Innovative Helix Research Lab
INTRODUCTION
ADAPTIVE SYSTEMS
How It All Started
The purpose of the project is to explore the dynamics of multi-layered systems, organizational capacity, implementation of complex monitoring systems, and organizational behavior during times of transformation in relation to the natural laws of physics. Eligibility Criteria to Participate: Participants will complete a 73 Multiple Choice Question Survey that will take ~ 15-20 minutes to complete. For more information please contact Dr. Luma Mahairi at luma_mahairi@yahoo.com or (630) 696-7211
RESEARCH QUESTION 1
How can the attributes of entropy be utilized to define randomness in the multi-layered matrix of complex systems, and how does this understanding contribute to our knowledge of system dynamics, growth, adaptability, and resilience?
Open business organizations, characterized by the unrestricted flow, sharing, and exchange of information, possess a unique capacity to confront and endure the challenges of disorder, uncertainty, and entropy; this is because their openness facilitates the acquisition and application of the knowledge required to navigate complex environments with agility and resilience. A comprehensive analysis of the research landscape during the period under consideration identifies five noteworthy lines of inquiry concerning entropy and its implications for complex systems. The study of information theory arises initially as a fundamental area of research; this field investigates the fundamental principles regulating information transmission, processing, and quantification in complex systems. Researchers seek to discover strategies for effectively managing and mitigating entropy by better comprehending how information flows within these systems (Abad-Segura et al., 2021; Hooker, 2011; Markyna & Dyachkov, 2014). Researchers have also investigated the concept of maximum entropy, using entropy as a potent instrument for making predictions and inferring missing information. By adhering to maximum entropy principles, scholars hope to reveal the hidden patterns and structures inherent to complex systems. This enhanced understanding of entropy-driven dynamics contributes to more precise decision-making processes in the presence of uncertainty and complexity. The third area of study focuses on information entropy, which investigates the quantification of uncertainty and unpredictability embedded within a system's information content. This field of study aims to establish a rigorous framework for measuring the disorder and complexity of complex systems. By elucidating the complex relationship between information entropy and system behavior, researchers obtain valuable insight into decision-making processes, system adaptability, and system resilience (Abad-Segura et al., 2021). Decision-making within complex systems is another area of research emphasis. Scholars investigate the impact of entropy on decision-making processes to decipher the cognitive fallacies, challenges, and strategies employed by individuals and organizations in uncertain and disordered environments. This line of inquiry reveals the intricate relationship between entropy and decision-making, thereby providing a nuanced comprehension of the intricate decision dynamics operating within complex systems (Hooker, 2011). Although not intrinsically related to entropy, research on enthalpy contributes to comprehending complex systems. The enthalpy of a system represents its entire energy content and significantly affects its potential for change and transformation. Researchers investigate the intricate interplay between enthalpy and entropy, aiming to develop a comprehensive framework depicting complex systems' dynamics and adaptability.
RESEARCH
QUESTION 2
What are the potential benefits and challenges of incorporating monitoring systems based on fuzzy logic models into complex systems, and how can these systems enhance our ability to manage chaos, complexity, and uncertainty?
Fuzzy logic models, a type of artificial intelligence designed to deal with uncertainty and imprecision, are effective for monitoring complex systems. Due to the inherent uncertainty that characterizes such systems, fuzzy logic models adapt to monitoring system behavior and anticipating potential problems. Incorporating fuzzy logic models into complex systems has numerous advantages. First, it improves the ability to monitor and trace system behavior, thereby providing valuable insights into the dynamics and performance of the system. In addition, it facilitates the early detection of prospective issues, facilitating proactive measures to mitigate or prevent negative outcomes. In addition, incorporating fuzzy logic models improves the management of disorder, complexity, and uncertainty within these complex systems. However, integrating fuzzy logic models into complex systems presents several obstacles. The inherent complexity and difficulty of developing these models is one such obstacle. Designing effective fuzzy logic models requires careful consideration and expert knowledge to represent the system's complexities accurately. In addition, these models may be sensitive to changes in the input data, necessitating constant adaptation and refinement to preserve their efficacy. In addition, the computational requirements of executing fuzzy logic models can be substantial, posing time and resource constraints (Bouloiz et al., 2013). Despite these obstacles, fuzzy logic models continue to be useful for monitoring and managing complex systems. Understanding both the advantages and disadvantages of these models allows for more informed decisions regarding their application and utilization. In a related context, the proposed system is a promising strategy for addressing problems characterized by increased model uncertainty or uncertain input data measurements. For example, the Kinematics data set from the University of Toronto's DELVE Repository concentrates on the forward Kinematics of an 8-link, all-revolve robot arm. In situations where even a small amount of undistorted training data is available, type-2 systems can be initially trained as type-1 fuzzy systems if even a small amount of undistorted training data is available. Consequently, the fuzzy-rough approximation method for generating type-2 fuzzy rules for ambiguous data is employed. Inadvertently resembling deep neural networks, the fuzzy system with smooth type-reduction has multiple layers aggregated by smooth maximum and smooth minimum functions (Tavana & Hajipour, 2020). Future research should seek to adapt model complexity reduction techniques initially developed for convolutional neural networks to fuzzy type-2 smooth systems. Researchers use these techniques to expedite and optimize fuzzy logic models' functionality when addressing complex systems' inherent complexities (Wang et al., 2017).
RESEARCH
QUESTION 3
What are the underlying mechanisms that act as catalytic processes in different complex
systems, and how do these mechanisms contribute to self-organization, absorptive
capacity, and the development of precise strategies and knowledge management
infrastructure?
Nobel Peace Prize winner Prigogine's work (Prigogine & Lefever, 2007) sought to bridge the divide between the physical and human sciences. Prigogine's main objective was to reconcile molecular evolution with the Second Law of Thermodynamics. The concept of order through fluctuations, which refers to the emergence of new ordering mechanisms in complex systems, was one of the main phenomena he investigated. Per Prigogine's research, complex systems can manifest a unique type of structure known as a dissipative structure. These structures maintain a critical distance from equilibrium and a minimum dissipation level due to the continuous passage of energy and matter from the external environment. Dissipative structures exhibit a greater degree of order or structure than their initial state, even though a substantial loss of energy in heat accompanies their formation. Examples of dissipative structures provided by Prigogine include the formation of convection currents and vortexes in a fluid subject to a temperature gradient. These structures exhibit a greater degree of order than the system previously possessed, but their rotational energies eventually dissipate, resulting in no enduring increase in order. The theory of Prigogine emphasized the interaction between the generation of dissipative structures and the dissipation of energy. Still, it did not provide a mechanism for increased order in an open system. In the context of the research question, the text highlights dissipative structures as an illustration of a mechanism that functions as a catalyst in complex systems, such as cities and innovation ecosystems (see Pulselli et al., 2005). By harnessing external energy and transforming it into higher-order structures, dissipative structures generate and maintain self-organization and absorptive capacity. To achieve permanent increased order in open systems, dissipative structures alone cannot function as a substrate (Prigogine & Lefever, 2007). Gilstrap (2007) addressed the interdependence of dissipative systems and their contributions to absorbent capacity. By harnessing external energy and transforming it into higher-order structures, dissipative systems improve their capacity to assimilate and ingest new information, knowledge, and resources. This assimilation and integration of external inputs contribute to the system's absorptive capacity, allowing it to adapt and formulate precise strategies. Gilstrap also alluded to the function of dissipative structures in developing infrastructure for knowledge management. Understanding dissipative structures' dynamics and limitations can provide insight into the mechanisms required for managing knowledge within complex systems. By examining alternative models of leadership theory in out-of-balance educational settings, as suggested by Gilstrap, one can unearth the intricate relationships between dissipative structures, leadership, organizational change, and the development of knowledge management strategies.Shoumei (2011) discussed the underlying mechanisms that
act as catalytic processes in various complex
systems and how these mechanisms contribute to self-organization, absorptive capacity, and the development of precise strategies and knowledge management infrastructure. They emphasized the existence of systemic fluctuations, which are deviations from equilibrium values. Prigogine's believed that fluctuations could lead to order. In the context of complex systems, such as the enterprise knowledge ecosystem, Shoumei claimed that such systems consist of numerous subsystems and measurable macroscopic quantities, such as knowledge stock and knowledge flow. Due to the constant alterations of these macroscopic quantities, there are arbitrary and chaotic deviations or fluctuations. The knowledge ecosystem is highlighted for its emphasis on knowledge sharing, transfer, and innovation, which induce substantial fluctuations and contribute to self-organizing effects. Shoumei suggested that system fluctuations are advantageous to its self-development, as they increase positive feedback and decrease negative feedback. In turn, this causes a qualitative change in the entire system, forming new ordered structures.KEY TERMS DEFINED From the Encyclopedia of Britannica (Britannica, 2018) Enthalpy = Enthalpy is the sum of the internal energy and the product of the pressure and volume of a thermodynamic system. Enthalpy is an energy-like property or state function—it has the dimensions of energy (and is thus measured in units of joules or ergs), and its value is determined entirely by the temperature, pressure, and composition of the system and not by its history. In symbols, the enthalpy, H, equals the sum of the internal energy, E, and the product of the pressure, P, and volume, V, of the system: H = E + PV. According to the law of energy conservation, the change in internal energy is equal to the heat transferred to, less the work done by, the system. If the only work done is a change of volume at constant pressure, the enthalpy change is exactly equal to the heat transferred to the system. When energy needs to be added to a material to change its phase from a liquid to a gas, that amount of energy is called the enthalpy (or latent heat) of vaporization and is expressed in units of joules per mole. Other phase transitions have similar associated enthalpy changes, such as the enthalpy (or latent heat) of fusion for changes from a solid to a liquid. As with other energy functions, it is neither convenient nor necessary to determine absolute values of enthalpy. For each substance, the zero-enthalpy state can be some convenient reference state. Entropy = is the measure of a system’s thermal energy per unit temperature that is unavailable for doing useful work. Because work is obtained from ordered molecular motion, the amount of entropy is also a measure of the molecular disorder, or randomness, of a system. The concept of entropy provides deep insight into the direction of spontaneous change for many everyday phenomena. The idea of entropy provides a mathematical way to encode the intuitive notion of which processes are impossible, even though they would not violate the fundamental law of conservation of energy. For example, a block of ice placed on a hot stove surely melts, while the stove grows cooler. Such a process is called irreversible because no slight change will cause the melted water to turn back into ice while the stove grows hotter. In contrast, a block of ice placed in an ice-water bath will either thaw a little more or freeze a little more, depending on whether a small amount of heat is added to or subtracted from the system. Such a process is reversible because only an infinitesimal amount of heat is needed to change its direction from progressive freezing to progressive thawing. First Law of Thermodynamics= Law of Conservation of Energy states that in an isolated system, the total energy of the system is constant, even if energy has been converted from one form to another. The first law of thermodynamics is put into action by considering the flow of energy across the boundary separating a system from its surroundings. In order to conserve the total energy U, there must be a counterbalancing change (ΔU = Q − W) in the internal energy of the gas. The first law provides a kind of strict energy accounting system in which the change in the energy account (ΔU) equals the difference between deposits (Q) and withdrawals (W). Second Law of Thermodynamics = is a statement that describes the amount of useful work that can be done from a process that exchanges or transfers heat. This can be precisely stated in the following two forms, as originally formulated in the 19th century by the Scottish physicist William Thomson (Lord Kelvin) and the German physicist Rudolf Clausius, respectively: A cyclic transformation whose only final result is to transform heat extracted from a source which is at the same temperature throughout into work is impossible.
Please contact Dr. Luma Mahairi at (luma_mahairi@yahoo.com) for more information on the project and to complete the study survey. Thank you~