Augmented Model Research Articles

Generalized Predictive Control with Added Zeros and Poles in Its Augmented Model for Power Electronics Applications

Generalized predictive control (GPC) became one of the most popular and useful control strategies for academic and industry applications. An augmented model is applied to predict the future plant responses. This augmented model can be designed to embed the model of the plant reference, allowing its tracking by the controller according to the internal model principle (IMP). On the other hand, the performance of many controllers can be improved by adding zeros and poles in their structures (e.g., lead and lag compensators). However, according to the authors’ research, adding arbitrary poles or zeros to the GPC augmented model has not been explored yet. This paper presents a simple methodology to add arbitrary zeros and poles in the GPC augmented model. A new augmented model state variable is defined. The control law of the proposed approach embeds zeros and poles when zero-pole cancellation is avoided. Simulation results (considering a LCL filter controlled by a single-phase inverter of 500 W and a polynomial reference tracking controller) and experimental tests (using a third-order linear plant controlled by a resonant controller) prove that the proposed approach improves the transient response of different kinds of predictive tracking controllers applied to control different plants (including power electronics applications), without affecting the steady-state tracking capabilities of the control systems.

Attracting tourists to Indonesia: how SEZs and visa-free policies influence international arrivals

ABSTRACT The tourism sector drives international connectivity, prompting countries to adopt policies to attract global visitors. This study examines factors influencing tourist arrivals to Indonesia through an augmented gravity model incorporating economic, geographic, and policy variables. Key determinants include Indonesia's GDP, origin countries’ GDP, geographic distance, visa-free entry, Special Economic Zones (SEZ), and the impact of COVID-19. Data from 37 countries (2006–2023) reveal that Indonesia's and origin countries’ GDP positively impact arrivals, while distance deters tourism. SEZ policies show a modest positive influence, whereas visa-free policies unexpectedly exhibit a negative effect, likely due to challenges in managing visitor quality and maintaining destination appeal. COVID-19 significantly reduced arrivals, underscoring the sector's vulnerability to global disruptions. These insights support refining visa and SEZ policies to foster sustainable tourism aligned with Indonesia's long-term goals.

Assessing Potential and Impact Factors Driving Chadian Gum Arabic International Trade Based on the Augmented Gravity Model

Assessing Potential and Impact Factors Driving Chadian Gum Arabic International Trade Based on the Augmented Gravity Model

Do stock returns respond to physical and transition climate risks? Evidence from emerging BRICS economies

This study examines how stock market returns in emerging BRICS economies respond to growing physical and transition climate risks. To capture the physical climate risk, we use the frequency of natural disasters, the number of people affected by natural disasters, temperature anomaly, and precipitation anomaly. For transition risk, we included two climate-policy uncertainty measures. First, we conduct a panel-level analysis using a cross-sectionally augmented autoregressive distributed lag model. Second, for country-level analysis, we applied the augmented autoregressive distributed lag model to the monthly dataset from January-2000 to March-2023. The empirical results show that an increase in transition climate risk causes a significant and negative shock to stock returns, both in the short- and long-term in the panel and across each BRICS country. Second, we find that physical climate risk indicators have a significant and negative impact on stock returns in China, India, and South Africa, but not in Brazil or Russia. We conclude that the impact of physical climate risk on stock returns is country-specific, and that the impact of transition climate risk is widespread. These findings provide important insights for investors, regulators, hedgers, portfolio managers, and policymakers regarding policy formulation and future investment strategies.

The inoculation dilemma: Partial vs Full immunization during the early rollout in a pandemic

COVID-19 demonstrated the extent to which a pandemic can affect billions of lives worldwide. Vaccinations are an effective intervention that reduces the burden of the disease on the population. However, the low availability of vaccine doses coupled with an emerging infection wave calls for efficient dose allocation. We study the tradeoffs between prioritization of partial or full immunization while allocating limited doses with the help of an augmented SIR model. We define the term allocation ratio as the ratio of doses allocated for partial immunization as a proportion of the total available doses. Optimal control theory is used to derive the path traversed by the allocation ratio throughout the vaccine administration program. Numerical insights are obtained by introducing the case study of the Indian state of Tamil Nadu. Results indicate a preference towards full immunization when the active infections are low, while a switch to exclusive partial immunization is observed as the infection wave grows. Sensitivity analysis shows that factors like reduced vaccine availability, higher transmission rate, and high first-dose efficacy promote a quicker switch. The results also indicate significant potential savings of around ₹710 billion (∼ $8.46 billion) in mortality losses compared to the more widely followed pro-rata allocation policies. Hence, our study contributes to the growing discussion around the optimal strategy for vaccine administration with a focus on dose prioritization. The results of our research can help policymakers determine the allocation of limited available doses when faced with rising infection numbers during future pandemics.

A progressive data-augmented RANS model for enhanced wind-farm simulations

The development of advanced simulation tools is essential, both presently and in the future, for improving wind-energy design strategies, paving the way for a complete transition to sustainable solutions. The Reynolds-averaged Navier–Stokes (RANS) models are pivotal in enhancing our comprehension of the complex flow within and around wind farms; hence, improving their capacity to accurately model turbulence within this context is a vital research goal. The enhancement is essential for a precise prediction of wake recovery and for capturing intricate flow phenomena such as secondary flows of Prandtl’s second kind behind the turbines. To reach these objectives, here, we propose a progressive data-augmentation approach. We first incorporate the turbine-induced forces in the turbulent kinetic energy equation of the widely used k−ωSST model. Afterward, we utilize data from large-eddy simulations to progressively enhance the Reynolds-stress prediction of this baseline model, accurately capturing the evolution of eddy viscosity in the wake, as well as the emergence of secondary flows. We then apply the optimized model to two unseen cases with distinct layouts and conduct a comparative analysis focusing on the obtained quantities such as normalized streamwise velocity deficit, turbulence intensity, and power output. We also evaluate the performance of the augmented model in predicting wake characteristics by comparing it with wind-tunnel measurement data. Our comparisons and validations demonstrate the superior performance of the progressive data-augmented model over the standard version in all cases considered in this study.

Enhancing collaborative advantage through critical systems thinking: An augmented viable system model intervention in a cross‐sector partnership social policy context

AbstractCross‐sector partnerships are essential for addressing wicked problems but are typically ineffective because of messy human and organisational dynamics. Systemic approaches are needed to re‐design collaborations for a collaborative advantage. While the Viable System Model (VSM) is suited to this task, little guidance is provided on managing multiple perspectives or marginalisation in VSM interventions. Hence, we trial and evaluate the utility of a novel multimethodology approach combining the VSM with Team Syntegrity (TS), which is a workshop protocol for supporting fair dialogue and stakeholder pluralism. Our findings show this combination supports joined‐up thinking about improvements for viability and synergies from combining these methodologies. We propose that before a VSM diagnosis, an understanding of ‘what a system does’ and existence of VSM functions is established to ensure more meaningful selection of intervention participants and consideration of the VSM's insights. We also identify four avenues for marginalisation that need careful consideration in critical systems interventions.

Use of Data Augmentation and Fine-tuned Transfer Learning Models for Classification of Cervical Cancer

Innovative strategies for early and accurate diagnosis of cervical cancer continue to be a global health priority. In this research, we present a powerful framework for improving cervical cancer classification by combining data augmentation methods with tailored transfer learning models. The lack of properly labelled medical data is a major roadblock to developing reliable diagnosis models. The geometric transformations, contrast tweaks, and noise addition that we use to increase the dataset artificially help us overcome this difficulty. The model's robustness is improved as a result of the additional exposure to diverse real-world circumstances provided by this supplemented dataset. We use a transfer learning strategy based on pre-trained convolutional neural networks (CNNs) to harness the potential of deep learning. These networks are well-suited for medical image analysis due to their ability to quickly and accurately identify features in a wide variety of images. Our expanded cervical cancer dataset is used to fine-tune these algorithms for their diagnostic purpose. Our experiments show that utilising both data augmentation and fine-tuned transfer learning considerably raises the accuracy of cervical cancer categorization. In terms of sensitivity, specificity, and overall accuracy, the model performs at a state-of-the-art level. This suggests that it may be useful in the diagnosis and early identification of cervical cancer. Additionally, our method demonstrates the significance of efficient data utilisation and model adaptability in the field of medicine. Our method is scalable and affordable, and it can be easily implemented in healthcare settings since we handle the problem of data scarcity and take advantage of the features of pre-trained deep learning models.

Graph-Based Methods for Forecasting Realized Covariances

Abstract We forecast the realized covariance matrix of asset returns in the U.S. equity market by exploiting the predictive information of graphs in volatility and correlation. Specifically, we augment the Heterogeneous Autoregressive model via neighborhood aggregation on these graphs. Our proposed method allows for the modeling of interdependence in volatility (also known as spillover effect) and correlation, while maintaining parsimony and interpretability. We explore various graph construction methods, including sector membership and graphical LASSO (for modeling volatility), and line graph (for modeling correlation). The results generally suggest that the augmented model incorporating graph information yields both statistically and economically significant improvements for out-of-sample performance over the traditional models. Such improvements remain significant over horizons up to 1 month ahead, but decay in time. The robustness tests demonstrate that the forecast improvements are obtained consistently over the different out-of-sample sub-periods and are insensitive to measurement errors of volatilities.

Liquid plugs in narrow tubes: application to airway occlusion

We study liquid plugs in the pulmonary airways based on the two-phase axisymmetric weighted residual integral boundary-layer model of Dietze et al. (J. Fluid Mech., vol. 894, 2020, A17), which was originally developed to study liquid films coating the inner surface of a cylindrical tube in interaction with a core gas flow. The augmented form of this model, which was never applied beyond a proof of concept, allows for the representation of liquid pseudo-plugs. Here, we demonstrate its predictive power vs experiments and direct numerical simulations, in terms of the dynamics of plug formation and the characteristics of developed liquid plugs, such as their shape, flow field, speed and length, as well as the associated wall stresses and their spatial derivatives. In particular, we show that the augmented model allows us to establish a direct continuation path from travelling-wave solutions (TWS) to travelling-plug solutions (TPS). We then apply the model to predict mucus plugs in the conducting zone of the tracheobronchial tree, based on the lung architecture model of Weibel. We proceed by numerical continuation of travelling-state solutions in terms of the airway generation, whereby we impose the wavelength of the linearly most-amplified convective instability (CI) mode or that of the absolute instability (AI) mode. We identify the critical airway generation for liquid-plug formation (TWS/TPS transition), maximum potential for wall-stress-induced epithelial cell damage and CI/AI transition, and investigate how these phenomena are affected by the main control parameters, i.e. airway orientation vs gravity, air flow rate, mucus properties and airway size.

Enhancing IoT cyber attacks intrusion detection through GAN-based data augmentation and hybrid deep learning models for MQTT network protocol cyber attacks

Enhancing IoT cyber attacks intrusion detection through GAN-based data augmentation and hybrid deep learning models for MQTT network protocol cyber attacks

The impact of trade liberalization on China–ASEAN trade relations along the belt and road: An augmented gravity model analysis

While trade liberalization has become increasingly important in global economic integration, little is known about its specific impact on China–ASEAN bilateral trade relations within the Belt and Road Initiative framework, particularly regarding cultural factors. This study addresses this gap by examining how trade liberalization affects bilateral trade dynamics between China and ASEAN nations, with special attention to linguistic and religious proximity as potential moderators. Using an augmented gravity model and panel data from 2012 to 2022, we find that increased trade liberalization significantly enhances bilateral trade volumes, with stronger effects in export trade compared to imports. Moreover, shared linguistic foundations and religious beliefs amplify these positive effects, particularly in ASEAN countries with lower economic development levels. These findings provide crucial insights for policymakers seeking to strengthen regional economic integration and optimize trade cooperation strategies along the Belt and Road.

A Space Mapping Augmented Compact Model for Uncertainty Quantification of GaN HEMT Devices and Circuits in the Presence of Trap Effects

A Space Mapping Augmented Compact Model for Uncertainty Quantification of GaN HEMT Devices and Circuits in the Presence of Trap Effects

Asset pricing in African frontier equity markets

This paper undertakes a horse races style comparison of the efficacy of a range of multifactor asset pricing models in explaining the cross section of stock returns in African securities markets. Valuation factors used include size, book-to-market value, momentum, operating profit, asset growth or investment, liquidity and investor protection. Using monthly returns of 375 blue chip firms from 8 African equity markets over 23 years, we undertake a horse-race style comparison of various classes of augmented CAPM models. We show that both the Fama & French (2015) five factor and Fama & French (2018) six factor framework yield the highest explanatory power. Analysis of costs of equity and optimized portfolio opportunity set simulations reveal substantial differences arising and borne by practitioners from the contrasting application of different asset pricing models underscoring the timely importance of our study.

FloodDamageCast: Building flood damage nowcasting with machine-learning and data augmentation

Near-real-time estimation of damages (a.k.a, damage nowcasting) to building and infrastructure is crucial during response and recovery efforts. Despite advancements in flood risk predictions, the majority of existing methods primarily focus on inundation estimation with limited damage nowcasting capabilities. Flooding damage nowcasting at fine spatial resolutions remains a very challenging problem with currently no existing model to perform the task. This limitation is mainly due to a number of technical challenges such as limited consideration of non-linear interactions between flood hazards and build-environment features, issues with imbalanced datasets, and the absence of reliable ground truth for model performance evaluation. To address this important gap, this study presents FloodDamageCast, a machine learning (ML) framework tailored for property flood damage nowcasting. The framework leverages heterogeneous data related to the built environment, topographic, and hydrological features to predict residential flood damage in a fine resolution of 500 m by 500 m in the context of Harris County, TX, during the 2017 Hurricane Harvey. To deal with data imbalance, FloodDamageCast includes a tabular data augmentation model based on Conditional Tabular Generative Adversarial Networks (CTGAN). The data augmentation model component addresses highly imbalanced class issues, where the majority class constitutes 96.4% of the dataset, potentially impairing model performance, By combining GAN-based data augmentation with an efficient ML model, Light Gradient-Boosting Machine (LightGBM), our results demonstrate the framework’s ability to identify high-damage spatial areas that would be overlooked by baseline models. the satisfactory performance of FloodDamageCast also shows its capability to be used for flood damage nowcasting at a fine spatial resolution to inform response and recovery efforts. The insights from flood damage nowcasting would help emergency management agencies and public officials to more efficiently identify repair needs and allocate resources, and also save time and efforts during on-the-ground inspections.

Contact fatigue life prediction of PEEK gears based on CTAB-GAN data augmentation

The contact fatigue life of PEEK gears is a critical fundamental data for the design of load-carrying capacity, which is significantly influenced by many factors such as material type, operating temperature, and loading level. Temperature effects must be considered during contact fatigue life prediction of PEEK gears. This article presents a predictive model for contact fatigue life of PEEK gears, utilizing the CTAB-GAN data augmentation. The CTAB-GAN data augmentation model expands the experimental dataset of contact fatigue lives of oil-lubricated PEEK gears. Then, a predictive formula was derived to estimate contact fatigue life of oil-lubricated PEEK gears considering the temperature dependent effect. Results revealed that the predicted fatigue lives of PEEK gears under different loading and temperature conditions based on the enhanced dataset are within a two-fold scatter band. The mean square error of predicted lives reduces by 30% compared to the life formula based on the original experimental data set. Furthermore a quantitative analysis was conducted on the influence of operating temperature and stress on the fatigue life of PEEK gears.

Do digitalization and green innovation limit carbon emissions? Evidences from BRICS economies

Rapidly evolving innovation and digitalization have captured the focus of policymakers and scholars regarding their potent role in influencing environmental quality. The present research analyzes the impact of these variables on the carbon emissions of Brazil, Russia, India, China, and South Africa economies from 1990 to 2021. This research also explores the impact of economic growth, quadratic green innovation, and green energy on carbon emissions. Using several panel diagnostic tests, this research validates heterogeneous slopes, the presence of cross-sectional dependence, and significant cointegration. Due to the mixed integration order, this research uses a cross-sectional augmented autoregressive distributed lag model, and the results show that economic expansion and green innovation are significant drivers of emissions in both the short and long run. However, digitalization, quadratic green innovation, environmental policy stringency, and green energy are significant in improving environmental quality and sustainability. The long-term results are tested by employing a series of parametric and nonparametric regressions. This research recommends further investment in environmental research and development, digital technologies, green innovation, and the strengthening of environmental policies to attain sustainable development.

Does Financial Integration Contribute to Bilateral Trade in Africa: An Empirical Investigation by the Augmented Gravity Model

Does Financial Integration Contribute to Bilateral Trade in Africa: An Empirical Investigation by the Augmented Gravity Model

Enhancing bond strength prediction at UHPC-NC interface: A data-driven approach with augmentation and explainability

Existing concrete structures often have difficulty reaching their design service life due to aging, increased loads, and natural disasters, and they need to be repaired and strengthened or replaced. Ultra-high-performance concrete (UHPC), known for its ultra-high strength and excellent toughness, offers a promising solution for repairing and strengthening normal concrete (NC) structures. It is expected to be a viable solution when applied to the repair and strengthening of NC structures. A reliable interfacial bonding between the two materials (NC substrate and UHPC) determines the overall performance of this composite structure. In this study, a data-driven approach is proposed to predict the bond performance at the UHPC-NC interface as accurately as possible. To overcome the problem of limited experimental data, a data augmentation model is introduced, combining kernel density estimation (KDE) and tabular generative adversarial networks (TGAN). The optimal model is determined from six decision tree-based ensemble learning models applied to two strategies: "synthetic training - real testing" and "real training - real testing." Finally, a parametric analysis is performed using SHapley Additive exPlanations (SHAP) to elucidate the importance and sensitivity of different features related to bond strength. The findings illustrate that the suggested KDE-TGAN model effectively captures the distribution of the original dataset, enhancing both the accuracy and robustness of the bond strength prediction models. Furthermore, the model's ability to explain the importance and sensitivity of different features provides valuable insights into bond strength prediction. Thus, the proposed data augmentation model provides a reliable approach for modeling experimental data with small samples in structural engineering applications.

IMPACT OF VOCATIONAL EDUCATION ON ECONOMIC GROWTH: AN ANALYSIS OF THE INDIAN ECONOMY

Vocational education can be seen as an important tool in leveraging India’s demographic dividend. Given the labor surplus in terms of the young population, it is important to examine how vocational education and training can serve as a critical driver of economic growth. This study aims at examining the impact of vocational education and training on economic growth in India, using the framework of the augmented Solow growth model by decomposing human capital into vocational education, along with primary, secondary and higher education. Using an ARDL model based on the annual data from 1990 to 1991 and 2019 to 2020, the study analyzes the short-run and long-run impact of vocational education. The results indicate that in the case of India, the impact of vocational education on economic growth is both a short-run and long-run phenomenon exhibiting a stable positive relation. Other control variables used in this study, such as Investment, Population, Openness and Inflation are found with a significant impact on economic growth. The study suggests that it is necessary to upgrade vocational education programs to create high-quality workers with a view to improving productivity and macroeconomic outcomes in India.