The evolution of retail banking services in United Kingdom: a retrospective analysis

The purpose of this paper is to assess the sequence of technological changes occurred in the retail banking sector of the United Kingdom against the emergence of customer services by developing an evolutionary argument. The historical paradigm of Information Technology provides useful insights into the ‘learning opportunities’ that opened the way to endogenous changes in the banking activity such as the reconfiguration of its organizational structure and the diversification of the product line. The central idea of this paper is that innovation never occurs without simultaneous structural change. Thus, a defining property of the banking activity is the diachronic adaptation of formal and informal practices to an evolving technological dimension reflecting the extent to which the diffusion of innovation (re)generates variety of micro level processes and induces industry evolution.Information Technology; Retail Banking; History of Technology; Innovation Systems.

How innovation systems emerge to solve ecological problems: Biofuels in the United States and Brazil

This paper discusses the re-emergence of biofuel innovation systems in the United States and Brazil. We argue that innovation systems emerge and evolve to solve a problem, and that the way the problem is framed and articulated has a significant impact on the direction and momentum of this evolution. Additionally, innovation sequences occur with a recurrent pattern of changing problems and innovative solutions. We consider the role of the State as a core actor in the mobilisation of innovation systems and discuss how specific institutional arrangements, political contexts and technological competencies influence how problems are framed. We find that role of the State varies across time as well as across different geographical regions. Finally, we suggest that as ecological problems intensify we might expect to see an increase in State intervention in innovation systems

Filtered Networks of Evolutionary Processors

* Supported by INTAS 00-626 and TIC 2003-09319-c03-03.This paper presents some connectionist models that are widely used to solve NP-problems. Most well known numeric models are Neural Networks that are able to approximate any function or classify any pattern set provided numeric information is injected into the net. Neural Nets usually have a supervised or unsupervised learning stage in order to perform desired response. Concerning symbolic information new research area has been developed, inspired by George Paun, called Membrane Systems. A step forward, in a similar Neural Network architecture, was done to obtain Networks of Evolutionary Processors (NEP). A NEP is a set of processors connected by a graph, each processor only deals with symbolic information using rules. In short, objects in processors can evolve and pass through processors until a stable configuration is reach. This paper just shows some ideas about these two models

Nonbanks in the payments system: innovation, competition, and risk - a conference summary

From the early days of automated card sorting to the more recent times of the Internet and check imaging, payments and payments processing have continually embraced new technology. At the same time, the industry has been shaped by its share of entry and exit, through startups, mergers, and the reorganization of businesses seeking the proper scope of horizontal and vertical integration. ; These changes have enabled nonbank organizations to play a larger role in the payments system. Nonbanks have followed a number of pathways to more prominence: purchasing bank payment processing subsidiaries, carving out niches in the payments market through innovation, and taking advantage of economies of scale made possible by shifting to electronic forms of payment. ; Nonbanks have introduced some of the most far-reaching innovations to the payments system in recent years, leading to greater efficiencies in payments processing. At the same time, nonbanks have changed the dynamics of competition in payments, leading to a significant change in the system’s risk profile. ; Sullivan and Wang summarize the proceedings of a conference on nonbanks in the payments system held by the Federal Reserve Bank of Kansas City in Santa Fe, New Mexico, on May 2-4, 2007. The conference addressed many of the key questions raised by the growing presence of nonbanks in payments, including: Have recent payment innovations been more likely to come from nonbanks? Have nonbanks improved or harmed competition in payments? Have nonbanks increased risk or helped to develop tools to manage it? How should public policy respond as increasingly more activity in payments lies outside of the banking system?Payment systems ; Nonbank financial institutions

Energy-aware scheduling in distributed computing systems

Distributed computing systems, such as data centers, are key for supporting modern computing demands. However, the energy consumption of data centers has become a major concern over the last decade. Worldwide energy consumption in 2012 was estimated to be around 270 TWh, and grim forecasts predict it will quadruple by 2030. Maximizing energy efficiency while also maximizing computing efficiency is a major challenge for modern data centers. This work addresses this challenge by scheduling the operation of modern data centers, considering a multi-objective approach for simultaneously optimizing both efficiency objectives. Multiple data center scenarios are studied, such as scheduling a single data center and scheduling a federation of several geographically-distributed data centers. Mathematical models are formulated for each scenario, considering the modeling of their most relevant components such as computing resources, computing workload, cooling system, networking, and green energy generators, among others. A set of accurate heuristic and metaheuristic algorithms are designed for addressing the scheduling problem. These scheduling algorithms are comprehensively studied, and compared with each other, using statistical tools to evaluate their efficacy when addressing realistic workloads and scenarios. Experimental results show the designed scheduling algorithms are able to significantly increase the energy efficiency of data centers when compared to traditional scheduling methods, while providing a diverse set of trade-off solutions regarding the computing efficiency of the data center. These results confirm the effectiveness of the proposed algorithmic approaches for data center infrastructures.Los sistemas informáticos distribuidos, como los centros de datos, son clave para satisfacer la demanda informática moderna. Sin embargo, su consumo de energético se ha convertido en una gran preocupación. Se estima que mundialmente su consumo energético rondó los 270 TWh en el año 2012, y algunos prevén que este consumo se cuadruplicará para el año 2030. Maximizar simultáneamente la eficiencia energética y computacional de los centros de datos es un desafío crítico. Esta tesis aborda dicho desafío mediante la planificación de la operativa del centro de datos considerando un enfoque multiobjetivo para optimizar simultáneamente ambos objetivos de eficiencia. En esta tesis se estudian múltiples variantes del problema, desde la planificación de un único centro de datos hasta la de una federación de múltiples centros de datos geográficmentea distribuidos. Para esto, se formulan modelos matemáticos para cada variante del problema, modelado sus componentes más relevantes, como: recursos computacionales, carga de trabajo, refrigeración, redes, energía verde, etc. Para resolver el problema de planificación planteado, se diseñan un conjunto de algoritmos heurísticos y metaheurísticos. Estos son estudiados exhaustivamente y su eficiencia es evaluada utilizando una batería de herramientas estadísticas. Los resultados experimentales muestran que los algoritmos de planificación diseñados son capaces de aumentar significativamente la eficiencia energética de un centros de datos en comparación con métodos tradicionales planificación. A su vez, los métodos propuestos proporcionan un conjunto diverso de soluciones con diferente nivel de compromiso respecto a la eficiencia computacional del centro de datos. Estos resultados confirman la eficacia del enfoque algorítmico propuesto