Why and how do project management offices change? A structural analysis approach

The growing popularity of Project Management Offices (PMOs) as organizational structures is grounded in the assumption they support more efficient and effective project management for better strategy implementation. However, research emphasizes they fail to deliver expected value: their unstable nature precludes the delivery of long-term benefits. This is compounded by the absence of a theory of PMO change and adaptation. Recent research, taking a co-evolution lens rooted in evolutionary theory, suggests that PMOs should be studied in relation to the broader organizational context, in order to better capture the dynamic interplay and fit between them. In this study, taking a routine perspective as micro-foundation and unit of analysis, we focus on the co-evolution between PMO and Project Portfolio Management (PfM) as organizational capability for six case studies. A structural analysis of the relational routines' system between PMO, PfM and the Organizational context allow us to unveil dynamics at stake, i.e. why and how changes occur, as well as eigen behaviors and the changing states of various routines elements (influential, mediating, dependent or not-influential). This study makes five contributions. We show that: 1) PMO and PfM can be conceptualized as collections of routines, 2) PMO and PfM co-evolve over time to adapt to organizational context influence, 3) the co-evolution of a routines' system, abstracted as a non-trivial machine, exhibits an eigen behavior, 4) applying a structural analysis approach allows to simulate the dynamics of a routines' system and to unveil the role of key routine elements and 5) eigen values of routines' systems allow to characterize their eigen behavior.     

A predictive hybrid force modeling in turning: application to stainless steel dry machining with a coated groove tool

The International Journal of Advanced Manufacturing Technology - This paper presents an hybrid numerical/analytical modeling for estimation of cutting forces in machining process. The approach...     

Stress corrosion cracking susceptibility of P285NH and API 5L X65 steel grades in the high-level radioactive waste repository cell concept

Since 2014, the concept developed for the disposal of high-level radioactive waste in the French deep geological repository project Cigéo includes a cement-based grout material. This cement-based grout material will be injected between the casing and the claystone to neutralize the potential acidity resulting from the claystone oxidation induced by the drilling process of the disposal cell. In these conditions of pH (around 10.5) and temperature (90°C, maximum expected during the disposal), the metallic materials could be sensitive to stress corrosion cracking (SCC). In this project, different environments (aerated or deaerated, at room temperature or at 90°C) and synthetic solutions are considered to reproduce the different periods expected during the long life repository. The project is based on electrochemical measurements (polarization curves to define the SCC critical domain of potentials), slow strain rate tensile tests, and long-term immersion for crack initiation and propagation tests.     

Using fuzzy coding with qualitative data: example with subjective data in human-computer interaction

This article shows the role that fuzzy sets may play in the prospect of analysing qualitative data. To underline this role, a human-computer interaction (HCI) study is presented. The data coming from 20 experts concerns their judgment regarding 33 questions related to the use of HCI approaches in order to support interactive system development phases. Each response scale features three main modalities, that is Agree, Partially agree and Disagree. The dataset example is analysed using multiple correspondence analysis (MCA) with both crisp and fuzzy coding models where the intermediate modality, Partially agree, is removed and considered with ½ membership values to the two extreme modalities. A comparative analysis is performed and the discussion states the interest of fuzzy coding with several kinds of qualitative factors or measurement variables. With qualitative measurement variables (our example), the main drawback of fuzzy coding could be the information loss, which is counterbalanced by the possibility of having fewer modalities and therefore of simplifying the multivariate analysis.     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

Visual model‐predictive localization for computationally efficient autonomous racing of a 72‐g drone

Drone racing is becoming a popular e‐sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a novel sensor fusion method called visual model‐predictive localization (VML). Within a small time window, VML approximates the error between the model prediction position and the visual measurements as a linear function. Once the parameters of the function are estimated by the RANSAC algorithm, this error model can be used to compensate the prediction in the future. In this way, outliers can be handled efficiently and the vision delay can also be compensated efficiently. Theoretical analysis and simulation results show the clear advantage compared with Kalman filtering when dealing with the occasional large outliers and vision delays that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named “Trashcan,” which was equipped with a Jevois smart camera for a total of 72 g. An average speed of 2 m/s is achieved while the maximum speed is 2.6 m/s. To the best of our knowledge, this flying platform is currently the smallest autonomous racing drone in the world, while still being one of the fastest autonomous racing drones.     

Gaussian Product Sampling for Rendering Layered Materials

To increase diversity and realism, surface bidirectional scattering distribution functions (BSDFs) are often modelled as consisting of multiple layers, but accurately evaluating layered BSDFs while accounting for all light transport paths is a challenging problem. Recently, Guo et al. [GHZ18] proposed an accurate and general position-free Monte Carlo method, but this method introduces variance that leads to longer render time compared to non-stochastic layered models. We improve the previous work by presenting two new sampling strategies, pair-product sampling and multiple-product sampling. Our new methods better take advantage of the layered structure and reduce variance compared to the conventional approach of sequentially sampling one BSDF at a time. Our pair-product sampling strategy importance samples the product of two BSDFs from a pair of adjacent layers. We further generalize this to multiple-product sampling, which importance samples the product of a chain of three or more BSDFs. In order to compute these products, we developed a new approximate Gaussian representation of individual layer BSDFs. This representation incorporates spatially varying material properties as parameters so that our techniques can support an arbitrary number of textured layers. Compared to previous Monte Carlo layering approaches, our results demonstrate substantial variance reduction in rendering isotropic layered surfaces.