Behavior Of Systems Research Articles

Robust optimisation for vertiport location problem considering travel mode choice behaviour in urban air mobility systems

Robust optimisation for vertiport location problem considering travel mode choice behaviour in urban air mobility 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

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.

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.

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

Abstract 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.

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.

Dynamic Analysis and Sliding Mode Synchronization Control of Chaotic Systems with Conditional Symmetric Fractional-Order Memristors

In this paper, the characteristics of absolute value memristors are verified through the circuit implementation and construction of a chaotic system with a conditional symmetric fractional-order memristor. The dynamic behavior of fractional-order memristor systems is explored using fractional-order calculus theory and the Adomian Decomposition Method (ADM). Concurrently, the investigation probes into the existence of coexisting symmetric attractors, multiple coexisting bifurcation diagrams, and Lyapunov exponent spectra (LEs) utilizing system parameters as variables. Additionally, the system demonstrates an intriguing phenomenon known as offset boosting, where the embedding of an offset can adjust the position and size of the system’s attractors. To ensure the practical applicability of these findings, a fractional-order sliding mode synchronization control scheme, inspired by integer-order sliding mode theory, is designed. The rationality and feasibility of this scheme are validated through a theoretical analysis and numerical simulation.

Asymptotic behavior of thermoelastic systems of laminated Timoshenko beams with Kelvin-Voigt damping

Asymptotic behavior of thermoelastic systems of laminated Timoshenko beams with Kelvin-Voigt damping

Uncoupling of Damped Linear Potential Multi-degree-of-freedom Structural and Mechanical Systems

Abstract This paper provides the necessary and sufficient conditions for a multi-degree-of-freedom linear potential system with an arbitrary damping matrix to be uncoupled into independent subsystems of at most two degrees-of-freedom using a real orthogonal transformation. The incorporation of additional information about the matrices, which many structural and mechanical systems commonly possess, shows a reduction in the number of these conditions to three. Several new results are obtained and are illustrated through examples. A useful general form for the damping matrix is developed that guarantees uncoupling of such systems when they satisfy just two conditions. The results provided herein lead to new physical insights into the dynamical behavior of potential systems with general damping matrices and to robust computational procedures. It is shown that the dynamics of a damped potential system in which the damping matrix may be arbitrary is identical to that of a damped gyroscopic potential system with a symmetric damping matrix. This brings, for the first time, these two systems, which are seen today as belonging to different categories of dynamical systems, under a unified framework.

Sampling the user controls in neural modeling of audio devices

This work studies neural modeling of nonlinear parametric audio circuits, focusing on how the diversity of settings of the target device user controls seen during training affects network generalization. To study the problem, a large corpus of training datasets is synthetically generated using SPICE simulations of two distinct devices, an analog equalizer and an analog distortion pedal. A proven recurrent neural network architecture is trained using each dataset. The difference in the datasets is in the sampling resolution of the device user controls and in their overall size. Based on objective and subjective evaluation of the trained models, a sampling resolution of five for the device parameters is found to be sufficient to capture the behavior of the target systems for the types of devices considered during the study. This result is desirable, since a dense sampling grid can be impractical to realize in the general case when no automated way of setting the device parameters is available, while collecting large amounts of data using a sparse grid only incurs small additional costs. Thus, the result provides guidance for efficient collection of training data for neural modeling of other similar audio devices.

Seismic performance of vertical strut and lateral brace suspended continuous‐plasterboard ceiling system with all edges free in the plane of ceiling

AbstractSuspended ceilings are critical nonstructural elements, and their seismic damage in buildings highlighted the incompatibility between the design and performance of structural and nonstructural elements. In order to study the performance of less researched continuous‐plasterboard ceilings, shake table testing was conducted on a ceiling system vertically supported at grid ends with free edges in its plane and suspended by vertical struts and lateral braces. The system had a clearance of 20 mm at the grid ends to accommodate ceiling movements. The ceiling performed satisfactorily for floor accelerations ranging from 0.2 g to 1.4 g without any visible damage. However, the ceiling was slightly rotated and lightly damaged at its perimeter for an extreme dynamic loading of sinusoidal excitation at its natural frequencies. In addition, the experimental performance of the ceiling was numerically validated using nonlinear and linearized responses of sub‐assemblage test data of critical components and connections. It was observed that the developed numerical models can be used to predict the behavior of such ceiling systems as an alternative to evaluation by shake table testing.

The power of sound: unravelling how acoustic communication shapes group dynamics.

Acoustic signalling is a key mode of communication owing to its instantaneousness and rapid turnover, its saliency and flexibility and its ability to function strategically in both short- and long-range contexts. Acoustic communication is closely intertwined with both collective behaviour and social network structure, as it can facilitate the coordination of collective decisions and behaviour, and play an important role in establishing, maintaining and modifying social relationships. These research topics have each been studied separately and represent three well-established research areas. Yet, despite the close connection of acoustic communication with collective behaviour and social networks in natural systems, only few studies have focused on their interaction. The aim of this theme issue is therefore to build a foundation for understanding how acoustic communication is linked to collective behaviour, on the one hand, and social network structure on the other, in non-human animals. Through the building of such a foundation, our hope is that new questions in new avenues of research will arise. Understanding the links between acoustic communication and social behaviour seems crucial for gaining a comprehensive understanding of sociality and social evolution. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Revealing trends and persistent cycles of non-autonomous systems with autonomous operator-theoretic techniques

An important problem in modern applied science is to characterize the behavior of systems with complex internal dynamics subjected to external forcings. Many existing approaches rely on ensembles to generate information from the external forcings, making them unsuitable to study natural systems where only a single realization is observed. A prominent example is climate dynamics, where an objective identification of signals in the observational record attributable to natural variability and climate change is crucial for making climate projections for the coming decades. Here, we show that operator-theoretic techniques previously developed to identify slowly decorrelating observables of autonomous dynamical systems provide a powerful means for identifying nonlinear trends and persistent cycles of non-autonomous systems using data from a single trajectory of the system. We apply our framework to real-world examples from climate dynamics: Variability of sea surface temperature over the industrial era and the mid-Pleistocene transition of Quaternary glaciation cycles.

Linear and nonlinear axial behaviour of internal replacement pipe systems for pipeline rehabilitation

Internal replacement pipe (IRP) is an innovative trenchless technology for the rehabilitation of legacy steel and cast-iron pipelines. This advanced IRP system must be properly designed so that it can safely and effectively restore the service of damaged pipelines. This paper studies the axial behaviour of IRP systems for repairing pipelines with circumferential host-pipe discontinuities under seasonal and extreme levels of temperature change. Analytical solutions along with a total of 270 linear and nonlinear finite element (FE) simulations, validated against experimental results and available closed-form solutions, were used for a parametric study on the effects of geometrical and material properties on axial stresses and deformations of IRP systems subjected to temperature change. Effects of the internal pressure, material and geometric nonlinearities, and different modes of IRP-host pipe unbonding were also investigated. An analytical model was developed for the prediction of temperature change induced loading and response of the IRP system. Using the results obtained from a comprehensive FE-based parametric study, three modification factors were extracted for the application to the developed analytical model in order to accurately predict the maximum axial stress of IRP and the opening of a circumferential host-pipe discontinuity subjected to various levels of temperature change.

COMPLEX CONTAGION IN SOCIAL SYSTEMS WITH DISTRUST

Social systems are characterized by the presence of group interactions and by the existence of both trust and distrust relations. Although there is a wide literature on signed social networks, where positive signs associated to the links indicate trust, friendship, agreement, while negative signs represent distrust, antagonism, and disagreement, very little is known about the effect that signed interactions can have on the spreading of social behaviors when, not only pairwise, but also higher-order interactions are taken into account. In this paper, we introduce a model of complex contagion on signed simplicial complexes, and we investigate the role played by trust and distrust on the dynamics of a social contagion process, where exposure to multiple sources is needed for the contagion to occur. The presence of higher-order signed structures in our model naturally induces new infection and recovery mechanisms, thus increasing the richness of the contagion dynamics. Through numerical simulations and analytical results in the mean-field approximation, we show how distrust determines the way the system moves from a state where no individuals adopt the social behavior, to a state where a finite fraction of the population actively spreads it. Interestingly, we observe that the fraction of spreading individuals displays a non-monotonic dependence with respect to the average number of connections between individuals. We then investigate how social balance affects social contagion, finding that balanced triads have an ambivalent impact on the spreading process, either promoting or impeding contagion based on the relative abundance of fully trusted relations. Our results shed light on the nontrivial effect of trust on the spreading of social behaviors in systems with group interactions, paving the way to further investigations of spreading phenomena in structured populations.

Decomposition solutions and Bäcklund transformations of the B-type and C-type Kadomtsev–Petviashvili equations

This paper introduces a modified formal variable separation approach, showcasing a systematic and notably straightforward methodology for analyzing the B-type Kadomtsev–Petviashvili (BKP) equation. Through the application of this approach, we successfully ascertain decomposition solutions, Bäcklund transformations, the Lax pair, and the linear superposition solution associated with the aforementioned equation. Furthermore, we expand the utilization of this technique to the C-type Kadomtsev–Petviashvili (CKP) equation, leading to the derivation of decomposition solutions, Bäcklund transformations, and the Lax pair specific to this equation. The results obtained not only underscore the efficacy of the proposed approach, but also highlight its potential in offering a profound comprehension of integrable behaviors in nonlinear systems. Moreover, this approach demonstrates an efficient framework for establishing interrelations between diverse systems.

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

Advancing interactive systems with liquid crystal network-based adaptive electronics.

Achieving adaptive behavior in artificial systems, analogous to living organisms, has been a long-standing goal in electronics and materials science. Efforts to integrate adaptive capabilities into synthetic electronics traditionally involved a typical architecture comprising of sensors, an external controller, and actuators constructed from multiple materials. However, challenges arise when attempting to unite these three components into a single entity capable of independently coping with dynamic environments. Here, we unveil an adaptive electronic unit based on a liquid crystal polymer that seamlessly incorporates sensing, signal processing, and actuating functionalities. The polymer forms a film that undergoes anisotropic deformations when exposed to a minor heat pulse generated by human touch. We integrate this property into an electric circuit to facilitate switching. We showcase the concept by creating an interactive system that features distributed information processing including feedback loops and enabling cascading signal transmission across multiple adaptive units. This system responds progressively, in a multi-layered cascade to a dynamic change in its environment. The incorporation of adaptive capabilities into a single piece of responsive material holds immense potential for expediting progress in next-generation flexible electronics, soft robotics, and swarm intelligence.