Revisiting Ghoshal and Bartlett’s Theory of the Multinational Corporation as an Interorganizational Network

More than 25 years after its appearance, Ghoshal and Bartlett’s seminal paper ‘The Multinational Corporation as an Interorganizational Network’ stands as a widely recognized but insufficiently understood contribution to the international management literature. This paper reviews related literature, celebrates and critically discusses Ghoshal and Bartlett’s work and proposes ways to address its main limitations, which are a lack of a defined set of parameters describing networks and the delineation of network boundaries. We then translate their theory into concrete propositions and thereby bring it closer to empirical testing. Taking a subsidiary-network perspective, we expand the authors’ work by combining network and entrepreneurship literature and theoretically deducting positive effects of networks on subsidiary entrepreneurial orientation. This yields entirely new insights and a more holistic view of the effects of subsidiary networks.     

Reduced dimension GDSW coarse spaces for monolithic Schwarz domain decomposition methods for incompressible fluid flow problems

Monolithic preconditioners for incompressible fluid flow problems can significantly improve the convergence speed compared with preconditioners based on incomplete block factorizations. However, the computational costs for the setup and the application of monolithic preconditioners are typically higher. In this article, several techniques are applied to monolithic two-level generalized Dryja-Smith-Widlund (GDSW) preconditioners to further improve the convergence speed and the computing time. In particular, reduced dimension GDSW coarse spaces, restricted and scaled versions of the first level, hybrid, and parallel coupling of the levels, and recycling strategies are investigated. Using a combination of all these improvements, for a small time-dependent Navier-Stokes problem on 240 message passing interface (MPI) ranks, a reduction of 86% of the time-to-solution can be obtained. Even without applying recycling strategies, the time-to-solution can be reduced by more than 50% for a larger steady Stokes problem on 4608 MPI ranks. For the largest problems with 11 979 MPI ranks, the scalability deteriorates drastically for the monolithic GDSW coarse space. On the other hand, using the reduced dimension coarse spaces, good scalability up to 11 979 MPI ranks, which corresponds to the largest problem configuration fitting on the employed supercomputer, could be achieved.     

Integrated laser based pre-tempering at laser welding of AISI 1045 steel by using 3D-scanner optics

Laser-based pre-heating of laser beam welding with a 3D scanning optics, applied to AISI 1045 steel, is studied. Laser beam welding of heat-treatable steel is challenging due to martensitic hardening in combination with defects. Pre-tempering aims the reduction of the cooling rates and martensitic microstructure within the weld seam. An oscillating defocused laser beam was guided over the surface for pre-heating by means of a 3D scanner optics. During pre-heating, the laser power, the scanning speed and the number of cycles were varied. Welding with 4000 W and 2 m/min with a focused laser beam was executed. Thus the resulting temperature profile behind the ongoing laser beam and cooling time T_8|5 between 800 °C–500 °C was significantly extended. Two parameter combinations (15 cycles|600 W|50 mm/s(²) and 10 cycles|800 W|50 mm/s²) succeeded in a microstructure of bainite and martensite. By extending the cooling time T_8|5 to 3.11 s(²) and 4.17 s². Thus, average hardness for laser based pre-tempering of 487 HV 0.5(²) and 455 HV 0.5² was achieved. As a reference, global pre-heating at 400 °C using a heating plate can reduce the average hardness of the weld zone from 729 HV 0.5 at room temperature to 304 HV 0.5 at a cooling time T_8|5 of 5.63 s.     

A grey wolf optimizer approach for optimal weight design problem of the spur gear

The weight of gears is an important issue for high performance of the machine elements. Thus, the design of the gear pairs with respect to the minimum weight is an important and attractive subject to study. Various analytical and heuristic approaches were utilized to optimize the weight of gears in previous studies. In this study, the problem of gear optimization as per the minimum weight was solved with Grey Wolf Optimizer (GWO). GWO is a meta-heuristic optimization method that stands out with its successful performance in engineering applications. In this study, the performance of GWO was investigated in the case of constituting a spur gear with respect to minimum weight for the first time. Compared with the previous studies, the results obtained in the study show that it is possible to design a lighter gear with GWO.     

Artificial Microbial Arenas: Materials for Observing and Manipulating Microbial Consortia

From the smallest ecological niche to global scale, communities of microbial life present a major factor in system regulation and stability. As long as laboratory studies remain restricted to single or few species assemblies, however, very little is known about the interaction patterns and exogenous factors controlling the dynamics of natural microbial communities. In combination with microfluidic technologies, progress in the manufacture of functional and stimuli‐responsive materials makes artificial microbial arenas accessible. As habitats for natural or multispecies synthetic consortia, they are expected to not only enable detailed investigations, but also the training and the directed evolution of microbial communities in states of balance and disturbance, or under the effects of modulated stimuli and spontaneous response triggers. Here, a perspective on how materials research will play an essential role in generating answers to the most pertinent questions of microbial engineering is presented, and the concept of adaptive microbial arenas and possibilities for their construction from particulate microniches to 3D habitats is introduced. Materials as active and tunable components at the interface of living and nonliving matter offer exciting opportunities in this field. Beyond forming the physical horizon for microbial cultivates, they will enable dedicated intervention, training, and observation of microbial consortia.     

Variable neighborhood search based approaches to a vehicle scheduling problem in agriculture

A vehicle scheduling problem (VSP) that arises from sugar beet transportation within minimum working time under the set of constraints reflecting a real‐life situation is considered. A mixed integer quadratically constrained programming (MIQCP) model of the considered VSP and reformulation to a mixed integer linear program (MILP) are proposed and used within the framework of Lingo 17 solver, producing optimal solutions only for small‐sized problem instances. Two variants of the variable neighborhood search (VNS) metaheuristic—basic VNS (BVNS) and skewed VNS (SVNS) are designed to efficiently deal with large‐sized problem instances. The proposed VNS approaches are evaluated and compared against Lingo 17 and each other on the set of real‐life and generated problem instances. Computational results show that both BVNS and SVNS reach all known optimal solutions on small‐sized instances and are comparable on medium‐ and large‐sized instances. In general, SVNS significantly outperforms BVNS in terms of running times.     

Artificial neural network model for ground vibration amplitudes prediction due to light railway traffic in urban areas

The growth of density and circulation speed of railway transportation systems in urban areas increases the importance of the research issues of the produced environmental impacts. This study presents a field data analysis, obtained during monitoring campaigns of ground vibration, due to light railway traffic in urban areas, based on the artificial neural network (ANN) approach, using quantitative and qualitative predictors. Different ANN-based models, using those predictors, were evaluated/trained and validated. Using several criteria, including those that measures the possibility of ANN overfitting (RR²) and complexity (AIC), the best ANN model was successfully obtained for Lisbon area. This model, with 16 input elements (quantitative and qualitative predictors), 2 neurons on the hidden layer with a hyperbolic tangent sigmoid transfer function, and 1 neuron on the output layer considering a linear transfer function, has 0.9720 for the coefficient of determination and 0.5293 for the sum squared error.

     

A Nyström-based finite element method on polygonal elements

We consider families of finite elements on polygonal meshes, that are defined implicitly on each mesh cell as solutions of local Poisson problems with polynomial data. Functions in the local space on each mesh cell are evaluated via Nyström discretizations of associated integral equations, allowing for curvilinear polygons and non-polynomial boundary data. Several experiments demonstrate the approximation quality of interpolated functions in these spaces.     

Applying robotic frameworks in a simulated multi-agent contest

In the Multi-Agent Programming Contest 2017 the TUBDAI team of the Technische Universität Berlin is using the complex multi-agent scenario to evaluate the application of two frameworks from the field (multi-)robot systems. In particular the task-level decision-making and planning framework ROS Hybrid Behaviour Planner (RHBP) is used to implement the execution and decision-making for single agents. The RHBP framework builds on top of the framework Robot Operating System (ROS) that is used to implement the communication and scenario specific coordination strategy of the agents. The united team for the official contest is formed by volunteering students from a project course and their supervisors.