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Jiawen Hu,1,2 Yiwen Wang,1 Xiaojian Ji,1 Yinan Zhang,1 Kunpeng Li,1 Feng Huang1 1Department of Rheumatology and Immunology, First Medical Center of Chinese PLA General Hospital, Beijing, China; 2Medical School of Chinese PLA, Beijing, ChinaCorrespondence: Feng Huang, Department of Rheumatology and Immunology, First Medical Center of Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China, Email frankfhuang@126.comAbstract: This article focuses on a range of non-pharmacological strategies for managing sarcopenia in chronic diseases, including exercise, dietary supplements, traditional Chinese exercise, intestinal microecology, and rehabilitation therapies for individuals with limited limb movement. By analyzing multiple studies, the article aims to summarize the available evidence to manage sarcopenia in individuals with chronic diseases. The results strongly emphasize the role of resistance training in addressing chronic diseases and secondary sarcopenia. Maintaining the appropriate frequency and intensity of resistance training can help prevent muscle atrophy and effectively reduce inflammation. Although aerobic exercise has limited ability to improve skeletal muscle mass, it does have some positive effects on physical function. Building upon this, the article explores the potential benefits of combined training approaches, highlighting their helpfulness for overall quality of life. Additionally, the article also highlights the importance of dietary supplements in combating muscle atrophy in chronic diseases. It focuses on the importance of protein intake, supplements rich in essential amino acids and omega-3, as well as sufficient vitamin D to prevent muscle atrophy. Combining exercise with dietary supplements appears to be an effective strategy for preventing sarcopenia, although the optimal dosage and type of supplement remain unclear. Furthermore, the article explores the potential benefits of intestinal microecology in sarcopenia

The remaining useful lifetime (RUL) estimated from the in-situ degradation data has shown to be useful for online predictive maintenance. In the literature, the RUL is often estimated by assuming a soft-failure threshold for the degradation data. In practice, however, systems may not be subject to the degradation-induced soft failures. Instead, the systems are deemed to be fail when they cannot perform the intended function, and such failures are known as hard failures. Because there are no fixed thresholds for hard failures, the corresponding RUL estimation is not an easy task, which causes difficulties in finding the optimal maintenance schedule. In this study, a Weibull proportional hazards model is proposed to jointly model the degradation data and the failure time data. The degradation data are treated as the time-varying covariates so that the degradation does not directly lead to system failures, but increases the hazard rate of hard failures. A random-effects Wiener process is proposed to model the degradation data by considering the system heterogeneities. Based on the developed proportional hazards model, closed-form distribution of the RUL is derived upon each inspection and the optimal maintenance schedule is then obtained by minimizing the system maintenance cost. The proposed maintenance strategy is successfully applied to predictive maintenance of lead-acid batteries., Statistics

A simple and general relation between the solubility of quartz and the density of solution is derived rigorously. Based on this relation and the pressure-volume-temperature-composition model of Mao et al. (2010), an accurate density-based model is developed for the solubility of quartz in aqueous NaCl and/or CO2 solution up to 1273 K and 20,000 bar. The model parameters are regressed with carefully assessed experimental data. Compared to a large number of experimental data, the average absolute deviations of calculated quartz solubilities in water, aqueous NaCl solution and aqueous CO2 solution are 5.50%, 5.24% and 7.55%, respectively, which are within experimental uncertainties, and are much better than the most competitive models in literature. Particularly, this model can predict the experimental solubility of quartz in aqueous NaCl and CO2 solution without using any additional parameter. This model can be incorporated in software for accurate geochemical modeling. The strategy of this model should be promising for the solubilities of other minerals in water or multicomponent aqueous solutions.