技术重塑治理关系与数字治理“内卷化”——以“12345”热线驱动市域治理为例

目前,推动来自数据资源的技术驱动与政府行政体系融合,已成为推进国家治理体系和治理能力现代化的应有之义。数字治理对民众和政府具有双向赋能意义,但需要有效协同数字技术与科层体制之间的关系、平衡多元治理主体之间的关系,避免陷入数字治理“内卷化”的困境。以A市“12345”热线驱动市域治理为例,数字治理的“内卷化”生发于数字信息在行政体制内运行的不同阶段。在诉求信息输入阶段,信息传播的跨时空性使群众从逐级传递变为直接面对上级政府,上级政府实现对诉求处理全流程的监督和考核,形塑了去中间化治理主体关系,导致治理内容、数量和成本不断增长,同时基层政府自主治理的空间减少。在诉求信息筛选阶段,现实诉求以简化和标准化的信息形式在热线数字系统中闭环流转,治理实践中诉求解决的情境关系出现脱域。原先可以在治理情境中被识别的不合理诉求和反复提交的诉求,伪装成单一诉求从而突破原有治理结构的抑制,挤占了大量的治理注意力和治理资源,破坏原有的治理传统与治理规则,造成“无理”诉求工单在热线系统中的空转。在诉求信息处理或执行阶段,受到科层压力与信息传递的乘数效应影响,诉求治理的责任关系发生改变,基层组织由于压力过载产生目标替代行为。数字治理“内卷化”是数字技术逻辑对治理逻辑与治理关系的改造,使数字技术从辅助治理的工具手段转变为主导基层工作的关键变量。数字技术对治理的反噬后果,不仅表现为治理效能的低效增长,还体现在工具理性思维对人本思维的侵蚀与替代。破解数字治理的“内卷化”问题,需要审视数字技术引发的治理关系变革,发挥数字信息在治理体系与治理能力现代化中的支撑作用,在市域层面重点发挥数据挖掘的决策辅助功能,厘清各个治理主体关系责权利边界,避免数字化的过度监督。数字治理要辅助场景式治理,重点激发基层组织和群众的主体性。

基于“12345”热线转办的投诉分析及措施探讨

目的归纳总结“12345”热线转办投诉的情况,分析原因,提出处理措施。方法选取2020年1月—2022年12月“12345”热线转办的启东市中医院投诉763件进行分析,分析投诉类型,总结发生投诉的科室分布情况,同时总结引起投诉的相关原因,提出处理措施。结果763件投诉情况中,涉及院感防控方面的总计380件,占比49.80%,其他涉及医院诊疗服务、就医流程、医疗收费等情况。涉及投诉的科室包括门诊医技科室、病区、总务、药房、收费等。分析结果显示,院感防控是3年内引起投诉的主要原因;其次是医患沟通不到位、医疗质量问题、服务态度问题、收费不合理等。结合原因提出相应处理对策。结论通过“12345”热线能够一定程度使医院了解投诉信息,拓宽投诉通道,改进服务机制,提升服务质量,以建立和谐、融洽的医患关系。

高职大思政课“12345”实践育人模式的构建与实施——以西安航空职业技术学院为例

在世界百年未有之大变局和中华民族伟大复兴战略全局时代背景下,思政课教学要与社会大课堂结合起来,充分挖掘鲜活的育人素材,培养担当民族复兴重任的时代新人。本文以西安航空职业技术学院发挥航空文化资源优势、开展大思政课实践教学、构建“12345”实践育人模式为例,为解决高职思政课教学中存在的专职教学团队与特聘教授之间沟通不畅、理论教学与实践教学相脱节、课程资源开发与利用不充分、教学形式单一等问题提供思路。

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。

政民互动“连心线”助推社区治理能力现代化——接诉即办的12345热线问题研究

12345政务服务便民热线一端连着政府,一端连着民众,成为政府听民声、察民情、解民忧的重要桥梁,可称之为政民互动“连心线”。热线在提高政府形象、应诉效率和治理水平的同时,也面临着公共资源挤占、客服人员业务水平不高、合规诉求办理不到位等诸多挑战。通过搭建“12345+遇事先商量”平台,完善“民呼我应”工作体系,创新实施民声接听、民意分析、民情观察、民诉协调、民事督办“五民服务工程”,健全“12345+接诉即办”机制,采取“12345+数据研判”分析等措施,能够为政府高效决策提供咨询,进而有效提升政府的社区治理能力。

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。

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。

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。

12345平台的法律地位——数字法治政府的组织法观察

12345平台内生出一种不同于行政权力、社会权力的新型权力——数字权力,其产生的主要原因是国家治理需要,基础是合作行政,载体是数字技术。12345平台数字权力的扩张带来行政组织法难题,包括数字权力改变传统权力结构,挑战组织法的职权法定原则;数字权力模糊传统权力边界,带来行政合法性难题;数字权力带来去中心化,引发主体归责机制模糊化。12345平台是一种新的行政组织运作模式,“新”表现为一种以行政任务为导向的整体运作模式,形成一个动态化的数字生态系统;是以新公共管理为基础、逐渐转向网络治理范式的新型合作模式。12345平台作为数字政府建设的产物,应受行政组织法调整。在数字法治政府建设背景下,应以整体政府改革为面向,科学调整行政组织法体系;以数字素养、监督机制、技术和法律手段解决行政合法性难题;从采购思维转向规则制定思维,依法设计负责任的人工智能。

“12345”热线诊疗投诉特征、产生原因及改进建议分析——以江苏省某口腔医院为例

正视口腔患者的合理诉求,及时、有效地处理患者的诊疗投诉,关系到口腔医疗服务质量提升与患者就医体验改善,具有重要意义。通过对江苏省某三甲口腔专科医院2019-2022年“12345”热线受理的1 778例诊疗投诉进行分析发现,诊疗投诉数和每万人次诊疗投诉数均逐年上升,单月诊疗投诉数受到疫情防控政策影响呈现波动趋势,患者投诉内容主要集中在工作流程、疫情防控、医疗质量、停诊问题等方面。导致上述问题的原因主要包括突发公共卫生事件的影响、医疗资源配置不合理、医务人员医疗技术不足与职业倦怠、医患双方的认知存在差异、投诉处理机制与医疗纠纷管理不适配。为此,建议提升突发公共卫生事件的处置能力;合理配置医疗资源,持续优化诊疗流程;提升医疗服务水平,积极缓解职业倦怠;消除医患认知差异,推动共同决策模式构建;优化投诉管理机制,避免医患矛盾加剧。

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).

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.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

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.

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.

Geostatistical seismic inversion and 3D modelling of metric flow units,porosity and permeability in Brazilian presalt reservoir

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.

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

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.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

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.

Mshpy23:a user-friendly,parameterized model of magnetosheath conditions

Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind−Magnetosphere−Ionosphere Link Explorer(SMILE)will observe magnetosheath and its boundary motion in soft X-rays for understanding magnetopause reconnection modes under various solar wind conditions after their respective launches in 2024 and 2025.Magnetosheath conditions,namely,plasma density,velocity,and temperature,are key parameters for predicting and analyzing soft X-ray images from the LEXI and SMILE missions.We developed a userfriendly model of magnetosheath that parameterizes number density,velocity,temperature,and magnetic field by utilizing the global Magnetohydrodynamics(MHD)model as well as the pre-existing gas-dynamic and analytic models.Using this parameterized magnetosheath model,scientists can easily reconstruct expected soft X-ray images and utilize them for analysis of observed images of LEXI and SMILE without simulating the complicated global magnetosphere models.First,we created an MHD-based magnetosheath model by running a total of 14 OpenGGCM global MHD simulations under 7 solar wind densities(1,5,10,15,20,25,and 30 cm)and 2 interplanetary magnetic field Bz components(±4 nT),and then parameterizing the results in new magnetosheath conditions.We compared the magnetosheath model result with THEMIS statistical data and it showed good agreement with a weighted Pearson correlation coefficient greater than 0.77,especially for plasma density and plasma velocity.Second,we compiled a suite of magnetosheath models incorporating previous magnetosheath models(gas-dynamic,analytic),and did two case studies to test the performance.The MHD-based model was comparable to or better than the previous models while providing self-consistency among the magnetosheath parameters.Third,we constructed a tool to calculate a soft X-ray image from any given vantage point,which can support the planning and data analysis of the aforementioned LEXI and SMILE missions.A release of the code has been uploaded to a Github repository.

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.