Second Generation General System Theory: Perspectives in Philosophy and Approaches in Complex Systems

Following the classical work of Norbert Wiener, Ross Ashby, Ludwig von Bertalanffy and many others, the concept of System has been elaborated in different disciplinary fields, allowing interdisciplinary approaches in areas such as Physics, Biology, Chemistry, Cognitive Science, Economics, Engineering, Social Sciences, Mathematics, Medicine, Artificial Intelligence, and Philosophy. The new challenge of Complexity and Emergence has made the concept of System even more relevant to the study of problems with high contextuality. This Special Issue focuses on the nature of new problems arising from the study and modelling of complexity, their eventual common aspects, properties and approaches—already partially considered by different disciplines—as well as focusing on new, possibly unitary, theoretical frameworks. This Special Issue aims to introduce fresh impetus into systems research when the possible detection and correction of mistakes require the development of new knowledge. This book contains contributions presenting new approaches and results, problems and proposals. The context is an interdisciplinary framework dealing, in order, with electronic engineering problems; the problem of the observer; transdisciplinarity; problems of organised complexity; theoretical incompleteness; design of digital systems in a user-centred way; reaction networks as a framework for systems modelling; emergence of a stable system in reaction networks; emergence at the fundamental systems level; behavioural realization of memoryless functions

Foundations of Multi-Paradigm Modelling for Cyber-Physical Systems

This open access book coherently gathers well-founded information on the fundamentals of and formalisms for modelling cyber-physical systems (CPS). Highlighting the cross-disciplinary nature of CPS modelling, it also serves as a bridge for anyone entering CPS from related areas of computer science or engineering. Truly complex, engineered systems—known as cyber-physical systems—that integrate physical, software, and network aspects are now on the rise. However, there is no unifying theory nor systematic design methods, techniques or tools for these systems. Individual (mechanical, electrical, network or software) engineering disciplines only offer partial solutions. A technique known as Multi-Paradigm Modelling has recently emerged suggesting to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s), and then weaving the results together to form a representation of the system. If properly applied, it enables, among other global aspects, performance analysis, exhaustive simulation, and verification. This book is the first systematic attempt to bring together these formalisms for anyone starting in the field of CPS who seeks solid modelling foundations and a comprehensive introduction to the distinct existing techniques that are multi-paradigmatic. Though chiefly intended for master and post-graduate level students in computer science and engineering, it can also be used as a reference text for practitioners

Exploring low-carbon futures: A web service approach to linking diverse climate-energy-economy models

© 2019 by the authors. The use of simulation models is essential when exploring transitions to low-carbon futures and climate change mitigation and adaptation policies. There are many models developed to understand socio-environmental processes and interactions, and analyze alternative scenarios, but hardly one single model can serve all the needs. There is much expectation in climate-energy research that constructing new purposeful models out of existing models used as building blocks can meet particular needs of research and policy analysis. Integration of existing models, however, implies sophisticated coordination of inputs and outputs across different scales, definitions, data and software. This paper presents an online integration platform which links various independent models to enhance their scope and functionality. We illustrate the functionality of this web platform using several simulation models developed as standalone tools for analyzing energy, climate and economy dynamics. The models differ in levels of complexity, assumptions, modeling paradigms and programming languages, and operate at different temporal and spatial scales, from individual to global. To illustrate the integration process and the internal details of our integration framework we link an Integrated Assessment Model (GCAM), a Computable General Equilibrium model (EXIOMOD), and an Agent Based Model (BENCH). This toolkit is generic for similar integrated modeling studies. It still requires extensive pre-integration assessment to identify the ‘appropriate’ models and links between them. After that, using the web service approach we can streamline module coupling, enabling interoperability between different systems and providing open access to information for a wider community of users

Hardware Implementations of Spiking Neural Networks and Artificially Intelligent Systems

Artificial spiking neural networks are gaining increasing prominence due to their potential advantages over traditional, time-static artificial neural networks. Custom hardware implementations of spiking neural networks present many advantages over other implementation mediums. Two main topics are the focus of this work. Firstly, digital hardware implementations of spiking neurons and neuromorphic hardware are explored and presented. These implementations include novel implementations for lowered digital hardware requirements and reduced power consumption. The second section of this work proposes a novel method for selectively adding sparsity to a spiking neural network based on training set images for pattern recognition applications, thereby greatly reducing the inference time required in a digital hardware implementation

Online Raman spectroscopy for bioprocess monitoring

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Author received the S.B. degree, June 2005 and the M. Eng. degree, Sept. 2005.Includes bibliographical references (p. 195-202).Online monitoring of bioprocesses is essential to expanding the potential of biotechnology. In this thesis, a system to estimate concentrations of chemical components of an Escherichia Coli fermentation growth medium via a remote fiber-optic Raman spectroscopy probe was studied in depth. The system was characterized to determine sources of instability and systematic error. A complete first-order error analysis was conducted to determine the theoretical sensitivity of the instrument. A suite of improvements and new features, including an online estimation of optical density and biomass, a method to correct for wavelength shifts, and a setup to increase repeatability and throughput for offline and calibration methods was developed accordingly. The theoretical and experimental ground work for developing a correction for spectrum distortions caused by elastic scattering, a fundamental problem for many spectroscopic applications, was laid out. In addition, offline Raman spectroscopy was used to estimate concentrations of fructose, glucose, sucrose, and nitrate in an oil palm (Elais guineensis) bioreaction. Finally, an expansion of optical techniques into new scale-up applications in plant cell bioprocesses, such as plant call differentiation was explored.by Gustavo Adolfo Gil.M.Eng.and S.B

Computer Science & Technology Series : XVI Argentine Congress of Computer Science - Selected papers

CACIC’10 was the sixteenth Congress in the CACIC series. It was organized by the School of Computer Science of the University of Moron. The Congress included 10 Workshops with 104 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. (http://www.cacic2010.edu.ar/). CACIC 2010 was organized following the traditional Congress format, with 10 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 195 submissions. An average of 2.6 review reports were collected for each paper, for a grand total of 507 review reports that involved about 300 different reviewers. A total of 104 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

New perspectives on learning, inference, and control in brains and machines

The work presented in this thesis provides new perspectives and approaches for problems that arise in the analysis of neural data. Particular emphasis is placed on parameter fitting and automated analysis problems that would arise naturally in closed-loop experiments. Part one focuses on two brain-computer interface problems. First, we provide a framework for understanding co-adaptation, the setting in which decoder updating and user learning occur simultaneously. We also provide a new perspective on intention-based parameter fitting and tools to extend this approach to higher dimensional decoders. Part two focuses on event inference, which refers to the decomposition of observed timeseries data into interpretable events. We present application of event inference methods on voltage-clamp recordings as well as calcium imaging, and describe extensions to allow for combining data across modalities or trials

Towards Agent-based Multi-scale Tumor Growth Modeling: Software Environment and Computational Complexity

Understanding tumor development crossing multiple spatial-temporal scales is of great practical importance to better fighting against cancers. It is hard to attack this problem with pure biological means. In recent decades, computer-based modeling and simulation techniques have been playing an increasingly important role in addressing it. After reviewing the literature, however, we notice that existing tumor models are either highly simplified or too complicated to be scaled to large tumor systems. In light of these problems, we have developed a software environment TUGME to facilitate the multi-scale modeling and simulation of tumor development based on the agent-based method. The most important feature of this software environment is its flexibility which enables straight-forward model reuse and extension. Tumor models of TUGME are hybrid as discrete and continuous approaches are coupled to model the discrete and continuous nature of the tumor system. TUGME is highly modularized, thus changing one module only requires few or no modifications to the others. Using TUGME, we have simulated the avascular growth of a multicellular tumor spheroid system of the tumor cell line, EMT6/Ro. Our tumor models treat individual tumor cells as single agents. The cell morphology and topology are represented by a 3D Voronoi tessellation. Cell motion, which is driven by mechanical interactions between a cell and its surroundings, is modeled using Newton's second law. Oxygen and glucose are treated as nutrients for cell energy production. Their transport and metabolism by cells are described by reaction-diffusion equations. Cell proliferation is defined considering the availability of both oxygen and glucose as well as the availability of space as its controllers. Based on these models, a series of simulations have been carried out. Good agreements between our simulations and experiments indicate the applicability of TUGME and the validity of our tumor models. In addition, the investigation of the invasive tumor morphology under different nutrient conditions shows that a lower nutrient concentration gives rise to a rougher tumor surface. One of the key challenges of agent-based multi-scale cancer modeling and simulation is the sharp increase of the computational cost of model solving with increasing system size (the number of tumor cells). According to our tests, the main computational bottleneck of our tumor models consists in solving the linear system of cell motion. To better understand this problem, we look into the properties of the matrix of the linear system. Our conclusion is that its matrix is extremely sparse, symmetric and positive-definite. These properties can help find a more efficient solver for the linear system. This work can be important reference for people who intend to work on individual-cell-oriented cancer modeling