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The estimation of material performance plays a crucial role in practical life, enabling the rational allocation of time and resources while enhancing the practical application of materials. Therefore, this study investigates the analytical and predictive capabilities of five machine learning (ML) algorithms (the Random Forest algorithm – RF, the Adaboost algorithm – AB, the Gradient Boosting algorithm – GB, the Extra Trees algorithm – ET and linear models – LM) for comprehensive performance parameters of wood ceramics (ammonia nitrogen adsorption quantity - Q , open porosity - P , burn-off , and hardness). In the analysis of model prediction capabilities, five key statistical parameters (Root Mean Square Error - RMSE , Mean Square Error - MSE , Coefficient of Correlation - R , Determination Coefficient - R 2 , and Mean Absolute Relative Error - MARE) were calculated. The results indicate the following: (1) Among various ML models, the GB model exhibits the most superior performance with an R 2 ≥ 0.9594. (2) However, in predicting the four performance parameters of wood ceramics, the AB model with an R 2 ranging from 0.0078 to 0.45, indicating notably poor predictive capabilities. Despite integrating the Support Vector Regression (SVR) module, no enhancement in predictive accuracy was observed. (3) While forecasting the four performance parameters of wood ceramics, the LM model demonstrates predictive accuracy relatively akin to that of the RF and ET models among the ML models. (4) Though the feature importance scores vary across distinct input variable models, there is a consistent trend of change. (5) Variables B and D exhibit some level of correlation with other variables. In summary, the results of this study suggest that, for the analysis and prediction of wood ceramic performance, the GB model among the five regression models demonstrates outstanding simulation performance. [ABSTRACT FROM AUTHOR]

• This study examined the genes related to PD pathogenesis by tissues and blood samples datasets. • WGCNA and protein–protein interaction (PPI) network were used to pinpoint hub genes. • Elevation of FOXO3 , HIST2H2BE , and HDAC1 expression associated with PD pathogenesis. Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein–protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3 , HIST2H2BE , and HDAC1 , all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3 , HIST2H2BE , and HDAC1 , were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3 , HIST2H2BE , and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches. • We evaluated process-based (PB) and machine-learning (ML) models for SOC prediction. • Random forest & support vector machine predicted SOC superior to PB models. • Reduced data availability and quality significantly weakened ML model performance. • ML-assisted PB model ensembles strongly improved model performance. [ABSTRACT FROM AUTHOR]

• FFS maps were produced using CTREE, KNN, SVM, RF, GBM and XGBoost algorithms. • The performance of six machine learning techniques was evaluated. • This study will be the first to use the CTREE algorithm for FFS mapping. • XGBoost gave the best results compared to other models. • The most important factor for producing FFS maps in XGBoost models was annual temperature. Antalya is one of the provinces with the highest number of forest fires in Türkiye. In 2021, 278 forest fires occurred within the administrative boundaries of Antalya Regional Directorate of Forestry. The main objective of this study is to produce forest fire susceptibility (FFS) maps of Antalya province using machine learning (ML) models. In addition to forest fire inventory data, 16 factors, including topographic, environmental, meteorological, and human-driven, were used in the study. Inventory data included 2166 fire ignition points from the General Directorate of Forestry. 70 % of the inventory dataset was used to train the ML models and 30 % to validate the models. Overall accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) approaches were considered as validation metrics. FFS maps of Antalya were produced using stand-alone ML algorithms, K-Nearest Neighbors, and Support Vector Machines, as well as tree-based Conditional Inference Trees (CTREE), Random Forest (RF), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. To the best of our knowledge, this is the first study using the CTREE algorithm for forest fire susceptibility mapping. Therefore, this study is important for the related literature. The validation results revealed that the XGBoost model outperformed other models. It is thought that the FFS map produced using the XGBoost model will guide forest engineers, wildland firefighting teams, and firefighters to minimize damage and control forest fires. [ABSTRACT FROM AUTHOR]

VEGF is prototypic marker of neovascularization, repeatedly proposed as intrinsic characteristic of peri-implantitis. This study aimed to assess pattern of VEGF in peri-implantitis, its correlation with titanium particles (TPs) and capacity as respective biomarker. Pathological specificity of VEGF was assessed in peri-implant granulations using immunohistochemistry, periodontal granulations represented Ti-free positive controls. VEGF was correlated to TPs, identified using scanning electron microscopy coupled with dispersive x-ray spectrometry. Diagnostic accuracy, sensitivity and specificity of VEGF were estimated in PICF specimens from peri-implantitis, peri-implant mucositis (PIM) and healthy peri-implant tissues (HI) using machine learning algorithms. Peri-implantitis exhibited rich neovascular network with expressed density in contact zones toward neutrophil infiltrates without specific pattern variations around TPs, identified in all peri-implantitis specimens (mean particle size 8.9 ± 24.8 µm2; Ti-mass (%) 0.380 ± 0.163). VEGF was significantly more expressed in peri-implantitis (47,065 ± 24.2) compared to periodontitis (31,14 ± 9.15), and positively correlated with its soluble concentrations in PICF (p = 0.01). VEGF was positively correlated to all clinical endpoints and significantly increased in peri-implantitis compared to both PIM and HI, but despite high specificity (96%), its overall diagnostic capacity was average. Two patient clusters were identified in peri-implantitis, one with 8-fold higher VEGF values compared to HI, and second with lower values comparable to PIM. VEGF accurately reflects neovascularization in peri-implantitis that was expressed in contact zones toward implant surface without specific histopathological patter variation around TPs. VEGF answered requests for biomarker of peri-implantitis but further research is necessary to decrypt its exact underlying cause. [Display omitted] • Neovascularization was expressed in contact zones toward implant surface. • VEGF is highly specific to peri-implantitis and accurately reflects neovascularization. • VEGF could not be directly associated to titanium particles. • In-depth research is required to decipher the specific VEGF pattern in peri-implantitis. [ABSTRACT FROM AUTHOR]

In the industrial production process, it is important to improve the productivity of blast furnaces in response to the worldwide call for energy saving and emission reduction. In this study, a data-driven method using computational fluid dynamics (CFD) is developed for the rapid prediction of nitrate decomposition in a blast furnace. The method includes the creation of a CFD database, for which nozzle arrangement patterns, relative distances, spray cone angles and temperatures are simulated and collected. Support vector regression (SVR) hyperparameters are optimized using genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimizer (GWO). Machine learning interpretability methods shapley additive explanations (SHAP) and partial dependence plots (PDP) are used to analyze the impact of features on prediction results. The results show that the hybrid GWO-SVR model has a better performance, with the degree of fit (R2) improving from 0.7752 to 0.9719. When the distance is taken around 0.58, increasing the temperature and cone angle is more favorable to improve the decomposition rate of the blast furnace. SHAP method shows that only the arrangement 2 and 4 have a positive effect on improving the decomposition rate. The response time for accurate prediction of nitrate decomposition rate is 259200 times shorter than that of industrial-scale CFD simulations. The method is also used to determine the best combination of solutions. This cross-disciplinary approach provides a time-saving and cost-effective tool for improving the response of blast furnace productivity to changes in nozzle arrangement. [ABSTRACT FROM AUTHOR]

The quest to attain net-zero emissions has increased the drive for more renewable energy potential from the co-gasification of biomass. The co-gasification of coconut shell and oil palm wastes is a potential technological route to produce hydrogen-rich syngas. However, the complexity of the gaseous-phase reaction often results in process uncertainties which could lead to energy and material wastage. Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. The co-gasification of coconut shell and oil palm wastes blend catalyzed by Portland cement, dolomite, and limestone resulted in the maximum syngas production of 42 mol.%, 38 mol.%, 45 mol.%, respectively. The co-gasification process was modeled using SVM regression incorporated with linear, quadratic, and cubic kernel functions, GPR incorporated with rotational, squared, Matern 5/2, and exponential kernel functions, and non-linear response quadratic model (NLRQM) using sequential quadratic programming (SQP), and Levenberg-Marquardt (LM). The performance analysis of the models revealed that the SVM incorporated with linear kernel had the least performance with R2 in the range of 0.3–0.7. Whereas the best performance in terms of prediction of the syngas composition was obtained using the NLRQM algorithm with an inbuilt SQP and LM algorithms. The observed syngas composition was consistent with predicted values with R2 > 0.97 for the three catalyzed co-gasification processes. The low RMSE (<1) and MAE (<1) obtained from the models are indications of the robustness of the accurate prediction of the NLRQM-LM and NLRQM-SQP algorithms. The sensitivity analysis revealed that the co-gasification temperature, catalysts loading, and the blending amount play a significant role in the predicted syngas composition. However, the co-gasification temperature had the highest influence as indicated by the level of importance values. • Hydrogen-rich syngas production from co-gasification of biomass blend was investigated. • Twelve machine learning algorithms were employed to model the co-gasification process. • Support vector machine and Gaussian process regression performed moderately. • Non-linear response quadratic model embedded with inbuilt algorithms perform best. • Gasification temperature significantly influenced the predicted syngas composition. [ABSTRACT FROM AUTHOR]

[Display omitted] • An automatic sorting machine is designed for grading pomegranates. • Machine vision is utilized in order to detect sunburn on pomegranates. • Three different image processing algorithms are proposed for feature extraction. • ANN and SVM classification models are used to sort the fruits automatically. • The experimental results of algorithms and overall sorting machine are provided. Sunburn is a widespread problem in pomegranate marketing. About 30 % of the harvested pomegranates suffer from sunburn. In this research, an automatic sorting machine was designed, developed, and tested to separate the sunburn pomegranates. To detect sunburn on pomegranates, three novel image processing algorithms were proposed: skin spot detection, brightness intensity distribution, and statistical surface parameter. Two supervised machine learning algorithms comprising artificial neural networks and support vector machines were utilized to classify the fruits. The proposed algorithms were used to control the developed sorting machine to separate the sunburn pomegranates from the healthy ones. Finally, the performance of the proposed algorithms was evaluated by the accuracy and response time of the sunburn sorting function of the developed machine. The brightness intensity distribution algorithm with the accuracy and response time of 98 % and 0.88 s, respectively, is suggested as the fastest and the most accurate algorithm. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of the DTPA chelating agent on the rock/oil contact angle and rock surface charge was examined under varying concentrations, salinity, and potential-determining ions. • An experimental database comprising 240 sets of wettability data was acquired. • Two advanced machine-learning models, namely Random Forest and Boosted Regression Tree, were developed to accurately estimate the alteration in rock wettability. • Statistical analysis was conducted, demonstrating outstanding predictions for a wide range of input variables. Chelating agents have garnered increasing attention due to their unique capabilities in addressing challenges associated with chemical enhanced oil recovery (CEOR) materials. This research investigates the transformative potential of diethylenetriaminepentaacetic acid (DTPA) chelating agent in altering sandstone rock wettability. Wettability and zeta potential measurements were used to assess the impact of crucial factors including DTPA concentration, salinity, and potential determining ions (PDIs) on contact angle and rock surface charges. The findings revealed that there is an optimal level for DTPA concentration and salinity that can shift rock wettability from an oil-wet to strongly water-wet state. Moreover, while PDIs in their natural concentrations had no significant impact on DTPA performance, a threefold increase in their concentration was found to adversely affect DTPA efficacy. After conducting rock wettability tests, two advanced machine learning (ML) techniques, namely Random Forest (RF) and Boosted Regression Tree (BRT), were employed to assess rock/oil contact angle based on the aforementioned factors. Data were collected from 240 experimental datasets of contact angles. The findings indicated that both models performed well, but BRT exhibited exceptional performance. Sensitivity analysis, conducted using the Jackknife method, revealed the order of importance for parameters affecting wettability as follows: PDIs > salinity > time > DTPA concentration. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of diethyl ether on the interfacial tension (IFT) of crude oil-brine was examined under varying salinity and temperature conditions. • An experimental database comprising 7,017 sets of crude oil/brine/DEE IFT data was acquired. • Six advanced machine-learning models were developed to accurately estimate the IFT of Crude oil–Brine-DEE. • Statistical analysis was conducted, demonstrating outstanding predictions for a wide range of input variables. Solvent injection, a well-established method for enhanced oil recovery (EOR), has demonstrated significant improvements in oil recovery when compared with conventional water flooding techniques. The interfacial tension (IFT) is pivotal in determining the displacement efficiency and overall performance of innovative techniques like dimethyl ether-enhanced waterflooding (DEW), which has gained substantial attention in recent years. In this study, following laboratory measurements of IFT, six advanced machine learning (ML) techniques were employed: Generalized Linear Model (GLM), Generalized Additive Model (GAM), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Boosted Regression Tree (BRT) to model the IFT in both oil-brine and oil-brine-diethyl ether (DEE) systems. The analysis is based on an extensive dataset comprising 7,017 data points for oil-brine and 6,949 data points for oil-brine-DEE systems obtained from experimental studies. The findings indicate that the developed RF model excels in predicting IFT, boasting a remarkable coefficient of determination (R2 = 0.99) along with the lowest root mean squared error (RMSE = 0.2), mean squared error (MSE = 0.04), and mean absolute error (MAE = 0.13). The study underscores the significance of optimizing salinity levels to achieve the most substantial reduction in IFT. This reduction is attributed to the enhanced migration of polar components, such as asphaltene molecules, to the interface of the oil-brine system. Moreover, the research highlights a synergistic decrease in IFT when both DEE and soluble ions are present, resulting in the lowest IFT at around 2 mN/m in 40,000 ppm salinity (S 2) at 70 °C (T 3). This indicates that the adsorption of DEE at the water–oil interface forms a layer capable of adsorbing ions, thereby enhancing the layer's thickness. As a result, the oil–solvent-ion layer becomes thicker compared to the oil-ion layer, leading to the maximum decrease in IFT. Additionally, with increasing temperature up to 70 °C, the IFT of both systems demonstrated a downward trend, as evidenced by all experiments. The outcomes of this study have the potential to enhance our comprehension of the underlying mechanisms involved in water-soluble solvent EOR techniques. [ABSTRACT FROM AUTHOR]

It is nowadays a hot topic to apply machine learning (ML) algorithms to illustrate water temperature dynamics in lentic waters. Due to the limited amount of in-situ temperature measurements from traditional sampling programmes, most of the related studies, however, restricted their analysis within the surface and rarely checked the results for whole depth profiles. Moreover, capability of such methods in projecting thermal dynamics under future climate conditions is even less illustrated. To fill the gap, we collected an unparalleled huge database including 9 million water temperature measurements, from 2013 to 2022, in Rappbode Reservoir, Germany as well as the corresponding climatic and hydrological observations, to train three commonly used ML models (Random Forest, XGBoost, and Long Short-Term Memory) and comprehensively evaluate their performance in reproducing thremal structure within the whole water column. We also systemically compared such simulation results with a well-established process-driven model (CE-QUAL-W2). Going beyond a pure reproduction of observatiosn, we further evaluated the ability of CE-QUAL-W2 and ML models in projecting thermal dynamics under RCP8.5 climate scenario up to 2100. Our results suggested three ML methods yielded high accuracy in capturing water temperature dynamics from the surface (epilimnion) to bottom (hypolimnion), and also satisfactorily reproduced temporal development of the stratification pattern, with the results corresponding well with those from CE-QUAL-W2. By contrast, the projections by ML models were rather insensitive to future climate warming under RCP8.5 comparing with those by CE-QUAL-W2, pointing to an important risk whenever ML-based models are extrapolated beyond their training data range. Supported by millions of in situ measurements, our research clearly illustrated the opportunities (limits) of ML models in projecting thermal dynamics in deep waters, and the conclusion also provides key guidance for scientists to select the appropriate tools in projecting water temperature, and other environmental factors, under changing climate. [Display omitted] • An unparalleled temperature database was collected to test performance of ML models. • ML models captured the seasonal temperature pattern from top to bottom water layers. • ML models accurately reproduced the temporal development of stratification structure. • Extrapolation in ML models should be cautious under climate warming conditions. [ABSTRACT FROM AUTHOR]

• Hybrid models successfully predicted key performance metrics for antibody production in mammalian cells. • Such models allow predictions under varying process conditions, without the need to re-estimate parameters. • Performance could be predicted for a different product under similar process conditions with reasonable accuracy. • Algorithm choice did not significantly impact model performance, but computational requirements differed. Among the many approaches to build predictive models for bioprocesses, hybrid models have gained increased attention due to their ability to combine data driven approaches with process knowledge. This study develops and compares hybrid models that predict the performance of a mammalian CHO cell culture producing a therapeutic product. Three machine learning algorithms, MLP, Random Forest and XGBoost regressors are compared to understand the effect of algorithm choice on the rates predicted and overall performance of the hybrid model. When combined in series with mechanistic equations, all three algorithms could predict next day Viable Cell Density, titer and the cumulative amount of glucose consumed with low error, although decision tree-based algorithm required less computational time for training. The models performed well using a dataset for a new product, without further model training. Retraining the model on a subset of data from the new product improved the prediction accuracy of VCD and titer. The potential for application and further model developments are also outlined. [Display omitted] [ABSTRACT FROM AUTHOR]

• Relationships among amyloid PET, MRI, and cognition are unclear in Alzheimer. • Deep-SHAP is proposed to investigate regional imaging vs. cognition relationships. • Deep-SHAP identified the modality-specific brain regions relevant to cognition. Mild cognitive impairment (MCI)/Alzheimer's disease (AD) is associated with cognitive decline beyond normal aging and linked to the alterations of brain volume quantified by magnetic resonance imaging (MRI) and amyloid-beta (Aβ) quantified by positron emission tomography (PET). Yet, the complex relationships between these regional imaging measures and cognition in MCI/AD remain unclear. Explainable artificial intelligence (AI) may uncover such relationships. We integrate the AI-based deep learning neural network and Shapley additive explanations (SHAP) approaches and introduce the Deep-SHAP method to investigate the multivariate relationships between regional imaging measures and cognition. After validating this approach on simulated data, we apply it to real experimental data from MCI/AD patients. Deep-SHAP significantly predicted cognition using simulated regional features and identified the ground-truth simulated regions as the most significant multivariate predictors. When applied to experimental MRI data, Deep-SHAP revealed that the insula, lateral occipital, medial frontal, temporal pole, and occipital fusiform gyrus are the primary contributors to global cognitive decline in MCI/AD. Furthermore, when applied to experimental amyloid Pittsburgh compound B (PiB)-PET data, Deep-SHAP identified the key brain regions for global cognitive decline in MCI/AD as the inferior temporal, parahippocampal, inferior frontal, supratemporal, and lateral frontal gray matter. Deep-SHAP method uncovered the multivariate relationships between regional brain features and cognition, offering insights into the most critical modality-specific brain regions involved in MCI/AD mechanisms. [ABSTRACT FROM AUTHOR]

Landslides pose a significant threat to lives and socio-economic stability globally. In this study, we conducted a comprehensive landslide susceptibility mapping (LSM) in the western region of Cameroon, focusing on Bafoussam and its surroundings. The integration of multi-criteria decision analysis models (AHP), statistical methods (Information Value IV, Shannon Entropy SE, Frequency Ratio FR), and machine learning algorithms (Naïve Bayes and Logistic Regression) provided a robust assessment of landslide risk. Our analysis, based on 54 recorded landslides, carefully selected Landslide Conditioning Factors (LCF), and influential parameters such as lithology, slope, altitude, and precipitation, resulted in susceptibility maps categorizing the area into five risk zones. The Spatial distribution shows the centre and northwestern regions as high-risk areas. Model sensitivity differences underscore the need for tailored LSM selection. Validation using the Area Under Curve/Receiver Operating Characteristics (AUC/ROC) method indicates the LR and NB methods have the highest accuracy (82.7% and 84.1%, respectively). Comparative analysis of landslide events in Gouaché, Sichuan, Souk Ahras, and Kekem reveals correlations between heavy rainfall and geological conditions. The study supplies valuable insights for decision-makers in landslide-prone areas, emphasizing the importance of integrating multiple methodologies for comprehensive risk assessment and management. [Display omitted] • A comprehensive landslide susceptibility mapping (LSM) was conducted in the west region of Cameroon, focusing on Bafoussam and its surroundings. • The study utilized a combination of Multi-Criteria Decision Analysis models, statistical methods, and machine learning algorithms for a robust assessment of landslide risk. • Susceptibility maps classified the area into five risk zones, with the centre and northwestern regions identified as high-risk areas. • Validation using the AUC/ROC method showed the Logistic Regression and Naïve Bayes methods had the highest accuracy (82.7% and 84.1%, respectively). • The study underscores the need for a tailored approach to LSM selection and the importance of integrating multiple methodologies for comprehensive risk assessment and management. [ABSTRACT FROM AUTHOR]

Diabetic vascular complications (DVC) are the main cause of death in diabetic patients. However, there is a lack of effective biomarkers or convenient methods for early diagnosis of DVC. In this study, the salivary glycopatterns from 130 of healthy volunteers (HV), 139 patients with type 2 diabetes mellitus (T2DM) and 167 patients with DVC were case-by-case analyzed by using lectin microarrays. Subsequently, diagnostic models were developed using logistic regression and machine learning algorithms based on the data of lectin microarrays in training set. The performance of diagnostic models was evaluated in an independent blind cohort. The results of lectin microarrays indicated that the glycopatterns identified by 16 lectins (e.g. BS-I, PWM and EEL) were significantly altered in DVC patients compared with patients with T2DM, which suggested the alterations in salivary glycopatterns could reflect onset of DVC. Notably, K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort. The integrated classifier, which combined three machine learning models, exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort. Our study provided a cost-effective and non-invasive method for auxiliary diagnosis DVC based on the combination of salivary glycopatterns and machine learning algorithms. • There were no convenient methods for early diagnosis of diabetic vascular complications (DVC) • Salivary glycopatterns showed significantly altered in DVC patients compared to type 2 diabetes mellitus patients • The K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort • The integrated classifier exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort [ABSTRACT FROM AUTHOR]

Even though the World Health Organization declared the end of the pandemic, COVID-19 is still considered an endemic disease, affecting many people worldwide. As a result, hospitalization rates can continue to strain healthcare systems, particularly in low and middle-income countries. Thus, this article presents two predictive Machine Learning (ML) models to identify COVID-19 patients more likely to be hospitalized or face a higher mortality risk, aiming to assist physicians and healthcare administrators in making informed decisions. Previous research primarily focused on using expensive input data, such as clinical examinations and images, to implement ML models. In contrast, we implemented the models using information on patients' demographics, vaccination history, symptoms, and underlying health conditions. We trained the ML algorithms using data from the OpenDataSus database, focusing on patients undergoing hospital treatment in São Paulo, Brazil. Our study tested 14 different ML algorithms to determine their effectiveness: gradient boosting, light gradient boosting machine, logistic regression, linear discriminant analysis, ridge, AdaBoost, random forest, support vector machine, k-nearest neighbor, extra tree, decision tree, naive Bayes, quadratic discriminant analysis, and dummy. The results showed that the gradient-boosting model outperformed the others, achieving an accuracy rate of 83% and an Area Under the Curve (AUC) of 0.89 for predicting mortality risk. Moreover, the model achieved an accuracy rate of 71% and an AUC of 0.75 for predicting hospitalization risk. Our analysis evidence that including information on the number of vaccine doses can enhance the prediction results for hospitalization and mortality of patients. Based on these significant findings, we have developed a web application to assist healthcare professionals in identifying patients at a higher risk of severe outcomes from COVID-19. • Models for identifying COVID-19 patients at risk of mortality and hospitalization. • Gathered São Paulo hospital data for COVID-19 model training. • Evaluated 14 machine Learning algorithms; gradient boosting proved most effective. • Vaccine doses can improve the risk of mortality and hospitalization. • Web app aids healthcare professionals in identifying high-risk COVID-19 cases. [ABSTRACT FROM AUTHOR]

Soft 2D tactile sensors are becoming increasingly important in robotics and human-machine interaction. In this paper, we propose a new approach to develop a soft tactile sensor using fiber Bragg gratings (FBGs) and machine learning algorithms. The sensor consists of a layer of silicone elastomer with embedded 192 FBGs that can detect deformations caused by point impact. The FBG responses are then processed by machine learning algorithms to measure the position and the force of impacts with the mean absolute errors of 2.1 mm and 0.34 N, respectively. [Display omitted] • 2D flexible tactile sensor with array of 192 FBG sensors was shown. • Experimental calibration of 2D tactile sensors with assistance of machine learning algorithm was presented. • Prediction of pressing force with mean absolute error of 0.34 N was demonstrated. • Prediction of position of the impact with mean absolute error of 2.1 mm was achieved. [ABSTRACT FROM AUTHOR]

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

[Display omitted] • A dual-scale non-invasive technique for crack detection is demonstrated. • Resonance-based device is evaluated for its attenuation and frequency shift. • A comparison between sensors operating in microwave and radiowave regime is evaluated. • The microwave regime device show superior quality factor with ∼250 % improved sensitivity. • Out of multiple machine learning algorithms, k-NN algorithm proved to be most accurate with accuracy of around ∼99 %. Condition monitoring of structures built using metallic supports is a significant issue in structural engineering. Through this work, design, fabrication, and optimization of a dual-scale resonance-based microwave sensor is discussed for monitoring cracks developed over metallic surfaces. These sensors were initially simulated and later fabricated in micro-wave (4.59 GHz) and radio-wave region (418 MHz) for which the power attenuation and frequency shifts were observed. With the structure having crack depth of 2 mm, microwave sensor showed the power shift ∼24 %, while the radio-wave range sensors showed ∼9 %. The corresponding frequency shifts were 2.4 % and 0.85 %, respectively. The microwave sensor (power shift data) showed ∼280 % more sensitivity than the radio-wave sensor. By using dual scale, comprising of both frequency and power shifts, the microwave sensor classification was further improved to 99.72 %, as compared to their individual counterparts. A non-destructive crack detection system in microwave regime is hence demonstrated, which can be further explored for range of crack studies in metallic structures. [ABSTRACT FROM AUTHOR]

A soft fiber-reinforced bending actuator (SFRBA) is a multi-material system that plays a crucial role in robotics applications due to its high output force and robust bending motion. However, the multi-material composition of SFRBAs causes significant structural nonlinearity. This nonlinear behavior is difficult to capture using traditional analytical models. This study presents a method for modeling the SFRBA using finite element method (FEM) and nonlinear machine learning algorithms (MLAs). First, the key structural parameters of the SFRBA are defined and selected as input variables for the method. An accurate FEM, considering varying actuation pressure, is then employed to generate a simulation training dataset. Subsequently, three nonlinear MLAs, including polynomial regression (PLR), extreme gradient boosting regression (XGBoostR), and normalized multilayer perceptron regression (NMLPR), are developed to model the bending angle of the SFRBA. Moreover, transfer learning is deployed to improve the accuracy and convergence speed of the optimal NMLPR. Experimental measurements are conducted to validate the established MLAs, and the results demonstrate that the refined NMLPR outperforms the other models, yielding an average Root Mean Square Error (RMSE) of 7.935° and Mean Absolute Percentage Error (MAPE) of 6.45%. Furthermore, the refined NMLPR is implemented in a feedback control loop to showcase its real-time ability to convert actuation pressure information into bending angles. The results exhibit excellent control performance, with an average MAPE of less than 6% and a low time delay of 0.0875 s. This work exemplifies a methodology that combines FEM and MLAs for modeling SFRBAs, paving the way for their further development and practical application. [Display omitted] • Multiple compositions of the SFRBAs increase the structural nonlinearity. • FEM-based MLAs are proposed for modeling the bending angle of SFRBA. • The result of optimal MLA yields an average RMSE of 7.935°. • The best MLA is used in a feedback control loop to showcase its real-time ability. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable surge in the prevalence of cardiovascular diseases (CVD), presenting a significant global public health challenge and a leading cause of mortality worldwide. Among the myriad complications stemming from CVD, heart failure stands out as a critical concern. Addressing heart failure through surgical means poses considerable challenges. The primary objective of this research is to identify pivotal attributes linked to heart failure and employ diverse machine learning methodologies to predict its occurrence, thereby enabling early estimation of mortality rates associated with heart failure. Leveraging a heart failure dataset, we conducted comprehensive model construction using pre-processing techniques such as feature scaling and correlation analysis. The Extreme Gradient Boosting (XGBoost) method was instrumental in evaluating feature relevance, leading to the selection of two distinct datasets: the whole dataset and the XGBoost dataset. In conclusion, we employed thirteen machine learning methods to predict the occurrence of death events within these datasets. Fine-tuning hyperparameters significantly enhanced model performance. Notably, our model demonstrated exceptional performance on this dataset, achieving the highest accuracy of 85.23% with Random Forest on the whole dataset and 86.36% with Flexible Discriminant Analysis on the XGBoost dataset. [Display omitted] • This research addresses the rising global prevalence of cardiovascular diseases, focussing heart failure. • The study aims to predict heart failure occurrence, enabling early estimation of mortalityrates. • Employing model, feature scaling and correlation analysis method to identify pivotal attributes linked to heart failure. [ABSTRACT FROM AUTHOR]

Continuous monitoring of wastewater treatment processes is key to mitigate the risk of inadequately treated wastewater on the environment and public health. However, effective control of wastewater treatment processes is challenging because of the numerous relevant variables and their complex physio-chemical-biological interdependence. Most published related studies focused on correlating the effluent concentration of chemical oxygen demand and/or suspended solids using only a limited number of wastewater influent variables. In addition, recent machine learning- (ML) based studies in wastewater treatment systems considered some individual classification algorithms rather than providing a comparison between different algorithm performances. In the current study, different algorithms were developed to categorize a range of wastewater treatment effluent characteristics based on multiple influent variables. To demonstrate their application, 23 ML classification algorithms were deployed on a wastewater treatment reactor-generated dataset and their performances were evaluated considering two different group of metrics related to the removal efficiency and effluent quality. The analysis results showed that, among all considered algorithms, the ensemble bagged trees algorithm had the most superior performance in terms of its overall classification accuracy. An interpretability analysis was further performed on the treatment process variables to detect the correlation between the input and output variables and to assess variable importance. In practice, the developed algorithms can facilitate optimal operation and effective management of wastewater treatment plants. ML algorithms also present efficient tools for rapidly classifying the effluent characteristics in lieu of typical sampling and laboratory analysis processes. [ABSTRACT FROM AUTHOR]

[Display omitted] Continuous monitoring of wastewater treatment processes is key to mitigate the risk of inadequately treated wastewater on the environment and public health. However, effective control of wastewater treatment processes is challenging because of the numerous relevant variables and their complex physio-chemical-biological interdependence. Most published related studies focused on correlating the effluent concentration of chemical oxygen demand and/or suspended solids using only a limited number of wastewater influent variables. In addition, recent machine learning- (ML) based studies in wastewater treatment systems considered some individual classification algorithms rather than providing a comparison between different algorithm performances. In the current study, different algorithms were developed to categorize a range of wastewater treatment effluent characteristics based on multiple influent variables. To demonstrate their application, 23 ML classification algorithms were deployed on a wastewater treatment reactor-generated dataset and their performances were evaluated considering two different group of metrics related to the removal efficiency and effluent quality. The analysis results showed that, among all considered algorithms, the ensemble bagged trees algorithm had the most superior performance in terms of its overall classification accuracy. An interpretability analysis was further performed on the treatment process variables to detect the correlation between the input and output variables and to assess variable importance. In practice, the developed algorithms can facilitate optimal operation and effective management of wastewater treatment plants. ML algorithms also present efficient tools for rapidly classifying the effluent characteristics in lieu of typical sampling and laboratory analysis processes. [ABSTRACT FROM AUTHOR]

The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods; (ii) high-frequency data increases the performance of estimation models; (iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data; (iv) the pandemic causes an adverse effect on the performance of estimation models; (v) energy-related variables are more important than other variables although all are significant; (vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed. • The study examines role of data frequency and prediction methods in electricity prices. • This study applies machine learning algorithms and time series econometric models. • Machine learning algorithms perform better than time series econometric models. • High-frequency data increases performance of prediction models. • COVID-19 pandemic causes an adverse effect on performance of prediction models. [ABSTRACT FROM AUTHOR]

The present study provided the first-time comprehensive evaluation of 12 advanced statistical and machine learning (ML) algorithms for the Soil Moisture (SM) estimation from dual polarimetric Sentinel-1 radar backscatter. The ML algorithms namely support vector machine (SVM) with linear, polynomial, radial and sigmoid kernel, random forest (RF), multi-layer perceptron (MLP), radial basis function (RBF), Wang and Mendel's (WM), subtractive clustering (SBC), adaptive neuro fuzzy inference system (ANFIS), hybrid fuzzy interference system (HyFIS), and dynamic evolving neural fuzzy inference system (DENFIS) were used. Extensive field samplings were performed for collection of in-situ SM data and other parameters from the selected sites for seven different dates and at two different locations (Varanasi and Guntur District, India), concurrent to Sentinel-1 overpasses. The backscattering coefficients were considered as input variables and SM as output variable for the training, validation and testing of the ML algorithms. The site at Varanasi was used for the training, validation and testing of the models. On the other hand, the Guntur site was used as an independent site for checking the model performance, before finalizing the algorithms. The performances of different trained algorithms were evaluated in terms of correlation coefficient (r), root mean square error (RMSE) (in m3/m3) and bias (in m3/m3). The study identified the RF, SBC and ANFIS as the top three best performing models with comparable and promising SM estimation. In order to test the robustness of these best models (RF, SBC and ANFIS), further performance analysis was performed to the independent datasets of the Varanasi and Guntur test sites, which indicates that the performance of these three models were consistent and SBC can be recommended as the best among all for SM estimation. [ABSTRACT FROM AUTHOR]

[Display omitted] • The high consumption of maize requires primarily the identification of transgenic products. • Elemental Spectroscopy techniques provide sufficient information about the samples. • Both non-supervised and supervised multivariate analyses are effective for discrimination. • Internal and external validation provide reliability for classification tasks. In the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four varieties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175–330 nm and 275–770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combinations of one GM variety against one NGM variety, the best outcome achieved was 100 % LOOCV and external validation accuracy. These results showed that LIBS supported by appropriate chemometric methods represents an alternative screening technique for identifying and discriminating transgenic from non-transgenic maize. [ABSTRACT FROM AUTHOR]

In this study, we apply and compare different algorithms from machine learning to describe and predict NEET rates in 31 European countries in the period from 2005 to 2020. With this aim, we considered eleven indicators describing the socio-economic national context and the level of innovation of the economies. Besides improving knowledge about the use of machine learning algorithms for the description of the NEET phenomenon, we discuss the connections between NEETs and other indicators that connect with other relevant sustainable development goals (SDGs), such as education, the reduction of inequalities, and decent work for everyone. The reduction of NEET rates is the only goal directly addressed to young people, The article underscores the need for evidence-based approaches to measure SDG achievement, especially concerning the heterogeneous NEET population. It emphasizes the importance of machine learning algorithms as a modern methodology for understanding and addressing the NEET phenomenon within the framework of SDGs, considering the complex interrelationships of socio-economic factors contributing to social and economic sustainability. • We identify the Best Machine Learning Algorithms for NEET and SDG studies. • We connect NEET paths in 2005–2020 with other relevant SDG goals. • We propose a complex approach, combining 31 countries and various indicators. [ABSTRACT FROM AUTHOR]

The progression of the fuzzy domain is propelled synergistically by the evolution of science via academic papers and the advancement of technology through technical patents. However, the intrinsic interrelationships within the science and technology (S&T) interactive landscape of this domain remain largely unknown, as few studies attempt to explore these close interrelationships. To bridge this research gap, based on 161,060 academic publications and 36,693 granted patents, we measured their mutual attribution between S&T from the perspective of textual semantics and structure information on top of a quantitative network monitoring process. The results showed that the fuzzy domain lacked technology-oriented topics in terms of semantic relations and presented an interactive pattern where science was ahead of technology; that is, science worked as an engineer to expand the knowledge boundaries of the research field, and technology existed more as a beneficiary. This article presents cutting-edge insights into the intrinsic S&T interactions of the fuzzy domain in a visual and quantitative approach for the first time and aims to aid researchers in theoretically grasping the S&T innovation laws. [ABSTRACT FROM AUTHOR]

• Experimentation of AWJ milling of alumina ceramic using Box-Behnken RSM. • Data augmentation using a bootstrapping technique. • Hyperparameter tuning using a grid search with 5-fold cross validation. • Development of machine learning algorithms using the augmented data samples. • The methodology was tested on dataset of WEDM of metal matrix composites. In abrasive waterjet (AWJ) milling process, the ability to accurately predict outcomes such as surface roughness (R a) and depth of cut (DoC) holds immense significance for cost reduction, resource optimization, and minimizing material wastage. However, a major challenge lies in the limited availability of AWJ milling datasets, hindering the development of robust machine learning models. To address the predicament of outcome, the present study employs a bootstrapping data augmentation technique to expand small AWJ milling datasets. Augmented data is used to train machine learning models, and hyperparameter fine-tuning is conducted using grid search and 5-fold cross-validation. In the prediction of surface roughness (R a) for AWJ milling of alumina ceramic, the study finds that a Multi-Layer Perceptron (MLP) Regression model with a 4–100-100–1 architecture, 'relu' activation function, 'adam' solver, alpha value of 0.0001, and 'adaptive' learning rate outperforms the decision tree and random forest regression models. In the case of prediction of AWJ milling depth of cut (DoC) in alumina ceramic, the random forest regression model surpasses MLP and decision tree regression models. It employs hyperparameters including 100 estimators, 'gini' criterion, 'log2′ for maximum features, a maximum depth of 50, a minimum sample split of 10, a minimum sample leaf of 2, bootstrap as 'True', and a minimum impurity decrease of 0. The methodology followed to develop the machine learning models in this study is also tested in wire electrical discharge machining (WEDM) process in the machining aspects of metal matrix composites (MMC) for the prediction of surface roughness (R a). The experimental dataset was adopted from (Satishkumar et al., 2011). In case of prediction of surface roughness (R a) in WEDM, the random forest regression model outperforms MLP and decision tree regression models. These developed machine learning models hold the potential to greatly assist manufacturing engineers in selecting optimal input parameters for AWJ milling and WEDM, thereby reducing the wastage of machine resources. [ABSTRACT FROM AUTHOR]

• Multi-sensor input machine learning vastly outperforms single-senor input models. • Recursive feature elimination reduces machine learning model complexity. • Gaussian process regression model best estimates wheat nitrogen nutrition index. • Transfer component analysis was used to improve model transferability. The nitrogen nutrition index (NNI) has been extensively applied for the diagnosis of crop nitrogen status, providing insights into efficient nitrogen utilization and plant growth. In this study, we utilized a low-altitude unmanned aerial vehicle (UAV) platform, equipped with multispectral (MS), red–green–blue (RGB), and thermal infrared (TIR) cameras, to comprehensively capture wheat spectral information. The analysis of the relationship between NNI and relative yield revealed an initially linear relationship, which saturated for high NNI values. To enhance accuracy and minimize complexity, we employed a random forest (RF) – recursive feature elimination (RFE) method to select features as inputs for four machine learning (ML) models: back propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR), and Gaussian process regression (GPR). After feature selection, the prediction accuracies of single-sensor models were ranked as: MS > RGB > TIR. The R2 values for the four ML models were in the range of 0.54–0.75. Among multi-sensor combinations, the GPR with MS + RGB + TIR input features achieved the best results with R2 = 0.89 and RPD = 2.52. Further, the dataset was partitioned into six subsets based on location and cultivar variety to evaluate model transferability. The results showed that the transferability largely suffered during the bivariate conditions of different varieties at different locations; the transferability of the model was average improved by 11 % when GPR was combined with transfer component analysis (TCA). The accuracy and transferability of the NNI estimation models significantly improved, offering valuable guidance and methodological support for diagnosing the nitrogen nutrient status of wheat. [ABSTRACT FROM AUTHOR]

• DSPA and CNN are the best ML algorithms for sepsis prediction. • Imbalance-XGBoost outperforms other traditional ML algorithms. • DSPA can improve sepsis diagnosis and management. • Further research needed on poor-performing ML algorithms. With the integration of artificial intelligence and clinical medicine, machine learning (ML) algorithms have been applied to develop sepsis predictive models for sepsis management. The purpose is to systematically summarize existing evidence to determine the effectiveness of ML algorithms in sepsis. We conducted a systematic electronic search of databases including PubMed, Cochrane Library, Embase, and the Web of Science, and included all case-control and cohort studies using terms reflecting sepsis and ML up to September 2023. statistical software STATA was used for network meta -analysis, and QUADAS-2 tool was used to assess the certainty of evidence. The SUCRA results for sensitivity, specificity, and predictive accuracy of various models are as follows: DSPA (77.0 %) > Imbalance-XGBoost (72.9 %) > CNN + Bi-LSTM (69.7 %) > CNN (67.3 %) > LR (62.4 %) > Ensemble model (55.9 %) > RF (53.2 %) > ET (51.3 %) > XGBoost (49.1 %) > DNN (48.1 %) > MLP (47.5 %) > RBF (47.1 %) > KNN (45.8 %) > NB (33.3 %) > SVM (13.7 %) > Bi-LSTM (5.7 %); CNN (78.3 %) > CNN + Bi-LSTM (77.6 %) > DSPA (75.1 %) > ET (69 %) > Bi-LSTM (68.5 %) > MLP (51 %) > RBF (50.2 %) > KNN (47.3 %) > RF (47 %) > Ensemble Model (43.4 %) > XGBoost (38.1 %) > SVM (37.3 %) > NB (34.2 %) > DNN (31.1 %) > LR (30.4 %) > Imbalance-XGBoost (21.5 %); DSPA (85.9 %) > CNN + Bi-LSTM (82.6 %) > CNN (81.9 %) > Imbalance-XGBoost (76.8 %) > ET (67.8 %) > RF (51.1 %) > Ensemble model (47.7 %) > XGBoost (44.4 %) > LR (42.7 %) > MLP (38.1 %) > RBF (37.8 %) > KNN (37.3 %) > DNN(35.8 %) > Bi-LSTM(33.3 %) > NB(21.5 %) > SVM(15.3 %). DSPA and CNN may be the best ML algorithms for predicting sepsis. Imbalance-XGBoost algorithm outperformed other traditional ML algorithms in terms of sensitivity and predictive accuracy. This study has several implications for clinical practice and research, highlighting the potential benefits of using ML algorithms in sepsis management, particularly in improving sepsis detection and reducing mortality rates. Through our systematic review and network meta -analysis, we have provided a comprehensive and accurate assessment of the effectiveness of ML algorithms in sepsis prediction, emphasizing the need for further exploration and evaluation of these algorithms to advance sepsis management. [ABSTRACT FROM AUTHOR]

The transformative impact of Artificial Intelligence (AI) technologies, particularly Machine Learning (ML), on the analysis of spectroscopic data in water quality assessment cannot be overstated. We remark the ways in which AI and ML have revolutionized the analysis and prediction of water quality parameters. These technologies efficiently process spectral data from various sources, identify contaminants, and support early detection systems. However, AI tools have limitations, including the need for a large and diverse dataset for optimal performance, and some studies used small datasets, limiting the predictive power of the models. Open databases can aid in expanding AI applications in water quality control and treatment. The potential of AI and spectroscopic techniques reduce costs, promote environmentally sustainable water treatment, and enhance water and environmental quality. Finally, we emphasize the need for legislative changes and collaboration between organizations to harness the synergy between these technologies, and its vital water resources. • Evaluation of machine learning techniques as tools for water quality control. • Establishment of ANN as the most used ML algorithm in water quality control. • Highlighting the need to implement regulations that include AI-based solutions for water quality control and treatment. [ABSTRACT FROM AUTHOR]

Whole-life carbon emissions (WLCE) studies are critical in assessing the environmental impact of buildings and promoting sustainable design practices. However, existing methods for estimating WLCE are time-consuming and data-intensive, limiting their usefulness in the early building design stages. In response to this, this research introduces a novel approach by harnessing various machine learning algorithms to predict WLCE and WLCE intensity (normalised by floor area) for buildings. To evaluate the suitability of machine learning algorithms, we conducted an experiment involving ten algorithms to build the prediction models. These models were trained using data from 150 typical residential properties in Cornwall, UK, along with 28 features obtained from a comprehensive survey, including floor area, heating type, and occupant characteristics. The ten algorithms include Multiple Linear Regression, and non-linear algorithms such as Decision Tree, Random Forest. Performance evaluation metrics, such as coefficient of determination (R2), mean absolute error (MAE), means squared error (MSE), root-mean-square error (RMSE), and elapsed time, were employed. Our research contributes to the field by showcasing the effectiveness of machine learning models in predicting building WLCE. We reveal that all the tested machine learning algorithms have the capability to predict WLCE and WLCE intensity, non-linear models outperform linear ones, and the Random Forest (RF) model demonstrates superior performance in terms of accuracy, stability, and efficiency. This research encourages the integration of life cycle studies into the early design stage, even within tight building design schedules, offering practical guidance to architects and designers. Furthermore, these results also benefit a wide range of stakeholders, not only the architects but also the engineers, policymakers, and life cycle assessment (LCA) researchers, contributing to the advancement of data-driven sustainability approaches within the building sector. • Novel method using machine learning algorithms for building WLCE prediction. • Promotes WLCE integration into the early stages of building design. • Leverage a comprehensive UK residential dataset for robust model development. [ABSTRACT FROM AUTHOR]

• Vibration-based method: changes in structural frequencies utilized to estimate the residual fatigue life. • Comparison of theoretical and machine learning algorithms such as Support Vector Machines (SVM) and ANN. • Check prediction accuracy with different input schemes, including single-mode and multi-mode frequencies. Fiber reinforced polymer (FRP) structures may experience cumulative fatigue damage during their service life which can lead to structural failure. This paper focuses on predicting the residual fatigue life of FRP structures using vibration parameters. The relationship between residual fatigue life and natural frequencies is examined through modal testing and fatigue measurements on FRP beam specimens. Two prediction methods; semi-empirical models and machine learning (ML) algorithms, are utilized. The semi-empirical models are derived from existing "residual stiffness" models based on the relationship between bending stiffness and flexural frequencies. ML algorithms Support Vector Machine (SVM) and Artificial Neural Network (ANN), are developed for fatigue life prediction. Experimental validation is performed using measured frequencies during fatigue testing of FRP beams. The ML algorithms can use multimode frequencies unlike single mode of semi-empirical models. The verification results show that the ML algorithms can be used to predict the residual fatigue life with the selection of the appropriate mode of frequency. The results show that ML algorithms outperform single-mode frequency inputs, and the use of higher modes of measured frequencies improves the precision of fatigue life prediction. An inverse algorithm based on SVM exhibits higher prediction accuracy and stability, even with limited training samples. [ABSTRACT FROM AUTHOR]

U.S. background ozone is defined as ground-level ozone in the absence of any domestic anthropogenic emissions. It plays an important role in the U.S. regulatory framework, as it corresponds to the theoretical minimum ozone that can be achieved at a location. Although some studies estimated background ozone from observations, it cannot be measured directly, and therefore air quality models have been used to estimate its levels across different parts of the country. These estimates are uncertain given the inherent uncertainty in air quality modeling. Recently data fusion techniques have been used to improve those estimates using ground-level ozone observations and satellite data. Here, we used two adjustment methods to improve on air quality model-derived estimates of background ozone: a Multi-Variate Linear Regression (MVLR) model and a machine learning (ML) algorithm, called Random Forest (RF), with a focus on thirteen urban areas in the U.S. These adjustments regress observed ozone on the simulated background and anthropogenic ozone (by a regional air quality model) and their combinations with spatial and temporal variables. The RF-ML algorithm showed the biggest improvement in model performance, compared to MVLR and the base air quality model predictions. Applying the MVLR adjustment, the background ozone estimates increased in most locations. Furthermore, applying the RF ML model adjustment increased background ozone estimates even more, highlighting the enhanced contribution of background ozone in recent years that constitutes a larger portion of total ozone and exceeds NAAQS in some regions. Our results are consistent with other observational and modeling studies in that the highest background ozone levels were estimated in the western U.S. and over higher elevations. • We first estimated background ozone in the U.S. using air quality models. • We used data fusion methods and machine learning to adjust the background ozone. • Adjustment methods improved the air quality model performance for total ozone. • Applying adjustment methods increased background ozone estimates in most locations. • The highest background ozone levels were estimated in the western U.S. [ABSTRACT FROM AUTHOR]

• An enhanced estimation of temperature-dependent radiative properties of heterogenous porous media. • Using Monte Carlo ray tracing algorithm for ground truth data generation. • Developing expertly-designed engineered features for geometry abstraction. • Implementing artificial neural networks for quick and efficient predictions. The accurate and efficient prediction of radiative properties of materials plays a crucial role in numerous industrial and engineering applications. However, experimental measurement can be cumbersome, particularly for complex materials. As a result, numerical techniques such as Monte Carlo ray tracing (MCRT) have been developed to assess radiative properties. MCRT is a well-known tool for solving radiation heat transport (RHT). Despite its advantages, MCRT requires tracing a large bundle of light beams for convergency and veracity of outcomes, which necessitates further advancements in its employment. To this end, supervised learning algorithms can be used in conjunction with MCRT to provide a quick and accurate substitution for typical MCRT simulations. In this study, machine learning algorithms employing expertly designed features are used to evaluate the radiative properties of heterogeneous porous media, while considering the effects of conductive heat transfer. Directional features have been introduced into the sets of the features implemented in our previous study for the enhancement of predictions. The results show that the applied method can improve prediction accuracy compared to prior studies. Moreover, this approach is generalizable to other radiative heat transfer problems by extracting relevant characteristics in accordance with the involved physics. [ABSTRACT FROM AUTHOR]

• QSPR models for predicting critical temperature of binary mixtures are built. • Linear and non-linear methods are adopted for QSPR modeling. • Internal and external validations and application domain are conducted. • The R ext 2 and AARD of the best QSPR model are 0.98 and 1.27%, respectively. The critical temperature is an important parameter in the design and selection of binary organic mixtures. Rapid and accurate prediction has been a focus of research. Strong nonlinear relationships exist between molecular characteristics and critical temperature. Developing nonlinear models is an important measure to improve the prediction accuracy. In this paper, 56 different QSPR models are developed to predict the critical temperature using 14 types of mixture descriptors and four modeling methods (MLR, XGBoost, RBFNN and SVM). A dataset containing 2540 data points is adopted. Results show x 1 d 1 + x 2 d 2 is the optimum mixture descriptor type, and the accuracy of the nonlinear models is better than that of the linear models. The model combining x 1 d 1 + x 2 d 2 and SVM exhibits the best predictive power; the R ext 2 , RMSE ext , and AARD are 0.988, 10.057 K, and 1.27%, respectively. For this model, 78.98% of the data have an absolute deviation of less than 5 K, and the accuracy is better than that of existing QSPR models for critical temperature of mixtures. The application domain analysis shows the model has good performance for novel binary organic mixtures. In addition, compared with the empirical calculation methods for predicting the critical temperature, the results show the developed model has higher reliability. [ABSTRACT FROM AUTHOR]

This review provides an overview of recent advances in hydrothermal liquefaction (HTL) biocrude production processes using plastics as feedstock, seawater as the processing medium, and microwave irradiation as a process intensification method. Additionally, the review examines the application of aid-in investigation tools such as kinetics, machine learning, and feasibility analysis to HTL research. All these aspects have been underexplored in review literature compared to process optimization, biocrude upgrading, continuous HTL, and aqueous phase reutilization. The potential of HTL as an effective method for the depolymerization of plastics is initially evaluated. The ease of plastic depolymerization follows the order of polycarbonate (300 °C) > polystyrene (350 °C) > polyethylene = polypropylene (420 °C) > polyethylene terephthalate (>450 °C). Both synergism and antagonism are observed for co-HTL of plastics with biomass, ranging from −48.3% to 79.2%. Using seawater as an alternative HTL processing medium shows promising potential, while the effect of sea salts on biocrude yield/quality is still controversial especially when carbohydrate-rich feedstocks are utilized, necessitating more comprehensive examination. Microwave irradiation has been shown to increase biocrude yield from lipid, produce comparable yields from protein and lignin, and decrease yield from carbohydrate compared to conventional heating. As for the aid-in investigation tools, limited efforts have been made to apply kinetic modeling to the HTL of plastics, which could be particularly useful when synergism or antagonism is observed during co-HTL of plastics and biomass. Machine learning-enabled predictions of product yield and quality have been found to be more accurate than traditional mathematical models. Future research could focus on using machine learning algorithms to elucidate product formation mechanisms. The techno-economic and life cycle assessment reveal that the commercialization of HTL technology remains a distant prospect, further improvements in product yield, quality, and process energy efficiency are essential. Overall, this review offers augmented insights into HTL technology and facilitates the identification of novel opportunities, which is of value to promote the biocrude production. [Display omitted] • Plastics as feedstock, seawater as the processing medium, and microwave irradiation as a heating method. • Aid-in investigation tools such as kinetics, machine learning, and feasibility analysis to HTL research. • Augmented insights into HTL technology and the identification of novel opportunities. [ABSTRACT FROM AUTHOR]

• The performance of five techniques for estimating lettuce LA was compared. • Artificial intelligence-based models could be a reliable alternative to conventional leaf area estimation methods. • The proposed image analysis algorithm performed best among other methods examined. • The ANFIS method was more accurate than the ANNs method. Artificial intelligence (AI) as a simulation approach is increasingly being used in precision agriculture. The current study evaluated the performance of five methods for estimating lettuce leaf area (LA), including simple linear regression (SLR), artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), support vector regression (SVR), and image analysis technique. The standardized Ranking Performance Index (sRPI) was used to determine the overall ranking of the simulation approaches. Image analysis and ANFIS methods were found to have superior performance to other methods evaluated, according to the evaluation criteria of R2 values of 0.98 and 0.92, and RRMSE values of 4.3 and 2.32, respectively. The SLR, on the other hand, was found to be the least ranked method. Overall, the results indicated that the image analysis method could be a cost-effective option, and the ANFIS method could serve as a non-destructive alternative to conventional LA determination methods. [ABSTRACT FROM AUTHOR]

• Breast cancer, which predominantly affects women and can result in death, can be identified at an early stage using mammography using Artificial neural network algorithms that imitates human for extracting knowledge from the dataset and favorable to data scientists. • Mammography image is enhanced and smoothed using the NLM filtering and AHE approaches. The features are extracted from the mammography picture in accordance with GLRLM features after the pre processed image has been grouped with morphological procedures for feature extraction. • Kernel-based convolutional neural network algorithm suits for collecting the tumour from the aberrant optical image and categorized as benign or malignant tumour early and has great accuracy and a low mistake rate. The suggested approach could save the patient's life by offering the treating physician helpful direction. • This approach automatically detects breast cancer using efficient image processing methods. The quality of mammography pictures is increased using Non-Local-Means (NLM) filtering, adaptive histogram equalization (AHE), and clustering with a morphological technique. The Gray Level Run Length Matrix (GLRLM), which considers the shape, surface, and location of the breast tumour cells, is utilized to extract important details from the mammography image. • The automated breast cancer detection system achieved an accuracy of 96.5%, a sensitivity of 95.4%, and a specificity of 93.3%, which outperformed the current methods. These findings suggest that the proposed approach efficiently identifies malignant tumors in mammogram images. • The developed tumuor detection method shows good stability and reproducibility with less response time making it a promising malignant tumor identification. The current use of medical image processing can be employed to detect various diseases or tumors present in the human body. Breast cancer, which predominantly affects women and can result in death, can be identified at an early stage using mammography, a special screening method of computer tomography (CT) scan, which may potentially save the patient's life. Despite advancements in medical imaging technology, radiologists encounter difficulties in precisely discriminating between non-cancerous and cancerous brain tumors owing to their cellular composition. This study aims to solve the issue of automatic detection of breast cancer utilizing efficient image processing methods. The quality of mammography pictures is increased using Non-Local-Means (NLM) filtering, adaptive histogram equalization (AHE), and clustering with a morphological technique. The Gray Level Run Length Matrix (GLRLM), which considers the shape, surface, and location of the breast tumour cells, is utilized to extract important details from the mammography image. The K-nearest neighbor (K-NN) method is used to categorize the image as normal or abnormal based on the selected attributes. Using a kernel-based convolutional neural network, the tumour is then isolated from the aberrant image and categorized as benign or malignant (KBCNN). This approach has demonstrated improved precision compared to existing methods, achieving more than 95% accuracy. [ABSTRACT FROM AUTHOR]

• The developed numerical model can accurately simulate the gas-particle flow and decomposition process of magnesium nitrate in the pyrolysis furnace under multi-jet flow. • Coupling CFD and MLAs methods to predict the decomposition rate of magnesium nitrate in a pyrolysis furnace. • RFR algorithm has better performance in predicting decomposition rate compared to other machine learning algorithms. • The optimal combination is obtained by optimizing the selected parameters using a genetic algorithm. This paper investigates the effect of multi-jet arrays on the decomposition of magnesium nitrate in a pyrolysis furnace through computational fluid dynamics (CFD) and machine learning algorithms (MLAs). The effects of temperature, arrangement, cone angle of injections and distance between pyrolysis gas inlets on the decomposition rate were studied in the CFD method. Then, regression models for decomposition rates were built using four machine learning algorithms: Neural Networks (NN), Decision Tree Regressor (DTR), Random Forest Regression (RFR), and Polynomial Regression (PR). RFR was integrated with a genetic algorithm for parameter optimization since it was judged to have the best performance after comparison. Specifically, the arrangement of gas inlets mainly affects the residence time of the particles rather than the temperature uniformity. Increasing the temperature and cone angle both favor the decomposition of magnesium nitrate in the pyrolysis furnace. The interaction of gas-solid multiple jets prolongs the residence time of particles in the pyrolysis furnace thus increasing the decomposition rate when the value of d/D is taken around 0.625. The maximum decomposition rate of 99.67% is achieved when the parameters are set to arrangement 2, temperature = 897 °C, d/ D = 0.63, and cone angle = 73.8° [ABSTRACT FROM AUTHOR]

• Visibility forecast has been tackled by means of machine and deep learning methods. • Influence of reanalysis variables addiction as predictive variables has been assessed. • A feature selection algorithm has been used to select the optimal reanalysis variables. • Evolutionary optimization algorithms (DE and PSO) were employed. • Improvements of up to 17.3% with respect to the baseline prediction have been achieved. Low visibility events are a severe problem for road transport, causing accidents and major economic losses. Their accurate prediction may help prevent these problems. For that purpose, machine and deep learning techniques have been applied for fog prediction using in situ meteorological data and persistence variables as baseline predictors. These techniques have been evaluated for different prediction time-horizons: 1 h, 3 h and 6 h. The effect of including data extracted from ERA5 Reanalysis as predictive variables has been studied. A database, covering 23 months, has been used, which contains visibility and other meteorogical variables measured in Mondoñedo, Galicia, Spain. A 222000 km2 region around Mondoñedo has been delimited. Thus, a proposed iterative forward selection algorithm based on evolutionary algorithms has been applied to determine the optimal variables and nodes in the region for each regressor model. Both Differential Evolution and Particle Swarm Optimization have been used as optimization algorithms, and an improvement of up to 17.3% with respect to the baseline databases have been obtained. Finally, an analysis of the most frequently selected variables by the evolutionary algorithms has been conducted, leading us to conclude which variables and geographical nodes provide better information to the prediction models. [ABSTRACT FROM AUTHOR]

• A machine learning algorithm aided by molecular kinetic theory is proposed. • The CO 2 /CH 4 competitive adsorption is revealed through posture analysis of the molecules. • The fast predictions on competitive adsorption behavior with various conditions are performed. • The potential implications are discussed for CO 2 sequestration and enhanced gas recovery. Understanding the competitive adsorption behavior of CO 2 and CH 4 in shale nanopores is crucial for enhancing the recovery of shale gas and sequestration of CO 2 , which is determined by both the inherent characteristics of the molecules and external environmental factors such as pore size, temperature, and partial pressures of CO 2 and CH 4. While the competitive adsorption behavior of CO 2 /CH 4 has been analyzed by previous studies, a comprehensive understanding from the perspective of molecular kinetic theory and the efficient calculation for competitive adsorption behavior considering various geological situations is still challenging, limited by the huge computation cost of classical molecular dynamics (MD) methods. In this work, the theoretical connection between inherent characteristics of molecules and adsorption behavior is firstly built to reveal the general laws in the behavior of CO 2 /CH 4 competitive adsorption through posture analysis of the molecules. A machine learning algorithm, aided by molecular kinetic theory, is proposed to facilitate the fast and accurate predictions of competitive adsorption behavior, and detailed analyses of the influencing factors are conducted accordingly. The insights gained from this work provide a foundation for expeditiously optimizing the competitive adsorption behavior of CO 2 /CH 4 , with potential implications for CO 2 sequestration and enhanced gas recovery (CSEGR) process. [ABSTRACT FROM AUTHOR]

• Adasyn and t-SNE are used to pre-process the collected rockburst data. • SVM, BP, RBF, RF and ELM optimized by global optimization algorithm are used to predict rockburst intensity. • An improved D-S evidence theory rockburst prediction fusion model based on multiple machine learning algorithms is raised. • The fusion model predicts much better than single algorithm model in engineering application. Accurate prediction of rockburst hazards is an effective means to improve the development and utilization of underground space. When using a single machine learning algorithm for rockburst prediction, the reliability of the classification results can hardly be guaranteed. In this paper, a fusion model for rockburst prediction based on multiple machine learning algorithms is proposed in combination with D-S evidence theory. Six characteristic parameters (σ θ , σ c , σ t , SR , BR and Wet) were statistically analyzed for 304 sets of rockburst cases, and the data had outliers and class imbalances. The box plot method was used to detect replacement of a small number of outliers, and the Adasyn oversampling method eliminated the effect of data class imbalance. The visualization of the data by using the t-SNE method showed improved classifiability. SVM, BP, RBF, RF and ELM rockburst prediction models were developed with the six characteristic parameters as model inputs. In addition, the global optimization algorithms were used to find the optimal hyperparameters of the five machine learning algorithms. The accuracy of the optimized models was improved by 2.63%, 5.27%, 2.63%, 6.58% and 7.5% compared to the unoptimized models. Based on the prediction results, the precision was proposed as the fusion index. Additionally, based on improved D-S evidence theory, a fusion model for rockburst prediction was developed with five optimized machine learning algorithm models as base classifiers. Finally, the fusion model was applied to the rockburst prediction of Jiangbian Hydropower Station and Sanshan Island Gold Mine in China with the accuracy of 92.86%, and the prediction was outperformed by a single base classifier. The fusion model compensated for the uncertainty and poor robustness of the prediction results of the single-base classifier, and improved the accuracy and reliability of rockburst prediction. [ABSTRACT FROM AUTHOR]

In this paper, three machine learning algorithms (Random Forest (RF), Support Vector Regression (SVR), and Long Short Term Memory (LSTM)) were used to predict and compare the thermodynamic performance of a 1 kW Organic Rankine Cycle (ORC) system under cryogenic operating conditions (i.e., cold source temperature < −160 °C). The cryogenic ORC system uses liquid nitrogen as the cold source, hot water as the heat source, and propane as the working fluid. Ten key operating parameters were selected as input parameters through Variable Importance Measures, and the output work of the expander, the cold exergy efficiency, and the system exergy destruction were used as output parameters. Moreover, the multi-objective optimization of this experimental system was conducted by applying the Non-dominated Sorting Genetic Algorithm III. The results showed that the RF algorithm was the most suitable algorithm among the three machine learning algorithms. According to the optimal results of the prediction model, the maximum error was 5.0251%, which was relatively small compared to the optimal results under experimental conditions. The related results demonstrate the feasibility of machine learning for cryogenic ORC data prediction, which can guide the design and improvement of ORC systems under low-temperature cold source conditions. • Performance of a 1 kW-scale cryogenic Organic Rankine Cycle (ORC) is predicted. • Three machine learning algorithms are used for prediction and system optimization. • Random Forest is more suitable for predicting output work and exergy destruction. • Support Vector Regression is more suitable for predicting cold exergy efficiency. • Maximum error for predicted optimal results and experimental results is 5.0251%. [ABSTRACT FROM AUTHOR]

This paper explores the utilisation of machine learning in architecture, focusing on the addressed problems and commonly employed programming languages, software, platforms, libraries, and algorithms. Eight major academic search and publishing platforms were systematically reviewed, covering the period from 2007 to 2022, resulting in the selection of 60 relevant articles from a pool of 175. The articles were categorised based on their thematic focus, primarily centring around Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), and Computer-Aided Manufacturing (CAM). By evaluating the current state of machine learning in architecture, this study provides valuable insights into its usage and identifies potential areas for future research. This paper contributes to a comprehensive understanding of the evolving landscape of machine learning in the field by investigating subfields within architecture and the specific tools used to tackle architectural challenges. • Conducting a systematic review to investigate the application of machine learning in architecture. • Examining the subfields of architecture that have been studied through machine learning and the problems addressed within them. • Analysing the utilisation of programming languages, software, machine learning platforms, programming libraries, and machine learning algorithms in architecture. • Focusing on published works between 2007 and 2022 that utilize machine learning in the field of architecture. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic highlights the need for effective and non-intrusive methods to monitor the well-being of elderly individuals in their homes, especially for early detection of potential viral infections. Conspicuously, the present paper develops a Multi-scaled Long Short Term Memory (Ms-LSTM) model for the routine health monitoring of elderly patients to detect COVID-19. The proposed method offers home-based health diagnostics through urine analysis by leveraging the IoT-Fog-Cloud paradigm. Mainly, the proposed model constitutes a four-layered architecture: data acquisition, fog layer, cloud layer, and interface layer. Each layer serves distinct functionalities and provides specific services, thereby collectively enhancing the overall effectiveness of the model. The statistical results of the study demonstrate the superior performance of the proposed Ms-LSTM model in comparison to state-of-the-art methods, including Artificial Neural Networks (ANN), K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, and LSTM. Further, the proposed model attains a mean temporal efficiency of 39.23 seconds. It exhibits high reliability (92.97%), stability (70.06%), and predictive accuracy (93.25%). • The emergence of new viruses has put health systems under immense pressure. • The current article offers an IFC health framework for health monitoring. • It endeavours to offer an innovative IFC-assisted intelligent toilet system. • It envisions advancing healthcare to achieve better home-based care outcomes for the elderly. [ABSTRACT FROM AUTHOR]

• PPBL's morphological hallmark is binucleated lymphocytes in peripheral blood. • High SDV and SDAL2 cell population data values imply morphological heterogeneity. • PPBL presented a "round bottom flask" shape scattergram in the DxH 800 analyzer. • Our ML model combines easily accessible laboratory data to detect PPBL and SMZL. The diagnosis of persistent polyclonal B-cell lymphocytosis (PPBL) is often challenging because of the lack of features and the overlap with the peripheral expression of splenic marginal zone lymphomas (SMZL). To obtain new clues for PPBL detection and diagnosis, all data provided by the DxH 800 analyzer (including scatter and cell population data (CPD)) was exploited and combined using a machine learning (ML) approach. A total 211 samples from 101 normal controls and 110 patients (PPBL and SMZL) were assessed. Age, gender, full blood count, CPD, scatter, flags and CellaVision differentials were also considered. A ML model was built for true classification purposes. PPBL and SMZL shared increased absolute lymphoid counts, atypical lymphoid flag presence and CPD values (8 out of 14). A typical "round-bottom-flask" shape scattergram was described for the first time for PPBL which was also present in 51.4% of SMZL cases. The developed ML model render a global classification accuracy of 93.4%, allowing the detection of all pathological cases, with mean misclassification rates of 12% among PPBL and SMZL. Our ML model represents a new unbiased tool than can be widely applied in the laboratory as an aid for detection of PPBL. [ABSTRACT FROM AUTHOR]

Stone dust induced risk and vulnerability in the developing nations is very thought-provoking and therefore is necessary to address scientifically to manage it. Identifying vulnerable areas using the robust method is preliminary and essential steps. The present study has intended to delineate Human health vulnerability models (HHVMs) using machine learning algorithms and justify whether ensemble prediction can provide improved results in stone quarry and crushing dominated Middle catchment of Dwarka river basin. Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Reduced Error Pruning (REP) Tree, Gaussian Process algorithms and ensemble prediction have been applied for HHV modelling. Total 6.9 to 7.37% in cluster 1, 9.11 to 9.6% in cluster 2, 19.63 to 26.79% and 5.34 to 12.85% areas in cluster 3 and 4 is found into very high human health vulnerable class as per the five applied models. REPTree model is found consistent for predicting vulnerability in case of all the clusters followed by GP model. As per the result of RMSE, the RF model is appeared as the most consistent model followed by REPTree. Some of the ensemble prediction models are potential for yielding improved result than individual algorithm. [ABSTRACT FROM AUTHOR]