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We investigated the optimal performance of an irreversible Stirling-like heat engine described by both overdamped and underdamped models within the framework of stochastic thermodynamics. By establishing a link between energy dissipation and Wasserstein distance, we derived the upper bound of maximal power that can be delivered over a complete engine cycle for both models. Additionally, we analytically developed an optimal control strategy to achieve this upper bound of maximal power and determined the efficiency at maximal power in the overdamped scenario.

To determine distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards in Wuhan, China, we tested air and surface samples. Contamination was greater in intensive care units than general wards. Virus was widely distributed on floors, computer mice, trash cans, and sickbed handrails and was detected in air ≈4 m from patients.

At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

Building a human-like car-following model that can accurately simulate drivers’ car-following behaviors is helpful to the development of driving assistance systems and autonomous driving. Recent studies have shown the advantages of applying reinforcement learning methods in car-following modeling. However, a problem has remained where it is difficult to manually determine the reward function. This paper proposes a novel car-following model based on generative adversarial imitation learning. The proposed model can learn the strategy from drivers’ demonstrations without specifying the reward. Gated recurrent units was incorporated in the actor-critic network to enable the model to use historical information. Drivers’ car-following data collected by a test vehicle equipped with a millimeter-wave radar and controller area network acquisition card was used. The participants were divided into two driving styles by K-means with time-headway and time-headway when braking used as input features. Adopting five-fold cross-validation for model evaluation, the results show that the proposed model can reproduce drivers’ car-following trajectories and driving styles more accurately than the intelligent driver model and the recurrent neural network-based model, with the lowest average spacing error (19.40%) and speed validation error (5.57%), as well as the lowest Kullback-Leibler divergences of the two indicators used for driving style clustering.

Previous studies have suggested that asthmatic patients often have comorbid depression; however, temporal associations remain unclear. To determine whether depression predicts asthma and, conversely, whether asthma predicts depression. A literature search was conducted without language restrictions using Pubmed, Embase, Cochrane and PsycINFO for studies published before January, 2015. Papers referenced by the obtained articles were also reviewed. Only comparative prospective studies with reported risk estimates of the association between depression and asthma were included. In order to investigate whether one of these conditions was predictive of the other, studies were excluded if enrolled participants had pre-existing depression or asthma. A random-effects model was used to calculate the pooled risk estimates for two outcomes: depression predicting asthma and asthma predicting depression. Seven citations, derived from 8 cohort studies, met our inclusion criteria. Of these, six studies reported that depression predicted incident adult-onset asthma, including 83684 participants and 2334 incident cases followed for 8 to 20 years. Conversely, two studies reported that asthma predicted incident depression. These studies involved 25566 participants and 2655 incident cases followed for 10 and 20 years, respectively. The pooled adjusted relative risks (RRs) of acquiring asthma associated with baseline depression was 1.43 (95% CI, 1.28-1.61) (P<0.001). The adjusted RRs for acquiring depression associated with baseline asthma was 1.23 (95% CI, 0.72-2.10) (P = 0.45). Depression was associated with a 43% increased risk of developing adult-onset asthma. However, asthma did not increase the risk of depression based on limited studies. Further prospective studies ascertaining the true association between asthma and subsequent risk of depression are warranted.

Background: Balancing nutrition security with environmental sustainability is a key priority in global food policy, with Sustainable Healthy Diets (SHDs) serving as a critical framework aligned with the UN Sustainable Development Goals (SDGs). Traditional Japanese cuisine reflects SHD principles through its emphasis on plant-based, seasonal, and minimally processed dishes. However, modern, globalized dietary patterns increasingly feature ultra-processed foods, raising concerns about health risks such as high sodium intake. Methods: This study adopts a novel dish-level content analysis of 120 contemporary recipes from NHK Today’s Cooking between 2023 and 2025, a TV program by Japan’s national public broadcaster that is widely regarded as reflecting the practices of Japanese home cooking, to examine how SHDs pillars—nutritional diversity (e.g., varied protein sources), environmental sustainability (e.g., low-carbon ingredients), and cultural continuity (e.g., traditional techniques)—are embedded in Japanese home cooking. Unlike macro-level consumption or nutrition data, this dish-level approach reveals how individual dishes embody sustainability through ingredient selection, preparation methods, and cultural logic. Results: Quantitatively, pork (33.3%) and seafood (19.2%) together dominated main protein sources, with minimal beef (2.5%) and a notable presence of soy-based foods (12.5%), supporting lower reliance on environmentally intensive red meat; mean salt content per person in main dishes was 2.16 ± 1.09 g (28.9% for men, 33.3% for women of Japan’s daily salt targets), while recipe patterns emphasizing fermentation and seasonal alignment highlight possible pathways through which Japanese dietary practices can be considered ecologically efficient. Simultaneously, the analysis identifies emerging challenges, encompassing environmental issues such as overfishing and public health concerns like excessive sodium consumption. Conclusions: By centering dishes as culturally meaningful units, and using media recipes as reproducible, representative datasets for monitoring dietary change, this approach offers a reproducible framework for assessing dietary sustainability in evolving global food systems.

Chronic constipation is a common gastrointestinal disorder, with a worldwide incidence of 14–30%. It negatively affects quality of life and is associated with a considerable economic burden. As a disease with multiple etiologies and risk factors, it is important to understand the pathophysiology of chronic constipation. The purpose of this review is to discuss latest findings on the roles of gut motility, fluid, and electrolyte transport that contribute to chronic constipation, and the main drugs available for treating patients. We conducted searches on PubMed and Google Scholar up to 9 February 2021. MeSH keywords “constipation”, “gastrointestinal motility”, “peristalsis”, “electrolytes”, “fluid”, “aquaporins”, and “medicine” were included. The reference lists of searched articles were reviewed to identify further eligible articles. Studies focusing on opioid-induced constipation, evaluation, and clinic management of constipation were excluded. The occurrence of constipation is inherently connected to disorders of gut motility as well as fluid and electrolyte transport, which involve the nervous system, endocrine signaling, the gastrointestinal microbiota, ion channels, and aquaporins. The mechanisms of action and application of the main drugs are summarized; a better understanding of ion channels and aquaporins may be helpful for new drug development. This review aims to provide a scientific basis that can guide future research on the etiology and treatment of constipation.

The incidence rate of cardio-cerebrovascular diseases (CCVDs) is increasing worldwide, causing an increasingly serious public health burden. The pursuit of new promising treatment options is thus becoming a pressing issue. Hydroxysafflor yellow A (HSYA) is one of the main active quinochalcone C -glycosides in the florets of Carthamus tinctorius L., a medical and edible dual-purpose plant. HSYA has attracted much interest for its pharmacological actions in treating and/or managing CCVDs, such as myocardial and cerebral ischemia, hypertension, atherosclerosis, vascular dementia, and traumatic brain injury, in massive preclinical studies. In this review, we briefly summarized the mode and mechanism of action of HSYA on CCVDs based on these preclinical studies. The therapeutic effects of HSYA against CCVDs were presumed to reside mostly in its antioxidant, anti-inflammatory, and neuroprotective roles by acting on complex signaling pathways.

As higher education becomes increasingly prevalent and accessible in China, a growing number of residents are afforded the option to pursue advanced studies. Can higher education genuinely enhance residents’ subjective well-being? The response to this enquiry necessitates additional investigation. This study selected 5 wave data of Chinese General Social Survey (CGSS), a total of 53,874 samples. Machine learning methodologies, including XGBoost and GBDT, were utilised for the inaugural correlation investigation between higher education and subjective well-being in China. Feature importance sorting elucidated the nonlinear correlations and interaction effects, such as the threshold effect of social fairness cognition on happiness, that typical regression models struggle to capture. (1) The average subjective well-being of the higher education group (4.005309) was significantly higher than that of the non-higher education group (3.835478), and the education level had a significant positive predictive role on subjective well-being ( p  = 0.000 < 0.05); (2) Machine learning uncovers substantial correlations between higher education and subjective well-being ( =0.008, p  < 0.01), with social justice cognition (feature weight=0.32) and self-rated health (0.28) identified as primary mediators. (3) The proportion of women in the highest level of well-being ([4.0,5.0)] was slightly higher than that of men. Higher education can markedly enhance the subjective well-being of individuals. Furthermore, it improves residents’ subjective well-being via social justice perception, self-assessed health, social class identity, job satisfaction, and socio-economic status. This establishes a scientific foundation for the government and all societal sectors to augment investment in education, enhance the distribution of educational resources, and foster the comprehensive development of individuals.

Touch displays are ubiquitous in modern technologies. However, current protective methods for emerging flexible displays against static, scratches, bending, and smudge rely on multilayer materials that impede progress towards flexible, lightweight, and multifunctional designs. Developing a single coating layer integrating all these functions remains challenging yet highly anticipated. Herein, we introduce an organic–inorganic covalent–ionic hybrid network that leverages the reorganizing interaction between siloxanes (i.e., trifluoropropyl–funtionalized polyhedral oligomeric silsesquioxane and cyclotrisiloxane) and fluoride ions. This nanoscale organic–inorganic covalent–ionic hybridized crosslinked network, combined with a low surface energy trifluoropropyl group, offers a monolithic layer coating with excellent optical, antistatic, anti–smudge properties, flexibility, scratch resistance, and recyclability. Compared with existing protective materials, this all–in–one coating demonstrates comprehensive multifunctionality and closed–loop recyclability, making it ideal for future flexible displays and contributing to ecological sustainability in consumer electronics. Lin et al. report a nanoscale organic-inorganic covalent-ionic hybrid network leveraging the reorganising interaction between siloxanes and fluoride ions, enabling a single layer coating with excellent optical, antistatic, anti-smudge, anti-scratch, and mechanical properties for touch displays.