元宇宙赋能数字经济高质量发展:基础、机理、路径与应用场景

新时代中国经济由高速增长向高质量发展转变,数字经济的高质量发展是推动中国经济发展模式转变的重要途径。元宇宙以大数据、云计算、物联网、区块链等高新技术为基础,有效整合人才、资本、技术等生产资料,为数字经济的高质量发展提供可靠的技术支撑。数据成为数字经济的关键性生产要素,改变了生产要素的供应体系,能够提供海量可重复利用的资源,有利于数字经济的可持续增长。在元宇宙的作用下,资源配置的时空阻隔效应被打破,供需实现精准预测和匹配,要素组合效率和生产效率实现质的飞跃,资源配置实现帕累托最优,数字经济效益显著提升。依托数字货币构建的去中心化交易支付系统以及区块链辅助构建的低信用成本、优质信用环境的平台,元宇宙为数字经济发展构建了便捷的支付方式、安全的交易环境、完善的信用体系与良好的竞合关系。在开源思维、共享理念、通证技术的支撑下催生出通证经济、开源经济和元宇宙经济等新兴数字经济业态,数字经济不断进行横向聚合和纵向延展性扩容。数字经济的边际成本逐渐变成沉没成本,在消费引导供给的模式下数字经济发展模式从规模经济向范围经济转变,其发展空间得到拓展,促进了数字经济的质变与量变。数据作为关键性生产要素渗透到数字经济的全行业和全产业链,既促进数字经济向智能化、高级化和众包化转型,又充分释放生产端和消费端之间的市场潜力,同时,通过对大数据的深入挖掘,构建企业最佳生产路径,从而达到节能降耗、低碳绿色发展的目标,推动数字经济与元宇宙的深度融合。元宇宙赋能数字经济的高质量发展契合了创新、协调、绿色、开放、共享的发展理念,在高端制造、医疗健康、智慧农业等方面具有广阔的前景,为供给侧结构性改革开辟出新途径,为实现我国数字经济的高质量发展提供新思路。目前,在元宇宙赋能数字经济高质量发展的过程中还存在两者融合不平衡不充分、核心技术和智能装备不足、重商业娱乐应用轻核心产业发展以及运用安全等问题,未来需要突破关键技术的短板,培育元宇宙与数字经济深度融合的样板以及优势产业集群,向民生保障、国家战略产业、高端制造业等方面深入,为中国制造向中国智造、制造大国向制造强国的转变奠定基础。     

我国统计数据质量的评估方法研究————————趋势模拟评估法及其应用

 本文采用组合模型的形式对时间序列数据的变化特点建模，在模型通过各种检验、具有良好统计预测功能的基础上，从检验异常值的角度来分析预测值与实际值之间差异的程度，找出离群数据，利用数理统计中检验实验观测数据异常值的方法，对离群数据的误差进行统计上的显著检验，从而评估统计数据的质量。文章以我国国内生产总值（GDP）为研究对象，选取我国1978-2003年间的GDP作为样本，运用趋势模拟评估法来评估我国2004年国内生产总值的准确性。对我国经济指标的时间序列数据进行了实证分析。     

航位推算系统非线性过程处理新方法研究

针对航位推算系统中利用扩展Kalman滤波方法解决非线性问题在算法复杂性上的缺陷和精度上的不稳定性,该文从数据预先处理的角度出发,合理规避线性化过程,提出一种充分利用现成Kalman滤波公式的方法,即数据进入滤波器之前就预先进行处理,获得每一组量测值,按照极坐标-笛卡儿坐标的转换方式转为相应的位置信息,再按照标准的Kalman滤波公式构建滤波器,进行状态的最优估计。计算机仿真结果证明该算法的有效性。     

鞍山市生态资源与环境质量的遥感数据分析

利用美国陆地卫星资源LandsatTM遥感监测数据,参考相关图件,进行解译判别,监测鞍山市区及各县区土地类型、土地退化类型,并结合水资源的调查结果,对鞍山市生态环境质量进行评价,分析结果表明,鞍山市全境存在一定程度的土壤侵蚀现象,但基本上为微度侵蚀,重度侵蚀和中度侵蚀面积较小;鞍山市区和台安县生态环境状况指数一般,较适合人类生存,岫岩县和海城市生态环境状况指数良好,生物多样性较好,基本适合人类生存。     

Estimation of nonseparable models with censored dependent variables and endogenous regressors

In this article we develop a nonparametric estimator for the local average response of a censored dependent variable to endogenous regressors in a nonseparable model where the unobservable error term is not restricted to be scalar and where the nonseparable function need not be monotone in the unobservables. We formalize the identification argument put forward in Altonji, Ichimura, and Otsu (2012 Altonji, J. G., Ichimura, H., Otsu, T. (2012). Estimating derivatives in nonseparable models with limited dependent variables. Econometrica 80:1701–1719.[Crossref], [Web of Science ®] , [Google Scholar]), construct a nonparametric estimator, characterize its asymptotic property, and conduct a Monte Carlo investigation to study its small sample properties. Identification is constructive and is achieved through a control function approach. We show that the estimator is consistent and asymptotically normally distributed. The Monte Carlo results are encouraging.     

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose‐response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the “hybrid” method proposed by Crump, two strategies of BMA, including both “maximum likelihood estimation based” and “Markov Chain Monte Carlo based” methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose‐response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose‐response data.     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.     

张澍西北竹枝词民俗文化价值论略

张澍是清代竹枝词发展史上重要的作家,也是唯一一位既创作西南竹枝词,又创作西北竹枝词的诗人。他以西北人的眼光来审视西北,对西北地区的文化习俗进行了历史性扫描,勾画出了丰富多彩的社会民俗画面,涵盖了山川风物、民间文学、饮食习俗和宗教信仰等多个领域,反映了西北少数民族独特的习俗和生活方式,形成一幅包罗万象的清代西北民族风情画卷,多角度、全方位地展现了西北地区独特的地域精神和原生态的民俗文化,具有很高的思想史、民族史和民俗学价值。