Influencing Factors of Total Factor Energy Efficiency in Bohai Rim Region Based on DEA-Tobit Model

Bohai Rim region is an important economic development area and a large carbon emission area in China.It is of great significance to explore the total factor energy efficiency and its influencing factors for the low carbon transformation and high-quality development of the Bohai Rim region.Based on the total factor energy efficiency framework,the DDF-DEA model was used to calculate the total factor energy efficiency,and the internal and external differences of the total factor energy efficiency were further analyzed.The internal and external influencing factors were determined by ML index method and classical endogenous growth theory,and then the Tobit panel model was used to empirically analyze the action mechanism of all influencing factors of total factor energy efficiency in the Bohai Rim region.The results show that the pure technical efficiency,scale efficiency and technological progress among the internal influencing factors contribute to the improvement of energy efficiency in the Bohai Rim region.Industrial structure,industrial internal structure and ownership structure inhibit the improvement of energy efficiency.Energy consumption structure and energy endowment also have a negative impact on energy efficiency.Therefore,measures such as promoting technological progress,adjusting economic structure and optimizing energy structure will effectively improve total factor energy efficiency in the Bohai Rim region.

安徽省旅游效率和旅游竞争力的耦合协调演化及影响因素——基于DEA-Tobit模型的分析

通过构建旅游效率与旅游竞争力综合评价指标体系,以安徽省16市为案例地,采用DEA模型、熵值法、耦合协调模型分析安徽省2011—2019年各市旅游效率系统与旅游竞争力系统的耦合协调关系,运用Tobit模型定量识别其影响因素。结果表明:安徽省旅游效率整体水平较高,在空间格局上呈现稳定性和规律性的演化特征;安徽省旅游竞争力水平整体上不断提升,呈现由双核主导(合肥市、黄山市)向单核极化(合肥市)演变的格局特征;安徽省旅游综合效率系统和旅游竞争力系统的耦合协调时序演化较为稳定,空间差异较大;产业结构水平、市场规模、对外开放程度、旅游产业专业化程度对旅游效率和旅游竞争力系统的耦合协调发展起正向促进作用,而城市化水平、信息化水平反向抑制其协调发展。

海南自贸港上市公司融资效率的影响因素研究——基于DEA-Tobit模型

发挥资本市场支持海南自贸港高质量发展的作用,提高上市公司质量,需要重点解决好上市公司融资效率问题。以2018—2022年20家海南自贸港上市公司为研究样本,运用DEA-Tobit模型对它们的融资效率进行静态和动态分析,研究影响融资效率的因素,结果显示:海南自贸港上市公司的融资效率整体处于较低水平,但呈上升趋势,主要得益于技术进步;宏观经济因素、股权结构和企业成长能力等对融资效率的影响不明显。海南自贸港上市公司可通过强化上市公司管理水平及创新能力,提升上市公司财务管理水平,重视人才与科技的作用,牢抓全面注册制改革发展机遇,加快推进资本市场改革与开放等措施提高融资效率。

福建省专利质量对海洋技术创新效率的影响——基于DEA-Tobit模型的实证研究

海洋技术创新是推动海洋经济转型升级、提升海洋经济高质量发展的重要驱动力,如何提升海洋技术创新效率是当前面临的重要问题。为探讨专利质量是否影响海洋技术创新效率的提升,本文基于2007-2021年海洋统计数据,运用二阶段DEA模型测算福建省海洋技术创新效率,综合分析海洋科技研发效率和科技成果转化效率,进一步分析专利质量对海洋技术创新效率的影响。研究结果表明:2007-2021年福建省海洋技术创新整体效率较高,呈上升趋势;分阶段看,海洋科技研发效率总体平均水平低于科技成果转化效率。Tobit模型回归结果表明专利质量的提高对海洋技术创新整体效率及分阶段的研发效率和成果转化效率的提升均具有促进作用。此外,海洋经济规模、海洋产业结构、科研平台数、科研院校专利占比等因素对海洋技术创新效率也具有一定的影响。

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。

基于24Model的地铁内涝事故原因分析与评估

为降低地铁内涝事故灾害风险,基于事故致因“2-4”模型,分析了地铁内涝事故致灾因子,采用层次分析法构建了地铁内涝事故原因分析指标体系,确定了各风险因子的权重,并利用模糊综合评价法对地铁内涝事故进行了定量评估,识别出关键的影响因素。结果表明:地铁内涝事故一级指标中不安全动作与物态因素最重要,其中影响最大的指标包括擅自更改建筑设计、未按照要求检查水位情况、未及时排水、出入口不符合防汛标准等因素;习惯性不安全行为的权重位居第二,表明该指标因素较为重要,同时安全管理体系得分位居第二,表明该指标因素较易发生。对关键指标采取防范措施,可有效降低风险,从而减少地铁内涝事故的发生。

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。

金融助推体育产业发展的效率测算及影响因素研究——基于DEA-Tobit模型的实证分析

经济新常态背景下,金融业在推动体育产业发展上起到重要作用,研究二者之间的关系对促进金融-体育系统协调发展具有重要意义。本文以我国2007-2023年上市体育公司金融支持效率为研究对象,采用DEA-BCC模型测算金融支持效率,Tobit面板模型进行影响因素分析,检验结果显示:我国金融支持体育产业效率均大于0.6,但整体呈波动式走向;东部地区和主板市场金融支持体育产业的发展水平具有一定的代表,且与全样本走势基本一致;企业属性、股权集中度对金融支持体育产业效率有显著的促进作用,企业规模、宏观经济风险会显著抑制金融支持效率,融资约束则对其具有负向不显著影响;对所有样本进行分地区、分市场回归依然得到相似的结论。最后,本文从政府、金融监管当局、体育企业自身提出一定的建议,以促进金融支持体育产业效率的提升。

基于DEA-Tobit模型的中国体育公共服务财政支出效率及其影响因素研究

为探究我国体育公共服务财政支出效率及其存在问题,选取2013—2021年中国31个省份的面板数据,利用CCR、BCC和Malmquist模型进行效率评价,并构建Tobit模型分析其影响因素。研究表明:体育公共服务财政支出综合效率不高,波动较大,规模效率普遍高于纯技术效率,地区间存在“中部>西部>东部”的异质性特征;除江西和西藏外均存在投入和产出冗余现象;全要素生产率前期波动大,后期趋于稳定;外部影响因素中,人均GDP、人口密度、财政自主权、体育管理人员比例与体育公共服务财政效率呈正相关,未通过显著性检验,城镇化率为负相关且通过显著性检验。据此提出加快财税改革、完善转移支付、加快供给侧改革、坚持新发展理念等发展建议,以期推动体育公共服务高质量发展。

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。

基于DEA-Tobit模型的华东地区公共体育服务效率研究

公共体育服务是政府为满足公民体育需求所提供的一种体育服务措施,而效率评价是衡量其发展的重要手段。科学的公共体育服务效率评价,能够客观清晰地反映公共体育服务发展情况。构建DEA模型对2016—2018年我国华东地区六省一市的公共体育服务效率评价指标进行测算分析,并运用Tobit模型进行实证检验,探究可能对公共体育服务效率带来影响的外部因素。结果显示:(1)华东地区2016—2018年公共体育服务综合效率值为0.931,纯技术效率值为0.992,规模效率值为0.937,规模报酬呈现递减趋势;(2)地区经济发展水平、人口密度和体育管理人员规模与公共体育服务效率呈正相关,与受教育水平呈负相关。根据研究结果分析华东地区公共体育服务效率问题与差距,提出以下对策建议:减少区域短板,促进协同发展;调整资源配置,提升供给质量;健全监督机制,完善管理体系。

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.

云南山地碳汇造林投入产出效率研究——基于DEA-Tobit模型的腾冲市农户实证分析

在考虑碳汇生态价值补偿和林业生产效率的基础上,将碳汇收益纳入林业产出中,分析农户碳汇林投入产出效率的变动趋势。基于云南省腾冲市清洁发展机制小规模造林和再造林项目数据,分别运用三阶段DEA和Tobit模型对区域农户投入产出效率及其影响因素进行研究。研究表明,目前腾冲市农户碳汇造林生产经营处于规模效益上升阶段,投入产出效率值为0.598,较低的纯技术效率是制约生产效率提升的关键因素,不同项目区的生产效率存在显著差异;林业补贴政策对碳汇林生产效率具有显著正向影响,林地细碎化程度和采伐指标申请也对生产效率有积极作用,而市场距离和苗木价格对投入产出效率有负面影响。建议优化林业碳汇政策,提高农户参与碳汇造林的意愿,鼓励通过优化资源配置实现更高的投入产出效率,从而实现生态发展和农民增收的双赢。

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.

基于DEA-Tobit模型的生猪产业绿色发展效率研究

近年来,生猪产业绿色化取得长足进展。为了解生猪产业绿色发展效率变化情况及相关影响因素,研究首先通过DEA模型对生猪产业绿色发展效率进行测算,研判样本期内生猪产业绿色发展效率变化情况;再利用面板Tobit模型分析生猪产业绿色发展效率的影响因素。结果表明:从时间维度观察,生猪产业绿色发展效率在研究期间展现出波动上升的趋势,而在2019年之后,这一趋势则趋于稳定并持续上升。若从空间角度审视,内蒙古、黑龙江等9个省份在生猪产业绿色发展效率方面的提升最为显著。进一步分析动态效率,我们发现规模效率在推动生猪产业绿色发展效率提升中起到了关键作用。此外,资源禀赋、经济发展水平、环境规制、开放程度、交通通达度和规模化水平等要素,均对生猪产业绿色发展效率产生了显著的正向影响,为其提升提供了强大的驱动力。资源禀赋、经济发展水平、环境规制、开放程度、交通通达度和规模化水平均可显著正向提升生猪产业绿色发展效率。因此,应从释放区域资源禀赋、差异化落实环境规制和推动规模化养殖等方面提高生猪产业绿色发展效率。

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。