Regression Algorithm Research Articles

A deep learning approach for prediction of air quality index in smart city

Industrial developments and consumption of massive amount of fossil fuels, vehicle pollution, and other calamities upsurges the AQI (Air Quality Index) of major cities in a drastic manner. Owing to these factors, it is important to take proactive measures for reducing the air pollution in order to avoid life- threatening consequence. Therefore, prediction of air quality is significant for improving the health of living beings as highly polluted regions have a higher concentration of pollutants mixed in the air, affecting the respiratory system and reducing the lifetime. To control pollution, AQI is used as a measure for estimating the pollutant content in the air. Even though many existing techniques have predicted AQI, enhancement is required in prediction algorithms with minimized loss. To address the challenges in traditional algorithms, the proposed smart cities-based AQI prediction intends to utilize the proposed regression algorithm in the dataset, namely Air- Quality-Data, which collected harmful pollutants on an hourly and daily basis from multiple cities in India between 2015 to 2020. To achieve prediction efficiency with reduced loss, pre-processing of input data is being performed using Deep GAN (Generative Adversarial Network). It performs the imputation of data in place of missing values to improve accurate prediction. Additionally, feature scaling normalizes independent real-data features to a fixed scale. With the processed data, regression is done through modified Stacked Attention GRU with KL divergence, which predicts Ernakulam, Chennai and Ahmedabad cities with higher, medium, and low levels of AQI in India. The performance of the proposed regression algorithm is measured using metrics such as MAE (Mean Absolute Error), MSE (Mean Square Error), R2 (Coefficient of determination), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) and better MAE, MSE, R2, MAPE and RMSE obtained by the model is 0.1013, 0.0134, 0.9479, 0.1152 and 0.1156. Internal assessment and comparative analysis performed with existing regression algorithms exhibit lower loss values obtained from the present research, which determines the efficacy of the proposed model.

Digital twin-based tig welding quality prediction using electrode tip angle degradation influencing Industry 5.0 in manufacturing sector

Automation in tungsten inert gas (TIG) welding is important to achieve high production rates and quality in manufacturing industries. To improve the welding process and quality inspection methodologies, the intelligent welding robot and vision-based inspection system have been researched and deployed in many engineering fields. Hence to enhance the performance and production, a digital twin-based welding system with the prediction of weld quality based on the consideration of electrode tip angle degradation. The proposed system will capture real-time electrode tip angle and weld pool temperature using a forward looking infrared (FLIR) camera along with welding current and speed correlated with tensile strength as the output parameter. To validate the analysis, support vector machine (SVM) and random forest (RF) algorithms were implemented in which the RF model performs well on the prediction of welding quality by mapping with tensile strength. RF model confirms maximum accuracy of 90% with 0.29 seconds computation time to perform prediction on the next execution of welding operation. It is inferred that if the tip angle degradation increases consecutively welding current decreases drastically impacting the weld quality from good to poor. To forecast the need for immediate or scheduled maintenance to reduce the tip angle degradation, a linear regression algorithm is implemented to enable the inspection engineer to perform maintenance without delay in production.

DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm

DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

ABSTRACT Objectives To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. Materials and Methods Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. Results In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. Conclusions AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

SPOTTING OF CYBERBULLYING ON SOCIAL MEDIA USING MACHINE LEARNING

Cyberbullying occurs when someone harasses, threatens, or mistreats someone else online or on social media. Cyberbullying leads to threats, public embarrassment, and reprimands. Cyberbullying has caused a rise in youth suicide and mental health issues. It has lowered my self-esteem and escalated my suicidal thoughts. If cyberbullying continues, an entire generation of young adults will suffer from low self-esteem and mental health issues. Many machine learning algorithms are used to automatically identify social media cyberbullying. Social media monitoring allows this. These models don't account for all the factors that could make a comment or post bullying. Bullying is still ambiguous. This study presents a cyberbullying diagnosis model based on several factors. Cyberbully is a misuse of technology advantage to bully a person. This project is used Dataset namely,’ Cyberbullying-tweets’. This project used Machine Learning Classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Neural Networks (NN), Logistic Regression(LR) Algorithms and calculated performance results for comparing the performance for dataset. Keywords: Cyberbullying Detection, Social Media Analysis, Machine Learning Models, Natural Language Processing (NLP), Text Classification , Feature Extraction, Sentiment Analysis, Textual Data Mining, Online Harassment, User Profiling, Toxic Language Detection, Hate Speech Detection, Content Moderation, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Neural Network Algorithms, Lexicon-based Features, Syntactic Features, N-gram Analysis, Arabic Social Media (if focusing on Arabic language),User Behavior Analysis, Feature Engineering, Cross- Platform Analysis, Real-time Monitoring, Social Network Analysis

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation

In the context of carbon neutrality and carbon peaking, molecular management has become a focus of the petrochemical industry. The key to achieving molecular management is molecular reconstruction, which relies on rapid and accurate calculation of oil properties. Focusing on naphtha, we proposed a novel property prediction model construction procedure (MDs-NP) employing molecular dynamics simulations for property collections and gamma distribution from real analytical data for calculating mole fractions of simulation mixtures. We calculated 348 sets of mixture properties data in the range of 273 K–300 K by molecular dynamics simulations. Molecular feature extraction was based on molecular descriptors. In addition to descriptors based on open-source toolkits (RDKit and Mordred), we designed 12 naphtha knowledge (NK) descriptors with a focus on naphtha. Three machine learning algorithms (support vector regression, extreme gradient boosting and artificial neural network) were applied and compared to establish models for the prediction of the density and viscosity of naphtha. Mordred and NK descriptors + support vector regression algorithm achieved the best performance for density. The selected RDKFp and NK descriptors + artificial neural network algorithm achieved the best performance for viscosity. Using ablation studies, T, P_w and CC(C)C are three effective descriptors in NK that can improve the performance of the property prediction models. MDs-NP has the potential to be extended to more properties as well as more-complex petroleum systems. The models from MDs-NP can be used for rapid molecular reconstruction to facilitate construction of data-driven models and intelligent transformation of petrochemical processes.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Method for Monitoring Wheat Growth Status and Estimating Yield Based on UAV Multispectral Remote Sensing

An efficient and accurate estimation of wheat growth and yield is important for wheat assessment and field management. To improve the accuracy and stability of wheat growth and yield estimation, an estimation method based on a genetic algorithm-improved support vector regression (GA-SVR) algorithm was proposed in this study. The correlation analysis between vegetation indices calculated from spectral data and wheat growth phenotypes and yields was performed to obtain the optimal combination of vegetation indices with high correlation and good estimation performance. At the same time, the optimal model for wheat growth monitoring was screened and constructed in experiments with 12 wheat varieties and 3 gradient nitrogen fertilizer application levels. Then, the yield estimation model was established and its applicability was verified under different nitrogen fertilizer application levels. The results showed that the constructed models for the leaf area index, plant height, and yield estimation performed well, with coefficients of determination of 0.82, 0.71, and 0.70, and root mean square errors of 0.09, 2.7, and 68.5, respectively. This study provided an effective UAV remote sensing technique for monitoring wheat growth status and estimating yield. This study provides an effective unmanned aerial remote sensing technique for monitoring wheat growth and estimating yield, and provides technical support for wheat yield assessment and field management.

Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma

BackgroundTo develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression.MethodsWe retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis.ResultsFor the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was − 0.03 and cumulative risk rates were calculated.Data conclusionTo assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

Acoustic impedance prediction based on extended seismic attributes using multilayer perceptron, random forest, and extra tree regressor algorithms

AbstractAcoustic impedance is the product of the density of a material and the speed at which an acoustic wave travels through it. Understanding this relationship is essential because low acoustic impedance values are closely associated with high porosity, facilitating the accumulation of more hydrocarbons. In this study, we estimate the acoustic impedance based on nine different inputs of seismic attributes in addition to depth and two-way travel time using three supervised machine learning models, namely extra tree regression (ETR), random forest regression, and a multilayer perceptron regression algorithm using the scikit-learn library. Our results show that the R2 of multilayer perceptron regression is 0.85, which is close to what has been reported in recent studies. However, the ETR method outperformed those reported in the literature in terms of the mean absolute error, mean squared error, and root-mean-squared error. The novelty of this study lies in achieving more accurate predictions of acoustic impedance for exploration.

Thyroglobulin measurement is the most powerful outcome predictor in differentiated thyroid cancer: a decision tree analysis in a European multicenter series.

An accurate prognostic assessment is pivotal to adequately inform and individualize follow-up and management of patients with differentiated thyroid cancer (DTC). We aimed to develop a predictive model for recurrent disease in DTC patients treated by surgery and 131I by adopting a decision tree model. Age, sex, histology, T stage, N stage, risk classes, remnant estimation, thyroid-stimulating hormone (TSH), thyroglobulin (Tg), administered 131I activities and post-therapy whole body scintigraphy (PT-WBS) were identified as potential predictors and put into regression algorithm (conditional inference tree, c-tree) to develop a risk stratification model for predicting persistent/recurrent disease over time. The PT-WBS pattern identified a partition of the population into two subgroups (PT-WBS positive or negative for distant metastases). Patients with distant metastases exhibited lower disease-free survival (either structural, DFS-SD, and biochemical, DFS-BD, disease) compared to those without metastases. Meanwhile, the latter were further stratified into three risk subgroups based on their Tg values. Notably, Tg values >63.1 ng/mL predicted a shorter survival time, with increased DFS-SD for Tg values <63.1 and <8.9 ng/mL, respectively. A comparable model was generated for biochemical disease (BD), albeit different DFS were predicted by slightly different Tg cutoff values (41.2 and 8.8 ng/mL) compared to DFS-SD. We developed a simple, accurate and reproducible decision tree model able to provide reliable information on the probability of structurally and/or biochemically persistent/relapsed DTC after a TTA. In turn, the provided information is highly relevant to refine the initial risk stratification, identify patients at higher risk of reduced structural and biochemical DFS, and modulate additional therapies and the relative follow-up.

Assessing Pipe Condition in Water Distribution Networks

The condition assessment of water distribution pipes is of utmost importance for the prioritization of rehabilitation interventions. However, the application of available methodologies for condition assessment by water utilities with limited human, technological and financial resources is becoming increasingly complex. The current paper aims at the development and application of a methodology for the prediction of the physical condition of water distribution pipes without the need for visual inspection. The methodology includes the development and application of three different algorithms (heuristic, linear regression and support vector regression). The methodology is applied to a water distribution network located in the Algarve region, Portugal. The results obtained from each algorithm are compared with a well-known performance indicator, the ratio of useful life, and present significant differences in its overall pipe condition classification. Results have demonstrated the following: the ratio of useful life tends to distribute pipe classification more equally in the three classes (i.e., good, average and unsatisfactory); the heuristic algorithm classifies most pipes as average condition; and the linear regression algorithm classifies with unsatisfactory conditions. The support vector regression algorithm stands out as the main classifier for identifying pipes in good condition when compared to other algorithms.

Machine learning models to predict submucosal invasion in early gastric cancer based on endoscopy features and standardized color metrics

Conventional endoscopy is widely used in the diagnosis of early gastric cancers (EGCs), but the graphical features were loosely defined and dependent on endoscopists’ experience. We aim to establish a more accurate predictive model for infiltration depth of early gastric cancer including a standardized colorimetric system, which demonstrates promising clinical implication. A retrospective study of 718 EGC cases was performed. Clinical and pathological characteristics were included, and Commission Internationale de l’Eclariage (CIE) standard colorimetric system was used to evaluate the chromaticity of lesions. The predicting models were established in the derivation set using multivariate backward stepwise logistic regression, decision tree model, and random forest model. Logistic regression shows location, macroscopic type, length, marked margin elevation, WLI color difference and histological type are factors significantly independently associated with infiltration depth. In the decision tree model, margin elevation, lesion located in the lower 1/3 part, WLI a*color value, b*color value, and abnormal thickness in enhanced CT were selected, which achieved an AUROC of 0.810. A random forest model was established presenting the importance of each feature with an accuracy of 0.80, and an AUROC of 0.844. Quantified color metrics can improve the diagnostic precision in the invasion depth of EGC. We have developed a nomogram model using logistic regression and machine learning algorithms were also explored, which turned out to be helpful in decision-making progress.

An Interwell Connectivity Assessment Model for Polymer Flooding Short-Term Development Data Based on A-LSTM and EFAST Methods

Interwell connectivity assessment in polymer-driven reservoirs is critical for setting appropriate injection rates and improving oil recovery. Traditional deep learning techniques often lack accuracy and reliability when applied to short-term oilfield production data. In response, the A-LSTM algorithm is proposed, which integrates the attention mechanism with a long- and short-term memory network (LSTM). The predictive accuracy of A-LSTM is assessed and juxtaposed with LSTM and support vector regression (SVR) algorithms for short-term single-well daily oil production analysis. The Huber loss function was utilized to quantify the difference between predicted and actual results, resulting in a dynamic production prediction model. An interwell connectivity (IWC) assessment model is then obtained by fusing the dynamic production prediction model with the EFAST method, thus demonstrating the superior prediction accuracy of A-LSTM in oil production prediction and connectivity assessment. Moreover, the credibility of the assessment is further corroborated through numerical simulations and interwell tracer tests. The study results showed that the interwell connectivity evaluation model based on the A-LSTM algorithm and EFAST method is not only capable of accurately predicting the single-well daily oil production using a small sample dataset but also a highly reliable method for interwell connectivity evaluation, and the application of the interwell connectivity assessment model can further guide polymer flooding work in oilfields.

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves the synthetic generation of comprehensive patient discharge messages, setting a new standard in the field with GPT autonomously generating 20 fields. Through a meticulous review of the existing literature, we systematically explore GPT’s aptitude for synthetic data generation and feature extraction, providing a robust foundation for subsequent phases of the research. The empirical demonstration showcases the transformative potential of our proposed solution, presenting over 70 patient discharge messages with synthetically generated fields, including severity and chances of hospital re-admission with justification. Moreover, the data had been deployed in a mobile solution where regression algorithms autonomously identified the correlated factors for ascertaining the severity of patients’ conditions. This study not only establishes a novel and comprehensive methodology but also contributes significantly to medical machine learning, presenting the most extensive patient discharge summaries reported in the literature. The results underscore the efficacy of GPT in overcoming data scarcity challenges and pave the way for future research to refine and expand the application of GPT in diverse medical contexts.

147 The interaction between growth and immunity: Insights into the immune response of finisher pigs with divergent feed efficiency

Abstract This work evaluated the immune response of finisher pigs with divergent feed efficiency to identify immune pathways that were associated with efficiency. Blood gene expression data from a previous trial (Liu et al., 2019) were re-analyzed with novel methods that quantified changes in biological pathways and associated them with animal performance. Data were quality control checked and aligned to the Sus scrofa reference genome (Sscrofa11.1) using Kallisto. The dataset had 32 samples from eight pigs with high efficiency (HE) or low efficiency (LE) that had been injected with lipopolysaccharide (LPS). Blood samples were taken before challenge, and at 2, 6, and 24 h after challenge. After normalization and evaluation of differential gene expression, activation or inhibition of pathways was quantified using a topology-based pathway activation method and a customization of the Reactome database. Pathways were associated with feed efficiency using a regularized regression algorithm to identify immune pathways that were associated with efficiency. For statistical analysis, RNA integrity number, feed efficiency and time post LPS challenge were included in the model. At 2 h post LPS, HE pigs showed greater activation in pathways related to heat stress and creatine metabolism, but inhibition of pathways related to lysine and threonine catabolism, and immune resolving mediators (P-adj &amp;lt; 0.01), compared with the response in LE pigs. At 6 h post challenge there was a striking difference in cell cycle response, whereby cell cycle was activated in HE pigs but inhibited in LE pigs (P-adj &amp;lt; 0.01). The biggest difference in immune response was at 24h, with a strong response to LPS still evident in HE pigs, but a diminishing response in LE pigs. High efficiency pigs had activation in pathways related to oxidative stress, the metabolism of tryptophan, serine, tyrosine, threonine, glycogen, vitamin B5, and fibrin clot formation (P-adj &amp;lt; 0.01), which were not evident in LE pigs. The regression of pathways with feed efficiency showed that vitamin B5 metabolism was positively associated with gain:feed, whilst calcium-dependent signaling was negatively associated with gain:feed at multiple timepoints post-challenge. High efficiency pigs appeared to expand leukocyte populations at 6 h post LPS challenge, which was not evident in LE pigs. By 24 h, the response of LE pigs to LPS was minimal, but HE pigs still showed a strong response with greater utilization of energy, amino acids, and more oxidative stress. Low efficiency pigs appeared to recover faster from the LPS challenge, which was partially explained by differences in vitamin B5 utilization and calcium homeostasis. These findings confirm differences in the immune response of HE and LE pigs and provide insights related to amino acid, vitamin B5, and calcium homeostasis, which could help optimize the immune response of HE pigs. The novel analysis methods provided additional insights, which complement the original findings (T1).

Application of machine learning in predicting the thermal conductivity of single-filler polymer composites

Polymer composites with superior thermal conductivity and low electrical conductivity are pivotal in the cooling of electronic devices. Despite their prevalence, the accurate prediction of the thermal conductivity of these composites remains a challenge. The emergence of machine learning (ML) provides a groundbreaking approach to solving this issue. In this study, we constructed a comprehensive dataset collected from previous experimental papers and successfully predicted the thermal conductivity of single-filler polymer composites using four ML regression algorithms: random forest regression (RFR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and Gaussian process regression (GPR). By employing feature engineering to select pertinent features from the original datasets, the accuracy of the four models on the test set was improved, among which GBDT exhibited the highest accuracy with the Pearson correlation coefficient value of 0.981. Factors such as filler volume fraction and matrix thermal conductivity significantly influence the thermal conductivity of composite materials, while the thermal conductivity of the fillers has a relatively minor impact. Additionally, we identified the topological polar surface area (TPSA) as a crucial descriptor for surface modifications, quantifying diverse surface-modifying agents. Due to the incorporation of more descriptors, ML models exhibit higher precision and broader applicability compared to empirical formulas. Our study provides an effective tool for predicting the thermal conductivity of polymer composites with single fillers and underscores the potential of machine learning in accelerating materials design.

Clinical validation of automated depth camera-based measurement of the Fugl-Meyer assessment for upper extremity.

Depth camera-based measurement has demonstrated efficacy in automated assessment of upper limb Fugl-Meyer Assessment for paralysis rehabilitation. However, there is a lack of adequately sized studies to provide clinical support. Thus, we developed an automated system utilizing depth camera and machine learning, and assessed its feasibility and validity in a clinical setting. Validation and feasibility study of a measurement instrument based on single cross-sectional data. Rehabilitation unit in a general hospital. Ninety-five patients with hemiparesis admitted for inpatient rehabilitation unit (2021-2023). Scores for each item, excluding those related to reflexes, were computed utilizing machine learning models trained on participant videos and readouts from force test devices, while the remaining reflex scores were derived through regression algorithms. Concurrent criterion validity was evaluated using sensitivity, specificity, percent agreement and Cohen's Kappa coefficient for ordinal scores of individual items, as well as correlations and intraclass correlation coefficients for total scores. Video-based manual assessment was also conducted and compared to the automated tools. The majority of patients completed the assessment without therapist intervention. The automated scoring models demonstrated superior validity compared to video-based manual assessment across most items. The total scores derived from the automated assessment exhibited a high coefficient of 0.960. However, the validity of force test items utilizing force sensing resistors was relatively low. The integration of depth camera technology and machine learning models for automated Fugl-Meyer Assessment demonstrated acceptable validity and feasibility, suggesting its potential as a valuable tool in rehabilitation assessment.