Lithuania is a Baltic European country which shares borders with Poland, Belarus, Latvia, and Russia and has a geothermal anomaly in the southwestern region. It consists of two main geothermal complexed, i.e., Devonia...
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Lithuania is a Baltic European country which shares borders with Poland, Belarus, Latvia, and Russia and has a geothermal anomaly in the southwestern region. It consists of two main geothermal complexed, i.e., Devonian and Cambrian with a temperature of up to 40 degrees C (at a depth of 1000 m) and 96 degrees C (at a depth of 2000 m), respectively. The Devonian complex is composed of an unconsolidated sandstone formation with porosity and permeability in the range of 4-31% and 200 mD-6000 mD, respectively, and these make it a favorable candidate for a low enthalpy geothermal complex because of the high water production rates. This study evaluates the geothermal potential in the Devonian complex of the selected sites for commercial development. The study utilizes the mechanistic modelling approach including uncertainty management to forecast the water production rates and estimate the power generation capacity. Lastly, the study reveals that it is feasible to produce 6 MW to 60 MW of power from the existing vertical wells for a period of 25 years. Furthermore, reactive transport modelling also proves that there is dissolution and precipitation of the minerals near and away from the wellbore, respectively, which impairs the reservoir quality and further concludes that there is an effect of time on re-injection which should be considered to enhance the reservoir quality for future operations. In addition to that, no effect of the re-injection temperature of the produced water is observed.
In this note, we consider port-Hamiltonian structures in numerical optimal control of ordinary differential equations. By introducing a novel class of nonlinear monotone port-Hamiltonian (pH) systems, we show that the...
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In this note, we consider port-Hamiltonian structures in numerical optimal control of ordinary differential equations. By introducing a novel class of nonlinear monotone port-Hamiltonian (pH) systems, we show that the primal-dual gradient method maybe viewed as an infinite-dimensional nonlinear pH system. The monotonicity and the particular block structure arising in the optimality system is used to prove exponential stability of the dynamics towards its equilibrium, which is a critical point of the first-order optimality conditions. Leveraging the port-based modeling, we propose an optimization-based controller in a suboptimal receding horizon control fashion. To this end, the primal-dual gradient based optimizer-dynamics is coupled to a pH plant dynamics in a power-preserving manner. We show that the resulting model is again monotone pH system and prove that the closed-loop exhibits local exponential convergence towards the equilibrium.
Port-Hamiltonian systems provide an energy-based modeling paradigm for dynamical input- state-output systems. At their core, they fulfill an energy balance relating stored, dissipated and supplied energy. To accuratel...
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Port-Hamiltonian systems provide an energy-based modeling paradigm for dynamical input- state-output systems. At their core, they fulfill an energy balance relating stored, dissipated and supplied energy. To accurately resolve this energy balance in time discretizations, we propose an adaptive grid refinement technique based on a posteriori error estimation. The evaluation of the error estimator includes the computation of adjoint sensitivities. To interpret this adjoint equation as a backwards-in-time equation, we show piecewise weak differentiability of the dual variable. Then, leveraging dissipativity of the port-Hamiltonian dynamics, we present a parallelizable approximation of the underlying adjoint system in the spirit of a block-Jacobi method to efficiently compute error indicators. We illustrate the performance of the proposed scheme by means of numerical experiments showing that it yields a smaller violation of the energy balance when compared to uniform refinements and traditional step size controlled time stepping.
The aim of the present study is to investigate numerically the 2D unsteady transient convection of cold water near its highest density value in an inclined square porous enclosed space. The horizontal walls are kept i...
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The aim of the present study is to investigate numerically the 2D unsteady transient convection of cold water near its highest density value in an inclined square porous enclosed space. The horizontal walls are kept in adiabatic condition. The temperature of left sidewall varies in sinusoidal manner whereas the opposing right sidewall is sustained with constant lower temperature. The range of angle of inclination is considered from 0 degrees to 90 degrees. The finite volume method (FVM) was applied to portray all math.matical equations. The special outcome of inclination angle, Darcy number, maximum density parameter and porosity are obtained in the present study with the use of Brinkmann-Forchheimer extended Darcy model. The change in the angle of inclination is noteworthy in the structure and direction of the flow of the fluid. The bicellular structure of the fluid is identified because of the density inversion effect. Increasing Darcy number and porosity values enhance the average rate of energy transport across the chamber. The lowest heat transfer is noticed for T-m = 0:4 due to supreme density effect. Here the energy transfer from one cell to another cell is by conduction mode due to the existence of dual cell structure.
Pension funds are an essential part of retirement planning, and their performance and risks play a significant role in ensuring financial stability for retirees. This study aims to analyse the connectedness and spillo...
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Pension funds are an essential part of retirement planning, and their performance and risks play a significant role in ensuring financial stability for retirees. This study aims to analyse the connectedness and spillover effects in the Lithuanian second-pillar pension fund market. The findings of this study provide insight on the interdependence within the second-pillar pension funds market and with other financial markets, and contribute to a better understanding of the risk-return trade-off of pension funds, especially during high-volatility periods. Differently from other studies in this paper market regimes are identified using Hidden Markov Models (HMM). Interdependence (including multivariate and non-linear) and causality between pension funds are analysed in different market regimes. Finally, returns spillover in different regimes is estimated using VAR and VECM models. The results of this paper are expected to be useful for pension fund managers, participants, and pension system supervisors in making decisions about investment strategies and in practices of systemic risk management regulation.
The aim of this work is to find an effective combination of modelling based on the boosting technique and Shapley value computation with the practise of evaluating an undirected graph model. To this end, we created an...
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The aim of this work is to find an effective combination of modelling based on the boosting technique and Shapley value computation with the practise of evaluating an undirected graph model. To this end, we created an XGBoost-SHAP regression model in which the target variable is the cyanobacteria concentration and the model variables consist of 20 environmental factors. Two partial correlation-based graphs were then created. Firstly, a preliminary network containing all the features (with the target variable) with the original datasets of the parameters, and secondly, a network called SHAP-NET based on the Shapley values of the independent variables from the SHAP model. It seems that by using new combined machine learning and network tools such as SHAPNET, it will be possible to further improve the idea of explainability of models in the field of XAI (eXplainable Artificial Intelligence), and attempts to solve practical domain problems, as in this work, can contribute to progress in this area.
This paper theoretically explores the coexistence of synchronization and state estimation analysis through output sampling measures fora class of memristive neural networks operating within the flux-charge domain. The...
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This paper theoretically explores the coexistence of synchronization and state estimation analysis through output sampling measures fora class of memristive neural networks operating within the flux-charge domain. These networks are subject to constant delayed responses in self-feedback loops and time-varying delayed responses incorporated into the activation functions. A contemporary output sampling controller is designed to discretize system dynamics based on available output measurements, which enhances control performance by minimizing update frequency, thus overcoming network bandwidth limitations and addressing network synchronization and state vector estimation. By utilizing differential inclusion mapping to capture weights from discontinuous memristive switching actions and an input-delay approach to bound nonuniform sampling intervals, we present linear matrix inequality-based sufficient conditions for synchronization and vector estimation criteria under the Lyapunov-Krasovskii functional framework and relaxed integral inequality. Finally, by utilizing the preset experimental data-set, we visually verify the adaptability of the proposed theoretical findings concerning synchronization, anti-synchronization, and vector state estimation of delayed memristive neural networks operating in the flux-charge domain. Furthermore, numerical validation through simulation demonstrates the impact of leakage delay and output measurement sampling by comparative with scenarios and measurements.
Individual biology influences environment-dependent population dynamics through life history. Population models that consider individual physiology are therefore popular for modelling dynamics under various environmen...
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Individual biology influences environment-dependent population dynamics through life history. Population models that consider individual physiology are therefore popular for modelling dynamics under various environments. In recent years, a quantitative framework integrating metabolic theory (dynamic energy budget theory) into individual-based models (DEB-IBMs) has emerged to investigate the link from individual physiology to demography. However, this link shows substantial variation, some of which may be explained by individual behaviours operating in local environments. Current DEB-IBMs focusing on population modelling do not consider individual-scale behaviours and instead resort to imposed population-level relationships. We therefore propose to extend the DEB-IBM approach to consider the role of individual-scale behaviour by replacing the functional response - a population-averaged phenomenological relationship - with individual-scale foraging mechanisms in a spatially heterogeneous environment. Using this model, we simulate consumer dynamics in a consumer-resource system for different individual behaviours across a range of temperature, resource carrying capacity and individual variability values. We further illustrate the model in a case study by comparing simulated population dynamics with both the classical DEB-IBM and experimental data for a laboratory Daphnia magna population. Simulations reveal that temperature- and resource-dependent consumer extinction probability patterns change with individual behaviour. Moreover, simulations agree with experimental data on D. magna populations: dynamics after the initial growth peak were better captured under random walk movement behaviour compared to the classical DEB-IBM. Both the simulation and case study showed how fine-scale behaviour mediates the metabolism's impact on population dynamics by allowing for the emergence of different functional responses. Our model thus provides a link between metabolism, life hist
Nowadays, transfer learning has shown promising results in many applications. However, most deep transfer learning methods such as parameter sharing and fine-tuning are still suffering from the lack of parameters tran...
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Nowadays, transfer learning has shown promising results in many applications. However, most deep transfer learning methods such as parameter sharing and fine-tuning are still suffering from the lack of parameters transmission strategy. In this paper, we propose a new optimization model for parameter-based transfer learning in convolutional neural networks named STP-CNN. Indeed, we propose a Lasso transfer model supported by a regularization term that controls transferability. Moreover, we opt for the proximal gradient descent method to solve the proposed model. The suggested technique allows, under certain conditions, to control exactly which parameters, in each convolutional layer of the source network, which will be used directly or adjusted in the target network. Several experiments prove the performance of our model in locating the transferable parameters as well as improving the data classification.
We present a convergent fourth-order finite difference method (FDM) for the Black-Scholes (BS) equation. The proposed numerical scheme is constructed based on an implicit Euler method and a fourth-order accurate FDM. ...
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We present a convergent fourth-order finite difference method (FDM) for the Black-Scholes (BS) equation. The proposed numerical scheme is constructed based on an implicit Euler method and a fourth-order accurate FDM. Many options used in the financial market have payoff functions with discontinuous differentials. Because the second and first derivatives in the BS equation cause high errors and low convergence rates, we propose the numerical method to overcome this difficulty by applying a fine mesh at the early stage of the numerical method. For a sufficiently smooth option price function, a coarse mesh is employed. We can achieve a fourth-order convergence rate applying the proposed algorithm. The effectiveness and capability of the proposed algorithm are validated through various computational results using a European call option.
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