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The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability. [ABSTRACT FROM AUTHOR]

Nanoconfined water plays an important role in broad fields of science and engineering. Classical molecular dynamics (MD) simulations have been widely used to investigate water phases under nanoconfinement. The key ingredient of MD is the force field. In this study, we systematically investigated the performance of a recently introduced family of globally optimal water models, OPC and OPC3, and TIP4P/2005 in describing nanoconfined two-dimensional (2D) water ice. Our studies show that the melting points of the monolayer square ice (MSI) of all three water models are higher than the melting points of the corresponding bulk ice Ih. Under the same conditions, the melting points of MSI of OPC and TIP4P/2005 are the same and are ∼90 K lower than that of the OPC3 water model. In addition, we show that OPC and TIP4P/2005 water models are able to form a bilayer AA-stacked structure and a trilayer AAA-stacked structure, which are not the cases for the OPC3 model. Considering the available experimental data and first-principles simulations, we consider the OPC water model as a potential water model for 2D water ice MD studies. [ABSTRACT FROM AUTHOR]

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

Multi-scale predictive models for the shock sensitivity of energetic materials connect energy localization ("hotspots") in the microstructure to macro-scale detonation phenomena. Calculations of hotspot ignition and growth rely on models for chemical reaction rates expressed in Arrhenius forms; these chemical kinetic models, therefore, are foundational to the construction of physics-based, simulation-derived meso-informed closure (reactive burn) models. However, even for commonly used energetic materials (e.g., HMX in this paper) there are a wide variety of reaction rate models available. These available reaction rate models produce reaction time scales that vary by several orders of magnitude. From a multi-scale modeling standpoint, it is important to determine which model best represents the reactive response of the material. In this paper, we examine three global Arrhenius-form rate models that span the range of reaction time scales, namely, the Tarver 3-equation, the Henson 1-equation, and the Menikoff 1-equation models. They are employed in a meso-informed ignition and growth model which allows for connecting meso-scale hotspot dynamics to macro-scale shock-to-detonation transition. The ability of the three reaction models to reproduce experimentally observed sensitivity is assessed by comparing the predicted criticality envelope (Walker–Wasley curve) with experimental data for pressed HMX Class V microstructures. The results provide a guideline for model developers on the plausible range of time-to-ignition that are produced by physically correct Arrhenius rate models for HMX. [ABSTRACT FROM AUTHOR]

We present a theoretical model to study the effect of counterion size on the effective charge, size, and thermodynamic behavior of a single, isolated, and flexible polyelectrolyte (PE) chain. We analyze how altering counterion size modifies the energy and entropy contributions to the system, including the ion-pair free energy, excluded volume interactions, entropy of free and condensed ions, and dipolar attraction among monomer–counterion pairs, which result in competing effects challenging intuitive predictions. The PE self-energy is calculated using the Edwards–Muthukumar Hamiltonian, considering a Gaussian monomer distribution for the PE. The condensed ions are assumed to be confined within a cylindrical volume around the PE backbone. The dipolar and excluded volume interactions are described by the second and third virial coefficients. The assumption of freely rotating dipoles results in a first-order coil–globule transition of the PE chain. A more realistic, weaker dipolar attraction, parameterized in our theory, shifts it to a second-order continuous transition. We calculate the size scaling-exponent of the PE and find exponents according to the relative dominance of the electrostatic, excluded volume, or dipolar effects. We further identify the entropy- and energy-driven regimes of the effective charge and conformation of the PE, highlighting the interplay of free ion entropy and ion-pair energy with varying electrostatic strengths. The crossover strength, dependent on the counterion size, indicates that diminishing sizes favor counterion condensation at the expense of free ion entropy. The predictions of the model are consistent with trends in simulations and generalize the findings of the point-like counterion theories. [ABSTRACT FROM AUTHOR]

Portevin–Le Chatelier effect, associated with dynamic strain aging, is widely found in various alloys, and the maximum serration magnitude from the tensile stress–strain curve can be considered as the point closest to the failure threshold. Due to the difficulty to define solute and solvent atoms in multicomponent alloys, it is a great challenge to explore the serration mechanism. In this study, the atoms that play the role of pinning are defined as solute atoms. Aided by mean-field theory, an in situ pinning model coupled with the twinning effect in a CoNiV alloy with low stacking fault energies is successfully established, which can well predict the failure threshold, i.e., the maximum serration magnitude. The present study paves a new way to correlate the serration dynamics and in situ pinning, and further predicts the failure threshold upon loading for multicomponent high- and medium-entropy alloys. [ABSTRACT FROM AUTHOR]

Structural integrity and robustness are key parameters to evaluate microfabrication techniques. Bending and collapsing of 2D/3D microstructures are commonly noted in solvent-involved procedures, e.g., liquid-based post-treatment in wet-etching, lithography, and Two Photon Polymerizations (TPPs). Such structural failures are caused by excessive solution-imposed capillary forces, where multiple kinds of liquids may intensively participate. Current pieces of the literature focus on the mechanical one-axis models to illustrate their deformation process. To date, there exists an emerging demand for dual-axis models to satisfy rapidly developed micro/nano-engineerings. Here, utilizing polymer micro-pillars distributed in a square array as an illustration example, a dual-axis beam-sway model is proposed considering the influences of structure arrangement as well as the solvent. Specifically, a simplified criterion for judging structural stability is identified. For verifications, the TPP-based experimental data show excellent consistency with model predictions. All in all, the extended model offers reliable guidance for the fabrication of delicate structures and further benefits the optimization of related microfabrication processes. [ABSTRACT FROM AUTHOR]

A model predicting the number of emission sites and total current from a porous conical electrospray emitter as functions of voltage is derived. A pressure balance between capillary and electric forces is used to determine an onset criterion for individual menisci, and an ionic emission scaling law is invoked to predict the current each meniscus emits. These submodels are integrated over a phenomenological meniscus size distribution and the area of the emitter to yield a model for emitter performance as a function of five free parameters, two for the ionic emission submodel and three for the meniscus size distribution. Bayesian inference is applied to determine these model parameters from an existing dataset [Dressler et al., J. Propul. Power 38, 809 (2022)]. The model predictions after training are compared to the experimental data, and it is found that the majority of the data are within a 90% credible interval. The ability of the model to capture key trends in the experimental data is attributed to the interplay of two effects: the distribution over meniscus size on the emitter and the position-dependent electric field. The calibrated model results also suggest that the emitter surface is wetted by a series of large but sparsely distributed pools of propellant. The performance and extensibility of the model are examined within the context of model-based design for porous electrospray array thrusters. [ABSTRACT FROM AUTHOR]

Introduction: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation. Methods: We studied pseudo‐values based on the Aalen‐Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR‐IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR‐IPCW). The MLR‐IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. Results: The pseudo‐value, BLR‐IPCW, and MLR‐IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low‐density regions of predicted transition probability. Conclusions: We recommend implementing either the pseudo‐value or BLR‐IPCW approaches to produce a calibration curve, combined with the MLR‐IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN. [ABSTRACT FROM AUTHOR]

The unique structure of the human foot is seen as a crucial adaptation for bipedalism. The foot's arched shape enables stiffening the foot to withstand high loads when pushing off, without compromising foot flexibility. Experimental studies demonstrated that manipulating foot stiffness has considerable effects on gait. In clinical practice, altered foot structure is associated with pathological gait. Yet, experimentally manipulating individual foot properties (e.g. arch height or tendon and ligament stiffness) is hard and therefore our understanding of how foot structure influences gait mechanics is still limited. Predictive simulations are a powerful tool to explore causal relationships between musculoskeletal properties and whole-body gait. However, musculoskeletal models used in three-dimensional predictive simulations assume a rigid foot arch, limiting their use for studying how foot structure influences three-dimensional gait mechanics. Here, we developed a four-segment foot model with a longitudinal arch for use in predictive simulations. We identified three properties of the ankle-foot complex that are important to capture ankle and knee kinematics, soleus activation, and ankle power of healthy adults: (1) compliant Achilles tendon, (2) stiff heel pad, (3) the ability to stiffen the foot. The latter requires sufficient arch height and contributions of plantar fascia, and intrinsic and extrinsic foot muscles. A reduced ability to stiffen the foot results in walking patterns with reduced push-off power. Simulations based on our model also captured the effects of walking with anaesthetised intrinsic foot muscles or an insole limiting arch compression. The ability to reproduce these different experiments indicates that our foot model captures the main mechanical properties of the foot. The presented four-segment foot model is a potentially powerful tool to study the relationship between foot properties and gait mechanics and energetics in health and disease. Author summary: During every step, the foot absorbs the shock as it strikes the ground, supports the body weight, and transfers calf muscle forces to the ground to push off towards the next step. Although experiments have shown that foot structure affects both foot and whole-body movement, a deep understanding of the role of different muscles, ligaments, and other tissues in the foot is still lacking. Examining the relation between alterations in individual foot properties, e.g. muscle strength or arch height, and walking is challenging in experiments, but can be done in model-based simulations. Yet, existing models that are suitable for simulating walking oversimplify the foot. Here, we present a new, more detailed foot model. Simulations with our model captured many features of human walking not captured by simulations with simple models. We found that the stiffness of the foot-ankle complex and especially the ability to stiffen the foot had a large effect on the walking pattern. A reduced ability to stiffen the foot resulted in simulated walking patterns resembling those observed in patients with flexible feet. In the future, we will use our model to investigate how foot deformities alter walking in patients and to design personalized treatments. [ABSTRACT FROM AUTHOR]

In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique. [ABSTRACT FROM AUTHOR]

Operation at high current density of polymer electrolyte membrane electrolysis cell (PEMEC) can reduce its electrode area and capital expense. The high current density operation is limited by the limiting current density (LCD), where electrolysis voltage abruptly increases. Effects of operating temperature and pressure on the LCD were evaluated in a lab-scale PEMEC within a range from 70 to 90 °C and from 0.1 to 0.3 MPa. Also, a theoretical model was developed to predict the LCD, and to clarify what determines LCD and how the operating conditions influence the LCD. The theoretical analysis suggests that when current density is close to the LCD, water mass transport at anode reaches a limitation, which indicates that water saturation reaches almost zero at anodic catalyst layer. The analysis also finds that lower operating temperature and higher operating pressure can reduce oxygen gas saturation and raise the water saturation in porous transport layer at anode, and finally enlarge the LCD. • Limiting current density (LCD) is decided by mass transport limitation. • Oxygen gas saturation near catalyst layer is close to 1 at LCD. • Bubble overvoltage abruptly rises at LCD. • Lower temperature and higher pressure enlarge LCD. [ABSTRACT FROM AUTHOR]

This article reports on development of an adaptive framework for predicting the erosion performance of polymer composites using certain statistical and machine learning (ML) models. For this, ramie‐epoxy composites reinforced with variations (0–30 wt%) of sponge iron slag (an iron industry waste) are considered. The composites are fabricated and then subjected to high temperature solid particle erosion wear trials following Taguchi's L27 orthogonal array. The effects of different control factors on the erosion rate in an interactive environment are appraised by analysis of variance (ANOVA) which reveals the filler content as the most significant factor contributing 66.21%, followed by impact velocity (22.86%) and impingement angle (2.28%). A regression model based on the input–output parameters obtained from experimentation is constructed for prediction of erosion rate. Further, four predictive models using different machine learning algorithms are also proposed to predict the erosion rate of the composites. The feasibility and performance of each ML model is assessed using appropriate performance metrics. Among all the models, the gradient boosting machine model is found to be the most reliable model exhibiting the highest prediction accuracy and least errors. Highlights: Development of novel class of composites reinforced with sponge iron slag.Database creation based on erosion wear experimentation on the composites.Data‐driven modeling for prediction of erosion rates using machine learning.Comparison of performance of different models and identifying the best one. [ABSTRACT FROM AUTHOR]

Despite documented associations between social determinants of health and outcomes post–congenital heart surgery, clinical risk models typically exclude these factors. The study sought to characterize associations between social determinants and operative and longitudinal mortality as well as assess impacts on risk model performance. Demographic and clinical data were obtained for all congenital heart surgeries (2006-2021) from locally held Congenital Heart Surgery Collaborative for Longitudinal Outcomes and Utilization of Resources Society of Thoracic Surgeons Congenital Heart Surgery Database data. Neighborhood-level American Community Survey and composite sociodemographic measures were linked by zip code. Model prediction, discrimination, and impact on quality assessment were assessed before and after inclusion of social determinants in models based on the 2020 Society of Thoracic Surgeons Congenital Heart Surgery Database Mortality Risk Model. Of 14,173 total index operations across New York State, 12,321 cases, representing 10,271 patients at 8 centers, had zip codes for linkage. A total of 327 (2.7%) patients died in the hospital or before 30 days, and 314 children died by December 31, 2021 (total n = 641; 6.2%). Multiple measures of social determinants of health explained as much or more variability in operative and longitudinal mortality than clinical comorbidities or prior cardiac surgery. Inclusion of social determinants minimally improved models' predictive performance (operative: 0.834-0.844; longitudinal 0.808-0.811), but significantly improved model discrimination; 10.0% more survivors and 4.8% more mortalities were appropriately risk classified with inclusion. Wide variation in reclassification was observed by site, resulting in changes in the center performance classification category for 2 of 8 centers. Although indiscriminate inclusion of social determinants in clinical risk modeling can conceal inequities, thoughtful consideration can help centers understand their performance across populations and guide efforts to improve health equity. [Display omitted] [ABSTRACT FROM AUTHOR]

Tensile testing (aka tension testing) is a widely employed mechanical testing technique for analyzing materials' properties and behavior under applied stress. Tensile testing plays a pivotal role in helping engineers to make informed decision about material selection and usage. Despite its importance, there is a limited numbers of studies that explored the potential of AI techniques for real time monitoring and material behavior prediction in tensile testing. To this end, this work presents a deep learning model designed to predict the material's condition throughout tensile testing and provide an early warning prior to fracture. By leveraging a comprehensive dataset of tension test video samples, the proposed model utilizes both convolution and recurrent neural networks to extract pertinent spatial and temporal visual features, thereby predicting the frames at which material deformation and fracture occur. The evaluation results of our research showed that the proposed model achieved a predictive ability with an F1-score of 97%, on average. The implications of our research are significant for industries and researchers in the field of materials science and engineering. By accurately predicting material status, our model enables autonomous, real time analysis of material behavior during tensile testing, leading to better time and cost efficiency in various applications. [ABSTRACT FROM AUTHOR]

To achieve smart and sustainable construction goals, machine learning (ML) techniques can serve as a cost-effective and efficient substitute for labour-intensive, laboratory, or in situ approaches in parameter estimation essential for infrastructure design. Of these, soil's shear strength parameters, notably cohesion (c) and friction angle (φ), typically govern the design of geo-structures. For quick and cost-effective estimation of these parameters, the earlier studies proposed ML-based predictive models that were less practical and accurate or consider an excessive number of input variables. To minimize these limitations, our study proposes new models of c and φ using gene expression programming (GEP) based on readily available soil attributes such as sand content (S), depth (D), specific gravity (Gs), liquid limit (LL), plastic limit (PL), and fine content (FC). The newly proposed models show excellent accuracy as the values of R2, RMSE (root mean square error), MAE (mean absolute error), RSE (relative standard error) for c-predictive model were 0.984, 1.13, 0.878, 0.017, respectively, and were 0.927, 1.123, 0.922, 0.072, respectively, for φ-predictive model. Through sensitivity analysis, FC and LL emerged as the most critical parameters influencing c, while Gs and PL proved sensitive for determining φ. In comparison with existing models, the c-predictive model displays R2 enhancements of 11.84–45.87% and RMSE improvements of 65.9–92.03%, while the φ-predictive model showcases R2 gains of 13.16–23.75% and RMSE improvements of 58.79–69.29%. By integrating predictive prowess with sustainable and smart construction principles, our study plots a realistic course for efficient geo-structural design. [ABSTRACT FROM AUTHOR]

Objective: To compare how different imputation methods affect the estimates and performance of a prediction model for premature mortality. Study Design and Setting: Sex-specific Weibull accelerated failure time survival models were run on four separate datasets using complete case, mode, single and multiple imputation to impute missing values. Six performance measures were compared to access predictive accuracy (Nagelkerke R2, integrated brier score), discrimination (Harrell's c-index, discrimination slope) and calibration (calibration in the large, calibration slope). Results: The highest proportion of missingness for a single variable was 10.86% for the female model and 8.24% for the male model. Comparing the performance measures for complete case, mode, single and multiple imputation: the Nagelkerke R2 values for the female model was 0.1084, 0.1116, 0.1120 and 0.111–0.1120 with the male model exhibited similar variation of 0.1050, 0.1078, 0.1078 and 0.1078–0.1081. Harrell's c-index also demonstrated small variation with values of 0.8666, 0.8719, 0.8719 and 0.8711–0.8719 for the female model and 0.8549, 0.8548, 0.8550 and 0.8550–0.8553 for the male model. Conclusion: In the scenarios examined in this study, mode imputation performed well when using a population health survey compared to single and multiple imputation when predictive performance measures is the main model goal. To generate unbiased hazard ratios, multiple imputation methods were superior. This study shows the need to consider the best imputation approach for a predictive model development given the conditions of missing data and the goals of the analysis. [ABSTRACT FROM AUTHOR]

Background: Ubiquitin/ubiquitin-like (Ub/UBL)-related genes have been reported to be associated with the survival of osteosarcoma patients but have not yet been systematically explored. Methods: The prognostic value of Ub/UBL-related genes, immune cell infiltration and clinicopathological features of patients were explored by Cox and LASSO regression analyses. A prognostic model was established and then validated in the GSE21257 dataset. The differential expression of hub genes in osteosarcoma was confirmed by qRT-PCR, western blotting and immunohistochemistry. Results: Tripartite Motif Containing 8 (TRIM8) and Ubiquitin Like With PHD And Ring Finger Domains 2 (UHRF2) were screened as genes with prognostic value in osteosarcoma. Kaplan–Meier analysis and scatter plots indicated that patients in the high gene significance score group tended to have a worse prognosis. The concordance index, calibration analysis and receiver operating characteristic analysis suggested that the model had good prediction accuracy and high sensitivity and specificity. Decision curve analysis revealed that patients could obtain greater net benefit from this model. Functional analyses of the differentially expressed genes indicated that they were involved in important functions and pathways. TRIM8 and UHRF2 were confirmed to be highly expressed in osteosarcoma cell lines and tissues. Conclusions: TRIM8 and UHRF2 are potential prognostic genes in osteosarcoma, and these results provide insights into the roles of these genes and their implications for patient outcomes. [ABSTRACT FROM AUTHOR]

Summary: Primary immune thrombocytopenia (ITP) is linked to specific pathogenic mechanisms, yet its relationship with mitophagy and ferroptosis is poorly understood. This study aimed to identify new biomarkers and explore the role of mitophagy and ferroptosis in ITP pathogenesis. Techniques such as differential analysis, Mfuzz expression pattern clustering, machine learning, gene set enrichment analysis, single‐cell RNA sequencing (scRNA‐seq) and immune infiltration analysis were employed to investigate the molecular pathways of pivotal genes. Two‐sample Mendelian randomization (TSMR) assessed the causal effects in ITP. Key genes identified in the training set included GABARAPL1, S100A8, LIN28A, and GDF9, which demonstrated diagnostic potential in validation sets. Functional analysis indicated these genes' involvement in ubiquitin phosphorylation, PPAR signalling pathway and T‐cell differentiation. Immune infiltration analysis revealed increased macrophage presence in ITP, related to the critical genes. scRNA‐seq indicated reduced GABARAPL1 expression in ITP bone marrow macrophages. TSMR linked S100A8 with ITP diagnosis, presenting an OR of 0.856 (95% CI = 0.736–0.997, p = 0.045). The study pinpointed four central genes, GABARAPL1, S100A8, LIN28A, and GDF9, tied to mitophagy and ferroptosis in ITP. It posits that diminished GABARAPL1 expression may disrupts ubiquitin phosphorylation and PPAR signalling, impairing mitophagy and inhibiting ferroptosis, leading to immune imbalance. [ABSTRACT FROM AUTHOR]

Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis. [ABSTRACT FROM AUTHOR]

With the plentiful information available in the Gaia BP/RP spectra, there is significant scope for applying discriminative models to extract stellar atmospheric parameters and abundances. We describe an approach to leverage an 'Uncertain Neural Network' model trained on APOGEE data to provide high-quality predictions with robust estimates for per-prediction uncertainty. We report median formal uncertainties of 0.068 dex, 69.1 K, 0.14 dex, 0.031 dex, 0.040 dex, and 0.029 dex for [Fe/H], T eff, log  g , [C/Fe], [N/Fe], and [α/M], respectively. We validate these predictions against our APOGEE training data, LAMOST, and Gaia GSP- phot stellar parameters, and see a strong correlation between our predicted parameters and those derived from these surveys. We investigate the information content of the spectra by considering the 'attention' our model pays to different spectral features compared to expectations from synthetic spectra calculations. Our model's predictions are applied to the Gaia data set, and we produce a publicly available catalogue of our model's predictions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has profoundly impacted healthcare systems and economies worldwide, leading to the implementation of travel restrictions and social measures. Efforts such as vaccination campaigns, testing, and surveillance have played a crucial role in containing the spread of the virus and safeguarding public health. There needs to be more research exploring the transmission dynamics of COVID-19, particularly within European nations. Therefore, the primary objective of this research was to examine the spread patterns of COVID-19 across various European countries. Doing so makes it possible to implement preventive measures, allocate resources, and optimize treatment strategies based on projected case and mortality rates. For this purpose, a hybrid prediction model combining CNN and LSTM models was developed. The performance of this hybrid model was compared against several other models, including CNN, k-NN, LR, LSTM, MLP, RF, SVM, and XGBoost. The empirical findings revealed that the CNN-LSTM hybrid model exhibited superior performance compared to alternative models in effectively predicting the transmission of COVID-19 within European nations. Furthermore, examining the peak of case and death dates provided insights into the dynamics of COVID-19 transmission among European countries. Chord diagrams were drawn to analyze the inter-country transmission patterns of COVID-19 over 5-day and 14-day intervals. [ABSTRACT FROM AUTHOR]

This article investigates an observer-based robust model predictive control (RMPC) design to control the uncertain discrete-time linear systems with disturbances. To make a more practical scheme, it is supposed that the uncertain system has been faced with unknown disturbance and input constraints. The proposed RMPC approach is based on a state feedback control design that ensures the H ∞ performance criterion to attenuate disturbance affections. Furthermore, in view of practical application, the control law is constructed based on the estimated states obtained from the Luenberger observer. Based on Lyapunov's theory, the input to state practically stability (ISPS) of the closed-loop system is ensured. Appropriate conditions for the ISPS of the closed-loop system and the estimation error are obtained in terms of online linear matrix inequalities (LMIs) which lead to obtaining the time-varying gain matrices of both controller and observer. Finally, to validate the obtained results, the proposed approach is applied to a numerical example and it is compared with the existing control scheme and the superiority is proved. [ABSTRACT FROM AUTHOR]

Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]

Background: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions. Methods: A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts. Results: The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis. Conclusions: In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes. [ABSTRACT FROM AUTHOR]

Background: To analyze the risk factors for the development of avascular necrosis (AVN) of the femoral head after reduction surgery in children with developmental hip dysplasia (DDH), and to establish a prediction nomogram. Methods: The clinical data of 134 children with DDH (169 hips) treated with closure reduction or open reduction from December 2016 to December 2019 were retrospectively analyzed. Independent risk factors for AVN after DDH reduction being combined with cast external immobilization were determined by univariate analysis and multivariate logistic regression and used to generate nomograms predicting the occurrence of AVN. Results: A total of 169 hip joints in 134 children met the inclusion criteria, with a mean age at surgery of 10.7 ± 4.56 months (range: 4–22 months) and a mean follow-up duration of 38.32 ± 27.00 months (range: 12–94 months). AVN developed in 42 hip joints (24.9%); univariate analysis showed that the International Hip Dysplasia Institute (IHDI) grade, preoperative development of the femoral head ossification nucleus, cartilage acetabular index, femoral head to acetabular Y-shaped cartilage distance, residual acetabular dysplasia, acetabular abduction angle exceeding 60°, and the final follow-up acetabular index (AI) were associated with the development of AVN (P < 0.05). Multivariate logistic regression analysis showed that the preoperative IHDI grade, development of the femoral head ossification nucleus, acetabular abduction angle exceeding 60°, and the final follow-up AI were independent risk factors for AVN development (P < 0.05). Internal validation of the Nomogram prediction model showed a consistency index of 0.833. Conclusion: Preoperative IHDI grade, preoperative development of the femoral head ossification nucleus, final AI, and acetabular abduction angle exceeding 60° are risk factors for AVN development. This study successfully constructed a Nomogram prediction model for AVN after casting surgery for DDH that can predict the occurrence of AVN after casting surgery for DDH. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a chronic disease of unknown etiology that lacks a specific treatment. In IPF, macrophages play a key regulatory role as a major component of the lung immune system, especially during inflammation and fibrosis. However, our understanding of the cellular heterogeneity and molecular characterization of macrophages in IPF, as well as their relevance in the clinical setting, is relatively limited. In this study, we analyzed in-depth single-cell transcriptome sequencing (scRNA-seq) data from lung tissues of IPF patients, identified macrophage subpopulations in IPF, and probed their molecular characteristics and biological functions. hdWGCNA identified co-expressed gene modules of a subpopulation of IPF-associated macrophages (IPF-MΦ), and probed the IPF-MΦ by a machine-learning approach. hdWGCNA identified a subpopulation of IPF-associated macrophage subpopulations and probed the IPF-MΦ signature gene (IRMG) for its prognostic value, and a prediction model was developed on this basis. In addition, IPF-MΦ was obtained after recluster analysis of macrophages in IPF lung tissues. Coexpressed gene modules of IPF-MΦ were identified by hdWGCNA. Then, a machine learning approach was utilized to reveal the characteristic genes of IPF-MΦ, and a prediction model was built on this basis. In addition, we discovered a type of macrophage unique to IPF lung tissue named ATP5-MΦ. Its characteristic gene encodes a subunit of the mitochondrial ATP synthase complex, which is closely related to oxidative phosphorylation and proton transmembrane transport, suggesting that ATP5-MΦ may have higher ATP synthesis capacity in IPF lung tissue. This study provides new insights into the pathogenesis of IPF and provides a basis for evaluating disease prognosis and predictive medicine in IPF patients. [ABSTRACT FROM AUTHOR]

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional l 2 -norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods. [ABSTRACT FROM AUTHOR]

Purpose: Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). Methods: A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. Results: Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. Conclusions: We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy. [ABSTRACT FROM AUTHOR]

To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients' readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients. [ABSTRACT FROM AUTHOR]

The shock index (SI) has been associated with predicting transfusion needs in trauma patients. However, its utility in penetrating thoracic trauma (PTTrauma) for predicting the Critical Administration Threshold (CAT) has not been well-studied. This study aimed to evaluate the prognostic value of SI in predicting CAT in PTTrauma patients and compare its performance with the Assessment of Blood Consumption (ABC) and Revised Assessment of Bleeding and Transfusion (RABT) scores. We conducted a prognostic type 2, single-center retrospective observational cohort study on patients with PTTrauma and an Injury Severity Score (ISS) > 9. The primary exposure was SI at admission, and the primary outcome was CAT. Logistic regression and decision curve analysis were used to assess the predictive performance of SI and the PTTrauma score, a novel model incorporating clinical variables. Of the 620 participants, 53 (8.5%) had more than one CAT. An SI > 0.9 was associated with CAT (adjusted OR 4.89, 95% CI 1.64–14.60). The PTTrauma score outperformed SI, ABC, and RABT scores in predicting CAT (AUC 0.867, 95% CI 0.826–0.908). SI is a valuable predictor of CAT in PTTrauma patients. The novel PTTrauma score demonstrates superior performance compared to existing scores, highlighting the importance of developing targeted predictive models for specific injury patterns. These findings can guide clinical decision-making and resource allocation in the management of PTTrauma. [ABSTRACT FROM AUTHOR]

Background: Numerous studies have developed or validated prediction models aimed at estimating the likelihood of amputation in diabetic foot (DF) patients. However, the quality and applicability of these models in clinical practice and future research remain uncertain. This study conducts a systematic review and assessment of the risk of bias and applicability of amputation prediction models among individuals with DF. Methods: A comprehensive search was conducted across multiple databases, including PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedical Literature Database (CBM), and Weipu (VIP) from their inception to December 24, 2023. Two investigators independently screened the literature and extracted data using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability. Results: A total of 20 studies were included in this analysis, comprising 17 development studies and three validation studies, encompassing 20 prediction models and 11 classification systems. The incidence of amputation in patients with DF ranged from 5.9 to 58.5%. Machine learning-based methods were employed in more than half of the studies. The reported area under the curve (AUC) varied from 0.560 to 0.939. Independent predictors consistently identified by multivariate models included age, gender, HbA1c, hemoglobin, white blood cell count, low-density lipoprotein cholesterol, diabetes duration, and Wagner's Classification. All studies were found to exhibit a high risk of bias, primarily attributed to inadequate handling of outcome events and missing data, lack of model performance assessment, and overfitting. Conclusions: The assessment using PROBAST revealed a notable risk of bias in the existing prediction models for amputation in patients with DF. It is imperative for future studies to concentrate on enhancing the robustness of current prediction models or constructing new models with stringent methodologies. [ABSTRACT FROM AUTHOR]

Research has substantiated that the presence of outliers in data usually introduces additional errors and biases, which typically leads to a degradation in the precision of volatility forecasts. However, correcting outliers can mitigate these adverse effects. This study corrects the additive outliers through a weighting method and let these corrected values to replace the original outliers. Then, the model parameters are re-estimated based on this new return series. This approach reduces the extent to which outliers distort volatility estimates, allowing the model to better adapt to market conditions and improving the accuracy of volatility forecasts. This study introduces this approach for the first time to generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) models, so as to establish an additional outliers corrected GARCH-MIDAS model (AO-GARCH-MIDAS). This pioneering approach marks a unique innovation. The research employs a diverse array of evaluation methods to validate the model's robustness and consistently demonstrates its dependable performance. Findings unequivocally reveal the substantial influence of outliers on the model's predictive capacity, with the AO-GARCH-MIDAS model exhibiting consistent superiority across all evaluation criteria. Additionally, while the GARCH model showcases stronger estimation capabilities compared to the GARCH-MIDAS model, the latter demonstrates heightened predictive prowess. Notably, regarding variable selection, the results underscore the greater predictive informational value inherent in realized volatility over other low-frequency factors. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a pressing need for improved therapeutic response and prognosis prediction. This study delves into a novel predictive model related to ferroptosis, a regulated cell death mechanism disrupting metabolic processes. Results: Single-cell sequencing data analysis identified subpopulations of HCC cells exhibiting activated ferroptosis and distinct gene expression patterns compared to normal tissues. Utilizing the LASSO-Cox algorithm, we constructed a model with 10 single-cell biomarkers associated with ferroptosis, namely STMN1, S100A10, FABP5, CAPG, RGCC, ENO1, ANXA5, UTRN, CXCR3, and ITM2A. Comprehensive analyses using these biomarkers revealed variations in immune infiltration, tumor mutation burden, drug sensitivity, and biological functional profiles between risk groups. Specific associations were established between particular immune cell subtypes and certain gene expression patterns. Treatment response analyses indicated potential benefits from anti-tumor immune therapy for the low-risk group and chemotherapy advantages for the high-risk group. Conclusions: The integration of this single-cell level model with clinicopathological features enabled accurate overall survival prediction and effective risk stratification in HCC patients. Our findings illuminate the potential of ferroptosis-related genes in tailoring therapy and prognosis prediction for HCC, offering novel insights into the intricate interplay among ferroptosis, immune response, and HCC progression. [ABSTRACT FROM AUTHOR]

The thermal management of proton exchange membrane fuel cell crucially impacts the performance and durability of fuel cell vehicle systems. Optimized temperature control is critical to the water balance and electrochemical reaction inside the fuel cell stack under dynamic operating conditions. This work develops a dynamic thermal management system model with uncertain parameters for the dual coolant circulation fuel cell system to simulate unforeseeable disturbances in real systems. With the proposed model, the tube-based robust model predictive control (tube-RMPC) is innovatively applied to optimize dynamic thermal management. This strategy can identify the optimal trajectories of thermostat angle, heater power, water pump speed, and cooling fan speed, thus maximizing the temperature dynamic response speed while minimizing the stack temperature difference fluctuations in the presence of unforeseeable disturbances. The result shows that during the load ascending and descending operations, the temperature dynamic response time is 8 and 15 s, with temperature fluctuations below 1 °C. The root mean square error for stack temperature is reduced by 10.4% and 24.4% compared to the closed-loop MPC. The fuel cell system exhibits excellent temperature dynamic response capability using the tube-RMPC method. • Tube-RMPC is applied in fuel cell vehicle systems to effectively suppress stack temperature fluctuations. • Robust dynamic thermal management strategy is proposed to determine optimal action trajectories of critical TMS components. • Proposed method controls temperatures at different stack locations precisely under rapid load changes. • Robustness of tube based MPC under unpredictable disturbances is validated. [ABSTRACT FROM AUTHOR]

Background: Diabetic kidney disease (DKD) is the main cause of end-stage renal disease and has a high mortality rate. Currently, no effective treatments are available to reduce the progression of kidney damage associated with diabetes. Objectives: To explore the influence and predictive value of the atherogenic index of plasma (AIP) on early chronic kidney disease and liver injury in patients with type 2 diabetes mellitus (T2DM). Methods: Medical records of 1057 hospitalized adult patients with T2DM between January 2021 and December 2022 were collected. The predictive value of AIP, renal function, and liver injury in patients with T2DM were analyzed using Pearson's correlation, multiple logistic regression, and receiver operating characteristic (ROC) curve analyses. Results: AIP was a sensitive indicator of early liver and kidney injury in patients with T2DM. Patients in the DKD group showed increased AIP that positively correlated with serum creatinine, uric acid, and β2-microglobulin levels. Increased AIP negatively correlated with estimated glomerular filtration rate (eGFR). AIP significantly correlated with alanine aminotransferase and aspartate aminotransferase levels and glutamyl transpeptidase-to-platelet ratio (GPR). An eGFR of 60-100 mL/min/1.73 m2 significantly increased the risk of DKD as the AIP increased. At lower GPR levels, the risk of DKD significantly increased with increasing AIP. However, no significant difference was found between the 2 groups when the GPR was >0.1407. The ROC curve analysis showed that AIP could predict early liver injury. Conclusions: AIP is directly involved in early liver and kidney injury in T2DM and may be a sensitive indicator for early detection. Plain Language Summary: Diabetes and its complications are a global public health concern. Diabetic kidney disease (DKD) is the main cause of end-stage renal disease, and metabolism-related disease factors are found throughout the progression of DKD. This study identified common sensitive indicators of early metabolism-related damage to liver and kidney function in patients with T2DM. [ABSTRACT FROM AUTHOR]

Background: Postoperative urinary retention (POUR), a common condition after prolapse surgery with potential serious sequelae if left untreated, lacks a clearly established optimal timing for catheter removal. This study aimed to develop and validate a predictive model for postoperative urinary retention lasting > 2 and > 4 days after prolapse surgery. Methods: We conducted a retrospective review of 1,122 patients undergoing prolapse surgery. The dataset was divided into training and testing cohorts. POUR was defined as the need for continuous intermittent catheterization resulting from a failed spontaneous voiding trial, with passing defined as two consecutive voids ≥ 150 mL and a postvoid residual urine volume ≤ 150 mL. We performed logistic regression and the predicted model was validated using both training and testing cohorts. Results: Among patients, 31% and 12% experienced POUR lasting > 2 and > 4 days, respectively. Multivariable logistic model identified 6 predictors. For predicting POUR, internal validation using cross-validation approach showed good performance, with accuracy lasting > 2 (area under the curve [AUC] 0.73) and > 4 days (AUC 0.75). Split validation using pre-separated dataset also showed good performance, with accuracy lasting > 2 (AUC 0.73) and > 4 days (AUC 0.74). Calibration curves demonstrated that the model accurately predicted POUR lasting > 2 and > 4 days (from 0 to 80%). Conclusions: The proposed prediction model can assist clinicians in personalizing postoperative bladder care for patients undergoing prolapse surgery by providing accurate individual risk estimates. [ABSTRACT FROM AUTHOR]