Behavior Of Systems Research Articles

State Machines for Large Scale Computer Software and Systems

The behavior and architecture of large scale discrete state systems found in computer software and hardware can be specified and analyzed using a particular class of primitive recursive functions. This article begins with an illustration of the utility of the method via a number of small examples and then via longer specification and verification of the “Paxos” distributed consensus algorithm [ 26 ]. The “sequence maps” are then shown to provide an alternative representation of deterministic state machines and products of state machines. Distributed and composite systems, parallel and concurrent computation, and real-time behavior can all be specified naturally with these methods—which require neither extensions to the classical state machine model nor any axiomatic methods or other techniques from formal logic or other foundational methods. Compared to state diagrams or tables or the standard set-tuple-transition-maps, sequence maps are more concise and better suited to describing the behavior and compositional architecture of computer systems. Staying strictly within the boundaries of classical deterministic state machines anchors the methods to the algebraic structures of automata and makes the specifications faithful to engineering practice.

Elements of Timed Pattern Matching

The rise of machine learning and cloud technologies has led to a remarkable influx of data within modern cyber-physical systems. However, extracting meaningful information from this data has become a significant challenge due to its volume and complexity. Timed pattern matching has emerged as a powerful specification-based runtime verification and temporal data analysis technique to address this challenge. In this paper, we provide a comprehensive tutorial on timed pattern matching that ranges from the underlying algebra and pattern specification languages to performance analyses and practical case studies. Analogous to textual pattern matching, timed pattern matching is the task of finding all time periods within temporal behaviors of cyber-physical systems that match a predefined pattern. Originally we introduced and solved several variants of the problem using the name of match sets, which has evolved into the concept of timed relations over the past decade. Here we first formalize and present the algebra of timed relations as a standalone mathematical tool to solve the pattern matching problem of timed pattern specifications. In particular, we show how to use the algebra of timed relations to solve the pattern matching problem for timed regular expressions and metric compass logic in a unified manner. We experimentally demonstrate that our timed pattern matching approach performs and scales well in practice. We further provide in-depth insights into the similarities and fundamental differences between monitoring and matching problems as well as regular expressions and temporal logic formulas. Finally, we illustrate the practical application of timed pattern matching through two case studies, which show how to extract structured information from temporal datasets obtained via simulations or real-world observations. These results and examples show that timed pattern matching is a rigorous and efficient technique in developing and analyzing cyber-physical systems.

Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment.

This roadmap reviews the new, highly interdisciplinary research field studying the behavior of condensed matter systems exposed to radiation. The Review highlights several recent advances in the field and provides a roadmap for the development of the field over the next decade. Condensed matter systems exposed to radiation can be inorganic, organic, or biological, finite or infinite, composed of different molecular species or materials, exist in different phases, and operate under different thermodynamic conditions. Many of the key phenomena related to the behavior of irradiated systems are very similar and can be understood based on the same fundamental theoretical principles and computational approaches. The multiscale nature of such phenomena requires the quantitative description of the radiation-induced effects occurring at different spatial and temporal scales, ranging from the atomic to the macroscopic, and the interlinks between such descriptions. The multiscale nature of the effects and the similarity of their manifestation in systems of different origins necessarily bring together different disciplines, such as physics, chemistry, biology, materials science, nanoscience, and biomedical research, demonstrating the numerous interlinks and commonalities between them. This research field is highly relevant to many novel and emerging technologies and medical applications.

Gradient-based adaptive neural network technique for two-dimensional local fractional elliptic PDEs

This paper introduces gradient-based adaptive neural networks to solve local fractional elliptic partial differential equations. The impact of physics-informed neural networks helps to approximate elliptic partial differential equations governed by the physical process. The proposed technique employs learning the behaviour of complex systems based on input-output data, and automatic differentiation ensures accurate computation of gradient. The method computes the singularity-embedded local fractional partial derivative model on a Hausdorff metric, which otherwise halts the computation by available approximating numerical methods. This is possible because the new network is capable of updating the weight associated with loss terms depending on the solution domain and requirement of solution behaviour. The semi-positive definite character of the neural tangent kernel achieves the convergence of gradient-based adaptive neural networks. The importance of hyperparameters, namely the number of neurons and the learning rate, is shown by considering a stationary anomalous diffusion-convection model on a rectangular domain. The proposed method showcases the network’s ability to approximate solutions of various local fractional elliptic partial differential equations with varying fractal parameters.

Phase Transition in Silicon from Machine Learning Informed Metadynamics.

Investigating reconstructive phase transitions in large-sized systems requires a highly efficient computational framework with computational cost proportional to the system size. Traditionally, widely used frameworks such as density functional theory (DFT) have been prohibitively expensive for extensive simulations on large systems that require long-time scales. To address this challenge, this study employed well-trained machine learning potential to simulate phase transitions in a large-size system. This work integrates the metadynamics simulation approach with machine learning potential, specifically deep potential, to enhance computational efficiency and accelerate the study of phase transition and consequent development of grains and dislocation defects in a system. The new method is demonstrated using the phase transitions of bulk silicon under high pressure. This approach has revealed the transition path and formation of polycrystalline silicon systems under specific stress conditions, demonstrating the effectiveness of deep potential-driven metadynamics simulations in gaining insights into complex material behaviors in large-sized systems.

Theoretical studies and temperature-dependent Raman spectroscopy of Schiff's bases 5O.12 liquid crystalline compound

Comparative Raman spectra and different physical properties of 5O.12 liquid crystal compound using density functional theory is studied in this article. The molecular structure of 5O.12 liquid crystal has been optimized by using density functional theory (DFT). The Raman spectroscopy examination is critical for observing the properties and behavior of liquid crystals. 5O.12 is a Schiff-based with several liquid crystalline phases, including Nematic, Smectic A, and Smectic B. The phases of the liquid crystal vary as the temperature changes, providing essential information about the liquid crystal's molecular arrangement. The density functional theory with standard basis set 6–31 G (d, p) paired with B3LYP functional is used to study the theoretical Raman spectra and various physical properties. Theoretical details of Raman spectra give excellent agreement with experimental results. Experimental Raman spectra were examined with temperature in all of the observed phases in this article. Comparative Raman studies at different temperatures help in understanding thermodynamic and structural behavior of molecular systems. Peak positions and line width are investigated further as temperature varies, yielding precise and significant information about the molecule's ordering and behaviour. DFT is used to do research on different physical properties like HOMO LUMO, relative energy gaps and various thermochemical properties such as entropy, heat capacity, thermal energy. These characteristics give an understanding of the stability of molecules, as well as the optical properties and structure of liquid crystalline compounds.

Structural, topological, and rheological characteristics of entangled short-chain branched polymer melts under shear flow in comparison with the linear analog

We present a detailed analysis of the general influence of short branches on the structural, topological, and rheological behaviors of entangled short-chain branched (SCB) polyethylene (PE) melt systems under shear flow via direct comparison with the corresponding linear analogs using extensive atomistic nonequilibrium molecular dynamics (NEMD) simulations, for a wide range of flow strengths. In comparison with the linear melt, the SCB systems generally exhibit more compact chain structures and larger dynamic resistance, in response to an imposed flow field at all flow strengths. These features essentially arise from (i) the increased chain stiffness due to the torsional restriction of backbone atoms around the branch points and (ii) the fast random Brownian motion of short branches via their very short characteristic relaxation time. We analyzed various structural and rheological properties, such as anisotropic chain dimension and orientation and their detailed distributions, topological characteristics of the entanglement network, material functions, chain rotation dynamics, and flow birefringence. Distinctive physical characteristics of the entangled SCB systems exposed by these individual properties can be consistently understood based on the fundamental structural and dynamical roles of short branches. These findings are considered informative in our systematic understanding and prediction for the general rheological behaviors of long entangled SCB polymer systems under flow, and in tuning the material properties of SCB polymers in practical applications.

Observation of nonlinear response and Onsager regression in a photon Bose-Einstein condensate

The quantum regression theorem states that the correlations of a system at two different times are governed by the same equations of motion as the single-time averages. This provides a powerful framework for the investigation of the intrinsic microscopic behaviour of physical systems by studying their macroscopic response to a controlled external perturbation. Here we experimentally demonstrate that the two-time particle number correlations in a photon Bose-Einstein condensate inside a dye-filled microcavity exhibit the same dynamics as the response of the condensate to a sudden perturbation of the dye molecule bath. This confirms the regression theorem for a quantum gas, and, moreover, demonstrates it in an unconventional form where the perturbation acts on the bath and only the condensate response is monitored. For strong perturbations, we observe nonlinear relaxation dynamics which our microscopic theory relates to the equilibrium fluctuations, thereby extending the regression theorem beyond the regime of linear response.

Beyond dynamics: learning to discover conservation principles

The discovery of conservation principles is crucial for understanding the fundamental behavior of both classical and quantum physical systems across numerous domains. This paper introduces an innovative method that merges representation learning and topological analysis to explore the topology of conservation law spaces. Notably, the robustness of our approach to noise makes it suitable for complex experimental setups and its aptitude extends to the analysis of quantum systems, as successfully demonstrated in our paper. We exemplify our method’s potential to unearth previously unknown conservation principles and endorse interdisciplinary research through a variety of physical simulations. In conclusion, this work emphasizes the significance of data-driven techniques in deepening our comprehension of the principles governing classical and quantum physical systems.

Human Action Recognition System Using LRCN

Abstract: The importance of human action recognition is significant across various domains, driving advancements in safety, healthcare, sports analytics, and interactive technologies. Leveraging machine learning techniques like Long Recurrent Convolutional Neural Networks (LRCN) trained on datasets such as UCF50, our project focuses on action prediction from YouTube videos. This technology plays a pivotal role in enhancingsafety and security by enabling the detection of anomalies and suspicious behaviours in surveillance systems. In healthcare, it supports remote patient monitoring, rehabilitation assessment, and personalized care plans based on observed actions. Addi- tionally, in sports analytics and entertainment, human action recognition informs performance evaluation, content creation, and immersive experiences. Our integration of a pre-trained LRCN model into a Flask web application signifies the tangible impact of machine learning on human action recognition, with ongoing efforts to optimize functionality through input validation, error handling, user feedback, security measures, and performance enhancements, illustrating the practical application and societal benefits of this innovative technology

Autonomous assembly and disassembly of gliding molecular robots regulated by a DNA-based molecular controller.

In recent years, there has been a growing interest in engineering dynamic and autonomous systems with robotic functionalities using biomolecules. Specifically, the ability of molecular motors to convert chemical energy to mechanical forces and the programmability of DNA are regarded as promising components for these systems. However, current systems rely on the manual addition of external stimuli, limiting the potential for autonomous molecular systems. Here, we show that DNA-based cascade reactions can act as a molecular controller that drives the autonomous assembly and disassembly of DNA-functionalized microtubules propelled by kinesins. The DNA controller is designed to produce two different DNA strands that program the interaction between the microtubules. The gliding microtubules integrated with the controller autonomously assemble to bundle-like structures and disassemble into discrete filaments without external stimuli, which is observable by fluorescence microscopy. We believe this approach to be a starting point toward more autonomous behavior of motor protein-based multicomponent systems with robotic functionalities.

Modelling the behaviour in socio-technical systems for policy assessment - A comparison of modelling approaches using the example of Sponge City concept implementation

How are policies affecting the social behaviour of actors and thus the dynamics of the corresponding socio-technical systems? This question is of central relevance to understand what policies might be effective for sustainable transitions and transformation where social changes are immanent. Exploratory modelling approaches such as agent-based modelling (ABM) are suitable and established to analyse how individual, especially heterogeneous behaviour emerges to affect social systems. In contrast to this micro-level perspective of understanding how social behaviour influences system behaviour, System Dynamics models (SDM) focus on the macro-level perspective by analysing how system interactions drive complex system behaviour. Qualitative modelling approaches such as Causal Loop Diagrams belonging to the Systems Thinking Modelling (STM) toolbox remain descriptive but need much less effort for model development. In this article, we compare how the different approaches ABM, SDM and STM can model social behaviour in socio-technical systems and what different kind of insights can be gained to assess the efficacy of policies. It discusses the advantages and limitations of the different approaches for their application in behavioural public policy modelling for sustainability transition. Therefore, three exemplary models (STM, SDM, ABM) are created for the same topic of implementation dynamics of adaptation measures to foster rainwater retention (Sponge City concept) in a multi-residential quarter. As a result, the risk of green gentrification is identified. Three hypothetical policies are implemented to illustrate the differences between the three modelling approaches for their use in policy assessment. Based on that, we discuss how the three modelling approaches conceptualise behaviour in general.

An experimental study on aging effects of fuel-cut events including sound optimized torque reduction on modern three-way catalysts

Continuously increasing country-specific emission standards for passenger vehicles demand additional development steps in complex exhaust gas aftertreatment systems for a more effective reduction of harmful gases. The efficiency of the aftertreatment system declines over its lifecycle due to specific load profiles and other boundary conditions, resulting in increasing emissions over vehicle mileage. As a result, the durability of the exhaust system becomes more important. Fuel cut is an intended, temporary interruption of the fuel supply of modern combustion engines of passenger cars to reduce fuel consumption and emissions. During this fuel reduction event, unconsumed oxygen of rich and cool fresh air is introduced to the combustion chamber and to the exhaust system. Besides a catalyst cool down, oxidation effects are provoked by enhanced oxygen reactions processes within the catalyst’s washcoat and surface layers. In this publication, specific aging cycles with a variation of fuel-cut programs were examined on their effects on a modified modern eight-cylinder turbo-charged engine, especially on their oxidation effects correlating to catalyst aging. Light-off curves and conversion heat maps were used to evaluate possible damaging impacts of fuel-cut events on the aging behavior of the catalyst systems. Results indicate that higher frequency of fuel-cut events results in a reduced catalyst conversion efficiency, whereas thermal sintering might be reduced due to overall lower temperature load. Temporary oxidation processes on catalyst’s surfaces occur only in a short timespan after changing the air fuel ratio to lean, resulting in a weaker thermal sintering during longer fuel-cut events. Sound optimized torque reduction functions, used in sports cars for a better drivability and powertrain response, exhibit a stronger aging influence due to additional thermal shock effects on the catalysts front face, as demonstrated via optical infrared sensor technology during catalyst aging cycles on the modified engine bench setup. A theoretical calculation of aging processes as a combination of thermal sintering by Arrhenius equation as well as oxidation process is discussed to describe an aging classification induced via oxidation processes. Additional physisorption measurements of different catalyst probes support the results which indicate the direction of future experimental approaches.

Safe Insulation from the Inside as a Solution to the Energy and Climate Crisis

Along with rapid climate changes, issues related to energy efficiency and efficient use of energy are becoming more and more relevant in the EU. One of the energy-consuming sectors is buildings, which are responsible for about 40% of the EU final energy consumption and 36% of CO2 emissions. In addition to this, the current energy crisis has acutely raised the issue of energy poverty. Therefore, one of the ways to fight with energy poverty and high energy consumption in buildings is insulation from the inside. However, warming from the inside is risky due to hygrothermal processes and mold risks. This study assesses the hygrothermal performance of masonry walls with 9 interior insulation systems exposed to different external conditions in the climate chambers. Masonry walls were tested in a steady cycle, a dynamic cycle, a dynamic cycle with rain and a steady cycle as drying. Also, an identical simulation of the hygrothermal process was carried out in the DELPHIN software to compare results of both testing methods. Both vapor-open and vaportight systems were chosen as insulation systems. Results show that the hygrothermal behavior of vapor-open and vapor-tight insulation systems are different under different test cycles regarding different vapor diffusion resistance of materials. Mathematical simulations results are different from the climate chamber simulations, because of the change in material humidity, that are changing during climate chambers simulations. From this it can be concluded that mathematical simulations do not give a complete vision of hydrothermal processes, because they are dynamic, but in modeling the properties of materials are defined. The study provides valuable data on hygrothermal processes in different wall insulation systems from the inside.

Towards accelerating particle‐resolved direct numerical simulation with neural operators

AbstractWe present our ongoing work aimed at accelerating a particle‐resolved direct numerical simulation model designed to study aerosol–cloud–turbulence interactions. The dynamical model consists of two main components—a set of fluid dynamics equations for air velocity, temperature, and humidity, coupled with a set of equations for particle (i.e., cloud droplet) tracing. Rather than attempting to replace the original numerical solution method in its entirety with a machine learning (ML) method, we consider developing a hybrid approach. We exploit the potential of neural operator learning to yield fast and accurate surrogate models and, in this study, develop such surrogates for the velocity and vorticity fields. We discuss results from numerical experiments designed to assess the performance of ML architectures under consideration as well as their suitability for capturing the behavior of relevant dynamical systems.

Robust Phototaxis by Harnessing Implicit Communication in Modular Soft Robotic Systems

AbstractIn robotics, achieving adaptivity in complex environments is challenging. Traditional robotic systems use stiff materials and computationally expensive centralized controllers, while nature often favors soft materials and embodied intelligence. Inspired by nature's distributed intelligence, this study explores a decentralized approach for robust behavior in soft robotic systems without knowledge of their shape or environment. It is demonstrated that only a few basic rules implemented in identical modules that shape the soft robotic system can enable whole‐body phototaxis, navigating on a surface toward a light source, without explicit communication between modules or prior system knowledge. The results reveal the method's effectiveness in generating robust and adaptive behavior in dynamic and challenging environments. Moreover, the approach's simplicity makes it possible to illustrate and understand the underlying mechanism of the observed behavior, paying particular attention to the geometry of the assembled system and the effect of learning parameters. Consequently, the findings offer insights into the development of adaptive, autonomous robotic systems with minimal computational power, paving the way for robust and useful behavior in soft and microscale robots, as well as robotic matter, that operate in real‐world environments.

Rheology and physical properties of sonicated chickpea concentrate with fructans in aqueous media and stable emulsions

Based on the rheological and other physical properties of chickpea concentrate (CPC)/fructan aqueous mixtures containing 0.2% carboxymethyl cellulose (CMC) and their respective emulsions, the macrostructure of the system was elucidated, and its effect on the stability of the proposed emulsions was evaluated. The zeta potential and particle size of the CPC dispersions subjected to ultrasonic treatment were analyzed. Neither agave (AGF) nor chicory fructans (CHF) affected the rheological behavior (Newtonian) of aqueous systems containing only CPC. A Carreau behavior was observed when CMC was added. A synergistic effect occurred when 6% CHF was mixed with CMC. Furthermore, CHF led to a higher overall viscosity than AGF in the systems studied. Fructans changed the total charge of the protein dispersion and increased the particle size. The viscoelastic properties confirmed the liquid-like behavior of the aqueous mixtures. Emulsions containing CPC/CMC mixtures and fructans exhibited thixotropic behavior; emulsions containing 30% oil followed the Carreau model, while emulsions containing 60% oil were fitted to the Hershel-Bulkley model for upward and downward shear rate curves. The latter behavior corresponded to the formation of a weak gel structure and a flocculated oil droplet network, as confirmed by the mechanical spectra. The droplet size in the emulsions decreased in the presence of fructans, particularly for 3% CHF and 6% AGF. However, 6% CHF led to the formation of the most kinetically stable emulsions. These results confirm the possibility of incorporating CPC and fructans as oil-in-water emulsions stabilized with CMC into healthy plant-based products.

Magma titanium and iron contents dictate crystallization timescales and rheological behaviour in basaltic volcanic systems

Magma ascending through Earth’s crust undergoes complex chemical and physical changes that may induce crystallization, a process that contributes to lead the magmatic system toward a thermodynamic state of equilibrium. The diverse cooling and deformative regimes suffered by magmas heavily influence crystallization rates, solidification timescales, and consequently, the rheological evolution of magma. This, in turn, significantly impacts the dynamics of volcanic plumbing systems and the associated eruptive styles. Here, we investigate the rheological changes in Stromboli magma (Italy) during disequilibrium crystallization under non-isothermal subliquidus conditions. By systematically varying the cooling rate (1-10 °C/min) and the shear rate (1-10 s−1), we find that cooling rates significantly influence the solidification path of the basalt, whereas shear rates have a subordinate effect. By comparing our results with literature data on basalts from Mt. Etna (Italy), characterized by higher TiO2 and FeOtot contents, we observed distinct timescales and rates of solidification, contributing to unique eruptive dynamics in these volcanic plumbing systems.

The expanding scope of ship design practice

As the former International Chair of IMDC, the initiator of the continuing series of IMDC State of Art (SoA) Reports and the lead author of most IMDC SoA Reports on design methodology from 1997 to 2018, the author has both pioneered and observed an increasingly broader scope in the practice of the design of particularly complex vessels. The paper commences with reviewing some key publications, not just to recent IMDCs, that have tracked the manner in which “ship design” (in the broadest sense) has become more sophisticated – especially in the crucial early stages of design. The diversity of ship design practice, not just due to computer-based methods, is readily observable. Moreover, the impact of computer aided design in ship design has not just been to better analyse ship performance (e. g. in hydrodynamics, strength and ship infrastructure systems behaviour) but also in the increasing use of graphical tools and design methods to enable “better ship design”. In a growing number of, mainly, academic centres, but also in some government agencies and design consultancies, there is a clear desire to better understand how to design “ships” and to manage the ship design process, especially for the most complex and novel classes of vessels. In particular, the objectives being sought when conceptualising and synthesising a range of ship options (as part of the requirement elucidation approach) in an ever-increasing scoping of the relevant issues, amounts to developing a more holistic approach. This is not just due to an increasingly complex ship acquisition and ownership environment, but also due to environmental and socio-economic (especially system safety) concerns. Overlaying all this are the opportunities or the spectre of Artificial Intelligence (or perhaps more immediately those of Machine Learning) and its likely impact on engineering practice as well as those other professions in the “marine design enterprise”. The paper concludes by emphasising that while ship design has distinct differences, when compared with most other large scale engineering design practice, the lead ship design profession of the naval architect has somehow to deal with this expanding scope in the practice of “ship design”. This means the education and on the job development thrust must broaden if the ship design profession is not to be side-lined into acting as mere hull engineers. It is argued, such a specific role will be more vulnerable in an increasingly Machine Learning dominated future, than the holistic ship creating and systems architectural alternative. Finally an ambitious vision for future ship designers is given alongside a summary of the specific main contributions by the author to ship design methodology.

The detection of unconventional quantum oscillations in insulating 2D materials

In strongly correlated quantum materials, electrons behave in ways that often extend beyond the confines of conventional Fermi-liquid theory. Interesting results include the observation of low-temperature metallic behavior in systems that are highly resistive. Here we provide an overview of experiments in which insulators exhibit characteristics of a metal such as the Shubnikov–de Haas-like quantum oscillations, focusing on recent findings in the correlated insulating states of two-dimensional WTe2. We discuss the status of current research, clarify the debates and challenges in interpreting the experiments, rule out extrinsic explanations and discuss promising future directions.