Analysis of Influence on Aerodynamic Noise of Wind Turbine Blades under Different Pitch Angles

Aiming at the influence of blade pitch Angle on aerodynamic noise of wind turbines, the sound field and flow field distribution at 0˚, 5˚, 10˚ and 15˚ are calculated by numerical simulation. Then, through the distribution of pressure field and velocity field calculated by flow field, the influence of different pitch angles on wind turbine blade aerodynamic noise and the reasons for its influence are analyzed. The results show that when the pitch Angle increases within 0˚ - 10˚, the aerodynamic noise pressure level of the blade decreases. However, the sound pressure level of aerodynamic noise increases in the range of 10˚ - 15˚. The changes of static pressure gradient and pressure pulsation on the blade surface make the aerodynamic noise change, and the changes of the two are positively correlated. At the same time, the fluid velocity and fluid motion state on the blade surface are closely related to the aerodynamic noise of the blade. The greater the fluid velocity, the more complex the fluid motion state and the greater the turbulent kinetic energy of the wind turbine blade, and the aerodynamic noise of the wind turbine blade will also increase.

Mining Unknown Porcine Protein Isoforms by Tissue-based Map of Proteome Enhances Pig Genome Annotation

A lack of the complete pig proteome has left a gap in our knowledge of the pig genome and has restricted the feasibility of using pigs as a biomedical model.In this study,we developed a tissue-based proteome map using 34 major normal pig tissues.A total of 5841 unknown protein isoforms were identified and systematically characterized,including 2225 novel protein isoforms,669 protein isoforms from 460 genes symbolized beginning with LOC,and 2947 protein isoforms without clear NCBI annotation in the current pig reference genome.These newly identified protein isoforms were functionally annotated through profiling the pig transcriptome with high-throughput RNA sequencing of the same pig tissues,further improving the genome annotation of the corresponding protein-coding genes.Combining the well-annotated genes that have parallel expression pattern and subcellular witness,we predicted the tissue-related subcellular locations and potential functions for these unknown proteins.Finally,we mined 3081 orthologous genes for 52.7% of unknown protein isoforms across multiple species,referring to 68 KEGG pathways as well as 23 disease signaling pathways.These findings provide valuable insights and a rich resource for enhancing studies of pig genomics and biology,as well as biomedical model application to human medicine.

Statistical considerations for genomic selection

Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased prediction, Bayesian alphabet, and least absolute shrinkage and selection operator.Then it discusses the measurement of the performance of genomic selection and factors affecting the prediction of performance. Among the measurements of prediction performance, the most important and commonly used measurement is prediction accuracy. In simulation studies where true breeding values are available, accuracy of genomic estimated breeding value can be calculated directly. In real or industrial data studies, either trainingtesting approach or k-fold cross-validation is commonly employed to validate methods. Factors influencing the accuracy of genomic selection include linkage disequilibrium between markers and quantitative trait loci, genetic architecture of the trait, and size and composition of the training population. Genomic selection has been implemented in the breeding programs of dairy cattle, beef cattle, pigs and poultry. Genomic selection in other species has also been intensively researched, and is likely to be implemented in the near future.