In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he enco...
Dynamic random network models are presented as a mathematical framework for modelling and analyzing the time evolution of complex networks. Such framework allows the time analysis of several network characterizing features such as link density, clust...
Cronbach’s (1957) famous division of scientific psychology into two disciplines is still apparent for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields...
This paper elaborates on the Random Network Model (RNM) as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features...
Bi-factor models of intelligence tend to outperform higher-order g factor models statistically. The literature provides the following rivalling explanations: (i) the bi-factor model represents or closely approximates the true underlying data-generati...
Time evolving Random Network Models are presented as a mathematical framework for modelling and analyzing the evolution of complex networks. This framework allows the analysis over time of several network characterizing features such as link density,...
Mathematical modeling has served as an epidemiological tool to enhance the modeling efforts of the social and economic impacts of the pandemic. This article reviews epidemiological network models, which are conceived as a flexible way of representing...
This study examines the impact of cooperative development levels and collaboration models on social network relations in İzmir, Türkiye. The research aims to uncover how the developmental stages of cooperatives affect their relational posit...
This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the r...
Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor,...
The Lightning Network (LN) has emerged as one of the prominent solutions to overcome the biggest limit of blockchain based on PoW: scalability. LN allows for creating a layer on top of an existing blockchain where users can send payments and micro-pa...
Hydraulic models of water distribution networks are the basis of a digital twin, but they are still built mostly by hand. HydraKG is a software pipeline that constructs an EPANET model from a network description and checks the result against that des...
Cascading failures are the significant cause of network breakdowns in a variety of complex infrastructure systems. Given such a system, uncovering the dependence of cascading failures on its underlying topology is essential but still not well explore...
Due to the complexity of district heating systems, improved novel methods of district heating network (DHN) simulation are still being introduced in the literature. The subject of validation strategies for DHN simulation models emerges as one that ha...
In underwater clustering and benchmark networks, nodes need to reduce the rate and energy consumption of acoustic communication while ensuring synchronization accuracy. In large-scale networks, the improvement in the efficiency of existing network ti...
Data-driven neural networks (NNs) have gained significant attention across engineering disciplines, particularly in design optimization and experimental settings, where they are widely used to construct surrogate models for high-dimensional regressio...
In recent years, there has been debate about the optimal conceptualisation of psychopathology. Structural models of psychopathology have been developed to counter issues, including comorbidity and poor diagnostic stability prevalent within the tradit...
The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues,...
(1) Background: The expansion that most cities have been showing for more than half a century has also brought with it an increase in the density of buildings, most of the time at the expense of green areas. This has led to negative effects, such as...
The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing s...
The lymphatic system is a body-wide network of lymphatic vessels and lymphoid organs. The complexity of the structural and functional organization of the lymphatic system implies the necessity of using computational modeling approaches to unravel the...
Based on complex network theory, this paper establishes a Boolean competitive relationship network model and a weighted competitive relationship network model for building material enterprises based on big data and performs empirical analyses with co...
Affiliation networks, with their bipartite structure and non-binary features, pose unique challenges due to their complex relationships and diverse node attributes. These challenges differ from those in symmetric one-mode networks. To address them, w...
Network vector AutoRegressive models play a vital role in multivariate time series analysis. However, previous research in the classic Network vector AutoRegressive (NAR) model is limited to strict assumptions of linearity and time-invariance of node...
We provide a non-asymptotic analysis of the spiked Wishart and Wigner matrix models with a generative neural network prior. Spiked random matrices have the form of a rank-one signal plus noise and have been used as models for high dimensional Princip...
A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS)...
Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision, in particular. Compared to such examples, however, there have been more limited applications of GAN...
Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given ref...
Over the past decade, new technologies have driven the rise of what is being termed as the fourth industrial revolution. The introduction of this new revolution is amalgamating the cyber and physical worlds, bringing with it many benefits, such as th...
We study the structural correlations in the Italian overnight money market over the period 1999–2010. We show that the structural correlations vary across different versions of the network. Moreover, we employ different configuration models and exami...
Coherence evaluation of texts falls into a category of natural language processing tasks. The evaluation of texts’ coherence implies the estimation of their semantic and logical integrity; such a feature of a text can be utilized during the solving o...
Reservoir simulation remains a major computational bottleneck for production optimization, history matching, and uncertainty quantification, particularly as geological models become increasingly detailed and recovery processes more complex. Coarse-gr...
Many charge controlled models of memristor have been proposed for various applications. First, the original linear dopant drift model suffers discontinuities close to the memristor layer boundaries. Then, the nonlinear dopant drift model improves the...
Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. H...
The CAN (Controller Area Network) protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to attac...
Network trace is a comprehensive record of data packets traversing a computer network, serving as a critical resource for analyzing network behavior. However, in practice, the limited availability of high-quality network traces, coupled with the pres...
The aim of the research, the results of which are presented in the article, is to discover new knowledge allowing for the description and design of business models 4.0 using network effects. In their research, the authors reviewed the literature, car...
With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Ne...
25 July 2023
Background: Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application o...
In this current study, we formulate a kind of new fractional BAM neural network model concerning five neurons and time delays. First, we explore the existence and uniqueness of the solution of the formulated fractional delay BAM neural network models...
The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous...
In this paper a modified wavelet synthesis algorithm for continuous wavelet transform is proposed, allowing one to obtain a guaranteed approximation of the maternal wavelet to the sample of the analyzed signal (overlap match) and, at the same time, a...
Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models...
One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool...
This paper explores a stochastic model of noisy observations with a sparse true signal structure. Such models arise in a wide range of applications, including signal processing, anomaly detection, and performance monitoring in telecommunication netwo...
Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, ris...
Mobile edge computing has been proposed as a solution for solving the latency problem of traditional cloud computing. In particular, mobile edge computing is needed in areas such as autonomous driving, which requires large amounts of data to be proce...
Various control approaches for building thermal controls have been studied to improve the energy use which determines a large part of the spatial thermal quality. This research compares the performance of deterministic models and a network-based mode...
The existing optimization approaches regarding network-constrained unit commitment with large wind power integration face great difficulties in reconciling the two crucial but contradictory objectives: computational efficiency and the economy of the...
The weighted mean temperature (Tm) is crucial for converting zenith wet delay to precipitable water vapor in global navigation satellite system meteorology. Mainstream Tm models have the shortcomings of poor universality and severe local accuracy los...
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