An interdisciplinary approach to the study of kiln firing: a case study from the Campus Galli open-air museum (southern Germany)

Pottery kilns are a common feature in the archaeological record of different periods. However, these pyrotechnological installations are still seldom the target of interdisciplinary investigations. To fill this gap in our knowledge, an updraft kiln firing experiment was run at the Campus Galli open-air museum (southern Germany) by a team consisting of experimental archaeologists, material scientists, geoarchaeologists, and palaeobotanists. The entire process from the preparation of the raw materials to the firing and opening of the kiln was carefully recorded with a particular focus on the study of the raw materials used for pottery making, as well as on fuel usage. The temperatures were monitored by thermocouples placed at different positions in the combustion and firing chambers. In addition, thermocouples were installed within the kiln wall to measure the temperature distribution inside the structure itself. Unfired raw materials as well as controlled and experimentally thermally altered ceramic samples were then characterised with an integrated analysis including ceramic petrography, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and portable X-ray fluorescence (pXRF). Our work provides data about mineralogical and microstructural developments in both pottery kiln structures and the ceramics produced in this type of installations. This is helpful to discuss the limits and potential of various scientific analyses commonly used in ancient ceramic pyrotechnological studies. Overall, our work contributes to a better understanding of updraft kiln technology and offers guidelines on how to address the study of this type of pyrotechnological installations using interdisciplinary research strategies

Future air quality in Europe: a multi-model assessment of projected exposure to ozone

In order to explore future air quality in Europe at the 2030 horizon, two emission scenarios developed in the framework of the Global Energy Assessment including varying assumptions on climate and energy access policies are investigated with an ensemble of six regional and global atmospheric chemistry transport models. <br><br> A specific focus is given in the paper to the assessment of uncertainties and robustness of the projected changes in air quality. The present work relies on an ensemble of chemistry transport models giving insight into the model spread. Both regional and global scale models were involved, so that the ensemble benefits from medium-resolution approaches as well as global models that capture long-range transport. For each scenario a whole decade is modelled in order to gain statistical confidence in the results. A statistical downscaling approach is used to correct the distribution of the modelled projection. Last, the modelling experiment is related to a hind-cast study published earlier, where the performances of all participating models were extensively documented. <br><br> The analysis is presented in an exposure-based framework in order to discuss policy relevant changes. According to the emission projections, ozone precursors such as NO<sub>x</sub> will drop down to 30% to 50% of their current levels, depending on the scenario. As a result, annual mean O<sub>3</sub> will slightly increase in NO<sub>x</sub> saturated areas but the overall O<sub>3</sub> burden will decrease substantially. Exposure to detrimental O<sub>3</sub> levels for health (SOMO35) will be reduced down to 45% to 70% of their current levels. And the fraction of stations where present-day exceedences of daily maximum O<sub>3</sub> is higher than 120 μg m<sup>−3</sup> more than 25 days per year will drop from 43% down to 2 to 8%. <br><br> We conclude that air pollution mitigation measures (present in both scenarios) are the main factors leading to the improvement, but an additional cobenefit of at least 40% (depending on the indicator) is brought about by the climate policy

Mobile Manipulation Hackathon: Moving into Real World Applications

The Mobile Manipulation Hackathon was held in late 2018 during the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) to showcase the latest applications of wheeled robotic manipulators. The challenge had an open format, where teams developed an application using simulation tools and integrated it into a robotic platform. This article presents the competition and analyzes the results, with information gathered during the event and from a survey circulated among the finalist teams. We provide an overview of the mobile manipulation field, identify key areas required for further development to facilitate the implementation of mobile manipulators in real applications, and discuss ideas about how to structure future hackathon-style competitions to enhance their impact on the scientific and industrial communities.Peer ReviewedPostprint (published version

Dynamics of coupled cell networks: synchrony, heteroclinic cycles and inflation

Copyright © 2011 Springer. The final publication is available at www.springerlink.comWe consider the dynamics of small networks of coupled cells. We usually assume asymmetric inputs and no global or local symmetries in the network and consider equivalence of networks in this setting; that is, when two networks with different architectures give rise to the same set of possible dynamics. Focussing on transitive (strongly connected) networks that have only one type of cell (identical cell networks) we address three questions relating the network structure to dynamics. The first question is how the structure of the network may force the existence of invariant subspaces (synchrony subspaces). The second question is how these invariant subspaces can support robust heteroclinic attractors. Finally, we investigate how the dynamics of coupled cell networks with different structures and numbers of cells can be related; in particular we consider the sets of possible “inflations” of a coupled cell network that are obtained by replacing one cell by many of the same type, in such a way that the original network dynamics is still present within a synchrony subspace. We illustrate the results with a number of examples of networks of up to six cells

A view of Neural Networks as dynamical systems

We consider neural networks from the point of view of dynamical systems theory. In this spirit we review recent results dealing with the following questions, adressed in the context of specific models. 1. Characterizing the collective dynamics; 2. Statistical analysis of spikes trains; 3. Interplay between dynamics and network structure; 4. Effects of synaptic plasticity.Comment: Review paper, 51 pages, 10 figures. submitte

Impact of Single Links in Competitive Percolation -- How complex networks grow under competition

How a complex network is connected crucially impacts its dynamics and function. Percolation, the transition to extensive connectedness upon gradual addition of links, was long believed to be continuous but recent numerical evidence on "explosive percolation" suggests that it might as well be discontinuous if links compete for addition. Here we analyze the microscopic mechanisms underlying discontinuous percolation processes and reveal a strong impact of single link additions. We show that in generic competitive percolation processes, including those displaying explosive percolation, single links do not induce a discontinuous gap in the largest cluster size in the thermodynamic limit. Nevertheless, our results highlight that for large finite systems single links may still induce observable gaps because gap sizes scale weakly algebraically with system size. Several essentially macroscopic clusters coexist immediately before the transition, thus announcing discontinuous percolation. These results explain how single links may drastically change macroscopic connectivity in networks where links add competitively.Comment: non-final version, for final see Nature Physics homepag

Dendritic Slow Dynamics Enables Localized Cortical Activity to Switch between Mobile and Immobile Modes with Noisy Background Input

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity – called a bump – can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability

Energy efficient sparse connectivity from imbalanced synaptic plasticity rules

It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum