Single Network Research Articles

Robust liquid crystal semi-interpenetrating polymer network with superior energy-dissipation performance.

Liquid crystal networks (LCN) have attracted surging interest as extraordinary energy-dissipation materials owning to their unique dissipation mechanism based on the re-orientation of mesogens. However, how to integrate high Young's modulus, good dissipation efficiency and wide effective damping temperature range in energy-dissipation LCN remains a challenge. Here, we report a strategy to resolve this challenge by fabricating robust energy-dissipation liquid crystal semi-interpenetrating polymer network (LC-semi-IPN) consisting crystalline LC polymers (c-LCP). LC-semi-IPN demonstrates a superior synergistic performance in both mechanical and energy-dissipation properties, surpassing all currently reported LCNs. The crystallinity of c-LCP endows LC-semi-IPN with a substantial leap in Young's modulus (1800% higher than single network). The chain reptation of c-LCP also promotes an enhanced dissipation efficiency of LC-semi-IPN by 200%. Moreover, its effective damping temperature reaches up to 130 °C, which is the widest reported for LCNs. By leveraging its exceptional synergistic performance, LC-semi-IPN can be further utilized as a functional architected structure with exceptional energy-dissipation density and deformation-resistance.

An intellectual model of pest detection and classification using enhanced optimization-assisted single shot detector and graph attention network

An intellectual model of pest detection and classification using enhanced optimization-assisted single shot detector and graph attention network

Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio

ABSTRACT Methane is the second most significant greenhouse gas after carbon dioxide. As global warming intensifies, the quantification of methane point sources is becoming increasingly crucial. However, retrieving high-quality methane signals from remote sensing data remains challenging due to various factors, including surface reflectance, atmospheric interference, sensor noise, wind speed, and sensor sensitivity. In real-world scenarios, methane remote sensing quantification often encounters unfavourable conditions that lead to low signal-to-noise ratio (SNR) signals, resulting in reduced quantification accuracy. To address these challenges, we introduce a novel multi-task learning-based approach. Specifically, we incorporate a denoising auxiliary task into the quantification network by introducing an additional denoising branch that recovers clean plume column concentration maps. The network is trained with supervision using both denoising and quantification losses, which enables it to acquire robust feature representations to noise and benefits the quantification of low SNR images. During the inference phase, the denoising branch is removed, resulting in an efficient and robust single quantification network with reduced inferring time, supporting onboard computation. We construct a low SNR database based on the AVIRIS-NG sensor and evaluate the generalization ability of our method. DQNet achieves the highest RMSE, MAPE, and R of 16.478 kg/h, 15.296%, and 95.167% respectively on the synthetic dataset. Across the entire range of SNR, DQNet exhibits a greater relative advantage as SNR decreases. However, our approach is not limited to AVIRIS-NG and can be easily extended to various multispectral and hyperspectral satellites.

Constructing representative group networks from tractography: lessons from a dynamical approach

Human group connectome analysis relies on combining individual connectome data to construct a single representative network which can be used to describe brain organisation and identify differences between subject groups. Existing methods adopt different strategies to select the network structural features to be retained or optimised at group level. In the absence of ground truth, however, it is unclear which structural features are the most suitable and how to evaluate the consequences on the group network of applying any given strategy. In this investigation, we consider the impact of defining a connectome as representative if it can recapitulate not just the structure of the individual networks in the cohort tested but also their dynamical behaviour, which we measured using a model of coupled oscillators. We applied the widely used approach of consensus thresholding to a dataset of individual structural connectomes from a healthy adult cohort to construct group networks for a range of thresholds and then identified the most dynamically representative group connectome as that having the least deviation from the individual connectomes given a dynamical measure of the system. We found that our dynamically representative network recaptured aspects of structure for which it did not specifically optimise, with no significant difference to other group connectomes constructed via methods which did optimise for those metrics. Additionally, these other group connectomes were either as dynamically representative as our chosen network or less so. While we suggest that dynamics should be at least one of the criteria for representativeness, given that the brain has evolved under the pressure of carrying out specific functions, our results suggest that the question persists as to which of these criteria are valid and testable.

Metrics of physiological network topology are novel biomarkers to capture functional disability and health.

Physiological networks are highly complex, integrating connections among multiple organ systems and their dynamic changes underlying human aging. It is unknown whether individual-level network could serve as robust biomarkers for health and aging. We used personalized network analysis to construct single sample network and examine the associations between network properties and functional disability in the Rugao Longevity and Aging Study (RuLAS), the China Health and Retirement Longitudinal Study (CHARLS), the Chinese Longitudinal Healthy Longevity Survey (CLHLS), and the National Health and Nutrition Examination Survey (NHANES). We observed impairments in interconnected physiological systems among long-lived adults in RuLAS. Single sample network analysis was applied to reflect the co-occurrence of these multi-system impairments at the individual level. The ADL-disabled individuals' networks exhibited notably increased connectivity among various biomarkers. Significant associations were found between network topology and functional disability across RuLAS, CHARLS, CLHLS and NHANES. Additionally, network topology served as novel biomarkers to capture risks of incident ADL disability in CHARLS. Furthermore, these metrics of physiological network topology predicted mortality across four cohorts. Sensitivity analysis demonstrated that prediction performance of network topology remained robust, regardless of the chosen biomarkers and parameters. These findings showed that metrics of network topology were sensitive and robust biomarkers to capture risks of functional disability and mortality, highlighting the role of single sample physiological networks as novel biomarker for health and aging.

Holographic Communication: A Study of the Quality of Holographic Copies Perception

Relevance. Holography is becoming one of the most promising areas of visualization of three-dimensional objects, which justifies the emergence of a certain scientific interest in this area of research. There is a general global trend of intensifying the work of specialists on the problem of using holographic technologies in various areas of human activity. Trends in the implementation of holographic services and holographic communication today already require a revision of the principles of planning, designing and building existing communication networks, as well as approaches to the implementation of sixth-generation networks 6G, which are based on the integration of various technologies and communication networks into a single network. A separate issue is the assessment of the quality of service and the quality of perception of holographic services by both objective and subjective assessment methods. There are practically no criteria for assessing the quality of a holographic image, including scales and methods for subjective assessment of the quality of holographic services. Moreover, the properties of the holographic flow are poorly understood, and even less so its influence on communication networks and requirements for network parameters, which makes the tasks of studying traffic characteristics and assessing the quality of service of holographic services very relevant.The aim of the work is to evaluate the quality of perception of holographic conference calls using a subjective evaluation method on a model network. The work uses methods of subjective assessment of the quality of perception. The materials presented in the article reflect the results of the authors' experimental research work on studying the problem of the quality of perception of holographic copies. A description of the developed scheme of a full-scale experiment is given.Results. The data obtained as a result of the work of an expert group on assessing the quality of perception are presented. The subjective assessment of the quality of perception of a holographic image begins to deteriorate with 8 connections and becomes unsatisfactory with 12 connections, which must be taken into account when planning experimental studies of work on assessing the quality of perception. Novelty. For the first time, an assessment of the quality of perception of the provision of a holographic conferencing service was carried out using a subjective assessment method. Theoretical significance. The influence of increasing the number of holographic traffic flows on the quality of perception of the received content is analyzed. Practical significance. Expanding the possibilities for assessing the degree of user satisfaction with holographic services.

Artificial Neural Network Model to Predict the Exportation of Traditional Products of Colombia

This article develops the design, training, and validation of a computational model to predict the exportation of traditional Colombian products using artificial neural networks. This work aims to obtain a model using a single multilayer neural network. The number of historical input data (delays), the number of layers, and the number of neurons were considered for the neural network design. In this way, an experimental design of 64 configurations of the neural network was performed. The main arduousness addressed in this work is the significant difference (in tons) in the values of the considered products. The results show the effect that occurs due to the different range values, and one of the proposals made allows this limitation to be handled appropriately. In summary, this work seeks to provide essential information for formulating a model for efficient and practical application.

Lithium battery SOC estimation based on improved sparrow search algorithm and backpropagation neural network

Accurate state-of-charge (SOC) estimation is crucial for optimal battery management. This paper proposes a novel method, the Improved Sparrow Search Algorithm-Backpropagation (ISSA-BP) neural network, to address the issue of low estimation accuracy encountered with a single BP neural network. ISSA is used to optimize the initial weights and thresholds of the BP neural network, effectively overcoming its tendency to get stuck in local minima. Compared to the single BP neural network, ISSA-BP demonstrates significantly improved accuracy under two conditions (DST and BJDST), with reductions in root mean square error by 64.0% and 50.9% and mean absolute error by 69.8% and 51.1%, respectively. These results highlight the superior robustness and accuracy of the ISSA-BP algorithm for SOC estimation in lithium batteries.

Deep reinforcement learning for autonomous SideLink radio resource management in platoon-based C-V2X networks: An overview

Dynamic and autonomous SideLink (SL) Radio Resource Management (RRM) is essential for platoon-based cellular vehicular networks. However, this task is challenging due to several factors. These include the limited spectrum below 6 GHz, stringent vehicle-to-everything (V2X) communications requirements, uncertain and dynamic environments, limited vehicle sensing capabilities, and inherent distributed operation. These limitations often lead to resource collisions, data packet loss, and increased latency. Current standardized approaches in Long-Term Evolution-V2X (LTE-V2X) and New Radio-V2X (NR-V2X) rely on random resource selection, limiting their efficiency. Moreover, RRM is inherently a complex combinatorial optimization problem. It may involve conflicting objectives and constraints, making traditional approaches inadequate. Platoon-based communication necessitates careful resource allocation to support a diverse mix of communication types. These include safety-critical control messaging within platoons, less time-sensitive traffic management information between platoons, and even infotainment services like media streaming. Optimizing resource sharing inter- and intra- platoons is crucial to avoid excessive interference and ensure overall network performance. Deep Reinforcement Learning (DRL), combining Deep Learning (DL) and Reinforcement Learning (RL), has recently been investigated for network resource management. It offers a potential solution for these challenges. A DRL agent, represented by deep neural networks, interacts with the environment and learns optimal decision-making through trial and error. This paper overviews proposed DRL-based methods for autonomous SL RRM in single and multi-agent platoon-based C-V2X networks. It considers both intra- and inter-platoon communications with their specific requirements. We discuss the components of Markov Decision Processes (MDP) used to model the sequential decision-making of RRM. We then detail the DRL algorithms, training paradigms, and insights on the achieved results. Finally, we highlight challenges in existing works and suggest strategies for addressing them.

Broad Distributed Game Learning for intelligent classification in rolling bearing fault diagnosis

As a new Single Layer Feedforward Network (SLFN) architecture, Broad Learning System (BLS) has been widely used in the field of fault diagnosis because of its fast-training speed and high generalization capability. However, when features in different classes of signals are similar or weak, BLS generates a large number of redundant features that may be difficult to classify accurately. In view of this, a new Broad Distributed Game Learning (BDGL) method is proposed in this paper, which maps data into the game space by constructing two non-parallel game hyperplanes to achieve game and segmentation of different similar features, thereby making the data linearly differentiable in the game space. Meanwhile, a linear distribution constraint term is designed to reduce noise fitting and weak feature learning in training data learning by limiting the complexity of model parameters, thereby making the solution of the objective function simpler and faster. By comparing the Precision, Recall, F-score, Kappa and Accuracy of BDGL and the comparison methods on the two types of rolling bearing experimental data, the results show that BDGL has a high classification accuracy. In addition, the experimental results on small and noisy samples once again demonstrate the effectiveness of BDGL, which provides an efficient solution for rolling bearing fault diagnosis.

DAPNet: A Dual-Attention Parallel Network for the Prediction of Ship Fuel Consumption Based on Multi-Source Data

The precise prediction of ship fuel consumption (SFC) not only serves to enhance energy efficiency to benefit shipping enterprises but also to provide quantitative foundations to aid in carbon emission reduction and ecological environment protection. On the other hand, SFC-related data represent typical multi-source characteristics and heterogeneous features, which lead to several methodological issues (e.g., feature alignment and feature fusion) in SFC prediction. Therefore, this paper proposes a dual-attention parallel network named DAPNet to solve the above issues. Firstly, we design a parallel network structure containing two kinds of long short-term memory (LSTM) and improved temporal convolutional networks (TCNs) for time-series analysis tasks so that different source data can be applied to suitable networks. Secondly, a local attention mechanism is included in each single parallel network so as to improve the ability of feature alignment from different-scale training data. Finally, global attention is employed for the fusion of all parallel networks, which can enrich representation features and simultaneously enhance the performance of SFC prediction. In experiments, DAPNet is compared with 10 methods, including baseline and attention models. The comparison results show that DAPNet and several of its variants obtain the highest accuracy in SFC prediction.

Role of molecular damage in crack initiation mechanisms of tough elastomers

Tough soft materials such as multiple network elastomers (MNE) or filled elastomers are typically stretchable and include significant energy dissipation mechanisms that prevent or delay crack growth. Yet most studies and fracture models focus on steady-state propagation and damage is assumed to be decoupled from the local stress and strain fields near the crack tip. We report an in situ spatial-temporally resolved 3D measurement of molecular damage in mechanophore-labeled MNE just before a crack propagates. This technique, complemented by digital image correlation, allows us to compare the spatial distribution of both damage and deformation in single network (SN) elastomers and in MNE. Compared to SN, MNE have a wide-spread damage in front of the crack and, surprisingly, delocalize strain concentration. A continuum model, where damage distribution is fully coupled to the crack tip fields, is proposed to explain these results. Additional measurements of time-dependent molecular damage during fixed grips relaxation in the presence of a crack reveal that the less localized damage distribution delays fracture initiation. The observations and exploratory modeling reveal the dynamic fracture mechanism of MNE, providing guidance for rational design of high-performance tough elastomers.

Traffic Sign Detection Using You Only Look Once (YOLOv3) Technique

Although deep learning-based object detection has produced excellent performance, there are still many issues with images from real-world capture, including rotating jitter, blurring, and noise deletion. The impact of these issues on object detection is significant. The main goal of this paper is to develop a real-time “You Only Look Once” (YOLOv3) algorithm to detect traffic signs. Compared to all other object detection algorithms, the YOLO method has a number of benefits. In contrast to different algorithms, YOLO looks at the image entirely by making predictions of the bounding boxes utilizing a convolutional neural network (CNN), determining the probability of each class for these boxes, and detecting the image more quickly. The proposed method applies a single neural network to the entire image. Then this network divides that image into regions, which provide the bounding boxes and also predict probabilities for each region. These generated bounding boxes are weighted by the predicted probabilities. The proposed method achieves 99% accuracy in the detection process.

Improved prediction accuracy for compressive strength of recycled aggregate concrete using optimization-based algorithms and cascade forward neural network

This study proposed a data driven approach to predict the compressive strength (CS) of recycled aggregate concrete (RAC) for sustainable construction using an elite single genetic optimization algorithm-based cascade forward neural network (ESGA-CFNN) model. It was applied to 272 RAC samples under different conditions and compositions focusing on key parameters for CS prediction: water-to-cement ratio (WCR), water absorption (WA), recycled coarse aggregate (RCA) density, fine aggregate (FA) density, naturally occurring coarse aggregate (NCA) density and water-to-total material ratio (WTMR). These parameters were used to develop the ESGA-CFNN model which was then evaluated for its performance. To compare the ESGA-CFNN model, two other models were developed and compared: particle swarm optimization-based CFNN (PSO-CFNN) and artificial bee colony-based CFNN (ABC-CFNN). K-fold cross-validation was used during model development to prevent overfitting. Results showed that ESGA-CFNN model performed better with an RMSE (root-mean-squared error) of 1.144, R2 (determination coefficient) of 0.991 and a10-index of 1.000. ABC-CFNN model had an RMSE of 1.434, R2 of 0.987 and a10-index of 0.982 while PSO-CFNN had an RMSE of 1.561, R2 of 0.984 and a10-index of 0.982. Practical validation with 6 RAC samples confirmed the real world applicability of these models. The findings of this study showed that the proposed ESGA-CFNN model is important for quality control in RAC production and optimizing mix designs to achieve required compressive strength to meet standards and reduce cost and increase sustainability in concrete construction. This study introduces a novel hybrid approach combining ESGA-CFNN, PSO-CFNN, and ABC-CFNN algorithms for accurately predicting the compressive strength of RAC. These models outperform traditional methodologies by offering enhanced predictive accuracy and generalization capability, especially in complex, real-world datasets.

How Does Supply Network Structure Influence Firms’ Financial Performance During Disruption?

In a post pandemic world, supply chain mapping has become a key concern for almost any supply chain operation. A single focal supply chain network might have hundreds of suppliers including those that supply both goods and services. These increase exponentially as one traces the network downstream. Using a pair of networks from the auto industry (Ford and General Motors) as a basis for the analysis, we apply a data mining technique to filter for significant supply chain relationships in each of these networks over the period spanning 2018–2022. We then examine the structural metrics for each of these networks and compare these measures with applicable financial measures to elucidate how supply chain network structure affects financial performance. Our regression analyses reveal that supplier network structures significantly affect customers’ market value. Specifically, Tobin’s Q is negatively related to the number of suppliers they source from and the importance of the customer (e.g., Ford) to its suppliers, but it is positively associated with the tightness of supplier customer connection and the density of the network.

Optimization study of diesel engine emission prediction based on neural network model cluster

This study aims to reduce the testing volume and cost of engine bench tests. By combining neural network models and ensemble learning algorithms, a model cluster prediction method is proposed to predict engine emissions. The core of this method is to use a large number of neural network models for prediction, taking the average of the prediction results as the final prediction result, thereby reducing prediction errors and improving accuracy. The findings reveal that this cluster-based approach significantly outperforms a single optimal model in forecasting steady-state emissions of NOx, HC, and CO in diesel engines. Notably, the performance of the model cluster is highly dependent on both the quality and the number of constituent sub-models, with enhanced predictive capabilities observed when higher-quality sub-models are included. Additionally, the study identifies a stabilization in predictive accuracy as the number of models increases, particularly within the range of 50 to 100 models, and achieving robust stability beyond 100 models. The research also underscores the importance of the training dataset size on the effectiveness of neural network modeling. A reduction in the training dataset from 28% to 18% leads to a decline in the coefficient of determination ( R2) for NOx emissions prediction in a single neural network model from 0.6954 to 0.6539, a 5.97% decrease. For the model cluster method, the R2 similarly drops from 0.8633 to 0.8154, a reduction of 5.55%. Notably, the neural network model suffers from distortion in predicting the peak position of NOx emissions. After supplementing specific operating condition training data in a targeted manner, the model training amount was increased from 18% to 25%. The model cluster method showed a good improvement in the prediction of NOx emissions, with the prediction coefficient increasing from 0.8154 to 0.8997, with an improvement rate of 10.34%. Through planning of training data, the study demonstrates that employing just 25% of experimental data for model clustering can effectively predict the engine emission trends for the remaining 75% of data in the target conditions. This approach offers a substantial reduction in experimental requirements while maintaining high prediction accuracy.

Deep learning-based spike sorting: a survey.

Objective.Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically. This survey provides an in-depth evaluation of the approaches, methodologies and outcomes presented in recent articles, shedding light on the current state-of-the-art.Approach.Twenty-four articles published until December 2023 on deep learning-based spike sorting have been examined. The proposed methods are divided into three sub-problems of spike sorting: spike detection, feature extraction and classification. Moreover, integrated systems, i.e., models that detect spikes and extract features or do classification within a single network, are included.Main Results.Although most algorithms have been developed for single-channel recordings, models utilising multi-channel data have already shown promising results, with efficient hardware implementations running quantised models on ASICs and FPGAs. Convolutional neural networks have been used extensively for spike detection and classification as the data can be processed spatiotemporally while maintaining low-parameter models and increasing generalisation and efficiency. Autoencoders have been mainly utilised for dimensionality reduction, enabling subsequent clustering with standard methods. Also, integrated systems have shown great potential in solving the spike sorting problem from end to end.Significance.This survey explores recent articles on deep learning-based spike sorting and highlights the capabilities of deep neural networks in overcoming associated challenges, but also highlights potential biases of certain models. Serving as a resource for both newcomers and seasoned researchers in the field, this work provides insights into the latest advancements and may inspire future model development.

The Current Status of Digital Currency Development in China

Abstract: Digital currencies, as innovative products in the field of financial technology, are characterized by their decentralized structure and strong encryption based on blockchain and other technologies. Although your mention of relying on local area networks may be a bit of a misnomer in the broad sense, as the operation of digital currencies relies more broadly on the Internet rather than a single local area network, the concepts of digital encryption and decentralization it emphasizes are accurate. Legal digital currencies, such as the Digital Currency Electronic Payment (DCEP) issued by the People's Bank of China, represent a milestone in this field. They not only integrate the security and transparency of blockchain technology but also achieve a perfect combination of controllable anonymity and legal tender status through the deep integration of the generalized account system with the banking system. The issuance of DCEP marks an important step forward for China in the field of digital currencies and is expected to have a profound impact on the financial market, promoting the modernization and upgrading of the payment system, enhancing transaction efficiency and security, and at the same time, providing the public with more convenient and secure payment options, further integrating into and changing people's daily lifestyles.

Data-driven computation of adjoint sensitivities without adjoint solvers: An application to thermoacoustics

Adjoint methods have been the pillar of gradient-based optimization for decades. They enable the accurate computation of a gradient (sensitivity) of a quantity of interest with respect to all system parameters in one calculation. When the gradient is embedded in an optimization routine, the quantity of interest can be optimized for the system to have the desired behavior. Adjoint methods, however, require the system's governing equations and their Jacobian. In this paper, we propose a computational strategy to infer the adjoint sensitivities from data when the governing equations might be unknown (or partly unknown), and noise might be present. The key component of this strategy is an echo state network, which learns the dynamics of nonlinear regimes with varying parameters, and it evolves dynamically via a hidden state. Although the framework does not make assumptions on the dynamical system, we focus on thermoacoustics, which are governed by nonlinear and time-delayed systems. First, we show that a parameter-aware echo state network (ESN) infers the parametrized dynamics. Second, we derive the adjoint of the ESN to compute two types of sensitivity: (i) parameter sensitivity, which is the gradient of a time-averaged cost functional with respect to physical or design parameters of the system, and (ii) initial condition sensitivity, which is the gradient of a cost functional of the final state with respect to the initial condition. Third, we propose the thermoacoustic echo state network (T-ESN), which embeds the physical knowledge in the network architecture for improved generalization. Fourth, we apply the framework to a variety of nonlinear thermoacoustic regimes of a prototypical system. We show that the T-ESN accurately infers the correct adjoint sensitivities of the acoustic energy with respect to the flame parameters and initial conditions. The results are robust to noisy data, from periodic, through quasiperiodic, to chaotic regimes. The inferred adjoint sensitivities are employed to suppress an instability via steepest descent. We show that a single network predicts the nonlinear bifurcations on unseen scenarios, which allows it to converge to the minimum of the acoustic energy. This work opens new possibilities for gradient-based data-driven design optimization. Published by the American Physical Society 2024

Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson's Disease.

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Recent studies have highlighted the significant role of cerebrospinal fluid (CSF) in reflecting pathophysiological PD brain conditions by analyzing the components of CSF. Based on the published literature, we created a single network with altered metabolites in the CSF of patients with PD. We analyzed biological functions related to the transmembrane of mitochondria, respiration of mitochondria, neurodegeneration, and PD using a bioinformatics tool. As the proteome reflects phenotypes, we collected proteome data based on published papers, and the biological function of the single network showed similarities with that of the metabolomic network. Then, we analyzed the single network of integrated metabolome and proteome. In silico predictions based on the single network with integrated metabolomics and proteomics showed that neurodegeneration and PD were predicted to be activated. In contrast, mitochondrial transmembrane activity and respiration were predicted to be suppressed in the CSF of patients with PD. This review underscores the importance of integrated omics analyses in deciphering PD's complex biochemical networks underlying neurodegeneration.