Genetic networks with canalyzing Boolean rules are always stable

We determine stability and attractor properties of random Boolean genetic network models with canalyzing rules for a variety of architectures. For all power law, exponential, and flat in-degree distributions, we find that the networks are dynamically stable. Furthermore, for architectures with few inputs per node, the dynamics of the networks is close to critical. In addition, the fraction of genes that are active decreases with the number of inputs per node. These results are based upon investigating ensembles of networks using analytical methods. Also, for different in-degree distributions, the numbers of fixed points and cycles are calculated, with results intuitively consistent with stability analysis; fewer inputs per node implies more cycles, and vice versa. There are hints that genetic networks acquire broader degree distributions with evolution, and hence our results indicate that for single cells, the dynamics should become more stable with evolution. However, such an effect is very likely compensated for by multicellular dynamics, because one expects less stability when interactions among cells are included. We verify this by simulations of a simple model for interactions among cells.Comment: Final version available through PNAS open access at http://www.pnas.org/cgi/content/abstract/0407783101v

Under-approximating Cut Sets for Reachability in Large Scale Automata Networks

In the scope of discrete finite-state models of interacting components, we present a novel algorithm for identifying sets of local states of components whose activity is necessary for the reachability of a given local state. If all the local states from such a set are disabled in the model, the concerned reachability is impossible. Those sets are referred to as cut sets and are computed from a particular abstract causality structure, so-called Graph of Local Causality, inspired from previous work and generalised here to finite automata networks. The extracted sets of local states form an under-approximation of the complete minimal cut sets of the dynamics: there may exist smaller or additional cut sets for the given reachability. Applied to qualitative models of biological systems, such cut sets provide potential therapeutic targets that are proven to prevent molecules of interest to become active, up to the correctness of the model. Our new method makes tractable the formal analysis of very large scale networks, as illustrated by the computation of cut sets within a Boolean model of biological pathways interactions gathering more than 9000 components

Optimal synthesis of active distributed RC low-pass filters

Although synthesis procedures for active distributed RC networks are well developed, the approximation problem is largely unsolved. Previously proposed solutions have several disadvantages. A better solution to the approximation problem is obtained by developing an error expression involving the difference between the ideal specification and the exact realization, then minimizing this error by numerical techniques. The method is illustrated by designing a set of active distributed RC low-pass filters --Abstract, page ii

Non-linear Machine Learning with Active Sampling for MOX Drift Compensation

Abstract—Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, extreme gradient boosting (XGBoost) and radial kernel support vector machines (SVM). Applied on the UCI’s HT detectors dataset, the study evaluates methods for active sampling, makes an assessment of suitable neural networks architectures and compares the performance of neural networks, XGBoost and radial kernel SVM to classify gas mixtures (banana and wine odours, clean air) in the presence of humidity and temperature changes. The results show high classification accuracy levels (above 90%) and confirm that active sampling can provide a suitable solution. Index Terms—Neural Networks, Extreme Gradient Boosting, XGBoost, Support Vector Machines, Non-Linear Learning Methods, Machine Learnin

A component-based model and language for wireless sensor network applications

Wireless sensor networks are often used by experts in many different fields to gather data pertinent to their work. Although their expertise may not include software engineering, these users are expected to produce low-level software for a concurrent, real-time and resource-constrained computing environment. In this paper, we introduce a component-based model for wireless sensor network applications and a language, Insense, for supporting the model. An application is modelled as a composition of interacting components and the application model is preserved in the Insense implementation where active components communicate via typed channels. The primary design criteria for Insense include: to abstract over low-level concerns for ease of programming; to permit worst-case space and time usage of programs to be determinable; to support the fractal composition of components whilst eliminating implicit dependencies between them; and, to facilitate the construction of low footprint programs suitable for resource-constrained devices. This paper presents an overview of the component model and Insense, and demonstrates how they meet the above criteria.Preprin

Flow measurement inside a zinc-nickel flow cell battery using FBG based sensor system

Downloading of the abstract is permitted for personal use only. A detailed knowledge of the internal flow distribution inside a zinc-nickel flow battery is of critical importance to ensure smooth flow of the electrolyte through the battery cell and better operation of the device. Information of this type can be used as a useful means of early detection of zinc deposition and dendrite formation inside the cell, negative factors which affect the flow and thus which can lead to internal short circuiting, this being a primary failure mode of these types of batteries. This deposition occurs at low pH levels when zinc reacts with the electrolyte to form solid zinc oxide hydroxides. Traditionally, manual inspection is conducted, but this is time consuming and costly, only providing what are often inaccurate results-overall it is an impractical solution especially with the wider use of batteries in the very near future. Fibre Bragg grating (FBG) sensors integrated inside the flow cell offer the advantage of measuring flow changes at multiple locations using a single fibre and that then can be used as an indicator of the correlation between the internal flow distribution and the deposition characteristics. This work presents an initial study, where two networks of FBGs have been installed and used for flow change detection in an active zinc-nickel flow battery. Data have been obtained from the sensor networks and information of battery performance completed and summarized in this paper. The approach shows promising results and thus scope for the future research into the development of this type of sensor system

Barrier information coverage with wireless sensors

Abstract—Sensor networks have been deployed for many barrier coverage applications such as intrusion detection and border surveillance. In these applications, it is critical to operate a sensor network in an energy-efficient manner so the barrier can be covered with as few active sensors as possible. In this paper, we study barrier information coverage which exploits collaborations and information fusion between neighboring sensors to reduce the number of active sensors needed to cover a barrier and hence to prolong the network lifetime. Moreover, we propose a practical solution to identify the barrier information coverage set which can information-cover the barrier with a small number of active sensors. The effectiveness of the proposed solution is demonstrated by numerical and simulation results. I

GPS- GIS and Neural Networks for Monitoring Control, Cataloging the Prediction and Prevention in Tectonically Active Areas☆

Abstract Monitoring the system of active faults in Castrovillari, carried out in time by the Geomatics of the University Mediterranean of Reggio Calabria, through GPS measurement onsite on test networks, created a database of crustal movements, useful for different studies and analysis of tectonic and deformation type. With the help of the powerful spatial and temporal data processing tools offered by GIS, and integration with traditional artificial intelligence models of neural networks, we created a platform that can not only to handle the huge amount of data processing, analysis and visualization tools, but also can get the first results for predicting displacements/distortions also useful for civil protection