Search-based system architecture development using a holistic modeling approach

This dissertation presents an innovative approach to system architecting where search algorithms are used to explore design trade space for good architecture alternatives. Such an approach is achieved by integrating certain model construction, alternative generation, simulation, and assessment processes into a coherent and automated framework. This framework is facilitated by a holistic modeling approach that combines the capabilities of Object Process Methodology (OPM), Colored Petri Net (CPN), and feature model. The resultant holistic model can not only capture the structural, behavioral, and dynamic aspects of a system, allowing simulation and strong analysis methods to be applied, it can also specify the architectural design space. Both object-oriented analysis and design (OOA/D) and domain engineering were exploited to capture design variables and their domains and define architecture generation operations. A fully realized framework (with genetic algorithms as the search algorithm) was developed. Both the proposed framework and its suggested implementation, including the proposed holistic modeling approach and architecture alternative generation operations, are generic. They are targeted at systems that can be specified using object-oriented or process-oriented paradigm. The broad applicability of the proposed approach is demonstrated on two examples. One is the configuration of reconfigurable manufacturing systems (RMSs) under multi-objective optimization and the other is the architecture design of a manned lunar landing system for the Apollo program. The test results show that the proposed approach can cover a huge number of architecture alternatives and support the assessment of several performance measures. A set of quality results was obtained after running the optimization algorithm following the proposed framework --Abstract, page iii

Executable system architecting using systems modeling language in conjunction with Colored Petri Nets - a demonstration using the GEOSS network centric system

Models and simulation furnish abstractions to manage complexities allowing engineers to visualize the proposed system and to analyze and validate system behavior before constructing it. Unified Modeling Language (UML) and its systems engineering extension, Systems Modeling Language (SysML), provide a rich set of diagrams for systems specification. However, the lack of executable semantics of such notations limits the capability of analyzing and verifying defined specifications. This research has developed an executable system architecting framework based on SysML-CPN transformation, which introduces dynamic model analysis into SysML modeling by mapping SysML notations to Colored Petri Net (CPN), a graphical language for system design, specification, simulation, and verification. A graphic user interface was also integrated into the CPN model to enhance the model-based simulation. A set of methodologies has been developed to achieve this framework. The aim is to investigate system wide properties of the proposed system, which in turn provides a basis for system reconfiguration --Abstract, page iii

An Executable System Architecture Approach to Discrete Events System Modeling Using SysML in Conjunction with Colored Petri Net

This paper proposes an executable system architecting paradigm for discrete event system modeling and analysis through integration of a set of architecting tools, executable modeling tools, analytical tools, and visualization tools. The essential step is translating SysML-based specifications into colored Petri nets (CPNs) which enables rigorous static and dynamic system analysis as well as formal verification of the behavior and functionality of the SysML-based design. A set of tools have been studied and integrated that enable a structured architecture design process. Some basic principles of executable system architecture for discrete event system modeling that guide the process of executable architecture specification and analysis are discussed. This paradigm is aimed at general system design. Its feasibility was demonstrated with a C4- type network centric system as an example. The simulation results was used to check the overall integrity and internal consistency of the architecture models, refine the architecture design, and, finally, verify the behavior and functionality of the system being modeled

Simple and High-Accurate Schemes for Hyperbolic Conservation Laws

The paper constructs a class of simple high-accurate schemes (SHA schemes) with third order approximation accuracy in both space and time to solve linear hyperbolic equations, using linear data reconstruction and Lax-Wendroff scheme. The schemes can be made even fourth order accurate with special choice of parameter. In order to avoid spurious oscillations in the vicinity of strong gradients, we make the SHA schemes total variation diminishing ones (TVD schemes for short) by setting flux limiter in their numerical fluxes and then extend these schemes to solve nonlinear Burgers’ equation and Euler equations. The numerical examples show that these schemes give high order of accuracy and high resolution results. The advantages of these schemes are their simplicity and high order of accuracy

Graphical Image Classification Combining an Evolutionary Algorithm and Binary Particle Swarm Optimization

Biomedical journal articles contain a variety of image types that can be broadly classified into two categories: regular images, and graphical images. Graphical images can be further classified into four classes: diagrams, statistical figures, flow charts, and tables. Automatic figure type identification is an important step toward improved multimodal (text + image) information retrieval and clinical decision support applications. This paper describes a feature-based learning approach to automatically identify these four graphical figure types. We apply Evolutionary Algorithm (EA), Binary Particle Swarm Optimization (BPSO) and a hybrid of EA and BPSO (EABPSO) methods to select an optimal subset of extracted image features that are then classified using a Support Vector Machine (SVM) classifier. Evaluation performed on 1038 figure images extracted from ten BioMedCentral® journals with the features selected by EABPSO yielded classification accuracy as high as 87.5%

Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities

Graph theory effectively explains urban structures via street–street connectivity. However, systematic comparisons of street structures across cities remain challenging. This study employs graph convolutional networks (GCNs) to analyze street network structures. A two-branch GCN was used as the backbone to extract comparable features among street networks. The proposed approach was used to examine the structures of different urban road networks in a case study of polycentricity prediction across 298 Chinese cities. The model transformed approximately 4.5-million street segments into natural streets to create urban street graphs, which were subsequently analyzed to extract local and global embeddings. The extracted embeddings – with a portion labeled with a known urban polycentricity score – were used to predict the score for each city through a single-layer perceptron (SLP) model. Our results show consistency between the predicted polycentricity scores based on the derived street embeddings and those based on the population. Thus, the proposed GCN-based method can effectively predict the complexity and interconnection of street networks in different cities. This innovative integration of GCNs into urban studies demonstrates that deep learning techniques can analyze and comprehend the intricate patterns of street networks on a large scale

Flexible and Intelligent Learning Architectures for SOS (FILA-SoS)

Multi-faceted systems of the future will entail complex logic and reasoning with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized. Our quest continues to handle complexities, design and operate these systems. The challenge in Complex Adaptive Systems design is to design an organized complexity that will allow a system to achieve its goals. This report attempts to push the boundaries of research in complexity, by identifying challenges and opportunities. Complex adaptive system-of-systems (CASoS) approach is developed to handle this huge uncertainty in socio-technical systems

Eff ect of a comprehensive programme to provide universal access to care for sputum-smear-positive multidrugresistant tuberculosis in China: a before-and-after study

Background China has a quarter of all patients with multidrug-resistant tuberculosis (MDRTB) worldwide, but less than 5% are in quality treatment programmes. In a before-and-after study we aimed to assess the eff ect of a comprehensive programme to provide universal access to diagnosis, treatment, and follow-up for MDRTB in four Chinese cities (population 18 million). Methods We designated city-level hospitals in each city to diagnose and treat MDRTB. All patients with smear-positive pulmonary tuberculosis diagnosed in Center for Disease Control (CDC) clinics and hospitals were tested for MDRTB with molecular and conventional drug susceptibility tests. Patients were treated with a 24 month treatment package for MDRTB based on WHO guidelines. Outpatients were referred to the CDC for directly observed therapy. We capped total treatment package cost at US$4644. Insurance reimbursement and project subsidies limited patients’ expenses to 10 (2011) to those from a retrospective survey of all patients with MDRTB diagnosed in the same cities during a baseline period (2006–09). Findings 243 patients were diagnosed with MDRTB or rifampicin-resistant tuberculosis during the 12 month programme period compared with 92 patients (equivalent to 24 per year) during the baseline period. 172 (71 243 individuals were enrolled in the programme. Time from specimen collection for resistance testing to treatment initiation decreased by 90 started on appropriate drug regimen increased 2·7 times (from nine [35 172), and follow-up by the CDC after initial hospitalisation increased 24 times (from one [4 163 [99 increased ten times (from two [8 programme period had negative cultures or clinical–radiographic improvement. Patients’ expenses for hospital admission after MDRTB diagnosis decreased by 78$796 to $174), reducing the ratio of patients’ expenses to annual household income from 17·6% to 3·5% (p<0·0001 for all comparisons between baseline and programme periods). However, 36 (15%) patients did not start or had to discontinue treatment in the programme period because of fi nancial diffi culties. Interpretation This comprehensive programme substantially increased access to diagnosis, quality treatment, and aff ordable treatment for MDRTB. The programme could help China to achieve universal access to MDRTB care but greater fi nancial risk protection for patients is needed

Anti-Inflammatory Effect of IL-37b in Children with Allergic Rhinitis

Background. Interleukin-37 (IL-37), a newly described member of IL-1family, functioned as a fundamental inhibitor of innate inflammatory and immune responses, especially its isoform IL-37b. Objective. This study was undertaken to evaluate the expression and regulation of IL-37b in children with allergic rhinitis (AR). Methods. Forty children with AR and twenty-five normal controls were included. The relationship between IL-37b and Th1/2 cytokines production in serum and nasal lavage was examined by enzyme-linked immunosorbent assay (ELISA). Peripheral blood mononuclear cells (PBMCs) were purified for in vitro regulation experiment of IL-37b. Intranasal mometasone furoate was given in AR children and IL-37b change after one-month treatment was detected using ELISA. Results. We observed significantly decreased IL-37b expression levels in both serum and nasal lavage compared to controls. IL-37b was negatively correlated with Th2 cytokines. Our results also showed that IL-37b downregulated Th2 cytokine expressed by PBMCs and this modulation was through mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K) pathway. We also found that intranasal mometasone furoate therapy can promote nasal IL-37b expression. Conclusion. IL-37b may be involved in Th2 cytokine regulation in AR and its expression was related to the efficacy of intranasal steroid therapy

Anatomical and Physiological Plasticity in Leymus chinensis (Poaceae) along Large-Scale Longitudinal Gradient in Northeast China

Although it has been widely accepted that global changes will pose the most important constrains to plant survival and distribution, our knowledge of the adaptive mechanism for plant with large-scale environmental changes (e.g. drought and high temperature) remains limited.An experiment was conducted to examine anatomical and physiological plasticity in Leymus chinensis along a large-scale geographical gradient from 115° to 124°E in northeast China. Ten sites selected for plant sampling at the gradient have approximately theoretical radiation, but differ in precipitation and elevation. The significantly increasing in leaf thickness, leaf mass per area, vessel and vascular diameters, and decreasing in stoma density and stoma index exhibited more obvious xerophil-liked traits for the species from the moist meadow grassland sites in contrast to that from the dry steppe and desert sites. Significant increase in proline and soluble sugar accumulation, K(+)/Na(+) for the species with the increasing of stresses along the gradient showed that osmotic adjustment was enhanced.Obvious xerophytic anatomical traits and stronger osmotic adjustment in stress conditions suggested that the plants have much more anatomical and physiological flexibilities than those in non-stress habitats along the large-scale gradient