Network Structure Parameters Research Articles

Contribution of 80 MeV silicon swift heavy ion irradiation for reforming of optical and structural properties of amorphous Ge23Se62As15 thin films for telecommunication and sensing applications

Linear and non-linear optical properties are investigated for amorphous Ge23Se62As15 thin films irradiated under 80 MeV Silicon swift heavy ions. The changes in optical properties are understood in terms of glass network structure and parameters of incident ion. Besides, the study verifies the relation between the mean coordination number of glass network and electronic energy loss of the ion. The Swanepoel method and semi-empirical relations are used to determine the optical parameters. The study indicates that the most useful fluence to optimize optical properties is around 3 × 1012 ions/cm2. The amorphous phase of thin film samples is retained till the highest fluence (1 × 1013 ions/cm2) as confirmed using XRD measurements. A highly roughed surface is found at fluence 1 × 1013 ions/cm2 (SEM image observation). The micro Raman study reveals that Ge-Ge, Se-Se bonds, and GeSe4/2 tetrahedral corner-shared structural units are responsible for optical properties modification. Modification in surface morphology and optical/structural properties with the glass network structure and ion treatment parameters is useful for telecom and sensing applications.

Evolution of Publications, Subjects, and Co-Authorships in Network-on-Chip Research From a Complex Network Perspective

The academia and industry have been pursuing network-on-chip (NoC) related research since two decades ago when there was an urgency to respond to the scaling and technological challenges imposed on intra-chip communication in SoC designs. Like any other research topic, NoC inevitably goes through its life cycle: A. it started up (2000–2007) and quickly gained traction in its own right; B. it then entered the phase of growth and shakeout (2008–2013) with the research outcomes peaked in 2010 and remained high for another four/five years; C. NoC research was considered mature and stable (2014–2020), with signs showing a steady slowdown. Although from time to time, excellent survey articles on different subjects/aspects of NoC appeared in the open literature, yet there is no general consensus on where we are in this NoC roadmap and where we are heading, largely due to lack of an overarching methodology and tool to assess and quantify the research outcomes and evolution. In this paper, we address this issue from the perspective of three specific complex networks, namely the citation network, the subject citation network, and the co-authorship network. The network structure parameters ( e.g. , modularity, diameter, etc. ) and graph dynamics of the three networks are extracted and analyzed, which helps reveal and explain the reasons and the driving forces behind all the changes observed in NoC research over 20 years. Additional analyses are performed in this study to link interesting phenomena surrounding the NoC area. They include: (1) relationships between communities in citation networks and NoC subjects, (2) measure and visualization of a subject’s influence score and its evolution, (3) knowledge flow among the six most popular NoC subjects and their relationships, (4) evolution of various subjects in terms of number of publications, (5) collaboration patterns and cross-community collaboration among the authors in NoC research, (6) interesting observation of career lifetime and productivity among NoC researchers, and finally (7) investigation of whether or not new authors are chasing hot subjects in NoC. All these analyses have led to a prediction of publications, subjects, and co-authorship in NoC research in the near future, which is also presented in the paper.

An improved Faster R-CNN for defect recognition of key components of transmission line.

In a national power grid system, it is necessary to keep transmission lines secure. Detection and identification must be regularly performed for transmission tower components. In this paper, we propose a defect recognition method for key components of transmission lines based on deep learning. First, based on the characteristics of the transmission line image, the defect images are preprocessed, and the defect dataset is created. Then, based on the TensorFlow platform and the traditional Faster R-CNN based on the R-CNN model, the concept-ResNet-v2 network is used as the basic feature extraction network to improve the network structure adjustment and parameter optimization. Through feature extraction, target location, and target classification of aerial transmission line defect images, a target detection model is obtained. The model improves the feature extraction on transmission line targets and small target component defects. The experimental results show that the proposed method can effectively identify the defects of key components of the transmission lines with a high accuracy of 98.65%.

Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model.

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

Small-Object Detection Based on YOLO and Dense Block via Image Super-Resolution

Small-object detection is a basic and challenging problem in computer vision tasks. It is widely used in pedestrian detection, traffic sign detection, and other fields. This paper proposes a deep learning small-object detection method based on image super-resolution to improve the speed and accuracy of small-object detection. First, we add a feature texture transfer (FTT) module at the input end to improve the image resolution at this end as well as to remove the noise in the image. Then, in the backbone network, using the Darknet53 framework, we use dense blocks to replace residual blocks to reduce the number of network structure parameters to avoid unnecessary calculations. Then, to make full use of the features of small targets in the image, the neck uses a combination of SPPnet and PANnet to complete this part of the multi-scale feature fusion work. Finally, the problem of image background and foreground imbalance is solved by adding the foreground and background balance loss function to the YOLOv4 loss function part. The results of the experiment conducted using our self-built dataset show that the proposed method has higher accuracy and speed compared with the currently available small-target detection methods.

Strength of stochastic fibrous materials under multiaxial loading.

Many biological and engineering materials are made from fibers organized in the form of a stochastic crosslinked network, and the mechanics of the network controls the behavior of the material. In this work we investigate the strength of stochastic networks without pre-existing damage which fail due to crosslink rupture. Athermal networks ranging from approximately affine to strongly non-affine are subjected to multiaxial loading and the strength is evaluated using numerical models. It is observed that once the stress is normalized by the strength measured in uniaxial tension, the failure surface becomes approximately independent of network parameters. This extends the relation between strength and network parameters previously established in (S. Deogekar, M. R. Islam, R. C. Picu, Parameters controlling the strength of stochastic fibrous materials, Int. J. Solids Struct., 2019, 168, 194-202) to the multiaxial case. The failure surface depends on both first two invariants of the stress. Strongly non-affine networks behave somewhat different from the affine networks under loadings close to the hydrostatic and pure shear loading modes, while the difference disappears in the first quadrant of the principal stress space. The results are compared with experimental data from the literature.

Study on 3D Image Reconstruction Model of Sparring Action Based on Graph Neural Network (GNN).

With the advent of the information age, human demand for information is increasing day by day. The emergence of the concept of big data has triggered a new round of technological revolution, and visual information plays an important role in information. In order to obtain a better 3D model, this paper studies the reconstruction model of training motion 3D images based on a graphical neural network algorithm. This paper studies the problem of Sanda from the following two aspects. First, we try to apply two deep learning algorithms, graphical neural network and recurrent neural network, to the boxing movement recognition task and compare the effects with quadratic discriminant analysis and support vector machine. By comparing and analyzing the influence of different network structures on the deep learning algorithm, it is concluded that recurrent neural network has more practical application advantages than graph neural network in network structure parameter tuning.

Stock Selection and Prediction Using Deep Learning

This paper offers systematic analysis on selection process of stocks and prediction of same using Deep Learning. The ability to learn huge raw dataset and extract important data from it, without any prior knowledge makes it attractive. The performance of Deep Learning Model is highly dependent on optimizer, loss function, network structure, activation function, and other parameters. This paper is an attempt to study the performance of Autoencoder Neural Network in stock selection process, and future prediction of these stocks using Long Short-Term Memory (LSTM) model. The study involves static 5 years daily data from 2013 to 2018 of each company in S & P 500. Assumption for this study is that user is willing to invest a fix amount in stocks and owns a stock already. Autoencoder has been applied on stocks which are primarily filtered according to the amount user wants to invest and the last day closing price of all the stocks to consider affordability. RMSE score, between original data and recreated data, will be used to select stocks. Considering behaviour of user is neutral, top 50 stocks with least RMSE score and bottom 50 stocks with most RMSE score have been selected. Latent Features of these selected stocks will be examined. Among these 100 stocks, 20 stocks from different sectors will be selected by checking the correlation of them with the stock user already owns. LSTM model will predict the next day future price of these 20 stocks with high accuracy using adam optimiser. Empirical results suggest that top 50 stocks with least RMSE score will give low return with low risk. This implies that top 50 stocks from the result of autoencoder gives blue chip stocks. Similarly bottom 50 stocks with high RMSE score will give high return with high risk. This implies that top 50 stocks from the result of autoencoder gives small cap stocks. Correlation estimation is noticeably improved after Autoencoder Neural Network. Future one day stock prediction using LSTM gives high accuracy with Root Mean Square Error 2.22 by using adam optimizer. Our study offers handful insights and useful road-map for further investigation of stock market analysis and prediction using Deep Learning Networks.

Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

Populations of multipopulation genetic algorithms (MPGAs) parallely evolve with some interaction mechanisms. Previous studies have shown that the interaction structures can impact on the performance of MPGAs to some extent. This paper introduces the concept of complex networks such as ring-shaped networks and small-world networks to study how interaction structures and their parameters influence the MPGAs, where subpopulations are regarded as nodes and their interaction or migration of elites between subpopulations as edges. After solving the flexible job-shop scheduling problem (FJSP) by MPGAs with different parameters of interaction structures, simulation results were measured by criteria, such as success rate and average optimal value. The analysis reveals that (1) the smaller the average path length (APL) of the network is, the higher the propagation rate will be; (2) the performance of MPGAs increased first and then decreased along with the decrease of APL, indicating that, for better performance, the networks should have a proper APL, which can be adjusted by changing the structural parameters of networks; and (3) because the edge number of small-world networks remains unchanged with different rewiring possibilities of edges, the change in performance indicates that the MPGA can be improved by a more proper interaction structure of subpopulations as other conditions remain unchanged.

The network boundary specification problem in the global and world city research: investigation of the reliability of empirical results from sampled networks

Despite the well-known dependence of vertex and network structural parameters on network boundary specification employed by researchers, there has so far been effectively no discussion of this methodological caveat in the global and world city literature. Given the reliance of empirical studies of urban networks on the sampling of underlying actors that form these networks by their interactions, we consider it of key importance to examine the dependence of network centralities of cities on network boundary specification. We consider three distinctive modelling approaches based on: (a) office networks, (b) ownership ties and (c) inter-organisational projects. Our results indicate that city network centralities obtained from sampled networks are highly consistent with those obtained from whole network analysis for samples featuring as little as 4% (office networks), 10% (ownership ties) and 25% (inter-organisational projects) of the underlying actors.

Antenna Optimization Design Based on Deep Gaussian Process Model

When using Gaussian process (GP) machine learning as a surrogate model combined with the global optimization method for rapid optimization design of electromagnetic problems, a large number of covariance calculations are required, resulting in a calculation volume which is cube of the number of samples and low efficiency. In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. In this network, GP is used to replace the fully connected layer of the CNN, the convolutional layer and the pooling layer of the CNN are used to reduce the dimension of the input parameters and GP is used to predict output, while particle swarm optimization (PSO) is used algorithm to optimize network structure parameters. The modeling method proposed in this paper can compress the dimensions of the problem to reduce the demand of training samples and effectively improve the modeling efficiency while ensuring the modeling accuracy. In our study, we used the proposed modeling method to optimize the design of a multiband microstrip antenna (MSA) for mobile terminals and obtained good optimization results. The optimized antenna can work in the frequency range of 0.69–0.96 GHz and 1.7–2.76 GHz, covering the wireless LTE 700, GSM 850, GSM 900, DCS 1800, PCS1900, UMTS 2100, LTE 2300, and LTE 2500 frequency bands. It is shown that the DGP network model proposed in this paper can replace the electromagnetic simulation software in the optimization process, so as to reduce the time required for optimization while ensuring the design accuracy.

Evaluating aircraft cockpit emotion through a neural network approach

AbstractStudies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.

Social interaction, and not group size, predicts parasite burden in mammals

Although parasitism is often considered a cost of sociality, the evidence is mixed, possibly because sociality is multivariate. Here we contrast the dependence of parasitism costs on major social variables such as group size and social structure, as measured by network metrics. We conduct two robust phylogenetic meta-analyses, comprising 43 published results for studies with group size and 32 results with social structure metrics. This is the first meta-analytical test of this hypothesis for mammals as a whole. Contrarily to theoretical expectations and previous meta-analyses, there is no relationship between group size and parasitism, but we find conflicting results when analysing different aspects of sociality. Our analysis reveals that social structure is connected to parasite load, possibly because contact between group members, and not group size, is linked to parasite transmission. While more intensely interconnected groups facilitate parasite transmission, large groups are frequently fragmented into smaller, weakly connected subgroups. Strong social modularisation should thus be favoured by natural selection to hamper parasite overload. Future empirical studies should focus on specific parameters of social network structure and on parasite transmissibility. If social structure can evolve fast, even culturally, then host/parasites evolutionary games enter into a whole new fast dynamics, and animal conservation studies should take advantage of this possibility.

Effects of core-periphery structure on explosive synchronization

We investigate the explosive synchronization in networks with core-periphery structure. The effects of different patterns of core-periphery networks on explosive synchronization are studied by altering network structural parameters. With the variation of two core-periphery structural parameters, the relative connection strength between core and peripheral nodes, and the relative connection strength among peripheral nodes, we find distinct roles played by structural parameters in the path toward explosive synchronization. Our results show that the order parameter of periphery is closer to that of the core in the synchronization phase with the increment of connections between core and peripheral nodes. In addition, we find that sparser the connections among peripheral nodes are, the easier the whole dynamic network is to reach explosive synchronization. We also discover that if the number of connections between core and periphery scales vary sublinearly with the network size, there exists a novel two-jump behavior of the order parameter of the whole network. Moreover, as the level of the sublinearity increases, the order parameter starts to oscillate in a decaying manner, rather than being increasing monotonically and slowly as in the case of normal explosive synchronization when the coupling strength exceeds a critical threshold. Hence in this regime, it becomes increasingly difficult for the network to maintain stable explosive synchronization even though the underlying network topology is connected.

Sequential Network with Residual Neural Network for Rotatory Machine Remaining Useful Life Prediction Using Deep Transfer Learning

Deep learning has a strong feature learning ability, which has proved its effectiveness in fault prediction and remaining useful life prediction of rotatory machine. However, training a deep network from scratch requires a large amount of training data and is time-consuming. In the practical model training process, it is difficult for the deep model to converge when the parameter initialization is inappropriate, which results in poor prediction performance. In this paper, a novel deep learning framework is proposed to predict the remaining useful life of rotatory machine with high accuracy. Firstly, model parameters and feature learning ability of the pretrained model are transferred to the new network by means of transfer learning to achieve reasonable initialization. Then, the specific sensor signals are converted to RGB image as the specific task data to fine-tune the parameters of the high-level network structure. The features extracted from the pretrained network are the input into the Bidirectional Long Short-Term Memory to obtain the RUL prediction results. The ability of LSTM to model sequence signals and the dynamic learning ability of bidirectional propagation to time information contribute to accurate RUL prediction. Finally, the deep model proposed in this paper is tested on the sensor signal dataset of bearing and gearbox. The high accuracy prediction results show the superiority of the transfer learning-based sequential network in RUL prediction.

Mixing and Elastic Contributions to the Diffusion Coefficient of Polymer Networks

Because the swelling of polymer gels is a diffusion process of the polymer network, the swelling rate is determined by the diffusion coefficient of a polymer network. The diffusion coefficient (D) includes the polymer–solvent mixing contribution (Dmix) and the elastic contribution from the polymer network (Del). In this study, we experimentally investigate D, Dmix, and Del, using tetra gels whose network structural parameters are systematically controllable. We measured D and the shear modulus (G) using dynamic light scattering and dynamic viscoelastic measurement, respectively. According to the Tanaka–Fillmore theory, we evaluate Dmix and Del using the linear relationship between D and G in the as-prepared state. Consequently, we demonstrate a relation of D = Dmix + Del in the as-prepared, swelling, and equilibrium swollen states.

Design and Application of Deep Belief Network Based on Stochastic Adaptive Particle Swarm Optimization

Due to the problem of poor recognition of data with deep fault attribute in the case of traditional superficial network under semisupervised and weak labeling, a deep belief network (DBN) was proposed for deep fault detection. Due to the problems of deep belief network (DBN) network structure and training parameter selection, a stochastic adaptive particle swarm optimization (RSAPSO) algorithm was proposed in this study to optimize the DBN. A stochastic criterion was proposed in this method to make the particles jump out of the original position search with a certain probability and reduce the probability of falling into the local optimum. The RSAPSO-DBN method used sample data to train the DBN and used the final diagnostic error rate to construct the fitness value function of the particle swarm algorithm. By comparing the minimum fitness value of each particle to determine the advantages and disadvantages of the model, the corresponding minimum fitness value was selected. Using the number of network nodes, learning rate, and momentum parameters, the optimal DBN classifier was generated for fault diagnosis. Finally, the validity of the method was verified by bearing data from Case Western Reserve University in the United States and data collected in the laboratory. Comparing BP (BP neural network), support vector machine, and heterogeneous particle swarm optimization DBN methods, the proposed method demonstrated the highest recognition rates of 87.75% and 93.75%. This proves that the proposed method possesses universality in fault diagnosis and provides new ideas for data identification with different fault depth attributes.

The emergence of the higher education research field (1976–2018): preferential attachment, smallworldness and fragmentation in its collaboration networks

The field of higher education research is fast-growing, both in number of publications and in geographical reach. There is however limited evidence on how this growth in publication influences the structure of the underlying co-authorship network. This is important as structural network parameters can change quickly in a fast-growing network, leading to fundamental different network structures, e.g., in terms of hierarchy, fragmentation, and inequality. Ultimately, these network structures can influence the current and future innovation and knowledge production in the field. Empirically, we construct 34 different co-authorship networks of all authors published in 28 higher education journals listed in Web of Science between 1976 and 2018 and perform bibliometric network analyses. We find that the growth of publications and authors in the higher education research field leads to increased clustering among authors, creating a dense core of well-connected author clusters. At the same time, we observe an increasing inequality in the network. The co-authorship network is characterized by high fragmentation and reveals a core-periphery structure. Our analysis shows that co-authorship is a selective process, driven by a Matthew effect based on previous publications. As a result, core authors are unlikely to co-author with newer, less established authors. Moreover, we also detect a growing inequality in the average impact of an article. We conclude the paper by discussing possible explanations and by offering some suggestions for future research.

Head-to-Head Comparison of Social Network Assessments in Stroke Survivors.

Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale-Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member. In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET's network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size. We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5. Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

Alteration of the Intra- and Inter-Lobe Connectivity of the Brain Structural Network in Normal Aging.

The morphological changes in cortical parcellated regions during aging and whether these atrophies may cause brain structural network intra- and inter-lobe connectivity alterations are subjects that have been minimally explored. In this study, a novel fractal dimension-based structural network was proposed to measure atrophy of 68 parcellated cortical regions. Alterations of structural network parameters, including intra- and inter-lobe connectivity, were detected in a middle-aged group (30–45 years old) and an elderly group (50–65 years old). The elderly group exhibited significant lateralized atrophy in the left hemisphere, and most of these fractal dimension atrophied regions were included in the regions of the “last-in, first-out” model. Globally, the elderly group had lower modularity values, smaller component size modules, and fewer bilateral association fibers. They had lower intra-lobe connectivity in the frontal and parietal lobes, but higher intra-lobe connectivity in the temporal and occipital lobes. Both groups exhibited similar inter-lobe connecting pattern. The elderly group revealed separations, sparser long association fibers, commissural fibers, and lateral inter-lobe connectivity lost effect, mainly in the right hemisphere. New wiring and reconfiguring modules may have occurred within the brain structural network to compensate for connectivity, decreasing and preventing functional loss in cerebral intra- and inter-lobe connectivity.