Modeling Of Systems Research Articles

Investigating institutional and expenditure patterns on debt and economic growth in Ghana

AbstractThis study investigates the influence of government expenditure and institutions on public debt accumulation and economic growth in Ghana between 1990 and 2019. The paper examines how public debt is utilized for government spending on capital and recurrent expenditure, and the role bureaucratic quality plays in improving economic growth. The employed estimation technique is structural equation modelling because it effectively assesses complex situations and allows the modelling of complex systems by utilizing simultaneous equations, mediator analysis and modification indices to improve the goodness of fit of a model. The study indicates that public debt negatively affects GDP growth, whereas trade openness positively influences GDP growth and public debt accumulation. Bureaucratic quality impacts GDP growth positively and capital expenditure negatively. Recurrent expenditure positively predicts GDP growth and capital expenditure. Capital expenditure's effect on GDP growth is negative but statistically insignificant. The right checks and balances should be implemented to mitigate rent‐seeking and corrupt activities associated with bureaucracy. Tax revenue mobilization should be improved to reduce budget deficits and borrowing, whereas exports should be promoted through improving local industries. Finally, all infrastructural projects must be completed to serve their purpose.

Photovoltaic Model Parameters Estimation Via the Fully Informed Search Algorithm

Effective parameter estimation for photovoltaic (PV) systems holds significant importance for both researchers and industry professionals. An accurate understanding of PV models, achieved through modeling and simulation, plays a pivotal role in optimizing the design, control, testing, and forecasting of PV system performance. Developing a precise and robust parameter identification method significantly contributes to enhancing the modeling, control, and optimization of photovoltaic systems. In this context, our research contribution introduces a novel version of Rao metaheuristic algorithm named the Fully Informed Search Algorithm (FISA). Which demonstrate acceptable performance to solving optimization problems in several applied fields. While, maintaining the simplicity and non-parametric nature of the original algorithm. The proposed algorithm holds promise for various industrial applications, particularly in optimizing complex systems such as photovoltaic (PV) systems. For which, we used it to efficiently identifying the parameters of the single-diode model (SDM). Thus, we demonstrate its effectiveness through the application in two distinct case studies within our simulation research. in the end, we compared the results of FISA algorithm to seven other well-known algorithms, the obtained results indicate the superiority of the proposed algorithm in term of the stability of the system, a faster convergence and higher accuracy.

Impact of temperature dependent coefficient of performance of heat pumps on heating systems in national and regional energy systems modelling

To successfully transition towards fully renewable energy systems, energy sectors that today are primarily autonomous should be closely integrated to increase system flexibility and maximise synergy from effective sector coupling. Heat pumps are one of the sector coupling technologies which enable a mutual integration of power and heat sectors. Heat pumps are viewed as a low-cost source of sustainable heat for both space heating and industrial processes. However, representation of heat pumps in energy system models is typically oversimplified by using a uniform coefficient of performance, which limits perception of the hourly temperature changes on heat pump efficiency, or by using generic heat pumps for all individual residential, district, or industrial heat supplies. The LUT Energy System Transition Model was expanded to examine the impact of utilisation of hourly coefficient of performance profiles and specific heat pumps for different applications. The results indicate that, in cold climate countries, the use of hourly coefficient of performance profiles has little impact on the heat supply structure and overall primary energy demand, but has a noticeable impact on the electricity supply and energy storage system. Though the cost of the optimised system stays on the same level, application of hourly COP profiles leads to a more accurate simulation of electricity consumption and energy storage operation. The separation of district and industrial heat pumps leads to a higher share of heat pumps in industrial heat supply and allows for a reduction in overall energy system primary energy demand by 1.8%.

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-point simulation implies the sequential modeling of individual points on the simulation grid, one at a time, by borrowing spatial information from the training image and honoring the conditioning data points. The developed methodology is based on the mapping of the training image patterns database using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm for dimensionality reduction, and the clustering of patterns by applying the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, as an efficient unsupervised classification technique. For the automation, optimization, and input parameter tuning, multiple stages are implemented, including entropy-based determination of the template size and a k-nearest neighbors search for clustering parameter selection, to ensure the proposed method does not require the user’s interference. The proposed model is validated using synthetic two- and three-dimensional datasets, both for conditional and unconditional simulations, and runtime information is provided. Finally, the method is applied to a case study gold mine for stochastic orebody modeling. To demonstrate the computational efficiency and accuracy of the proposed method, a two-dimensional training image with 101 by 101 pixels is simulated for 100 conditional realizations in 453 s (~4.5 s per realization) using only 361 hard data points (~3.5% of the simulation grid), and the resulting average simulation has a good visual match and only an 11.8% pixel-wise mismatch with the training image.

The circular bioeconomy: a driver for system integration

BackgroundHuman and earth system modeling, traditionally centered on the interplay between the energy system and the atmosphere, are facing a paradigm shift. The Intergovernmental Panel on Climate Change’s mandate for comprehensive, cross-sectoral climate action emphasizes avoiding the vulnerabilities of narrow sectoral approaches. Our study explores the circular bioeconomy, highlighting the intricate interconnections among agriculture, forestry, aquaculture, technological advancements, and ecological recycling. Collectively, these sectors play a pivotal role in supplying essential resources to meet the food, material, and energy needs of a growing global population. We pose the pertinent question of what it takes to integrate these multifaceted sectors into a new era of holistic systems thinking and planning.ResultsThe foundation for discussion is provided by a novel graphical representation encompassing statistical data on food, materials, energy flows, and circularity. This representation aids in constructing an inventory of technological advancements and climate actions that have the potential to significantly reshape the structure and scale of the economic metabolism in the coming decades. In this context, the three dominant mega-trends—population dynamics, economic developments, and the climate crisis—compel us to address the potential consequences of the identified actions, all of which fall under the four categories of substitution, efficiency, sufficiency, and reliability measures. Substitution and efficiency measures currently dominate systems modeling. Including novel bio-based processes and circularity aspects might require only expanded system boundaries. Conversely, paradigm shifts in systems engineering are expected to center on sufficiency and reliability actions. Effectively assessing the impact of sufficiency measures will necessitate substantial progress in inter- and transdisciplinary collaboration, primarily due to their non-technological nature. In addition, placing emphasis on modeling the reliability and resilience of transformation pathways represents a distinct and emerging frontier that highlights the significance of an integrated network of networks.ConclusionsExisting and emerging circular bioeconomy practices can serve as prime examples of system integration. These practices facilitate the interconnection of complex biomass supply chain networks with other networks encompassing feedstock-independent renewable power, hydrogen, CO2, water, and other biotic, abiotic, and intangible resources. Elevating the prominence of these connectors will empower policymakers to steer the amplification of synergies and mitigation of tradeoffs among systems, sectors, and goals.

Approaches to automating processes of working with ontological resources

Ontological models are extensively used in information support systems that offer resources and services for solving management, design, and scientific and technical problems. Specifically, domain ontologies are commonly utilized in decision support systems. In the ontological modeling of complex systems, there arises a need to automate the process-es of handling ontological resources. This work discusses the main software systems and methodologies for ontological modeling, approaches to automating the processes of creating, populating, and using ontological models, and considers the temporal aspect of the ontological representation of objects. The aim of the work is to explore methods for auto-mating the life cycle of ontological resources and to analyze the extent of their adaptation in applied ontologies. The work highlights a relatively high degree of automation in the process of populating ontologies and the use of large lan-guage models in this process. However, it points out the lack of methods for automating the conversion of information from tables and diagrams into ontological models, as well as for validating and processing the content of the model. Promising directions for automating work with ontological resources are also indicated.

Interpretable multi-task neural network modeling and particle swarm optimization of process parameters in laser welding

The process parameters directly affect the quality and performance of laser welding (LW) by shaping the molten pool appearance. However, with the increase in the dimensions of process parameters and the number of samples, the relationship between process parameters and molten pool often exhibits high levels of uncertainty and nonlinearity. Machine learning (ML) techniques have been used for complex LW systems modeling, but they may be limited and unable to fully capture the complex relationships when faced with complex systems. Additionally, traditional ML techniques may lack interpretability within the model, making it difficult to understand the input features and their impact on the output. To address these issues, a method that combines an interpretable multi-task neural network (MTNN) and particle swarm optimization (PSO) is proposed. Firstly, an LW experimental platform is constructed, and the orthogonal design is conducted for data collection. In the data preparation stage, the curriculum learning strategy is employed to reorganize the raw experimental data. Subsequently, the MTNN is constructed to automatically learn feature representations among input process parameters, capture nonlinear relationships between data, and share weights across multiple related tasks to improve efficiency and performance. Furthermore, the PSO is designed to determine the constraints and objective functions. In addition, the shapley additive explanation (SHAP) method is utilized to calculate the importance of process parameters and enhance the interpretability. Finally, the optimization results are validated, and the results demonstrate that the optimized results of the proposed method have achieved satisfactory performance compared to the traditional ML methods.

Noise modelling and mitigation for broadband in‐door power line communication systems

AbstractCommunication systems are greatly hampered by many disruptive noises in powerline communication systems (PLC), which come with strong interference, resulting in the malfunction of PLC systems. Hence, there is a need to model noise and its effect on communication systems. This paper presents noise modelling and mitigation techniques for indoor broadband powerline communication systems. To model the PLC noise, frequency domain measurements employing the GSP‐930 spectrum analyser were carried out to determine the noise frequency response in the frequency range of 1–30 MHz. The results obtained were plotted. While the analytical model for the noise model is presented, furthermore, noise mitigation techniques for multiple input multiple output PLC (MIMO‐PLC) systems in the form of spatial modulation PLC systems have been proposed. The SM‐PLC system employs the indices of the individual transmit lines to increase the data rate, as opposed to the traditional MIMO‐PLC systems, where the symbol to be transmitted is transmitted by duplicating the symbol across all lines. The proposed system uses the maximum likelihood (ML) detector at the receiver to obtain estimates of the transmitted symbols. The simulation results of the SM‐PLC system are compared with the already existing MIMO‐PLC system and show a significant improvement of ≈6 dB and 5.2 dB in signal‐to‐noise ratio (SNR) at a bit error rate of 10(−5) for spectral efficiencies of 4 bits per channel use (bpcu) and 6 bpcu, respectively. On comparison of the SM‐PLC system having a combination of additive white Gaussian noise and impulse noise at the receiver, the SM‐PLC system outperformed the traditional MIMO‐PLC by 3.5 and 3.8 dB in SNR for 4 and 6 bpcu, respectively.

Hybridized machine learning models for phosphate pollution modeling in water systems for multiple uses

Phosphate pollution in water bodies is a significant environmental concern, especially in regions with extensive agricultural practices. Hence, a tool for accurately assessing the phosphate concentration is essential. This research paper explores the effectiveness of machine learning (ML) models combined with nature-inspired optimization algorithms for predicting phosphate levels in water systems. The novelty consists of integrating the power of machine learning models, which have been presenting excellent performance at capturing complex relationships in environmental pollution data, with the Harris Hawks Optimizer (HHO) optimization capabilities inspired by hawks' hunting behavior. Four hybrid implementations combining the HHO were evaluated, and four feature subsets were assessed to identify the most influential variables in the modeling process. Using water quality data from Brazilian upstream watersheds, the hybrid models were trained and validated, enabling accurate and robust predictions of phosphate concentrations. The elastic net (EN) model optimized by Harris Hawk Optimizer (HHO-EN) produced the best-averaged performance among all experiments (R = 0.825, R2 = 0.670, Root Mean Square Error (RMSE) = 0.049 mg/L, Mean Absolute Error (MAE) = 0.037 mg/L). A parametric and feature importance analysis identified the most influential parameters in the contamination modeling process. Hybrid machine learning models represent a novel and efficient strategy for water quality monitoring and environmental management, supporting the preservation of aquatic ecosystems.

Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop.

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

Probabilistic and maximum entropy modeling of chemical reaction systems: Characteristics and comparisons to mass action kinetic models.

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell.

Understanding user intent modeling for conversational recommender systems: a systematic literature review

Understanding user intent modeling for conversational recommender systems: a systematic literature review

Model and Implementation of a Novel Heat-Powered Battery-Less IIoT Architecture for Predictive Industrial Maintenance

The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment.

A systematic methodology for port-Hamiltonian modeling of multidimensional flexible linear mechanical systems

This article introduces a novel systematic methodology for modeling a class of multidimensional linear mechanical systems that directly allows to obtain their infinite-dimensional port-Hamiltonian representation. While the approach is tailored to systems governed by specific kinematic assumptions, it encompasses a wide range of models found in current literature, including ℓ-dimensional elasticity models (where ℓ = 1, 2, 3), vibrating strings, torsion in circular bars, classical beam and plate models, among others. The methodology involves formulating the displacement field using primary generalized coordinates via a linear algebraic relation. The non-zero components of the strain tensor are then calculated and expressed using secondary generalized coordinates, enabling the characterization of the skew-adjoint differential operator associated with the port-Hamiltonian representation. By applying Hamilton's principle and employing a specially developed integration by parts formula for the considered class of differential operators, the port-Hamiltonian model is directly obtained, along with the definition of boundary inputs and outputs. To illustrate the methodology, the plate modeling process based on Reddy's third-order shear deformation theory is presented as an example. To the best of our knowledge, this is the first time that a port-Hamiltonian representation of this system is presented in the literature.

Predicting Long-Time-Scale Kinetics under Variable Experimental Conditions with Kinetica.jl.

Predicting the degradation processes of molecules over long time scales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic data. Here, we showcase Kinetica.jl, a new software package for constructing large-scale chemical reaction networks in a fully automated fashion by exploring chemical reaction space with a kinetics-driven algorithm; coupled to efficient machine-learning models of activation energies for sampled elementary reactions, we show how this approach readily enables generation and kinetic characterization of networks containing ∼103 chemical species and ≃104-105 reactions. Symbolic-numeric modeling of the generated reaction networks is used to allow for flexible, efficient computation of kinetic profiles under experimentally realizable conditions such as continuously variable temperature regimes, enabling direct connection between bottom-up reaction networks and experimental observations. Highly efficient propagation of long-time-scale kinetic profiles is required for automated reaction network refinement and is enabled here by a new discrete kinetic approximation. The resulting Kinetica.jl simulation package therefore enables automated generation, characterization, and long-time-scale modeling of complex chemical reaction systems. We demonstrate this for hydrocarbon pyrolysis simulated over time scales of seconds, using transient temperature profiles representing those of tubular flow reactor experiments.

Preparatory study for carbon sequestration modelling of agroforestry systems in Hungary: The assessment of the yield class distribution of windbreaks

The escalating carbon dioxide emissions leading to global climate change are acknowledged as a paramount environmental challenge in the twenty-first century. The significance of land use systems in stabilising carbon dioxide levels and enhancing carbon sink potential has gained noteworthy attention from both the scientific and political communities. The Intergovernmental Panel on Climate Change emphasises that agroforestry systems present vital prospects for synergising climate change adaptation and mitigation efforts, offering substantial technical mitigation potential. Windbreaks are well-known agroforestry systems in Hungary and form an important part of agricultural landscapes. The improved agroforestry subsidy system in our country makes it relevant to model the carbon sequestration potential of windbreaks. In the framework of the ForestLab project we plan to develop a carbon sequestration model specific for Hungarian agroforestry systems. In this study, as a preparatory step of the model development, we assessed the yield class distribution of Hungarian windbreaks by tree species group and identified variables that had significant effect on yield class based on the data of the National Forestry Database. Our results show that among the examined effects the most important predictor of the yield class of windbreaks was the tree species group, followed by the thickness of the productive soil layer and the hydrology of the site.

Modeling of double ground fault modes in power supply systems of non-traction consumers of railway transport

THE PURPOSE. Development of digital models to determine the modes of double ground faults in 6-10 kV overhead signaling lines supplying signaling, centralization and automatic blocking facilities.METHODS. To create the models, methods of multiphase modeling of electric power systems were used, based on the use of lattice equivalent circuits from RLCelements connected according to complete graph circuits. For modeling, the industrial software package Fazonord, developed at the Irkutsk State Transport University, was used.RESULTS. The developed models provide correct accounting of all factors influencing the modes of double ground faults, including increased electromagnetic influences of the traction network. In contrast to known approaches, the dynamics of changes in these influences caused by variations in traction loads during train movement are modeled.CONCLUSION. The proposed approach to modeling double ground fault modes is universal and can be used for standard traction power supply systems, as well as promising high-voltage traction networks. The results obtained can be used in practice to configure relay protection devices for overhead signaling lines in order to reliably detect double ground fault modes and take measures to eliminate them. In addition, the modeling methodology presented in the article can be used when selecting measures to improve the reliability of power supply to signaling and automatic blocking facilities that ensure the safety of train traffic, as well as to develop methods and algorithms for determining distances to places of double ground faults in overhead power lines feeding signaling facilities , centralization and autoblocking. The presented results confirmed the effectiveness of the proposed approach for modeling double ground fault modes.

Geographic Information Systems for Water Quality Modeling in the Zhytomyr District Communities

Abstract To ensure safe and quality drinking water for residents of rural settlements who use their own wells, boreholes, and natural sources for domestic water supply, a comprehensive approach to evaluating the quality of underground drinking water using geographic information system (GIS) technologies is necessary. The purpose of the study was to assess the quality of drinking water sources of noncentralized water supply in rural settlements of the united territorial communities (UTCs) of Zhytomyr district and to create geoinformation models based on the research results. The following research methods were used during the research: analytical, field, laboratory, statistical, calculation, and cartographic. The research was conducted in 129 settlements of 12 UTCs of Zhytomyr district, where drinking water samples were collected from noncentralized water supply sources for further analysis in the Measurement Laboratory of Polissia National University, and the creation of geoinformation models using the ArcGIS Pro software package. It has been proven that the average pH level in none of the studied settlements exceeded the norm. The average nitrate concentration in the drinking water from noncentralized water sources exceeded the norm by 1.4–3.5 times, specifically in the water of the Pulyny, Cherniakhiv, Vilshanka, Volytsia, and Oliivka communities, exceeding the maximum acceptable concentration (MAC) limit by more than two times. Only in rural settlements in the Liubar community was the average iron content found to be above the norm by more than 1.9 times. Overall, it was established that the calculated value of the overall water quality class in the Zhytomyr district was 2.03, which is determined as “good,” clean water of acceptable quality. The best water quality was found in the Vilshanka, Cherniakhiv, and Stanyshivka communities, with a quality class range of 1.85–1.93, while the worst water quality was recorded in the Oliivka, Teterivka, and Liubar communities, with a quality class range of 2.13–2.31. It was determined that the highest contribution to the overall water quality was made by nitrate and iron content. The obtained research results and models based on them can be used by local governments of the studied communities to inform the population about the quality of drinking water and to develop a plan for improving the state of drinking water supply with the aim of increasing the level of environmental safety of drinking water.

Oil families, oil–source rock correlation, basin modeling, and implication for petroleum systems, Termit Basin, Niger

Oil families, oil–source rock correlation, basin modeling, and implication for petroleum systems, Termit Basin, Niger

A modeling method for the control circuit of a double-fed induction generator in a wind power system based on multimodel fusion

Abstract The current modeling techniques of a Double-fed induction generator (DFIG) for wind power systems have shortcomings in the idealization of equipment internal parameters and unitization of system coupling. In this paper, a multi-model fusion method of doubly-fed induction wind turbine modeling for wind power systems is proposed. Firstly, the 1.5 MW DFIG body is constructed according to the parameters of the physical prototype. Then, to realize the system’s control of DFIG, the DIFG external circuit and control circuit of the wind power system are built to realize multi-system coupling simulation modeling. Through simulation analysis, the effectiveness of the multi-model coupling simulation system and the availability of the method are proved.