Determination of uncertainties of geomechanical parameters of metamorphic rocks using petrographic analyses

Geomechanical parameters of intact metamorphic rocks determined from laboratory testing remain highly uncertain because of the great intrinsic variability associated with the degrees of metamorphism.The aim of this paper is to develop a proper methodology to analyze the uncertainties of geomechanical characteristics by focusing on three domains,i.e.data treatment process,schistosity angle,and mineralogy.First,the variabilities of the geomechanical laboratory data of Westwood Mine(Quebec,Canada)were examined statistically by applying different data treatment techniques,through which the most suitable outlier methods were selected for each parameter using multiple decision-making criteria and engineering judgment.Results indicated that some methods exhibited better performance in identifying the possible outliers,although several others were unsuccessful because of their limitation in large sample size.The well-known boxplot method might not be the best outlier method for most geomechanical parameters because its calculated confidence range was not acceptable according to engineering judgment.However,several approaches,including adjusted boxplot,2MADe,and 2SD,worked very well in the detection of true outliers.Also,the statistical tests indicate that the best-fitting probability distribution function for geomechanical intact parameters might not be the normal distribution,unlike what is assumed in most geomechanical studies.Moreover,the negative effects of schistosity angle on the uniaxial compressive strength(UCS)variabilities were reduced by excluding the samples within a specific angle range where the UCS data present the highest variation.Finally,a petrographic analysis was conducted to assess the associated uncertainties such that a logical link was found between the dispersion and the variabilities of hard and soft minerals.

Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.

Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

Adaptive Leader-Follower Consensus Control of Multiple Flexible Manipulators With Actuator Failures and Parameter Uncertainties

In this paper,the leader-follower consensus problem for a multiple flexible manipulator network with actuator failures,parameter uncertainties,and unknown time-varying boundary disturbances is addressed.The purpose of this study is to develop distributed controllers utilizing local interactive protocols that not only suppress the vibration of each flexible manipulator but also achieve consensus on joint angle position between actual followers and the virtual leader.Following the accomplishment of the reconstruction of the fault terms and parameter uncertainties,the adaptive neural network method and parameter estimation technique are employed to compensate for unknown items and bounded disturbances.Furthermore,the Lyapunov stability theory is used to demonstrate that followers’angle consensus errors and vibration deflections in closed-loop systems are uniformly ultimately bounded.Finally,the numerical simulation results confirm the efficacy of the proposed controllers.

Influence of uncertainties: A focus on block volume and shape assessment for rockfall analysis

Block size and shape depend on the state of fracturing of the rock mass and,consequently,on the geometrical features of the discontinuity sets(mainly orientation,spacing,and persistence).The development of non-contact surveying techniques applied to rock mass characterization offers significant advantages in terms of data numerosity,precision,and accuracy,allowing for performing a rigorous statistical analysis of the database.This fact is particularly evident when dealing with rockfall phenomena:uncertainties in spacing and orientation data could significantly amplify the uncertainties connected with in situ block size distribution(IBSD),which represents a relation between each possible value of the volume and its probability of not being exceeded.In addition to volume,block shape can be considered as a derived parameter that suffers from uncertainties.Many attempts to model the possible trajectories of blocks considering their actual shape have been proposed,aiming to reproduce the effect on motion.The authors proposed analytical equations for calculating the expected value and variance of volume distributions,based on the geometrically correct equation for block volume in the case of three discontinuity sets.They quantify and discuss the effect of both volume and shape variability through a synthetic case study.Firstly,a fictitious rock mass with three discontinuity sets is assumed as the source of rockfall.The IBSDs obtained considering different spacing datasets are quantitatively compared,and the overall uncertainty effect is assessed,proving the correctness of the proposed equations.Then,block shape distributions are obtained and compared,confirming the variability of shapes within the same IBSD.Finally,a comparison between trajectory simulations on the synthetic slope is reported,aiming to highlight the effects of the propagation of uncertainties to block volume and shape estimation.The benefits of an approach that can quantify the uncertainties are discussed from the perspective of improving the reliability of simulations.

Non-Negative Adaptive Mechanism-Based Sliding Mode Control for Parallel Manipulators with Uncertainties

In this paper,a non-negative adaptive mechanism based on an adaptive nonsingular fast terminal sliding mode control strategy is proposed to have finite time and high-speed trajectory tracking for parallel manipulators with the existence of unknown bounded complex uncertainties and external disturbances.The proposed approach is a hybrid scheme of the online non-negative adaptive mechanism,tracking differentiator,and nonsingular fast terminal sliding mode control(NFTSMC).Based on the online non-negative adaptive mechanism,the proposed control can remove the assumption that the uncertainties and disturbances must be bounded for the NFTSMC controllers.The proposed controller has several advantages such as simple structure,easy implementation,rapid response,chattering-free,high precision,robustness,singularity avoidance,and finite-time convergence.Since all control parameters are online updated via tracking differentiator and non-negative adaptive law,the tracking control performance at high-speed motions can be better in real-time requirement and disturbance rejection ability.Finally,simulation results validate the effectiveness of the proposed method.

Adaptive recurrent neural network for uncertainties estimation in feedback control system

In this paper,a recurrent neural network(RNN)is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems.The neural network approximates the uncertainties related to unmodeled dynamics,parametric variations,and external disturbances.The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance.The RNN weights are online adapted,and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation.The used activation function,at the hidden layer,has an expression that simplifies the adaptation laws from the stability analysis.It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses.The proposed RNN based feedback control is applied to a DC–DC converter for current regulation.Simulation and experimental results are provided to show its effectiveness.Compared to the feedforward neural network and the conventional feedback control,the RNN based feedback control provides good tracking performance.

A Novel Two-Stage Scheduling Approach for a Hybrid Floating Photovoltaic-Battery-Hydropower Plant Considering Uncertainties

The application of floating photovoltaics (PVs) in hydropower plants has gained increasing interest in forming hybrid energy systems (HESs). It enhances the operational benefits of the existing hydropower plants. However, uncertainties of PV and load powers can present great challenges to scheduling HESs. To address these uncertainties, this paper proposes a novel two-stage optimization approach that combines distributionally robust chance-constrained (DRCC) and robust-stochastic optimization (RSO) approaches to minimize the operational cost of an HES. In the first stage, the scheduling of each device is obtained via the DRCC approach considering the PV power and load forecast errors. The second stage provides a robust near real time energy dispatch according to different scenarios of PV power and load demand. The solution of the RSO problem is obtained via a novel double-layer particle swarm optimization algorithm. The performance of the proposed approach is compared to the traditional stochastic and robust-stochastic approaches. Simulation results de- monstrate the superiority of the proposed two-stage approach and its solution method in terms of operational cost and execution time.

Fourth-Order Predictive Modelling: II. 4th-BERRU-PM Methodology for Combining Measurements with Computations to Obtain Best-Estimate Results with Reduced Uncertainties

This work presents a comprehensive fourth-order predictive modeling (PM) methodology that uses the MaxEnt principle to incorporate fourth-order moments (means, covariances, skewness, kurtosis) of model parameters, computed and measured model responses, as well as fourth (and higher) order sensitivities of computed model responses to model parameters. This new methodology is designated by the acronym 4th-BERRU-PM, which stands for “fourth-order best-estimate results with reduced uncertainties.” The results predicted by the 4th-BERRU-PM incorporates, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizes the results produced by extant data assimilation and data adjustment procedures.

知识资本对企业数字化转型的影响机制

基于知识基础理论,采用2010—2021年中国A股上市公司为研究对象,实证考察知识资本对企业数字化转型的直接影响与通过缓解融资约束而产生的间接影响以及环境动态性和丰富性在知识资本与企业数字化转型之间的调节作用。研究表明,知识资本有效促进企业数字化转型,在一系列稳健性检验后依旧成立。知识资本对企业数字化转型的促进作用是通过缓解融资约束来实现的;环境动态性负向调节、环境丰富性正向调节该促进作用。进一步检验发现,在国有产权、高科技行业样本中,知识资本对企业数字化转型的影响更显著。研究结论为指导企业利用知识资本促进数字化转型提供经验启示。

高等教育集群化发展与经济韧性:机制、途径及贡献

随着全球进入新的动荡期,经济韧性发展愈发重要。基于2008—2020年31省面板数据,从高等教育质量集聚和规模集聚角度出发,研究评价了我国高等教育集群化发展情况。在此基础上,实证研究了高等教育集群化水平对经济韧性改善的影响。研究发现,高等教育集群化与经济韧性显著正相关;东、中、西部三大地区高等教育集群化发展对经济韧性的影响存在明显差异;高等教育集群通过人才效应、创新效应和产业效应等途径影响经济韧性发展,其中,人才效应贡献最大。研究有助于厘清高等教育集群对经济韧性影响的机制和途径,因地制宜地实施高等教育集群化发展政策,充分发挥高等教育集群在人才、科技和产业等方面的经济韧性提升效应。

数字化转型如何影响组织韧性--一个有调节的中介效应模型

研究以2009—2022年沪深A股上市企业为研究样本,考察了在异质环境视角下,数字化转型对组织韧性的影响及异质性特征,并对企业创新能力在其中的中介传导机制、环境不确定性的调节机制以及两者兼具的有调节的中介机制进行了检验。研究发现:①企业数字化转型对组织韧性的稳定性和灵活性维度均有着显著正向影响。②企业创新在数字化转型与组织韧性的影响效应中发挥部分中介作用。③环境不确定性能够调节数字化转型与组织韧性稳定性与灵活性之间的基准效应。④环境不确定性可以调节中介效应的前半段路径,即企业面临的环境不确定性越强,数字化转型越能促进企业创新产出。同时,环境不确定性还能调节组织韧性稳定性中介效应的后半段路径,即企业面临的环境不确定性越强,企业创新越能促进组织韧性稳定性的提升。

三维成矿预测关键问题

三维成矿预测是当前深部找矿预测和勘查的重要方法和手段,其方法体系和实践应用均已取得大量成果,但同时存在若干关键科学技术问题,导致其进一步发展受到制约。本文从多尺度三维成矿预测方法体系不完善、不确定性分析与优化研究薄弱、三维成矿预测要素挖掘存在瓶颈、缺少针对三维成矿预测的三维深度学习模型和方法等关键问题出发,对目前三维成矿预测领域相关方面的研究进展进行综合分析,并提出针对上述关键问题可能的解决方案和研究方向。预期未来三维成矿预测领域的研究工作将创新发展出多种方法,实现对三维预测信息的深度挖掘;构建形成适用的三维深度学习模型和训练方法,有效增强三维成矿预测结果的预测能力;通过系统性地开展三维成矿预测不确定性研究,进一步优化预测过程和结果,有效提高三维成矿预测方法的可靠性和准确性;形成面向多尺度三维成矿预测的方法体系,更有效地指导矿集区-矿田-勘查区块(矿床)等不同级别的深部矿产资源找矿勘查工作。相关关键问题的解决将进一步深化和完善三维成矿预测理论和方法体系,促进三维成矿预测理论方法的实践应用,显著提升深部找矿预测和勘查工作的效率与水平,助力深部找矿突破。

基于数字孪生与元宇宙技术的能源互联网态势感知系统论方法研究(一):概念、挑战与研究框架

态势感知是能源互联网的核心任务,为运管调控各项决策提供重要辅助信息。能源互联网的复杂定性对其态势感知任务提出严峻挑战——受其所辖单元日益增长的规模、种类、主动性、耦合性以及外界环境等不确定因素的影响,能源互联网其复杂性与日俱增。系统复杂性所衍生的一些问题超出经典简化论的讨论范畴,亟需一种新的认知手段。该文通过关联数据、数据科学、物理系统与具体应用,提出能源互联网数字孪生系统;进一步,延伸出一种新型数据驱动的态势感知方法论,即数字孪生态势感知(digitaltwinsituation awareness,DT-SA)。其核心思想是将真实世界的固有难题转化到虚拟空间,继而借助复杂系统理论和大数据分析等工具予以解决,具体涉及孪生体建模、分析和认知等核心环节。此外,该文也讨论元宇宙技术对孪生体互联的增强作用,以及相关的科学问题。该系列研究有益于推进能源系统领域和数据科学领域的交叉融合,对于企业数字化转型、数字城市建设等具备参考价值。

建设性冲突与破坏性冲突对联盟治理机制选择倾向性的非对称影响研究

伙伴间冲突普遍存在于战略联盟中,如何根据冲突的不同类型选取与之匹配的治理机制对联盟后续的运营与发展至关重要。本文结合交易成本理论与社会交换理论,探究不同类型的伙伴间冲突(建设性冲突与破坏性冲突)对联盟治理机制(契约治理机制与关系治理机制)选择倾向性的差异性影响,并进一步检验外部环境不确定性及伙伴行为不确定性对上述选择决策的权变作用。选取国内制造业企业联盟数据进行实证分析发现:当联盟面临建设性冲突时,合作伙伴更倾向于选择关系治理机制;而当联盟面临破坏性冲突时,合作伙伴更倾向于选择契约治理机制。环境不确定性显著加强了建设性冲突与关系治理机制选择倾向性间的关系,而行为不确定性显著增强了破坏性冲突与契约治理机制选择倾向性间的关系。本文研究结论丰富了联盟治理机制动态调整的理论研究,也为联盟企业在不同情境下对联盟治理机制进行适应性调整提供了指导建议。

Top-down法评定EDTA络合滴定法测定海洋沉积物中碳酸钙含量的不确定度

依据Top-down技术中的质控图法对EDTA络合滴定法测定海洋沉积物中碳酸钙含量的不确定度进行评定。利用2021年质控样标准物质GBW 07333、GBW 07314和GBW 07316中CaCO_(3)测定值的时序数据进行评定,3组时序数据正态统计量A^(2*)(s)和独立性统计量A^(2*)(RM)均小于1,CaCO_(3)测定值、A_(EWMi)和移动极差控制图没有失控点,3组时序数据处于受控状态。当CaCO_(3)质量分数分别为2.05%、6.20%和38.56%时,计算得其扩展不确定度分别为0.08%、0.07%和0.20%。将测量不确定度与对应含量值进行线性拟合建立了不确定度预测模型,GBW 07309和GBW 07334中CaCO_(3)的测量不确定度预测值分别为0.09%和0.18%,预测值与Top-down技术获取的评定结果较为吻合。

供给与需求不确定情形下的供应链回购契约与补偿契约比较

在供应链存在不确定性的情况下,企业之间通过签订有效的契约(如补偿契约或回购契约)对供应链成员的行为进行约束。研究了需求和生产不确定同时存在时,一个两级供应链中的制造商和零售商在一个定价-订购-生产-销售周期内进行斯坦伯格博弈的模型,各成员的目标均为实现自身利润最大化。从理论上证明了使利润最大化的最优投入产量和最优订购量均衡存在且唯一,揭示了生产成本、批发价格、生产与需求的不确定性等因素对均衡的影响。最后,从制造商和零售商角度比较了其在不同契约机制下的利润水平。对于制造商,当零售商补偿单价相对于制造商生产成本较高时,多数情况下补偿契约优于回购契约;当零售商补偿单价相对于制造商生产成本较小时则恰好相反。对于零售商,多数情况下回购契约优于补偿契约,唯一的特殊情况是制造商产品盈利空间相对较小、生产不确定较大以及需求不确定较小。

基于全纯嵌入的电力系统不确定性仿射潮流方法

仿射潮流算法是求解新型电力系统状态量分布特征的重要手段。针对现有仿射潮流算法保守度较高、算法效率较低且需要选择合适的迭代初始值等不足之处,该文提出一种基于全纯嵌入的不确定性仿射潮流计算方法。首先,构建电力系统节点电压和功率仿射模型,并建立系统仿射潮流方程。在此基础上,将嵌入因子引入到仿射潮流方程中,构造具有泰勒级数形式的全纯嵌入仿射潮流模型;接着,将仿射潮流求解问题转换为泰勒幂级数系数的求解问题,获取状态量初始解及递推关系,计算不确定性潮流的仿射解。最后,算例验证所提算法具有保守性低、计算效率高和量化分析分布式电源出力对电压影响等优势。

数字化转型、服务化融合与制造业企业绩效提升——兼论经济政策不确定性的调节效应

引入经济政策不确定性作为调节变量,从嵌入式服务化和混入式服务化的双重融合视角,研究数字化转型提升制造业企业绩效的理论机制和作用路径。在此基础上,采用2008—2020年制造业A股上市公司数据对理论机制进行实证检验。研究结果显示:数字化转型能够提升企业绩效,嵌入式服务化发挥了正向中介作用,但在区域分布、所有制结构和技术水平等方面具有明显的异质性;数字化转型能够抑制混入式服务化中制造业企业的“脱实向虚”行为;经济政策的不确定性会增强数字化转型对嵌入式服务化的推动作用和对混入式服务化的抑制作用。

创新生态系统不确定性条件下后发企业何以实现颠覆性创新——基于SOR模型的动态能力中介作用

创新生态系统被视为培养颠覆性创新的沃土,其日益增加的不确定性为后发企业提供了实现颠覆性创新的机会窗口。将动态能力细分为认知性动态能力与非认知性动态能力两个维度,利用SOR模型构建“条件刺激—能力构建—颠覆行为”的理论框架,试图打开创新生态系统不确定性条件下后发企业实现颠覆性创新的“黑箱”。基于创新生态系统中376份后发企业的样本数据,研究发现:①创新生态系统不确定性不仅对颠覆性创新有显著正向影响,而且对认知性动态能力和非认知性动态能力也有显著正向影响;②认知性动态能力与非认知性动态能力对颠覆性创新有显著正向影响,并且分别在创新生态系统不确定性与颠覆性创新关系中起部分中介作用;③创新生态系统开放度能够正向调节创新生态系统不确定性与认知性动态能力关系。研究有助于指导面对不确定性的后发企业实现“弯道超车”,并且为创新生态系统突破创新迟滞困境提供新思路。