Behavior Of Model System Research Articles

Effect of oscillating magnetic field (OMF) on the supercooling behavior of iron-oxide nanoparticle (IONP) agar model system.

Freezing extends the shelf life of foods but often leads to structural damage due to ice crystal formation, negatively impacting quality attributes. Oscillating magnetic field (OMF)-assisted supercooling has emerged as a potential technique to overcome these limitations by inhibiting ice nucleation and maintaining foods in a supercooled state. Despite its potential, the effectiveness and underlying mechanisms of OMF-assisted supercooling remain subjects of debate. In this study, the effects of OMF on the supercooling behavior of an agar-based food model system containing iron(III)-oxide nanoparticles (IONP) were investigated. Agar samples containing IONPs at various concentrations (3, 6, 12 and 15mg per 100mL) were prepared to simulate the presence of ferric materials responsive to OMF. The samples were exposed to an external OMF (10mT, 10Hz) at -8°C for 24h. Higher supercooling probabilities were achieved in the IONP-containing samples, with probabilities of 75%, 75%, and 90% for the 3mg, 6mg, and 12mg concentrations, respectively. In contrast, lower supercooling probabilities of 60% and 55% were exhibited by the control samples (without nanoparticles) and samples containing zinc nanoparticles (ZNPs), respectively. It is suggested that the enhanced supercooling stability in IONPsamples is due to the interaction between the magnetic nanoparticles and the OMF, inhibiting ice nucleation possibly through the magneto-mechanical motion affecting water molecule orientation and hydrogen bonding networks.

Expanding model transparency and learning potential through structural and behavioural debriefings

AbstractSimulation‐based learning environments have been recommended by researchers as educational tools to support learning in complex domains. However, studies have evidenced that subjects may nevertheless have great difficulty understanding and managing dynamic systems, with several assertions are being made regarding the impact of employing transparent model simulations on learning and performance outcomes. This research, therefore, examines the moderating impacts of structural and behavioural debriefings, which are concentrated respectively on critical variables and relations, and on the connection between model structure, action patterns and system behaviour. We propose a series of hypotheses regarding the impact of model transparency and prior debriefing conditions on participants' performance and their comprehension of the system dynamics. Three out of the six hypotheses were validated: prior structural debriefing alongside model transparency positively impacts performance; prior behavioural debriefing, when combined with model transparency and prior structural debriefing, positively impacts both the comprehension of model dynamics and performance.

РАЗРАБОТКА МОДЕЛИ BP-СХЕМЫ НА ОСНОВЕ ТЕОРИИ АВТОМАТОВ И С ПРИМЕНЕНИЕМ МЕТОДОВ КВАНТОВОГО ПРОГРАММИРОВАНИЯ

The paper describes the concept of a new type of abstract automata “BP-scheme” (Business Process) that effectively distributes management commands between entities with their permanent growth in the paradigm of complexly modelled systems. The paper investigates the proposed model of the BP-scheme implementation, which is an artificial neural network, functioning based on fuzzy logic rules using the Takagi-Sugeno algorithm, for deriving rules in the form of functional dependencies. The model uses QPU (Quantum Processing Unit) in computing system behaviour, which implies a quick response to changes in the system entities. QPU used taking into account the developed rules of fuzzy neural network allows improving scenarios and predictive models of a complex system behaviour.

ACCESS: Assurance Case Centric Engineering of Safety–critical Systems

Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created documents, which are evaluated by system stakeholders through lengthy and complicated processes. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. This becomes increasingly important, as systems becomes more complex, it is a challenge to manage their development life-cycles, including coordination of development, verification and validation activities, and change impact analysis in inter-connected system assurance artifacts. Moreover, there is a need for assurance cases that support evolution during the operational life of the system, to enable continuous assurance in the face of an uncertain environment, as Robotics and Autonomous Systems (RAS) are adopted into society. In this paper, we contribute ACCESS — Assurance Case Centric Engineering of Safety–critical Systems, an engineering methodology, together with its tool support, for the development of safety–critical systems around evolving model-based assurance cases. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts (e.g. system architectural models, system safety analysis, system behaviour models, etc.), and how formal methods can be integrated during the development process. We demonstrate how assurance cases can be automatically evaluated both at development and runtime. We apply our approach to a case study based on an Autonomous Underwater Vehicle (AUV).

ReptiLearn: An automated home cage system for behavioral experiments in reptiles without human intervention.

Understanding behavior and its evolutionary underpinnings is crucial for unraveling the complexities of brain function. Traditional approaches strive to reduce behavioral complexity by designing short-term, highly constrained behavioral tasks with dichotomous choices in which animals respond to defined external perturbation. In contrast, natural behaviors evolve over multiple time scales during which actions are selected through bidirectional interactions with the environment and without human intervention. Recent technological advancements have opened up new possibilities for experimental designs that more closely mirror natural behaviors by replacing stringent experimental control with accurate multidimensional behavioral analysis. However, these approaches have been tailored to fit only a small number of species. This specificity limits the experimental opportunities offered by species diversity. Further, it hampers comparative analyses that are essential for extracting overarching behavioral principles and for examining behavior from an evolutionary perspective. To address this limitation, we developed ReptiLearn-a versatile, low-cost, Python-based solution, optimized for conducting automated long-term experiments in the home cage of reptiles, without human intervention. In addition, this system offers unique features such as precise temperature measurement and control, live prey reward dispensers, engagement with touch screens, and remote control through a user-friendly web interface. Finally, ReptiLearn incorporates low-latency closed-loop feedback allowing bidirectional interactions between animals and their environments. Thus, ReptiLearn provides a comprehensive solution for researchers studying behavior in ectotherms and beyond, bridging the gap between constrained laboratory settings and natural behavior in nonconventional model systems. We demonstrate the capabilities of ReptiLearn by automatically training the lizard Pogona vitticeps on a complex spatial learning task requiring association learning, displaced reward learning, and reversal learning.

Visualization, transformation, and analysis of execution traces with the eclipse TRACE4CPS trace tool

An execution trace is a model of a single system behavior. Execution traces occur everywhere in the system’s lifecycle as they can typically be produced by executable models, by prototypes of (sub)systems, and by the system itself during its operation. An execution trace can be visualized and analyzed with various techniques, providing insight into the dynamic behavior, performance, bottlenecks, etc., of the system. In this paper, we present the Trace tool of the Eclipse Trace4cps project for the visualization and analysis of execution traces. A prominent application is the trace-based performance engineering of embedded or cyber-physical systems. Performance is an important system quality, as it can give a competitive advantage. Reasoning about system-level performance in such systems, however, is hard due to its cross-cutting nature. We show how the Trace tool can support this by various examples. Performance engineering is not the only application of the Trace tool, however: it supports system analysis in a wide range of situations.

Unraveling the Complexity with Applications of Nonlinear Analysis in Signal Processing and Communication Engineering

Nonlinear analysis is an essential area of signal and communication engineering. Many different kinds of nonlinear analysis can be used in this study. Linear signal processing techniques had previously struggled to accurately capture complex data pattern and communication signals. However, the development of non-linear analysis is a game-changing advancement since it provides a solid framework for understanding and manipulating these complex and non-trivial dynamics. In order to understand the behaviour of a wide variety of signals, from financial market trends to biological and environmental data, non-linear techniques are essential in signal processing. Improved understanding of system behaviour and predictive models may result from the use of such techniques to extract complex data patterns that are not often used. Non-linear approaches also contribute to the development of image and video processing by easing the development of advanced compression, tracking, and object recognition algorithms. Non-linear analysis is crucial in communication engineering as it aids in comprehending and optimising the flow of information across various channels. A greater understanding of signal distortion is made possible by the nonlinear perspective, which makes it possible to build reliable communication systems that can withstand interference and noise. By facilitating the creation of sophisticated modulation systems, adaptive equalisation methods, and error control coding, it raises the communication networks' dependability and effectiveness. Designing complex systems that can handle the complexities of today's data and communication demands is made possible by the paradigm-shifting synergy of nonlinear analysis, signal processing, and communication engineering. This abstract highlights the role that nonlinear analysis plays in transforming various fields and highlights how it can be used to solve changing problems related to information comprehension, processing, and transmission in a technologically sophisticated world.

Virtual brain network modeling in AD: from pathophysiology towards treatment

AbstractBackgroundWhile much is known about the pathology and clinical symptoms of AD, their mutual relationship to impaired brain function during the AD disease course is incompletely understood. For deriving a coherent, integrated multi‐scale disease mechanism, computational neurophysiology is a promising candidate. Virtual models of neurons, neuronal circuits and large‐scale networks are being used at different scales and for different purposes. Ranging from biologically plausible neural mass models to more abstract models of complex system behaviour or pathology spreading patterns, their diversity and versatility can help to address issues that are hard to tackle experimentally.MethodIn this presentation I will review several recent developments that illustrate how virtual models can help to translate local neuronal activity to brain‐wide network level data, probe the effects of AD pathology on brain network behaviour, and predict neurophysiological outcomes of different types of treatment. More specifically, I will show how amyloid‐induced hyperexcitability is compatible with large‐scale oscillatory slowing in AD, and how we can simulate counteracting activity‐targeting interventions such as non‐invasive brain stimulation using virtual models.ResultAD‐like adaptations in neuronal excitability in neural mass model parameters led to the combination of high absolute power and large‐scale oscillatory slowing in nearly all tested scenarios. In contrast, the hypoexcitable neural mass scenarios did not produce AD‐like oscillatory changes. Furthermore, simulating various counteracting tDCS‐induced excitability changes led to highly variable ‘virtual treatment’ outcomes, supporting the idea that electrode location choice can play a substantial role in tDCS treatment success, and generating specific predictions to be falsified in patient studies.ConclusionVirtual models of altered brain activity can help to understand AD pathophysiology and facilitate treatment development.

A hybrid approach to system verification in early design for complex mechatronic systems based on formal functional semantics

Model-based systems engineering (MBSE) has been adopted as a mainstream design methodology for complex mechatronic systems. According to this paradigm, structured models, typically represented in SysML are used to describe the system of interest from various aspects. Among these models, the system behavior model is fundamental for the subsequent design and hence should be verified against system functions in the early design stage. However, due to the lack of formal semantics of the involved SysML models and software-physical hybrid characteristics of the mechatronic systems, it is not a trivial task to apply existing formal verification methods to solve this problem. In this study, a novel approach relying on hybrid functional semantics is proposed. First, this verification problem is formally defined based on an in-depth analysis of the architectures and traceability between the involved SysML models. Then, a graphical modeling language to represent the hybrid functional semantics in SysML is defined so that the function and behavior models can be enhanced and explicitly correlated by the formal semantics. Finally, based on the semantically enhanced models, the model verification problem is formally re-defined as a semantics verification problem and further divided into two sub-problems, i.e., continuous and discrete semantics verification problems, which are automatically solved by leveraging existing verification techniques including functional-effect compatibility check and symbolic model checking. A mobile robot is illustrated to show the effectiveness of the proposed approach.

Consistency Constraints for Mapping Dataflow Graphs to Hybrid Dataflow/von Neumann Architectures

Dataflow process networks (DPNs) provide a convenient model of computation that is often used to model system behavior in model-based designs. With fixed sets of nodes, they are also used as dataflow graphs as an intermediate program representation by compilers to uncover instruction-level parallelism of sequential programs. Many recent processor architectures, which are still von Neumann architectures, also use dataflow computing to increase their exploitation of instruction-level parallelism by exposing their datapaths so that the compiler can take care of the allocation of processing units (PUs), the execution schedules of instructions on the PUs, and the communication of intermediate values between PUs. If the communication paths are buffered, these architectures can be abstracted into a DPN architecture whose PUs and interconnection network are DPN nodes. In this article, we introduce a DPN abstraction of hybrid dataflow/von Neumann architectures and consider the mapping of the nodes of a given dataflow graph to the PUs of such a DPN architecture such that there are no conflicts due to the mapping of different nodes to the same PU. We express the allocation and scheduling constraints in terms of propositional logic for the original dataflow graph and for a modified version of the dataflow graph that simplifies the constraints by introducing levels using copy nodes, such that all nodes receive inputs only from nodes of the previous level. We also formulate equisatisfiable SMT constraints using integer variables to reason directly about the parallel runtime. On this basis, we further present alternative SAT constraints that explicitly encode concurrency, and discuss variants of the constraints for a better understanding of the same.

Minimising conflicts among run‐time non‐functional requirements within DevOps

AbstractSignificant contributions in the existing literature highlight the potential of softgoal interdependency graphs towards analyzing conflicting non‐functional requirements (NFRs). However, such analysis is often at a very abstract level and does not quite consider the run‐time performance statistics of NFR operationalizations. On the contrary, some initial empirical evaluations demonstrate the importance of the run‐time statistics. In this paper, a framework is proposed that uses these statistics and combines the same with NFR priorities for computing the impact of NFR conflicts. The proposed framework is capable of identifying the best possible set of NFR operationalizations that minimizes the impact of conflicting NFRs. A detailed space analysis of the solution framework helps proving the efficiency of the proposed pruning mechanism in terms of better space management. Furthermore, a Dynamic Bayesian Network (DBN) ‐ based system behavioral model that works on top of the proposed framework, is defined and analyzed. An appropriate tool prototype for the framework is implemented as part of this research.

How to combat atmospheric carbon dioxide along with development activities? A mathematical model

Depletion in forest density due to development activities is one of the reasons for the elevated level of carbon dioxide (CO2). In this scenario, the plantation of leafy trees, which absorb more CO2 compared to regular trees, under a planned strategy may help to attain the desired level of atmospheric carbon dioxide. We have formulated a non-linear mathematical model concerning the strategy of maintaining the atmospheric level of CO2 along with development activities and analyzed the proportion of deforested land needed for the plantation of leafy trees. This strategy will ensure that the absorption of carbon dioxide remains at its previous level. To study the long-term behavior of the formulated model system, we employ the qualitative theory of differential equations. We have derived sufficient conditions under which all the considered dynamical variables settle to their equilibrium level. After analyzing the formulated model system, we find that the system undergoes Hopf and transcritical bifurcations with respect to the deforestation rate. Furthermore, numerical simulations have been carried out to support the analytically obtained results. Through simulation, we have determined the proportion of cleared land that should be utilized for the plantation of leafy trees to maintain the level of atmospheric CO2.

Behavior-Based Anomaly Detection in Log Data of Physical Access Control Systems

Behavior-based anomaly detection (AD) approaches for enterprise-IT security are not easily applicable to other domains, such as embedded devices and IoT nodes in cyber-physical systems. AD approaches are usually highly optimized for specific purposes, tightly bound to domain-specific technologies and rely on a specific syntax of investigated data. Data from cyber-physical systems is however highly diverse, often poorly documented and not easily ingested for automated analysis. AECID provides an anomaly detection approach, that monitors unstructured textual event data (i.e., log data), and implements self-learning for autonomous operation. A parser generator establishes a model of normal system behavior on top of observed events, which then can be leveraged to detect anomalies as deviations from that baseline. The unsupervised anomaly detection approaches of AECID apply machine learning techniques to perform sequence analysis, correlation analysis and statistical tests of events represented in log data. This paper discusses AECID's applicability in a building security system use case. A proof of concept demonstrates the effective detection of anomalies in log data of a building access control system stemming from card misuse, including stolen access cards and cloned cards.

Insights on Simulating Summer Warming of the Great Lakes: Understanding the Behavior of a Newly Developed Coupled Lake‐Atmosphere Modeling System

AbstractThe Laurentian Great Lakes are the world's largest freshwater system and regulate the climate of the Great Lakes region, which has been increasingly experiencing climatic, hydrological, and ecological changes. An accurate mechanistic representation of the Great Lakes thermal structure in Regional Climate Models (RCMs) is paramount to studying the climate of this region. Currently, RCMs have primarily represented the Great Lakes through coupled one‐dimensional (1D) column lake models; this approach works well for small inland lakes but is unable to resolve the realistic hydrodynamics of the Great Lakes and leads to inaccurate representations of lake surface temperature (LST) that influence regional climate and weather patterns. This work overcomes this limitation by developing a fully two‐way coupled modeling system using the Weather Research and Forecasting model and a three‐dimensional (3D) hydrodynamic model. The coupled model system resolves the interactive physical processes between the atmosphere, lake, and surrounding watersheds; and validated against a range of observational data. The model is then used to investigate the potential impacts of lake‐atmosphere coupling on the simulated summer LST of Lake Superior. By evaluating the difference between our two‐way coupled modeling system and our observation‐driven modeling system, we find that coupled‐lake atmosphere dynamics can lead to a higher LST during June‐September through higher net surface heat flux entering the lake in June and July and a lower net surface heat flux entering the lake in August and September. The unstratified water in June distributes the entering surface heat flux throughout the water column leading to a minor LST increase, while the stratified waters of July create a conducive thermal structure for the water surface to warm rapidly under the higher incoming surface heat flux. This research provides insight into the coupled modeling system behavior, which is critical for enhancing our predictive understanding of the Great Lakes climate system.

Design and Fabrication of a High Performance Microfluidic Chip for Blood Plasma Separation: Modelling and Prediction of System Behaviour via CFD Method.

This paper presents a single-step microfluidic system designed for passive separation of human fresh blood plasma using direct capillary forces. Our microfluidic system is composed of a cylindrical well between upper and lower channel pairs produced by soft photolithography. The microchip was fabricated based on hydrophobicity differences upon suitable cylindrical surfaces using gravitational and capillary forces and lateral migration of plasma and red blood cells. The plasma radiation was applied to attach the polymeric segment (polydimethylsiloxane (PDMS)) to the glass. Meanwhile, Tween 80 was used as a surfactant to increase the hydrophobicity of the lateral channel surfaces. This led to the higher movement of whole blood, including plasma. Fick's law of diffusion was validated for this diffusion transfer, the Navier-Stokes equation was used for the momentum balance, and the Laplace equation was utilized for the dynamics of the mesh. A model with high accuracy using the COMSOL Multiphysics software was created to predict the capillary forces and chip model validation. RBCs (red blood cells) were measured by the H3 cell counter instrument, by which 99% plasma purity was achieved. Practically, 58.3% of the plasma was separated from the blood within 12 min. Correlation between plasma separation results obtained from software and experimental data showed a coefficient of determination equal to 0.9732. This simple, rapid, stable, and reliable microchip can be considered as a promising candidate for providing plasma in point-of-care diagnostics.

Design of a Takagi–Sugeno Fuzzy Exact Modeling of a Buck–Boost Converter

DC–DC converters are used in many power electronics applications, such as switching power supply design, photovoltaic, power management systems, and electric and hybrid vehicles. Traditionally, DC–DC converters are linearly modeled using a typical operating point for their control design. Some recent works use nonlinear models for DC–DC converters, due to the inherent nonlinearity of the switching process. In this sense, a standout modeling technique is the Takagi–Sugeno fuzzy exact method due to its ability to represent nonlinear systems over the entire operating range. It is more faithful to system behavior modeling, and allows a nonlinear closed-loop control design. The use of nonlinear models allows the testing of controllers obtained by linear methods to operate outside their linearization point, corroborating with robust controllers for specific applications. This work aims to perform the exact fuzzy Takagi–Sugeno modeling of a buck–boost converter with non-ideal components, and to design a discrete proportional–integral–derivative (PID) controller from the pole cancellation technique, obtained linearly, to test the controller at different operating points. The PID control ensured a satisfactory result compared with the stationary value of the different operating points, but it did not reach the desired transient response. Since the proposed model closely represents the operation of the buck–boost converter by considering the components’ non-idealities, other control techniques that consider the system’s nonlinearities can be applied and optimized later.

The influence of piperine on oxidation-induced porcine myofibrillar protein gelation behavior and fluorescent advanced glycation end products formation in model systems

This study investigated the effects of piperine on oxidation-induced porcine myofibrillar protein (MP) gelation behavior and fluorescent advanced glycation end products (fAGEs) formation. Model systems were prepared, lipid oxidation, MP gelling behavior, and fAGEs content were determined daily. The results indicated that lipid oxidation, carbonyl content, S0, cooking loss, and tryptophan fluorescence intensity of MP significantly decreased, whereas gel strength, WHC, and whiteness markedly increased as the concentration of piperine increased (from 0 to 30 μM/g protein), indicating that piperine could reduce lipid oxidation and oxidative damage to MP. The fluorescence intensity of fAGEs markedly decreased (P < 0.05), from 93.1 ± 4.4 to 61.2 ± 3.0, as the concentration of piperine increased from 0 μM/g protein to 30 μM/g protein after 5 days of incubation. These results in model systems suggest that the presence of piperine has an important role in the inhibition of MP oxidation and fAGEs formation.

Effects of sucrose particle size on the microstructure and bloom behavior of chocolate model systems

Effects of sucrose particle size on the microstructure and bloom behavior of chocolate model systems

Testing, Validation, and Verification of Robotic and Autonomous Systems: A Systematic Review

We perform a systematic literature review on testing, validation, and verification of robotic and autonomous systems (RAS). The scope of this review covers peer-reviewed research papers proposing, improving, or evaluating testing techniques, processes, or tools that address the system-level qualities of RAS. Our survey is performed based on a rigorous methodology structured in three phases. First, we made use of a set of 26 seed papers (selected by domain experts) and the SERP-TEST taxonomy to design our search query and (domain-specific) taxonomy. Second, we conducted a search in three academic search engines and applied our inclusion and exclusion criteria to the results. Respectively, we made use of related work and domain specialists (50 academics and 15 industry experts) to validate and refine the search query. As a result, we encountered 10,735 studies, out of which 195 were included, reviewed, and coded. Our objective is to answer four research questions, pertaining to (1) the type of models, (2) measures for system performance and testing adequacy, (3) tools and their availability, and (4) evidence of applicability, particularly in industrial contexts. We analyse the results of our coding to identify strengths and gaps in the domain and present recommendations to researchers and practitioners. Our findings show that variants of temporal logics are most widely used for modelling requirements and properties, while variants of state-machines and transition systems are used widely for modelling system behaviour. Other common models concern epistemic logics for specifying requirements and belief-desire-intention models for specifying system behaviour. Apart from time and epistemics, other aspects captured in models concern probabilities (e.g., for modelling uncertainty) and continuous trajectories (e.g., for modelling vehicle dynamics and kinematics). Many papers lack any rigorous measure of efficiency, effectiveness, or adequacy for their proposed techniques, processes, or tools. Among those that provide a measure of efficiency, effectiveness, or adequacy, the majority use domain-agnostic generic measures such as number of failures, size of state-space, or verification time were most used. There is a trend in addressing the research gap in this respect by developing domain-specific notions of performance and adequacy. Defining widely accepted rigorous measures of performance and adequacy for each domain is an identified research gap. In terms of tools, the most widely used tools are well-established model-checkers such as Prism and Uppaal, as well as simulation tools such as Gazebo; Matlab/Simulink is another widely used toolset in this domain. Overall, there is very limited evidence of industrial applicability in the papers published in this domain. There is even a gap considering consolidated benchmarks for various types of autonomous systems.

A machine learning approach to identifying suicide risk among text-based crisis counseling encounters.

With the increasing utilization of text-based suicide crisis counseling, new means of identifying at risk clients must be explored. Natural language processing (NLP) holds promise for evaluating the content of crisis counseling; here we use a data-driven approach to evaluate NLP methods in identifying client suicide risk. De-identified crisis counseling data from a regional text-based crisis encounter and mobile tipline application were used to evaluate two modeling approaches in classifying client suicide risk levels. A manual evaluation of model errors and system behavior was conducted. The neural model outperformed a term frequency-inverse document frequency (tf-idf) model in the false-negative rate. While 75% of the neural model's false negative encounters had some discussion of suicidality, 62.5% saw a resolution of the client's initial concerns. Similarly, the neural model detected signals of suicidality in 60.6% of false-positive encounters. The neural model demonstrated greater sensitivity in the detection of client suicide risk. A manual assessment of errors and model performance reflected these same findings, detecting higher levels of risk in many of the false-positive encounters and lower levels of risk in many of the false negatives. NLP-based models can detect the suicide risk of text-based crisis encounters from the encounter's content.