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We investigate the gas flows near to solid surfaces in terms of the local spatial variation in the molecular mean free path (MFP). Molecular dynamics (MD) is the appropriate scientific tool for obtaining molecularly-accurate dynamic information in micro and nano-scale gas flows, and has been used to evaluate the molecular mean free path of gases. In the calibration procedure, the viscosity of a gas in the homogeneous case can be recovered in our MD simulations and reach good agreement with the theoretical prediction and data from NIST. In surface-bounded gas flows, if the collisions between gas molecules and walls are counted, a spatially-varying mean free path is presented, and for the first time we have observed that the distribution of the free paths deviates from the exponential one and spikes appear in their distributions at larger Kn, i.e. in the transition flow regime. Based on elementary kinetic theory, the effective viscosity of the gas derived from the mean free path has been incorporated into the framework of the continuum-fluid dynamics equations, and micro-Couette flows are performed to demonstrate this potential application.

Three-dimensional (3D) computational fluid dynamic (CFD) simulations have been performed to investigate the complex flow features and stripping of fluid materials from a cylindrical water drop at the late-stage in a Shock Liquid Drop Interaction (SLDI) process when the drop’s downstream end experiences compression after it is impacted by a supersonic shock wave (Ma = 1.47). The drop trajectory/breakup has been simulated using a Lagrangian model and the unsteady Reynolds-averaged Navier–Stokes (URANS) approach has been employed for simulating the ambient airflow. The Kelvin–Helmholtz Rayleigh–Taylor (KHRT) breakup model has been used to capture the liquid drop fragmentation process and a coupled level-set volume of fluid (CLSVOF) method has been applied to investigate the topological transformations at the air/water interface. The predicted changes of the drop length/width/area with time have been compared against experimental measurements, and a very good agreement has been obtained. The complex flow features and the qualitative characteristics of the material stripping process in the compression phase, as well as disintegration and flattening of the drop are analyzed via comprehensive flow visualization. Characteristics of the drop distortion and fragmentation in the stripping breakup mode, and the development of turbulence at the later stage of the shock drop interaction process are also examined. Finally, this study investigated the effect of increasing Ma on the breakup of a water drop by shear stripping. The results show that the shed fluid materials and micro-drops are spread over a narrower distribution as Ma increases. It illustrates that the flattened area bounded by the downstream separation points experienced less compression, and the liquid sheet suffered a slower growth.

In any healthcare setting, it is important to monitor and control airflow and ventilation with a thermostat. Computational fluid dynamics (CFD) simulations can be carried out to investigate the airflow and heat transfer taking place inside a neonatal intensive care unit (NICU). In this present study, the NICU is modeled based on the realistic dimensions of a single-patient room in compliance with the appropriate square footage allocated per incubator. The physics of flow in NICU is predicted based on the Navier–Stokes conservation equations for an incompressible flow, according to suitable thermophysical characteristics of the climate. The results show sensible flow structures and heat transfer as expected from any indoor climate with this configuration. Furthermore, machine learning (ML) in an artificial intelligence (AI) model has been adopted to take the important geometric parameter values as input from our CFD settings. The model provides accurate predictions of the thermal performance (i.e., temperature evaluation) associated with that design in real time. Besides the geometric parameters, there are three thermophysical variables of interest: the mass flow rate (i.e., inlet velocity), the heat flux of the radiator (i.e., heat source), and the temperature gradient caused by the convection. These thermophysical variables have significantly recovered the physics of convective flows and enhanced the heat transfer throughout the incubator. Importantly, the AI model is not only trained to improve the turbulence modeling but also to capture the large temperature gradient occurring between the infant and surrounding air. These physics-informed (Pi) computing insights make the AI model more general by reproducing the flow of fluid and heat transfer with high levels of numerical accuracy. It can be concluded that AI can aid in dealing with large datasets such as those produced in NICU, and in turn, ML can identify patterns in data and help with the sensor readings in health care.

This qualitative study aimed to gain a deep understanding of the spiritual needs of patients with advanced cancer. A qualitative study using semi-structured interviews was conducted. The interviews were audio-recorded, transcribed verbatim, and subjected to thematic analysis. Two researchers coded the interviews independently in NVivo 12 plus and developed major themes and subthemes by inductive and constant comparison. This study was conducted in the inpatient ward of a tertiary cancer hospital in Hunan Province, Chinese Mainland. Eligible participants with advanced cancer were recruited using the purposive sampling method. The sample size was determined by data saturation. All interviews were conducted face-to-face individually from May 2021 to July 2021. A total of 13 patients with advanced cancer patients were interviewed. Six themes were identified, namely being treated as normal and independent individuals, receiving and giving love, seeking inner peace, connecting with spiritual sources, finding meaning and purpose, and preparing for death. Different categories of spiritual needs of patients with advanced cancer were identified in this study. Healthcare professionals need to develop interventions that aim to meet patients’ spiritual needs.

From the perspective of positive psychology, our study aimed to explore depressive symptoms and psychological well-being among Chinese nurses, as well as analyze the impacts of character strengths, self-efficacy and social support on the mental health of nurses. A cross-sectional and descriptive design using five self-reported questionnaires was used to investigate a cohort of 4238 nurses during 2018. A structural equation modeling analysis was used to verify a hypothetical model linking character strengths, self-efficacy, social support, depressive symptoms, and psychological well-being. The prevalence of depression among this cohort of Chinese nurses was 58.1%. The mean scores for caring, inquisitiveness, and self-control were 19.93 (SD = 2.82), 15.94 (SD = 3.00), and 16.34 (SD = 2.95), respectively. The hypothesized model was a good fit of the data (χ2/df = 1.77, p = .183, root mean square error of approximation = 0.04, goodness of fit index = 1.00, comparative fit index = 1.00, Tucker–Lewis index = 1.00). Except for the path from self-control to depression, the other hypothetical paths investigated were statistically significant. Character strengths were directly and positively associated with psychological well-being. Inquisitiveness was the strongest direct protective factor for depression. In addition, character strengths indirectly alleviated depression and increased psychological well-being through mediating variables of social support and self-efficacy. This study should alert nurse managers that more attention should be paid to the character strengths and mental health of nurses. This study provides evidence for interventions based on character strengths as a management strategy to support the mental health of nurses.

ObjectivesThis study aimed to investigate the knowledge and attitudes of patients with cancer of palliative care and their preferences regarding end-of-life care in mainland China.DesignA cross-sectional study.SettingThis study was conducted in a tertiary cancer hospital.ParticipantsTwo hundred forty-seven patients with cancer were recruited and consented to fill out the questionnaires.Outcome measuresThe participants’ knowledge and attitudes of palliative care and their preferences of end-of-life care involving place of care, place of death, truth disclosure and treatments during end-of-life were measured.ResultsIn total, 239 questionnaires were valid. The vast majority of patients with cancer (81.2%) had never heard about palliative care or related policies, and only a few of them (5.8%) had somewhat or totally understanding of palliative care. Most participants (75.3%) had supportive attitudes towards palliative care. In terms of preferences for end-of-life care, most patients with cancer preferred to be cared for at home at the end of their life and to die at home. The majority of patients with cancer (65.7%) wanted to know their diagnosis or prognosis of the disease, regardless of the type of disease. More than half of the participants (54%) wished to improve their quality of life rather than prolong their life expectancy. More than a third of the patients with cancer preferred to entrust a family member or agent to sign medical decision agreements for them.ConclusionsIt is essential for healthcare providers to improve the understanding of patients with cancer of palliative care and be aware of the end-of-life care preferences of patients with cancer, in order to provide support that enables patients with cancer to receive end-of-life care that is accordant with their wishes.

End-stage kidney disease (ESKD) imposes a significant global health burden, with patients often experiencing poor quality of life (QoL) due to psychological distress and low self-efficacy. Digital health interventions (DHIs) offer potential to address these challenges. However, their effects in this population remain inconsistent, and a comprehensive synthesis of the evidence is lacking. The present study aims to assess the impact of DHIs on the psychological health, self-efficacy, and QoL of patients with ESKD and to evaluate engagement, adherence, and satisfaction with these interventions. A comprehensive search was conducted across six electronic databases (PubMed, Web of Science, Cochrane Library, PsycINFO, Embase, and CINAHL) up to January 21, 2025. Randomized controlled trials (RCTs) examining the effects of DHIs on psychological health, self-efficacy, or QoL in patients with ESKD were included. Two reviewers independently screened studies, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias Tool (RoB 2). A meta-analysis was performed using Review Manager 5.4, with subgroup analyses by treatment modality, intervention type, and duration. Evidence quality was assessed using the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. Twenty-three RCTs involving 2407 patients with ESKD from 12 countries were included. DHIs significantly improved depression (standardized mean differences [SMD] -0.41, 95% CI -0.63 to -0.19, P=.003) and overall QoL (SMD 0.55, 95% CI 0.07-1.03, P=.03). While DHIs did not significantly improve overall self-efficacy (SMD 0.56, 95% CI -0.06 to 1.18, P=.08), a benefit was observed in patients on hemodialysis (SMD 0.59, 95% CI 0.34-0.83, P<.001). Engagement was favorable, with completion rates above 63%, adherence rates of 54%-79%, and generally positive patient feedback on DHIs. Application-based interventions improved self-efficacy (SMD 0.66, 95% CI 0.31-1.02, P<.001) and overall QoL (SMD 0.50, 95% CI 0.04-0.96, P=.003); telemedicine improved depression (SMD -0.88, 95% CI -1.21 to -0.56, P<.001) and self-efficacy (SMD 2.76, 95% CI 2.32-3.20, P<.001); and video-based interventions improved depression (SMD -0.34, 95% CI -0.55 to -0.13, P=.002) and overall QoL (SMD 0.31, 95% CI 0.15-0.46, P<.001). Due to high heterogeneity and risk of bias, evidence quality was rated as low for depression and overall QoL, moderate for general anxiety, and very low for stress and self-efficacy. DHIs can significantly improve the psychological health and QoL of patients with ESKD, particularly when tailored to patients' needs and delivered through interactive platforms such as apps and telemedicine. High engagement and positive patient feedback suggest good acceptability in clinical practice. However, low evidence quality warrants cautious interpretation. Future research should involve more high-quality RCTs and design DHIs that address the unique needs of older patients, patients on peritoneal dialysis, and kidney transplant recipients.

Three-dimensional (3D) computational fluid dynamics (CFD) simulations have been carried out to investigate the complex interaction of a planar shock wave (Ma = 1.22) with a cylindrical bubble. The unsteady Reynolds-averaged Navier–Stokes (URANS) approach with a level set coupled with volume of fluid (LSVOF) method has been applied in the present study. The predicted velocities of refracted wave, transmitted wave, upstream interface, downstream interface, jet, and vortex filaments are in very good agreement with the experimental data. The predicted non-dimensional bubble and vortex velocities also have great concordance with the experimental data compared with a simple model of shock-induced Rayleigh–Taylor instability (i.e., Richtmyer–Meshkov instability) and other theoretical models. The simulated changes in the bubble shape and size (length and width) against time agree very well with the experimental results. Comprehensive flow analysis has shown the shock–bubble interaction (SBI) process clearly from the onset of bubble compression up to the formation of vortex filaments, especially elucidating the mechanism on the air–jet formation and its development. It is demonstrated for the first time that turbulence is generated at the early phase of the shock cylindrical bubble interaction process, with the maximum turbulence intensity reaching about 20% around the vortex filament regions at the later phase of the interaction process.

Background Low-diversity diets and sedentary status are risk factors for depressive symptoms, while knowledge workers were ignored before. The purpose of this current study was to examine the relationship between dietary diversity, sedentary time spent outside of work, and depressive symptoms among knowledge workers. Study design and methods This was a multicenter and cross‐sectional design that included 118,723 knowledge workers. Participants self-reported online between January 2018 and December 2020. Demographic information, the Dietary Diversity Scale, the Patient Health Questionnaire-9, dietary habits (which included eating three meals on time, midnight snacking, overeating, social engagement, coffee consumption, sugary drink consumption, smoking and alcohol use), sedentary time spent outside of work and physical activity were investigated. Results The relationships between demographic information, dietary habits and dietary diversity, and depressive symptoms were estimated. Compared with the first and second levels of dietary diversity, the third level of dietary diversity (OR: 0.91; 95% CI: 0.84–0.98) reduced the risk of depressive symptoms. Knowledge workers with different degrees of sedentary status (2–4 h (OR: 1.11; 95% CI: 1.07–1.14), 4–6 h (OR: 1.21; 95% CI: 1.17–1.26), and > 6 h (OR: 1.49; 95% CI: 1.43–1.56), presented a progressively higher risk of depressive symptoms. Conclusion High amounts of sedentary time spent after work and low levels of dietary diversity are risk factors for depressive symptoms. In addition, an irregular diet and overeating are also major risk factors for knowledge workers.

Background Nurses in tertiary hospitals are at high risk for depression. Understanding sleep quality and perceived stress may contribute to nurses’ mental health and health-related nursing productivity. The aim of this study was to investigate the role of sleep quality and perceived stress on depressive symptoms among nurses in tertiary hospitals. Methods A total of 2,780 nurses (overall response rate = 91.1%) were recruited through a cross-sectional survey in 23 tertiary hospitals in China. Questionnaires included the Self-Rating Depression Scale, the Pittsburgh Sleep Quality Index, and the Chinese Perceived Stress Scale. Variables that were significant in Chi-square tests were further entered into binary logistic stepwise regression. Results The prevalence of depressive symptoms was 60.3% (n = 1,676), of which 97.4% (n = 1,633) were female, and 77.8% were younger than 35 years (n = 1,304). Nurses who had moderate, poor, severe sleep quality and poor perceived pressure were more likely to be depressed. Master’s degree, 6–10 years of work, and physical activity were protective factors, while the opposite was the case for shift work and high dissatisfaction. Conclusions More than half of nurses working in tertiary care hospitals reported depressive symptoms, and lower sleep quality and higher perceived stress were more associated with this. Perceived stress is an interesting concept, which may provide a new entry point for the well-known idea that there is a relationship between poor sleep quality and depression. It is possible to reduce depressive symptoms among public hospital nurses by providing information on sleep health and stress relief. Highlights The prevalence of depressive symptoms is high among tertiary hospital nurses (60.3%). Lower sleep quality and worse perceived stress are modifiable variables relevant to depressive symptoms and may be useful for depressive symptom management strategies. Taking job characteristics (i.e., work experience, shifts, and satisfaction) into consideration may help achieve fewer depressive symptoms in the nurse population.