Random Forest Regression Model Research Articles

Influence of Typical Regional Land Use/Landscape Pattern on Water TN of the Upper Yellow River

This study aimed to explore the relationship between land use landscape pattern and water quality in the upstream of the Gansu water conservation, water and soil erosion, and ecological fragile areas. Based on the land use data and water quality monitoring section in 2020 in the 200 m, 500 m, 1 km, 2 km, 50 km, and 10 km riparian buffer area, the single-factor index evaluation method, random forest regression model, and BP neural network were used to quantify the response relationship between land use and landscape pattern of the upper Yellow River in Gansu province and water quality index and to carry out the basin water quality prediction based on land use landscape index data. The results showed that： ① through the single-factor index method, the major indicators of the total nitrogen （TN） in July and September, dissolved oxygen （DO）, permanganate index, ammonia nitrogen （NH4+ -N）, total phosphorus （TP）, and other surface indexes met the surface water environment class Ⅲ water quality standard. ② The random forest regression model was used to analyze the influence of land use and landscape index on TN, and the difference in TN in different typical areas was obtained. The land use types with the highest influence on the TN index in water conservation areas, soil and soil erosion areas, and ecological fragile areas were cultivated land, grassland, and construction land, respectively. ③ The BP neural network was used to predict the water quality index based on different typical areas of land use landscape index. The result of water conservation areas was good, the error rate between the predicted value and the actual value was below 10%, and the prediction accuracy was high. The study showed that water quality prediction based on land use and landscape index/water quality quantitative relationship model had a good water quality prediction effect.

Predicting 24-hour intraocular pressure peaks and averages with machine learning.

Predicting 24-hour peak and average intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma. This study aimed to develop and assess a machine learning model for predicting 24-hour peak and average IOP, leveraging advanced techniques to enhance prediction accuracy. We also aimed to identify relevant features and provide insights into the prediction results to better inform clinical practice. In this retrospective study, electronic medical records from January 2014 to May 2024 were analyzed, incorporating 24-hour IOP monitoring data and patient characteristics. Predictive models based on five machine learning algorithms were trained and evaluated. Five time points (10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM) were tested to optimize prediction accuracy using their combinations. The model with the highest performance was selected, and feature importance was assessed using Shapley Additive Explanations. This study included data from 517 patients (1,034 eyes). For predicting 24-hour peak IOP, the Random Forest Regression (RFR) model utilizing IOP values at 10:00 AM, 12:00 PM, 2:00 PM, and 4:00 PM achieved optimal performance: MSE 5.248, RMSE 2.291, MAE 1.694, and R2 0.823. For predicting 24-hour average IOP, the RFR model using IOP values at 10:00 AM, 12:00 PM, 4:00 PM, and 6:00 PM performed best: MSE 1.374, RMSE 1.172, MAE 0.869, and R2 0.918. The study developed machine learning models that predict 24-hour peak and average IOP. Specific time point combinations and the RFR algorithm were identified, which improved the accuracy of predicting 24-hour peak and average intraocular pressure. These findings provide the potential for more effective management and treatment strategies for glaucoma patients.

Predicting response to immunotherapy in oral squamous cell carcinoma via a CT-based radiomics model

BackgroundTo investigate whether radiomics models derived from pretreatment CT could help to predict response to immunotherapy in oral squamous cell carcinoma (OSCC).MethodsRetrospectively, a total of 40 patients with measurable OSCC were included. The patients were divided into responder group and non-responder group according to the comparison of pre-treatment and post-treatment CT findings. Radiomics features were extracted from pre-treatment CT images, and optimal features were selected by univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis. Neural network, support vector machine, random forest and logistic regression models were used to predict response to immunotherapy in OSCC, and leave-one-out cross validation was employed to assess the performance of the classifiers. The area under the curve (AUC), accuracy, sensitivity and specificity were calculated to quantify the predictive efficacy.ResultsA total of 7 features were selected to build models upon machine learning methods. By comparing different machine learning based models, the neural network model achieved the best predictive ability, with an AUC of 0.864, an accuracy of 82.5%, a sensitivity of 82.5%, and a specificity of 82.5%.ConclusionsThe pretreatment CT-based radiomics model showed good performance in predicting response to immunotherapy in OSCC. Pretreatment CT-based radiomics model might provide an alternative approach for the selection of patients who benefit from immunotherapy.

Generating tyre tread designs using a sensory evaluation regression model and a generative model

This study introduces a method for generating tyre tread images that are highly rated in terms of ‘slip resistance’ sensory evaluations. It involves constructing a regression model to predict ‘slip resistance’ sensory evaluations in tyre tread images and subsequently developing an image generation model informed by this regression model. The regression model was developed using a machine learning framework based on ensemble learning and SHapley Additive exPlanations. The model was then used to filter training data for the image generation process, leading to the creation of a tyre tread image generation model aimed at producing images with high ‘slip resistance’ sensory evaluations, using StyleGAN. The proposed method was applied in a case study using real data, and its effectiveness was validated by comparison with a multiple regression analysis approach. The results revealed that more accurate results were achieved with the random forest (RF) regression model than with the multiple regression model due to the RF regression model’s emphasis on non-linear features. Furthermore, a significant difference was observed between the ‘slip resistance’ sensory evaluation values of the images generated by StyleGAN trained with training data screened by the RF regression model and those from the training data.

Refrigeration capacity modeling of europium titanate based magnetocaloric compounds using computational single hidden layer intelligent and random forest regression methods

Europium titanate (EuTiO3) is a quantum para-electric magnetocaloric compound with potential application in green magnetic cooling technology. Super-exchange and indirect interactions existing between the magnetic moments of this compound are responsible for the observed magnetic properties such as the refrigeration capacity which falls among the key factors that characterize the usefulness of magnetic system of cooling in addressing energy crisis. Crystallographic substitutions and ionic replacement potentially shift magnetic ordering between anti-ferromagnetism to ferromagnetism and ultimately alters the refrigeration (magnetic) capacity of EuTiO3 compound. This work proposes extreme learning machine (ELM) and random forest regression (RFR) computational approaches to model refrigeration capacity of europium titanate magnetic refrigerant with the aid of applied magnetic field, ionic concentrations and ionic radii predictors. Sigmoid activation based extreme learning machine algorithm(SGA-ELM) demonstrates superior performance over sine function activation based extreme learning machine algorithm(SIA-ELM) with improvement of 0.03 %, 16.04 % and 35.03 % on the basis of Pearson correlation coefficient (CC), root mean square error (RMSE) and mean absolute error (MAE) performance yardsticks, respectively on testing set of EuTiO3 based magnetocaloric compounds. SGA-ELM performs better than SIA-ELM and RFR model with improvement of 35.03 % and 66.84 %, respectively while SIA-ELM model performs better than RFR model with improvement of 59.97 %. SGA-ELM model developed in this work outperforms the existing swarm based support vector regression (SW-SVR) models with performance improvement of 15.38 % (for SW-SVR-Gas with Gaussian function) and 42. 71 % % (for SW-SVR-Pol with polynomial function) using mean absolute deviation (MAD) performance yardstick. Significance of magnetic field and dopants concentrations on refrigeration capacity of different EuTiO3 perovskite was investigated with SGA-ELM model. The accuracy of developed models in determining refrigeration capacity of EuTiO3 compounds would ultimately circumvents experimental challenges and explores future potentials of these classes of compound for addressing present and future energy crisis in cooling industries.

Precipitation changes during the last glacial period in the Ili Basin, northern Central Asia, as inferred from the records of loess dolomite

Understanding the climatic evolution in Central Asia (CA) and its drivers is crucial for informed decision-making and predicting global changes due to its significant contribution as a global dust source. Unfortunately, our current understanding of the pre-Holocene precipitation patterns in CA is lacking due to the limited availability of reliable proxy indicators, and our knowledge of future precipitation projections in the region, based on paleoclimatic dynamics, is also quite limited. In this study, we analyzed variations in carbonate and dolomite contents of a loess section in the Ili Basin, northern CA, to reveal precipitation changes during the last glacial period. The results showed that changes in carbonate minerals were mainly influenced by the source material supply, driven by reduced precipitation and eluviation during glacial period. We thereby established a precipitation index by removing the influence of provenance signals from the dolomite records. The index indicated lower precipitation during mid-marine isotope stage (MIS) 3 compared to MIS2, likely due to meridional shifts and intensity changes of the westerlies caused by changes in precession and obliquity, with precession playing a major role. Through the comparison of the precipitation index with the δ18O records of the Greenland ice core on a millennial timescale, it was observed that the precipitation in northern CA exhibited a positive correlation with the North Atlantic Oscillation (NAO) mode due to migration of the westerlies. By leveraging our understanding of orbital- and millennial-scale precipitation patterns, we utilized the random forest (RF) regression model and the autoregressive integrated moving average model to forecast precipitation changes for the upcoming 5000–10,000 years. The results indicated a variable pattern marked by a general upward trend, suggesting the possibility of favorable development of agricultural-based economies in the Ili River Valley. People should realize that some integrated measures are designed to improve resilience of agricultural sector in the region and enhance its capacity to adapt to challenges posed by climate change. However, more extensive research is necessary to verify these results through thorough examination and comparisons of loess sections in our research location.

Prediction of emergency department presentations for acute coronary syndrome using a machine learning approach

The relationship between weather and acute coronary syndrome (ACS) incidence has been the subject of considerable research, with varying conclusions. Harnessing machine learning techniques, our study explores the relationship between meteorological factors and ACS presentations in the emergency department (ED), offering insights into seasonal variations and inter-day fluctuations to optimize patient care and resource allocation. A retrospective cohort analysis was conducted, encompassing ACS presentations to Dutch EDs from 2010 to 2017. Temporal patterns were analyzed using heat-maps and time series plots. Multivariable linear regression (MLR) and Random Forest (RF) regression models were employed to forecast daily ACS presentations with prediction horizons of one, three, seven, and thirty days. Model performance was assessed using the coefficient of determination (R²), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The study included 214,953 ACS presentations, predominantly unstable angina (UA) (94,272; 44%), non-ST-elevated myocardial infarction (NSTEMI) (78,963; 37%), and ST-elevated myocardial infarction (STEMI) (41,718; 19%). A decline in daily ACS admissions over time was observed, with notable inter-day (estimated median difference: 41 (95%CI = 37–43, p = < 0.001) and seasonal variations (estimated median difference: 9 (95%CI 6–12, p = < 0.001). Both MLR and RF models demonstrated similar predictive capabilities, with MLR slightly outperforming RF. The models showed moderate explanatory power for ACS incidence (adjusted R² = 0.66; MAE (MAPE): 7.8 (11%)), with varying performance across subdiagnoses. Prediction of UA incidence resulted in the best-explained variability (adjusted R² = 0.80; MAE (MAPE): 5.3 (19.1%)), followed by NSTEMI and STEMI diagnoses. All models maintained consistent performance over extended prediction horizons. Our findings indicate that ACS presentation exhibits distinctive seasonal changes and inter-day differences, with marked reductions in incidence during the summer months and a distinct peak prevalence on Mondays. The predictive performance of our model was moderate. Nonetheless, we obtained good explanatory power for UA presentations. Our model emerges as a potentially valuable supplementary tool to enhance ED resource allocation or future predictive models predicting ACS incidence in the ED.

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Database. The caloric content of foods was estimated using the nutritional composition (i.e., carbohydrates, protein, total lipid, and total sugar content) and classified based on their weighted health risks, considering their carbohydrate, lipid, and glycemic index levels. The algorithms used for modeling include multiple linear regression (MLR), K-nearest neighbors, support vector machine, random forest regression (RFR), gradient-boosted regression, decision trees (DT), and deep neural networks. The MLR model demonstrated high accuracy on the training dataset (R2=0.99, mean absolute error [MAE]=7.71kcal, and root mean squared error [RMSE]=17.89kcal) and testing dataset (R2=0.99, MAE=7.75kcal, and RMSE=18kcal) in energy estimation, indicating its effectiveness in dietary assessment. The RFR and DT models were useful in categorizing foods into low-health-risk foods, but their performance was reduced in medium and high-health-risk groups. This research contributes to developing tools that could aid in personalized dietary planning and public health interventions to mitigate diet-related health risks. PRACTICAL APPLICATION: This study applies machine learning to estimate how many calories are in food and to understand the health risks different foods might have. By investigating the fats, cholesterol, and sugars in food items listed in a public database, we can better plan diets or develop apps that help people make healthier eating choices. This work aims to improve public access to nutritional information, supporting efforts to combat diet-related diseases through educational materials and applications that guide dietary choices in various settings.

Estimation and Spatial Distribution of Individual Tree Aboveground Biomass in a Chinese Fir Plantation in the Dabieshan Mountains of Western Anhui, China

Understanding aboveground biomass (AGB) and its spatial distribution is key to evaluating the productivity and carbon sink effect of forest ecosystems. In this study, a 123-year-old Chinese fir forest in the Dabieshan Mountains of western Anhui Province was used as the research subject. Using AGB data calculated from field measurements of individual Chinese fir trees (diameter at breast height [DBH] and height) and spectral vegetation indices derived from unmanned aerial vehicle (UAV) remote sensing images, a random forest regression model was developed to predict individual tree AGB. This model was then used to estimate the AGB of individual Chinese fir trees. Combined with digital elevation model (DEM) data, the effects of topographic factors on the spatial distribution of AGB were analyzed. We found that remote sensing spectral vegetation indices obtained by UAVs can be used to predict the AGB of individual Chinese fir trees, with the normalized difference vegetation index (NDVI) and the optimized soil-adjusted vegetation index (OSAVI) being two important predictors. The estimated AGB of individual Chinese fir trees was 339.34 Mg·ha−1 with a coefficient of variation of 23.21%. At the local scale, under the influence of elevation, slope, and aspect, the AGB of individual Chinese fir trees showed a distribution pattern of decreasing from the middle to the northwest and southeast along the northeast-southwest trend. The effect of elevation on AGB was influenced by slope and aspect; AGB on steep slopes was higher than on gentle slopes, and the impact of slope on AGB was influenced by aspect. Additionally, AGB on north-facing slopes was higher than on south-facing slopes. Our results suggest that local environmental factors such as elevation, slope, and aspect should be considered in future Chinese fir plantation management and carbon sink assessments in the Dabieshan Mountains of western Anhui, China.

Optimization of Rock Breaking Parameters by Combing Mechanical Specific Energy Theory and Random Forest Algorithm

Abstract As the key indicators to evaluate drilling efficiency, mechanical rate of penetration (ROP) and mechanical specific energy (MSE) are affected by many uncertain factors. In the actual drilling process, it is often necessary to adjust the parameters to achieve speed and efficiency. A dual-objective model is established considering the maximization of ROP and the minimization of MSE. Which combined with the theory of mechanical specific energy and the principle of multi-objective optimization, the model parameter weights are determined by the feature importance in the random forest regression model, and the value range of rock breaking parameter is optimized, realize the fusion of physical model and data model. The results show that, in the four strata of well X, the average ROP of T2k1 stratum is increased by 48.6% and the average MSE is decreased by 26.6%, the average ROP of T1b3 stratum is increased by 89.9% and the average MSE is decreased by 33.8%, the average ROP of the T1b2 stratum was increased by 41.3% and the average MSE was decreased by 39.0%, the average ROP of the T1b1 stratum was increased by 29.2% and the average MSE was decreased by 37.3%, which met the dual objectives of maximizing the ROP and minimizing the MSE. Compared with the traditional single-objective optimization method, the established dual-objective model is more in line with the needs of complex drilling engineering, and the fusion of physical model and data model can reduce certain human error.

Spectral Diversity as a Predictor of Tree Diversity: Exploring Challenges and Opportunities Across Forest Ecosystems

Forests are crucial for ecosystem health, climate regulation, and biodiversity. However, many tree species face extinction threats, requiring active monitoring for conservation. The spectral variation hypothesis (SVH) suggests that spectral diversity can serve as a proxy for ground-measured biodiversity. Despite its promise, SVH’s application has shown inconsistent results, complicating its use in biodiversity monitoring. This study examines the relationship between tree diversity and Sentinel-2-derived spectral diversity across Quebec’s forests, analyzing 2531 inventory plots using a combination of spectral analysis, cluster analysis and random forest (RF) regressions. We evaluate four biodiversity indices: species richness, Shannon diversity, functional dispersion, and percent conifer. Our analysis reveals overlapping spectral signatures that make it challenging to differentiate between varying levels of species richness, Shannon diversity, and functional dispersion. However, percent conifer shows spectral separability and can be stratified using unsupervised k-means clustering. Using RF regression models, only the percent conifer models demonstrated strong performance (R2 = 0.77), while models for the other biodiversity indices did not exceed an R2 of 0.46. This study highlights the complex relationship between spectral diversity and tree diversity, and suggests that future research should aim to improve the understanding of the relationship, or lack thereof, between ground-measured biodiversity indices and relatable spectral metrics.

Machine learning model predicting factors for incisional infection following right hemicolectomy for colon cancer

Background and aimColorectal cancer is a prevalent malignancy worldwide, and right hemicolectomy is a common surgical procedure for its treatment. However, postoperative incisional infections remain a significant complication, leading to prolonged hospital stays, increased healthcare costs, and patient discomfort. Therefore, this study aims to utilize machine learning models, including random forest, support vector machine, deep learning models, and traditional logistic regression, to predict factors associated with incisional infection following right hemicolectomy for colon cancer.MethodsClinical data were collected from 322 patients undergoing right hemicolectomy for colon cancer, including demographic information, preoperative chemotherapy status, body mass index (BMI), operative time, and other relevant variables. These data are divided into training and testing sets in a ratio of 7:3. Machine learning models, including random forest, support vector machine, and deep learning, were trained using the training set and evaluated using the testing set.ResultsThe deep learning model exhibited the highest performance in predicting incisional infection, followed by random forest and logistic regression models. Specifically, the deep learning model demonstrated higher area under the receiver operating characteristic curve (ROC-AUC) and F1 score compared to other models. These findings suggest the efficacy of machine learning models in predicting risk factors for incisional infection following right hemicolectomy for colon cancer.ConclusionsMachine learning models, particularly deep learning models, offer a promising approach for predicting the risk of incisional infection following right hemicolectomy for colon cancer. These models can provide valuable decision support for clinicians, facilitating personalized treatment strategies and improving patient outcomes.

Mass spectrometry unveils heat-induced changes in yolk oxylipins and key lipid molecules during home cooking

IntroductionOxylipins, as a widespread class of metabolic markers following oxidative stress, and several studies have reported dietary regulation of lipid metabolism. However, there is a lack of investigation of dietary oxylipins, especially cooking-induced changes in food lipid oxidation. ObjectivesInvestigated the effects of cooking methods and lipid profiles on polyunsaturated fatty acids derived oxylipins generation within egg yolks. MethodsThe lipid profile of egg yolk was determined by UPLC-QTOF-MS/MS, oxylipins were detected by HPLC-QTRAP-MS/MS, while the total fatty acid content was quantified by GC-FID. Random Forest (RF) and Partial Least Squares (PLS) regression models were employed to explore the association between oxidized lipids and key lipid species. ResultsHeating reduced egg yolk docosahexaenoic acid (DHA) content, and no consistent trends for arachidonic acid (AA), linoleic acid (LA), and linolenic acid (ALA). Yolk lipid composition affected triacylglycerol (TG), phosphatidylethanolamine (PE), and LA-monoepoxide contents after cooking. 9- and 13-HODE (hydroxyoctadecadienoic acid), 9,10,13-TriHOME (trihydroxyoctadecenoic acid), 9,10- and 12,13-EpOME (epoxyoctadecenoic acid), 9,10- and 12,13-DiHOME (dihydroxyoctadecenoic acid), 5-HETE (hydroxyeicosatetraenoic acid), and 4-HDHA (hydroxydocosahexaenoic acid) were the prevalent oxylipins with high concentrations, accounting for 1.08 %–29.58 % of the total content of 29 oxylipins. Steaming resulted in a 1.9-fold increase in oxylipin concentrations in yolks compared to raw yolks, and boiling with or without shells (poaching) resulted in a 1.30- to 1.76-fold increase in oxylipin concentrations. In contrast, pan-fried yolks exhibited the lowest and least variable levels of total oxylipins, while still retaining some epoxides, including epoxyeicosatrienoic acid (EET) and EpOME. Utilizing big data analysis, we mapped the oxylipin network in both ordinary and DHA-enriched egg yolks, revealing a strong correlation between cooking-induced oxylipin production and variations in 24 lipid species. ConclusionRevealed the potential mechanisms and key lipid molecules for heating-induced oxylipin production of yolk through lipidomics and big data analysis.

Comparison of Air Fare Prediction Using Various Machine Learning Techniques

Due to a rise in air service links globally, air travel is now a popular, significant, and faster form of transportation. Airline rates are thought to be impacted by several factors and are subject to frequent fluctuation, making estimation of these rates a challenging but essential task. The airline uses a pricing structure for the plane ticket. The cost of airline tickets varies based on the time of day, according to the survey. Even the weekends, the tourist season, and the pageant season have an impact on this. An aircraft ticket's price is influenced by several different elements. The buyer only has access to a limited amount of information, which is truly insufficient to estimate flight pricing, but the seller is aware of all the requirements. In this study, we use machine learning regression approaches such as K Nearest Neighbourhood, Decision Tree, and Random Forest to estimate flight fares based on basic data including source, destination, departure time and date, arrival timings, halts, and airline type. The results of the investigation show that the Random Forest Regression Model yields extremely optimal results.

Dynamics of rain-triggered lahars and destructive power inferred from seismo-acoustic arrays and time-lapse camera correlation at Volcán de Fuego, Guatemala

AbstractLahars, or volcanic mudflows, are one of the most devastating natural, volcanic hazards. Deadly lahars, such as the one that occurred after the Nevado del Ruiz, Columbia eruption in 1985, in which at least 23,000 people tragically lost their lives, threaten the safety and well-being of humans, the economy, and the infrastructure of many of the communities living in the vicinity of volcanoes. Due to their complex flow behaviors, lahars remain a major challenge to those studying them. We present an analysis of several rain-triggered lahar events at Volcán Fuego in Guatemala using both seismic and infrasound monitoring to quantify both ground vibrations and low-frequency atmospheric sound waves associated with these mudflows. Geophysical data collected over this field campaign quantifies flow parameters such as velocities, stage and the frequency of these rain-triggered lahars. Time-lapse imagery of lahar flows is compared with filtered seismo-acoustic signal characteristics to ascertain stage predictions and relationship to stage fluxes. Using random forest regression models, we establish moderate correlations (correlation coefficient modes 0.48–0.53) with statistical significance (p value = 0.01–0.02) between signal energetics and respective stage. Compiling a catalog of rain-triggered lahar events in Volcán de Fuego’s drainages over a season permits a dataset amenable to statistical analysis. Our goal is the development of new-generation geophysical monitoring tools that will be capable of remote and real-time estimation of flow parameters.

Digital color analysis and machine learning for ballpoint pen ink clustering and aging investigation

Fraudulent activities often involve document manipulation, which poses a significant challenge to forensic science. To address this issue, a novel method was developed that combines intended artificial UV pre-degradation, digital color analysis (DCA) of stroke images, and various machine learning (ML) models. This method can cluster blue ballpoint pen inks and predict their photodegradation time. The results of the study indicate that the k-shape clustering method is highly effective in differentiating between inks based on their degradation curve patterns and HSV or RBS color features, aligning well with results from chromatography analyses. Furthermore, the random forest regression model demonstrated superior performance in predicting age, exhibiting the highest coefficients of determination. The DCA-ML method is a straightforward, cost-effective, and highly accurate solution for clustering blue pen inks. Using photodegradation curves to predict document age could eliminate the need for conventional physicochemical analysis techniques.

Urethral tissue characterization using multiparametric ultrasound imaging

A decrease in urethral closure pressure is one of the primary causes of stress urinary incontinence in women. Atrophy of the urethral muscles is a primary factor in the 15 % age-related decline in urethral closure pressure per decade. Incontinence not only affects the well-being of women but is also a leading cause of nursing home admission. The objective of this research was to develop a noninvasive test to assess urethral tissue microenvironmental changes using multiparametric ultrasound (mpUS) imaging technique. Transperineal B-scan ultrasound (US) data were captured using clinical scanners equipped with curvilinear or linear transducers. Imaging was performed on volunteers from our institution medical center (n = 15, 22 to 76 y.o.) during Valsalva maneuvers. After expert delineation of the region of interest in each frame, the central axis of the urethra was automatically defined to determine the angle between the urethra and the US beam for further analysis. By integrating angle-dependent backscatter with radiomic texture feature analysis, a mpUS technique was developed to identify biomarkers that reflect subtle microstructural changes expected within the urethral tissue. The process was repeated when the urethra and US beam were at a fixed angle. Texture selection was conducted for both angle-dependent and angle-independent results to remove redundancies. Ultimately, a distinct biomarker was derived using a random forest regression model to compute the urethra score based on features selected from both processes. Angle-dependent backscatter analysis shows that the calculated slope of US mean image intensity decreased by 0.89 (±0.31) % annually, consistent with the expected atrophic disorganization of urethral tissue structure and the associated reduction in urethral closure pressure with age. Additionally, textural analysis performed at a specific angle (i.e., 40 degrees) revealed changes in gray level nonuniformity, skewness, and correlation by 0.08 (±0.04) %, −2.16 (±1.14) %, and −0.32 (±0.35) % per year, respectively. The urethra score was ultimately determined by combining data selected from both angle-dependent and angle-independent analysis strategies using a random forest regression model with age, yielding an R2 value of 0.96 and a p-value less than 0.001. The proposed mpUS tissue characterization technique not only holds promise for guiding future urethral tissue characterization studies without the need for tissue biopsies or invasive functional testing but also aims to minimize observer-induced variability. By leveraging mpUS imaging strategies that account for angle dependence, it provides more accurate assessments. Notably, the urethra score, calculated from US images that reflect tissue microstructural changes, serves as a potential biomarker providing clinicians with deeper insight into urethral tissue function and may aid in diagnosing and managing related conditions while helping to determine the causes of incontinence.

Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to death-one using a comprehensive database and another simpler model based on demographic and clinical data without laboratory tests. A retrospective cohort study was conducted from January 2017 to June 2023. Two models were created: Model A with 85 variables and Model B with 22 variables. We assessed the models using random forest (RF), support vector machine, and logistic regression, comparing their performance via the AU-ROC. The RF regression model was used to predict time to death. To identify the most relevant factors for prediction, the Shapley value method was used. Among 359 HD patients, the RF model provided the most reliable prediction. The optimized Model A showed an AU-ROC of 0.86 ± 0.07, a sensitivity of 0.86, and a specificity of 0.75 for predicting all-cause mortality. It also had an R2 of 0.59 for predicting time to death. The optimized Model B had an AU-ROC of 0.80 ± 0.06, a sensitivity of 0.81, and a specificity of 0.70 for predicting all-cause mortality. In addition, it had an R2 of 0.81 for predicting time to death. Two new interpretable clinical tools have been proposed to predict all-cause mortality and time to death in HD patients using machine learning models. The minimal and readily accessible data on which Model B is based makes it a valuable tool for integrating into clinical decision-making processes.

Machine Learning Approach to Investigate High Temperature Corrosion of Critical Infrastructure Materials

AbstractDegradation of coatings and structural materials due to high temperature corrosion in the presence of molten salt environment is a major concern for critical infrastructure applications to meet its commercial viability. The choice of high value coatings and structural (construction parts) materials comes with challenges, and therefore data centric approach may accelerate change in discovery and data practices. This research aims to use machine learning (ML) approach to estimate corrosion rates of materials when operated at high temperatures conditions (e.g., nuclear, geothermal, oxidation (dry/wet), solar applications) but geared towards nuclear thermochemical cycles. Published data related to materials (structural and coatings materials), their composition and manufacturing, including corrosion environment were gathered and analysed. Analysis demonstrated that random forest regression model is highly precise compared to other models. Assessment indicates that very limited sets of materials are likely to survive high temperature corrosive environment for extended period of exposure. While a higher quality and larger dataset are required to accurately predict the corrosion rate, the findings demonstrated the value of ML’s regression and data mining capabilities for corrosion data analysis. With the research gap in material selection strategies, proposed research will be critical to advancing data analytics approach exploiting their properties for high temperature corrosion applications. Graphical Abstract

Machine learning-enabled attapulgite/polyimide nanofiber composite aerogels-based colorimetric sensor array for real-time monitoring of balsa fish freshness

This paper presents the development and application of attapulgite/polyimide nanofiber composite aerogels (ATP/PI NFAs) integrated with a range of acid-base indicators, fabricated using electrospinning and freeze-drying technologies. A detailed characterization of the ATP/PI NFAs revealed a 3D multi-level pore structure that enhanced the mass transfer of target gas molecules and their interaction with probe molecules. Utilizing machine learning approaches, we designed an ATP/PI NFAs-based colorimetric sensor array capable of real-time evaluation of balsa fish freshness. Color features sensitive to changes in freshness were selected using principal component analysis and random forest. Partial least squares regression and random forest regression models were established, achieving the prediction of total volatile basic nitrogen content in balsa fish. The system was validated using a national standard method to demonstrate its accuracy and practicality. The combination of advanced ATP/PI NFAs-based colorimetric sensor array with robust machine learning models paves the way for food safety monitoring.