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Introduction: Tuberculosis (TB) morbidity and mortality are significantly increasing in the elderly worldwide. Their optimal health outcomes are hampered by medication related burden (MRB) and poor treatment adherence. Understanding th e MRB status from patients' perspectives and its association with adherence among elderly TB patients will help achieve the End TB targets. Thus, we aimed to identify the incidence of MRB and nonadherence among elderly TB patients in Guizhou, and determine their association., Methods: A cross-sectional study was conducted in three prefectures with high TB notifications in Guizhou in 2022. The data were collected via face-to-face structured interviews. MRB was measured using the Living with Medicines Questionnaire version 3 (LMQ-3), which consists of eight domains. Nonadherence was assessed by treatment interruption, which was defined as any interruption lasting at least 1 day at any time within the last 3 months. A binary unconditional logistic regression model was used to determine the association between variables., Results: Of the 405 elderly TB patients enrolled, 49.4% and 42.7% of the respondents perceived suffering from moderate and high MRB, respectively. The incidence of nonadherence among patients was 33.6%. Patients with higher scores in domain 2 (practical difficulties) [ OR adj = 1.19; 95% CI (1.11-1.28)] and domain 4 (side effects burden of prescribed medications) [ OR adj = 1.16; 95% CI (1.06-1.27)] were more likely to experience nonadherence. But, patients with higher scores in domain 8 (control/autonomy of medicine use) [ OR adj = 0.70; 95% CI (0.61, 0.81)] were more likely to occur adherence. Patients with a high education level [ OR adj = 0.29; 95% CI (0.08, 0.92)] had a decreased risk of nonadherence, but those with a living expense from a retirement salary [ OR adj = 2.55; 95% CI (1.16, 5.71)] had an increased risk of nonadherence., Discussion: The incidence of MRB and medication nonadherence is high among elderly TB patients in Guizhou. The significant associations between the three domains of MRB and nonadherence highlight that measuring MRB in multiple dimensions using the LMQ-3 in elderly TB patients could assist clinicians in providing patient-centered care, and multifaceted interventions targeting the identified problems should be implemented to reduce MRB and nonadherence among elderly TB patients in Guizhou., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Jian, Huang, Chen, Hu and Fang.)

As the market penetration rate of automated vehicles (AVs) increases, there will be a transition period when the traffic stream is composed of both AVs and human-driven vehicles (MVs) in the near future. However, the interactions between MVs and AVs, especially whether MVs will behave differently when following AVs compared to following MVs, have not been fully understood. Previous studies in this field mainly conducted traffic/numerical simulations or field experiments to investigate human drivers' behavior changes, but these approaches all have critical drawbacks such as simplified driving environments and limited sample sizes. To fill in the knowledge gap, this study uses the high-resolution (10 Hz) Waymo Open Dataset to reveal differences in car-following behaviors between MV-following-AV and MV-following-MV cases. Driving volatility measures, time headways and time-to-collision (TTC) are adopted to quantify and compare MV-following-AV and MV-following-MV interactions. The principal component analysis (PCA) is applied on the high-dimensional feature space, followed by the hierarchical clustering on the dimension-reduced feature set to categorize MV driving styles when following AVs. The comparison results indicate that MV-following-AV events have lower driving volatility in terms of velocity and acceleration/deceleration, smaller time headways and higher TTC values. Furthermore, the clustering results reveal that human drivers when following AVs exhibit four different car-following styles: high-velocity-non-aggressive, high-velocity-aggressive, low-velocity-non-aggressive, and low-velocity-aggressive. These findings highlight the vital importance of taking into account the heterogeneity of MV-following-AV behaviors when designing mixed traffic control algorithms and can be beneficial for AV fleet operators to improve their algorithms., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Drive cycles in vehicle systems are important determinants for energy consumption, emissions, and safety. Estimating the frequency of the drive cycle quickly is important for control applications related to fuel efficiency, emission reduction and improving safety. Quantum computing has established the computational efficiency that can be gained. A drive cycle frequency estimation algorithm based on the quantum Fourier transform is exponentially faster than the classical Fourier transform. The algorithm is applied on real world data set. We evaluate the method using a quantum computing simulator, demonstrating remarkable consistency with the results from the classical Fourier transform. Current quantum computers are noisy, a simple method is proposed to mitigate the impact of the noise. The method is evaluated on a 15 qubit IBM-q quantum computer. The proposed method for a noisy quantum computer is still faster than the classical Fourier transform., (© 2022. The Author(s).)

A current issue within the driver distraction community centres around different findings regarding the impact of mobile phone conversation on driving found in driving simulators versus instrumented vehicles employed in real-world naturalistic driving studies (NDSs). This paper compares and contrasts the two types of studies and aims to provide reasons for the differences in findings that have been documented. A comprehensive review of literature and consultations with human factors experts highlighted that simulator studies tend to show degradation in driving performance, suggestive of increased crash risk as a result of mobile phone conversation. Whilst NDSs, at times, present data suggesting that mobile phone conversation distraction actually reduces crash risk. This study identifies that these differences may be attributed to behavioural hypotheses associated with driver self-regulation, arousal from cognitive loading, task displacement and gaze concentration - all of which need to be explicitly tested in future driving studies. Metric estimation and application was also revealed to be polarising results and the subsequent assessment of the crash risk. A common metric applied in this domain is the 'Odds Ratio', particularly prevalent in NDSs. This study presents a detailed investigation into the assumptions and application of the Odds Ratio which revealed the potential for over- and under-estimation of the metric depending on the core data and sampling assumptions. Furthermore, this research presents a comparative analysis of select driving simulator studies and an NDS considering only driving behaviour data as a means to consistently compare the findings of both methodologies. The findings from this investigation implores the need for greater consistency in the application of analysis methods and metrics across both simulator and NDSs. Improvements can yield a more robust platform to systematically compare and interpret data across both approaches, ultimately leading to enhanced planning and safety regarding mobile phone use while driving., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Autonomous Vehicles have captured the imagination of our society and have promised a future of safe and efficient mobility. However, there is a need to understand behaviour and its consequences in the use of autonomous vehicles. Using paradigms of behavioural and experimental economics, we show that risk attitudes play a role in acceptability of autonomous vehicles, productivity in autonomous vehicles and safety under risk of failures of autonomous systems. We found that risk attitudes and age have a significant impact on these. We believe these findings will help provide guidance to insurance agencies, licensing, vehicle design, and policies around automated vehicles., (Copyright © 2019 Elsevier Ltd. All rights reserved.)