Augmented Model Research Articles

Does information and communication technology improve labor productivity? Recent evidence from the Southeast Asian emerging economies

AbstractThe comprehension of the textbook and augmented Solow growth model has been studied to picture the consequences of workforce growth and capital. In the age of digitalization, Information and Communications Technology (ICT) is also the first necessity identified factors affecting labor productivity. This paper revisits the augmented Solow growth equation by integrating ICT as an explanator that improves labor productivity in Southeast Asian emerging economies. We used a Two‐Way Random‐Effects Model to regress the impact of workforce growth rate, physical capital, and human capital on labor productivity. The results show that using human capital is essential to improve output in emerging market countries rather than only using physical capital. According to our adjusted ICT factors, our analysis reveals that using the Internet and mobile cellular significantly boosts labor productivity. Moreover, the analysis of total factor productivity shows that most Southeast Asian economies highly depend on implementing ICT, especially the contribution of Internet usage and mobile cellular. Therefore, this study contributes to further studies and policy recommendations in improving the introduction of Internet usage and installation, especially for the emerging Southeast Asian.

Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks.

Human opisthorchiasis is a dangerous infectious chronic disease distributed in many Asian areas in the water-basins of large rivers, Siberia, and Europe. The gold standard for human opisthorchiasis laboratory diagnosis is the routine examination of Opisthorchis spp. eggs under a microscope. Manual detection is laborious, time-consuming, and dependent on the microscopist's abilities and expertise. Automatic screening of Opisthorchis spp. eggs with deep learning techniques is a useful diagnostic aid. Herein, we propose a convolutional neural network (CNN) for classifying and automatically detecting O. viverrini eggs from digitized images. The image data acquisition was acquired from infected human feces and was processed using the gold standard formalin ethyl acetate concentration technique, and then captured under the microscope digital camera at 400x. Microscopic images containing artifacts and O.viverrini egg were augmented using image rotation, filtering, noising, and sharpening techniques. This augmentation increased the image dataset from 1 time to 36 times in preparation for the training and validation step. Furthermore, the overall dataset was subdivided into a training-validation and test set at an 80:20 ratio, trained with a five-fold cross-validation to test model stability. For model training, we customized a CNN for image classification. An object detection method was proposed using a patch search algorithm to detect eggs and their locations. A performance matrix was used to evaluate model efficiency after training and IoU analysis for object detection. The proposed model, initially trained on non-augmented data of artifacts (class 0) and O. viverrini eggs (class 1), showed limited performance with 50.0% accuracy, 25.0% precision, 50.0% recall, and a 33.0% F1-score. After implementing data augmentation, the model significantly improved, reaching 100% accuracy, precision, recall, and F1-score. Stability assessments using 5-fold cross-validation indicated better stability with augmented data, evidenced by an ROC-AUC metric improvement from 0.5 to 1.00. Compared to other models such as ResNet50, InceptionV3, VGG16, DenseNet121, and Xception, the proposed model, with a smaller file size of 2.7 MB, showed comparable perfect performance. In object detection, the augmented data-trained model achieved an IoU score over 0.5 in 139 out of 148 images, with an average IoU of 0.6947. This study demonstrated the successful application of CNN in classifying and automating the detection of O. viverrini eggs in human stool samples. Our CNN model's performance metrics and true positive detection rates were outstanding. This innovative application of deep learning can automate and improve diagnostic precision, speed, and efficiency, particularly in regions where O. viverrini infections are prevalent, thereby possibly improving infection sustainable control and treatment program.

Parametrized guaranteed cost method with prescribed performance via critic‐only ADP for uncertain nonlinear systems and disturbances

AbstractIn this paper, a novel parametrized guaranteed cost control (PGCC) method with prescribed performance control (PPC) is proposed for nonlinear systems with unknown bounded uncertainties and external disturbances described by Euler‐Lagrange (EL) equations. The proposed method is capable of ensuring a robust optimal performance in the presence of dynamics constraints without the specific information of nonlinear perturbation. As a consequence, the conditions in the form of disturbances and uncertainties are also relaxed. The augmented model is converted by transforming the uncertainties and disturbances in the original system to a mismatched term using asymmetric PPC. By designing and modifying the novel guaranteed cost function to account for the maximum value of the nonlinear perturbation, an infinite‐horizon PGCC problem is proposed by the unconstrained stationary optimal control problem. The relevant proposed linear parametrized Hamilton‐Jacobian‐Bellman (PHJB) equation is approximated to solve by a critic‐only neural network (NN) with efficient computationally. Uniformly ultimately bounded stability is guaranteed via a Lyapunov‐based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme.

The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2

Abstract. Sensitivity analysis in chemical transport models quantifies the response of output variables to changes in input parameters. This information is valuable for researchers engaged in data assimilation and model development. Additionally, environmental decision-makers depend upon these expected responses of concentrations to emissions when designing and justifying air pollution control strategies. Existing sensitivity analysis methods include the finite-difference method, the direct decoupled method (DDM), the complex variable method, and the adjoint method. These methods are either prone to significant numerical errors when applied to nonlinear models with complex components (e.g. finite difference and complex step methods) or difficult to maintain when the original model is updated (e.g. direct decoupled and adjoint methods). Here, we present the implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2 as CMAQ-hyd. CMAQ-hyd can be applied to compute numerically exact first- and second-order sensitivities of species concentrations with respect to emissions or concentrations. Compared to CMAQ-DDM and CMAQ-adjoint, CMAQ-hyd is more straightforward to update and maintain, while it remains free of subtractive cancellation and truncation errors, just as those augmented models do. To evaluate the accuracy of the implementation, the sensitivities computed by CMAQ-hyd are compared with those calculated with other traditional methods or a hybrid of the traditional and advanced methods. We demonstrate the capability of CMAQ-hyd with the newly implemented gas-phase chemistry and biogenic aerosol formation mechanism in CMAQ. We also explore the cross-sensitivity of monoterpene nitrate aerosol formation to its anthropogenic and biogenic precursors to show the additional sensitivity information computed by CMAQ-hyd. Compared with the traditional finite difference method, CMAQ-hyd consumes fewer computational resources when the same sensitivity coefficients are calculated. This novel method implemented in CMAQ is also computationally competitive with other existing methods and could be further optimized to reduce memory and computational time overheads.

Hybrid carbon price forecasting using a deep augmented FEDformer model and multimodel optimization piecewise error correction

Accurate and robust carbon price forecasting is crucial for stabilizing the carbon financial market and reducing reliance on fossil resources. This study introduces a novel hybrid model, combining a deep augmented frequency enhanced decomposed transformer (DA-FEDformer), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and a multimodel optimization piecewise error correction, for accurate carbon price prediction. To leverage frequency domain information, the proposed DA-FEDformer model enhances the decoder and encoder layers by incorporating a multilayer perceptron layer. A novel improved kernel mean square error loss function is devised for the model, and the evolved sign momentum optimizer is employed for further optimization. To further improve the prediction accuracy, a data postprocessing method is integrated into the model. The DA-FEDformer predicts the carbon price, and the ICEEMDAN method decomposes the error sequence into subsequences. These subsequences are forecasted using five different models. Finally, the DA-FEDformer prediction results and the error correction results are combined to obtain the final carbon price sequence. This hybrid model is systematically evaluated using carbon price data from Hubei, Guangzhou, and Beijing in China. The results demonstrate that the hybrid model achieves favorable performance on three datasets. This innovative approach provides a promising solution for carbon price forecasting in China.

Improving Recognition of Defective Epoxy Images in Integrated Circuit Manufacturing by Data Augmentation

This paper discusses the problem of recognizing defective epoxy drop images for the purpose of performing vision-based die attachment inspection in integrated circuit (IC) manufacturing based on deep neural networks. Two supervised and two unsupervised recognition models are considered. The supervised models examined are an autoencoder (AE) network together with a multi-layer perceptron network (MLP) and a VGG16 network, while the unsupervised models examined are an autoencoder (AE) network together with k-means clustering and a VGG16 network together with k-means clustering. Since in practice very few defective epoxy drop images are available on an actual IC production line, the emphasis in this paper is placed on the impact of data augmentation on the recognition outcome. The data augmentation is achieved by generating synthesized defective epoxy drop images via our previously developed enhanced loss function CycleGAN generative network. The experimental results indicate that when using data augmentation, the supervised and unsupervised models of VGG16 generate perfect or near perfect accuracies for recognition of defective epoxy drop images for the dataset examined. More specifically, for the supervised models of AE+MLP and VGG16, the recognition accuracy is improved by 47% and 1%, respectively, and for the unsupervised models of AE+Kmeans and VGG+Kmeans, the recognition accuracy is improved by 37% and 15%, respectively, due to the data augmentation.

Eurozone inflation: fresh projections from global factors

This study identifies the contributions of global factors in determining eurozone inflation. The analysis makes use of an augmented Phillips curve model of inflation, as well as panel data estimates. The findings document the importance of such factors in forecasting inflation, thus, confirming the substantial role of external factors in eurozone inflation. The findings survived certain model specifications. Therefore, monetary, and fiscal policymakers should explicitly consider both domestic and global conditions to anchor inflation expectations in forming specific strategies for taming inflation expectations.

Generative Adversarial Network-Based Data Augmentation for Enhancing Wireless Physical Layer Authentication.

Wireless physical layer authentication has emerged as a promising approach to wireless security. The topic of wireless node classification and recognition has experienced significant advancements due to the rapid development of deep learning techniques. The potential of using deep learning to address wireless security issues should not be overlooked due to its considerable capabilities. Nevertheless, the utilization of this approach in the classification of wireless nodes is impeded by the lack of available datasets. In this study, we provide two models based on a data-driven approach. First, we used generative adversarial networks to design an automated model for data augmentation. Second, we applied a convolutional neural network to classify wireless nodes for a wireless physical layer authentication model. To verify the effectiveness of the proposed model, we assessed our results using an original dataset as a baseline and a generated synthetic dataset. The findings indicate an improvement of approximately 19% in classification accuracy rate.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Many societal entities now have more excellent standards for the efficacy and dependability of identification systems due to the ongoing advancement of computer technology. Traditional identification methods, such as keys and smart cards, have been supplanted by biometric systems in highly secure environments. This research presents a smart computational method for automatically authenticating fingerprints for identity (ID) verification and personal identification. Compared to more traditional machine learning algorithms, the results from applying Deep learning (DL) in areas like computer vision, image identification, robotics, and voice processing have generally been positive. Due to their capacity to analyse big data size and deal with fluctuations in biometric data (such as ageing or expression problems), DL has been heavily used by the artificial intelligence research community. Several biometric systems have succeeded with automatic feature extraction employing deep learning approaches like Convolutional Neural Networks (CNNs). In this research, we provide a biometric process that uses convolutional neural networks. This work introduces a deep learning-based biometric identification system that uses Monte Carlo Dropout (MC Dropout). Combining these two systems makes the authentication process more secure and dependable. Fingerprint image enhancement techniques involve the application of Gabor filters and structure-adaptive anisotropic filters, which have proven to be effective in enhancing the clarity and distinctiveness of fingerprint patterns. To improve the efficiency of deep learning models, this work proposes the Inception-Augmentation GAN (IAGAN) model for data augmentation. The study adds to security development by integrating novel biometric identification and authentication approaches with cutting-edge neural network technology. In this research, we provide a new activation function to speed up the convergence of deep neural networks. The results of 99.6% on Gabor filters and 99.8% on the structure-adaptive anisotropic filter with GACNN with MCD show that deep neural networks can excel over competing approaches with enough training data.

Application of augmented reality models of canine skull in veterinary anatomical education.

Veterinary anatomy plays a crucial role in the curriculum for veterinary medicine and surgery. The integration of modern information technology in veterinary education can greatly benefit from innovative tools such as augmented reality (AR) applications. The aim of this study was to develop an accurate and interactive three-dimensional (3D) digital model of an animal skull using AR technology, aiming to enhance the learning of skull anatomy in veterinary anatomy education. In this study, a canine skull specimen was isolated, and the skull bones were scanned using a structured light scanner to create a 3D digital model of the canine skull, which was found to be indistinguishable from the original specimen by measurement of skull proportions. Furthermore, the interactive AR model of the canine skull, displayed using Unity3D, was subjected to testing and evaluation by 60 first-year veterinary medical students attending the gross anatomy of the animal. The students were divided into two groups: the traditional group and AR group. Both groups completed an objective test and a questionnaire. The evaluation of learning effectiveness in the test revealed no significant difference between the traditional group (which learned using textbooks and a canine skull specimen) and AR group (which learned using AR tools). However, in the questionnaire, students displayed high enthusiasm and interest in using the AR tool. Therefore, the application of AR tools can improve students' motivation for learning and enhance the comprehension of anatomical structures in three dimensions. Furthermore, this study exemplifies the use of AR as an auxiliary tool for teaching and learning in veterinary anatomy education.

Global Polarimetric Synthetic Aperture Radar Image Segmentation with Data Augmentation and Hybrid Architecture Model

Machine learning and deep neural networks have shown satisfactory performance in the supervised classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, the PolSAR image classification task still faces some challenges. First, the current form of model input used for this task inevitably involves tedious preprocessing. In addition, issues such as insufficient labels and the design of the model also affect classification performance. To address these issues, this study proposes an augmentation method to better utilize the labeled data and improve the input format of the model, and an end-to-end PolSAR image global classification is implemented on our proposed hybrid network, PolSARMixer. Experimental results demonstrate that, compared to existing methods, our proposed method reduces the steps for the classification of PolSAR images, thus eliminating repetitive data preprocessing procedures and significantly improving classification performance.

Proposed design of an augmented deep learning model for estimation of sustainable development goals via satellite image analysis

Proposed design of an augmented deep learning model for estimation of sustainable development goals via satellite image analysis

Investigating the agriculture-induced environmental Kuznets curve hypothesis in South Asian economies

PurposeThe main purpose of this study is to investigate the agriculture-induced environmental Kuznets curve (EKC) hypothesis in South Asian economies (SAE).Design/methodology/approachThis study employs econometric techniques, including Westerlund cointegration tests, cross-sectional augmented distributive lag model (CS-ARDL) and Dumitrescu and Hurlin (DH) causality tests to investigate the relationship between renewable and non-renewable energy consumption, agriculture, economic growth, financial development and carbon emissions in SAE from 1990 to 2019.FindingsThe CS-ARDL test outcome supports the presence of the agriculture-induced EKC hypothesis in SAE. Additionally, through the application of the DH causality test, the study confirms a unidirectional causality running from renewable energy consumption (REC), fossil fuel consumption (FFC), economic growth (GDP) and squared economic growth (GDP2) to carbon dioxide (CO2) emissions.Research limitations/implicationsThis study proposes that future research should extend comparisons to worldwide intergovernmental bodies, use advanced econometric methodologies for accurate estimates, and investigate incorporating the service or primary sector into the EKC. Such multidimensional studies can inform various methods for mitigating global climate change and ensuring ecological sustainability.Originality/valueEnvironmental degradation has been extensively studied in different regions and countries, but SAE face significant constraints in addressing this issue, and comprehensive studies in this area are scarce. This research is pioneering as it is the first study to investigate the applicability of the agriculture-induced EKC in the South Asian region. By filling this gap in the current literature, the study provides valuable insights into major SAE and their environmental challenges.

Optimal control strategy for traffic platoon longitudinal coordination around equilibrium state enabled by partially automated vehicles

This paper presents an internal model-Kalman filtering-based optimal hybrid feedforward/feedback control strategy for traffic platoon control coordination enabled by SAE Automation Level 2 or Level 3 vehicles, i.e., partially automated vehicles (PAVs). Based on the Helly linear car-following model, a PAV platoon is established. Taking each vehicle’s characteristic polynomial as the dominant internal model polynomial, an augmented system state model with filtered inputs is formed, and then both the system states and the external disturbance/internal perturbation from previous vehicle can be estimated by a delicately designed Kalman filter. By using a linear quadratic regulation approach, a distributed hybrid optimal feedforward/feedback controller utilizing the local estimated states is constructed to achieve a practically decoupled platoon regulation around an equilibrium state. String stability of the control system thus obtained is further analyzed. Finally, extensive numerical simulations verified the theoretical analysis in decoupling between successive vehicles, in suppressing the influence of various external disturbances, and in maintaining string stability.

A model predictive solution to cooperative guidance of hypersonic reentry vehicle with impact angle and distance coordination

This paper studied the cooperative guidance problem for a group of hypersonic reentry vehicles with the coordination of impact angle and relative distance. As is well established, the most salient challenge of the underlying problem is how to perform, with minimal effort, the bank reversal as well as angle-of-attack profiles to ensure the desired formation. Toward this, we formulate the problem as a clear reflection of the optimized sequences of bank reversals under the guideline of quasi-equilibrium gliding principle. Then, we proceed to introduce an augmented model predictive algorithm to strike a balance between efficiency and precision in the planned objectives. Simulation results demonstrate the effectiveness of the proposed algorithm.

State of charge estimation for lithium-ion batteries based on data augmentation with generative adversarial network

Ever-increasing efforts in state of charge (SOC) of lithium-ion battery estimation techniques have been centered on machine learning-based methods, and a prerequisite guaranteeing the effectiveness of these methods demands an abundance of high-quality datasets. However, performing battery-related experiments is time-consuming and expensive, and manufacturers are reluctant to release their datasets due to confidentiality constraints. This limited data availability has hindered further research in this area. This paper proposes a time series augmentation model (TS-DCGAN) based on a deep convolutional generative adversarial framework. By converting the data generation problem into image generation and utilizing the advancements in GAN-based image generation, the proposed method can produce high-quality battery data. The distributional similarity and diversity of generated datasets are evaluated quantitatively and qualitatively with several metrics. The SOC estimator trained on the synthetic datasets achieved comparable execution to the actual data. Additionally, when integrated with a few real datasets, the performance of baseline SOC estimators improved to varying degrees. Extensive experiments demonstrate that the proposed method can generate synthetic data that accurately reflects the original feature distributions and temporal dynamics. This has the potential to greatly assist researchers in developing reliable battery SOC estimators.

Saturated H∞ control of networked systems under stochastic transmission delays: The random sampling periods case

AbstractIn this paper, the saturated control problems of networked systems with random sampling periods and stochastic transmission delays are investigated. The main issue is to design an saturated controller such that the controlled close‐loop system is exponentially mean‐square stable with a desired performance. A matrix exponential which is different from some existing literature is used to reconstruct the discrete‐time stochastic system to an equivalent yet tractable augmented model without any additional conditional constraint. In virtue of reduced‐order confluent Vandermonde matrix approach and Kronecker product operation, the stability condition in terms of linear matrix inequality is derived and then the desired saturated controller is designed. It should be emphasized that the improved method can also be used for the stability analysis and controller design when the system matrix contains complex roots. Finally, two numerical examples and a three‐axis milling machine system are utilized to manifest the reliability and practicality of the designed strategy.

PhraseAug: An Augmented Medical Report Generation Model with Phrasebook.

Medical report generation is a valuable and challenging task, which automatically generates accurate and fluent diagnostic reports for medical images, reducing workload of radiologists and improving efficiency of disease diagnosis. Fine-grained alignment of medical images and reports facilitates the exploration of close correlations between images and texts, which is crucial for cross-modal generation. However, visual and linguistic biases caused by radiologists' writing styles make cross-modal image-text alignment difficult. To alleviate visual-linguistic bias, this paper discretizes medical reports and introduces an intermediate modality, i.e. phrasebook, consisting of key noun phrases. As discretized representation of medical reports, phrasebook contains both disease-related medical terms, and synonymous phrases representing different writing styles which can identify synonymous sentences, thereby promoting fine-grained alignment between images and reports. In this paper, an augmented two-stage medical report generation model with phrasebook (PhraseAug) is developed, which combines medical images, clinical histories and writing styles to generate diagnostic reports. In the first stage, phrasebook is used to extract semantically relevant important features and predict key phrases contained in the report. In the second stage, medical reports are generated according to the predicted key phrases which contain synonymous phrases, promoting our model to adapt to different writing styles and generating diverse medical reports. Experimental results on two public datasets, IU-Xray and MIMIC-CXR, demonstrate that our proposed PhraseAug outperforms state-of-the-art baselines.

Effects of Economic Uncertainty on the Asset Quality of Banking Sector: Evidence from a Developing Country

ABSTRACT: Extant literature investigates on the determinants of NPL (non-performing loan) many a time, however, little is known on the relationship between economic uncertainty and NPL for any developing country. Historically, Bangladesh is suffering from high level of NPL in the banking sector. In addition to that, Bangladesh observes high level of macroeconomic uncertainty as characterized by high and volatile Gross Domestic Products and inflation, continuous exchange rate devaluations, and shallow financial markets. Motivated by this, we investigate the nexus between economic uncertainty and NPL for Bangladesh. Based on the data availability, we use the annual dataset covering 1990–2018. Furthermore, we use autoregressive distributed lag (ARDL) model considering its benefit accommodating both I(0) and I(1) variables. The empirical results show that there is positive relationship between economic uncertainty and NPL in the long run. The results are robust to the alternative measures of economic uncertainty and specifications. Consistent with the expectation, financial development carries a negative sign on the asset quality albeit the effect of economic uncertainty is actually pronounced (both the size and significance changes) in the augmented models. Key policy implication of this study is that government should formulate well anchored, rule-based policies to reduce inflation and interest related uncertainties. With a rule-based policy (for example, inflation targeting), people believe that central bank can achieve its targets. Government should adopt floating exchange rate regime that will adjust the external shocks well via exchange rates. In addition to this, government can monitor, and regulate stock market so that uncertainty in stock market is reduced. Finally, monetary and fiscal policies should be communicated well to the people. Thus, it is possible to reduce asymmetric information among the people, which may reduce economic uncertainty, and thus reduce NPL.

Artificial neural network-based model for sleep quality prediction for frontline medical staff during major medical assistance.

Background: The sleep quality of medical staff was severely affected during COVID-19, but the factors influencing the sleep quality of frontline staff involved in medical assistance remained unclear, and screening tools for their sleep quality were lacking. Methods: From June 25 to July 14, 2022, we conducted an Internet-based cross-sectional survey. The Pittsburgh Sleep Quality Index (PSQI), a self-designed general information questionnaire, and a questionnaire regarding the factors influencing sleep quality were combined to understand the sleep quality of frontline medical staff in Fujian Province supporting Shanghai in the past month. A chi-square test was used to compare participant characteristics, and multivariate unconditional logistic regression analysis was used to determine the predictors of sleep quality. Stratified sampling was used to divide the data into a training test set (n = 1061, 80%) and an independent validation set (n = 265, 20%). Six models were developed and validated using logistic regression, artificial neural network, gradient augmented tree, random forest, naive Bayes, and model decision tree. Results: A total of 1326 frontline medical staff were included in this survey, with a mean PSQI score of 11.354 ± 4.051. The prevalence of poor sleep quality was 80.8% (n = 1072, PSQI >7). Six variables related to sleep quality were used as parameters in the prediction model, including type of work, professional job title, work shift, weight change, tea consumption during assistance, and basic diseases. The artificial neural network (ANN) model produced the best overall performance with area under the curve, accuracy, sensitivity, specificity, precision, F1 score, and kappa of 71.6%, 68.7%, 66.7%, 69.2%, 34.0%, 45.0%, and 26.2% respectively. Conclusions: In this study, the ANN model, which demonstrated excellent predictive efficiency, showed potential for application in monitoring the sleep quality of medical staff and provide some scientific guidance suggestions for early intervention.