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Abstract Background The relationship of testosterone and estradiol concentrations with cognitive function among community-dwelling older men was inconclusive. To examine the association of serum testosterone and estradiol concentrations with cognitive function in older men with or without vascular risk factors (VRFs). Methods This cross-sectional study consisted of 224 community-dwelling men aged 65–90 years in the Songjiang District of Shanghai, China. Serum testosterone and estradiol were measured by electrochemiluminescence immunoassay. The following five factors were defined as VRFs in this study: obesity, history of hypertension, diabetes, stroke, and coronary heart disease. Multivariable linear regression was used to examine the association of testosterone and estradiol with the Mini-Mental State Examination (MMSE) in participants with or without VRF. Restricted cubic spline (RCS) regression was performed to account for the nonlinearity of these associations. Results An inverted “U” shaped non-linear relationship was found between testosterone concentration and MMSE score in men with one VRF (P overall =.003, non-linear P =.002). Estradiol showed an inverted “U” shaped non-linear relationship with MMSE score independent of VRFs (men without VRF, P overall =.049, non-linear P =.015; men with one VRF, overall P =.007, non-linear P =.003; men with two or more VRFs, overall P =.009, non-linear P =.005). Conclusion In older men, an optimal level of sex steroid concentration may be beneficial to cognitive function and the VRFs should be considered when interpreting the relationship between sex steroid and cognitive function.

Accurate vehicle acceleration prediction is useful for developing reliable Advanced Driving Assistance Systems (ADAS) and improving road safety. The existence of driver heterogeneity magnifies the variations in acceleration data, leading to consequential impacts on the precision of vehicle acceleration prediction. However, few studies have fully considered the driver heterogeneity when predicting vehicle acceleration. To model the characteristics of individual drivers, this study first identifies the driving behavior semantics which is defined as the underlying patterns of driving behaviors. The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. To validate the accuracy of the proposed prediction framework, vehicle acceleration data in car-following conditions is extracted from the safety pilot model deployment (SPMD) dataset. The segmentation results indicate that the CHMM possesses a robust capacity for modeling driving behavior. The prediction results demonstrate that the proposed framework, which incorporates driver clustering before prediction, significantly improves the accuracy of predictions. And the CNN-LSTM outperforms the LSTM in predicting vehicle acceleration during car-following scenarios. The findings from this study can enhance the development of personalized functionalities within ADAS to promote its deployment, thereby improving its acceptance and safety.

BackgroundAge-related muscle atrophy and adipose accumulation begin to occur in young and middle-aged individuals, and exercise at an early age improves body composition. Pyroptosis may play an essential role in age-related low-grade inflammation. This study aimed to explore the alleviation of muscle atrophy by weight-bearing training with increasing age via inhibition of pyroptosis.MethodsNinety 8-month-old male SD rats were randomly divided into three groups: (1) normal baseline group (N group, n = 10), sacrificed after adaptive feeding; control group (C group, n = 40); and weight-bearing running group (R group, n = 40). Blood samples, adipose tissue (AT), and extensor digitorum longus (EDL) were collected after 8, 16, 24, and 32-weeks intervention.ResultsThe body weight, muscle mass, fat mass, plasma lipid, AT wet weight, adipocyte cross-sectional area (CSA), and apoptosis rates of AT and EDL were increased, while the muscle mass, wet weight, and fiber CSA of EDL were decreased by aging, which were reversed by exercise. Weight-bearing training promoted protein synthesis in EDL, inhibited protein degradation in EDL, and expression of pyroptotic key proteins in EDL and AT in rats.ConclusionWeight-bearing training improves body composition and alleviates age-related muscle atrophy in rats, and its mechanism may be related to the inhibition of pyroptosis in the EDL and AT and the improvement of muscle protein metabolism.

Accurate identification and analysis of traffic conflicts through surrogate safety measures (SSMs) are crucial for safety evaluation in road systems. Existing SSMs for conflict identification and analysis mostly consider the temporal–spatial proximity of conflicts without taking into account the severity of potential collisions. This makes SSMs unsuitable for traffic safety evaluation in complex road environments. In order to address the shortcomings above, this study first introduces a new SSM called the Potential Conflict Risk Index (PCRI). To validate the effectiveness of PCRI, the inD dataset is adopted for conflict identification comparison between time-to-collision (TTC) and PCRI. Using PCRI, this study conducts a conflict analysis in the freeway merging areas based on the data from the Outer Ring Expressway Dataset (ORED), accounting for differences between cars and trucks. The comparative results between TTC and PCRI show that PCRI can provide a more comprehensive identification of conflicts and a more accurate identification of the moment with the highest conflict risk. The results of conflict analysis suggest that conflicts occur more frequently in situations involving trucks, and these conflicts commonly occur in closer proximity to the on-ramp at freeway merging areas. The findings from this study can improve the accuracy of conflict identification under different conflict patterns, enhancing the specificity of traffic safety measures and ultimately ensuring the safety of road systems.

Abstract As early as 1910, Weyl gave a classification of the singular Sturm–Liouville equation, and divided it into the Limit Point Case and the Limit Circle Case at infinity. This led to the study of singular Sturm–Liouville spectrum theory. With the development of applications, the importance of singular Sturm–Liouville problems with a weighted function becomes more and more significant. This paper focuses on the study of singular Sturm–Liouville problems with a weighted function. Finally, an example of singular Sturm–Liouville problems with a weighted function is given.

Parking duration analysis is an important aspect of evaluating parking demand. Identifying accurate distribution characteristics of parking duration can not only enhance parking efficiency and parking facility planning, but also provide essential support for parking delicacy management. Previous studies have proposed various statistical distributions to depict parking duration data. However, it is difficult to find a certain type of distribution to describe the characteristics of parking duration in diverse parking facilities, since model uncertainty is caused by stochastic parking behaviors and diverse parking environments. To address the model uncertainty, a Bayesian model averaging (BMA) was applied to integrate the advantages of different statistical distributions to depict parking duration characteristics. The parking dataset was collected from a commercial parking lot in Chengdu, China, and the dataset was categorized into two groups (i.e., temporary users and long-term users) to analyze. A set of statistical distributions was chosen as candidate models, and their corresponding unknown parameters were estimated. The posterior model probability for each candidate model was calculated according to the goodness-of-fit (GOF) metric. The findings of the study illustrate that there is no universally applicable distribution form (e.g., log-normal distribution) to depict the parking duration distribution for both user types, whereas the BMA approach assigns weights to candidate models and always provides an accurate description of the parking duration characteristics. The parking duration analysis is useful for improving parking management strategies and optimizing parking pricing policies.

Improving urban ecological efficiency is an integral part of ecological protection and high-quality development of the Yellow River Basin. We used the super-efficiency slacks-based model with unexpected output to measure the ecological efficiency of 62 prefecture-level cities in the Yellow River Basin from 2005 to 2018. Its spatial distribution characteristics and convergence are discussed. The influencing factors and spatial spillovers of the ecological efficiency are assessed through the spatial Dubin model. The results show that: 1) The ecological efficiency is highest in cities in the lower reaches of the Yellow River Basin, mid-level in the upper reaches, and lowest in the middle reaches. 2) There is no σ convergence in the ecological efficiency of the Yellow River Basin, but there is absolute β convergence and conditional β convergence. 3) Economic development, financial development, and technological innovation significantly promote ecological efficiency, and the spatial spillover effects are significant. Increased connection with the outside world can significantly improve ecological efficiency, but there is no apparent spatial spillover effect for these connections. The industrial structure and urbanization rate reduced ecological efficiency and have a significant negative spillover effect. The energy consumption structure reduces the level of ecological efficiency, and the spillover effect is not significant.