Network Of Processes Research Articles

Tough and biodegradable C-lignin cross-linked polyvinyl alcohol supramolecular composite films with closed-looping recyclability

Catechyl lignin (C-lignin) is a unique natural biopolymer that is considered to be the best alternative to conventional lignin due to its linear homogeneous molecular structure. Based on the structural properties of C-lignin, the development of a new biodegradable plastic to replace traditional petroleum-based plastics is a sustainable route. However, the maximum filling fraction of lignin is required to achieve excellent biodegradability of lignin-based bio-plastics, yet resulting in decreased mechanical strength of composites. Herein, we report a novel strategy to fabricate a closed-loopling recyclability supramolecular composite films through casting and dry molding process of intermolecular covalent-noncovalent cross-linking networks with polyvinyl alcohol by the epoxidation of C-lignin. The intermolecular cross-linking mechanism analyses reveal that the resultant bio-material not only display improved mechanical properties (maximum tensile strength of 133.5 MPa and strain at break of 690.2 %), but also show UV-resistant, antimicrobial, thermally stable, and rapid biodegradable properties. Notably, the fabricated films demonstrate the characteristics of closed-loop recyclability, which paves a new way toward rational design of bioplastics using renewable lignin. These integrated properties make the lignin-based biocomposites a promising alternative to traditional plastics.

Variance of the Infection Number of Heterogeneous Malware Spread in Network

The Susceptible–Infected–Susceptible (SIS) model in complex networks is one of the critical models employed in the modeling of virus spread. The study of the heterogeneous SIS model with a non-homogeneous nodal infection rate in finite-size networks has attracted little attention. Investigating the statistical properties of heterogeneous SIS epidemic dynamics in finite networks is thus intriguing. In this paper, we focus on the measure of variability in the number of infected nodes for the heterogeneous SIS epidemic dynamics in finite-size bipartite graphs and star graphs. Specifically, we investigate the metastable-state variance of the number of infected nodes for the SIS epidemic process in finite-size bipartite graphs and star graphs with heterogeneous nodal infection rates. We employ an extended individual-based mean-field approximation to analyze the heterogeneous SIS epidemic process in finite-size bipartite networks and star graphs. We derive the approximation solutions of the variance of the infected number. We verify the proposed theory by simulations. The proposed theory has the potential to help us better understand the fluctuations of SIS models like epidemic dynamics with a non-homogeneous infection rate.

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

MTTLA‐DLW: Multi‐task TCN‐Bi‐LSTM transfer learning approach with dynamic loss weights based on feature correlations of the training samples for short‐term wind power prediction

AbstractWind power prediction for newly built wind farms is usually faced with the problem of no sufficient historical data. To efficiently extract the useful features from related wind farms, a novel transfer learning method based on temporal convolutional network (TCN)‐Bi‐long short‐term memory (LSTM) with dynamic loss weights is proposed. Firstly, a novel multi‐task TCN‐Bi‐LSTM model is designed to extract common features. The separate TCNs, and common Bi‐LSTM layers of the proposed model are designed to extract the temporal features from related wind farms. Secondly, in the pre‐training stage, to optimize the training process of the neural networks, a dynamic loss‐weighting strategy is proposed for multi‐task learning (MTL) to select the most related features, which increase the prediction accuracy by providing a suitable optimization object. Thirdly, the multi‐task TCN‐Bi‐LSTM model is re‐trained based on the samples from the target wind farm. Finally, a dataset of seven wind farms was employed to evaluate the efficiency of the proposed MTL structure and the dynamic loss‐weighting strategy. The result shows that the root mean squared error of the 12‐h short‐term prediction can be decreased by 4.19% compared with the traditional single‐task learning model, which verifies the validity of the proposed multi‐task transfer learning method.

Classification of plant leaf diseases using deep neural networks in color and grayscale images

For a long time, plants have shown a crucial role in our life, manufacturing and industry. However, a large number of diseases have significantly affected to the production of plants. The detection and diagnosis of the diseases are necessary to improve the production of plants. In recent years, the automatic classification of plant diseases using artificial intelligence and computer vision have attracted a huge number of researches. The paper presents a classification method of plant leaf diseases using various Deep Neural Networks (e.g., Alexnet, Resnet-50, Densenet-121). The color features have significantly affected the classification accuracy. Therefore, we analyzed and compared the classification accuracy on two public datasets (Plant village leaf datasets) that consist of color and grayscale images. The method obtained the classification accuracy of 98.08% and 92% on color and grayscale images, respectively. The obtained results showed the effectiveness of the method. Based on the obtained results, the impact of color features to the classification accuracy of various Deep Neural Networks is analyzed in the paper. Moreover, the paper compares the performance of various optimization algorithms during the training process of deep neural networks to classify leaf diseases.

Abstract PO1-23-11: Differential m6A modification identified by direct RNA sequencing in endocrine- resistant and sensitive breast cancer cells

Abstract A major limitation of current adjuvant treatments for the ~ 70% of breast cancer patients with estrogen receptor α–expressing (ER+) primary breast tumors is the development of acquired and adaptive endocrine therapy (ET) resistance and metastatic spread. Chemical modifications of transcribed RNAs alters their cellular location, stability, and, for mRNAs, translation into proteins. N-6 methyladenosine (m6A) is the most common modification of mRNA. We tested the hypothesis that m6A epitranscriptomic changes alters the transcriptome resulting in changes in targets in pathways contributing to the metastatic cascade in ET-resistant breast cancer cells. We used an unbiased approach of direct mRNA sequencing using the Oxford Nanopore MinION platform on polyA-selected mRNA from MCF-7 luminal A breast cancer cells and ER+, ET-resistant LCC9 breast cancer cells treated with vehicle control or 100 nM 4-hydroxytamoxifen (4-OHT) for 24 h. In addition, MCF-7 cells were treated with the METTL3 (the catalytic methyltransferase that adds the methyl group from S-adenosylmethionine (SAM) to the adenosine on the N6 position) inhibitor STM2457 to identify m6A positions and transcripts directly regulated by METTL3 activity. Computational analysis directly identified m6A locations in transcripts. We examined the differential modification rate (DMR) between MCF-7 cells +/STM2457 treatment, between MCF-7 and LCC9 cells with vehicle control treatment or with 4-OHT treatment and within each cell line with 4-OHT treatment. We identified 183 and 3,097 genes with m6A DMRs between vehicle-treated and 4-OHT-treated MCF-7 and LCC9 cells, respectively. For 4-OHT-treated cells, we identified 2,401 and 2,638 genes with m6A DMRs in MCF-7 and LCC9 cells respectively with 1,760 genes in common. We identified many genes with multiple m6A modifications, e.g., AMIGO2 appeared to have 14 m6A sites at DRACH motifs in MCF-7 cells, but none were m6A modified in LCC9 cells. Enrichment by Pathway Maps, Process Networks, and GO Processes were performed. Overall, our data suggest a role for altered m6A positions in transcripts and pathways involved in ET-resistance in breast cancer. Citation Format: Belinda Petri, Bailey Valdes, Kellianne Piell, Eric Rouchka, Carolyn Klinge. Differential m6A modification identified by direct RNA sequencing in endocrine- resistant and sensitive breast cancer cells [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-23-11.

A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation.

The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.

FAGSO-BP neural network model for settlement prediction of soft soil foundation

To address the problem of difficulty in theoretical formulas and numerical analysis methods to consider the prediction of foundation settlement in the case of multiparameter coupling, the fireworks algorithm (FWA) with a gravity search operator (FAGSO) is introduced into the back propagation (BP) neural network model, and the weight and threshold processes in the neural network are improved by the FAGSO algorithm. A soft soil foundation settlement prediction model based on a FWA optimized BP neural network (FAGSO-BP) with a gravity search operator is proposed. Based on the second phase of the soft foundation treatment project of the Shatian Port Area, Humen City, and Dongguan City, the parameters of pile load height, pore water pressure, and groundwater level were collected and tested. The results indicate that the average relative error of the predicted cumulative settlement value of the soft soil foundation was 0.06%, root mean square error was 7.68, mean absolute error was 4.30, mean absolute percentage error was 1.40%, and mean square error was 59.98. Compared with the BP neural network, GA-BP neural network, and FWA-BP neural network, the error was smaller and the model stability was higher.

Physics-Constraint Variational Neural Network for Wear State Assessment of External Gear Pump.

Most current data-driven prognosis approaches suffer from their uncontrollable and unexplainable properties. To address this issue, this article proposes a physics-constraint variational neural network (PCVNN) for wear state assessment of the external gear pump. First, a response model of the pressure pulsation of the gear pump is constructed via a spectral method, and a compound neural network is utilized to extract features from the pressure pulsation signal. Then, the response model is formulated into an objective function to softly constrain the learning process of the neural network, forcing the learned features to have explicit physics meaning. Meanwhile, to characterize the system uncertainty, the variational inference is utilized to extend a Kullback-Leibler (KL) divergence into the objective function. Finally, the wear state is evaluated based on the distance of learned physics features. Experimental results on an external gear pump validate the merits of the proposed method in explainable representation learning and system uncertainty estimation. It also offers a controllable and explainable perspective to understand the dynamic behavior of the system.

СПРОЩЕНА ІМІТАЦІЙНА МОДЕЛЬ НАВАНТАЖЕННЯ В ЗАМКНЕНІЙ МЕРЕЖІ КОМУТАЦІЇ ПАКЕТІВ

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a dis-cretized description of the load states of the network nodes and corresponds to the repre-sentation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially se-lected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (en-sures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained re-sults are generalized, but attention is focused on the example of satellite packet switching networks.

Proposed solutions for grapes development in Ninh Thuan by Fuzzy-ANP-SWOT

Ninh Thuan is one of the extremely dry regions in Vietnam, and choosing farming solutions in the agricultural sector is one of the top priorities. Research chooses grapes to propose solutions for development in drought because this is considered one of the local staple crops. The study used a multi-criteria decision-making method (MCDM) with a combination of Fuzzy Analytic Network Processes (Fuzzy - ANP). Through the study of published documents and surveys, the study has identified 24 factors belonging to four groups: natural, economic, social and policy and total as a SWOT matrix. Through the Fuzzy ANP model and expert assessment, the research has proposed eight strategies, and the study identified priority strategies for the sustainable development of vines in Ninh Thuan. The results are ranked as follows: WO2 (0.162), ST1 (0.148), WO1 (0.137), SO2(0.124), WT2(0.128), ST2 (0.122), WT1(0.119) and SO1 (0.059). Research results are the basis for helping local governments have more quantitative decision-making tools, thereby helping proposed solutions to be more objective and suitable to the actual situation.

Multi-granularity short circuit fault diagnosis for tokamak power converters under small sample conditions and noise interference

The power supply system of a tokamak device is a crucial subsystem responsible for providing the magnetic field for plasma confinement. The timely application of fault diagnosis techniques to identify abnormal states of the power supply is of crucial importance for the safe and efficient operation of tokamak devices. Intelligent diagnostic methods based on Convolutional Neural Networks (CNN) are widely employed nowadays. However, within the operational environment of the device, there exist multiple physical field couplings, and inevitable noise will impact the data. Moreover, the measurement equipment exhibits instability, resulting in limited availability of data that meets the required criteria. To address these issues, this paper investigates a fault diagnosis approach utilizing an Auxiliary Classifier Generative Adversarial Network (ACGAN) for data augmentation, and proposes a Multi-Granularity Denoising CNN (MGDCNN) that accounts for noise interference, enabling diagnosis of faults at different granularities. In our approach, we first construct a multi-granularity structure of the data based on prior knowledge. Then, a hierarchical structure corresponding to the multi-granularity data and robust to noise is built by introducing an embedded Discrete Wavelet Transform (DWT) into the one-dimensional (1-D) CNN. Finally, the natural stratification of the network is combined with individual granularity branches for fault diagnosis. Experimental results on simulated datasets and public benchmark datasets demonstrate that the proposed model achieves a diagnostic accuracy of 98.81 % for short-circuit faults in power supply converter at the finest granularity. Even with noisy data, the model maintains a diagnostic accuracy of approximately 97 %, indicating superior noise resistance performance in the inference process of the MGDCNN network.

Driver Drowsiness Detection System Based on Eye State Analysis

Driver Drowsiness Detection System Based on Eye State Analysis

Attentional decoder networks for chest X-ray image recognition on high-resolution features

Background and objective:This paper introduces an encoder–decoder-based attentional decoder network to recognize small-size lesions in chest X-ray images. In the encoder-only network, small-size lesions disappear during the down-sampling steps or are indistinguishable in the low-resolution feature maps. To address these issues, the proposed network processes images in the encoder–decoder architecture similar to U-Net families and classifies lesions by globally pooling high-resolution feature maps. However, two challenging obstacles prohibit U-Net families from being extended to classification: (1) the up-sampling procedure consumes considerable resources, and (2) there needs to be an effective pooling approach for the high-resolution feature maps. Methods:Therefore, the proposed network employs a lightweight attentional decoder and harmonic magnitude transform. The attentional decoder up-samples the given features with the low-resolution features as the key and value while the high-resolution features as the query. Since multi-scaled features interact, up-sampled features embody global context at a high resolution, maintaining pathological locality. In addition, harmonic magnitude transform is devised for pooling high-resolution feature maps in the frequency domain. We borrow the shift theorem of the Fourier transform to preserve the translation invariant property and further reduce the parameters of the pooling layer by an efficient embedding strategy. Results:The proposed network achieves state-of-the-art classification performance on the three public chest X-ray datasets, such as NIH, CheXpert, and MIMIC-CXR. Conclusions:In conclusion, the proposed efficient encoder–decoder network recognizes small-size lesions well in chest X-ray images by efficiently up-sampling feature maps through an attentional decoder and processing high-resolution feature maps with harmonic magnitude transform. We open-source our implementation at https://github.com/Lab-LVM/ADNet.

A high‐performance cellular automata model for urban expansion simulation based on convolution and graphic processing unit

AbstractCellular automata (CA) models are effective tools for simulating future urban expansion. With the widespread use of high‐resolution geospatial data for CA simulation, the computational intensity of CA models has increased. Additionally, due to the continuous development of CA modeling research, many scholars have made improvements to the models to enhance their simulation accuracy, resulting in an increasing computational complexity of the model. Consequently, the simulation task based on CA requires vast computing time and memory space. In recent years, deep learning (DL) has experienced rapid development. Many open‐source DL frameworks support graphic processing unit (GPU) parallel computing and provide efficient application programming interfaces (APIs) that can be easily called to handle tasks of interest. In this study, a high‐performance CA model was constructed based on the similarity between the neighborhood effect calculation process of the CA model and the convolutional process in a convolutional neural network (CNN). The convolution function in the DL library is used to calculate the neighborhood effect of the CA model to reduce the time and memory consumption of CA‐based simulation. The experimental results show that compared with the conventional CA model, the execution time of the GPU‐convolution‐CA model proposed in this study has been reduced by more than 98%.

The development process of urban agricultural networks to ensure urban food security in Hat Yai City, Songkhla Province, Thailand

ABSTRACT The purpose study was to examine the process, results, factors affecting the development process of the Hat Yai urban agricultural network for food security, Hat Yai District, Songkhla Province, Thailand, using qualitative research. Participant observation, in-depth interviews, group discussions, and inductive data analysis were conducted. Data were collected from thirty-one participants. The study found that the process of agricultural network development consists of three stages: network formation, seeking cooperation, learning and integration. The results are reflected at the individual, group, and network levels, linking them to the sustainable development dimension. The positive factors affecting the development process are the urban context, common goals, organisational culture, management, and building trust. The negative factors include the nature of Hat Yai urban residents in the context of developing countries, resource inadequacy, and public policy. The recommendations involve having holistic and targeted local public policy regarding urban agriculture to establish sustainable urban food security.

SIMPLIFIED SIMULATION MODEL OF LOAD IN A CLOSED PACKET SWITCHING NETWORK

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a discretized description of the load states of the network nodes and corresponds to the representation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially selected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (ensures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained results are generalized, but attention is focused on the example of satellite packet switching networks.

An adding‐points strategy surrogate model for well control optimization based on radial basis function neural network

AbstractThis work introduces a new adding points strategy for augmenting the accuracy of reservoir proxy model and improving the effect of well control optimization. The method is based on the optimization process of a radial basis function neural network and genetic algorithm (GA), which aids in identifying the more important points to be included in the sample space. Notably, the uniqueness of this method lies in selecting the points of higher importance for subsequent optimization processes across the entire sample space. These selected points are then added to the surrogate model. The surrogate model is updated for each generation until the termination condition is satisfied, enabling the surrogate model to achieve improved accuracy. The results show that the new method is more effective, superior, and converges faster than the traditional method.

End-to-End Differentiable Physics Temperature Estimation for Permanent Magnet Synchronous Motor

Differentiable physics is an approach that effectively combines physical models with deep learning, providing valuable information about physical systems during the training process of neural networks. This integration enhances the generalization ability and ensures better consistency with physical principles. In this work, we propose a framework for estimating the temperature of a permanent magnet synchronous motor by combining neural networks with the differentiable physical thermal model, as well as utilizing the simulation results. In detail, we first implement a differentiable thermal model based on a lumped parameter thermal network within an automatic differentiation framework. Subsequently, we add a neural network to predict thermal resistances, capacitances, and losses in real time and utilize the thermal parameters’ optimized empirical values as the initial output values of the network to improve the accuracy and robustness of the final temperature estimation. We validate the conceivable advantages of the proposed method through extensive experiments based on both synthetic data and real-world data and then provide some further potential applications.

Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions

Recent research has delved into the biological parallels between deep neural networks (DNNs) in vision and human perception through the study of visual illusions. However, the bulk of this research is currently constrained to the investigation of visual illusions within a single model focusing on a singular type of illusion. There exists a need for a more comprehensive explanation of visual illusions in DNNs, as well as an expansion in the variety of illusions studied. This study is pioneering in its application of representational dissimilarity matrices and feature activation visualization techniques for a detailed examination of how five classic visual illusions are processed by DNNs. Our findings uncover the potential of DNNs to mimic human visual illusions, particularly highlighting notable differences in how these networks process illusions pertaining to color, contrast, length, angle, and spatial positioning. Although there are instances of consistency between DNNs and human perception in certain illusions, the performance distribution and focal points of interest within the models diverge from those of human observers. This study significantly advances our comprehension of DNNs’ capabilities in handling complex visual tasks and their potential to emulate the human biological visual system. It also underscores the existing gaps in our understanding and processing of intricate visual information. While DNNs have shown progress in simulating human vision, their grasp of the nuance and intricacy of complex visual data still requires substantial improvement.