Complex adaptive structures, which are ubiquitous in nature, are now being emulated for non-biological applications. The theory, analysis and design of such structures represent a challenge for those wishing to create them due to their very complexity. This paper will give an overview of the concepts inherent in such structures in nature and created by design, and why such structures can represent the solution for a number of particular applications.
This paper endeavours to set the scene for the design of complex adaptive structures. The need to address complexity emerges as we look at the history of engineering design and the ever increasing importance of the use of information in the broad context of designing artefacts. The combination of the need and the availability of the tools to meet the need will doubtless trigger impressive progress. Complex adaptive structural design builds upon established engineering design in four domains: the use of computational tools; the exploitation of comprehensive databases; the evolution of broadening performance criteria and specifications; and the enhancement of physical hardware through both increased control of conventional mechanical and materials processes and the selective emulation of biological examples. Whilst many of these undoubtedly need substantially more research and development their potential availability will trigger progress in areas where the exploitation of complexity can be beneficial.
Utilizing a general two-step procedure, a new class of porphodimethene macrocycles has been prepared, which can be easily converted to porphyrins bearing two 8-functionalized naphthalene spacers. Through straightforward modifications in the precursor molecules, macrocycles with a wide range of steric and electronic attributes can be isolated. With these simple ligands, metal-porphyrin complexes exhibiting interesting properties can be produced. For example, when two reactive groups are poised above the porphyrin, a reversible ring closure can take place under mild conditions, allowing for potential recognition sites close to a metal center to be electrochemically and chemically activated and deactivated. This intramolecular porphodimethene-porphyrin interconversion offers many exciting possibilities for the development of catalysis adept at specific transformations and for the design of novel sensors or photosensitizers.
Mixed-metal supramolecular complexes are of interest in that they link multiple structural components into a large supramolecular array. Each subunit is designed to perform a simple act and those acts combine together to give rise to more complicated device functions. By variation of the nature or type of components used and their structural position within the supramolecular assembly, the type of functioning of the molecular device can be controlled. Our molecular design uses transition metal polyazine light absorbers (LA) and couples them through bridging ligands (BL) to other metal centers of interest. These additional metals can function as bioactive sites (BAS), electron acceptors (EA) and electron collectors (EC). An overview of our work in this area will be described with a focus on how component modulation allows these systems to be applicable to a large array of problems of interest including multifunctional DNA binding agents and photochemical molecular devices for light energy conversion.
Proc. SPIE 4512, Synthesis and characterization of novel acid-sensitive tert-butyl methacrylate and isobutyl methacrylate containing star-shaped polymers, 0000 (23 October 2001); https://doi.org/10.1117/12.446778
Star-shaped polymers containing poly(isobutyl methacrylate) (iBMA) and poly(tert-butyl methacrylate) (t-BMA) arms coupled to a 2,5-dimethyl-2,5-hexanediol dimethacrylate (DHDMA) core were synthesized using arm-first living anionic polymerization. Gel permeation chromatography (GPC) indicated that coupling efficiencies were high and coupled products exhibited a monomodal molecular weight distribution. The star-shaped polymer number--average molecular weights were 8-10 times higher than the precursor arm molecular weights. The ratio of coupling reagent to living chain end concentration controlled the molecular weight of the star-shaped polymer and the number of coupled arms. The molecular weight distributions of the star-shaped polymers ranged from 1.5-2.0. Due to the labile tertiary- butyl esters contained in the DHDMA cores, these star-shaped polymers were readily hydrolyzed in the presence of acid catalysts. For example, poly(iBMA) star-shaped polymers were hydrolytically stable at 25 degree(s)C and hydrolyzed readily at 65 degree(s)C in the presence of hydrochloric acid. In addition, the poly(t-BMA) containing star--shaped polymers degraded under similar conditions. The degradation process for the iBMA and t-BMA containing star-shaped polymers was confirmed using 1H NMR spectroscopy, and poly(iBMA)-block- poly(methacrylic acid) and poly(methacrylic acid) were obtained, respectively.
The high binding affinity of cisplatin toward DNA has led to its popularity as an anticancer agent. Due to cumulative drug resistance and toxic side effects, researchers are exploring related metallodrugs. Our approach involves the use of supramolecular complexes. These mixed-metal complexes incorporate a reactive platinum moiety bridged by a polyazine ligand to a light absorbing metal-based chromophore. The presence of the light absorber allows excitation of these systems, opening up the possibility of photoactivation. The use of a supramolecular design allows components of the assembly to be varied to enhance device function and light absorbing properties. Aspects of our molecular design process and results on the DNA binding properties for a number of these mixed-metal complexes will be discussed.
During the last decade constant improvements have been made in materials and structures design and control. But now some performance objectives cannot be achieved using classical technologies and require the use of the smart materials concept. But it is at the actuation end of the equation that smart materials and structures present the greatest challenge. It is here in particular that improved and even new materials have a leading role to play. Piezoelectrics, electrostrictives, photostrictives, magnetostrictives, electroactive polymers, shape memory materials, carbon nanotubes, rheological fluids,...all have their important contributions to make. So this paper aims to perform a brief review of the physical basis of the active materials behaviour.
Electrostatic self-assembly (ESA) methods have been used to synthesize thin and thick film organic/inorganic materials and devices. The ESA method involves the dip coating of charged substrates with alternating layers of anionic and cationic molecules, and the properties of the resulting multilayered structures depend on both the characteristics of the individual molecules and the spatial order of the layers. Since the process is performed at room temperature and pressure by dipping substrates into separate solutions containing the charged molecules, coatings may be formed on substrates of virtually any composition, shape and size. Materials that have been investigated for incorporation into such coatings include noble metal nanoclusters, metal oxide nanoclusters, polymers, cage-structured molecules such as fullerenes, proteins, and dipolar chromophore molecules. In this paper we investigate the self-organization that occurs in such materials at the molecular level, and show experimental examples of such self-organization made possible through atomic force microscopy, TEM and other visualization methods. In particular, we focus on the formation of ordered dipolar molecules that distribute electro-optic behavior, but discuss other ordered self- assembly observations.
Eukaryotic cells are remarkably adaptable entities. Whether embedded in solid tissues or freely suspended in blood or other fluids, cells principally exist in an aqueous environment but maintain a hydrophobic barrier, the plasma membrane, across which changes in the environment are detected. Utilizing specialized macromolecular components, cells can sense changes in temperature, hydrostatic pressure, oxygen tension, shear, shape, osmolarity, pH, electrical potential, electromagnetic radiation, and the concentrations of specific chemical compounds. Modes of response are equally varied, ranging from rapid secretion of stored substances to irreversible functional differentiation to self-destruction. Recent research has elucidated many of the enzymatic and genetic programs that accomplish these adaptations and suggests novel targets for therapeutic intervention.
It is understood that once human tooth erupts into the oral cavity it models or adapts to the functional requirements imposed on it. In this study, experiments were conducted to evaluate the nature of dentine mineralization and mechanical property gradients using fluoroscopic X-ray imaging and instrumented micro-indentation techniques respectively. It was found that dentine adapts as a complex structure with significant gradients in its mineralization and elastic modulus. A significant relationship between the pattern of mineralization and the spatial gradients in mechanical properties was observed in the sagittal and cross-sections of the dentine. The natural gradation in the mechanical properties is explained by the two-dimensional and three- dimensional stress analysis conducted in anatomical scaled dento-osseous models using digital photoelasticity. This work highlights dentine structure as a biologically adapted Functionally Graded Material.
The human brain is comprised of over 100 billion neurons organized into tracts, nuclei, circuits and systems. This provides innumerable elegant abilities that rely on the nervous system to act as a complex adaptive structure (CAS). This property is apparent with respect to overall function, the function of individual neurons and the function of sensory and motor systems. At the overall functional level, the nervous system monitors the environments and can alter that environment. Alterations such as turning on a light switch or changing the diameter of neural vasculature, can improve the performance or chance for survival of the nervous system. Individual neurons can alter the activity of their electrogenic pumps, their rate of transmitter synthesis, their neurotransmitter release and their receptor density in order to maintain optimal functioning in a circuit following changes in their micro-environment. At the systems level, the visual system adjusts the orientation of the eyes or pupillary diameter to receive the highest quality visual information. In the motor system, the myotatic reflex maintains muscle position in the face of changing load, and the gain of the muscle organ responsible for the myotatic reflex can also be automatically adjusted. Internal homeostasis, essential for optimal performance of the nervous system, can be achieved through complex behavioral actions such as feeding. The hypothalamus plays an important role in such behaviors and in the type of sensorimotor integration responsible for the CAS nature of overall nervous system function. Thinking about the CAS characteristics of the nervous system may lead to development of non-biological CAS prostheses for the brain.
In order to maintain homeostasis, the heart must pump blood commensurate with the metabolic needs of the body and do so at a pressure that is adequate to perfuse the vital organs. Basic cardiovascular physiology is reviewed and emphasis is place on those factors that are important in the control of cardiac output, heart rate and blood pressure.
Normal renal function allows mammals to exist in diverse environments. Collections of nephrons, the basic unit of renal structure, regulate body fluid and electrolytes, blood pressure, and red cell mass. Nephron, and hence renal function also allows removal of toxic waste products generated during metabolism. The kidney is a major site of detoxification for many exogenous drugs and chemicals and also has important functions as an endocrine organ. Without precisely regulated renal function, mammals would quickly dehydrate in relatively arid environments (air) or internally overhydrate and drown in marine environments.
Immunity to pathogenic organisms is a complex process involving interacting factors within the immune system including circulating cells, tissues and soluble chemical mediators. Both the efficiency and adaptive responses of the immune system in a dynamic, often hostile, environment are essential for maintaining our health and homeostasis. This paper will present a brief review of one of nature's most elegant, complex adaptive systems.
Nature builds by 1) use of local, inexpensive, available often recycled materials which 2) are self-ordering or growing by attributes shared between the material and environment, 3) repair themselves, 4) sense and adapt to changes in the environment daily, seasonally, and yearly; 5) easily disintegrate and recycle back into the material sink when their usefulness is at an end; and 6) do not harm the environment, but perhaps enhance it or resolve problems.
Newly developed evolutionary computational techniques have emerged as important tools for complex design tasks. In this paper, the basic concepts involved with artificial neural networks, fuzzy logic, the genetic algorithm and cellular automata will be presented. The origins of these techniques in natural complex adaptive systems will be identified, and a number of particular examples of the use of these techniques in practical applications will be discussed. Finally, the potential for the use of these techniques for the design of complex adaptive structures will be addressed.
Actuated systems such as robots take many forms and sizes but each requires solving the difficult task of utilizing available control inputs to accomplish desired system performance. Coordinated groups of robots provide the opportunity to accomplish more complex tasks, to adapt to changing environmental conditions, and to survive individual failures. Similarly, groups of simulated robots, represented as graphical characters, can test the design of experimental scenarios and provide autonomous interactive counterparts for video games. The complexity of writing control algorithms for these groups currently hinders their use. A combination of biologically inspired heuristics, search strategies, and optimization techniques serve to reduce the complexity of controlling these real and simulated characters and to provide computationally feasible solutions.
The current exponential growth of the Internet precipitates a need for improved tools to help people cope with the volume of information available. Existing search engines such, as Yahoo, Alta vista and Excite are efficient in terms of high recall (percentage of relevant document that are retrieved from Internet), and fast response time, at the cost of poor precision (percentage of documents retrieved that are considered relevant). The problem is due to the lack of filtering, lack of specialisation, lack of relevance feedback, lack of adaptation and lack of exploration. One solution for the above problems is to use intelligent agents, which can operate autonomously and become better over time. The agents rely on a user model to improve their performance in retrieving the information. This paper presents an adaptive information retrieval (IR) that learns from the user feedback through an evolutionary method, namely, genetic algorithms (GA).
Complex Adaptive Structures can be viewed as a combination of Complex Adaptive Systems and fully integrated autonomous Smart Structures. Traditionally when designing a structure, one combines rules of thumb with theoretical results to develop an acceptable solution. This methodology will have to be extended for Complex Adaptive Structures, since they, by definition, will participate in their own design. In this paper we introduce a new methodology for Emergent System Identification that is concerned with combining the methodologies of self-organizing functional networks (GMDH - Alexy G. Ivakhnenko), Particle Swarm Optimization (PSO - James Kennedy and Russell C. Eberhart) and Genetic Programming (GP - John Koza). This paper will concentrate on the utilization of Particle Swarm Optimization in this effort and discuss how Particle Swarm Optimization relates to our ultimate goal of emergent self-organizing functional networks that can be used to identify overlapping internal structural models. The ability for Complex Adaptive Structures to identify emerging internal models will be a key component for their success.
Conflicting levels of spatial and temporal scales often hamper using sensor systems to monitor the health of large structures. Some structures, such as dams, bridges and pipelines can be huge, with spans often measured in kilometers. These structures also have lifetimes that can be measured in terms of decades and occasionally even centuries. However, damage to the structure is often localized both spatially and temporally. Cracks are very local events. The critical loading on the structure and/or the occurrence of critical damage may occur on time scales that are very short compared to the lifetime of the structure. Detecting and determining the extent of damage in a structure under these circumstances is often difficult. It is usually uneconomical to cover a large structure with a dense array of sensors that sample at high speed continuously. One possible solution is to have the sensor system be adaptable to changes in the structural health and to key events. This paper will discuss several strategies that can be used in adaptive structural sensing systems. One approach is to use an array of localized data processors with sophisticated trigger and data preprocessing algorithms that only send pertinent data to a central data logger/processor. Another approach is to use imaging systems, such as visible light images or those obtained from ground penetrating radar, to identify potential damage sites that require closer inspection, or squinting, of the imaging system. These could be coupled with a robotic inspection system that changes its inspection route based on the condition of the structure, or the occurrence of a possible damage-causing event, such as an earthquake.
Genetic algorithms (GAs) are becoming increasingly popular due to their ability to solve large complex optimization problems which other methods have difficulty solving. In this paper, an introduction to the theory of GAs and its operators are presented. A brief overview of the current research using GAs in aerospace engineering applications is given. Based on the author's previous work, optimal piezoelectric actuator placement for space telescope mirrors using GAs is discussed. The problem discussed here involves finding optimal locations and optimal voltages for 15 piezoelectric actuators, selected from a maximum of 193 candidate locations. The GA was found to be effective and robust in solving this problem with more than 8.4*1021 possible solutions. Two sets of actuator placements are given as solutions to the multi-criteria optimization problem. The use of GAs for structural damage detection inverse problems for concentrated damage of a continuous beam is also shown. A real number encoded GA was found to provide relatively accurate solutions for this damage detection problem.
The microelectronics industry has seen explosive growth during the last thirty years. Extremely large markets for logic and memory devices have driven the development of new materials, and technologies for the fabrication of even more complex devices with feature sizes now down at the sub micron and nanometer level. Recent interest has arisen in employing these materials, tools and technologies for the fabrication of miniature sensors and actuators and their integration with electronic circuits to produce smart devices and systems. This effort offers the promise of: 1) increasing the performance and manufacturability of both sensors and actuators by exploiting new batch fabrication processes developed including micro stereo lithographic and micro molding techniques; 2) developing novel classes of materials and mechanical structures not possible previously, such as diamond like carbon, silicon carbide and carbon nanotubes, micro-turbines and micro-engines; 3) development of technologies for the system level and wafer level integration of micro components at the nanometer precision, such as self-assembly techniques and robotic manipulation; 4) development of control and communication systems for MEMS devices, such as optical and RF wireless, and power delivery systems, etc. A novel composite structure can be tailored by functionalizing carbon nanotubes and chemically bonding them with the polymer matrix e.g. block or graft copolymer, or even cross-linked copolymer, to impart exceptional structural, electronic and surface properties. Bio- and mechanical-MEMS devices derived from this hybrid composite provide a new avenue for future smart systems.
It is well known that holographic data storage can significantly increase data storage capacity. However, the technological maturity of holographic data storage is believed to be impeded by the lack of good holographic material that can be erased and recorded optically with almost unlimited rewriting cycles, large index modulation for large capacity multiplexed data recording, long lifetime, and immunity to destructive readout for archival applications. The performance of an azobenzene polymer is presented for holographic data storage applications. Initial experiments demonstrated that it is capable of satisfying many of above requirements. Recording of holograms without follow-up processing and being stable in application environment are its most attractive features. Applications of such material to other adaptive structures are possible.
The design or architecture of bone is quite complex and diverse, ranging from a very porous cellular solid (trabecular bone) to a very dense solid (cortical bone). Significant adaptations to cortical and trabecular bone mass and architecture have been observed in response to changes in stresses acting on the tissue. The purpose of this paper is to examine bone stress-adaptation schemes, including so- called self-optimization theories of bone, within two- dimensional (2D) and three-dimensional (3D) finite element modeling (FEM) domains. Stress-adaptive FEM simulations are implemented using Matlab and involve analysis of stresses and strains, followed by successive iterations with the goal to globally minimize stress-strain objective functions (strain energy density, von Mises, maximum shear) without imposing constraints other than bounds on the relative density. Both isotropic and anisotropic material properties are considered while applying time-independent loading conditions for simple geometry domains with isoparametric elements. Application of a uniform tension/shear loading to 2D rectangular domains produced heterogeneous material, complex lattice structures that were qualitatively similar to trabecular bone. Three-dimensional cantilever beam analyses using isotropic and anisotropic material properties produced density-optimized, but not necessarily stiffness and strength optimized, structures. Finite element analysis simulations can assist in understanding complex adaptive structures, including bone.
A concept to sequence DNA without tagging the molecule is developed. The fabrication process is compatible with current microelectronics and (emerging) soft-material fabrication technologies, allowing the method to be integrable with MEMS and lab-on-a-chip devices. The preliminary results indicate sensitivity in the nano-gram regime for 100 micron-square pixels. The technology can be extended to perform combinatorial analysis with on-line measurement in real-time during the hybridization process.
Loss of mobility in the elderly causes a significant economic burden to caregivers and is one of the most significant determinants of depression and loss of muscle strength and productivity in this age group. Mobility aids can assist with locomotion by providing physical support, however they fail to provide direction guidance and avoidance of obstacles and hazards. This talk will focus on design of intelligent adaptive wheeled walkers. By allowing the user varying degrees of control, from complete to collaborative, these walkers afford the user with the feeling of control, while helping to increase the ease and safety of their daily travels. The control systems of these walkers differ from those of other mobility aids and mobile robots because they must both assist in mobility and provide balance and support, but also give directional aid if necessary. These functions must be performed in a tight loop adaptation with a human whose input may be difficult to predict. Through the use of a wheeled walker equipped with force and sonar sensors, we were able to develop an intelligent self-guided mobility aid that can provide improved independence, autonomy, and quality of life for the elderly.
Photodynamic Therapy (PDT) is a very exciting treatment modality that offers the possibility of a highly targeted treatment for cancer and other diseases. A major issue in the use of PDT is the inability to deliver the required dose of light to deep areas of the tumor. The turbid nature of tissue causes the light to be highly scattered before reaching the base of the tumor. In this paper, we first present the basics of PDT for an interdisciplinary audience only vaguely familiar with PDT. We will then examine the use of a complex adaptive system to increase the penetration depth and control of light in the tissue. By using a feedback mechanism, the light path can be adjusted to yield superior illumination within the tissue.