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Over the past few years, due to the rapid development of machine learning (ML) models for weather forecasting, state-of-the-art ML models have shown superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a spatial resolution of 0.25 degree. However, the challenge remains to perform comparably to the ECMWF ensemble mean (EM) in 15-day forecasts. Previous studies have demonstrated the importance of mitigating the accumulation of forecast errors for effective long-term forecasts. Despite numerous efforts to reduce accumulation errors, including autoregressive multi-time step loss, using a single model is found to be insufficient to achieve optimal performance in both short and long lead times. Therefore, we present FuXi, a cascaded ML weather forecasting system that provides 15-day global forecasts with a temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi is developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance evaluation, based on latitude-weighted root mean square error (RMSE) and anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first ML-based weather forecasting system to accomplish this achievement.

Although research on the osteal signaling pathway has progressed, understanding of gut microbial-dependent signaling pathways for metabolic and immune bone homeostasis remains elusive. In recent years, the study of gut microbiota has shed light on our understanding of bone homeostasis. Here, we review microbiota-mediated gut-bone crosstalk via bone morphogenetic protein/SMADs, Wnt and OPG/receptor activator of nuclear factor-kappa B ligand signaling pathways in direct (translocation) and indirect (metabolite) manners. The mechanisms underlying gut microbiota involvement in these signaling pathways are relevant in immune responses, secretion of hormones, fate of osteoblasts and osteoclasts and absorption of calcium. Collectively, we propose a signaling network for maintaining a dynamic homeostasis between the skeletal system and the gut ecosystem. Additionally, the role of gut microbial improvement by dietary intervention in osteal signaling pathways has also been elucidated. This review provides unique resources from the gut microbial perspective for the discovery of new strategies for further improving treatment of bone diseases by increasing the abundance of targeted gut microbiota.

A new-type multiple-source heat pump cycle with two-stage compression was established on the basis of the problems of similarly existing heat pumps. The equivalent temperature levels of typical evaporators are applied to the different heat sources of the proposed cycle, and the high-temperature heat sources are shown to enhance vapor injection. Then, the mathematical model and prototype are developed, and the results from experimental simulation and validation showed that the solar collector can improve the heating performance of the proposed heat pump system. In the middle-temperature heating period, the outdoor temperature is less than −25°C, and the average coefficient of performance (COP) value of the proposed heat pump was 4.2, which was greater than the COPs of conventional ground source heat pumps.

Background: Sciatica is one of the common clinical diseases. Studies have proved the efficacy of Chinese patent medicine (CPM) in the treatment of sciatica, so far, there has not been a complete systematic review of its effectiveness and safety, and the comparative efficacy and safety of CPM have not been ranked. Therefore, it is necessary to evaluate the efficacy and safety of these CPM by means of systematic review and network meta-analysis (NMA), and to compare them in order. Methods: We will search PubMed, Cochrane Library, EMbase, Web of Science, CNKI, Wanfang, VIP, CBM and other databases for RCTs of CPM in the treatment of sciatica, (database established until December 30, 2020). In addition, we will manually search the “Pharmaceutical Information”, “National Essential Drug List”, “Chinese Pharmacopoeia”, etc. to inquire about drug instructions, and screen the market circulation and clinically commonly used CPM. We will use RevMan software, gemtc package, GeMTC software for statistical analysis, and draw the surface under cumulative ranking area (SUCRA) to predict the order of curative effect of treatment measures. Results: Our study will compare and evaluate the effectiveness of CPM in the treatment of sciatica, and rank different CPM. The outcome indicators will include clinical efficacy, pain degree, lumbar spine function and adverse events. Conclusion: Our research will provide support for clinical practice. INPLASY registration number: INPLASY2020110073.

Recently, energy storage system (ESS) with carbon dioxide (CO2) as working fluid has been proposed as a new method to deal with the application restrictions of Compressed Air Energy Storage (CAES) technology, such as dependence on geological formations and low energy storage density. A novel ESS named as Compressed CO2 Energy Storage (CCES) based on transcritical CO2 Brayton cycle is presented in this paper. The working principle of CCES system is introduced and thermodynamic model is established to assess the system performance. Parametric analysis is carried out to study the effect of some key parameters on system performance. Results show that the increase of turbine efficiency is more favorable for system optimization and the effect of minimum pressures on system performance is more significant compared with maximum pressures. A simple comparison of CCES system, liquid CO2 system and Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) system is conducted. It is shown that the system efficiency of CCES is lower than that of AA-CAES system but 4.05% higher than that of liquid CO2 system, while the energy density of CCES system is 2.8 times the value of AA-CAES system, which makes CCES a novel ESS with potential application.

In summertime mornings, marine boundary layer (MBL) stratocumulus clouds commonly cover the southern California coast. The formation and dissipation of MBL stratocumulus affect the photovoltaic (PV) power. Increasing rooftop solar PV generation over the coast necessitates accurate solar forecasts to facilitate the reliable and economical integration of solar PV into the electric grid. For forecast horizons of hours to days, numerical weather prediction (NWP) and machine learning techniques are considered as the most accurate methods and widely used. However, the comparisons of NWP irradiance forecasts with ground measurements show that NWP models consistently overestimate the solar irradiance at the surface due to both clear sky biases and cloud modelling issues. As for machine learning techniques, most researchers either deliberately ignore meteorological conditions (for endogenous forecast models) or lack meteorological expertise to select meteorological input variables that go beyond classical weather station measurements such as air temperature, wind speed, and relative humidity. In this study, several methods are proposed to improve both NWP and machine learning forecast accuracy. Firstly, the clear sky irradiance bias in the New Goddard Shortwave (SW) scheme of Weather Research and Forecasting (WRF) scheme are found to be missing absorption of water vapor continuum. Use of the new parameterization of water vapor including water vapor continuum reduced WRF’s clear sky biases. Secondly, we confirmed the positive correlation between temperature inversion base height (IBH) and inland extent of MBL stratocumulus, and postulated that WRF underprediction of cloud cover extent is due to underprediction of IBH. A thermodynamic method was developed to modify the boundary layer temperature and moisture profiles to better represent the boundary layer structure in WRF. Validation against satellite global horizontal irradiance (GHI) demonstrated that the best IBH ensemble improves GHI accuracy by 23% mean absolute error compared to the baseline WRF model and is similar to 24-hour persistence forecasts for coastal marine layer region. The spatial error maps showed deeper inland cloud cover. Thirdly, we focused on selecting appropriate meteorological input variables based on the characteristics of MBL clouds and studied how accurately support vector machine (SVM), random forest (RF), and gradient boosting (GB) machine learning models predict solar radiation. All three models significantly outperform physics-based NWP models and 24-hour persistence in predicting solar radiation, especially during cloudy periods in the morning. The most important meteorological variables are found to be liquid water path, IBH, and thickness between 1000 and 500 mb pressure levels.

With increasing development of geographical information technology, the means and methods of geography class teaching are developing as well. This paper aims at the study how Google Earth assists us with geographical teaching in middle school. Firstly, this paper makes a brief introduction of the function of Google Earth as well as their advantages applied in geography class teaching. Then, the methods of applying Google Earth to geography educational content in middle school are described. Finally, taking the Yangzi River in junior textbook as an example, the applying process of the mentioned above is achieved.