Detailed Design Research Articles

Designing the Australian Cancer Atlas: visualizing geostatistical model uncertainty for multiple audiences.

The Australian Cancer Atlas (ACA) aims to provide small-area estimates of cancer incidence and survival in Australia to help identify and address geographical health disparities. We report on the 21-month user-centered design study to visualize the data, in particular, the visualization of the estimate uncertainty for multiple audiences. The preliminary phases included a scoping study, literature review, and target audience focus groups. Several methods were used to reach the wide target audience. The design and development stage included digital prototyping in parallel with Bayesian model development. Feedback was sought from multiple workshops, audience focus groups, and regular meetings throughout with an expert external advisory group. The initial scoping identified 4 target audience groups: the general public, researchers, health practitioners, and policy makers. These target groups were consulted throughout the project to ensure the developed model and uncertainty visualizations were effective for communication. In this paper, we detail ACA features and design iterations, including the 3 complementary ways in which uncertainty is communicated: the wave plot, the v-plot, and color transparency. We reflect on the methods, design iterations, decision-making process, and document lessons learned for future atlases. The ACA has been hugely successful since launching in 2018. It has received over 62000 individual users from over 100 countries and across all target audiences. It has been replicated in other countries and the second version of the ACA was launched in May 2024. This paper provides rich documentation for future projects.

Scale effects on core design, fuel costs, and spent fuel volume of pressurized water reactors

The desire to improve the economic competitiveness and deployment pace of nuclear energy through modularization, manufacturing, and series production had led to the development of smaller size reactors. As the standard 17x17 fuel technology is mainly maintained in the pressurized water reactors (PWRs) category, this translates into a lower number of fuel assemblies in the core and sometimes a reduced fuel height. To assess the impact of such scale change in core design on fuel cycle cost and spent fuel volume, a scoping analysis tool is developed based on infinite lattice calculations, leakage, fuel management reduced models, and levelized unit cost of electricity (LCOE) estimate. As such, cost dynamics driven by fuel specific power, burnup, core leakage, feed, cycle length, fuel assembly height as well as uranium market data are captured with consistent set of assumptions and analysis methods. A selection of 5 reactor designs representative of leading PWR developers is assessed and compared. Pursuing higher specific powers and optimal burnups are highlighted as the main fuel cost reduction drivers, nevertheless, practical limitations and opportunities must be evaluated to establish the feasibility of such enhanced fuel operation. In consequence, a detailed core design is performed using SIMULATE3 code for 5 PWR variations including natural and forced coolant circulation modes, two reactor scales, power densities of 73, 112, and 123 kW/l and higher discharge burnups. Design and optimization are performed at the lattice level, for the reflector, and at the core loading level. Satisfactory steady-state operation including power distribution, coolant operating limits, and reactivity requirements are analyzed and reported in this paper. The fuel economics of the detailed core designs confirm the scoping analysis findings. Despite the unlocked power uprates in small PWRs, the achievable burnup for a given fuel specific power requires more enrichment and shorter fuel height results in higher fabrication costs per mass of fuel, which makes scaling down core size a more expensive endeavor on the fuel cycle front. Spent fuel volumes are reported for the PWRs designed in this paper. These volumes are driven by the core average discharge burnup regardless of the scale in consideration. Additional cost and core performance aspects related to heavy reflector gains, fuel-reflector substitution, and disposal cost policy in the U.S. are examined.

Ethane integration project—a study for Egyptian NGLs plants and their economics

For Egypt, ethane is a vital chemical precursor, with the potential to significantly attract financial resources and drive economic growth. Enhancing its added value necessitates efficient recovery of ethane before it enters the national grid without processing. This research investigates the technical and economic feasibility of integrating natural gas liquids (NGLs) from three Egyptian NGLs plants—South Western Desert NGLs (SWD NGLs), North Western Desert NGLs (NWD NGLs), and East Mediterranean NGLs (EMD NGLs)—to improve ethane recovery from natural gas fields. The study focuses on revamping these NGLs plants using various NGLs recovery techniques, conducting a comprehensive comparison to identify the most effective methods for processing rich feed gas. Detailed design of the NGLs recovery processes was developed using HYSYS V.14 simulation software and supplementary calculation sheets. The design criteria encompass both technical and economic parameters to ensure a holistic approach in meeting project requirements. Results indicate a significant increase in ethane recovery: 16% for SWD NGLs, 10% for NWD NGLs (A, B, C), and 85% for EMD NGLs. This improvement will boost ethane feedstock for petrochemical companies from 2000 ton/day to 3500 ton/day, enabling increased polyethylene production. Economic analysis for the base case shows an expected internal rate of return (IRR) of 26%, a net present value (NPV) of USD 73 million, and a payback period of approximately 3 years. Sensitivity analysis on product prices, feed gas prices, and total capital investment confirms the project’s economic viability, with all cases showing feasibility (IRR ≥ 15%) except when product prices fall below USD 225/ton for propane, USD 215/ton for liquefied petroleum gas (LPG), and USD 25/bbl for condensate. This study highlights the potential for enhanced ethane recovery, offering substantial economic benefits and supporting the growth of Egypt’s petrochemical industry.

Sensing Classroom Temperature, Relative Humidity, Illuminance, CO2, and Noise: An Integral Solution Based on an IoT Device for Dense Deployments.

Maintaining optimal Indoor Environmental Quality (IEQ) requires continuous measurement of certain variables. To this end, ASHRAE and BPIE recommend that at least the following areas of interest be considered when measuring IEQ: thermal comfort, illuminance, indoor air quality, and noise. At this time, it is not common to find an IoT device that is suitable for dense deployments in schools, university campuses, hospitals, and office buildings, among others, that measures variables in all of the above areas of interest. This paper presents a solution to the problem previously outlined by proposing an IoT device that measures variables across all of the aforementioned areas of interest. Moreover, in a radio frequency network with a tree-like structure of IoT devices, this device is able to assume the roles of sensor and hub node, sensor and router node, and only sensor node. The experimental results are satisfactory, and the detailed system design ensures the replicability of the device. Furthermore, the theoretical analysis paves the way for high scalability.

Crystal structure of the class A extended-spectrum β-lactamase CTX-M-96 in complex with relebactam at 1.03 Angstrom resolution.

The use of β-lactam/β-lactamase inhibitors constitutes an important strategy to counteract β-lactamases in multidrug-resistant (MDR) Gram-negative bacteria. Recent reports have described ceftazidime-/avibactam-resistant isolates producing CTX-M variants with different amino acid substitutions (e.g., P167S, L169Q, and S130G). Relebactam (REL) combined with imipenem has proved very effective against Enterobacterales producing ESBLs, serine-carbapenemases, and AmpCs. Herein, we evaluated the inhibitory efficacy of REL against CTX-M-96, a CTX-M-15-type variant. The CTX-M-96 structure was obtained in complex with REL at 1.03 Å resolution (PDB 8EHH). REL was covalently bound to the S70-Oγ atom upon cleavage of the C7-N6 bond. Compared with apo CTX-M-96, binding of REL forces a slight displacement of the deacylating water inwards the active site (0.81 Å), making the E166 and N170 side chains shift to create a proper hydrogen bonding network. Binding of REL also disturbs the hydrophobic patch formed by Y105, P107, and Y129, likely due to the piperidine ring of REL that creates clashes with these residues. Also, a remarkable change in the positioning of the N104 sidechain is also affected by the piperidine ring. Therefore, differences in the kinetic behavior of REL against class A β-lactamases seem to rely, at least in part, on differences in the residues being involved in the association and stabilization of the inhibitor before hydrolysis. Our data provide the biochemical and structural basis for REL effectiveness against CTX-M-producing Gram-negative pathogens and essential details for further DBO design. Imipenem/REL remains an important choice for dealing with isolates co-producing CTX-M with other β-lactamases.

What teachers can do to enhance students’ well-being: Discussion

BackgroundThis special issue sheds light on how students’ well-being can be enhanced through teaching practices, emphasizing the significance of well-being in the educational context. My discussion was rooted in extensive well-being literature, which allowed me to outline key findings about the importance, effects, and conceptualizations of well-being, and factors that may influence well-being. MethodThe issue comprises seven empirical studies, each exploring the relationship between teacher-related variables (e.g., emotions, growth mindset) or teaching-related variables (e.g., intercultural education, need-supportive teaching) and student well-being. It utilizes studies with methodologies ranging from large-scale assessments like the Program for International Student Assessment (PISA) to detailed longitudinal designs across various educational levels and contexts. ResultsFindings underscore the pivotal role of teaching in influencing student well-being. That is, specific teacher behaviors and pedagogical approaches were identified that correlate with student well-being. Key aspects included intercultural education, need-supportive teaching, and positive teacher–student relationships. The methodological strengths of the included studies were their use of large datasets, longitudinal designs, and multi-informant data. Based on these findings, I presented some suggestions for possible methodological improvements. ConclusionThis SI contained impressive articles that can be used as excellent models for research in this area. The findings can also expand our current knowledge by providing a multifaceted, multinational, and multi-informant understanding of the topic. Practical applications are suggested how teachers can support their students’ well-being and highlighted the importance of integrating well-being into educational discourse to provide a foundation for future investigations of teaching and learning processes.

Data-driven stock forecasting models based on neural networks: A review

As a core branch of financial forecasting, stock forecasting plays a crucial role for financial analysts, investors, and policymakers in managing risks and optimizing investment strategies, significantly enhancing the efficiency and effectiveness of economic decision-making. With the rapid development of information technology and computer science, data-driven neural network technologies have increasingly become the mainstream method for stock forecasting. Although recent review studies have provided a basic introduction to deep learning methods, they still lack detailed discussion on network architecture design and innovative details. Additionally, the latest research on emerging large language models and neural network structures has yet to be included in existing review literature. In light of this, this paper comprehensively reviews the literature on data-driven neural networks in the field of stock forecasting from 2015 to 2023, discussing various classic and innovative neural network structures, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Transformers, Graph Neural Networks (GNNs), Generative Adversarial Networks (GANs), and Large Language Models (LLMs). It analyzes the application and achievements of these models in stock market forecasting. Moreover, the article also outlines the commonly used datasets and various evaluation metrics in the field of stock forecasting, further exploring unresolved issues and potential future research directions, aiming to provide clear guidance and reference for researchers in stock forecasting.

New design techniques on matching couplers for traveling-wave accelerating structures

Numerical optimizations on couplers of the traveling-wave (TW) accelerating structures usually require lots of calculation resources. This paper proposes a new technique for matching couplers to an accelerating structure in a more efficient and accurate way. It combines improved Kroll method with improved Kyhl method, thereby simplifying simulation process while achieving a high accuracy. This paper also presents the detailed design on couplers for a C-band constant-gradient (CG) accelerating structure based on this new technique. Such a new technique can be widely used for any TW accelerating structures working at different frequencies of S-band, C-band, and X-band including CG, constant-impedance (CI), and other structures with either electric couplers or magnetic couplers. Published by the American Physical Society 2024

A Prediction about Radio Frequency Envelope Detectors for Implementing a 2.4 GHz Rectenna for IEEE 802.15.4 with MOS Transistors

This study introduces a rectenna, functioning as an RF envelope detector, utilizing a 16 nm bulk MOS transistor (metal-oxide-semiconductor field-effect transistor) for nonlinear detection. A circuit architecture is presented alongside a detailed design methodology and simulations. The detector efficiently demodulates a 2.4 GHz OOK (On/Off Keying) encoded signal, comprising a 32-bit word, within 320 μs. Remarkably, the circuit operates passively, requiring no voltage supply or bias current, and functions effectively with −53 dBm input power at the antenna. This capability enables the decoding of 32-bit unsigned integer radio packets as a wakeup radio event. The effectiveness of the envelope detector is substantiated through comprehensive simulations.

Revisiting the cost analysis of importing liquefied green hydrogen

Hydrogen emerges as a promising energy carrier in achieving carbon neutrality, especially in countries such as South Korea and Japan, where renewable energy resources are scarce. Consequently, economically importing hydrogen from overseas is paramount to a stable supply. However, previous studies have often oversimplified the estimation of hydrogen import costs with optimistic assumptions, raising doubts about their reliability. This study comprehensively assesses costs incurred from green hydrogen production to liquefaction and transportation. The cost analysis includes detailed design considerations for hydrogen liquefaction and re-conditioning plants, alongside thermodynamic simulations to estimate the boil-off gas rate of liquid hydrogen during transport. Focusing on a case study of importing liquefied green hydrogen from Australia to South Korea, our findings reveal that the levelized cost of hydrogen is approximately 30.2 USD/kgH2 as of 2023. Projections suggest a decrease to around 18.3 USD/kgH2 by 2050, assuming technological advancements, significantly exceeding values reported in the existing literature. Sensitivity analysis highlights the renewable electricity price in Australia and the boil-off rate during shipping as key factors that significantly influence the levelized cost of hydrogen.

Design of a recirculating water channel for the development of a hydrokinetic turbine

This study presents the design, construction, and operational details of an experimental recirculating water channel dedicated to the assessment of hydrokinetic turbines, including a propeller-type turbine and a H-Darrieus turbine, since it offers a controlled testing environment. The recirculating water channel had overall dimensions of 5 m in length, 0.35 m in width, and 0.5 m in depth, it allows for the replication of various water flow conditions and permits a maximum achievable flow velocity of 0.6 m/s, which closely resembles real-world scenarios. Afterwards, a propeller- type turbine and a H-Darrieus turbine underwent characterization within the channel, and a performance curve constructed through the coefficient versus the blade tip speed ratio (TSR) revealed a peak efficiency of 0.2129 at a TSR of 2.952 for the propeller turbine, and a higher peak efficiency of 0.3076 at a TSR of 0.38 for the H-Darrieus turbine. The versatility of this experimental setup makes it a valuable platform for in-depth performance studies of hydrokinetic turbines. This water channel and its systematic characterization process represent a vital resource for conducting efficiency and behavior analyses across a wide range of operational parameters related to hydrokinetic turbine technologies.

Responses analysis and structural optimization of a semisubmersible platform based on the Taguchi experiment method

Responses analysis and structural optimization of a semisubmersible platform based on the Taguchi experiment method

Design and characterization of the time-resolved opacity spectrometer (OpSpecTR) for the NIF iron opacity campaign.

A new time-resolved opacity spectrometer (OpSpecTR) is currently under development for the National Ignition Facility (NIF) opacity campaign. The spectrometer utilizes Icarus version 2 (IV2) hybridized complementary metal-oxide-semiconductor sensors to collect gated data at the time of the opacity transmission signal, unlocking the ability to collect higher-temperature measurements on NIF. Experimental conditions to achieve higher temperatures are feasible; however, backgrounds will dominate the data collected by the current time-integrating opacity spectrometer. The shortest available OpSpecTR integration time of ∼2ns is predicted to reduce self-emission and other late-time backgrounds by up to 80%. Initially, three Icarus sensors will be used to collect data in the self-emission, backlighter, and absorption regions of the transmission spectrum, with plans to upgrade to five Daedalus sensors in future implementations with integration times of ∼1.3ns. We present the details of the diagnostic design along with recent characterization results of the IV2 sensors.

Atmospheric Influences on Li6PS5Cl Separators and the Resulting Ionic Conductivity for All-Solid-State Batteries

All-solid-state batteries (ASSBs), defined through a solid electrolyte, are emerging as a promising solution to address current challenges in energy and power density demands for electromobility. Within the various possible types of solid electrolytes, sulfide-based materials exhibit advantageous high ionic conductivities. However, due to the strong reactivity of sulfides, atmospheric exposure can lead to the formation of toxic hydrogen sulfide and additionally negatively impact the resulting battery performance. Both factors present key challenges for ASSB production, as they necessitate the development of a material-adapted, economically viable and safe process atmosphere. In the present study, the influence of different production atmospheres on sulfide-based solid electrolytes is experimentally investigated. For this purpose, sulfide sheets are exposed to defined atmospheres with dynamic air fluctuations at dew points ranging from −60 °C to 0 °C. The resulting ionic conductivities indicate a dependency on the prevailing dew point and exposure time with a discernible impact on performance even at dew points of −60 °C within atmospheres with constant air circulation. With the acquired results, a detailed and knowledge-based selection and design of dry room production atmospheres for ASSB cell assembly is possible, which is a necessary step for further industrialization.

Aqua Breast Augmentation (ABA): Hydrodissection Breast Augmentation Technique.

Breast augmentation is a prevalent procedure in plastic surgery. In this article we present the Aqua Breast Technique (ABA), an approach aimed at enhancing patient satisfaction and reducing downtime. This technique leverages tumescent anesthesia and a small incision in the inframammary fold, avoiding electrocautery and surgical drains, to fulfill the growing demand for painless procedures with minimal recovery periods. The study retrospectively analyzed 238 patients who underwent breast augmentation using the ABA technique at Instituto Rubi between 2021 and 2024. The ABA protocol involves detailed preoperative design, local anesthesia, a precise 3-centimeter incision, careful dissection, and specific postoperative care, emphasizing hydrodissection for tissue separation and the use of smooth round silicone implants through a Keller funnel. The application of the ABA technique to 238 patients, with a mean follow-up time 14months. Ten patients required surgical intervention due to complications. Hematoma and infection rates were 0.42%, while capsulectomies were required in 2.1% of cases. Aqua Breast Augmentation technique has been successfully applied as ahydrodissection-based breast augmentation technique method, with low complication rates, short downtime, and reduced postoperative pain. Level V, therapeutic study. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Design and implementation of college students’ physical education teaching information management system by data mining technology

This study intends to improve the efficiency of physical education teaching management, accelerate the normal teaching process, and meet the modern management requirements that traditional teaching management methods cannot meet. Based on data mining technology, this study designs a college student physical education teaching information management system, and makes a detailed design of each functional module. The main task of this study is to investigate how to effectively integrate data mining techniques with existing university student physical education teaching databases. Then, this study finds useful data information from massive data information to provide information support for university student physical education teaching. In order to effectively mine the relevant information of the data, the student evaluation module in the system is designed based on decision trees, and the teacher-student related data analysis module in the system is designed based on association rules. The research results indicate that 1039 records and 8205 student records are extracted from the teaching management database as mining objects. Rule 1: The support rate for “a professor's degree is a doctoral degree” is 20.4 %, indicating that there are 20.4 % of records in the teacher database that “the title is a professor and a doctoral degree”; the confidence level of Rule 1 is 78.2 %, indicating that 78.2 % of professors have a doctoral degree. Through the analysis of the rules that evaluate teaching as good, it can be found that the three attributes of professional title, education level, and teaching experience are the most important relevant factors affecting teaching effectiveness. Research has shown that the longer and richer the teaching experience, the stronger the teaching ability. Secondly, the mining results obtained through data mining techniques are analyzed. The maximum difference between the original algorithm's support mining results and the true values is 0.08, while the maximum difference between the improved algorithm's support mining results and the true values is 0.01. Compared to the original algorithm, the improved algorithm's mining results are accurate and effective. The application of data mining ideas in this system has laid a solid foundation for the development of physical education and teaching. Moreover, a three-layer system architecture model is adopted to better adapt to the development of school physical education, which is beneficial for later system maintenance and greatly reduces the work pressure of teachers. The system has been successfully launched and running in universities, and it is in good working condition.

An automated approach for carbon integration in industrial park

Industrial park is a hub for various industrial activities. Industrial emissions are one of the main sources of carbon emissions. The reduction of carbon emissions is crucial to achieve carbon neutrality for industrial parks. This paper presents an automated approach to maximize the utilization of CO2 among different plants in industrial parks through carbon allocation network. The minimum target of external fresh CO2 is determined based on automated composite table algorithm (ACTA), which is then materialized with the detailed design of carbon allocation network. A case study with four scenarios were analyzed to systematically guide the carbon reduction process towards its carbon neutrality target. Direct utilization scenario refers to CO2 utilization between sources and sinks in the parks without any purification scheme. Sink manipulation and source reduction scenarios indicate that process parameter changes, equipment modifications and other measures in the sinks or sources lead to the reduction of CO2 demands or supplies. In the purification scenario, CO2 is purified to different concentrations to maximize its allocations among different plants. A case study based on chemical industrial park in Fuyang, China is presented to illustrate the approach. Four factories (coal-fired power, cement, ammonia, ethanol) were identified as carbon sources, while three others (urea, methanol, dimethyl ether) as carbon sinks. By implementing sink manipulation and source reduction, the fresh CO2 resources and CO2 emissions are effectively reduced to 205.25 t/h and 149.61 t/h, respectively. The result for purification scenario indicates that CO2 emissions can be decreased from 317.33 t/h to 105.72 t/h, with an overall reduction of 66.68 %, while the fresh carbon requirement is reduced from 357.41 t/h to 161.34 t/h. This work provides a useful tool for the utilization of excess CO2 within an industrial park.

Envisioning scenarios in designs for Networked Learning

This article presents an application of a design methodology to envision implicit value hierarchies in the design process of a digital learning platform meant to encompass learning processes and activities conducive to experiential collaborative learning (ECL). The authors argue that many technologies for the field of education fall short of their purposes and neglect intended underpinning pedagogy and didactics. Previous research efforts in networked learning have primarily focused on conceptual critiques of the implementation of EdTech and warned of technological euphoria undermining relevant caution. This means, that when a design team tries to conceptualize technological artefacts into a script the more ethical and value-oriented parts of the learning process tend to be ignored. While we agree with the conceptual critiques, our approach has instead been to engage with the design process and implement appropriate methodologies in an attempt to highlight implicit value hierarchies in the underlying learning theory. When using technologies in Networked learning we thus emphasize that both designers and stakeholders should engage in a systematic discussion and reflection of values and related judgements while constructing a value hierarchy. Through a Value-based design methodology based on semantic zooming we thus present 7 interconnected envisioning scenarios developed in the UnFoLD project to demonstrate how it is possible to operationalize values into detailed design briefs or technological scripts. This article will through presented experiences from a design process, show how the methodology of envisioning scenarios can be applied to mitigate the risks of implementation technology in a learning situation. We argue that an awareness and mapping of values as a part of the design process is essential and that an increased focus on the ethical and moral responsibilities of designers and involved researchers are important as technologies should not be seen as isolated, value-neutral, or uncomplicated translations of analogue teaching activities. The purpose of the article is to inspire other researchers and designers to implement value hierarchies, envisioning scenarios, or other similar methods to ensure that pedagogical and didactic priorities are not lost in accommodating marketability, practicalities, or technological constraints.

Numerical investigation for shear behavior of pretensioned spun precast concrete pile

Pretensioned spun precast concrete (SPC) pile has emerged as a popular solution for deep foundation systems due to its ease of construction, enhanced bearing capacity, and overall cost-effectiveness. However, the hollowness of SPC piles, which reduces their sectional area, may become critical near the pile cap zone, especially in liquefaction scenarios during earthquake events. This study numerically investigates the shear behavior of hollow pre-tensioned SPC piles using the Finite Element (FE) technique. Primarily, the numerical results are validated with the test results of actual SPC piles where they were manufactured by high-strength concrete and high-tensioned steel strands for both with and without spirals. The numerical results show a good agreement with the experimental data in terms of strength, stiffness, and failure pattern for both types of spiral arrangements. The numerically obtained shear capacity is also found to be within close range of the code recommendations. A series of parametric studies have been conducted to understand the influence of essential pile parameters such as spiral’s spacing, effective prestress, number of strands, thickness-to-diameter ratio, concrete strength, and test orientation angle on the shear resistance of SPC pile. The study claimed that the addition of shear reinforcements to SPC piles boosts their shear capability, whereas the addition of strands and angle of test orientation have little effect on the piles' shear resistance. The optimum applied pre-stressing was found in a range of 4–8 MPa. The optimum values of t/d ratios in terms of material savings to shear resistance of the SPC pile fall in a range of 0.20 – 0.30. Morris Sensitivity Analysis (MSA) reveals that effective prestress and pile concrete strength are the most influential parameters of the shear capacity of SPC piles. This study provides important details for the SPC pile's shear design, illustrating its applicability in deep foundations in seismic areas.

Multifidelity Methodology for Reduced-Order Models with High-Dimensional Inputs

In the early stages of aerospace design, reduced-order models (ROMs) are crucial for minimizing computational costs associated with using physics-rich field information in many-query scenarios requiring multiple evaluations. The intricacy of aerospace design demands the use of high-dimensional design spaces to capture detailed features and design variability accurately. However, these spaces introduce significant challenges, including the curse of dimensionality, which stems from both high-dimensional inputs and outputs necessitating substantial training data and computational effort. To address these complexities, this study introduces a novel multifidelity, parametric, and nonintrusive ROM framework designed for high-dimensional contexts. It integrates machine learning techniques for manifold alignment and dimension reduction—employing proper orthogonal decomposition and model-based active subspace—with multifidelity regression for ROM construction. Our approach is validated through two test cases: the 2D RAE 2822 airfoil and the 3D NASA CRM wing, assessing various fidelity levels, training data ratios, and sample sizes. Compared to the single-fidelity principal component–active subspace (PCAS) method, our multifidelity solution offers improved cost-accuracy benefits and achieves better predictive accuracy with reduced computational demands. Moreover, our methodology outperforms the manifold-aligned ROM method by 50% in handling scenarios with large input dimensions, underscoring its efficacy in addressing the complex challenges of aerospace design.