Emerging strategy recognition in agile portfolios

Agile practices become increasingly popular for projects and project portfolios offering firms a higher flexibility to adapt to dynamic environments. This study investigates the antecedents and consequences of agile practices' relevance for strategy formulation in project portfolio management processes. Building on complex adaptive systems theory, we hypothesize a positive relationship between agile capabilities and emerging strategy initiatives and eventually portfolio success. Agile capabilities refer to both the project portfolio organization's intensity of and competence in applying agile practices. Using a sample of 135 portfolios and multiple informants for each portfolio, the results support entrepreneurial orientation and voice behavior as antecedents for agile capabilities. Furthermore, the findings support an agile portfolio's positive relationship with emergence recognition and overall portfolio success. The findings contribute to the literature by identifying two significant antecedents of agile capabilities as well as empirically demonstrating the positive relationship between agile portfolios and emerging strategy recognition. For practitioners, the study encourages the application of agile practices by stressing the general positive influence of agile capabilities and underlines entrepreneurial orientation and voice behavior as important methods of empowerment for agile portfolio processes.     

并联有源滤波器的改进重复控制策略研究

针对三相整流负载产生的6k±1次谐波,提出静止坐标系下的改进型6k±1重复控制策略。同时将比例控制与改进型重复控制相结合,设计改进重复控制并联比例的复合控制结构。为减小改进重复控制中延时环节不为整数的影响,采用基于Lagrange插值近似的FIR滤波器逼近分数延时特性。最后对系统进行稳定性分析和详细设计方法进行推导。通过Matlab仿真验证改进重复控制策略能有效跟踪6k±1次谐波且具有良好的补偿效果,动态响应较快。     

Effects of ethylene-octene copolymer (POE) on the brittle to ductile transition of high-density polyethylene/POE blends

Three kinds of ethylene-octene copolymers (POE) were melt-blended with high-density polyethylene (PE-HD) in different proportions. Detailed characterizations were conducted to analyze their structural differences of POE and its effects in toughening PE-HD. The higher molecular weight POE can improve the toughness of PE-HD. 60:40 PE-HD/POE is elongated to break up to 700% while impact strength is 84.7 kJ/m² at −30°C, which is 21-fold of PE-HD. In the brittle to ductile transition (BDT) during impact, the fracture mechanism changes from the crazing mode to the shear yield-plastic deformation mode. The BDT temperature decreases as the POE molecular weight and its content increase. The interface strength in tension is estimated to access their effects. The Boltzmann-type models were successfully extended to describe the typical S-shaped curves in BDT of notched impact strength vs POE content or temperature. The supplementary decay model is suggested for the attenuation in toughening. Transition map in impact is proposed to select the use range of composition (c ) and temperature (T ) for high toughness. The curves are converted into 3D graph of T -c -impact strength for illustrating their coupling-separate effects, and further into the contour map of impact strength in T -c space for finding their partial equivalence.     

基于VOF模型的层叠器流动过程分析及流道结构优化

基于FLUENT软件的流体体积(VOF)模型研究了不同壁面滑移程度以及不同流道结构参数下4层等厚熔体通过层叠器倍增为8层熔体时,流道中熔体的分层情况变化。结果表明,壁面滑移程度的降低会促使熔体在上下壁面聚集,进而导致上下壁面处熔体层厚增加;汇流段与出口段间圆角半径、汇流段扩压角和平衡段长度这3个结构参数则只会影响熔体在左右壁面的聚集,进而影响各层熔体的尺寸精度,且其中扩压角的影响程度最大,平衡段长度次之,圆角半径的影响较小。     

Convergence analysis of the alternating RGLS algorithm for the identification of the reduced complexity Volterra model

In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS).     

Kinetic analysis of magnesium aluminate spinel formation: Effect of MgO:Al2O3 ratio and titania dopant

The mechanistic pathway of MgO-Al₂O₃ reaction in solid state to form MgAl₂O₄ spinel was investigated to correlate the kinetic parameters with ratio of reactants (MgO:Al₂O₃) and with the presence of a doping agent, TiO₂. The time-temperature-expansion data of oxide compacts was analyzed using several model free analyses and model based (linear and non-linear) kinetic algorithms. These indicated that spinel formation process can be best described by single step with n-dimensional Avrami equation for every MgO:Al₂O₃ ratio, irrespective of titania dopant. The activation energy (E_a) of the process was proportional to % spinel formed in each system and validated with quantitative XRD analysis. The higher value of Avrami coefficient (n) in 90 wt% Al₂O₃ compositions has been explained with geometric considerations of powder packing. Incorporations of 1% TiO₂ in the MgO: Al₂O₃ oxide compact did not markedly affect the reaction model, frequency factor and Activation energy.     

Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading

To date, the only effective means to respond to the spreading of the COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reducing their social and economic consequences. Here, we introduce a meta-population model based on temporal networks, calibrated on the COVID-19 outbreak data in Italy and applied to evaluate the outcomes of these two types of NPIs. Our approach combines the advantages of granular spatial modelling of meta-population models with the ability to realistically describe social contacts via activity-driven networks. We focus on disentangling the impact of these two different types of NPIs: those aiming at reducing individuals’ social activity, for instance through lockdowns, and those that enforce mobility restrictions. We provide a valuable framework to assess the effectiveness of different NPIs, varying with respect to their timing and severity. Results suggest that the effects of mobility restrictions largely depend on the possibility of implementing timely NPIs in the early phases of the outbreak, whereas activity reduction policies should be prioritized afterwards.     

Group sequential crossover trial designs with strong control of the familywise error rate

Crossover designs are an extremely useful tool to investigators, and group sequential methods have proven highly proficient at improving the efficiency of parallel group trials. Yet, group sequential methods and crossover designs have rarely been paired together. One possible explanation for this could be the absence of a formal proof of how to strongly control the familywise error rate in the case when multiple comparisons will be made. Here, we provide this proof, valid for any number of initial experimental treatments and any number of stages, when results are analyzed using a linear mixed model. We then establish formulae for the expected sample size and expected number of observations of such a trial, given any choice of stopping boundaries. Finally, utilizing the four-treatment, four-period TOMADO trial as an example, we demonstrate that group sequential methods in this setting could have reduced the trials expected number of observations under the global null hypothesis by over 33%.     

水文序列相依变异识别的RIC定阶准则——以自回归模型为例

水文过程相依性是水文变异的主要表现形式之一,应用自回归模型对其进行拟合时合理确定模型阶数是一个难点问题。本文在分析AIC和BIC准则的基础上,提出了一种以原序列与其相依成分的相关系数作为拟合度指标,同时借用信息熵形式的函数式,作为模型不确定性度量指标的自回归模型定阶准则(简称RIC准则)。以AR(1)、AR(2)、AR(3)和AR(4)模型为例进行统计试验,将不同序列长度下该准则的定阶准确率与其他定阶准则进行比较,试验结果表明,RIC准则对于上述模型均具有较好的适应性,且定阶准确率远高于AIC准则,其中对于前三阶模型RIC准则优于BIC准则,但四阶模型略低于BIC准则。RIC准则的优势是可以同时满足模型定阶、相依程度分级与模型检验的需求,将其应用于实测水文序列分析,结果显示,该准则能较准确地识别自回归模型的阶数,且符合提出的"相依有变异而残差无变异的最小阶数"的检验标准。