On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

Bayesian Evidence and Model Selection

In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Specific attention is paid to the Laplace approximation, variational Bayes, importance sampling, thermodynamic integration, and nested sampling and its recent variants. Analogies to statistical physics, from which many of these techniques originate, are discussed in order to provide readers with deeper insights that may lead to new techniques. The utility of Bayesian model testing in the domain sciences is demonstrated by presenting four specific practical examples considered within the context of signal processing in the areas of signal detection, sensor characterization, scientific model selection and molecular force characterization.Comment: Arxiv version consists of 58 pages and 9 figures. Features theory, numerical methods and four application

Interests Diffusion in Social Networks

Understanding cultural phenomena on Social Networks (SNs) and exploiting the implicit knowledge about their members is attracting the interest of different research communities both from the academic and the business side. The community of complexity science is devoting significant efforts to define laws, models, and theories, which, based on acquired knowledge, are able to predict future observations (e.g. success of a product). In the mean time, the semantic web community aims at engineering a new generation of advanced services by defining constructs, models and methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to come from a hybrid approach merging the disciplines above. Along this line, this work focuses on the propagation of individual interests in social networks. The proposed framework consists of the following main components: a method to gather information about the members of the social networks; methods to perform some semantic analysis of the Domain of Interest; a procedure to infer members' interests; and an interests evolution theory to predict how the interests propagate in the network. As a result, one achieves an analytic tool to measure individual features, such as members' susceptibilities and authorities. Although the approach applies to any type of social network, here it is has been tested against the computer science research community. The DBLP (Digital Bibliography and Library Project) database has been elected as test-case since it provides the most comprehensive list of scientific production in this field.Comment: 30 pages 13 figs 4 table

Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits

Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only continuous optimization variables, the representational power of the variational ansatz can sometimes be significantly enhanced by adding certain discrete optimization variables, as is exemplified by the generalized quantum approximate optimization algorithm (QAOA). However, the hybrid discrete-continuous optimization problem in the generalized QAOA poses a challenge to the optimization. We propose a new algorithm called MCTS-QAOA, which combines a Monte Carlo tree search method with an improved natural policy gradient solver to optimize the discrete and continuous variables in the quantum circuit, respectively. We find that MCTS-QAOA has excellent noise-resilience properties and outperforms prior algorithms in challenging instances of the generalized QAOA

A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?

The potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting "molecular communication" (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, "bottom-up" SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT

A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?

he potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting “molecular communication” (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, “bottom-up” SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT

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Department of Mechanical EngineeringThe potential danger of invisible hazardous substance leakage accident is increasing, such as hazardous chemical leakage accidents in industrial complexes, potential risks of aging nuclear power plants, and international chemical terrorism threats. In particular, hazardous chemical, biological, or radioactive substances leaked into the atmosphere cause irreversible damage to nature, and there is a risk of human damage if prompt action is not taken. Therefore, estimating the emission source and the amount of invisible hazardous substances is required to minimize human casualties and increase public safety. As the risk of hazardous material leakage and potential terrorism increases in random places, it is difficult using traditional systems such as pre-installed ground sensors in a specific area. This thesis proposes autonomous search method for estimating the source of hazardous materials using a mobile sensor attached to an unmanned aerial vehicle (UAV). Since the mobile sensor can be freely deployed to any arbitrary places, it is possible to monitor a wider area with a relatively low cost. Besides, this approach is an unmanned autonomous system, so it has the advantage of minimizing secondary human casualties that may additionally occur during search. The source term estimation (STE) using mobile sensors is considered to be a challenging problem because the sensor measurements from atmospheric gas dispersion are sparse, intermittent, and time-varying due to the turbulence and the sensor noise. Thus, Bayesian inference-based estimation technique, sequential Monte Carlo method (i.e., particle filter), is used to estimate the source by using the inaccurate measurements which is easily influenced by air turbulence and sensor noise in this thesis. The autonomous search algorithms using information theory are also proposed. In the proposed algorithms, the information entropy (i.e., uncertainty of estimation) is calculated by using information theory and the agent choose the action to move to the next sensing location that can minimize the expected uncertainty. In other words, the proposed information-theoretic search algorithm is reward-based decision making approaches that use information entropy as a reward. The receding horizon and Gaussian mixture model clustering approaches are adopted to improve the search performance in various environment. Since the time required to compute all of the respective rewards increases as the number of action candidates increases, the policy-based autonomous source term search and estimation algorithm is proposed using deep neural network and reinforcement learning approach to determine efficient search path considering continuous action space. Furthermore, this thesis proposes a cooperative search method for multiple unmanned mobile vehicles based on game theory. The inaccuracy of sensor measurement values can be reduced by using multiple mobile sensors with the fusion approach, so the source of hazardous substances can be quickly estimated. The negotiation based on the game theory can improve the group search performance for source term estimation and search. Finally, to verify the performance of the proposed algorithm, numerical simulation and flight test results using an actual gas measurement sensor and multicopter drone are presented.ope