Development of a Multi-Hour Ahead Wind Power Forecasting System

Wind energy, as a renewable and green energy source with substantial value that is vital for sustainable human development, is gaining more and more attention around the world. The variability of wind implies that wind power is random, intermittent, and volatile. In order to overcome the unfavourable factors brought by wind power and enhance the reliable, stable, and secure operation of electrical grids that incorporate wind power systems, a multi-hour ahead wind power forecasting system consisting of an optimal combination of statistical, physical, and artificial intelligence (AI) models for real wind farm applications was proposed in this research. Except for a direct persistence model that was able to produce wind power forecasts directly, an indirect persistence, an autoregressive integrated moving average (ARIMA), and a Weather Research and Forecasting (WRF) model were used to provide wind speed forecasts which, in turn, could be converted to wind power forecasts by using a power curve model. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was applied to construct a novel 5-in-1 (ensemble) WRF model for wind speed and wind power forecasting. An adaptive neuro-fuzzy inference system (ANFIS) model was employed to determine the power curve model, and another ANFIS model was utilised to build a wind speed correction model exclusively for correcting the wind speed forecasts provided by the 5-in-1 (ensemble) WRF model. By using a set of 24-day historical wind speed and wind power measurements acquired from an operational wind turbine in a real wind farm located in North China, the multi-hour ahead wind power forecasting system was proposed comprising the following components over various forecast time horizons: the direct and indirect persistence models for 30-minute ahead forecasting, the ARIMA model for 1-hour ahead forecasting, and the WRF-TOPSIS model (with corrections obtained from the ANFIS-based wind speed correction model) for 1.5-hour to 24-hour (with a 30-minute temporal resolution) ahead forecasting. The primary contribution of this research is the novel WRF-TOPSIS model strategy used to select and combine the best-performing WRF models from a vast ensemble of possible models. The results demonstrated that the proposed multi-hour ahead wind power forecasting system has excellent predictive performance and is of practical relevance

The Overseeing Mother: Revisiting the Frontal-Pose Lady in the Wu Family Shrines in Second Century China

Located in present-day Jiaxiang in Shandong province, the Wu family shrines built during the second century in the Eastern Han dynasty (25–220) were among the best-known works in Chinese art history. Although for centuries scholars have exhaustively studied the pictorial programs, the frontal-pose female image situated on the second floor of the central pavilion carved at the rear wall of the shrines has remained a question. Beginning with the woman’s eyes, this article demonstrates that the image is more than a generic portrait (“hard motif ”), but rather represents “feminine overseeing from above” (“soft motif ”). This synthetic motif combines three different earlier motifs – the frontal-pose hostess enjoying entertainment, the elevated spectator, and the Queen Mother of the West. By creatively fusing the three motifs into one unity, the Jiaxiang artists lent to the frontal-pose lady a unique power: she not only dominated the center of the composition, but also, like a divine being, commanded a unified view of the surroundings on the lofty building, hence echoing the political reality of the empress mother’s “overseeing the court” in the second century during Eastern Han dynasty

A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model

Wind is a renewable and green energy source that is vital for sustainable human development. Wind variability implies that wind power is random, intermittent, and volatile. For the reliable, stable, and secure operation of an electrical grid incorporating wind power systems, a multi-hour ahead wind power forecasting system comprising a physics-based model, a multi-criteria decision making scheme, and two artificial intelligence models was proposed. Specifically, a Weather Research and Forecasting (WRF) model was used to produce wind speed forecasts. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was employed to construct a 5-in-1 (ensemble) WRF model relying on 1334 initial ensemble members. Two adaptive neuro-fuzzy inference system (ANFIS) models were utilised to correct the wind speed forecasts and determine a power curve model converting the improved wind speed forecasts to wind power forecasts. Moreover, three common statistics-based forecasting models were chosen as references for comparing their predictive performance with that of the proposed WRF-TOPSIS-ANFIS model. Using a set of historical wind data obtained from a wind farm in China, the WRF-TOPSIS-ANFIS model was shown to provide good wind speed and power forecasts for 30-min to 24-h time horizons. This paper demonstrates that the novel forecasting system has excellent predictive performance and is of practical relevance

Optimized Therapeutic 177Lu-Labeled PSMA-Targeted Ligands with Improved Pharmacokinetic Characteristics for Prostate Cancer

Clinical trials have shown the significant efficacy of [177Lu]Lu-PSMA-617 for treating prostate cancer. However, the pharmacokinetic characteristics and therapeutic performance of [177Lu]Lu-PSMA-617 still need further improvement to meet clinical expectations. The aim of this study was to evaluate the feasibility and therapeutic potential of three novel 177Lu-labeled ligands for the treatment of prostate cancer. The novel ligands were efficiently synthesized and radiolabeled with non-carrier added 177Lu; the radiochemical purity of the final products was determined by Radio-HPLC. The specific cell-binding affinity to PSMA was evaluated in vitro using prostate cancer cell lines 22Rv1and PC-3. Blood pharmacokinetic analysis, biodistribution experiments, small animal SPCET imaging and treatment experiments were performed on normal and tumor-bearing mice. Among all the novel ligands developed in this study, [177Lu]Lu-PSMA-Q showed the highest uptake in 22Rv1 cells, while there was almost no uptake in PC-3 cells. As the SPECT imaging tracer, [177Lu]Lu-PSMA-Q is highly specific in delineating PSMA-positive tumors, with a shorter clearance half-life and higher tumor-to-background ratio than [177Lu]Lu-PSMA-617. Biodistribution studies verified the SPECT imaging results. Furthermore, [177Lu]Lu-PSMA-Q serves well as an effective therapeutic ligand to suppress tumor growth and improve the survival rate of tumor-bearing mice. All the results strongly demonstrate that [177Lu]Lu-PSMA-Q is a PSMA-specific ligand with significant anti-tumor effect in preclinical models, and further clinical evaluation is worth conducting

Mussel-Inspired Dopamine and Carbon Nanotube Leading to a Biocompatible Self-Rolling Conductive Hydrogel Film

We report a novel self-rolling, conductive, and biocompatible multiwall carbon nanotube (MWCNT)-dopamine-polyethylene glycol (PEG) hydrogel film. The gel can self-fold into a thin tube when it is transferred from a glass slide to an aqueous environment, regardless of the concentrations of the MWCNT. The film presents a highly organized pattern, which results from the self-assembly of hydrophilic dopamine and hydrophobic carbon nanotubes. By exploring the biomedical potential, we found that MWCNT-included rolled film is nontoxic and can promote cell growth. For further functional verification by qPCR (quantitative polymerase chain reaction), bone marrow derived mesenchymal cells present higher levels of osteogenic differentiations in response to a higher concentration of CNTs. The results suggest that the self-rolling, conductive CNT-dopamine-PEG hydrogel could have multiple potentials, including biomedical usage and as a conductive biosensor

New insights into the growth mechanism of 3D-printed Al2O3–Y3Al5O12 binary eutectic composites

We investigated the complex solidification behaviour of AlO–YAlO eutectics prepared by laser engineered net shaping. Colony structure consisting of coupled irregular eutectic was dominant at interior region of as-fabricated specimens, and its growth behaviour was depicted by Magnin–Kurz model. In each deposited layer, The YAlO phase within irregular eutectic grew through a competitive mechanism from random to oriented, whereas the orientation of AlO remained unchanged. Transition from anomalous to colony coupled eutectics occurred at the bottom of each layer, and irregular to regular eutectics transition was recognized at outer region of as-built sample

Nanostructured Al2O3-YAG-ZrO2 ternary eutectic components prepared by laser engineered net shaping

The present work proposed a novel 3D printing approach for fabricating ternary eutectic ceramics (Al O -YAG-ZrO , or AYZ in short) using laser engineered net shaping (LENS) technique. Highly dense (≥98%) thin wall components with refined eutectic microstructures were successfully fabricated. The as-fabricated ceramic eutectics mainly consist of cellular eutectics with three phases interpenetrated. In each deposited layer, the solidified microstructure changes from planar to cellular along building direction. The evolution of characteristic dimension of microstructure can be well interpreted using Jackson-Hunt relationship. Irregular interpenetrating to regular fibrous eutectic structure transition occurs at boundary region of LENSed specimens, primarily due to much higher solidification rate. The texture of // // is recognized at the interior region of LENSed eutectic part along building direction, but gradually tilts from central to edge due to change of cellular growth direction. The LENSed AYZ samples exhibit almost isotropic property, and have mechanical performances comparable to those fabricated using conventional direction solidification methods