Visual media have dominated sensory communications for decades,and the resulting“visual hegemony”leads to the call for the“auditory return”in order to achieve a holistic balance in cultural acceptance.Romance of t...Visual media have dominated sensory communications for decades,and the resulting“visual hegemony”leads to the call for the“auditory return”in order to achieve a holistic balance in cultural acceptance.Romance of the Three Kingdoms,a classic literary work in China,has received significant attention and promotion from leading audio platforms.However,the commercialization of digital audio publishing faces unprecedented challenges due to the mismatch between the dissemination of long-form content on digital audio platforms and the current trend of short and fast information reception.Drawing on the Business Model Canvas Theory and taking Romance of the Three Kingdoms as the main focus of analysis,this paper argues that the construction of a business model for the audio publishing of classical books should start from three aspects:the user evaluation of digital audio platforms,the establishment of value propositions based on the“creative transformation and innovative development”principle,and the improvement of the audio publishing infrastructure to ensure the healthy operation and development of the digital audio platforms and consequently improve their current state of development and expand the boundaries of cultural heritage.展开更多
Many modern Chinese libraries have established distribution agencies in other cities to distribute books published or sold on consignment.Huating Bookstore,which was once the Shanghai General Distribution Office of Zh...Many modern Chinese libraries have established distribution agencies in other cities to distribute books published or sold on consignment.Huating Bookstore,which was once the Shanghai General Distribution Office of Zhejiang Library in modern times,is one of them.On the basis of a brief description of modern Zhejiang Library and its book publishing,as well as the overview of Huating Bookstore,the paper introduces and analyzes the ZPL Publishing Book Catalogue sent by Huating Bookstore,and the ZPL publishing and selling consignment books issued by Huating Bookstore.It points out that Huating Bookstore is a bridge between the ZPL located in Hangzhou and various retail ZPL publishing bookstores in Shanghai.Their production and sales relationship is a mutually beneficial one.展开更多
SNAPSHOTS OF NOTABLE BOOKS ON AFRICA.Africa’s Fashion Diaspora By ELIZABETH WAY Yale University Press.The author,Elizabeth Way,associate curator of costume at The Museum at the Fashion Institute of Technology,is the ...SNAPSHOTS OF NOTABLE BOOKS ON AFRICA.Africa’s Fashion Diaspora By ELIZABETH WAY Yale University Press.The author,Elizabeth Way,associate curator of costume at The Museum at the Fashion Institute of Technology,is the curator behind Africa’s Fashion Diaspora(2024).She has also curated several prominent exhibitions.Africa’s Fashion Diaspora is among the first books to delve into the vast influence of the African Diaspora on global fashion.展开更多
Travel in Northern Xizang(Revised Edition of 2024)Author:Ma Lihua.This book is one of the series of books about Ma Lihua's trip to Xizang.It describes the nomadic culture,festivals and daily life of local people,a...Travel in Northern Xizang(Revised Edition of 2024)Author:Ma Lihua.This book is one of the series of books about Ma Lihua's trip to Xizang.It describes the nomadic culture,festivals and daily life of local people,and the vast and magnificent scenery in northern Xizang.Published by China Tibetology Publishing House(ISBN:9787521105094).展开更多
The rapid development and popularization of Internet technology has provided important support for the informatization reform of various industries.University books and materials have the characteristics of large quan...The rapid development and popularization of Internet technology has provided important support for the informatization reform of various industries.University books and materials have the characteristics of large quantities and difficult storage and management.Its digital storage and management platform construction can effectively realize the digital storage and real-time retrieval of books and materials,and help students and teachers to find and use books and materials more quickly.Especially in recent years,with the update of intelligent platforms and other technologies,the resource-sharing platform of university books and materials can realize more convenient book information retrieval without the limitation of time and space.This paper mainly explores the optimization path and platform construction scheme of university libraries and information management under the background of the Internet,hoping to provide some theoretical references for university library managers.展开更多
This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)empowerment.Fi...This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)empowerment.Firstly,the relevant concepts were defined,and then the unique attributes of mobile community e-commerce were analyzed.As a typical representative of mobile community e-commerce,Little Red Book introduces the background and characteristics of its platform,analyzes its mobile community operation mode,and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology,to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.展开更多
安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事...安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。展开更多
Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Ar...Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).展开更多
Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,...Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,30°,45°,60°,and 90°),under multiple levels of direct shearing for the first time.The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior.Under a low shear stress,the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations,and the increase depends on the bedding orientation and creep time.Under high shear stress conditions,the creep compliance of shale is minimal when the bedding orientation is 0°,and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°,45°,60°,and 90°.The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation.A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed.The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate.The applicability of the five classical empirical creep models is quantitatively evaluated.It shows that the logarithmic model can well explain the experimental creep strain and creep rate,and it can accurately predict long-term shear creep deformation.Based on an improved logarithmic model,the variations in creep parameters with shear stress and bedding orientations are discussed.With abovementioned findings,a mathematical method for constructing an anisotropic shear creep model of shale is proposed,which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.展开更多
Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the ...Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.展开更多
BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still...BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.展开更多
Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation ...Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.展开更多
As the demand for used books has grown in recent years,various online/offline market platforms have emerged to support the trade in used books.The price of used books can depend on various factors,such as the state of...As the demand for used books has grown in recent years,various online/offline market platforms have emerged to support the trade in used books.The price of used books can depend on various factors,such as the state of preservation(i.e.,condition),the value of possession,and so on.Therefore,some online platforms provide a reference document to evaluate the condition of used books,but it is still not trivial for individual sellers to determine the price.The lack of a standard quantitative method to assess the condition of the used book would confuse both sellers and consumers,thereby decreasing the user experience of the online secondhand marketplace.Therefore,this paper discusses the automatic examination of the condition of used books based on deep learning approaches.In this work,we present a book damage detection system based on various You Only Look Once(YOLO)object detection models.Using YOLOv5,YOLOR,and YOLOX,we also introduce various training configurations that can be applied to improve performance.Specifically,a combination of different augmentation strategies including flip,rotation,crop,mosaic,and mixup was used for comparison.To train and validate our system,a book damage dataset composed of a total of 620 book images with 3,989 annotations,containing six types of damages(i.e.,Wear,Spot,Notch,Barcode,Tag,and Ripped),collected from the library books is presented.We evaluated each model trained with different configurations to figure out their detection accuracy as well as training efficiency.The experimental results showed that YOLOX trained with its best training configuration yielded the best performance in terms of detection accuracy,by achieving 60.0%(mAP@.5:.95)and 72.9%(mAP@.5)for book damage detection.However,YOLOX performed worst in terms of training efficiency,indicating that there is a trade-off between accuracy and efficiency.Based on the findings from the study,we discuss the feasibility and limitations of our system and future research directions.展开更多
The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use ...The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.展开更多
Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of ...Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.展开更多
Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currentl...Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.展开更多
基金This study is a phased achievement of the“Research on Innovative Communication of Romance of the Three Kingdoms under Audio Empowerment”project(No.23ZGL16)funded by Zhuge Liang Research Center,a key research base of social sciences in Sichuan Province.
文摘Visual media have dominated sensory communications for decades,and the resulting“visual hegemony”leads to the call for the“auditory return”in order to achieve a holistic balance in cultural acceptance.Romance of the Three Kingdoms,a classic literary work in China,has received significant attention and promotion from leading audio platforms.However,the commercialization of digital audio publishing faces unprecedented challenges due to the mismatch between the dissemination of long-form content on digital audio platforms and the current trend of short and fast information reception.Drawing on the Business Model Canvas Theory and taking Romance of the Three Kingdoms as the main focus of analysis,this paper argues that the construction of a business model for the audio publishing of classical books should start from three aspects:the user evaluation of digital audio platforms,the establishment of value propositions based on the“creative transformation and innovative development”principle,and the improvement of the audio publishing infrastructure to ensure the healthy operation and development of the digital audio platforms and consequently improve their current state of development and expand the boundaries of cultural heritage.
基金the research results of Humanities and Social Science Planning Fund Project of the Ministry of Education of P.R.China,titled“Research on the Books Publishing of Modern Chinese Library from the Perspective of Generalized Technology”(Project number:19YJA870014).
文摘Many modern Chinese libraries have established distribution agencies in other cities to distribute books published or sold on consignment.Huating Bookstore,which was once the Shanghai General Distribution Office of Zhejiang Library in modern times,is one of them.On the basis of a brief description of modern Zhejiang Library and its book publishing,as well as the overview of Huating Bookstore,the paper introduces and analyzes the ZPL Publishing Book Catalogue sent by Huating Bookstore,and the ZPL publishing and selling consignment books issued by Huating Bookstore.It points out that Huating Bookstore is a bridge between the ZPL located in Hangzhou and various retail ZPL publishing bookstores in Shanghai.Their production and sales relationship is a mutually beneficial one.
文摘SNAPSHOTS OF NOTABLE BOOKS ON AFRICA.Africa’s Fashion Diaspora By ELIZABETH WAY Yale University Press.The author,Elizabeth Way,associate curator of costume at The Museum at the Fashion Institute of Technology,is the curator behind Africa’s Fashion Diaspora(2024).She has also curated several prominent exhibitions.Africa’s Fashion Diaspora is among the first books to delve into the vast influence of the African Diaspora on global fashion.
文摘Travel in Northern Xizang(Revised Edition of 2024)Author:Ma Lihua.This book is one of the series of books about Ma Lihua's trip to Xizang.It describes the nomadic culture,festivals and daily life of local people,and the vast and magnificent scenery in northern Xizang.Published by China Tibetology Publishing House(ISBN:9787521105094).
文摘The rapid development and popularization of Internet technology has provided important support for the informatization reform of various industries.University books and materials have the characteristics of large quantities and difficult storage and management.Its digital storage and management platform construction can effectively realize the digital storage and real-time retrieval of books and materials,and help students and teachers to find and use books and materials more quickly.Especially in recent years,with the update of intelligent platforms and other technologies,the resource-sharing platform of university books and materials can realize more convenient book information retrieval without the limitation of time and space.This paper mainly explores the optimization path and platform construction scheme of university libraries and information management under the background of the Internet,hoping to provide some theoretical references for university library managers.
基金Phased Research Key Project of Shanghai China Vocational Education Association“Research on Digital Transformation Path of Vocational Education Driven by AIGC from the Perspective of New Quality Productivity”,Phased Research Project of Shanghai Computer Industry Association“The Reform and Exploration of Cross-border E-commerce Talent Cultivation in Vocational Colleges from the Perspective of Industry Education Integration”(Project No.sctakt202404)。
文摘This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)empowerment.Firstly,the relevant concepts were defined,and then the unique attributes of mobile community e-commerce were analyzed.As a typical representative of mobile community e-commerce,Little Red Book introduces the background and characteristics of its platform,analyzes its mobile community operation mode,and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology,to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.
文摘安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。
基金supported by the Chinese–Norwegian Collaboration Projects within Climate Systems jointly funded by the National Key Research and Development Program of China (Grant No.2022YFE0106800)the Research Council of Norway funded project,MAPARC (Grant No.328943)+2 种基金the support from the Research Council of Norway funded project,COMBINED (Grant No.328935)the National Natural Science Foundation of China (Grant No.42075030)the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX23_1314)。
文摘Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).
基金funded by the National Natural Science Foundation of China(Grant Nos.U22A20166 and 12172230)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515012654)+1 种基金funded by the National Natural Science Foundation of China(Grant Nos.U22A20166 and 12172230)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515012654)。
文摘Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,30°,45°,60°,and 90°),under multiple levels of direct shearing for the first time.The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior.Under a low shear stress,the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations,and the increase depends on the bedding orientation and creep time.Under high shear stress conditions,the creep compliance of shale is minimal when the bedding orientation is 0°,and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°,45°,60°,and 90°.The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation.A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed.The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate.The applicability of the five classical empirical creep models is quantitatively evaluated.It shows that the logarithmic model can well explain the experimental creep strain and creep rate,and it can accurately predict long-term shear creep deformation.Based on an improved logarithmic model,the variations in creep parameters with shear stress and bedding orientations are discussed.With abovementioned findings,a mathematical method for constructing an anisotropic shear creep model of shale is proposed,which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.
基金We acknowledge funding from NSFC Grant 62306283.
文摘Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.
基金Supported by the Project of NINGBO Leading Medical Health Discipline,No.2022-B11Ningbo Natural Science Foundation,No.202003N4206Public Welfare Foundation of Ningbo,No.2021S108.
文摘BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.
文摘Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.
文摘As the demand for used books has grown in recent years,various online/offline market platforms have emerged to support the trade in used books.The price of used books can depend on various factors,such as the state of preservation(i.e.,condition),the value of possession,and so on.Therefore,some online platforms provide a reference document to evaluate the condition of used books,but it is still not trivial for individual sellers to determine the price.The lack of a standard quantitative method to assess the condition of the used book would confuse both sellers and consumers,thereby decreasing the user experience of the online secondhand marketplace.Therefore,this paper discusses the automatic examination of the condition of used books based on deep learning approaches.In this work,we present a book damage detection system based on various You Only Look Once(YOLO)object detection models.Using YOLOv5,YOLOR,and YOLOX,we also introduce various training configurations that can be applied to improve performance.Specifically,a combination of different augmentation strategies including flip,rotation,crop,mosaic,and mixup was used for comparison.To train and validate our system,a book damage dataset composed of a total of 620 book images with 3,989 annotations,containing six types of damages(i.e.,Wear,Spot,Notch,Barcode,Tag,and Ripped),collected from the library books is presented.We evaluated each model trained with different configurations to figure out their detection accuracy as well as training efficiency.The experimental results showed that YOLOX trained with its best training configuration yielded the best performance in terms of detection accuracy,by achieving 60.0%(mAP@.5:.95)and 72.9%(mAP@.5)for book damage detection.However,YOLOX performed worst in terms of training efficiency,indicating that there is a trade-off between accuracy and efficiency.Based on the findings from the study,we discuss the feasibility and limitations of our system and future research directions.
基金This work was partly supported by the Basic Ability Improvement Project for Young andMiddle-aged Teachers in Guangxi Colleges andUniversities(2021KY1800,2021KY1804).
文摘The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.
基金supported by the National Natural Science Foundation of China(Grant Nos.42141019 and 42261144687)and STEP(Grant No.2019QZKK0102)supported by the Korea Environmental Industry&Technology Institute(KEITI)through the“Project for developing an observation-based GHG emissions geospatial information map”,funded by the Korea Ministry of Environment(MOE)(Grant No.RS-2023-00232066).
文摘Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.
基金supported by National Natural Science Foundation of China,China(No.42004016)HuBei Natural Science Fund,China(No.2020CFB329)+1 种基金HuNan Natural Science Fund,China(No.2023JJ60559,2023JJ60560)the State Key Laboratory of Geodesy and Earth’s Dynamics self-deployment project,China(No.S21L6101)。
文摘Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.
