Revolutionizing biochar synthesis for enhanced heavy metal adsorption: Harnessing machine learning and Bayesian optimization

The best practice of watershed management is through the understanding of the hydrological processes. As a matter of fact, hydrological processes are highly associated with stochastic, non-linear, and non-stationary phenomena. Hydrological processes simulation and modeling are challenging issues in the domains of hydrology, climate and environment. Hence, the development of machine learning (ML) models for solving those complex hydrological problems took essential place over the past couple decades. It can be observed, hydrological data availability has increased remarkably, and thus computational resources has led to a resurgence in ML models’ development. It has been witnessed huge efforts on the hydrological processes modeling using the facility of ML models and several review researches have been conducted. Literature studies approved the capacity of ML models in the field of hydrology over the classical “traditional models” based on their forecastability, flexibility, precision, generalization, and modeling execution convergence speed. However, although several potential merits were observed in ML model’s development, several limitations are allied such as the interpretability of those black-box models, the practicality of the ML models in watershed management, and difficulty to explain the physical hydrological processes. In this survey, an exhibition for all the published review articles on the development of ML models for hydrological processes and recognize all the research gaps and potential research direction. The ultimate aim of the current survey is to establish a new milestone for the interested hydrology, environment and climate researchers on the applications of ML models.

Key-frame extraction is a key technique of video retrieval. In view of the low accuracy of currently traditional key-frame extraction algorithm, and low recall level and accuracy of video retrieval, a kind of model of network video retrieval of dynamic Bayesian network optimization for the mobile terminal was put forward. First of all, the study of dynamic Bayesian network optimization algorithm was conducted, and by using a Bayesian algorithm, the real data were used as the characteristic of the center of clustering, then the condensed coarsening and the condensed refining that are two important processes for network video retrieval model algorithm were improved, used to replace the top core set obtained in the original coarsening process, thereby designing a kind of new dynamic Bayesian network optimization algorithm, so as to realize the rapid and accurate positioning of the top core set, which can decrease the number of condensed layer and simplify the computational complexity of the algorithm. Then, the improved network video retrieval model algorithm was applied to the key-frame extraction of the video. The experimental results show that the proposed algorithm can more effectively extract key frames of video compared with the original algorithm.

Oil viscosity is an important parameter for estimating the flow behavior of crude oil under varying pressure and temperature regimes encountered in the subsurface and surface components of the petroleum production system. Although experimental data provides the most accurate characterization of this property, obtaining such data in the laboratory is often time-consuming and expensive. Alternatively, one of several empirical correlations can be used, albeit with often poor generalization to samples from differing thermodynamic systems or crude oil types. In this paper, we use a data-driven approach based on machine learning (ML) algorithms to create high-performing models for predicting the dead oil and bubblepoint oil viscosity of Nigerian crudes. Random Forest (RF) and Gradient Boosting (GB) ensembles, as well as Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks, are compared to several widely used correlations, and their performance is evaluated on a large dataset of crude oil samples collected from various Niger Delta oil fields. The ML models are built with minimal and easily accessible input parameters shared by the correlations, allowing for an objective assessment of their superiority to these correlations given the same input framework. These are reservoir temperature and oil API gravity for the dead oil viscosity modeling, and the solution gas-oil ratio and dead oil viscosity for the bubblepoint oil viscosity modeling. Furthermore, the model's reliability is tested by partitioning the dataset into training, validating, and testing sets, and optimal model configurations are achieved through Bayesian Optimization. On the dead oil viscosity modeling task, we achieve a modest improvement over the best-performing correlation developed by Al-Khafaji (1987). The RF model's R2 and mean absolute error (MAE) scores on the testing data are 0.831 and 3.423, respectively, versus 0.809 and 4.439 for the correlation method. However, for the bubblepoint oil viscosity modeling, the improvement recorded using the RF model over the best-performing correlation developed by Beggs and Robinson (1975) is remarkable. In this case, the R2 and MAE scores for the RF model are 0.924 and 0.441, respectively, compared to 0.574 and 0.813 for the correlation method. As a result, our ML models have outperformed conventional correlations for modeling oil viscosity. Ultimately, oil viscosity estimates from the developed ML models can provide a much more accurate characterization of the fluid behavior of Niger Delta crudes than the conventional correlations can. Furthermore, the ML models allow for continuous improvement of their generalization ability by introducing new data from various sources to the models.

Bayesian optimization (BO) is a versatile and robust global optimization method under uncertainty. However, most of the BO algorithms were developed for problems with only continuous variables. For practical engineering optimization, discrete variables are also prevalent. BO methods based on Gaussian process (GP) surrogates also suffers from the curse-of-dimensionality problem. To address these challenges, in this paper, a Bayesian discrete optimization algorithm is introduced to solve permutation-based combinatorial problems. A new kernel function is developed based on position distances for permutation. To improve the efficiency and scalability of the algorithm, a sparse GP model based on inducing points is further developed, where the simulated annealing algorithm is applied to select inducing points. The new algorithm is demonstrated and tested with the production scheduling problem for additive manufacturing. Experimental results show that the proposed algorithm can find a better solution with limited evaluations than state-of-the-art algorithms.

This study aimed to develop a machine learning model for accurately predicting postoperative VTE risk in these patients. The data of this study retrospectively collected the inpatients of cervical cancer in the Affiliated Cancer Hospital of Chongqing University from January 2020 to December 2023. We utilized 1,044 observations and six variables to develop seven machine learning (ML) models and selected the best-performing model for assessing postoperative venous thromboembolism (VTE) risk. The models were evaluated using ROC, PR, and DCA curves, and the prediction of the model's process was explained using SHAP values. Among 1,044 postoperative cervical cancer patients, 82 (7.85%) developed VTE. Seven machine learning algorithms were developed and evaluated, with the random forest (RF) model showing the best overall performance (AUC = 0.852, AUPR = 0.332). Compared with other models, including logistic regression (AUC = 0.767) and XGBoost (AUC = 0.836), the RF model demonstrated superior discrimination, calibration, and generalizability. Decision curve analysis confirmed that the RF model yielded the highest net clinical benefit across a wide range of threshold probabilities (5-80%). SHAP analysis revealed that D-dimer, neutrophil-to-lymphocyte ratio (NLR), and age were the most influential variables, indicating their substantial contributions to model prediction. Our study suggests that machine learning algorithms can be practical tools for postoperative VTE risk assessment and can learn from patient characteristics to provide personalized evaluations. The RF model outperformed the other six algorithms evaluated. We developed the final RF model as a user-friendly web tool for healthcare professionals.

To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm (IWOA). By initializing the Bayesian parameter population using Tent chaotic mapping and the reverse learning strategy, employing a radial basis kernel function hyperparameter training mechanism based on the Adam optimizer and optimizing the Expected Improvement (EI) function using the Limited-memory Broyden–Fletcher– Goldfarb–Shanno with Bounds (L-BFGS-B) method, IBO was proposed to obtain the optimal candidate set with the smallest objective function value. By introducing a nonlinear convergence factor and the adaptive Levy flight perturbation strategy, IWOA was proposed to obtain locally optimized optimal solutions. By using the reverse-guided optimization mechanism and employing a fitness-oriented selection strategy, the optimal solution was chosen to complete the closed-loop process of reverse learning feedback. Nine standard test functions and the Proportional Integral Derivative (PID) parameter identification of the electro-hydraulic servo system in a 330 MW steam turbine were presented as examples. Compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Bayesian Optimization (BO) and Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO), the Improved Bayesian Optimization-Whale Optimization Algorithm (IBO-WOA) proposed in this paper has been validated to effectively avoid the problem of getting stuck in local optima during complex optimization and has high parameter recognition accuracy. Meanwhile, an Out-Of-Distribution (OOD) Test based on noise injection had demonstrated that IBO-WOA had good robustness. The time constant identification of the steam turbine were carried out using IBO-WOA under two experimental conditions, and the identification results were input into the PFR model. The simulated power curve can track the experimental measured curve well, proving that the parameter identification results obtained by IBO-WOA have high accuracy and can be used for the modeling and response characteristic analysis of the steam turbine PFR.

Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples

Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ETo estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ETo at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ETo estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ETo estimation. Among the applied ML models, the ETo tree boost (TB) ML model outperformed the other ML models in estimating ETo in diverse climatic conditions based on statistical indicators (R2, NSE, r, RMSE, and MAE). Moreover, the observed R2, NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ETo point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ETo maps. Based on the ETo maps, this study suggests mainly a focus on areas with high ETo values and proper irrigation scheduling of crops to ensure water sustainability.

Development of machine learning models for prediction of antibody non-specificity.

Discovering a robust machine learning model for predicting the productivity of a solar-driven humidification-dehumidification system

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

BackgroundThe large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability: evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making.MethodsML models were developed using data of 404 ESG procedures performed at 12 centers across Europe. Collected data included clinical and demographic variables at the time of ESG and at follow-up. Multicentre/external and single center/internal and temporal validation were performed. Training and evaluation of the models were performed on Python’s scikit-learn library. Performance of models was quantified as receiver operator curve (ROC-AUC), sensitivity, specificity, and calibration plots.ResultsMulticenter external validation: ML models using preoperative data show poor performance. Best performances were reached by linear regression (LR) and support vector machine models for TWL% and EWL%, respectively, (ROC-AUC: TWL% 0.87, EWL% 0.86) with the addition of 6-month follow-up data.Single-center internal validation: Preoperative data only ML models show suboptimal performance. Early, i.e., 3-month follow-up data addition lead to ROC-AUC of 0.79 (random forest classifiers model) and 0.81 (LR models) for TWL% and EWL% achievement prediction, respectively. Single-center temporal validation shows similar results.ConclusionsAlthough preoperative data only may not be sufficient for accurate postoperative predictions, the ability of ML models to adapt and evolve with the patients changes could assist in providing an effective and personalized postoperative care. ML models predictive capacity improvement with follow-up data is encouraging and may become a valuable support in patient management and decision-making.

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

With accelerating climate change and urbanization, regional carbon balance faces increasing uncertainty. Terrestrial carbon sinks play a crucial role in advancing China’s sustainable development under the dual-carbon strategy. This study quantitatively modeled China’s terrestrial carbon sink capacity and analyzed the multidimensional relationships between impact factors and carbon sinks. After preprocessing multi-source raster data, we introduced kernel normalized the difference vegetation index (kNDVI) to the Carnegie–Ames–Stanford approach (CASA) model, together with a heterotrophic respiration (Rh) empirical equation, to simulate pixel-level net ecosystem productivity (NEP) across China. A light gradient-boosting machine (LightGBM) model, optimized via Bayesian algorithms, was trained to regress NEP drivers, categorized into atmospheric components (O3, NO2, and SO2) and subsurface properties (a digital elevation model (DEM), enhanced vegetation index (EVI), soil moisture (SM)), and human activities (land use/cover change (LUCC), POP, gross domestic product (GDP)). Shapley Additive Explanation (SHAP) values were used for model interpretation. The results reveal significant spatial heterogeneity in NEP across geographic and climatic contexts. The pixel-level mean and total NEP in China were 268.588 gC/m2/yr and 2.541 PgC/yr, respectively. The north tropical zone (NRZ) exhibited the highest average NEP (828.631 gC/m2/yr), while the middle subtropical zone (MSZ) and south subtropical zone (SSZ) demonstrated the most stable NEP distributions. LightGBM achieved high simulation accuracy, further enhanced by Bayesian optimization. SHAP analysis identified EVI as the most influential factor, followed by SM, NO2, DEM, and POP. Additionally, LightGBM effectively captured nonlinear relationships and variable interactions.

Permeability is a crucial parameter in reservoir engineering, as it determines how easily fluids can flow through subsurface rocks. Traditional methods of measuring permeability are expensive, error-prone and time-consuming, which has led to the development of machine learning models that can predict permeability based on well-log data. This study aims to determine the most effective machine learning models for predicting permeability based on available well-log data. The study covers a detailed explanation of the data-gathering and pre-processing techniques used. Four input features were used in the models, namely gamma ray (GR), bulk density (RHOB), neutron porosity (NPHI), and resistivity (RT). Six different models were trained and evaluated based on their performance, namely linear regression, support vector machine, decision trees, gradient boosting, multi-layer perceptron, and random forest models. The models were evaluated based on their Mean Squared Error and RSquare values. Our results showed that the random forest model outperformed the other models, achieving a Mean Squared Error (MSE) value of 0.88. The multi-layer perceptron and gradient boosting models also showed promising results, achieving MSE values of 0.81 each. These findings suggest that machine learning models can be a powerful tool for predicting permeability from well-log data, and the random forest model, in particular, holds great promise for future modelling efforts in this area.