Revisiting the Old Industrial Region: Adaptation and Adjustment in an Integrating Europe

The position of old industrial regions (OIRs) has been neglected in recent regional development research, partly as a result of dominant discourses concerned with concepts such as the knowledge economy, learning regions and the new regionalism. One outcome of this conceptual overload is that empirical research has typically been confined to all too familiar case studies of regional success that tell a rather partial story. Yet the extension of the European integration project eastwards alongside growing competition from the urban and regional ‘hotspots’ of the global south prompts a series of largely unconsidered questions about the ability of OIRs to achieve sustainable economic development and social cohesion in the years ahead. Lacking the capital, technological and labour assets of more dynamic cities and regions, and with the historic legacy of deindustrialisation and the decline of traditional sectors, OIRs face some important dilemmas of adjustment and adaptation. In this paper our purpose is to engage with these issues through some preliminary empirical research into the recent fortunes of OIRs in Western Europe’s largest economies: France, Germany, Spain and the UK. Drawing upon material from the Eurostat database, our results hint at interesting patterns of divergence in the performance of OIRs in terms of processes of economic restructuring, employment change and social cohesion. In particular some important variations emerge in the trajectory of regions within different national contexts. Drawing upon recent thinking relating to commodity chains and global production networks, our results lead us to pose a series of questions that relate to the way regions are being repositioned within broader political and economic networks as part of unfolding processes of uneven development and changing spatial divisions of labour

Artificial intelligence and machine learning in the era of digital transformer monitoring: Exciting developments at Hitachi Energy

The era of digitalization brings new challenges and new paradigms since transformer users and manufacturers alike are moving towards digital solutions. This transition requires new approaches, new architectures, and new ways of looking at data collection, storage, and assessment. Speed and reliability of actionable information become essential at a time when data is ubiquitous, loads are more complex, and energy production moves from traditional plants to distributed generation. This article intends to show some of the ongoing efforts at Hitachi Energy to address these and other demanding technical and economic issues. Our wind power forecast approach deals with the problem of uncertainty in upcoming power demand. We propose a machine learning model to predict power demand to improve the calculation of loadability and cooling / hotspot calculations. Similarly, our Bushing Tan δ and Capacitance Fault Detection solution uses the error of a model to detect problems with Tan δ and capacitance. Our Probabilistic Fault Tree describes an open-source approach that uses Bayesian networks to find the probability of failure of a specific transformer. Finally, we describe two publications made by our team regarding the use of synthetic data created using the Duval Pentagons to generate a model that diagnoses transformer faults; and a patent regarding the creation of an infrastructure that uses blockchain to anonymize users and provide them with information about their transformer fleet using artificial intelligence

Artificial intelligence and machine learning in the era of digital transformer monitoring: Exciting developments at Hitachi Energy

The era of digitalization brings new challenges and new paradigms since transformer users and manufacturers alike are moving towards digital solutions. This transition requires new approaches, new architectures, and new ways of looking at data collection, storage, and assessment. Speed and reliability of actionable information become essential at a time when data is ubiquitous, loads are more complex, and energy production moves from traditional plants to distributed generation. This article intends to show some of the ongoing efforts at Hitachi Energy to address these and other demanding technical and economic issues. Our wind power forecast approach deals with the problem of uncertainty in upcoming power demand. We propose a machine learning model to predict power demand to improve the calculation of loadability and cooling / hotspot calculations. Similarly, our Bushing Tan δ and Capacitance Fault Detection solution uses the error of a model to detect problems with Tan δ and capacitance. Our Probabilistic Fault Tree describes an open-source approach that uses Bayesian networks to find the probability of failure of a specific transformer. Finally, we describe two publications made by our team regarding the use of synthetic data created using the Duval Pentagons to generate a model that diagnoses transformer faults; and a patent regarding the creation of an infrastructure that uses blockchain to anonymize users and provide them with information about their transformer fleet using artificial intelligence

Spartan Daily, October 23, 1959

Volume 47, Issue 23https://scholarworks.sjsu.edu/spartandaily/3939/thumbnail.jp

Stag - Vol. 18, No. 07 - November 2, 1966

The Stag, the official student newspaper of Fairfield University, was published weekly during the academic year (September - June) and ran from September 23, 1949 (Vol. 1, No. 1) to May 6, 1970 (Vol. 21, No. 20).https://digitalcommons.fairfield.edu/archives-stag/1234/thumbnail.jp

Opera Mundi: a weekly report on the economy of the Common Market. No. 431, October 9 - October 15, 1967.

No abstract

Cash Box, March 21, 1964

The international music record weeklyPublication ceased with Nov. 199