Experimental study of relationship between average isotopic fractionation factor and evaporation rate

Isotopic fractionation is the basis of tracing the water cycle using hydrogen and oxygen isotopes. Isotopic fractionation factors in water evaporating from free water bodies are mainly affected by temperature and relative humidity, and vary significantly with these atmospheric factors over the course of a day. The evaporation rate （E） can reveal the effects of atmospheric factors. Therefore, there should be a certain functional relationship between isotopic fractionation factors and E. An average isotopic fractionation factor （ t~＊ ） was defined to describe isotopic differences between vapor and liquid phases in evaporation with time intervals of days. The relationship between or＊ and E based on the isotopic mass balance was investigated through an evaporation pan experiment with no inflow. The experimental results showed that the isotopic compositions of residual water were more enriched with time; tr＊ was affected by air temperature, relative humidity, and other atmospheric factors, and had a strong functional relation with E. The values of 0~＊ can be easily calculated with the known values of E, the initial volume of water in the pan, and isotopic compositions of residual water.

An Improved Non-isolated LED Converter with Power Factor Correction and Average Current Mode Control

A new type of high power LED drivers is proposed by adopting an improved two-stages non-isolated configuration. In order to improve power factor and achieve accurate average current control under universal input voltages ranging from 100 Vrms to 240 Vrms, the power factor correction and average current mode control methods operating in continuous current conduction mode are designed and implemented. With the LUMILEDS emitter type LEDs, a laboratory prototype is built and measured. And from the measured results, it could be concluded that the proposed driver has many better performances such as high power factor, low current harmonic, accurate average current control and switch protection.

MULTI-SCALE COHERENT STRUCTURES IN TURBULENT BOUNDARY LAYER DETECTED BY LOCALLY AVERAGED VELOCITY STRUCTURE FUNCTIONS

The time sequence of longitudinal velocity component at different vertical locations in turbulent boundary layer was finely measured in a wind tunnel. The concept of coarse_grained velocity structure functions, which describes the relative motions of straining and compressing for multi_scale eddy structures in turbulent flows, was put forward based on the theory of locally multi_scale average. Based on the consistency between coarse_grained velocity structure function and Harr wavelet transformation,detecting method was presented, by which the coherent structures and their intermittency was identified by multi_scale flatness factor calculated by locally average structure function. Phase_averaged evolution course for multi_scale coherent eddy structures in wall turbulence were extracted by this conditional sampling to educe scheme. The dynamics course of multi_scale coherent eddy structures and their effects on statistics of turbulent flows were studied.

Average of Distribution and Remarks on Box-Splines

A class of generalized moving average operators is introduced, and the integral representations of an average function are provided. It has been shown that the average of Dirac δ distribution is just the well known box spline. Some remarks on box splines, such as their smoothness and the corresponding partition of unity, are made. The factorization of average operators is derived. Then, the subdivision algorithm for efficient computing of box splines and their linear combinations follows.

Average modeling of Single Stage Flyback PFC ＋ Flyback DC／DC converter

With the use of this novel average model for Single Stage Flyback PFC+Flyback DC/DC converter, voltage control mode, peak current control mode and average current control mode can be simulated easily by changing the model's parameters. It can be used to do various analysis not only for small signal and static behavior but also for large signal and dynamic behavior of the converter. By using this average model the simulation speed can be improved by 2 orders of magnitude above that obtained by using the conventional switched model. It can be applied to optimize the trade\|off between high power factor, voltage stress, current stress and good output performance while designing this kind of single stage PFC converter. A 60W single stage power factor corrector was built to verify the proposed model. The modeling principle can be applied to other Single Stage PFC topologies.

Low-complexity PTS scheme based on phase factor sequences optimization

In this paper, a new partial transmit sequence（PTS）scheme with low computational complexity is proposed for the problems of high computational complexity in the conventional PTS method. By analyzing the relationship of candidate sequences in the PTS method under the interleaved partition method, it has been discovered that some candidate sequences generated by phase factor sequences have the same peak average power ratio（PAPR）. Hence, phase factor sequences can be optimized to reduce their searching times. Then, the computational process of generating candidate sequences can be simplified by improving the utilization of data and minimizing the calculations of complex multiplication. The performance analysis shows that, compared with the conventional PTS scheme, the proposed approach significantly decreases the computational complexity and has no loss of PAPR performance.

Computation of Impact Factor of High-Speed Railway Bridge by KTX Train Riding Test

The design live load of railway is divided into common railway and high-speed railway separately inKorea. Accordingly, the Korean design specification of railway specifies the impact factor for common railway and high-speed railway respectively. The impact factor for high-speed railway is based on Eurocode. Since the impact factor criteria inKoreawere established by adopting those of the Eurocode and without dedicated investigation relying on research results reflecting the domestic circumstances, thorough examination should be implemented on these criteria. Therefore the evaluation of impact factor based on field tests is required. Both dynamic and static vertical displacements are necessary to compute the impact factor. The dynamic response can be obtained from the measurement of deflection of the bridge slab crossed by the firstKoreahigh-speed train (KTX, Korea Train eXpress) running at high-speed. The main difficulties encountered are in obtaining static response because static response corresponds to the response of the bridge when the train remains immobile on the bridge or crosses the bridge at speed slower than5 km/hr. This study introduces the static response derived by applying the moving average method on the dynamic response signal. To that goal, field measurements was conducted under train speeds of5 km/hr and ranging from100 km/hr to300 km/hr on Yeonjae Bridge located in the trial section of the Gyeonbu High-Speed Railway Line before its opening. The validity of the application of the moving average method is verified from comparison of measured static response and derived static response by moving average method. Moreover, evaluation is conducted on the impact factor computed for a bridge crossed by the KTX train running at operational speed.

基于WBOA的3D-DV-Hop节点定位算法

针对三维(3D)-DV-Hop定位算法未知节点从最近的锚节点获取平均跳距估算距离时定位精度低的问题,提出了一种新的基于WBOA的3D-DV-Hop算法。首先,该算法去除了网络中无法通信和无法定位的节点;然后添加了修正因子,修正平均跳距。接着创建一个用于优化的蝴蝶初始种群,构建目标函数。采用自适应权重来平衡全局搜索与局部搜索的能力,使得算法不易陷入局部最优,加快了算法的收敛精度;最终求得全局最优平均跳距。仿真结果表明:尽管算法的平均运行时间略有增加,但算法的定位精度得到了有效提高。

基于DRGs指标全髋关节置换术患者平均住院日延长影响因素分析

目的基于疾病诊断相关分组(DRGs)指标分析全髋关节置换术(THA)患者平均住院日(ALOS)延长的影响因素。方法回顾性选取2022年1至12月在金华市中心医院行THA出院的399例患者为研究对象。按照住院天数14 d为时间点分为两组,≤14 d组260例和>14 d组139例。比较两组患者临床特征、DRGs数据和围术期并发症发生情况。结果>14 d组患者合并糖尿病、心脏相关性疾病、脑血管病比例均高于≤14 d组,股骨头缺血性坏死比例低于≤14 d组,输血费、护理费、康复费、自付金额均高于≤14 d组,手术室时长长于≤14 d组,围术期便秘、低蛋白血症、肺栓塞、下肢静脉血栓、泌尿道感染发生率均高于≤14 d组,差异均有统计学意义(均P<0.05)。结论合并基础疾病、手术室时长延长及围术期并发症的发生是影响THA住院时长的主要因素,并会显著增加医疗费用与人力成本。因此,有必要基于DRGs监测数据,制定和实施有效对策,优化诊疗流程,从而提升医院的服务能力与效率。

基于光环境仿真模拟的高校实验室竖向遮阳构件优化研究

以中国石油大学古镇口校区实验楼为例,以该校区实验楼的物理实验室为基础模型,将实验室外立面作为装饰构件的竖向外遮阳构件形体尺寸作为变量,结合相关建筑设计规范、绿色建筑设计规范和实际工程需求,设置试验的边界条件,试验方案设计采用正交试验法,以采光系数、空间天然采光百分比作为主要室内光照评价指标,结合其他相关光环境评价指标,使用三维建模软件Rhino建立基础实验模型,运用Grasshopper+Ladybug Tools设置变量并对实验室进行室内光环境定量模拟。优化结果使高校实验室的光环境得到改善。研究方法和思路有助于提高建筑项目落地实施的质量,同时为碳中和目标下的高校建筑设计提供参考。

包容性货币政策是否有助于推动中国经济高质量发展?

本文通过构建一个同时涵盖货币经济与就业搜寻匹配摩擦理论的动态均衡模型,分析了包容性货币政策的宏观经济调控效应,并在此基础上分析货币政策可信度在其中的作用机制。文章分析得出:在平均通胀目标制下,扩张性货币政策(更具包容性)更有助于促进充分就业和全要素生产率提高。特别是,相较传统通胀目标制,平均通胀目标制下政策调控效应的相对优势在政策可信度较高时可以更充分地实现。同时,实行平均通货膨胀目标制可以有效缓解外生冲击下的经济波动,有利于维护宏观稳定。文章的创新之处在于以高质量发展为目标,分析平均通货膨胀目标制对当前中国经济的适用性,为健全现代货币政策框架以推动中国经济向高质量发展的成功转型提供了一些新的思路和方法。

三相电压型VIENNA整流器建模及稳定性分析

基于电动汽车直流充电桩的VIENNA型前级整流电路,提出采用小信号方法对VIENNA整流器进行建模。该方法首先通过采用局部线性化技术和状态空间平均法推导dq0坐标系下VIENNA整流器的数学模型,得出相关传递函数矩阵,即VIENNA整流器小信号线性化模型,进而用于控制器的设计;然后,采用频域法分别对电压环、电流环进行设计,利用频域法及根轨迹曲线详细分析各项参数对系统的影响,并利用曲线拟合的方式给出了系统稳定域随各项参数变化的曲线;最后,在实验中验证所提方法的正确性。

基于SARIMAX-SVR的光伏发电功率预测

为提高光伏发电功率预测精度,提出一种基于外生因素及季节性的差分自回归移动平均SARIMAX(seasonal autoregressive integrated moving average with exogenous factors)并结合优化支持向量回归SVR(support vector regression)的光伏发电功率预测方法。首先,采用相关性特征法聚类气象条件中关键气象因子,以消除数据冗余并降低ARIMAX模型的复杂性;其次,在ARIMAX模型中引入季节性因素,构建SARIMAX模型来捕捉数据的季节性变化;最后,使用SARIMAX模型的拟合残差其作为SVR模型的输入,进一步拟合数据的非线性。通过仿真算例分析表明,所提方法可显著提高光伏发电功率预测精度。

重型柴油车NO_(x)排放因子与其浓度相关性研究

重型柴油车排放的NO_(x)污染物对环境空气质量影响较大,受排放标准、排放控制技术水平、检测方法、驾驶工况等众多因素的影响,当前较难整体评估在用重型柴油车的实际排放状况.本研究首先通过发动机排放台架试验及实际道路排放(PEMS)循环试验,探究了重型柴油车NO_(x)排放因子与NO_(x)平均浓度之间的内在联系;其次,研究了远程监控有效数据筛选规则,提出了一种基于NO_(x)日均浓度评估远程监控重型柴油车NO_(x)排放因子的方法.结果表明:重型柴油车发动机排放台架、PEMS循环试验的NO_(x)排放因子均与NO_(x)平均浓度呈较强相关性,相关系数(R^(2))为0.99.通过远程监控平台监测数据验证了重型柴油车的NO_(x)排放因子与NO_(x)日均浓度有一定相关性,R^(2)高于0.9(p<0.01).因此,可用重型柴油车排放的NO_(x)日均浓度来表征整车污染物排放情况.研究显示,安装远程终端的第五和第六排放阶段的重型柴油车,分别按照NO_(x)日均浓度低于200×10^(−6)和50×10^(−6)来判定排放稳定达标重型柴油车,按照NO_(x)日均浓度高于900×10^(−6)和500×10^(−6)来判定高排放重型柴油车,监管部门和车辆生产企业可利用该指标快速筛选排放稳定达标以及高排放重型柴油车.

1981-2020年陕西安康冬小麦生育期对农业气象条件的响应

为了明确陕西安康地区冬小麦生育期演变规律及主要影响因素,基于该地区农业气象观测站点的气象观测和小麦生育期观测数据,利用线性倾向估计法和线性多元回归法分析了1981-2020年陕西安康冬小麦生育期变化趋势以及冬小麦生长季(每年10月至翌年6月)气象要素的变化趋势,解析了冬小麦生育期变化与气象因子的关系。结果表明,1981-2020年陕西安康冬小麦生长季日平均气温、日平均地温、气温日较差、降水量和日照时数均呈现上升趋势,相对湿度呈下降趋势(P<0.001),其中3月份日平均气温上升幅度最明显,升幅达0.82℃·10 a^(-1),1和3月份相对湿度下降趋势最明显,降幅分别达1.62和1.65个百分点·10 a^(-1)。1981-2020年,陕西安康冬小麦各生育时期的日期均显著提前,平均每10 a约提前3.18~12.69 d。营养生长期持续时间平均每10 a缩短8.11 d,营养生殖生长期持续时间平均每10 a延长7.67 d。冬小麦生育期变化和气象因子极显著相关。其中生育期与温度相关性最强。冬小麦10个生育时期日序数与气温、地温呈显著负相关,乳熟期日序数与气温日较差呈显著负相关(P<0.001)。各生育时期日序数与相对湿度均呈正相关关系,与降水量、日照时数呈负相关(P<0.001)。影响陕西安康冬小麦生育期的主导气象因子主要是气温、地温和气温日较差(P<0.01),相对湿度、降水和日照时数影响较小。综上所述,温度升高可能是导致陕西安康冬小麦生育期日期提前、营养生长期持续时间缩短和营养生殖生长期延长的主要原因。

基于间接健康特征优化与多模型融合的锂电池SOH-RUL联合预测

准确预测锂电池健康状态(SOH)与电池剩余使用寿命(RUL)对提高电池安全性能具有重要意义。而当前针对SOH和RUL的预测,存在着间接健康特征选取困难,以及使用数据驱动方法缺乏不确定性表达的问题。为此,该文提出一种基于间接健康特征优化与多模型融合的锂电池SOH-RUL联合预测方法。首先从充电电压曲线中采集多个健康特征,并通过特征并行融合方法和注意力机制进行优化处理得到间接健康特征(IHF)。然后引入贝叶斯模型平均(BMA)方法来解决预测过程中的不确定性问题,将其与支持向量回归(SVR)和长短期记忆神经网络(LSTM)相结合,构建SVR-BMA融合模型和LSTM-BMA融合模型,并分别进行SOH和RUL预测;通过自适应噪声完备集合经验模态分解(CEEMDAN)方法从SOH预测阶段的容量预测结果中提取出RUL预测的输入特征,以实现SOH和RUL的联合预测。最后利用CALCE数据集进行性能测试,实验结果表明,所提方法能有效提高SOH和RUL预测的准确性和可靠性。

基于多种策略改进的鲸鱼优化算法

针对标准鲸鱼优化算法收敛速度慢、搜索与开发不平衡、种群之间信息交流匮乏、容易陷入局部最优的问题,提出了一种改进算法。首先,采用Tent混沌映射提高初始种群的分布均匀性,并引入非线性收敛因子,提升算法前期的全局搜索和中后期局部开发的能力,协调了搜索与开发的转换机制。然后,将种群的平均位置向量引入随机搜索过程中,有效改善个体与种群之间缺乏信息交流的问题。接着,将自适应惯性权重引入位置更新公式中,以加快算法的收敛速度,提高求解精度。最后,利用柯西算子对陷入局部最优的个体进行变异扰动。通过15个基准测试函数对改进算法进行仿真实验,实验结果表明,改进后的鲸鱼优化算法具有良好的性能,并通过Wilcoxon秩和检验证明了改进算法的有效性。

A moving average Cholesky factor model in joint mean-covariance modeling for longitudinal data

Modeling the mean and covariance simultaneously is a common strategy to efficiently estimate the mean parameters when applying generalized estimating equation techniques to longitudinal data. In this article, using generalized estimation equation techniques, we propose a new kind of regression models for parameterizing covariance structures. Using a novel Cholesky factor, the entries in this decomposition have moving average and log innovation interpretation and are modeled as the regression coefficients in both the mean and the linear functions of covariates. The resulting estimators for eovarianee are shown to be consistent and asymptotically normally distributed. Simulation studies and a real data analysis show that the proposed approach yields highly efficient estimators for the parameters in the mean, and provides parsimonious estimation for the covariance structure.

影响制种玉米机械化去雄的农艺因子

研究不同农艺因子对制种玉米机械化去雄的影响,为制种玉米生产标准化和全面推进制种玉米机械化去雄技术提供技术依据。选取4个代表性品种原种,根据农艺要求和种植模式,每个品种设置1个种植区,每个种植区分设不同种子分级处理、不同播种时间、不同播种方式、不同种植模式、不同去雄机械和去雄模式的对照重复,测定制种玉米长势整齐度和机械化去雄效果。制种玉米机械化去雄效果与种子分级、播种期、播种方式、种植模式、去雄机械和去雄模式相关,种子分级处理后种植机械化取净率(85%以上)显著高于混合种机械化取净率(48%);中晚期播种机械化取净率比早期播种机械化取净率高5个百分点;联合精量播种机播种比人工点播机械化去雄取净率高10个百分点以上;等行距种植模式取净率比宽窄行种植模式取净率高4个百分点;采用卫星导航智能驾驶系统控制农机作业,作业精度高,直线性好,有利于后续农机化作业。因此,在实际生产中要根据土壤条件选择合适的耕整地方式和机具,精选种子并对其进行分级处理和分级种植,适当推迟播种期,提高播种质量,采取等行距种植,并根据作物长势和种植规模选择合适去雄作业机械和去雄模式,可有效提高机械化去雄取净率。

新质生产力视角下广东省相关新兴产业的效率评价及其关键影响因素分析

新质生产力是实现中国式现代化和高质量发展的重要抓手,但目前学术界对培育新质生产力的产业讨论与定量研究仍有不足。通过深入讨论新质生产力的概念内涵,并立足于广东省,基于培育发展新质生产力视角选取14个相关新兴产业;在此基础上,使用DEA-Malmquist模型与BMA模型,从行业内部指标、新参与主体、新渗透要素与行业内部指标角度出发,对这些产业2015—2022年的科技效率与关键影响因素进行研究。研究表明:研究期间14个相关新兴产业的科技效率呈现曲折性发展态势,而企业自身抗风险能力、环境处置能力、政府定向支持、产业结构、政府融资支持与市场需求为关键影响因素。因此,政府应当加强对战略性新兴产业的定向支持、引导产业绿色发展、完善产业金融支持服务体系与扩大消费市场规模,从而促进相关新兴产业的发展,加快推动发展新质生产力。