Multiple Control Variables Research Articles

Operation Optimization Framework for Advanced Reactors Using a Data-Driven Digital Twin

Abstract To meet future energy demand, while producing safe, reliable, and carbon-free energy, nuclear reactors will be needed. To make next-generation reactors more economically competitive, the Artificial Intelligence and Machine Learning-based operation optimization module of the dynamic operation and maintenance optimization (DyOMO) framework is proposed. The operation optimization module of DyOMO consists of a data-driven digital twin coupled to a genetic algorithm (GA) optimizer to quickly and efficiently search the solution space for optimal control schemes. The digital twin consists of a Bayesian Network (BN) known as MVCBayes to incorporate uncertainty in the optimization and feedforward neural networks (FFNN) as MVCNet with GA to conduct the optimization. Over time as reactor systems are operating, component degradation will cause the system's electrical output to decrease or be shutdown entirely to perform maintenance. To prevent this, the DyOMO operation optimization module aims to prolong system operation until the next scheduled maintenance period using multiple variable control (MVC) that simultaneously perturbs all actuators to better control the reactor. Comparing this approach with traditional single variable control (SVC), MVC can extend reactor operation past 5% degradation while SVC begins to struggle once the pump and turbine degradation surpasses 0.85% for load-following (LF) operation. Given this extra operation time, the system can continue to run while maximizing its safety margin until the next scheduled shutdown and potentially decrease the total number of maintenance actions throughout the license period to decrease operational and maintenance (O&M) costs.

Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO

Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.

(Invited) Tunable Chemistry of 2D Carbide Mxenes for Hydrogen Evolution Reaction

Two-dimensional (2D) transition metal carbides and nitrides (MXenes) are a large family of earth-abundant materials with more than forty compositions synthesized since 2011, such as Ti2CT x , Mo2CT x , Nb2CT x , Ti3C2T x , Mo2TiC2Tx, and Mo2Ti2C3T x . MXenes have emerged as promising candidates for catalytic energy storage and conversion because their 2D surfaces are electrochemically active combined with MXenes’ hydrophilicity, high electrical conductivity, and affinity to bond to molecules and nanomaterials to form hybrid structures. MXenes electrochemical properties can be tuned by the control of multiple variables: in the MXene formula of M n +1X n T x , the transition metals (M) can be any of the groups 4, 5, 6 of the periodic table or their combination, X can be carbon, nitrogen, or a solid solution of both, surface terminations (T x ) can be any of the group 16 and 17, and the ‘n’ (MXene 2D flake thicknesses) can be 1 to 4. In this talk, we will discuss the control of MXene transition metals, their thicknesses, and surfaces to tune their electrocatalytic behavior. Specifically, we present a systematic study of 20 different MXenes, including novel compositions such as W2TiC2T x and Mo2Nb2C3T x and high-entropy MXenes, and discuss how we achieve low overpotential for hydron evolution reaction (as low as ~160 mV).

Design and Optimization of Low Impact Shift Control Strategy for Aviation Transmission Power System Based on Response Surface Methodology

The utilization of a variable-speed power system significantly improves the forward flight speed and cruising range of the helicopter. Nevertheless, the shock of speed and torque during the shift process brings stability and safety problems that cannot be ignored. Thus, swift and stable shift control is a key issue in the research on aviation power systems. This study focuses on the design and optimization of low-impact shift control strategies for a variable-speed power system, which involves multiple control variables, long adjustment times, and uncontrollable risks due to the nonsteady state. A comprehensive power system model that integrates the engine, a two-speed dual-clutch transmission system, and the main rotor was proposed. By selecting the engine fuel flow, friction clutch hydraulic pressure, and rotor pitch angle as input signals, regression fitting models between the input signals’ starting time points and speed or torque shock were obtained using Response Surface Methodology (RMS). The interaction effect of multiple time series was analyzed, and four kinds of low-impact nonlinear programming multi-objective optimized models for speed or torque are proposed. The results indicate that the P values of the RMS fitting models at upshift and downshift are less than 0.0001 and 0.05, respectively, which are highly significant and can effectively predict the shift dynamic response; under the optimized upshift and downshift control strategy, the speed and torque shock are reduced by 5–10%.

From crisis to resilience: strengthening climate action in OECD countries through environmental policy and energy transition

Climate change represents a grave challenge to the global economy, environment, and societal well-being, jeopardizing their long-term sustainability. In response to this urgent issue, the study emphasizes the significance of environmental policy and energy transitions as fundamental factors in addressing the climate change crisis. The research draws upon data from OECD countries spanning the period between 1990 and 2020, utilizing robust econometric techniques to assess data properties. The study utilizes a comprehensive CS-ARDL model, incorporating multiple control variables like non-renewable energy GDP, foreign direct investment (FDI), and research and development (R&D). The results show that environmental policy and energy transitions are effective in reducing climate change impacts in the form of CO2 emissions. The non-environmental factors like GDP and FDI are positively associated and thereby accelerate climate change processes, whereas R&D promotes environmental protection by reducing CO2 emissions. Based on these findings, the study advocates for the implementation of rigorous policy measures by OECD economies to strengthen and enforce environmental policies to ensure compliance and foster sustainable practices across sectors. The study also suggests that OECD must promote energy transitions by investing in renewable energy sources at the mass level (micro and macro) and phasing out reliance on non-renewable energy.

The Effect of Cross-Coupling on the Dynamic Performance of a Dual Phase Shift Controlled BiWPT System

In a bidirectional wireless power transfer (BiWPT) system, dual phase-shift (DPS) control is used to maintain the optimal efficiency at constant output power over varying misalignment. The presence of multiple control and output variables in DPS control creates cross-coupling between transmitter and receiver side, making BiWPT a multiple-input multiple-output (MIMO) system. Due to cross coupling, any change in control variable in one side of the BiWPT system will affect the output of the same side as well as output and control input in the other side. This results in an uncertain dynamic behaviour of the output response. The cross-coupling effect has not been adequately researched in the literature. Hence, this paper presents an accurate dynamic model using a generalized state-space averaging approach (GSSA) and an extended describing function (EDF) method for a multivariable control of the S-S compensated BiWPT system. This model is used to derive the system’s relative gain array (RGA), which is used to study the effect of cross-coupling terms on the output variables. A 1 kW S-S compensated BiWPT experimental set-up is used to validate the effect of cross-coupling on the dynamic behavior of the system by comparing DPS and single phase-shift (SPS) controlled experimental waveforms. The steady-state and transient behavior of the BiWPT system has also been investigated with simulation and experimental results.

Robust model agnostic predictive control algorithm for randomly excited dynamical systems

We propose a novel robust model agnostic predictive control (RoMAn-MPC) algorithm and illustrate its application in randomly excited dynamical systems. Unlike conventional model predictive control algorithms, the proposed RoMAn-MPC requires no information about the underlying physics of the system; instead, the governing physics is identified by using the recently proposed stochastic equation discovery framework. One key advantage of the proposed approach resides in its capability to generalize; this eliminates the repeated retraining phase — a major bottleneck with other machine learning-based model agnostic control algorithms. Overall, the proposed RoMAn-MPC (a) is robust against measurement noise, (b) works with sparse measurements, (c) can tackle set-point changes, (d) works with multiple control variables, and (e) can incorporate dead time. We have obtained state-of-the-art results on several benchmark examples. Finally, we use the proposed approach for vibration mitigation of a 76-storey building under seismic loading.

The Impact of Industry Concentration on Stability: The Case of Indonesian Islamic-Commercial Banks

This study aims at investigating the impact of the banking industry’s concentration on the stability of Islamic commercial banks in Indonesia. This study used a single country setting of 14 Islamic-commercial banks in Indonesia from 2011 to 2020. The data utilized in this study comprised all Islamic-commercial banks' assets in Indonesia, excluding commercial banks with Islamic business units. The influence of concentration level on the stability of Islamic-commercial banks was investigated using a panel data model. According to Hausman's test, the fixed-effect model is more suitable than the random-effect model. The findings indicate that the “concentration-stability” hypothesis was supported-robust using two concentration level measurements: CR4 and HHI. It is implied that banks tended to be more stable at the higher competition level. From the bank’s specific characteristics, only the cost-to-income ratio significantly influenced the bank's stability, as expected. Other bank-specific characteristics, such as bank size, credit risk, and income diversity, had no substantial influence on observed bank stability. A robustness check was performed by estimating new models that included multiple control variables that did not change the effect of concentration level on the bank’s stability. This study adds to the literature by demonstrating the “concentration-stability” hypothesis in the Indonesian Islamic-commercial banking industry. Moreover, this study’s results confirm the previous study’s findings using different methods and measures of industry concentration. In addition, this study is relevant in the context of the merger action of three large Islamic commercial banks.

Alcohol Use Disorder Visits and Suicide Ideation Diagnosis: Racial/Ethnic Differences at Emergency Departments

Nationally, suicide ideation prevalence is comparable among White, American Indian/Alaska Native, Black, and Hispanic adults experiencing alcohol use disorder. This study examines whether such comparability extends to the probability of receiving a suicide ideation diagnosis when presenting with alcohol use disorder at emergency departments. The probability of hospitalization following such diagnosis is examined as well. National Emergency Department Sample (2019) data were used. Logistic and multilevel logistic regression analyses were performed in 2022-2023 with suicide ideation diagnosis and subsequent hospitalization as the outcome variables. Control variables included demographics, payor, alcohol use disorder level, comorbidities, and emergency department facility. Adjusted probabilities were computed. Age-adjusted probabilities of suicide ideation diagnoses for American Indian/Alaska Native, Black, and Hispanic patients with alcohol use disorder were 5.4%, 6.7%, and 4.9% (95% CIs=3.7, 7.1; 6.0, 7.4; 4.4, 5.4), respectively; all less than that for White counterparts (8.7%; 95% CI=8.2, 9.2). Among patients with alcohol use disorder plus suicide ideation diagnoses, the age-adjusted probability of hospitalization for American Indians/Alaska Natives (32.4%; 95% CI=20.9, 44.0) was less than that for Whites, Blacks, and Hispanics (49.8%, 52.3%, and 49.9%; 95% CIs=46.7, 52.8; 47.1, 57.5; and 43.9, 55.8, respectively). In regressions with multiple control variables, the racial/ethnic differences remained statistically significant (p<0.05). Diagnosis of suicide ideation, a key step in emergency department suicide prevention care, occurred significantly less often for patients of color with alcohol use disorder than for White counterparts. American Indians/Alaska Natives, the racial/ethnic group known to have the nation's highest suicide rate, had the lowest probability of being hospitalized after a diagnosis of alcohol use disorder plus suicide ideation.

An econometric analysis of price elasticity and demand factors in the global honey and honey-based beverage markets

The contemporary discourse on global commodity markets presents a myriad of compelling quandaries for scholars, policy-makers, and practitioners alike. Among these markets, since 2015 the global honey and honey-based beverage sector embodies unique economic characteristics and dynamics. While the market has experienced expansive growth in a plethora of jurisdictions, Kazakhstan remains out of focus of researchers. For this reason, we decided to undertake an exhaustive econometric analysis, scrutinizing the price elasticity of demand and various demand determinants in Kazakhstan’s honey and honey-based beverage markets. Utilizing longitudinal data spanning over a decade, this investigation employs advanced econometric methodologies - including but not restricted to Ordinary Least Squares (OLS) regressions, co-integration analyses, and error-correction models - to rigorously interrogate the price elasticity of demand for honey and honey-based beverages. The research encompasses multiple control variables like GDP per capita, consumer behavior, inflation rates, and population dynamics to create a multi-faceted econometric model capable of rendering nuanced insights. It incorporates both macroeconomic and microeconomic variables to delineate the symbiotic relationship between price elasticity and demand determinants such as consumer income, advertising impact, and price points of substitute and complementary goods. The analysis showed that the honey market in Kazakhstan is fundamentally oligopolistic, and the triumvirate of top producers collectively wield a 60% market control, leaving a residual 40% for the aggregation of small and medium enterprises. However, the findings unveil a complex web of economic variables influencing the price elasticity of demand in Kazakhstan’s honey markets. Most notably, the study elucidates that the price elasticity of demand for honey and honey-based beverages is highly inelastic in the short term but transitions towards being elastic in the long run, a phenomenon predominantly influenced by changes in consumer income and preferences. A 12.3% increase in exports from 2020 to 2021 implies an increasing global demand for Kazakhstani honey, and thus should be a focus for stakeholders to leverage for market expansion. An Export-to-Production Ratio at 26% indicates that roughly one-fourth of honey produced is assigned for the international markets. The average production cost per kilogram of honey in Kazakhstan was found lower compared to the global average. Remarkably, digitization has been adopted as a salient strategy for market development. The penetration of digital platforms for honey trading increased from 2% in 2012 to 18% in 2021. The research identifies complex interplay between exogenous variables like global market trends, geopolitical factors, and cultural shifts, contributing to a comprehensive understanding of the market dynamics at play. This article makes several seminal contributions to the academic dialogue surrounding the economics of honey markets. Firstly, it adds a new geographic focus by centering on Kazakhstan, an understudied yet burgeoning market in the global honey industry. Secondly, the rigorous econometric methodology employed contributes to the methodological lexicon of commodity market analyses. Finally, the complex approach, incorporating both economic theory and real-world market trends, paves the way for future studies and aids in the formulation of policy directives aimed at stabilizing price volatility in commodity markets.

A critical reflection on computing the sampling variance of the partial correlation coefficient.

The partial correlation coefficient quantifies the relationship between two variables while taking into account the effect of one or multiple control variables. Researchers often want to synthesize partial correlation coefficients in a meta-analysis since these can be readily computed based on the reported results of a linear regression analysis. The default inverse variance weights in standard meta-analysis models require researchers to compute not only the partial correlation coefficients of each study but also its corresponding sampling variance. The existing literature is diffuse on how to estimate this sampling variance, because two estimators exist that are both widely used. We critically reflect on both estimators, study their statistical properties, and provide recommendations for applied researchers. We also compute the sampling variances of studies using both estimators in a meta-analysis on the partial correlation between self-confidence and sports performance.

A translational evaluation of component skills for the establishment of multiply controlled intraverbals.

Intraverbal behavior is a type of verbal behavior in which the response form has no point-to-point correspondence with its verbal stimulus. However, the form and occurrence of most intraverbals is under the control of multiple variables. Establishing this form of multiple control may depend on a variety of preestablished skills. The purpose of Experiment 1 was to evaluate these potential prerequisites with adult participants using a multiple probe design. The results suggest that training was not required for each putative prerequisite. In Experiment 2, probes for all skills were conducted following convergent intraverbal probes. The results showed that convergent intraverbals only emerged when proficiency of each skill was demonstrated. Finally, Experiment 3 evaluated alternating training of multiple tact and intraverbal categorization. The results showed that this procedure was effective for half of the participants.

The relationship between IFRS adoption and foreign direct investments in Gulf Cooperation Council countries

Purpose: This paper investigates the relationship between International Financial Reporting Standards (IFRS) adoption and Foreign Direct Investments (FDI) inflows in a sample of Gulf Cooperation Council (GCC) countries, namely Kuwait, Qatar, UAE, and Saudi Arabia over the period standing between 1990 and 2020. Methodology/approach: This study implements a statistical analysis, starting with a panel correlation matrix and then a panel unit root test to identify the integrating properties of variables. The Pedroni panel co-integration approach tests co-integration among variables, and the instrumental Generalized Method of Moments (GMM) estima-tion technique identifies the relationship among variables and their significance. Findings: The results imply that only Gross Domestic Product (GDP) per capita, exchange rate and trade enhance FDI inflows in the studied countries. Contrary to our expectations, the IFRS was found to be insignificant on FDI along with economic control variables. Originality/value: Using multiple control variables, this study provides original em-pirical evidence and statistical analysis on the effect of IFRS adoption on FDI inflows in the GCC area.

Do high housing prices crowd out young professionals?—Micro-evidence from China

Motivated by the growing trend of young professionals flowing out of high-profile cities in China, this study examines whether high housing prices have a crowding-out effect on young professionals across 235 Chinese cities. Using a conditional logit model with multiple control variables related to the economy, the social environment, and city location, the results showed an inverted U-shaped relationship between a city’s house prices and young professionals’ potential migration into the city. We also performed a series of robustness checks and categorised the sample by gender, academic qualification, and marital status. The results indicated the same inverted U-shaped relationship, but with different extreme points for house prices across the sub-samples. The findings offer suggestions for Chinese local governments to attract young professionals by regulating housing prices in cities where prices surpass the extreme point.

Intermittent faking of personality profiles in high-stakes assessments: A grade of membership analysis.

In high stakes assessments of personality and similar attributes, test takers may engage in impression management (aka faking). This article proposes to consider responses of every test taker as a potential mixture of "real" (or retrieved) answers to questions, and "ideal" answers intended to create a desired impression, with each type of response characterized by its own distribution and factor structure. Depending on the particular mix of response types in the test taker profile, grades of membership in the "real" and "ideal" profiles are defined. This approach overcomes the limitation of existing psychometric models that assume faking behavior to be consistent across test items. To estimate the proposed faking-as-grade-of-membership (F-GoM) model, two-level factor mixture analysis is used, with two latent classes at the response (within) level, allowing grade of membership in "real" and "ideal" profiles, each underpinned by its own factor structure, at the person (between) level. For collected data, units of analysis can be item or scale scores, with the latter enabling analysis of questionnaires with many measured scales. The performance of the F-GoM model is evaluated in a simulation study, and compared against existing methods for statistical control of faking in an empirical application using archival recruitment data, which supported the validity of latent factors and classes assumed by the model using multiple control variables. The proposed approach is particularly useful for high-stakes assessment data and can be implemented with standard software packages. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Simple Modulation Strategy for Full ZVS of Single-Stage Electrolytic Capacitor-Less EV Charger With Universal Input

The single-stage electrolytic capacitor-less on-board charger (OBC) is attracting attention with the possibility of achieving high efficiency, high power density, and low component count. However, under universal input and wide battery voltage, the single-stage OBC has a difficulty in achieving ZVS turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> while maintaining unity power factor. In order to achieve ZVS under wide voltage range, the conventional single-stage OBC uses multiple control variables including switching frequency where the switching frequency usually varies widely, which complicates the converter design and optimization. In this regard, a totem-pole based single-stage electrolytic capacitor-less OBC was introduced in [36]. It uses one control variable and fixed switching frequency. However, this topology is not able to achieve full ZVS under wide voltage range and has high grid current total harmonic distortion (THD). This article proposes a simple modulation strategy that uses an adaptive duty-cycle and phase-shift modulation to achieve full ZVS under universal input and wide battery voltage. In addition, the fourth-harmonic injection method greatly reduces the grid current THD. The effectiveness of the proposed modulation was validated on a 3.84-kW prototype with 100–240 V ac input and 460–800 V battery voltage. The efficiency is also increased up to 2.3%p under low-voltage grid compared to the previous work.

Interchange roles of formal and informal project management on business operational success

While there are numerous studies on factors affecting the project and business performance in the literature, no research published on the concurrent roles played by formal (FPM) and informal (IPM) project management on project success. The study’s three main objectives are: first, give a conceptual framework for evaluating the individual and concurrent contributions of FPM and IPM to project success. Second, the project management methods’ mediation effects on the link between senior management support and project success are evaluated. Third, the effects of using multiple environmental control variables are investigated. A sample of 103 small to medium-sized enterprises was used to collect managerial and business operational data. Using structural equation modelling and correlational relationships, ten different models are examined. The findings revealed that IPM has a partial mediation effect, whereas FPM has no effect. Furthermore, the analysis revealed that IPM has the greatest impact on project success. Integration of FPM and IPM with environmental factors is considered. These findings are explained in terms of business, economic, and commercial diversities.

Analysis on the Cognitive Impact of Social Mobile Games on Left-Behind Children in the Era of Big Data.

The popularity of mobile gaming has become a common sight in rural areas, and the problem of left-behind children's mobile gaming has become the biggest challenge faced by rural education, and has become a hot topic in the education sector and society. The stage of left-behind children is the golden period of learning and growth. However, this stage is also the period when they have the greatest fluctuations in their ideology due to various factors such as guardians and youths. With the development of big data, it has been applied to various aspects of people's life. This article is mainly based on qualitative research, with the interview and observation as the main methods, supplemented by a questionnaire survey method. In the empirical analysis part, this article has a certain degree of expansion in data selection and research methods. Compared with previous studies, we increased the scale of the research data, making the research results more meaningful. In the research method, the ordinary least squares method (OLS), the propensity score matching method (PSM) and the two-stage least square method (2SLS) are used, and multiple control variables are selected. The factor analysis of the original scores of the historical knowledge test and the original scores of the two-dimensional cultural value evaluation are carried out to obtain the factor scores of cognitive ability. The emotion of the child is expressed by the depression score, and factor analysis is also performed on the depression score. Cognitive abilities refer to reasoning or thinking, processing speeds, and one's ability to solve problems in novel situations, independent of acquired knowledge. OLS regression results show that left-behind children are inferior to non-left-behind ones in cognitive ability. Moreover, left-behind children are more likely to be emotionally depressed. And whether the children are accompanied by migrant or rural parents, there is no significant difference in their cognitive ability and emotions. Because there are unobservable factors that affect whether children are left-behind and children's cognitive abilities and emotions, the sample may have a self-selection bias. This research focuses on the phenomenon of left-behind children's mobile gaming, revealing the compromised cognitive abilities of these marginalized children groups. Our study might put a wake-up for authorities on the education in rural areas.

Multivariable-Modulation-Based Conduction Loss Minimization in a Triple-Active-Bridge Converter

This article presents the design, development, and optimization of pulsewidth modulation (PWM) scheme of an isolated bidirectional triple-active-bridge (TAB) dc–dc converter composed of three full-bridge modules and a high-frequency planar transformer. This article aims at improving the efficiency of the TAB converter by means of conduction loss minimization. The approach utilizes multiple control variables as degrees of freedom for the converter modulation. The optimization is based on the minimization of the true rms current, formulated using generalized harmonic approximation technique. The approach constitutes of two steps: the modulation pattern with least algorithmic complexity for efficiency maximization is first found depending on the operating load and gain condition, and, subsequently, the optimum control variables are calculated using the gradient descent algorithm applied on the identified modulation pattern. An 800-W TAB converter proof-of-concept is built to verify all theoretical considerations and model-oriented analysis. While the converter has an input dc bus voltage of 160 V, the two output ports of the converter can deliver 400 W each at voltage levels of 110–130 V and 18–27 V, respectively. With the implementation of the proposed optimal phase-duty control, the experimental results show a nonunity gain light load efficiency increment up to 6.1% compared with the conventional modulation technique.

Data-driven model-based calibration for optimizing electrically boosted diesel engine performance

Engine calibration is a process of optimizing its control variables, which is a crucial step to achieve the desired performance from the engine system. However, its complexity has increased dramatically due to the constant evolution of the engine architecture with multiple control variables interacting with each other, making it difficult to calibrate its control variables with significantly increased experimental costs. System complexity also affects the physics-based modeling process as it makes the system model highly non-linear and difficult to capture system dynamics under all engine operating conditions. To overcome these challenges, the current work proposes a machine learning-based framework to reduce the required calibration effort. The work implements a stochastic surrogate assisted optimization approach on an actual engine experimental test setup, which efficiently looks for the global optimal solution based on only the input-output data set. This work performs a steady-state calibration of a turbocharged diesel engine with an additional external electric-assisted boosting system (eBoost). Three control parameters are considered for this study: Variable Geometry Turbocharger (VGT) vane position, Exhaust Gas Recirculation (EGR) valve position, and eBoost speed to identify the optimal trade-off between engine efficiency (Brake Specific Fuel Consumption) and emissions ( NO x). The addition of eBoost makes the calibration problem difficult because the two control parameters, VGT and EGR, also depend on eBoost speed. Using the proposed data-driven calibration approach, the experimental results showed enhanced engine performance in terms of efficiency and emissions using the eBoost compared with the case without eBoost. At the same time, the experimental cost was reduced significantly due to the reduced number of testing points required to achieve the optimum.