Multi‐parameter design optimization of crash box for crashworthiness analysis

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.

A crash box design is developed to enhance the crash box’s abilities to absorb crash energy. Previous research has developed the crash box by adding filler material. Adding the filling material to the crash box will increase energy absorption. Aluminum honeycomb has a combination of lightweight mass and an ability to absorb crash energy. The addition of filler material to the crash box will also reduce the possibility of global bending in the crash box. The method of study is a computer simulation using ANSYS Academic software ver 18.1. This research used circular, square and hexagonal cross-section variations, which reached the same cross-sectional area design. Geometry model for the crash box and honeycomb filler is defined as crash box thickness (tc) 1.6 mm, honeycomb filler thickness (t) 0.5 mm for single layer and 1 mm for double layer and crash box length (l) 120 mm. The materials used were AA6063-T6 for crash boxes and AA3003 for honeycomb fillers. The test model consisted of two types, namely frontal load and oblique load test. The impactor velocity (v) is set to 15 m/s. The impactor and the fixed support are modeled as a rigid body, while the crash box is assumed as an elastic body. Observations were done by using the characteristics of deformation pattern and the absorption amount of produced energy due to the given loading model. Based on the deformation pattern results, it can be found that in the crash box model with square and hexagon honeycomb filler, the occurred deformation pattern was concertina, while the crash box with circular honeycomb filler was the mixed mode in the frontal load test. Regarding the oblique loads, the crash box remains to collapse the global bending on all models. Simulation results with the frontal load test model found that the crash box with circle-shaped honeycomb has the highest energy absorption while the crash box with hexagonal honeycomb filler has the highest Specific Energy Absorption (SEA). In the oblique load test, it was found that the crash box with hexagonal honeycomb filler has the highest energy absorption and SEA. By comparing the hexagonal crash box model with and without honeycomb filler, it is noted that the hexagonal crash box with honeycomb filler has higher Crash Force Efficiency

A novel microstructure-informed machine learning framework for mechanical property evaluation of SiCf/Ti composites

Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams

Prediction of the interpass temperature of a wire arc additive manufactured wall: FEM simulations and artificial neural network

Background and purposeThis study aims to develop a robust and user-friendly prediction model for radiation-induced hypothyroidism (RIHT) in nasopharyngeal carcinoma (NPC) patients.Materials and methodsNPC patients treated with IMRT between Jan. 2019 and Dec. 2021 were randomly assigned to a training cohort (n = 328) and a validation cohort (n = 141) at a ratio of 7:3. A total of 33 clinical and dose-volume variables were collected. Significant variables (p < 0.05) were identified through univariate Cox analysis and further refined using a 101-combination machine learning (ML) framework to develop a robust predictive model. The model was subsequently simplified through multivariate Cox analysis and a nomogram. Finally, the performance of the model was evaluated using the C-index, calibration plots, and decision curve analysis.ResultsUsing a 101-combination ML framework, we developed a predictive model for RIHT in NPC. The Coxboost + RSF method with 11 predictors achieved the best performance (C-index: 0.91 [training], 0.71 [validation]). A simplified five-variable model (pre-treatment TSH, TSH-to-thyroid-volume ratio, age, V45, V20) was created via multi-cox regression, with a C-index of 0.80 [training] and 0.71 [validation]. High-risk patients had significantly higher three-year RIHT incidences (72.3% vs. 18.6%, p < 0.0001) in the training cohort, and 67.9% versus 24.4% (p < 0.0001) in the validation cohort. The model showed strong calibration and confirmed clinical utility through decision curve analysis, supporting its use in personalized treatment planning.ConclusionWe developed a ML framework to identify key predictive factors for RIHT, which was simplified into a five-variable model for clinical use, offering a robust tool for predicting RIHT risk in decision-making.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13014-025-02725-5.

Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs. The aim of this study was to create a HAC risk prediction machine learning (ML) framework using hospital administrative data collections within North Metropolitan Health Service (NMHS), Western Australia. A retrospective cohort study was performed among 64,315 patients between July 2020 to June 2022 to develop an automated ML framework by inputting HAC and the healthcare site to obtain site-specific predictive algorithms for patients admitted to the hospital in NMHS. Univariate analysis was used for initial feature screening for 270 variables. Of these, 77 variables had significant relationship with any HAC. After excluding non-contemporaneous data, 37 variables were included in developing the ML framework based on logistic regression (LR), decision tree (DT) and random forest (RF) models to predict occurrence of four specific HACs: delirium, aspiration pneumonia, pneumonia and urinary tract infection. All models exhibited similar performance with area under the curve scores around 0.90 for both training and testing datasets. For sensitivity, DT and RF exceeded LR performance while on average, false positives were lowest for LR-based models. Patient's length of stay, Charlson Index, operation length and intensive care unit stay were common predictors. Integrating ML-based risk detection systems into clinical workflows can potentially enhance patient safety and optimise resource allocation. LR-based models exhibited best performance. We have successfully developed a "real-time" risk prediction model, where patient risk scores are calculated and reviewed daily.

Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

A comparative study of machine learning frameworks for predicting CO2 conversion into light olefins

Crash box design as one of the passive safety components in a vehicle had been developed to enhance energy absorption. Initial fold on the crash box is set to facilitate folding during the crash. The aims of this study is to investigate the initial folded crash box with length to thickness ratio subjected to frontal test. The frontal test is modelled by using finite element analysis. Through computer simulation using 9 models, the obtained result was used to provide the better design of crash box. The variations in this study were length to thickness ratio of crash box with length of tube (L) = 115; 132.5; 150 mm and the thickness of tube (t) = 1.6; 2.0; 2.5 mm. The crash box material was assumed as bilinear isotropic hardening material. The velocity used in the simulations was 7.67 m/s with impact mass of 103 kg. Based on the results, it can be shown that 1st model to 8th model produce deformation pattern as concertina mode and 9th model has diamond mode. The 3rd model has the largest energy absorption with value 18.29 kJ.

Yield Curve and Recession Forecasting in a Machine Learning Framework

In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e., non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China.

Occupant behavior prediction has become a significant part of smart building technologies. This research emphasizes on HVAC (Heating Ventilation and Air Conditioning) system as its operation is crucial in terms of both energy usage and user comfort. In previous researches, comfort prediction is developed using the filled questionnaires from occupants. The prediction output is employed to control the mechanical components of the system. On/Off control is mostly developed using rule-based distance triggering that turns on/off the system when occupants enter/exit a specific perimeter. This article proposes a plug-and-play HVAC control system with an adaptive cloud machine learning (ML) framework that utilizes room current ambient conditions together with the historical adjustment log to automatically adjust room temperature according to the user comfort using Support Vector Classification (SVC). This system can also turn on air conditioner automatically using predictive control which implements GPS location and the occupant's data on the Artificial Neural Network (ANN). Furthermore, cloud deployment can solve problems in processing power and storage of an end device, also provide scalability for future development. Findings show that an adaptive algorithm in the ML framework can perform well, even when the occupant's behavior changes slowly.

Hematology research has greatly benefited from the integration of diverse biological data resources and advanced machine learning (ML) frameworks. This integration has not only deepened our understanding of blood diseases such as leukemia and lymphoma, but also enhanced diagnostic accuracy and personalized treatment strategies. By applying ML algorithms to analyze large-scale biological data, researchers can more effectively identify disease patterns, predict treatment responses, and provide new perspectives for the diagnosis and treatment of hematologic disorders. Here, we provide an overview of the current landscape of biological data resources and the application of ML frameworks pertinent to hematology research.

Metal foams for improved energy absorption in passenger vehicles