Multilayer Network Model Research Articles

Optimization of annular microfluidic biosensor enhanced by active and passive effects using Taguchi’s method coupled with multi-layer perceptron neural networks (MLP-NN) models

Optimization of annular microfluidic biosensor enhanced by active and passive effects using Taguchi’s method coupled with multi-layer perceptron neural networks (MLP-NN) models

Mechanical properties and multi-layer perceptron neural networks of polyacrylonitrile fiber reinforced concrete cured outdoors

The application range of polyacrylonitrile fiber (PANF) in concrete engineering in alpine regions was clarified by performing six sets of mechanical tests of PANF-reinforced concrete (PANFRC) by outdoor curing. The results show that PANF can enhance the performance of concrete mainly in terms of the tensile performance. The splitting strength and flexural strength increased by 15.69% and 8.54% after 150 days, and the dosage used in the alpine region should be controlled between 1.2–1.5 kg/m3. Finally, based on the experimental results, the multi-linear regression prediction model (MLR) and multi-layer perceptron neural networks model (MLP) were used to predict the compressive strength (fcu), splitting strength (fsp), flexural strength (fts), tensile–compression ratio (fsp/fcu), ratio of flexural strength to compressive strength (fts/fcu), and ratio of flexural strength to splitting strength (fts/fsp) of each group of PANFRC specimens. The prediction model was constructed based on the fiber content (W), curing age (D), dynamic elastic modulus (E), and surface rebound hardness (R). The results of the model test show that MLR and MLP are reasonable for predicting the mechanical properties of PANFRC, and the prediction error of the latter is smaller. Moreover, R has a strong dependence on the mechanical evaluation index, the sensitivity coefficient Qik is 0.37, PANF has the least influence, and Qik is only 0.16. This study has reference significance for the application of PANFRC in alpine regions and provides useful ideas for the efficient and intelligent development of the construction industry in alpine regions.

Deep learning‐based high‐dimensional multiple regression estimator for chest x‐ray image classification in rapid cardiomegaly screening

AbstractChest x‐ray (CXR) examination is a common first‐line, non‐invasive, and rapid screening method in clinical examinations. The posteroanterior (PA) and anteroposterior (AP) view modes can both be used to detect related cardiopulmonary diseases, such as pneumonitis, tuberculosis, pulmonary fibrosis, lung tumors, and cardiomegaly. Compared with cardiac computed tomography and cardiac magnetic resonance imaging methods, CXR examination has a short scanning duration and costs less, and is suitable for routine and follow‐up health examinations. Cardiomegaly is an asymptomatic disease in the early stage and cannot be detected through electrocardiography measurements. Thus, early cardiomegaly classes detections, such as cardiac hypertrophy and ventricular dilatation, can help make decisions regarding drug treatments and surgeries. In addition, an automatic assistive tool is required to differentiate between normal individuals and those with cardiomegaly to address the problem of manual inspection and labor shortage. Hence, PA view‐based CXR classification is used to develop a deep learning (DL)‐based high‐dimensional multiple regression analysis (MRA) model for CXR image classification in rapid cardiomegaly screening. This multilayer network model uses a two‐channel three‐layer convolution‐normalization‐pooling process with two‐dimensional (2D) multi convolution operations to enhance images and to extract feature patterns; and then a one‐dimensional feature conversion is used to estimate the four coordinate points of the maximal horizontal cardiac diameter (MHCD) and maximal horizontal thoracic diameter (MHTD), which can be used to estimate cardiothoracic ratio and detect cardiomegaly. For experimental tests, the training and testing datasets are collected from the National Institutes of Health CXR Image Database (Clinical Center, USA), and 10‐fold cross‐validation was used for model evaluation in terms of precision (%), recall (%), accuracy (%), and F1 score. These indexes are used to evaluate the feasibility of the proposed MRA estimator. In addition, the performances of the proposed model are compared with those of conventional DL‐based multilayer classifiers.

Evaluation of Module Dynamics in Functional Brain Networks After Stroke.

The brain's functional network can be analyzed as a set of distributed functional modules. Previous studies using the static method suggested the modularity of the brain function network decreased due to stroke; however, how the modular network changes after stroke, particularly over time, is far from understood. This study collected resting-state functional MRI data from 15 stroke patients and 15 age-matched healthy controls. The patients exhibit distinct clinical symptoms, presenting in mild (n = 6) and severe (n = 9) subgroups. By using a multilayer network model, a dynamic modular structure was detected and corresponding interaction measurements were calculated. The results demonstrated that the module structure and interaction had changed following the stroke. Importantly, the significant differences in dynamic interaction measures demonstrated that the module interaction alterations were not independent of the initial degree of clinical severity. Mild patients were observed to have a significantly lower between-module interaction than severe patients as well as healthy controls. In contrast, severe patients showed remarkably lower within-module interaction and had a reduced overall interaction compared to healthy controls. These findings contributed to the development of post-stroke dynamics analysis and shed new light on brain network interaction for stroke patients.Clinical relevance- Dynamic module interaction analysis underpins the post-stroke functional plasticity and reorganization, and may enable new insight into rehabilitation strategies to promote recovery of function.

A combined scheme of parallel-reaction kinetic model and multi-layer artificial neural network model on pyrolysis of Reed Canary

A comprehensive understanding of pyrolysis kinetics is crucial for the design of biomass pyrolysis. In this study, a combined scheme was proposed for biomass pyrolysis. A kinetic model, serving as a knowledge-based model, was established to capture the primary trend of biomass pyrolysis. An ANN model, as an experience-based model, was developed to provide “details” on the process. The kinetic model incorporated a least-square optimization model to calculate optimal kinetic parameters for pyrolysis reaction steps. The ANN model utilized metaheuristic algorithms such as Particle Swarm Optimization, Pattern Search, Genetic Algorithms, and Surrogate Optimization to optimize the hyperparameters of the ANN model. By combining the kinetic model with the ANN model, comprehensive predictions of biomass pyrolysis were obtained, and the combined Kinetic-ANN model was validated with experimental data at 4 heating rates. Finally, the combined model was compared with the solo-kinetic and solo-ANN models, confirming the advanced performance of the combined approach.

Discontinuous control for exponential synchronization of complex-valued stochastic multi-layer networks

This article is concerned with the exponential synchronization issue of complex-valued stochastic multi-layer networks (CVSMLNs) via two discontinuous control approaches. At first, a new network model of complex-valued multi-layer network is built by integrating stochastic disturbances, intra-layer and inter-layer couplings. Different from previous aperiodic intermittent control (APIC) approaches, under the framework of APIC, novel impulsive feedback control (IFC) (IFC is executed at the activation time of APIC) and universal adaptive feedback control (adaptive control gain is different in different layers) are both developed to realize the exponential synchronization of CVSMLNs. Subsequently, some simple sufficient criteria are derived by virtue of Lyapunov method, graph theory and complex generalized Itô’s formula. Ultimately, the theoretical results are used in a practical complex-valued neural network and the availability of the obtained results is verified by giving some numerical examples.

An approach towards building robust neural networks models using multilayer perceptron through experimentation on different photovoltaic thermal systems

The study is aimed at training an artificial neural network to determine the specific Photo voltaic thermal (PV/T) system that will function optimally in a specific environmental situation by optimizing the input parameters. In this regard, four different PV/T systems based on the arrangement of the tubes and the presence of the copper absorber sheet, namely, vertically oscillating tube, vertically oscillating sheet tube, rectangular spiral tube and horizontally oscillating tube PV/T’s were considered. In the experimental study, wind velocity, solar irradiation, ambient temperature, relative humidity, and inlet temperature to the PV/T are considered the influencing factors. The response factors for evaluating the performance of the PV/T system were temperature outlet and power output. Water was used as the working fluid for the thermal management of the systems for all four test cases. The investigation discloses that the rectangular spiral tube photovoltaic thermal system is the best among the studied systems as it maintains both average thermal efficiency and average electrical efficiency at 50.7% and 12.15%, respectively, considering the trade-off between electrical and thermal efficiency. The ideal design can then be selected for a specific area that will be more appropriate with its environmental fluctuations by approximating the production for each scenario. The artificial neural network was designed to be capable of being successfully trained for this application based on Artificial Neural Network (ANN), Multilayer Perceptron using Levenberg Marquardt learning algorithm through MATLAB software based on the experimental data. The ANN model for the rectangular spiral tube PV/T system achieved an exceptional overall R-value of 0.99843 among the proposed prediction models. For the artificial neural network model formulated for vertically oscillating tube, vertically oscillating sheet tube and horizontally oscillating tube PV/T system, the R-value was found to be 0.99807, 0.9943, and 0.99593, respectively. In the training phase, the MSE for vertically oscillating tube, vertically oscillating sheet tube, rectangular spiral tube and horizontally oscillating tube PV/T’s using the multilayer perceptron artificial neural network models were found to be 1.1328, 3.5082, 0.3134 and 1.1328, respectively. For the same PV/T’s models, the MSE values for Cross validation phase were found to be 1.8497, 3.9072, 5.1419, and 4.6402, respectively. Finally, the suggested linear prediction models assist in quickly and easily determining the ideal conditions for every solar system while lowering the error in furcating future results.

Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study.

Iron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. <15% Supplemented Iron is absorbed in the small bowel, and the remaining iron is a source of dysbiosis. The gut microbiome signatures to the level of predicting anemia among low-middle-income populations are unknown. The present study was conducted to identify gut microbiome signatures that have predictive potential in association with Neutrophil to lymphocytes ratio (NLR) and Mean corpuscular volume (MCV) in anemia. One hundred and four participants between 10 and 70 years were recruited from Odisha's Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia. Significant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species. These findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings.

Convolutional neural network-based deep learning model for air quality prediction in October city of Egypt

Purpose Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis. Design/methodology/approach Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO2) and particulate matter 10 µm in diameter (PM10). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models. Findings The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO2 and PM10 are expected to reach 10 and 127 µg/m3, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment. Originality/value This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

How and when are topological explanations complete mechanistic explanations? The case of multilayer network models

The relationship between topological explanation and mechanistic explanation is unclear. Most philosophers agree that at least some topological explanations are mechanistic explanations. The crucial question is how to make sense of this claim. Zednik (Philos Psychol 32(1):23–51, 2019, https://doi.org/10.1080/09515089.2018.1512090) argues that topological explanations are mechanistic if they (i) describe mechanism sketches that (ii) pick out organizational properties of mechanisms. While we agree with Zednik’s conclusion, we critically discuss Zednik’s account and show that it fails as a general account of how and when topological explanations are mechanistic. First, if topological explanations were just mechanism sketches, this implies that they could be enriched by replacing topological terms with mechanistic detail. This, however, conflicts how topological explanations are used in scientific practice. Second, Zednik’s account fails to show how topological properties can be organizational properties of mechanisms that have a place in mechanistic explanation. The core issue is that Zednik’s account ignores that topological properties often are global properties while mechanistic explanantia refer to local properties. We demonstrate how these problems can be solved by a recent account of mechanistic completeness (Craver and Kaplan in Br J Philos Sci 71(1):287–319, 2020, https://doi.org/10.1093/bjps/axy015; Kohár and Krickel in Calzavarini and Viola (eds) Neural mechanisms—new challenges in the philosophy of neuroscience, Springer, New York, 2021, https://doi.org/10.1007/978-3-030-54092-0_17) and use a multilayer network model of Alzheimer’s Disease to illustrate this.

Modeling and Invulnerability Analysis of Multilayer Air Traffic Network considering Altitude Layer

In this paper, a network coupling mechanism is studied to couple the air sector network, airport network, and air route network into a multilayer air network model. Then, the altitude layers are divided into three: high, medium, and low, and the arrival and departure flight procedures are also considered. By defining the association between the altitude layers and the waypoints, a multilayer air network model considering the altitude layer is constructed. Then, the line graph theory is used to redefine the nodes and edges, and the network is reconstructed to obtain a new single-layer one which is easy to calculate. Finally, a case study is carried out in Chengdu control area. The results show that the proposed model is closer to the reality, and the invulnerability performance is more referential. Besides, it also reflects the impact of altitude layers on the efficiency of airspace. The results are helpful for air traffic controllers to manage airspace better and have significance for promoting air traffic safety and stability.

Визначення сили впливу іонного променя на об’єкт космічного сміття за контурами його зображень з використанням глибокого навчання

The goal of this article is to develop an effective image preprocessing algorithm and a neural network model for determining the force to be transmitted to a space debris object (SDO) for its non-contact deorbit. In the development and study of the algorithm, use was made of methods of theoretical mechanics, machine learning, computer vision, and computer simulation. The force is determined using a photo taken by an onboard camera. To increase the efficiency of the neural network, an algorithm was developed for feature recognition by the SDO edge in the photo. The algorithm, on the one hand, selects a sufficient number of features to describe the properties of the figure and, on the other hand, significantly reduces the amount of data at the neural network input. A dataset with the features and corresponding reference force values was created for model training. A neural network model was developed to determine the force to be exerted on a SDO from the SDO features. The model was tested using a set of eighteen calculated cases to determine the effectiveness, accuracy, and speed of the algorithm. The proposed algorithm was compared with two existing ones: the method of central projections onto an auxiliary plane and the multilayered neural network model that calculates the force using the SDO orientation parameters. The comparison was performed using the root mean square error, the maximum absolute error, and the maximum relative error. The test results are presented as tables and graphs. The proposed approach makes it possible to develop a system of SDO non-contact removal that does not need to determine the exact relative position and orientation with respect to the active spacecraft. Instead, the algorithm uses camera-taken photos, from which the features necessary for calculation are extracted. This makes it possible to reduce the requirements for its computing elements, to abandon sensors for determining the relative position and orientation, and to reduce the cost of the system.

Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability.

Dependencies in the global food production network can lead to shortages in numerous regions, as demonstrated by the impacts of the Russia-Ukraine conflict on global food supplies. Here we reveal the losses of 125 food products after a localized shock to agricultural production in 192 countries and territories using a multilayer network model of trade (direct) and conversion of food products (indirect), thereby quantifying 108 shock transmissions. We find that a complete agricultural production loss in Ukraine has heterogeneous impacts on other countries, causing relative losses of up to 89% in sunflower oil and 85% in maize via direct effects and up to 25% in poultry meat via indirect impacts. Whereas previous studies often treated products in isolation and did not account for product conversion during production, the present model considers the global propagation of local supply shocks along both production and trade relations, allowing for a comparison of different response strategies.

Enhancing Signature Verification through Neural Network Ensemble

This study presents a signature authentication mechanism to prevent forgery. In the actual world, handling a large collection of data and detecting genuine signatures with reasonable accuracy is often difficult for any verification system. As a result, artificial intelligence techniques are used that can learn from a large data set during the training phase and reply effectively during the application phase without wasting a lot of storage memory space or processing time. It should also be able to refresh its expertise based on real-world encounters on a regular basis. A Multi-Layered Neural Network Model is one such adaptive machine learning technique that is used in this study. Initially, a massive amount of data is gathered by photographing several authentic and fake signatures. The image quality is increased by applying image processing, which is followed by the feature extraction phase, which extracts specific unique standard statistical features.

Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy

The application of ensemble learning models has been continuously improved in recent landslide susceptibility research, but most studies have no unified ensemble framework. Moreover, few papers have discussed the applicability of the ensemble learning model in landslide susceptibility mapping at the township level. This study aims at defining a robust ensemble framework that can become the benchmark method for future research dealing with the comparison of different ensemble models. For this purpose, the present work focuses on three different basic classifiers: decision tree (DT), support vector machine (SVM), and multi-layer perceptron neural network model (MLPNN) and two homogeneous ensemble models such as random forest (RF) and extreme gradient boosting (XGBoost). The hierarchical construction of deep ensemble relied on two leading ensemble technologies (i.e., homogeneous/heterogeneous model ensemble and bagging, boosting, stacking ensemble strategy) to provide a more accurate and effective spatial probability of landslide occurrence. The selected study area is Dazhou town, located in the Jurassic red-strata area in the Three Gorges Reservoir Area of China, which is a strategic economic area currently characterized by widespread landslide risk. Based on a long-term field investigation, the inventory counting thirty-three slow-moving landslide polygons was drawn. The results show that the ensemble models do not necessarily perform better; for instance, the Bagging based DT-SVM-MLPNN-XGBoost model performed worse than the single XGBoost model. Amongst the eleven tested models, the Stacking based RF-XGBoost model, which is a homogeneous model based on bagging, boosting, and stacking ensemble, showed the highest capability of predicting the landslide-affected areas. Besides, the factor behaviors of DT, SVM, MLPNN, RF and XGBoost models reflected the characteristics of slow-moving landslides in the Three Gorges reservoir area, wherein unfavorable lithological conditions and intense human engineering activities (i.e., reservoir water level fluctuation, residential area construction, and farmland development) are proven to be the key triggers. The presented approach could be used for landslide spatial occurrence prediction in similar regions and other fields.

Construction of Sports Management System Based on Multi-Layer Neural Network Model

Abstract Sports administrators must do an excellent job of preparation before the game. The goal is to keep the game safe. This paper uses the BP neural network to establish the danger warning system for sports competitions. The aim is to reduce the probability of dangerous incidents in sports competitions. Then this paper determines the relevant evaluation indexes of the risk warning mode of sports competition activities. Finally, this paper makes an empirical study of the risk warning model of sports events. The results show that the risk prediction of sports matches based on the BP neural network is accurate.

A survey of multilayer networks modelled to assess robustness in infrastructure systems

The development of modern societies places particular demands on the consistent performance of infrastructure systems. Because multilayer network models are capable of representing the interdependencies between infrastructure components, they have been widely used to analyse the robustness of infrastructure systems. This study is a systematic review of the literature published since 2010. It aims to investigate how multilayer network models have been used in analysing the robustness of infrastructure systems. According to the findings, the percolation theory was the most popular method, used in about 57% of papers. Regarding the properties, coupling strength and node degree were the most common, while directed links and feedback conditions were the least common. The following gaps were identified, which provide opportunities for further research. These include the absence of models based on real-world data and the need for models that make fewer simplifying assumptions about complex systems. No papers considered all potential properties and their effect on boosting or weakening each other’s effect. By considering all properties, the importance of different properties on the robustness of infrastructure systems can be quantified and compared in future studies.

A prediction model for thermal conductivity of metallic nuclear fuel based on multiple machine learning models

In this work, the presence of the BCC phase and its thermal conductivity in uranium-based metallic nuclear fuels at higher temperatures were predicted by coupling a random forest classification model with a multilayer perceptron network model. To analyze the effect of phase constitution on thermal conductivity in uranium-based alloys, the pearson correlation coefficient was introduced. The data samples in the current small-sample database of metallic nuclear fuels, created by collecting experimental and computational data from the literature, were first pre-processed with the guide of professional knowledge before machine learning, involving data screening and data completion. Based on temperature and composition characteristics, the existence of the BCC phases and the thermal conductivities of the candidates were predicted by our models. The optimized thermal conductivities of the candidate alloys coincide well with the experimental results, which shows the reliability of our model.

Estimation and prediction of temperature in Iraq using the multi-layered neural network model

Estimation and prediction of temperature in Iraq using the multi-layered neural network model

A stacked machine learning model for multi-step ahead prediction of lake surface water temperature

Lake surface water temperature is one of the most important physical and ecological indices of lakes, which has frequently been used as the indicator to evaluate the impact of climate change on lakes. Knowing the dynamics of lake surface water temperature is thus of great significance. The past decades have witnessed the development of different modeling tools to forecast lake surface water temperature, yet, simple models with fewer input variables, while maintaining high forecasting accuracy are scarce. Impact of forecast horizons on model performance has seldom been investigated. To fill the gap, in this study, a novel machine learning algorithm by stacking multilayer perceptron and random forest (MLP-RF) was employed to forecast daily lake surface water temperature using daily air temperature as the exogenous input variable, with the Bayesian Optimization procedure applied for tuning the hyperparameters. Prediction models were developed using long-term observed data from eight Polish lakes. The MLP-RF stacked model showed very good forecasting capabilities for all lakes and forecast horizons, far better than shallow multilayer perceptron neural network, a model coupling wavelet transform and multilayer perceptron neural network, non-linear regression and air2water models. A reduction in model performance was observed as the forecast horizon increased. However, the model also performs well with a forecast horizon of several days (e.g., 7 days ahead, testing stage: R2 - [0.932, 0.990], RMSE °C - [0.77, 1.83], MAE °C - [0.55, 1.38]). In addition, the MLP-RF stacked model has proven to be reliable for both intermediate temperatures and minimum and maximum peaks. The model proposed in this study will be useful to the scientific community in predicting lake surface water temperature, thus contributing to studies on such sensitive aquatic ecosystems as lakes.