Behavior Of System Research Articles

The mediating role of behavioral intention on factors influencing user behavior in the E-government state financial application system at the Indonesian Ministry of Finance

The mediating role of behavioral intention on factors influencing user behavior in the E-government state financial application system at the Indonesian Ministry of Finance

Exploring dust-ion acoustic shocks in a plasma in the light of phase plane analysis

This paper presents a comprehensive exploration of shock waves in a dusty plasma, incorporating the dynamics of inertial dust and ions with Maxwellian distributed electrons. By deriving the Korteweg-de Vries-Burgers (KdV-B) equation and by using its standard solution we analyze the characteristics of the shock front under varying parameters. Furthermore, we conduct a phase plane analysis to elucidate the system's behavior and dynamics by tuning in the parameters that provide various types of perturbation in the system. The investigation sheds light on the intricate behavior of shock waves in dusty plasmas and contributes valuable insights to the understanding of plasma dynamics.

Analyzing bifurcation, stability, and wave solutions in nonlinear telecommunications models using transmission lines, Hamiltonian and Jacobian techniques

This study presents a comprehensive analysis of a nonlinear telecommunications model, exploring bifurcation, stability, and wave solutions using Hamiltonian and Jacobian techniques. The investigation begins with a thorough examination of bifurcation behavior, identifying critical points and their stability characteristics, leading to the discovery of diverse bifurcation scenarios. The stability of critical points is further assessed through graphical and numerical methods, highlighting the sensitivity to parameter variations. The study delves into the derivation of both numerical and analytical wave solutions, aligning them with energy orbits depicted in phase portraits, revealing a spectrum of wave behaviors. Additionally, the analysis extends to traveling wave solutions, providing insights into wave propagation dynamics. Notably, the study underscores the efficacy of the planar dynamical approach in capturing system behavior in harmony with phase portrait orbits. The findings have significant implications for telecommunications engineers and researchers, offering insights into system behavior, stability, and signal propagation, ultimately advancing our understanding of complex nonlinear dynamics in telecommunications networks.

Mortality risk information and health-seeking behavior during an epidemic

In a context where pessimistic survival perceptions have been widespread as a result of the HIV/AIDS epidemic (Fig. 1 A), we study vaccine uptake and other health behaviors during the recent COVID-19 pandemic. Leveraging a longitudinal cohort study in rural Malawi that has been followed for up to 25 y, we document that a 2017 mortality risk information intervention designed to reduce pessimistic mortality perceptions (Fig. 1 B) resulted in improved health behavior, including COVID-19 vaccine uptake (Fig. 1 C). We also report indirect effects for siblings and household members. This was likely the result of a reinforcing process where the intervention triggered engagement with the healthcare system and stronger beliefs in the efficacy of modern biomedical treatments, which led to the adoption of health risk reduction behavior, including vaccine uptake. Our findings suggest that health information interventions focused on survival perceptions can be useful in promoting health behavior and participation in the formal healthcare system, even during health crises-such as the COVID-19 pandemic-that are unanticipated at the time of the intervention. We also note the importance of the intervention design, where establishing rapport, tailoring the content to the local context, and spending time with respondents to convey the information contributed to the salience of the message.

Timed Automata-Based Strategy for Controlling Drone Access to Critical Zones: A UPPAAL Modeling Approach

Controlling access to critical zones by drones is crucial for ensuring safety and efficient operations in various applications. In this research, we propose a strategy for controlling the access of a set of drones to a critical zone using timed automata and UPPAAL. UPPAAL is a model checker and simulator for real-time systems, which allows for the modeling, simulation, and verification of timed automata. Our system consists of six drones, a controller, and a buffer, all modeled as timed automata. We present a formal model capturing the behavior and interactions of these components, considering the constraints of allowing only one drone in the critical zone at a time. Timed automata are a powerful formalism for modeling and analyzing real-time systems, as they can capture the temporal aspects of system behavior. The advantages of using timed automata include the ability to model time-critical systems, analyze safety and liveness properties, and verify the correctness of the system. We design a strategy that involves signaling the approaching drones, preventing collisions, and ensuring orderly access to the critical zone. We utilize UPPAAL for simulating and verifying the system, including the evaluation of properties such as validation properties, safety properties, liveness properties, and absence of deadlocks. However, a limitation of timed automata is that they can become complex and difficult to model for large-scale systems, and the analysis can be computationally expensive as the number of components and behaviors increases. Through simulations and formal verification, we demonstrate the effectiveness and correctness of our proposed strategy. The results highlight the ability of timed automata and UPPAAL to provide reliable and rigorous analysis of drone access control systems. Our research contributes to the development of robust and safe strategies for managing drone operations in critical zones.

From Genesis to Old Age: Exploring the Immune System One Cell at a Time with Flow Cytometry.

The immune system is a highly complex and tightly regulated system that plays a crucial role in protecting the body against external threats, such as pathogens, and internal abnormalities, like cancer cells. It undergoes development during fetal stages and continuously learns from each encounter with pathogens, allowing it to develop immunological memory and provide a wide range of immune protection. Over time, after numerous encounters and years of functioning, the immune system can begin to show signs of erosion, which is commonly named immunosenescence. In this review, we aim to explore how the immune system responds to initial encounters with antigens and how it handles persistent stimulations throughout a person's lifetime. Our understanding of the immune system has greatly benefited from advanced technologies like flow cytometry. In this context, we will discuss the valuable contribution of flow cytometry in enhancing our knowledge of the immune system behavior in aging, with a specific focus on T-cells. Moreover, we will expand our discussion to the flow cytometry-based assessment of extracellular vesicles, a recently discovered communication channel in biology, and their implications for immune system functioning.

Optimization on frequency constraints with FFT using automatic differentiation on hybrid ODE applications

PurposeThis study aims to optimize electrical systems represented by ordinary differential equations and events, using their frequency spectrum is an important purpose for designers, especially to calculate harmonics.Design/methodology/approachThis paper presents a methodology to achieve this, by using a gradient-based optimization algorithm. The paper proposes to use a time simulation of the electrical system, and then to compute its frequency spectrum in the optimization loop.FindingsThe paper shows how to proceed efficiently to compute the frequency spectrum of an electrical system to include it in an optimization loop. Derivatives of the frequency spectrum such as the optimization inputs can also be calculated. This is possible even if the sized system behavior cannot be defined a priori, e.g. when there are static converters or electrical devices with natural switching.Originality/valueUsing an efficient sequential quadratic programming optimizer, automatic differentiation is used to compute the model gradients. Frequency spectrum derivatives with respect to the optimization inputs are calculated by an analytical formula. The methodology uses a “white-box” approach so that automatic differentiation and the differential equations simulator can be used, unlike most state-of-the-art simulators.

Discrete element analysis of the dynamic behavior of ballast track-subgrade system under train dynamic load

To study the dynamic response law of the ballast track-subgrade coupling system under the action of train dynamic load, considering the real shape of ballast particles, a refined discrete element model of the rail-sleeper-ballast bed-subgrade coupling system was established. The established model can realistically simulate the coupling mechanism between irregular ballast particles, between track panel and ballast bed, and between ballast bed and subgrade under train dynamic load. The validity of the established model is verified using on-site measurement results. This study, from a microscopic perspective, focuses on particle contact forces, vibrations, and frictional energy consumption in the rail-sleeper-ballast bed-subgrade coupling system under train dynamic load and investigates the influence of train speed and axle weight. Results show that an increase in train speed and axle weight enhances the number and probability distribution of strong force chains between ballast particles, increasing the risk of particle breakage and deterioration. The influence range and depth in the ballast bed-subgrade system increase with the train's speed and axle weight. Due to the granular nature of the ballast bed, the vibration acceleration distribution of ballast particles under train dynamic loads is relatively random. Overall, the vibration level of the ballast bed increases with train speed and axle weight. The frictional energy consumption of ballast particles rapidly grows as the train's bogie load passes, and the effect of train axle weight on the frictional energy consumption of ballast particles is more significant than the train's speed.

Dissipative Quantum Hopfield Network: A numerical analysis

Abstract We present extensive simulations of a quantum version of the Hopfield Neural Network to explore its emergent behavior. The system is a network of $N$ qubits oscillating at a given $\Omega$ frequency and which are coupled via Lindblad jump operators built with local fields $h_i$ depending on some given stored patterns. Our simulations show the emergence of pattern-antipattern oscillations of the overlaps with the stored patterns similar (for large $\Omega$ and small temperature) to those reported within a recent mean-field description of such a system, and which are originated deterministically by the quantum term including $s_x^i$ qubit operators. However, in simulations we observe that such oscillations are stochastic due to the interplay between noise and the inherent metastability of the pattern attractors induced by quantum oscillations, and then are damped in finite systems when one averages over many quantum trajectories. In addition, we report the system behavior for large number of stored patterns at the lowest temperature we can reach in simulations (namely $T=0.005\, T_C$). Our study reveals that for small-size systems the quantum term of the Hamiltonian has a negative effect on storage capacity, decreasing the overlap with the starting memory pattern for increased values of $\Omega$ and number of stored patterns. However, it also impedes the system to be trapped for long time in mixtures and spin-glass states. Interestingly, the system also presents a range of $\Omega$ values for which, although the initial pattern is destabilized due to quantum oscillations, other patterns can be retrieved and remain stable even for many stored patterns, implying a quantum-dependent nonlinear relationship between the recall process and the number of stored patterns.

Bifurcation analysis and control study of improved full-speed differential model in connected vehicle environment

Abstract In recent years, the traffic congestion problem has become more and more serious, and the research on traffic system control has become a new hot spot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable pivots can alleviate the traffic congestion problem from a new perspective. In this work, the full-speed differential model considering the vehicle network environment is improved in order to adjust the traffic flow from the perspective of bifurcation control, the existence conditions of Hopf bifurcation and saddle-node bifurcation in the model are proved theoretically, and the stability mutation point for the stability of the transportation system is found. For the unstable bifurcation point, a nonlinear system feedback controller is designed by using Chebyshev polynomial approximation and stochastic feedback control method. The advancement, postponement, and elimination of Hopf bifurcation are achieved without changing the system equilibrium point, and the mutation behavior of the transportation system is controlled so as to alleviate the traffic congestion. The changes in the stability of complex traffic systems are explained through the bifurcation analysis, which can better capture the characteristics of the traffic flow. By adjusting the control parameters in the feedback controllers, the influence of the boundary conditions on the stability of the traffic system is adequately described, and the effects of the unstable focuses and saddle points on the system are suppressed to slow down the traffic flow. In addition, the unstable bifurcation points can be eliminated and the Hopf bifurcation can be controlled to advance, delay, and disappear, so as to realize the control of the stability behavior of the traffic system, which can help to alleviate the traffic congestion and describe the actual traffic phenomena as well.

Electromagnetic radiation control for nonlinear dynamics of Hopfield neural networks.

Electromagnetic radiation (EMR) affects the dynamical behavior of the nervous system, and appropriate EMR helps to study the dynamic mechanism of the nervous system. This paper uses a sophisticated four-dimensional Hopfield neural network (HNN) model augmented with one or more memristors to simulate the effects of EMR. We focus on the chaotic dynamics of HNN under the influence of EMR. Complex dynamical behaviors are found and transient chaotic phenomena have the same initial value sensitivity, showing how transient chaos is affected by EMR. Multiperiodic phenomena induced by quasi-periodic alternations are found in the dual EMR, as well as the suppression properties of the dual EMR for system chaos. This implies that the dynamical behavior of the HNN system can be controlled by varying the amount of EMR or the number of affected neurons in the HNN. Finally, a strong validation of our proposed model is provided by Multisim and Field Programmable Gate Array(FPGA) hardware.

Dynamical analysis of a periodically forced chaotic chemical oscillator.

We present a comprehensive dynamical analysis of a chaotic chemical model referred to as the autocatalator, when subject to a periodic administration of one substrate. Our investigation encompasses the dynamical characterization of both unforced and forced systems utilizing isospikes and largest Lyapunov exponents-based parameter planes, bifurcation diagrams, and analysis of complex oscillations. Additionally, we present a phase diagram showing the effect of the period and amplitude of the forcing signal on the system's behavior. Furthermore, we show how the landscapes of parameter planes are altered in response to forcing application. This analysis contributes to a deeper understanding of the intricate dynamics induced by the periodic forcing of a chaotic system.

Systems Perspective Outcomes from Aerospace Failure Investigations

AbstractMany tools are available for managing complexity in the development of large aerospace systems. Complexity manifests in components, software and so many possible human interactions that all the operational states of the system are not predicted ahead of time. System behaviors are seemingly unexpected, unpredictable, and often unwanted. However, finding these failure modes before they happen can be done by techniques that are qualitative, quantitative, or mixed methods, used situationally, as needed for each particular circumstance. Through studies of failure investigations from NASA and the aerospace industry, recommendations coalesce on using a systems perspective to increase communication and reduce the risk of failure. Overall, a consistent outcome of these investigations is the suggestion to listen to as many value‐added perspectives as possible. Preventing failures in operation is managed by improving collaboration within and among teams, which is an effective way to reveal those perspectives, through both formal and informal communication techniques properly contextualized.

A Framework to Use Bifurcation Analysis for Insight into Complex Systems Resilience

AbstractThe increasing complexity and integration of systems present challenges in understanding and managing these systems, as highlighted by the INCOSE Systems Engineering Vision for 2035. This complexity risks system failures due to insufficient understanding. Resilience Engineering is a field that focuses on system behavior in the face of disruptions. However, existing methods to measure resilience, such as probability‐based measures and linear recovery models, are limited by the non‐linear and dynamic nature of modern systems. Bifurcation Analysis, a mathematical system dynamics technique, offers a different perspective by examining how systems behave under changing conditions. This approach, developed in ecology and traditionally applied in fields including power systems and neural networks, can provide insights into the resilient characteristics of non‐linear systems. Despite its promise, Bifurcation Analysis is not usually associated with Resilience Engineering. This paper proposes using Bifurcation Analysis to understand system resilience better and support Resilience Engineering practitioners. It aims to bridge the gap between the two fields, offering a framework for integrating both approaches. This framework is then applied to a model system to demonstrate its potential to enhance the understanding of system resilience. Contributions are an overview of current Resilience Engineering and Bifurcation Analysis, a general‐use framework for practitioners, and an application of this framework. Future work will focus on applying the new framework to more cases, allowing for improved Resilience Engineering, and improving the framework to make it more accessible, versatile, and reliable.

Transient analysis of a bulk stream queue with arbitrarily distributed arrival intervals

We study the classical infinite buffer single server queueing model with renewal input of customers in batches of random size, having arbitrarily distributed arrival intervals and exponentially distributed service times. Using the technique of supplementary variable and shift operator we derive closed form expression of the time dependent system content distribution in terms of its Laplace transform. The analysis is mainly based on the root-finding technique of the non-linear characteristic equation in terms of the Laplace transform variable. Additionally, using asymptotic properties of Laplace transform, we deduce the corresponding steady-state distribution. We discuss some special cases of the model, thus providing an alternative approach in deriving the transient distribution. We further evaluate certain performance measures and present extensive numerical examples in tabular and graphical form to illustrate the applicability of our theoretical work. The effect of system parameters, interarrival time distribution and traffic intensity on the system behavior is also demonstrated.

Forecast Uncertainties Real-Time Data-Driven Compensation Scheme for Optimal Storage Control

This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.

Chaotic Dynamic Behavior of a Fractional-Order Financial System with Constant Inelastic Demand

The establishment of a financial system should not only consider the current situation, but also need to refer to the past. Due to the memory of the fractional derivative, a fractional-order system can more effectively describe the historical significance of the financial system. Most scholars use the prediction–correction scheme to study fractional-order systems. This paper provides a higher-precision numerical method for the financial system, which more effectively simulate the system. Based on the definition of the Grünwald–Letnikov fractional derivative, the integer-order system with nonconstant demand elasticity is extended to the fractional-order setting, and its dynamic behavior is studied, with some novel chaotic attractors found. The research results are helpful for improving the understanding of the financial system and the financial market and for predicting financial risks.

Molecular interaction mechanism and process analysis of separation of cyclohexane/ethyl acetate by extractive distillation

The selection of efficient and environmentally friendly extractants is crucial for extractive distillation (ED), and the microscopic mechanism of extractants in ED is still unclear. For the benefit of interpreting the microstructure distribution and phase behavior of the mixed system, this paper studied it by quantum chemical (QC) calculation and molecular dynamics (MD) simulation. Firstly, 1,3-dimethylimidazolium hexafluorophosphate [Mmim][PF6], 1-ethyl-3-methylimidazolium hexafluorophosphate [Emim][PF6] and organic solvent DMSO were screened based on COSMO-RS model and relative volatility. The bond length, bond energy, total electron density and deformation charge density of [Mmim][PF6], [Emim][PF6], DMSO extractant and ethyl acetate were analyzed by QC, the intrinsic properties of the chosen extractant and azeotropic system, as well as the interactions among the three extractants and ethyl acetate, were investigated. Then, the effect of extractant on the separation performance of cyclohexane ethyl acetate azeotrope in solvent environment was further studied by MD simulation. The two-dimensional mass density distributions (ANDDs) visualized the mixing behavior and structural distribution of the three extractants with the azeotropic system in the ternary system respectively, and the hydrogen bonding statistics indicated a high number of hydrogen bonds between [Mmim][PF6] and ethyl acetate. The radial distribution function (RDF) indicated that the extraction effect of [Mmim][PF6] was better and [PF6-] played a dominant role in the separation of azeotropic system. The strong interaction between [Mmim][PF6] extractant [PF6-] and ethyl acetate was further verified by the spatial distribution function (SDF). Ultimately, through Aspen Plus software simulation, it was found that the [Mmim][PF6] extractive distillation process was superior to the traditional solvent DMSO process, TAC was reduced by 8 %, and gas emissions were reduced by 5 %. Through the combination of QC, MD, and process simulation offer theoretical guidance for the efficient separation of the cyclohexane-ethyl acetate system and provide a specific reference value for the selection of the optimal extractant.

Model‐Based Systems Engineering Approach for Designing an Artificial Magnetic Field Generator System for Spacecraft Radiation Protection

AbstractThe hazardous environment of space, dominated by cosmic and solar radiation, poses a significant threat to spacecraft and their occupants. Traditional radiation shielding methods, like passive materials, have limitations in weight and effectiveness. An artificial magnetic field generator system emerges as a promising solution to replicate Earth's magnetosphere, providing a protective magnetic shield against harmful radiation. (Dick, Launius, 2007) This paper presents a Model‐Based Systems Engineering (MBSE) approach to the holistic modeling and design of such a system.Utilizing the MBSE approach, this study models the system's components, their interactions, and the offered services, incorporating Radiation Monitoring, Magnetic Shielding, Power Management, System Health & Diagnostics, and Crew Communication services. The conceptual data model captures key entities and their relationships, ensuring a coherent integration of the system's parts. The activity diagram illustrates the operational flow, providing clarity on the system's dynamic behavior under varying radiation conditions and power reserves. A services taxonomy is developed to hierarchically categorize and ensure the comprehensive functionality of the system.The application of MBSE methodology provides numerous advantages, including a unified visualization of the system's complexities, enhanced stakeholder communication, and a streamlined validation and verification process. Furthermore, the flexibility inherent in the MBSE approach ensures that the system can be easily updated or scaled based on advancements in technology or changing mission requirements. (ECLIPSE Suite, 2022) The MBSE approach's application to the design of an artificial magnetic field generator system for spacecraft presents a robust and systematic method to ensure optimal performance, safety, and adaptability in the perilous realm of space.

Graph Theoretical Description of Phase Transitions in Complex Multiscale Phases with Supramolecular Assemblies.

Phase transitions are typically quantified using order parameters, such as crystal lattice distances and radial distribution functions, which can identify subtle changes in crystalline materials or high-contrast phases with large structural differences. However, the identification of phases with high complexity, multiscale organization and of complex patterns during the structural fluctuations preceding phase transitions, which are essential for understanding the system pathways between phases, is challenging for those traditional analyses. Here, it is shown that for two model systems- thermotropic liquid crystals and a lyotropic water/surfactant mixtures-graph theoretical (GT) descriptors can successfully identify complex phases combining molecular and nanoscale levels of organization that are hard to characterize with traditional methodologies. Furthermore, the GT descriptors also reveal the pathways between the different phases. Specifically, centrality parameters and node-based fractal dimension quantify the system behavior preceding the transitions, capturing fluctuation-induced breakup of aggregates and their long-range cooperative interactions. GT parameterization can be generalized for a wide range of chemical systems and be instrumental for the growth mechanisms of complex nanostructures.