Development Of Predictive Models Research Articles

Subsidence associated with dewatering and gas extraction from coal seams: Contribution of desorption-induced coal shrinkage

Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management.

Precision forecasting of fertilizer components’ concentrations in mixed variable-rate fertigation through machine learning

Accurate monitoring of fertilizer concentration and variable irrigation components is crucial for achieving precision irrigation through variable-rate fertigation. However, technological and cost constraints pose challenges in effectively managing mixed variable-rate fertigation. This study addresses these challenges by integrating machine learning (ML) with easily monitored physical parameters, such as electrical conductivity (EC), pH, and temperature, to predict fertilizer solution components and concentrations in mixed variable-rate fertigation. Cubic spline interpolation (CSI) was used to enhance the training dataset. Six ML algorithms—Multivariate Linear Regression (MLR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETs), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGB)—were used to develop prediction models, with their performance evaluated using the coefficient of determination (R2), root mean square error (RMSE), and Akaike information criterion (AIC). The Extended Fourier Amplitude Sensitivity Test (EFAST) assessed the sensitivity of the ML models to physical parameters. All of the ML models, except for MLR, particularly SVM, demonstrated superior performance in predicting fertilizer solution components’ concentrations, with R2 values between 0.989 and 0.997 and RMSE values between 0.089 and 0.210. The CSI significantly enhanced model performance, resulting in larger R2 values and smaller RMSE values. The AIC results and sensitivity analysis confirmed the exceptional performance of SVM, emphasizing its suitability for predicting fertilizer components using easily measured physical parameters. The developed ML models offer valuable insights for decision-makers managing irrigation and fertilization in mixed variable-rate fertigation.

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

The Unmanned Aerial Vehicle (UAV) swarm cooperative networks across diverse domains present significant challenges in management and control to maximize collaborative efficiency, particularly in applications for large-scale and dynamic environments where real-time coordination is essential. This challenge has led to the development of a UAV swarm cooperative network flow prediction model, facilitating the strategic anticipation of network dynamics. A novel algorithm for distributed, finite-time cooperative control is introduced, grounded in this model. This algorithm optimizes the operational efficiency and control precision of UAV systems, while simultaneously augmenting the collaborative efficiency and stability of the UAV swarms. It marks a significant advancement in distributed computing strategies, offering a viable solution for the real-time coordination challenges in extensive UAV swarm cooperative networks. The proposed approach leverages multi-agent technology, integrating spatial-temporal constraints, location limitations, and path selection in cooperative behavior for dynamic and effective network management. This study not only enhances the understanding of UAV swarm dynamics but also contributes to the practical application of UAV swarms in areas requiring precise coordination and robust network stability.

Prediction of compressive strength of cross‐shaped stub CFSTs under axial loading: Numerical and analytical study

AbstractSpecial‐shaped concrete‐filled steel tubes (CFSTs) have been used in modern structures like high‐rise commercial and residential buildings due to their superior structural performance compared to steel and reinforced concrete members. Various shapes of special‐shaped CFSTs might be necessary to meet architectural and aesthetic needs. Cross‐shaped CFSTs could be used where two orthogonal walls cross in high‐rise buildings. However, at present, the research on the compressive performance of cross‐shaped CFSTs is limited, consequently, the unavailability of design guidelines and design‐oriented strength prediction models. Therefore, in this study, a finite element (FE) model of cross‐shaped CFSTs was developed following the past experimental data, and the model's accuracy was verified by the failure modes and load–strain curves of specimens. Sensitivity analysis was performed for some parameters of the concrete damaged plasticity model besides imperfections and residual stress. The parametric analysis was conducted considering various study parameters such as the width‐to‐thickness ratio, width‐to‐depth ratio, and steel and concrete strengths. The compressive strength of cross‐shaped CFSTs was predicted by different design codes and available design formulas, which gave unsatisfactory results necessitating the development of new strength prediction models. Finally, a new design formula was developed by performing a linear regression of FE and test results. The proposed formula predicted the strength of cross‐shaped CFSTs with great accuracy and can be used for design purposes.

Development and Validation of Machine Learning-Based Predictive Model for Prolonged Hospital Stay after Decompression Surgery for Lumbar Spinal Canal Stenosis

Development and Validation of Machine Learning-Based Predictive Model for Prolonged Hospital Stay after Decompression Surgery for Lumbar Spinal Canal Stenosis

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling.

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Machine learning for development of ECG-based prediction models for atrial fibrillation, thromboembolism and mortality in patients with heart failure

Abstract Background Heart failure (HF) is a worldwide epidemic affecting more than 64 million people. Given its massive burden both socially and economically, early identification of individuals at risk for complications is important. Purpose To test the predictive performance of non-invasive models from 12-lead digitalized electrocardiography (ECG) and cycle ergometer for adverse outcome in individuals with heart failure. Methods 598 Patients scheduled for right heart catheterization due to unexplained dyspnea were screened. All ECG recorded at admission were further analysed using the software "ECG-Precision-Analysis V-1-2023" to measure the duration of amplified p-wave (APWD at 175mm/sec, 80mm/mV) and to confirm the diagnosis of inter-atrial block. Ergometer was performed in a supine position with increasing intensity adapted to individual capacity (25 or 50W). The maximum physical capacity (Watt-max) was determined. Follow-up (FU) was scheduled at 12-,36-, 60- and 120-month after discharge with the primary endpoint as MACCE (a composite of new onset of AF, ischemic stroke and peripheral embolism) in at baseline event-free patients and the secondary endpoint as all-cause mortality in the total cohort. Three machine learning (ML) algorithms (COX regression, BSR and LASSO regression) were used to select candidate variables and establish predictive models for MACCE and all-cause mortality. Model evaluation was performed in terms of discriminatory power using C-statistics for each endpoint. Results Prediction models developed using machine learning (ML) algorithms (COX regression, BSR and LASSO regression) identified Watt-max and/or APWD as core components for MACCE and mortality prediction . APWD and Watt-max were key components of ML models for prediction of thromboembolic risk and AF (MACCE) and reached accurate diagnostic performances (C-statistics up to 0.74), moreover, another model consisted of APWD and aIAB (advanced inter-atrial block) was established to serve as a reference to ML models with moderate performance for MACCE (mean C-statistics <0.70). All ML models for prediction of all-cause mortality included Watt-max and achieved C-statistics > 0.75. Conclusions In patients with heart failure, the combination of APWD, aIAB and maximum excercice capacity allow to identify patients at risk for AF and stroke, whereas mortality is predominantly predicted by maximum exercise capacity but not APWD.Kaplan-Meier curves for MACCE/MortalitytimeROC curves for MACCE/Mortality

The Effect of Menopausal Status, Insulin Resistance and Body Mass Index on the Prevalence of Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is common and presents in a large proportion—up to 30%—of the global adult female population. Several factors have been linked with NAFLD in women, such as age, obesity, and metabolic syndrome. To extract appropriate details about the topic, we conducted an extensive search using various medical subject headings and entry terms including ‘Menopause’, ‘Non-alcoholic fatty liver disease’, ‘Insulin resistance’, and ‘BMI’. This exhaustive search resulted in a total of 180 studies, among which only 19 were able to meet the inclusion criteria. While most of these studies indicated a significant rise in NAFLD prevalence among postmenopausal women, two did not find strong evidence linking menopause with NAFLD. Moreover, it was observed that women with NAFLD had higher insulin resistance levels and BMIs compared to those without the condition. In summary, it is important to consider specific factors like risk profile, hormonal status, and age along with metabolic components when treating women presenting with NAFLD. There is need for data-driven research on how gender affects the sensitivity of biomarkers towards NAFLD as well as the development of sex-specific prediction models—this would help personalize management approaches for women, who stand to benefit greatly from such tailored interventions.

Calibration and development of safety performance functions for two-way stop-control intersections on rural two-lane highways in Louisiana

The first edition of the Highway Safety Manual (HSM) contains a simplistic version of the crash prediction model for two-way stop-controlled intersections (TWSC) on rural two-lane highways. This model considers AADT on major and minor roads, with the base conditions defined as no intersection skewness, no turning lanes, and no lighting. A crash modification factor (CMF) will be applied if an intersection has conditions different from the base condition. However, the HSM model does not take account of curvature. It is well known that curved TWSC intersections are less safe than non-curved ones, particularly on rural two-lane roadways. This paper presents the development of crash prediction models incorporating intersection geometrics for TWSC intersections on rural two-lane highways in Louisiana. Then, it compares the results from the developed model with the calibrated HSM model. The negative binomial model was used with 5126 TWSC intersections verified one by one, including both three- and four-leg intersections from all parishes (counties). The estimation results indicate that AADT, curve radius, and intersection skewness angle significantly impact expected crash frequency for both three- and four-leg intersections. This research utilizes cumulative residual plots, mean absolute error, and root mean square error for comparative analysis of HSM models, HSM models with calibration, and Louisiana-specific models. The results show that Louisiana-specific SPFs outperformed the calibrated SPFs with superior reliability. Calibration factors of 0.58 for three-leg intersections and 0.46 for four-leg intersections are estimated, suggesting that the original HSM model overpredicts crashes in Louisiana.

Development and validation of a machine learning-based prediction model for sudden cardiac death in the general population: insights from the community cardiovascular cohort

Abstract Background/Introduction Accurately predicting and managing the risk of sudden cardiac death (SCD) in the general population is a formidable challenge. Purpose This study aimed to develop and validate a machine learning (ML)-based prediction model for SCD using data from the Community Cardiovascular Cohort (CCCC) in Taiwan. Methods A community-based cohort study was used for model construction . Four ML algorithms, including extreme gradient boosting (XGBoost), random forests (RF), logistic regression, and deep neural network (DNN), underwent meticulous evaluation. Performance assessment encompassed receiver operating characteristic (ROC) curves, area under the curves (AUC), and accuracy metrics. Significant risk factors were identified through feature importance analysis. Results We employed a random partitioning strategy, allocating 1744 participants (55%) to the training dataset, 952 participants (30%) to the test dataset, and 476 participants (15%) to the validation dataset. In the data training stage, the XGBoost algorithm demonstrated outstanding prediction performance for SCD (AUC: 1.000); In the validation stage, the XGBoost algorithm demonstrated good prediction performance for SCD prediction (AUC: 0.791, 95%CI: 0.617-0.964; Accuracy: 0.971). Using XGBoost-generated predicted probabilities (P(XGB)), we identified individuals with P(XGB) ≥ 0.5 (Group 1) who displayed significantly higher SCD risk (HR 32.0; 95% CI: 9.29-109.9) compared to those with P(XGB) < 0.5 (Group 0). Conclusions Our study demonstrates the effectiveness of ML algorithms, particularly XGBoost, in predicting event of SCD in the general population. The identification of these pivotal risk factors holds profound implications, empowering the identification of high-risk individuals and facilitating the implementation of precisely tailored preventive strategies.Figure 1:KM sruvival curveFigure 2:Feature importance analysis

Machine learning–based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images

PurposeThis study aimed to assess tumor regression grade (TRG) in patients with rectal cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning–based radiomics analysis using baseline T2-weighted magnetic resonance (MR) images.Materials and methodsIn total, 148 patients with locally advanced rectal cancer(T2-4 or N+) who underwent MR imaging at baseline and after chemoradiotherapy between January 2010 and May 2021 were included. A region of interest for each tumor mass was drawn by a radiologist on oblique axial T2-weighted images, and main features were selected using principal component analysis after dimension reduction among 116 radiomics and three clinical features. Among eight learning models that were used for prediction model development, the model showing best performance was selected. Treatment responses were classified as either good or poor based on the MR-assessed TRG (mrTRG) and pathologic TRG (pTRG). The model performance was assessed using the area under the receiver operating curve (AUROC) to classify the response group.ResultsApproximately 49% of the patients were in the good response (GR) group based on mrTRG (73/148) and 26.9% based on pTRG (28/104). The AUCs of clinical data, radiomics models, and combined radiomics with clinical data model for predicting mrTRG were 0.80 (95% confidence interval [CI] 0.73, 0.87), 0.74 (95% CI 0.66, 0.81), and 0.75(95% CI 0.68, 0.82), and those for predicting pTRG was 0.62 (95% CI 0.52, 0.71), 0.74 (95% CI 0.65, 0.82), and 0.79 (95% CI 0.71, 0.87).ConclusionRadiomics combined with clinical data model using baseline T2-weighted MR images demonstrated feasible diagnostic performance in predicting both MR-assessed and pathologic treatment response in patients with rectal cancer after NCRT.

Forecasting Criminal Activity Using Machine Learning Approaches

Predicting criminal activity has long been a challenge for law enforcement agencies worldwide. Traditional methods often rely on historical data and human intuition, which may be limited in their accuracy and scope. In recent years, machine learning techniques have emerged as promising tools for forecasting criminal activity by leveraging large-scale datasets and advanced algorithms. This paper presents a novel machine learning approach to forecasting criminal activity, focusing on the development and evaluation of predictive models using various data sources, including crime reports, demographic information, and environmental factors. We explore the application of supervised and unsupervised learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, to identify patterns and trends in crime data. Furthermore, we discuss the challenges and ethical considerations associated with deploying predictive models in real-world law enforcement settings, emphasizing the importance of transparency, fairness, and accountability. Through empirical analysis and case studies, we demonstrate the potential of machine learning techniques to enhance crime prediction and prevention efforts, providing valuable insights for policymakers, law enforcement agencies, and researchers in the field of criminal justice.

Reactivity and Steric Parameters from 2D to 3D Bulky Pyridines : Increasing Steric Demand at Nitrogen with Chiral Azatriptycenes

Sterically hindered pyridines embedded in a three‐dimensional triptycene framework have been synthesized, and their resolution by chiral HPLC enabled access to unprecedented enantiopure pyridines exceeding the known steric limits. The design principles for new axially chiral pyridine derivatives are then described. To rationalize their associations with Lewis acids and transition metals, a comprehensive determination of the steric and electronic parameters for this new class of pyridines was performed. This led to the general parameterization of the steric parameters (percent buried volume %VBur, Tolman cone angle θ, and He8_steric descriptor) for a large set of two‐ and three‐dimensional pyridine derivatives. These parameters are shown to describe quantitatively their interactions with carbon‐ and boron‐centered Lewis acids and were used to predict the ΔG° of association with the prototypical B(C6F5)3 Lewis acid widely used in frustrated Lewis pair catalysis. This first parameterization of pyridine sterics is a fundamental basis for the future development of predictive reactivity models and for guiding new applications of bulky and chiral pyridines in organocatalysis, frustrated Lewis pairs, and transition‐metal catalysis.

Reactivity and Steric Parameters from 2D to 3D Bulky Pyridines : Increasing Steric Demand at Nitrogen with Chiral Azatriptycenes.

Sterically hindered pyridines embedded in a three-dimensional triptycene framework have been synthesized, and their resolution by chiral HPLC enabled access to unprecedented enantiopure pyridines exceeding the known steric limits. The design principles for new axially chiral pyridine derivatives are then described. To rationalize their associations with Lewis acids and transition metals, a comprehensive determination of the steric and electronic parameters for this new class of pyridines was performed. This led to the general parameterization of the steric parameters (percent buried volume %VBur, Tolman cone angle θ, and He8_steric descriptor) for a large set of two- and three-dimensional pyridine derivatives. These parameters are shown to describe quantitatively their interactions with carbon- and boron-centered Lewis acids and were used to predict the ΔG° of association with the prototypical B(C6F5)3 Lewis acid widely used in frustrated Lewis pair catalysis. This first parameterization of pyridine sterics is a fundamental basis for the future development of predictive reactivity models and for guiding new applications of bulky and chiral pyridines in organocatalysis, frustrated Lewis pairs, and transition-metal catalysis.

#1584 Development and validation of risk prediction models according to different clinical settings of IgA nephropathy: nationwide multicenter cohort study

Abstract Background and Aims IgA nephropathy (IgAN) is the most common glomerulonephritis with a high risk of progression to end-stage renal disease (ESRD). Because machine learning models developed for research have limited use in clinical settings, this study developed and validated prediction models for IgAN according to different clinical settings. Method In this study, we analyzed data from 1,174 patients in the Japanese Nationwide Retrospective Cohort Study in IgAN, focusing on a 10-year observation period. We defined the derivation data from January 2002 to April 2004, and temporal validation data from May 2004 to December 2004. The composite renal outcome was defined as a 1.5-fold increase in creatinine or progression to ESRD. We developed and evaluated the three prediction models according to the different clinical settings: the primary care model for general physicians in primary care settings, the tertiary care model for specialists in tertiary care hospitals, and the research institute model, a machine learning-based approach for academic research institute settings. Results After excluding 82 patients for missing data, 114 and 14 patients experienced the composite renal outcome in the derivation (n = 874) and validation (n = 218) cohorts, respectively. The primary care model identified three variables—eGFR <45 mL/min/1.73 m², proteinuria ≥0.5 g/day, and non-use of corticosteroids—as significant predictors (C-statistic: 0.796; 95% CI: 0.686-0.895). The tertiary care model composed of three variables including glomerular number, histological severity and tubular/interstitial severity showed a slightly higher C-statistic (0.807; 95% CI: 0.713-0.886). When stratifying patients into risk groups, the primary care model showed a higher incidence of composite renal events in high-risk patients, and the tertiary care model effectively discriminated outcomes in all groups. The research institute model, which included 38 variables including proteinuria level, hypertension, eGFR, and non-use of corticosteroids as key predictors, showed comparable performance (C-statistic: 0.808; 95% CI: 0.689-0.886). Conclusion The primary care and tertiary care models provide simple yet effective predictive tools, competitive with the machine learning-based research institution model. These models are critical for predicting renal outcomes in patients with IgA nephropathy and for patient management according to different clinical settings.

#560 The representation of multimorbidity and frailty in the development and validation of kidney failure risk prediction models – a systematic review

Abstract Background and Aims Prognostic models that identify individuals with chronic kidney disease (CKD) at highest risk of developing kidney failure help clinicians to make decisions and deliver precision medicine. People with CKD are very likely to have multiple long-term health conditions (multimorbidity) and often experience frailty. These factors impact progression of kidney disease and influence the risk of other outcomes such as death. It is unclear to what extent prognostic models, that estimate the risk of kidney failure, consider the impact of multimorbidity and frailty and whether these models are valid in such sub-populations. This systematic review (CRD42022347295) describes and evaluates the representation of multimorbidity and frailty within cohorts used to develop and/or validate prognostic models assessing the risk of kidney failure in individuals with CKD. The review aims to determine if multimorbidity or frailty has been considered in relation to prediction of kidney failure and if reliable prognostic models exist for use in this population. Method We included studies that described the derivation, validation or update of a kidney failure prognostic model (outcome assessed at ≥2 years) for use in adults with CKD and reported at least one measure of either discrimination or calibration. The primary outcome for the review was the representation of multimorbidity or frailty in these models. An electronic search for published peer-reviewed articles involved MEDLINE, CINAHL Plus and the Cochrane Library – CENTRAL and was supplemented with manual review of references from previous systematic reviews and clinical guidelines. Results A total of 97 studies, reporting 121 different kidney failure prognostic models were identified. A total of 2 925 413 participants and 149 380 kidney failure events were included across all studies. Included participants had a mean age of 58.9 years (SD 9.6), 44.4% were female, with a mean eGFR of 47.5 ml/min/1.73 m2 (SD 10.7). Only two studies reported multimorbidity, measured by Elixhauser Comorbidity Index and Charlson Co-morbidity Index (CCI), and a single study reported a self-reported proxy frailty measure. The rates of specific co-morbidities were reported in a greater proportion of studies: 67.0% (n = 65) studies reported baseline data on diabetes, 54.6% (n = 53) reported hypertension, and 39.2% (n = 38) reported cardiovascular disease. No studies included frailty in model development and only one study considered multimorbidity as a predictor variable (via CCI). No studies assessed model performance in populations with multimorbidity. A single study assessed clinical utility of a model and referral algorithms considering frailty and the risk of kidney failure and death. Conclusion There is a paucity of kidney failure risk prediction models that consider the impact of multimorbidity and/or frailty in adults with CKD, resulting in a lack of clear evidence-based practice and guidance for multimorbid or frail individuals. These knowledge gaps should be explored to help clinicians know whether these models can be used for patients who experience multimorbidity and/or frailty.

Integrating hybrid PV/wind-based electric vehicles charging stations with green hydrogen production in Kentucky through techno-economic assessment

The study presents a comprehensive analysis of the integration of Photovoltaic (PV)/wind systems with electric vehicle (EV) charging stations, enhanced by green hydrogen production based on existing EV load profiles across Kentucky. It explores the use of excess PV energy to charge Lithium-ion batteries, potentially supplemented by on-grid power during periods of lower electricity costs. The research includes the development of predictive models for hydrogen production and its subsequent use in hydrogen-powered vehicles. The techno-economic assessment reveals that such integration exhibits consistent economic viability, with minimal variations in key financial indicators like the levelized cost of electricity, payback period, and PV/wind energy fraction. The study also highlights green hydrogen production for on-grid PV/wind power charging the electrolyzer leads to the highest production cost (∼13 USD/Kg) compared to off-peak grid and a grid/PV/wind combination (∼6 USD/Kg). The projected hydrogen output is poised to support a significant number of light and heavy-duty vehicles by 2040, indicating promising potential for low-carbon transportation in Kentucky.

Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches

IntroductionOpen globe injuries (OGI) represent a main preventable reason for blindness and visual impairment, particularly in developing countries. The goal of this study is evaluating key variables affecting the prognosis of open globe injuries and validating internally and comparing different machine learning models to estimate final visual acuity.Materials and methodsWe reviewed three hundred patients with open globe injuries receiving treatment at Khatam-Al-Anbia Hospital in Iran from 2020 to 2022. Age, sex, type of trauma, initial VA grade, relative afferent pupillary defect (RAPD), zone of trauma, traumatic cataract, traumatic optic neuropathy (TON), intraocular foreign body (IOFB), retinal detachment (RD), endophthalmitis, and ocular trauma score (OTS) grade were the input features. We calculated univariate and multivariate regression models to assess the association of different features with visual acuity (VA) outcomes. We predicted visual acuity using ten supervised machine learning algorithms including multinomial logistic regression (MLR), support vector machines (SVM), K-nearest neighbors (KNN), naïve bayes (NB), decision tree (DT), random forest (RF), bagging (BG), adaptive boosting (ADA), artificial neural networks (ANN), and extreme gradient boosting (XGB). Accuracy, positive predictive value (PPV), recall, F-score, brier score (BS), Matthew correlation coefficient (MCC), receiver operating characteristic (AUC-ROC), and calibration plot were used to assess how well machine learning algorithms performed in predicting the final VA.ResultsThe artificial neural network (ANN) model had the best accuracy to predict the final VA. The sensitivity, F1 score, PPV, accuracy, and MCC of the ANN model were 0.81, 0.85, 0.89, 0.93, and 0.81, respectively. In addition, the estimated AUC-ROC and AUR-PRC of the ANN model for OGI patients were 0.96 and 0.91, respectively. The brier score and calibration log-loss for the ANN model was 0.201 and 0.232, respectively.ConclusionAs classic and ensemble ML models were compared, results shows that the ANN model was the best. As a result, the framework that has been presented may be regarded as a good substitute for predicting the final VA in OGI patients. Excellent predictive accuracy was shown by the open globe injury model developed in this study, which should be helpful to provide clinical advice to patients and making clinical decisions concerning the management of open globe injuries.

Development of a Cost Prediction Model for Design Changes: Case of Korean Apartment Housing Projects

Apartment buildings are significantly popular among South Korean construction companies. However, design changes present a common yet challenging aspect, often leading to cost overruns. Traditional cost prediction methods, which primarily rely on numerical data, have a gap in fully capitalizing on the rich insights that textual descriptions of design changes offer. Addressing this gap, this research employs machine learning (ML) and natural language processing (NLP) techniques, analyzing a dataset of 35,194 instances of design changes from 517 projects by a major public real estate developer. The proposed models demonstrate acceptable performance, with R-square values ranging from 0.930 to 0.985, underscoring the potential of integrating structured and unstructured data for enhanced predictive analytics in construction project management. The predictor using Extreme Gradient Boosting (XGB) shows better predictive ability (R2 = 0.930; MAE = 16.05; RMSE = 75.09) compared to the traditional Multilinear Regression (MLR) model (R2 = 0.585; MAE = 43.85; RMSE = 101.41). For whole project cost changes predictions, the proposed models exhibit good predictive ability, both including price fluctuations (R2 = 0.985; MAE = 605.1; RMSE = 1009.5) and excluding price fluctuations (R2 = 0.982; MAE = 302.1; RMSE = 548.5). Additionally, a stacked model combining CatBoost and Support Vector Machine (SVM) algorithms was developed, showcasing the effective prediction of cost changes, with or without price fluctuations.

Antepartum Intimate Partner Violence: Development of a Risk Prediction Model.

Objectives: To explore socio-behavioral, clinical, and imaging findings associated with antepartum intimate partner violence (IPV) and aid in risk stratification of at-risk individuals. Methods: We analyzed electronic medical records during indexed pregnancies for 108 pregnant patients who self-reported antepartum IPV (cases) and 106 age-matched pregnant patients who did not self-report antepartum IPV (controls). Sociodemographic, clinical, and radiology data were analyzed via chi-squared and Fisher's exact tests with p < 0.05 as the threshold for significance. Stepwise logistic regression was applied to derive a risk prediction model. Results: The proportion of cases reporting emotional IPV (76% vs. 52%) and/or physical IPV (45% vs. 31%) during pregnancy significantly increased from prior to pregnancy. Cases were significantly more likely to report prepregnancy substance use (odds ratio [OR] = 2.60; 95% confidence interval [CI]: 1.13-5.98), sexually transmitted infections (OR = 3.48; 95%CI: 1.64-7.37), abortion (OR = 3.17; 95%CI: 1.79, 5.59), and preterm birth (OR = 5.97; 95%CI: 1.69-21.15). During pregnancy, cases were more likely to report unstable housing (OR = 5.26; 95%CI: 2.67-10.36), multigravidity (OR = 2.83; 95%CI: 1.44-5.58), multiparity (OR = 3.75; 95%CI: 1.72-8.20), anxiety (OR = 3.35; 95%CI: 1.85-6.08), depression (OR = 5.58; 95%CI: 3.07-10.16), substance use (OR = 2.92; 95%CI: 1.28-6.65), urinary tract infection (UTI) (OR = 3.26; 95%CI: 1.14-9.32), intrauterine growth restriction (OR = 10.71; 95%CI: 1.35-85.25), and cesarean delivery (OR = 2.25; 95%CI: 1.26-4.02). Cases had significantly more OBGYN abnormalities on imaging and canceled more radiological studies (OR = 5.31). Logistic regression found housing status, sexually transmitted infection history, preterm delivery history, abortion history, depression, and antepartum UTI predictive of antepartum IPV. The risk prediction model achieved good calibration with an area under the curve of 0.79. Conclusions: This study identifies significant disparities among patients experiencing antepartum IPV, and our proposed risk prediction model can inform risk assessment in this setting.