Combination Of Variables Research Articles

Probabilistic effect of non-potable mixing water on characteristic strength of concrete

Across the world, the acceptance criterion for concrete made with non-potable water (SA) is stipulated for mean strength with reference to similar concrete made with potable water (SP). The basis for the strength design of concrete is the characteristic strength. The effect of the acceptance criteria on the characteristic strength of SA and SP was examined using statistical simulations, for the first time. It was found that, compared with a two-sample set, using a three-sample set would improve the characteristic strength of SA. A higher allowable ratio between the means of SA and SP and a lower deviation of individual samples from the mean would improve the characteristic strength of SA. The effect of the coefficient of variation (CoV) of the original population on the characteristic strength of SA varied. On the one hand, a higher CoV reduced the characteristic strength of SP, thereby increasing the probability of SA strength being higher than SP strength. On the other, a higher CoV of SP meant a wider spread of the samples and that could increase the CoV of SA, thereby reducing the characteristic strength of SA. Probabilistic charts were developed for the characteristic strengths of SA and SP for different combinations of the aforementioned variables, with guidance for practical application.

Estimating Left Ventricular Mass from the Electrocardiogram across the Spectrum of LV Mass from Normal to Increased LV Mass in an Older Age Group.

To examine the relationship of QRS voltages and left ventricular (LV) mass across the spectrum of individuals with different LV mass. Twenty QRS voltage measurements or combinations were determined in a consecutive series of 159 adults with an ECG and echocardiogram without previous myocardial infarction, left or right bundle branch block, pre-excitation, or electronic pacemaker. The four strongest and significant correlations between QRS and LV mass were S in V4, deepest S wave in any precordial lead plus S in V4, S in V3, and S in V3 plus R in AVL times QRS duration. For men, the strength of the relationships were S in V3 (F = 33.8), deepest S wave in any precordial lead plus S V4 (F = 33.7), S in V3 plus R aVL (F = 29.9), S in V4 (F = 29.79), and deepest S in precordial leads (F = 17.9). The R wave in AVL alone did not correlate with LV mass. Criteria using the R wave in lateral precordial leads did not correlate as strongly with LV mass. For women, only S in V4 significantly correlated with LV mass. Overall, the R wave voltage in limb leads (AVL I or II) did not correlate with precordial S wave amplitudes. Univariate and multivariate analysis showed that some but not all QRS voltages correlated with each other. In multivariate analysis, using only single variables and not combination of QRS variables, the only significant relationship between QRS voltage and left ventricular mass was for men the S in V3 (p = 0.04) and for women S in V4 (p = 0.016) and R in V6 (p = 0.04). The S wave in V3 and V4 correlate most strongly with LV mass while the R wave in limb leads, including AVL, do not correlate.

MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma.

To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS). This single-center, IRB-approved, HIPAA-compliant retrospective study included 108 patients (69 LM, 39 LMS) who had pathology, preoperative MRI, and clinical data available at our tertiary academic institution. Two radiologists independently evaluated 14 features on preoperative MRI. Texture features based on 3D segmentation were extracted from T2W-weighted MRI (T2WI) using commercially available texture software (TexRAD™, Feedback Medical Ltd., Great Britain). MRI conventional features, and clinical and MRI texture features were compared between LM and LMS groups. Dataset was randomly divided into training (86 cases) and testing (22 cases) cohorts (8:2 ratio); training cohort was further subdivided into training and validation sets using ten-fold cross-validation. Optimal radiomics model was selected out of 90 different machine learning pipelines and five models containing different combinations of MRI, clinical, and radiomics variables. 12/14 MRI conventional features and 2/2 clinical features were significantly different between LM and LMS groups. MRI conventional features had moderate to excellent inter-reader agreement for all but two features. Models combining MRI conventional and clinical features (AUC 0.956) and MRI conventional, clinical, and radiomics features (AUC 0.989) had better performance compared to models containing MRI conventional features alone (AUC 0.846 and 0.890) or radiomics features alone (0.929). While multiple MRI and clinical features differed between LM and LMS groups, the model combining MRI, clinical, and radiomic features had the best predictive ability but was only marginally better than a model utilizing conventional MRI and clinical data alone.

Prioritizing landscapes for mitigating the impacts of onshore wind farms on multidimensional waterbird diversity in the Yellow Sea

Abstract Ongoing wind energy developments play a key role in mitigating the global effects of climate change and the energy crisis; however, they have complex ecological consequences for many flying animals. The Yellow Sea coast is considered as an ecological bottleneck for migratory waterbirds along the East Asian–Australasian flyway (EAAF), and is also an important wind farm base in China. However, the effects of large-scale onshore wind farms along the EAAF on multidimensional waterbird diversity, and how to mitigate these effects, remain unclear. Here we examined how wind farms and their surrounding landscapes affected multidimensional waterbird diversity along the Yellow Sea coast. Taxonomic, functional, and phylogenetic diversity of the waterbird assemblages, and mean pairwise distances and nearest taxon distances with null models were quantified in relation to 4 different wind turbine densities. We also measured 6 landscape variables. Multi-dimensional waterbird diversity (taxonomic, functional, and phylogenetic diversity) significantly decreased with increasing wind turbine density. Functional and phylogenetic structures tended to be clustered in waterbird communities, and environmental filtering drove waterbird community assemblages. Furthermore, waterbird diversity was regulated by a combination of wind turbine density and landscape variables, with edge density of aquaculture ponds, in addition to wind turbine density, having the greatest independent contribution to waterbird diversity. These results suggest that attempts to mitigate the impact of wind farms on waterbird diversity could involve the landscape transformation of wind farm regions, for example, by including high-edge-density aquaculture ponds (i.e., industrial ponds) around wind farms, instead of traditional low-edge-density aquaculture ponds.

A brief mindfulness-based intervention, sleep quality, sleep duration, and fatigue among nurses: A randomized controlled trial.

The purpose of the current study was to investigate the effectiveness of a mindfulness-based intervention in improving sleep duration, sleep quality, and fatigue among Jordanian nurses. A randomized controlled trial was used to conduct this study. Data from 123 nurses (60 nurses in the interventional group and 63 nurses in the control group) were collected and analyzed. The primary outcome measures were compared between the intervention and control groups using repeated measure MANOVA to assess changes in sleep duration, sleep quality, and fatigue over time. The results showed that there was a statistically significant multivariate effect of the intervention on the linear combination of the dependent variables (sleep duration, sleep quality, and fatigue) (V = 0.23, F (3, 119) = 12.02, p < .001) with Partial η2 of 0.23. Follow-up analysis showed that the intervention group had lower sleep disturbances (Mean difference = -6.53, CI = -7.79 to -5.26) and less fatigue (Mean difference = -13.36, CI = -17.24 to -9.47) than the control group. The current study showed that the mindfulness-based intervention effectively addresses sleep quality and fatigue in nurses, which in turn is expected to positively impact the health care system. Mindfulness-based interventions effectively address sleep quality and fatigue associated with nurses' job performance and outcomes. Implementing brief mindfulness-based interventions is crucial as they can be self-implemented and fit into nurses' busy schedules.

Solvent- and solvothermal-induced phase transitions and crystallisations in syndiotactic polystyrene and multiblock copolymer syndiotactic polystyrene-co-cis-1,4-polybutadiene

The complex polymorphism of nanoporous syndiotactic polystyrene never stops to amaze. Simple and rapid solvent- and solvothermal-induced crystallisation of syndiotactic polystyrene (sPS) and syndiotactic polystyrene polymer segments in the multiblock copolymer syndiotactic polystyrene-co-cis-1,4-polybutadiene (sPSB) into the desired crystalline form have been disclosed and herein reported. Stirring polymer powders of sPS in the chloroform/water solvent mixture allows crystallisation of the amorphous phase and the conversion of the co-crystalline δ forms into the highly crystalline δ form; variable combinations of water with other common non-polar solvents, or of chloroform with methanol failed in this transformation. The γ form has been obtained by solvothermal treatment of the δ form by simple refluxing in acetonitrile. Stirring the γ form in chloroform/water mixture affords its rapid conversion into the ε form with elevated crystallinity. Finally, applying the chloroform/water treatment to the β form yields the δ form with high crystallinity. The complex polymorphism of the sPS was also found in sPSB copolymer, even with short styrene segments. The thermal behaviour, in terms of melting enthalpy and temperature, was investigated at the variance of styrene content.

Determining who military service members deem credible to discuss firearm safety for suicide prevention.

To examine rankings of credible sources for discussing secure storage within a representative sample of firearm-owning service members, and examine how combinations of demographic variables impact the ranking of credible sources. The probability-based sample was collected with the help of Ipsos. Participants were US service members who owned a firearm at the time of the survey (n = 719). The total sample ranked service members, Veterans, and members of law enforcement as the most credible sources and faith leaders, casual acquittances, and celebrities as the least credible sources. Black men ranked the NRA as a highly credible source whereas Black females ranked the NRA as one of the least preferred sources. Regardless of political preference, those who lived in non-metropolitan rural environments ranked members of law enforcement as highly credible sources. Those who lived in non-metropolitan rural and urban settings and identified as liberal ranked the National Shooting Sports Foundation as a highly credible source. Law enforcement officers, military members, and Veterans are ranked as highly credible sources by most subgroups of firearm-owning service members. Leveraging these voices in firearm safety conversations is necessary, may increase adherence to secure storage recommendations, and ultimately reduce suicide.

Potential exposure of adults and children to particles from resuspended nano-enabled consumer sprays

The increasing application of nanotechnology has resulted in a growing number of nano-enabled consumer products, and they could be important contributors to indoor particulate matter, with potential adverse health effects. This study investigated the exposure of adults and children to the released and resuspended manufactured particles from seven nano-enabled consumer sprays. Sedimentation and resuspension of released particles were investigated in a newly constructed 2.8 × 1.6 × 2.4 m3 chamber. The resuspension of deposited particles was investigated as a function of product type, flooring material (e.g., carpet and vinyl), resuspension force (e.g., walking by an adult and motion of a robotic sampler that simulated a child), and measurement height. The concentration of released and resuspended particles in the air was determined using Button Aerosol Samplers (SKC Inc.) with 25-mm 2 μm-pore PTFE filters. Samplers were positioned in the experimenter's breathing zone (e.g., 1.5 m for adults and 0.3 m for a child-simulating robot) and at fixed stations of 0.3 m and 1.1 m heights. Resuspended particle mass concentrations ranged from 28 to 905 μg/m3, and the resuspension rates of deposited spray particles for the same variable combinations varied from 10−4 to 10−1 h−1, depending on product type, flooring material, sampling height, and resuspension force. Particle resuspension rates from carpet were up to 320 % higher than resuspension rates from vinyl flooring, resuspension rates measured at 0.3 m were up to 195 % higher than the rates measured with a 1.1 m stationary sampler, and resuspension rates due to a walking adult were up to 243 % higher than resuspension rates caused to a moving robot that simulated a child. Overall, these data on the resuspension of particles from nano-enabled consumer sprays could help us understand the resulting exposures and support future studies on human exposure reduction.

Suspected primary bone marrow T-cell lymphoid neoplasia causing paraneoplastic hypercalcemia in 11 dogs (2014-2021).

To describe the clinical findings and outcome in hypercalcemic dogs that were diagnosed with T-cell lymphoid neoplasia by bone marrow evaluation. 11 client-owned dogs, identified retrospectively through 2 diagnostic laboratories between 2014 and 2021. Cases presented with hypercalcemia and lacked overt evidence of lymphoid neoplasia in the blood or nonmedullary tissues. T-cell lymphoid neoplasia was diagnosed once the bone marrow was investigated, using a variable combination of cytology, histology, and flow cytometry. The median age at presentation was 5.7 years (range, 4.0 to 8.6 years). All cases were large-breed dogs, and 4 of 11 cases were Golden Retrievers. Dogs presented most commonly for polyuria and polydipsia (72%). Eight cases had neutropenia, and 10 of 11 dogs had reported thrombocytopenia. In all cases, flow cytometry identified an expansion of neoplastic small- to intermediate-sized T cells in the bone marrow that expressed low-class-II major histocompatibility complex. Neoplastic T cells in 10 of 11 cases expressed CD4. Treatments ranged from prednisone alone to multiagent chemotherapy. The median overall survival time was 260 days (range, 25 to 792 days). T-cell lymphoid neoplasia diagnosed via bone marrow evaluation that may represent a unique bone marrow T-cell neoplastic entity should be considered in hypercalcemic dogs with isolated cytopenias that lack peripheral lymphocytosis, lymphadenopathy, and organomegaly. Clinical outcome in these cases was variable, which may be related to nonstandardized treatments, but a subset of patients had prolonged survival.

A Novel Fusion-Based Methodology for Drought Forecasting

Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R2) value of 0.845 in each month in 2018. The spatiotemporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena.

Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania.

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.

Intelligent design and buckling experiment of curvilinearly stiffened thin-walled structures

Curvilinearly stiffened variable-stiffness structures offer excellent load-bearing capacities and design flexibility, particularly for thin-walled structures with cutouts. Nevertheless, the complex combination of numerous variables, buckling failure modes, and unclear load-carrying mechanisms present significant challenges to structural analysis and optimization. Hence, a unified characterization method for nonuniform layouts and variable thicknesses is proposed and an intelligent optimization framework based on structural image learning is established. Buckling experiments on two stiffened panels and a corresponding numerical analysis are performed to verify the effectiveness of the method under axial compressive loading. Results show that the curvilinearly stiffened panel exhibits a superior load-bearing capacity of 28.8% compared with an orthogonally stiffened panel. Specifically, the curvilinearly stiffened panel efficiently transfers axial loads to a broader region and avoids interruptions in the force transmission path by cutouts, thereby resulting in material and buckling failures simultaneously.

AI monitoring and warning system for low visibility of freeways using variable weight combination model

AbstractIn intelligent vehicles, road environment perception technology is a key component of autonomous driving assistance systems. This component is the foundation for vehicle decision‐making and control, and is a guarantee of safety during the driving of the vehicle. The existing environment perception technology mainly targets well‐lit environments and requires visible light imaging equipment. Therefore, in low visibility environments, this technology cannot make good judgments about the external environment. Many existing perception systems mainly rely on sensors. Under low visibility conditions, these sensors weaken their effectiveness due to signal transmission, reflection, or absorption, resulting in incomplete or distorted data collection. Reduced visibility often affects the sensing range of various sensors, hindering the system's ability to detect and recognize distant objects, thereby limiting the necessary advance warning and response time for safe navigation. In response to this issue, this study proposed a combined method of infrared imaging and polarized imaging to collect feature data on road conditions in low visibility environments. Then, the obtained images were denoised and enhanced. The processed images were input into the system for recognition, and the images were analyzed and recognized using a low visibility road situation semantic segmentation algorithm based on deep learning. The research outcomes denoted that the pixel accuracy, average pixel accuracy, and average intersection ratio of the variable weight combination model in polarized degree images were 91.2%, 89.1%, and 71.6%, respectively. Those in infrared images were 83.6%, 90.6%, and 62.1%, respectively. The various indicators of the variable weight combination model were higher than those of the U‐shaped neural network model, indicating its performance is relatively excellent. The research results indicated that infrared imaging helps to acquire information at night or in low light conditions, while polarized imaging can provide better adaptation to cluttered light and reflections, enabling the system to provide more robust environmental sensing in complex weather conditions. It fills a critical gap in perception for autonomous driving systems in adverse weather conditions.

A physics-informed Bayesian optimization method for rapid development of electrical machines

Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.

Clustered Cardiometabolic Risk and the "Fat but Fit Paradox" in Adolescents: Cross-Sectional Study.

The "fat but fit paradox" states that people who are fit have a lower cluster cardiometabolic risk (CCMR), even if they are overweight or obese. Therefore, the objective was to investigate the CCMR between four categories based on the "fat but fit paradox" variable, in different fitness categories-cardiorespiratory fitness, muscular fitness, and physical fitness-in adolescents. Body composition, cardiorespiratory fitness, muscle fitness, blood samples, and blood pressure were assessed in 230 adolescents, and cardiometabolic risk and three different "fat but fit paradox" variables were calculated. Participants with a higher CRF exhibited a lower CCMR within their body mass index (BMI) category (p < 0.05). Participants with a high BMI and high muscular fitness showed a lower CCMR than participants with a low muscular fitness and a similar BMI, or low BMI and low muscular fitness (p < 0.05). When both variables, CRF and muscular fitness, were combined, their effectabove CCMR increased (p < 0.05). Across all fitness categories, the fat and unfit group, whether considered individually or combined, exhibited the highest risk of CCMR (p < 0.05). This study confirms the "fat but fit paradox" in different physical fitness categories, showing the importance of both CRF and muscular fitness as predictors of CCMR, with the combination of both variables showing a greater agreement.

Correlation of academic emotion and hardiness personality of undergraduate nursing students

BackgroundAcademic emotion is a fundamental emotional concept closely linked to academic achievement. Understanding the connection between academic emotion and the personality trait of hardiness is pivotal in maintaining a stable career orientation throughout one's educational career. Therefore, in pursuit of fostering the robust growth of nursing careers, it is imperative to delve into the academic emotions experienced by undergraduate nursing students. This study endeavors to mitigate the impact of gender differences among nursing students while investigating the intricate relationship between academic emotions and the trait of hardiness in their personalities.MethodsThis study employed a cross-sectional research design. We gathered data from a convenient sample of 292 nursing students enrolled at Shanxi University of Chinese Medicine. Each student provided demographic information and responded to a general academic mood questionnaire, as well as a Hardiness Personality Rating Scale. Subsequently, we used canonical correlation analysis to evaluate the correlation between academic emotion and tenacity personality in 292 undergraduate nursing students.ResultsWe discovered that academic emotions among nursing students are predominantly characterized by feelings of disappointment and boredom. Furthermore, personality hardiness is primarily influenced by the dimensions of engagement and control. It is important to note that a heightened level of negative, low-arousal academic emotions can diminish the level of engagement. The first typical correlation coefficient corresponding to academic emotion and hardiness were 0.660. The linear combination of standardized variables of the first typical variable corresponding to academic emotion (X1) = -0.444*negative hyperarousal -0.443 * positive hyperarousal + 0.694 * negative hypoarousal -0.260 * positive hypoarousal. The standardized variable equation of the first typical variable corresponding to hardiness personality (η1) = 0.235* hardiness -0.433* control -0.530* investment -0.303* challenge.ConclusionsNursing students generally believe that their input is out of proportion to the return, and this unbalanced emotional experience will seriously affect their academic emotions in China. It is suggested that paying attention to cultivating their tenacious personality traits in the teaching process may help to enhance their academic emotions and enhance the sense of belonging and identity of nursing students engaged in the nursing profession.

Stress and strain analysis and parameter optimization of pipe truss tower connection of super-large tower crane based on FEM

Clamping bushing structure is an internode connection mechanism designed for the standard section of tubular truss tower. In this paper, the clamping bushing structure of the connecting mechanism of super-large tower crane is taken as the research object, a three-dimensional model of clamping bushing structure is established and imported into ABAQUS, and its multi-body contact model is further constructed to study the contact and bearing relationship of the structure under multiple working conditions, and the accuracy of the calculation results of the model is verified by the experimental stress test under tensile working conditions. In addition, this study is based on the control variable method, and through the design of orthogonal test table, the influence degree of five variable parameters of clamping bushing on the bearing capacity of the structure is investigated. Finally, through the range analysis, the optimal horizontal combination of variables and parameters of clamping bushing structure is obtained, and the optimal matching relationship between the shape of the tower connecting mechanism and the bearing capacity is obtained. The results show that, compared with the original model, the stress concentration at the most dangerous section of the optimized joint and the bushing is obviously alleviated, in which the stress peaks of the upper and lower joints are kept below 500 MPa, and the stress peaks of the bushing groove are also reduced to between 573 and 722 MPa. Moreover, the designed and optimized lower joint can reduce the maximum equivalent plastic strain of the joint root circumference by 56.05% under the original maximum tensile condition, and the overall distribution trend of equivalent plastic strain is more uniform, and a more reliable structural design is obtained, which plays an important guiding role in the design, optimization and analysis of the connecting mechanism of the tower body of large tower crane.

Use of artificial intelligence and I-Score for prediction of recurrence before catheter ablation of atrial fibrillation

BackgroundBased solely on pre-ablation characteristics, previous risk scores have demonstrated variable predictive performance. This study aimed to predict the recurrence of AF after catheter ablation by using artificial intelligence (AI)-enabled pre-ablation computed tomography (PVCT) images and pre-ablation clinical data. MethodsA total of 638 drug-refractory paroxysmal atrial fibrillation (AF) patients undergone ablation were recruited. For model training, we used left atria (LA) acquired from pre-ablation PVCT slices (126,288 images). A total of 29 clinical variables were collected before ablation, including baseline characteristics, medical histories, laboratory results, transthoracic echocardiographic parameters, and 3D reconstructed LA volumes. The I-Score was applied to select variables for model training. For the prediction of one-year AF recurrence, PVCT deep-learning and clinical variable machine-learning models were developed. We then applied machine learning to ensemble the PVCT and clinical variable models. ResultsThe PVCT model achieved an AUC of 0.63 in the test set. Various combinations of clinical variables selected by I-Score can yield an AUC of 0.72, which is significantly better than all variables or features selected by nonparametric statistics (AUCs of 0.66 to 0.69). The ensemble model (PVCT images and clinical variables) significantly improved predictive performance up to an AUC of 0.76 (sensitivity of 86.7% and specificity of 51.0%). ConclusionsBefore ablation, AI-enabled PVCT combined with I-Score features was applicable in predicting recurrence in paroxysmal AF patients. Based on all possible predictors, the I-Score is capable of identifying the most influential combination.

Forecasting Post-Epidemic Air Passenger Flow Among Hub Cities in China Based on PLS and GA-SVR Model

For stakeholders and companies involved in civil aviation to plan and make wise decisions, accurate estimates of air passenger flow are necessary. The conclusions drawn from this model serve as an invaluable resource for pertinent choices. The top hub cities in mainland China's air travel network are predicted using a variable weight combination model that combines PLS and GA-SVR. According to the test results, the model developed in this study improves prediction accuracy. This demonstrates how well detailed information about social and economic development can be gleaned from linear development patterns and nonlinear fluctuation rules. Predictions can be made with more accuracy and a better fit as a result. Over the next five years, it is predicted that over 300 million passengers will fly between the top hub cities in mainland China, an increase of 6.51% per year. The growth in passenger traffic varies significantly between different routes. The routes from Beijing to Shanghai and Shanghai to Shenzhen saw the most travellers, while the Beijing-Chengdu route saw the fastest growth in traveller numbers. The study's findings provide useful advice for civil aviation businesses and people involved in their decision-making, fostering growth in the sector during the post-pandemic period.

Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis.

The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model's performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance.