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Numerical weather prediction (NWP) correction is one of the key technologies to improve the accuracy of wind and solar power forecasting. However, there are few researches on the correction of NWP irradiance. At the same time, most of the existing NWP wind speed correction methods only consider a single location, ignoring the spatio-temporal coupling of wind speed, which affects the correction effect. Aiming at this problem, this paper proposed an integrated correction method for multi-point NWP wind speed and irradiance considering the spatial and temporal correlation of regional wind and solar resources. Taking the measured wind speed and irradiance data of multiple wind farms and photovoltaic power plants in the region as the learning objective of the correction model, an integrated NWP correction model based on attentional neural network was established to simultaneously correct multiple station-level NWP data with certain correlation. The proposed method was verified by the NWP data and historical wind speed / irradiance data of 8 wind farms and 7 photovoltaic power plants in a region. The calculation results show that compared with the traditional single-point NWP correction method, the proposed method can effectively improve the accuracy of NWP.

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China's 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions. [ABSTRACT FROM AUTHOR]

INTRODUCTION: As aggregation underpins Tau toxicity, aggregation inhibitor peptides may have disease‐modifying potential. They are therefore currently being designed and target either the 306VQIVYK311 aggregation‐promoting hotspot found in all Tau isoforms or the 275VQIINK280 aggregation‐promoting hotspot found in 4R isoforms. However, for any Tau aggregation inhibitor to potentially be clinically relevant for other tauopathies, it should target both hotspots to suppress aggregation of Tau isoforms, be stable, cross the blood‐brain barrier, and rescue aggregation‐dependent Tau phenotypes in vivo. METHODS: We developed a retro‐inverso, stable D‐amino peptide, RI‐AG03 [Ac‐rrrrrrrrGpkyk(ac)iqvGr‐NH2], based on the 306VQIVYK311 hotspots which exhibit these disease‐relevant attributes. RESULTS: Unlike other aggregation inhibitors, RI‐AG03 effectively suppresses aggregation of multiple Tau species containing both hotspots in vitro and in vivo, is non‐toxic, and suppresses aggregation‐dependent neurodegenerative and behavioral phenotypes. DISCUSSION: RI‐AG03 therefore meets many clinically relevant requirements for an anti‐aggregation Tau therapeutic and should be explored further for its disease‐modifying potential for Tauopathies. Highlights: Our manuscript describes the development of a novel peptide inhibitor of Tau aggregation, a retro‐inverso, stable D‐amino peptide called RI‐AG03 that displays many clinically relevant attributes. We show its efficacy in preventing Tau aggregation in both in vitro and in vivo experimental models while being non‐toxic to cells. RI‐AG03 also rescues a biosensor cell line that stably expresses Tau repeat domains with the P301S mutation fused to Cer/Clo and rescues aggregation‐dependent phenotypes in vivo, suppressing neurodegeneration and extending lifespan.Collectively our data describe several properties and attributes of RI‐AG03 that make it a promising disease‐modifying candidate to explore for reducing pathogenic Tau aggregation in Tauopathies such as Alzheimer's disease. Given the real interest in reducing Tau aggregation and the potential clinical benefit of using such agents in clinical practice, RI‐AG03 should be investigated further for the treatment of Tauopathies after validation in mammalian models.Tau aggregation inhibitors are the obvious first choice as Tau‐based therapies as much of Tau‐mediated toxicity is aggregation dependent. Indeed, there are many research efforts focusing on this therapeutic strategy with aggregation inhibitors being designed against one of the two aggregation‐promoting hotspots of the Tau protein. To our knowledge, RI‐AG03 is the only peptide aggregation inhibitor that inhibits aggregation of Tau by targeting both aggregation‐promoting hotspot motifs simultaneously. As such, we believe that our study will have a significant impact on drug discovery efforts in this arena. [ABSTRACT FROM AUTHOR]

The short-term fluctuation of wind power can affect its prediction accuracy. Thus, a short-term segmentation prediction method of wind power based on ramp segment division is proposed. A time-series trend extraction method based on moving average iteration is proposed on the full-time period to analyze the real-time change characteristics of power time-series initially; secondly, a ramp segment extraction method based on its definition and identification technique is proposed based on the results of the trend extraction; and a segmentation prediction scheme is proposed to lean the power prediction under different time-series: the LightGBM-LSTM is proposed for the non-ramping segment using point prediction, and the CNN-BiGRU-KDE is proposed for probabilistic prediction of ramp segments. From the results, this ramp segment definition and identification technique can effectively identify the ramp process of wind power, which makes up for the misidentification and omission of the classical climbing event definition; meanwhile, the segment prediction scheme not only meets the prediction accuracy requirements of the non-ramping segment, but also provides the effective robust information for the prediction of the ramping period, which offers reliable reference information for the actual wind farms. In particular, it is well adapted to wind power prediction under extreme working conditions caused by ramping weather, which is a useful addition to short-term wind power prediction research. [ABSTRACT FROM AUTHOR]

The rapid development and wide application of wireless network technology have brought unprecedented opportunities to the reform of medical and health services in China. Many hospitals in China have widely applied wireless network technology to informatization construction of hospitals, mainly including wireless room inspection, wireless infusion, wireless nursing, telemedicine, and warehouse management. This has the advantages of being flexible, convenient, effective, and reliable, which is conducive to the comprehensive integration of hospital data and information, reducing the work pressure of medical personnel, improving medical diagnosis and quality of care, and promoting the development of China’s medical and health services. Wireless networks have made outstanding contributions in controlling medical defects and improving economic efficiency while improving work efficiency and reducing costs. However, there are still certain security risks in wireless networks. Only the safe and rational use of wireless network technology can ensure the hospital’s information security.

With the advent of the Internet era, traditional teaching forms, teaching methods and teaching concepts have undergone tremendous changes, and the teaching of medical information retrieval courses has also undergone good changes. This paper will explore the new teaching model of medical information retrieval under the Internet era from the teaching modes of PICOS, MOOC, SPOC, etc., in order to change the current status of poor classroom interaction and lack of high-quality teaching resources in college medical information retrieval courses, and improve the teaching quality of medical information retrieval courses and graduate students’ information literacy; and effectively change the traditional teaching evaluation system, give full play to the advantages of the network environment, and better innovate and rectify the teaching mode of medical information retrieval courses.