NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus

Modeling and Analysis of the tilt behavior of the cylinder block in a high-speed axial piston pump

Increasing the rotating speed of the axial piston pump is effective in improving the power-to-weight ratio. However, the cylinder block tilts severely at high speed, which causes significant leakage. In this paper, a dynamic model of the rotating assembly in a high-speed axial piston pump is established to investigate the tilt behavior of the cylinder block when fully considering the relevant factors within the whole rotating assembly, such as the tilt moments due to the inertia of piston-slipper assemblies and the periodic pressure in piston chambers, the elastic deformation of the shaft, and the nonlinear bearing characteristics of the oil film. Furthermore, the cylinder block tilt behavior is measured to validate the established dynamic model. The theoretical and experimental results show that the tilt angle of the cylinder block increases with the increasing rotating speed. And at high rotating speed, the cylinder block tilts much more severely under low outlet pressure. Finally, the bearing capacities of the oil film and the spline coupling are analyzed to find out the dominant factors affecting the tilt behavior of the cylinder block

Modeling and Analysis of the tilt behavior of the cylinder block in a high-speed axial piston pump

Increasing the rotating speed of the axial piston pump is effective in improving the power-to-weight ratio. However, the cylinder block tilts severely at high speed, which causes significant leakage. In this paper, a dynamic model of the rotating assembly in a high-speed axial piston pump is established to investigate the tilt behavior of the cylinder block when fully considering the relevant factors within the whole rotating assembly, such as the tilt moments due to the inertia of piston-slipper assemblies and the periodic pressure in piston chambers, the elastic deformation of the shaft, and the nonlinear bearing characteristics of the oil film. Furthermore, the cylinder block tilt behavior is measured to validate the established dynamic model. The theoretical and experimental results show that the tilt angle of the cylinder block increases with the increasing rotating speed. And at high rotating speed, the cylinder block tilts much more severely under low outlet pressure. Finally, the bearing capacities of the oil film and the spline coupling are analyzed to find out the dominant factors affecting the tilt behavior of the cylinder block

NERO: A biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades, the most dramatic advances in MR have followed in the wake of critical corpus development. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus