Dynamical Research Articles

Entropy Generation Modeling in Dynamic Local Thermal Non-Equilibrium Systems Using Neural Networks

The study of entropy generation in thermal non-equilibrium (TNE) states has significant implications for optimizing thermal management systems and understanding heat transfer mechanisms in permeable media. This study investigates the entropy properties in a thermal non-equilibrium (TNE) state within double-lid-driven enclosures filled with a permeable medium. Unlike the temperature equilibrium state, the entropy approach is described by two equations: one for the irreversibility of the mixture phase and one for the irreversibility of the medium phase. High mixed convection is considered due to the motion of the non-facing edges (left-side and upper edges). Four cases based on the direction of motion are examined: Case 1, where the left-side and top edges move in the negative and positive directions of the Y- and X-axes, respectively; Case 2, where the upper and left-side edges move in the negative and positive directions of the X- and Y-axes, respectively; and Cases 3 and 4, where the edges move in the positive and negative directions of the respective axes. Heat generation within the flow domain is considered for both the suspension and medium phases. The governing system is solved numerically using finite volume techniques with the SIMPLER algorithm. The obtained data are used to predict key quantities, such as the heat transfer rate, under the influence of major factors using an effective artificial neural network (ANN) analysis. The main findings show that the solid phase entropy is higher in Case 3 compared to the other cases. Additionally, Case 2 results in a minimum solid phase Nusselt coefficient at the center of the active boundary.

Transitions in intensive care: Investigating critical slowing down post extubation.

Complex biological systems undergo sudden transitions in their state, which are often preceded by a critical slowing down of dynamics. This results in longer recovery times as systems approach transitions, quantified as an increase in measures such as the autocorrelation and variance. In this study, we analysed paediatric patients in intensive care for whom mechanical ventilation was discontinued through removal of the endotracheal tube (extubation). Some patients failed extubation, and required a re-intubation within 48 hours. We investigated whether critical slowing down could be observed post failed extubations, prior to re-intubation. We tested for significant increases (p <.05) between extubation and re-intubation, in the variance and autocorrelation, over the time series data of heart rate, respiratory rate and mean blood pressure. The autocorrelation of the heart rate showed a significantly higher proportion of increases in the group that failed extubation, compared who those who did not. It also showed a significantly higher magnitude of increase for the failed extubation group in a t-test. Moreover, incorporating these magnitudes significantly improved the fit of a logistic regression model when compared to a model that solely used the mean and standard deviation of the vital signs. While immediate clinical utility is limited, the work marks an important first step towards using dynamical systems theory to understand the dynamics of signals measured at the bedside during intensive care.

Integrated Design and Dynamic Response Analysis of the EV Reducer Considering Mass Eccentricity

To meet the high-performance demands of electric vehicle reducers, the authors present a novel approach for parameter matching in electric drive systems for electric vehicles. Based on fuzzy evaluation, the method identifies the optimal performance parameters for the entire vehicle and determines the best transmission ratio scheme for the reducer. The gear geometrical parameters of the reducer are derived using the transmission ratio scheme as the constraint condition. An 18-degree-of-freedom dynamic system model, incorporating factors such as time-varying mesh stiffness, the backlash, time-varying transmission error and mass eccentricity, has been built using Matlab. The dynamic response of the gear system under the designed gear parameter scheme have been analyzed and compared with the gear parameter results obtained using KISSsoft, demonstrating the accuracy and rigour of the design. This research has practical implications for the design and performance optimization of electric vehicle reducers.

Reachability of time‐varying fractional dynamical systems with prescribed control

AbstractThe focus of this article revolves around investigating reachability results in the context of time‐varying fractional dynamical systems with prescribed or predetermined control by employing Riemann–Liouville's fractional derivative. The investigation delves into the necessary and sufficient conditions for reachability results concerning time‐varying linear fractional dynamical systems, employing both the Gramian technique and fractional calculus. Additionally, Schauder's fixed‐point technique is employed to establish the sufficient conditions for the reachability results of time‐varying nonlinear fractional dynamical systems. The validity of the theoretical findings is confirmed through the successive approximation technique, accompanied by a series of numerical examples.

Stability analysis of cosmological model in f(T) gravity

In this paper, we examine the stability condition of [Formula: see text] gravity theory where T is the Torsion scalar using interacting and non-interacting models with the help of dynamical system. We let the [Formula: see text] function be [Formula: see text], where T is Torsion scalar and [Formula: see text] and [Formula: see text] are the arbitrary constants. We calculated the critical points and study the stability behavior for this model. We analyze the system’s phase graphs and examined the physical interpretation. We demonstrate all of the cosmological parameters including [Formula: see text], [Formula: see text], q, and [Formula: see text] at each critical point and contrast them with values from observations. Then, we assume hybrid scale factor and transform it into the equation of redshift and time. Using this equation, we convert all the parameters into terms of redshift and analyze their behavior. Our model illustrates the Universe’s accelerating expansion. The energy conditions are examined in terms of redshift for the model. As a result, we conclude that our [Formula: see text] model is stable and all the observed values are in agreement with observations.

Dynamic behaviour of an eco-epidemiological model of fractional-order with a fear effect

Abstract In this paper, we propose a fractional-order prey-predator model with fear effects on the dynamic behavior of the populations. The model is used as a functional response of Holling type II in a non-delayed model. First, we prove several important results such as the existence, uniqueness, and boundedness of the solutions to the fractional-order dynamical system. Next, we discuss both the local and global stabilities of the fractional-order prey-predator model. The occurrence of Hopf bifurcation for fractional order is examined. Finally, the analytical solutions are confirmed through numerical simulations.

Investigation of the Dynamical Analysis, Stability, and Bifurcation for a Connected Damped Oscillator with a Piezoelectric Harvester

PurposeThe present work investigates and analyzes the mathematical modeling of a dynamical system attached to a piezoelectric device. It is well-established that piezoelectric transducers are effective energy harvesting devices commonly utilized in practical applications with mechanical systems. The structure of the dynamical model contains a damped Duffing oscillator acting as the major component, which is connected to an un-stretched pendulum and at the same time, to the piezoelectric harvester.MethodLagrange's equations are employed to deduce the governing equations of motion (EOM) based on the overall generalized coordinates characterizing the system. This model has been solved analytically using a perturbation technique known multiple-scales (MS) up to the third approximation. This indicates a level of complexity and detail in the model’s analysis. Moreover, the obtained solutions are compared with the numerical ones for more transparency and to highlight the accuracy of the approximate solutions.ResultsDetailed graphical figures have been executed to study the nonlinear stability analysis of the equations of modulation. Phase portrait diagrams, bifurcation ones, and spectrums of Lyapunov are exhibited to illustrate various types of systems’ behavior, complemented by Poincaré maps for further insight. Additionally, the varied ranges of the stabilities are explored and discussed.ApplicationsThe mechanical vibrations are converted to electricity due to the existence of the piezoelectric transducer that is connected to the dynamic model, which has wide uses and applications like crystal oscillators, medical ultrasound applications, gas igniters, and displacement transducers.

Selkov’s Dynamic System of Fractional Variable Order with Non-Constant Coefficients

This article uses an approach based on the triad model–algorithm–program. The model is a nonlinear dynamic Selkov system with non-constant coefficients and fractional derivatives of the Gerasimov–Caputo type. The Adams–Bashforth–Multon numerical method from the predictor–corrector family of methods is selected as an algorithm for studying this system. The ABMSelkovFracSim 1.0 software package acts as a program, in which a numerical algorithm with the ability to visualize the research results is implemented to build oscillograms and phase trajectories. Examples of the ABMSelkovFracSim 1.0 software package operation for various values of the model parameters are given. It is shown that with an increase in the values of the parameter responsible for the characteristic time scale, regular and chaotic modes are observed. Further in this work, bifurcation diagrams are constructed, which confirm this. Aperiodic modes are also detected and a singularity is revealed.

A wavelet analysis of monetary policy transmission channels in Morocco

PurposeThis paper examines the effects of monetary policy and the interdependencies between the real economy, the financial sector and monetary variables.Design/methodology/approachThe authors employ the continuous wavelet transform to examine how the relationship between the variables in question evolves over time and across various frequencies.FindingsThe results underscore the ineffectiveness of the exchange rate channel. Despite monetary policy consistently leading long-term interest rates, its limited impact on investment suggests the interest rate channel may be less operational. Conversely, the study presents evidence supporting the credit channel’s effectiveness, not only in low market volatility but also during economic downturns. Finally, the examination of the asset prices channel uncovers that the stock market was influenced by monetary policy during its brisk ascent, while real estate led monetary policy.Practical implicationsThe study yields several practical implications. It emphasizes employing advanced mathematical methods to capture the dynamic relationships between monetary policy and economic variables. It highlights the importance of a resilient banking sector given its significant role in the economic advancement of the country. It underscores the need to develop a more inclusive and dynamic financial system and diversify funding alternatives for the private sector to make asset prices a pivotal channel for achieving monetary policy objectives. And with Morocco’s transition to an inflation targeting framework and a flexible exchange rate regime, assessing the effectiveness of the interest rate and exchange rate channels becomes crucial. While the innovative use of wavelet coherence methodology sets a precedent for evaluating monetary policy transmission channels in developing countries, urging researchers and policymakers to explore novel quantitative approaches.Originality/valueThis article stands out as one of the few to employ the wavelet coherence methodology to investigate monetary policy transmission across multiple channels, specifically within the context of a developing country.

Continuation of periodic orbits in conservative hybrid dynamical systems and its application to mechanical systems with impulsive dynamics

Continuation of periodic orbits in conservative hybrid dynamical systems and its application to mechanical systems with impulsive dynamics

Experiment-Guided Refinement of Milestoning Network.

Milestoning is an efficient method for calculating rare event kinetics by constructing a continuous-time kinetic network that connects the reactant and product states. Its accuracy depends on both the quality of the underlying force fields and the trajectory sampling. The sampling error can be effectively controlled through various methods. However, the force fields are often not accurate enough, leading to quantitative discrepancies between simulations and experimental data. To address this challenge, we present a refinement approach for Milestoning network based on the maximum caliber (MaxCal), a general variational principle for dynamical systems, to combine simulations and experimental data. The Kullback-Leibler divergence rate between two Milestoning networks is analytically evaluated and minimized as the loss function. Meanwhile, experimental thermodynamic (equilibrium constants) and kinetic (rate constants) data are incorporated as constraints. The use of MaxCal implies that the refined kinetic network is minimally perturbed from the original one while satisfying the experimental constraints. The refined network is expected to align better with available experimental data. The refinement approach is demonstrated using the binding and unbinding dynamics of a series of six small molecule ligands for the model host system, β-cyclodextrin.

Biological arrow of time: emergence of tangled information hierarchies and self-modelling dynamics

Abstract We study open-ended evolution by focusing on computational and information-processing dynamics underlying major evolutionary transitions. In doing so, we consider biological organisms as hierarchical dynamical systems that generate regularities in their phase-spaces through interactions with their environment. These emergent information patterns can then be encoded within the organism's components, leading to self-modelling "tangled hierarchies". Our main conjecture is that when macro-scale patterns are encoded within micro-scale components, it creates fundamental tensions (computational inconsistencies) between what is encodable at a particular evolutionary stage and what is potentially realisable in the environment. A resolution of these tensions triggers an evolutionary transition which expands the problem-space, at the cost of generating new tensions in the expanded space, in a continual process. We argue that biological complexification can be interpreted computation-theoretically, within the Gödel--Turing--Post recursion-theoretic framework, as open-ended generation of computational novelty. In general, this process can be viewed as a meta-simulation performed by higher-order systems that successively simulate the computation carried out by lower-order systems. This computation-theoretic argument provides a basis for hypothesising the biological arrow of time.

Multi-Observer Fusion Based Minimal-Sensor Adaptive Control for Ship Dynamic Positioning Systems

This paper proposes an adaptive dynamic positioning (DP) control method based on a multi-observer fusion architecture with minimal sensor requirements. A sliding mode observer is designed based on a high- and low-frequency superposition model to filter high-frequency state variables, while a finite-time convergence disturbance observer estimates unknown time-varying low-frequency disturbances online. For efficient handling of model uncertainties, a single-parameter learning neural network is implemented that requires only one parameter to be estimated online. The control system employs auxiliary dynamic systems to handle input saturation constraints and considers thruster system dynamics. Theoretical analysis demonstrates the stability of the observer-fusion control strategy, while simulation results based on the SimuNPS platform validate its effectiveness in state estimation and disturbance rejection compared to traditional sensor-dependent methods.

Integrating ecological philosophy into ideological and political education in universities: Bridging with biomechanics for sustainable development and human health considerations

The ideological and political education (IPE) ecosystem in universities is not only an essential component of the broader educational ecosystem but also a microcosm of it. In an era that emphasizes ecology and sustainability, integrating the concepts of the biomechanics field into the ecological environment of IPE in universities holds significant practical implications. Biomechanics, a discipline dedicated to studying the mechanical behavior of organisms, has a profound impact on aspects such as the ecological environment and human health. From the perspective of ecological philosophy, the ecological environment of IPE in universities should fully consider the sustainable development of biomechanical technologies and their influence on human health. This paper presents a dynamic early warning system for IPE in universities based on an improved Support Vector Machine (SVM) algorithm. This system aims to optimize the IPE model and enhance educational effectiveness while incorporating concepts related to biomechanics. We have constructed an evaluation index system for the quality of IPE in universities across five dimensions: basic quality, teaching attitude, teaching method, teaching ability, and teaching effect. These indicators are evaluated through expert scoring to generate a comprehensive assessment of the teaching quality of IPE. Taking into account the close connection between human mechanical responses and the ecological environment in the field of biomechanics, during the evaluation process, we incorporate the impact of the ecological environment on the human biomechanical state (such as bone development and muscle function) into consideration. This approach guides students to establish a correct ecological view and understanding of biomechanical technologies. Model simulations and performance verifications show that the fuzzy neural network undergoes 779 training iterations, with a training target set at 0.05 and a learning rate of 0.09. This model demonstrates a strong ability to provide comprehensive dynamic early warnings for IPE in universities. It has obvious advantages in adaptability, model fitting accuracy, and processing efficiency in the face of information overload, contributing to the continuous development of IPE in universities from the integrated perspective of ecological philosophy and biomechanics.

RNA folding kinetics control riboswitch sensitivity in vivo

Riboswitches are ligand-responsive gene-regulatory RNA elements that perform key roles in maintaining cellular homeostasis. Understanding how riboswitch sensitivity to ligand (EC50) is controlled is critical to explain how highly conserved aptamer domains are deployed in a variety of contexts with different sensitivity demands. Here we uncover roles by which RNA folding dynamics control riboswitch sensitivity in cells. By investigating the Clostridium beijerinckii pfl ZTP riboswitch, we identify multiple mechanistic routes of altering expression platform sequence and structure to slow RNA folding, all of which enhance riboswitch sensitivity. Applying these methods to riboswitches with diverse aptamer architectures and regulatory mechanisms demonstrates the generality of our findings, indicating that any riboswitch that operates in a kinetic regime can be sensitized by slowing expression platform folding. Our results add to the growing suite of knowledge and approaches that can be used to rationally program cotranscriptional RNA folding for biotechnology applications, and suggest general RNA folding principles for understanding dynamic RNA systems in other areas of biology.

Energy Performance Study of a Data Center Combined Cooling System Integrated with Heat Storage and Waste Heat Recovery System

The energy efficiency of data centers has become an urgent problem as it is enjoying rapid development. This study proposes an integrated energy system involving a data center with different renewable energy sources and waste heat recovery, which can consider the partial and unsteady working load of data center. A dynamic and sophisticated system simulation model is established, which can provide both reliable and fast evaluations but also allow flexible extension of additional components. It is found that the free natural resource cooling system can cover about 28% of cooling demand. Compared to the reference condition, the proposed energy system achieves significant energy-saving benefits, with an energy-saving rate of 16.4%. The system COP increases from 3.88 to 4.64, and the PUE decreases from 1.36 to 1.30, resulting in a 23.45% reduction in electricity expenses. By integrating a waste heat recovery system, the heat pump can absorb approximately 3.57 million kWh of heat from the data center, providing approximately 4.587 million kWh of heating energy for users. The rooftop PV system generates approximately 370,000 kWh of electricity annually, covering approximately 8% of the total electricity consumption of the data center. This study can offer a new channel for the energy efficiency enhancement of data centers.

Economic Complexity and Sectoral Performance in Africa: The Role of Economic Governance Institutions

ABSTRACTIn line with earlier research on economic development, which viewed development as a process of structural modification of the productive structure, this study concentrates on economic complexity and its role in the developmental process. We studied how economic complexity and economic governance institutions affect sectoral performance in Africa, and how economic governance institutions are moderating the economic complexity‐sectoral performance nexus in the region. The study covered a sample of 30 African countries over the period 2010–2021, and used both the dynamic system GMM and the Driscoll and Kraay (1998) standard errors fixed effect estimation techniques. We find that economic complexity and economic governance institutions individually have negative unconditional effects on the agricultural, manufacturing, and services sectors in Africa. We also find that economic governance institutions on the continent failed to moderate the adverse effect of economic complexity on sectoral performance. These findings indicate that the low level of economic complexity in African economies and the weak governance institutions that have plagued the continent have both contributed to its poor sectoral performance. In line with these findings, we provided policy recommendations, which emphasized the need for economic diversification and governance institutional reform in Africa.

Global sensitivity analysis of high-speed train dynamics system with high-dimensional inputs and multiple outputs

A high-speed train dynamics system involves numerous uncertainty parameters, and there are multiple evaluation indices for its dynamic performance. In this paper, we conduct high-dimensional and multi-output global sensitivity analysis for the dynamic performance of high-speed trains by comprehensively considering these dynamics parameters and indices. Firstly, the dynamics simulation model of the high-speed train is established and six dynamics indices for evaluating the safety and comfort of the train are obtained under operating conditions. Subsequently, considering the multi-output and high-dimensional characteristics of train sensitivity analysis, we propose a multi-output global sensitivity analysis method based on the correlation-analysis-guided Kriging model (CA-K). Finally, global sensitivity analysis of 96 uncertainty parameters on six dynamics indices is conducted under three scenarios utilising the proposed method. The results show that (1) CA-K achieves higher accuracy in approximating high-dimensional train dynamics indices than existing conventional surrogate models. (2) The multi-output global sensitivity analysis method can simultaneously identify all important dynamics parameters and exclude unimportant parameters.

UMA ANÁLISE SISTEMÁTICA SOBRE O FUNCIONAMENTO DA ECONOMIA E SUA RELAÇÃO COM A EVOLUÇÃO TECNOLÓGICA

The global economy is a dynamic system shaped by transactions, credit, and economic cycles, fundamental pillars for understanding its functioning. Although often seen as a technical field reserved for specialists, economics directly impacts the lives of all individuals. This article examines economic fundamentals and contextualizes them within a scenario profoundly transformed by the digital revolution and the Information Age. Technological evolution, especially the advancement of disruptive technologies and artificial intelligence (AI), plays a central role in this process, accelerating innovation, automating processes, and reconfiguring markets on a global scale. Additionally, the impact of blockchain and cryptocurrencies is analyzed as a driving force for decentralization and transparency in financial markets, while AI reshapes productive processes and personalizes financial services. The study also addresses the role of individuals in the new economy, emphasizing how access to digital and educational tools empowers economic agents to make more informed decisions and actively participate in innovative models, such as the sharing economy. These emerging technologies challenge traditional economic structures, drive productive efficiency, and create new opportunities while raising ethical and social questions. By connecting technological advancements to contemporary economic dynamics, the study provides a comprehensive analysis of the complex interactions between economics and technology.

Static and dynamic systems in problem-solving

PurposeParadigm change in systems thinking is justified, and a new direction is suggested. The change of state of equilibrium from unsatisfactory to acceptable driven by a dynamic system is described as problem-solving which is innate and universal in the living sphere. An empirical theory of problem-solving is proposed which integrates by design or chance the mental effort of creating the envisaged change with the symbolic structure of “product – dynamic system” which drives the former.Design/methodology/approachThe elementary constituent of structures and systems is identified as 1 – and 2 – place sentences or ordered pairs, the smallest parts of natural language which make sense and of which complex, linguistic structures can be constructed by following the stages of “linguistic modelling”. This leads to sequences of predicate logic conditionals representing the propagation of state or the reasoning structure which may be related to AI. A “story” of a problematic scenario in natural language is transformed into its analytical equivalent.FindingsProblem-solving approaches have a long historical background ending with the current, extensive “systems thinking” but none has come up with a comprehensive, empirical approach the reasons for which is discussed. The proposed theory clarifies the role of conventional science, the arts and engineering in problem-solving. It is rooted in current branches of knowledge, involves the use of mathematics and other disciplines and is highly teachable, needs debate, software and further developments.Originality/valueMathematical modelling is restricted to the use of quantitative properties. The symbolic structure of the proposed theory is processed, natural language which admits the use of emotive properties as part of logical conditionals derived from semantic diagrams. Systems thinking is now part of problem-solving. The analytical content allows the inclusion into teaching curriculum at school and higher education level, at the same time the method can be adopted to informal applications. An integrated theory of creative and methodical problem-solving driven by matching systems/product by design as well as static systems as part of dynamic systems is presented. Problem-solving is innate and universal in living things; it is the primary phenomenon to be identified driving its own means of resolution: the system.