Predictive Outcome Research Articles

Beyond the AJR: Can MRI Predict Outcomes in Children With Severe Traumatic Brain Injury?

Beyond the AJR: Can MRI Predict Outcomes in Children With Severe Traumatic Brain Injury?

Deep Hybrid Intelligence: CNN-LSTM for Accurate Software Bug Prediction

This research aimed to explore the potential of applying deep learning to software bug prediction. The study utilized various data preprocessing techniques that were essential in preparing the data for analysis, using a set of commonly available software bug reports and related metrics. In the data collection and preprocessing phase, the dataset was filtered to focus on critical software metrics, scaled for consistency, and additional techniques such as feature engineering and standardization were employed to enhance data variability. In order to analyze the effectiveness of the model in predicting software faults, the dataset was split so that it could be used for testing and training purposes. Several deep learning models, include CNN and LSTM architectures, were developed utilizing the preprocessed dataset in order to enhance the performance of the models. Subsequently, a hybrid ensemble technique was employed, combining the prediction outcomes of the best-performing individual models to form an ensemble model. Using test datasets, each model's performance was assessed using common assessment measures including precision, F1 score, accuracy, and recall. The ensemble models outperformed individual models in bug prediction, as demonstrated by higher accuracy and F1 scores. The final model achieved an accuracy of 96%, which was considered highly satisfactory for predicting software defects.

Revolutionizing healthcare: a comparative insight into deep learning’s role in medical imaging

Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the model computational constraints, Cloud Computing (CC) is integrated to operate with the DL models. Recent articles on DL-based MRI have not discussed datasets specific to different diseases, which makes it difficult to build the specific DL model. Thus, the article systematically explores a tutorial approach, where we first discuss a classification taxonomy of medical imaging datasets. Next, we present a case-study on AD MRI classification using the DL methods. We analyse three distinct models-Convolutional Neural Networks (CNN), Visual Geometry Group 16 (VGG-16), and an ensemble approach-for classification and predictive outcomes. In addition, we designed a novel framework that offers insight into how various layers interact with the dataset. Our architecture comprises an input layer, a cloud-based layer responsible for preprocessing and model execution, and a diagnostic layer that issues alerts after successful classification and prediction. According to our simulations, CNN outperformed other models with a test accuracy of 99.285%, followed by VGG-16 with 85.113%, while the ensemble model lagged with a disappointing test accuracy of 79.192%. Our cloud Computing framework serves as an efficient mechanism for medical image processing while safeguarding patient confidentiality and data privacy.

Development and validation of a nomogram for predicting cerebrospinal fluid leak after endoscopic craniopharyngioma resection.

To identify risk factors for cerebrospinal fluid (CSF) leak after extended endoscopic endonasal surgery for craniopharyngiomas and develop a predictive model for predicting postoperative CSF leak. Six hundred and sixty cases of craniopharyngioma (training cohort: n = 462; validation cohort: n = 198) were retrospectively reviewed between October 2018 and May 2024, and relevant risk factors were identified. A nomogram was built using a stepwise logistic regression method based on the Akaike information criterion. The performance of the nomogram was evaluated using area under the curve (AUC), calibration curve, and decision curve analysis. The overall rate of postoperative CSF leak was 4.5%. Higher prognostic nutritional index (PNI) level (OR 0.819, 95% confidence interval [CI] 0.735-0.912; p < 0.001) and larger dural defect (OR 6.789, 95% CI 3.112-14.807; p < 0.001) were identified as independent predictors for postoperative CSF leak in multivariable logistic regression analysis. The AUCs of the nomogram were 0.870 (95% CI, 0.782-0.957; p < 0.001) and 0.842 (95% CI, 0.722-0.963; p < 0.001) in the training and validation sets, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between predictive and actual outcomes (p = 0.608 and p = 0.564, respectively).Decision curve analysis further confirmed the clinical usefulness of the nomogram. Higher PNI levels may help reduce the risk of postoperative CSF leak, while a larger dural defect size was demonstrated as an independent risk factor. We developed and validated a nomogram for predicting CSF leak after endoscopic craniopharyngioma resection, which showed strong predictive performance and could assist clinicians in formulating personalized treatment strategies.

High-fidelity prediction of forming quality for self-piercing riveted joints in aluminum alloy based on machine learning

The effect of rivet length and sheet thickness on the cross-sectional formation and tensile-shear performance of self-piercing riveted joints in AA5754 aluminum alloy was examined through experimental investigation. The influence degree of joining parameters on the forming quality was analyzed. It was revealed that rivet length and sheet thickness are pivotal factors influencing the tensile-shear strength of the joint, culminating in the identification of four optimal riveting process schemes:LCh1Ah2A、LAh1Ah2B、LAh1Ah2B and LBh1Ah2B. A simulation model for self-piercing riveting was established, employing the GISSMO failure model and the modified Mohr-Coulomb (MMC) failure criteria to predict the damage and fracture of the aluminum alloy. A plethora of high-quality datasets depicting the cross-sections of the joints were derived from simulation analysis. Subsequently, the structure and hyperparameter determination method of traditional neural network prediction models were elucidated. By amalgamating the Aquila Optimization (AO) algorithm with the African Vultures Optimization Algorithm (AVOA), a hybrid optimization algorithm model known as MIC_AOAVOA was developed. This model effectively harnesses the strengths of various algorithms to augment search efficiency and optimization capabilities. Strategies for population initialization and adaptive weight adjustments were incorporated to enhance the algorithm's convergence velocity and the quality of solutions. The cauchy opposition-based learning (COBL) and fitness-distance balance (FDB) strategy further refined the composite algorithm, bolstering its global search capabilities and population diversity. Comparative analyses were performed with single algorithm models and traditional BP neural network models, with an in-depth examination of the MIC_AOAVOA_BP model's prediction outcomes. Comprehensive evaluations utilizing error statistics and composite evaluation indicators demonstrated that the model consistently achieved mean absolute percentage error (MAPE) values below 10%, correlation coefficients (R²) exceeding 0.98, and stable mean squared error (MSE) values around 0.0002 across the prediction of three metrics. These results underscore the model's high precision and stability. Consequently, the proposed enhanced model offers a solution that is more stable, accurate, and robust for the prediction of forming quality in self-piercing riveted joints within engineering applications.

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing significant financial, economic, and human burdens. In Europe, stroke is the second leading cause of death and the primary cause of disability, with numbers expected to rise due to ageing and improved survival rates. Functional recovery from AF-related stroke is often unsatisfactory, leading to prolonged hospital stays, severe disability, and high mortality. Despite advances in AF and stroke research, the full pathophysiological and management issues between AF and stroke increasingly need innovative approaches such as artificial intelligence (AI) and machine learning (ML). Current risk assessment tools focus on static risk factors, neglecting the dynamic nature of risk influenced by acute illness, ageing, and comorbidities. Incorporating biomarkers and automated ECG analysis could enhance pathophysiological understanding. This paper highlights the need for personalised, integrative approaches in AF and stroke management, emphasising the potential of AI and ML to improve risk prediction, treatment personalisation, and rehabilitation outcomes. Further research is essential to optimise care and reduce the burden of AF and stroke on patients and healthcare systems.

Evaluating suitability of regression models in small data regimes using concrete with recycled copper tailings as a case study

The utilization of regression models for the prediction of construction material properties is well-established, yet their performance when applied to small datasets is still unclear. This study investigates the performance of different regression models combined with various data preprocessing techniques in contexts where data is limited. Specifically, the research focuses on evaluating the suitability of five regression models across nine different data processing scenarios using concrete with recycled copper tailing as a case study. This study aims to determine which combinations of regression models and preprocessing methods yield the most accurate predictions in small data regimes. This research is motivated by the necessity to enhance prediction reliability in the field of construction materials, where experimental data can often be scarce or costly to obtain. Within the study context, a dataset comprising 21 experimental specimens is used to evaluate the performance of the models on various concrete properties, including fresh density, compressive strength, flexural strength, pull-off strength, abrasion resistance, water penetration, rapid chloride ion permeability, and air permeability. Through rigorous evaluation involving a 10-fold cross-validation process to verify accuracy, the research demonstrates that selecting the optimal regression model and data preprocessing technique selection substantially improves prediction outcomes, even with limited data. The findings highlight the importance of this research, suggesting that even small datasets, when handled correctly, can provide robust insights.

A multitask interpretable model with graph attention mechanism for activity prediction of low-data PIM inhibitors.

The aberrant expression of proviral integration site for Moloney murine leukemia virus (PIM) kinases is closely related to various tumors and chemotherapy resistance, making them attractive targets for cancer therapy. However, due to the extremely high homology among the three PIM isoforms (PIM1, PIM2, PIM3) and the limited availability of existing bioactivity data, screening and designing selective PIM inhibitors remain a daunting challenge. To address this issue, this study constructed a multitask regression model that can simultaneously predict the half-maximal inhibitory concentration (IC50 values). The model utilizes an attention mechanism to capture effects within local atomic groups and the interactions between different groups of atoms. Through weight sharing, the model enhances the accuracy of predicting PIM3 inhibitors by leveraging the rich and highly correlated data from PIM1 and PIM2 isoforms. Additionally, visualizing the weights of nodes (atoms in the molecule) in the model helps us to intuitively understand the relationship between molecular features and prediction outcomes, thereby enhancing the interpretability of the model. In summary, this work provides new insights and methods for performing activity prediction tasks for multiple similar targets in low-data scenarios.

Optimal facial regions for remote heart rate measurement during physical and cognitive activities

Remote photoplethysmography (rPPG) has gained prominence as a non-contact and real-time technology for heart rate monitoring. A critical factor in rPPG’s accuracy is the selection of regions of interest (ROI), as it can significantly influence prediction outcomes. Most studies typically use the forehead and cheeks as ROIs, but little research has explored other facial regions or how stable these ROIs are during physical movement and cognitive tasks. In this study, we analyzed 28 facial regions based on anatomical definitions using two mixed datasets derived from three public databases: LGI-PPGI, UBFC-rPPG, and UBFC-Phys. We applied rPPG algorithms such as orthogonal matrix image transformation (OMIT), plane-orthogonal-to-skin (POS), chrominance-based (CHROM), and local group invariance (LGI). Our findings show that the glabella, medial forehead, lateral forehead, malars, and upper nasal dorsum consistently perform well, with the glabella achieving the highest overall evaluation score. These results offer valuable insights for advancing remote heart rate monitoring technologies.

Prediction of Beijing's PM2.5 Concentration Based on the LSTM Model

PM2.5 has serious impacts on cardiovascular and respiratory health. As people's attention to physical health increases, the issue of PM2.5 has become increasingly prominent. The goal of this research is to create a prediction model for Beijing's PM2.5 concentrations using the Long Short-Term Memory (LSTM) deep learning algorithm. This paper utilizes PM2.5 measurements from the US Embassy in Beijing and meteorological data from Beijing Capital International Airport from 2010 to 2014. The study forecasts PM2.5 concentrations via the LSTM model by integrating variables such as temperature, pressure, and wind speed. The results of this study validate the feasibility of the LSTM model in predicting PM2.5 and yield relatively good prediction outcomes. It is evident that concentrations are lower in the summer and higher in the winter. However, the prediction results are lower compared to the actual data and are not effective in predicting drastic changes caused by other influencing factors. The results provide information for the creation of more efficient air quality management plans by exposing the connections between PM2.5 and different meteorological variables.

Predictive Modeling in IoT-Driven Agriculture: A Comparative Study of Regression and Classification with LIME Interpretability

Abstract. This study aims to compare the performance of regression and classification machine learning models in crop prediction by analyzing data from IoT-driven agriculture. Linear regression and random forest regression models were used to predict the percentage of root growth dry matter, while logistic regression and support vector machines were employed to classify crop production regions. To enhance model interpretability, the LIME tool was applied to analyze feature importance. The experimental results demonstrate that the models perform well in terms of prediction accuracy, and LIME provided clear feature explanations, helping identify the variables with the greatest impact on prediction outcomes. This research offers data-driven insights for optimizing resource management in smart agricultural systems.

Prediction of the End of a Romantic Relationship in Peruvian Youth and Adults: A Machine Learning Approach

This study explores the effectiveness of machine learning models in predicting the end of romantic relationships among Peruvian youth and adults, considering various socioeconomic and personal attributes. The study implements logistic regression, gradient boosting, support vector machines, and decision trees on SMOTE-balanced data using a sample of 429 individuals to improve model robustness and accuracy. Using stratified random sampling, the data is split into training (80%) and validation (20%) sets. The models are evaluated through 10-fold cross-validation, focusing on accuracy, F1-score, AUC, sensitivity, and specificity metrics. The Random Forest model is the preferred algorithm because of its superior performance in all evaluation metrics. Hyperparameter tuning was conducted to optimize the model, identifying key predictors of relationship dissolution, including negative interactions, desire for emotional infidelity, and low relationship satisfaction. SHAP analysis was utilized to interpret the directional impact of each variable on the prediction outcomes. This study underscores the potential of machine learning tools in providing deep insights into relationship dynamics, suggesting their application in personalized therapeutic interventions to enhance relationship quality and reduce the incidence of breakups. Future research should incorporate larger and more diverse datasets to further validate these findings.

Dissecting the Tumor Microenvironment (TME) to Decipher New Immunotherapy Targets by Using Artificial Intelligence

Cancer remains one of the leading causes of death worldwide, second only to cardiovascular diseases. Standard cancer treatments, such as chemotherapy, radiation, and surgery, are effective for primary tumors but often fail to eliminate disseminated tumor cells responsible for metastasis. This limitation underscores the necessity for advanced therapeutic strategies, leading to the rise of immunotherapy, which leverages the immune system to combat cancer. Despite its promise, immunotherapy faces challenges, including variable patient responses and immune-related toxicities, complicating the prediction of treatment efficacy. Here, artificial intelligence (AI) emerges as a vital tool that can enhance the precision and effectiveness of immunotherapy by analyzing the intricate tumor microenvironment (TME). This paper explores the limitations of current immunotherapies and examines how AI can address these challenges. It discusses the TME’s role in shaping immune responses, highlighting how understanding its complexities can improve predictive power and treatment outcomes. Furthermore, we address the limitations of AI in cancer research and propose future directions for its integration into clinical practice, with the potential to revolutionize personalized cancer therapy and improve overall patient care.

Extended Fourier Neural Operators to learn stiff chemical kinetics under unseen conditions

The solution of stiff chemical kinetics is recognized as the computational bottleneck for direct simulations of reacting flows. In this study, we extend the concept of Fourier Neural Operator (FNO) to learn stiff chemical kinetics. Specifically, element and mass conservation are introduced as the physical constraints in the extended FNO (EFNO). In addition, the training data are transformed by a Box–Cox strategy to rectify the skewed distribution of the species in the stiff problems. Finally, balanced loss functions are formulated to address the unbalanced sampling data points in complex reacting flow problems. The EFNO model is leveraged to forecast the temporal evolution of chemical species, utilizing an iterative approach wherein the prediction outcome from the previous time step is employed as a new input for subsequent time step prediction. The results in this work demonstrate the significant use of an EFNO approach to solving stiff chemical dynamics in reacting flow simulations, with a time step size comparable to the typical flow time step size. Its prediction accuracy and generalization ability are evaluated by comparing with the original FNO, Deep Nueral Network (DNN) and DeepONet models, in solving toy problems, zero-dimensional hydrogen autoignition, and a three-dimensional hydrogen/ammonia turbulent jet flame. The EFNO is shown to be highly accurate. More importantly, compared with other deep learning models, it can be generalized to stiff chemical kinetic states under unseen conditions, which the model has never trained for. The great performance of EFNO in terms of accuracy and generalization ability suggests that EFNO is a promising solution algorithm for stiff chemical kinetics problems in reacting flows.Novelty and Significance Statement: The novelty of this work lies in the newly developed extended Fourier neural operators (EFNO) to learn stiff chemical kinetics. Specifically, we for the first time evaluated and tested the performance of Fourier neural operators in solving stiff chemical kinetics. More importantly, we extended the original Fourier neural operators to accurately solve for stiff chemical kinetics problems under unseen conditions, which was a very challenging problem for deep learning methods in the literature. Our results demonstrated that the EFNO model solves chemical kinetics in both simple 0D autoignition and complex 3D turbulent jet flames with great accuracy and generalization ability, even for conditions which the training dataset has never encompassed. This work is significant because it developed a neural operator-based algorithm that can significantly accelerate the stiff chemical kinetic solution process in reacting flow simulations with great accuracy even for unseen initial conditions.

IDHNet: Ultra‐Short‐Term Wind Power Forecasting With IVMD–DCInformer–HSSA Network

ABSTRACTThe variability and unpredictability of wind power generation present significant challenges for grid management and planning. Enhancing the accuracy of wind power forecasting is crucial for improving the reliability of renewable energy systems. To enhance the accuracy of temporal wind power predictions, the IVMD–DCInformer–HSSA framework has been introduced. Initially, the original wind power data is decomposed into multiple intrinsic mode function (IMF) components using the improved variational mode decomposition (IVMD) technique. Subsequently, the Sparrow search algorithm (HSSA) is employed to optimize the parameters of the enhanced Informer deep neural network, which are then integrated into the improved Informer model. The predictions of each IMF component resulting from the IVMD decomposition are then combined to generate the final prediction outcome. The experimental results show that the R‐squared value of the proposed combined model is increased to 0.9903, and the accuracy is increased by 1%–3% compared with other models, which has a good prediction effect.

Breaking the barriers of technology adoption: Explainable AI for requirement analysis and technology design in smart farming

Understanding the factors that drive and hinder technology adoption is critical for companies that try to access customer segments or governmental agencies that want to foster economic, ecological, or social change. By assessing the technological readiness of customer groups, common and individual barriers or opportunities for technology adoption can be observed and translated into technological requirements, business strategies, or policy interventions. Current approaches to assessing such barriers do not provide information on which factors influence technological readiness more than others, limiting the prioritization of targeted technological or political interventions. This research introduces an Explainable Machine Learning (XAI) approach to overcome this limitation. It exemplifies its usability for the Precision Livestock Farming domain, particularly for smart technologies incorporating novel advances in Artificial Intelligence and Internet of Things. A random forest machine learning model is introduced to identify clusters of different farmers' technological readiness based on the available features (survey questions). XAI techniques are then deployed to understand the influence of individual features on the prediction outcome, highlighting factors that increase or decrease technological readiness of farmers. The results are assessed for their potential for requirement and business analysis while providing targeted suggestions for technology design.

Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.

Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes. Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction. A retrospective cohort study was conducted from January 2018 to October 2020. A total cohort of 240,000 patients' medical records was collected. The data were collected exclusively for preoperative variables to accurately analyze the predictive factors impacting the postoperative length of stay. The main outcome of this study is an analysis of the length of stay (in days) after surgery until discharge. The prediction was performed with ridge regression, random forest, XGBoost, and multi-layer perceptron neural network models. The XGBoost resulted in the best performance with an average error within 3days. Moreover, we explain each feature's contribution over the XGBoost model and further display distinct predictors affecting the overall prediction outcome at the patient level. The risk factors that most importantly contributed to the stay after surgery were as follows: a direct bilirubin laboratory test, department change, calcium chloride medication, gender, and diagnosis with the removal of other organs. Our results suggest that healthcare providers take into account the risk factors such as the laboratory blood test, distributing patients, and the medication prescribed prior to the surgery. We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.

ROVIGA: Model-Driven Soil Moisture Sensor for Internet-Connected Plant Pot

The soil moisture sensor provides numerical measurements to detect changes in soil moisture using an analog voltage output. This research aims to develop a capacitive sensor based on a statistical model to detect soil moisture for plant watering, leveraging the Internet of Things (IoT). The analysis was conducted using polynomial and linear regression models. The modeling process was based on primary gravimetric test results from dried soil. The best model coefficients, selected based on the highest adjusted R-squared value, were used for sensor recalibration. A watering system was then developed using an Arduino and a model-driven capacitive soil moisture sensor integrated into an internet-connected smart plant pot, enabling remote control via a mobile phone. The research findings indicate that the 8th-order polynomial model, with the highest adjusted R-squared value of 0.9583, is the most accurate. The smart watering system using the model-driven capacitive sensor achieved soil moisture prediction outcomes ranging from 0.08 to 1.01 for 150 to 418 sensor data points. The internet-connected smart plant pot allows precise and real-time control, delivering notifications and enabling actions when plants require watering.

A Comparative Study on Machine Learning Classifiers for Cervical Cancer Prediction: A Predictive Analytic Approach

INTRODUCTION: Cervical cancer is a significant global health concern, particularly in underdeveloped nations where preventive healthcare measures are limited. Early identification of the risks associated with cervical cancer is essential for both prevention and treatment. OBJECTIVES: In recent years, machine-learning algorithms have gained popularity as potential techniques for determining a person's risk of developing cancer based on demographic and medical information. This study uses a dataset that contains patient demographics, clinical history, and results from diagnostic tests to examine how machine learning-based algorithms can be used to predict the risks of cervical cancer. METHODS: Various machine learning approaches are used to create predictive systems, including Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), Nearest Centroid (NC), Multilayer Perceptron(MP), and AdaBoost (AB). RESULTS: The prediction capability of these models is assessed using performance metrics such as accuracy, sensitivity, specificity, f-measure, precision, and area under the receiver operating characteristic curve (AUC-ROC). Our results show that the decision tree has the highest accuracy, precision, and f1-score (98.91%, 97.81%, and 0.9889). Additionally, model performance was optimized by the use of hyperparameter tuning. After hyperparameter adjustment, the Support Vector Machine (SVM) showed superior accuracy of 99.64%, precision of 99.26%, and an F1-score of 0.9963, thereby indicating its potential in cervical cancer probability prediction. We also created a web application that uses a machine-learning model to estimate the risk of cervical cancer. CONCLUSION: The findings of this study highlight the significance of SVM and demonstrate the potential and capabilities of machine learning techniques to enhance accurate prediction and patient outcomes for cervical cancer screening.

Compressive behavior of concrete jacketed in basalt TRM shell: Experiments and predictions

Numerous studies have investigated the behavior of textile reinforced mortar (TRM)-confined concrete, yielding various stress-strain models predominantly derived from fiber-reinforced polymer (FRP)-confined concrete. Yet, these models frequently overlook the intricate constitutive behavior of TRM materials and inadequately capture how TRM delivers its confinement effects. This often leads to inaccurate representation of the true characteristics of the confinement system, particularly the crucial role of mortar. This paper focuses on the impact of variations in textile layers and mortar matrix strength on the stress-strain behavior of basalt TRM (BTRM)-confined concrete, aiming to enhance our understanding of the confinement mechanism. Additionally, the study critically evaluates the key components of an existing analysis-oriented stress-strain model for FRP-confined concrete and introduces a refined version that more precisely depicts the behavior of confined concrete. This refined model integrates an identified confinement mechanism to provide accurate predictions of BTRM-confined concrete behavior. Our results reveal that low-grade mortar significantly decreases the actual confinement stiffness of BTRM jackets, inducing a steeper decline in the stress-strain curve, while high-strength mortar slightly diminishes the hoop rupture strain. To address the challenges of quantifying the complex confinement effect-compounded by the variable stress state and constitutive behavior of TRM-a novel coefficient, termed km, is introduced to gauge the influence of mortar strength on the confinement stiffness within the confining pressure equation. Predictive outcomes, including stress-strain and axial-to-lateral strain curves, show close alignment with experimental data, particularly in the slope at the plateau stage. This research significantly advances the quantitative understanding of TRM confinement effects and proposes the potential for designing more ductile structures using these materials, which could lead to enhanced resilience against seismic events and other structural challenges.