wonderful. I go to school at seven in the morning. The campus is always filled of(1) vitality.I have many interesting classes everyday(2). In English class, we learn new words and practice speaking. Math class challenges my mind and art class is my favorite.
基金the Natural Science and Engineering Research Council of Canada(RGPIN/4052-16,ALLRP 560390-20).
文摘This paper addresses a fundamental question in rock mechanics:Are there Class II rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing machines.The pioneering work of some researchers is reviewed,and the misconception of classifying rocks into Class I and Class II is discussed.The mechanism of post-peak Class II behavior is discussed based on some recent test results.When a brittle hard rock is tested using a soft testing machine under axial-strain-controlled loading,violent failure can occur when the peak strength is reached,and the post-peak stress-strain curve cannot be obtained.However,a Class II post-peak stress-strain curve can be obtained when the rock is tested under lateral-strain-controlled loading.If a stiff testing machine is used,Class I and Class II post-peak stress-strain curves will be obtained under axial-and lateral-strain-controlled loadings,respectively.It is therefore not appropriate to classify rocks into Class I or Class II rocks.The influences of other conditions,such as rock type,confinement,and specimen height-to-diameter ratio,on the type(Class I or Class II)of post-peak stress-strain curves are also discussed.Finally,some misconceptions in the rock mechanics community,stemming from the concept of“Class II rock”,are discussed.By clarifying these concepts related to Class I and Class II behaviors,this paper seeks to clarify misunderstandings and misapplications related to post-peak strength and deformation properties in the field.
文摘Hello,everyone.My English name is Molly.I am a girl.I am 7 years old.I'm a primary school student.I'm in Class 4,Grade 2 at Beijing Jianhua ExperimentalSchooll.
文摘Jiang Shaohong is my best friend.He is 14years old and he is in Class 1,Grade 8.He is clever and can learn things quickly.He works hard at all his subjects.He has many hobbies,such as reading,singing,dancing,and playing sports.He loves animals,so he keeps a small dog as a pet and takes good care of it at home.He is kind and helpful.He often helps his classmates with their studies.
基金supported by the Hospital-level Nursing Research Project of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine(xhhlcx2023-017)the third period of the 14th Five-Year nursing talent project of Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine(Xhlxm014)the Ministry of Education of Humanities and Social Science Project(23YJC630002)and High-level local university construction project founded by Shanghai Municipal Education Commission.
文摘Objectives:To identify the subgroups of self-reported outcomes and associated factors among breast cancer patients undergoing surgery and chemotherapy.Methods:A cross-sectional study was conducted between January and November 2021.We recruited patients from two tertiary hospitals in Shanghai,China,using convenience sampling during their hospitalization.Patients were assessed using a questionnaire that included sociodemographic and clinical characteristics,the Patient Reported Outcomes Measurement Information System profile-29(PROMIS-29),and the PROMIS-cognitive function short form 4a.Latent class analysis was performed to examine possible classes regarding self-reported outcomes.Multiple logistic regression analysis was used to determine the associated factors.Analysis of variance(ANOVA)was conducted for symptoms across the different classes.Results:A total of 640 patients participated in this study.The findings revealed three subgroups in terms of self-reported outcomes among breast cancer patients undergoing surgery and chemotherapy:low physical-social-cognitive function,high physical-low cognitive function,and high physical-socialcognitive function.Multivariable logistic regression analysis showed that age(≥60 years old),menopause,the third chemotherapy cycle,undergoing simple mastectomy and breast reconstruction,duration of disease 3-12 months,stageⅢ/Ⅳcancer,and severe pain were associated factors of the functional decline groups.Besides,significant differences in depression and sleep disorders were observed among the three groups.Conclusions:Breast cancer patients receiving surgery and chemotherapy can be divided into three subgroups.Aging,menopause,chemotherapy cycle,surgery type,duration and stage of disease,and severe pain affected the functional decline groups.Consequently,healthcare professionals should make tailored interventions to address the specific functional rehabilitation and symptom relief needs.
文摘Calvin Wee first set foot in China at the age of 14—not for a diplomatic forum or a youth initiative,but on a week-long school trip to Guangzhou.Yet even then,the experience left a lasting impression.“We visited a local school and sat in on several classes,”he recalled.“The math lessons stood out—the students’intensity and focus were striking.In Singapore,we study hard,but this was on another level.”That early glimpse into China’s education system stayed with him,and years later,it resurfaced as he began to engage more deeply with the Chinese language and culture.A pocket-sized wuxia xiaoshuo(martial arts novel),picked up on a whim in a Guangzhou bookstore,opened the door to martial arts fiction—and sparked a lasting interest in Chinese literature and,eventually,China Studies.
文摘When I caught myself deliberately or unintentionally showing off my knowledge of luxury brands,I began to feel wary of myself.As a small-town girl raised in a working-class family,Tve always harbored a kind of intellectual pride in being poor.High consumption is wrong,and spending extravagantly on luxury goods is even worse.My expectation for myself was never to be like a movie character who could rattle off details about luxury brands.Tve always felt that the ideal version of me should have more depth.
