Combining Machine Learning Research Articles

Enhancing mix proportion design of low carbon concrete for shield segment using a combination of Bayesian optimization-NGBoost and NSGA-III algorithm

The demand for segment concrete increases rapidly with the expansion of urban rail transit and underground space, which may lead to carbon emissions (CE). In the production of segment concrete, using supplement cementitious materials (SCM) can be used to reduce CE instead of cement. However, SCM contents are usually determined by many orthogonal experiments, which are often time-consuming and only consider the limited experimental variables. Therefore, a novel hybrid intelligent algorithm combining Bayesian optimization (BO), natural gradient boosting (NGBoost) and non-dominated sorting genetic algorithm (NSGA)-III is proposed, which overcomes these disadvantages. Firstly, NGBoost model after hyperparameter optimization using BO is used to establish a nonlinear mapping relationship between segment concrete mix proportion and performance, which was used as the fitness function of the non-dominated sorting genetic algorithm (NSGA-III). Secondly, the optimal Pareto solutions were obtained through NSGA-III, and the reasonable range of segment concrete mixing proportion was obtained. Finally, a segment concrete production case was used to verify the effectiveness of the proposed algorithm. The results showed that the proposed hybrid algorithm combining proposed scheme decision strategy can obtain an optimal design scheme for concrete mix proportion. In addition, the comprehensive optimization rate of the obtained optimal scheme compared to the experimental optimal group was 11.5%. Moreover, the amount of phosphorous slag (PS) and granulated blast furnace slag (GBFS) in the optimal scheme was 26%, with a mass ratio of 1:2. Compared to the experimental optimal group, the optimal scheme can reduce the cost and CE per cubic meter by 31.64 yuan and 31.04 kg, respectively. Furthermore, the hybrid algorithm proposed can accurately obtains an optimal amount of SCM, which could be used as a supporting tool to reduce the production cost and CE of segment concrete. Overall, this research contributes to combining machine learning algorithms with actual production processes, which overcomes the data limitations of traditional experiments. It also provides a new intelligent approach and reference cases for the low-carbon design and sustainable development of building materials such as concrete.

Prediction of CCL2 in breast cancer based on enhanced MRI radiomics model with integrated bioinformatics analysis and machine learning.

e12509 Background: Breast cancer is the most common malignant tumor in women. According to the 2023 Cancer Statistics Report, breast cancer has surpassed lung cancer to become the most common malignant tumor. Its incidence and mortality both rank first among female malignant tumors. This study proposes non-invasive prediction of CCL2 mRNA expression in breast cancer tissue through enhanced MRI radiomics, while integrating bioinformatics analysis to explore the underlying molecular mechanisms and their association with the immune microenvironment. Methods: Excluding criteria were applied to select primary solid tumor samples with RNA-seq data (n = 840) from the TCGA-BRCA database. Enhanced MRI imaging data (n = 98) intersecting with the TCGA-BRCA data were obtained from TCIA-BRCA. The imaging data were randomly divided into training (n = 70) and testing sets (n = 28) in a 7:3 ratio. A feature subset was selected, and a model was constructed using the support vector machine (SVM) algorithm in the training set. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer-Lemeshow goodness-of-fit test. The calibration of the radiomics prediction model was assessed using the Brier score, and the clinical utility of the radiomics prediction model was demonstrated by drawing decision curves (DCA). External validation of the model was performed using a validation set. The radiomics score (RS) was calculated using the LR radiomics model and categorized as a binary variable (Low/High RS). Subsequently, KEGG pathway, immune-related genes, and gene mutation analysis were conducted based on the CCL2 high and low expression levels. Results: A total of 1810 radiomic features were obtained with ICC values ≥ 0.75. Subsequently, the mRMR (Maximum relevance, minimum redundancy) method, and the optimal 7 feature subsets were further identified using the RFE (Recursive feature elimination) algorithm. The SVM model demonstrated favorable predictive performance, with an AUC value of 0.822 for the training set and 0.779 for the validation set, as indicated by the ROC curve. Calibration curve and Hosmer-Lemeshow goodness-of-fit test revealed good consistency between the radiomic predictive model for the probability of high gene expression and the actual values (P > 0.05). Additionally, the decision curve analysis (DCA) demonstrated high clinical utility of the model. The TNF-α signaling pathway was found to influence the occurrence and development of breast cancer. Furthermore, the degree of Plasma Cell infiltration was observed to be highest in breast cancer. Conclusions: The CCL2 model constructed using a combination of machine learning and radiomic features, can non-invasively predict the prognosis of breast cancer treatment and exhibits good generalizability.

Predicting carbon and oil price returns using hybrid models based on machine and deep learning

SummaryPredicting carbon and oil prices is recently gaining relevance in the climate change literature. This is due to the fact that conventional energy market analysis and the design of mechanisms for climate change mitigation constitute key variables for artificial carbon markets. Yet, modelling non‐linear effects in time series remains a major challenge for carbon and oil price forecasting. Hence, hybrid models seem to be appealing alternatives for this purpose. This study evaluates the performance of 12 hybrid models, which weigh results from random forest, support vector machine, autoregressive integrated moving average and the non‐linear autoregressive neural network models. The weights are determined by (i) assuming equal weights, (ii) using a neural network to optimise individual weights and (iii) employing deep learning techniques. The findings of our work confirm the salient characteristics of modelling the non‐linear effects of time series and the potential of hybrid models based on neural networks and deep learning in predicting carbon and oil price returns. Furthermore, the best results are obtained from hybrid models that combine machine learning and traditional econometric techniques as inputs, which capture the linear and non‐linear effects of time series.

Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.

Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating diverse cell populations in 3D cell culture over time necessitates non-toxic staining methods, as specific fluorescent tags may not be suitable, and immunofluorescence staining can be cytotoxic for prolonged live cell cultures. We aim to perform machine learning-based cell classification within a live heterogeneous cell culture population grown in a 3D tissue culture relying only on reflectance, transmittance, and nuclei counterstained images obtained by confocal microscopy. In this study, we employed a supervised convolutional neural network (CNN) to classify tumor cells and fibroblasts within 3D-grown spheroids. These cells are first segmented using the marker-controlled watershed image processing method. Training data included nuclei counterstaining, reflectance, and transmitted light images, with stained fibroblast and tumor cells as ground-truth labels. Our results demonstrate the successful marker-controlled watershed segmentation of 84% of spheroid cells into single cells. We achieved a median accuracy of 67% (95% confidence interval of the median is 65-71%) in identifying cell types. We also recapitulate the original 3D images using the CNN-classified cells to visualize the original 3D-stained image's cell distribution. This study introduces a non-invasive toxicity-free approach to 3D cell culture evaluation, combining machine learning with confocal microscopy, opening avenues for advanced cell studies.

Optimizing loss functions for improved energy demand prediction in smart power grids

In this paper, our aim is to improve the accuracy and effectiveness of energy demand forecasting, particularly within modern electricity transmission systems and smart grid technology. To achieve this, we developed a hybrid approach that combines machine learning, representation learning, and other deep learning techniques. This approach is based on extracting essential features, including time-based attributes, identifiable trends, and optimal lags. The outcome of our investigation is the observation that triplet losses demonstrate remarkable accuracy, particularly when employed with a larger margin size and for longer prediction lengths. This finding signifies a substantial improvement in the precision and reliability of energy demand forecasting within modern electricity transmission systems. Our research not only improves predictive modeling in the power grid but also demonstrates the practical use of advanced analytics in addressing renewable energy integration challenges, refining energy demand forecasting for efficient management, system operation, and market analysis.

Adopting combination machine learning models could reduce hospital length of stay for oncology patients.

11084 Background: Integra Connect previously created in-patient (IP) admission prediction model based on OCM data. One of the challenges for practices in a value-based care (VBC) program is to provide continuous care-coordination during and after an IP admission. Our objective is to show how adopting combination machine learning (ML) models can predict IP length of stay (LOS). Potential benefits include an overall reduction in LOS which could minimize the risk of hospital acquired conditions. Methods: ML models were trained on 5 major cancer types (Lung Cancer, Multiple Myeloma, Lymphoma, Small Intestine / Colorectal Cancer, High-risk Breast Cancer') from 7 OCM practices of PP4-PP9 data, excluding surgery IP admission. The top 5 cancer types accounted for ~50% of total in-patient admissions and ~50% of total LOS in days. The IP admissions were divided into 4 major cohorts in terms of LOS in days (1-3: class 1, 4-8: class 2, 9-15: class 3, and more than 15: class 4). To reduce the overall LOS, we adopted two ML models: 1) To classify LOS, a multi-classification model (Model1, an eXtreme Gradient Boosted Trees Classifier) and 2) To predict the LOS for classes 1-3, a regression-based model (Model2, an eXtreme Gradient Boosted Trees Regressor with Early Stopping). Class 4 cohort had a wide range (16- 90+ days) for LOS and therefore was excluded from Model2. The other three cohorts belonged to Q1, IQR, and Q4, respectively. Model1 was trained on 4,280 randomly selected sample IP admissions to balance each class and Model2 was trained on 19,636 IP admissions, both with 102 features. Results: The models were tested on PP10-PP11 claims excluding 0 days LOS. Model1 predicted 296 (3,945) IP admissions in class 4 of which 88 were true positives. Assuming at least a 10% reduction in associated total LOS translated to lower LOS of 1-3% for 5 (7) practices. The remaining 3,649 IP admissions were used to predict LOS by Model2. The difference between actual LOS and Predicted LOS was found as 18%-24%. To address model prediction errors, we defined the target LOS to be predicted LOS with an upper bound of at least 10%. This led to reductions of 6% -16% across practices. Finally, combining the results of both ML models we determined that the potential to lower LOS was at least 6% and at most 19%. Conclusions: Our ML models identified opportunities to reduce LOS across multiple OCM cancer practices for 5 major cancer types. We also identified a cohort of patients with critical condition (class 4) that is vital for practice transformation initiatives. These identified reduction in LOS for oncology patients provides cost reduction and quality improvement opportunities in VBC programs. In the future, more opportunities to lower LOS will be explored with a re-admission prediction model.

Perception-based constraint solving for sudoku images

We consider the problem of perception-based constraint solving, where part of the problem specification is provided indirectly through an image provided by a user. As a pedagogical example, we use the complete image of a Sudoku grid. While the rules of the puzzle are assumed to be known, the image must be interpreted by a neural network to extract the values in the grid. In this paper, we investigate (1) a hybrid modeling approach combining machine learning and constraint solving for joint inference, knowing that blank cells need to be both predicted as being blank and filled-in to obtain a full solution; (2) the effect of classifier calibration on joint inference; and (3) how to deal with cases where the constraints of the reasoning system are not satisfied. More specifically, in the case of handwritten user errors in the image, a naive approach fails to obtain a feasible solution even if the interpretation is correct. Our framework identifies human mistakes by using a constraint solver and helps the user to correct these mistakes. We evaluate the performance of the proposed techniques on images taken through the Sudoku Assistant Android app, among other datasets. Our experiments show that (1) joint inference can correct classifier mistakes, (2) overall calibration improves the solution quality on all datasets, and (3) estimating and discriminating between user-written and original visual input while reasoning makes for a more robust system, even in the presence of user errors.

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant’s nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

P‐6: Redefining Pixel Circuit Analysis: Causal Discovery and Probabilistic Modeling

When analyzing data using existing machine learning models without explicit causal information, several limitations often arise, particularly in the misinterpretation of correlations as causal relationships. These limitations are more pronounced in complex scenarios or in situations where outcomes are influenced by sequential effects. This paper presents an advanced methodology for analyzing pixel circuit design and its driving conditions, a domain characterized by complex interactions and multiple variables. Traditional machine learning methods, when applied in isolation, have shown limitations in unraveling the intricate causal relationships inherent in such systems. To address this challenge, we integrated Explainable AI (XAI) techniques, particularly SHAP (SHapley Additive exPlanations) values, into our analysis. In collaboration with domain experts, we constructed a Directed Acyclic Graph (DAG) that effectively reduced the complexity of interconnections and ensured consistency with empirical data. This approach facilitated a more accurate identification of the impact of each parameter and its causal influence. By decomposing the joint distribution of the variables into conditional distributions, taking into account their parental relationships, we gained a deeper understanding of the changes in causal mechanisms. Our methodology significantly enhanced the accuracy of causal analysis under realistic pixel driving conditions. The findings not only offer novel insights into pixel circuitry but also demonstrate the efficacy of combining machine learning with XAI in complex systems analysis, gaining wide acceptability among relevant experts.

Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors

To ensure the structural integrity of concrete and prevent unanticipated fracturing, real-time monitoring of early-age concrete's strength development is essential, mainly through advanced techniques such as nano-enhanced sensors. The piezoelectric-based electro-mechanical impedance (EMI) method with nano-enhanced sensors is emerging as a practical solution for such monitoring requirements. This study presents a strength estimation method based on Non-Destructive Testing (NDT) Techniques and Long Short-Term Memory (LSTM) and artificial neural networks (ANNs) as hybrid (NDT-LSTMs-ANN), including several types of concrete strength-related agents. Input data includes water-to-cement rate, temperature, curing time, and maturity based on interior temperature, allowing experimentally monitoring the development of concrete strength from the early steps of hydration and casting to the last stages of hardening 28 days after the casting. The study investigated the impact of various factors on concrete strength development, utilizing a cutting-edge approach that combines traditional models with nano-enhanced piezoelectric sensors and NDT—LSTMs-ANN enhanced with nanotechnology. The results demonstrate that the hybrid provides highly accurate concrete strength estimation for construction safety and efficiency. Adopting the piezoelectric-based EMI technique with these advanced sensors offers a viable and effective monitoring solution, presenting a significant leap forward for the construction industry's structural health monitoring practices.

A hybrid approach with customized machine learning classifiers and multiple feature extractors for enhancing diabetic retinopathy detection

Diabetic retinopathy (DR) is a severe global issue causing blindness if untreated, affecting millions worldwide and worsening over time. Addressing this growing concern necessitates early and accurate DR identification. This study introduces a novel approach to DR detection, combining machine learning algorithms with deep feature extraction techniques. A hybrid model is proposed by stacking predictions from diverse classifiers, such as Decision Trees, Random Forests, Support Vector Machines (SVMs), and more. Three deep learning models – MobileNetV2, DenseNet121, and InceptionResNetV2 – are employed as feature extractors from retinal images. Each classifier undergoes hyperparameter tuning for optimal performance. Using the APTOS 2019 Blindness Detection dataset, including preprocessing techniques like data augmentation and standardization, this hybrid model demonstrates promising accuracy in multi-class (95.50%) and binary classification (98.36%). Notably, DenseNet121 outperforms others. The results suggest the effectiveness of this hybrid technique in early diabetic retinopathy detection, holding significant promise for improved medical intervention.

A Named Entity Recognition System for the Marathi Language

Named entity recognition is a complex task in developing many NLP applications. This is one of the essential requirements of language modeling in NLP; without it, it is not possible to proceed further and achieve better results. In this proposed task, we have designed a hybrid technique that is a combination of machine learning and a rule-based approach. This system is to identify such named entities that belong under a specific class, creating a special identification and importance in the meaning generation as well as understanding of the language. This is concerned with the input text. Named entity recognition is important for different group items, such as a person’s name, location or place, animals, organization, time or date, etc. Named entities are informative and good representatives of knowledge. NE also explores the knowledge of artificial intelligence-based systems or expert systems. Using the proposed hybrid model, we have achieved 59.40% performance in identifying named entities and properly labeling for the Marathi

AI‐Assisted Design of Printed Edge‐Fed Non‐Uniform Zig‐Zag Antenna for mm‐Wave Automotive Radar

AbstractIn this paper, the design of a novel horizontally polarized single‐layer antenna for 77 (GHz) automotive radar applications is4 addressed. An innovative non‐uniform zig‐zag parametrization of the antenna layout is considered to enable a more flexible control on both the impedance matching in the working frequency band and the shaping of the radiated beam pattern with respect to a standard (uniform) one without compromising the linear (horizontal) polarization of the radiated field. Such a polarization guarantees a lower back‐scattering from road pavements, resulting in a reduced amount of clutter and thus allowing a more robust target detection. Moreover, the single‐layer layout has several advantages in terms of fabrication simplicity/costs and mechanical robustness to vibrations. The design of the proposed non‐uniform zig‐zag antenna (NZA) is performed through a customized implementation of the System‐by‐Design (SbD) approach that fruitfully combines machine learning and evolutionary optimization to efficiently deal with the computational complexity at hand. An extensive numerical validation, dealing with designs of different lengths, verifies the high performance of the NZA in terms of beam direction deviation (e.g., BDD < 1 (deg)), sidelobe level (e.g., SLL < −18.2 (dB)), and polarization ratio (e.g., PR > 20 (dB)) within the working frequency band (GHz), as well as its superiority over competitive designs. Finally, the realization of a prototype and its experimental test, validate the proposed NZA concept for automotive mm‐wave radar applications in advanced driver assistance systems and autonomous vehicles such as, for instance, adaptive cruise control, collision avoidance, and blind spot detection.

Research on Visualization Techniques Based on High-Dimensional Attribute Data of Chemical Materials

Machine learning is currently used to analyze and predict materials in fully automated applications in the materials domain. However, human interpretation and involvement are limited due to the opaque nature of these algorithms. This study explores for the first time the use of semi-human and semi-automated analysis techniques in materials data research. The analytics approach combines machine learning techniques with data visualization and analysis so that the analysis of material properties is dominated by human intelligence, with machine learning techniques as a complementary method. This helps experts understand the associations between material properties from a broader perspective, enabling them to generalize the concept of micro-properties to macro-properties.

A predictive study of the efficacy of transcutaneous auricular vagus nerve stimulation in the treatment of major depressive disorder: An fMRI-based machine learning analysis

BackgroundIn order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the appropriate MDD population before treatment. MethodsA total of 86 MDD patients were recruited in this study, and all subjects were conducted with the clinical scales and resting-state functional magnetic resonance imaging (fMRI) scan before and after 8 weeks’ taVNS treatment. A two-stage feature selection strategy combining Machine Learning and Statistical was used to screen out the critical brain functional connections (FC) that were significantly associated with efficacy prediction, then the efficacy prediction model was constructed for taVNS treating MDD. Finally, the model was validated by separated the responding and non-responding patients. ResultsThis study showed that taVNS produced promising clinical efficacy in the treatment of mild and moderate MDD. Eleven FCs were selected out and were found to be associated with the cortico-striatal-pallidum-thalamic loop, the hippocampus and cerebellum and the HAMD-17 scores. The prediction model was created based on these FCs for the efficacy prediction of taVNS treatment. The R-square of the conducted regression model for predicting HAMD-17 reduction rate is 0.44, and the AUC for classifying the responding and non-responding patients is 0.856. ConclusionThe study demonstrates the validity and feasibility of combining neuroimaging and machine learning techniques to predict the efficacy of taVNS on MDD, and provides an effective solution for personalized and precise treatment for MDD.

Phase division and recognition of crystal HRTEM images based on machine learning and deep learning

The High Resolution Transmission Electron Microscope (HRTEM) images provide valuable insights into the atomic microstructure, dislocation patterns, defects, and phase characteristics of materials. However, the current analysis and research of HRTEM images of crystal materials heavily rely on manual expertise, which is labor-intensive and susceptible to subjective errors. This study proposes a combined machine learning and deep learning approach to automatically partition the same phase regions in crystal HRTEM images. The entire image is traversed by a sliding window to compute the amplitude spectrum of the Fast Fourier Transform (FFT) in each window. The generated data is transformed into a 4-dimensional (4D) format. Principal component analysis (PCA) on this 4D data estimates the number of feature regions. Non-negative matrix factorization (NMF) then decomposes the data into a coefficient matrix representing feature region distribution, and a feature matrix corresponding to the FFT magnitude spectra. Phase recognition based on deep learning enables identifying the phase of each feature region, thereby achieving automatic segmentation and recognition of phase regions in HRTEM images of crystals. Experiments on zirconium and oxide nanoparticle HRTEM images demonstrate the proposed method achieve the consistency of manual analysis. Code and supplementary material are available at https://github.com/rememberBr/HRTEM2.

Insurance Premium Prediction Using Machine Learning

RiskInsight represents an innovative company poised to transform the world of insurance by applying state-of-the-art machine learning techniques. The focus of this project is the Insurance Risk Assessment System, a high-level framework carefully designed using the Python programming language and the sklearn library. Anchored by unprecedented accuracy and operational efficiency, RiskInsight enters the field of risk assessment with unwavering determination. Using the power of advanced algorithms inspired by artificial neural networks, the system navigates huge historical data and evaluates real-time risk dynamics. The cornerstone of RiskInsight is its in-depth analysis of various risk factors carefully curated in a robust database. Combining machine learning capabilities and domain expertise, RiskInsight seeks to redefine the boundaries of risk assessment by transcending traditional paradigms. The goal is to bring about a paradigm shift in super forecasting and provide stakeholders with insights that promote fairer payment structures and informed decision-making. With its innovative design and flexible functionality, RiskInsight promises to catalyze major changes in the insurance ecosystem. By providing accurate payment forecasts and advanced risk management strategies, the platform aims to enhance the efficiency of insurance operations. A testament to its transformative potential, RiskInsight is poised to usher in a new era of efficiency and equity in the insurance industry and sets a precedent for technological innovation in risk assessment and predictive systems. Keywords: Machine Learning, Fairer Payment Structure, Efficiency, Transformative Potential Acronyms: sklearn - Scikit-learn

Machine Learning-Enhanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Metalloproteins.

Understanding the fine structural details of inhibitor binding at the active site of metalloenzymes can have a profound impact on the rational drug design targeted to this broad class of biomolecules. Structural techniques such as NMR, cryo-EM, and X-ray crystallography can provide bond lengths and angles, but the uncertainties in these measurements can be as large as the range of values that have been observed for these quantities in all the published structures. This uncertainty is far too large to allow for reliable calculations at the quantum chemical (QC) levels for developing precise structure-activity relationships or for improving the energetic considerations in protein-inhibitor studies. Therefore, the need arises to rely upon computational methods to refine the active site structures well beyond the resolution obtained with routine application of structural methods. In a recent paper, we have shown that it is possible to refine the active site of cobalt(II)-substituted MMP12, a metalloprotein that is a relevant drug target, by matching to the experimental pseudocontact shifts (PCS) those calculated using multireference ab initio QC methods. The computational cost of this methodology becomes a significant bottleneck when the starting structure is not sufficiently close to the final one, which is often the case with biomolecular structures. To tackle this problem, we have developed an approach based on a neural network (NN) and a support vector regression (SVR) and applied it to the refinement of the active site structure of oxalate-inhibited human carbonic anhydrase 2 (hCAII), another prototypical metalloprotein target. The refined structure gives a remarkably good agreement between the QC-calculated and the experimental PCS. This study not only contributes to the knowledge of CAII but also demonstrates the utility of combining machine learning (ML) algorithms with QC calculations, offering a promising avenue for investigating other drug targets and complex biological systems in general.

Optimizing the mechanical performance of A356–Sc–Sr alloy via combining machine learning and mechanical stirring under vacuum

In this study, a machine learning design system (MLDS) with a property-oriented optimization strategy was first established to predict the mechanical properties of the A356 alloys with adding Sc and Sr elements. Based on the experimental verification from the MLDS, the addition of 0.2 wt% Sc and 0.067 wt% Sr elements led to the refinement of α-Al grains and eutectic Si phases. Then, the vacuum–stirring was introduced to obtain the semi-solid microstructure of the A356–0.2Sc–0.067Sr alloy. The α-Al grains displayed the spherical morphology and the reduction in pores helped improve the mechanical properties of the alloy. In addition, the effects of stirring time and stirring temperature on the microstructure and mechanical properties of the alloy were investigated. The results demonstrated that the α-Al grains of the alloy were further spheroidized, resulting in the improved mechanical properties. The ultimate tensile strength and elongation of the alloy were 220 MPa and 6.0%, respectively, which were increased by 16.8% and 26.7% in comparison to those of the alloy without vacuum–stirring. The aim of this work is to provide a new method to prepare high-performance A356 alloy through composition design and microstructural control.

Amino acid metabolomics and machine learning for assessment of post-hepatectomy liver regeneration

ObjectiveAmino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH).MethodsThe liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography–tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index.ResultsEleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively.ConclusionThe KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH.