Association of composite dietary antioxidant index with cardiovascular disease in adults: results from 2011 to 2020 NHANES.

Background: Oxidative stress is a known pathogenic mechanism in cardiovascular disease (CVD), yet the association between dietary antioxidants and CVD in the general population remains underexplored. This study leverages data from the National Health and Nutrition Examination Survey (NHANES) to investigate the association of a composite dietary antioxidant index with CVD in US adults. Methods: Analyzing data from 25,997 adults (2011-2020 NHANES), we employed weighted generalized linear models, subgroup analysis, threshold effect analyses, and sensitivity analysis to assess the association between dietary antioxidants and CVD. Nonlinear associations were explored through a restricted cubic spline, with gender-specific stratification and threshold effect analysis to identify critical inflection points. Results: Increasing levels of the composite dietary antioxidant index corresponded with decreased CVD prevalence (P < 0.001). In all models, weighted generalized linear models revealed a consistent negative association between CVD prevalence. And in Model 3, Quartile 4 had a 29% lower CVD prevalence than Quartile 1[0.71 (0.59, 0.85), P < 0.001]. Meanwhile, the findings of the unweighted logistic regression model demonstrated stability. Various characteristics such as sex, age, race, PIR, education, BMI, alcohol consumption, hypertension, hyperlipidemia, and diabetes did not influence this inverse association (P for interaction >0.05). Notably a nonlinear association was observed, with a significant inflection point at 3.05 among women. Conclusion: This study demonstrates a strong negative association between the composite dietary antioxidant index and CVD prevalence, suggesting the potential protective role of dietary antioxidants. These findings underscore the need for prospective studies to further understand the impact of oxidative stress on cardiovascular health.

Association between triglyceride glucose body mass index and cardiovascular disease in adults: evidence from NHANES 2011- 2020.

Background: The association between insulin resistance and cardiovascular diseases (CVD) is of significant interest. However, there is limited published research on the relationship between CVD and the triglyceride glucose-body mass index (TyG-BMI). This study aims to examine the association between TyG-BMI and CVD in US adults. Method: We analyzed data from 11016 adults collected through the 2011-2020 NHANES. Employing weighted generalized linear models, subgroup analysis, sensitivity analysis, and receiver operating characteristic curves, we examined the association between the TyG-BMI index and CVD. Nonlinear associations were investigated using restricted cubic splines. Results: Higher TyG-BMI values were significantly associated with an increased prevalence of CVD (P<0.001). Weighted generalized linear models consistently demonstrated a positive association across all models. Specifically, individuals in the highest tertile of TyG-BMI had a 38% higher CVD prevalence than those in the lowest quartile (OR=1.380; 95% CI=1.080, 1.763). Unweighted logistic regression models further confirmed these findings. Sex, race, education, family income to poverty ratio, smoking, hypertension, and diabetes did not modify this positive association (P for interaction >0.05). Incorporating the TyG-BMI index into traditional risk factor models marginally improved the prediction of CVD prevalence (P for comparison <0.05). Conclusions: The TyG-BMI index, an indicator of insulin resistance, is significantly positive associated with a higher prevalence of CVD. These findings underscore the importance of managing insulin resistance to prevent CVD and highlight the need for further research into the underlying mechanisms of this association.

Traffic accident duration prediction using multi-mode data and ensemble deep learning.

Predicting the duration of traffic accidents is a critical component of traffic management and emergency response on expressways. Traffic accident information is inherently multi-mode data in terms of data types. However, most existing studies focus on single-mode data, and the influence of multi-mode data on the prediction performances of models has been the subject of only very limited quantitative analysis. The present work addresses these issues by proposing a heterogeneous deep learning architecture employing multi-modal features to improve the accuracy of predictions for traffic accident durations on expressways. Firstly, six unique data modes are obtained based on the structured data and the text data. Secondly, a hybrid deep learning approach is applied to build classification models with reduced prediction error. Finally, a rigorous analysis of the influence for multi-mode data on the accident duration prediction performances is conducted using a variety of deep learning models. The proposed method is evaluated using survey data collected from an expressway monitoring system in Shaanxi Province, China. The experimental results show that Word2Vec-BiGRU-CNN is a suitable and better model using text features for traffic accident duration prediction, as the F1-score is 0.3648. This study confirms that the newly established structured features extracted from text data substantially enhance the prediction effects of deep learning algorithms. However, these new features were a detriment to the prediction effects of conventional machine learning algorithms. Accordingly, these results demonstrate that the processing and extraction of text features is a complex issue in the field of traffic accident duration prediction.

Traffic accident duration prediction using text mining and ensemble learning on expressways.

Predicting traffic accident duration is necessary for ensuring traffic safety. Several attempts have been made to achieve high prediction accuracy, but researchers have not considered traffic accident text data in much detail. The limited text data of the first report on an incident describes the characteristics of an accident that are initially available. This paper uses text data fusing and ensemble learning algorithms to build a model to predict an accident's duration, and a preprocessing scheme of accident duration text data is established. Next, the random forest (RF) algorithm is applied to select feature variables of text data related to the traffic incident duration. Last, a text feature vector is introduced to models such as decision tree, k nearest neighbor, support vector regression, random forest, Gradient Boosting Decision Tree, and Xtreme Gradient Boosting. Our results show that the improved RF model has good prediction accuracy with RMSE, MAPE and R2. From this, the textual factors important to determining the duration of the accident are identified. Further, we investigated that the cumulative importance of 60% is sufficient for traffic accident prediction using text data. These results provide insights into minimizing traffic congestion related to accidents and contribute to the input optimization in text prediction.

Development of a True-Biaxial Split Hopkinson Pressure Bar Device and Its Application.

Although highly desirable, the experimental technology of the dynamic mechanical properties of materials under multiaxial impact loading is rarely explored. In this study, a true-biaxial split Hopkinson pressure bar device is developed to achieve the biaxial synchronous impact loading of a specimen. A symmetrical wedge-shaped, dual-wave bar is designed to decompose a single stress wave into two independent and symmetric stress waves that eventually form an orthogonal system and load the specimen synchronously. Furthermore, a combination of ground gaskets and lubricant is employed to eliminate the shear stress wave and separate the coupling of the shear and axial stress waves propagating in bars. Some confirmatory and applied tests are carried out, and the results show not only the feasibility of this modified device but also the dynamic mechanical characteristics of specimens under biaxial impact loading. This novel technique is readily implementable and also has good application potential in material mechanics testing.

Gait analysis using wearable sensors.

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.