Abstract: This paper describes part of a project about Learningby-
Modeling (LbM). Studying complex systems is increasingly
important in teaching and learning many science domains. Many
features of complex systems make it difficult for students to develop
deep understanding. Previous research indicates that involvement
with modeling scientific phenomena and complex systems can play a
powerful role in science learning. Some researchers argue with this
view indicating that models and modeling do not contribute to
understanding complexity concepts, since these increases the
cognitive load on students. This study will investigate the effect of
different modes of involvement in exploring scientific phenomena
using computer simulation tools, on students- mental model from the
perspective of structure, behavior and function. Quantitative and
qualitative methods are used to report about 121 freshmen students
that engaged in participatory simulations about complex phenomena,
showing emergent, self-organized and decentralized patterns. Results
show that LbM plays a major role in students' concept formation
about complexity concepts.
Abstract: Enterprise applications are complex systems that are hard to develop and deploy in organizations. Although software application development tools, frameworks, methodologies and patterns are rapidly developing; many projects fail by causing big costs. There are challenging issues that programmers and designers face with while working on enterprise applications. In this paper, we present the three of the significant issues: Architectural, technological and performance. The important subjects in each issue are pointed out and recommendations are given. In architectural issues the lifecycle, meta-architecture, guidelines are pointed out. .NET and Java EE platforms are presented in technological issues. The importance of performance, measuring performance and profilers are explained in performance issues.
Abstract: The ability of the brain to organize information and generate the functional structures we use to act, think and communicate, is a common and easily observable natural phenomenon. In object-oriented analysis, these structures are represented by objects. Objects have been extensively studied and documented, but the process that creates them is not understood. In this work, a new class of discrete, deterministic, dissipative, host-guest dynamical systems is introduced. The new systems have extraordinary self-organizing properties. They can host information representing other physical systems and generate the same functional structures as the brain does. A simple mathematical model is proposed. The new systems are easy to simulate by computer, and measurements needed to confirm the assumptions are abundant and readily available. Experimental results presented here confirm the findings. Applications are many, but among the most immediate are object-oriented engineering, image and voice recognition, search engines, and Neuroscience.
Abstract: Complex systems are composed of several plain interacting independent entities. Interaction between these entities creates a unified behavior at the global level that cannot be predicted by examining the behavior of any single individual component of the system. In this paper we consider a welded frame of an automobile trailer as a real example of Complex Technical Systems, The purpose of this paper is to introduce a Statistical method for predicting the life cycle of complex technical systems. To organize gathering of primary data for modeling the life cycle of complex technical systems an “Automobile Trailer Frame" were used as a prototype in this research. The prototype represents a welded structure of several pieces. Both information flows underwent a computerized analysis and classification for the acquisition of final results to reach final recommendations for improving the trailers structure and their operational conditions.
Abstract: Every 2-3 years the influenza B virus serves
epidemics. Neuraminidase (NA) is an important target for influenza
drug design. Although, oseltamivir, an oral neuraminidase drug, has
been shown good inhibitory efficiency against wild-type of influenza
B virus, the lower susceptibility to the R152K mutation has been
reported. Better understanding of oseltamivir efficiency and
resistance toward the influenza B NA wild-type and R152K mutant,
respectively, could be useful for rational drug design. Here, two
complex systems of wild-type and R152K NAs with oseltamivir
bound were studied using molecular dynamics (MD) simulations.
Based on 5-ns MD simulation, the loss of notable hydrogen bond and
decrease in per-residue decomposition energy from the mutated
residue K152 contributed to drug compared to those of R152 in wildtype
were found to be a primary source of high-level of oseltamivir
resistance due to the R152K mutation.
Abstract: Control of complex systems is one of important files in complex systems, that not only relies on the essence of complex systems which is denoted by the core concept – emergence, but also embodies the elementary concept in control theory. Aiming at giving a clear and self-contained description of emergence, the paper introduces a formal way to completely describe the formation and dynamics of emergence in complex systems. Consequently, this paper indicates the Emergence-Oriented Control methodology that contains three kinds of basic control schemes: the direct control, the system re-structuring and the system calibration. As a universal ontology, the Emergence-Oriented Control provides a powerful tool for identifying and resolving control problems in specific systems.
Abstract: Complexity, as a theoretical background has made it
easier to understand and explain the features and dynamic behavior
of various complex systems. As the common theoretical background
has confirmed, borrowing the terminology for design from the
natural sciences has helped to control and understand urban
complexity. Phenomena like self-organization, evolution and
adaptation are appropriate to describe the formerly inaccessible
characteristics of the complex environment in unpredictable bottomup
systems. Increased computing capacity has been a key element in
capturing the chaotic nature of these systems.
A paradigm shift in urban planning and architectural design has
forced us to give up the illusion of total control in urban
environment, and consequently to seek for novel methods for
steering the development. New methods using dynamic modeling
have offered a real option for more thorough understanding of
complexity and urban processes. At best new approaches may renew
the design processes so that we get a better grip on the complex
world via more flexible processes, support urban environmental
diversity and respond to our needs beyond basic welfare by liberating
ourselves from the standardized minimalism.
A complex system and its features are as such beyond human
ethics. Self-organization or evolution is either good or bad. Their
mechanisms are by nature devoid of reason. They are common in
urban dynamics in both natural processes and gas. They are features
of a complex system, and they cannot be prevented. Yet their
dynamics can be studied and supported.
The paradigm of complexity and new design approaches has been
criticized for a lack of humanity and morality, but the ethical
implications of scientific or computational design processes have not
been much discussed. It is important to distinguish the (unexciting)
ethics of the theory and tools from the ethics of computer aided
processes based on ethical decisions. Urban planning and architecture
cannot be based on the survival of the fittest; however, the natural
dynamics of the system cannot be impeded on grounds of being
“non-human".
In this paper the ethical challenges of using the dynamic models
are contemplated in light of a few examples of new architecture and
dynamic urban models and literature. It is suggested that ethical
challenges in computational design processes could be reframed
under the concepts of responsibility and transparency.
Abstract: The deviation between the target state variable and the
practical state variable should be used to form the state tending factor
of complex systems, which can reflect the process for the complex
system to tend rationalization. Relating to the system of basic
equations of complete factor synergetics consisting of twenty
nonlinear stochastic differential equations, the two new models are
considered to set, which should be called respectively the
rationalizing tendency model and the non- rationalizing tendency
model. Therefore we can extend the theory of programming with the
objective function & constraint condition suitable only for the realm
of man-s activities into the new analysis with the tendency function &
constraint condition suitable for all the field of complex system.
Abstract: Within the domain of Systems Engineering the need
to perform property aggregation to understand, analyze and manage
complex systems is unequivocal. This can be seen in numerous
domains such as capability analysis, Mission Essential Competencies
(MEC) and Critical Design Features (CDF). Furthermore, the need
to consider uncertainty propagation as well as the sensitivity of
related properties within such analysis is equally as important when
determining a set of critical properties within such a system.
This paper describes this property breakdown in a number of
domains within Systems Engineering and, within the area of CDFs,
emphasizes the importance of uncertainty analysis. As part of this, a
section of the paper describes possible techniques which may be used
within uncertainty propagation and in conclusion an example is
described utilizing one of the techniques for property and uncertainty
aggregation within an aircraft system to aid the determination of
Critical Design Features.
Abstract: The requirements analysis, modeling, and simulation have consistently been one of the main challenges during the development of complex systems. The scenarios and the state machines are two successful models to describe the behavior of an interactive system. The scenarios represent examples of system execution in the form of sequences of messages exchanged between objects and are a partial view of the system. In contrast, state machines can represent the overall system behavior. The automation of processing scenarios in the state machines provide some answers to various problems such as system behavior validation and scenarios consistency checking. In this paper, we propose a method for translating scenarios in state machines represented by Discreet EVent Specification and procedure to detect implied scenarios. Each induced DEVS model represents the behavior of an object of the system. The global system behavior is described by coupling the atomic DEVS models and validated through simulation. We improve the validation process with integrating formal methods to eliminate logical inconsistencies in the global model. For that end, we use the Z notation.
Abstract: The deterministic quantum transfer-matrix (QTM)
technique and its mathematical background are presented. This
important tool in computational physics can be applied to a class of
the real physical low-dimensional magnetic systems described by the
Heisenberg hamiltonian which includes the macroscopic molecularbased
spin chains, small size magnetic clusters embedded in some
supramolecules and other interesting compounds. Using QTM, the
spin degrees of freedom are accurately taken into account, yielding
the thermodynamical functions at finite temperatures.
In order to test the application for the susceptibility calculations to
run in the parallel environment, the speed-up and efficiency of
parallelization are analyzed on our platform SGI Origin 3800 with
p = 128 processor units. Using Message Parallel Interface (MPI)
system libraries we find the efficiency of the code of 94% for
p = 128 that makes our application highly scalable.
Abstract: The majority of existing predictors for time series are
model-dependent and therefore require some prior knowledge for the
identification of complex systems, usually involving system
identification, extensive training, or online adaptation in the case of
time-varying systems. Additionally, since a time series is usually
generated by complex processes such as the stock market or other
chaotic systems, identification, modeling or the online updating of
parameters can be problematic. In this paper a model-free predictor
(MFP) for a time series produced by an unknown nonlinear system or
process is derived using tracking theory. An identical derivation of the
MFP using the property of the Newton form of the interpolating
polynomial is also presented. The MFP is able to accurately predict
future values of a time series, is stable, has few tuning parameters and
is desirable for engineering applications due to its simplicity, fast
prediction speed and extremely low computational load. The
performance of the proposed MFP is demonstrated using the
prediction of the Dow Jones Industrial Average stock index.