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AimTo assess, from a United States (US) payer's perspective, the cost-effectiveness of gels designed to separate the endometrial surfaces (intrauterine spacers) placed following intrauterine surgery.Materials and methodsA decision tree model was developed to estimate the cost-effectiveness of intrauterine spacers used to facilitate endometrial repair and prevent the formation (primary prevention) and reformation (secondary prevention) of intrauterine adhesions (IUAs) and associated pregnancy- and birth-related adverse outcomes. Event rates and costs were extrapolated from data available in the existing literature. Sensitivity analyses were conducted to corroborate the base case results.ResultsIn this model, using intrauterine spacers for adhesion prevention led to net cost savings for US payers of $2,905 per patient over a 3.5-year time horizon. These savings were driven by the direct benefit of preventing procedures associated with IUA formation ($2,162 net savings) and the indirect benefit of preventing pregnancy-related complications often associated with IUA formation ($3,002). These factors offset the incremental cost of intrauterine spacer use of $1,539 based on an assumed price of $1,800 and the related increase in normal deliveries of $931. Model outcomes were sensitive to the probability of preterm and normal deliveries. Budget impact analyses show overall cost savings of $19.96 per initial member within a US healthcare plan, translating to $20 million over a 5-year time horizon for a one-million-member plan.LimitationsThere are no available data on the effects of intrauterine spacers or IUAs on patients' quality of life. Resultingly, the model could not evaluate patients' utility related to treatment with or without intrauterine spacers and instead focused on costs and events avoided.ConclusionThis analysis robustly demonstrated that intrauterine spacers would be cost-saving to healthcare payers, including both per-patient and per-plan member, through a reduction in IUAs and improvements to patients' pregnancy-related outcomes.

A well-configured spare parts supply chain (SC) can reduce costs and increase the competitiveness of spare parts retailers. A structured method for configuring spare parts SCs should be used to determine whether to centralise or decentralise inventory management, also considering hybrid configurations. Moreover, such a method should define whether or not to switch the production of spare parts from Conventional Manufacturing (CM) technologies to Additive Manufacturing (AM) ones. Indeed, AM is considered the next revolution in the field of spare parts, and the adoption of AM technologies strongly affects the characteristics of SCs. However, the choice between centralisation and decentralisation is not the subject of much scientific research, and it is also not clear when AM would be the preferable manufacturing technology for spare parts. This paper aims to assist managers and practitioners in determining how to design their spare parts SCs, thus defining both the spare parts SC configuration and the manufacturing technology to adopt through the development of a decision support system (DSS). The proposed DSS is a user-friendly decision tree, and, for the first time, it allows comparison of the total costs of SCs characterised by different degrees of centralisation with both AM and CM spare parts. [ABSTRACT FROM AUTHOR]

The detection of fraudulent URLs that lead to malicious websites using addresses similar to those of legitimate websites is a key form of defense against phishing attacks. Currently, in the case of Internet of Things devices is especially relevant, because they usually have access to the Internet, although in many cases they are vulnerable to these phishing attacks. This paper offers an overview of the most relevant techniques for the accurate detection of fraudulent URLs, from the most widely used machine learning and deep learning algorithms, to the application, as a proof of concept, of classification models based on quantum machine learning. Starting from an essential data preparation phase, special attention is paid to the initial comparison of several traditional machine learning models, evaluating them with different datasets and obtaining interesting results that achieve true positive rates greater than 90%. After that first approach, the study moves on to the application of quantum machine learning, analysing the specificities of this recent field and assessing the possibilities it offers for the detection of malicious URLs. Given the limited available literature specifically on the detection of malicious URLs and other cybersecurity issues through quantum machine learning, the research presented here represents a relevant novelty on the combination of both concepts in the form of quantum machine learning algorithms for cybersecurity. Indeed, after the analysis of several algorithms, encouraging results have been obtained that open the door to further research on the application of quantum computing in the field of cybersecurity. [ABSTRACT FROM AUTHOR]

The Wisconsin Card Sorting Test (WCST) is a popular neuropsychological test that is complicated to score and interpret. In an attempt to make scoring of the WCST simpler, Berry (The Clinical Neuropsychologist 10, 117–121, 1996) developed a diagrammatic scoring procedure, particularly to aid scoring of perseverative responses. We identified key limitations of Berry's diagram, including its unnecessary ambiguity and complexity, use of terminology different from that used in the standardized WCST manual, and lack of distinction between perseverative errors and perseverative responses. Our new diagrammatic scoring procedure scores each response one-by-one; we strongly suggest that the diagram is used in conjunction with the 1993 WCST manual. Our new diagrammatic scoring procedure aims to assist novice users in learning how to accurately score the task, prevent scoring errors when using the manual version of the task, and help scorers verify whether other existing computerized versions of the task (apart from the PAR version) conform to the Heaton et al. (1993) scoring method. Our diagrammatic scoring procedure holds promise to be incorporated into any future versions of the WCST manual. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate the correlations among traits in biomass sorghum, estimate the direct and indirect effects, and check for a cause/effect relationship of the traits on the theoretical calorific value (TCV) through pathway analysis. An additional aim is to provide a complementary technique for analysis of the traits and orientation of breeding programs of biomass sorghum through data mining. A total of 25 hybrids of biomass sorghum were evaluated in a triple square lattice design. The following traits were measured: flowering (FLOW); plant height (PH); fresh biomass yield of the stalk (FBYs), of leaves, and of panicles; dry matter of the stalk and of the leaves (DMl); neutral detergent fiber of the stalk and of the leaves; acid detergent fiber of the stalk and of the leaves; lignin of the stalk and of the leaves; hemicellulose of the stalk and of the leaves; cellulose concentration of the stalk and of the leaves; and the TCV. The data were placed under exploratory analysis, the mixed model approach, pathway analysis based on genotypic correlations, and the regression tree technique. The traits FLOW, PH, FBYs, DMl, and LIG were considered the main determinants of the changes in the TCV and, therefore, with potential for use in indirect selection. The technique of data mining allowed better visualization of the relationships among the traits, assisting in some explanations regarding the effect of the traits on the TCV. [ABSTRACT FROM AUTHOR]

This research investigates machine learning models for predicting mental health consequences using survey data. The study employs a two-phase approach first, it utilizes TensorFlow for initial Deep Neural Network (DNN) model building, and then it applies Random Forest (RF), Naive Bayes classifier, and decision tree methods for comparative analysis. The DNN model demonstrates strong performance, achieving high accuracy in mental health prediction. Metrics such as testing time, precision, mean absolute error, and accuracy are compared to provide insight into the advantages and disadvantages of each model. While the DNN model excels in accuracy and precision, other models offer trade-offs in computational efficiency. The results clarify the role of machine learning in mental wellness evaluation and intervention, providing guidance for further research and real-world applications. This research enhances the discourse on predictive modeling for mental health outcomes, facilitating advancements in leveraging machine learning to improve mental health assessment and intervention strategies. [ABSTRACT FROM AUTHOR]

The prevalence of diabetes is increasing globally, necessitating efficient methods to enhance the timely identification and treatment of diabetes, Focusing on early detection and effective management strategies for complications is essential. This study presents an integrated solution comprising two modules: diabetic detection and diabetic retinopathy detection. The diabetic detection module employs machine learning techniques like decision trees, random forests, and KNN for forecasting presence of diabetes based on patient data. The diabetic retinopathy detection module utilizes deep learning techniques, specifically the ResNet50 model architecture, to analyze retinal images and identify signs of diabetic retinopathy. A comprehensive implementation of both modules, including data preprocessing, model training, and evaluation, using Python libraries such as TensorFlow, Keras, and scikit- learn. The trained models are then integrated into a web application. This web application allows users to input their medical data and retinal images, and receive real- time predictions regarding their diabetic status and risk of diabetic retinopathy. The integration of these modules into a web application provides an intuitive interface tailored for both healthcare professionals and patients to assess diabetic risks conveniently. Furthermore, it facilitates early intervention and management of diabetic complications, ultimately improving patient outcomes and reducing healthcare burdens. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is listed among the top 20 leading causes of death worldwide, highlighting the urgent need for effective methods of early disease prediction for chronic conditions. This paper presents a novel strategy for the early diagnosis of CKD, leveraging machine learning (ML) methods to support researchers in their pursuit of preventative approaches. The study incorporates data from 400 patients, analyzing a comprehensive set of 25 attributes. The missing values in the dataset handled by mean and mode statistics were employed for numerical and nominal dimensions respectively. The recursive feature elimination (RFE) with cross-validation (CV) technique was utilized to identify the most crucial attributes. The resulting predictive model achieved positive outcomes in terms of accuracy, precision, recall, and F -measure across all implemented classification algorithms (random forest (RF), Naive Bayes (NB), decision tree (DT), multi-layer neural network (MLNN), partial decision tree algorithm (PART), repeated incremental pruning to produce error reduction (RIPPER), k  Star and extreme gradient boosting (XGBoost)). Notably, the XGBoost classification algorithm demonstrated an impressive accuracy of up to 99.5%. The utilization of predictive models is indispensable for healthcare professionals, as they play a pivotal role in enabling early detection of CKD and mitigating the risk of kidney failure occurrence. This research offers valuable insights into the development of reliable methods for the early identification of CKD, providing a promising avenue for enhancing patient outcomes and reducing the burden of this prevalent chronic condition. [ABSTRACT FROM AUTHOR]

In contrast to multi-class classification, in multi-label classification (MLC), each data sample is a member of several classes. In this regard, the classifiers should discover more complex patterns within the data; accordingly, the complexity grows remarkably. Recently, scholars used the binary relevance, the label powerset, and the ensemble method (EM) techniques to extend state-of-the-art classifiers or develop new ones to solve real-world MLC problems more optimally. The current paper recruits the ridge regression (RR) and a decision tree (DT) and presents a new MLC model named PHMLC. In the PHMLC, the Laplacian Eigenmaps Nonlinear Dimensionality Reduction Algorithm is employed to omit the redundant and irrelevant features. Next, the data is encoded by the principal label space transformation (PLST) and singular value decomposition. Then, the RR is recalled to predict the first set of labels through the encoded data. Besides, the Binary Relevance technique is applied to the original DT, and an MLC model called BR-DT is introduced. The BR-DT is responsible for predicting the second set of labels. Eventually, the greedy selection mechanism is utilized to obtain final labels based on the predicted labels by the PLST-based RR and the BR-DT models. To demonstrate the PHMLC's effectiveness, the PHMLC model is applied to nine real-life multi-label datasets, and the results are compared with the BR-SVM, CPLST, BR-Ridge, BR-DR, PLST, and BR-DT models regarding the AP, EM, HS, Macro, and Micro F1 metrics. [ABSTRACT FROM AUTHOR]

Stroke is an acute cerebrovascular disease characterized by a high incidence, disability rate, recurrence rate, and mortality rate. Depression, as one of the main types of mood disorders, can manifest as symptoms such as a loss of interest in daily activities, reduced energy, diminished self-esteem, insomnia, and decreased appetite. There exists a significant relationship between these two conditions in terms of their pathogenesis. To delve deeper into the factors associated with the occurrence of both diseases, particularly examining the influence of depression on stroke risk, the authors conducted a large-scale investigation and data collection in the population of Shanghai, obtained 5599 valid data points, including information on hypertension, abnormal blood lipid levels, diabetes, atrial fibrillation, body mass index (BMI), a history of stroke, family history of stroke, previous transient ischemic attacks, smoking history, and exercise habits. Ultimately, the authors employed the decision tree C4.5 algorithm to construct a risk analysis model for both stroke and depression, analyzing the risk factors contributing to their occurrence and inferring the correlation between depression and stroke. The results revealed a close association between depression and a history of stroke, BMI, gender, and physical activity. Additionally, in the nonelderly population, psychological factors such as anxiety, depression, mood disorders, and stress were found to be closely linked to stroke onset. In many instances, regular exercise may mitigate the adverse effects of depression on stroke risk. [ABSTRACT FROM AUTHOR]

The objective of this study was to construct a prediction model for early diagnosis and classification of lung cancer based on multi-dimensional clinical data. Three advanced machine learning models — Random Forest, Decision Tree, and Support Vector Machine (SVM) — were employed to predict lung cancer using features such as age, chronic disease history, and clinical symptoms. The models were optimized through various strategies to improve predictive performance. The results demonstrated that all three models achieved high accuracy and sensitivity in lung cancer prediction. Furthermore, a detailed analysis of feature importance identified key factors such as age and chronic disease history that significantly influenced prediction outcomes. Among the models, the SVM exhibited particularly strong performance, providing robust support for accurate lung cancer prediction. Future work will focus on integrating multimodal data and optimizing model architecture and hyperparameters to further enhance the predictive accuracy and clinical utility of the model, thereby contributing to the early diagnosis and treatment of lung cancer. [ABSTRACT FROM AUTHOR]

In this study, the linear and non‐linear multivariate relationships between 25 teleconnection indices (tele‐indices) as independent variables and annual P24max as the dependent variable were analyzed using multivariate linear regression (MLR) and decision tree regression models (M5), in selected synoptic weather stations of Iran over a statistical period of 30 years (1992–2021). No strong and statistically significant correlation between each tele‐index and P24max was observed. Therefore, it is not appropriate to attribute climate changes in the region to a single factor such as El Niño, but rather consider the combined influence of multiple factors. The M5 model demonstrated higher performance, indicating a non‐linear relationship between tele‐indices and P24max. The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. The normalized root mean square error (NRMSE) of P24max estimation was found to be 15%, 13%, 15%, 8%, 20%, 14%, and 12% with the coefficients of determination of 0.78, 0.79, 0.72, 0.85, 0.81, 0.82, and 0.84 in Hashemabad‐Gorgan, Rasht, Kermanshah, Ahvaz, Bandar Abbas, Isfahan, and Birjand, respectively. Finally, it is possible to forecast P24max using tele‐indices measured in the previous year. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the factors influencing vitamin D (VD)-related knowledge, attitude, and practice (KAP) in middle-aged and elderly patients with ankle fractures using the binary logistic regression and decision tree models, thereby providing scientific evidence for VD-related recovery strategies. A Chinese version of the nutrition KAP questionnaire was administered in a Grade III A Hospital using random sampling for the investigation. Meanwhile, the factors influencing KAP were analyzed using the aforementioned two models. The results of the binary logistic regression model showed that the patient age [odds ratio (OR)=0.186; 95% confidence interval (CI): 0.117–0.295], spouse education (OR=0.336; 95% CI: 0.267–0.422), and spouse occupation (OR=1.351; 95% CI: 1.078–1.693) were the factors influencing VD-related KAP. The decision tree chi-squared automatic interaction detector (CHAID) algorithm showed that spouse education, patient age, and living room lighting were the influencing factors, among which spouse education was the most important. VD in middle-aged and elderly patients with ankle fractures was affected by the age and the education level of the spouse. These two models can analyze the influencing factors of knowledge and practice, and can be used together to complement each other. [ABSTRACT FROM AUTHOR]

Background/Objectives: In this study, a medical decision support system is presented to assist physicians in epileptic focus detection by correlating MRI and EEG data of temporal lobe epilepsy patients. Methods: By exploiting the asymmetry in the hippocampus in MRI images and using voxel-based morphometry analysis, gray matter reduction in the temporal and limbic lobes is detected, and epileptic focus prediction is realized. In addition, an epileptic focus is also determined by calculating the asymmetry score from EEG channels. Finally, epileptic focus detection was performed by associating MRI and EEG data with a decision tree. Results: The results obtained from the proposed algorithm provide 100% overlap with the physician's finding on the EEG data. Conclusions: MRI and EEG correlation in epileptic focus detection was improved compared with physicians. The proposed algorithm can be used as a medical decision support system for epilepsy diagnosis, treatment, and surgery planning. [ABSTRACT FROM AUTHOR]

Background: After its introduction in the ICD-10-CM in 2016, sarcopenia is a condition widely considered to be a medical disease with important consequences for the elderly. Considering its high prevalence in older adults and its detrimental effects on health, it is essential to identify its risk factors to inform targeted interventions. Methods: Taking data from wave 2 of the ELSA, using ML-based methods, this study investigates which factors are significantly associated with sarcopenia. The Minimum Redundancy Maximum Relevance algorithm has been used to allow for an optimal set of features that could predict the dependent variable. Such a feature is the input of a ML-based prediction model, trained and validated to predict the risk of developing or not developing a disease. Results: The presented methods are suitable to identify the risk of acquired sarcopenia. Age and other relevant features related with dementia and musculoskeletal conditions agree with previous knowledge about sarcopenia. The present classifier has an excellent performance since the "true positive rate" is 0.81 and the low "false positive rate" is 0.26. Conclusions: There is a high prevalence of sarcopenia in elderly people, with age and the presence of dementia and musculoskeletal conditions being strong predictors. The new proposed approach paves the path to test the prediction of the incidence of sarcopenia in older adults. [ABSTRACT FROM AUTHOR]

Polarimetric measurement has been proven to be of great importance in various applications, including remote sensing in agriculture and forest. Polarimetric full waveform LiDAR is a relatively new yet valuable active remote sensing tool. This instrument offers the full waveform data and polarimetric information simultaneously. Current studies have primarily used commercial non-polarimetric LiDAR for tree species classification, either at the dominant species level or at the individual tree level. Many classification approaches combine multiple features, such as tree height, stand width, and crown shape, without utilizing polarimetric information. In this work, a customized Multiwavelength Airborne Polarimetric LiDAR (MAPL) system was developed for field tree measurements. The MAPL is a unique system with unparalleled capabilities in vegetation remote sensing. It features four receiving channels at dual wavelengths and dual polarization: near infrared (NIR) co-polarization, NIR cross-polarization, green (GN) co-polarization, and GN cross-polarization, respectively. Data were collected from several tree species, including coniferous trees (blue spruce, ponderosa pine, and Austrian pine) and deciduous trees (ash and maple). The goal was to improve the target identification ability and detection accuracy. A machine learning (ML) approach, specifically a decision tree, was developed to classify tree species based on the peak reflectance values of the MAPL waveforms. The results indicate a re-substitution error of 3.23% and a k-fold loss error of 5.03% for the 2106 tree samples used in this study. The decision tree method proved to be both accurate and effective, and the classification of new observation data can be performed using the previously trained decision tree, as suggested by both error values. Future research will focus on incorporating additional LiDAR data features, exploring more advanced ML methods, and expanding to other vegetation classification applications. Furthermore, the MAPL data can be fused with data from other sensors to provide augmented reality applications, such as Simultaneous Localization and Mapping (SLAM) and Bird's Eye View (BEV). Its polarimetric capability will enable target characterization beyond shape and distance. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications. [ABSTRACT FROM AUTHOR]

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The aim of this study was to assess the sleep status of middle-aged and elderly adults in Bengbu City, Anhui Province, China, to identify the major dietary patterns, and to analyze the association of different sleep duration and dietary patterns with metabolic syndrome (MetS) and its related influencing factors, as well as to explore the predictive value of sleep duration and dietary patterns. A cross-sectional analysis was performed utilizing data collected from the Community-based Cardiovascular and Health Promotion Study 2019 (COCHPS 2019) conducted in Bengbu. The definition of MetS adhered to the criteria of Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults (2016 Revision). Dietary information was obtained using the Food Frequency Questionnaire (FFQ) to assess dietary intake over the past year. Principal component analysis (PCA) was performed to identify dominant dietary patterns. A logistic regression model was developed to analyze the associations among sleep duration, dietary patterns, and MetS, and a decision tree (DT) model was developed to compare factors affecting MetS and screen people at high risk for MetS. The prevalence of MetS was 13.4% among the 9132 middle-aged and elderly residents over 45 years of age included in COCHPS 2019. Participants were divided into short (< 6 h/d), normal (6–8 h/d), and long (> 8 h/d) groups based on their daily sleep duration. Three dietary patterns were identified by PCA, the fruit-milk pattern, the tubers-meat pattern, and the vegetable-cereal pattern. After adjusting for covariables, logistic regression analysis showed that long sleep duration was significantly negatively associated with MetS. The fruit-milk and vegetable-cereal patterns were negatively associated with MetS, whereas the tubers-meat pattern was positively correlated with MetS. The results of the DT model analysis showed that the vegetable-cereal pattern is the most important factor impacting MetS, followed by marital status, the tubers-meat pattern, the fruit-milk pattern, exercise, sleep duration, and gender. The DT model also screened out five types of MetS high-risk groups. The results of our study indicate that normal sleep duration and consumption of either a fruit-milk or vegetable-cereal diet may lower the likelihood of developing MetS in middle-aged and elderly adults. [ABSTRACT FROM AUTHOR]

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field. [ABSTRACT FROM AUTHOR]

• As arousal levels decrease, the stability of the driver's operation relatively improves. • An increase in risk level weakens the influence of emotions on driving behavior. • The impact of risk factors exhibits a more stable trend. • Emotions and risk factors improve the accuracy of the hazard recognition model. • By enhancing emotion detection, promptly adjust the state of the driver. As the contradictions among the human-vehicle-environment elements in road traffic systems intensify, the resulting traffic safety issues are becoming increasingly severe. Emotions, as critical psychological factors influencing safe driving, directly impact drivers' perceptions and judgments of surrounding information. Simultaneously, potential risks during the driving process can affect drivers' decisions and driving behavior. On the basis of the definition of dimensional emotions, this study analyzes the impact of different valence, arousal, and risk level scenarios on drivers' collision avoidance behavior. A total of 21 drivers aged 18 to 50 years, with driving experience ranging from 1 to 10 years, participated in a simulated driving experiment. This study employs the decision tree (DT) algorithm to define the quantitative relationship between the occurrence of different driving behaviors and their influencing factors. It predicts vehicle avoidance behavior in two emergency conditions to determine whether collision avoidance can be achieved. The results indicate that the model's prediction accuracy improves when emotion and risk level information are combined, reaching 88.89 %. This is a 5.56 % improvement over the model using only emotion information and a 2.53 % improvement over the model using only risk level information. Compared with the impact of subjective emotional factors on driving behavior, risk scenarios exhibit a more stable trend in influencing driving behavior. Under the interaction of emotions and risk factors, the accuracy and generalization ability of the driving behavior prediction model based on the Decision Tree (DT) algorithm have been greatly improved. This research provides a theoretical basis for addressing adverse driving behavior caused by driver emotions and analyzing the factors influencing collision avoidance processes. [ABSTRACT FROM AUTHOR]

This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with a built-in IMU. Physical frailty was assessed using exercise-related items extracted from the Kihon Check List. The efficacy of five machine learning models in distinguishing older people with physical frailty was investigated. This study included 48 older people, of which 24 were frail and 24 were not. Compared with the non-frail participants, the older people with physical frailty had a small root mean square value in the vertical and anteroposterior directions and angular velocity in the anteroposterior direction (p < 0.001, r = 0.36; p < 0.001, r = 0.29; p < 0.001, r = 0.30, respectively) and a large mean power frequency value in the vertical direction (p = 0.042, r = 0.18). The decision tree model could most effectively classify physical frailty, with an accuracy, F1 score, and area under the curve of 78.6%, 91.8%, and 0.81, respectively. The characteristics of IMU-attached cane usage by older adults with physical frailty can be utilized to effectively evaluate and determine physical frailty in their usual environments. [ABSTRACT FROM AUTHOR]

Conducting high‐quality overviews of reviews (OoR) is time‐consuming. Because the quality of systematic reviews (SRs) varies, it is necessary to critically appraise SRs when conducting an OoR. A well‐established appraisal tool is A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2, which takes about 15–32 min per application. To save time, we developed two fast‐and‐frugal decision trees (FFTs) for assessing the methodological quality of SR for OoR either during the full‐text screening stage (Screening FFT) or to the resulting pool of SRs (Rapid Appraisal FFT). To build a data set for developing the FFT, we identified published AMSTAR 2 appraisals. Overall confidence ratings of the AMSTAR 2 were used as a criterion and the 16 items as cues. One thousand five hundred and nineteen appraisals were obtained from 24 publications and divided into training and test data sets. The resulting Screening FFT consists of three items and correctly identifies all non‐critically low‐quality SRs (sensitivity of 100%), but has a positive predictive value of 59%. The three‐item Rapid Appraisal FFT correctly identifies 80% of the high‐quality SRs and correctly identifies 97% of the low‐quality SRs, resulting in an accuracy of 95%. The FFTs require about 10% of the 16 AMSTAR 2 items. The Screening FFT may be applied during full‐text screening to exclude SRs with critically low quality. The Rapid Appraisal FFT may be applied to the final SR pool to identify SR that might be of high methodological quality. [ABSTRACT FROM AUTHOR]

• Results indicated that the F 2 plants had wide variations in most characteristics. • Multivariate methods successfully determined selection criteria. • The decision tree successfully predicted the PN and SW range for superior genotypes. • Selecting PN 87 – 119 per plant and SW ≥ 14.55 g can obtain superior genotypes. The existence of sufficient genetic variability and knowing the suitable selection criterion ranges are indispensable requirements of breeding studies. This study aimed to explore genetic diversity in ten F 2 populations (heterozygous–heterogeneous), five parents, and three commercial check cultivars by multivariate methods and to predict useful selection ranges using a decision tree model for further breeding studies. Hybridizations were made in 2019, F 1 populations were grown, and F 2 seeds were obtained in 2020. Genotypes were grown according to an Augmented Randomized Complete-Block Design in 2021 at Aydın Adnan Menderes University Faculty of Agriculture. Results revealed considerable genetic diversity and transgressive segregation in key characteristics across the F 2 populations. Seed yields of F 2 plants are higher than those of the parents. The seed protein and oil content of the F 2 plants were not different from the parents and commercial checks. Higher phenotypic than genotypic variance indicated a high influence of environment on the inheritance of all studied characteristics. The highest broad heritability was observed in the number of shattered pods per plant (94.81 %) and the lowest in infertile pods per plant (2.16 %). Seed yield showed a significant and positive genotypic correlation with the number of fertile pods per plant (0.61**) and a significant negative association with the number of fertile branches per plant (−0.53**). Principal component analysis (PCA) of 14 quantitative characteristics showed that soybean genotypes separated by the first four components contributed 71.12 % of total genetic variation, with important yield-attributing characteristics present in PC1 and PC3, while important seed quality characteristics were noted in PC2. The decision tree model indicated that genotypes with high yield and optimum quality could be obtained by selecting the number of fertile pods 87–119 per plant and a hundred-seed weight greater than 14.55 g. [ABSTRACT FROM AUTHOR]

Multidimensional proximity has been considered the important factor affecting regional scientific collaboration network (RSCN) and has received extensive attention from scholars. This paper constructs a cross-RSCN based on co-authorship across 31 regions in China, incorporates traditional multidimensional proximity and network embedding proximity into a unified framework and proposes a five-factor driving model for dissecting the mechanism of cross-regional scientific collaborative innovation (CI). The results show that the scientific collaboration network is gradually becoming saturated with connections, and the polarisation of cross-regional collaboration is increasingly serious. The collaboration intensity is affected by the co-movement of several proximity factors, among which geographic proximity (GP) is the main but not the sole one that positively affects the cross-regional scientific collaboration. Based on the decision tree model, we conclude that structure proximity may compensate for low GP and low economic proximity (EP) on cross-regional CI performance. The positive effect of network location proximity on regional scientific collaboration is more sensitive when the EP is not high. The findings provide significant guidance for scientific research institutions, universities and regional policy-makers to select appropriate partners as well as network embedding strategies, and further contribute to long-term improvement of scientific innovation performance and stable regional economic growth. [ABSTRACT FROM AUTHOR]

Accurately predicting lithium-ion batteries' state of temperature (SOT) is crucial for effective battery safety and health management. This study introduces a novel approach to SOT prediction based on voltage and temperature profiles during the abusive discharging process, aiming for enhanced prediction accuracy and evaluating the safety range. The duration of equal voltage discharge and temperature variation during discharge are considered temperature indicators. Linear regression and R2 analyses are employed to assess the relationship and variance over different discharge–charge cycles of varied duration between the complete life cycle and its temperature variance. In this study, a decision tree (DT) and an artificial neural network (ANN) are employed to estimate the SOT of a Li-ion battery. The effectiveness and accuracy of the proposed methods are validated using ageing data from eVTOL charge–discharge cycles through numerical simulations. The results demonstrate that for the short cruise range of 600 s, the DT algorithm with an R2 regression value of 6.17% demonstrates better performance than ANN, whereas for the bigger cruise range of 1000 s, the ANN model with an R2 regression value of 5.06 percent was better suited than DT. It is concluded that both DT and ANN outperform other methods in predicting the SOT of lithium-ion batteries. [ABSTRACT FROM AUTHOR]