Network Of Processes Research Articles

NITRINET: A predictive model for nitrification in reclaimed water distribution in pressurised irrigation networks

In this work, a new model (NITRINET) to simulate nitrification processes in pressurised irrigation distribution networks fed with reclaimed water is developed and applied to the Tintín Irrigation District distribution network as case study (Montilla, southern Spain). The importance of this model relies on the fact that the nutrient chemical composition of reclaimed water varies spatially along the network. The water quality analyses carried out in the Irrigation District have shown that nitrate concentrations increase along the distribution network in contrast to the reduction observed in the ammoniacal forms. This confirms that nitrification processes occur inside the pipes. Thus, the model combines the hydraulic simulation of the distribution network and the modelling of nitrification processes to predict the concentration of ammoniacal nitrogen (NH4+-N) and nitric nitrogen (NO3--N) in water arriving at farms. This model is of great applicability to the irrigation sector, given that to carry out precision fertigation strategies and optimise the amount of fertiliser applied, it is necessary to determine the concentration of nutrients present in water arriving at farms. The nutrients that reclaimed water already carries must be considered when planning fertilisation. This allows for a reduction in the amount of fertiliser applied to the soil, which has a positive impact both on the environment and on farmers’ incomes. Simulations performed with NITRINET have shown promising results, predicting the concentration of NH4+-N and NO3--N in irrigation water with mean absolute errors of 1.49 mg·L−1 and 1.25 mg·L−1, respectively.

Identification of challenges to Industry 5.0 adoption in Indian manufacturing firms: an emerging economy perspective

ABSTRACT This study aims to identify a comprehensive list of challenges and empirically evaluate their impact on Industry 5.0 adoption, particularly in developing countries in Indian manufacturing firms. Challenges were classified into four significant categories: human, environmental, legal and ethical, and financial through an extensive literature review conducted and experts’ inputs. Multi-criteria decision-making (MCDM) is a method for analyzing complex issues by assessing multiple alternatives based on specific criteria. The analytic hierarchy Process (AHP) aids in selecting suitable options, while the analytical network processes (ANP) technique determines relevant criteria. A sensitivity analysis was carried out to validate the research findings. The most preeminent challenges were identified as human critical thinking, human-machine teams, incorporation of ethics, investment in Industry 5.0 technologies, human well-being perspective, over-dependency on technology, and personalization and hyper-customization 5.0, respectively, in the adoption of Industry 5.0. The comprehensive challenges proposed in the study will assist decision-makers, practitioners, and managers in determining fair benchmarking strategies to precipitate Industry 5.0. This research helps to gauge manufacturing firms’ readiness index to adopt Industry 5.0, provides a distinct perspective, and foregrounds the significance of research to assist the industry in its long-term to anticipate Production 6.0.

Entropy-based guidance of deep neural networks for accelerated convergence and improved performance

Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can be visualized and identified. Entropy-based loss terms are developed to improve dense and convolutional model accuracy and efficiency by promoting the ideal entropy patterns. Experiments in image compression, image classification, and image segmentation on benchmark datasets demonstrate these losses guide neural networks to learn rich latent data representations in fewer dimensions, converge in fewer training epochs, and achieve higher accuracy.

Probabilistic Discrete‐Time Models for Spreading Processes in Complex Networks: A Review

AbstractResearch into network dynamics of spreading processes typically employs both discrete and continuous time methodologies. Although each approach offers distinct insights, integrating them can be challenging, particularly when maintaining coherence across different time scales. This review focuses on the Microscopic Markov Chain Approach (MMCA), a probabilistic f ramework originally designed for epidemic modeling. MMCA uses discrete dynamics to compute the probabilities of individuals transitioning between epidemiological states. By treating each time step—usually a day—as a discrete event, the approach captures multiple concurrent changes within this time frame. The approach allows to estimate the likelihood of individuals or populations being in specific states, which correspond to distinct epidemiological compartments. This review synthesizes key findings from the application of this approach, providing a comprehensive overview of its utility in understanding epidemic spread.

BP Neural Network-Enhanced System for Employment and Mental Health Support for College Students

This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.

Relational hyperevent models for the coevolution of coauthoring and citation networks

Abstract The development of appropriate statistical models has lagged behind the ambitions of empirical studies analysing large scientific networks—systems of publications connected by citations and authorship. Extant research typically focuses on either paper citation networks or author collaboration networks. However, these networks involve both direct relationships, as well as broader dependencies between references linked by multiple citation paths. In this work, we extend recently developed relational hyperevent models to analyse networks characterized by complex dependencies across multiple network modes. We introduce new covariates to represent theoretically relevant and empirically plausible mixed-mode network configurations. This model specification allows testing hypotheses that recognize the polyadic nature of publication data, while accounting for multiple dependencies linking authors and references of current and prior papers. We implement the model using open-source software to analyse publicly available data on a large scientific network. Our findings reveal a tendency for subsets of papers to be cocited, indicating that the impact of these papers may be partly due to endogenous network processes. More broadly, the analysis shows that models accounting for both the hyperedge structure of publication events and the interconnections between authors and references significantly enhance our understanding of the mechanisms driving scientific production and impact.

Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M2Net) model

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions; the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs; its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M2Net), and it consists of two major steps. Following image preprocessing, the 2D mixed-scale ZoomNet segments ILT from each orthogonal view (i.e., Axial, Sagittal, and Coronal views) to enhance the prior information. Then, the proposed context-aware volume integration network (CVIN) effectively fuses the multiview results.Using contrast-enhanced computed tomography angiography (CTA) data from human subjects with AAAs, we evaluated the proposed M2Net. A quantitative analysis shows that the proposed deep-learning M2Net model achieved superior performance (e.g., DICE scores of 0.88 with a sensitivity of 0.92, respectively) compared with other state-of-the-art deep-learning models. In closing, the proposed M2Net model can provide high-quality delineation of ILT in an automated fashion and has the potential to be translated into the clinical workflow.

A Multi-Agent Reinforcement Learning-Based Task-Offloading Strategy in a Blockchain-Enabled Edge Computing Network

In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme’s comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively.

Influence of structural properties and connectivity of initial fracture network on incipient karst genesis

A karst system is generally difficult to characterize especially regarding its conduits pattern and morphology. Even though speleology allows to map the geometry of cave systems, a large part of karst conduit networks remains unreachable underground. In this study, we use a reactive transport model that simulates dissolution processes in fracture networks to investigate the effect of discrete fracture network (DFN) properties (i.e., anisotropy, connectivity, fracture intersection types) on incipient karst conduit geometry under different boundary conditions (constant hydraulic head versus constant flow rate conditions, directional versus concentrated recharge conditions). We focused on single fracture (with/without cementation) and ladder like fracture networks which are common on limestone. Results show that fracture network properties, play a major role in the final geometry and topology of the incipient karst conduit patterns. Also, the presence of cement on a single fracture favors tortuous and curvilinear wormholes, while the anisotropy of the fracture network favors the formation of anisotropic incipient karst geometries. Finally, the type of hydraulic BC remains a major factor that governs dissolution processes and thus incipient karst conduit geometry.

Research on the Evolution Process of New Distribution Network Forms Considering Low Carbon and Sustainable Development

To obtain the planning and development status of new distribution networks under the low-carbon and sustainable development goals, research the evolution process of new distribution network forms under the “carbon peak” and “carbon neutrality” goals (dual carbon goals). Firstly, combining the characteristics of high proportion renewable energy, construct a new distribution network morphology evolution system under the “dual carbon target”. Explore the energy pattern and structural form of the current and medium/long-term new distribution network, and obtain the corresponding evolution process. Then, considering the transmission characteristics of the new distribution network under the “dual carbon target”, an evaluation index system is established in three aspects: power supply, distribution, and transmission. And construct an evaluation model for the development form of distribution networks based on SWOT analysis, to evaluate the new development form of distribution networks. Finally, numerical examples were verified and analyzed using MATLAB simulation software. The method proposed in this article not only intuitively describes the evolution process of the new distribution network morphology, but also obtains the evolution of the new distribution network at different time stages under the “dual carbon target”, as well as the evolution results of the distribution network morphology in terms of carbon emissions and cost investment. The final research results of this article can provide theoretical basis for distribution network planning under the “dual carbon goal”.

The eye of artificial intelligence - Convolutional Neural Networks

Inspired by the biological visual system, the convolutional neural network has been widely studied and invented in the field of artificial intelligence. As one of the important algorithms in artificial neural networks, convolutional neural networks have shown outstanding application potential in fields such as image recognition, computer vision, and natural language processing. This article will focus on exploring the powerful capabilities of convolutional neural networks in image processing. By delving into the implementation process of a convolutional neural network, readers will gain a deeper understanding of its working principles. In addition, this article will briefly introduce three classic models of convolutional neural networks, providing readers with more background knowledge. Next, this paper will analyze in detail two typical application cases of convolutional neural networks in the field of image processing: intelligent transportation systems and dental imaging technology. These cases demonstrate the successful application of convolutional neural networks in practical scenarios, pointing the way for their future development. In the future, convolutional neural networks will be more widely used in fields such as image and video processing as data scale increases and computing power improves. By using techniques such as model compression and hardware optimization, it is made more suitable for low-power and high-efficiency environments, and its interpretability and applicability are enhanced through data augmentation and model interpretation techniques.

Nucleation phenomena and extreme vulnerability of spatial k-core systems

K-core percolation is a fundamental dynamical process in complex networks with applications that span numerous real-world systems. Earlier studies focus primarily on random networks without spatial constraints and reveal intriguing mixed-order transitions. However, real-world systems, ranging from transportation and communication networks to complex brain networks, are not random but are spatially embedded. Here, we study k-core percolation on two-dimensional spatially embedded networks and show that, in contrast to regular percolation, the length of connections can control the transition type, leading to four different types of phase transitions associated with interesting phenomena and a rich phase diagram. A key finding is the existence of a metastable phase where microscopic localized damage, independent of system size, can cause a macroscopic phase transition, a result which cannot be achieved in traditional percolation. In this case, local failures spontaneously propagate the damage radially until the system collapses, a phenomenon analogous to the nucleation process.

Disentangling high-order effects in the transfer entropy

Transfer entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias—either in deficiency or excess—during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. Here, we propose a novel approach. Instead of conditioning TE on all network processes except the driver and the target, as in its fully conditioned version, or not conditioning at all, as in the pairwise approach, our method searches for both the multiplets of variables that maximize information flow and those that minimize it. This provides a decomposition of TE into unique, redundant, and synergistic atoms. Our approach enables the quantification of the relative importance of high-order effects compared to pure two-body effects in information transfer between two processes, while also highlighting the processes that contribute to building these high-order effects alongside the driver. We demonstrate the application of our approach in climatology by analyzing data from El Niño and the Southern Oscillation. Published by the American Physical Society 2024

Power-enhanced residual network for function approximation and physics-informed inverse problems

In this study, we investigate how the updating of weights during forward operation and the computation of gradients during backpropagation impact the optimization process, training procedure, and overall performance of the neural network, particularly the multi-layer perceptrons (MLPs). This paper introduces a novel neural network structure called the Power-Enhancing residual network, inspired by highway network and residual network, designed to improve the network's capabilities for both smooth and non-smooth functions approximation in 2D and 3D settings. By incorporating power terms into residual elements, the architecture enhances the stability of weight updating, thereby facilitating better convergence and accuracy. The study explores network depth, width, and optimization methods, showing the architecture's adaptability and performance advantages. Consistently, the results emphasize the exceptional accuracy of the proposed Power-Enhancing residual network, particularly for non-smooth functions. Real-world examples also confirm its superiority over plain neural network in terms of accuracy, convergence, and efficiency. Moreover, the proposed architecture is also applied to solving the inverse Burgers' equation, demonstrating superior performance. In conclusion, the Power-Enhancing residual network offers a versatile solution that significantly enhances neural network capabilities by emphasizing the importance of stable weight updates for effective training in deep neural networks. The codes implemented are available at: https://github.com/CMMAi/ResNet_for_PINN.

A physics-informed neural network approach for predicting fatigue life of SLM 316L stainless steel based on defect features

Defects in additively manufactured materials severely limit the performance of parts in practical applications, often exposing them to the risk of fatigue failure. In order to improve the reliability and performance of additively manufactured parts, it becomes crucial to accurately predict the fatigue life of the material. Although traditional semi-empirical formulas can assess the effect of defects on the fatigue performance of parts, they still lack detailed research and consideration of defect morphology features. Therefore, this study proposes a method based on Physics-Informed Neural Networks (PINN). This method improves the predictive capability of the model and enhances its interpretability by extracting the sensitive features of critical defects and embedding known physical knowledge or fracture mechanics methods as loss functions into the training process of the neural network. Additionally, the method effectively captures the complex relationship between defect features and fatigue life, providing a deeper understanding of the model prediction results. The results show that the PINN model considering feature-related knowledge has higher prediction accuracy and reliability, and all predicted fatigue life are narrowed within 2-factor bands, enabling more accurate prediction of fatigue life for SLM 316L stainless steel under different processing conditions.

A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity during Brain Development.

Brain dynamic effective connectivity (dEC), characterizes the information transmission patterns between brain regions that change over time, which provides insight into the biological mechanism underlying brain development. However, most existing methods predominantly capture fixed or temporally invariant EC, leaving dEC largely unexplored. Herein we propose a deep dynamic causal learning model specifically designed to capture dEC. It includes a dynamic causal learner to detect time-varying causal relationships from spatio-temporal data, and a dynamic causal discriminator to validate these findings by comparing original and reconstructed data. Our model outperforms established baselines in the accuracy of identifying dynamic causalities when tested on the simulated data. When applied to the Philadelphia Neurodevelopmental Cohort, the model uncovers distinct patterns in dEC networks across different age groups. Specifically, the evolution process of brain dEC networks in young adults is more stable than in children, and significant differences in information transfer patterns exist between them. This study highlights the brain's developmental trajectory, where networks transition from undifferentiated to specialized structures with age, in accordance with the improvement of an individual's cognitive and information processing capability. The proposed model consists of the identification and verification of dynamic causality, utilizing the spatio-temporal fusing information from fMRI. As a result, it can accurately detect dEC and characterize its evolution over age.

Overcoming organizational fragmentation in environmental education – the networking role of local education offices

Although environmental education (EE) scholars celebrate the diversity of organizations that engage in this work, the challenge remains how to bring these organizations together. Over the last decade, scholars have called for building bridges, but we still know very little about organizational networking and integration processes in EE. In this paper, I analyzed a case study of the first local education office (LEO) to manage an EE network in California. Drawing on institutional theory, I found that the LEO cultivated a local EE organizational field through three complementary strategies of relational work: enabling connections, aligning the field, and holding space. These three strategic tools enabled EE organizations to connect, develop a shared meaning system, and grow their capacity to serve schools. Based on my findings, I propose a model for managing local EE networks, as well as theoretical constructs for understanding organizational processes within the evolving EE landscape.

Broadening the scope: Multiple functional connectivity networks underlying threat conditioning and extinction

Abstract Threat learning processes are thought to be foundational to anxiety and fear-related disorders. However, the study of these processes in the human brain has largely focused on specific brain regions, owing partly to the ease of translating between these regions in human and nonhuman animals. Moving beyond analyzing focal regions of interest to whole-brain dynamics and connectivity during threat learning is essential for understanding the neuropathology of fear-related disorders in humans. In this study, 223 participants completed a 2-day Pavlovian threat conditioning paradigm while undergoing fMRI. Participants completed threat acquisition and extinction. Extinction recall was assessed 48 hours later. Using a data-driven group independent component analysis (ICA), we examined large-scale functional connectivity networks during each phase of threat learning. Connectivity networks were tested to see how they responded to conditioned stimuli during early and late phases of threat acquisition and extinction as well as during early trials of extinction recall. A network overlapping with the default mode network involving hippocampus, ventromedial prefrontal cortex (vmPFC), and posterior cingulate was implicated in threat acquisition and extinction. Another network overlapping with the salience network involving dorsal anterior cingulate cortex (dACC), mPFC, and inferior frontal gyrus was implicated both in threat acquisition and in extinction recall. Other networks overlapping with parts of the salience, somatomotor, visual, and frontoparietal networks were involved in the acquisition or in the extinction of learned threat responses. These findings help support the functional cooperation of specific brain regions during threat learning in a model-free fashion while introducing new findings of spatially independent functional connectivity networks during threat and safety learning. Rather than being a single process in a core network of regions, threat learning involves multiple brain networks operating in parallel performing different functions at different timescales. Understanding the nature and interplay of these dynamics will be critical for comprehensive understanding of the multiple processes that may be at play in the neuropathology of anxiety and fear-related disorders.

A dual-head output network attack detection and classification approach for multi-energy systems

In today’s digital age, multi-energy systems (MES) have become an indispensable part of the social infrastructure, providing people with diversified energy support such as electricity, gas, water and so on. However, with the increasing popularity and networking of MES, the network security threats they face are becoming more and more serious, especially the threat of network attacks. This makes it essential to detect attacks on MES and precisely classify attack types in order to establish effective defense strategies. In this paper, a Dual-Head output network attack detection and classification method based on parallel CNN-BiLSTM network is proposed. The method adopts a parallel structure and can process different aspects of information at the same time, speeding up the training and inference process of the whole network, making the system respond more quickly to potential network attacks, and improving real-time and efficiency. The multi-model fusion structure can give full play to the advantages of CNN and BiLSTM in processing different types of data, so that the system can capture attack characteristics more comprehensively in many aspects, and improve the overall detection and classification performance. The dual-head output not only improves the system’s ability to accurately detect attacks, but also can effectively classify different types of attacks in detail, which helps to formulate more targeted defense strategies. In addition, in order to effectively evaluate our proposed method, the network traffic data required for the experiment were collected in an environment very similar to the actual operating environment of a multi-energy system. Finally, the experiment verifies that our method can not only realize effective detection of network attacks, but also accurately classify different types of attacks.

Social Opportunity Structures in Hacktivism: Exploring Online and Offline Social Ties and the Role of Offender Convergence Settings in Hacktivist Networks

ABSTRACT Hacktivism represents the promotion in the cyber landscape of ideologically motivated agendas using hacking techniques. Despite research on the topic has provided some clues on how hacktivist networks develop, the processes behind their evolution remain mostly unknown. This gap in the literature prompted us to research the role of online/offline social relationships and of the offender convergence settings in the creation, recruitment process and development of hacktivist networks. This study is based on 30 interviews with hacktivists, and it uses the social opportunity structures framework to analyze the development of 21 hacktivist networks. The results show that said networks can be divided in sub-categories based on the type of connections used to create them. Online social relationships and online convergence settings (particularly social media platforms and IRC channels) seem to play a key role in the development of hacktivist networks, while offline contacts are limited. For the recruitment process, hacktivists use comparable strategies to any organization, but three different categories were identified when discussing the level of sophistication applied to the selection of new candidates.