Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

Economical Optimization of Grid Power Factor Using Predictive Data

We present an electrical grid optimization method for economical benefit. After simplifying an IEEE feeder diagram, we build a compact smart grid system including a photovoltaic-inverter system, a shunt capacitor, an on-load tapchanger(OLTC) and transmission lines. The system power factor(PF) regulation and reactive power dispatching are indispensable to improve power quality. Our control method uses predictive weather and load data to decide engaging or tripping the shunt capacitor, or reactive power injection by the photovoltaic-inverter system, ultimately to keep the system PF in a good range. From the perspective of economics, the economical model is considered as a decision maker in our predictive data control method.Capacitor-only control strategy is a common photovoltaic(PV)regulation method, which is treated as a baseline case. Simulations with GridLAB-D on profiled loads and residential loads have been carried out. The comparison results with baseline control strategy and our predictive data control method show the appreciable economical benefit of our method.

基于改进的ISODATA的超球覆盖仿生模式分类算法

现有仿生模式识别分类器难以解决含有多个聚集点、非线性和稀疏性样本的分类问题。因此,引入特征分类贡献度,提出了基于改进的迭代自组织数据分析(M-ISODATA)的超球覆盖仿生模式识别算法。首先引入马氏距离对自组织数据分析方法(ISODATA)的欧氏距离替换,并引入熵权法对马氏距离进行加权以赋予各特征不同的贡献度;同时为了去除干扰样本点,引入改进的局部离群因子检测方法(M-LOF)对样本进行训练,减少了不同类别流形之间的重叠区域。再利用改进的自组织数据分析方法(M-ISODATA)对每类训练样本点动态聚类,寻找到同一类的多个小类覆盖区中心后,用超球进行该类的有效覆盖,并对落入重叠区域的测试样本点进行二次划分,实现测试样本的正确分类。最后在iris数据集上验证该算法的有效性,并将该算法应用于雷达辐射源信号的分类识别。实验结果表明,该算法具有很好的拒识、免重训能力,对于雷达信号的识别率能达到97.29%,相比于传统典型模式识别算法具有更好的识别能力。

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Development of Safety Factors for the UT Data Analysis Method in Plant Piping

There are several thousand piping components in a nuclear power plant. These components are affected by degradation mechanisms such as FAC (Flow-Accelerated Corrosion), cavitation, flashing, and LDI (Liquid Droplet Impingement). Therefore, nuclear power plants implement inspection programs to detect and control damages caused by such mechanisms. UT (Ultrasonic Test), one of the non-destructive tests, is the most commonly used method for inspecting the integrity of piping components. According to the management plan, several hundred components, being composed of as many as 100 to 300 inspection data points, are inspected during every RFO (Re-Fueling Outage). To acquire UT data of components, a large amount of expense is incurred. It is, however, difficult to find a proper method capable of verifying the reliability of UT data prior to the wear rate evaluation. This study describes the review of UT evaluation process and the influence of UT measurement error. It is explored that SAM (Square Average Method), which was suggested as a method for reliability analysis in the previous study, is found to be suitable for the determination whether the measured thickness is acceptable or not. And, safety factors are proposed herein through the statistical analysis taking into account the components’ type.

数据要素化背景下的档案数字转型行动空间展望

数据要素化已成为我国引领全球数字转型的关键路径。档案领域作为数据的关键参与者和贡献者,为了及时跟进档案数字转型进程,必须充分协调与数据要素化的衔接。文章从数据要素化背景出发,解析数据作为生产要素的基本认知、流通交易、安全治理以及试点先行的基本内容,进一步探讨档案数字转型同其关联要素,主要展现为档案作为数据要素的内涵扩充、多维参与数据流通交易、档案工作融入数据治理体系、实践推进导向下的试点示范场景构建,并基于此对档案数字转型可展开的行动空间提出展望。

数据要素市场化配置的法治推进——兼论《数据二十条》相关条款设计

数据要素市场化配置的程度与水平决定了充分释放数据要素动能的程度,对加快数据产业链、价值链重构与转型升级意义重大。数据要素市场化配置的前提是明确数据产权归属,基础是构建统一开放的数据要素市场,发展是健全高效的定价机制,底线是保障数据要素安全发展。我国虽已明确了数据这一新型生产要素的重要地位,但相关法律尚未明确数据的产权归属,全国统一的数据要素市场尚未建立,数据要素定价机制尚未明确,数据要素市场交易与监管的边界也尚未清晰,所有这些阻碍了数据要素市场化配置的充分实现。故需立足数据要素市场的特征与现实,以数据产权结构性分置制度促进数据动态权属流动,构建统一开放的数据要素交易市场机制,同时厘清数据监管边界,保障数据要素市场安全发展,实现数据要素高水平、高质量、高效率的市场化配置。

Agricultural Total Factor Productivity and Income Gap between Urban and Rural Residents--An Empirical Study Based on Provincial Panel Data

Taking the relevant data of 27 provinces in China during 2013 and 2017 as samples,this paper firstly measured the agricultural total factor productivity( TFP) using Malmquist index method. Then,it built the panel data model,and empirically tested the impacts of agricultural TFP on the income gap between urban and rural residents. The results show that the improvement in agricultural TFP can promote the narrowing of the income gap between urban and rural residents,and the factors such as urbanization level and industrial structure also have significant impacts on the income gap between urban and rural residents. On the basis of these,it came up with recommendations,including increasing agricultural human capital investment and establishing agricultural production research institutions.

发展新质生产力 推动我国经济高质量发展

中华人民共和国(新中国)成立以来,从毛泽东的《论十大关系》,到邓小平的“科学技术是第一生产力”,再到习近平的“整合科技创新资源,引领发展战略性新兴产业和未来产业,加快形成新质生产力”,我党对经济工作规律性的认识,随着时代的发展而不断深化。习近平总书记在2024年1月31日召开的中央政治局第十一次集体学习会议上的重要讲话,更是把这种认识推向了全新的高度。总书记在主持学习时明确指出“必须牢记高质量发展是新时代的硬道理”,“高质量发展需要新的生产力理论来指导,而新质生产力已经在实践中形成并展示出对高质量发展的强劲推动力、支撑力,需要我们从理论上进行总结、概括,用以指导新的发展实践”,并强调“科技创新能够催生新产业、新模式、新动能,是发展新质生产力的核心要素”。为了深入学习贯彻总书记讲话精神,围绕“发展新质生产力推动我国经济高质量发展”这个新时代经济发展的核心课题,本刊邀请国内著名专家、学者,撰写一组笔谈文章,以飨读者。

数据要素、新型基础设施与产业结构调整路径

数据要素和新型基础设施在各个产业发挥日益重要的作用。不同产业对数据要素和新型基础设施的应用方式和利用效率存在差异,影响产业结构调整路径。文章构建包含两个生产部门的动态一般均衡模型,用于代表不同产业或同一产业内的不同企业。每个部门将数据作为新生产要素、将新型基础设施作为数据扩展型技术,与传统生产要素结合形成新的生产函数。不同部门的数据产出弹性、数据和传统要素的替代弹性存在差异,这是影响产业结构调整路径的重要因素。通过理论模型推导和数值模拟研究,文章发现:在不同产业之间,由于产品替代弹性较低,提高数据要素和新型基础设施的数量会导致数据产出弹性较高、数据和传统要素的替代弹性较高的产业产出提高,但其产品价格和名义产出降低,促使资本和劳动力流向数据利用效率低的产业。这一过程将改变资本和劳动力的价格,影响劳动收入份额。在同一产业内部,由于不同企业间的产品替代弹性较高,数据要素和新型基础设施导致资本和劳动力流向数据利用效率高的企业,优化生产要素配置,推动整个产业的数字化转型。新型基础设施建设对产业结构调整的推动作用受到数据要素的调节,且最终的影响方向和路径与数据要素一致,可以通过调整需求结构引导更具针对性和长效的产业结构调整。最后,文章提出数据要素、新型基础设施建设的相关政策建议,并对产业结构调整可能导致的问题提出对策。

数据要素市场化进程中的数据流通与价值创造——基于上海数据交易所的案例研究

数据要素市场化是数字经济发展的重要方向,也是推动数字化转型和创新的重要手段。数据要素市场化进程中数据流通是数据要素市场化的基础和前提,价值创造是数据要素市场化的目的和结果。作为数据要素流通的主要平台和数据价值实现的主要渠道,数据交易平台起着至关重要的作用。数据要素市场仍存在数据确权、估值定价、资源配置和风险防控等问题,基于上海数据交易所案例经验,提出数据要素市场化发展的对策建议。

企业高质量数字化转型管理:理论前沿

在相关领域理论前沿与企业实践的基础上,首先,基于数据生产要素和数实融合的中国数字经济原创思想及理论,按“基础理论—应用基础理论—应用理论”的科学研究递进逻辑,提出企业高质量数字化转型管理的研究框架;然后,归纳提炼出7个重要研究领域及关键科学问题;最后,通过与已有相关研究框架和结论的文献对话,论述该框架理论的学术价值和实践指导潜力,以期为今后的理论研究提供方向性指导。

建筑基本周期多因素机器学习预测模型

建筑物基本周期是其最重要的动力特性参数,影响因素众多。受限于曲线拟合的传统建模手段,目前的基本周期预测模型表达式中仅能包含高度或层数等单一因素,而忽略其他因素的影响。数据驱动机器学习方法的出现,为建筑周期多因素预测模型的建立提供了新思路。研究从大量文献中收集整理了2561条建筑周期的实测数据,形成了包含建筑高度、层数、材料、功能、地区等多因素的建筑周期实测数据库。建立了具有自学习能力的建筑基本周期多因素机器学习预测模型,避免了一般机器学习模型中繁琐的参数调节过程,提升模型的鲁棒性和适用性。与传统模型结果的对比表明:所提预测模型的适用结构类型范围广、准确性更高,配合云端服务器可形成一种全新的、开放式自学习的建筑周期预测模式。

Using Factor Analysis to Determine the Factors Impacting Learning Python for Non-Technical Business Analytics Graduate Students

This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus behind this study stems from the escalating demand for data-driven professionals, the diverse academic backgrounds of students, the imperative for adaptable pedagogical methods, the ever-evolving landscape of curriculum designs, and the overarching commitment to fostering educational equity. To investigate these multifaceted dynamics, we employed a data collection method that included the distribution of an online survey on platforms such as LinkedIn. Our survey reached and engaged 74 graduate students actively pursuing degrees in Business Analytics within the United States. This comprehensive research is the first and only one of its kind conducted in this context, and it serves as a vanguard exploration into the challenges and influences that shape the learning journey of Python among non-technical graduate Business Analytics students. The analytical insights derived from this research underscore the pivotal role of hands-on learning strategies, exemplified by practice exercises and assignments. Moreover, the study highlights the positive and constructive influence of collaboration and peer support in the process of learning Python. These invaluable findings significantly augment the existing body of knowledge in the field of business analytics. Furthermore, they offer an essential resource for educators and institutions seeking to optimize the educational experiences of non-technical students as they acquire essential Python skills.

数据价值释放:现状、问题和建议

数据要素作为一种新兴生产要素,是新质生产力发展的重要动力。我国当前面临数据价值释放不充分的问题,有必要从完善数据要素市场的视角拓展至促进数据价值释放的更一般的理论体系。数据要素的价值释放总是经历供给、需求、供需匹配三个基本环节,并依赖政策法律形成全面健康的价值释放生态。提高数据要素价值释放效率,需正视并解决当前我国数据价值释放过程中面临的诸多现实问题,如数据供给成本高且开发激励不足、数据利用能力不够、供需匹配机制与相关制度法规尚不完善等。针对这些问题所反映出的市场失灵与政策失灵,政府应把握数据要素在价值创造与释放过程中的核心规律,兼顾交易交互两大主线,保障市场有效运行,健全定价与收益分配体系,推动数据价值的充分释放。

基于扎根理论的政府开放数据价值影响因素研究

[目的/意义]政府开放数据价值是数字经济情境下个体数据行为嬗变的反馈性标志。从政府开放数据使用者视角识别其价值要素,对于促进政府数据管理水平,提升数据交易常态化具有理论和实践意义。[方法/过程]利用会议、访谈与网络数据获取原始资料,结合扎根理论,构建“三维—多层”的数据价值影响因素模型,涵盖121个初始概念、30项范畴和9项主范畴,各领域受访者共33位。[结果/结论]个体感知数据价值影响因素主要涉及需求、进路、市场化三个维度,综合考察各项主范畴,针对数据价值的内在关联以及外部机制,可以提炼出直接、间接、跨层、双元4种关系结构。这个过程在理论上弥补了传统政务体系下政府开放数据与个体需求二元割裂之不足,延展了政府开放数据的价值使用行为与价值释放路径。

数字经济与实体经济深度融合:联动机制与政策优化

以数据作为关键生产要素的数字经济正在引领高质量发展。实体经济的数字化正朝着数字经济运行的模式转变。文章阐述了数字经济与实体经济融合的联动机制,即传统实体产业的数字化转型拓展了数字经济的发展空间,与此同时,数字经济的发展促进传统实体产业平台化、网络化、数字化运行和效率提升。数字经济的发展催生了规模巨大的新的市场需求,刺激了实体经济的进一步发展,减缓了空间对经济活动的约束,扩大了资源配置的空间范围,实体经济将数字空间里数据驱动的资源配置在现实的地理空间中实现,这表明数字经济与实体经济具有互补性。文章丰富了数字经济与实体经济深度融合机制的研究,也拓展了数字经济运行模式的研究,为推进数字中国战略提供了理论启示。

数字经济赋能中国式现代化:逻辑机理、现实梗阻与创新路径

合理发挥数字经济优势是全面推进与拓展中国式现代化的必由之路。数字经济可为中国式现代化提供市场体系、基础设施、技术网络、经济支柱支持。研究发现:数据要素融入、数字基建布设、数字技术下沉及数字业态创新为中国式现代化注入动能。尚需注意,数字治理局限性、城乡发展脱轨化、技术应用片面化、数字文化偏向性等诸多梗阻并存,限制数字经济对中国式现代化的赋能作用发挥。为此,应寻求数字组织再嵌入社会基层治理、重塑数字基建组织性网络动态结构、探索数字技术全流程生态治理应用、构建文化产业数字化新型发展业态,以扎实推进中国式现代化。

基于高通量监测数据的PMF源解析数据输入量研究

为探究数据输入量的变化对源解析结果的影响,以上海某工业区的大气重金属高通量监测数据为例,按不同数据量将监测数据分别输入至正定矩阵因子分解(Positive Matrix Factorization, PMF)模型中,通过考察模型中Q理论值(Qtheo)与Q计算值(Qtrue)的接近程度、源分类以及源贡献与研究区污染源分布特点的吻合情况,分析数据输入量对源解析结果的影响。结果显示:该区域大气重金属污染受工业生产主导(64.44%),其次是扬尘(19.60%)和交通运输(15.96%)。通过对数据量的考察,发现输入量为60~120时能够得出研究区域的污染源数量与贡献率,但考虑到测试成本、获取数据的时间,认为输入量为60~80时,也能得出合理的源解析结果。短期高通量的分钟级数据集,有益于PMF模型输出高精密度、高时效性的源解析结果,是解决应急污染监控的最佳手段。

新质生产力形成中企业信息资源服务创新模式——基于汽车制造企业的案例研究

[目的/意义]探究新质生产力背景下数据价值挖掘过程中的生产力要素组合,构建新的生产关系并创新企业的信息资源服务模式,为数字化企业培育新质生产力提供理论与实践指导。[方法/过程]文章基于DIKW价值链理论深入探讨新质生产力背景下数据价值挖掘的过程,构建了新质生产力形成过程中企业信息资源创新服务框架,运用单案例研究方法结合中国第一汽车集团公司的数据要素形成过程,分析采用一手和二手资料分析各阶段的生产力要素最优组合与新生产关系,对新质生产力形成过程中企业信息资源服务模式进行解析。[结果/结论]案例分析结果表明,通过人智协创的信息资源服务模式企业能够实现数智技术的创新应用与数字化人才的系统培育;将数据要素与业务流程深度融合,能够进一步推动企业科技创新、生产过程和产品与服务的高质量发展;新质生产力形成过程中企业信息资源服务模式,有助于企业发挥数据要素作为新生产要素的牵引力作用,为其全要素数字化转型与新质生产力的培育提供重要支持。[局限]然而,由于仅对单案例进行分析,其结果的可复制性和可推广性存在一定的不足。因此在后续研究中,将通过对多个行业的企业在新质生产力背景下的信息资源服务模式创新实践进行分析,以对信息资源创新服务模式有更为全面的见解。