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يتناول كتاب (ثقافة الأطعمة الصينية) والذي قام بتأليفه (زهاو رونغقوانغ) في حوالي (103) صفحة من القطع المتوسط موضوع (عادات الطعام في الصين) مستعرضا المحتويات التالية : الباب الأول : ثقافة مواد الأطعمة الصينية، الباب الثاني : الخاصية الإقليمية لثقافة الأطعمة الصينية، الباب الثالث : التقاليد والطقوس الشعبية للأطمعة الصينية، الباب الرابع : فنون الطبخ الصيني وثقافة التوابل.

Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. A systematic scoping review was conducted according to the 5-stage framework by Arksey and O'Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted. Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study. The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.

Traditional Chinese ideology - Traditional virtues of China - Ancient Chinese literature - Science and technology of ancient China - Traditional Chinese art - Chinese cultural relics - Ancient Chinese architecture - Chinese arts and crafts - Chinese folk customs - Life of the Chinese people.

Background Studies on the relationship between insulin resistance (IR) surrogates and long-term all-cause mortality in patients with coronary heart disease (CHD) and hypertension are lacking. This study aimed to explore the relationship between different IR surrogates and all-cause mortality and identify valuable predictors of survival status in this population. Methods The data came from the National Health and Nutrition Examination Survey (NHANES 2001–2018) and National Death Index (NDI). Multivariate Cox regression and restricted cubic splines (RCS) were performed to evaluate the relationship between homeostatic model assessment of IR (HOMA-IR), triglyceride glucose index (TyG index), triglyceride glucose-body mass index (TyG-BMI index) and all-cause mortality. The recursive algorithm was conducted to calculate inflection points when segmenting effects were found. Then, segmented Kaplan–Meier analysis, LogRank tests, and multivariable Cox regression were carried out. Receiver operating characteristic (ROC) and calibration curves were drawn to evaluate the differentiation and accuracy of IR surrogates in predicting the all-cause mortality. Stratified analysis and interaction tests were conducted according to age, gender, diabetes, cancer, hypoglycemic and lipid-lowering drug use. Results 1126 participants were included in the study. During the median follow-up of 76 months, 455 participants died. RCS showed that HOMA-IR had a segmented effect on all-cause mortality. 3.59 was a statistically significant inflection point. When the HOMA-IR was less than 3.59, it was negatively associated with all-cause mortality [HR = 0.87,95%CI (0.78, 0.97)]. Conversely, when the HOMA-IR was greater than 3.59, it was positively associated with all-cause mortality [HR = 1.03,95%CI (1.00, 1.05)]. ROC and calibration curves indicated that HOMA-IR was a reliable predictor of survival status (area under curve = 0,812). No interactions between HOMA-IR and stratified variables were found. Conclusion The relationship between HOMA-IR and all-cause mortality was U-shaped in patients with CHD and hypertension. HOMA-IR was a reliable predictor of all-cause mortality in this population.

The vehicle semi-active suspension system plays an important role in improving the driving safety and ride comfort by adjusting the coefficients of the damping and spring. The main contribution of this paper is the proposal of a PPO-based vibration control strategy for a vehicle semi-active suspension system, in which the designed reward function realizes the dynamic adjustment according to the road condition changes. More specifically, for the different suspension performances caused by different road conditions, the three performances of the suspension system, body acceleration, suspension deflection, and dynamic tire load, were taken as the state space of the PPO algorithm, and the reward value was set according to the numerical results of the passive suspension, so that the corresponding damping force was selected as the action space, and the weight matrix of the reward function was dynamically adjusted according to different road conditions, so that the agent could have a better improvement effect at different speeds and road conditions. In this paper, a quarter–car semi-active suspension model was analyzed and simulated, and numerical simulations were performed using stochastic road excitation for different classes of roads, vehicle models, and continuously changing road conditions. The simulation results showed that the body acceleration was reduced by 46.93% under the continuously changing road, which proved that the control strategy could effectively improve the performance of semi-active suspension by combining the dynamic changes of the road with the reward function.

Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss. This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement. A scoping review was conducted using the 5-stage framework by Arksey and O'Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched. A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35%) focused on using AI chatbots to promote both a healthy diet and exercise, 13% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48% (11/23) of the studies used only text-based AI chatbots, 52% (12/23) operationalized AI chatbots through smartphones, and 39% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87%), motivational messages (18/23, 78%), gamification (6/23, 26%), and emotional support (6/23, 26%). Study participants who experienced speech- and augmented reality-based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances. AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss.

In this paper, we investigate the polarization images of a synchrotron-emitting fluid ring surrounding a static hairy black hole within the framework of Horndeski’s theory. Our findings indicate that the characteristics of these polarization images are predominantly influenced by the hairy parameter, alongside the magnetic field near the black hole, the fluid’s velocity, and the observer’s inclination angle. Specifically, the hairy parameter primarily affects the polarized intensity and the apparent radius of the ring in the images. Conversely, the impacts of the magnetic field, fluid velocity, and inclination angle on the polarization images are found to be independent of the hairy parameter and closely resemble those observed in the context of Schwarzschild black holes. Additionally, the polarization direction is significantly influenced by the magnetic field orientation, while the inclination angle crucially determines the apparent flatness of the images. Variations in the fluid velocity direction also markedly affect the trend in polarized intensity. Furthermore, we explore how these parameters influence the Stokes Q - U loops, revealing distinct behaviors in response to changes in the aforementioned variables. This comprehensive analysis enhances our understanding of the intricate dynamics and observational signatures of black holes within alternative gravitational theories.