Network Model Research Articles

The influencing factors of green technology innovation in renewable energy companies based on hyper-network

Green technology innovation is a critical factor in ensuring the long-term stable development of renewable energy enterprises. Based on the super network theory, this paper constructs a network model of green technology innovation influencing factors of renewable energy enterprises, which includes the knowledge sub-network of green technology innovation of renewable energy enterprises, the research and development member sub-network of green technology innovation team of renewable energy enterprises and the policy sub-network of green technology innovation of renewable energy enterprises. It explores the mechanism of its influence on innovation in the preparation stage. Simulation analysis by Netlogo software concludes that innovation knowledge sharing, R&D membership, and innovation policy all have a significant positive impact on green technology innovation in renewable energy companies.

Domain adaptation of transformer-based neural network model for clinical note classification in Indian healthcare

Domain adaptation of transformer-based neural network model for clinical note classification in Indian healthcare

A Novel Hybrid Model for Financial Forecasting Based on CEEMDAN-SE and ARIMA-CNN-LSTM

Financial time series data are characterized by non-linearity, non-stationarity, and stochastic complexity, so predicting such data presents a significant challenge. This paper proposes a novel hybrid model for financial forecasting based on CEEMDAN-SE and ARIMA- CNN-LSTM. With the help of the CEEMDAN-SE method, the original data are decomposed into several IMFs and reconstructed via sample entropy into a lower-complexity stationary high-frequency component and a low-frequency component. The high-frequency component is predicted by the ARIMA statistical forecasting model, while the low-frequency component is predicted by a neural network model combining CNN and LSTM. Compared to some classical prediction models, our algorithm exhibits superior performance in terms of three evaluation indexes, namely, RMSE, MAE, and MAPE, effectively enhancing model accuracy while reducing computational overhead.

YOLO-HyperVision: A vision transformer backbone-based enhancement of YOLOv5 for detection of dynamic traffic information

With the increase of traffic flow in modern urban areas, traffic congestion has become a serious problem that affects people’s normal production and life. Using target detection technology instead of manual labor can quickly detect the road traffic situation and provide timely information about the traffic flow. However, when using drones to observe the traffic flow in the air, the perspective effect will cause the detected vehicles and pedestrians to be very small, and the scale difference between different categories of targets is large, which increases the detection difficulty of a single convolutional neural network model. In order to solve the problem of low accuracy of traditional single-stage target detection models, this study proposes an improved Yolov5 vehicle target detection model with Vision Transformer (VIT) backbone, You Only Look Once-HyperVision (YOLO-HV), which aims to solve the problem of poor multi-scale target recognition performance caused by the inability of traditional CNN networks to integrate contextual information, and help drones achieve more efficient and accurate traffic flow recognition functions. This study deeply integrates the Vision Transformer (VIT) backbone and Convolutional Neural Network (CNN), effectively combining the multi-scale detection advantages of Vision Transformer and the inductive bias ability of Convolutional Neural Network, and adds multi-scale residual modules and context correlation enhancement modules, which greatly improves the recognition accuracy of single-stage detectors for drone images. Through comparative experiments on the VisDrone dataset, it is found that the detection performance of this model is improved compared with several commonly used detection models. YOLO-HV can increase the mean average precision (mAP) by 3.3% compared with the pure convolutional network of the same model size. YOLO-HV model has achieved excellent performance in the task of traffic flow image detection taken by drones, and can more accurately identify and classify road vehicles than various target detection models.

Influence Maximization Based on Adaptive Graph Convolution Neural Network in Social Networks

The influence maximization problem is a hot issue in the research on social networks due to its wide application. The problem aims to find a small subset of influential nodes to maximize the influence spread. To tackle the challenge of striking a balance between efficiency and effectiveness in traditional influence maximization algorithms, deep learning-based influence maximization algorithms have been introduced and have achieved advancement. However, these algorithms still encounter two key problems: (1) Traditional deep learning models are not well-equipped to capture the latent topological information of networks with varying sizes and structures. (2) Many deep learning-based methods use the influence spread of individual nodes as labels to train a model, which can result in an overlap of influence among the seed nodes selected by the model. In this paper, we reframe the influence maximization problem as a regression task and introduce an innovative approach to influence maximization. The method adopts an adaptive graph convolution neural network which can explore the latent topology information of the network and can greatly improve the performance of the algorithm. In our approach, firstly, we integrate several network-level attributes and some centrality metrics into a vector as the presentation vector of nodes in the social network. Next, we propose a new label generation method to measure the influence of nodes by neighborhood discount strategy, which takes full account of the influence overlapping problem. Subsequently, labels and presentation vectors are fed into an adaptive graph convolution neural network model. Finally, we use the well-trained model to predict the importance of nodes and select top-K nodes as a seed set. Abundant experiments conducted on various real-world datasets have confirmed that the performance of our proposed algorithm surpasses that of several current state-of-the-art algorithms.

Building the fracture network model for the Okuaizu geothermal field based on microseismic data analysis

Understanding flow behavior in a geothermal reservoir is important for managing sustainable geothermal energy extraction. Fluid flow in geothermal reservoirs generally occurs in complex existing fracture systems in which the reservoirs are situated in highly fractured rocks. To simulate a discrete fracture model, the location and orientation of the fracture were computed using statistical processes and observational data. In many cases, estimating the location and orientation of fractures from 1D borehole logging data is challenging. In this study, we used microseismic data to build the fracture network systems and extract the detailed positions and dimensions of the fractures. We used the microseismic data recorded at the Okuaizu Geothermal Field, Fukushima Prefecture, Japan, from 2019 to 2021. First, we located the hypocenters, removing the effect of uncertainty in the velocity structure of the geothermal fluids. We relocated and clustered the seismic events based on waveform similarity. We analyzed each cluster to define the fracture orientation using principal component analysis (PCA) and focal mechanism (FM) analysis. We used the P polarity with the S/P ratio as a constraint for a better fault-plane solution. With PCA, we can extract the fracture dimension of each cluster. Our cluster analysis showed that the clusters were not always planar fractures, and we interpreted them as fracture zones. Based on the consistency between PCA and FM, each cluster/fracture zone was classified into three conceptual models to characterize the fracture network system in this field. This model showed variations in the orientation of small fractures within the fracture zone. We characterized the spatial variation in fracture distribution and orientations in the reservoir and demonstrated the fracture network system of this field. The fracture zone near the injection well has a N–S strike, and the dip is above 80°; however, the fracture zone in the northeastern part of the injection well has a NW–SE strike with a dip between 60° and 80°. The fracture network system estimated in this study is crucial for robust reservoir modeling because our model is more realistic, observation-orientated, and includes local anomalies of reservoir properties.Graphical

Multiscale modeling of neuronal dynamics in hippocampus CA1

The development of biologically realistic models of brain microcircuits and regions constitutes currently a very relevant topic in computational neuroscience. One of the main challenges of such models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. In this paper we introduce a multiscale modeling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modeling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. Then, we analyze the implementation of synaptic plasticity within our framework, a key aspect to study the role of hippocampus in learning and memory consolidation, and we demonstrate the capability of our framework to incorporate the variations at synaptic level. Finally, we present an example of the implementation of our model to study a stimulus propagation at the macro-scale level, and we show that the results of our framework can capture the dynamics obtained in the corresponding spiking network model of the whole CA1 area.

Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours

The scientific and rational assessment of the evolution of node importance in rail transit line networks is important for the sustainability of transportation systems. Based on the complex network theory, this study develops a weighted network model using the Space L method. It first considers the network topology, the mutual influence of neighboring nodes of the transportation system, and the land use intensity in the station influence domain to construct a comprehensive index evaluation system of node importance. It then uses the covariance-weighted principal component analysis algorithm to more comprehensively evaluate the node importance evolution mechanism and analyzes the similarity and difference of the sorting set by adopting three different methods. The interaction mechanism between the distribution of important nodes and the evolution of land use intensity is explored in detail based on the fractal dimension theory. The Xi’an rail transit network is considered an example of qualitative and quantitative analysis. The obtained results show that the importance of nodes varies at different times of the day and the complexity of the morning peak is more prominent. Over time, articulated fragments with significance values greater than 0.5 are formed around the station, which are aligned with the direction of urban development, creating a sustainable mechanism of interaction. As the network’s crucial nodes in the center of gravity increase and the southern network expands, along with the increased intensity of the city’s land utilization, the degree of alignment in evolution becomes increasingly substantial. Different strategies for attaching the network, organized based on the size of Si can lead to the rapid damage of the network (reducing it to 0.2). The identification of crucial nodes highlighted in this paper serves as an effective representation of the functional characteristics of the nodes in transportation networks. The results obtained can provide a reference for the operation and management of metro systems and further promote the sustainable development of transportation networks.

Data as an enabler for better recycling in a circular economy

Recycling plays a crucial role in the circular economy (CE), as effective recycling ensures that materials enter a sustainable loop. Industry 4.0 (I4.0) has the capability to enhance recycling efficiency and support the development of more comprehensive infrastructure. However, existing value chain (VC) models do not form a closed material and product loop, and there is insufficient information exchange between recyclers and other stakeholders. This paper extends an existing VC model and discusses the information flow required under the background of the CE. It provides a reference model for a value network in a closed material loop, which enhances the traceability of resource use and location. By mapping out the information flow, the model facilitates enhanced communication and coordination among various stakeholders.

Image forgery detection by combining Visual Transformer with Variational Autoencoder Network

Recently, the applications and artificial intelligences used for image manipulation have become quite successful. In this case, the manipulation of personal data can lead to problems of insurmountable magnitude. Such problems not only put personal data at risk, but also lead us to unethical practices, with potentially irreversible negative consequences. For this reason, the reliability of image or video data is highly questionable. To solve this challenging problem, we introduce a Visual Transformer based Visual Transformer with Variational Autoencoder Network (ViT-VAE Net) model. The model includes Visual Transformer, one of the state-of-the-art architectures. In addition to this architecture, a Variational Auto Encoder structure is also included. is much more effective than models developed with the classical Convolutional Neural Network (CNN). Unlike models developed with CNN, it can perform operations on images of any size without being bound by a standard image resolution. In addition, thanks to the self-attention mechanism in the Visual Transformer architecture, manipulations on the image are caught more easily than CNN. The ViT-VAE Net model was trained with a large dataset and tested with 4 different datasets. With a success rate of 67 % on the training dataset, the model provided promising results. Very high rates were also obtained with the test datasets.

Improved Arithmetic Optimization Algorithm with Transfer Learning based Arabic Sign Language Identification System

The Arabic sign language (ArSL) has witnessed ground-breaking research activities to identify hand gestures and signs through the deep learning (DL) model. SL is a unique communication tool that bridges the gaps between people with hearing impairment and ordinary people. The ArSL recognition system is of immense importance for different groups of people since it enables individuals with hearing impairment to communicate effectively. In SLs, signs are characterized by discrepancies in hand positions, shapes, motions, body parts, and facial expressions, posing a crucial threat to visual recognition in computer vision (CV). An automated sign detection technique needs two primary courses of action: the recognition of specific features and the classification of the input dataset. Previously, several approaches for detecting and classifying SLs had been proposed. In this study, an Improved Metaheuristics with Transfer Learning based Arabic Sign Language Identification System (IMTL-ArSL) method is developed. The primary objective of the IMTL-ArSL method is to detect and classify the existence of various signs in the Arabic language. In the IMTL-ArSL model, the bilateral filtering (BF) model is initially used for preprocessing. Besides, the Residual Network (ResNet50v2) model is applied for extracting features, and an improved arithmetic optimization algorithm (IAOA) model is utilized for hyperparameter tuning. Finally, a gated recurrent unit (GRU) network is exploited to identify sign languages. The empirical analysis of the IMTL-ArSL approach is tested using a benchmark dataset. The experimental values of the IMTL-ArSL approach portrayed a superior accuracy value of 93.87% over other techniques.

The impact of deep learning based- psychological capital with ideological and political education on entrepreneurial intentions

The aim of this study is to investigate the influence of psychological capital on college students’ entrepreneurial intentions. Through a combination of relevant analysis and linear regression, the primary focus is on exploring the relationship between psychological capital and its four dimensions with entrepreneurial intentions. Firstly, the items in the psychological capital questionnaire were revised to align more closely with entrepreneurial contexts. Subsequently, the average deviations and standard deviations of each dimension of psychological capital were analyzed. Then, the correlation between psychological capital and entrepreneurial intentions was examined to explore the extent of their relationship. Finally, regression analysis was conducted on both psychological capital and entrepreneurial intentions, and utilizing a recurrent neural network model, the covariant relationship between entrepreneurial psychological capital and intentions was explored. The results indicated that the average scores for entrepreneurial self-efficacy, optimism, hope, and resilience were 3.91, 4.27, 4.19, and 4.15, respectively. The average value of psychological capital was 4.13, indicating a moderately high level. The correlation analysis between psychological capital and entrepreneurial intentions yielded a result of 0.562, indicating a moderate degree of correlation. The correlation coefficients of the four dimensions with entrepreneurial intentions were 0.390, 0.494, 0.531, and 0.467, respectively. The standardized coefficients for psychological capital and its four dimensions were 0.564, 0.382, 0.510, 0.536, and 0.468, all of which were statistically significant. Overall, psychological capital exhibited better predictive power for entrepreneurial intentions than its individual dimensions. The results from the deep learning model similarly demonstrated the positive role of psychological capital in entrepreneurial intentions, though the influence of ideological and political education (IPE) factors was relatively weaker. In conclusion, both psychological capital and IPE have a promotive effect on entrepreneurial intentions. This study provides a reference for the accurate evaluation of college students’ entrepreneurial intentions.

Prediction of Bonding Strength of Heat-Treated Wood Based on an Improved Harris Hawk Algorithm Optimized BP Neural Network Model (IHHO-BP)

In this study, we proposed an improved Harris Hawks Optimization (IHHO) algorithm based on the Sobol sequence, Whale Optimization Algorithm (WOA), and t-distribution perturbation. The improved IHHO algorithm was then used to optimize the BP neural network, resulting in the IHHO-BP model. This model was employed to predict the bonding strength of heat-treated wood under varying conditions of temperature, time, feed rate, cutting speed, and grit size. To validate the effectiveness and accuracy of the proposed model, it was compared with the original BP neural network model, WOA-BP, and HHO-BP benchmark models. The results showed that the IHHO-BP model reduced the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 51.16%, 40.38%, and 51.93%, respectively, while increasing the coefficient of determination (R2) by at least 10.85%. This indicates significant model optimization, enhanced generalization capability, and higher prediction accuracy, better meeting practical engineering needs. Predicting the bonding strength of heat-treated wood using this model can reduce production costs and consumption, thereby significantly improving production efficiency.

Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism

Heart murmurs play a critical role in assessing the condition of the heart. Murmur quality reflects the subjective human perception of heart murmurs and is an important characteristic strongly linked to cardiovascular diseases (CVDs). This study aims to use deep neural networks to classify the patients’ murmur quality (i.e., harsh and blowing) from phonocardiogram (PCG) signals. The phonocardiogram recordings with murmurs used for this task are from the CirCor DigiScope Phonocardiogram dataset, which provides the murmur quality labels. The recordings were segmented, and a dataset of 1266 segments with average lengths of 4.1 s from 164 patients’ recordings was obtained. Each patient usually has multiple segments. A deep neural network model based on convolutional neural networks (CNNs) with channel attention and gated recurrent unit (GRU) networks was first used to extract features from the log-Mel spectrograms of segments. Then, the features of different segments from one patient were weighted by the proposed “Feature Attention” module based on the attention mechanism. The “Feature Attention” module contains a layer of global pooling and two fully connected layers. Through it, the different features can learn their weight, which can help the deep learning model distinguish the importance of different features of one patient. Finally, the detection results were produced. The cross-entropy loss function was used to train the model, and five-fold cross-validation was employed to evaluate the performance of the proposed methods. The accuracy of detecting the quality of patients’ murmurs is 73.6%. The F1-scores (precision and recall) for the murmurs of harsh and blowing are 76.8% (73.0%, 83.0%) and 67.8% (76.0%, 63.3%), respectively. The proposed methods have been thoroughly evaluated and have the potential to assist physicians with the diagnosis of cardiovascular diseases as well as explore the relationship between murmur quality and cardiovascular diseases in depth.

Investigation of Machine Learning Regression Techniques to Predict Critical Heat Flux Over a Large Parameter Space

A unifying and accurate model to predict critical heat flux (CHF) over a wide range of conditions has been elusive since wall boiling research emerged. With the release of the experimental data utilized in the development of the 2006 Groeneveld CHF lookup table (LUT), by far the most extensive public CHF database available to date (nearly 25 000 data points), the development of data-driven prediction models over a large parameter space in simple geometry (vertical, uniformly heated round tubes) can be achieved. Furthermore, the popularization of machine learning techniques to solve regression problems has led to more advanced tools for analyzing large and complex databases. This work compares three machine learning algorithms to predict the CHF database. For each selected regression algorithm (ν-Support vector, Gaussian process, and neural network), a set of optimized hyperparameters is applied. Among the investigated algorithms, the neural network emerges as the most effective, achieving a CHF predicted/measured factor standard deviation of 12.3%, three times less than that of the LUT. In comparison, the Gaussian process regression and the ν-support vector regression achieve a standard deviation of 17.7%, about two times lower than the LUT. Hence, all considered algorithms significantly outperform the LUT prediction performance. The neural network model and training methodology are designed to prevent overfitting, which is confirmed by data analyses of the CHF predictions. Finally, future development directions (including data coverage, transfer learning, and uncertainty quantification) are discussed.

A systematic approach to tuning a neural network model and its application in estimating layer parameters from VES Schlumberger data

A systematic approach to tuning a neural network model and its application in estimating layer parameters from VES Schlumberger data

Drug–target interaction prediction through fine-grained selection and bidirectional random walk methodology

The study of drug–target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug–target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization.

Prediction of number of insured having work accident in Turkish construction industry

Accurate forecasting of work accidents is of paramount importance in promoting workplace safety and improving risk-management strategies. This study proposes a novel approach based on a neural network fitted with the Levenberg–Marquardt algorithm to predict future accident numbers in 22 different occupational groups within the Turkish construction industry. By utilising historical official data spanning the years 2014–2022, the aim is to provide insights into the potential accident rates that may arise in different job categories. The constructed neural network model consists of two hidden layers. Leveraging the powerful capabilities of the Levenberg–Marquardt algorithm, the network is trained to capture effectively the complex dynamics underlying work accidents in the construction industry. The findings demonstrate the effectiveness of the proposed approach in forecasting future accident numbers with a high degree of precision. The neural network model successfully leverages the temporal trends and underlying factors present in the historical data. By employing an advanced neural network framework and the Levenberg–Marquardt algorithm, this study offers a robust methodology for predicting work accident rates across diverse job categories. The results obtained from this study can guide the development of targeted preventive measures, tailored training programmes and efficient resource allocation strategies.

Application of Digital Twin Technology in Synthetic Aperture Radar Ground Moving Target Intelligent Detection System

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously.

The Immigrant Mortality Advantage Among Over-65 Middle Eastern and North African Immigrants to the United States.

Immigrants to the United States often have longer life expectancies than their U.S.-born counterparts, however it is unclear whether a similar "immigrant advantage" exists for immigrants from the Middle East and North Africa (MENA). This study uses a novel machine-learning name classifier to offer one of the first national-level examinations of MENA mortality patterns by nativity in the United States. A recurrent neural network model was developed to identify MENA individuals based on given name and surname characteristics. The model was trained on more than 2.5 million mortality-linked social security records in the Berkeley Unified Numident Mortality Database (BUNMD). Mortality rates and life expectancy were estimated using a Gompertz distribution and maximum likelihood estimation, focusing on high-coverage years between 1988 and 2005 and deaths over age 65. Foreign-born MENA men over 65 showed a significant immigrant mortality advantage with a hazard ratio (HR) of 0.64 and an estimated 3.13 additional years of life expectancy at age 65 compared to U.S.-born counterparts. Foreign-born MENA women also exhibited an advantage, with a HR of 0.71 and an additional 2.24years of life expectancy at age 65. This study is one of the first national-level analyses of mortality outcomes among the over-65 MENA population in the United States, finding a MENA immigrant mortality advantage. The results suggest further research is needed to identify and disaggregate the MENA population in health research.