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BackgroundAn increasing number of patients with chronic heart failure (CHF) are demanding more convenient and efficient modern health care systems, especially in remote areas away from central cities. Telehealth is receiving increasing attention, which may be useful to patients with CHF. ObjectiveThis study aimed to evaluate the feasibility of a hospital-community-family (HCF)–based telehealth program, which was designed to implement remote hierarchical management in patients with CHF. MethodsThis was a single-arm prospective study in which 70 patients with CHF participated in the HCF-based telehealth program for remote intervention for at least 4 months. The participants were recruited from the clinic and educated on the use of smart health tracking devices and mobile apps to collect and manually upload comprehensive data elements related to the risk of CHF self-care management. They were also instructed on how to use the remote platform and mobile app to send text messages, check notifications, and open video channels. The general practitioners viewed the index of each participant on the mobile app and provided primary care periodically, and cardiologists in the regional central hospital offered remote guidance, if necessary. The assessed outcomes included accomplishments of the program, usability and satisfaction, engagement with the intervention, and changes of heart failure–related health behaviors. ResultsAs of February 2018, a total of 66 individuals, aged 40-79 years, completed the 4-month study. Throughout the study period, 294 electronic medical records were formed on the remote monitoring service platform. In addition, a total of 89 remote consultations and 196 remote ward rounds were conducted. Participants indicated that they were generally satisfied with the intervention for its ease of use and usefulness. More than 91% (21/23) of physicians believed the program was effective, and 87% (20/23) of physicians stated that their professional knowledge could always be refreshed and enhanced through a library hosted on the platform and remote consultation. More than 60% (40/66) of participants showed good adherence to the care plan in the study period, and 79% (52/66) of patients maintained a consistent pattern of reporting and viewing their data over the course of the 4-month follow-up period. The program showed a positive effect on self-management for patients (healthy diet: P=.046, more fruit and vegetable intake: P=.02, weight monitoring: P=.002, blood pressure: P

The cultivation of microalgae from municipal wastewater, while simultaneously removing nutrients from the water column, has the potential to aid biodiesel production and carbon dioxide fixation, thereby alleviating the pressure of energy shortages. In this research, different ratios of sodium bicarbonate and glucose were used to prepare simulated municipal wastewater. The results demonstrated that microalgae were most effectively treated under one-stage direct treatment conditions. During direct culture, the most effective treatment was observed for IAA-3, which exhibited a dry weight of 1.4363 g/L and a lipid content of 25.05% after stimulation with 0.0005 M NaHCO3. In contrast, NaHCO3-2 demonstrated optimal performance during the secondary culture, with a dry weight of 1.6844 g/L and a lipid content of 18.05%. Finally, the economic, social and environmental benefits of direct treatment (IAA-3) and secondary treatment NaHCO3-2 were analyzed. The benefits of direct treatment were found to be USD 0.50989/L, while those of secondary treatment were USD 0.43172/L. For each tonne of municipal wastewater treated, the carbon sequestration benefits of IAA-3 during direct treatment and NaHCO3-2 during secondary treatment were USD 0.45645 and USD 0.85725, respectively. [ABSTRACT FROM AUTHOR]

Granitic rocks forming in the syn- to post-orogenic stages can trace the compositional and structural complexity of the crust beneath an orogenic belt. The Qinling orogenic belt undertook multiple stages of tectonics and magmatism, resulting in the multifaceted evolution and compositional diversity of the crust. In the present study, the Guangtoushan and Miba plutons in South Qinling were chosen to reveal the crustal heterogeneity in study area via isotopic geochemistry and zircon geochronology. The Guangtoushan pluton was emplaced between ~215 Ma and ~202 Ma and the Miba pluton formed at ~213 Ma, as constrained by zircon U-Pb isotopic dating. Granitic rocks of the Miba pluton are characterized by amphibole bearing and homogeneous composition, with relatively depleted Sr-Nd isotopic compositions (initial 87Sr/86Sr values of 0.7060 to 0.7084 and initial εNd values of −5.4 to −9.5) and high Pb isotopic values. The Guangtoushan pluton contains muscovite and complex inherited zircon grains and has variable Sr-Nd isotopic composition (initial 87Sr/86Sr values of 0.7050 to 0.7091 and initial εNd values of −4.5 to −12.9) and low Pb isotopic values. Felsic magmas of the Guangtoushan pluton should be derived mainly from meta-sedimentary rocks beneath South Qinling, while the Miba pluton originated primarily from partial melting of meta-igneous rocks. The compositional diversity recorded in the Early Mesozoic plutons was caused by the heterogeneous crust, and partial melting was induced by heating of the up-welling asthenosphere in a post-collision setting. [ABSTRACT FROM AUTHOR]

Considering the development of deep learning and the emergence of intelligent control demands in nuclear reactors, along with the presence of plant‐level real‐time information monitoring systems in most nuclear power plants, there is a considerable accumulation of sensor measurements from long‐term operation. This makes it feasible to conduct medium to long‐term predictions for various real‐time conditions in nuclear power plants. Therefore, this paper proposes the utilization of a gate‐based recurrent neural network called GRU (Gated Recurrent Unit) and its variants for parameter prediction of LOCA (Loss of Coolant Accident) scenarios. The main content of this paper consists of two parts: (1) Experimental verification is conducted to demonstrate that GRU has excellent capability in capturing long‐term sequential information and generalization ability, making it suitable for predicting accident conditions in nuclear power plants. Two accident trend prediction methods based on the GRU network are proposed for scenarios with limited data. The results show that these methods can effectively provide short‐term development trends for accident conditions. Additionally, by considering the feature extraction capacity of CNN, the fusion of CNN and GRU models is employed for parameter prediction under different sizes of broken area. The results indicate an improvement in the model's generalization ability. (2) In scenarios with limited and incomplete data, a more robust variant of GRU called GRU‐D model is utilized for both univariate and multivariate synchronous prediction of accident conditions with different missing values. Experimental results demonstrate that even with a data missing rate of 90%, the GRU‐D network exhibits excellent predictive accuracy and generalization ability in parameter prediction for the given conditions. [ABSTRACT FROM AUTHOR]