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To study the impact of vigorous vs. moderate exercise on metabolic parameters in polycystic ovary syndrome (PCOS).

The electrification of vehicle power systems has become a dominant trend worldwide. However, with current technologies, range anxiety is still a major obstacle to the popularization of battery electric vehicles (BEVs). Previous research has found that users’ trust in the BEVs’ range estimation system (RES) is associated with their range anxiety. However, influential factors of trust in RES have not yet been explored. Thus, a questionnaire was designed to model the factors that are directly (i.e., implicit factors) and indirectly (i.e., explicit factors) associated with BEV users’ trust in RES. Following the three-layer automation trust framework (i.e., dispositional trust, situational trust, and learned trust), a questionnaire was designed and administrated online. In total, 367 valid samples were collected from BEV users in mainland China. A mixed approach combining Bayesian network (BN) and regression analyses (i.e., BN–regression mixed approach) was proposed to explore the potential topological relationships among factors. Four implicit factors (i.e., sensitivity to BEV brand, knowledge of RES, users’ emotional stability, and trust in the battery estimation system of their phones) have been found to be directly associated with BEV users’ trust in RES. Furthermore, four explicit factors (i.e., users’ highest education, regional charging infrastructure development, BEV brand, and household income) were found to be indirectly associated with users’ trust in RES. This study further demonstrates the effectiveness of using a BN–regression mixed approach to explore topological relationships among social–psychological factors. Future strategies aiming to modulate trust in RES can target toward factors in different levels of the topological structure.

The utilization of remote photoplethysmography (rPPG) technology has gained attention in recent years due to its ability to extract blood volume pulse (BVP) from facial videos, making it accessible for various applications such as health monitoring and emotional analysis. However, the BVP signal is susceptible to complex environmental changes or individual differences, causing existing methods to struggle in generalizing for unseen domains. This article addresses the domain shift problem in rPPG measurement and shows that most domain generalization methods fail to work well in this problem due to ambiguous instance-specific differences. To address this, the article proposes a novel approach called Hierarchical Style-aware Representation Disentangling (HSRD). HSRD improves generalization capacity by separating domain-invariant and instance-specific feature space during training, which increases the robustness of out-of-distribution samples during inference. This work presents state-of-the-art performance against several methods in both cross and intra-dataset settings.

With the rapid growth of online food delivery services, the population of delivery workers has reached 13 million in China. Most of them use electric bicycles (e-bikes) as their primary transportation tool. On the other hand, mobile phone call (MPC), as the primary and most effective communication method for delivery workers, induces distracted riding and threatens traffic safety. Although MPC has been extensively investigated in driving-related research, MPC among delivery workers has not yet been investigated. In this study, a questionnaire based on the theory of planned behavior has been designed to investigate social-psychological factors leading to MPC among delivery workers. 150 delivery workers participated in this study. The structural equation model method was adopted to analyze the data. The results show that injunctive norm is positively associated with MPC behaviors; but descriptive norm is negatively associated with MPC behaviors, potentially because of the selective perception phenomenon.

Deep ultraviolet (DUV) LEDs have great potential in sterilization, water, air purification, and other fields. In this work, DUV LED wafers with different quantum well (QW) widths were grown by metal–organic chemical vapor deposition. It is found that the light output power (LOP) and peak wavelength of all chips are not only related to the QW thickness but also affected by warpage. For the first time, to the best of our knowledge, a positive correlation between the LOP and peak wavelength of DUV LED chips on the same wafer was observed, which is very important for improving the yield of DUV LEDs and reducing costs. Furthermore, the influence of QW thickness on the external quantum efficiency (EQE) of DUV LED has also been investigated. As the thickness of the QW increases, the exciton localization effect decreases and the quantum confinement Stark effect increases. Consequently, DUV LED wafers with a QW thickness of 2 nm have the highest EQE and yield. These findings not only help to improve the efficiency of DUV LEDs but also provide new insights for evaluating the performance of DUV LED wafers.

To evaluate evidence for the role of probiotic supplementation in enhancing natural killer (NK) cell function in healthy elderly individuals. Five electronic databases were searched, and references of included articles and eligible reviews up to December 2019, with English language and human subject restrictions, were examined. Two independent reviewers identified randomized control trials (RCTs) of probiotic supplementation influencing NK cell function in healthy elderly individuals, assessed the quality of every article, and extracted data for subsequent meta-analysis. We identified six eligible trials including 364 healthy elderly subjects. Trials were heterogeneous in study design and probiotic supplementation (including genus, strain, dose, and duration). Five trials used Lactobacillusinterventions alone or in combination with Bifidobacterium. Only one trial focused on Bacillus coagulans. The duration of supplementation ranged from 3 to 12 weeks, and the doses, from 1?×?109to 4?×?1010colony-forming units. Pooling data of eligible trials showed that probiotics significantly (P?

Although automated vehicles (AVs) were considered a promising solution to enhance traffic safety by eliminating human errors, AV crashes still happen in mixed traffic consisting of human-driven vehicles and AVs. Thus, to reduce AV-involved crashes, it is necessary to understand the factors leading to AV crashes. However, traditional regression-based methods may not reveal a structured relationship among leading factors of AV crashes, which hinders the exploration of countermeasures to AV crashes. Based on the 246 AV crash records collected by the National Highway Traffic Safety Administration, this study investigated the factors associated with AV crashes. An additive Bayesian network (ABN) approach was utilized to construct the topological relationship among potential influential factors of AV crashes, followed by post-ABN regression analyses. Results show that, though AV technologies have developed rapidly in the past few years, rear-end crashes are still dominant among AV-involved crashes, potentially because of the discrepancy in the driving behaviors between AV and human-driven vehicles. The crash type of AV-involved crashes is more related to the pre-crash movements of crash partners than it is to the pre-crash movements of AVs, while crash outcomes (e.g., injury severity) are associated with the environmental factors (e.g., operating entities) and crash-procedure-related factors (e.g., crash type). Findings from this study aid in understanding AV crash patterns, which can inform targeted interventions and technology advancements to improve safety outcomes for all road users.

Though driving automation promises to improve driving safety, drivers are still required to be ready to retake control in conditionally automated vehicles, which are defined by the Society of Automotive Engineers (SAE) as SAE L3 vehicles. Thus, drivers’ states can still affect driving safety in SAE L3 vehicles. Previous research found that a high cognitive load may impair drivers’ takeover performance. Thus, it is still necessary to estimate drivers’ cognitive load in SAE L3 vehicles. However, existing driver cognitive load estimation algorithms mostly focus on vehicles with a lower level of driving automation (e.g., SAE L0), which may not be relevant when estimating driver states in SAE L3 vehicles, given that drivers’ responsibilities are different, and several commonly used measures (e.g., driving performance) are unavailable when drivers are not continuously controlling the vehicle. Further, previous driver cognitive load estimation algorithms rarely considered the temporal information in the input features. Thus, we proposed a deep-learning algorithm to estimate driver cognitive load in SAE L3 vehicles, which integrated multiple physiological features (i.e., electrocardiogram, electrodermal activity, respiration) and considered the temporal correlation of the data using a transformer-encoder-based network. The performance of our algorithm was compared with baseline models on an open data set. Results showed that our algorithm outperformed baseline models and achieved an accuracy of 94.4% using within-subject data partition (proportionally splitting data from the same subject into the training and testing data sets) and an accuracy of 89% using across-subjects data partition (dividing the training and testing data sets based on individual subjects).

With the development of conditionally automated driving, drivers will be allowed to perform non-driving-related tasks. Under such circumstances, continuous monitoring of driver cognitive load will play an increasingly important role in ensuring that drivers have sufficient mental resources to take over control of the vehicle should the driving automation fail. However, estimation of cognitive load is challenging because of the difficulties in identifying high-level feature representation and accounting for interindividual differences. Physiological measures are believed to be promising candidates for cognitive load estimation in partially automated vehicles. However, current estimation methods are mainly based on the manual feature extraction of time- or frequency-domain indicators from physiological signals, which may not adapt to dynamic driving conditions. With the development of deep learning, the neural network has shown good performance in automatically capturing high-level features from input data. Inspired by this, we adopted a novel approach to classify driver cognitive load based on electrocardiogram (ECG) spectrograms, in which the driver’s ECG signal was collected and transformed into a 2D spectrogram by a short-time Fourier transform. A squeeze-and-excitation network-based deep-learning framework that can capture high-level features and pays more attention to the cognition-related features of the spectrogram was proposed for classification. Experiments on a publicly available dataset demonstrated that our model achieved an accuracy of 96.76% in differentiating two levels of cognitive load for a within-subject evaluation and 71.50% accuracy with an across-subjects evaluation. The results demonstrated the feasibility of detecting drivers’ cognitive load through deep learning using ECG spectrogram alone.

Chromhidrosis is a rare idiopathic disorder characterized by colored secretions most typically from the malar cheeks, axilla, or areolar regions. Histologically, chromhidrosis is notable for glandular structures with decapitation secretion indicating ectopic apocrine glands in the dermis, and the presence of lipofuscin pigments under ultraviolet fluorescence and in cytology smears. This case report describes a 26-year-old man who presented with a 2- to 3-year history of black-colored secretions on the bilateral malar cheeks, present on exertion or with squeezing of the cheeks. A 3-mm punch biopsy of the left cheek demonstrated histopathologic findings characteristic of chromhidrosis under hematoxylin and eosin staining and ultraviolet fluorescence. To our best knowledge, this is the second case report in the literature of an adult male being affected by chromhidrosis, and the first of an adult male with black-colored malar cheek secretions in chromhidrosis.

A case–control study of five classes of antihypertensive medication used among 2,851 women shows that taking calcium-channel blockers for ≥10 years is associated with increased risk of invasive ductal and lobular breast carcinoma. These results are intriguing, but warrant additional investigation given the study limitations and inconsistent findings in the literature.