Machine Learning Regression Models Research Articles

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors across Multiple Spectral Wavelengths.

Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT)-based sensors for chemically specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing data sets predict the change in the fluorescence emission in response to serotonin, ΔF/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training data set leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery.

Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions

Vibrio parahaemolyticus and Vibrio vulnificus are bacteria with a significant public health impact. Identifying factors impacting their presence and concentrations in food sources could enable the identification of significant risk factors and prevent incidences of foodborne illness. In recent years, machine learning has shown promise in modeling microbial presence based on prevalent external and internal variables, such as environmental variables and gene presence/absence, respectively, particularly with the generation and availability of large amounts and diverse sources of data. Such analyses can prove useful in predicting microbial behavior in food systems, particularly under the influence of the constant changes in environmental variables. In this study, we tested the efficacy of six machine learning regression models (random forest, support vector machine, elastic net, neural network, k-nearest neighbors, and extreme gradient boosting) in predicting the relationship between environmental variables and total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater and oysters. In general, environmental variables were found to be reliable predictors of total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater, and pathogenic V. parahaemolyticus in oysters (Acceptable Prediction Zone >70 %) when analyzed using our machine learning models. SHapley Additive exPlanations, which was used to identify variables influencing Vibrio concentrations, identified chlorophyll a content, seawater salinity, seawater temperature, and turbidity as influential variables. It is important to note that different strains were differentially impacted by the same environmental variable, indicating the need for further research to study the causes and potential mechanisms of these variations. In conclusion, environmental variables could be important predictors of Vibrio growth and behavior in seafood. Moreover, the models developed in this study could prove invaluable in assessing and managing the risks associated with V. parahaemolyticus and V. vulnificus, particularly in the face of a changing environment.

Analytical results for uncertainty propagation through trained machine learning regression models

Machine learning (ML) models are increasingly being used in metrology applications. However, for ML models to be credible in a metrology context they should be accompanied by principled uncertainty quantification. This paper addresses the challenge of uncertainty propagation through trained/fixed ML regression models. Analytical expressions for the mean and variance of the model output are obtained/presented for certain input data distributions and for a variety of ML models. Our results cover several popular ML models including linear regression, penalised linear regression, kernel ridge regression, Gaussian Processes (GPs), support vector machines (SVMs) and relevance vector machines (RVMs). We present numerical experiments in which we validate our methods and compare them with a Monte Carlo approach from a computational efficiency point of view. We also illustrate our methods in the context of a metrology application, namely modelling the state-of-health of lithium-ion cells based upon Electrical Impedance Spectroscopy (EIS) data.

Comparison of methods to aggregate climate data to predict crop yield: an application to soybean

High-dimensional climate data collected on a daily, monthly, or seasonal time step are now commonly used to predict crop yields worldwide with standard statistical models or machine learning models. Since the use of all available individual climate variables generally leads to calculation problems, over-fitting, and over-parameterization, it is necessary to aggregate the climate data used as predictors. However, there is no consensus on the best way to perform this task, and little is known about the impacts of the type of aggregation method used and of the temporal resolution of weather data on model performances. Based on historical data from 1981 to 2016 of soybean yield and climate on 3447 sites worldwide, this study compares different temporal resolutions (daily, monthly, or seasonal) and dimension reduction techniques (principal component analysis (PCA), partial least square regression, and their functional counterparts) to aggregate climate data used as inputs of machine learning and linear regression (LR) models predicting yields. Results showed that random forest models outperformed and were less sensitive to climate aggregation methods than LRs when predicting soybean yields. With our models, the use of daily climate data did not improve predictive performance compared to monthly data. Models based on PCA or averages of monthly data showed better predictive performance compared to those relying on more sophisticated dimension reduction techniques. By highlighting the high sensitivity of projected impact of climate on crop yields to the temporal resolution and aggregation of climate input data, this study reveals that model performances can be improved by choosing the most appropriate time resolution and aggregation techniques. Practical recommendations are formulated in this article based on our results.

Slurry Erosion Resistance, Morphology, and Machine Learning Modeling of Plasma-Sprayed Si3N4+TiC+VC and CrNi based Ceramic Coatings

This study presents a comprehensive analysis of the morphology, mechanical properties, erosion resistance, and machine learning modeling of Si3N4+TiC+VC and CrNi based ceramic coatings deposited via plasma spraying. The cross-sectional morphology analysis revealed good mechanical interlocking between the coatings and the substrate, with pores predominantly observed in the Si3N4+TiC+VC coating. The coating thicknesses were measured as follows: 260 μm for Si3N4+TiC+VC, 243 μm for Si3N4+TiC+VC blended with CrNi, and 232 μm for pure CrNi coating. Mechanical characterization showed that microhardness values ranged from 1933 HV for Si3N4+TiC+VC coating to 499 HV for pure CrNi coatings. Slurry erosion tests demonstrated that pure Si3N4+TiC+VC coating exhibited superior erosion resistance at 90º impingement angle compared to other coatings. Additionally, a rigorous evaluation of over 20 machine learning regression models revealed that Gaussian Process Regressors and Artificial Neural Network (ANN) with a layer size of 20, displayed satisfactory performance in modeling mass loss prediction. The erosion mechanisms observed in the coatings provided valuable insights into their response to slurry erosion, highlighting the influence of coating composition and impingement angles. The findings underscore the potential of Si3N4+TiC+VC ceramic coatings to enhance the erosion resistance of stainless steel, offering promising applications in various industries.

Using Machine Learning to Predict Axial Pile Capacity

Accurate estimation of the ultimate axial load bearing capacity of piles is necessary to ensure the safety of the supported structures and to prevent cost overruns. Traditional mechanics-based design methods do not always predict pile capacity accurately, or precisely, leaving room for improvement. This study focuses on the potential of machine learning (ML) in estimating pile capacity. A dataset of 546 load tests was compiled from three databases. The baseline performance of traditional design methods was first established by comparing the capacities computed using four traditional approaches, against the capacities interpreted from load tests using Davisson’s criterion. Sixteen different ML techniques were explored. First, the optimal feature selection technique for model training was investigated. Second, hyperparameters of each technique were optimized. The process involved the training of 32,000 different models and tuning their hyperparameters. Next the dataset was randomly split into training (70%) and testing (30%) for comparing the 16 different ML regression models. Each of the optimized models was then trained using six feature sets. The performance of each of the 16 ML models with the best performing feature subset was compared with the baseline performance from traditional methods. Evaluation criteria included measured versus predicted capacities, influence of soil type on accuracy, as well as the absence of pile diameter, or length effects on accuracy and precision. In general, the ML methods performed significantly better than the best traditional method. The current research demonstrated that ML may offer advantages in geotechnical design when large datasets are available.

Enhancing hydrogen production prediction from biomass gasification via data augmentation and explainable AI: A comparative analysis

Hydrogen production for clean energy is gaining a foothold, notably through the gasification of biomass. Machine learning aids in its accurate production predictions, yet its opaque nature limits explanation. This study looked into machine learning models' transparency and evaluated the impact of data augmentation on hydrogen production prediction accuracy using a publicly available numerical dataset. Fifteen (15) machine learning regression models including linear and tree-based were evaluated leading to the introduction of a hybrid model. The proposed hybrid model is based on ensemble and averaging techniques in mathematics. The deployed model's prediction was evaluated using the Mean Absolute Error (MAE), the Mean Square Error (MSE), the Root Mean Squared Error (RMSE), the Root Mean Squared Log Error (RMSLE), and R-squared (R2) based on K-Fold cross-validation. Furthermore, the effect of the grid search, random search, and Bayesian optimization techniques for model hyperparameter optimization were carried out. The evaluated models demonstrate superior predictions and explore data augmentation's potential in refining machine learning accuracy with the proposed hybrid model recording an MAE of 2.187, MSE of 9.788, RMSE of 3.129, R2 of 0.250, MSLE of 0.005, and RMSLE of 0.070 against the original data result of MAE of 2.665, MSE of 11.877, RMSE of 3.446, R2 of 0.112, MSLE of 0.006 and RMSLE of 0.076. However, when the proposed model hyperparameter training setting was optimized, the yielded result improved further with an MAE of 0.044, MSE of 0.040, RMSE of 0.215, R2 of 0.996, MSLE of 0.000 and RMSLE of 0.005. By unraveling the inner workings of black box models using the SHAP, LIME, and ELI-5 interpretable AI models, this study advances hydrogen production prediction using biomass gasification. Insights gained in this study foster the development of more transparent and effective models, aiming for the widespread adoption of sustainable hydrogen energy.

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

Predicting Stellar Masses of the First Galaxies Using Graph Neural Networks

Theoretical models of galaxy formation and evolution are primarily investigated through cosmological simulations and semi-analytical models. The former method consumes O(106) core-hours explicitly modeling the dynamics of the galaxies, whereas the latter method only requires O(103) core-hours foregoing directly simulating internal structure for computational efficiency. In this work, we present a proof-of-concept machine learning regression model, using a graph neural network architecture, to predict the stellar mass of high-redshift galaxies solely from their dark matter merger trees, trained from a radiation hydrodynamics cosmological simulation of the first galaxies.

Automated group constant parameterization for low sample sizes using different Machine learning approaches

This paper deals with group constant parameterization, a necessary step to utilize the results of assembly-level neutronics calculations at the full-core level. The focus is on low sample size problems when the commonly used linear interpolation approach is inadequate, a typical situation of using Monte Carlo codes for group constant generation. This work presents a newly developed code package for automated group constant parameterization. It implements several machine learning regression models − including a novel polynomial regression algorithm − performs hyperparameter optimization and selects the best model based on a detailed evaluation. The applicability of the new code package is demonstrated in a case study for a VVER-1200 fuel assembly covering both normal operation and transient conditions. In this example, the novel polynomial regression model provides a 73 pcm average error in kinf that leads to reactivity coefficients well within the desired precision.

UAV remote sensing phenotyping of wheat collection for response to water stress and yield prediction using machine learning

Water stress is a significant challenge for global food production. Rainfall pattern is becoming unpredictable due to climate change that causes unprecedent water stress conditions in cereals production including wheat which is one of the important staple food crops. To sustain wheat production under water limiting conditions, there is an urgent need to develop drought-tolerant wheat varieties. For this, screening large numbers of wheat genotype for traits related to growth and yield under water stressed conditions is crucial. In this study, we deployed high-throughput phenotyping approaches, including uncrewed aerial vehicle (UAV)-based multispectral imaging, advanced machine and deep learning regression models. Two separate field experiments, irrigated and rainfed, were conducted comprising 553 wheat genotypes, and collected dataset for traits such as plant height, phenology, grain yield, and timeseries multispectral imaging. UAV-multispectral imagery derived plant height measurements showed a high correlation (R2=0.75) with manual measurements. Vegetation indices derived from multispectral data differentiated growth pattern of genotypes under rainfed and irrigated conditions and were used in yield prediction modeling. Wheat genotypes were effectively ranked, and their response differentiated for water stress tolerance based on yield index, stress susceptibility index, and yield loss%. Importantly, yield prediction in genotypes was computed using four machine learning regression algorithms i.e., linear regression, support vector machine, random forest, and deep learning H2O-3, where H2O-3 was the most accurate model with R2=0.80. Results show that multispectral-driven traits combined with machine learning models effectively phenotyped large wheat population and such approaches can be integrated in crop breeding program to develop varieties tolerant to water stress.

Machine learning-based multi-objective optimization and thermal assessment of supercritical CO2 Rankine cycles for gas turbine waste heat recovery

Technologies for utilizing waste heat for power generation have attracted significant attention in recent years due to their potential to enhance energy efficiency and reduce greenhouse gas emissions. This research focuses on the comparative and optimization analysis of three supercritical carbon dioxide (sCO2) Rankine cycles (simple, cascade, and split) for gas turbine waste heat recuperation. The study begins with parametric analysis, investigating the significant effects of key variables, including turbine inlet temperature, condenser inlet temperature, and pinch point temperature, on the thermal performance of advanced sCO2 power cycles. To identify the most efficient cycle configuration, a multi-objective optimization approach is employed. This approach combines a Genetic Algorithm with machine learning regression models (Random Forest, XGBoost, Artificial Neural Network, Ridge Regression, and K-Nearest Neighbors) to predict cycle performance using a dataset extracted from cycle simulations. The decision-making process for determining the optimal cycle configuration is facilitated by the TOPSIS (technique for order of preference by similarity to the ideal solution) method. The study's major findings reveal that the split cycle outperforms the simple and cascade configurations in terms of power generation across various operating conditions. The optimized split cycle not only demonstrates superior power output but also exhibits enhanced net power output, heat recovery, system and exergy efficiency of 7.99 MW, 76.17 %, 26.86 % and 57.96 %, respectively, making it a promising choice for waste heat recovery applications. This research has the potential to contribute to the advancement and widespread adoption of waste heat recovery in energy technologies boosting system efficiency and economic feasibility. It provides a new perspective for future research, contributing to the improvement of energy generation infrastructure.

Dissecting unique and common variance across body and brain health indicators using age prediction.

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

Machine-learning-assisted design of high strength steel I-section columns

High strength steel has been attracting attention in the building industry due to its superior mechanical properties. The accurate design of high strength steel structures is crucial to boost its wide application. In this paper, an accurate and unified design approach for high strength steel I-section columns with different material grades, boundary conditions, geometric dimensions (including cross-section sizes and member lengths) and failure modes is proposed based on machine learning. Firstly, 871 experimental and numerical data were collected from the literature to establish a database. Then, seven machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, Adaptive Boosting, Extreme Gradient Boosting and Categorical Boosting, were applied to establish machine learning regression models to predict buckling resistances of high strength steel I-section columns. The model performance was then evaluated through statistic indices, with the evaluation results indicating that the Categorical Boosting trained model yields the highest level of accuracy. Based on the data in the collected database, the regression model trained by Categorical Boosting and existing codified design provisions, as given in the European code and American specification, were assessed and compared. The European code and American specification were found to yield scattered and inaccurate failure load predictions, while the Categorical Boosting trained model led to substantially more accurate and consistent failure load predictions for high strength steel I-section columns with different material grades, boundary conditions, geometric dimensions and failure modes.

Streamflow prediction using support vector regression machine learning model for Tehri Dam

Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model’s performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model.

Predicting Cryptocurrency Returns Using Classification and Regression Machine Learning Model

People are starting to see the cryptocurrency market as a viable source of income and investment, similar to the stock market, as the concept of cryptocurrencies continues to gain popularity. Predicting Bitcoin returns is related to financial machine learning, which uses time series to forecast price variance. This study starts with the daily close price of Bitcoin for its initial dataset. The price is transformed into percentages and binary classes, which categorize into “Up” and “Down”, after which a time series is applied to produce two datasets: a categorical dataset for classification and a numerical dataset for regression. For classification that represents a Binary classification in asset-price forecasting, k-fold cross-validation is applied to ensure that the best classifiers are selected for testing and analysis. Most of the regression analysis was based on visualization, which displayed the predicted prices by each regressor in front of the original values and helped analyse the models’ results more accurately. The outcomes of this study were achieved by anticipating bitcoin returns using classification and regression machine learning models, despite the approaches’ low accuracy and significant precision rate to the “Up” class. At this stage, with a significant limitation regarding the dataset and a lack of other indicators, a model capable of predicting future variations is considered a beneficial addition for many trading tools or even for crypto market analysts.

Prototyping of a Machine Learning–Based Burnup Measurement Capability for Pebble Bed Reactor Fuel

The pebble bed reactor is a unique reactor design due to its capacity for continuous multipass circulation of the fuel elements, without causing interruption to reactor operation, with the assistance of the burnup measurement system. Such a system necessarily requires an accurate knowledge of the burnup of each fuel pebble upon ejection from the core so as to inform the reloading decision and to ensure that no pebble exceeds the regulated discharge burnup limit at any point following reinsertion into the reactor core. In this work, we conceptualize, develop, and demonstrate a machine learning–based fuel burnup prediction framework leveraging advanced modeling and simulation capabilities. At its core, machine learning regression models are learned from simulated data to establish the correlation among the irradiated fuel composition (hence burnup), the gamma leakage spectrum, and the gamma spectroscopy results. Sensitivity analysis is conducted to quantify the impact of unknown design parameters, such as fuel enrichment, and irradiation environment, including power density, temperature, and neighboring materials, on the prediction accuracy of various supervised regression algorithms. The effects of a short cooldown period on machine learning prediction accuracy are also investigated. A test data set is used to validate that the data generation methodology proposed in this work successfully results in a machine learning model capable of interpolating its prediction of burnup onto a much wider range of irradiation conditions than were explicitly represented in the training database. The inclusion of a cooldown period of just 2 h leads to a prediction root-mean-square error of <5 MWd/kgU when the fuel enrichment is known and <9 MWd/kgU otherwise.

Prediction of Intracranial Pressure in Patients with an Aneurysmal Subarachnoid Hemorrhage Using Optic Nerve Sheath Diameter via Explainable Predictive Modeling.

Background: The objective of this investigation was to formulate a model for predicting intracranial pressure (ICP) by utilizing optic nerve sheath diameter (ONSD) during endovascular treatment for an aneurysmal subarachnoid hemorrhage (aSAH), incorporating explainable predictive modeling. Methods: ONSD measurements were conducted using a handheld ultrasonography device during the course of endovascular treatment (n = 126, mean age 58.82 ± 14.86 years, and female ratio 67.46%). The optimal ONSD threshold associated with an increased ICP was determined. Additionally, the association between ONSD and ICP was validated through the application of a linear regression machine learning model. The correlation between ICP and various factors was explored through the modeling. Results: With an ICP threshold set at 20 cmH2O, 82 patients manifested an increased ICP, with a corresponding ONSD of 0.545 ± 0.08 cm. Similarly, with an ICP threshold set at 25 cmH2O, 44 patients demonstrated an increased ICP, with a cutoff ONSD of 0.553 cm. Conclusions: We revealed a robust correlation between ICP and ONSD. ONSD exhibited a significant association and demonstrated potential as a predictor of ICP in patients with an ICP ≥ 25 cmH2O. The findings suggest its potential as a valuable index in clinical practice, proposing a reference value of ONSD for increased ICP in the institution.

466 Development of Machine Learning Algorithms to Predict Symptomatic VTE at Time of Admission and Time of Discharge after Severe Traumatic Injury

OBJECTIVES/GOALS: Clinical indicators predictive of venous thromboembolism (VTE) in trauma patients at multiple time points are not well outlined, particularly at time of discharge. We aimed to describe and predict inpatient and post-discharge risk factors of VTE after trauma using a multi-variate regression model and best of class machine learning (ML) models. METHODS/STUDY POPULATION: In a prospective, case-cohort study, all trauma patients (pts) who arrived as level 1 or 2 trauma activations, from June 2018 to February 2020 were considered for study inclusion. A subset of pts who developed incident, first time, VTE and those who did not develop VTE within 90 days of discharge were identified. VTE were confirmed either by imaging or at autopsy during inpatient stay or post-discharge. Outcomes were defined as the development of symptomatic VTE (DVT and/or PE) within 90 days of discharge.A multi-variate Cox regression model and a best in class of a set of 5 different ML models (support-vector machine, random-forest, naives Bayes, logistic regression, neural network]) were used to predict VTE using models applied a) at 24 hours of injury date or b) on day of patient discharge. RESULTS/ANTICIPATED RESULTS: Among 393 trauma pts (ISS=12.0, hospital LOS=4.0 days, age=48 years, 71% male, 96% with blunt mechanism, mortality 2.8%), 36 developed inpatient VTE and 36 developed VTE after discharge. In a weighted, multivariate Cox model, any type of surgery by day 1, increased age per 10 years, and BMI per 5 points were predictors of overall symptomatic VTE (C-stat 0.738). Prophylactic IVC filter placement (4.40), increased patient age per 10 years, and BMI per 5 points were predictors of post-discharge symptomatic VTE (C-stat= 0.698). A neural network ML model predicted VTE by day 1 with accuracy and AUC of 0.82 and 0.76, with performance exceeding those of a Cox model. A naīve Bayesian ML model predicted VTE at discharge, with accuracy and AUC of 0.81 and 0.77 at time of discharge, with performance exceeding those of a Cox model. DISCUSSION/SIGNIFICANCE: The rate of inpatient and post-discharge VTEs remain high. Limitations: single institution study, limited number of patients, internal validation only, with the use of limited number of ML models. We developed and internally validated a ML based tool.Future work will focus on external validation and expansion of ML techniques.

Chitosan-coated coconut shell composite: A solution for treatment of Cr(III)-contaminated tannery wastewater

BackgroundTannery industry generates a large amount of Cr(III)-contaminated wastewater daily. Unless properly treated, not only this effluent contaminates the water body, but also damages the environment and threatens public health. This batch study investigates the feasibility of chitosan-coated coconut shells as a low-cost material for removing Cr(III) from tannery wastewater. Both chitosan and coconut shell (CS) waste are abundantly available from local agricultural and fishery industries. MethodsTo enhance its treatment performance for Cr(III) removal, the CS was coated with chitosan as a composite. To sustain its cost-effectiveness, the saturated composite was regenerated with HNO3. Its performance for Cr(III) removal was evaluated and compared to other low-cost adsorbents in previous work. FindingsAt the same initial concentration of 20 mg/L, it was found that the composite had a higher Cr(III) removal (97%) than the chitosan alone under the optimized conditions of 4 g/L of dose, pH 6.5, 200 rpm of agitation speed, and 1 h of reaction time. The isotherm of Cr(III) removal by the adsorbents followed the Langmuir model, while the pseudo-second order reaction was representative to simulate the adsorption data. The Cr(III) removal by the composite was based on attractive columbic forces between the negative charge of the adsorbent's surface and the positive charge of the metal cation. The negative value of ΔG thermodynamic parameter suggests the spontaneous nature of adsorption. The efficiency of machine learning regression (MLR) model was assessed in predicting the experimental data of adsorption. In spite of promising results, treated effluents still could not comply with the required limit of discharge standards of less than 0.5 mg/L mandated by local legislation. Therefore, a subsequent treatment using activated sludge is required. Overall, this work reveals a contribution of unused resources from the coconut and shrimp industries in the form of composite for protecting the aquatic environment.