Improved meshing technique and its application in the analysis of large and complex MEMS systems

The high selectivities of liquid chromatography and mass spectrometry make liquid chromatography-mass spectrometry one of the most popular tools for quantitative analysis in complex chemical, biological, and environmental systems, while the potential mathematical selectivity of liquid chromatography-mass spectrometry is rarely investigated. This work discussed the mathematical selectivity of liquid chromatography-mass spectrometry by three-way calibration based on the trilinear model, with an application to quantitative analysis of coeluting aromatic amino acids in human plasma. By the trilinear decomposition of the constructed liquid chromatography-mass spectrometry-sample trilinear model and individual regression of the decomposed relative intensity versus concentration, the proposed three-way calibration method successfully achieved quantitative analysis of coeluting aromatic amino acids in human plasma, even in the presence of uncalibrated interferent(s) and a varying background. This analytical method can ease the requirements for sample preparation and complete chromatographic separation of components, reduce the use of organic solvents, decrease the time of chromatographic separation, and increase the peak capacity of liquid chromatography-mass spectrometry. As a "green analytical method", the liquid chromatography-mass spectrometry three-way calibration method can provide a promising tool for direct and fast quantitative analysis in complex systems containing uncalibrated spectral interferents, especially for the situation where the coelution problem is difficult to overcome.

Scenario-driven analysis of systems specified through graph transformations

Worst Case Execution Time (WCET) analysis of large and complex hybrid systems can be time consuming. Contract based design allows for compositional reasoning of complex systems. Contracts justify the behavior of a system by way of assumptions (which are to be satisfied by the system environment) and guarantees, which are to be met by the system. Contracts also play a major role in compositional reasoning, refinement and re-usability of the system components. In this paper, we present a formal framework to enforce real-time contracts using Hoare triples, for synchronous system design and verification. In that regard, we propose real-time Hoare rules which are based on the WCET of the system and its components. We verify the real-time behavior of the system by applying these rules. These rules not only justify the system behavior and the behavior of its components but their timing as well. We also show that these Hoare rules are sound. Then we show that the synchronous composition of component level Hoare rules based contracts justify a system level contract. This real-time contract composition and reasoning technique which is based on real-time Hoare logic rules is the first ever attempt in synchronous system design and verification.

Managing international mining — a case study in the application of system dynamics to complex, multiple ownership systems

Rigorous mcnp based shutdown dose rate calculations: computational scheme, verification calculations and application to ITER

Enhanced robust stability analysis of large hydraulic control systems via a bifurcation-based procedure

We propose an algorithm for computing the influence matrix and rank distribution of nodes of a weighted directed graph by calculating the nodes’ mutual impact. The algorithm of accumulative impact solves problems of dimension and computational complexity arising in the analysis of large complex systems. The algorithm calculates the mutual impact of each pair of vertices, making it possible to rank the nodes according to their importance within the system and to determine the most influential components. It produces results similar to those of the commonly used impulse method when applied to graphs that are impulse-stable in an impulse process, while overcoming the disadvantages of the impulse method in other situations. Results are always obtained regardless of impulse stability; they do not depend on the initial impulse, so that the initial values of the weights affect the calculation results. When elements in the adjacency matrix of the weighted directed graph are multiplied by a constant factor, scale invariance is not violated, and the full affect for each of the nodes scales proportionally. Several examples of analyses of weighted directed graphs, including one related to the practical problem of urban solid waste removal, are provided to demonstrate the advantages of the proposed algorithm.

A computer-oriented procedure for efficient stability analysis of very large systems was developed by modifying a method due to Mikhailov. The method allows pure time delays in the system model. The state variable model of the system is used, and the computational procedure is tailored to take advantage of the sparesness of the system matrices that are applicable for many systems. The practicality of the procedure has been demonstrated in the analysis of several systems, the largest of which consisted of a 553rd order model of a multistage flash evaporator plant.

A computational model for complex systems analysis: Causality estimation

“Complexity and Disaster Forensics: New Paradigms, Models and Approaches for Natural Hazards Management in the Pacific Islands Region” assesses and applies complex systems theory, modeling and analysis to disaster forensics policy and research. To better understand the root cause and complex causality of disasters, complex systems theory, with roots in the fields of statistical physics, information theory, and non-linear dynamics, and systems analysis, is applied to help communities and nations achieve important social development goals, reduce institutional brittleness and increase disaster resilience by promoting positive transformations in the co-evolving and mutually dependent human-environmental condition, and by capitalizing on opportunities provided by human creativity, diplomatic openings, technologic capacities and environmental change. The case studies, investigations and models outlined in this chapter collectively demonstrate the quality, breadth and depth of complex systems and disaster forensics methodologies. Game-theoretic (“Small World”) decision analyses, “Large world” (complex systems) models of mutually interactive game design are put forth to capture the complexity of climate related disasters and to reduce the threat of climate refugees in the Pacific Island region. The resulting risk management lessons learned were applied to communities in the Pacific Island of Vanuatu, the most natural disaster prone country in the world.

The paper describes a decision support system for solving multicriteria problems during the analysis of complex systems, in the case where a finite set of alternatives whose characteristics are given in tabular form is considered. It is based on the analysis of the phasing of such studies whose results determine the requirements for the types of problems to be solved with its use and the list of methods that are expedient to implement. The considered decision support system is designed to solve problems under certainty, as well as stochastic and non-stochastic uncertainty. In accordance with the main stages of the analysis of complex systems, it implements a number of multicriteria methods that allow assessment of relationships between the factors associated with its operation, determine the importance of the indicators that characterize it, generate a priority series of the considered alternatives, or form the kernel of alternatives that are promising for further research.

Recent advances in theory and applications of complex dynamical systems have contributed much to the successful handling of certain problems in biology, physics, economics, engineering, and so forth that until recently were thought too difficult to be analyzed. These complex systems may be characterized by systems with uncertainty, impulse, time delay, stochastic perturbation, hybrid dynamics, distributed dynamics, and chaotic dynamics. The overall aim of this special issue is to bring together the latest or innovative knowledge and advances in mathematics for handling complex systems, whichmay depend largely onmethods frommathematical analysis, artificial intelligence, statistics, and engineering, including nonlinear dynamics, time series analysis, neural networks, and evolutionary computation. The solicited papers in this special issue should provide solutions tomodeling, analysis, control, and applications of real-world complex systems. The topics in this special issue included, but are not limited to, modelling, analysis, and applications of complex systems such as impulsive systems, pulse-modulated systems, switching systems, delayed systems, discontinuous control system theory, bifurcation theory and chaotic dynamics. The response to this special issue on modelling, analysis, and applications of complex systemswas beyond our expectation. We received 39 papers in this research field.This special issue includes thirty-one high-quality peer-reviewed articles. These articles contain some new, novel, and innovative techniques and ideas that may stimulate further research in every branch of theory and applications of complex systems. Acknowledgments

Computer systems are becoming increasingly complex, especially interactive software systems, namely software user interfaces. The scientific community relies on different methods to assess their safety. This article provides an updated literature survey on hazard analysis approaches used to improve the safety of complex systems. To support the survey, we conceptualise complex systems, highlighting the challenge in terms of assessing their safety. We provide a brief overview on the approaches historically available to tackle issues in those systems, along with their most common methods. Finally, the article focuses in one method of a non-traditional approach, which is described in more details, along with some of its extensions, which seeks to improve the hazard analysis in complex systems.

Complex systems abound on this planet, in the composition of the human body, in ecosystems, in social interaction, in political decision-making, and more. Analytical methods allowing us to better understand how these systems operate and, consequently, to have a chance to intervene and change the undesirable behavior of some of the more pernicious systems have developed and continue to be enhanced via quickly changing technology. Some of these analytical methods are accessible by pre-college students, but have not been widely used at that level of education. Jay Forrester, the founder of one of the methodologies, System Dynamics (SD), used to study complex system behavior involving feedback, laments the lack of understanding of complex systems evident in short-sited decisions made by legislators -- global climate change and fiscal policies being cases in point. In order to better prepare future decision makers with tools that could allow them to make more informed decisions about issues involving complex systems efforts have been underway to increase pre-college teacher understanding of the SD method. The research described in this dissertation introduces the mathematics education community to the value of System Dynamics modeling in pre-college algebra classes, indicates a path by which a traditional mathematics curriculum could be enhanced to include small SD models as a new representation for elementary functions studied in algebra classes, and provides an empirical study regarding conceptual understanding of functions by students. Chapter 2 indicates the numerous beneficial learning outcomes that empirical studies have shown accompany model-building activities. Chapter 3 indicates the need for students to become familiar with complex systems analysis, how SD modeling (one method of complex systems analysis) aligns with the Common Core State Standards in Mathematics, and the work that has transpired over the past two decades using SD in K-12. Chapter 4 focuses on the importance of the concept of function in high school mathematics, some limitations of exclusive reliance on the closed form equation representation for mathematizing problems and the SD stock/flow representations of some of the elementary functions that are studied in algebra classes. Chapter 5 looks at the issues affecting two traditional teachers and the challenges they faced when trying to reintroduce SD modeling into their algebra classes. Chapter 6 explains the student component of the classroom experiment that was conducted by the teachers who are highlighted in Chapter 5. The analysis of the results of student model-building activities in the two classroom

Complex systems exhibit dynamics that typically evolve on low-dimensional attractors and may have sparse representation in some optimal basis. Recently developed compressive sensing techniques exploit this sparsity for state reconstruction and/or categorical identification from limited measurements. We argue that data-driven dimensionality reduction methods integrate naturally with sparse sensing in the context of complex systems. This framework works equally well with a physical model or in an equation-free context, where data is available but the governing equations may be unknown. We demonstrate the advantages of combining these methods on three prototypical examples: classification of dynamical regimes, optimal sensor placement, and equation-free dynamic model reduction. These examples motivate the potentially transformative role that state-of-the-art data methods and machine learning can play in the analysis of complex systems.