Production Networks Linkages, Innovation Processes and Social Management Technologies. A Methodological Approach Applied to the Volkswagen case in Argentina

The purpose of this paper -as a part of a wider research project - is to analyze the concept of production network from a methodological and theoretical viewpoint based on a three-plane perspective. These dimensions are the linkages among agents, the innovation activities, and the social management technology, including work process organization and the social agreement generation model in force. It is an experimentally methodological approach that tries to go from a theoretical conceptualization of the phenomenon to its empirical evaluation. The questions guiding this research are as follows: What are the variables and dimensions to be observed in the analysis of a group of interconnected firms in order to define a production network? Is it a unique definition or, on the contrary, does it involve a range of alternatives? What are the externalities generated by the agents who belong to one network? What is the relationship between the network’s firms’ technological behavior and their organizational counterpart? How are learning processes in the business firms linked to their own training systems? Has the social management technology some differential role in the learning process and in the development of skills? How do knowledge transmission processes manifest themselves within the “network”? What indicators are useful for the empirical identification of the different means of manifestation of the network according to the theoretical viewpoint adopted? How can those indicators be articulated in order to elaborate typologies intended for the identification of “hybrid” models? How can a complex indicator be built in order to show the different levels of circulation of intangible assets, development of learning processes and work process organization? In the first section, the conceptualization of the production “network” used in this paper is discussed. In the second section, most relevant variables and indicators are presented in order to feature the business firms and the network in terms of: a) type, quantity and quality of tangible and intangible exchanges among the agents; b) innovative capacity and learning; c) social management technology. Then we elaborate a typology of networks based on the consideration of the previous parameters. Lastly, in the fourth section, we discuss how the three dimensions interact in the case of Volkswagen and his forty main local suppliers.Innovation, production process, case study

An information adaptive system study report and development plan

The purpose of the information adaptive system (IAS) study was to determine how some selected Earth resource applications may be processed onboard a spacecraft and to provide a detailed preliminary IAS design for these applications. Detailed investigations of a number of applications were conducted with regard to IAS and three were selected for further analysis. Areas of future research and development include algorithmic specifications, system design specifications, and IAS recommended time lines

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 140

This bibliography lists 306 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1975

Using growing RBF-nets in rubber industry process control

This paper describes the use of a Radial Basis Function (RBF) neural network in the approximation of process parameters for the extrusion of a rubber profile in tyre production. After introducing the rubber industry problem, the RBF network model and the RBF net learning algorithm are developed, which uses a growing number of RBF units to compensate the approximation error up to the desired error limit. Its performance is shown for simple analytic examples. Then the paper describes the modelling of the industrial problem. Simulations show good results, even when using only a few training samples. The paper is concluded by a discussion of possible systematic error influences, improvements and potential generalisation benefits. Keywords: Adaptive process control; Parameter estimation; RBF-nets; Rubber extrusio

Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data

Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.Comment: To appear in proceedings of The 14th International Conference on Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA, 201

On the Antitrust Remedies to Promote Retail Innovation in the EU Car Sector

One of the main objectives behind the recent Block Exemption Regulation for the motor vehicle sector is the promotion of innovative retail formats. This article explores the question whether the new competition rules are apt to attain this objective or should have adopted a more restrictive approach towards qualitative selective distribution