Dynamical Research Articles

Network transformation-based analysis of biochemical systems.

A dynamical system obtains a wide variety of kinetic realizations, which is advantageous for the analysis of biochemical systems. A reaction network, derived from a dynamical system, may or may not possess some properties needed for a thorough analysis. We improve and extend the work of Johnston and Hong et al. on network translations to network transformations, where the network is modified while preserving the dynamical system. These transformations can shrink, extend, or retain the stoichiometric subspace. Here, we show that a positive dependent network can be translated to a weakly reversible network. Using the kinetic realizations of (1) calcium signaling in the olfactory system and (2) metabolic insulin signaling, we demonstrate the benefits of transformed systems with positive deficiency for analyzing biochemical systems. Furthermore, we present an algorithm for a network transformation of a weakly reversible non-complex factorizable kinetic (NFK) system to a weakly reversible complex factorizable kinetic (CFK) system, thereby enhancing the Subspace Coincidence Theorem for NFK systems of Nazareno et al. Finally, using the transformed kinetic realization of monolignol biosynthesis in Populus xylem, we study the structural and kinetic properties of transformed systems, including the invariance of concordance and variation of injectivity and mono-/multi-stationarity under network transformation.

Retreatment and aesthetic restoration of maxillary incisor with calcified root canal using a dynamic navigation system: a case report

BackgroundPulp canal occlusion (PCO) increases the difficulty of root canal treatment as well as tooth preservation and restoration. This is the first case report of successful localization of a calcified root canal under the guidance of a dynamic navigation system (DNS) for complete root canal retreatment and aesthetic restoration after a failed attempt to locate the calcified root canal with a traditional dental operating microscope (DOM).Case presentationThe patient was scheduled for root canal treatment for a labially inclined maxillary central incisor and post-core crown restoration with resin veneers in another hospital, but the calcified root canal could not be located with a microscope, so the patient was referred to our department. Root canal retreatment was planned for tooth #8, and the calcified root canal of tooth #9 was planned to be located and dredged under the guidance of a DNS. We used a DNS for successful positioning and to complete the root canal retreatment operation in the following 4 steps: preoperative CBCT imaging, preoperative plan design, calibration and registration, and real-time dynamic navigation. Three months later, teeth #8 and #9 presented no clinical symptoms, after which the fiber post-cores and zirconia all-ceramic crown restorations were completed. At the 12-month follow-up visit, the patient continued to be symptom­free, and satisfied with the final aesthetic restoration.ConclusionsThis case report suggests that DNS may be a promising technique with high accuracy and effectiveness to reduce the risk of iatrogenic errors and provide maximal preservation of dentin, when the traditional treatment of calcified root canals approaches failure (lateral perforation).

Riparian Methylmercury Production Increases Riverine Mercury Flux and Food Web Concentrations.

The production and uptake of toxic methylmercury (MeHg) impacts aquatic ecosystems globally. Rivers can be dynamic and difficult systems to study for MeHg production and bioaccumulation, hence identifying sources of MeHg to these systems is both challenging and important for resource management within rivers and main-stem reservoirs. Riparian zones, which are known biogeochemical hotspots for MeHg production, are understudied as potential sources of MeHg to rivers. Here, we present a comprehensive quantification of the hydrologic and biogeochemical processes governing MeHg concentrations, loads, and bioaccumulation at 16 locations along 164 km of the agriculturally intensive Snake River (Idaho, Oregon USA) during summer baseflow conditions, with emphasis on riparian production of MeHg. Approximately one-third of the MeHg load of the Snake River could not be attributed to inflowing waters (upgradient, tributaries, or irrigation drains). Across the study reach, increases in MeHg loads in surface waters were significantly correlated with MeHg concentrations in riparian porewaters, suggesting riparian zones were likely an important source of MeHg to the Snake River. Across all locations, MeHg concentrations in surface waters positively correlated with MeHg concentrations in benthic snails and clams, supporting that riparian produced MeHg was assimilated into local aquatic food webs. This study contributes new insights into riparian MeHg production within rivers which can inform mitigation efforts to reduce MeHg bioaccumulation in fish.

Object detection and tracking for drones: A system design using dynamic visual SLAM

Abstract. Drones can play a quite crucial role in many walks of life today. Enhancing the visual perception ability of drones is crucial to their intelligence level. Among them, it is necessary to focus on strengthening the detection, tracking and mapping capabilities of drones for dynamic objects. However, the existing visual SLAM systems carried by drones do not perform well in dynamic environments. This project designs a monocular visual SLAM system specifically for drones, aiming to achieve efficient three-dimensional mapping and target tracking, surpassing the limitations of simple static mapping and positioning. Besides, this project constructs a drone dynamic SLAM system developed on the ORB-SLAM3 structure, uses drone images to detect, track and map object motion models, and reconstructs environmental maps to obtain motion parameters with real physical scales. This project strives to optimize the input pre-processing module, improve the validity of data and output environmental maps and raster maps. The outcomes demonstrate the system's strong accuracy and adaptability in dynamic installation procedures.

Thermophoretic particle deposition effect on a squeezed flow of radiative Jeffrey fluid past a sensor surface with uniform heat source/sink and chemical reaction

Abstract This anticipated model investigates the time-dependent, and incompressible 2D magnetized Jeffrey liquid flowing on a sensor surface placed between two infinite parallel plates with the existence of a uniform heat source/sink. The lower plate remains fixed while the upper plate is subject to squeezing. For the heat and mass transmission processes, the consequences of radiative heat flux, chemical reaction, and thermophoretic particle deposition are applied and analyzed. The envisioned model is supported by prescribed heat and mass flux conditions. The uniqueness of the proposed model is the analysis of the unsteady squeezed flow of Jeffrey liquid over a sensor surface, accounting for radiative heat flux, thermophoretic particle deposition, and variable thermal conductivity. The model possesses applications in microfluidic sensors, biomedical devices, thermal management systems, and inkjet printing. The equations comprising the model are subject to similarity transformations. The bvp4c package is utilized to analyze the dynamic flow system. The connotation of arising parameters with the associated profiles is depicted through graphs and tables. It is examined that fluid velocity, temperature, and concentration profiles are dwindling functions of squeezed parameter. It is also witnessed that the concentration of liquid drops with an enhancement of thermophoretic and chemical reaction parameters. The validation of the truthfulness of the envisioned model is an added feature of this study.

The Effect of Network Delay and Contagion on Mobile Banking Users: A Dynamical Analysis

This paper analyses how network contagion affects the acceptance or rejection of mobile banking, emphasizing the risk of contagion that hinders the growth of users among bank clients. As a research method, a mathematical model based on the Susceptible, Infected, and Recovered framework is used within a nonlinear dynamic system with a distributed time delay and the optimal control strategy. The approach aims to find the behaviour dynamics of mobile banking users, non-users, and undecided ones. Our model adds value to the existing literature by employing a qualitative dynamic analysis, as opposed to other studies that study contagion empirically. The steady states and their conditions for stability are found, and the critical value for time delay causing oscillatory is determined. The optimal control strategies are identified to enlarge the number of mobile banking users and decrease the number of non-users. Numerical simulations support the theoretical findings. The research has practical implications given that the model predicts mobile banking user behaviour in assisting policy decision-making and addresses the possible policy measures that aim to increase the number of mobile banking users. The conclusions suggest that financial inclusion can contribute to the expansion of mobile banking users.

Kowalevskaya exponents and normal forms of planar dynamical systems. Application to integrability problem"

Kowalevskaya exponents and normal forms of planar dynamical systems. Application to integrability problem"

Optimizing inverted pendulum control: Integrating neural network adaptability

This study explores the implementation and efficacy of a neural network controller for an inverted pendulum system, contrasting it with traditional state feedback control. Initially, state feedback control exhibited limitations in managing complex system dynamics. Subsequently, a neural network controller was developed, trained using datasets from both uncontrolled and refined state space models. The refined model yielded lower training loss and superior control performance. This research demonstrates the neural network controllers enhanced adaptability and precision, offering significant improvements over traditional methods in controlling dynamic systems like inverted pendulums.

A unified framework for the analysis of accuracy and stability of a class of approximate Gaussian filters for the Navier–Stokes equations

Abstract Bayesian state estimation of a dynamical system utilising a stream of noisy measurements is important in many geophysical and engineering applications. In these cases, nonlinearities, high (or infinite) dimensionality of the state space, and sparse observations pose key challenges for deriving efficient and accurate data assimilation (DA) techniques. A number of DA algorithms used commonly in practice, such as the Ensemble Kalman Filter (EnKF) or Ensemble Square Root Kalman Filter (EnSRKF), suffer from serious drawbacks such as catastrophic filter divergence and filter instability. The analysis of stability and accuracy of these DA schemes has thus far focused either on finite-dimensional dynamics, or on complete observations in case of infinite-dimensional dissipative systems. We develop a unified framework for the analysis of several well-known and empirically efficient DA techniques derived from various Gaussian approximations of the Bayesian filtering schemes for geophysical-type dissipative dynamics with quadratic nonlinearities. We establish rigorous results on (time-asymptotic) accuracy and stability of these algorithms with general covariance and observation operators. The accuracy and stability results for EnKF and EnSRKF for dissipative PDEs are, to the best of our knowledge, completely new in this general setting. It turns out that a hitherto unexploited cancellation property involving the ensemble covariance and observation operators and the concept of covariance localization in conjunction with covariance inflation play a pivotal role in the accuracy and stability for EnKF and EnSRKF. Our approach also elucidates the links, via determining functionals, between the approximate-Bayesian and control-theoretic approaches to DA. We consider the ‘model’ dynamics governed by the two-dimensional incompressible Navier–Stokes equations (NSEs) and observations given by noisy measurements of averaged volume elements or spectral/modal observations of the velocity field. In this setup, several continuous-time DA techniques, namely the so-called 3DVar, EnKF and EnSRKF reduce to a stochastically forced NSEs. For the first time, we derive conditions for accuracy and stability of EnKF and EnSRKF. The derived bounds are given for the limit supremum of the expected value of the L 2 norm and of the H 1 Sobolev norm of the difference between the approximating solution and the actual solution as the time tends to infinity. Moreover, our analysis reveals an interplay between the resolution of the observations (roughly, the ‘richness’ of the observation space) associated with the observation operator underlying the DA algorithms and covariance inflation and localization which are employed in practice for improved filter performance.

Transient Response of MDOF Nonlinear Dynamical System under Stationary Random Excitation

Transient Response of MDOF Nonlinear Dynamical System under Stationary Random Excitation

Ionic polymer-metal composite (IPMC) as a flexible capacitor: an experimental study

Abstract In recent years, significant research has been directed towards the development of energy storage devices that are thin, lightweight, and flexible, catering to the diverse demands of modern smart electronics. This study investigates the remarkable adaptability of ionic polymer metal composite (IPMC) capacitors when subjected to bending conditions. Under investigation was the capacitor’s functionality within a broad spectrum of bending angles, ranging from 0 ∘ to 100 ∘ . The study aims to reveal the capacitor’s response and stability across this expansive curvature range, providing insights into its potential applications in flexible and dynamic systems. The flexibility of the supercapacitor allows it to bend or fold up to 180 ∘ without experiencing permanent mechanical deformation. The calculated specific capacitance values obtained by bending the cell at angles of 0, 40, 70, and 100 ∘ are found to be 4.3, 6.4, 8.2, and 8.4 mF g−1 respectively. The result displayed the IPMC capacitor exhibited notable resilience under varying bending angles, affirming its capacity to maintain operational integrity despite significant mechanical stress. Through a systematic exploration of bending conditions, this research unveils the capacitor’s ability to flexibly conform to various bending angles. This adaptability is particularly significant for applications where traditional rigid capacitors may be impractical or inefficient.

First evidence of circulation of multiple arboviruses in Algeria.

Algeria like other North African countries is experiencing recurrent episodes of West Nile Virus (WNV) emergences and new health threats associated with the introduction of Aedes albopictus in 2010 are to be feared. To improve the surveillance of mosquito-borne pathogens, we performed a study using innovative tools based on multiplex molecular methods. We combined two approaches: a high-throughput chip based on the BioMark Dynamic array system to detect arboviruses in mosquitoes, and a set of immunologic methods (ELISA, microsphere immunoassays (MIA) and virus microneutralization tests (MNT)) for serological surveys in animal hosts. We investigated two distinct regions: a first zone located in the coastal humid region and a second one in the Saharan desert region. We collected a total of 1,658 mosquitoes belonging to nine different species and found predominantly Culex pipienss. l. (56.5%) and Cx. perexiguus (27.5%). From 180 pools of 10 mosquitoes, we detected four arboviruses: Banna virus (BAV), chikungunya virus (CHIKV), Sindbis virus (SINV), and Usutu virus (USUV). Moreover, we examined 389 blood samples from equids and poultry and found that 52.4% were positive for flavivirus antibodies in ELISA, while 30.8% were positive for WNV and two chickens and two equids were positive for USUV by MNT and MIA respectively. To our knowledge, this is the first report of five arboviruses circulating in Algeria, with three reported for the first time (CHIKV, BAV, and USUV). Our study brings evidence that reinforcing surveillance using more discriminant tools may help in anticipating future emergences and propose adapted control measures.

The convolutional neural network for Pacific decadal oscillation forecast

Abstract The Pacific Decadal Oscillation (PDO) is often described as a long-lived El Niño-like pattern of Pacific climate variability, and it has widespread climate and ecosystem impacts. PDO forecasts can provide useful information for policymakers on how to handle PDO impacts. Nevertheless, due to the long duration of the PDO cycles and their complex formation mechanisms, it remains a challenge to predict long lead time PDO. In this paper, we propose a transfer-learning-enhanced Convolutional Neural Network (CNN) to tackle complex ocean dynamic forecasting and predict PDO events with up to a one-year lead time. Our method first trains the CNN on historical simulations from Coupled Model Intercomparison Project 6 (CMIP6), covering the period from 1850 to 1972. This prior knowledge is then refined by further training the model with observational data from 1854 to 1972, ensuring robust performance on unseen data. Additionally, k-fold cross-validation is also employed to evaluate the model's performance across diverse subsets of data, enhancing its reliability. Throughout the testing phase from 1983 to 2022, the CNN model consistently outperforms existing dynamical forecast systems, exhibiting superior correlation skills in predicting annual mean PDO indices and PDO phases, including displaying resilience to seasonal variations. The transferred CNN is thus a powerful method to predict PDO events and is potentially valuable for a wide range of applications. This work directly supports the objectives of the World Climate Research Programme (WCRP) Grand Challenge on Climate Prediction.

Equilibria of large random Lotka-Volterra systems with vanishing species: a mathematical approach.

Ecosystems with a large number of species are often modelled as Lotka-Volterra dynamical systems built around a large interaction matrix with random part. Under some known conditions, a global equilibrium exists and is unique. In this article, we rigorously study its statistical properties in the large dimensional regime. Such an equilibrium vector is known to be the solution of a so-called Linear Complementarity Problem. We describe its statistical properties by designing an Approximate Message Passing (AMP) algorithm, a technique that has recently aroused an intense research effort in the fields of statistical physics, machine learning, or communication theory. Interaction matrices based on the Gaussian Orthogonal Ensemble, or following a Wishart distribution are considered. Beyond these models, the AMP approach developed in this article has the potential to describe the statistical properties of equilibria associated to more involved interaction matrix models.

Wasserstein convergence rates in the invariance principle for sequential dynamical systems

Wasserstein convergence rates in the invariance principle for sequential dynamical systems

Software and Data Support for Computer Forensic Analysis

Introduction: the paper highlights the role of software and data support for computer forensic analysis carried out at the stage of judicial investigation and preliminary investigation, emphasizing its importance in the era of digitalization. Purpose: disclose the questions that modern technologies of computer-technical expertise are able to answer, and necessary data sets for research. Methods: the methodological framework for the study is based on the methods of consistency and analysis. Results: the paper presents a methodology for auditing computer equipment (CE) and information and communication technologies (ICT) in the context of criminal investigations. It also elucidates the techniques and methods employed in the study of CE and ICT hardware and software. Conclusions: the author analyzes the state of crime related to the use of computer technology in telematics networks and information and communication technologies, identifies ways to commit crimes related to the use of malware and legal software, the functioning of a dynamic system representing the mechanism of these crimes, as well as systemic aspects of the emergence and investigation of threats of this type.

Dynamical theory for adaptive systems

Abstract The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former’s dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin–Siggia–Rose-De Dominicis–Janssen to analyse non-equilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors—including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.

System Design of Three-coordinate Dynamic Synchronous Tester Based on STM32

In this paper, a three-coordinate dynamic synchronous tester system based on STM32 is developed. STM32F103C8T6 chip is used as the control system. The control system mainly consists of three modules: motion control module, magnetic encoder measurement module and displacement data collection module. The STM32 main control controls the three-axis stepper table to move according to the specified instructions, the AS5600 magnetic encoder is used to measure the data of the three-way stepper motor, and the TCA9548A module is used to expand the IIC channel and realize the communication between the STM32 and the host computer, and the data is transmitted to the host computer through serial communication. The test results show that the system meets the needs of the existing domestic industrial level, the cost is moderate, the performance is reliable and the operation is simple, and meets the practical application requirements.

The Diffusive Lotka–Volterra Competitive Model with Advection Term: Exclusion, Coexistence and Multiplicity

Earlier, the research was mainly focused on the classic diffusive two-species Lotka–Volterra competitive system (without advection). Averill et al. [2017] regarded a diffusive two-species Lotka–Volterra competitive system with advection term (with one species having a combination of random dispersal and biased movement upward along the resource gradient, while the other species adopts purely random dispersal). In this paper, we consider a more general diffusive two-species Lotka–Volterra competitive system with advection term (involving both species with a combination of random dispersal and biased movement upward along their respective resource gradients). By utilizing the theory of monotone dynamical systems, spectral analysis, methods of super- and sub-solutions and some nontrivial analytic skills, we finally obtain a rather deep understanding on the global dynamics. Moreover, our results reveal that the dynamical behavior of the system is very complex: exclusion, coexistence and bistability all may happen depending closely on the interspecific competition intensities. More interestingly, by bifurcation theory, we find that the system always admits multiple coexistence steady states when the interspecific competition intensities are near the critical point.