Economic resilience : including a case study of the global transition network

This paper explores the dynamic properties of organisms and ecosystems that make them so resilient and capable of adapting to changing circumstances, allowing them to maintain an overall condition of coherence, wholeness and health while living in balance within the resources of the planet. Key principles of resilient ecological systems are explored including: self-regulation; positive and negative feedback; diversity; scale and context; cooperation; emergence and novelty; and ecological tipping points. In contrast, market based economic systems can produce unstable growth with unintended destruction of cultural and species diversity and homogenisation of global life-styles. The paper re-examines fundamental economic principles using insights from biological evolution and ecosystem dynamics to establish a foundation for more resilient economies. This involves experimenting with different models in different communities to find patterns of sustainable production and exchange appropriate to local regions. Fundamental steps in this direction include the emergence of self-organising local communities based on creative experimentation, re-localisation of core sectors of the economy (food, energy, health and education), evolution of local currencies and banking practices that support local enterprise and investment in green technologies, stimulation of decentralised renewable energy networks and economic reform aligned with ecological principles. The Transition Network provides a case study of an international community based movement that has been experimenting with putting some of these principles into practice at the local level. The aim of the Transition Network is to support community led responses to peak oil and climate change, building resilience and well-being. The concept of ecological resilience and its application to local economy is hard wired into the values and emerging structure of the network of transition communities across the globe. The movement started in the UK in 2005 and there are now over 1000 Transition initiatives spanning 34 countries across the world. Many attribute the success and phenomenal growth of the Transition Network to its emerging holographic structure that mimics cell growth within living organisms. Growing a more resilient food system in the face of the twin challenges of natural resource scarcity and climate change is central to the Transition movement. A set of principles for a post carbon resilient food economy in the UK are offered. These include an 80% cut in carbon emission in the food sector by 2050, agricultural diversification, prioritization of farming methods that establish and enhance carbon sinks, phasing out of dependence on fossil fuels in food growing, processing and distribution, promoting access to nutritious and affordable food, as well as promoting greater access to land for growing food in urban and peri-urban areas. Practical examples of Transition related projects in the food sector are presented across the following themes: access to land, low carbon production methods, food distribution systems, health and community gardens and orchards, and collaborative ownership models

Quick-cast: A method for fast and precise scalable production of fluid-driven elastomeric soft actuators

Fluid-driven elastomeric actuators (FEAs) are among the most popular actuators in the emerging field of soft robotics. Intrinsically compliant, with continuum of motion, large strokes, little friction, and high power-to-weight ratio, they are very similar to biological muscles, and have enabled new applications in automation, architecture, medicine, and human-robot interaction. To foster future applications of FEAs, in this paper we present a new manufacturing method for fast and precise scalable production of complex FEAs of high quality (leak-free, single-body form, with <0.2 mm precision). The method is based on 3d moulding and supports elastomers with a wide range of viscosity, pot life, and Young's modulus. We developed this process for two different settings: one in laboratory conditions for fast prototyping with 3d printed moulds and using multi-component liquid elastomers, and the other process in an industrial setting with 3d moulds micromachined in metal and applying compression moulding. We demonstrate these methods in fabrication of up to several tens of two-axis, three-chambered soft actuators, with two types of chamber walls: cylindrical and corrugated. The actuators are then applied as motion drivers in kinetic photovoltaic building envelopes

Estimation, Analysis and Smoothing of Self-Similar Network Induced Delays in Feedback Control of Nuclear Reactors

This paper analyzes a nuclear reactor power signal that suffers from network induced random delays in the shared data network while being fed-back to the Reactor Regulating System (RRS). A detailed study is carried out to investigate the self similarity of random delay dynamics due to the network traffic in shared medium. The fractionality or selfsimilarity in the network induced delay that corrupts the measured power signal coming from Self Powered Neutron Detectors (SPND) is estimated and analyzed. As any fractional order randomness is intrinsically different from conventional Gaussian kind of randomness, these delay dynamics need to be handled efficiently, before reaching the controller within the RRS. An attempt has been made to minimize the effect of the randomness in the reactor power transient data with few classes of smoothing filters. The performance measure of the smoothers with fractional order noise consideration is also investigated into.Comment: 6 pages, 6 figure

Technofixing the Future: Ethical Side Effects of Using AI and Big Data to meet the SDGs

While the use of smart information systems (the combination of AI and Big Data) offer great potential for meeting many of the UN’s Sustainable Development Goals (SDGs), they also raise a number of ethical challenges in their implementation. Through the use of six empirical case studies, this paper will examine potential ethical issues relating to use of SIS to meet the challenges in six of the SDGs (2, 3, 7, 8, 11, and 12). The paper will show that often a simple “technofix”, such as through the use of SIS, is not sufficient and may exacerbate, or create new, issues for the development community using SIS

Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators

In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications’ output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.This work was supported by the STIC-AmSud/CAPES scientific cooperation program under the EnergySFE research project grant 99999.007556/2015-02, EU H2020 Programme, and MCTI/RNP-Brazil under the HPC4E Project, grant agreement n° 689772. Tested K40 boards were donated thanks to Steve Keckler, Timothy Tsai, and Siva Hari from NVIDIA.Postprint (author's final draft