A Method to Determine Generalized Basic Probability Assignment in the Open World

Dempster-Shafer evidence theory (D-S theory) has been widely used in many information fusion systems since it was proposed by Dempster and extended by Shafer. However, how to determine the basic probability assignment (BPA), which is the main and first step in D-S theory, is still an open issue, especially when the given environment is in an open world, which means the frame of discernment is incomplete. In this paper, a method to determine generalized basic probability assignment in an open world is proposed. Frame of discernment in an open world is established first, and then the triangular fuzzy number models to identify target in the proposed frame of discernment are established. Pessimistic strategy based on the differentiation degree between model and sample is defined to yield the BPAs for known targets. If the sum of all the BPAs of known targets is over one, then they will be normalized and the BPA of unknown target is assigned to 0; otherwise the BPA of unknown target is equal to 1 minus the sum of all the known targets BPAs. IRIS classification examples illustrated the effectiveness of the proposed method

Over-expression of a gamma-tocopherol methyltransferase gene in vitamin E pathway confers PEG-simulated drought tolerance in alfalfa

α-Tocopherol is one of the most important vitamin E components present in plant. α-Tocopherol is a potent antioxidant, which can deactivate photoproduced reactive oxygen species (ROS) and prevent lipids from oxidation when plants suffer drought stress. γ-Tocopherol methyltransferase (γ-TMT) catalyzes the formation of α-tocopherol in the tocopherol biosynthetic pathway. Our previous studies showed that over-expression of γ-TMT gene can increase the accumulation of α-tocopherol in alfalfa (Medicago sativa). However, whether these transgenic plants confer increased drought tolerance and the underlying mechanism are still unknown.This work was financially supported by Earmarked Fund for China Agriculture Research System (CARS-34), the National Natural Science Foundation of China (31872410), National Crop Germplasm Resources Center (NICGR-78), and the Agricultural Science and Technology Innovation Program (ASTIPIAS10)

Phosphorylation of Icariin Can Alleviate the Oxidative Stress Caused by the Duck Hepatitis Virus A through Mitogen-Activated Protein Kinases Signaling Pathways

The duck virus hepatitis (DVH) caused by the duck hepatitis virus A (DHAV) has produced extensive economic losses to the duck industry. The currently licensed commercial vaccine has shown some defects and does not completely prevent the DVH. Accordingly, a new alternative treatment for this disease is urgently needed. Previous studies have shown that icariin (ICA) and its phosphorylated derivative (pICA) possessed good anti-DHAV effects through direct and indirect antiviral pathways, such as antioxidative stress. But the antioxidant activity showed some differences between ICA and pICA. The aim of this study is to prove that ICA and pICA attenuate oxidative stress caused by DHAV in vitro and in vivo, and to investigate their mechanism of action to explain their differences in antioxidant activities. In vivo, the dynamic deaths, oxidative evaluation indexes and hepatic pathological change scores were detected. When was added the hinokitiol which showed the pro-oxidative effect as an intervention method, pICA still possessed more treatment effect than ICA. The strong correlation between mortality and oxidative stress proves that ICA and pICA alleviate oxidative stress caused by DHAV. This was also demonstrated by the addition of hydrogen peroxide (H2O2) as an intervention method in vitro. pICA can be more effective than ICA to improve duck embryonic hepatocytes (DEHs) viability and reduce the virulence of DHAV. The strong correlation between TCID50 and oxidative stress demonstrates that ICA and pICA can achieve anti-DHAV effects by inhibiting oxidative stress. In addition, the superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) of ICA and pICA showed significant difference. pICA could significantly inhibit the phosphorylation of p38, extra cellular signal regulated Kinase (ERK 1/2) and c-Jun N-terminal kinase (JNK), which were related to mitogen-activated protein kinases (MAPKs) signaling pathways. Ultimately, compared to ICA, pICA exhibited more antioxidant activity that could regulate oxidative stress-related indicators, and inhibited the phosphorylation of MAPKs signaling pathway

A modified belief entropy in Dempster-Shafer framework.

How to quantify the uncertain information in the framework of Dempster-Shafer evidence theory is still an open issue. Quite a few uncertainty measures have been proposed in Dempster-Shafer framework, however, the existing studies mainly focus on the mass function itself, the available information represented by the scale of the frame of discernment (FOD) in the body of evidence is ignored. Without taking full advantage of the information in the body of evidence, the existing methods are somehow not that efficient. In this paper, a modified belief entropy is proposed by considering the scale of FOD and the relative scale of a focal element with respect to FOD. Inspired by Deng entropy, the new belief entropy is consistent with Shannon entropy in the sense of probability consistency. What's more, with less information loss, the new measure can overcome the shortage of some other uncertainty measures. A few numerical examples and a case study are presented to show the efficiency and superiority of the proposed method

A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory

Due to the incomplete knowledge, how to handle the uncertain risk factors in failure mode and effects analysis (FMEA) is still an open issue. This paper proposes a new generalized evidential FMEA (GEFMEA) model to handle the uncertain risk factor, which may not be included in the conventional FMEA model. In GEFMEA, not only the conventional risk factors, the occurrence, severity, and detectability of the failure mode, but also the other incomplete risk factors are taken into consideration. In addition, the relative importance among all these risk factors is well addressed in the proposed method. GEFMEA is based on the generalized evidence theory, which is efficient in handling incomplete information in the open world. The efficiency and some merit of the proposed method are verified by the numerical example and a real case study on aircraft turbine rotor blades

A modified method for risk evaluation in failure modes and effects analysis of aircraft turbine rotor blades

Failure mode and effects analysis is an important methodology, which has been extensively used to evaluate the potential failures, errors, or risks in a system, design, or process. The traditional method utilizes the risk priority number ranking system. This method determines the risk priority number by multiplying failure factor values. Dempster–Shafer evidence theory has been combined with failure mode and effects analysis due to its effectiveness in dealing with uncertain and subjective information. However, since the risk evaluation of different experts may be different and some even conflict with each other, Dempster’s combination rule may become invalid. In this article, for better performance of application of evidence theory in failure mode and effects analysis, a modified method is proposed to reassign the basic believe assignment taking into consideration a reliability coefficient based on evidence distance. We illustrate several numerical examples and use the modified method to obtain the risk priority numbers for risk evaluation in failure modes of aircraft engine rotor blades. The results show that the proposed method is more reasonable and effective for real applications

An evidential sensor fusion method in fault diagnosis

Dempster–Shafer evidence theory is widely used in information fusion. However, it may lead to an unreasonable result when dealing with high conflict evidence. In order to solve this problem, we put forward a new method based on the credibility of evidence. First, a novel belief entropy, Deng entropy, is applied to measure the information volume of the evidence and then the discounting coefficients of each evidence are obtained. Finally, weighted averaging the evidence in the system, the Dempster combination rule was used to realize information fusion. A weighted averaging combination role is presented for multi-sensor data fusion in fault diagnosis. It seems more reasonable than before using the new belief function to determine the weight. A numerical example is given to illustrate that the proposed rule is more effective to perform fault diagnosis than classical evidence theory in fusing multi-symptom domains