Priori Information Based Support Vector Regression and Its Applications

In order to extract the priori information (PI) provided by real monitored values of peak particle velocity (PPV) and increase the prediction accuracy of PPV, PI based support vector regression (SVR) is established. Firstly, to extract the PI provided by monitored data from the aspect of mathematics, the probability density of PPV is estimated with ε-SVR. Secondly, in order to make full use of the PI about fluctuation of PPV between the maximal value and the minimal value in a certain period of time, probability density estimated with ε-SVR is incorporated into training data, and then the dimensionality of training data is increased. Thirdly, using the training data with a higher dimension, a method of predicting PPV called PI-ε-SVR is proposed. Finally, with the collected values of PPV induced by underwater blasting at Dajin Island in Taishan nuclear power station in China, contrastive experiments are made to show the effectiveness of the proposed method

Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

Prior knowledge, such as wind speed probability distribution based on historical data and the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, provides much more information about the wind speed, so it is necessary to incorporate it into the wind speed prediction. First, a method of estimating wind speed probability distribution based on historical data is proposed based on Bernoulli’s law of large numbers. Second, in order to describe the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, the probability distribution estimated by the proposed method is incorporated into the training data and the testing data. Third, a support vector regression model for wind speed prediction is proposed based on standard support vector regression. At last, experiments predicting the wind speed in a certain wind farm show that the proposed method is feasible and effective and the model’s running time and prediction errors can meet the needs of wind speed prediction

Combustion Synthesis of Large Bulk Nanostructured Ni 65

A large bulk nanostructured Ni65Al21Cr14 alloy with dimensions of Φ 100 mm × 6 mm was produced by combustion synthesis technique followed with rapid solidification. The Ni65Al21Cr14 alloy was composed of γ′-Ni3Al/γ-Ni(Al, Cr) eutectic matrix and γ-Ni(Al, Cr) dendrite. The eutectic matrix consisted of 80–150 nm cuboidal γ′-Ni3Al and 2–5 nm γ-Ni(Al, Cr) boundary. The dentrite was comprised of high-density growth twins with about 3–20 nm in width. The nanostructured Ni65Al21Cr14 alloy exhibited simultaneously high fracture strength of 2200 MPa and good ductility of 26% in compression test

A New Uncertainty Evaluation Method and Its Application in Evaluating Software Quality

Uncertainty theory is a branch of axiomatic mathematics dealing with experts’ belief degree. Considering the uncertainty with experts’ belief degree in the evaluation system and the different roles which different indices play in evaluating the overall goal with a hierarchical structure, a new comprehensive evaluation method is constructed based on uncertainty theory. First, index scores and weights of indices are described by uncertain variables and evaluation grades are described by uncertain sets. Second, weights of indices with respect to the overall goal are introduced. Third, a new uncertainty comprehensive evaluation method is constructed and proved to be a generalization of the weighted average method. Finally, an application is developed in evaluating software quality, which shows the effectiveness of the new method

Significant decrease of maternal mitochondria carryover using optimized spindle-chromosomal complex transfer.

Mutations in mitochondrial DNA (mtDNA) contribute to a variety of serious multi-organ human diseases, which are strictly inherited from the maternal germline. However, there is currently no curative treatment. Attention has been focused on preventing the transmission of mitochondrial diseases through mitochondrial replacement (MR) therapy, but levels of mutant mtDNA can often unexpectedly undergo significant changes known as mitochondrial genetic drift. Here, we proposed a novel strategy to perform spindle-chromosomal complex transfer (SCCT) with maximal residue removal (MRR) in metaphase II (MII) oocytes, thus hopefully eliminated the transmission of mtDNA diseases. With the MRR procedure, we initially investigated the proportions of mtDNA copy numbers in isolated karyoplasts to those of individual oocytes. Spindle-chromosomal morphology and copy number variation (CNV) analysis also confirmed the safety of this method. Then, we reconstructed oocytes by MRR-SCCT, which well developed to blastocysts with minimal mtDNA residue and normal chromosomal copy numbers. Meanwhile, we optimized the manipulation order between intracytoplasmic sperm injection (ICSI) and SCC transfer and concluded that ICSI-then-transfer was conducive to avoid premature activation of reconstructed oocytes in favor of normal fertilization. Offspring of mice generated by embryos transplantation in vivo and embryonic stem cells derivation further presented evidences for competitive development competence and stable mtDNA carryover without genetic drift. Importantly, we also successfully accomplished SCCT in human MII oocytes resulting in tiny mtDNA residue and excellent embryo development through MRR manipulation. Taken together, our preclinical mouse and human models of the MRR-SCCT strategy not only demonstrated efficient residue removal but also high compatibility with normal embryo development, thus could potentially be served as a feasible clinical treatment to prevent the transmission of inherited mtDNA diseases

Interior regularity criterion for incompressible Ericksen-Leslie system

Abstract An interior regularity criterion of suitable weak solutions is formulated for the Ericksen-Leslie system of liquid crystals. Such a criterion is point-wise, with respect to some appropriate norm of velocity u and the gradient of d, and it can be viewed as a sort of simply sufficient condition on the local regularity of suitable weak solutions

Regularized Discrete Optimal Transport for Class-Imbalanced Classifications

Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that leverages optimal transport for handling imbalanced data. Specifically, we establish a transport plan between training and testing data without modifying the original data distribution, drawing upon the principles of optimal transport theory. Additionally, we introduce a non-convex interclass regularization term to establish connections between testing samples and training samples with the same class labels. This regularization term forms the basis of a regularized discrete optimal transport model, which is employed to address imbalanced classification scenarios. Subsequently, in line with the concept of maximum minimization, a maximum minimization algorithm is introduced for regularized discrete optimal transport. Subsequent experiments on 17 Keel datasets with varying levels of imbalance demonstrate the superior performance of the proposed approach compared to 11 other widely used techniques for class-imbalanced classification. Additionally, the application of the proposed approach to water quality evaluation confirms its effectiveness

Fabrication and Thermal Degradation Kinetics of PBT/BEO/Nano-Sb2O3 Composites

Developing polybutylene terephthalate (PBT) with high thermal stability and flame-retardant properties is crucial for automotive, biomedical devices, electronics, and other fields. Herein, we focus on a PBT/brominated epoxy resin (BEO)/nano-Sb2O3 composites by a melt-blending method. The effects of heating rate and nano-Sb2O3 content on the thermal stability and thermal degradation kinetics of PBT composites were studied by TG-DSC. With the increasing of heating rate, the thermal hysteresis effect of temperature gradient is produced, which is eliminated when the temperature exceeds 400°C. With the increase of nano-Sb2O3 content, the Ea of PBT/BEO/nano-Sb2O3 composites increases at first and then decreases. When the content of nano-Sb2O3 is 3 wt%, the Ea of PBT/BEO/nano-Sb2O3 is the highest, which is 66.18 kJ/mol (31.43%) higher than that of neat PBT. Also, the exploration of the thermal degradation kinetics of PBT/BEO/nano-Sb2O3 composites is expected to provide research ideas for new high flame-retardant materials

Behavior of solid matters and heavy metals during conductive drying process of sewage sludge

Behavior of solid matters and heavy metals during conductive drying process of sewage sludge was evaluated in a sewage sludge disposal center in Beijing, China. The results showed most of solid matters could be retained in the dried sludge after drying. Just about 3.1% of solid matters were evaporated with steam mainly by the form of volatile fatty acids. Zn was the dominant heavy metal in the sludge, followed by Cu, Cr, Pb, Ni, Hg, and Cd. The heavy metals in the condensate were all below the detection limit except Hg. Hg in the condensate accounted for less than 0.1% of the total Hg. It can be concluded that most of the heavy metals are also retained in the dried sludge during the drying process, but their bioavailability could be changed significantly. The results are useful for sewage sludge utilization and its condensate treatment