Internal Variables Research Articles

Stabilization of complex‐valued neural networks subject to semi‐Markov jumping parameters: A dynamic event‐triggered protocol

AbstractFor continuous‐time complex‐valued neural networks, this paper addresses the state‐feedback stabilization issue via dynamic event‐triggered protocol. Aiming at random parameters' switching, semi‐Markov jump model surpasses the Markov jump model in terms of its generality, enabling us to effectively capture the occurrence of random abrupt alterations in both the structure and parameters of complex‐valued neural networks. To optimize packet transmission, a new dynamic event‐based protocol is introduced to judge whether the previous signal transmission continues. The design of this protocol takes into full consideration the imaginary part characteristics of the system, while also integrating the system modes and dynamic variables. Utilizing an appropriate Lyapunov functional that contains auxiliary internal dynamical variables, the desired stability is proposed. Eventually, the effectiveness of theoretical findings is ultimately validated through two numerical simulations.

Determinants of Bank Profitability and Implications for Company Value with Moderating Dividend Policy

The research is quantitative and the objective is to determine the internal (Loan Quality, Third Party Fund, Managerial Efficiency) and external (BI Rate, Inflation) variables that determine Bank profitability, and the impact of this profitability on company value by moderating the Bank's stock dividend policy. The method used is panel data regression and moderated regression analysis using Eviews 10. The data used is secondary data with a sample of 10 Book 4 banks listed on the Indonesian Stock Exchange for the 2011-2020 period. The results obtained are first Loan Quality, Third Party Funds, Managerial Efficiency, Bank Indonesia Interest Rates and Inflation Rates simultaneously have a significant and positive effect on Banking Profitability, second Banking Profitability partially has a significant and positive effect on Company Value and third Banking Profitability which is moderated by Dividend Policy partially has a significant and positive effect on Company Value

Mechanical Properties and Mesoscopic Numerical Simulation of Local Weakening in High-Performance Concrete after 10 Years of Alkali Solution Immersion

The natural environment in the high-altitude regions of Northwest China is extremely harsh, characterized by numerous salt lakes. The high concentrations of chloride salts, sulfates, and alkali metal ions in these areas can induce alkali–silica reactions (ASRs) in concrete. These reactions generate harmful gel within the concrete, causing expansion and cracking, which significantly impacts the durability of concrete structures. This study investigates the evolution of the mechanical properties in high-performance concrete (HPC) under long-term ASR by incorporating different admixtures and varying the equivalent alkali content. A three-dimensional random aggregate mesoscopic model was used to simulate static compression tests under various operational conditions. Non-destructive testing methods were utilized to determine the expansion rate, internal, and surface damage variables of the concrete. The experimental results indicate that the 10-year expansion rate differs from the 1-year rate by approximately 1%, and under long-term ASR mitigation measures, the internal damage in the HPC is minimal, though the surface damage is more severe. As the equivalent alkali content increases, the compressive strength of the concrete cubes decreases, initially rising before falling by 5–15% over time. The HPC with only air-entraining agent added exhibited better mechanical performance than the HPC with both air-entraining and corrosion inhibitors added, with the poorest performance observed in the HPC with only a corrosion inhibitor. A relationship was established between the surface and internal damage variables, with the surface damage initially increasing rapidly before stabilizing as the internal damage rose. Numerical simulations effectively describe the damage behavior of HPC under static uniaxial compression. Comparisons with actual failure morphologies revealed that, in the cube compression tests, crack propagation directly penetrated both coarse and fine aggregates rather than circumventing them. The simulations closely matched the experimental outcomes, demonstrating their accuracy in modeling experiments. This study discusses the compressive mechanical properties of concrete under prolonged ASR through a combination of experimental and simulation approaches. It also delves into the impact of surface damage on the overall mechanical performance and failure modes of concrete. The findings provide experimental and simulation support for the concrete structures in regions with high alkali contents.

Constructing Memristive Hindmarsh–Rose Neuron with Countless Coexisting Firings

Memristor synapses have been widely introduced into neuronal models to investigate the effects of external magnetic fields. However, there is a relative lack of research on the external-induced electric fields in neurons. In this paper, a 4D-memristive Hindmarsh–Rose neuron model is constructed by introducing a memristor and an electric field variable, which can generate complex neural firing. Notably, numerical simulations reveal that the initial conditions of the memristor can induce different firing patterns, exhibiting a unique fractal structure in the basin of attractions. Remarkably, the offset parameters of the internal variables of the neuron can be canceled out so that the offset boosting of the variables can be achieved according to the initial values, giving rise to an uncountably many hidden attractors with homogeneous multistability. This model provides the first example of generating uncountably many attractors in a memristive neuron model without relying on trigonometric functions, significantly advancing our understanding of neuronal dynamics. Finally, a digital circuit is designed and implemented on the RISC-V platform to verify the numerical simulation and theoretical analysis. The findings of this study have a certain implication for the development of advanced neuromorphic computing systems and the understanding of complex neuronal behaviors in the presence of external electric fields.

Multistability and synchronicity of memristor coupled adaptive synaptic neuronal network

Memristor coupled neuronal networks can simulate the complex interactions between neurons in biological neuronal networks. These capture the current and synaptic transmission between neurons through mathematical equations, thus reflecting the signal transmission and integration in biological neuronal networks. This paper presents a five-dimensional isomorphic memristor coupled adaptive synaptic neuron (mCASN) network using a non-ideal memristor to couple two-dimensional adaptive synaptic neurons. The theoretical analysis of equilibrium points shows that the mCASN network has many different stability types. The complex parameters-dependent bifurcation behaviors are studied using various numerical methods, and the initial state-dependent coexistence behaviors and the riddled-like basins of attraction sensitive to initial states are further revealed, which proves that the coupled network has multistability. In addition, using the normalized average synchronization error method, we reveal that the mCASN network has synchronization characteristics controlled by parameters and initial states. The complete synchronization and lag synchronization induced by the network parameters, the internal variable of the coupled memristor, and the state variable of the sub-neuron are numerically analyzed. Finally, the mCASN network is implemented using an MCU-based digital hardware platform, and the numerical results are verified.

Modified error-in-constitutive-relation (MECR) framework for the characterization of linear viscoelastic solids

We develop an error-in-constitutive-relation (ECR) approach toward the full-field characterization of linear viscoelastic solids described within the framework of standard generalized materials. To this end, we formulate the viscoelastic behavior in terms of the (Helmholtz) free energy potential and a dissipation potential. Assuming the availability of full-field interior kinematic data, the constitutive mismatch between the kinematic quantities (strains and internal thermodynamic variables) and their “stress” counterparts (Cauchy stress tensor and that of thermodynamic tensions), commonly referred to as the ECR functional, is established with the aid of Legendre–Fenchel gap functionals linking the thermodynamic potentials to their energetic conjugates. We then proceed by introducing the modified ECR (MECR) functional as a linear combination between its ECR parent and the kinematic data misfit, computed for a trial set of constitutive parameters. The affiliated stationarity conditions then yield two coupled evolution problems, namely (i) the forward evolution problem for the (trial) displacement field driven by the constitutive mismatch, and (ii) the backward evolution problem for the adjoint field driven by the data mismatch. This allows us to establish compact expressions for the MECR functional and its gradient with respect to the viscoelastic constitutive parameters. For generality, the formulation is established assuming both time-domain (i.e. transient) and frequency-domain data. We illustrate the developments in a two-dimensional setting by pursuing the multi-frequency MECR reconstruction of (i) piecewise-homogeneous standard linear solid, and (b) smoothly-varying Jeffreys viscoelastic material.

Transient computational homogenization of heterogeneous poroelastic media with local resonances

AbstractA computational homogenization framework is proposed for solving transient wave propagation in the linear regime in heterogeneous poroelastic media that may exhibit local resonances due to microstructural heterogeneities. The microscale fluid‐structure interaction problem and the macroscale are coupled through an extended version of the Hill‐Mandel principle, leading to a variationally consistent averaging scheme of the microscale fields. The effective macroscopic constitutive relations are obtained by expressing the microscale problem with a reduced‐order model that contains the longwave basis and the so‐called local resonance basis, yielding the closed‐form expressions for the homogenized material properties. The resulting macroscopic model is an enriched porous continuum with internal variables that represent the microscale dynamics at the macroscale, whereby the Biot model is recovered as a special case. Numerical examples demonstrate the framework's validity in modeling wave transmission through a porous layer.

External Market and Internal Market Efficiency in Increasing Hospital Visit Numbers

Hospital health institutions have experienced developments that have led to professional services, in line with improvements in various factors, both internal and external, including the increasing awareness of hospital managers implementing professional management and the increasing demands of the public for the quality of hospital services. This study aims to determine External Market and Internal Market Efficiency in Increasing Hospital Visit Numbers. This type of research is quantitative research. The population in this study was the entire community of Malang district. The sampling technique used purposive sampling technique. Determining the sample in this study used the Slovin formula. The research instrument uses a questionnaire and data analysis uses multiple linear regression. The results of the analysis show that the R value shows a close relationship between the independent variables (X1, X2) and the dependent variable (Y) of 0.823. The adjusted R square value is 0.467, meaning that 46.7% of the community's decision variables in returning visits can be explained by external marketing variables. This can be seen from the significance level being smaller than α (0.000<0.05), so that H0 is accepted and H1 is rejected. Conclusions in this research include external marketing variables, internal marketing have a significant effect on the variable intention to revisit at Hospital X in Malang district, both simultaneously and partially dan the variable that more dominantly influences patient satisfaction is the internal marketing variable.

Practical application of a heuristic model for swim coaches to optimize training

Swim coaches routinely monitor the performance and performance determining variables of their athletes to optimize training programs in an individual-specific manner with the ultimate aim to swim faster and win races. To interpret the collected data, they require a suitable, and practically useful, conceptual framework, which can be found in the power balance of swimming. For coaches a heuristic model for training and performance optimization based on the power balance has been proposed. We build on this model and illustrate how it can be employed in the training practice using data of an exemplary sub-elite swimmer (700 FINA points), which was collected continuously during two training sessions. Variables that are used in daily swim training practice, such as heart rate (HR), stroke count (SC), stroke rate (SR), and lap time (LT), were measured. By combining external load variables (e.g. LT) and internal load variables (e.g. HR) with technical variables (e.g. SR), the degree to which the swimmer complies with the training program can be determined. The measured values of these variables are presented using a traffic light feedback system indicating the degree of compliance. The traffic light system enables coaches to adjust the program if deemed necessary. It is thus shown how the model and commonly measured variables can assist swim coaches in the design and evaluation of training sessions in their pursuit of personal performance improvement leading to greater athletic success.

Optimal scheduling of energy routers based on distributed CAES systems

Abstract With the rise of renewable energy, distributed energy, and other different forms of energy, the architecture and function of the energy router are relatively perfect, for the topology of the energy router itself, different forms of energy in the energy router flow, conversion to realize the large-scale use of distributed energy equipment has also gradually become a hot spot of research. However, there are fewer studies on the energy router with a distributed CAES system to participate in the optimal scheduling of energy for integrated energy systems. Therefore, this paper is based on the operational domain model of an energy router with a distributed CAES system and analyzes the physical characteristics and coupling relationship of non-energy storage components, takes air pressure, temperature, and oil volume as state variables, takes charging, power generation, and heating power as external interfaces, and utilizes internal interface variables such as air quality and flow rate to realize energy storage, flow, and other internal interface variables to realize the communication between energy storage and non-energy storage elements, and finally determine the operation domain of the system. Secondly, the feasible domain of the energy router based on the distributed CAES system is portrayed, and the optimization of the system parameters is guided with the help of the relative distance by establishing the electric and thermal power constraints and the storage state constraints. Finally, the effectiveness of the feasible domain analysis method in the energy router of a distributed CAES system is verified by taking a northern region as an example.

Event-Triggered Consensus of Uncertain Euler-Lagrange Multiagent Systems Over Jointly Connected Digraphs.

In this article, a fully distributed event-triggered protocol is proposed to solve the consensus problem of uncertain Euler-Lagrange (EL) multiagent systems (MASs) under jointly connected digraphs. First, distributed event-based reference generators are proposed to generate continuously differentiable reference signals via event-based communication under jointly connected digraphs. Unlike some existing works, only the states of agents rather than virtual internal reference variables need to be transmitted among agents. Second, adaptive controllers are exploited based on the reference generators so that each agent can track the reference signals. The uncertain parameters converge to their real values under an initially exciting (IE) assumption. It is proved that the uncertain EL MAS achieves state consensus asymptotically under the proposed event-triggered protocol composed of the reference generators and the adaptive controllers. A unique feature of the proposed event-triggered protocol is its fully distributed property: the protocol does not depend on global information about the jointly connected digraphs. Meanwhile, a minimum interevent time (MIET) is guaranteed. Finally, two simulations are conducted to show the validity of the proposed protocol.

Combined EORTC FDG-PET/CT and RECIST 1.1 response criteria and prediction of pathological response to neoadjuvant nivolumab and ipilimumab for clinical stage III melanoma.

9590 Background: Accurate identification of pathological response to neoadjuvant therapy for clinical stage III melanoma before surgery may allow us to better tailor the treatment. RECIST 1.1 has shown poor concordance rates with pathological responses and EORTC FDG-PET/CT response criteria using SUV max is susceptible to internal and external variables leading to difficulty in determining accuracy appropriately. Methods: We conducted a multicenter retrospective analysis of patients with clinical stage III melanoma treated with neoadjuvant nivolumab 3mg/kg + ipilimumab 1mg/kg(N3+I1) for two cycles who did baseline and pre-operative 18F-FDG-PET/CT. RECIST 1.1 and EORTC PETCT criteria were used to classify patients in terms of response. The pathological results were correlated to the image findings. Results: Between Jan 2019 and Jan 2024, 37 patients with clinical stage III melanoma treated with neoadjuvant N3+I1 were identified. Gender: 25 males (67%), median age (years): 58 (range: 31-78), BRAFV600 mut: 25(67%), positive nodes on baseline exam:1=30 (81%), 2=4(11%), and 3 or more =3(8%). Stage AJCC 8th edition: IIIB=23 (62%), IIIC=12 (32%), IIID=2(6%). Among them, 34 had baseline and post-neoadjuvant therapy PET-CT. Twenty-one (62%) had major pathological response (MPR) and 13(38%) had non-MPR. Using objective response and non-objective criteria to capture MPR and non-MPR, respectively, we found the following results: RECIST 1.1 (sensitivity= 48%, specificity=100%, accuracy=68%), EORTC-PET (sensitivity=90%, specificity=69%, accuracy=82%). Combining RECIST 1.1 and EORTC-PET/CT analysis, we found that: double objective responders (CR/PR and CMR/PMR, n=10) had 100% specificity to MPR and double progressors (PD/PMD, n=9) had 95% specificity to non-MPR. The only patient classified as double progressor that did not have non-MPR presented an intense inflammatory response with fever, pain and erythema leading to an increase in the size and number of lymph-nodes. No viable cells were found in the pathological report. Conclusions: Using combined RECIST 1.1 and EORTC-PET/CT response criteria may help to predict pathological response in patients with clinical stage III melanoma who undergo to neoadjuvant nivolumab and ipilimumab.

Efficient and accurate uncertainty quantification in engineering simulations using time-separated stochastic mechanics

A robust method for uncertainty quantification is undeniably leading to a greater certainty in simulation results and more sustainable designs. The inherent uncertainties of the world around us render everything stochastic, from material parameters, over geometries, up to forces. Consequently, the results of engineering simulations should reflect this randomness. Many methods have been developed for uncertainty quantification for linear elastic material behavior. However, real-life structure often exhibit inelastic material behavior such as visco-plasticity. Inelastic material behavior is described by additional internal variables with accompanying differential equations. This increases the complexity for the computation of stochastic quantities, e.g., expectation and standard deviation, drastically. The time-separated stochastic mechanics is a novel method for the uncertainty quantification of inelastic materials. It is based on a separation of all fields into a sum of products of time-dependent but deterministic and stochastic but time-independent terms. Only a low number of deterministic finite element simulations are then required to track the effect of (in)homogeneous material fluctuations on stress and internal variables. Despite the low computational effort the results are often indistinguishable from reference Monte Carlo simulations for a variety of boundary conditions and loading scenarios.

A thermo-mechanical constitutive model for triple-shape and two-way shape memory polymers

Semicrystalline polymers often have diverse molecular configurations in response to the variation of temperature, making it easier to realize multiple or two-way shape memory effects (SMEs). In order to provide guidance for the design of relevant smart structures, it is necessary to develop corresponding constitutive models to better characterize the thermal-mechanical behavior of such shape memory polymers (SMPs). For the first time, a thermo-mechanical finite deformation constitutive model is presented to reveal the deformation mechanism of the triple-shape two-way SME of semicrystalline SMPs in our work, based on the theory of thermodynamics with internal state variables. To verify the validity of the model, the model results are compared with the test data for a typical shape memory cycle. It is found that the model can be employed to describe the non-equilibrium response of semicrystalline SMPs with two types of crystallites in the vicinity of crystallization and melting. Since the model results fit well with the test data, the effectiveness of the model in predicting the triple two-way shape memory behavior is demonstrated.

Thermodynamics of Irreversible Processes: Fundamental Constraints, Representations, and Formulation of Boundary Conditions

Starting from the analysis of the lack of positivity of the Cattaneo heat equation, this work addresses the thermodynamic relevance of the positivity constraint in irreversible thermodynamics, that is at least as significant as the entropic constraints. The fulfillment of this condition in hyperbolic models leads to the parametrization of the concentration fields with respect to internal variables associated with the microscopic dynamics. Using Brownian motion theory as a landmark example for deriving macroscopic transport equations from the equations of motion at the particle/molecular level, we discuss two typical problems involving hydrodynamic interactions at the microscale: surface chemical reactions at a solid interface of a diffusing reactant, and mass-balance equations in a complex viscoelastic fluid, in which the physics of the interaction leads either to overcoming the parabolic diffusion model or to considering the parametrization of the concentration with respect to the degrees of freedom associated with the relaxation dynamics of the solvent fluid.

Thermomechanics of phase transformation induced localization in NiTi tubes. Part II constitutive modeling and simulations

In an isobaric test, typically used for establishing the transformation temperatures of SMAs, a specimen is taken through a cool/heat cycle at a prescribed stress level. The companion paper, Part I, demonstrated that such tests on NiTi tubes result in rather complex interactions between the helical bands of localized deformation induced by phase transformations, the associated latent heat, and the thermal exchange with the environment. These interactions were quantified using accurately controlled testing conditions and full-field diagnostics, and provide a challenging platform for evaluating analyses. In Part II this challenge is met by first extending the thermomechanical constitutive framework developed by our group to include variations of transformation stress, strain, and latent heat with temperature. Unique features of the model include: modeling the reversible phase transformations of NiTi through a single surface in the deviatoric stress–temperature space with the transformation strain and entropy as the internal variables; and use of softening to model the inhomogeneous deformation exhibited in tension. The model is calibrated to the isothermal results of Part I. The constitutive model is then incorporated into a fully coupled static displacement-thermally transient finite element analysis that is used to simulate the isobaric experiments on NiTi tubes of Part I over a range of stresses. The clamped ends of the experiment are idealized as radial constraints. Heat exchange between the structure and the environment is strictly by convection. Isobaric testing is simulated by taking the model tube through the cool/heat cycle of the experiment at the prescribed stress level. A small thickness depression at one of the ends is used to initiate localized transformation. The temperature-strain response is reproduced with the two transformations initiating at essentially the same temperatures as in the experiments, producing the correct transformation strains for all stress levels. Transformation of M propagates via a helical band at similar speeds as in the experiments, while transformation of A is via multipronged fronts for all cases. Successful reproduction of the velocities of the banded transformations is governed by the value of the convection coefficient. Overall, the reproduction of the isobaric experiments over a range of stress levels, validates the constitutive and structural models. It also points to the limitations of modeling the heat exchange between the specimen and the environment only by convection.

Combination of fuzzy control and reinforcement learning for wind turbine pitch control

Abstract The generation of the pitch control signal in a wind turbine (WT) is not straightforward due to the nonlinear dynamics of the system and the coupling of its internal variables; in addition, they are subjected to the uncertainty that comes from the random nature of the wind. Fuzzy logic has proved useful in applications with changing system parameters or where uncertainty is relevant as in this one, but the tuning of the fuzzy logic controller (FLC) parameters is neither straightforward nor an easy task. On the other hand, reinforcement learning (RL) allows systems to automatically learn, and this capability can be exploited to tune the FLC. In this work, a WT pitch control architecture that uses RL to tune the membership functions and scale the output of a fuzzy controller is proposed. The RL strategy calculates the fuzzy controller gains in order to reduce the output power error of the WT according to the wind speed. Different reward mechanisms based on the output power error have been considered. Simulation results with different wind profiles show that this architecture performs better (123.7 W) in terms of power errors than an FLC without RL (133.2 W) or a simpler PID (208.8 W). Even more, it provides a smooth response and outperforms other hybrid controllers such as RL-PID and radial basis function neural network control.

Improving Electricity Theft Detection Using Electricity Information Collection System and Customers’ Consumption Patterns

Electricity theft detection (ETD) techniques employed to identify fraudulent consumers often fail to accurately pinpoint electricity thieves in real time. The patterns associated with electricity use are leveraged to identify anomalies indicative of electricity theft. However, challenges in the benchmark ETD include overfitting and a high incidence of false positives (FPs) resulting from incorrect usage patterns formed by considering only electricity consumption patterns without accounting for external factors that contribute to variations in normal consumption patterns. Further investigation is required to precisely detect instances of electricity theft, thereby minimizing nontechnical losses and forecasting future electricity demand to maintain a stable supply. This study employs a master energy meter located on the transformer side to monitor the amount of energy distributed to the region. Zones with a high likelihood of energy theft are detected by comparing the sum of readings from all the smart meters with the readings from the master energy meter installed on the HV of the substation transformer within the same electric feeder. Ensemble XGBoost machine-learning algorithm and K-Means algorithm are used for the classification of malicious and nonmalicious samples and grouping similar types of consumers together, respectively. This makes the proposed model resistant to false-positive rates caused by changes in usage patterns that aren’t done on purpose. Furthermore, energy thieves are identified by detecting anomalies in consumption behavior while maintaining constant internal and external environmental variables. This novel model proposed here mitigates the FP rate found in the present research of electricity usage data. Our approach outperforms support vector machines, convolution neural network, and logistic regression in simulations. Precision, F1-score, recall, Matthews Correlation Coefficient, Receiver Operating Characteristics (ROC)-Area Under The Curve (AUC), and Precision Recal (PR)-Area Under The Curve (AUC) validate our model. The simulation results show that the proposed K-Means-LSTM-XGBoost model improved the classifier’s F1-score, precision, and recall to 93.75%, 95.16%, and 92.38%, respectively. Our model classifies huge time series data with high precision and can be utilized by the utility for real time electricity theft detection.

Elasticity of bilayer lipid membranes from their density correlation function

ABSTRACT We study the density correlation function (DCF) of DPPC lipid bilayers. We compare Molecular Dynamics (MD) results with theoretical predictions obtained with a mesoscopic description, in terms of the lipid membrane elasticity. One key objective of our work is the quantification of the lipid membrane elasticity directly from the DCF, both for the membrane undulations and local membrane thickness. Our method does not require the definition of instantaneous surfaces or internal variables defining lipid orientations. Building on our previous work, here we focus on the intralayer correlations, i.e. the DCF of lipids residing on the same monolayer, by tracking only the position of the phosphorus atoms in a lipid head group. We demonstrate the relevance of the intralayer two-dimensional (2D) correlations to the total DCF. We further show that all-atom (AA) and coarse grained (CG) lipid forcefields, feature distintively different DCFs. The CG forcefield predicts results in good agreement with the mesoscopic predictions, for the entire wavevector range; the AA forcefield (CHARMM36) predict strong peristaltic fluctuations at long wavevectors q ≳ 0.8 nm − 1 , which are absent in the CG lipid model (MARTINI).

Extension of Prandtl’s solution to a general isotropic model of plasticity including internal variables

Extension of Prandtl’s solution to a general isotropic model of plasticity including internal variables