基于Blending-Clustering集成学习的大坝变形预测模型

【目的】变形是反映大坝结构性态最直观的效应量,构建科学合理的变形预测模型是保障大坝安全健康运行的重要手段。针对传统大坝变形预测模型预测精度低、误报率高等问题导致的错误报警现象,【方法】选取不同预测模型和聚类算法集成,构建了一种Blending-Clustering集成学习的大坝变形预测模型,该模型以Blending对单一预测模型集成提升预测精度为核心,并通过Clustering聚类优选预测值改善模型稳定性。以新疆某面板堆石坝变形监测数据为实例分析,通过多模型预测性能比较,对所提出模型的预测精度和稳定性进行全面评估。【结果】结果显示:Blending-Clustering模型将预测模型和聚类算法集成,均方根误差(RMSE)和归一化平均百分比误差(nMAPE)明显降低,模型的预测精度得到显著提高;回归相关系数(R~2)得到提升,模型具备更强的拟合能力;在面板堆石坝上22个测点变形数据集上的预测评价指标波动范围更小,模型的泛化性和稳定性得到有效增强。【结论】结果表明:Blending-Clustering集成预测模型对于预测精度、泛化性和稳定性均有明显提升,在实际工程具有一定的应用价值。

Multi-objective Design of Blending Fuel by Intelligent Optimization Algorithms

Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded.Herein,a complete workflow for designing a fuel blending scheme is presented,which is theoretically supported,efficient,and reliable.Based on the data distribution of the composition and properties of the blending fuels,a model of polynomial regression with appropriate hypothesis space was established.The parameters of the model were further optimized by different intelligence algorithms to achieve high-precision regression.Then,the design of a blending fuel was described as a multi-objective optimization problem,which was solved using a Nelder–Mead algorithm based on the concept of Pareto domination.Finally,the design of a target fuel was fully validated by experiments.This study provides new avenues for designing various blending fuels to meet the needs of next-generation engines.

A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty

The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.

基于Prophet算法和Blending集成学习的实时负荷中期预测

目前的中期负荷预测一般未考虑负荷实时状态,而负荷数据的非线性、季节性、随机性、时序性特征将影响实时负荷的中期预测。构建一个实时负荷中期预测的框架,采用Prophet算法提取负荷数据的季节性部分,采用Blending集成学习对负荷数据的非季节部分进行滚动预测,将季节性部分和非季节性部分合成中期负荷实时数据。爱尔兰电力系统的算例结果验证了模型的有效性和稳定性。

基于FPFF-Blending模型融合的个体工商户信用评价研究

个体工商户信用评价研究往往通过单一机器学习模型建立,其预测精确率较低,抗干扰能力较弱。基于特征金字塔的FPFF特征融合算法,应用于Blending模型融合框架,建立个体工商户信用评价异质融合模型,并赋予模型可解释性,综合解决单一模型稳定性较差、原有Blending框架融合模型过拟合、融合模型缺乏可解释性的问题。通过对个体工商户数据集进行实证实验,结果表明:融合模型较单一机器学习模型在个体工商户信用评价场景下具有更优的预测性能和泛化能力。

Umami taste components in chicken-spices blends and potential effect of aroma on umami taste intensity

In this study,umami taste intensity(UTI)and umami taste components in chicken breast(CB)and chickenspices blends were characterized using sensory and instrumental analysis.Our main objective was to assess the aroma-umami taste interactions in different food matrices and reconcile the aroma-taste perception to assist future product development.The impact of key aroma,including vegetable-note"2-pentylfuran",meaty"methional",green"hexanal",and spicy-note-estragole and caryophyllene"on UTI was evaluated in monosodium glutamate and chicken extract.We found that spices significantly decreased UTI and umami taste components in CB.Interestingly,the perceptually similar odorants and tastants exhibited the potential to enhance UTI in food matrices.Methional was able to increase the UTI,whereas spicy and green-note components could reduce the UTI significantly.This information would be valuable to food engineers and formulators in aroma selection to control the UTI perceived by consumers,thus,improving the quality and acceptability of the chicken products.

Preparation and Properties of Regenerated Cellulose/Amylopectin Blend Fibers from 1-Butyl-3-Methylimidazolium Chloride with Controlled Biodegradation

Regenerated cellulose/amylopectin blend fibers with controlled biodegradation were produced using dry-jet wet-spinning technology from cellulose/amylopectin/1-butyl-3-methylimidazolium chloride blends.Morphological,structural and chemical analyses revealed that dense,homogeneous and void-free blend fibers were prepared in a two-stage dissolution process.The blend fibers were regenerated from water and treated with water or 95%(volume fraction)ethanol.However,cellulose-amylopectin interactions caused crystalline rearrangements in the blend fibers,resulting in a general decrease in crystallinity.Generally,tensile properties decreased with increasing amylopectin content,except that the blend fibers with 10%(mass fraction)amylopectin exhibited higher tensile strength than the regenerated cellulose control fibers.Ethanol treatment reduced the hydrophilicity of the blend fibers,increasing the crystallinity of the blend fibers.The blend fibers exhibited remarkable degradation,directly proportional to the amylopectin content.Despite higher crystallinity,ethanol-treated blend fibers degraded faster than water-treated fibers,indicating amylopectin and ethanol regulated the degradation.

Investigating Transport Properties of Environmentally Friendly Azeotropic Binary Blends Based on Evaporation in Auto-Cascade Refrigeration

The exploration of performance and prediction of environmentally friendly refrigerant physical properties represents a critical endeavor.Equilibriummolecular dynamics simulationswere employed to investigate the density and transport properties of propane and ethane at ultra-low temperatures under evaporative pressure conditions.The results of the density simulation of the evaporation conditions of the blends proved the validity of the simulation method.Under identical temperature and pressure conditions,increasing the proportion of R170 in the refrigerant blends leads to a density decrease while the temperature range in which the gas-liquid phase transition occurs is lower.The analysis of simulated results pertaining to viscosity,thermal conductivity,and self-diffusion coefficient reveals heightened deviation levels within the phase transition temperature zone.This increase in deviation attributed to intensified molecular activity.In terms of uncovering the physical mechanism of gas-liquid phase transition,the work illustrates the macroscopic phenomenon of the intensified existing disorder during phase transitions at the molecular level.Molecular dynamics simulations analyzing the thermophysical properties of refrigerant blends from a microscopic point of view can deepen the comprehension of the thermal optimization of refrigeration processes.

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries.However,the optimization model is complex and difficult to build,which is a typical mixed integer nonlinear programming(MINLP)problem.Considering the large scale of the MINLP model,in order to improve the efficiency of the solution,the mixed integer linear programming-nonlinear programming(MILP-NLP)strategy is used to solve the problem.This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model,and a relaxed MILP model is constructed on this basis.The particle swarm optimization algorithm with niche technology(NPSO)is proposed to optimize the solution,and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes,the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results,thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications.The optimization result of the MILP model provides a good initial point.By fixing the integer variables to the MILPoptimal value,the approximate MINLP optimal solution can be obtained through a NLP solution.The above solution strategy has been successfully applied to the actual gasoline production case of a refinery(3.5 million tons per year),and the results show that the strategy is effective and feasible.The optimization results based on the closed-loop scheduling optimization strategy have higher reliability.Compared with the standard particle swarm optimization algorithm,NPSO algorithm improves the optimization ability and efficiency to a certain extent,effectively reduces the blending cost while ensuring the convergence speed.

The Effectiveness of the Havriliak-Negami Model in Predicting the Master Curves of the Asphalt Blends with SBS Triblock Copolymer and Organic-Montmorillonite at Different Temperatures and Frequencies

The dynamic viscoelastic properties of asphalt AC-20 and its composites with Organic-Montmorillonite clay (OMMt) and SBS were modeled using the empirical Havriliak-Negami (HN) model, based on linear viscoelastic theory (LVE). The HN parameters, α, β, G0, G∞and τHN were determined by solving the HN equation across various temperatures and frequencies. The HN model successfully predicted the rheological behavior of the asphalt and its blends within the temperature range of 25˚C - 40˚C. However, deviations occurred between 40˚C - 75˚C, where the glass transition temperature Tg of the asphalt components and the SBS polymer are located, rendering the HN model ineffective for predicting the dynamic viscoelastic properties of composites containing OMMt under these conditions. Yet, the prediction error of the HN model dropped to 2.28% - 2.81% for asphalt and its mixtures at 100˚C, a temperature exceeding the Tg values of both polymer and asphalt, where the mixtures exhibited a liquid-like behavior. The exponent α and the relaxation time increased with temperature across all systems. Incorporating OMMt clay into the asphalt blends significantly enhanced the relaxation dynamics of the resulting composites.

基于Blending集成学习的多源信息液压系统多类故障诊断研究

针对传统故障诊断方法准确性不高、耗时长问题,研究通过多个EfficientNet模型对传感器数据进行预训练,并使用XGBoost作为元学习器,提出了一种基于Blending集成学习的多源信息液压系统多类故障诊断方法。实验结果表明,各个子分类器在训练次数达到300次后趋于收敛,准确率均达到95%左右。该方法具有较高的准确性和鲁棒性,为液压系统故障诊断提供了一种有效的解决方案。

Conceptual Blending and Meme Humor on the Internet:The Example of COVID-19 Publicity Posters in Chinese Microblogging

Internet memes,as multimodal cultural products disseminated through the Internet,usually take the form of short videos or images that express humor or satire.The creation and dissemination of humor in memes are both creative and complex,and the successful perception of meme humor reflects humans’universal thinking capacity.Based on the theoretical framework of conceptual blending,this paper selects a set of COVID-19 publicity posters from the official Weibo account of China Guangzhou Fabu(Guangzhou Internet Information Office),analyses the multi-level structure of Internet memes,and explores the dynamic cognitive process in the interpretation of humorous memes to reveal people’s ability to make simultaneous analogies and integration between elements in different mental spaces.

The application study of dual-energy CT nonlinearblending technique in pulmonary angiography

Objective This study aimed to explore the feasibility of enhancing image quality in computed tomography(CT) pulmonary angiography (CTPA) and reducing radiation dose using the nonlinear blending (NLB)technique of dual-energy CT.Methods A total of 61 patients scheduled for CTPA were enrolled, and 30 patients underwent dual-energyscanning. Nonlinear blending images (NLB group) and three groups of linear blending images (LB group,80 kV group, and 140 kV group) were reconstructed after scanning;31 patients underwent single-energyscanning (120 kV group). The CT values and standard deviations of the pulmonary trunk, left and rightpulmonary arteries, and ipsilateral back muscle at the bifurcation level of the left and right pulmonaryarteries were measured. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the fivegroups were calculated. The subjective image quality of the five groups was assessed. The radiation dosesof the dual- and single-energy groups were recorded and calculated.Results The CNR and SNR values of blood vessels in the NLB group were significantly higher than thosein the LB, 140 kV, and 80 kV groups (CNR of pulmonary artery trunk: t = 3.50, 4.06, 7.17, all P < 0.05;SNRof pulmonary trunk: t = 3.76, 4.71, 6.92, all P < 0.05). There were no statistical differences in the CNR andSNR values between the NLB group and 120 kV group (P > 0.05). The effective radiation dose of the dualenergygroup was lower than that of the single-energy group (t = –4.52, P < 0.05). The subjective scores ofimages in the NLB group were the highest (4.28 ± 0.74).Conclusion The NLB technique of dual-energy CT can improve the image quality of CTPA and reducethe radiation dose, providing more reliable imaging data for the clinical diagnosis of pulmonary embolism.

基于SPI-RRV指数中国气象干旱及其风险时空演变特征研究

为全面揭示变化环境下我国多维气象干旱特征,耦合气象干旱指数(SPI)和可靠性-回弹性-脆弱性(RRV)指数,提出了一种基于SPI-RRV指数的干旱风险评价方法,定量评价了中国气象干旱及其风险的时空演变特征。结果表明:SPI-RRV指数具有特征稳定和时空可比性强的特点,能够较为准确地评估气象干旱风险时空演变特征;南方平均干旱栅格比、干旱月占比和频次大于北方,湿润区和半湿润区干旱历时短、烈度大,半干旱区和干旱区干旱历时长、烈度相对较小;干旱高风险区转移具有显著年代际变化规律,空间上从西北向西南地区转移。

深部煤储层孔裂隙结构对煤层气赋存的影响-以鄂尔多斯盆地东缘大宁-吉县区块为例

深部煤储层孔隙–裂缝结构对深部煤层气资源潜力评价和勘探开发具有重要意义。选取鄂尔多斯盆地东缘大宁–吉县区块DJ57井本溪组5个煤岩样品为研究对象,在煤岩煤质参数测试的基础上,采用气体吸附法、高压压汞法和微米CT扫描等测试手段,对深部煤储层中的纳米级孔隙-微米级裂缝进行多尺度定量表征,综合评价不同尺度的孔裂隙结构特征。再结合渗透率和甲烷等温吸附试验,探讨了微观孔裂隙对深部煤储层中煤层气的赋存和渗流的影响。研究结果表明:基于多种孔隙表征方法对深部煤储层孔裂隙进行多尺度定量表征,其孔裂隙体积分布类型主要以“U”型为主,呈现出微孔与微裂缝并存双峰态,主要集中在0.3~1.5 nm和>100μm的范围内。其中,微孔(<2 nm)、介孔(2~50 nm)、宏孔(50 nm~10μm)和微裂缝(>10μm)体积平均分别占总孔裂隙体积的80.18%,6.70%,1.65%和11.47%。随着微孔发育而吸附气量呈增大的趋势,微孔可以提供大量吸附点位,为深部煤层气的吸附和赋存提供场所。随着微裂缝发育而游离气量呈增大的趋势,微裂缝可以提供大量储集空间,为深部煤层气的富集提供空间条件。此外,微裂缝在三维空间中相互连通,形成网状结构,连通性强。随着微裂缝越发育,煤储层渗透率越大,微裂缝增强了煤层气的渗流能力。纳米级孔隙和微米级裂隙发育特征分别控制着深部煤层气吸附能力和开发潜力。

水资源-能源-粮食-生态系统耦合协调及驱动力分析

为加深对我国水资源、能源、粮食、生态系统协同演变趋势的认识,构建水资源-能源-粮食-生态多维系统指标体系,运用耦合协调度模型对我国2005—2020年水资源-能源-粮食-生态系统耦合协调度进行评价,并采用多因素归因分析法进行驱动力分析。结果表明:我国水资源-能源-粮食-生态系统耦合协调度从2005年的0.55增长到2020年的0.84,各地区耦合协调度从勉强协调发展水平过渡到中级协调发展水平,各子系统对耦合协调度上升的驱动分别经历了由粮食子系统到生态子系统再到水资源子系统主导的过程;能源子系统的贡献率虽然比较小,但是未来可能是各地区提升水资源-能源-粮食-生态系统多维系统协调发展水平的突破口。

基于DEMATEL-ISM的水利工程EPC项目价值增值机理研究

采用设计-采购-施工(engineering-procurement-construction,EPC)总承包模式的水利工程项目日益增多,研究其价值增值机理有利于总承包方做好项目的价值管理。从水利工程EPC项目的价值链分析入手,基于文献和专家访谈得到23个价值增值影响因素。采用问卷调查方式得到各因素间的影响程度评分,用决策实验和评估实验(decision-making trial and evaluation laboratory,DEMATEL)法计算了各因素的中心度和原因度,结合解释结构模型(interpretative structural modeling,ISM)对数据进行分析,将价值增值因素划分为6类,构建了水利工程EPC项目价值增值的递阶模型,探究和揭示了项目价值增值的路径和机理。研究结果对水利工程EPC总承包方制订管理策略有一定启示作用。

基于混合式教学的“教-学-评”一体化实验教学体系探索

实验能力是医学专业研究生的重要科研能力,实验能力的培养与实验教学的方法与质量密切相关。文章针对传统实验教学受到时间和空间限制且评价指标较为单一的现状,提出构建基于线上自主学习和现场实践相结合的“教-学-评”一体化实验教学体系,建立覆盖课前线上自主学习、课堂现场实践、课后作业提交与考核教学全过程的评价体系,以评促学,以评促教,通过评价反馈激发学生的学习热情,改进教学内容和方法,不断探索与完善实验教学体系,提高实验教学质量,以期为培养具有实践能力与创新精神的医学科研人才发挥积极作用。

基于UPLC-QTOF-MS解析苦荞黄酮组成及药理学网络分析

为优化苦荞黄酮提取工艺并明确其具体成分结构和含量及药理作用,本研究以四川省凉山彝族自治州甘洛县优质苦荞为研究对象,对超声提取苦荞黄酮工艺进行了优化,采用超高效液相色谱-四级杆-飞行时间质谱(UPLC-QTOF-MS)对主要黄酮成分进行结构鉴定,并采用超高效液相色谱-三重四级杆质谱联用仪对鉴定到的黄酮进行准确定量,同时利用中药系统药理学(TCMSP)系统分析其网络药理学特性。结果表明,超声辅助乙醇提取苦荞黄酮的最优条件为乙醇浓度90%(V/V)、超声功率140 W、超声温度70℃、超声时间60 min、提取溶剂与苦荞原料比50∶1(mL·g^(-1)),该条件下,提取液中共鉴定到黄酮16种,其中芦丁、表儿茶素和儿茶素含量分别达到115.81、72.99和23.08μg·mL^(-1)。这16种黄酮在防治心血管疾病、慢性炎症性疾病上具有重要作用。本研究可为苦荞精深加工综合利用尤其是功能性苦荞产品开发利用奠定基础。

信息技术与化学课堂教学整合的探讨——从e-Learning到Blending Learning

Blending Learning强调把传统学习方式的优势和e-Learning(即数字化或网络化学习)的优势结合起来,既发挥教师的主导作用,又充分启动学生主动性。在当前的化学课堂教学中,要在化学教学中实行Blending Learning的教育思想,需从三个方面来进行:运用信息技术于化学课堂教学,促进学生的有意义学习,打破单一地运用讲授法的局面,促进师生之间课堂内外的交流和对话。