酒精性肝病中医病机治法及中药防治规律的多维度分析

目的:分析酒精性肝病(alcoholic liver disease,ALD)中医病机、治法及中药防治规律。方法:筛选CNKI、维普网、万方数据库中有关中医药治疗ALD的文献,并建立数据库。通过Excel、GraphPad Prism、VOS viewer和SPSS Modeler软件进行数据统计,分别从ALD中医病机、治法及用药规律3个维度可视化分析。结果:共检索得到相关文献1890篇,根据纳排标准筛选后,最终纳入文献312篇。分析显示偏嗜饮酒、瘀血、痰饮是引发ALD的主要病因,气滞血瘀、肝郁脾虚、痰凝水停、湿热内蕴、湿困脾胃是该病的主要病机,应采取行气活血、疏肝健脾、化痰利水、清热解毒、化湿醒脾等治法。按疾病发展阶段分析可知,初期过度饮酒,酒毒湿热内蕴致脾失健运、肝气郁结,治宜解毒化湿、健脾疏肝;中期气滞日久,血行不畅,瘀血内停,气血湿热酒毒相互搏结,治宜行气活血、解毒利湿;末期气血湿热酒毒相互搏结日久,气滞、血瘀、水停,久病及肾,肝脾肾同病,治宜扶正祛邪、攻补兼施。用药规律分析显示,丹参、茯苓、柴胡、葛根、甘草及山楂等应用频次较高,配伍较多的是补益、利水渗湿、活血化瘀、解表、清热及理气等功效类药。关联规则分析显示,关联度最高的两药组合是葛花和砂仁,三药组合是丹参、山楂和泽泻,四药组合为丹参、山楂、柴胡和泽泻。结论:本研究全面分析了ALD中医病机及治法,并揭示了中药防治配伍规律,可为深入开展中医药防治ALD研究提供一定借鉴。

采用STAMP-24Model的多组织事故分析

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。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

复杂系统视角下洞室施工人员安全认知仿真研究

为制定有效的安全认知提升策略及减少安全事故的发生,需深入探究洞室施工人员安全认知的演化过程和特征。首先,基于复杂社会技术系统和“2-4”模型(24Model)等相关知识,识别安全认知影响因素,构建洞室施工人员安全认知指标体系;然后,通过Super Decisions软件计算指标权重;随后利用系统动力学(SD)理论,明确各因素间的相互关系,建立包含组织安全管理等4个子系统的SD仿真模型,将以安全投入为基础的认知纠偏机制应用到SD仿真模型;最后,结合实际工程算例开展SD仿真以及敏感性分析。研究表明:随着工程进展,通过增加安全投入,并发挥认知纠偏机制作用,施工人员的整体安全认知由下降趋势转变为缓慢上升趋势;组织安全管理对洞室施工人员安全认知影响程度最大,结果与24Model的基本假设一致,从而验证了SD仿真模型的科学性和有效性。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

基于综合对抗解释结构模型的煤矿透水事故致因分析

为了更加科学高效地对煤矿透水事故致因进行辨识和分类管理,以2021年丰源煤矿“4·10”重大透水事故为例,运用24Model法对事故致因进行分类提取,采用综合对抗解释结构模型(简称TAISM),对致因进行层级结构划分和因果关系分析。通过计算得到了该起事故致因的1组7层对抗型有向拓扑层级图。研究结果表明:监督机构监督检查不力、当地政府安全发展意识不强是造成此次事故发生的最根本原因。此外,在事故致因层级图中出现了2个回路,而同一个回路中的不同事故致因存在较强的关联性,应对其进行一体化控制,最大限度减少煤矿安全事故的发生。为今后煤矿事故致因分析提供了新的思路。

基于Revit平台的隔震支座快速建模模块开发与应用

随着隔震技术的推广应用以及建筑业信息化水平的持续提升,在隔震工程中对隔震层建筑信息模型(building information modeling, BIM)建模的需求逐渐增长,然而针对性的研究工作相对较少。为此,围绕隔震支座BIM模型的高效建模方法和应用模块开展了研究。首先,综合隔震支座应用情况和力学特性,可将其分为橡胶隔震支座、滑移摩擦隔震支座和其他类型隔震支座,据此提出了隔震支座BIM快速建模模块基本架构;随后,基于Revit和Visual Studio平台开发了三类隔震支座BIM模型的快速建模功能,并实现了连接节点参数化建模和支座批量/手动布置的操作功能;最后,开展了某化工公司的库房隔震加固项目的隔震层BIM模型建模实践,结果表明:利用快速建模模块可将隔震层BIM建模操作从7个步骤降低至2个步骤,且使用过程中对隔震支座构造细节的认知要求相对较低。同时,建成后的BIM模型与实际工程在建筑信息的多个方面具有较好的一致性。相关研究可为建筑和桥梁隔震工程的BIM建模提供参考和借鉴。

基于WordCloud技术的MBSE发展态势研究

基于IEEE及国际系统工程协会(International Council on Systems Engineering,INCOSE)社区会刊,提取与基于模型的系统工程(model based systems engineering,MBSE)领域相关的167篇顶刊的关键词和摘要。采用Python及其第三方库WordCloud技术,以可视化形式展示MBSE领域研究内容并对MBSE发展态势进行研究。研究结果表明,MBSE在产品研发全生命周期,应用建模技术来支持系统需求、设计、分析、验证与确认等活动,在系统架构设计方面具有重要作用,将MBSE与安全性分析、可靠性分析方法结合也是MBSE的重要研究内容;系统建模语言(system modeling language,SysML)和对象过程方法(object process method,OPM)分别是目前MBSE研究领域中最受欢迎的建模语言和建模方法;将MBSE方法与本体进行结合是规范MBSE模型表达的重要手段,将MBSE与信息物理系统、数字孪生、并行工程领域进行融合研究是MBSE的重要发展方向。所提研究为使用WordCloud文本分析技术来探索当前的MBSE研究提供了技术路线参考,有助于对MBSE的未来发展态势进行预测。

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Effects of mesenchymal stem cell on dopaminergic neurons,motor and memory functions in animal models of Parkinson's disease:a systematic review and meta-analysis

Parkinson’s disease is chara cterized by the loss of dopaminergic neurons in the substantia nigra pars com pacta,and although restoring striatal dopamine levels may improve symptoms,no treatment can cure or reve rse the disease itself.Stem cell therapy has a regenerative effect and is being actively studied as a candidate for the treatment of Parkinson’s disease.Mesenchymal stem cells are considered a promising option due to fewer ethical concerns,a lower risk of immune rejection,and a lower risk of teratogenicity.We performed a meta-analysis to evaluate the therapeutic effects of mesenchymal stem cells and their derivatives on motor function,memory,and preservation of dopamine rgic neurons in a Parkinson’s disease animal model.We searched bibliographic databases(PubMed/MEDLINE,Embase,CENTRAL,Scopus,and Web of Science)to identify articles and included only pee r-reviewed in vivo interve ntional animal studies published in any language through J une 28,2023.The study utilized the random-effect model to estimate the 95%confidence intervals(CI)of the standard mean differences(SMD)between the treatment and control groups.We use the systematic review center for laboratory animal expe rimentation’s risk of bias tool and the collaborative approach to meta-analysis and review of animal studies checklist for study quality assessment.A total of 33studies with data from 840 Parkinson’s disease model animals were included in the meta-analysis.Treatment with mesenchymal stem cells significantly improved motor function as assessed by the amphetamine-induced rotational test.Among the stem cell types,the bone marrow MSCs with neurotrophic factor group showed la rgest effect size(SMD[95%CI]=-6.21[-9.50 to-2.93],P=0.0001,I^(2)=0.0%).The stem cell treatment group had significantly more tyrosine hydroxylase positive dopamine rgic neurons in the striatum([95%CI]=1.04[0.59 to 1.49],P=0.0001,I^(2)=65.1%)and substantia nigra(SMD[95%CI]=1.38[0.89 to 1.87],P=0.0001,I^(2)=75.3%),indicating a protective effect on dopaminergic neurons.Subgroup analysis of the amphetamine-induced rotation test showed a significant reduction only in the intracranial-striatum route(SMD[95%CI]=-2.59[-3.25 to-1.94],P=0.0001,I^(2)=74.4%).The memory test showed significant improvement only in the intravenous route(SMD[95%CI]=4.80[1.84 to 7.76],P=0.027,I^(2)=79.6%).Mesenchymal stem cells have been shown to positively impact motor function and memory function and protect dopaminergic neurons in preclinical models of Parkinson’s disease.Further research is required to determine the optimal stem cell types,modifications,transplanted cell numbe rs,and delivery methods for these protocols.

Anisotropic time-dependent behaviors of shale under direct shearing and associated empirical creep models

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.

Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

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.

结合SAM视觉分割模型与随机森林机器学习的无人机影像盐沼植被“精灵圈”提取

“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。

Cell reprogramming therapy for Parkinson’s disease

Parkinson’s disease is typically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta.Many studies have been performed based on the supplementation of lost dopaminergic neurons to treat Parkinson’s disease.The initial strategy for cell replacement therapy used human fetal ventral midbrain and human embryonic stem cells to treat Parkinson’s disease,which could substantially alleviate the symptoms of Parkinson’s disease in clinical practice.However,ethical issues and tumor formation were limitations of its clinical application.Induced pluripotent stem cells can be acquired without sacrificing human embryos,which eliminates the huge ethical barriers of human stem cell therapy.Another widely considered neuronal regeneration strategy is to directly reprogram fibroblasts and astrocytes into neurons,without the need for intermediate proliferation states,thus avoiding issues of immune rejection and tumor formation.Both induced pluripotent stem cells and direct reprogramming of lineage cells have shown promising results in the treatment of Parkinson’s disease.However,there are also ethical concerns and the risk of tumor formation that need to be addressed.This review highlights the current application status of cell reprogramming in the treatment of Parkinson’s disease,focusing on the use of induced pluripotent stem cells in cell replacement therapy,including preclinical animal models and progress in clinical research.The review also discusses the advancements in direct reprogramming of lineage cells in the treatment of Parkinson’s disease,as well as the controversy surrounding in vivo reprogramming.These findings suggest that cell reprogramming may hold great promise as a potential strategy for treating Parkinson’s disease.

一种街景图像中建筑物高度估算方法

建筑物高度信息是城市三维建模的基础数据,但已有的建筑物高度估算研究多采用LiDAR和SAR等遥感影像。随着计算机和互联网的快速发展,街景数据因采集容易和成本低等特点成为了一种新兴的建筑物高度估算数据源。文章提出一种街景图像中建筑物高度估算方法,首先利用segment anything model实现图像中建筑物像素高度提取;然后利用图像元数据和电子地图数据获取建筑物与相机之间的距离、图像焦距,根据街景图像与建筑物实体的几何关系改进针孔相机模型,构建建筑物高度估算方法;最后选取北京、柏林的Mapillary街景图像开展实验验证。结果表明,与改进前相比,改进后针孔相机模型明显提升了高度估算准确度,RMSE降低了11.31 m,R^(2)提高了0.4,具备实用价值。

Genetically modified non-human primate models for research on neurodegenerative diseases

Neurodegenerative diseases(NDs)are a group of debilitating neurological disorders that primarily affect elderly populations and include Alzheimer's disease(AD),Parkinson's disease(PD),Huntington's disease(HD),and amyotrophic lateral sclerosis(ALS).Currently,there are no therapies available that can delay,stop,or reverse the pathological progression of NDs in clinical settings.As the population ages,NDs are imposing a huge burden on public health systems and affected families.Animal models are important tools for preclinical investigations to understand disease pathogenesis and test potential treatments.While numerous rodent models of NDs have been developed to enhance our understanding of disease mechanisms,the limited success of translating findings from animal models to clinical practice suggests that there is still a need to bridge this translation gap.Old World nonhuman primates(NHPs),such as rhesus,cynomolgus,and vervet monkeys,are phylogenetically,physiologically,biochemically,and behaviorally most relevant to humans.This is particularly evident in the similarity of the structure and function of their central nervous systems,rendering such species uniquely valuable for neuroscience research.Recently,the development of several genetically modified NHP models of NDs has successfully recapitulated key pathologies and revealed novel mechanisms.This review focuses on the efficacy of NHPs in modeling NDs and the novel pathological insights gained,as well as the challenges associated with the generation of such models and the complexities involved in their subsequent analysis.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

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).

Converting TREx-RGB green-channel data to 557.7 nm auroral intensity:Methodology and initial results

The recently deployed Transition Region Explorer(TREx)-RGB(red-green-blue)all-sky imager(ASI)is designed to capture“true color”images of the aurora and airglow.Because the 557.7 nm green line is usually the brightest emission line in visible auroras,the green channel of a TREx-RGB camera is usually dominated by the 557.7 nm emission.Under this rationale,the TREx mission does not include a specific 557.7 nm imager and is designed to use the RGB green-channel data as a proxy for the 557.7 nm aurora.In this study,we present an initial effort to establish the conversion ratio or formula linking the RGB green-channel data to the absolute intensity of 557.7 nm auroras,which is crucial for quantitative uses of the RGB data.We illustrate two approaches:(1)through a comparison with the collocated measurement of green-line auroras from the TREx spectrograph,and(2)through a comparison with the modeled green-line intensity according to realistic electron precipitation flux measurements from low-Earth-orbit satellites,with the aid of an auroral transport model.We demonstrate the procedures and provide initial results for the TREx-RGB ASIs at the Rabbit Lake and Lucky Lake stations.The RGB response is found to be nonlinear.Empirical conversion ratios or formulas between RGB green-channel data and the green-line auroral intensity are given and can be applied immediately by TREx-RGB data users.The methodology established in this study will also be applicable to the upcoming SMILE ASI mission,which will adopt a similar RGB camera system in its deployment.

Toward a Learnable Climate Model in the Artificial Intelligence Era

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.