Multilayer Model Research Articles

DRADTiP: Drug repurposing for aging disease through drug-target interaction prediction

MotivationThe greatest risk factor for many non-communicable diseases is aging. Studies on model organisms have demonstrated that genetic and chemical perturbation alterations can lengthen longevity and overall health. However, finding longevity-enhancing medications and their related targets is difficult. MethodIn this work, we designed a novel drug repurposing model by identifying the interaction between aging-related genes or targets and drugs similar to aging disease. Each disease is associated with certain specific genetic factors for the occurrence of that disease. The factors include gene expression, pathway, miRNA, and degree of genes in the protein-protein interaction network. In this paper, we aim to find the drugs that prolong the life span of humans with their aging-related targets using the above-mentioned factors. In addition, the contribution or importance of each factor may vary among drugs and targets. Therefore, we designed a novel multi-layer random walk-based network representation learning model including node and edge weight to learn the features of drugs and targets respectively. ResultThe performance of the proposed model is demonstrated using k-fold cross-validation (k = 5). This model achieved better performance with scores of 0.93 and 0.91 for precision and recall respectively. The drugs identified by the system are evaluated to be potential candidates for aging since the degree of interaction between the potential drugs and their gene sets are high. In addition, the genes that are interacting with drugs produce the same biological functions. Hence the life span of the human will be increased or prolonged.

Automated diagnosis of atherosclerosis using multi-layer ensemble models and bio-inspired optimization in intravascular ultrasound imaging.

Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physically or using VH-IVUS software. Since manual or VH-IVUS software-based diagnosis is time-consuming, automated plaque characterization tools are essential for accurate atherosclerosis detection and classification. Recently, deep learning (DL) and computer vision (CV) approaches are promising tools for automatically classifying plaques on IVUS images. With this motivation, this manuscript proposes an automated atherosclerotic plaque classification method using a hybrid Ant Lion Optimizer with Deep Learning (AAPC-HALODL) technique on IVUS images. The AAPC-HALODL technique uses the faster regional convolutional neural network (Faster RCNN)-based segmentation approach to identify diseased regions in the IVUS images. Next, the ShuffleNet-v2 model generates a useful set of feature vectors from the segmented IVUS images, and its hyperparameters can be optimally selected by using the HALO technique. Finally, an average ensemble classification process comprising a stacked autoencoder (SAE) and deep extreme learning machine (DELM) model can be utilized. The MICCAI Challenge 2011 dataset was used for AAPC-HALODL simulation analysis. A detailed comparative study showed that the AAPC-HALODL approach outperformed other DL models with a maximum accuracy of 98.33%, precision of 97.87%, sensitivity of 98.33%, and F score of 98.10%.

Toward security quantification of serverless computing

Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing.

Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement

ObjectivesThis study aimed to assess the accuracy of machine learning (ML) models with feature selection technique in classifying cervical vertebral maturation stages (CVMS). Consensus-based datasets were used for models training and evaluation for their model generalization capabilities on unseen datasets.MethodsThree clinicians independently rated CVMS on 1380 lateral cephalograms, resulting in the creation of five datasets: two consensus-based datasets (Complete Agreement and Majority Voting), and three datasets based on a single rater’s evaluations. Additionally, landmarks annotation of the second to fourth cervical vertebrae and patients’ information underwent a feature selection process. These datasets were used to train various ML models and identify the top-performing model for each dataset. These models were subsequently tested on their generalization capabilities.ResultsFeatures that considered significant in the consensus-based datasets were consistent with a CVMS guideline. The Support Vector Machine model on the Complete Agreement dataset achieved the highest accuracy (77.4%), followed by the Multi-Layer Perceptron model on the Majority Voting dataset (69.6%). Models from individual ratings showed lower accuracies (60.4–67.9%). The consensus-based training models also exhibited lower coefficient of variation (CV), indicating superior generalization capability compared to models from single raters.ConclusionML models trained on consensus-based datasets for CVMS classification exhibited the highest accuracy, with significant features consistent with the original CVMS guidelines. These models also showed robust generalization capabilities, underscoring the importance of dataset quality.

A statistical physics-based physicochemical study of L-phenylalanine adsorption on activated carbon.

In this comparative study of the adsorption of L-phenylalanine (L-Phe) on two modified low-activated carbons (ACK and ACZ) at four temperatures (293-313K), steric and energetic characteristics of adsorption were investigated. An advanced statistical physics multilayer model involving single-layer and double-layer adsorption scenarios was developed to interpret the L-Phe adsorption phenomenon. Modeling results indicate that two and three L-Phe layers were arranged depending on the tested adsorption systems. The estimated number of L-Phe molecules per leading adsorption site varied from 1.71 to 2.09 and from 1.76 to 1.86 for systems L-Phe-ACK and L-Phe-ACZ, respectively. The results show that a multimolecular adsorption mechanism might connect this amino acid molecule on ACZ and ACK surfaces in a non-parallel location. These parameters changed as follows, according to the adsorbed quantity at saturation analysis: Qasat (L-Phe-ACK) ˃ Qasat (L-Phe-ACZ). This indicates that ACK material was more efficient and valuable for L-Phe adsorption than ACZ. It was also shown that the adsorption capacity decreases as the temperature increases, proving the exothermicity of the adsorption process. This analytical substantiation is confirmed by calculating the binding energies, suggesting the occurrence of physical bonds between L-Phe amino acid molecules and ACK/ACZ binding sites and among L-Phe-L-Phe molecules. Pore size distribution was interpreted and calculated by applying the Kelvin theory to data from single adsorption isotherms. All used temperatures depicted a distribution of pores below 2nm. The docking analysis involving L-Phe and the ACZ and ACK adsorbents reveal a significant resemblance in how receptors detect ligands. Consequently, the findings from the docking process confirm that the calculated binding affinities fall within the spectrum of adsorption energy. This study analyzed the adsorption capacity of the L-Phe through a model proposed by statistical physics formalism. Molecular docking was used to determine the various types of interactions between the two activated carbons. Two aspects, including orientation of L-Phe on the site, number of molecules per site n, interaction energy, density of receptor site, and adsorption capacity, were discussed to elucidate the influence of activation on the two adsorbents.

Image based Modeling and Control for Batch Processes

This manuscript addresses the problem of leveraging thermal images for modeling and feedback control, specifically tailored for terminal quality control of batch processes. The primary objective, common in many batch processes, is to produce products with quality variables aligning with user specifications, available for measurement only at batch termination, precluding the direct use of classical control strategies. Furthermore, in many instances, traditional online sensors such as thermocouples may not be available, but instead spectral inputs like thermal images or acoustic data may be more readily available for feedback control. The challenge is to not only use the non-traditional sensor data for building a dynamic model but also to use that model for terminal quality control. The proposed approach involves a multi-layered modeling strategy. Initially, a dimensionality reduction technique is employed to condense the high-dimensional image into a set of representative outputs. Subsequently, subspace identification (SSID) is applied to develop a Linear Time-Invariant (LTI) State Space (SS) model between the inputs and the reduced outputs. Finally, a Partial Least Squares (PLS) model is constructed linking the terminal states of a batch (identified using SSID) with the product qualities obtained for that specific batch. This model is then incorporated into a Model Predictive Control (MPC) formulation. The effectiveness of the MPC is illustrated by showcasing its capability to generate products of high quality by deploying the MPC on a bi-axial lab-scale rotational molding setup.

Prediction method for 4D trajectory of large fixed wing UAV under multi-layer convolution and bidirectional gating model

Abstract With the increase of low-altitude flight tasks, the importance of research on low-altitude flight safety and efficient operation has gradually become prominent. Therefore, it is necessary to try to establish a low-altitude airspace flight path pre-planning method based on 4D trajectory prediction, which can effectively support the above research. In order to improve the integrity, accuracy and reliability of the long-range trajectory prediction results of large fixed wing UAV (LFW UAV), based on the standard neural network as the basic concept to form a fusion model of improved convolutional neural network (CNN) and bidirectional gated recurrent neural network (BIGRU). At the same time, the flight trajectory fitting preprocessing is added to build the completed multilayer cyclic convolutional improvement model (MCCI). The experimental results show that the MCCI model has obvious advantages in terms of prediction accuracy, deviation range and predictable duration when dealing with the LFW UAV 4D trajectory prediction problem, and better reflects the characteristics of 3D position and spatiotemporal elements. The comparative analysis shows that the error values of the MCCI model are smaller than those of the comparison prediction models in any dimension, and the prediction results have better fit and model performance.

Predicting Prayagraj's Urbanization Trajectory using CA-ANN Modelling: Population Pressures and Land Use Dynamics

Land Use/Land Cover (LULC) dynamics provide a crucial role in the monitoring, planning, and management of resources. They also offer valuable information for developing strategies to balance conservation efforts, resolve conflicts between different land uses, and address pressures from growth. The present study focuses on the assessment of LULC dynamics, their forecasting, and their changes for Prayagraj city (including its surroundings) of India. Using long-term spatiotemporal Landsat datasets (1988–2018), we have explored the interlinkages between the change dynamics and human population pressure to explore the impact of agriculture and urbanization on the city landscape. Future growth prediction is carried out by incorporating Cellular Automaton (CA) and Artificial Neural Network (ANN) models. Six exploratory layers (viz., roads, educational institutes, railway transition, slope, river, and restricted area) are used in the learning process to determine LULC change (1997–2008) simulation. The validation of real and predicted LULC is carried out for 2018, where the correctness percentage and kappa value are found to be 90.29% and 0.87, respectively. Then, the ANN- Multilayer perceptron (MLP) and CA model are applied to predict LULC–2028 using the same trained transition probabilities. Results show that Built-land has grown highly by 10.03%, whereas Agriculture land and Forest land have significantly decreased by 13.43% and 3.03%, respectively, from 1988 to 2018. The predicted LULC of 2028 reveals that Built-land will keep growing by 2.83% during 2018–2028 at the cost of Agriculture land and Forest land, especially in northern, south–western and southern region, including city's inner sphere. United Nations' human population projection reveals that the city is expected to reach a population of 1.625 million by 2028. This indicates that tremendous pressure will be placed on land resources, particularly on agricultural, barren, and forested areas. To address this alarming scenario, it is imperative to delineate future development areas, ensuring better urban planning for the environmental sustainability and economic prosperity of Prayagraj city.

Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters

The security and privacy of a system are urgent issues in achieving secure and efficient learning-based systems. Recent studies have shown that these systems are susceptible to subtle adversarial perturbations applied to inputs. Although these perturbations are difficult for humans to detect, they can easily mislead deep learning classifiers. Noise injection, as a defense mechanism, can offer a provable defense against adversarial attacks by reducing sensitivity to subtle input changes. However, these methods face issues of computational complexity and limited adaptability. We propose a multilayer filter defense model, drawing inspiration from filter-based image denoising techniques. This model inserts a filtering layer after the input layer and before the convolutional layer, and incorporates noise injection techniques during the training process. This model substantially enhances the resilience of image classification systems to adversarial attacks. We also investigated the impact of various filter combinations, filter area sizes, standard deviations, and filter layers on the effectiveness of defense. The experimental results indicate that, across the MNIST, CIFAR10, and CIFAR100 datasets, the multilayer filter defense model achieves the highest average accuracy when employing a double-layer Gaussian filter (filter area size of 3×3, standard deviation of 1). We compared our method with two filter-based defense models, and the experimental results demonstrated that our method attained an average accuracy of 71.9%, effectively enhancing the robustness of the image recognition classifier against adversarial attacks. This method not only performs well on small-scale datasets but also exhibits robustness on large-scale datasets (miniImageNet) and modern models (EfficientNet and WideResNet).

Modon solutions in an N-layer quasi-geostrophic model

Modons, or dipolar vortices, are common and long-lived features of the upper ocean, consisting of a pair of counter-rotating monopolar vortices moving through self-advection. Such structures remain stable over long times and may be important for fluid transport over large distances. Here, we present a semi-analytical method for finding fully nonlinear modon solutions in a multi-layer quasi-geostrophic model with arbitrarily many layers. Our approach is to reduce the problem to a multi-parameter linear eigenvalue problem which can be solved using numerical techniques from linear algebra. The method is shown to replicate previous results for one- and two-layer models and is applied to a three-layer model to find a solution describing a mid-depth propagating, topographic vortex.

Multi-Objective Route Planning Model for Ocean-Going Ships Based on Bidirectional A-Star Algorithm Considering Meteorological Risk and IMO Guidelines

In this study, a new route planning model is proposed to help ocean-going ships avoid dangerous weather conditions and ensure safe ship navigation. First, we integrate ocean-going ship vulnerability into the study of the influence of meteorological and oceanic factors on navigational risk. A multi-layer fuzzy comprehensive evaluation model for weather risk assessment is established. A multi-objective nonlinear route planning model is then constructed by comprehensively considering the challenges of fuel consumption, risk, and time during ship navigation. The International Maritime Organization (IMO) guidelines are highlighted as constraints in the calculations, and wind, wave, and calm water resistance to ships in the latest ITTC method is added to the fuel consumption and sailing time in the objective function. Finally, considering the large amount of data required for ocean voyages, the bidirectional A* algorithm is applied to solve the model and reduce the planning time. Furthermore, our model is applied to the case of an accident reported in the Singapore Maritime Investigation Report, and the results show that the model-planned route is very close to the original planned route using the Towing Manual, with an average fit of 98.22%, and the overall meteorological risk of the model-planned route is 11.19% smaller than the original route; our model can therefore be used to plan a safer route for the vessel. In addition, the importance of risk assessments and the IMO guidelines as well as the efficiency of the bidirectional A* algorithm were analyzed and discussed. The results show that the model effectively lowers the meteorological risk, is more efficient than the traditional route planning algorithm, and is 86.82% faster than the Dijkstra algorithm and 49.16% faster than the A* algorithm.

A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity.

Three major types of GABAergic interneurons, parvalbumin-, somatostatin-, and vasoactive intestinal peptide-expressing (PV, SOM, VIP) cells, play critical but distinct roles in the cortical microcircuitry. Their specific electrophysiology and connectivity shape their inhibitory functions. To study the network dynamics and signal processing specific to these cell types in the cerebral cortex, we developed a multi-layer model incorporating biologically realistic interneuron parameters from rodent somatosensory cortex. The model is fitted to in vivo data on cell-type-specific population firing rates. With a protocol of cell-type-specific stimulation, network responses when activating different neuron types are examined. The model reproduces the experimentally observed inhibitory effects of PV and SOM cells and disinhibitory effect of VIP cells on excitatory cells. We further create a version of the model incorporating cell-type-specific short-term synaptic plasticity (STP). While the ongoing activity with and without STP is similar, STP modulates the responses of Exc, SOM, and VIP cells to cell-type-specific stimulation, presumably by changing the dominant inhibitory pathways. With slight adjustments, the model also reproduces sensory responses of specific interneuron types recorded in vivo. Our model provides predictions on network dynamics involving cell-type-specific short-term plasticity and can serve to explore the computational roles of inhibitory interneurons in sensory functions.

Risk Assessment and Diagnosis Code Prediction from Electronic Health Records (EHR) using deep learning

Knowledge in the medical field is often expressed by distinct and subjective norms. Much research and development efforts have focused on using deep learning algorithms to predict the likelihood of illnesses from EHR in recent years. When it comes to risk prediction, deep learning-based techniques outperform more conventional machine learning models. But nothing in the literature fully accounts for what doctors already know, such as the connections between illnesses and their risk factors. This research examines the use of Multi-layer Perceptron models for the categorisation of diagnoses in electronic health records. The raw data and a modified version of the EHR dataset are used to train two MLPs with distinct topologies. For comparative purposes, a Random Forest is used as a baseline. To phenotype patients using their electronic health records, we provide a deep learning method. Predictive modelling of chronic illnesses is the particular scenario used to verify the suggested model on a real-world EHR data warehouse. Many deep learning applications on EHRs have been effective, and there is still a lot of potential to be realised. It has been discovered that deep learning models can learn from the limited EHR dataset, but not to a level where they outperform the baseline model.

Deciphering the complex mechanics of atherosclerotic plaques: A hybrid hierarchical theory-microrheology approach

Understanding the viscoelastic properties of atherosclerotic plaques at rupture-prone scales is crucial for assessing their vulnerability. Here, we develop a Hybrid Hierarchical theory-Microrheology (HHM) approach, enabling the analysis of multiscale mechanical variations and distribution changes in regional tissue viscoelasticity within plaques across different spatial scales. We disclose a universal two-stage power-law rheology in plaques, characterized by distinct power-law exponents (αshort and αlong), which serve as mechanical indexes for plaque components and assessing mechanical gradients. We further propose a self-similar hierarchical theory that effectively delineates plaque heterogeneity from the cytoplasm, cell, to tissue levels. Moreover, our proposed multi-layer perceptron model addresses the viscoelastic heterogeneity and gradients within plaques, offering a promising diagnostic strategy for identifying unstable plaques. These findings not only advance our understanding of plaque mechanics but also pave the way for innovative diagnostic approaches in cardiovascular disease management. Statement of significanceOur study pioneers a Hybrid Hierarchical theory-Microrheology (HHM) approach to dissect the intricate viscoelasticity of atherosclerotic plaques, focusing on distinct components including cap fibrosis, lipid pools, and intimal fibrosis. We unveil a universal two-stage power-law rheology capturing mechanical variations across plaque structures. The proposed hierarchical model adeptly captures viscoelasticity changes from cytoplasm, cell to tissue levels. Based on the newly proposed markers, we further develop a machine learning (ML) diagnostic model that sets precise criteria for evaluating plaque components and heterogeneity. This work not only reveals the comprehensive mechanical heterogeneity within plaques but also introduces a mechanical marker-based ML strategy for assessing plaque conditions, offering a significant leap towards understanding and diagnosing atherosclerotic risks.

Galerkin boundary element method for simulating lightning response of grounding grid in horizontal multilayered soil model considering soil ionization effect

AbstractThis work proposes a novel mathematical model based on the Galerkin time‐domain boundary element method for accurately calculating the lightning current distribution and lightning impulse response of the buried substation grounding grid, in a multi‐layer horizontal layered soil model, by taking into account the soil ionization effect. To improve computational efficiency, the quasi‐static complex image method and its closed form time‐domain Green's function have been introduced into the model that has the ability to analytically calculate the mutual inductance coefficient between the branch currents of any two conductor segments and the mutual resistance coefficient between the leakage currents. The Galerkin time‐domain boundary element method proposed in this work can simulate the transient lightning impulse response of a substation grounding grid buried in the multi‐layer horizontal layered soil.

Stock market index prediction based on market trend using LSTM

The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, single-layer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings.

Research on reconfigurable systems in discrete manufacturing workshops

Abstract With the development of smart manufacturing, the demand for personalized production has made reconfigurable systems in the manufacturing industry a focal point of research and application in high-end manufacturing. The paper analyzes the current status of the manufacturing industry and the challenges of reconfigurable technology. Furthermore, it designs and develops a reconfigurable system for discrete manufacturing of automotive components. The paper focuses on issues such as creating reconfigurable models, designing adaptive reconfiguration algorithms, accomplishing the creation of multi-layered models, the design of rule libraries, and algorithm implementation. The reconfiguration result scheme can be input into commercial software for performance simulation, and can directly integrate with production systems to complete production planning. This demonstrates the rationality of the reconfigurable models in the system, the correctness of the reconfiguration algorithms, and the effectiveness of the system, providing a theoretical basis for reconfigurable production lines.

Carbon Price Forecasting Using Optimized Sliding Window Empirical Wavelet Transform and Gated Recurrent Unit Network to Mitigate Data Leakage

Precise forecasts of carbon prices are crucial for reducing greenhouse gas emissions and promoting sustainable, low-carbon development. To mitigate noise interference in carbon price data, hybrid models integrating data decomposition techniques are commonly utilized. However, it has been observed that the improper utilization of data decomposition techniques can lead to data leakage, thereby invalidating the model’s practical applicability. This study introduces a leakage-free hybrid model for carbon price forecasting based on the sliding window empirical wavelet transform (SWEWT) algorithm and the gated recurrent unit (GRU) network. First, the carbon price data are sampled using a sliding window approach and then decomposed into more stable and regular subcomponents through the EWT algorithm. By exclusively employing the data from the end of the window as input, the proposed method can effectively mitigate the risk of data leakage. Subsequently, the input data are passed into a multi-layer GRU model to extract patterns and features from the carbon price data. Finally, the optimized hybrid model is obtained by iteratively optimizing the hyperparameters of the model using the tree-structured Parzen estimator (TPE) algorithm, and the final prediction results are generated by the model. When used to forecast the closing price of the Guangdong Carbon Emission Allowance (GDEA) for the last nine years, the proposed hybrid model achieves outstanding performance with an R2 value of 0.969, significantly outperforming other structural variants. Furthermore, comparative experiments from various perspectives have validated the model’s structural rationality, practical applicability, and generalization capability, confirming that the proposed framework is a reliable choice for carbon price forecasting.

Enhancing air quality classification using a novel discrete learning-based multilayer perceptron model (DMLP)

Enhancing air quality classification using a novel discrete learning-based multilayer perceptron model (DMLP)

Prediction Model using a Multi-Layer Perceptron Neural Network for Military Plastic Explosive PE4 Blast Performance in a Significant Effect of Tropicalisation

Over the years, plastic explosive PE4 has been imported from the United Kingdom and is fully utilised in military activities and drills. This study is to discover a ratio of the plastic explosive PE4 explosion performance in tropical conditions and develop a multilayer perceptron neural network model for the prediction of plastic explosive in Malaysia. Several environmental tests have been performed in Kem Kongkoi, Jelebu, Negeri Sembilan. Six parameters were considered, including environmental temperature, distance measurement, explosive material, variety of shapes, weight and ignition point. In this paper, the Bayesian Regularization, Levenberg-Marquardt algorithm and Scaled Conjugate Gradient model were developed. Every model was tested with 24 datasets to discover the root mean square error and regression performance. The Bayesian Regularization model provides the best prediction model as it has a mean square error of 0.0005 and a regression performance value that is close to 1 at 0.9992.