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.

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.

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.

数据经纪人赋能数据要素交易的实践图景与风险规制

数据要素交易是一种市场化的数据流通方式,旨在释放数据要素价值,激发市场主体创新活力。受制于数据权属不清、交易标的不明、定价标准缺失等理论难题,以及交易体量小、业务类型单一、供需错配等实践困境,数据要素交易并没有想象中活跃。造成数据要素交易乏力的成因冗杂但具有共通性,其中信任赤字、技术壁垒、监管失能等因素占据着主导地位。从数据价值链的视角来看,可以通过数据经纪人制度推进数据要素市场化配置改革。作为数字经济的重要参与者,数据经纪人在聚合数据资源、促进数据流通、实现数据价值等方面扮演着重要角色,不仅与国家政策导向高度契合,同时还具备地方实践基础,确立数据经纪人具有必要性与可行性。但风险与价值并存,数据经纪人在选任标准、行业透明度以及监督考评层面存在诸多不确定性,贸然赋能数据要素交易可能引发新的安全风险。对此,应当有针对性地进行风险防范。一是要划分数据经纪人的类型与等级,根据生态协同、数据运营、技术创新等能力遴选适格主体担任数据经纪人;二是要实施年度注册登记制度,通过定期报送和披露组织结构、业务范围、管理制度等相关信息提升数据处理透明度;三是要建立体系化的监督考评机制,从外部考核与内部评价两方面入手检验数据经纪人的工作成效。

基于Panel-data的区际产业转移粘性分析

利用面板数据模型,以江苏省为例,对28个制造业的科技进步、产业集群、区域人力资本积累、沉没成本和资产专用性、劳动力跨区域流动、制度创新、政府阻力等因素对产业转移粘性的关系进行了实证研究,并依据各个因素的贡献率得出了跨区域产业转移的初步结论。

基于Panel Data模型的内蒙古房地产价格影响因素的区域性差异研究

以地方GDP、地方财政收入、职工平均工资、房地产开发投资额为影响内蒙古房地产价格的主要指标,选取2001—2014年内蒙古12个盟市的相关数据,运用面板数据模型,将12个盟市分为中、东、西三个区域,并对影响三个区域房地产价格的因素进行差异性分析。结果表明,内蒙古房地产价格的影响因素确实存在明显的区域差异。地方GDP对房地产价格的影响在西部最强,中部次之,东部不显著;财政收入和房地产开发投资额对房地产价格的影响程度并不大,明显小于另外两个变量;职工平均工资对中、东、西部的影响东部最大,中部次之,西部不显著。

基于三维地球模型的中国大陆重力潮汐因子研究

潮汐现象是地球对日、月等星体引力的响应,重力潮汐因子对于揭示地球内部结构具有重要作用.中国地震局、中国科学院等自20世纪80年代开始布设首批重力仪,用于研究中国大陆固体潮空间分布特征,到目前为止已建成超过50个台站的重力观测网络.本研究利用该网络的重力潮汐因子观测结果,结合考虑了横向非均匀效应的潮汐理论和三维地球模型,构建了中国大陆及周边地区的重力M_(2)、O_(1)因子分布模型.基于微扰理论,本研究发现P波速度扰动、S波速度扰动、密度扰动及综合效应对重力M_(2)因子影响的幅值范围分别为-0.12%至0.14%、-0.19%至0.17%、-0.08%至0.06%和-0.09%至0.11%(对O1因子的影响为-0.13%至0.28%、-0.27%至0.15%、-0.12%至0.10%和-0.15%至0.05%),影响最显著的区域为喜马拉雅造山带(负值)和华夏古陆东南缘(正值),体现了这些地区的特殊构造.本研究利用最小二乘配置方法对重力潮汐因子的理论值与实测值进行了融合,发现中国大陆及周边地区的重力M_(2)因子幅值范围为1.130至1.175,总体呈现周围低、中心高的特征.重力O1因子幅值范围为1.145至1.195,总体呈现从西北向东南逐渐升高的特征.以上结果可为中国大陆及周边地区重力潮汐因子、地球模型等相关研究提供参考.

基于Panel-data的江苏省区域经济增长要素分析

利用江苏省2002—2010年的面板数据,研究资本要素、制度因素、消费结构对江苏省区域经济增长的影响。研究表明:资本和外贸依存度对经济增长的贡献显著,对3大区域的经济增长都起到积极促进的作用;农村恩格尔系数对于苏中地区的贡献是显著的,说明提高苏中地区农村的消费水平有助于促进苏中经济的增长。

GEO公共数据库中4例多指畸形患者细胞间质和上皮细胞的单细胞转录组分析

背景:多指畸形患者中参与Heghog信号通路的一些转录因子发生异常表达,这些转录因子调控大量靶基因的表达,从而影响细胞的功能。目的:通过单细胞转录组分析多指畸形患者的转录组特征。方法:从GEO公共数据库中下载4例多指畸形患者细胞间质和上皮细胞的单细胞转录组数据,将成纤维细胞和角质细胞划分为细胞亚群,并分析不同亚群的转录因子,构建转录因子及其靶基因的调控网络,分析调控因子的功能。结果与结论:对单细胞的转录谱数据进行分析发现,与多指畸形中细胞功能高度相关的调控因子有HOXD13、MSX2、LHX2、EMX2、LEF1、CREB3L2以及LHX2等HOX家族成员和GLI2转录因子。成纤维细胞中的HOXD13、MSX2和LHX2在多指畸形过程中发挥作用,而HES2和GLIS1在角质细胞的形成和发育过程中起重要作用。结果表明:HOXD13、MSX2和LHX2等转录因子的高表达可能与多指畸形的发育密切相关。通过靶向特定的转录因子或调节其活性,可能为纠正或预防多指畸形提供潜在的治疗策略。

基于Panel Data模型的住宅价格影响因素研究——以河北省为例

文章引入Panel Data模型,结合河北省住宅市场相关数据,建立住宅价格影响因素模型,通过实证分析找出了影响河北省住宅价格的主要因素。在此基础之上,结合河北省住宅市场的具体情况,提出了调控河北省住宅价格的措施和建议。

LinkMF:结合Linked Data的协同过滤推荐算法

协同过滤(CF)是推荐系统中应用最为广泛的推荐算法之一,然而数据稀疏性和冷启动问题是协同过滤方法的两个主要挑战。由于Linked Data整合了关于实体的丰富且结构化的特征,可以作为额外的信息源来缓解以上两种挑战。该文中我们首次提出了结合Linked Data改进CF推荐算法,基于矩阵分解提出了一种新的CF模型——LinkMF,在保证推荐准确度的基础上利用Linked Data缓解数据稀疏性和冷启动问题。首先,我们从Linked Data中抽取项目的特征表示并为项目建模;然后提出新的相似度度量方法计算项目相似度;最后利用项目相似度约束和指导MF分解过程产生推荐。在MovielLens和YAGO标准数据集上的大量实验结果表明,LinkMF优于现有的一些CF方法,特别在缓解数据稀疏性和冷启动问题上取得很好地效果。

Data Flow&Transaction Mode Classification and An Explorative Estimation on Data Storage&Transaction Volume

The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various perspectives.By utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)Data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world total.All sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional systems.Adapting to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.

Gender-specific associations between coronary heart disease and other chronic diseases: cross-sectional evaluation of national survey data from adult residents of Germany

Background Combinations of coronary heart disease(CHD) and other chronic conditions complicate clinical management and increase healthcare costs. The aim of this study was to evaluate gender-specific relationships between CHD and other comorbidities. Methods We analyzed data from the German Health Interview and Examination Survey(DEGS1), a national survey of 8152 adults aged 18-79 years. Female and male participants with self-reported CHD were compared for 23 chronic medical conditions. Regression models were applied to determine potential associations between CHD and these 23 conditions. Results The prevalence of CHD was 9%(547 participants): 34%(185) were female CHD participants and 66%(362) male. In women, CHD was associated with hypertension(OR = 3.28(1.81-5.9)), lipid disorders(OR = 2.40(1.50-3.83)), diabetes mellitus(OR = 2.08(1.24-3.50)), kidney disease(OR = 2.66(1.101-6.99)), thyroid disease(OR = 1.81(1.18-2.79)), gout/high uric acid levels(OR = 2.08(1.22-3.56)) and osteoporosis(OR = 1.69(1.01-2.84)). In men, CHD patients were more likely to have hypertension(OR = 2.80(1.94-4.04)), diabetes mellitus(OR = 1.87(1.29-2.71)), lipid disorder(OR = 1.82(1.34-2.47)), and chronic kidney disease(OR = 3.28(1.81-5.9)). Conclusion Our analysis revealed two sets of chronic conditions associated with CHD. The first set occurred in both women and men, and comprised known risk factors: hypertension, lipid disorders, kidney disease, and diabetes mellitus. The second set appeared unique to women: thyroid disease, osteoporosis, and gout/high uric acid. Identification of shared and unique gender-related associations between CHD and other conditions provides potential to tailor screening, preventive, and therapeutic options.

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。

Weight Data Fusion Based on Mutual Support Applied in Large Diameter Measurement

The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature field, and the difficulty to collimate and locate by usual method. By improving the measurement accuracy of larger axis accessories, it is useful to raise axis and hole＇s industry produce level. Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method, the measurement results may not support or even contradict each other. To the situation, this paper puts forward a mutual support deviation distinguish data fusion method, including mutual support deviation detection and weight data fusion. The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data. So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system. By using the weight data fusion based on the mutual support （DFMS） to the simulation and experiment data, both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results. The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement, and can be used as a reference in the data distinguish of other area. The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5 ×10^-6D or even higher when the measured axis workpiece＇s diameter is 1-5 m （ 1 m ≤ D ≤5 m ）.

粮食主产区农民收入增长要素的Panel Data模型分析

运用Panel Data模型对中国粮食主产区农民收入增长要素进行了计量分析,结果表明,农业生产条件、农业生产资料的使用、农产品价格水平、农业生产结构、资本投入等因素对粮食主产区农民人均农业纯收入增长具有显著影响.

基于Panel Data模型的生产者服务业区域发展影响因素

利用面板数据误差修正模型考察了制造业规模、投资、就业、区域经济水平、城市化水平等因素对我国东部、中部和西部的生产者服务业发展的长期和短期影响.结果发现,除了区域经济水平在长期和短期都对本区域生产者服务业具有显著的促进作用以外,其他影响因素在不同区域的影响方向和大小有所不同.从长期来看,东部地区制造业规模对生产者服务业具有挤出效应,而投资和就业能显著地促进行业发展;中部地区投资、就业和城市化水平都对生产者服务业产生了负向影响;西部地区除了制造业规模影响为负值之外,其他因素都能促进行业发展.从短期来看,东部除了投资、中部和西部除了城市化之外,其他因素基本上对行业发展具有正效应.

基于Panel Data的山地城市立交基本段通行能力影响因素研究

通过统计学分析,基于重庆市主城区不同立交基本段采集的交通流与道路线型等数据,建立Panel Data(面板数据)模型,研究山地城市立交基本段通行能力的主要影响因素,确定通行能力与主要影响因素间的数学关系,对主要影响因素的敏感度排序.结果表明:竖曲线半径、设计速度、圆曲线半径、大车比例是影响山地城市立交基本段通行能力的主要因素,其中,竖曲线半径对通行能力的影响最大,敏感度最高,为20.66%;道路坡度由于影响权重较小,被大车比例取代.