11,429 research outputs found
A new approach to understanding the frequency response of mineral oil
Dielectric spectroscopy is non-invasive diagnostic method and can give information about dipole relaxation, electrical conduction and structure of molecules. Since the creation of charge carriers in mineral oil is not only from dissociation but also injection from electrodes, the injection current cannot be simply ignored. The polarization caused by the charge injection has been studied in this paper. Based on our research, if the mobility of the injected charge carriers is fast enough so that they can reach the opposite electrode, the current caused by the injection will contribute only to the imaginary part of the complex permittivity and this part of the complex permittivity will decrease with the frequency with a slope of -1 which is in a good agreement with the experimental result. The classic ionic drift and diffusion model and this injection model will be combined to make an improved model. In this paper, the frequency responses of three different kinds of mineral oils have been measured, and this modified model has been used to simulate the experiment result. Since there is only one unknown parameter in this improved model, a better understanding of the frequency response in mineral oil can be achieve
Effective Inter-Residue Contact Definitions for Accurate Protein Fold Recognition.
Background
Effective encoding of residue contact information is crucial for protein structure prediction since it has a unique role to capture long-range residue interactions compared to other commonly used scoring terms. The residue contact information can be incorporated in structure prediction in several different ways: It can be incorporated as statistical potentials or it can be also used as constraints in ab initio structure prediction. To seek the most effective definition of residue contacts for template-based protein structure prediction, we evaluated 45 different contact definitions, varying bases of contacts and distance cutoffs, in terms of their ability to identify proteins of the same fold. Results
We found that overall the residue contact pattern can distinguish protein folds best when contacts are defined for residue pairs whose CĪ² atoms are at 7.0 Ć
or closer to each other. Lower fold recognition accuracy was observed when inaccurate threading alignments were used to identify common residue contacts between protein pairs. In the case of threading, alignment accuracy strongly influences the fraction of common contacts identified among proteins of the same fold, which eventually affects the fold recognition accuracy. The largest deterioration of the fold recognition was observed for Ī²-class proteins when the threading methods were used because the average alignment accuracy was worst for this fold class. When results of fold recognition were examined for individual proteins, we found that the effective contact definition depends on the fold of the proteins. A larger distance cutoff is often advantageous for capturing spatial arrangement of the secondary structures which are not physically in contact. For capturing contacts between neighboring Ī² strands, considering the distance between CĪ± atoms is better than the CĪ²ābased distance because the side-chain of interacting residues on Ī² strands sometimes point to opposite directions. Conclusion
Residue contacts defined by CĪ²āCĪ² distance of 7.0 Ć
work best overall among tested to identify proteins of the same fold. We also found that effective contact definitions differ from fold to fold, suggesting that using different residue contact definition specific for each template will lead to improvement of the performance of threading
Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
Wind turbinesā economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-short-term predictions of wind power and speed with a wind farm in the Arctic, including and excluding wind turbulence, within three hours by employing several different machine learning algorithms. A rigorous and detailed statistical comparison of the predictions is conducted. The results show that the algorithms achieve reasonably accurate predictions, but turbulence intensity does not statistically contribute to wind power or speed forecasts. This observation illustrates the uncertainty of turbulence in wind power generation. Besides, differences between the types of algorithms for ultra-short-term wind forecasts are also statistically insignificant, demonstrating the unique stochasticity and complexity of wind speed and power
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