Accurate Model Research Articles

Analytical prediction of electromagnetic performance for surface-embedded permanent magnet in-wheel machines considering iron’s nonlinearity

Accurate magnetic field calculation is the premise of electromagnetic performance prediction. Conventional subdomain (SD) techniques assume that the iron’s relative permeability is infinite, leading to falsely overestimated flux density. We propose an accurate magnetic field analytical model for permanent magnet (PM) in-wheel machines considering iron’s magnetization nonlinearity and saturation. Specifically, according to the excitation source and topology, the entire solution domain of the machine is divided into sub-regions such as stator slots/teeth, stator slot-openings/tooth-tips, air-gap, and rotor slots/teeth, etc. Poisson’s or Laplace’s magnetic vector potential (MVP) equations are solved using Maxwell’s electromagnetic theory and complex Fourier series methods in each sub-region. Specifically, in our approach, The Cauchy product theorem addresses the discontinuous magnetic permeability change at the slot and tooth interface. The machine’s magnetic saturation effect is considered by combining the actual magnetization characteristics of iron with an iterative algorithm. The general solution for the MVP is solved using the boundary conditions between adjacent subregions. Subsequently, electromagnetic properties such as air-gap flux density, back electromotive force (EMF), and electromagnetic torque are obtained. The accuracy of the analytical model is verified by finite element analysis (FEA) and prototype tests, which proved that the proposed analytical model can consider the iron’s nonlinearity and the magnetic saturation. In addition, the inaccurate overestimation of electromagnetic torque and air-gap magnetic flux density by the conventional SD techniques has also been proven.

Lava flow field development and lava tube formation during the 1858–1861 eruption of Vesuvius (Italy), unravelled by historical documentation, lidar data and 3D mapping

Somma-Vesuvius is well known for its powerful Plinian explosive eruptions, however during the last eruptive cycle (1631–1944), persistent activity took place on the stratovolcano as mild and violent Strombolian, and effusive eruptions, forming more than one hundred lava flow fields. An important mechanism of lava transport within lava flow fields is the formation and development of lava tubes. The presence of lava tubes in a flow field can greatly increase their distance of emplacement. Observations of lava tubes at Vesuvius have been documented in historical records and speleological reports but no modern scientific studies are available. This work focuses on lava tubes formed in the compound lava flow field of the long-lived 1858 eruption (from 27 May 1858 to 12 April 1861) that was fed by seven eruptive fissures. The temporal and spatial evolution of the 1858 lava flow field was reconstructed using historical documentation. The exposed lava flow field surface was analysed using a 1-m resolution lidar Digital Surface Model (DSM). Surveys to fully digitize the interior and the overlying surface of the largest lava tube found in the 1858 lava flow field were conducted using a terrestrial laser scanner, optical cameras, and an Unmanned Aerial Vehicle (UAV). The accurate 3D model obtained was used to precisely quantify the inner dimensions and to better constrain the morphologies of the lava tube. Observed internal features were described and used to gain information on the formation and activity of the lava tube. Our data allowed us to understand that the described lava tube formed as an inflated lava flow inside which lava flowed through during an extended period ultimately draining out completely at the end of the eruption. Understanding how lava flow fields develop and how lava tubes form on Vesuvius is crucial to re-evaluate the last effusive activity of the volcano and its impact on hazard assessment.

Design and Performance Analysis of A Linear Switched Reluctance Motor Considering End Effects

Introduction: In recent years, linear actuators have gained significant attention due to their ability to provide direct linear movement without the need for motion transformation devices. One significant challenge in the design and modeling of linear actuators is the occurrence of longitudinal end-effects. The finite length of the translator stack in linear actuators causes an unbalanced phase force, leading to discrepancies between the phases at the end and center of the actuator. Neglecting these end-effects can lead to inaccuracies in the actuator's performance and control. Methods: The objective of this study is to develop a comprehensive approach for the design, sizing, and modeling of the actuator to ensure optimal performance. An energy conversion procedure calculates the thrust force and determines the actuator's geometrical parameters. A numerical model based on the finite element method is developed to analyze the actuator's magnetic behavior and establish its characteristics. Furthermore, a thorough analysis of the end effect is conducted using two-dimensional finite element analysis. Results: To accurately capture the actuator's dynamic response and enable precise control, a mathematical model is formulated, incorporating the nonlinear behavior of inductance and incorporating the end effect factor. The proposed model demonstrates high accuracy and provides a solid foundation for the control of the linear switched reluctance actuator. Conclusion: Overall, this study contributes to the advancement of direct drive systems for industrial applications by providing a detailed design procedure and an accurate modeling approach for the linear actuator, enabling more efficient and precise control strategies.

Physics-informed ensemble learning with residual modeling for enhanced building energy prediction

Accurate modeling of building energy use is important to support a diverse spectrum of its downstream applications, such as building energy efficiency assessment, resilience analysis, smart control, etc. As mainstream approaches of building energy modeling, physics-based modeling builds on different fidelities of physics rules yet are usually compromised in modeling accuracy due to insufficiency of physics rules to capture real-world dynamics and incomplete input information. Data-driven approaches are computationally efficient, but black box (uninterpretable) in nature. For improved modeling of building energy use, this work proposes a physics-informed ensemble learning approach in building energy prediction through residual modeling. Specifically, we first analyze the components of building energy use data. Evidence suggests that the building energy use data can be decomposed into the physics-driven part, occupant-driven part, and white noise. Second, high-fidelity physics-based building models (EnergyPlus) and low-fidelity ones (RC models) are developed to capture the physics-driven part while time series methods are explored as the residual modeling approach to capture the occupant-driven discrepancies between physics-based simulation and measured building energy use (i.e., residuals). Finally, the physics-informed ensemble learning is proposed to integrate physics-based and data-driven models for enhanced accuracy of building energy modeling. Results demonstrate 40–90% decrease of discrepancies between modeling and field observations compared to traditional physics-based modeling methods. Moreover, when the training dataset size is small, the proposed ensemble model overperforms the pure data-driven models, demonstrating its higher robustness in extrapolation scenarios. This work makes fundamental contributions to the development of convergent modeling approaches in the building modeling field.

Multiple lubricated joints with long and short bearings in multibody mechanical systems - Modeling, simulation, and performance analysis

This study examines the effect of multiple lubricated imperfect long and short bearings on the performance of a multibody mechanical system. Unlike the typical assumption of a single perfect or imperfect lubricated joint due to modeling difficulties, this research considers the practical impacts of clearances in multiple joints. While unlubricated joints generally cause significant performance issues, lubricants reduce these effects, creating more localized peaks. The findings show that as the number of lubricated joints increases, both the magnitude and frequency of these peaks rise. In systems with multiple journal bearings, torque peaks become more noticeable due to the additional degrees of freedom introduced by the clearances. These degrees of freedom amplify acceleration, leading to higher lubricant reaction forces, which in turn require greater motor torque peaks to maintain the system's kinematics. Shorter lubricated joints exhibit more severe peaks than longer ones, mainly due to side leakage causing axial pressure variation and reduced damping capacity. The study highlights the need to replace idealized joints with imperfect ones for more accurate modeling of practical systems.

Probing Na in giant exoplanets with ESPRESSO and 3D NLTE stellar spectra

Neutral sodium was the first atom that was detected in an exoplanetary atmosphere using the transmission spectroscopy technique. To date, it remains the most successfully detected species due to its strong doublet in the optical at 5890\ A and 5896\ A . However, the center-to-limb variation (CLV) of these lines in the host star can bias the Na I detection. When combined with the Rossiter-McLaughlin (RM) effect, the CLV can mimic or obscure a planetary absorption feature if it is not properly accounted for. This work aims to investigate the impact of three-dimensional (3D) radiation hydrodynamic stellar atmospheres and non-local thermodynamic equilibrium (NLTE) radiative transfer on the modeling of the CLV+RM effect in single-line transmission spectroscopy to improve the detection and characterization of exoplanet atmospheres. We produced a grid of 3D NLTE synthetic spectra for Na I for FGK-type dwarfs within the following parameter space: $T_ eff =4500-6500$\,K, $ g =4.0-5.0$, and Fe/H . This grid was then interpolated to match the stellar parameters of four stars hosting well-known giant exoplanets, generating stellar spectra to correct for the CLV+RM effect in their transmission spectra. We used archival observations taken with the high-resolution ESPRESSO spectrograph. Our work confirms the Na I detections in three systems, namely WASP-52b, WASP-76b, and WASP-127b, also improving the accuracy of the measured absorption depth. Furthermore, we find that 3D NLTE stellar models can explain the spectral features in the transmission spectra of HD 209458b without the need for any planetary absorption. In the grid of stellar synthetic spectra, we observe that the CLV effect is stronger for stars with low $T_ eff $ and high $ g$. However, the combined effect of CLV and RM is highly dependent on the orbital geometry of the planet-star system. With the continuous improvement of instrumentation, it is crucial to use the most accurate stellar models available to correct for the CLV+RM effect in high-resolution transmission spectra to achieve the best possible characterization of exoplanet atmospheres. This will be fundamental in preparation for instruments such as ANDES at the Extremely Large Telescope to fully exploit its capabilities in the near future. We make our grid of 3D NLTE synthetic spectra for Na I publicly available.

Towards realistic blood cell biomechanics in microvascular thrombosis simulations

Abstract The paper is devoted to a three-dimensional mesoscale hemodynamic model for simulations of microvascular blood flows at cellular resolution. The focus is on creating a more accurate biomechanical model of red blood cells for further use in models of hemostasis and thrombosis. The presented model effectively and accurately reproduces peculiarities of blood flow under realistic hydrodynamic conditions in arterioles, venules, and capillaries, including the Fahraeus–Lindquist effect and subsequent platelet margination. In addition, shear-dependent platelet aggregation can also be captured using the proposed approach.

Circular rubber aggregate CFST stub columns under axial compression: prediction and reliability analysis

Extensive studies support using steel tubes to enhance the structural integrity of rubber aggregate concrete (RBAC), namely RBAC-filled steel tubes (RCFST). However, current design codes for assessing the axial compressive behaviour of circular stub RCFST (CS-RCFST) columns are limited. Furthermore, there is a scarcity of studies focused on ensuring the structural safety of these columns. Based on an extensive experimental database comprising 145 columns, this study explores machine learning (ML) capabilities for predicting the axial strength of CS-RCFST columns, using six typical machine-learning models, i.e., symbolic regression (SR), XGBoost, CatBoost, random forest, LightGBM, and Gaussian process regression models. The hyperparameter tuning of the introduced ML models is performed using the Bayesian Optimization technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R2 = 0.999 and 0.993 for the training and testing sets, respectively). In addition, a simple and practical design expression for CS-RCFST columns has been developed with acceptable accuracy based on the SR model (an average test-to-prediction ratio of 0.99 and CoV of 0.132). Meanwhile, the axial strength predicted by ML models was compared with two prominent practice codes (i.e., AISC360 and EC4). The comparison results indicated that the ML models could introduce a highly reliable and accurate approach over current design standards for strength prediction. Furthermore, a reliability analysis is conducted on two different ML models to evaluate the reliability of utilising ML models in practical design applications. This assessment involves identifying the statistical properties associated with the compressive strength of RBAC, as well as introducing the required resistance design factors aligned with the target reliability recommended by code standards.

Combining transfer learning and statistical measures to predict performance of composite materials with limited data

AbstractPredicting the performance of composite materials is crucial for their application in civil infrastructure, yet limited experimental data often hinder the development of accurate and generalizable models. This study introduces a deep neural network (DNN) approach that combines summarizing statistics (SS) and transfer learning (TL)—termed the SSTL‐DNN approach—to address data scarcity in modeling composite materials. The computational novelty lies in the SS method's ability to extract comprehensive information from limited datasets by converting complex constitutive laws into concise statistical representations, thereby enabling efficient and effective model training. Simultaneously, the TL method enhances computational efficiency by leveraging knowledge from related tasks with abundant data to improve learning in the target task with scarce data. This combination not only reduces dependency on large datasets but also significantly improves model generalization. The proposed SSTL‐DNN approach is validated through two case studies: fiber‐reinforced polymer confined concrete and engineered cementitious composites. In both case studies, the SSTL‐DNN model reduces the required dataset size by up to 75% and decreases the validation error by 39%, compared to traditional deep learning models. These results demonstrate that the SSTL‐DNN approach not only overcomes data scarcity but also provides accurate predictions and generalization to unseen data, offering a practical solution for modeling composite materials with limited data.

Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population

As the population ages, the increase in the number of middle-aged and older adults with diabetes poses new challenges to the allocation of resources in the healthcare system. Developing accurate diabetes prediction models is a critical public health strategy to improve the efficient use of healthcare resources and ensure timely and effective treatment. In order to improve the identification of diabetes in middle-aged and older patients, a Bagging-RF model is proposed. In the study, two diabetes datasets on Kaggle were first preprocessed, including unique heat coding, outlier removal, and age screening, after which the data were categorized into three age groups, 50–60, 60–70, and 70–80, and balanced using the SMOTE technique. Then, the machine learning classifiers were trained using the Bagging-RF integrated model with eight other machine learning classifiers. Finally, the model’s performance was evaluated by accuracy, F1 score, and other metrics. The results showed that the Bagging-RF model outperformed the other eight machine learning classifiers, exhibiting 97.35%, 95.55%, 95.14% accuracy and 97.35%, 97.35%, 95.14% F1 Score at the Diabetes Prediction Dataset for diabetes prediction for the three age groups of 50–60, 60–70, and 70–80; and 97.03%, 94.90%, 93.70% accuracy and 97.03%, 94.90%, 93.70% F1 Score at the Diabetes Prediction Dataset. 95.55%, 95.13% F1 Score; and 97.03%, 94.90%, 93.70% accuracy; and 97.03%, 94.89%, 93.70% F1 Score at Diabetes Prediction Dataset. In addition, while other integrated learning models, such as ET, RF, Adaboost, and XGB, fail to outperform Bagging-RF, they also show excellent performance.

Rearranging Deformable Linear Objects for Implicit Goals with Self‐Supervised Planning and Control

The robotic manipulation of deformable linear objects is a frontier problem with many potential applications in diverse industries. However, most existing research in this area focuses on shape control for a provided explicit goal and does not consider physical constraints, which limits its applicability in many real‐world scenarios. In this study, a self‐supervised planning and control approach are proposed to address the challenge of rearranging deformable linear objects for implicit goals. Specifically, the context of making both ends of the object reachable (inside the robotic access range) and graspable (outside potential collision regions) by dual‐arm robots is considered. Firstly, the object is described with sequential keypoints and the correspondence‐based action is parameterized. Secondly, a generator capable of producing multiple explicit targets is developed, which adhere to implicit conditions. Thirdly, value models are learnt to assign the most promising explicit target as guidance and determine the goal‐conditioned action. All models within the policy are trained in a self‐supervised manner based on data collected from simulations. Importantly, the learned policy can be directly applied to real‐world settings since we do not rely on accurate dynamic models. The performance of the new method is validated with simulations and real‐world experiments.

Artificial Neural Network-Based Data-Driven Parameter Estimation Approach: Applications in PMDC Motors

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.

Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.

(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q2, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and R2. We simulated all EXSCEL trial participants (N = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period. UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q2: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q2 facilitated comparisons between subgroups and (unlike R2) was lower for biased predictors. Q2 for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas. Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q2, the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q2 or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.

Spillover: Mechanisms, Genetic Barriers, and the Role of Reservoirs in Emerging Pathogens

Viral spillover represents the transmission of pathogen viruses from one species to another that can give rise to an outbreak. It is a critical concept that has gained increasing attention, particularly after the SARS-CoV-2 pandemic. However, the term is often used inaccurately to describe events that do not meet the true definition of spillover. This review aims to clarify the proper use of the term and provides a detailed analysis of the mechanisms driving zoonotic spillover, with a focus on the genetic and environmental factors that enable viruses to adapt to new hosts. Key topics include viral genetic variability in reservoir species, biological barriers to cross-species transmission, and the factors that influence viral adaptation and spread in novel hosts. The review also examines the role of evolutionary processes such as mutation and epistasis, alongside ecological conditions that facilitate the emergence of new pathogens. Ultimately, it underscores the need for more accurate predictive models and improved surveillance to better anticipate and mitigate future spillover events.

Ex Vivo Intestinal Organoid Models: Current State-of-the-Art and Challenges in Disease Modelling and Therapeutic Testing for Colorectal Cancer

Despite improvements in participation in population-based screening programme, colorectal cancer remains a major cause of cancer-related mortality worldwide. Targeted interventions are desirable to reduce the health and economic burden of this disease. Two-dimensional monolayers of colorectal cancer cell lines represent the traditional in vitro models for disease and are often used for diverse purposes, including the delineation of molecular pathways associated with disease aetiology or the gauging of drug efficacy. The lack of complexity in such models, chiefly the limited epithelial cell diversity and differentiation, attenuated mucus production, lack of microbial interactions and mechanical stresses, has driven interest in the development of more holistic and physiologically relevant in vitro model systems. In particular, established ex vivo patient-derived explant and patient-derived tumour xenograft models have been supplemented by progress in organoid and microfluidic organ-on-a-chip cultures. Here, we discuss the applicability of advanced culturing technologies, such as organoid systems, as models for colorectal cancer and for testing chemotherapeutic drug sensitivity and efficacy. We highlight current challenges associated with organoid technologies and discuss their future for more accurate disease modelling and personalized medicine.

A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis

BackgroundDNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach.MethodsIn the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach.ResultsBy applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager’s theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced.ConclusionsThe combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis.Clinical trial numberNot applicable.

Recursive dynamic state estimation for power systems with an incomplete nonlinear DAE model

AbstractPower systems are highly complex, large‐scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This article introduces a novel centralized dynamic state estimator designed specifically for power systems where some component models are missing. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, the least squares criterion is applied to estimate all dynamic and algebraic states recursively. The approach generalizes the iterated extended Kalman filter and does not require static network observability, relying on the network topology and parameters. Furthermore, a topological criterion is established for placing phasor measurement units (PMUs), termed topological estimability, which guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature with computational times in accordance with the typical PMU sampling rates.

Illuminating Substrate Preferences of Promiscuous F420H2-Dependent Dehydroamino Acid Reductases with 4-Track mRNA Display.

Stereoselective reduction of dehydroamino acids is a common biosynthetic strategy to introduce d-amino acids into peptidic natural products. The reduction, often observed during the biosynthesis of lanthipeptides, is performed by dedicated dehydroamino acid reductases (dhAARs). Enzymes from the three known dhAAR families utilize nicotinamide, flavin, or F420H2 coenzymes as hydride donors, and little is known about the catalysis performed by the latter family proteins. Here, we perform a bioinformatics-guided identification and large-scale in vitro characterization of five F420H2-dependent dhAARs. We construct an mRNA display-based pipeline for ultrahigh throughput substrate specificity profiling of the enzymes. The pipeline relies on a 4-track selection strategy to deliver large quantities of clean data, which were leveraged to build accurate substrate fitness models. Our results identify a remarkably promiscuous enzyme, referred to as MaeJC, that is capable of installing d-Ala residues into arbitrary substrates with minimal recognition requirements. We integrate MaeJC into a thiopeptide biosynthetic pathway to produce d-amino acids-containing thiopeptides, demonstrating the utility of MaeJC for the programmable installation of d-amino acids in ribosomal peptides.

Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

This research explores the application of Artificial Intelligence (AI) techniques to assess the mechanical properties of geopolymer concrete made from a blend of Banana Peel-Ash (BPA) and Sugarcane Bagasse Ash (SCBA), using a sodium silicate (Na2SiO3) to sodium hydroxide (NaOH) ratio ranging from 1.5 to 3. Utilizing three AI methodologies—Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP)—the study aims to enhance prediction accuracy for the mechanical properties of geopolymer concrete based on 104 datasets. By optimizing mix designs through varying proportions of BPA and SCBA, alkaline activator molarity, and aggregate-to-binder ratios, the research identified combinations that significantly enhance mechanical properties, demonstrating notable international relevance as it contributes to global efforts in sustainable construction by effectively utilizing industrial by-products. The experimental results demonstrated that increasing the molarity of the alkaline activator from 4 to 10 M significantly enhanced both the compressive and flexural strengths of the geopolymer concrete. Specifically, a mixture containing 52.5% SCBA and 47.5% BPA at a 10 M molarity achieved a maximum compressive strength of 33.17 MPa after 20 h of curing. In contrast, a mixture composed of 95% SCBA and 5% BPA at a 4 M molarity exhibited a substantially lower compressive strength of only 21.27 MPa. Additionally, the highest recorded flexural strength of 9.95 MPa (77.25% SCBA and 22.5 BPA) was observed at the 10 M molarity, while the flexural strength at 4 M was lowest, at 4.12 MPa (95% SCBA and 5% BPA). Microstructural analysis through Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (ED-SEM) revealed insights into the pore structure and elemental composition of the concrete, while Thermogravimetric Analysis (TGA) provided data on the material’s thermal stability and decomposition characteristics. Performance analysis of the AI models showed that the ANN model had an average MSE of 1.338, RMSE of 1.157, MAE of 3.104, and R2 of 0.989, while the ANFIS model outperformed with an MSE of 0.345, RMSE of 0.587, MAE of 1.409, and R2 of 0.998. The GEP model demonstrated an MSE of 1.233, RMSE of 1.110, MAE of 1.828, and R2 of 0.992, confirming that ANFIS is the most accurate model for predicting the mechanical and rheological properties of geopolymer concrete. This study highlights the potential of integrating AI with experimental data to optimize the formulation and performance of geopolymer concrete, advancing sustainable construction practices by effectively utilizing industrial by-products.

Assessing methods for telluric removal on atmospheric retrievals of high-resolution optical exoplanetary transmission spectra

Recent advancements in ultra-stable ground-based high-resolution spectrographs have propelled ground-based astronomy to the forefront of exoplanet detection and characterisation. However, the resultant transmission and emission spectra of exoplanetary atmospheres are inevitably contaminated by telluric absorption and emission lines due to the light's transmission through the Earth's atmosphere above the observatory. Retrieving accurate atmospheric parameters depends on accurate modelling and removal of this telluric contamination while preserving the faint underlying exoplanet signal. There exist many methods to model telluric contamination, whether directly modelling the Earth's transmission spectrum via radiative transfer modelling, or using a principal component analysis (PCA)-like reconstruction to fit the time-invariant features of a spectrum, and removing these models from the observations. We aimed to assess the efficacy of these various telluric removal methods in preserving the underlying exoplanetary spectra. We compared two of the most common telluric modelling and removal methods molecfit and the PCA-like algorithm SysRem using planetary transmission spectra injected into three high-resolution optical observations taken with ESPRESSO. These planetary signals were injected at orbital periods of P = 2 days and P = 12 days, resulting in differing changes in radial velocity during transit. We then retrieved various injected atmospheric model parameters in order to determine the efficacy of the telluric removal methods, as well as their effect on the transmission spectra of exoplanets with varying orbital architectures. For the close-in, high velocity injected signal, we found that SysRem performed better for species that are also present in the Earth's atmosphere--with accurate and precise retrieval of atmospheric abundances and $T$-$P$ profiles, across each of the datasets. As we moved to slower moving signals at larger orbital separations, for one of the three datasets SysRem dampened the planetary H$_2$O signal, leaving the retrieved abundance value unconstrained. In contrast, the H$_2$O signal was preserved for the telluric modelling method molecfit . However, this behaviour was not ubiquitous across all three of the injected datasets, with another dataset showing a more precise H$_2$O/Fe ratio when preprocessed with SysRem . These conflicts highlight the importance of testing individual high-resolution dataset reduction routines independently to ensure real exoplanetary signals are preserved.