Model Predictive Technique Research Articles

Comparative Analysis of Predictive Modeling Techniques for Mechanical Properties in Dissimilar Friction Stir Welding of AA6061 and AZ31B

Comparative Analysis of Predictive Modeling Techniques for Mechanical Properties in Dissimilar Friction Stir Welding of AA6061 and AZ31B

Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

Enhancing Selenium Removal Using Pre-Treated Natural Clay: Experimental Investigation and Predictive Modeling

Abstract A heat treatment methodology was adopted as a pretreatment strategy, altering the porous structure of the clay to minimize leaching for selenium adsorption in an aqueous system. Rigorous experiments were carried out in batch mode to determine optimal parameters across various variables, including contact time, adsorbent dosages, selenium concentrations, pH, temperature, and stirring speed during selenium removal using natural clay. Investigating several kinetic and isotherm models revealed the best fitting for the pseudo-second-order and the Langmuir isotherm. Endothermic and spontaneous characteristics of the adsorption process were shown during thermodynamic analysis. In this study, a predictive model for the efficiency of selenium separation was established using Response Surface Methodology (RSM). Additionally, an Artificial Neural Network (ANN), a data-driven model, was employed for comparative analysis. The predictive model exhibited a high degree of agreement with experimental data, demonstrated by a low relative error of <0.10, a high regression coefficient of >0.97), and a substantial Willmott-d index of >0.95. Moreover, the efficacy of pre-activated clay in selenium removal was assessed, revealing the superior performance of ANN models over RSM models in forecasting the efficiency of the adsorption process. This research significantly advances an effective and sustainable material for selenium removal, providing valuable insights into predictive modeling techniques applicable to similar contexts to boost scale-up confidence during industrial implementation in affected regions.

Accelerated Prediction of Phase Behavior for Block Copolymer Libraries Using a Molecularly Informed Field Theory.

Solution formulations involving polymers are the basis for a wide range of products spanning consumer care, therapeutics, lubricants, adhesives, and coatings. These multicomponent systems typically show rich self-assembly and phase behavior that are sensitive to even small changes in chemistry and composition. Longstanding computational efforts have sought techniques for predictive modeling of formulation structure and thermodynamics without experimental guidance, but the challenges of addressing the long time scales and large length scales of self-assembly while maintaining chemical specificity have thwarted the emergence of general approaches. As a consequence, current formulation design remains largely Edisonian. Here, we present a multiscale modeling approach that accurately predicts, without any experimental input, the complete temperature-concentration phase diagram of model diblock polymers in solution, as established postprediction through small-angle X-ray scattering. The methodology employs a strategy whereby atomistic molecular dynamics simulations is used to parametrize coarse-grained field-theoretic models; simulations of the latter then easily surmount long equilibration time scales and enable rigorous determination of solution structures and phase behavior. This systematic and predictive approach, accelerated by access to well-defined block copolymers, has the potential to expedite in silico screening of novel formulations to significantly reduce trial-and-error experimental design and to guide selection of components and compositions across a vast range of applications.

Application of Artificial Intelligence in Supply Chain Management: A Review on Strengths and Weaknesses of Predictive Modeling Techniques

Application of Artificial Intelligence in Supply Chain Management: A Review on Strengths and Weaknesses of Predictive Modeling Techniques

Predictive models for properties of hybrid blended modified sustainable concrete incorporating nano-silica, basalt fibers, and recycled aggregates: Application of advanced artificial intelligence techniques

The main objective of this work is to improve the compressive strength of concrete, specially in sustainable construction is to develop more precise predictive modeling techniques. The compressive strength prediction of basalt fiber reinforced concrete filled with nano-silica and recycled aggregates can be done using a hybrid deep learning model suggesting the use of the combination of Convolutional Neural Networks and Long Short-Term Memory networks. The CNN captures microstructural features from SEM images, while the LSTM models temporal dependencies from sequential curing data samples. To enhance the prediction accuracy, PCA was performed on feature dimensionality reduction and GA optimized hyperparameters both for the model as well as the concrete mix design for improved strength with cost effectiveness. With an R² value of 0.92–0.95, the performance results of the presented model came out better than the baseline models, as well as reducing the MAE by 20 %. Besides, there existed a 5–8 % better compressive strength in GA-optimized mix designs. Robustness comes into play with the model that shows steady strength predictions, regardless of conditions of curing under multiple conditions and at different material composition levels. Furthermore, the reutilization of recycled aggregates and nano-silica gives a real environmental benefit as less waste is produced but the material performance is maximized. This kind of outcome indicates how the proposed model can be practically applied in optimizing concrete design in terms of strength and sustainability features, thus providing an accessible instrument for decision-making in the construction field. It is an effective tool to improve the performance of concrete while minimizing environmental and material wastes.

Analytical modelling techniques for efficient broadband noise prediction of high-speed trains

Analytical modelling techniques are presented for broadband noise prediction of high-speed trains. For interior noise problems, train body structures are modelled exactly by the dynamic stiffness method (DSM); whereas the interior acoustic cavities of the train body are modelled by the SDSM where the boundary conditions are described by modified Fourier series with a rapid convergence rate. Finally, the vibro-acoustic coupling model of the train body structures is constructed. For exterior noise problems, exterior train noise models are first formed by experimental data considering the Doppler effect; then both the DSM and theoretical attenuation formulas are used to build noise radiation models for different external environment conditions. The performance of the method is evaluated by predicting the interior sound field distribution of typical cross sections of the train body and the external radiated noise in a tunnel, or in an environment where the acoustic barrier or buildings along railways exist. Numerical results are in good agreement with the those obtained by the finite element method. Thus, the proposed methods can achieve broadband noise prediction highly accurately and efficiently, which can also serve as the benchmark for predicting the radiated noise generated by high‐speed trains.

Comparative QSAR and q-RASAR modeling for aquatic toxicity of organic chemicals to three trout species: O. Clarkii, S. Namaycush, and S. Fontinalis

Oncorhynchus clarkii, Salvelinus fontinalis, and Salvelinus namaycush are vital trout species in North America, crucial for maintaining ecological balance, economic stability, and human health. These species thrive in cold, unpolluted waters and are highly vulnerable to contaminants. Given the rapid proliferation of industrial organic chemicals, traditional in vivo toxicity testing methods are inadequate to ensure timely and comprehensive risk assessments. Therefore, we employed in silico tools, namely Quantitative Structure-Activity Relationship (QSAR) and Quantitative Read-Across Structure-Activity Relationship (q-RASAR), to efficiently predict the aquatic toxicity of chemicals. Utilizing acute median lethal concentration (LC50) data from the US EPA’s ToxValDB, we developed the first-ever species-specific QSAR and q-RASAR models. The q-RASAR models outperformed traditional QSAR models by achieving higher internal and external statistical quality for each species. Key toxicity-determining descriptors included electrotopological state indices, autocorrelation descriptors, and similarity-based RASAR descriptors. For O. clarkii, the presence of chlorine atoms and rotatable bonds significantly influenced toxicity. S. fontinalis toxicity was strongly affected by polarizability, and van der Waals volumes, while S. namaycush showed sensitivity to weak hydrogen bond acceptors and topological complexity. The models predicted the toxicity of 1172 external compounds, identifying the most and least toxic chemicals for each species. This study not only offers the first comprehensive q-RASAR models for predicting trout species-specific toxicity but also provides novel insights into species-specific toxicological modes of action. The results contribute significantly to chemical screening and prioritization in aquatic risk assessments, effectively filling critical data gaps and advancing predictive modeling techniques.

Navigating OpenDOAR Distribution: A Deep Dive into Predictive Modeling and Trend Forecasting

Effective navigation and strategic foresight are imperative in the dynamic realm of academic repositories. This study aims to comprehensively understand global scholarly communication dynamics by analyzing repository distribution and forecasting future trends using predictive modeling methodologies. Employing a mixed-methods approach, this research blends quantitative analysis and predictive modeling techniques. It encompasses phases such as distribution analysis of repositories across continents, examination of repository counts across countries using GeoPandas and Matplotlib, optimization of Long short-term memory (LSTM) networks, application of Random Forest Regressor models, and time series analysis using Autoregressive Integrated Moving Average (ARIMA) models. The study reveals profound disparities in repository counts across continents, reflecting variations in research infrastructure. Visualizations vividly depict geographical patterns and disparities in repository distribution. Predictive modeling techniques offer insights into future trends in repository development, empowering decision-makers with strategic insights. While this study provides valuable insights, limitations exist, such as the scope limited to OpenDOAR repositories and the need for further refinement in predictive modeling techniques; future research should explore additional factors influencing repository development.

Prediction of Heart Disease Risk among Patients in Federal Medical Centre, Abeokuta Using Naïve Bayes

Heart disease is a silent killer, which can cause sudden death of individuals without obvious symptoms. The risk of heart disease is a sign in human beings that must not be neglected. Therefore, this study aimed to predict heart disease among patients in the Federal Medical Hospital Centre (FMC), Abeokuta. Descriptive statistics and data visualization techniques were used to gain insights into the distribution and relationships among the variables. Subsequently, a Naive Bayes classifier model was built using 80% of the data for training and 20% for testing. In addition, a Decision Tree Algorithm (DT) model was used to compare the performance of the Naive Bayes model. The performances of the two models were evaluated using accuracy, sensitivity, ROC-AUC, specificity, precision, and the F1-score. The Naive Bayes model achieved an overall accuracy of 83.61%, precision of 89.29%, recall of 78.12%, F1-Score of 83.33%, ROC-AUC of 90%, sensitivity of 78.12%, and specificity of 89.66. On the other hand, they were compared with the Decision Tree (DT) model which achieved an overall accuracy of 75.41%, precision of 77.42%, recall of 75%, F1-Score of 76.19%, ROC-AUC of 84.54%, sensitivity of 75%, and specificity of 75.86%. Similarly, the confusion matrix for both analyses gave the correct classification of 25 and 22 cases of patients who have heart disease while their wrong classification was 7 and 10 cases of patients who have no heart disease respectively. Furthermore, the importance of features carried out on both showed that the most significant features are the maximum heart rate achieved, fasting blood sugar, resting blood pressure, and chest pain respectively for Naive Bayes and Decision Tree. The findings of the analysis showed that the Naive Bayes model outperformed the Decision Tree in every aspect of the analyses in predicting the risk of heart disease based on the data used and, it suggested that medical health insurance should consider incorporating predictive modelling techniques like Naive Bayes into their risk assessment algorithms, which can be of great use in the medical line.

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.

Enhancing Multi-Junction Solar Cell Performance: Advanced Predictive Modeling and Cutting-Edge CIGS Integration Techniques

Historically, multi-junction solar cells have evolved to capture a broader spectrum of sunlight, significantly enhancing efficiency beyond conventional solar technologies. In this study, we utilized Silvaco TCAD tools to optimize a five-junction solar cell composed of AlInP, AlGaInP, AlGaInAs, GaInP, GaAs, InGaAs, and Ge, drawing on advancements documented in the literature. Our research focused on optimizing these cells through sophisticated statistical modeling and material innovation, particularly examining the relationship between layer thickness and electrical yield under one sun illumination. Employing III-V tandem solar cells, renowned for their superior efficiency in converting sunlight to electricity, we applied advanced statistical models to a reference solar cell configured with predefined layer thicknesses. Our analysis revealed significant positive correlations between layer thickness and electrical performance, with correlation coefficients (R2 values) impressively ranging from 0.86 to 0.96 across different regions. This detailed statistical insight led to an improvement in overall cell efficiency to 44.2. A key innovation in our approach was replacing the traditional germanium (Ge) substrate with Copper Indium Gallium Selenide (CIGS), known for its adjustable bandgap and superior absorption of long-wavelength photons. This strategic modification not only broadened the absorption spectrum but also elevated the overall cell efficiency to 47%. Additionally, the optimization process involved simulations using predictive profilers and Silvaco Atlas tools, which systematically assessed various configurations for their spectral absorption and current–voltage characteristics, further enhancing the cell’s performance. These findings underscore the critical role of precise material engineering and sophisticated statistical analyses in advancing solar cell technology, setting new efficiency benchmarks, and driving further developments in the field.

Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes in Resource Allocation, Disease Prevalence and High-Risk Populations

Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. Methodology: The research employs a comprehensive approach, utilizing various sources of healthcare data such as electronic health records (EHRs) and public health databases. Advanced analytical techniques, including machine learning, artificial intelligence, and big data analytics, are applied to derive actionable insights. Findings: The study reveals that predictive modeling significantly enhances resource optimization, enables accurate disease prevalence forecasting, and improves the identification of high-risk populations. Case studies demonstrate how these technologies lead to more efficient healthcare delivery, cost reduction, and better patient care outcomes. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theoretical understanding of healthcare data analytics by integrating advanced predictive modeling techniques with real-world healthcare applications. It offers valuable insights for policymakers on the importance of investing in data infrastructure and promoting data-driven decision-making. Practically, the study provides healthcare organizations with actionable strategies to implement predictive analytics for improved resource allocation and patient care.

Assessing cracking resistance and threshold limits of bituminous mixtures with IDEAL-CT and predictive modeling techniques

This paper presents a comprehensive study to establish the threshold limit of CTIndex for the Marshall mixes. The study also aims to assess the impact of different design factors on the cracking resistance of bituminous mixtures using the Indirect Tensile Asphalt Cracking Test (IDEAL-CT) test. This study considers 2 different aggregate sources, 2 different gradations, 5 different types of Design Aggregate Gradation (DAG), 3 binder types, and 5 levels of compactive effort. The test results show that higher cracking resistance can be achieved using a smaller nominal maximum size of the aggregate (NMAS), finer gradation, modified binder, and decreased compactive effort with aggregates having low abrasion and absorptive characteristics. This study also comprehends the influence of volumetric parameters of the bituminous mixes on fracture resistance. It was found that at a particular Optimum Binder Content (OBC), higher bulk specific gravity of the compacted specimen (Gmb) and voids filled with asphalt (VFA) result in reduced CTIndex, specifying poor cracking performance. While higher air voids (AV) and voids in mineral aggregate (VMA) lead to increased CTIndex, indicating better-cracking resistance. Statistical analysis tools were used to evaluate the significance of the influential factors that are affecting the cracking potential of the mix. Different Machine learning models were also developed to predict CTIndex based on the design factors considered in the study. The random forest (RFR) model showed strong accuracy, reflected by low Mean Absolute Error (MAE=3.16), Mean Absolute Percentage Error (MAPE=9.57), Root Mean Square Error (RMSE=4.23), and a high coefficient of determination (R²=0.95) value, notifying a precise fit and reliable predictions. Additionally, a GUI has been also developed to enhance the practical usability of the model for wider usage. Further, the present study proposes the threshold value of CTIndex for the selection of crack-resistant bituminous mixtures. Moreover, the study investigated the correlation between laboratory and field compaction methods and validated the initial threshold specification of CTIndex for the Marshall mixes. Despite of the variations in different compaction methodologies and specimen thickness, a strong positive correlation (R² > 0.76) between laboratory and field cores of BC-1 and DBM-2 indicates that the performance criteria are adequate and justified.

An In-Depth Look at Rising Temperatures: Forecasting with Advanced Time Series Models in Major US Regions

With growing concerns over climate change, accurately predicting temperature trends is crucial for informed decision-making and policy development. In this study, we perform a comprehensive comparative analysis of four advanced time series forecasting models—Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Multilayer Perceptron (MLP), and Gaussian Processes (GP)—to assess changes in minimum and maximum temperatures across four key regions in the United States. Our analysis includes hyperparameter optimization for each model to ensure peak performance. The results indicate that the MLP model outperforms the other models in terms of accuracy for temperature forecasting. Utilizing this best-performing model, we conduct temperature projections to evaluate the hypothesis that the rates of change in temperatures are greater than zero. Our findings confirm a positive rate of change in both maximum and minimum temperatures, suggesting a consistent upward trend over time. This research underscores the critical importance of refining time series forecasting models to address the challenges posed by climate change and supporting the development of effective strategies to mitigate the impacts of rising temperatures. The insights gained from this work emphasize the need for continuous advancement in predictive modeling techniques to better understand and respond to the dynamics of climate change.

Advancing oncological practice with innovative cancer data analytics: A new exponential-generated class of distributions

Traditional predictive modeling techniques have significantly influenced the analysis of survival times and predictive markers in oncology. However, these models often do not fully address the complexities of cancer data, leading to a noticeable gap in accurately modeling both cancer survival and mortality. The aim of this study is to show a model that offers the best fit for cancer mortality and survival data, thereby improving treatment strategies and patient prognoses. I propose the a New Exponential-Weibull (NEWE) distribution within the New Exponential-Generated (NE-G) class of distributions to enhance oncological analytics with greater accuracy and comprehensiveness. An empirical analysis of Brazilian cancer mortality data is conducted., This data set covers children, head and neck, and cervical cancer. Concurrently, survival time-to-event data for bladder and advanced lung cancers patients are assessed to evaluate the model’s effectiveness. Based on standard metrics, extensive simulation experiments show the Maximum Product of Spacing Estimator as the best of seven-point estimation techniques. The NEWE distribution shows superior modeling capabilities, surpassing traditional models with lower values of Log-likelihood, Cramer-von Mises, and Anderson-Darling and higher Kolmogorov-Smirnov (KS) p-values. The study also discerns the most fitting estimators for distinct types of cancer mortality and survival data. This includes the Right Tail Anderson-Darling for child cancer deaths, the Maximum Product Spacing for head and neck cancer deaths, the Least Squares for cervical cancer deaths, the Weighted Least Squares for bladder cancer survival, and the Anderson-Darling for advanced lung cancer deaths. This shows that the NEWE distribution can be used in a number of different cancers settings. The development and implementation of the NEWE distribution marks a significant advancement in oncological modeling. By analyzing both cancer mortality and survival data with enhanced accuracy and flexibility, this novel approach surpasses traditional models, offering deeper insights into cancer progression and treatment outcomes. As a result, the NEWE distribution equips healthcare professionals with a powerful tool for improving clinical decisions, leading to better prognostic assessments and patient care in cancer treatment.

5 Hands-on 3: Introduction to developing Python computational pipelines for predictive machine learning modelling of livestock data

Abstract This hands-on workshop aims to offer examples of computational pipelines that apply traditional predictive machine learning modelling techniques to animal science datasets with the purpose of solving classification and/or regression problems. The objective of this workshop is to provide the attendants with fully functional and customizable computational pipelines developed in Python. The workshop is structured in a hands-on format and includes a combination of basic notions about machine learning and relevant algorithms, evaluation measures, evaluation strategies and Python code examples. To avoid technical problems related to installing a Python environment on personal computers, we recommend the registrants to acquire access on the Repl.it platform (https://replit.com/) by creating a free account prior to attending the workshop. Detailed information will be provided before the beginning of the workshop at the following URL: http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2024/

Statistical and machine learning models for predicting spalling in CRCP

Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling—a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP’s functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling.

Evaluating Prediction Models with Hearing Handicap Inventory for the Elderly in Chronic Otitis Media Patients.

This retrospective, cross-sectional study aimed to assess the functional hearing capacity of individuals with Chronic Otitis Media (COM) using prediction modeling techniques and the Hearing Handicap Inventory for the Elderly (HHIE) questionnaire. This study investigated the potential of predictive models to identify hearing levels in patients with COM. We comprehensively examined 289 individuals diagnosed with COM, of whom 136 reported tinnitus and 143 did not. This study involved a detailed analysis of various patient characteristics and HHIE questionnaire results. Logistic and Random Forest models were employed and compared based on key performance metrics. The logistic model demonstrated a slightly higher accuracy (73.56%), area under the curve (AUC; 0.73), Kappa value (0.45), and F1 score (0.78) than the Random Forest model. These findings suggest the superior predictive performance of the logistic model in identifying hearing levels in patients with COM. Although the AUC for the logistic regression did not meet the benchmark, this study highlights the potential for enhanced reliability and improved performance metrics using a larger dataset. The integration of prediction modeling techniques and the HHIE questionnaire shows promise for achieving greater diagnostic accuracy and refining intervention strategies for individuals with COM.

Multi-Objective Robust Design Optimization for Crashworthiness Enhancement of Hybrid 2D Triaxially Braided Composite Tube Using Evolutionary Algorithms.

An innovative optimal design framework is developed aiming at enhancing the crashworthiness while ensuring the lightweight design of a hybrid two-dimensional triaxial braided composite (2DTBC) tube, drawing insights from the mesostructure of the composite material. To achieve these goals, we first compile the essential mechanical properties of the 2DTBC using a concentric cylinder model (CCM) and an analytical laminate model. Subsequently, a kriging surrogate model to elucidate the intricate relationship between design variables and macroscopic crashworthiness is developed and validated. Finally, employing multi-objective evolutionary optimization, we identify Pareto optimal solutions, highlighting that reducing the total fiber volume and increasing the glass fiber content in the total fiber volume are crucial for optimal crashworthiness and the lightweight design of the hybrid 2DTBC tube. By integrating advanced predictive modeling techniques with multi-objective evolutionary optimization, the proposed approach not only sheds light on the fundamental principles governing the crashworthiness of hybrid 2DTBC but also provides valuable insights for the design of robust and lightweight composite structures.