Modeling Of Networks Research Articles

A Graph-Based Technique for the Automated Control-Oriented Modeling of District Heating Networks

Abstract Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an acceptable average normalized root-mean-square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology.

Optimizing Brain-Computer Interfaces through Spiking Neural Networks and Memristors

Brain-computer interfaces (BCIs) have emerged as a transformative conduit bridging the human brain's intricate realms and computing systems' capabilities. However, numerous challenges remain in improving BCI accuracy, efficiency, and adaptability. This paper investigates the integration of spiking neural networks (SNNs) and memristors to optimize BCI performance. SNNs offer exceptional potential to enhance BCI accuracy through biomimetic modeling of biological neural networks. By emulating the brain's spatio-temporal signaling patterns, SNNs may significantly improve neural decoding precision. Meanwhile, memristors can simulate synaptic plasticity and potentially enable real-time adaptive learning in BCIs. Preliminary studies demonstrate substantially improved signal processing, feature extraction, and classification capabilities when using SNNs and memristors in BCIs. This neuroinspired integration offers a compelling vision for personalized BCIs that continuously adapt to individual users. However, realizing the full potential relies on addressing lingering technical hurdles as well as emerging ethical considerations around user autonomy, privacy, responsibility, and access. Ultimately, interdisciplinary collaboration remains imperative to harness the promising trajectory of optimized BCIs while navigating the multifaceted challenges ahead.

Macroscopic modeling of mixed bi-modal urban networks: A hybrid model of accumulation- and trip-based principles

Network-level traffic flow models either assume steady-state urban flows (i.e. accumulation-based models) or track the movement of all vehicles (i.e. trip-based models). The steady-state assumption present in the accumulation-based models may pose a challenge in light of the multi-modal nature of urban flows. It might be indeed a rough assumption for the flow of some transportation modes like buses, cruising-for-parking vehicles, taxis, and on-demand vehicles. Trip-based models address this concern, however, they need significant parameter calibration effort and are not computationally efficient, which substantially reduces the practicality of these models in real-world applications. Nevertheless, despite the critical importance of developing multi-modal traffic flow models, few attempts have been made to investigate these models in network macroscopic fundamental diagram (NMFD)-related literature. This paper bridges this gap by developing a hybrid network-level traffic flow model for mixed bi-modal (i.e. car and bus) networks. The present hybrid model reproduces the dynamics of car flows via accumulation-based model principles while tracking the movement of buses using the trip-based model. This effort also includes the development of a new FIFO-based entrance function to ensure different modes experience the same delay under saturated traffic conditions. Different numerical experiments are conducted to study the hybrid model performance and to compare it with that of accumulation-based and trip-based models in both steady-state and transition periods under different traffic conditions. Our observations reveal that the hybrid model simulates the dynamics of cars and buses by closely following the behavior of its components under free-flow conditions. The model also outperforms the accumulation-based model under saturated traffic conditions while being considerably less demanding than the trip-based model. A further investigation of the model performance is performed for networks with different bus shares in both free-flow and saturated traffic conditions, confirming the results of the initial numerical experiments. The hybrid model’s computational efficiency is demonstrated. The potential real-world applications of the hybrid model in development of bi-modal network-level simulation models, NMFD-based control strategies along with bus space allocation policies, public transport operation problems, modeling of cruising-for-parking vehicles, taxis, and on-demand vehicles, and modeling and application of autonomous modular vehicles are discussed and future research directions are highlighted.

Numerical and Mathematical Modeling of a Gene Network with Nonlinear Degradation of the Components

Numerical and Mathematical Modeling of a Gene Network with Nonlinear Degradation of the Components

A case study on transformer ferroresonance for subsea cable connected 230 kV substations using PSCAD

This research paper presents a comprehensive procedure on how to perform a ferroresonance study in a system dealing with the addition of subsea cable interconnector-2 between two 230 kV substations which is previously linked by subsea cable interconnector-1. Addition of a subsea cable interconnector-2 needs to be investigated for ferroresonance in planning and designing stages itself since the interaction between cable capacitive reactance and power transformers-magnetizing core which is a non-linear inductance may cause ferroresonance. The investigation is conducted in three stages namely energisation, de-energisation and stuck pole conditions of circuit breaker. Each stage concentrates on the overvoltage patterns as ferroresonance is characterized by overvoltage. The analysis is conducted in PSCAD/EMTDC software and the modelling of entire study network is done according to the CIGRE WG C4.307. The maximum overvoltage for each stage is found by performing 200 cases of statistical simulation and it is compared with the rated withstand voltage of the equipment. Through a rigorous analysis of overvoltage characteristics under different scenarios, suggestion has been given whether the subsea cable interconnector-2 can be safely operated during the studied cases without the risk of ferroresonance.

Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation

Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, overparameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training data set can render a DNN, evaluated purely on the training set, ineffective in distinguishing a cost-sensitive solution from its overall accuracy maximization counterpart. This necessitates rethinking cost-sensitive classification in DNNs. To address this challenge, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) framework to make overparameterized models cost sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs. Experiments on well-known data sets and a pharmacy medication image (PMI) data set, made publicly available, show that our method can effectively minimize the overall cost and reduce critical errors while achieving comparable overall accuracy. Funding: Research reported in this publication was supported by the National Library of medicine of the National Institutes of Health in the United States under award number R01LM013624. Data Ethics & Reproducibility Note: This paper abides by data ethics requirements. Data used are publicly available online at https://deepblue.lib.umich.edu/data/concern/data_sets/6d56zw997 . Codes to replicate the results of this paper are available at https://doi.org/10.24433/CO.2139841.v1 .

K-Medoids clustering applications for high-dimensionality multiphase probabilistic power flow

Actual power systems’ planning studies require the stochastic modeling of networks due to their increasing penetration of uncertain parameters, such as probabilistic-nature loads and renewable generation features. Although numerous Probabilistic Power Flow (PPF) methods have been presented in the literature, the scalability of such tools is still under study due to the ‘curse of dimensionality’, which states that the probabilistic methods lose their accuracy with the increase of random input variables. Since many authors are using clustering algorithms to solve the PPF, mainly because of their easy implementation and good computational precision, this paper proposes for the first time the K-Medoids clustering method to model uncertainties in unbalanced distribution systems. Modifications in the clustering algorithm are included to improve its accuracy in view of a large set of input variables. The multiphase modeling of networks is also considered to represent the inherent unbalance of distribution systems correctly. Simulations were carried out using the IEEE unbalanced test-feeders IEEE 123 and the large-scale system IEEE 8500, which includes more than 2000 input variables. Results show that K-Medoids are a better alternative in relation to other numerical/approximation approaches, such as Monte Carlo Simulation or Point Estimate Methods, because it is faster and presents great accuracy, especially for large-scale applications. Comparisons were also made regarding other clustering approaches, highlighting the benefits of the proposed one in scalability.

Coupled Modeling of the Surface Pipeline Network in a Low-Enthalpy Geothermal Field

This paper addresses the often overlooked, yet critical, aspect of designing and optimizing the surface pipeline network for the transportation of geothermal fluids from the wellheads to the delivery point, such as greenhouses, food drying plants, or fish farming units. While research on the geothermal industry predominately focuses on the reservoir and well engineering aspects of exploitation, insufficient attention has been given to the design of the pipeline network, leading to improper design and significant, yet avoidable, energy losses. Thus, this paper presents a comprehensive methodology for modeling and simulating geothermal fluid flow within the pipeline network by fully considering all hydraulic (friction, viscous flow, and gravity effects) and thermal (open air and underground pipeline heat loss) phenomena. These two aspects are handled simultaneously by setting up and solving the coupled set of the governing (differential) equations. We also demonstrate the difficulties that arise when attempting the solution of the mathematical problem, such as potential instability or lack of convergence. Finally, a fully detailed study of the real-world geothermal production system is presented utilizing the developed methodology to optimize the design and operation conditions of the system. By integrating debottlenecking strategies into the analysis, this approach not only maximizes power output, but also identifies and mitigates constraints within the system, ensuring efficient operation and performance increase.

Hybrid dynamic modeling of an industrial reactor network with first-principles and data-driven approaches

Dynamic modeling for complex, hazardous, difficult-to-operate chemical processes can often be challenging. This paper introduces a dynamic model that utilizes a hybrid approach, combining first-principles and data-driven methodologies, for modeling a complex reactor network comprising seven reactors in a counterflow connection. Specifically, a first-principles model is developed through mechanism analysis for each reactor. Real-time measurements are utilized by an unscented Kalman filter (UKF) to facilitate the co-estimation of both model states and parameters. A quadratic programming optimization is performed to address the physical constraints in the model parameters. Finally, a Seq2Seq neural network is employed in a serial configuration to compensate for the unmodeled dynamics by the first-principles model. The performance of the proposed dynamic model is compared with several other methods on an industrial nitration process under load-changing scenarios. The results demonstrate that our proposed model exhibits superior performance in terms of prediction accuracy and interpretability over other methods.

Modeling and Regulation of Thyroid Feedback Network Based on DNA Strand Displacement

Modeling and Regulation of Thyroid Feedback Network Based on DNA Strand Displacement

Empirical performance analysis and ML-based modeling of 5G non-standalone networks

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models.

SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction.

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.

Modeling default mode network patterns via a universal spatio-temporal brain attention skip network

Designing a comprehensive four-dimensional resting-state functional magnetic resonance imaging (4D Rs-fMRI) based default mode network (DMN) modeling methodology to reveal the spatio-temporal patterns of individual DMN, is crucial for understanding the cognitive mechanisms of the brain and the pathogenesis of psychiatric disorders. However, there are still two limitations of existing approaches for DMN modeling. The approaches either (1) simply split the spatio-temporal components and ignore the overall character of the spatio-temporal patterns or (2) are biased in the process of feature extraction for DMN modeling, and their spatio-temporal accuracy is thus not warranted. To this end, we propose a novel Spatio-Temporal Brain Attention Skip Network (STBAS-Net) to model the personalized spatio-temporal patterns of the DMN. STBAS-Net consists of spatial and temporal components, where the multi-head attention skip connection block in the spatial component achieves detailed feature extraction and enhancement in the shallow stage. Under the guidance of spatial information, we technically fuse multiple spatio-temporal information in the temporal component, which dexterously exploits the overall spatio-temporal features and achieves mutual constraints of spatio-temporal patterns to characterize the spatio-temporal patterns of the DMN. We verify the proposed STBAS-Net on a publicly released 4D Rs-fMRI dataset and an EMCI dataset. The experimental results show that compared with existing advanced methods, the proposed network can more accurately model the personalized spatio-temporal patterns of the human brain DMN and successfully identify abnormal spatio-temporal patterns in EMCI patients. This study provides a potential tool for revealing the spatio-temporal patterns of the human brain DMN and is expected to provide an effective methodological framework for future exploration of abnormal brain spatio-temporal patterns and modeling of other functional brain networks.

Counterparty choice, maturity shifts and market freezes: Lessons from the European interbank market

We explore the impact of relationship lending on the interbank debt maturity structure of banks using data from the e-MID market covering both pre- and post-Lehman periods. We study the term structure and maturity shortening of interbank lending as an indicator of risk in times of stress. We identify bank-level and pair-level variables which are shown to contain information about the behaviour of lending relations during times of stress. Using a two-part fractional response model we show that durable liquidity relationships increase the probability of contracting term loans, but do not prevent maturity shortening during periods of acute stress. Finally, we find that lenders with concentrated short-term interbank liability structure tend to reduce their own long term lending, which confirms the rollover risk viewpoint of term interbank market freeze. Our findings are relevant for the modelling of interbank networks under stress and the design of forward looking stress tests for the banking system.

Remarks on the Mathematical Modeling of Gene and Neuronal Networks by Ordinary Differential Equations

In the theory of gene networks, the mathematical apparatus that uses dynamical systems is fruitfully used. The same is true for the theory of neural networks. In both cases, the purpose of the simulation is to study the properties of phase space, as well as the types and the properties of attractors. The paper compares both models, notes their similarities and considers a number of illustrative examples. A local analysis is carried out in the vicinity of critical points and the necessary formulas are derived.

Transmission line switching spread modelling of synchrophasor smart grid network with load uncertainty inspired by epidemic model: a study of reliability and vulnerability

ABSTRACT Monitoring and controlling of transmission lines become easier in real time with synchrophasor measurements technology. Phasor Measurement Units (PMUs) provide the time-stamped synchrophasor data of the system buses and transmission lines with a high reporting rate. In this paper, a mathematical model is implemented that allows the simulation of the characteristics of the spread of transmission line switching with load variations. In the model, the transmission lines are divided into three parts defined as susceptible, infected and recovered transmission lines inspired from epidemic model. In this paper, Reliability Test System (RTS − 1979) is divided into three zones to simulate the line switching spread behaviour with load variation for reliability and vulnerability analysis of the synchrophasor smart grid network. The anomaly exists with synchrophasor measuring devices due to cyber intrusion in communication network or failure of devices makes the system observable with the characterization of vulnerable behaviour of transmission lines.

Characterization and reconstruction of rough fractures based on vector statistics

The characteristics of fracture networks are of great significance for unconventional geo-energy exploitation such as shale oil, shale gas and geo-thermal extraction. Natural fractures and induced fractures after reservoir stimulation control the mechanical properties and fluid flow of the reservoirs. The quantitative characterization and reconstruction of rough fractures are essential for studying the fluid flow and mechanical properties of the reservoirs with complicated fracture networks. Based on vector statistics, a vector statistical method (VSM) for quantitative characterization of rough fractures is proposed and tested using 10 standard joint profiles. Furthermore, the growth vector counting method (counting method) and growth vector probability method (probability method) for single rough fractures in two-dimensional (2D) and three-dimensional (3D) models are proposed. Afterward, a morphology comparison and quantitative evaluation of single rough fractures before and after reconstruction are conducted. When the counting and probability methods are used to reconstruct single rough fractures, the difference in the tortuosity and fractal dimensions of the original and reconstructed fractures are less than 5% and 2.5%, respectively. It implies the counting and probability methods proposed herein can reconstruct rough fractures with approximate statistical characteristics and quantitative characterization parameters. Subsequently, the counting and probability methods for single rough fractures are further developed for 2D and 3D modeling of conventional and rough discrete fracture networks. The results indicate that the growth vector counting and probability methods proposed in this study have significant potential for rough discrete fracture network modeling. In addition, the merits and limitations of the proposed algorithms in modeling discrete fracture network models are discussed.

Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity.

The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose a novel joint modeling approach for the analysis of growth biomarkers and metabolite associations, to unveil metabolic pathways related to childhood obesity. Within a Bayesian framework, we flexibly model the temporal evolution of growth trajectories and metabolic associations through the specification of a joint nonparametric random effect distribution, with the main goal of clustering subjects, thus identifying risk sub-groups. Growth profiles as well as patterns of metabolic associations determine the clustering structure. Inclusion of risk factors is straightforward through the specification of a regression term. We demonstrate the proposed approach on data from the Growing Up in Singapore Towards healthy Outcomes cohort study, based in Singapore. Posterior inference is obtained via a tailored MCMC algorithm, involving a nonparametric prior with mixed support. Our analysis has identified potential key pathways in obese children that allow for the exploration of possible molecular mechanisms associated with childhood obesity.

Modeling and Simulation of Distribution Networks under Lightning Transients: A Comparative Study of Accuracy and Complexity

Nowadays, electricity consumption is closely linked to the economic growth of cities, states, and countries, providing a better quality of life and security. Overhead power distribution networks at medium- and low-voltage levels are responsible for serving and supplying electricity to the largest consumer class, as they represent most consumer installations. Due to this reason, there is a vast expanse of exposed networks vulnerable to the effects of atmospheric discharges. Therefore, the objective of this article is to implement and evaluate the models available in the literature to represent distribution networks during transient events caused by lightning, using simulation in ATPDraw. These structures pose a significant complexity in their representation, and there are studies that assess more accurate methods for simulating transients in transmission lines, which have different structural and configuration characteristics. Ultimately, the results demonstrate that the choice of which model to implement in a simulation heavily depends on the analysis to be conducted by the researcher, and it can be observed that more accurate methods do not always yield substantial differences in the obtained outcomes.

High-impedance arc fault modeling for distribution networks based on dynamic geometry dimension

Accurate modeling of high-impedance arc fault (HIAF) is of great significance for studying the characteristics of arc fault and suppressing its harm. In traditional arc models, the arc column is usually considered a cylindrical channel with constant length and diameter. However, the arc-burning process is susceptible to the environment. The changes in arc length and diameter present complex characteristics. Therefore, this study proposes a new HIAF model for distribution networks based on the arc's dynamic geometry dimension (DGD). First, the arc length, diameter, and field strength expressions are improved based on the classical cybernetic model. Next, an automatic parameter optimization method for the DGD model is proposed, and then this model is compared with existing advanced models. After that, the effects of the variable parameters of this model on arc characteristics are analyzed. Finally, the practical application effect of this model is tested. The experimental results show that the DGD model can accurately describe the dynamic arc development process, approximate the given arc waveform closely, and generate high-quality samples, which has certain advantages.