Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

An important aspect of neuroscience is to characterize the underlying connectivity patterns of the human brain (i.e., human connectomics). Over the past few years, researchers have demonstrated that by combining a variety of different neuroimaging technologies (e.g., structural MRI, diffusion MRI and functional MRI) with sophisticated analytic strategies such as graph theory, it is possible to noninvasively map the patterns of structural and functional connectivity of human whole-brain networks. With these novel approaches, many studies have shown that human brain networks have nonrandom properties such as modularity, small-worldness and highly connected hubs. Importantly, these quantifiable network properties change with age, learning and disease. Moreover, there is growing evidence for behavioral and genetic correlates. Network analysis of neuroimaging data is opening up a new avenue of research into the understanding of the organizational principles of the brain that will be of interest for all basic scientists and clinical researchers. Such approaches are powerful but there are a number of challenging issues when extracting reliable brain networks from various imaging modalities and analyzing the topological properties, e.g., definitions of network nodes and edges and reproducibility of network analysis. We assembled contributions related to the state-of-the-art methodologies of brain connectivity and the applications involving development, aging and neuropsychiatric disorders such as Alzheimer’s disease, schizophrenia, attention deficit hyperactivity disorder and mood and anxiety disorders. It is anticipated that the articles in this Research Topic will provide a greater range and depth of provision for the field of imaging connectomics.

connectomics --- connectivity --- graph theory --- Small-world --- MRI

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

According to the Semantic Web a formal representation of knowledge is described by an ontology. This formal representation enables a unique identification of elements within an ontology. By the use of natural language for element annotation, ambiguity occurs and a unique element identification based on natural language cannot be guaranteed. This book describes an approach to identify the most relevant element described by a natural language term by reducing the aspect of ambiguity to a minimum.

Semantic Web, Mehrdeutigkeit, Disambiguierung, Graphentheorie, Spreading ActivationSemantic Web, Word-sense disambiguation, Graph theory, Spreading Activation

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book constitutes the proceedings of the 21st International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2018, which took place in Thessaloniki, Greece, in April 2018, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2018.The 31 papers presented in this volume were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections named: semantics; linearity; concurrency; lambda-calculi and types; category theory and quantum control; quantitative models; logics and equational theories; and graphs and automata.

artificial intelligence --- computer software --- selection and evaluation --- formal logic --- graph theory --- modal logic --- petri nets --- program compilers --- programming language --- semantics --- separation logic --- software engineering --- theorem proving --- type systems --- verification

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

As the ultimate information processing device, the brain naturally lends itself to being studied with information theory. The application of information theory to neuroscience has spurred the development of principled theories of brain function, and has led to advances in the study of consciousness, as well as to the development of analytical techniques to crack the neural code—that is, to unveil the language used by neurons to encode and process information. In particular, advances in experimental techniques enabling the precise recording and manipulation of neural activity on a large scale now enable for the first time the precise formulation and the quantitative testing of hypotheses about how the brain encodes and transmits the information used for specific functions across areas. This Special Issue presents twelve original contributions on novel approaches in neuroscience using information theory, and on the development of new information theoretic results inspired by problems in neuroscience.

neural network --- Potts model --- latching --- recursion --- functional connectome --- graph theoretical analysis --- eigenvector centrality --- orderness --- network eigen-entropy --- information entropy production --- discrete Markov chains --- spike train statistics --- Gibbs measures --- maximum entropy principle --- pulse-gating --- channel capacity --- neural coding --- feedforward networks --- neural information propagation --- information theory --- mutual information decomposition --- synergy --- redundancy --- integrated information theory --- integrated information --- minimum information partition --- submodularity --- Queyranne’s algorithm --- consciousness --- maximum entropy --- higher-order correlations --- neural population coding --- Ising model --- brain network --- complex networks --- connectome --- information theory --- graph theory --- free-energy principle --- internal model hypothesis --- unconscious inference --- infomax principle --- independent component analysis --- principal component analysis --- goodness --- categorical perception --- perceptual magnet --- information theory --- perceived similarity --- mutual information --- synergy --- redundancy --- neural code --- hippocampus --- entorhinal cortex --- navigation --- neural code --- representation --- decoding --- spike-time precision --- discrimination --- noise correlations --- information theory --- mismatched decoding --- information theory --- neuroscience

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.

process model validation --- partial least square regression --- phytochemicals --- natural extracts --- wheat germ --- fluidized bed drying --- mathematical model --- moisture content --- condensation --- simulation --- Pharmaceutical Processes --- Mammalian Cell Culture --- sensitivity analysis --- parameter estimation --- Design of Experiments --- algebraic modeling language --- dynamic optimization --- model predictive control --- moving horizon estimation --- fluid bed granulation --- heat and mass balance --- population balance model --- binder dissolution --- kernel development --- robust optimization --- uncertainty --- point estimation method --- equality constraints --- parameter correlation --- barley --- simulation --- hydration --- swelling --- cooking --- porridge --- extents --- graph theory --- model identification --- observability --- optimal clustering --- parameter estimation --- state decoupling --- data-mining --- machine learning --- neural networks --- chemistry --- materials --- engineering --- energy --- grey-box model --- machine learning --- SOS programming --- process modeling --- scrap dissolution --- scrap melting --- thermodynamics --- kinetics --- dynamic converter modelling --- Combined Heat and Power --- gray-box model --- utility management --- CHP legislation --- optimization --- polyacrylonitrile-based carbon fiber --- coagulation bath --- dry-jet wet spinning process --- computational fluid dynamics --- wave resonance --- maximum wave amplitude --- reactor coolant pump --- vane --- costing stopping --- mathematical model --- idling test --- n/a