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Solar interfacial evaporation, as a novel practical freshwater production method, requires continuous research on how to improve the evaporation rates to increase water production. In this study, sets of data were obtained from molecule dynamics simulation and literature, in which the parameters included height, diameter, height–radius ratio, evaporation efficiency, and evaporation rate. Initially, the correlation between the four input parameters and the output of the evaporation rate was examined through traditional pairwise plots and Pearson correlation analysis, revealing weak correlations. Subsequently, the accuracy and generalization performance of the evaporation rate prediction models established by neural network and random forest were compared, with the latter demonstrating superior performance and reliability confirmed via random data extraction. Furthermore, the impact of different percentages (10%, 20%, and 30%) of the data on the model performance was explored, and the result indicated that the model performance is better when the test set is 20% and all the constructed model converge. Moreover, the mean absolute error and mean squared error of the evaporation rate prediction model for the three ratios were calculated to evaluate their performance. However, the relationship between the height- radius ratio and optimal evaporation rate was investigated using the enumeration method, and it was determined that the evaporation efficiency was optimal when the height–radius ratio was 6. Finally, the importance of height, diameter, height– radius ratio, and evaporation efficiency were calculated to optimize evaporator structure, increase evaporation rate, and facilitate the application of interfacial evaporation in solar desalination. [ABSTRACT FROM AUTHOR]

In this article, we present a machine learning model to obtain fast and accurate estimates of the molecular Hessian matrix. In this model, based on a random forest, the second derivatives of the energy with respect to redundant internal coordinates are learned individually. The internal coordinates together with their specific representation guarantee rotational and translational invariance. The model is trained on a subset of the QM7 dataset but is shown to be applicable to larger molecules picked from the QM9 dataset. From the predicted Hessian, it is also possible to obtain reasonable estimates of the vibrational frequencies, normal modes, and zero point energies of the molecules. [ABSTRACT FROM AUTHOR]

Pre-tightening torque has a significant impact on the performance of a bolt-clamped high-frequency piezoelectric ultrasonic transducer widely used in wire bonding. To develop a vibratory response-based method for the determination of optimal pre-tightening torque, the vibration amplitude signals of the transducer under different excitation conditions were monitored, and the characteristic parameters of the response, involving the resonance frequency, settling time within the initial transient response, and vibration amplitude of the steady-state stage, were extracted. Based on a series of in-depth tests, analyses, and discussions, the influences that the pre-tightening torque has on the vibratory response properties were investigated. Then, the experimental measurement and the random forest method were combined to determine the optimal pre-tightening torque. The performance test results show that the developed method can give the optimal pre-tightening torque according to the desired vibrational response properties, and it is feasible, effective, and reliable as well as improves the adaptability and flexibility of the high-frequency ultrasonic transducer in the application. [ABSTRACT FROM AUTHOR]

The advent of massive broad-band photometric surveys enabled photometric redshift estimates for unprecedented numbers of galaxies and quasars. These estimates can be improved using better algorithms or by obtaining complementary data such as narrow-band photometry, and broad-band photometry over an extended wavelength range. We investigate the impact of both approaches on photometric redshifts for quasars using data from Southern Photometric Local Universe Survey (S-PLUS) DR4, Galaxy Evolution Explorer (GALEX) DR6/7, and the unWISE catalog for the Wide-field Infrared Survey Explorer (WISE) in three machine learning methods: Random Forest, Flexible Conditional Density Estimation (FlexCoDE), and Bayesian Mixture Density Network (BMDN). Including narrow-band photometry improves the root-mean-square error by 11 per cent in comparison to a model trained with only broad-band photometry. Narrow-band information only provided an improvement of 3.8 per cent when GALEX and WISE colours were included. Thus, narrow bands play a more important role for objects that do not have GALEX or WISE counterparts, which respectively makes 92 per cent and 25 per cent of S-PLUS data considered here. Nevertheless, the inclusion of narrow-band information provided better estimates of the probability density functions obtained with FlexCoDE and BMDN. We publicly release a value-added catalogue of photometrically selected quasars with the photo- z predictions from all methods studied here. The catalogue provided with this work covers the S-PLUS DR4 area (∼3000 square degrees), containing 645 980, 244 912, 144 991 sources with the probability of being a quasar higher than, 80 per cent, 90 per cent, 95 per cent up to r < 21.3 and good photometry quality in the detection image. More quasar candidates can be retrieved from the S-PLUS data base by considering less restrictive selection criteria. [ABSTRACT FROM AUTHOR]

BACKGROUND: Roots, tubers and bananas (RTB) play an essential role as staple foods, particularly in Africa. Consumer acceptance for RTB products relies strongly on the functional properties of, which may be affected by the size and shape of its granules. Classically, these are characterized either using manual measurements on microscopic photographs of starch colored with iodine, or using a laser light‐scattering granulometer (LLSG). While the former is tedious and only allows the analysis of a small number of granules, the latter only provides limited information on the shape of the starch granule. RESULTS: In this study, an open‐source solution was developed allowing the automated measurement of the characteristic parameters of the size and shape of yam starch granules by applying thresholding and object identification on microscopic photographs. A random forest (RF) model was used to predict the starch granule shape class. This analysis pipeline was successfully applied to a yam diversity panel of 47 genotypes, leading to the characterization of more than 205 000 starch granules. Comparison between the classical and automated method shows a very strong correlation (R2 = 0.99) and an absence of bias for granule size. The RF model predicted shape class with an accuracy of 83%. With heritability equal to 0.85, the median projected area of the granules varied from 381 to 1115 μm2 and their observed shapes were ellipsoidal, polyhedral, round and triangular. CONCLUSION: The results obtained in this study show that the proposed open‐source pipeline offers an accurate, robust and discriminating solution for medium‐throughput phenotyping of yam starch granule size distribution and shape classification. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Summary: Aims: A multi‐stakeholder consensus has proposed MASLD (metabolic dysfunction‐associated steatotic liver disease). We aimed to investigate the pathological findings related to the mid‐term mortality of patients with biopsy‐proven MASLD in Japan. Methods: We enrolled 1349 patients with biopsy‐proven MASLD. The observational period was 8010 person years. We evaluated independent factors associated with mortality in patients with MASLD by Cox regression analysis. We also investigated pathological profiles related to mortality in patients with MASLD using data‐mining analysis. Results: The prevalence of MASH and stage 3/4 fibrosis was observed in 65.6% and 17.4%, respectively. Forty‐five patients with MASLD died. Of these, liver‐related events were the most common cause at 40% (n = 18), followed by extrahepatic malignancies at 26.7% (n = 12). Grade 2/3 lobular inflammation and stage 3/4 fibrosis had a 1.9‐fold and 1.8‐fold risk of mortality, respectively. In the decision‐tree analysis, the profiles with the worst prognosis were characterised by Grade 2/3 hepatic inflammation, along with advanced ballooning (grade 1/2) and fibrosis (stage 3/4). This profile showed a mortality at 8.3%. Furthermore, the random forest analysis identified that hepatic fibrosis and inflammation were the first and second responsible factors for the mid‐term prognosis of patients with MASLD. Conclusions: In patients with biopsy‐proven MASLD, the prevalence of MASH and advanced fibrosis was approximately 65% and 20%, respectively. The leading cause of mortality was liver‐related events. Hepatic inflammation and fibrosis were significant factors influencing mid‐term mortality. These findings highlight the importance of targeting inflammation and fibrosis in the management of patients with MASLD. [ABSTRACT FROM AUTHOR]

Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ~106x/10x and ~106x/2.5x improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer. [ABSTRACT FROM AUTHOR]

Background: Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict OSAHS. Materials and methods: Clinical data of 2064 snoring patients who underwent physical examination in the Health Management Center of the First Affiliated Hospital of Shanxi Medical University from July 2018 to July 2023 were retrospectively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3. By analyzing the importance of these features, it was concluded that LDL-C, Cr, common carotid artery plaque, A1c and BMI made major contributions to OSAHS. Moreover, five kinds of machine learning algorithm models such as logistic regression, support vector machine, Boosting, Random Forest and MLP were further established, and cross validation was used to adjust the model hyperparameters to determine the final prediction model. We compared the accuracy, Precision, Recall rate, F1-score and AUC indexes of the model, and finally obtained that MLP was the optimal model with an accuracy of 85.80%, Precision of 0.89, Recall of 0.75, F1-score of 0.82, and AUC of 0.938. Conclusion: We established the risk prediction model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models. This predictive model helps to identify patients with OSAHS and provide early, personalized diagnosis and treatment options. [ABSTRACT FROM AUTHOR]

Traditional computational approaches such as Monte Carlo simulation, molecular dynamics, and density functional theory (DFT) have contributed to understanding the role of hole mobility in the development of suitable hole transporting materials (HTMs) for perovskite solar cell efficiency. However, these methods often involve significant computational expenses, thereby limiting the number of feasible studies and hindering the extraction of valuable structure−property guidelines for a rational design of novel HTMs. To address these challenges, this study proposes an ultrafast predictive model that balances prediction accuracy and time efficiency, a critical aspect for predicting the hole mobility of HTMs for perovskite optimization. The model, which leverages the Random Forest learning algorithm, enables comprehensive and rapid analysis of photovoltaic features taken from previous experiments/literature and processed with the RDKit Python library. Notably, recognizing the challenges associated with high correlation in the dataset, a method to improve the calculation of feature importance in random forests is applied. The results highlight bertz_ct, chi1, and logP as key predictive features of HTM performance. However, the model underscores the need to uncover additional predictive features based on structure–property relationships to predict the optimized hole mobility. This approach significantly accelerates the discovery process, outperforming prevalent statistical methods in the prediction of hole mobility and HTM performance. This research signifies a pivotal step toward cost‐effective, accelerated stability and efficiency of HTMs, with implications for the advancement of optoelectronic devices. [ABSTRACT FROM AUTHOR]

The current research is dedicated to harnessing cutting-edge technologies within the paradigm of Industry 5.0. The objective is to capitalize on advancements in Machine and Deep Learning techniques. This research endeavors to construct robust predictive models, utilizing historical data, for precise real-time predictions in estimating material quantities within a cement workshop. Machine Learning regressors evaluated based on several metrics, SVR (R-squared 0.9739, MAE 0.0403), Random Forest (R-squared 0.9990, MAE 0.0026), MLP (R-squared 0.9890, MAE 0.0255), Gradient Boosting (R-squared 0.9989, MAE 0.0042). The time series models LSTM and GRU yielded R-squared 0.9978, MAE 0.0100, and R-squared 0.9980, MAE 0.0099, respectively. The ultimate outcomes include improved and efficient production, optimization of production processes, streamlined operations, reduced downtime, mitigation of potential disruptions, and the facilitation of the factory's evolution towards intelligent manufacturing processes embedded within the framework of Industry 5.0. These achievements underscore the potential impact of leveraging advanced machine learning techniques for enhancing the operational dynamics and overall efficiency of manufacturing facilities [ABSTRACT FROM AUTHOR]

The development of fragility curves is an important step in seismic risk assessment within the scope of performance‐based earthquake engineering. The goal of this work is to use machine learning methods (regression‐based tools) to forecast the large‐span precast parking structural responses and fragility curves. This study proposes five predicting models based on machine learning to evaluate the seismic performance of the large‐span precast parking structures, including: neural networks, genetic algorithm‐based neural networks, support vector machine, decision tree and random forest. A database that includes 453 numerical synthetic results was used to train and test the machine learning models. The seismic performance of large‐span precast parking structures were predicted using the constructed machine learning models. Finally, the sensitivity analysis of input parameters was conducted. From this paper we can conclude that: (1) the genetic optimization‐based neural networks' predicting model has the most accurate predictive ability for seismic fragility estimation and (2) the structural responses and the fragility curves of parking structures are related to the differences of the stiffness of the connectors and the number of floors, of which the stiffness of the connectors should be given special attention. [ABSTRACT FROM AUTHOR]

Machine learning occupies a central position in the modeling of fish distribution patterns. The augmentation of explanatory variables in fish habitat through many kinds of observational methodologies necessitates the discernment of an optimal combination of these variables for fish distribution modeling. We proposed a feature selection technique, recursive feature elimination with cross-validation (RFECV), to determine optimal variables combinations for yellowfin tuna distribution in the Pacific Ocean. Four tree-based models, random forest, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and categorical boosting driven by RFECV, were developed using comprehensive fisheries and biotic/abiotic data. Habitat variables including sea temperature, dissolved oxygen concentration, chlorophyll-a concentration, sea salinity, and sea surface height were identified as significant features by all models. The models were trained using the corresponding selected variables, and these trained models were employed to predict the spatiotemporal distribution of yellowfin tuna from 1995 to 2019. The results obtained could inform useful knowledge for the sustainable exploitation of yellowfin tuna in the Pacific Ocean and furnish a benchmark of feature selection for machine-learning-based distribution modeling of other pelagic species. [ABSTRACT FROM AUTHOR]

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not (least absolute shrinkage and selection operator regression [LASSO]) model interactions. Cases made a nonfatal suicide attempt (n  = 6032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n  = 11 963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver-operating characteristic curve (AUC) values were higher in LASSO models (in men, 0.85, 95% CI, 0.84-0.86; in women, 0.89, 95% C, 0.88-0.90) than random forests (in men, 0.76, 95% CI, 0.74-0.78; in women, 0.79, 95% CI = 0.78-0.81). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

Climate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns. [ABSTRACT FROM AUTHOR]

Aim: The incidence of Helicobacter pylori‐negative gastric cancer (HPNGC) is increasing worldwide. Recently, metabolic dysfunction‐associated fatty liver disease (MAFLD) has been reported to be associated with various cancers, but its association with HPNGC has not been reported. We aimed to identify important independent factors associated with HPNGC, including MAFLD. Methods: This multicenter observational cohort study enrolled patients with gastric cancer (n = 1078) and health checkup examinees (n = 17 408). We analyzed patients with HPNGC (n = 26) and healthy participants with no H. pylori infection or any abnormal findings on upper gastrointestinal endoscopy (n = 1130). A logistic regression model was used to identify independent factors associated with HPNGC. The priority of the factors associated with HPNGC was evaluated using a decision‐tree algorithm and random forest analysis. Results: Among all patients with gastric cancer, 2.4% (26/1078) were diagnosed with HPNGC (mean age, 64 years; male/female, 13/13). In the logistic regression analysis, age, smoking, and MAFLD (odds ratio, 6.5359; 95% confidence interval, 2.5451–16.7841; p < 0.0001) were identified as independent factors associated with HPNGC. Metabolic dysfunction‐associated fatty liver disease was also identified as the most important classifier for the presence of HPNGC in decision‐tree analyses. Helicobacter pylori‐negative gastric cancer was observed in 5.2% of patients with MAFLD and 0.8% of patients without MAFLD. In the random forest analysis of the HPNGC, MAFLD was identified as the distinguishing factor with the highest variable importance (0.32). Conclusions: Metabolic dysfunction‐associated fatty liver disease was the most influential independent factor associated with HPNGC. These findings suggest that fatty liver and metabolic dysfunction could be involved in the pathogenesis of HPNGC. [ABSTRACT FROM AUTHOR]

Summary: The Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%. [ABSTRACT FROM AUTHOR]

The mineral zircon has a robust crystal structure, preserving a wealth of geological information through deep time. Traditionally, trace elements in magmatic and hydrothermal zircon have been employed to distinguish between different primary igneous or metallogenic growth fluids. However, classical approaches based on mineral geochemistry are not only time consuming but often ambiguous due to apparent compositional overlap for different growth environments. Here, we report a compilation of 11 004 zircon trace element measurements from 280 published articles, 7173 from crystals in igneous rocks, and 3831 from ore deposits. Geochemical variables include Hf, Th, U, Y, Ti, Nb, Ta, and the REEs. Igneous rock types include kimberlite, carbonatite, gabbro, basalt, andesite, diorite, granodiorite, dacite, granite, rhyolite, and pegmatite. Ore types include porphyry Cu-Au-Mo, skarntype polymetallic, intrusion-related Au, skarn-type Fe-Cu, and Nb-Ta deposits. We develop Decision Tree, XGBoost, and Random Forest algorithms with this zircon geochemical information to predict lithology or deposit type. The F1-score indicates that the Random Forest algorithm has the best predictive performance for the classification of both lithology and deposit type. The eight most important zircon elements from the igneous rock (Hf, Nb, Ta, Th, U, Eu, Ti, Lu) and ore deposit (Y, Eu, Hf, U, Ce, Ti, Th, Lu) classification models, yielded reliable F1-scores of 0.919 and 0.891, respectively. We present a web page portal (http://60.205.170.161:8001/) for the classifier and employ it to a case study of Archean igneous rocks in Western Australia and ore deposits in Southwest China. The machine learning classifier successfully determines the known primary lithology of the samples, demonstrating significant promise as a classification tool where host rock and ore deposit types are unknown. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) ranks as the second most prevalent neurodegenerative disorder globally, and its incidence is rapidly rising. The diagnosis of PD relies on clinical characteristics. Although current treatments aim to alleviate symptoms, they do not effectively halt the disease's progression. Early detection and intervention hold immense importance. This study aimed to establish a new PD diagnostic model. Methods: Data from a public database were adopted for the construction and validation of a PD diagnostic model with random forest and artificial neural network models. The CIBERSORT platform was applied for the evaluation of immune cell infiltration in PD. Quantitative real-time PCR was performed to verify the accuracy and reliability of the bioinformatics analysis results. Results: Leveraging existing gene expression data from the Gene Expression Omnibus (GEO) database, we sifted through differentially expressed genes (DEGs) in PD and identified 30 crucial genes through a random forest classifier. Furthermore, we successfully designed a novel PD diagnostic model using an artificial neural network and verified its diagnostic efficacy using publicly available datasets. Our research also suggests that mast cells may play a significant role in the onset and progression of PD. Conclusion: This work developed a new PD diagnostic model with machine learning techniques and suggested the immune cells as a potential target for PD therapy. [ABSTRACT FROM AUTHOR]

Aim: To investigate the prevalence and clinical manifestations of reading, writing, and mathematics disorders in children with cerebral palsy (CP). We explored how the clinical profile of these children differed from those with specific learning disorders (SLDs), taking into account several factors, particularly IQ scores, neuropsychological aspects, and the presence of a visual impairment. Method: A prospective cross‐sectional study was conducted in 42 children with CP (mean age 9 years 8 months; SD = 2 years 2 months) and 60 children with SLDs (mean age 10 years; SD = 1 year 7 months). Clinical characteristics, neuromotor and cognitive profiles, neuropsychological aspects (speech performance, academic skills, visual attention, phonological awareness, working memory), and signs of visual impairment (visual acuity, contrast sensitivity, visual field, oculomotor functions) were assessed. A machine learning approach consisting of a random forest algorithm, where the outcome was the diagnosis and the covariates were the clinical variables collected in the sample, was used for the analyses. Results: About 59% of the children with CP had reading, writing, or mathematics disorders. Children with CP with learning disorders had a low performance IQ, normal phonological awareness, and working memory difficulties, whereas children with SLDs had normal performance IQ, impaired phonological awareness, and mild working memory difficulties. There were no differences in verbal IQ between the two groups. Interpretation: Learning disorders are frequently associated with CP, with different clinical characteristics, compared with SLDs. Assessment of academic skills is mandatory in these children, even if the IQ is normal. At school age, specific interventions to promote academic skills in children with CP could be a major rehabilitative goal. What this paper adds: Reading, writing, and mathematics disorders in cerebral palsy have specific clinical characteristics.Their underlying mechanisms differ from those described in specific learning disorders.Working memory impairment can be considered a hallmark of learning disorders in children with cerebral palsy. [ABSTRACT FROM AUTHOR]

Execution of a marine operation (MO) requires coordinated actions of several vessels conducting simultaneous and sequential offshore activities. These activities have their operational limits given in terms of environmental parameters. Wave parameters are important because of their high energetic level. During the execution of a MO, forecast wave spectral parameters, i.e., significant wave height (Hs), peak period (Tp), and peak direction, are used to make an on-board decision. For critical operations, the use of forecasts can be complemented with buoy measurements. This paper proposes to use synthetic statistics of vessel dynamic responses to predict "real-time" wave spectral parameters using multi-output machine learning (ML) regression algorithms. For a case study of a vessel with no forward speed, it is observed that the random forest model predicts accurate Hs and Tp parameters. The prediction of wave direction is not very accurate but it can be corrected with on-board observations. The random forest model has good performance; it is efficient, useful for practical purposes, and comparable with other deep learning models reported in the scientific literature. Findings from this research can be valuable for real-time assessment of wave spectral parameters, which are necessary to support decision-making during the execution of MOs. [ABSTRACT FROM AUTHOR]

This study examines Free Space Optical (FSO) communication's performance in different fog conditions, focusing on Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) setups. In low fog, SISO handles signal degradation well. MIMO improves link robustness in moderate fog. High fog challenges traditional FSO, leading to ML integration to optimize communication parameters. For weather channel classification, a preprocessing scheme reduces features from 12 to 2 given that the 12 features are Bit Error Rate (BER), Quality Factor (Q-factor) and Received Optical Power (ROP) for different 4 users. A Gaussian Process Classifier (GPC) achieves an accuracy greater than 99%, surpassing SVM, Decision Tree, and Random Forest. GPC's fit and predict functions execute in 0.15 s, outperforming NuSVM (0.2 s). This study highlights FSO, SISO, MIMO, and Machine Learning (ML) practicality in enhancing communication resilience in adverse weather, especially in fog-prone areas. [ABSTRACT FROM AUTHOR]

This study investigates monthly streamflow modeling at Kale and Durucasu stations in the Black Sea Region of Turkey using remote sensing data. The analysis incorporates key meteorological variables, including air temperature, relative humidity, soil wetness, wind speed, and precipitation. The study also investigates the accuracy of multivariate adaptive regression (MARS) with Kmeans clustering (MARS-Kmeans) by comparing it with single MARS, M5 model tree (M5Tree), random forest regression (RF), multilayer perceptron neural network (MLP). In the first modeling stage, principal component regression is applied to diverse input combinations, both with and without lagged streamflow (Q), resulting in twenty-three and twenty input combinations, respectively. Results demonstrate the critical role of including lagged Q for improved model accuracy, as models without lagged Q exhibit significant performance degradation. The second stage involves a comparative analysis of the MARS-Kmeans model with other machine-learning models, utilizing the best-input combination. MARS-Kmeans, incorporating three clusters, consistently outperforms other models, showcasing superior accuracy in predicting monthly streamflow. [ABSTRACT FROM AUTHOR]

Aims: Peanut (Arachis hypogaea L.) straw decomposition and nutrient release (N, K, and P) processes were investigated using a 3-pool model (labile, intermediate, and resistant) to understand the determinant factors. Methods: A two-year field experiment was carried out with a split-plot design: the main plot contained two irrigation regimes, while the subplot contained three peanut straw incorporation rates. A total of 216 nylon-mesh bags consisting of peanut straw were buried at a depth of 20 cm, and were removed at 6 winter wheat growth stages (overwintering, double ridge, jointing, flowering, grain filling, and maturity), and the nutrient release (N, P and K) from the peanut straw was measured. Results: The decomposition dynamics of the labile and intermediate pools were similar in both years. The straw incorporation rate, rather than the irrigation regime, controlled the decomposition process, which increased with increased straw incorporation rates. At a high incorporation rate, the released N, P, and K from the peanut straw were approximately 39%, 30%, and 87% of the required regional fertilizer input for winter wheat, respectively. Furthermore, the N released from straw decomposition was strongly related to the released K as indicated by the stoichiometry ratio. The random forest model predicted that temperature, precipitation, and initial straw nutrients were the main drivers of peanut straw decomposition. Conclusions: We determined the nutrient stoichiometry and release characteristics of peanut straw decomposition, and found that in comparison to irrigation, the straw incorporation rate exhibits a more profound effect on the peanut straw decomposition process. Highlights: Peanut straw decomposition increases with incorporation rate than irrigation. The relative N release is synergistic with relative K release. High straw incorporation rate provides 30–40% of required N and P for wheat growth. Temperature, precipitation, and initial nutrient amount are the main drivers of straw decomposition. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to estimate trust from conversations using both lexical and acoustic data. Background: As NASA moves to long-duration space exploration operations, the increasing need for cooperation between humans and virtual agents requires real-time trust estimation by virtual agents. Measuring trust through conversation is a novel and unintrusive approach. Method: A 2 (reliability) × 2 (cycles) × 3 (events) withinsubject study with habitat system maintenance was designed to elicit various levels of trust in a conversational agent. Participants had trust-related conversations with the conversational agent at the end of each decision-making task. To estimate trust, subjective trust ratings were predicted using machine learning models trained on three types of conversational features (i.e., lexical, acoustic, and combined). After training, model explanation was performed using variable importance and partial dependence plots. Results: Results showed that a random forest algorithm, trained using the combined lexical and acoustic features, predicted trust in the conversational agent most accurately ðR2 adj ¼ 0:71Þ. The most important predictors were a combination of lexical and acoustic cues: average sentiment considering valence shifters, the mean of formants, and Mel-frequency cepstral coefficients (MFCC). These conversational features were identified as partial mediators predicting people’s trust. Conclusion: Precise trust estimation from conversation requires lexical cues and acoustic cues. [ABSTRACT FROM AUTHOR]

Large-scale digital soil maps are essential for rational and sustainable land management as well as accurate fertilizer application. This study focuses on digital mapping of soil properties, namely soil organic carbon (SOC), pH, nitrogen (N), phosphorus (P), and potassium (K) in Chernozem topsoil and subsoil. The study was conducted on two arable fields in the Cis-Ural forest-steppe zone of the Republic of Bashkortostan (Russia). The random forest algorithm in combination with terrain attributes and Sentinel-2A satellite data was applied for spatial prediction of soil properties. The root-mean-square error (RMSE) and coefficient of determination (R2) were used to determine the model performance. According to the Pearson correlation, a significant positive relationship between SOC and N content was found at all sites and depths (R = 0.76–0.92). A cross-validation revealed that SOC (R2 = 0.22–0.62, RMSE = 0.35–0.89%) and N (R2 = 0.16–0.60, RMSE = 21.11–36.6 mg kg−1) were best predicted among other properties at all depths using remote sensing data, whereas the performance of predictive models decreased with depth. However, a relationship between the content of some soil properties and their spatial distribution at study depths was observed, which can be used as an additional explanatory variable. We suppose that digital mapping of soil properties at the arable field scale should not be limited to topographic and remote sensing variables. Based on this information, the use of auxiliary variables, such as collocated soil information in combination with relief and remote sensing data can be effective in more accurately estimating the spatial distribution of properties across arable fields at different depths. Overall, this study provides valuable insights into spatial modelling of the vertical distribution of soil properties, highlighting the significance of remote sensing data at the arable field scale. The findings can be valuable for land managers, agronomists, and policymakers seeking sustainable land management practices and efficient fertilizer application, as well as for developing further mapping procedures for arable fields. [ABSTRACT FROM AUTHOR]

Early monitoring of within-field yield variability and forecasting yield potential is critical for farmers and other key stakeholders such as policymakers. Remote sensing techniques are progressively being used in yield prediction studies due to easy access and affordability. Despite the increasing use of remote sensing techniques for yield prediction in agriculture, there is still a need for medium-resolution satellite imagery when predicting canola yield using a combination of crop and soil information. In this study, we investigated the utility of remotely sensed flowering information from PlanetScope (at 4 m) satellite imagery combined with derived soil and topography parameters to predict canola yield. Our yield prediction model was trained and validated using data from 21 fields managed under variable rate seed and fertilizer application, including cleaned harvester yield maps, soil, and topography maps. To quantify the flowering intensity of canola, 9 vegetation indices (VIs) were calculated using spectral bands from PlanetScope imagery acquired for the reproductive stages of canola. We created five random forest regression models using different subsets of covariates, including VIs, soil, and topography features, to predict canola yield within the season. Using a random forest regression algorithm, we recorded accuracies ranging from poor to best performing using coefficient of determination and root mean squared error (R2: 0.47 to 0.66, RMSE: 325 to 399 kg ha−1). The optimal subset of covariates identified electrical conductivity (EC), Normalized Difference Yellowness Index, and Canola Index as the key variables explaining within-spatial variability in canola yield. Our final model exhibited a validation R2 of 0.46 (RMSE = 730 kg ha−1), demonstrating the potential of medium-resolution satellite imagery during the flowering stage to detect and quantify sub-field spatial and temporal floral phenology changes when predicting canola yield. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent model inversion attacks and to protect intellectual property. The privacy-preserving evaluation of ML models is possible through multi-key homomorphic encryption (MKHE), that allows both the client data and the model to be encrypted under different keys. In this paper, we propose an MKHE evaluation method for decision trees and we extend the proposed method for random forests. Each decision tree is evaluated as a single lookup table, and voting is performed at the level of groups of decision trees in the random forest. We provide both theoretical and experimental evaluations for the proposed method. The aim is to minimize the performance degradation introduced by the encrypted model compared to a plaintext model while also obtaining practical classification times. In our experiments with the proposed MKHE random forest evaluation method, we obtained minimal (less than 0.6%) impact on the main ML performance metrics considered for each scenario, while also achieving reasonable classification times (of the order of seconds). [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a high-resolution lidar instrument, facilitating landscape monitoring through forest structure and biomass assessments. Previous research has mainly focused on estimating forest attributes such as height and biomass from GEDI data. In this comprehensive study, we empirically investigate how Eucalyptus plantation characteristics and environmental factors collectively influence GEDI waveform metrics. Using a diverse dataset that includes measurements of canopy height, planting density, foliage characteristics, species composition, understorey presence and environmental conditions such as climate and soil properties, we explore the complex relationships between these attributes and GEDI waveform metrics using a two-step approach that includes assessing the linear relationship between each in-situ parameter and GEDI metrics, and also random forest modelling. Key findings include: 1) While canopy height (Ht) plays a significant role in shaping the GEDI waveform, forest wood volume (Volume), which incorporates height and diameter at breast height (DBH) of the trees and also tree stem density (Ntree/ha), exerts an even greater influence on GEDI metrics, in particular on the waveform extent (WE) and relative heights (RH); 2) The influence of factors such as foliage biomass and wood volume on GEDI waveform varies vertically from the forest floor up to forest top, and the model was more precise to predict the top part of the waveform; 3) No single attribute can solely account for the observed variations in the waveform characteristics, but multiple interactions between different forest and environmental features contribute to the complex patterns in the received waveforms. This research contributes to a deeper understanding of the complex relationships between forest plantation characteristics and GEDI waveform metrics and highlights the need to consider a wide range of forest attributes in the analyse of GEDI waveform to produce more accurate canopy height or volume estimates. [ABSTRACT FROM AUTHOR]

This paper introduces an assumption-lean method that constructs valid and efficient lower predictive bounds for survival times with censored data. We build on recent work by Candès et al. (2023) , whose approach first subsets the data to discard any data points with early censoring times and then uses a reweighting technique, namely, weighted conformal inference (Tibshirani et al. 2019), to correct for the distribution shift introduced by this subsetting procedure. For our new method, instead of constraining to a fixed threshold for the censoring time when subsetting the data, we allow for a covariate-dependent and data-adaptive subsetting step, which is better able to capture the heterogeneity of the censoring mechanism. As a result, our method can lead to lower predictive bounds that are less conservative and give more accurate information. We show that in the Type-I right-censoring setting, if either the censoring mechanism or the conditional quantile of the survival time is well estimated, our proposed procedure achieves nearly exact marginal coverage, where in the latter case we additionally have approximate conditional coverage. We evaluate the validity and efficiency of our proposed algorithm in numerical experiments, illustrating its advantage when compared with other competing methods. Finally, our method is applied to a real dataset to generate lower predictive bounds for users' active times on a mobile app. [ABSTRACT FROM AUTHOR]

Background: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia. Methods: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography–mass spectrometry (LC–MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model. Results: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16–18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0). Conclusions: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis. [ABSTRACT FROM AUTHOR]

We propose a random forest based estimation procedure for Quantile-Oriented Sensitivity Analysis—QOSA. In order to be efficient, a cross-validation step on the leaf size of trees is required. Our full estimation procedure is tested on both simulated data and a real dataset. Our estimators use either the bootstrap samples or the original sample in the estimation. Also, they are either based on a quantile plug-in procedure (the R-estimators) or on a direct minimization (the Q-estimators). This leads to 8 different estimators which are compared on simulations. From these simulations, it seems that the estimation method based on a direct minimization is better than the one plugging the quantile. This is a significant result because the method with direct minimization requires only one sample and could therefore be preferred. [ABSTRACT FROM AUTHOR]

Identifying a fish's artificial or natural origin is crucial for stock enhancement assessments. In this study, we collected specimens of hatchery (including the early fry, reared, and released juveniles) and wild mangrove snapper Lutjanus argentimaculatus to explore the feasibility of using δ13C and δ15N values in scales as a non-lethal alternative to muscle tissues for discriminating between hatchery and wild origins. The results showed scales generally enriched in δ13C value and relatively consistent in δ15N value compared to muscle. Significant correlations were found between the two tissues for both δ13C and δ15N values (R2 = 0.80–0.88). Based on the isotopic composition of the scale core, individuals with different origins could be effectively discriminated, achieving a classification success rate of 77.6%. The random forest analysis revealed that a δ13C value of −15.8‰ could be a reference for determining the hatchery and wild origins of mangrove snapper. This study provides an isotopic approach for assessing the benefit of stock enrichment in mangrove snapper. It highlights the importance of regular sampling and scales analysis to expand fishery management and conservation practices. [ABSTRACT FROM AUTHOR]

• Auto-encoder-enhanced Random Forest models with network biomarkers accurately predict IgAN prognosis. • Personalized IgAN treatment guidance system developed using network biomarkers. • System's performance on par with nephrologists, improving healthcare decision-making. The ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies. Analyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80–20 split for training and testing purposes. In the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies. Our system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients. [ABSTRACT FROM AUTHOR]

In recent years, the focus has shifted towards leveraging physics-based modelling and data-driven analysis to predict drilling incidents and anomalies in real time, with the goal of reducing non-productive periods. However, much of this attention has directed at specific drilling operations like drilling and tripping, leaving other vital processes, such as wellbore conditioning, comparatively overlooked. The primary objective of this study is to employ data-driven techniques for predicting the dynamic state of the wellbore by utilising sensor data, operating parameters, and surface measurements. Accurate predictions are pivotal for automating these processes, promising significant savings in both redundant time and associated costs, ultimately elevating operational efficiency. In this research, the surface drilling parameters such as flowrate, rotation speed, block position, and drill string length are incorporated with the surface measurements such as hookload, pressure, and torque during wellbore conditioning operation to predict further surface sensor measurements. Different parameter settings are evaluated to find the best approach. Six supervised learning algorithms are used to select the best prediction method. The findings reveal that considering all surface parameters and measurements yields the most accurate predictions. Among various single and multi-target regression methods, including deep learning approaches, the Gaussian process and random forest models exhibit the lowest prediction errors. By reliably predicting and understanding wellbore behaviour, this research paves the way for more efficient and autonomous drilling operations in the future, bridging a critical gap in the industry's automation capabilities. [ABSTRACT FROM AUTHOR]

Dolphins are social animals that depend on sound to communicate, navigate, and find food. The aim of our research was to describe dolphin whistle characteristics and compare whistles between two isolated populations of bottlenose dolphins in New Zealand. We focused on three aspects of dolphin bioacoustics (1) classification of whistles; (2) comparison of the whistles of two discrete bottlenose dolphin populations; and (3) comparison of whistles of dolphin populations from around the world. At Great Barrier Island, the most common type of whistle was upsweep followed by sine, while at Doubtful Sound, convex and sine whistles were the most common whistle types. Random Forest models discriminated between whistles from the two populations with an estimated accuracy of 90%. The key differences were whistle-type contour, duration, and end frequency. Linear Mixed models show that whistle acoustic parameters were influenced by location but not by group size or the presence of immatures. Whistle parameters of both populations fell within the range of multiple populations from around the world. This studyprovides a benchmark for future research into drivers of geographic variation in dolphin vocal communication. [ABSTRACT FROM AUTHOR]

Optimizing production–living–ecological space (PLES) can benefit territorial space planning and sustainable development in China. Thus, spatiotemporal evolution characteristics and influence factor analyses are vital. Laiwu, China, was selected as the study area due to its complex terrain and development history. A multitemporal land-use and land-cover classification extracted from Landsat remote-sensing images for 2001–2018 and the self-organizing map method were combined to identify the area's multitemporal PLES. The PLES evolution trajectories were extracted based on the multitemporal PLES. The main factors influencing the PLES spatiotemporal evolution differences were revealed using the random-forest-based factor importance evaluation method. The results showed that changes in the PLES trajectories occupied ∼ 18% of the study area. The ecological space recovery type was mainly observed in previously stable ecological space areas. Changes in the production space expansion (PSE) and living space expansion were mainly agglomerated in the fringe areas of previously stable production–ecological and living space lands. Distance from early built-up areas was the main factor influencing PLES expansion. Ecological space was mainly distributed far away from built-up areas, and living and production spaces were mainly distributed near the built-up areas. Additionally, the slope of the terrain affected the spatial distribution of PLES expansion to a certain extent, and the gross domestic product clearly affected the distribution of PSE. [ABSTRACT FROM AUTHOR]

The increase in penetration levels of distributed generation (DG) into the grid has raised concern about undetected islanding operations. Islanding is a phenomenon in which the grid-tied inverter of a distributed generation system, and some of the local loads are disconnected from the grid. If this condition is not detected and the generation (e.g. from a photovoltaic energy source) remains operative, the isolated DG system will stay energised by the inverter. The phase mismatch between the grid and the inverter voltage makes this scenario undesirable since it could be hazardous for the maintenance operator and could harm the inverter and loads in the event of an unsynchronised reconnection of the grid. Consequently, the article presented a novel hybrid active anti-islanding approach for fast and reliably detecting unintended islanding. For the modelling and experimental setup, a multiphase grid-tied photovoltaic distributed generating system was employed, and it was regarded as a viable application. Initially, the study introduces a fault-tolerant control (FTC) technique of data-driven predictive control to limit the impact of grid faults on inverters. Furthermore, the article suggested the Sandia frequency and voltage shift (SFVS) approach for inverter-based distributed generation to identify an islanding state. The approach employs a positive feedback gain to minimise NDZ and THD; moreover, the system does not affect the power quality. To minimise system damage, this scenario necessitates the use of effective islanding detection algorithms. This study suggests using empirical mode decomposition (EMD) to enhance power quality to extract detailed coefficients, which are subsequently processed to detect common transient fluctuations during islanding. In the grid-tied inverter, random forest (RF) is also utilised to categorise the condition as islanding or non-islanding. This exhibits an acceptable trade-off between output power quality and islanding detection effectiveness. The quality of the data and the selection of hyperparameters impact the performance of ML models. Following that, hyperparameter adjustment of the random forest model is done using the memetic firefly algorithm (MFA), which has a considerable impact on overall classification performance. The hyperparameters of the suggested RF model have been successfully calculated to attain the maximum accuracy and the least loss. Results demonstrate that the suggested method produces less THD (2.44) than the conventional detection, leading to faster islanding detection, a better non-detection zone of 0.3639 Hz, and a shorter detection time of 0.89 ms, respectively. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]

The development of the smart grid (SG) offers a way to improve the generation of electrical energy as well as the corresponding transmission and distribution. Due to the versatile nature, the installing of SG consumes less area and time when compared to traditional grids. The major aim of SG is to provide controllability of assets and grid observability and enhance the security and performance of power system. An integration of internet of things (IoT) in smart grid ensures intelligent features with reduced human intervention, cost-effectiveness, and reliability. In order to communicate the useful information with the internet and other web applications, IoT-enabled sensors are widely utilized in the power grid system, which enables better grid management. A photovoltaic (PV) system and a high-gain integrated Luo converter are included in the IoT-based power monitoring system for the smart grid that is recommended as a result of these variables. The maximum solar power is tracked with a help of grey wolf optimized artificial neural network (GWO-ANN) which aids in enhanced operation of converter. The obtained converter output is applied to 3 ϕ grid through a 3 ϕ voltage source inverter (VSI). The parameters like temperature, intensity, converter voltage and current, and grid voltage and current are monitored by sensors and further stored in IoT webpage. The random forest (RF) classifier is used for the prediction of shortest path through which the monitored data are transmitted. The whole set-up contributes an improved monitoring of smart grid parameters which is proved by the generated results. [ABSTRACT FROM AUTHOR]

Background: Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision‐making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT. Methods: We studied allogenic HSCTs performed on children with malignant disorders at a third‐level hospital between 1991 and 2021. Survival was analyzed using the Kaplan–Meier method, log‐rank test for the univariate analysis, and Cox regression for the multivariate analysis. A prognostic index was constructed based on these findings. Lastly, we constructed a predictive model using a random forest algorithm to forecast 1‐year survival after HSCT. Results: We analyzed 229 HSCTs in 201 patients with a median follow‐up of 1.64 years. Variables that impacted on the multivariate analysis were older age (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.12–1.76, p =.003), oldest period of HSCT (HR 0.46, 95% CI 0.29–0.73, p <.001), and mismatched donor (HR 2.65, 95% CI 1.51–4.65, p =.001). Our prognostic index was associated with 3‐year overall survival (OS; p <.001). A random forest was developed using as variables: diagnosis, age, year of HSCT, time from diagnosis to HSCT, disease stage, donor type, and conditioning. This achieved 72% accuracy in predicting 1‐year OS. Conclusions: Our index and random forest was effective in predicting 1‐year survival. However, further validation in diverse populations is necessary to establish their generalizability. [ABSTRACT FROM AUTHOR]

The Storm Water Management Model (SWMM) is a hydrological model for simulating and predicting runoff. Although powerful, SWMM can be computationally demanding. Therefore, we develop machine learning (ML) models to approximate the behavior of SWMM and expedite the task of predicting runoff. We perform a case study for the First Creek watershed in Knoxville, Tennessee, USA. We train ML models using rainfall data and subcatchment characteristics and apply feature engineering and clustering to objectively compare the outputs from SWMM and ML models. The results show that random forests can predict runoff volume accurately, with a Mean Absolute Error (MAE) of 0.006 (0.001) ${10^6}$ 10 6 gallons, where predictions are made almost instantaneously. Hence, our proposed ML-based approach can accurately predict runoff while greatly reducing computational requirements, filling a critical need in the field. [ABSTRACT FROM AUTHOR]

Background: Vestibular migraine (VM) and Menière's disease (MD) are two common causes of recurrent spontaneous vertigo. Using history, video-nystagmography and audiovestibular tests, we developed machine learning models to separate these two disorders. Methods: We recruited patients with VM or MD from a neurology outpatient facility. One hundred features from six "feature subsets": history, acute video-nystagmography and four laboratory tests (video head impulse test, vestibular-evoked myogenic potentials, caloric testing and audiogram) were used. We applied ten machine learning algorithms to develop classification models. Modelling was performed using three "tiers" of data availability to simulate three clinical settings. "Tier 1" used all available data to simulate the neuro-otology clinic, "Tier 2" used only history, audiogram and caloric test data, representing the general neurology clinic, and "Tier 3" used history alone as occurs in primary care. Model performance was evaluated using tenfold cross-validation. Results: Data from 160 patients with VM and 114 with MD were used for model development. All models effectively separated the two disorders for all three tiers, with accuracies of 85.77–97.81%. The best performing algorithms (AdaBoost and Random Forest) yielded accuracies of 97.81% (95% CI 95.24–99.60), 94.53% (91.09–99.52%) and 92.34% (92.28–96.76%) for tiers 1, 2 and 3. The best feature subset combination was history, acute video-nystagmography, video head impulse test and caloric testing, and the best single feature subset was history. Conclusions: Machine learning models can accurately differentiate between VM and MD and are promising tools to assist diagnosis by medical practitioners with diverse levels of expertise and resources. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of constructing a nonparametric predictor when the latent variables are given with incomplete information. The convenient predictor for this task is the random forest algorithm in conjunction to the so-called CART criterion. The proposed technique enables a partial imputation of the missing values in the data set in a way that suits both a consistent estimator of the regression function as well as a partial recovery of the missing values. The imputation is done through iterative assignation of the missing values to the tree's cells, maximising the CART criterion. A proof of the consistency of the random forest estimator is given in the case where each latent variable is missing completely at random (MCAR). [ABSTRACT FROM AUTHOR]

The escalating frequency and sophistication of cyber threats pose significant challenges to traditional intrusion detection methods. Signature-based misuse detection, hybrid detection, and anomaly detection, while effective in isolation, often struggle to keep pace with the ever-evolving tactics employed by attackers. This research stems from the pressing need to develop intrusion detection models that offer improved accuracy, adaptability, and robustness in the face of rapidly changing attack vectors. Our study leverages a comprehensive set of experiments conducted on diverse datasets, including N-BaIoT, NSL-KDD, and CICIDS2017. The primary focus is on the evaluation and comparison of machine learning algorithms such as Random Forest, XGBoost, and decision trees. Notably, our research culminates in the development of a hybrid intrusion detection model that combines the strengths of these algorithms. The results of our experiments indicate that the hybrid model, particularly when combining Random Forest and XGBoost, exhibits exceptional accuracy. This approach outperforms individual algorithms, achieving an accuracy rate of 97% in certain cases. We attribute this success to the ensemble learning approach, which capitalizes on the consensus of diverse classifiers. In conclusion, our research not only demonstrates the effectiveness of ensemble learning in enhancing intrusion detection but also underscores the importance of continuous adaptation in the face of evolving threats. By leveraging network traffic data, our hybrid model offers a promising avenue for bolstering intrusion classification in Big Data environments. [ABSTRACT FROM AUTHOR]

The demand for high-performance computing (HPC) resources in computing fields such as machine learning has increased significantly in recent years. Computing power has been growing exponentially to keep up with this demand. However, these gains have not been able to translate to performance improvement in real-world applications. One of the biggest reasons for this is performance degradation in terms of input/output (I/O) due to the increased storage latency and complex parallel I/O architecture of accessing data in distributed storage systems. In this study, we analyze application-specific I/O patterns to gain a deeper understanding of I/O throughput and the interaction between applications and the I/O system. Specifically, we analyze the importance of each feature of I/O patterns through feature analysis based on the collected monitoring information. We also investigate the feasibility of identifying the application based on the analyzed key features. To this end, we present the analysis accuracy and confusion matrix of four algorithms, including random forest, which are widely used as classification algorithms in the experimental results. The experiment results confirm that we can distinguish applications with an accuracy of more than 90% by using application-specific I/O patterns. [ABSTRACT FROM AUTHOR]

Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods. We analyzed five tumor marker test results: alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). A total of 246,261 records were used in the analysis. Of these, 179,929 records were used for model training and 66,332 records for performance evaluation. We developed a misidentification error detection model based on the random forest (RF) and deep neural network (DNN) methods. We performed an in silico simulation with 1 % random sample shuffling. The performance of the developed models was evaluated and compared to conventional delta check methods such as delta percent change (DPC), absolute DPC (absDPC), and reference change values (RCV). The DNN model outperformed the RF, DPC, absDPC, and RCV methods in detecting sample misidentification errors. It achieved balanced accuracies of 0.828, 0.842, 0.792, 0.818, and 0.833 for AFP, CA19-9, CA125, CEA, and PSA, respectively. Although the RF method performed better than DPC and absDPC, it showed similar or lower performance compared to RCV. Our research results demonstrate that an ML-based delta check method can more effectively detect sample misidentification errors compared to conventional delta check methods. In particular, the DNN model demonstrated superior and stable detection performance compared to the RF, DPC, absDPC, and RCV methods. [ABSTRACT FROM AUTHOR]

Background: Socio‐economic status is one of the most important factors shaping students' motivation and achievement but has seldom been explored in relation to achievement goals. Aims: This study aimed to investigate whether mastery‐approach goals explain the link between SES and key learning‐related outcomes (mediation) and whether SES modifies the relationship between mastery‐approach goals and these outcomes (moderation). Sample: Data came from 595,444 students nested in 21,322 schools across 77 countries. Methods: Data were analysed using multilevel‐moderated mediation analyses. Results: We found significant mediation and moderation. In terms of mediation, mastery‐approach goals mediated the association between family SES and learning‐related outcomes. However, a different pattern emerged for school SES, as students in higher SES schools had lower mastery‐approach goals. In terms of moderation, we found that family SES strengthened the association between mastery‐approach goals and learning‐related outcomes. However, the association between mastery‐approach goals and learning‐related outcomes was weaker in higher SES schools. Conclusion: Theoretical and practical implications for the achievement goal approach to achievement motivation are discussed. [ABSTRACT FROM AUTHOR]

Background: Life satisfaction is a key component of students' subjective well‐being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches. Objective: Using ML algorithms, the current study predicts secondary students' life satisfaction from individual‐level variables. Method: Two supervised ML models, random forest (RF) and k‐nearest neighbours (KNN), were developed based on the UK data and the Japan data in PISA 2018. Results: Findings show that (1) both models yielded better performance on the UK data than on the Japanese data; (2) the RF model outperformed the KNN model in predicting students' life satisfaction; (3) meaning in life, student competition, teacher support, exposure to bullying and ICT resources at home and at school played important roles in predicting students' life satisfaction. Conclusions: Theoretically, this study highlights the multi‐dimensional nature of life satisfaction and identifies several key predictors. Methodologically, this study is the first to use ML to explore the predictors of life satisfaction. Practically, it serves as a reference for improving secondary students' life satisfaction. [ABSTRACT FROM AUTHOR]