17,712 research outputs found
Disentangling scale approaches in governance research: comparing monocentric, multilevel, and adaptive governance
The question of how to govern the multiscale problems in today’s network society is an important topic in the fields of public administration, political sciences, and environmental sciences. How scales are defined, studied, and dealt with varies substantially within and across these fields. This paper aims to reduce the existing conceptual confusion regarding scales by disentangling three representative approaches that address both governance and scaling: monocentric governance, multilevel governance, and adaptive governance. It does so by analyzing the differences in (1) underlying views on governing, (2) assumptions about scales, (3) dominant problem definitions regarding scales, and (4) preferred responses for dealing with multiple scales. Finally, this paper identifies research opportunities within and across these approaches
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution
framework to generate continuous (grid-free) spatio-temporal solutions from the
low-resolution inputs. While being computationally efficient, MeshfreeFlowNet
accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet
allows for: (i) the output to be sampled at all spatio-temporal resolutions,
(ii) a set of Partial Differential Equation (PDE) constraints to be imposed,
and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal
domains owing to its fully convolutional encoder. We empirically study the
performance of MeshfreeFlowNet on the task of super-resolution of turbulent
flows in the Rayleigh-Benard convection problem. Across a diverse set of
evaluation metrics, we show that MeshfreeFlowNet significantly outperforms
existing baselines. Furthermore, we provide a large scale implementation of
MeshfreeFlowNet and show that it efficiently scales across large clusters,
achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of
less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2
On the Universality of Mesoscience: Science of 'the in-between'
The universality of mesoscales, ranging between elemental particles and the
universe, is discussed here by reviewing widely disparate fields and presenting
four cases, at differing hierarchical levels, from chemistry, chemical
engineering, meteorology, through to astronomy. An underpinning concept,
"Compromise in competition", is highlighted between various dominant, but
competing mechanisms, and is identified here to be the universal origin of
complexity and diversity in such examples. We therefore advance this as a key
underlying principle of an emerging science -- Mesoscience.Comment: 8 pages, 1 figur
The role of Computer Aided Process Engineering in physiology and clinical medicine
This paper discusses the potential role for Computer Aided Process Engineering (CAPE) in developing engineering analysis and design approaches to biological systems across multiple levels—cell signalling networks, gene, protein and metabolic networks, cellular systems, through to physiological systems. The 21st Century challenge in the Life Sciences is to bring together widely dispersed models and knowledge in order to enable a system-wide understanding of these complex systems. This systems level understanding should have broad clinical benefits. Computer Aided Process Engineering can bring systems approaches to (i) improving understanding of these complex chemical and physical (particularly molecular transport in complex flow regimes) interactions at multiple scales in living systems, (ii) analysis of these models to help to identify critical missing information and to explore the consequences on major output variables resulting from disturbances to the system, and (iii) ‘design’ potential interventions in in vivo systems which can have significant beneficial, or potentially harmful, effects which need to be understood. This paper develops these three themes drawing on recent projects at UCL. The first project has modeled the effects of blood flow on endothelial cells lining arteries, taking into account cell shape change resulting in changes in the cell skeleton which cause consequent chemical changes. A second is a project which is building an in silico model of the human liver, tieing together models from the molecular level to the liver. The composite model models glucose regulation in the liver and associated organs. Both projects involve molecular transport, chemical reactions, and complex multiscale systems, tackled by approaches from CAPE.
Chemical Engineers solve multiple scale problems in manufacturing processes – from molecular scale through unit operations scale to plant-wide and enterprise wide systems – so have an appropriate skill set for tackling problems in physiology and clinical medicine, in collaboration with life and clinical scientists
Design of the Artificial: lessons from the biological roots of general intelligence
Our desire and fascination with intelligent machines dates back to the
antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought
(syllogism) and Heron of Alexandria's mechanical machines and automata.
However, the quest for Artificial General Intelligence (AGI) is troubled with
repeated failures of strategies and approaches throughout the history. This
decade has seen a shift in interest towards bio-inspired software and hardware,
with the assumption that such mimicry entails intelligence. Though these steps
are fruitful in certain directions and have advanced automation, their singular
design focus renders them highly inefficient in achieving AGI. Which set of
requirements have to be met in the design of AGI? What are the limits in the
design of the artificial? Here, a careful examination of computation in
biological systems hints that evolutionary tinkering of contextual processing
of information enabled by a hierarchical architecture is the key to build AGI.Comment: Theoretical perspective on AGI (Artificial General Intelligence
Making space for proactive adaptation of rapidly changing coasts: a windows of opportunity approach
Coastlines are very often places where the impacts of global change are felt most keenly,
and they are also often sites of high values and intense use for industry, human habitation, nature
conservation and recreation. In many countries, coastlines are a key contested territory for planning
for climate change, and also locations where development and conservation conflicts play out. As
a “test bed” for climate change adaptation, coastal regions provide valuable, but highly diverse
experiences and lessons. This paper sets out to explore the lessons of coastal planning and
development for the implementation of proactive adaptation, and the possibility to move from
adaptation visions to actual adaptation governance and planning. Using qualitative analysis of
interviews and workshops, we first examine what the barriers are to proactive adaptation at the coast,
and how current policy and practice frames are leading to avoidable lock-ins and other maladaptive
decisions that are narrowing our adaptation options. Using examples from UK, we then identify
adaptation windows that can be opened, reframed or transformed to set the course for proactive
adaptation which links high level top-down legislative requirements with local bottom-up actions.
We explore how these windows can be harnessed so that space for proactive adaptation increases
and maladaptive decisions are reduced
- …