Conventional Neural Network Models Research Articles

Automated Crack Detection in Monolithic Zirconia Crowns Using Acoustic Emission and Deep Learning Techniques

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures.

Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty

The modernization of power systems faces uncertainties due to fluctuating renewable energy sources, electric vehicle expansion, and demand response initiatives. These uncertainties require consideration in power flow analyses by calculating power flow across a spectrum of generation and load values. Traditional power flow methods, which rely on iterative solutions of non-linear equation sets, become computationally burdensome under these uncertain conditions. Surrogate models have been used to reduces the computational cost of power flow analysis under uncertainty. Recently, graph neural networks (GNNs) have gained increasing attention as surrogates for power system simulations. However, GNNs, similar to other machine learning models, require significant amounts of training data. This paper proposes an innovative approach combining a multi-fidelity methodology with a GNN-based surrogate model for efficient power flow calculations. This model is trained using both high-fidelity power flow simulations and low-fidelity power flow simulations. Our multi-fidelity GNN model not only reduces the cost of generating training data, but also outperforms both single-fidelity GNN models trained solely on high-fidelity data and conventional neural network models. Additionally, the proposed model exhibits robustness to minor topology changes and achieves reasonable performance with unseen topologies. The model’s effectiveness is validated through simulations on standard IEEE systems.

Climbing robot-assisted quantitative identification of sheath surface defects of stay cables

Sheath surface defect identification of stay cables using climbing robots or drones is gaining popularity. However, effective quantitative identification approaches remain challenging. This study proposes a cable climbing robot assisted quantitative identification method to address sheath surface defects combining the deep learning method and image processing techniques. First, standard and high-resolution cable sheath images, collected by an improved cable climbing robot with exclusive scales indicating the defect size, were categorised into three datasets according to defect types. Subsequently, a mask region-based conventional neural network (Mask R-CNN) model was developed for defect capture, with a connected domain algorithm quantifying defect areas. Perspective projection was applied to the defect masks to reduce the image distortion effect. The test results show that the model can achieve over 89% precision, recall and F 1-score for three defect types, with masks presenting overlapping rates reach up to 87% compared to the ground-truth regions. The quantitative identification results are promising to facilitate stay cable assessment and maintenance. Future study will be focused on extending the volume and categories of the dataset and improving the performance of the model.

The technological assessment of green buildings using artificial neural networks

This study aims to construct a comprehensive evaluation model for efficiently assessing appropriate technologies within green buildings. Initially, an Internet of Things (IoT)-based environmental monitoring system is devised and implemented to collect real-time environmental parameters both inside and outside the building. To evaluate the technical suitability of green buildings, this study employs a multifaceted approach encompassing various criteria, including energy efficiency, environmental impact, economic benefits, user comfort, and sustainability. Specifically, it involves real-time monitoring of environmental parameters, analysis of energy consumption data, and indoor environmental quality indicators derived from user satisfaction surveys. Subsequently, a Multi-Layer Perceptron (MLP) is selected as a conventional artificial neural network (ANN) model, while a Long Short-Term Memory (LSTM) model is chosen as an advanced recurrent neural network model in the realm of deep learning. These models are utilized to process and explore the collected data and assess the technical suitability of green buildings. The dataset comprises physical quantities such as temperature, humidity, and light intensity, as well as economic indicators including energy efficiency and building operating costs. Furthermore, the assessment process considers the building's life cycle assessment and indoor environmental quality factors such as health, comfort, and safety. By incorporating these comprehensive criteria, a holistic evaluation of green building technologies is achieved, ensuring the selected technologies' suitability and effectiveness. The model prediction results demonstrate that the proposed hybrid evaluation model exhibits high accuracy and robust stability in predicting building environmental parameters. For instance, the Root Mean Square Error (RMSE) for temperature prediction is 1.2 °C, the Mean Absolute Error (MAE) is 0.9 °C, and the determination coefficient (R2) reaches 0.95. Similarly, for humidity prediction, the RMSE, MAE, and R2 are 3.5 %, 2.8 %, and 0.88. Compared to the traditional MLP and LSTM models alone, the proposed hybrid model shows significant improvements in predicting building energy consumption, with approximately 15 % and 12 % reductions in RMSE and MAE, respectively, and an increase in R2 values of approximately 7 percentage points. These findings indicate that by amalgamation of the IoT and ANNs, this study successfully establishes a comprehensive model for accurately assessing technologies suitable for green buildings. This approach offers a novel perspective and methodology for the design and evaluation of green buildings.

Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability.

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash-Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation-based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

Spiking neural networks for physiological and speech signals: a review.

The integration of Spiking Neural Networks (SNNs) into the analysis and interpretation of physiological and speech signals has emerged as a groundbreaking approach, offering enhanced performance and deeper insights into the underlying biological processes. This review aims to summarize key advances, methodologies, and applications of SNNs within these domains, highlighting their unique ability to mimic the temporal dynamics and efficiency of the human brain. We dive into the core principles of SNNs, their neurobiological underpinnings, and the computational advantages they bring to signal processing, particularly in handling the temporal and spatial complexities inherent in physiological and speech data. Comparative analyses with conventional neural network models are presented to underscore the superior efficiency, lower power consumption, and higher temporal resolution of SNNs. The review further explores challenges and future prospects, highlighting the potential of SNNs to revolutionize wearable healthcare monitoring systems, neuroprosthetic devices, and natural language processing technologies. By providing a comprehensive overview of current strategies, this review aims to inspire innovative approaches in the field, fostering advances in real-time and energy-efficient processing of complex biological signals.

End-to-end heterogeneous graph neural networks for traffic assignment

The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment, specifically user equilibrium traffic assignment problems. Our model integrates an adaptive graph attention mechanism with auxiliary “virtual” links connecting origin–destination node pairs, This integration enables the model to capture spatial traffic patterns across different links, By incorporating the node-based flow conservation law into the overall loss function, the model ensures the prediction results in compliance with flow conservation principles, resulting in highly accurate predictions for both link flow and flow-capacity ratios. We present numerical experiments on urban transportation networks and show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy. Notably, by introducing two different training strategies, the proposed heterogeneous graph neural network model can also be generalized to different network topologies. This approach offers a promising solution for complex traffic flow analysis and prediction, enhancing our understanding and management of a wide range of transportation systems.

Higher-Order Convolutional Neural Networks for Essential Climate Variables Forecasting

Earth observation imaging technologies, particularly multispectral sensors, produce extensive high-dimensional data over time, thus offering a wealth of information on global dynamics. These data encapsulate crucial information in essential climate variables, such as varying levels of soil moisture and temperature. However, current cutting-edge machine learning models, including deep learning ones, often overlook the treasure trove of multidimensional data, thus analyzing each variable in isolation and losing critical interconnected information. In our study, we enhance conventional convolutional neural network models, specifically those based on the embedded temporal convolutional network framework, thus transforming them into models that inherently understand and interpret multidimensional correlations and dependencies. This transformation involves recasting the existing problem as a generalized case of N-dimensional observation analysis, which is followed by deriving essential forward and backward pass equations through tensor decompositions and compounded convolutions. Consequently, we adapt integral components of established embedded temporal convolutional network models, like encoder and decoder networks, thus enabling them to process 4D spatial time series data that encompass all essential climate variables concurrently. Through the rigorous exploration of diverse model architectures and an extensive evaluation of their forecasting prowess against top-tier methods, we utilize two new, long-term essential climate variables datasets with monthly intervals extending over four decades. Our empirical scrutiny, particularly focusing on soil temperature data, unveils that the innovative high-dimensional embedded temporal convolutional network model-centric approaches markedly excel in forecasting, thus surpassing their low-dimensional counterparts, even under the most challenging conditions characterized by a notable paucity of training data.

LEVERAGING DEEP LEARNING FOR ACCURATE RICE LEAF DISEASE RECOGNITION

: Imagine a Rice Field, seemingly healthy, yet harboring invisible foes. This paper delves into the world of crop disease detection for the Tan spot, Leaf blight, Sheath decay, Bacterial leaf rot, and False smut Dataset consists of 3546 images. The image segmentation is done by BIRCH clustering followed by GMM clustering; various texture features have been extracted through this. The classification is done using existing models such as VGG16, VGG19, RESNET50, and INCEPTION V3. After comparing various models and validation, the observation was made that VGG19 performs well compared to other models. The overall accuracy obtained for the VGG19 is 95.88.%. The achieved accuracy surpasses that of conventional backpropagation neural network models, indicating significant advancements in crop disease diagnosis. This study introduces a novel approach that opens avenues for future research in the field of deep learning for crop disease diagnosis.

A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

Deep learning is a highly useful technique for the early identification of diabetes mellitus, according to the study done by numerous authors over the past few decades. By using pre-processing techniques on the dataset to get rid of various anomalies like over-fitting, under-fitting, redundancy, missing values, and non-significant features to make it more efficient for analysis, it is possible to increase the effectiveness of deep learning algorithms for diagnosing the disease. This work addresses the global problem of diabetes by exploring a revolutionary deep-learning method for early identification. Conventional convolutional neural network (CNN) models have drawbacks when used with numerical medical datasets, like this study's PIMA Indians Diabetes Database. The article suggests a technique for transforming numerical data into visual representations depending on feature relevance to get over this obstacle. This conversion makes it possible to use strong CNN models for diabetes early diagnosis. Classifying the created diabetic images after feeding them into CNN architectures that have already been trained on VGG16 and ResNet50. The promising outcomes with an accuracy of 97.19% demonstrate the possibility of the suggested strategy for improving diabetes detection and validating the effectiveness of diabetes imaging in obtaining an early diagnosis.

Multi-perspective interpretation for one-dimensional conventional neural network model to identify iron-bearing waste material

Identification of iron-bearing waste material (IWM) is challenging, as the physical and chemical properties of IWMs vary greatly with sources and lacking of common markers. Here, we proposed an interpretable method based on a one-dimensional convolutional neural network (1D CNN) and laser-induced breakdown spectroscopy (LIBS) that can recognize IWM. The proposed 1D CNN model obtained a test accuracy of 99.08%±1.37% with five-time repetition and showed sensitivities and specificities per class within the range of 98%-99%. To shed light into the black-box nature of neural network, a multi-perspective interpretation of CNN algorithm towards the analysis of spectroscopy was performed to provide a more complete view of feature importance, intermediate outputs and decision process. As for identifying IWM, the most critical elements captured by the CNN using a visualization algorithm (class activation map) were Na, Mn, Al, Ca, Fe, Mg, K, Cr, Zn, F and Cl. In the intermediate outputs, it was found that the kernels in the same convolution extract different local informative variables and contribute to learning high-level features. In the final decision process, the CNN was proved to accumulate more valuable information over the increased convolutions using another visualization algorithm (t-SNE) and the output was calculated through mathematical inference. This is the first study in which CNN-assisted LIBS spectroscopy was used to identify IWM while providing a multi-perspective interpretable method for CNN algorithm, which can lay a foundation for widening the application of CNN-based spectroscopic techniques in waste identification.

DeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma.

Lung cancer is the first leading cause of cancer-related death in the United States, with lung adenocarcinoma as the major subtype accounting for 40% of all cases. To improve patient survival, image-based prognostic models were developed due to the ready availability of pathological images at diagnosis. However, the application of these models is hampered by two main challenges: the lack of publicly available image datasets with high-quality survival information and the poor interpretability of conventional convolutional neural network models. Here, we integrated matched transcriptomic and H&E staining data from TCGA (The Cancer Genome Atlas) to develop an image-based prognostic model, termed Deep-learning based Cell Graph (DeepCG) model. Instead of survival data, we used a gene signature to predict patient prognostic risks, which was then used as labels for training DeepCG. Importantly, by employing graph structures to capture cell patterns, DeepCG can provide cell-level interpretation, which was more biologically relevant than previous region-level insights. We validated the prognostic values of DeepCG in independent datasets and demonstrated its ability to identify prognostically informative cells in images.

Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions

This study presents a novel event-triggered relearning framework for neural network modeling, designed to improve prediction precision in dynamic stochastic complex industrial systems under non-stationary and variable conditions. Firstly, a sliding window algorithm combined with entropy is applied to divide the input and output datasets across different operational conditions, establishing clear data boundaries. Following this, the prediction errors derived from the neural network under different operational states are harnessed to define a set of event-triggered relearning criteria. Once these conditions are triggered, the relevant dataset is used to recalibrate the model to the specific operational condition and predict the data under this operating condition. When the predicted data fall within the training input range of a pre-trained model, we switch to that model for immediate prediction. Compared with the conventional BP neural network model and random vector functional-link network, the proposed model can produce a better estimation accuracy and reduce computation costs. Finally, the effectiveness of our proposed method is validated through numerical simulation tests using nonlinear Hammerstein models with Gaussian noise, reflecting complex stochastic industrial processes.

Fault prediction of unmanned aerial vehicles based on entropy weight fusion and temporal graph convolutional network with non-Euclidian structure

Predicting weak and hidden faults in unmanned aerial vehicles (UAVs) is challenging due to their variable operation conditions and complex mechanisms. Conventional neural network models process the multisensory data in the form of Euclidean structure, the intrinsic connections among the individual data points are easy to be disregarded. Additionally, multisensory data are always directly fed into the model without adequately considering the importance or contribution of each sensor. Hence, an UAV fault prediction method is proposed by combining entropy weight fusion with a temporal graph convolutional network (GCN) to address the above problems. Firstly, the importance of multisensory data of UAVs are evaluated by each entropy value, and the multisensory data fusion is further realized by multiplying corresponding signal and entropy weight. Secondly, the multisensory data combined with fusion data are transferred together into graph-structure by adjacent matrix based on the node connection between different sensor data. Finally, the graph-structure data with non-Euclidian distance properties are input into temporal GCN to both capture the spatial and temporal relationship of the data, achieving better fault prediction results of UAVs. It is demonstrated that the proposed method is both applicable and superior in characterizing and predicting fault time series information of UAVs through parameter analysis and comparison studies with various existing algorithms.

A quality control system for logistic ports goods movable harbor cranes based on internet of things and deep learning

<span>The growth of commercial activity and the transportation of goods around the world has increased the challenges of stevedoring within ports. In case of loading and unloading ships safely, quickly, and efficiently, goods movable harbor cranes play an important role. This work aims to propose an industrial internet of things (IIoT)-based quality control system for logistic services ports goods movable harbor crane (QC-GMHC). The GMHC system based on using programmable logic controller (PLC), along with a multi-sensor data collecting system. Several operations have been done to establish the QC-GMHC system as: GMHC sensors real-time data storage, and data sharing; monitoring the GMHC status (remote-local); and the efficiency reporting. In order to validate the proposed system’s hardware, it was used in an already operational GMHC for six months, during which data were collected and analyzed. The results revealed that the proposed hardware system worked efficiently for 24 hours. To forecast the efficiency of the GMHC, a deep learning (DL) conventional long short-term memory (LSTM) and neural network model was trained and validated using synthetic data generated from acquired real data. The results showed that QC-GMHC can calculate efficiency with an accuracy of 80%, which is sufficient for our application.</span>

An Intrusion Detection Model Based on Feature Selection and Improved One-Dimensional Convolutional Neural Network

The problem of intrusion detection has new solutions, thanks to the widespread use of machine learning in the field of network security, but it still has a few issues at this time. Traditional machine learning techniques to intrusion detection rely on expert experience to choose features, and deep learning approaches have a low detection efficiency. In this paper, an intrusion detection model based on feature selection and improved one-dimensional convolutional neural network was proposed. This model first used the extreme gradient boosting decision tree (XGboost) algorithm to sort the preprocessed data, and then it used comparison to weed out 55 features with a higher contribution. Then, the extracted features were fed into the improved one-dimensional convolutional neural network (I1DCNN), and this network training was used to complete the final classification task. The feature selection and improved one-dimensional convolutional neural network (FS-I1DCNN) intrusion detection model not only solved the traditional machine learning method of relying on expert experience to extract features but also improved the detection efficiency of the model, reduced the training time while reducing the dimension, and increased the overall accuracy. In comparison to the I1DCNN model without feature extraction and the conventional one-dimensional convolutional neural network (1DCNN) model, the experimental results demonstrate that the FS-I1DCNN model’s overall accuracy increases by 0.67% and 2.94%, respectively. Its accuracy, precision, recall, and F1-score were significantly better than those of the other intrusion detection models, including SVM and DBN.

Hybrid model-based prediction of biomass density in case studies in Turkiye

Growing global concern over natural resource degradation due to urbanisation and population growth emphasizes the critical need for innovative solutions. Addressing this imperative, our study pioneers the integration of cutting-edge artificial intelligence (AI) methods to investigate crucial changes in vegetation density. In this context, a hybrid model, which harmoniously integrates conventional artificial neural network (ANN) models with the innovative Wavelet-ANN (W-ANN) approach, was employed in two case pilot areas, namely on Alanya in Antalya and Iznik in Bursa, Turkiye, renowned for their distinct ecosystems and land cover patterns. By employing diverse data sources, encompassing satellite-derived metrics such as the Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) from the MODIS/Terra satellite, alongside atmospheric data, our investigation intricately models temporal vegetation dynamics extending to the year 2030. Remarkably, the W-ANN model demonstrates better predictive performance compared to conventional methodologies. It anticipates a substantial 21.4% reduction in vegetation biomass density for Iznik, achieving a minimal 5.4% error probability. Similarly, for Alanya, the model forecasts a notable 6.6% decrease with a remarkably low 2% error probability, both projections extending to the year 2030. Our study reveals a significant reduction in vegetation biomass density by comparing the projected values of the W-ANN model for 2030 with the observed data from 2018. These findings gain further support from an analysis of the Normalised Difference Built-up Index (NDBI) derived from Landsat satellites, affirming the exceptional efficacy of our innovative AI-driven approach in advancing the understanding of urbanisation's impact on ecosystems.

CAPTIVE: Constrained Adversarial Perturbations to Thwart IC Reverse Engineering

Reverse engineering (RE) in Integrated Circuits (IC) is a process in which one will attempt to extract the internals of an IC, extract the circuit structure, and determine the gate-level information of an IC. In general, the RE process can be done for validation as well as Intellectual Property (IP) stealing intentions. In addition, RE also facilitates different illicit activities such as the insertion of hardware Trojan, pirating, or counterfeiting a design, or developing an attack. In this work, we propose an approach to introduce cognitive perturbations, with the aid of adversarial machine learning, to the IC layout that could prevent the RE process from succeeding. We first construct a layer-by-layer image dataset of 45 nm predictive technology. With this dataset, we propose a conventional neural network model called RecoG-Net to recognize the logic gates, which is the first step in RE. RecoG-Net is successful in recognizing the gates with more than 99.7% accuracy. Our thwarting approach utilizes the concept of adversarial attack generation algorithms to generate perturbation. Unlike traditional adversarial attacks in machine learning, the perturbation generation needs to be highly constrained to meet the fab rules such as Design Rule Checking (DRC) Layout vs. Schematic (LVS) checks. Hence, we propose CAPTIVE as a constrained perturbation generation satisfying the DRC. The experiments show that the accuracy of reverse engineering using machine learning techniques can decrease from 100% to approximately 30% based on the adversary generator.

FA -WSI -CNN Model for Predicting Breast Cancer using Deep Learning

Deep Learning is used for predicting a large volume of data sets in the medical field particularly for breast cancer prediction and diagnosis. The most effective and broadly applied model for detecting breast cancer is the Conventional Neural Network (CNN) among the various deep learning algorithms available. The existing CNN models are lacking in the analysis of a fully labeled Whole Set Image (WSI) data set. The proposed Fully Automate WSI with the CNN model will analyze the whole slide images and patch the input image for improving the accuracy. Then CNN model will get input from patched images and creates classified data for predicting breast cancer. The scikit-learn deep learning framework with Python is used to analyze the result and build a generalized tissue classifier, the WSI data set should include tissues generated under numerous different preparation circumstances. The proposed model experimental results shows promising WSI patch values, accuracy, precision, re-call, and F1 score of the breast cancer tissues which are used for diagnosis purposes. The FA -WSI -CNN model can reduce the training time by evaluating the inference time

Predicting the spatiotemporal characteristics of atmospheric rivers: A novel data-driven approach

Atmospheric Rivers (ARs) are narrow bands of high-water vapor content in the low troposphere of mid-latitude regions through which most of the poleward moisture is being transported. ARs have been represented statistically as the regions of intense vertically integrated horizontal water vapor transport (IVT) in the atmosphere. These ARs have been found positively correlated with extreme precipitation and flood events at some coastal mid-latitude regions and thus have been linked to several socioeconomic implications. The robust and accurate forecasts of AR availability at a significant lead time can be a useful tool for managing AR-associated floods and water resources. To enhance the knowledge of data-driven methods for modelling nonlinear atmospheric dynamics associated with ARs, we have explored some popular deep-learning architectures for predicting AR availability. AR availability maps derived from the statistical characterization of IVT using ERA5 reanalyses data of ECMWF from the testing dataset are taken as ground truth for the prediction. The predictions of the models have been analyzed based on popularly adopted performance evaluation metrics structural similarity index measure (SSIM), mean square error (MSE), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR). Our proposed autoencoder model outperforms the conventional convolutional neural network (CNN) and Conv-LSTM model. We have got comparatively higher scores (average) of SSIM (0.739) and PSNR (64.424) as well as lower scores (average) of RMSE (0.155) and MSE (0.025) for the predictions which signify the ability of our model to learn spatiotemporal features linked with AR-dynamics.