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This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogeneous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR's role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy. [ABSTRACT FROM AUTHOR]

The consistency and duration of the menstrual cycle exhibit significant associations with specific psychiatric conditions throughout an individual’s lifespan. The proposed methodology surveys the relationship between psychiatric disorders and the length or regularity of the menstrual cycle and analyzes the difficulties undergone by the women. A comprehensive dataset is generated and a mathematical model using an exploratory data analytics approach is developed, in order to establish a correlation between these variables. It utilizes a cyclic methodology, leveraging shared menstrual data and a predictive model derived from vehicles to enhance network learning. A decentralized secure learning procedure is implemented to ensure data privacy and security. The transfer learning techniques helps to enhance the ability to learn from diverse data distributions in IoMT (Internet of Medical Things) networks, improve the robustness of the learning process. This approach presents a practical and effective solution for IoMT network learning, allowing each participant to contribute their individual features to collectively extract valuable insights from the data. The decentralization facilitates end-users in accessing their personal medical records while ensuring privacy, irrespective of their location and time. This system also achieves a minimal delay sensitivity of 3.2%, by providing timely access to the required information. [ABSTRACT FROM AUTHOR]

This research presents a novel fault-tolerant predictive power control method for a Doubly-fed induction generator (DFIG) used in wind turbine control systems. Due to the proposed control mechanism, the system can continue to function effectively despite open-circuit or short-circuit faults in the insulated-gate bipolar transistors (IGBTs) of the MPC controller. Depending on the type of problem and its location, the tolerant IGBT overcomes power oscillations and limits the power converter’s potential switching states. By monitoring the optimal generator speed, wind turbine control systems strive to maximize power output. For wind turbines operating in the partial-load area, a fault-tolerant model predictive control strategy is recommended in order to achieve control goals despite disturbances, uncertainties, sensor, and actuator difficulties. A high order sliding mode observer (HOSMO) is used to evaluate both the actual states and sensor-faults at the same time. A high order sliding mode (HOSM) control strategy based on the MPC controller is used to regulate the speed of wind turbines in order to harness the wind’s maximum power. [ABSTRACT FROM AUTHOR]

Accurately predicting lane changes, a crucial driving activity for preventing accidents and ensuring driver safety, is addressed in this study. An innovative predictive model that integrates game theory for precise lane change intention detection and an optimized Convolutional Neural Network (CNN) for trajectory prediction is proposed in this study. The CNN’s efficiency is enhanced through metaheuristic optimization of both the convolution and fully connected layers using the Whale Optimization Algorithm (WOA). Emphasizing robust data processing, a Wiener filter is applied for pre-processing, and the Cascaded Fuzzy C means (CFCM) technique is employed for segmentation. The resulting Whale Optimization Algorithm-based CNN (WOA-CNN) effectively forecasts the trajectory of lane-changing vehicles. Validation of the proposed approach in Python demonstrates exceptional accuracy, reaching 96.5%. This study showcases the effectiveness of the WOA-CNN model in advancing the prediction accuracy of lane-changing behaviour, contributing to enhanced driver safety and accident prevention. [ABSTRACT FROM AUTHOR]

Road traffic crashes are becoming thorny issues being faced worldwide. Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out. However, the impact of built environment on traffic crash spatiotemporal trends has not received much attention. Moreover, the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected. To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes, this paper proposed a method of "ST-GWLR" for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression (GWLR) modeling methods. Firstly, the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model, hotspot analysis, and Mann-Kendall trend test. Then, the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression (GLR) model, to improve the accuracy of the model estimation. GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors, to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes. The performance of the GWLR models and GLR models was examined and compared sufficiently. The results showed that the proposed ST-GWLR, which captured spatial non-stationarity, performed better than the classic GLR combined with spatiotemporal analysis, and improved the prediction accuracy of the models by 14.9%, 13.9%, and 15.1%, respectively. There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors. The findings of this paper have positive implications for traffic safety management and urban built environment planning. [ABSTRACT FROM AUTHOR]

In structural concrete, diagonal tension failure is a significant mode of shear failure in slender flexural members. As a result, an accurate estimate of the slender beam's resistance to shear is required. This study presents a statistical characterisation of the uncertainty associated with various resistance predictive models for slender stirrupless beams with a distinct mode of failure (diagonal tension failure). The investigated models include code- and literature-based shear models. Through a comparative analysis of statistical moments and probabilistic representative models of the uncertainty factor using a substantial number of test cases, the study found that the mechanical model proposed by Tran (2020) demonstrated the best consistency in predicting shear strength. It exhibited a mean of 1.02, standard deviation of 0.16, and coefficient of variation of 16%. To effectively manage the uncertainties identified in the EC2 and AS3600–18 shear models, model uncertainty partial and reduction factors following the EN1990 guidelines were proposed for design practices. However, concerns remain regarding the performance of other shear models, necessitating further reliability investigation. [ABSTRACT FROM AUTHOR]

AbstractAimMethodsResultsConclusionsMachine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aims to utilize machine learning models to detect high-risk groups of excessive health and social services utilization at early stage, facilitating the implementation of preventive interventions.We used pseudonymized data covering a four-year period and including information on a total of 33,374 senior citizens from Southern Finland. The endpoint was defined based on the occurrence of unplanned healthcare visits and the total number of different services used. Input features included individual’s basic demographics, health status and past usage of healthcare resources. Logistic regression and eXtreme Gradient Boosting (XGBoost) methods were used for binary classification, with the dataset split into 70% training and 30% testing sets.Subgroup-based results mirrored trends observed in the full cohort, with age and certain health issues, e.g. mental health, emerging as positive predictors for high service utilization. Conversely, hospital stay and urban residence were associated with decreased risk. The models achieved a classification performance (AUC) of 0.61 for the full cohort and varying in the range of 0.55–0.62 for the subgroups.Predictive models offer potential for predicting future high service utilization in the older adult population. Achieving high classification performance remains challenging due to diverse contributing factors. We anticipate that classification performance could be increased by including features based on additional data categories such as socio-economic data. [ABSTRACT FROM AUTHOR]

Machine learning is a computer technique that automatically learns from experience and enhances the effectiveness of producing more precise diabetes predictions. However, large, inclusive, high-quality datasets are needed for training the machine learning networks. In this research work, attention-based approaches are designed for predicting diabetes in the affected individuals. Initially, the collected diabetes data is given into the data cleaning to get noise-free data for the prediction task. Here, extracted feature set 1 is extracted from the Auto encoder, and extracted feature set 2 is extracted from the 1-Dimensional Convolutional Neural Network (1D-CNN). These two sets of extracted features are fused in the adaptive way that is weighted feature fusion. Here, the weight of the selected features is optimized by an Enhanced Path Finder Algorithm (EPFA) to get more accurate results. The weighted fused features are employed for the diabetes prediction phase, in which the developed Attention-based Long Short Term Memory (ALSTM) with architecture optimization by improved PFA for predicting diabetes in affected one. Throughout the result analysis, the designed method attains 95% accuracy and 92%precision rate. Finally, the analysis is made by the proposed and existing prediction methods to showcase the effective performance. [ABSTRACT FROM AUTHOR]

The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients. Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA). The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets. The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients. [ABSTRACT FROM AUTHOR]

Intradialytic hypotension (IDH) is a common and serious complication in patients with Maintenance Hemodialysis (MHD). The purpose of this study is to externally verify three IDH risk prediction models recently developed by Ma et al. and recalibrate, update and present the optimal model to improve the accuracy and applicability of the model in clinical environment. A multicenter prospective cohort study of patients from 11 hemodialysis centers in Sichuan Province, China, was conducted using convenience sampling from March 2022 to July 2022, with a follow-up period of 1 month. Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of updating the model. The final cohort comprised 2235 individuals undergoing maintenance hemodialysis, exhibiting a 14.6% occurrence rate of IDH. The externally validated Area Under the Curve (AUC) values for the three original prediction models were 0.746 (95% CI: 0.718 to 0.775), 0.709 (95% CI: 0.679 to 0.739), and 0.735 (95% CI: 0.706 to 0.764) respectively. Conversely, the AUC value for the recalibrated and updated columnar plot model reached 0.817 (95% CI: 0.791 to 0.842), accompanied by a Brier score of 0.081. Furthermore, Decision Curve Analysis (DCA) exhibited a net benefit within the threshold probability range of 15.2% to 87.1%. Externally validated, recalibrated, updated, and presented IDH prediction models may serve as a valuable instrument for evaluating IDH risk in clinical practice. Furthermore, they hold the potential to guide clinical providers in discerning individuals at risk and facilitating judicious clinical intervention decisions. [ABSTRACT FROM AUTHOR]

The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life. This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model. 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure. Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model's area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer–Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927). The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies. [ABSTRACT FROM AUTHOR]

Vascular calcification (VC) is highly prevalent in patients undergoing hemodialysis, and is a significant contributor to the mortality rate. Therefore, biomarkers that can accurately predict the onset of VC are urgently required. Our study aimed to investigate serum miR-15a levels in relation to VC and to develop a predictive model for VC in patients undergoing hemodialysis at the Beijing Friendship Hospital hemodialysis center between 1 January 2019 and 31 December 2020. The patients were categorized into two groups: VC and non-VC. Logistic regression (LR) models were used to examine the risk factors associated with VC. Additionally, we developed an miR-15a-based nomogram based on the results of the multivariate LR analysis. A total of 138 patients under hemodialysis were investigated (age: 58.41 ± 13.22 years; 54 males). VC occurred in 79 (57.2%) patients. Multivariate LR analysis indicated that serum miR-15a, age, and WBC count were independent risk factors for VC. A miR-15a-based nomogram was developed by incorporating the following five predictors: age, dialysis vintage, predialysis nitrogen, WBC count, and miR-15a. The receiver operating characteristic (ROC) curve had an area under the curve of 0.921, diagnostic threshold of 0.396, sensitivity of 0.722, and specificity of 0.932, indicating that this model had good discrimination. This study concluded that serum miR-15a levels, age, and white blood cell (WBC) count are independent risk factors for VC. A nomogram constructed by integrating these risk factors can be used to predict the risk of VC in patients undergoing hemodialysis. [ABSTRACT FROM AUTHOR]

The International IgA Nephropathy (IgAN) Network developed and validated two prognostic prediction models for IgAN, one incorporating a race parameter. These models could anticipate the risk of a 50% reduction in estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease (ESRD) subsequent to an IgAN diagnosis via renal biopsy. This investigation aimed to validate the International IgA Nephropathy Prediction Tool (IIgANPT) within a contemporary Chinese cohort. Within this study,185 patients diagnosed with IgAN via renal biopsy at the Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, between January 2012 and December 2021, were encompassed. Each patient's risk of progression was assessed utilizing the IIgANPT formula. The primary outcome, a 50% decline in eGFR or progression to ESRD, was examined. Two predictive models, one inclusive and the other exclusive of a race parameter, underwent evaluation via receiver-operating characteristic (ROC) curves, subgroup survival analyses, calibration plots, and decision curve analyses. The median follow-up duration within our cohort spanned 5.1 years, during which 18 patients encountered the primary outcome. The subgroup survival curves exhibited distinct separations, and the comparison of clinical and histological characteristics among the risk subgroups revealed significant differences. Both models demonstrated outstanding discrimination, evidenced by the areas under the ROC curve at five years: 0.882 and 0.878. Whether incorporating the race parameter or not, both prediction models exhibited acceptable calibration. Decision curve analysis affirmed the favorable clinical utility of both models. Both prognostic risk evaluation models for IgAN exhibited remarkable discrimination, sound calibration, and acceptable clinical utility. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP. Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation. We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM's performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903). The GBM model's precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care. [ABSTRACT FROM AUTHOR]

Cross-validation is a widely used technique to estimate prediction error, but its behavior is complex and not fully understood. Ideally, one would like to think that cross-validation estimates the prediction error for the model at hand, fit to the training data. We prove that this is not the case for the linear model fit by ordinary least squares; rather it estimates the average prediction error of models fit on other unseen training sets drawn from the same population. We further show that this phenomenon occurs for most popular estimates of prediction error, including data splitting, bootstrapping, and Mallow's C p . Next, the standard confidence intervals for prediction error derived from cross-validation may have coverage far below the desired level. Because each data point is used for both training and testing, there are correlations among the measured accuracies for each fold, and so the usual estimate of variance is too small. We introduce a nested cross-validation scheme to estimate this variance more accurately, and show empirically that this modification leads to intervals with approximately correct coverage in many examples where traditional cross-validation intervals fail. Lastly, our analysis also shows that when producing confidence intervals for prediction accuracy with simple data splitting, one should not refit the model on the combined data, since this invalidates the confidence intervals. for this article are available online. [ABSTRACT FROM AUTHOR]

The shear behaviour of fibre-reinforced polymer reinforced concrete (FRP-RC) beams without web reinforcement suffers from low strength, low stiffness, more brittleness and wide and quick propagated cracks. Fortunately, the addition of various types of fibres could improve most of these weaknesses. On the other hand, the shear strength prediction of FRP-RC beams with various types of fibres is one of the most complex cases in structural engineering applications. Developing generalised, precise and consistent prediction models are necessary and very limited. This paper investigates the impacts of various types of fibres on shear strength and presents proposing four new prediction models, utilising artificial neural networks and empirical nonlinear regression analysis, and modifying the combination of available models based on a collected database of 49 shear test results of FRP-RC members with various types of fibres. The comparison of the developed models with the available equations from the literature indicates that the developed models yielded excellent performance, great efficiency and a high level of accuracy over all other existing models. Additionally, the parametric study confirmed that all the developed models have great abilities to accurately predict the actual response of each parameter, in spite of its complexity, on the shear strength of FRP-reinforced fibrous concrete beams without stirrups. [ABSTRACT FROM AUTHOR]

Optimal nutritional support in childhood cancer relies on the adequate provision of energy. This study investigated the impact of chemotherapy on resting energy expenditure (REE) during the first six months of treatment and the accuracy of predictive equations in calculating said requirements of newly diagnosed children with cancer. REE was measured at diagnosis utilising a validated bioelectrical impedance analysis (BIA) mobile unit and compared with three predictive equations (Schofield 1985, World Health Organization [WHO] 1985 and the Recommended Dietary Allowance [RDA] 1989). Agreement and accuracy of these equations were tested by determining bias and agreement rates and displayed using the Bland–Altman plot. Baseline values were plotted against monthly follow-up measurements over time. Statistical significance was 5% and a priori limits of agreement set between 90% and 110% of measured REE. Forty-three newly diagnosed children with median age 4 years (IQR 2.0–7.6) were measured prior to chemotherapy initiation. Compared with measured REE (mean ± SD) 719.53 ± 206.29 kcal/day, all predictive equations significantly overestimated REE: WHO 1985 (889.75 ± 323.31 kcal/day; 23% overestimation), Schofield 1985 (899.62 ± 336.10 kcal/day; 25% overestimation) and RDA (1647.67 ± 481.06 kcal/day; 129% overestimation) (p < 0.001). Despite significant proportionate bias in all three equations (p < 0.001), the intra-class consistency coefficient showed good reliability for the Schofield 1985 (0.864) and WHO 1985 (0.849) equations. Though statistically significant (chi-square = 23.11, p < 0.003), the overall 1 kcal/kg (1.3%) increase for all cancer types at six months may not be clinically significant. Existing predictive equations are unable to calculate REE accurately at childhood cancer diagnosis, highlighting the need for future investigations into the development of cancer-specific equations. [ABSTRACT FROM AUTHOR]

This study investigates the effect of the black level and color gamut on the overall display brightness using a psychophysical experiment. Two stimuli with different color gamut were matched until the overall brightness appeared the same by controlling the peak display luminance. Three experimental sessions were conducted with varying black levels and color gamut. The results indicate that the effect of the color gamut on perceived brightness was significant, unlike that of the black level. The matched luminance increased by 6% to 8% as the color gamut widened from sRGB to DCI-P3. The equivalent luminance prediction models were tested using the average stimuli luminance, exhibiting poor performances. [ABSTRACT FROM AUTHOR]

Sentiment analysis is critical for classifying users on social media and reviewing products through comments and reviews. At the same time, rating prediction is a popular and valuable topic in research on recommendation systems. This study improves the accuracy of ratings in recommendation systems through the combination of rating prediction and sentiment analysis from customer reviews. New ratings have been generated based on original ratings and sentiment analysis. Experimental results show that in almost all cases, revised ratings using a deep learning-based algorithm called LightGCN on 7 various real-life datasets improve rating prediction. In particular, rating prediction metrics (RMSE and MAE, R2, and explained variance) of the proposed approach (with revised ratings) are better than those of the typical approach (with unrevised ratings). Furthermore, evaluating ranking metrics (also top-k item recommendation metrics) for this model also shows that our proposed approach (with revised ratings) is much more effective than the original approach (with unrevised ratings). Our significant contribution to this research is to propose a better rating prediction model that uses a supplement factor sentiment score to enhance the accuracy of rating prediction. [ABSTRACT FROM AUTHOR]

Out-of-sample prediction is the acid test of predictive models, yet an independent test dataset is often not available for assessment of the prediction error. For this reason, out-of-sample performance is commonly estimated using data splitting algorithms such as cross-validation or the bootstrap. For quantitative outcomes, the ratio of variance explained to total variance can be summarized by the coefficient of determination or in-sample R2, which is easy to interpret and to compare across different outcome variables. As opposed to in-sample R2, out-of-sample R2 has not been well defined and the variability on out-of-sample R ̂ 2 has been largely ignored. Usually only its point estimate is reported, hampering formal comparison of predictability of different outcome variables. Here we explicitly define out-of-sample R2 as a comparison of two predictive models, provide an unbiased estimator and exploit recent theoretical advances on uncertainty of data splitting estimates to provide a standard error for R ̂ 2 . The performance of the estimators for R2 and its standard error are investigated in a simulation study. We demonstrate our new method by constructing confidence intervals and comparing models for prediction of quantitative Brassica napus and Zea mays phenotypes based on gene expression data. Our method is available in the R-package oosse. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP. Methods: Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation. Results: We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM’s performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903). Conclusions: The GBM model’s precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care. [ABSTRACT FROM AUTHOR]

Objective: Intradialytic hypotension (IDH) is a common and serious complication in patients with Maintenance Hemodialysis (MHD). The purpose of this study is to externally verify three IDH risk prediction models recently developed by Ma et al. and recalibrate, update and present the optimal model to improve the accuracy and applicability of the model in clinical environment. Methods: A multicenter prospective cohort study of patients from 11 hemodialysis centers in Sichuan Province, China, was conducted using convenience sampling from March 2022 to July 2022, with a follow-up period of 1month. Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of updating the model. Results: The final cohort comprised 2235 individuals undergoing maintenance hemodialysis, exhibiting a 14.6% occurrence rate of IDH. The externally validated Area Under the Curve (AUC) values for the three original prediction models were 0.746 (95% CI: 0.718 to 0.775), 0.709 (95% CI: 0.679 to 0.739), and 0.735 (95% CI: 0.706 to 0.764) respectively. Conversely, the AUC value for the recalibrated and updated columnar plot model reached 0.817 (95% CI: 0.791 to 0.842), accompanied by a Brier score of 0.081. Furthermore, Decision Curve Analysis (DCA) exhibited a net benefit within the threshold probability range of 15.2% to 87.1%. Conclusion: Externally validated, recalibrated, updated, and presented IDH prediction models may serve as a valuable instrument for evaluating IDH risk in clinical practice. Furthermore, they hold the potential to guide clinical providers in discerning individuals at risk and facilitating judicious clinical intervention decisions. [ABSTRACT FROM AUTHOR]

Background: The International IgA Nephropathy (IgAN) Network developed and validated two prognostic prediction models for IgAN, one incorporating a race parameter. These models could anticipate the risk of a 50% reduction in estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease (ESRD) subsequent to an IgAN diagnosis via renal biopsy. This investigation aimed to validate the International IgA Nephropathy Prediction Tool (IIgANPT) within a contemporary Chinese cohort. Methods: Within this study,185 patients diagnosed with IgAN via renal biopsy at the Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, between January 2012 and December 2021, were encompassed. Each patient’s risk of progression was assessed utilizing the IIgANPT formula. The primary outcome, a 50% decline in eGFR or progression to ESRD, was examined. Two predictive models, one inclusive and the other exclusive of a race parameter, underwent evaluation via receiver-operating characteristic (ROC) curves, subgroup survival analyses, calibration plots, and decision curve analyses. Results: The median follow-up duration within our cohort spanned 5.1years, during which 18 patients encountered the primary outcome. The subgroup survival curves exhibited distinct separations, and the comparison of clinical and histological characteristics among the risk subgroups revealed significant differences. Both models demonstrated outstanding discrimination, evidenced by the areas under the ROC curve at five years: 0.882 and 0.878. Whether incorporating the race parameter or not, both prediction models exhibited acceptable calibration. Decision curve analysis affirmed the favorable clinical utility of both models. Conclusions: Both prognostic risk evaluation models for IgAN exhibited remarkable discrimination, sound calibration, and acceptable clinical utility. [ABSTRACT FROM AUTHOR]

Background: The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients. Methods: Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA). Results: The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets. Conclusions: The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients. [ABSTRACT FROM AUTHOR]

Vascular calcification (VC) is highly prevalent in patients undergoing hemodialysis, and is a significant contributor to the mortality rate. Therefore, biomarkers that can accurately predict the onset of VC are urgently required. Our study aimed to investigate serum miR-15a levels in relation to VC and to develop a predictive model for VC in patients undergoing hemodialysis at the Beijing Friendship Hospital hemodialysis center between 1 January 2019 and 31 December 2020. The patients were categorized into two groups: VC and non-VC. Logistic regression (LR) models were used to examine the risk factors associated with VC. Additionally, we developed an miR-15a-based nomogram based on the results of the multivariate LR analysis. A total of 138 patients under hemodialysis were investigated (age: 58.41±13.22years; 54 males). VC occurred in 79 (57.2%) patients. Multivariate LR analysis indicated that serum miR-15a, age, and WBC count were independent risk factors for VC. A miR-15a-based nomogram was developed by incorporating the following five predictors: age, dialysis vintage, predialysis nitrogen, WBC count, and miR-15a. The receiver operating characteristic (ROC) curve had an area under the curve of 0.921, diagnostic threshold of 0.396, sensitivity of 0.722, and specificity of 0.932, indicating that this model had good discrimination. This study concluded that serum miR-15a levels, age, and white blood cell (WBC) count are independent risk factors for VC. A nomogram constructed by integrating these risk factors can be used to predict the risk of VC in patients undergoing hemodialysis. [ABSTRACT FROM AUTHOR]

Background: The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life. Objective: This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model. Methods: 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n=210) and the non-MCI (n=206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure. Results: Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model’s area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer– Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927). Conclusion: The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies. [ABSTRACT FROM AUTHOR]

The laser power bed fusion (LPBF) forming process introduces heat accumulation and variations in powder layer thickness, which can destabilize the melt track and reduce surface quality. This phenomenon is especially more serious in the early printing stage. To tackle this stability problem, we proposed a novel approach optimizing process parameters on a layer-specific basis. At first, a numerical database was constructed through a set of numerical simulations. Then, a neural network prediction model was trained based on the database. Finally, this prediction model was embedded into a genetic algorithm for layer-based prediction. To verify the prediction results on processing parameters and calibrate the prediction model, physical experiments were prepared. The developed model consistently exhibited relative errors mostly within 6%. It is noteworthy that the relative errors between the numerical simulation results and the expected values were only 0.77% in width and 1.11% in depth. Printing optimization test was applied for an LPBF machine with a Invar alloy powder. The proposed method yielded positive results in both numerical simulations and the printing test. It can be further adopted for new material printing parameter optimization due to an efficient printing stability in the early-stage for LPBF process. [ABSTRACT FROM AUTHOR]

This paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R² values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R² value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests. [ABSTRACT FROM AUTHOR]

Concrete-filled steel tubes (CFSTs) have demonstrated superior performance compared to other types of composite columns. The evaluation of interactions between concrete and steel composites is crucial due to their significant impact on the overall structural behavior in various loadings. This study develops an artificial neural network (ANN) that predicts the ultimate interfacial bond strength (Pu) of circular and square CFSTs. The length of the interfacial bond between the tube and the concrete; the thickness, shape, and inner perimeter of the tube; and the cubic compression strength and age of the concrete are considered as model inputs. The modeling process uses 397 experimental datasets from 18 studies of push-out tests, more specifically, 143 square and 254 circular CFSTs. ANN with a hidden layer of error-propagation, feed-forward, and sigmoidal activation function is trained, tested, optimized, and validated to achieve a good estimate. The resulting model can predict the Pu with a satisfactory coefficient of determination (R²) of 0.87. The ability of the developed model to predict the Pu is compared to the proposed formulas in the literature. It is found that the ANN model provides the most accurate predictions among all suggested formulas, in terms of R² and Taylor diagram analyses. Furthermore, the inclusion of the shape factor enabled the ANN model to predict the Pu of both squared and circular shapes of CFSTs. The validated ANN model is then used to examine the sensitivity of the parameters to the Pu. The correlations agreed well with the expected trend of published studies. [ABSTRACT FROM AUTHOR]

The fuel consumption model serves as a valuable tool for estimating real-time fuel consumption levels. An accurate fuel consumption model is crucial in providing precise information regarding fuel utilisation and motivating experts to assess the vehicle's economy. The goal of this study was to develop a novel approach by eliminating the measurement of real-time tractor PTO (power take-off) power for fuel consumption prediction and to develop a cloud-infused, server-less, machine learning (ML) based real-time generalised tractor fuel consumption prediction model for any tractor between the power range of 8-48 hp. The fuel consumption prediction models from 18 Machine Learning algorithms were developed, and the extensive data analysis with hyperparameter tuning concluded that the Gradient Boosting Regressor Machine Learning model outperformed the other Machine Learning models with reasonable accuracy (R² = 0.999 for training and 0.914 for testing). Cloud-based serverless Web App and Android App integrated with the Gradient Boosting Regressor based fuel consumption prediction model were developed for the real-time fuel consumption prediction and monitoring of a tractor during field operations. The developed Machine Learning model predicted fuel consumption with a Mean Absolute Percentage Error of 11.43% during real-time field experiments with Mouldboard plough operation. The field validation showed the generalisation ability and efficacy of the developed model, and it can be implemented as a user advisory system for real-time energy-efficient agricultural operations. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular calcification includes cardiac valve calcification (CVC) and vascular calcification. We aimed to analyze risk factors for CVC, and construct a predictive model in maintenance peritoneal dialysis (MPD) patients. Methods: We retrospectively analyzed MPD patients who began peritoneal dialysis between January 2014 and September 2021. Patients were randomly assigned to the derivation cohort and validation cohort in a 7:3 ratio. The patients in the derivation cohort were divided into the CVC group and non-CVC group. Logistic regression was used to analyze risk factors, then the rms package in R language was used to construct a nomogram model to predict CVC. Results: 1,035 MPD patients were included, with the age of 50.0 ± 14.2 years and 632 males (61.1%). Their median follow-up time was 25 (12, 46) months. The new-onset CVC occurred in 128 patients (12.4%). In the derivation cohort, multivariate logistic regression indicated old age, female, high systolic blood pressure (SBP), high calcium-phosphorus product (Ca × P), high Charlson comorbidity index (CCI) and long dialysis time were independent risk factors for CVC (p < 0.05). We constructed a nomogram model for predicting CVC in the derivation cohort, with a C index of 0.845 (95% CI 0.803-0.886). This model was validated with a C index of 0.845 (95%CI 0.781-0.909) in the validation cohort. Conclusion: We constructed a nomogram model for CVC in MPD patients, using independent risk factors including age, sex, SBP, Ca × P, CCI and dialysis time. This model achieved high efficiency in CVC prediction. [ABSTRACT FROM AUTHOR]

Background: This study aimed to explore the performance of renal resistive index (RRI), semiquantitative power Doppler ultrasound (PDU) score and renal venous Doppler waveform (RVDW) pattern in predicting acute kidney injury (AKI) in critically ill patients and establish prediction models. Methods: This prospective observational study included 234 critically ill patients. Renal ultrasound was measured within 24 h after intensive care unit admission. The main outcome was the highest AKI stage within 5 days after admission according to the Kidney Disease Improving Global Outcomes criteria. Results: Patients in the AKI stage 3 group had significantly higher RRI, RVDW pattern and lower PDU score (p < 0.05). Only lactate, urine volume, serum creatinine (SCr) on admission, PDU score and RVDW pattern were statistically significant predictors (p < 0.05). Model 1 based on these five variables (area under the curve [AUC] = 0.938, 95% confidence interval [CI] 0.899-0.965, p < 0.05) showed the best performance in predicting AKI stage 3, and difference in AUC between it and the clinical model including lactate, urine volume and SCr (AUC = 0.901, 95% CI 0.855-0.936, p < 0.05) was statistically significant (z statistic = 2.224, p = 0.0261). The optimal cut-off point for a nomogram based on Model 1 was ≤127.67 (sensitivity: 95.8%, specificity: 82.3%, Youden's index: 0.781). Conclusions: The nomogram model including SCr, urine volume, lactate, PDU score and RVDW pattern upon admission exhibited a significantly stronger capability for AKI stage 3 than each single indicator and clinical model including SCr, urine volume and lactate. [ABSTRACT FROM AUTHOR]

Introduction: To establish a prediction model to predict immunosuppressive medication (IM) nonadherence in kidney transplant recipients (KTRs) based on a combined theory framework. Methods: This polycentric, cross-sectional study included 1191 KTRs from October 2020 to February 2021 in China, with 1011 KTRs enrolled in the derivation set and 180 in the external validation set. Variables selected based on the combined theory of planned behavior (TPB)/health belief model (HBM) theory were analyzed by the least absolute shrinkage and selection operator (LASSO). Internal 10 cross-validation was conducted to determine the optimal lambda value. The receiver operating characteristic (ROC) curve, specificity, and sensitivity were used to evaluate the prediction model, and further assessment was run by external validation. Results: IM nonadherence rate was 38.48% in the derivation set and 37.22% in the validation set. The LASSO model was developed with eight predictors for IM nonadherence: age, preoperative drinking history, education, marital status, perceived barriers, social support, perceived behavioral control, and perceived susceptibility. The model demonstrated acceptable discrimination with the area under the ROC curve of 0.797 (95% CI: 0.745-0.850) in the internal validation set and 0.757 (95% CI: 0.684-0.829) in the external validation set. The specificity and sensitivity in the internal validation and external validation set were 0.741, 0.748, 0.673, and 0.716, respectively. Conclusions: The LASSO model was developed to guide identifying high-risk nonadherent patients and timely and effective interventions to improve their prognosis and survival. [ABSTRACT FROM AUTHOR]

Objective: To explore the correlation between neutrophil-to-lymphocyte ratio (NLR) and contrast-induced acute kidney injury (CI-AKI). To develop machine-learning (ML) methods based on NLR and other relevant high-risk factors to establish new and effective predictive models of CI-AKI. Methods: The data of 2230 patients, who underwent elective vascular intervention, coronary angiography and percutaneous coronary intervention were retrospectively collected. The patients were divided into a CI-AKI group and a non-CI-AKI group. Logistic regression was used to analyze the correlation of NLR with CI-AKI and high-risk factors for CI-AKI, and logistic regression (LR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and naïve Bayes (NB) models based on NLR and the high-risk factors were established. Results: A high NLR(>2.844) was an independent risk factor for CI-AKI (odds ratio = 2.304, p < 0.001). The area under the ROC curve (AUC) of the NB model was the largest (0.774), indicating that it had the best performance. NLR, serum creatinine concentration, fasting plasma glucose concentration, and use of ß-blocker all accounted for a large proportion of the predictive performance of each model and were the four most important factors affecting the occurrence of CI-AKI. Conclusions: There was a significant correlation between NLR and CI-AKI The NB model exhibited the best predictive performance out of the five ML models based on NLR exhibited the best predictive performance out of the five ML models. [ABSTRACT FROM AUTHOR]

We aimed to explore factors associated with mortality of diabetic kidney disease (DKD), and to establish a prediction model for predicting the mortality of DKD. This was a cohort study. In total, 1,357 DKD patients were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, with 505 DKD patients being identified from the MIMIC-III as the testing set. The outcome of the study was 1-year mortality. COX proportional hazard models were applied to screen the predictive factors. The prediction model was conducted based on the predictive factors. A receiver operating characteristic (ROC) curve with the area under the curve (AUC) was calculated to evaluate the performance of the prediction model. The median follow-up time was 365.00 (54.50,365.00) days, and 586 patients (43.18%) died within 1 year. The predictive factors for 1-year mortality in DKD included age, weight, sepsis, heart rate, temperature, Charlson Comorbidity Index (CCI), Simplified Acute Physiology Score (SAPS) II, and Sequential Organ Failure Assessment (SOFA), lymphocytes, red cell distribution width (RDW), serum albumin, and metformin. The AUC of the prediction model for predicting 1-year mortality in the training set was 0.771 [95% confidence interval (CI): 0.746-0.795] and the AUC of the prediction model in the testing set was 0.795 (95% CI: 0.756-0.834). This study establishes a prediction model for predicting mortality of DKD, providing a basis for clinical intervention and decision-making in time. [ABSTRACT FROM AUTHOR]

Objective: Identifying patients at high risk for cardiac arrest-associated acute kidney injury (CA-AKI) helps in early preventive interventions. This study aimed to establish and validate a high-risk nomogram for CA-AKI. Methods: In this retrospective dataset, 339 patients after cardiac arrest (CA) were enrolled and randomized into a training or testing dataset. The Student's t-test, non-parametric Mann-Whitney U test, or χ2 test was used to compare differences between the two groups. Optimal predictors of CA-AKI were determined using the Least Absolute Shrinkage and Selection Operator (LASSO). A nomogram was developed to predict the early onset of CA-AKI. The performance of the nomogram was assessed using metrics such as area under the curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC). Results: In total, 150 patients (44.2%) were diagnosed with CA-AKI. Four independent risk predictors were identified and integrated into the nomogram: chronic kidney disease, albumin level, shock, and heart rate. Receiver operating characteristic (ROC) analyses showed that the nomogram had a good discrimination performance for CA-AKI in the training dataset 0.774 (95%CI, 0.715-0.833) and testing dataset 0.763 (95%CI, 0.670-0.856). The AUC values for the two groups were calculated and compared using the Hanley-McNeil test. No statistically significant differences were observed between the groups. The calibration curve demonstrated good agreement between the predicted outcome and actual observations. Good clinical usefulness was identified using DCA and CIC. Conclusion: An easy-to-use nomogram for predicting CA-AKI was established and validated, and the prediction efficiency of the clinical model has reasonable clinical practicability. [ABSTRACT FROM AUTHOR]

Background: Primary membranous nephropathy (PMN) has a heterogeneous natural course. Immunosuppressive therapy is recommended for PMN patients at moderate or high risk of renal function deterioration. Prediction models for the treatment failure of PMN have rarely been reported. Methods: This study retrospectively studied patients diagnosed as PMN by renal biopsy at Sichuan Provincial People's Hospital from January 2017 to December 2020. Information on clinical characteristics, laboratory test results, pathological examination, and treatment was collected. The outcome was treatment failure, defined as the lack of complete or partial remission at the end of 12 months. Simple logistic regression was used to identify candidate predictive variables. Forced-entry stepwise multivariable logistic regression was used to develop the prediction model, and performance was evaluated using C-statistic, calibration plot, and decision curve analysis. Internal validation was performed by bootstrapping. Results: In total, 310 patients were recruited for this study. 116 patients achieved the outcome. Forced-entry stepwise multivariable logistic regression indicated that PLA2Rab titer (OR = 1.002, 95% CI: 1.001-1.004, p = 0.003), inflammatory cells infiltration (OR = 2.753, 95% CI: 1.468-5.370, p = 0.002) and C3 deposition on immunofluorescence (OR = 0.217, 95% CI: 0.041-0.964, p = 0.049) were the three independent risk factors for treatment failure of PMN. The final prediction model had a C-statistic (95% CI) of 0.653 (0.590-0.717) and a net benefit of 23%-77%. Conclusions: PLA2R antibody, renal interstitial inflammation infiltration, and C3 deposition on immunofluorescence were the three independent risk factors for treatment failure in PMN. Our prediction model might help identify patients at risk of treatment failure; however, the performance awaits improvement. [ABSTRACT FROM AUTHOR]

Prediction models for identifying emergency department (ED) patients at high risk of poor outcome are often not externally validated. We aimed to perform a head-to-head comparison of the discriminatory performance of several prediction models in a large cohort of ED patients. In this retrospective study, we selected prediction models that aim to predict poor outcome and we included adult medical ED patients. Primary outcome was 31-day mortality, secondary outcomes were 1-day mortality, 7-day mortality, and a composite endpoint of 31-day mortality and admission to intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Finally, the prediction models with the highest performance to predict 31-day mortality were selected to further examine calibration and appropriate clinical cut-off points. We included 19 prediction models and applied these to 2185 ED patients. Thirty-one-day mortality was 10.6% (231 patients), 1-day mortality was 1.4%, 7-day mortality was 4.4%, and 331 patients (15.1%) met the composite endpoint. The RISE UP and COPE score showed similar and very good discriminatory performance for 31-day mortality (AUC 0.86), 1-day mortality (AUC 0.87), 7-day mortality (AUC 0.86) and for the composite endpoint (AUC 0.81). Both scores were well calibrated. Almost no patients with RISE UP and COPE scores below 5% had an adverse outcome, while those with scores above 20% were at high risk of adverse outcome. Some of the other prediction models (i.e. APACHE II, NEWS, WPSS, MEWS, EWS and SOFA) showed significantly higher discriminatory performance for 1-day and 7-day mortality than for 31-day mortality. Head-to-head validation of 19 prediction models in medical ED patients showed that the RISE UP and COPE score outperformed other models regarding 31-day mortality. [ABSTRACT FROM AUTHOR]

Corona Virus Disease 2019 (COVID-19) has a significant impact on sleep quality. However, the effects on sleep quality in the post-COVID-19 pandemic era remain unclear, and there is a lack of a screening tool for Chinese older adults. This study aimed to understand the prevalence of poor sleep quality and determine sensitive variables to develop an effective prediction model for screening sleep problems during infectious diseases outbreaks. The Peking University Health Cohort included 10,156 participants enrolled from April to May 2023. The Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality. The data were randomly divided into a training-testing cohort (n = 7109, 70%) and an independent validation cohort (n = 3027, 30%). Five prediction models with 10-fold cross validation including the Least Absolute Shrinkage and Selection Operator (LASSO), Stochastic Volatility Model (SVM), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost model based on the area under curve (AUC) were used to develop and validate predictors. The prevalence of poor sleep quality (PSQI >7) was 30.69% (3117/10,156). Among the generated models, the LASSO model outperformed SVM (AUC 0.579), RF (AUC 0.626), ANN (AUC 0.615) and XGBoost (AUC 0.606), with an AUC of 0.7. Finally, a total of 12 variables related to sleep quality were used as parameters in the prediction models. These variables included age, gender, ethnicity, educational level, residence, marital status, history of chronic diseases, SARS-CoV-2 infection, COVID-19 vaccination, social support, depressive symptoms, and cognitive impairment among older adults during the post-COVID-19 pandemic. The nomogram illustrated that depressive symptoms contributed the most to the prediction of poor sleep quality, followed by age and residence. This nomogram, based on twelve-variable, could potentially serve as a practical and reliable tool for early identification of poor sleep quality among older adults during the post-pandemic period. The poor sleep quality (PSQI >7) was still prevalent among older adults during the post-COVID-19 pandemic. The LASSO model was the best model to predict poor sleep quality among older adults, compared with SVM, RF, ANN and XGBoost. This prediction model, based on twelve variables, may potentially serve as a practical and reliable tool for the early identification of poor sleep quality among older adults during the post-pandemic period. [ABSTRACT FROM AUTHOR]

The brain neuromechanism in maintenance hemodialysis patients (MHD) with cognitive impairment (CI) remains unclear. The study aimed to probe the relationship between spontaneous brain activity and CI by using resting-state functional magnetic resonance imaging (rs-fMRI) data. Here, 55 MHD patients with CI and 28 healthy controls were recruited. For baseline data, qualitative data were compared between groups using the χ2 test; quantitative data were compared between groups using the independent samples t-test, ANOVA test, Mann–Whitney U-test, or Kruskal–Wallis test. Comparisons of ALFF/fALFF/ReHo values among the three groups were calculated by using the DPABI toolbox, and then analyzing the correlation with clinical variables. p <.05 was considered a statistically significant difference. Furthermore, back propagation neural network (BPNN) was utilized to predict cognitive function. Compared with the MHD-NCI group, the patients with MHD-CI had more severe anemia and higher urea nitrogen levels, lower mALFF values in the left postcentral gyrus, lower mfALFF values in the left inferior temporal gyrus, and greater mALFF values in the right caudate nucleus (p <.05). The above-altered indicators were correlated with MOCA scores. BPNN prediction models indicated that the diagnostic efficacy of the model which inputs were hemoglobin, urea nitrogen, and mALFF value in the left central posterior gyrus was optimal (R2 = 0.8054), validation cohort (R2 = 0.7328). The rs-fMRI can reveal the neurophysiological mechanism of cognitive impairment in MHD patients. In addition, it can serve as a neuroimaging marker for diagnosing and evaluating cognitive impairment in MHD patients. [ABSTRACT FROM AUTHOR]

Acute pancreatitis (AP) is associated with a high incidence of acute kidney injury (AKI). This study aimed to develop a nomogram for predicting the early onset of AKI in AP patients admitted to the intensive care unit. Clinical data for 799 patients diagnosed with AP were extracted from the Medical Information Mart for Intensive Care IV database. Eligible AP patients were randomly divided into training and validation cohorts. The independent prognostic factors for the early development of AKI in AP patients were determined using the all-subsets regression method and multivariate logistic regression. A nomogram was constructed for predicting the early occurrence of AKI in AP patients. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA). Seven independent prognostic factors were identified as predictive factors for early onset AKI in AP patients. The AUC of the nomogram in the training and validation cohorts were 0.795 (95% CI, 0.758–0.832) and 0.772 (95% CI, 0.711–0.832), respectively. The AUC of the nomogram was higher compared with that of the BISAP, Ranson, APACHE II scores. Further, the calibration curve revealed that the predicted outcome was in agreement with the actual observations. Finally, the DCA curves showed that the nomogram had a good clinical applicability value. The constructed nomogram showed a good predictive ability for the early occurrence of AKI in AP patients. [ABSTRACT FROM AUTHOR]

With the progress of science and technology, Artificial Intelligence (AI) technology has become one of the mainstream technologies in the current society, providing an important driving force for human development. Thereby, in order to improve the effect of animal feeding, using AI technology to improve animal feed has become a necessary measure. Based on this, this work designs to use Long Short-Term Memory (LSTM) technology to build an intelligent prediction model of metabolic energy, which provides a reference for animal feed proportioning design. This work also explores the comprehensive performance of the LSTM model through simulation evaluation. The model is evaluated with different nodes as the main indicators. The results show that compared with the models with 5 and 20 nodes, the model with 10 nodes has better performance, and the highest data calculation accuracy of the model is about 90%. Meanwhile, the highest fitting degree of the model designed is 98.2%, and the lowest is 96.2%. It suggests that the model designed can better predict metabolic energy. This work provides technical support for expanding the application scope of AI technology and contributes to the intelligence of animal feeding. [ABSTRACT FROM AUTHOR]

Floods are frequent natural disasters, and the management of floods involves predicting flood hydrographs and inundation maps. However, the uncertainty associated with these predictions is a significant challenge in flood management. This study focuses on the uncertainty associated with the selection of mathematical models to estimate flood hydrographs and their impact on inundation map predictions. The study involved setting up 27 hydrological models using different mathematical models for the loss, transform, and routing methods within the Hydrologic Engineering center – Hydrologic Modelling system (HEC-HMS) model. The results showed that the peak flow estimation depends on the models adopted for predicting the flood hydrograph. This uncertainty was translated to the flood inundation maps, where the inundated area varied across different flood events. The study also found that the hydrological conditions prior to the start of the event impact the hydrological model predictive uncertainty, and ensemble predictions by Bayesian model averaging can reduce the uncertainty. [ABSTRACT FROM AUTHOR]

Hydrological modelling to support hypotheses on Earth system boundaries or the accelerating water crisis is nowadays done at the global scale, with difficulties associated to model uncertainties. Here we bring a robustness analysis of internal model variables as an additional tool for model evaluation using data from six Earth observation products and the global catchment model World-Wide HYPE in a comparative study. The assessment shows that: (i) variables have high agreement in mid-latitude temperate regions; (ii) the variables with higher agreement, and associated with good model performance in streamflow, were actual evapotranspiration, fractional snow cover and snow water equivalent; and (iii) changes in total water storage showed very poor agreement, probably due to an insufficient number of aquifers in the model set-up. We propose this procedure as a standard complementary method in global hydrological modelling, highlighting the importance of justifying models before using them for scenario analysis or water accounting. [ABSTRACT FROM AUTHOR]

Nowadays, extrusion-based additive manufacturing techniques have been utilised from domestic to industrial end-product fabrications. However, parts fabricated through these techniques are facing poorer part strength issues due to its high nature of anisotropic fabrication strategies. Moreover, it may be improved if it can be predicted in the initial stage of fabrication through part strength modelling technique. In this direction, many researchers have attempted to model part strength estimation models that could be utilised for prediction purposes and hence controlled and improved the part strength. However, due to the anisotropic nature of 3D printed parts, models developed in the existing literature are showing more prediction errors, which can affect the final part strength. To overcome this issue, a new part strength modelling technique based on ANN technique has been defined in this study, which reduces the prediction error significantly. For the validation of the developed model, a comparative study has been performed by considering previously developed models and corresponding data available in the existing literature. Results obtained from the proposed ANN model show that prediction errors reduced significantly and it can be one of the possible alternatives to predict part strength of anisotropic behaviour of 3D printed parts. [ABSTRACT FROM AUTHOR]

Existing forecasting methods employed for rainfall forecasting encounter many limitations, because the difficulty of the underlying mathematical proceeding in dealing with the patterning and imitation of rainfall data. This study attempts to provide a robust methodology for detecting the nonlinearity of the rainfall pattern by integrating several optimizer algorithms with an Artificial Neural Network (ANN). The Artificial Bee Colony, Particle Swarm Optimization, and Imperialism Competitive Algorithm have been integrated to improve and optimize the internal parameters of theANN method. In Malaysia, a real-world case study was set up, and the ANN model was created using 54 years (1967-2020) worth of local monthly data. The created artificial neural network method is being utilized for rainfall forecasting in real-time. A variety of network types were evaluated with various input information types with the goal of producing accurate rainfall forecasts. Statistical analysis was conducted using various statistical indicators to evaluate the model's accuracy in forecasting rainfall. The study revealed that the model based on the integration of the Imperial Competitive Algorithm with Artificial Neural Network (ICA-ANN) outperformed other predictive models. The results confirmed that the proposed model (ICA-ANN) is a promising predictive model for forecasting monthly rainfall with high accuracy. [ABSTRACT FROM AUTHOR]

Computational modelling is a technique for modelling and solving real-world problems by utilising computing to provide solutions. This paper presents a novel predictive model of cell survival/deathrelated effects of Extracellular Signal-Regulated Kinase Protein. The computational model was designed using Neural Networks and fuzzy system. Three hundred ERK samples were examined using ten different concentrations of three input proteins: EGF, TNF, and insulin. Based on the different concentrations of input proteins and different samples of ERK protein, adjustment Anderson darling (AD) statistics for multiple distribution functions were computed considering different test such as visual test, Pearson correlation coefficient, and uniformity tests. The results reveal that utilising different concentrations and samples, values such as 7.55 AD and 18.4 AD were obtained using the Weibull distribution function for 0 ng/ml of TNF, 100 ng/ml of EGF, and 0 ng/mL of insulin concentrations. The model was validated by predicting the various ERK protein values that fall within the observed range. The proposed model agrees with the deterministic model, which was developed using difference equations. [ABSTRACT FROM AUTHOR]

The biotic community predictive model proposed in the River Invertebrate Prediction And Classification System which compares observed values at test sites with expected values in reference sites is used to perform biological assessments of rivers. In 2021, a Korean River Macroinvertebrate Prediction and Assessment System (KRIMPAS) for river integrity assessment was developed. However, since it was the early stage of development, improvement of the accuracy and precision of assessment was required. Therefore, benthic macroinvertebrates and six environmental variables collected from 357 reference sites between 2010 to 2021 were used to compare the prediction mothods and attempt to optimize the KRIMPAS. Based on the number of taxa (species and family levels) and six biological indices, the existing KRIMPAS method (Original method) was compared with two weighted average methods (W-Avg 1 and 2) according to the probability that a test site was assigned. In terms of statistical significance, observed to expected (O/E) values of the number of taxa, but, there were difference for most of biological indices. When the probability density function based on the log-normal distribution for each derivation method was applied, no difference was observed in the number of taxa; however, there were differences in the mean, median, mode, and variance for each biological index. A fit verification based on the normalized root-mean-square error (NRMSE) for each derivation method was conducted, and the overall fit tended to be higher with the W-Avg 1 and 2 when compared with the Original method. Thus, to correct the current biological assessments of Korean rivers we propose the application of the W-Avg method as this would improve the derivation of the predictive model. [ABSTRACT FROM AUTHOR]

As COVID-19 is an emerging pandemic, analysing its evolution is necessary to understand it in order to find appropriate answers. In this paper, we aim to observe and analyse it at the Chadian-Senegalese level. Thus, we collect public data in order to present via curves, histograms and tables the main characteristics of this pandemic. In this way, we implement a python program to construct these. We focus only on extracting long-term data without predictive models. We observed that there are mainly two waves (outbreak) per year with stable or even decreasing infection and death rates. We also identified moments of growth and relaxation of the disease. These results can be used to identify times when treatment or prevention should be intensified. [ABSTRACT FROM AUTHOR]

This study aimed to generate prediction models to estimate the chemical composition of fresh and dry Brachiaria brizantha cv. Piatã grass using near infrared spectroscopy (NIRS). Chemical analyses of 249 samples were performed to determine oven-dried sample (ODS), dry matter (DM), crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), cellulose (CEL) and total digestible nutrients (TDN). The samples were scanned in an NIRS spectrometer and different percentages were used to compose and develop the models (100% fresh; 100% dry; 25% fresh:75% dry; 50% fresh:50% dry and 75% fresh:25% dry). The purpose of these mixed models is to know if it is possible to obtain reliable predictions from fresh samples in a database that contains dry samples. The calibration models were developed using modified partial least squares (MPLS) and evaluated by statistical parameters, including coefficient of determination (R²) and residual predictive deviation (RPD). The model with 100% dry samples obtained the best results in R² and RPD validations, for CP (0.94; 3.98), NDF (0.92; 3,49) and TDN (0.90; 3.12). The 100% fresh samples produced the best R² results in ODS (0.83), CP (0.85), ADF (0.84) and ADL (0.83). A screening model was validated to predict the characteristics and components of the fresh samples. The model using 100% dry grass was suitable for predicting all the variables, except ODS, DM and CEL. [ABSTRACT FROM AUTHOR]