Analyzing the influence of chemical components of incinerated bottom ash on compressive strength of magnesium phosphate cement using machine learning analysis

Magnesium Phosphate Cement (MPC) is recognized as an effective rapid repair material, with compressive strength serving as a key mechanical property indicator for its mortar formulations. Nevertheless, due to MPC's complex composition and formulation, predicting its compressive strength remains a significant challenge. In this study, a comprehensive database was developed, incorporating four key input variables: the magnesium-to-phosphate (M/P) molar ratio, water-to-cement (W/C) mass ratio, sand-to-binder (S/B) weight ratio, and the borax-to-magnesia(B/M) weight ratio. This dataset was used to train and validate eight machine learning models, including the Lightweight Gradient Boosting (LGB) algorithm, Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGB), Ridge Regression (RR), Random Forest (RF), Backpropagation Neural Network (BP), and Gradient Boosting (GB) models. The eight machine learning models were evaluated using performance metrics, including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Correlation Coefficient, and Root Mean Square Error (RMSE), to identify the optimal model, which was then optimized via the Gray Wolf Optimizer (GWO). The most accurate prediction of MPC compressive strength was attained using the XGB model, with the GWO-optimized XGB model showing enhancement in MAPE, MAE, R2, and RMSE by 21.8%, 60.6%, 43.9%, and 55.3% respectively, relative to the unoptimized XGB model. Employing Shapley Additive exPlanations (SHAP) values and Partial Dependence Plots (PDP), this study facilitates the identification of the most influential input variables and quantifies their effects on MPC compressive strength. The optimized model was validated against experimental data, demonstrating robust and conservative prediction behavior. While the model is trained solely to predict compressive strength, its interpretability enables rational insights into how formulation variables influence strength, thereby supporting informed mix design decisions. This framework offers a reliable and transparent computational tool for preemptive strength assessment of MPC and guides the optimization of mechanical performance in structurally demanding applications.

BackgroundMachine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients.MethodsWe retrospectively included patients who were admitted to intensive care units during 2015–2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data. Three ML models, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR), were used to establish mortality prediction model. Furthermore, we used feature importance, Shapley Additive exPlanations (SHAP) plot, partial dependence plot (PDP), and local interpretable model-agnostic explanations (LIME) to explain the established model.ResultsWe enrolled 6994 patients and found the accuracy was similar among the three ML models, and the area under the curve value of using XGBoost to predict 30-day, 90-day and 1-year mortality were 0.858, 0.839 and 0.816, respectively. The calibration curve and decision curve analysis further demonstrated accuracy and applicability of models. SHAP summary plot and PDP plot illustrated the discriminative point of APACHE (acute physiology and chronic health exam) II score, haemoglobin and albumin to predict 1-year mortality. The application of LIME and SHAP force plots quantified the probability of 1-year mortality and algorithm of key features at individual patient level.ConclusionsWe used an explainable ML approach, mainly XGBoost, SHAP and LIME plots to establish an explainable 1-year mortality prediction ML model in critically ill ventilated patients.

Climate signals, driven by complex interactions and nonlinear relationships, shape weather patterns and long-term trends, complicating the identification of dominant drivers due to collinearity. This study investigates the consistency and uncertainty of machine learning (ML) techniques for feature importance in climate science, comparing SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), and gain-based feature importance from Extreme Gradient Boosting (XGBoost). SHAP’s integration with Feed Forward Neural Networks (FFNN) and XGBoost is evaluated to assess model-specific uncertainties. Using winter precipitation data from Ohio, USA, as a case study, the relative contributions of global warming (GW) and the Interdecadal Pacific Oscillation (IPO) to precipitation changes are quantified. Results show GW consistently ranks higher than IPO in at least 60% of stations across all methods, with SHAP and PDPs agreeing in 89% of stations. Global SHAP importance from FFNN and XGBoost aligns in 82% of stations, with GW contributing 15% more than IPO on average, though disagreements in 18% of stations highlight model-dependent uncertainties. Temporal analysis using SHAP values indicates a moderate discrepancy in feature importance between FFNN and XGBoost models (Pearson correlation ≈ 0.5), despite their consensus on the increasing dominance of GW in recent decades, contributing to wetter winters. Regression analysis further confirms that GW accounts for approximately 70% of the multi-decadal variability in winter precipitation across Ohio, with PDPs indicating a strong monotonicity (ρ = 0.94) between warming levels and precipitation increase. PDPs visualize marginal effects but struggle with interactions, while gain-based methods tend to favor features with a greater number of effective split points that reduce loss. SHAP, though robust for ranking, varies with the base model. An ensemble framework is proposed, demonstrating the value of combining these ML techniques complementarily to account for uncertainties and enhance interpretability. This study highlights the importance of addressing methodological uncertainties in feature importance rankings to provide robust insights for climate modeling.

Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction

The early strength of magnesium phosphate cement (MPC) decreases sharply in severe cold environments ≤−10 °C, with the 2 h compressive strength falling to 3.5 MPa at−20 °C. Therefore, it cannot be used as a repair material for emergency repair construction in such environments. In this study, MPC is adapted for use in such cold environments by replacing part of the dead-burned magnesia (M) in the mixture with a small amount of light-burned magnesia (LBM) and introducing dilute phosphoric acid (PA) solution as the mixing water. The heat released by the highly active acid–base reaction of PA and LBM stimulates an MPC reaction. Moreover, the early strength of the MPC significantly improves with the increase in the Mg2+ concentration and the initial reaction temperature of the MPC paste, which enables MPC hardening in severe cold environments. Although the morphology of the reaction products of the MPC is poor and the grain plumpness and size of the struvite crystals are remarkably reduced, the early strength of MPC prepared in the severe cold environment is close to that of MPC prepared under normal temperature. Furthermore, the increases in the early reaction temperature and early strength of magnesium phosphate cement concrete (MPCC) are significantly improved when the PA concentration in the mixing water and the LBM/M ratio are 10% and 4–6% at −10 °C and 20% and 6–8% at −20 °C, respectively. Moreover, self-curing of MPCC can be realized even at −20 °C, at which temperature the 2 h and 24 h compressive strength of MPCC reach 36 MPa and 45 MPa, respectively.

Dual role of Interleukin-18 in linking metabolic and psychiatric symptoms: Insights from machine learning in schizophrenia.

Effect of raw materials and proportion on mechanical properties of magnesium phosphate cement

Improving the strength of bone cement is one of the critical goals in cement designing to maintain their integrity and stabilize connection between the cement and their surrounding tissue during the time that the cement has been replaced by matured bone tissue. To this aim, the authors decided to evaluate setting behavior and compressive strength of Magnesium Phosphate Cement (MPC) by adding car-boxylated Single-Walled Carbon Nanotubes (c-SWCNTs) and assess the biocompatibility of the composite cement. MPC containing 0 wt% to 0.5 wt% of c-SWCNTs at the Powder to Liquid Ratio (PLR) of 1 g-mL-1 to 2 g-mL-1 were produced. Adding c-SWCNTs to MPC postponed the setting time of the cement at the beginning of the cementation process and preserved the reaction with a high rate for a longer time. In addition, the compressive strength of MPC was enhanced to 28 MPa by adding 0.2 wt% c-SWCNTs because of producing cement with compact and uniform micro structure. In addition, cell behavior on MPC with/without c-SWCNTs indicated no cytotoxic effect alongside a suitable adhesion and proliferation of them.

Tree-based machine learning (ML) algorithms, such as Extra Trees (ET), Random Forest (RF), Gradient Boosting Machine (GBM), and XGBoost (XGB) are among the most widely used in early drug discovery, given their versatility and performance. However, models based on these algorithms often suffer from misclassification and reduced interpretability issues, which limit their applicability in practice. To address these challenges, several approaches have been proposed, including the use of SHapley Additive Explanations (SHAP). While SHAP values are commonly used to elucidate the importance of features driving models' predictions, they can also be employed in strategies to improve their prediction performance. Building on these premises, we propose a novel approach that integrates SHAP and features value analyses to reduce misclassification in model predictions. Specifically, we benchmarked classifiers based on ET, RF, GBM, and XGB algorithms using data sets of compounds with known antiproliferative activity against three prostate cancer (PC) cell lines (i.e., PC3, LNCaP, and DU-145). The best-performing models, based on RDKit and ECFP4 descriptors with GBM and XGB algorithms, achieved MCC values above 0.58 and F1-score above 0.8 across all data sets, demonstrating satisfactory accuracy and precision. Analyses of SHAP values revealed that many misclassified compounds possess feature values that fall within the range typically associated with the opposite class. Based on these findings, we developed a misclassification-detection framework using four filtering rules, which we termed "RAW", SHAP, "RAW OR SHAP", and "RAW AND SHAP". These filtering rules successfully identified several potentially misclassified predictions, with the "RAW OR SHAP" rule retrieving up to 21%, 23%, and 63% of misclassified compounds in the PC3, DU-145, and LNCaP test sets, respectively. The developed flagging rules enable the systematic exclusion of likely misclassified compounds, even across progressively higher prediction confidence levels, thus providing a valuable approach to improve classifier performance in virtual screening applications.

Background: Acute kidney injury (AKI), with an increase in serum creatinine, is a common adverse drug event. Although various clinical studies have investigated whether a combination of two nephrotoxic drugs has an increased risk of AKI using traditional statistical models such as multivariable logistic regression (MLR), the evaluation metrics have not been evaluated despite the fact that traditional statistical models may over-fit the data. The aim of the present study was to detect drug-drug interactions with an increased risk of AKI by interpreting machine-learning models to avoid overfitting.Methods: We developed six machine-learning models trained using electronic medical records: MLR, logistic least absolute shrinkage and selection operator regression (LLR), random forest, extreme gradient boosting (XGB) tree, and two support vector machine models (kernel = linear function and radial basis function). In order to detect drug-drug interactions, the XGB and LLR models that showed good predictive performance were interpreted by SHapley Additive exPlanations (SHAP) and relative excess risk due to interaction (RERI), respectively.Results: Among approximately 2.5 million patients, 65,667 patients were extracted from the electronic medical records, and assigned to case (N = 5,319) and control (N = 60,348) groups. In the XGB model, a combination of loop diuretic and histamine H2 blocker [mean (|SHAP|) = 0.011] was identified as a relatively important risk factor for AKI. The combination of loop diuretic and H2 blocker showed a significant synergistic interaction on an additive scale (RERI 1.289, 95% confidence interval 0.226–5.591) also in the LLR model.Conclusion: The present population-based case-control study using interpretable machine-learning models suggested that although the relative importance of the individual and combined effects of loop diuretics and H2 blockers is lower than that of well-known risk factors such as older age and sex, concomitant use of a loop diuretic and histamine H2 blocker is associated with increased risk of AKI.

Data-driven shear strength prediction of steel reinforced concrete composite shear wall

Multi-output machine learning for predicting the mechanical properties of BFRC

Experimental-computational approach to investigate compressive strength of magnesium phosphate cement with nanoindentation and finite element analysis

The incidence of early-onset gastroenteropancreatic neuroendocrine tumors (GEP-NETs) is increasing, with liver metastases often occurring early and adversely affecting prognosis. This study aimed to develop a predictive model for liver metastases detection in patients with early-onset GEP-NETs (<50 years) using an automated machine learning (AutoML) approach. A retrospective analysis was conducted on patients diagnosed with early-onset GEP-NETs [2000-2021] using data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into a training set (n=8,983) and a validation set (n=3,819) in a 7:3 ratio. A nomogram-based scoring system was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression. AutoML was applied to build predictive models using gradient boosting machine (GBM), generalized linear model (GLM), deep learning (DL), and distributed random forest (DRF) algorithms. Model performance was assessed using receiver operating characteristic (ROC), calibration, decision curve analysis (DCA), and interpretability tools including SHapley Additive exPlanations (SHAP), partial dependence plots (PDPs), and locally interpretable model-agnostic explanations (LIME) plots. A total of 12,802 patients were included, of whom 1,187 (9.3%) developed liver metastases, comprising 851 (9.5%) and 336 (8.8%) cases in the training and validation sets, respectively. Comparative analyses demonstrated that the AutoML models outperformed traditional logistic regression models, with the GBM algorithm achieving the highest performance. The GBM model achieved an area under the curve (AUC) of 0.961 in the training set and 0.953 in the validation set. Tumor location was identified as the most important predictor in the GBM model, followed by surgery, tumor size, chemotherapy, and T-staging. The AutoML model leveraging the GBM algorithm provides a robust and clinically valuable tool for the early prediction of liver metastases in patients with early-onset GEP-NETs.

Estimation of building project completion duration using a natural gradient boosting ensemble model and legal and institutional variables