Random Forest Machine Learning Algorithm Research Articles

High‐Gain Triple‐Band T‐Shaped Dielectric Resonator Based Hybrid Two‐Element MIMO Antenna for 5G New Radio, Wi‐Fi 6, V2X, and C‐Band Applications With a Machine Learning Approach

ABSTRACTIn this article, a rectangular dielectric resonator (DR) based multiple input multiple output (MIMO) hybrid antenna with a machine learning (ML) optimization approach for 5G New Radio (NR), WiMAX, WLAN, Wi‐Fi 6, Vehicle to Everything (V2X), and C‐band uplink applications are presented. The proposed antenna provides a triple band. It covers n1, n2, and n39 5G NR at a lower frequency (1.59–2.26 GHz), n48 5G NR at the middle frequency (3.1–3.87 GHz), and n46, n47, n96, WiMAX, WLAN, and C‐band uplink at higher frequency (5.25–6.91 GHz). It achieved more than 20% impedance bandwidth and 20 dB isolation in all frequency bands. The maximum gain of the proposed antenna is 15 dBi. The calculated MIMO parameters are within the accepted range. It is optimized through various ML algorithms, including random forest (RF), K‐nearest neighbor (KNN), artificial neural network (ANN), extreme gradient boosting (XGB) and decision tree (DT). The RF ML algorithm gives 98.09% accuracy compared to other ML algorithms for S parameter prediction.

Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina

Background/Objectives: The study of microbiome composition shows positive indications for application in the diagnosis and treatment of many conditions and diseases. One such condition is autism spectrum disorder (ASD). We aimed to analyze gut microbiome samples from children in Bosnia and Herzegovina to identify microbial differences between neurotypical children and those with ASD. Additionally, we developed machine learning classifiers to differentiate between the two groups using microbial abundance and predicted functional pathways. Methods: A total of 60 gut microbiome samples (16S rRNA sequences) were analyzed, with 44 from children with ASD and 16 from neurotypical children. Four machine learning algorithms (Random Forest, Support Vector Classification, Gradient Boosting, and Extremely Randomized Tree Classifier) were applied to create eight classification models based on bacterial abundance at the genus level and KEGG pathways. Model accuracy was evaluated, and an external dataset was introduced to test model generalizability. Results: The highest classification accuracy (80%) was achieved with Random Forest and Extremely Randomized Tree Classifier using genus-level taxa. The Random Forest model also performed well (78%) with KEGG pathways. When tested on an independent dataset, the model maintained high accuracy (79%), confirming its generalizability. Conclusions: This study identified significant microbial differences between neurotypical children and children with ASD. Machine learning classifiers, particularly Random Forest and Extremely Randomized Tree Classifier, achieved strong accuracy. Validation with external data demonstrated that the models could generalize across different datasets, highlighting their potential use.

Gravity Predictions in Data-Missing Areas Using Machine Learning Methods

Gravity data, comprising a key foundational dataset, are crucial for various research, including land subsidence monitoring, geological exploration, and navigational positioning. However, the collection of gravity data in specific regions is difficult because of environmental, technical, and economic constraints, resulting in a non-uniform distribution of the observational data. Traditionally, interpolation methods such as Kriging have been widely used to deal with data gaps; however, their predictive accuracy in regions with sparse data still needs improvement. In recent years, the rapid development of artificial intelligence has opened up a new opportunity for data prediction. In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms—random forest, support vector machine, and recurrent neural network—and compared their performances against the traditional Kriging interpolation method. The results indicate that machine learning methods exhibit a marked advantage in gravity data prediction, significantly enhancing the predictive accuracy.

Cross-sectional study on the diagnostic significance of plasma exosomal miRNAs in HBV-related hepatocellular carcinoma

PurposeHepatocellular carcinoma (HCC) associated with Hepatitis B Virus (HBV) is one of the most severe malignancies in East Asia, where early diagnosis is crucial for improving patient prognosis. So we aim to identify effective early diagnostic model for HCC.Design and methodsWe enrolled 108 early-stage HCC patients and 102 non-HCC individuals underlying HBV infection, collecting plasma exosomal miRNAs (exo-miRNAs) from all participants. These patients were randomly assigned to sequencing, screening, training, and validation group. After preliminary screening of candidate exo-miRNAs by next-generation high-throughput sequencing, qPCR data from the screening group were utilized in conjunction with the random forest machine learning algorithm to identify candidate exo-miRNAs with diagnostic potential. Subsequently, logistic regression diagnostic model was constructed using the relative expression levels of candidate exo-miRNAs, alpha-fetoprotein (AFP) levels and clinical parameters of gender and the presence of cirrhosis from the training group. The diagnostic accuracy of diagnostic model was subsequently validated in the validation group.ResultsFirstly, we identified miR-212-5p, miR-1248, and miR-1250-5p as candidate exo-miRNAs with potential diagnostic value. The exo-miRNAs panel, which consisted of miR-212-5p, miR-1248, miR-1250-5p, along with clinical parameters of gender and cirrhosis, achieved an AUC of 0.8634 (95% CI: 0.8027–0.9241), demonstrating diagnostic performance non-inferior to AFP in the independent dataset. Subsequently, by combining exo-miRNAs, AFP level and clinical parameter of gender, we enhanced the diagnostic panel, miRAGe, which exhibited an AUC of 0.9499 (95% CI: 0.9192–0.9806), sensitivity of 0.8900, and specificity of 0.9468.ConclusionOur study indicates that the miRAGe panel has low rate of both missed diagnosis and misdiagnosis rates, potentially serving as a useful diagnostic tool for HBV-related HCC in early stage, which may subsequently contribute to improve the prognosis.

Predictive modeling of gestational weight gain: a machine learning multiclass classification study

BackgroundGestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, and preterm birth. This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines.MethodsWe analyzed data from the Araraquara Cohort, Brazil, which comprised 1557 pregnant women with a gestational age of 19 weeks or less. Predictors included socioeconomic, demographic, lifestyle, morbidity, and anthropometric factors. Five machine learning algorithms (Random Forest, LightGBM, AdaBoost, CatBoost, and XGBoost) were employed for model development. The models were trained and evaluated using a multiclass classification approach. Model performance was assessed using metrics such as area under the ROC curve (AUC-ROC), F1 score and Matthew’s correlation coefficient (MCC).ResultsThe outcomes were categorized as follows: GWG within recommendations (28.7%), GWG below (32.5%), and GWG above recommendations (38.7%). The XGBoost presented the best overall model, achieving an AUC-ROC of 0.79 for GWG within, 0.76 for GWG below, and 0.65 for GWG above. The LightGBM also performed well with an AUC-ROC of 0.79 for predicting GWG within recommendations, 0.76 for GWG below, and 0.624 for GWG above. The most important predictors of GWG were pre-gestational BMI, maternal age, glycemic profile, hemoglobin levels, and arm circumference.ConclusionMachine learning models can effectively predict GWG categories, offering a valuable tool for early identification of at-risk pregnancies. This approach can enhance personalized prenatal care and interventions to promote optimal pregnancy outcomes.

Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico

Abstract. This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. During the period 2016-2019, and with alternating wet and dry season classifications, the research addresses challenges in identifying narrow rivers, using geospatial tools and it does notably the Pesqueria River, which is specially the most narrow and shallow river in the area. Five classes—Water, Vegetation, Urban, and Soil—were classified, achieving precision rates above 85%. Remarkably, SVM exhibited an excellent accuracy, particularly for narrow rivers, showcasing its utility in complex urban landscapes. The study utilizes high resolution satellite imagery with a spatial resolution of 4.7m, contributing to the reliability of the results. Emphasizing temporal dynamics, the research links LULC changes to urbanization, infrastructure, and seasonal variations, offering vital insights for sustainable urban development.

Dry mass grassland estimation using UAV ultra-wide RGB images

Abstract. Dry mass is an important parameter to optimise grassland management. Traditionally, dry mass values are estimated manually by cutting, drying, and weighing vegetation samples. In large areas of cultivation, this becomes a time-consuming and costly activity. In recent years, many researchers have studied different sensors embedded in Unmanned Aerial Vehicles (UAV) to collect spatial data and estimate biomass using machine learning algorithms for forest and agricultural applications. However, there needs to be more research dealing with estimating production indices for pasture, especially in Brazil, as stated. This study evaluates the feasibility of using the GoPro wide-angle RGB camera on UAVs (Unmanned Aerial Vehicles) to estimate the dry mass of pastures. Different data analysis methods were compared, including the combination of vegetation indices (VIs) values and three-dimensional metrics (3D) extracted from the Canopy Height Model (CHM): all metrics (ALL), three VIs plus four 3D metrics (VI3 + CHM4) and only 3D metrics. Random Forest (RF) machine learning algorithm was used to estimate dry mass. The best results were obtained when merging all the variables from the two flight campaigns, with a coefficient of determination (R2) of 0.80 for the model and a Pearson Correlation Coefficient (PCC) of 0.85 for validation, with a Root Mean Square Error (RMSE%) of 20.5%. In summary, using RGB sensors embedded in UAVs is a promising technique for estimating farm grazing parameters.

Leveraging Explainable Artificial Intelligence (XAI) for Expert Interpretability in Predicting Rapid Kidney Enlargement Risks in Autosomal Dominant Polycystic Kidney Disease (ADPKD)

Autosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents significant challenges in management, particularly with limited options for slowing cyst progression, as well as the use of tolvaptan being restricted to high-risk patients due to potential liver injury. However, determining high-risk status typically requires magnetic resonance imaging (MRI) to calculate total kidney volume (TKV), a time-consuming process demanding specialized expertise. Motivated by these challenges, this study proposes alternative methods for high-risk categorization that do not rely on TKV data. Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. The XGBoost model, combined with the Synthetic Minority Oversampling Technique (SMOTE), yielded the best performance. We also leveraged explainable artificial intelligence (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to visualize and clarify the model’s predictions. Furthermore, we generated text summaries to enhance interpretability. To evaluate the effectiveness of our approach, we proposed new metrics to assess explainability and conducted a survey with 27 doctors to compare models with and without XAI techniques. The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients.

Random Forest Prognostication of Survival and 6-Month Outcome In Pediatric Patients Following Decompressive Craniectomy For Traumatic Brain Injury

There is a dearth of literature regarding prognostic and predictive factors for outcome following pediatric decompressive craniectomy (DC) following traumatic brain injury (TBI). The aim of this study was to develop a random forest machine learning algorithm to predict outcomes following DC in pediatrics. This is a multi-institutional retrospective study assessing the 6-month postoperative outcome in pediatric patients that underwent DC. We developed a machine learning model using classification and survival random forest algorithms (CRF and SRF respectively) for the prediction of outcomes. Data was collected on clinical signs, radiographic studies, and laboratory studies. The outcome measures for the CRF were mortality and good or bad outcome based on Glasgow Outcome Scale (GOS) at 6-months. A GOS score of 4 or higher indicated a good outcome. The outcomes for the SRF model assessed mortality at during the follow-up period. A total of 40 pediatric patients were included. There was a hospital mortality rate of 27.5%, and 75.8% of survivors had a good outcome at 6-month follow up. The CRF for 6-month mortality had a ROC AUC of 0.984; whereas, the 6-month good/bad outcome had a ROC AUC of 0.873. The SRF was trained at the 6-month timepoint with a ROC AUC of 0.921. CRF and SRF models successfully predicted 6-month outcomes and mortality following DC in pediatric TBI patients. These results suggest random forest models may be efficacious for predicting outcome in this patient population.

Predicting ECGs masquerading AS STEMI with machine learning

Abstract Background Advancements in the management of ST elevation myocardial infarction (STEMI) in the last 15 years include early STEMI identification in the prehospital setting leading to expedited cardiac catheterisation laboratory (CCL) activation or fibrinolytic therapy. We have previously reported of patients who met the Glasgow algorithm (GA) ECG criteria for STEMI; 50% did not actually have an acute STEMI according to the 4th universal definition of MI (4UDMI)1. Identifying such patients could avoid unnecessary emergency CCL activations or inappropriate fibrinolytic administration. Methods From June 2010 to October 2021, we identified 2992 patients with ECG transmissions to Liverpool Hospital, Sydney Australia which met GA criteria for STEMI. These were adjudicated according to the 4UDMI for true-positive STEMI. We used univariate and multivariate logistic regression to identify likely patient characteristics that were predictive of true-positive STEMI. We then improved model performance using receiver-operator curve (ROC) with simple random forest machine learning algorithms. Results Among 2994 patients with prehospital ECG transmissions that met GA-criteria, 1553 (51%) were adjudicated as true-positive STEMI, which is comparable to previous findings1. All standard modifiable and non-modifiable risk factors were independently predictive of true-positive STEMI; however, in multivariate logistic regression gender, hypertension and diabetes lost significance. Dyslipidemia and current smoking were positively predictive of true-positive STEMI status (OR = 1.64 and 1.32 respectively), whereas age and previous MI were negatively associated (OR = 0.98 (per year increment) and 0.4). Using these identified predictors in a random forest to predict true-positive STEMI yielded an AUC of 0.65 (Figure 1). Conclusion Specific clarification of dyslipidaemia and current smoking status at first medical contact may be the most salient in predicting a true-positive STEMI during prehospital assessment which may assist in avoiding unnecessary emergency CCL activation or inappropriate fibrinolytic administration.ROC of ST elevation STEMI recognition

Comparisons of data-driven models for detecting slip occurrence and direction based on simulations of tactile sensing

Slip detection based on tactile sensing plays a crucial role for a robot to achieving stable grasps by promptly adjusting its grasping state. Detecting the occurrence of slippage has been a focus of previous research, but identifying the direction of slippage can provide richer reference information for correcting grasp issues. Due to the variations of experiment setup, the performances of these data-driven models have not been sufficiently compared. In this study, we compared the performance of three traditional machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN)—with four deep learning models—Gated Recurrent Unit (GRU), Convolutional Gated Recurrent Unit (CNN-GRU), Convolutional Long Short-Term Memory (ConvLSTM), and 3D Convolution(Conv3D)—in detecting slippage states and directions. We conducted experiments using a simulated dataset collected in Gazebo. Additionally, we compared the noise resistance capability of each model and their generalization performance when facing new objects. The results show that when facing known grasped objects, all models can effectively detect slippage occurrences and their directions. However, as noise increases, Conv3D exhibits the strongest robustness. When generalized to unknown grasped objects, the deep learning models outperforms the three traditional learning algorithms, with CNN-GRU showing the best performance.

Innovative use of depth data to estimate energy intake and expenditure in Adélie penguins.

Energy governs species' life histories and pace of living, requiring individuals to make trade-offs. However, measuring energetic parameters in the wild is challenging, often resulting in data collected from heterogeneous sources. This complicates comprehensive analysis and hampers transferability within and across case studies. We present a novel framework, combining information obtained from eco-physiology and biologging techniques, to estimate both energy expended and acquired on 48 Adélie penguins (Pygoscelis adeliae) during the chick-rearing stage. We employ the machine learning algorithm random forest (RF) to predict accelerometry-derived metrics for feeding behaviour using depth data (our proxy for energy acquisition). We also build a time-activity model calibrated with doubly labelled water data to estimate energy expenditure. Using depth-derived time spent diving and amount of vertical movement in the sub-surface phase, we accurately predict energy expenditure (R2=0.68, RMSE=344.67). Movement metrics derived from the RF algorithm deployed on depth data were able to accurately (accuracy=0.82) detect the same feeding behaviour predicted from accelerometry. The RF predicted accelerometry-estimated time spent feeding more accurately (R2=0.81) compared to historical proxies like number of undulations (R2=0.51) or dive bottom duration (R2=0.31). The proposed framework is accurate, reliable, and simple to implement on data from biologging technology widely-used on marine species. It enables coupling energy intake and expenditure, which is crucial to further assess individual trade-offs. Our work allows us to revisit historical data, to study how long-term environmental changes affect animals' energetics.

Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam.

Recurrent landslide events triggered by typhoons and tropical storms over Vietnam pose a longstanding threat to the nation's population and infrastructure. Changes in hydroclimatic conditions, especially the growing intensity and frequency of storms, have elevated landslide susceptibility in many parts of the country. This research examines the spatio-temporal variations in landslide susceptibility across central Vietnam over several years, using multi-temporal landslide inventories from Typhoon Ketsana (2009), Tropical Storm Podul (2013), and Typhoon Molave (2020). Additionally, the research explores the impact of individual landslide causative factors on the probabilistic occurrences of landslides. The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. These changes may be early indicators of increasing landslide susceptibility in response to changing hydro-climatic conditions. The research found that annual average rainfall and topographic elevation are the two most important variables influencing landslide prediction, showing a nonlinear relationship with landslide probability. The landslide susceptibility models achieved high Area Under the Receiver Operating Characteristic Curve (AUC) (>95%), accuracy (>89%), and sensitivity (>90%) scores, signifying the robustness of the models. Additionally, the uncertainty of the models was quantified and spatially mapped. This multi-temporal analysis of landslide susceptibility is crucial for understanding the regional susceptibility trends and identifying areas with increasing, decreasing, and consistently high susceptibility to landslides. These insights are invaluable for prioritizing mitigation and risk reduction strategies in landslide-prone regions and guiding appropriate land use planning.

Unraveling the spatio-temporal trajectories of urban growth in Asansol city, West Bengal: A geospatial exploration of the emerging urbanlandscape

The rapid urban growth of Asansol City has led to the rise of unplanned built-up areas, disrupting natural land covers and posing challenges to sustainable resource management. Hence, this study aims to quantify spatio-temporal trajectories of urban growth in Asansol City during 1991–2021, using integrated remote sensing and GIS-based techniques. Landsat 5 TM and 8 OLI/TIRS satellite imageries were used to delineate built-up and non-built-up areas using a random forest machine-learning algorithm. The study utilized the gradient approach, urban sprawl typologies, urban expansion indices, shannon’s entropy, and landscape fragmentation metrics to analyze the pattern, direction, intensity and dynamics of urban expansion in a space-time context. The findings showed that built-up growth increased significantly from 25.49 sq. km. to 57.18 sq. km. between 1991 and 2021, with the highest growth observed in the westward direction and within 3 km from the CBD. The study highlighted the prevalence of edge expansion and scatter development as dominant typologies of urban growth. Urban expansion indices, shannon's entropy and landscape fragmentation metrics analysis revealed the increasing urban sprawl towards the periphery and the formation of clustered built-up areas near the CBD. The outcomes of this study provide a basis for sustainable urban development by quantifying spatio-temporal trajectories and highlighting challenges from unrestrained growth of Asansol municipal corporation.

Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data

Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches. In addition, the time-intensive manual labeling can also be subjective rather than an objective identification of the landform. Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. The landform classification includes upland plateaus, ridges, convex slopes, planar slopes, concave slopes, stream channels, and valley bottoms, across a 400 km2 hilly landscape of the Ozark Mountains in northeastern Oklahoma. We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. We test the effectiveness of DEM resolution in mapping landforms via comparison of observed landforms with modeled landforms. Results showed that the approach mapped ∼84% of observed landforms when covariates were at 2 m resolution to ∼89% when they were at 10 m resolution. However, predicted maps showed that the 2 m resolution covariates performed better at capturing accurate landform boundaries and details of small-sized landforms such as stream channels and ridges. The approach presented here significantly reduces the time required for mapping landforms compared to traditional techniques using aerial imagery and field observations and allows incorporation of a wide variety of covariates. The landform map developed using this approach has several potential applications. It could be utilized in hydrological modeling, natural resource management, and characterizing soil-geomorphic processes and hazards in a hilly landscape.

Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing

Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.

Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models

Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.

Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach

Data-driven models have become powerful tools for structural and condition monitoring of engineering systems, particularly wind turbines. This paper presents a comparative analysis of common machine learning (ML) algorithms (artificial neural networks, linear regression, random forests, and gradient boosting) and a cointegration-based approach for fault detection using Supervisory Control and Data Acquisition (SCADA) data. While ML models offer early fault prediction, the cointegration method is simpler, requires less training data, and has lower computational costs. However, it is less effective for early detection. To balance these trade-offs, we propose a cascading monitoring framework, where the ML model provides long-term predictions (outer monitoring process) and the cointegration model offers short-term verification (inner monitoring process). The cointegration model serves to confirm anomalies flagged by the ML model. By combining both models in a cascade structure, the system reduces the risk of false alarms triggered by uncertainties in the ML model alone. Furthermore, the short-term cointegration-based prediction model helps pinpoint immediate risks and mitigate the issue of prolonged downtime. This combination enhances both accuracy and reliability, as demonstrated through testing on a five-year SCADA dataset from a commercial wind turbine with a known gearbox fault.

Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics

Objectives: This study aims to investigate MRI features predicting the grade of STS malignancy using conventional image reading and radiomics. Methods: Pretherapeutic imaging data regarding size, tissue heterogeneity, peritumoral changes, necrosis, hemorrhage, and cystic degeneration were evaluated in conventional image reading. Furthermore, the tumors’ apparent diffusion coefficient (ADC) values and radiomics features were extracted and analyzed. A random forest machine learning algorithm was trained and evaluated based on the extracted features. Results: A total of 139 STS cases were included in this study. The mean tumor ADC and the ratio between tumor ADC to healthy muscle ADC were significantly lower in high-grade tumors (p = 0.001 and 0.005, respectively). Peritumoral edema (p < 0.001) and peritumoral contrast enhancement (p < 0.001) were significantly more extensive in high-grade tumors. Tumor heterogeneity was significantly increased in high-grade sarcomas, particularly in T2w- and contrast-enhanced sequences using conventional image reading (p < 0.001) as well as in the radiomics analysis (p < 0.001). Our trained random forest machine learning model predicted high-grade status with an area under the curve (AUC) of 0.97 and an F1 score of 0.93. Biopsy-underestimated tumors exhibited differences in tumor heterogeneity and peritumoral changes. Conclusions: Tumor heterogeneity is a key characteristic of high-grade STSs, which is discernible through conventional imaging reading and radiomics analysis. Higher STS grades are also associated with low ADC values, peritumoral edema, and peritumoral contrast enhancement.

Risk factors of pancreatic fistula after distal pancreas resections: an observational retrospective study

BACKGROUND: The main complication of surgical pancreas interventions is the pancreatic fistula (PF) — abnormal secretion of pancreatic secretions into the abdominal cavity, which can lead to more severe complications. At the same time, some predictors of PF remain poorly understood. AIM: To investigate the risk factors for the development of PF after distal pancreatic resection. MATERIALS AND METHODS: The study included 40 patients who underwent distal resection. The influence of various clinical parameters on the development of PF after surgery was evaluated. Methods of mathematical modeling and correlation analysis were used. Mathematical modeling was carried out using the Random forest machine learning algorithm, and the indicator of the relative importance of the “importance” parameters was evaluated. RESULTS: According to the Random forest model, on day 1 after surgery, the most significant predictors of the development of PF were: the volume of neoplasm, age and stage of the oncological process, for which “importance” was 53.2, 13.7 and 12.5 (AUC ROC=62%); on day 3–5, “importance” was 61.7, 11.5 and 5.2 (AUC ROC=79%). An increase in the concentration of pancreatic amylase in blood plasma for 2–3 and 3–5 days correlated with its increase in the drainage discharge for 5-7 days after the intervention (r=0.48 and 0.76; p 0.05). A correlation was found between the level of amylase in the drainage discharge on 3–5 days after the intervention and the level of leukocytes according to the general blood test (r=0.62, p 0.05). CONCLUSION: An increase in plasma amylase levels is the main risk factor for the development of PF. An increase in the concentration of amylase in the drainage discharge can be considered as a potential risk factor for the development of pancreatic fistulas with clinical manifestations in the following days. The stage of the oncological process, the size of the neoplasm and the age of the patient are also risk factors.