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Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling—a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP's functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling. [ABSTRACT FROM AUTHOR]

Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms—specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893–0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable. [ABSTRACT FROM AUTHOR]

Prediction of water-conducting fractured zone (WCFZ) of mine overburden is the premise for reducing or eliminating water inrush hazards in undersea mining. To obtain a more robust and precise prediction of WCFZ in undersea mining, a WCFZ prediction dataset with 122 cases of fractured zones was constructed. Five machine learning algorithms (linear regression, XGBRegressor, RandomForestRegressor, LineareSVR, and KNeighborsRegressor) were employed to develop five corresponding predictive models, taking multiple factors into account.The optimal parameters for each model are obtained through ten-fold cross-validation (10CV). The model's predictive performance was validated and assessed using two metrics, namely the coefficient of determination (R2) and mean squared error (MSE). A comparison was made with the regression performance of commonly used empirical formulas. The results indicate that the constructed model outperforms reliance solely on theoretical criteria, showing a high R2 value of up to 0.925 and a low MSE value of 3.61. The proposed model was validated in a recently established mining area on Sanshan Island, China. It shows low absolute and relative errors of 0.71 m and 2.01%, respectively, between the predicted value from the model and observation result from the field, demonstrating a high level of consistency with on-site conditions. This paves a path to leveraging machine learning algorithms for predicting the height of WCFZ. [ABSTRACT FROM AUTHOR]

A dislocation-density based crystalline plasticity (DCP) and nonlinear finite element (FE) analysis were used to predict, and fundamentally understand how and why fracture nucleation and propagation are related to the interrelated microstructural mechanisms of dislocation-density pileups, GB structure, orientation, and total and partial dislocation density interactions within and adjacent for a random low angle grain boundary (LAGB) and a random high angle GB (HAGB). The GB orientations and structures were obtained from micropillar experiments, such that LAGBs and the HAGBs can be accurately represented and used for the modeling predictions. The normal stress, density of pileups, and dislocation-density accumulation along and within the GB were higher for the low angle GB bicrystal. These interrelated phenomena delineate how fracture for high angle GBs nucleate and propagate at lower nominal strains than the lower angle GB bicrystal case. These predictions underscore how fundamental mechanisms can be identified and used to understand how failure nucleates and propagates for different GB structures and orientations. [ABSTRACT FROM AUTHOR]

Microbes have been demonstrated to be closely linked to diseases that pose a major threat to human health. Computing technologies can help researchers find potential microbe-drug associations more quickly and precisely. In this study, we introduced a novel computational prediction model called GLNNMDA based on global and local features of microbes and drugs to infer possible microbe-drug correlations. In GLNNMDA, we first constructed a heterogeneous network based on known microbe-drug relationships by integrating multiple similarity metrics of drugs and microbes. Subsequently, low-dimensional features will be extracted for nodes in the heterogeneous network by adopting the graph attention encoder. Next, based on combining these low-dimensional features with multiple properties of microbes and drugs to form a new comprehensive feature matrix, we would utilize the GLF module to extract the global and local features for microbes and drugs respectively, and then, we would further fuse these global and local features to come up with predictions of possible microbe-drug associations. Moreover, in order to evaluate the prediction performance of GLNNMDA, under the framework of fivefold cross-validation, intensive comparative experiments and case studies were done on different well-known public databases. The results showed that GLNNMDA obtained the highest AUC values as well as AUPR values of 0.9802 ± 0.0011, 0.9773 ± 0.0021 and 0.8586 ± 0.0004, 0.8008 ± 0.0031 in the two databases, MDAD and aBiofilm, respectively, compared to the state-of-the-art competing prediction methods. In addition, case studies of well-known microorganisms and drugs have demonstrated the effectiveness of GLNNMDA in inferring potential microbial drug associations, which implies that GLNNMDA may be a useful tool for microbe-drug association prediction in the future. The source code is available at: "https://github.com/KuangHaiYue/GLNNMDA.git". [ABSTRACT FROM AUTHOR]

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients. [ABSTRACT FROM AUTHOR]

This study focuses on predicting the prognosis of acute ischemic stroke patients with focal neurologic symptoms using a combination of diffusion-weighted magnetic resonance imaging (DWI) and clinical information. The primary outcome is a poor functional outcome defined by a modified Rankin Scale (mRS) score of 3–6 after 3 months of stroke. Employing nnUnet for DWI lesion segmentation, the study utilizes both multi-task and multi-modality methodologies, integrating DWI and clinical data for prognosis prediction. Integrating the two modalities was shown to improve performance by 0.04 compared to using DWI only. The model achieves notable performance metrics, with a dice score of 0.7375 for lesion segmentation and an area under the curve of 0.8080 for mRS prediction. These results surpass existing scoring systems, showing a 0.16 improvement over the Totaled Health Risks in Vascular Events score. The study further employs grad-class activation maps to identify critical brain regions influencing mRS scores. Analysis of the feature map reveals the efficacy of the multi-tasking nnUnet in predicting poor outcomes, providing insights into the interplay between DWI and clinical data. In conclusion, the integrated approach demonstrates significant advancements in prognosis prediction for cerebral infarction patients, offering a superior alternative to current scoring systems. [ABSTRACT FROM AUTHOR]

Shear strength (SS) parameters are essential for understanding the mechanical behavior of materials, particularly in geotechnical engineering and rock mechanics. This study proposes a novel hierarchical ensemble model (HEM) to predict SS parameters: cohesion (C ) and angle of internal friction (φ ). The HEM addresses the limitations of traditional machine learning models. Its performance was validated using leave-one-out cross-validation (LOOCV) and out-of-bag (OOB) evaluation methods. The model's accuracy was assessed with R-squared correlation (R2), absolute average relative error percentage (AAREP), Taylor diagrams, and quantile–quantile plots. The computational results demonstrated that the proposed HEM outperforms previous studies using the same database. The model predicted φ and C with R2 values of 0.93 and 0.979, respectively. The AAREP values were 1.96% for φ and 4.7% for C . These results indicate that the HEM significantly improves the prediction quality of φ and C , and exhibits strong generalization capability. Sensitivity analysis revealed that σ_3maxσ3max (maximum principal stress) had the greatest impact on modeling both φ and C . According to uncertainty analysis, the LOOCV and OOB had the widest uncertainty bands for the φ and C parameters, respectively. [ABSTRACT FROM AUTHOR]

Obesity is an abnormal and potentially dangerous condition caused by excess body fat accumulation. The number of people with obesity is increasing worldwide. Obesity is the primary cause of various diseases; therefore, it is crucial to make efforts to control body weight. Identifying the factors that influence men with obesity to attempt to control and not control their weight is essential. The objective of this study was to create a prediction model for weight control experience among Korean men in their 30 s and 40 s. We analyzed data from the 2022 Community Health Survey and included 12,311 men who were overweight or obese. The men were divided into two groups based on their weight control experience: (1) Yes group (n = 9405) and (2) No group (n = 2906). Chi-square and independent t-tests were used to compare general and health-related characteristics between the groups. Decision tree analysis was used to build a prediction model for weight control experience. A split-sample test was conducted to validate the model. From the results of this study, various models predicting weight control experience were derived. From the decision tree model without setting the first node, those who weighed below average, had a high school diploma or less, and did not know their blood sugar levels had the highest probability of not controlling their weight at 55.3%. In the prediction model where the first node was set to age, those in their 40 s who thought their weight was below average and were unaware of their blood sugar levels had the highest rate of not trying to control their weight at 50.1%. In the prediction model where the first node was set to BMI, those who were overweight but thought their weight was below average and had a high school diploma or less had the highest rate of not trying to control their weight at 51.5%. There is an urgent need to provide obesity prevention and management education to those who have no weight control experience, particularly those at high risk, as identified in this study. [ABSTRACT FROM AUTHOR]

As we all know, momentum plays a crucial role in ball game. Based on the 2023 Wimbledon final data, this paper investigated momentum in tennis. Firstly, we initially trained a decision tree regression model on reprocessed data for prediction, and established the CBRF model based on CatBoost regression and random forest regression models to obtain prediction data. Secondly, significant non-zero autocorrelation coefficients were found, confirming the correlation between momentum and success. Thirdly, Based on these key factors, we proposed winning strategies for the players, conducted predictive analyses for six specific time intervals of the game. At last, by implementing these models to women's matches, championships, matches on different surfaces, the results demonstrated that the models have effective generalization ability. [ABSTRACT FROM AUTHOR]

Research on the severity and prognosis of sepsis with or without progressive delirium is relatively insufficient. We constructed a prediction model of the risk factors for 28-day mortality in patients who developed sepsis or sepsis-associated delirium. The modeling group of patients diagnosed with Sepsis-3 and patients with progressive delirium of related indicators were selected from the MIMIC-IV database. Relevant independent risk factors were determined and integrated into the prediction model. Receiver operating characteristic (ROC) curves and the Hosmer–Lemeshow (HL) test were used to evaluate the prediction accuracy and goodness-of-fit of the model. Relevant indicators of patients with sepsis or progressive delirium admitted to the intensive care unit (ICU) of a 3A hospital in Xinjiang were collected and included in the verification group for comparative analysis and clinical validation of the prediction model. The total length of stay in the ICU, hemoglobin levels, albumin levels, activated partial thrombin time, and total bilirubin level were the five independent risk factors in constructing a prediction model. The area under the ROC curve of the predictive model (0.904) and the HL test result (χ2 = 8.518) indicate a good fit. This model is valuable for clinical diagnosis and treatment and auxiliary clinical decision-making. [ABSTRACT FROM AUTHOR]

This study develops a systematic modeling framework, comprising a prediction model, a super-SBM model, and a spatial autocorrelation analysis model, to explore the spatial–temporal evolution tendencies of development efficiency within China's 30 regions in the low-carbon sports industry from 2006 to 2025. This framework aims to provide theoretical insights for the formulation of more targeted policies. Based on the empirical findings, the main conclusions of this study are as follows: (1) The optimal buffer operator grey prediction model demonstrates the highest accuracy among the prediction models examined. (2) The development efficiency curves of the 30 regions exhibit a significant increasing trend from 2006 to 2021, with values generally peaking between 0.4 and 0.6. (3) Notably, the disparity in development efficiency between developed and less developed regions is expected to progressively widen. (4) The development efficiency of the low-carbon sports industry across the 30 regions typically displays high-high clustering and low-low clustering during China's four five-year plan periods. This underscores the importance and urgency of promoting regional coordinated development within the low-carbon sports industry. [ABSTRACT FROM AUTHOR]

Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force. [ABSTRACT FROM AUTHOR]

With advancements in sustainable urban development, research on urban functional areas has garnered significant attention. In recent years, Point-of-Interest, with their large volume of information and ease of acquisition, have been widely applied in research on urban functional domains. However, scholars currently focus on the identification of urban functional areas, usually relying on data from a single period, whereas research on the prediction of functional areas has not yet been well validated. Therefore, in this study, we propose a new method based on several years of POI data to predict urban functional areas. Taking Nanjing City, Jiangsu Province, as an example, we first identified the functional area distribution of the old city of Nanjing over several years using POI data and then designed multiple sets of experiments to explore the CA–Markov model's ability to predict functional areas from various aspects, including model overall accuracy, robustness, and comparison analysis between predictions and actual situations. The results show that (1) for mixed or single functional areas, the model's predictions over several years tend to be stable, and the accuracy of the predictions over many years indicates the robustness of the model in predicting urban functional areas. (2) For mixed functional areas in cities, model predictions largely rely on the distribution of the base years used for prediction, leading to inaccurate results; thus, it is still not applicable for simulating and predicting mixed functional areas. (3) For single functional areas in cities or primary functions within an area, the model's predicted degree of change was close to the actual degree of change, making the results referable. [ABSTRACT FROM AUTHOR]

Precise forecasting of satellite clock bias is crucial for ensuring service quality and enhancing the efficiency of real-time precise point positioning (PPP).The grey model with many benefits is an excellent choice for predicting real-time clock bias. However, the absolute prediction error of a small number of satellites is very high in actual forecasting process. In order to address this issue, a non-homogeneous white exponential law grey model has been constructed specifically for predicting clock bias sequences with non-homogeneous class ratio approximating constants. Considering that any model is difficult to apply to different kinds of satellite clocks and clock bias signals, an adaptive selection strategy for forecast model is proposed to ensure more excellent prediction accuracy. Afterwards, a prediction scenario based on the observed products of the BeiDou satellite navigation system (BDS) is created to demonstrate the effectiveness of the approach described in this article. The outcomes of the method are then compared with those of a single grey model and the ultra-rapid predicted products. The outcomes demonstrate that this strategy's prediction accuracy is less than 1 ns/day and that its prediction uncertainty is much decreased, which may improve data selection for real-time applications like real-time kinematics (RTK) and PPP. [ABSTRACT FROM AUTHOR]

Ventricular septal rupture (VSR) is a mechanical complication of acute myocardial infarction (AMI), and its mortality has not decreased significantly in recent decades. However, no clinical model has been developed to predict short-term mortality in patients with post-infarction VSR (PIVSR). This study aimed to develop a nomogram to predict the 30-day mortality by using the clinical characteristics of hospitalized patients with PIVSR. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis was used to construct a nomogram by R. The model was evaluated by the area under the curve (AUC), calibration curve and decision curve analysis (DCA). The bootstrap method was used to validate the model internally. As a result, a nomogram was constructed by using six variables, including CRRT, mechanical ventilation, PPCI, WBC, PASP and methods of treatment. The AUC of the prediction model was 0.96 (0.93, 0.98). The prediction model was well calibrated. The DCA showed that if the threshold probability was between 15% and 95%, the nomogram model would provide a net benefit. The well-constructed and evaluated nomogram can be beneficial to clinicians to predict the risk of death within 30 days in patients with PIVSR. [ABSTRACT FROM AUTHOR]

Internal employees have always been at the core of organizational security management challenges. Once an employee exhibits behaviors that threaten the organization, the resulting damage can be profound. Therefore, analyzing reasonably stored behavioral data can equip managers with effective threat monitoring and warning solutions. Through data-mining, a knowledge graph for internal threat data is deduced, and models for detecting anomalous behaviors and predicting resignations are developed. Initially, data-mining is employed to model the knowledge ontology of internal threats and construct the knowledge graph; subsequently, using the behavioral characteristics, the BP neural network is optimized with the Sparrow Search Algorithm (SSA), establishing a detection model for anomalous behaviors (SBP); additionally, behavioral sequences are processed through data feature vectorization. Utilizing SBP, the LSTM network is further optimized, creating a predictive model for employee behaviors (SLSTM); ultimately, SBP detects anomalous behaviors, while SLSTM predicts resignation intentions, thus enhancing detection strategies for at-risk employees. The integration of these models forms a comprehensive threat detection technology within the organization. The efficacy and practicality of detecting anomalous behaviors and predicting resignations using SBP and SLSTM are demonstrated, comparing them with other algorithms and analyzing potential causes of misjudgment. This work has enhanced the detection efficiency and update speed of abnormal employee behaviors, lowered the misjudgment rate, and significantly mitigated the impact of internal threats on the organization. [ABSTRACT FROM AUTHOR]

Heavy metal accumulation in agricultural products has become a major concern. Previous studies have focused on the transport of heavy metals from the soil and their accumulation in crops. However, recent studies revealed that wheat leaves, ears, and awns can also transport and accumulate heavy metals. Wheat grains can be influenced by two sources of heavy metals: soil contamination and atmospheric deposition. To comprehend the transport characteristics of heavy metals in soil, atmospheric deposition, and wheat, 37 samples each for wheat rhizosphere soil, wheat roots, stems, leaves, and grains were collected. Fifteen samples of atmospheric dry deposition and atmospheric wet deposition were collected from Linshu County (northern area), China. Based on the test data, the characteristics of heavy metals and their distribution in the study area were analyzed. Migration patterns of heavy metals in crops from different sources were investigated using Pearson correlation and redundancy analysis. Finally, a predictive model for heavy metals in wheat grains was developed using multiple linear regression analysis. Significant disparities in the distribution of heavy metals existed among wheat roots, stems, leaves, and grains. The coefficient of variation of heavy metals in atmospheric deposition was relatively high, indicating discernible spatial patterns influenced by human activities. Notably, a positive correlation was observed between the concentration of heavy metals in wheat grains and atmospheric deposition of Hg, Cd, and Pb. Conversely, Zn and Ni levels in wheat grains were significantly negatively associated with soil Zn, Ni, pH, and OM content. The contribution of heavy metal elements from different sources varied in their impact on the grain's heavy metal content. Specifically, atmospheric deposition was the primary source of Hg and Pb in wheat grains, while Cd, Ni, Cu, and Zn were predominantly derived from soil. Using a multiple linear regression model, we could accurately predict Hg, Pb, Cd, Ni, Zn, and As concentrations in crop grains. This model can facilitate quantitative evaluation of ecological risk of heavy metals accumulation in crops in the study area. [ABSTRACT FROM AUTHOR]

Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the diagnosis, prognosis and prediction of the response to therapy has aroused the interest of researchers in their use together with machine learning and deep learning models. Most of the research in this field has been model-centric, meaning it has been based on finding better performing algorithms. However, in practice, improving data quality can result in a better model. This study investigates a data-centric machine learning approach to determine their potential benefits in predicting glioma grades. We report six performance metrics to provide a complete picture of model performance. Experimental results indicate that standardization and oversizing the minority class increase the prediction performance of four popular machine learning models and two classifier ensembles applied on a low-imbalanced data set consisting of clinical factors and molecular biomarkers. The experiments also show that the two classifier ensembles significantly outperform three of the four standard prediction models. Furthermore, we conduct a comprehensive descriptive analysis of the glioma data set to identify relevant statistical characteristics and discover the most informative attributes using four feature ranking algorithms. [ABSTRACT FROM AUTHOR]

To develop and validate a diagnostic prediction model based on blood parameters for predicting the pertussis in children. A retrospective study of 477 children with suspected pertussis at Zigong First People's Hospital was performed between January 2020 and December 2021. The patients were randomly divided into training cohort and validation cohort. Stepwise regression and R software was performed to develop and validate the model. Stepwise regression analysis showed that white blood cell (WBC), hematocrit (HCT), lymphocyte (LYMPH), C-reactive protein (CRP) and platelet distribution width to mean platelet volume ratio (PDW-MPV-R) were found to be independent factors associated with pertussis. The model containing WBC, CRP and PDW-MPV-R had the best performance. The area under curve (ROC, 0.77 for the training cohort and 0.80 for the validation cohort) of the model indicated satisfactory discriminative ability. The sensitivity and specificity of the model were 72.1% and 72.6% in training cohort and 74% and 72.1%, respectively, in validation cohort. Based on the ROC analysis, calibration plots, and decision curve analysis, we concluded that the model exhibited excellent performance. A model based on blood parameters is sufficiently accurate to predict the probability of pertussis in children, and may provide some reference for clinical decisions. [ABSTRACT FROM AUTHOR]

Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties. [ABSTRACT FROM AUTHOR]

This paper presents a thorough evaluation of health outcomes linked to water-related challenges in Islamic nations across East Asia and Central Asia from 2020 to 2030. It has been examined carefully that the trajectory of deaths and disability-adjusted life years associated with unsafe water sources, lack of sanitation, and absence of handwashing facilities is showing a potential rise in negative health impacts due to water pollution. The direct health influences of water-related problems are thoughtful. The increase in deaths and DALYs due to poor water quality and sanitation leads to a higher occurrence of waterborne diseases such as cholera, diarrhea, and dysentery. These conditions not only cause instant health disasters but also subsidize to long-term health issues which include chronic gastrointestinal disorders and malnutrition that is particularly among susceptible populations like children and the elderly. Employing various predictive models including autoregressive integrated moving average, exponential smoothing, support vector machines, and neural networks. The study evaluates their predictive capabilities by using mean absolute percentage error. Support vector machines is found to be the most accurate in forecasting deaths and disability-adjusted life years which is outperforming autoregressive integrated moving average, exponential smoothing, and neural networks. This research aims to inform stakeholders by providing insights into effective strategies for improving water resource management and public health interventions in the targeted regions. [ABSTRACT FROM AUTHOR]

The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC. [ABSTRACT FROM AUTHOR]

At present, tumor immunotherapy has been widely applied to treat various cancers. However, the accuracy of predicting treatment efficacy has not yet achieved a significant breakthrough. This study aimed to construct a prediction model based on the modified WGCNA algorithm to precisely judge the anti-tumor immune response. First, we used a murine colon cancer model to screen corresponding DEGs according to different groups. GSEA was used to analyze the potential mechanisms of the immune-related DEGs (irDEGs) in each group. Subsequently, the intersection of the irDEGs in every group was acquired, and 7 gene-modules were mapped. Finally, 4 gene-modules including cogenes, antiPD-1 immu-genes, chemo immu-genes and comb immu-genes, were selected for subsequent study. Furthermore, a clinical dataset of gastric cancer patients receiving immunotherapy was enrolled, and the irDEGs were identified. A total of 34 vital irDEGs were obtained from the intersections of the vital irDEGs and the four gene-modules. Next, the vital irDEGs were analyzed by the modified WGCNA algorithm, and the correlation coefficients between the 4 gene-modules and the response status to immunotherapy were calculated. Thus, a prediction model based on correlation coefficients was built, and the corresponding model scores were acquired. The AUC calculated according to the model score was 0.727, which was non-inferior to that of the ESTIMATE score and the TIDE score. Meanwhile, the AUC calculated according to the classification of the model scores was 0.705, which was non-inferior to that of the ESTIMATE classification and the TIDE classification. The prediction accuracy of the model was validated in clinical datasets of other cancers. [ABSTRACT FROM AUTHOR]

This study aimed to predict dental freeway space by examining the clinical history, habits, occlusal parameters, mandibular hard tissue movement, soft tissue motion, muscle activity, and temporomandibular joint function of 66 participants. Data collection involved video-based facial landmark tracking, mandibular electrognathography, surface electromyography of mandibular range of motion, freeway space, chewing tasks, phonetic expressions, joint vibration analysis, and 3D jaw scans of occlusion. This resulted in a dataset of 121 predictor features, with freeway space as the target variable. Six models were trained on synthetic data ranging from 500 to 25,000 observations, with 65 original observations reserved for testing: Linear Regression, Random Forest, CatBoost Regressor, XGBoost Regressor, Multilayer Perceptron Neural Network (MPNN), and TabNet. Explainable AI indicated that key predictors of freeway space included phonetics, resting temporalis muscle activity, mandibular muscle activity during clenching, body weight, mandibular hard tissue lateral displacements, and dental arch parameters. CatBoost excelled with a test error of 0.65 mm using 5000 synthetic data points, while a refined MPNN achieved the best performance with 25,000 synthetic data points and 121 unique predictors, yielding an absolute error of 0.43 mm on the 65 original observations. [ABSTRACT FROM AUTHOR]

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly. [ABSTRACT FROM AUTHOR]

Our research utilized deep learning to enhance the control of a 3 Degrees of Freedom biped robot leg. We created a dynamic model based on a detailed joint angles and actuator torques dataset. This model was then integrated into a Model Predictive Control (MPC) framework, allowing for precise trajectory tracking without the need for traditional analytical dynamic models. By incorporating specific constraints within the MPC, we met operational and safety standards. The experimental results demonstrate the effectiveness of deep learning models in improving robotic control, leading to precise trajectory tracking and suggesting potential for further integration of deep learning into robotic system control. This approach not only outperforms traditional control methods in accuracy and efficiency but also opens the way for new research in robotics, highlighting the potential of utilizing deep learning models in predictive control techniques. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0–30 and 30–60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority. [ABSTRACT FROM AUTHOR]

With the high yield of many wells represented by Well JT1 in the Maokou Formation, has catalyzed a surge in exploration activities along the platform margin facies of the Maokou Formation in central Sichuan and further showed the significant exploration potential of the Maokou Formation in the northern slope. However, the fracture cave body of the Maokou Formation exhibits a high degree of development, strong longitudinal and horizontal heterogeneity, large formation pressure differences, and drilling events such as gas kicks and lost circulation occur frequently, which seriously affects the efficient implementation of drilling. Understanding the spatial distribution of the three-pressure in the formation can help better deal with and solve the above problems. Therefore, in order to help the safe, high-quality and rapid drilling of the Maokou Formation in the study area, and enhance the efficiency of oil and gas development, this paper explores the research on the prediction method of the three-pressure of jointing well-seismic data based on the geomechanical experimental data and the actual drilling data. In the process of prediction of pore pressure, this study found that the pore pressure and formation velocity in the study area have an exponential relationship. In order to enhance the applicability of the Filippone's method in the study area and improve the prediction accuracy of pore pressure, the linear relationship between pore pressure and formation velocity in the Filippone's method is modified to an exponential relationship, and a pore pressure prediction model suitable for the work area was established. Based on the Mohr–Coulomb criterion and Huang's model, the prediction models of collapse pressure and fracture pressure applicable to the study area were established, respectively. Then, the elastic parameters were obtained through pre-stack inversion, and the three-pressure bodies were calculated based on the elastic parameter bodies. The results indicate that: (1) The three-pressure prediction method of the jointing well-seismic data in this paper can predict the formation's longitudinal and transverse pressure anomaly zones in advance. (2) The Maokou Formation in the study area is characterized by abnormally high pressure, to balance the pressure of the high-ground formation, high-density drilling fluid is necessary. (3) The prediction results of three-pressure in this paper are highly consistent with the actual drilling engineering events, which verifies the reliability of the three-pressure prediction results presented in this study. The results of the study can provide a basis for decision-making in drilling geological design, such as the determination of drilling fluid density, the evaluation of borehole stability and other engineering problems that require support from three-pressure data. [ABSTRACT FROM AUTHOR]

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics. [ABSTRACT FROM AUTHOR]

In order to improve the ability of intelligent reasoning and prediction of 3DR27 model which presented in our previous work, enhance the usability of this model, and better fulfill the demands of real applications for spatial database, we focused on the problem of reasoning with the inverse of 3D cardinal direction relations between spatial objects. In order to realize automated reasoning, an algorithm for computing the inverse of 3D cardinal direction relations based on matrices is proposed on the basis of the mapping between the 3D rectangular cardinal direction relations and 3D interval relation matrix. This algorithm improves the power of reasoning for this model by means of the excellent properties of matrix operations. Theorems are provided to prove formally that our algorithm is correct and complete. This study realized the automatic inference and calculation of the inverse of the 3D cardinal direction relations based on 3DR27 model and further improved the ability of spatial reasoning and spatial analysis of spatial database. [ABSTRACT FROM AUTHOR]

This study aimed to create and validate a predictive model for renal function following live kidney donation, using pre-donation factors. Accurately predicting remaining renal function post live kidney donation is currently insufficient, necessitating an effective assessment tool. A multicenter retrospective study of 2318 live kidney donors from two independent centers (May 2007–December 2019) was conducted. The primary endpoint was the reduction in eGFR to below 60 mL/min/m2 6 months post-donation. The primary endpoint was achieved in 14.4% of the training cohort and 25.8% of the validation cohort. Sex, age, BMI, hypertension, preoperative eGFR, and remnant kidney proportion (RKP) measured by computerized tomography (CT) volumetry were found significant in the univariable analysis. These variables informed a scoring system based on multivariable analysis: sex (male: 1, female: 0), age at operation (< 30: 0, 30–39: 1, 40–59: 2, ≥ 60: 3), preoperative eGFR (≥ 100: 0, 90–99: 2, 80–89: 4, < 80: 5), and RKP (≥ 52%: 0, < 52%: 1). The total score ranged from 0 to 10. The model showed good discrimination for the primary endpoint in both cohorts. The prediction model provides a useful tool for estimating post-donation renal dysfunction risk, factoring in the side of the donated kidney. It offers potential enhancement to pre-donation evaluations. [ABSTRACT FROM AUTHOR]

This paper studies the possibility of connecting Wind Farms (WF) to the electric grid with the use of finite space model predictive command (FS-MPC) to manage wind farms to improve the quality of the current output from the doubly-fed induction generator (DFIG) with considering fault ride-through technique. This proposed system can generate active power and enhance the power factor. Furthermore, the reduction of harmonics resulting from the connection of non-linear loads to the electrical grid is achieved through the self-active filtering mechanism in DFIGs-WF, facilitated by the now algorithm proposed. FS-MPC technique has the ability to improve system characteristics and greatly reduce active power ripples. Therefore, MATLAB software is used to implement and verify the safety, performance, and effectiveness of this designed technique compared to the conventional strategy. The results obtained demonstrated the effectiveness of the proposed algorithm in handling the four operational modes (Maximum power point tracking, Delta, Fault, and Filtering). Additionally, the suggested technique exhibited flexibility, robustness, high accuracy, and fast dynamic response when compared to conventional strategies and some recently published scientific works. On the other hand, the THD value of the current was significantly reduced, obtaining at one test time the values 56.87% and 0.32% before and after filtering, respectively 27.50% and 0.26% at another time of testing, resulting in an estimated THD reduction percentage of 99.43% and 99.05%, respectively. These high percentages prove that the quality of the stream is excellent after applying the proposed strategy. [ABSTRACT FROM AUTHOR]

This study aims to explore the risk factors associated with frozen shoulder (FS) and develop a predictive model for diagnosing FS, in order to facilitate early detection of the condition. A total of 103 patients diagnosed with FS and admitted to the Department of Joint Surgery at Suining Central Hospital between October 2021 and October 2023 were consecutively included in the study. Additionally, 309 individuals without shoulder joint diseases, matched for age and gender, who visited the department during the same time, were included as the control group.The complete recording of clinical data for all patients was followed by the utilization of statistical tests such as the Mann–Whitney U test, sample t test, and chi-square test to compare different groups. Additionally, multivariate binary logistic regression analysis was employed to identify risk factors associated with the occurrence of FS in patients, leading to the establishment of a prediction model and derivation of a simplified equation. The diagnostic effectiveness of individual indicators and prediction models was assessed through the use of receiver operating characteristic (ROC) curve analysis. In the sample of 103 individuals, 35 were identified as male and 68 as female, with an average age range of 40–70 years (mean age: 54.20 ± 6.82 years). The analysis conducted between different groups revealed that individuals with a low body mass index (BMI), in conjunction with other factors such as diabetes, cervical spondylosis, atherosclerosis, and hyperlipidemia, were more susceptible to developing FS. Logistic regression analysis further indicated that low BMI, diabetes, cervical spondylosis, and hyperlipidemia were significant risk factors for the occurrence of FS. These variables were subsequently incorporated into a predictive model, resulting in the creation of a simplified equation.The ROC curve demonstrated that the combined indicators in the predictive model exhibited superior diagnostic efficacy compared to single indicators, as evidenced by an area under the curve of 0.787, sensitivity of 62.1%, and specificity of 82.2%. Low BMI, diabetes, cervical spondylosis, and hyperlipidemia are significant risk factors associated with the occurrence of FS. Moreover, the utilization of a prediction model has demonstrated superior capability in forecasting the likelihood of FS compared to relying solely on individual indicators. This finding holds potential in offering valuable insights for the early diagnosis of FS. [ABSTRACT FROM AUTHOR]

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid. [ABSTRACT FROM AUTHOR]

Abstract This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms—specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p

Abstract Shear strength (SS) parameters are essential for understanding the mechanical behavior of materials, particularly in geotechnical engineering and rock mechanics. This study proposes a novel hierarchical ensemble model (HEM) to predict SS parameters: cohesion ( $$C$$ C ) and angle of internal friction ( $$\varphi$$ φ ). The HEM addresses the limitations of traditional machine learning models. Its performance was validated using leave-one-out cross-validation (LOOCV) and out-of-bag (OOB) evaluation methods. The model's accuracy was assessed with R-squared correlation (R2), absolute average relative error percentage (AAREP), Taylor diagrams, and quantile–quantile plots. The computational results demonstrated that the proposed HEM outperforms previous studies using the same database. The model predicted $$\varphi$$ φ and $$C$$ C with R2 values of 0.93 and 0.979, respectively. The AAREP values were 1.96% for φ and 4.7% for $$C$$ C . These results indicate that the HEM significantly improves the prediction quality of $$\varphi$$ φ and $$C$$ C , and exhibits strong generalization capability. Sensitivity analysis revealed that σ_3maxσ3max (maximum principal stress) had the greatest impact on modeling both $$\varphi$$ φ and $$C$$ C . According to uncertainty analysis, the LOOCV and OOB had the widest uncertainty bands for the $$\varphi$$ φ and $$C$$ C parameters, respectively.

Abstract The study of the dominant factors influencing moisture content is essential for investigating vacuum filtration mechanisms. In view of the present situation where there is insufficient experimental data and the dominant factors influencing the moisture content of a filter cake have not been identified, in this study a vacuum filtration apparatus was designed and constructed. Quartz sand particles were used as the filtration material. 300 datasets of moisture contents of a filter cake were obtained under different experimental conditions. Multiple Linear Regression, artificial neural network, decision tree, random forest, and extreme gradient boosting were used to establish a prediction model for moisture content during vacuum screening. By comprehensively analyzing the feature importance rankings and the effects of positive and negative correlations, the dominant factors influencing the moisture content of the filter cake during vacuum screening were the particle ratio, screen mesh, and airflow rate. This finding not only provides a scientific basis for the optimization of vacuum screening technology but also points the way for improving screening efficiency in practical applications. It is of significant importance for deepening the understanding of the vacuum screening mechanism and promoting its extensive application.

Abstract Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.

Abstract The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0–30 and 30–60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority.

Abstract This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-Z ICU : − 0.821 (p-value: 0.412); Spiegelhalter-Z death : 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection.

Abstract Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

This research introduces a methodology for data-driven regression modeling of components exhibiting nonlinear characteristics, utilizing the sparse identification of nonlinear dynamics (SINDy) method. The SINDy method is extended to formulate regression models for interconnecting components with nonlinear traits, yielding governing equations with physically interpretable solutions. The proposed methodology focuses on extracting a model that balances accuracy and sparsity among various regression models. In this process, a comprehensive model was generated using linear term weights and an error histogram. The applicability of the proposed approach is demonstrated through a case study involving a sponge gasket with nonlinear characteristics. By contrasting the predictive model with experimental responses, the reliability of the methodology is verified. The results highlight that the regression model, based on the proposed technique, can effectively establish an accurate dynamical system model, accounting for realistic conditions. [ABSTRACT FROM AUTHOR]

This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-ZICU: − 0.821 (p-value: 0.412); Spiegelhalter-Zdeath: 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection. [ABSTRACT FROM AUTHOR]

According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research. [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]

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]

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]