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The aim of this study was to develop a model for early prediction of adverse events and treatment effectiveness in patients with hyperkalemia. We collected clinical data from patients with hyperkalemia in the First Hospital of Zhejiang University School of Medicine between 2015 and 2021. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to analyze the predictors on the full dataset. We randomly divided the data into a training group and a validation group, and used LASSO to filter variables in the training set. Six machine learning methods were used to develop the models. The best model was selected based on the area under the curve (AUC). Shapley additive exPlanations (SHAP) values were used to explain the best model. A total of 1074 patients with hyperkalemia were finally enrolled. Diastolic blood pressure (DBP), breathing, oxygen saturation (SPO2), Glasgow coma score (GCS), liver disease, oliguria, blood sodium, international standardized ratio (ISR), and initial blood potassium were the predictors of the occurrence of adverse events; peripheral edema, estimated glomerular filtration rate (eGFR), blood sodium, actual base residual, and initial blood potassium were the predictors of therapeutic effect. Extreme gradient boosting (XGBoost) model achieved the best performance (adverse events: AUC = 0.87; therapeutic effect: AUC = 0.75). A model based on clinical characteristics was developed and validated with good performance. [ABSTRACT FROM AUTHOR]

A precise forecast of the need for blood transfusions (BT) in patients undergoing total hip arthroplasty (THA) is a crucial step toward the implementation of precision medicine. To achieve this goal, we utilized supervised machine learning (SML) techniques to establish a predictive model for BT requirements in THA patients. Additionally, we employed unsupervised machine learning (UML) approaches to identify clinical heterogeneity among these patients. In this study, we recruited 224 patients undergoing THA. To identify factors predictive of BT during the perioperative period of THA, we employed LASSO regression and the random forest (RF) algorithm as part of supervised machine learning (SML). Using logistic regression, we developed a predictive model for BT in THA patients. Furthermore, we utilized unsupervised machine learning (UML) techniques to cluster THA patients who required BT based on similar clinical features. The resulting clusters were subsequently visualized and validated. We constructed a predictive model for THA patients who required BT based on six predictive factors: Age, Body Mass Index (BMI), Hemoglobin (HGB), Platelet (PLT), Bleeding Volume, and Urine Volume. Before surgery, 1 h after surgery, 1 day after surgery, and 1 week after surgery, significant differences were observed in HGB and PLT levels between patients who received BT and those who did not. The predictive model achieved an AUC of 0.899. Employing UML, we identified two distinct clusters with significantly heterogeneous clinical characteristics. Age, BMI, PLT, HGB, bleeding volume, and urine volume were found to be independent predictors of BT requirement in THA patients. The predictive model incorporating these six predictors demonstrated excellent predictive performance. Furthermore, employing UML enabled us to classify a heterogeneous cohort of THA patients who received BT in a meaningful and interpretable manner. [ABSTRACT FROM AUTHOR]

High altitude exposure increases the risk of myocardial ischemia (MI) and subsequent cardiovascular death. Machine learning techniques have been used to develop cardiovascular disease prediction models, but no reports exist for high altitude induced myocardial ischemia. Our objective was to establish a machine learning-based MI prediction model and identify key risk factors. Using a prospective cohort study, a predictive model was developed and validated for high-altitude MI. We consolidated the health examination and self-reported electronic questionnaire data (collected between January and June 2022 in 920th Joint Logistic Support Force Hospital of china) of soldiers undergoing high-altitude training, along with the health examination and second self-reported electronic questionnaire data (collected between December 2022 and January 2023) subsequent to their completion on the plateau, into a unified dataset. Participants were subsequently allocated to either the training or test dataset in a 3:1 ratio using random assignment. A predictive model based on clinical features, physical examination, and laboratory results was designed using the training dataset, and the model's performance was evaluated using the area under the receiver operating characteristic curve score (AUC) in the test dataset. Using the training dataset (n = 2141), we developed a myocardial ischemia prediction model with high accuracy (AUC = 0.86) when validated on the test dataset (n = 714). The model was based on five laboratory results: Eosinophils percentage (Eos.Per), Globulin (G), Ca, Glucose (GLU), and Aspartate aminotransferase (AST). Our concise and accurate high-altitude myocardial ischemia incidence prediction model, based on five laboratory results, may be used to identify risks in advance and help individuals and groups prepare before entering high-altitude areas. Further external validation, including female and different age groups, is necessary. [ABSTRACT FROM AUTHOR]

The Quality of Recovery Score-40 (QoR-40) has been increasingly used for assessing recovery after patients undergoing surgery. However, a prediction model estimating quality of recovery is lacking. The aim of the present study was to develop and externally validate a clinical prediction model that predicts quality of recovery up to one week after surgery. The modelling procedure consisted of two models of increasing complexity (basic and full model). To assess the internal validity of the developed model, bootstrapping (1000 times) was applied. At external validation, the model performance was evaluated according to measures for overall model performance (explained variance (R2)) and calibration (calibration plot and slope). The full model consisted of age, sex, previous surgery, BMI, ASA classification, duration of surgery, HADS and preoperative QoR-40 score. At model development, the R2 of the full model was 0.24. At external validation the R2 dropped as expected. The calibration analysis showed that the QoR-40 predictions provided by the developed prediction models are reliable. The presented models can be used as a starting point for future updating in prediction studies. When the predictive performance is improved it could be implemented clinically in the future. [ABSTRACT FROM AUTHOR]

Protocol biopsy is a reliable method for assessing allografts status after kidney transplantation (KT). However, due to the risk of complications, it is necessary to establish indications and selectively perform protocol biopsies by classifying the high-risk group for early subclinical rejection (SCR). Therefore, the purpose of this study is to analyze the incidence and risk factors of early SCR (within 2 weeks) and develop a prediction model using machine learning. Patients who underwent KT at Samsung Medical Center from January 2005 to December 2020 were investigated. The incidence of SCR was investigated and risk factors were analyzed. For the development of prediction model, machine learning methods (random forest, elastic net, extreme gradient boosting [XGB]) and logistic regression were used and the performance between the models was evaluated. The cohorts of 987 patients were reviewed and analyzed. The incidence of SCR was 14.6%. Borderline cellular rejection (BCR) was the most common type of rejection, accounting for 61.8% of cases. In the analysis of risk factors, recipient age (OR 0.98, p = 0.03), donor BMI (OR 1.07, p = 0.02), ABO incompatibility (OR 0.15, p < 0.001), HLA II mismatch (two [OR 6.44, p < 0.001]), and ATG induction (OR 0.41, p < 0.001) were associated with SCR in the multivariate analysis. The logistic regression prediction model (average AUC = 0.717) and the elastic net model (average AUC = 0.712) demonstrated good performance. HLA II mismatch and induction type were consistently identified as important variables in all models. The odds ratio analysis of the logistic prediction model revealed that HLA II mismatch (OR 6.77) was a risk factor for SCR, while ATG induction (OR 0.37) was a favorable factor. Early SCR was associated with HLA II mismatches and induction agent and prediction model using machine learning demonstrates the potential to predict SCR. [ABSTRACT FROM AUTHOR]

The occurrence of systemic inflammatory response after percutaneous transhepatic gallbladder drainage brings great risks to patients and is one of the challenges faced by clinicians. Therefore, it is of great significance to find a suitable prediction method for clinicians to intervene early and reduce the transformation of serious complications. Easy-to-obtain and objectively measured clinical features were screened, and logistic regression was used to construct a prediction model. The predictive ability of the model was evaluated by using the receiver operating characteristic curve and the decision curve in the validation set and the training set, respectively. Nine clinical features (CRP, Fever, DBIL, Obstruction, Bile properties, PCT, Length, Width, and Volume factor) were used to construct the prediction model, and the validation results showed that the prediction model had good performance in the training set (AUC = 0.83) and the validation set (AUC = 0.81). The decision curve also showed that the predictive ability of the model incorporating nine clinical features is better than that of a single clinical feature. The model we constructed can accurately predict the occurrence of SIRS, which can guide clinicians to take treatment measures and avoid the deterioration of complications. [ABSTRACT FROM AUTHOR]

Conventional knowledge graph representation learn the representation of entities and relations by projecting triples in the knowledge graph to a continuous vector space. The vector representation increases the precision of link prediction and the efficiency of downstream tasks. However, these methods cannot process previously unseen entities during the knowledge graph evolution. In other words, the model trained on the source knowledge graph cannot be applied to the target knowledge graph containing new unseen entities. Recently, a few subgraph-based link prediction models obtained the inductive ability, but they all neglect semantic information. In this work, we propose an inductive representation learning model TGraiL which considers not only the topological structure but also semantic information. First, distance in the subgraph is used to encode the node's topological structure. Second, the projection matrix is used to encode the entity type information. Finally, both kinds of information are fused for training to acquire the ultimate vector representation of entities. The experimental results indicate that the model's performance has been significantly improved compared to the existing baseline models, demonstrating the method's effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Understanding the thermal decomposition behavior of TATB (1,3,5-triamino-2,4,6-trinitrobenzene) is a major focus in energetic materials research because of safety issues. Previous research and modelling efforts have suggested benzo-monofurazan condensation producing H2O is the initiating decomposition step. However, early evolving CO2 (m/z 44) along with H2O (m/z 18) evolution have been observed by mass spectrometric monitoring of head-space gases in both constant heating rate and isothermal decomposition studies. The source of the CO2 has not been explained, until now. With the recent successful synthesis of 13C6-TATB (13C incorporated into the benzene ring), the same experiments have been used to show the source of the CO2 is the early breakdown of the TATB ring, not adventitious C from impurities and/or adsorbed CO2. A shift in mass m/z 44 (CO2) to m/z 45 is observed throughout the decomposition process indicating the isotopically labeled 13C ring breakdown occurs at the onset of thermal decomposition along with furazan formation. Partially labeled (N18O2)3-TATB confirms at least some of the oxygen comes from the nitro-groups. This finding has a significant bearing on decomposition computational models for prediction of energy release and deflagration to detonation transitions, with respect to conditions which currently do not recognize this oxidation step. [ABSTRACT FROM AUTHOR]

Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of the 87 patients, 24 (27.6%) developed postoperative delirium. Age, use of psychotropic drugs, cognitive function (Mini-Cog < 4), index of activities of daily living (Barthel Index < 100), history of stroke or cerebral hemorrhage, and eGFR (estimated glomerular filtration rate) < 60 were selected to develop delirium prediction models. The Extra-trees model had the best area under the receiver operating characteristic curve (0.76 [standard deviation 0.11]; sensitivity: 0.63; specificity: 0.78). XGBoost showed the highest sensitivity (AUROC, 0.75 [0.07]; sensitivity: 0.67; specificity: 0.79). Machine learning algorithms could predict post-cardiovascular delirium using preoperative data. Trial registration: UMIN-CTR (ID; UMIN000049390). [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM) based time series forecasting methods suffer from multiple limitations, such as accumulated error, diminishing temporal correlation, and lacking interpretability, which compromises the prediction performance. To overcome these shortcomings, a fuzzy inference-based LSTM with the embedding of a fuzzy system is proposed to enhance the accuracy and interpretability of LSTM for long-term time series prediction. Firstly, a fast and complete fuzzy rule construction method based on Wang–Mendel (WM) is proposed, which can enhance the computational efficiency and completeness of the WM model by fuzzy rules simplification and complement strategies. Then, the fuzzy prediction model is constructed to capture the fuzzy logic in data. Finally, the fuzzy inference-based LSTM is proposed by integrating the fuzzy prediction fusion, the strengthening memory layer, and the parameter segmentation sharing strategy into the LSTM network. Fuzzy prediction fusion increases the network reasoning capability and interpretability, the strengthening memory layer strengthens the long-term memory and alleviates the gradient dispersion problem, and the parameter segmentation sharing strategy balances processing efficiency and architecture discrimination. Experiments on publicly available time series demonstrate that the proposed method can achieve better performance than existing models for long-term time series prediction. [ABSTRACT FROM AUTHOR]

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India. [ABSTRACT FROM AUTHOR]

Cell death-related genes indicate prognosis in cancer patients. PANoptosis is a newly observed form of cell death that researchers have linked to cancer cell death and antitumor immunity. Even so, its significance in lung adenocarcinomas (LUADs) has yet to be elucidated. We extracted and analyzed data on mRNA gene expression and clinical information from public databases in a systematic manner. These data were utilized to construct a reliable risk prediction model for six regulators of PANoptosis. The Gene Expression Omnibus (GEO) database validated six genes with risk characteristics. The prognosis of LUAD patients could be accurately estimated by the six-gene-based model: NLR family CARD domain-containing protein 4 (NLRC4), FAS-associated death domain protein (FADD), Tumor necrosis factor receptor type 1-associated DEATH domain protein (TRADD), Receptor-interacting serine/threonine-protein kinase 1 (RIPK1), Proline-serine-threonine phosphatase-interacting protein 2 (PSTPIP2), and Mixed lineage kinase domain-like protein (MLKL). Group of higher risk and Cluster 2 indicated a poor prognosis as well as the reduced expression of immune infiltrate molecules and human leukocyte antigen. Distinct expression of PANoptosis-related genes (PRGs) in lung cancer cells was verified using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, we evaluated the relationship between PRGs and somatic mutations, tumor immune dysfunction exclusion, tumor stemness indices, and immune infiltration. Using the risk signature, we conducted analyses including nomogram construction, stratification, prediction of small-molecule drug response, somatic mutations, and chemotherapeutic response. [ABSTRACT FROM AUTHOR]

Heating and cooling degree hours (HDH and CDH) are weather-based technical indexes designed to describe the need for energy requirements of buildings. Their calculation is the simplest method to estimate energy demand, providing the pattern of internal temperature variations in a building in response to weather conditions. The aim of the study is HDH and CDH prediction for Wrocław, Poland, based on outdoor air temperature using machine learning methods: artificial neural networks and support vector regression (ANN and SVR). The key issues raise in the study are: a detailed analysis of the most significant temperature lags (from 1 to 24 past hours) serving as predictors for modelling and an assessment of the impact of the database clustering on its accuracy. The best results are obtained with the clustering approach. The best predictor is the outdoor temperature observed 1 and 24 h before forecast demand (R2 = 0.981 and 0.904 for heating degree and cooling degree hours indices, respectively). Models with the highest quality are created using ANN, and the lowest with SVR. Prediction of heating/cooling degree hour indices provides building demand in advance, does not require knowledge about its characteristics, and expresses the possible impact of regional climate modifications. [ABSTRACT FROM AUTHOR]

In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models. [ABSTRACT FROM AUTHOR]

Eurasian forest-steppes form a 9000-km-long transitional zone between temperate forests and steppes, featuring a complex mosaic of herbaceous and woody habitats. Due to its heterogeneity regarding climate, topography and vegetation, the forest-steppe zone has been divided into several regions. However, a continental-scale empirical delineation of the zone and its regions was missing until recently. Finally, a map has been proposed by Erdős et al. based on floristic composition, physiognomy, relief, and climate. By conducting predictive distribution modeling and hierarchical clustering, here we compared this expert delineation with the solely macroclimate-based predictions and clusters. By assessing the discrepancies, we located the areas where refinement of the delineation or the inclusion of non-macroclimatic predictors should be considered. Also, we identified the most important variables for predicting the existence of the Eurasian forest-steppe zone and its regions. The predicted probability of forest-steppe occurrence showed a very high agreement with the expert delineation. The previous delineation of the West Siberia region was confirmed by our results, while that of the Inner Asia region was the one least confirmed by the macroclimate-based model predictions. The appropriate delineation of the Southeast Europe region from the East Europe region should be refined by further research, and splitting the Far East region into a southern and northern subregion should also be considered. The main macroclimatic predictors of the potential distribution of the zone and its regions were potential evapotranspiration (zone and regions), annual mean temperature (regions), precipitation of driest quarter (regions) and precipitation of warmest quarter (zone), but the importance of climatic variables for prediction showed great variability among the fitted predictive distribution models. [ABSTRACT FROM AUTHOR]

Our goal was to identify highly accurate empirical models for the prediction of the risk of febrile seizure (FS) and FS recurrence. In a prospective, three-arm, case–control study, we enrolled 162 children (age 25.8 ± 17.1 months old, 71 females). Participants formed one case group (patients with FS) and two control groups (febrile patients without seizures and healthy controls). The impact of blood iron status, peak body temperature, and participants' demographics on FS risk and recurrence was investigated with univariate and multivariate statistics. Serum iron concentration, iron saturation, and unsaturated iron-binding capacity differed between the three investigated groups (pFWE < 0.05). These serum analytes were key variables in the design of novel multivariate linear mixture models. The models classified FS risk with higher accuracy than univariate approaches. The designed bi-linear classifier achieved a sensitivity/specificity of 82%/89% and was closest to the gold-standard classifier. A multivariate model assessing FS recurrence provided a difference (pFWE < 0.05) with a separating sensitivity/specificity of 72%/69%. Iron deficiency, height percentile, and age were significant FS risk factors. In addition, height percentile and hemoglobin concentration were linked to FS recurrence. Novel multivariate models utilizing blood iron status and demographic variables predicted FS risk and recurrence among infants and young children with fever. [ABSTRACT FROM AUTHOR]

The roughness of the part surface is one of the most crucial standards for evaluating machining quality due to its relationship with service performance. For a preferable comprehension of the evolution of surface roughness, this study proposes a novel surface roughness prediction model on the basis of the unity of fuse d signal features and deep learning architecture. The force and vibration signals produced in the milling of P20 die steel are collected, and time and frequency domain feature from the acquired signals are extracted by variational modal decomposition. The GA-MI algorithm is taken to select the signal features that are relevant to the surface roughness of the workpiece. The optimal feature subset is analyzed and used as the input of the prediction model. DBN is adopted to estimate the surface roughness and the model parameters are optimized by ISSA. (Reviewer 1, Q1) The separate force, vibration and fusion signal information are brought into the DBN model and the ISSA-DBN model for the prediction of surface roughness, and the results show that the accuracy of the roughness prediction is as follows, respectively DBN: 78.1%, 68.8% and 84.4%, and ISSA-DBN: 93.8%, 87.5% and 100%. [ABSTRACT FROM AUTHOR]

Sepsis-associated acute kidney injury is a common complication of sepsis, but it is difficult to predict sepsis-associated acute kidney injury. In this retrospective observational study, adult septic patients were recruited from the MIMIC-III database as the training cohort (n = 4764) and from Xiangya Hospital (n = 1568) and Zhang's database as validation cohorts. We identified eleven predictors with seven independent risk predictors of sepsis-associated acute kidney injury [fluid input_day1 ≥ 3390 ml (HR hazard ratio 1.42), fluid input_day2 ≥ 2734 ml (HR 1.64), platelet_min_day5 ≤ 224.2 × 109/l (HR 0.86), length of ICU stay ≥ 2.5 days (HR 1.24), length of hospital stay ≥ 5.8 days (HR 1.18), Bun_max_day1 ≥ 20 mmol/l (HR 1.20), and mechanical ventilation time ≥ 96 h (HR 1.11)] by multivariate Cox regression analysis, and the eleven predictors were entered into the nomogram. The nomogram model showed a discriminative ability for estimating sepsis-associated acute kidney injury. These results indicated that clinical parameters such as excess input fluid on the first and second days after admission and longer mechanical ventilation time could increase the risk of developing sepsis-associated acute kidney injury. With our study, we built a real-time prediction model for potentially forecasting acute kidney injury in septic patients that can help clinicians make decisions as early as possible to avoid sepsis-associated acute kidney injury. [ABSTRACT FROM AUTHOR]

Offshore macroalgae production could provide an alternative source of biomass for food, materials and energy. However, the offshore environment in general, specifically the Eastern Mediterranean Sea (EMS) offshore, is a high energy and low nutrients environment, thus challenging for macroalgae farming. In this study, we experimentally investigated the impact of season, depth, and pre-cultivation fertilization duration on the growth rates and chemical composition of offshore Ulva biomass, and developed a predictive model tailored to offshore conditions, capable of estimating both biomass growth rate and nitrogen content. Specifically, we measured Ulva biomass growth rate and internal nitrogen in the nitrogen-poor EMS a few kilometers offshore the Israeli coast at various depths and on-shore pre-cultivation fertilization schedules. Based on these data, we constructed a predictive cultivation model of Ulva offshore growth, which allows for the optimization of fertilization requirements for offshore cultivation. This study provides new insights on the effects of seasonality, depth, and pre-cultivation fertilization duration on growth rates and chemical composition of offshore Ulva sp. biomass production. [ABSTRACT FROM AUTHOR]

College students' sports behavior is affected by many factors, and sports learning interest and sports autonomy support are potential psychological characteristic factors, which have important influence value on college students' sports behavior. Machine learning methods are widely used to construct prediction models and show high efficiency. In order to understand the impact of sports learning interest and sports autonomy support on college students' sports behavior (physical exercise level), the research decided to use the relevant methods of machine learning to build a prediction model, so as to find the internal relationship between them. This paper summarizes the relevant factors that affect college students' sports behavior (physical exercise level) from two aspects, namely, sports autonomy and sports learning interest, and surveys the demographic and sociological information of college students as a supplement. The research evaluates the level of the prediction model through the construction of the prediction model of the machine learning algorithm and the comparison method, so as to determine the optimal prediction model. The results show that the prediction accuracy of the logistic regression model is 0.7288, the recall rate is 0.7590, and F1 is 0.7397; The prediction accuracy of KNN model is 0.6895, the recall rate is 0.7596, and F1 is 0.7096; The prediction accuracy of naive Bayesian model is 0.7166, the recall rate is 0.6703, and F1 is 0.6864; the prediction accuracy of LDA model is 0.7263, the recall rate is 0.7290, and F1 is 0.7265; The prediction accuracy of the support vector machine model is 0.6563, the recall rate is 0.7700, and F1 is 0.6845; The prediction accuracy of GBDT model is 0.6953, the recall rate is 0.7039, and the F1 score is 0.6989; The prediction accuracy of the decision tree model is 0.6872, the recall rate is 0.6507, and F1 is 0.6672. The logistic regression model performs best in the combination of sports learning interest and motor autonomy support, due to the combination of its linear classification characteristics, better adaptability, high computational efficiency, and better adaptability to feature selection and outlier processing. The conclusion points out that the prediction level of logistic regression model is the highest when combining sports learning interest and sports autonomy support to predict college students' sports behavior (sports exercise grade), which also provides an important reference for improving college students' sports behavior (sports exercise grade). [ABSTRACT FROM AUTHOR]

Cusp-shaped fluctuations of the sea surface temperature (SST) front in the tropical Pacific, now known as tropical instability waves (TIWs), were discovered by remote sensing in the 1970s. Their discovery was followed by both theoretical and analytical studies, which, along with in situ observations, identified several possible generation mechanisms. Although modeling studies have shown that TIWs strongly influence the heat budget, their influence on local variations of realistically initialized predictions is not yet understood. We here evaluate a series of medium-range (up to ~ 10 days) coupled atmosphere–ocean predictions by a coupled model with different horizontal resolutions. Observational SST, surface wind stress, heat flux, and pressure data showed that representation of temporally and spatially local variations was improved by resolving fine-scale SST variations around the initialized coarse-scale SST front fluctuations of TIWs. Our study thus demonstrates the advantage of using high-resolution coupled models for medium-range predictions. In addition, analysis of TIW energetics showed two dominant sources of energy to anticyclonic eddies: barotropic instability between equatorial zonal currents and baroclinic instability due to intense density fronts. In turn, the eddy circulation strengthened both instabilities in the resolved simulations. This revealed feedback process refines our understanding of the generation mechanisms of TIWs. [ABSTRACT FROM AUTHOR]

In practical deep-learning applications, such as medical image analysis, autonomous driving, and traffic simulation, the uncertainty of a classification model's output is critical. Evidential deep learning (EDL) can output this uncertainty for the prediction; however, its accuracy depends on a user-defined threshold, and it cannot handle training data with unknown classes that are unexpectedly contaminated or deliberately mixed for better classification of unknown class. To address these limitations, I propose a classification method called modified-EDL that extends classical EDL such that it outputs a prediction, i.e. an input belongs to a collective unknown class along with a probability. Although other methods handle unknown classes by creating new unknown classes and attempting to learn each class efficiently, the proposed m-EDL outputs, in a natural way, the "uncertainty of the prediction" of classical EDL and uses the output as the probability of an unknown class. Although classical EDL can also classify both known and unknown classes, experiments on three datasets from different domains demonstrated that m-EDL outperformed EDL on known classes when there were instances of unknown classes. Moreover, extensive experiments under different conditions established that m-EDL can predict unknown classes even when the unknown classes in the training and test data have different properties. If unknown class data are to be mixed intentionally during training to increase the discrimination accuracy of unknown classes, it is necessary to mix such data that the characteristics of the mixed data are as close as possible to those of known class data. This ability extends the range of practical applications that can benefit from deep learning-based classification and prediction models. [ABSTRACT FROM AUTHOR]

To investigate the mechanical properties of fractured dolomite, this study analyzed the fracture characteristics (dip angle, length, position, quantity) using the Pearson coefficient and MIC coefficient. Subsequently, the data pertaining to fracture characteristics is preprocessed using a third-degree polynomial, and a three-classification strategy is implemented to improve the logistic regression algorithm to establish the strength prediction model of fractured dolomite. Furthermore, the significance order of the impact of fracture characteristics on rock strength was determined using the numerical simulation software PFC3D, and the dip angle effect was explained from the perspective of internal fracture propagation within the rock. The results show that: (1) When the regularization coefficient λ = 10,000, the algorithm has the highest prediction accuracy and the strongest model generalization ability. (2) The numerical simulation analysis software PFC3D can accurately invert rock failure process and characteristics, and the order of influence of fracture characteristics on rock strength is dip angle > length > position. [ABSTRACT FROM AUTHOR]

Prior to further processing, completed questionnaires must be screened for the presence of careless respondents. Different people will respond to surveys in different ways. Some take the easy path and fill out the survey carelessly. The proportion of careless respondents determines the survey's quality. As a result, identifying careless respondents is critical for the quality of obtained results. This study aims to explore the characteristics of careless respondents in survey data and evaluate the predictive power and interpretability of different types of data and indices of careless responding. The research question focuses on understanding the behavior of careless respondents and determining the effectiveness of various data sources in predicting their responses. Data from a three-month web-based survey on participants' personality traits such as honesty-humility, emotionality, extraversion, agreeableness, conscientiousness and openness to experience was used in this study. Data for this study was taken from Schroeders et al.. The gradient boosting machine-based prediction model uses data from the answers, time spent for answering, demographic information on the respondents as well as some indices of careless responding from all three types of data. Prediction models were evaluated with tenfold cross-validation repeated a hundred times. Prediction models were compared based on balanced accuracy. Models' explanations were provided with Shapley values. Compared with existing work, data fusion from multiple types of information had no noticeable effect on the performance of the gradient boosting machine model. Variables such as "I would never take a bribe, even if it was a lot", average longstring, and total intra-individual response variability were found to be useful in distinguishing careless respondents. However, variables like "I would be tempted to use counterfeit money if I could get away with it" and intra-individual response variability of the first section of a survey showed limited effectiveness. Additionally, this study indicated that, whereas the psychometric synonym score has an immediate effect and is designed with the goal of identifying careless respondents when combined with other variables, it is not necessarily the optimal choice for fitting a gradient boosting machine model. [ABSTRACT FROM AUTHOR]

The agricultural productivity of smallholder farmers in sub-Saharan Africa (SSA) is severely constrained by pests and pathogens, impacting economic stability and food security. An epidemic of cassava brown streak disease, causing significant yield loss, is spreading rapidly from Uganda into surrounding countries. Based on sparse surveillance data, the epidemic front is reported to be as far west as central DRC, the world's highest per capita consumer, and as far south as Zambia. Future spread threatens production in West Africa including Nigeria, the world's largest producer of cassava. Using innovative methods we develop, parameterise and validate a landscape-scale, stochastic epidemic model capturing the spread of the disease throughout Uganda. The model incorporates real-world management interventions and can be readily extended to make predictions for all 32 major cassava producing countries of SSA, with relevant data, and lays the foundations for a tool capable of informing policy decisions at a national and regional scale. [ABSTRACT FROM AUTHOR]

The accurate identification and real-time detection of obstacles have been considered the premise to ensure the safe operation of coal mine driverless electric locomotives. The harsh coal mine roadway environment leads to low detection accuracy of obstacles based on traditional detection methods such as LiDAR and machine learning, and these traditional obstacle detection methods lead to slower detection speeds due to excessive computational reasoning. To address the above-mentioned problems, we propose a deep learning-based ODEL-YOLOv5s detection model based on the conventional YOLOv5s. In this work, several data augmentation methods are introduced to increase the diversity of obstacle features in the dataset images. An attention mechanism is introduced to the neck of the model to improve the focus of the model on obstacle features. The three-scale prediction of the model is increased to a four-scale prediction to improve the detection ability of the model for small obstacles. We also optimize the localization loss function and non-maximum suppression method of the model to improve the regression accuracy and reduce the redundancy of the prediction boxes. The experimental results show that the mean average precision (mAP) of the proposed ODEL-YOLOv5s model is increased from 95.2 to 98.9% compared to the conventional YOLOv5s, the average precision of small obstacle rock is increased from 89.2 to 97.9%, the detection speed of the model is 60.2 FPS, and it has better detection performance compared with other detection models, which can provide technical support for obstacle identification and real-time detection of coal mine driverless electric locomotives. [ABSTRACT FROM AUTHOR]

Phyllachora maydis is a fungal pathogen causing tar spot of corn (Zea mays L.), a new and emerging, yield-limiting disease in the United States. Since being first reported in Illinois and Indiana in 2015, P. maydis can now be found across much of the corn growing regions of the United States. Knowledge of the epidemiology of P. maydis is limited but could be useful in developing tar spot prediction tools. The research presented here aims to elucidate the environmental conditions necessary for the development of tar spot in the field and the creation of predictive models to anticipate future tar spot epidemics. Extended periods (30-day windowpanes) of moderate mean ambient temperature (18–23 °C) were most significant for explaining the development of tar spot. Shorter periods (14- to 21-day windowpanes) of moisture (relative humidity, dew point, number of hours with predicted leaf wetness) were negatively correlated with tar spot development. These weather variables were used to develop multiple logistic regression models, an ensembled model, and two machine learning models for the prediction of tar spot development. This work has improved the understanding of P. maydis epidemiology and provided the foundation for the development of a predictive tool for anticipating future tar spot epidemics. [ABSTRACT FROM AUTHOR]

The certification of wine quality is essential to the wine industry. The main goal of this work is to develop a machine learning model to forecast wine quality using the dataset. We utilised samples from the red wine dataset (RWD) with eleven distinct physiochemical properties. With the initial RWD, five machine learning (ML) models were trained and put to the test. The most accurate algorithms are Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Using these two ML approaches, the top three features from a total of eleven features are chosen, and ML analysis is performed on the remaining features. Several graphs are employed to demonstrate the feature importance based on the XGBoost model and RF. Wine quality was predicted using relevant characteristics, often referred to as fundamental elements, that were shown to be essential during the feature selection procedure. When trained and tested without feature selection, with feature selection (RF), and with key attributes, the XGBoost classifier displayed 100% accuracy. In the presence of essential variables, the RF classifier performed better. Finally, to assess the precision of their predictions, the authors trained an RF classifier, validated it, and changed its hyperparameters. To address collinearity and decrease the quantity of predictors without sacrificing model accuracy, we have also used cluster analysis. [ABSTRACT FROM AUTHOR]

Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states. Physics informed neural networks (PINNs) leverage deep learning methods to provide an alternative approach to RT modeling. Applications of PINNs in monitoring of RTs for operator support requires near real-time model performance. However, as with all machine learning models, development of a PINN involves time-consuming model training. Here, we show that a transfer learning (TL-PINN) approach achieves significant performance gain, as measured by reduction of the number of iterations for model training. Using point kinetic equations (PKEs) model with six neutron precursor groups, constructed with experimental parameters of the Purdue University Reactor One (PUR-1) research reactor, we generated different RTs with experimentally relevant range of variables. The RTs were characterized using Hausdorff and Fréchet distance. We have demonstrated that pre-training TL-PINN on one RT results in up to two orders of magnitude acceleration in prediction of a different RT. The mean error for conventional PINN and TL-PINN models prediction of neutron densities is smaller than 1%. We have developed a correlation between TL-PINN performance acceleration and similarity measure of RTs, which can be used as a guide for application of TL-PINNs. [ABSTRACT FROM AUTHOR]

The mechanism of coal and gas outburst disasters is perplexing, and the evaluation methods of outburst disasters based on various sensitive indicators often have some imprecision and fuzziness. With the concept of accurate and intelligent mining in coal mines proposed in China, selecting quantifiable parameters for machine learning risk prediction can avoid the deviation caused by human subjectivity, and improve the accuracy of coal and gas outburst prediction. Aiming at the shortcomings of the support vector machine (SVM) such as low noise resistance and being prone to be influenced by parameters easily, this research proposed a prediction method based on a grey wolf optimizer to optimize the support vector machine (GWO-SVM). To coordinate the global and local optimization ability of the GWO, Tent Chaotic Mapping and DLH strategies were introduced to improve the optimization ability of the GWO and reduce the local optimal probability. The improved prediction model IGWO-SVM was used to predict the coal and gas outburst. The results showed that this model has faster training speed and higher classification prediction accuracy than the SVM and GWO-SVM models, which the accuracy rate reaching 100%. Finally, to obtain the correlation between the parameters of the coal and gas outburst prediction parameters, the random forest algorithm was used for training, and the three parameters with the highest feature importance were selected to rebuild the data set for machine learning. The accuracy of the IGWO-SVM outburst prediction model based on Random Forest was still 100%. Therefore, even if some prediction parameters are missing, the outburst can still be effectively predicted by using the RF-IGWO-SVM model, which is beneficial for the model application and underground safety management. [ABSTRACT FROM AUTHOR]

A model-based planning tool, integrated in an imaging system, is envisioned for CT-guided percutaneous microwave ablation. This study aims to evaluate the biophysical model performance, by comparing its prediction retrospectively with the actual ablation ground truth from a clinical dataset in liver. The biophysical model uses a simplified formulation of heat deposition on the applicator and a heat sink related to vasculature to solve the bioheat equation. A performance metric is defined to assess how the planned ablation overlaps the actual ground truth. Results demonstrate superiority of this model prediction compared to manufacturer tabulated data and a significant influence of the vasculature cooling effect. Nevertheless, vasculature shortage due to branches occlusion and applicator misalignment due to registration error between scans affects the thermal prediction. With a more accurate vasculature segmentation, occlusion risk can be estimated, whereas branches can be used as liver landmarks to improve the registration accuracy. Overall, this study emphasizes the benefit of a model-based thermal ablation solution in better planning the ablation procedures. Contrast and registration protocols must be adapted to facilitate its integration into the clinical workflow. [ABSTRACT FROM AUTHOR]

With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based and genetic risk factors(rs2227306/IL8). Our pipeline includes (1) leveraging phenotyping algorithm to minimize temporal bias, (2) performing simulation studies to determine the predictive power in samples without genetic information, (3) propensity score matching to control for the confoundings, (4) selecting machine learning algorithms to capture complex feature interactions, (5) performing oversampling to address data imbalance, and (6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups. [ABSTRACT FROM AUTHOR]

Customer churn remains a critical challenge in telecommunications, necessitating effective churn prediction (CP) methodologies. This paper introduces the Enhanced Gradient Boosting Model (EGBM), which uses a Support Vector Machine with a Radial Basis Function kernel (SVMRBF) as a base learner and exponential loss function to enhance the learning process of the GBM. The novel base learner significantly improves the initial classification performance of the traditional GBM and achieves enhanced performance in CP-EGBM after multiple boosting stages by utilizing state-of-the-art decision tree learners. Further, a modified version of Particle Swarm Optimization (PSO) using the consumption operator of the Artificial Ecosystem Optimization (AEO) method to prevent premature convergence of the PSO in the local optima is developed to tune the hyper-parameters of the CP-EGBM effectively. Seven open-source CP datasets are used to evaluate the performance of the developed CP-EGBM model using several quantitative evaluation metrics. The results showed that the CP-EGBM is significantly better than GBM and SVM models. Results are statistically validated using the Friedman ranking test. The proposed CP-EGBM is also compared with recently reported models in the literature. Comparative analysis with state-of-the-art models showcases CP-EGBM's promising improvements, making it a robust and effective solution for churn prediction in the telecommunications industry. [ABSTRACT FROM AUTHOR]

Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004–2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell's concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC. [ABSTRACT FROM AUTHOR]

Scientific analysis of regional agricultural carbon emission prediction models and empirical studies are of great practical significance to the realization of low-carbon agriculture, which can help revitalize and build up ecological and beautiful countryside in China. This paper takes agriculture in Guangdong Province, China, as the research object, and uses the extended STIPAT model to construct an indicator system for the factors influencing agricultural carbon emissions in Guangdong. Based on this system, a combined Isomap–ACO–ET prediction model combing the isometric mapping algorithm (Isomap), ant colony algorithm (ACO) and extreme random tree algorithm (ET) was used to predict agriculture carbon emissions in Guangdong Province under five scenarios. Effective predictions can be made for agricultural carbon emissions in Guangdong Province, which are expected to fluctuate between 11,142,200 tons and 11,386,000 tons in 2030. And compared with other machine learning and neural network models, the Isomap–ACO–ET model has a better prediction performance with an MSE of 0.00018 and an accuracy of 98.7%. To develop low-carbon agriculture in Guangdong Province, we should improve farming methods, reduce the intensity of agrochemical application, strengthen the development and promotion of agricultural energy-saving and emission reduction technologies and low-carbon energy sources, reduce the intensity of carbon emissions from agricultural energy consumption, optimize the agricultural planting structure, and develop green agricultural products and agro-ecological tourism according to local conditions. This will promote the development of agriculture in Guangdong Province in a green and sustainable direction. [ABSTRACT FROM AUTHOR]

Septic patients in the intensive care unit (ICU) often develop sepsis-associated delirium (SAD), which is strongly associated with poor prognosis. The aim of this study is to develop a machine learning-based model for the early prediction of SAD. Patient data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were divided into a training set and an internal validation set, while the eICU-CRD data served as an external validation set. Feature variables were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, support vector machines, decision trees, random forests, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. The performance of the models was evaluated in the validation set. The model was also applied to a group of patients who were not assessed or could not be assessed for delirium. The MIMIC-IV and eICU-CRD databases included 14,620 and 1723 patients, respectively, with a median time to diagnosis of SAD of 24 and 30 h. Compared with Non-SAD patients, SAD patients had higher 28-days ICU mortality rates and longer ICU stays. Among the models compared, the XGBoost model had the best performance and was selected as the final model (internal validation area under the receiver operating characteristic curves (AUROC) = 0.793, external validation AUROC = 0.701). The XGBoost model outperformed other models in predicting SAD. The establishment of this predictive model allows for earlier prediction of SAD compared to traditional delirium assessments and is applicable to patients who are difficult to assess with traditional methods. [ABSTRACT FROM AUTHOR]

Lymphedema is a disease that refers to tissue swelling caused by an accumulation of protein-rich fluid that is usually drained through the lymphatic system. Detection of lymphedema is often based on expensive diagnoses such as bioimpedance spectroscopy, shear wave elastography, computed tomography, etc. In current machine learning models for lymphedema prediction, reliance on observable symptoms reported by patients introduces the possibility of errors in patient-input data. Moreover, these symptoms are often absent during the initial stages of lymphedema, creating challenges in its early detection. Identifying lymphedema before these observable symptoms manifest would greatly benefit patients by potentially minimizing the discomfort caused by these symptoms. In this study, we propose to use new data, such as complete blood count, serum, and therapy data, to develop predictive models for lymphedema. This approach aims to compensate for the limitations of using only observable symptoms data. We collected data from 2137 patients, including 356 patients with lymphedema and 1781 patients without lymphedema, with the lymphedema status of each patient confirmed by clinicians. The data for each patient included: (1) a complete blood count (CBC) test, (2) a serum test, and (3) therapy information. We used various machine learning algorithms (i.e. random forest, gradient boosting, decision tree, logistic regression, and artificial neural network) to develop predictive models on the training dataset (i.e. 80% of the data) and evaluated the models on the external validation dataset (i.e. 20% of the data). After selecting the best predictive models, we created a web application to aid medical doctors and clinicians in the rapid screening of lymphedema patients. A dataset of 2137 patients was assembled from Seoul National University Bundang Hospital. Predictive models based on the random forest algorithm exhibited satisfactory performance (balanced accuracy = 87.0 ± 0.7%, sensitivity = 84.3 ± 0.6%, specificity = 89.1 ± 1.5%, precision = 97.4 ± 0.7%, F1 score = 90.4 ± 0.4%, and AUC = 0.931 ± 0.007). We developed a web application to facilitate the swift screening of lymphedema among medical practitioners: https://snubhtxt.shinyapps.io/SNUBH%5fLymphedema. Our study introduces a novel tool for the early detection of lymphedema and establishes the foundation for future investigations into predicting different stages of the condition. [ABSTRACT FROM AUTHOR]

Nowadays, exoskeleton is broadly used in the rehabilitation training of many postoperative patients. However, the uncertainty and disturbances caused by different patients and system itself may lead to incompletely rehabilitation training as planned, or even unsafety. This paper addresses the control problem of a lower limb exoskeleton, in the spirit of the recent progress on model predictive control (MPC) and extended state observer (ESO). More precisely, our approach is based on the strategy that designing an ESO to estimate the total disturbance of the dynamics model and compensating it in the design of the MPC process. To accomplish this, we introduce the virtual control quantity to decouple the dynamics model of the system and summarize the human disturbances, unmeasured states and system non-linearity as the total disturbance of the model. By doing so, the uncertainty can be estimated by our designed ESO. Based on the moving horizontal optimization and feedback mechanism of MPC, the output prediction of the system can be more accurate since the uncertainty are effectively compensated. The virtual experiment results demonstrate that proposed controller significantly improves the control accuracy on lower limb rehabilitation exoskeleton with disturbances (improved by over 34 % ), comparing with conventional MPC and fuzzy PID. As a result, our achievements will make contributions to better rehabilitation training for patients using rehabilitation exoskeletons. [ABSTRACT FROM AUTHOR]

We compared the predictive ability of the International Prognostic Index (IPI), a frequently used prognostic model for peripheral T-cell lymphoma (PTCL), with that of a type-specific prognostic model, the Prognostic Index for PTCL-U (PIT). We retrospectively analyzed 113 patients diagnosed with PTCL. The median age was 67 years (range, 16–88 years), 75 patients (66%) were male, and the most common disease type was PTCL, not otherwise specified (69%). With a median follow-up of 6.8 years (interquartile range, 2.7–9.9 years), 5-year survival rates for the four groups in IPI were 85%, 62%, 49%, and 13%, respectively. Similarly, 5-year survival rates for the four groups in PIT were 83%, 64%, 49%, and 19%, respectively. The area under the receiving operating characteristic curve for predicting mortality from PIT (0.725) was not significantly different from that from the IPI (0.685, P = 0.134). Multivariable analysis showed that performance status ≥ 2 (P < 0.0001) and extranodal lesions ≥ 2 (P = 0.029) were significantly associated with lower overall survival. The present study found no significant difference in prognostic ability between the IPI and PIT for PTCL, and both models appear useful as predictive models. [ABSTRACT FROM AUTHOR]

This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS. [ABSTRACT FROM AUTHOR]

Patients with parathyroid carcinoma (PC) are often diagnosed postoperatively, due to incomplete resection during the initial surgery, resulting in poor outcomes. The aim of our study was to investigate the pre-surgery indicators of PC and try to develop a predictive model for PC utilizing machine learning. Evaluation of pre-surgery neuropsychological function and confirmation of pathology were carried out in 133 patients with primary hyperparathyroidism in Beijing Chaoyang Hospital from December 2019 to January 2023. Patients were randomly divided into a training cohort (n = 93) and a validating cohort (n = 40). Analysis of the clinical dataset, two machine learning including the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression were utilized to develop the prediction model for PC. Logistic regression analysis was also conducted for comparison. Significant differences in elevated parathyroid hormone and decreased serum phosphorus in PC compared to (BP). The lower score of MMSE and MOCA was observed in PC and a cutoff of MMSE < 24 was the optimal threshold to stratify PC from BP (area under the curve AUC 0.699 vs 0.625). The predicted probability of PC by machine learning was similar to the observed probability in the test set, whereas the logistic model tended to overpredict the possibility of PC. The XGBoost model attained a higher AUC than the logistic algorithms and LASSO models. (0.835 vs 0.683 vs 0.607). Preoperative cognitive function may be a probable predictor for PC. The cognitive function-based prediction model based on the XGBoost algorithm outperformed LASSO and logistic regression, providing valuable preoperative assistance to surgeons in clinical decision-making for patients suspected PC. [ABSTRACT FROM AUTHOR]

Older adults are more likely to require emergency department (ED) visits than others, which might be attributed to their medication use. Being able to predict the likelihood of an ED visit using prescription information and readily available data would be useful for primary care. This study aimed to predict the likelihood of ED visits using extensive medication variables generated according to explicit clinical criteria for elderly people and high-risk medication categories by applying machine learning (ML) methods. Patients aged ≥ 65 years were included, and ED visits were predicted with 146 variables, including demographic and comprehensive medication-related factors, using nationwide claims data. Among the eight ML models, the final model was developed using LightGBM, which showed the best performance. The final model incorporated 93 predictors, including six sociodemographic, 28 comorbidity, and 59 medication-related variables. The final model had an area under the receiver operating characteristic curve of 0.689 in the validation cohort. Approximately half of the top 20 strong predictors were medication-related variables. Here, an ED visit risk prediction model for older people was developed and validated using administrative data that can be easily applied in clinical settings to screen patients who are likely to visit an ED. [ABSTRACT FROM AUTHOR]

Febrile seizures (FS) are the most prevalent type of seizures in children. Existing predictive models for FS exhibit limited predictive ability. To build a better-performing predictive model, a retrospective analysis study was conducted on febrile children who visited the Children's Hospital of Shanghai from July 2020 to March 2021. These children were divided into training set (n = 1453), internal validation set (n = 623) and external validation set (n = 778). The variables included demographic data and complete blood counts (CBCs). The least absolute shrinkage and selection operator (LASSO) method was used to select the predictors of FS. Multivariate logistic regression analysis was used to develop a predictive model. The coefficients derived from the multivariate logistic regression were used to construct a nomogram that predicts the probability of FS. The calibration plot, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were used to evaluate model performance. Results showed that the AUC of the predictive model in the training set was 0.884 (95% CI 0.861 to 0.908, p < 0.001) and C-statistic of the nomogram was 0.884. The AUC of internal validation set was 0.883 (95% CI 0.844 to 0.922, p < 0.001), and the AUC of external validation set was 0.858 (95% CI 0.820 to 0.896, p < 0.001). In conclusion, the FS predictive model constructed based on CBCs in this study exhibits good predictive ability and has clinical application value. [ABSTRACT FROM AUTHOR]

Acute exacerbation (AE) of chronic obstructive pulmonary disease (COPD) compromises health status; it increases disease progression and the risk of future exacerbations. We aimed to develop a model to predict COPD exacerbation. We merged the Korean COPD subgroup study (KOCOSS) dataset with nationwide medical claims data, information regarding weather, air pollution, and epidemic respiratory virus data. The Korean National Health and Nutrition Examination Survey (KNHANES) dataset was used for validation. Several machine learning methods were employed to increase the predictive power. The development dataset consisted of 590 COPD patients enrolled in the KOCOSS cohort; these were randomly divided into training and internal validation subsets on the basis of the individual claims data. We selected demographic and spirometry data, medications for COPD and hospital visit for AE, air pollution data and meteorological data, and influenza virus data as contributing factors for the final model. Six machine learning and logistic regression tools were used to evaluate the performance of the model. A light gradient boosted machine (LGBM) afforded the best predictive power with an area under the curve (AUC) of 0.935 and an F1 score of 0.653. Similar favorable predictive performance was observed for the 2151 individuals in the external validation dataset. Daily prediction of the COPD exacerbation risk may help patients to rapidly assess their risk of exacerbation and will guide them to take appropriate intervention in advance. This might lead to reduction of the personal and socioeconomic burdens associated with exacerbation. [ABSTRACT FROM AUTHOR]

In a first study, this paper argues and demonstrates that spiking neural networks (SNN) can be successfully used for predictive and explainable modelling of multimodal streaming data. The paper proposes a new method, where both time series and on-line news are integrated as numerical streaming data in the same time domain and then used to train incrementally a SNN model. The connectivity and the spiking activity of the SNN are then analyzed through clustering and dynamic graph extraction to reveal on-line interaction between all input variables in regard to the predicted one. The paper answers the main research question of how to understand the dynamic interaction of time series and on-line news through their integrative modelling. It offers a new method to evaluate the efficiency of using on-line news on the predictive modelling of time series. Results on financial stock time series and online news are presented. In contrast to traditional machine learning techniques, the method reveals the dynamic interaction between stock variables and news and their dynamic impact on model accuracy when compared to models that do not use news information. Along with the used financial data, the method is applicable to a wide range of other multimodal time series and news data, such as economic, medical, environmental and social. The proposed method, being based on SNN, promotes the use of massively parallel and low energy neuromorphic hardware for multivariate on-line data modelling. [ABSTRACT FROM AUTHOR]

This work presents the first attempt to create a physics-based digital twin model for predictive analysis of damage evolution during the use of ground stone tools (GSTs) in transformative tasks, encompassing the processing of raw resources for nutritional and non-alimentary purposes. The proposed methodology introduces a digital twin of the GSTs developed from 3D models generated using a photogrammetric technique based on Structure-from-Motion and Multi-View Stereo reconstruction. These models serve as the foundation for the development of the finite element (FE)-based digital twin model of the GSTs that exploits a contact formulation and the phase-field approach to simulate tool damage during pounding and grinding tasks. Defining the initial relative positions of the stones, their mechanical behaviour, and controlling the movement of the active stone in a way as close as possible to the real one, the digital twin model has been devised to evaluate how the surface damage is affected by perturbations in the loading conditions. The simulated damage is compared with the surface traces observed from experiments. The developed digital twin model aims at demonstrating its potentials for the GSTs investigations, as a supporting tool for experiments and for simulated tests on the archaeological records. [ABSTRACT FROM AUTHOR]

The mechanical properties of soil in fault-fracture zone areas are diverse and complex. Deep excavation projects often encounter adverse geological conditions such as reverse faulting, which can lead to surface subsidence and collapses, posing significant challenges to excavation safety. Currently, there is limited research in the field of deep excavation engineering that analyzes the influence of reverse faulting on the deformation of retaining piles, and the existing research methods are not systematic enough. Therefore, this study aims to investigate the characteristics of how reverse faulting affects the deformation of retaining piles in deep excavation projects. Various research methods were employed, including numerical simulation, on-site monitoring, and orthogonal experiments, using a deep excavation project in Shenzhen as a case study. The results of the study indicate that reverse faulting exacerbates the deformation of retaining piles, causing the trend of increased deformation to shift upward. The upper part of the pile is significantly more affected than the lower part, and the overall deformation of the pile exhibits an approximate spoon-shaped curve distribution, with the maximum deformation occurring in the upper-middle section of the excavation. Under the influence of reverse faulting, the deformation of retaining piles is positively correlated with fault slip distance and fault dip angle, while it is negatively correlated with fault position. The growth rate of the maximum deformation of retaining piles, denoted as r(ΔZmax/Δ), increases approximately logarithmically with increasing fault slip distance and exponentially with increasing fault dip angle, but decreases approximately logarithmically with increasing distance from the fault to the excavation. An analysis of the sensitivity of fault slip distance, fault dip angle, and fault position to the maximum deformation of retaining piles was conducted. It was determined that the fault dip angle has the highest sensitivity, followed by fault slip distance, while fault position has the lowest sensitivity. Based on the fitting of 64 sets of orthogonal experimental data, a good linear relationship was established between the maximum deformation of retaining piles (Uhm) and the indicator η( θ π T / 180 ∘ S ), leading to the development of a predictive model for the maximum deformation of retaining piles under the influence of reverse faulting. These research findings provide valuable insights and references for similar engineering projects. [ABSTRACT FROM AUTHOR]

Serum iron levels can be important contributors to health outcomes, but it is not often feasible to rely on blood-based measures for a large epidemiologic study. Predictive models that use questionnaire-based factors such as diet, supplement use, recency of blood donation, and medical conditions could potentially provide a noninvasive alternative for studying health effects associated with iron status. We hypothesized that a model based on questionnaire data could predict blood-based measures of iron status biomarkers. Using iron (mcg/dL), ferritin (mcg/dL), and transferrin saturation (%) based on blood collected at study entry, in a subsample from the U.S.-wide Sister Study (n = 3171), we developed and validated a prediction model for iron with multivariable linear regression models. Model performance based on these cross-sectional data was weak, with R2 less than 0.10 for serum iron and transferrin saturation, but better for ferritin, with an R2 of 0.13 in premenopausal women and 0.19 in postmenopausal women. When menopause was included in the predictive model for the sample, the R2 was 0.31 for ferritin. Internal validation of the estimates indicated some optimism present in the observed prediction model, implying there would be worse performance when applied to new samples from the same population. Serum iron status is hard to assess based only on questionnaire data. Reducing measurement error in both the exposure and outcome may improve the prediction model performance, but environmental heterogeneity, temporal variation, and genetic heterogeneity in absorption and storage may contribute substantially to iron status. [ABSTRACT FROM AUTHOR]

If the charging state of the lithium-ion battery can be accurately predicted, overcharge and overdischarge of the battery can be avoided, and the service life of the battery can be improved. In order to improve the prediction accuracy of SOC, a prediction method combined with convolutional layer, multi-head attention mechanism and gated cycle unit is proposed to extract data feature information from different dimensions of space and time. Using the data set of the University of Maryland, we simulated the battery in real vehicle operating conditions at different temperatures (0 °C, 25 °C, 45 °C). The test results showed that the mean absolute error, root mean square error and maximum prediction error of the model were 0.53%, 0.67% and 0.4% respectively. The results show that the model can predict SOC accurately. At the same time, the comparison with other prediction models shows that the prediction accuracy of this model is the highest. [ABSTRACT FROM AUTHOR]