IMF-PR: An Improved Morton-Filter-Based Pseudonym-Revocation Scheme in VANETs.

Vehicle ad hoc networks (VANETs) are special wireless networks which help vehicles to obtain continuous and stable communication. Pseudonym revocation, as a vital security mechanism, is able to protect legal vehicles in VANETs. However, existing pseudonym-revocation schemes suffer from the issues of low certificate revocation list (CRL) generation and update efficiency, along with high CRL storage and transmission costs. In order to solve the above issues, this paper proposes an improved Morton-filter-based pseudonym-revocation scheme for VANETs (IMF-PR). IMF-PR establishes a new distributed CRL management mechanism to maintain a low CRL distribution transmission delay. In addition, IMF-PR improves the Morton filter to optimize the CRL management mechanism so as to improve CRL generation and update efficiency and reduce the CRL storage overhead. Moreover, CRLs in IMF-PR store illegal vehicle information based on an improved Morton filter data structure to improve the compress ratio and the query efficiency. Performance analysis and simulation experiments showed that IMF-PR can effectively reduce storage by increasing the compression gain and reducing transmission delay. In addition, IMF-PR can also greatly improve the lookup and update throughput on CRLs.

Effects of the genetic knockout of the ß-1,3-galactosyltransferase 2 on spatial learning and neurons in the adult mouse hippocampus and somatosensory cortex.

OBJECTIVE: Glycosyltransferases contribute to the biosynthesis of glycoproteins, proteoglycans and glycolipids and play essential roles in various processes in the brain, such as learning and memory, brain development, neuronal survival and neurodegeneration. ß-1,3-galactosyltransferase 2 (B3galt2) belongs to the ß-1,3-galactosyltransferase gene family and is highly expressed in the brain. Recent studies have indicated that B3galt2 plays a vital role in ischemic stroke through several signaling pathways in a mouse model. However, the function of B3galt2 in the brain remains poorly understood. METHODS: The genotypes of mice were determined by PCR. To verify B3galt2 expression in an adult mouse brain, X-gal staining was performed in 6-month-old B3galt2 heterozygous (B3galt2+/-) mice. Using adult B3galt2 homozygous (B3galt2-/-), heterozygous and wild-type (WT) littermates, spatial learning and memory were determined by the Morris Water Maze test, and neurotoxicity and synaptic plasticity were examined by immunofluorescence. RESULTS: B3galt2 was highly expressed in the adult mouse hippocampus and cortex, especially in the hippocampal dentate gyrus. Compared to that of WT mice, the spatial learning ability of adult B3galt2-/- mice was impaired. B3galt2 mutations also caused neuronal loss and synaptic dysfunction in the hippocampus and somatosensory cortex, and these changes were more obvious in B3galt2-/- mice than in B3galt2+/- mice. CONCLUSIONS: The findings indicate that B3galt2 plays an important role in cognitive function, neuronal maintenance and synaptic plasticity in the adult mouse brain. This study suggests that genetic and/or pharmacological manipulation of glycosyltransferases may be a novel strategy for elucidating the mechanism of and managing various brain disorders.

PCP: A Pseudonym Change Scheme for Location Privacy Preserving in VANETs.

In vehicular ad hoc networks (VANETs), pseudonym change is considered as the vital mechanism to support vehicles' anonymity. Due to the complicated road conditions and network environment, it is a challenge to design an efficient and adaptive pseudonym change protocol. In this paper, a pseudonym change protocol for location privacy preserving (PCP) is proposed. We first present the requirements of pseudonym change in different scenarios. According to variable network states and road conditions, vehicles are able to take different pseudonym change strategies to resist the tracking by global passive adversaries. Furthermore, the registration protocol, authentication protocol, pseudonym issuance protocol, and pseudonym revocation protocol are introduced for the pseudonym management mechanism. As a consequence, it is not feasible for global passive adversaries to track a vehicle for a long time and obtain the trajectory of the vehicle. The analysis results show that the security and performance of PCP are improved compared with the traditional ones.

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion.

Dempster-Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster's combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.

A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement.

The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty of BPAs. However, the existing methods have some limitations. In this paper, a new total uncertainty measure from a perspective of maximum entropy requirement is proposed. The proposed method can measure both dissonance and non-specificity in BPA, which includes two components. The first component is consistent with Yager's dissonance measure. The second component is the non-specificity measurement with different functions. We also prove the desirable properties of the proposed method. Besides, numerical examples and applications are provided to illustrate the effectiveness of the proposed total uncertainty measure.

ß-1, 3-galactosyltransferase 2 ameliorates focal ischemic cerebral injury by maintaining blood-brain barrier integrity.

Blood-brain barrier (BBB) damage at the early stage of ischemic stroke is a vital cause of brain parenchymal injury. The mechanism of BBB disruption has been intensively investigated, but still not fully understood. ß-1, 3-galactosyltransferase 2 (B3galt2) is expressed in the brain, but its role in the pathogenesis of cerebral ischemia remains unknown. In this study, we investigated the role of B3galt2 in cerebral ischemia in mice. Focal cerebral ischemia was induced in mice by middle cerebral artery occlusion (MCAO). B3galt2 protein levels were determined in microvessels which were isolated from ischemic brain at 12, 24 and 72 h after MCAO. Mice were administered lentiviral vectors encoding B3galt2 (LV- B3galt2) or recombinant transforming growth factor-ß1 (r-TGF-ß1) by intracerebroventricular injection. We assessed infarct volume and neurologic deficits on days 1, 3, and 14 after MCAO, blood-brain barrier (BBB) integrity at 12 and 24 h after MCAO, and the levels of TGF-ß1, TGF-ßR(Ⅱ) and p-Smad2/3 at 24 and 72 h after MCAO. Our results indicated that B3galt2 was expressed in brain microvascular endothelial cells and increased in the ischemic microvessels. Overexpression of B3galt2 by LV- B3galt2 administration reduced infarct volume and improved functional outcome after cerebral ischemia. Moreover, the neuroprotective effects were associated with preventing BBB damage. Compared with wild-type (WT) mice, heterozygous B3galt2 knockout (B3galt2-/+) mice not only showed severe BBB damage, neurologic functional deficits, but also showed reduced expression of TGF-ß1, TGF-ßR(Ⅱ) and p-Smad2/3 in microvessels after cerebral ischemia. Pre-administration of r-TGF-ß1 reduced BBB damage, and improved the neurological outcomes in both WT mice and B3galt2-/+ mice after cerebral ischemia. Our results suggested B3galt2 protected against ischemic stroke in mice, and the underlying mechanism might include TGF-ß signaling pathway in brain microvascular endothelial cells.

ß-1, 3-galactosyltransferase 2 deficiency exacerbates brain injury after transient focal cerebral ischemia in mice.

Glycosyltransferases are enzymes that catalyze the formation of a variety of glycoconjugates. Glycoconjugates play vital roles in the nervous system. ß-1, 3-Galactosyltransferase 2 (B3galt2) is one of the major types of glycosyltransferases, which has not been reported in ischemia induced-brain injury. The purpose of this study was to explore the role of B3galt2 exerts and its underlying mechanism in cerebral ischemia in mice. Wild-type (WT) and heterozygous B3galt2 knockout (B3galt2-/+) mice were subjected to 90 min transient focal cerebral ischemia by middle cerebral artery occlusion (MCAO). The brain samples were analyzed at 24 h after reperfusion. The B3galt2 level in the peri-infarct penumbra was quantified. The cerebral infarct volume, neurological deficits, apoptosis and the levels of Reelin and Dab1 were assessed. Compared with control mice, B3galt2-/+ mice not only showed severe brain damage, neurologic functional deficits, but also showed severe neuronal apoptosis in the cortical penumbra after ischemia/reperfusion (I/R). The Caspase-3 activity was increased and the levels of Reelin and Dab1 were decreased in B3galt2-/+ mice. Recombinant human Reelin (rh-Reelin) administered intracerebroventricularly before MCAO significantly reduced infarct volume, and prevented neuronal loss in B3galt2-/+ mice after I/R. Our results suggest B3galt2 deficiency exacerbates ischemic brain damage in acute ischemic stroke in mice, and this was reversed by giving rh-Reelin. B3galt2 might play a beneficial role for neurons survival in the penumbra through modulation of Reelin pathway.

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method.

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

Zero-Sum Matrix Game with Payoffs of Dempster-Shafer Belief Structures and Its Applications on Sensors.

The zero-sum matrix game is one of the most classic game models, and it is widely used in many scientific and engineering fields. In the real world, due to the complexity of the decision-making environment, sometimes the payoffs received by players may be inexact or uncertain, which requires that the model of matrix games has the ability to represent and deal with imprecise payoffs. To meet such a requirement, this paper develops a zero-sum matrix game model with Dempster-Shafer belief structure payoffs, which effectively represents the ambiguity involved in payoffs of a game. Then, a decomposition method is proposed to calculate the value of such a game, which is also expressed with belief structures. Moreover, for the possible computation-intensive issue in the proposed decomposition method, as an alternative solution, a Monte Carlo simulation approach is presented, as well. Finally, the proposed zero-sum matrix games with payoffs of Dempster-Shafer belief structures is illustratively applied to the sensor selection and intrusion detection of sensor networks, which shows its effectiveness and application process.

A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An application in medical diagnosis.

OBJECTIVE: Recently, fuzzy soft sets-based decision making has attracted more and more interest. Although plenty of works have been done, they cannot provide the uncertainty or certainty of their results. To manage uncertainty is one of the most important and toughest tasks of decision making especially in medicine. In this study, we improve the performance of reducing uncertainty and raising the choice decision level in fuzzy soft set-based decision making. METHODS AND MATERIAL: We make use of two appropriate tools (ambiguity measure and Dempster-Shafer theory of evidence) to improve fuzzy soft set-based decision making. Our proposed approach consists of three procedures: primarily, the uncertainty degree of each parameter is obtained by using ambiguity measure; next, the suitable basic probability assignment with respect to each parameter (or evidence) is constructed based on the uncertainty degree of each parameter obtained in the first step; in the end, the classical Dempster's combination rule is applied to aggregate independent evidences into the collective evidence, by which the candidate alternatives are ranked and the best alternative will be obtained. RESULTS: We compare the results of our proposed method with the recent relative works. Through employing our presented approach, in Example 5, the belief measure of the uncertainty falls to 0.0051 from 0.0751; in Example 6, the belief measure of the uncertainty drops to 0.0086 from 0.0547; in Example 7, the belief measure of the uncertainty falls to 0.0847 from 0.1647; in application, the belief measure of the uncertainty drops 0.0001 from 0.0069. CONCLUSION: Three numerical examples and an application in medical diagnosis are provided to demonstrate adequately that, on the one hand, our proposed method is feasible and efficient; on the other hand, our proposed method can reduce uncertainty caused by people's subjective cognition and raise the choice decision level with the best performance.

A novel framework of classical and quantum prisoner's dilemma games on coupled networks.

Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner's dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner's dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner's dilemma is greatly impacted by the combined effect of entanglement and coupling.

Evidence Combination From an Evolutionary Game Theory Perspective.

Dempster-Shafer evidence theory is a primary methodology for multisource information fusion because it is good at dealing with uncertain information. This theory provides a Dempster's rule of combination to synthesize multiple evidences from various information sources. However, in some cases, counter-intuitive results may be obtained based on that combination rule. Numerous new or improved methods have been proposed to suppress these counter-intuitive results based on perspectives, such as minimizing the information loss or deviation. Inspired by evolutionary game theory, this paper considers a biological and evolutionary perspective to study the combination of evidences. An evolutionary combination rule (ECR) is proposed to help find the most biologically supported proposition in a multievidence system. Within the proposed ECR, we develop a Jaccard matrix game to formalize the interaction between propositions in evidences, and utilize the replicator dynamics to mimick the evolution of propositions. Experimental results show that the proposed ECR can effectively suppress the counter-intuitive behaviors appeared in typical paradoxes of evidence theory, compared with many existing methods. Properties of the ECR, such as solution's stability and convergence, have been mathematically proved as well.

Impact of roles assignation on heterogeneous populations in evolutionary dictator game.

The evolution of cooperation is a hot and challenging topic in the field of evolutionary game theory. Altruistic behavior, as a particular form of cooperation, has been widely studied by the ultimatum game but not by the dictator game, which provides a more elegant way to identify the altruistic component of behaviors. In this paper, the evolutionary dictator game is applied to model the real motivations of altruism. A degree-based regime is utilized to assess the impact of the assignation of roles on evolutionary outcome in populations of heterogeneous structure with two kinds of strategic updating mechanisms, which are based on Darwin's theory of evolution and punctuated equilibrium, respectively. The results show that the evolutionary outcome is affected by the role assignation and that this impact also depends on the strategic updating mechanisms, the function used to evaluate players' success, and the structure of populations.

A belief-based evolutionarily stable strategy.

As an equilibrium refinement of the Nash equilibrium, evolutionarily stable strategy (ESS) is a key concept in evolutionary game theory and has attracted growing interest. An ESS can be either a pure strategy or a mixed strategy. Even though the randomness is allowed in mixed strategy, the selection probability of pure strategy in a mixed strategy may fluctuate due to the impact of many factors. The fluctuation can lead to more uncertainty. In this paper, such uncertainty involved in mixed strategy has been further taken into consideration: a belief strategy is proposed in terms of Dempster-Shafer evidence theory. Furthermore, based on the proposed belief strategy, a belief-based ESS has been developed. The belief strategy and belief-based ESS can reduce to the mixed strategy and mixed ESS, which provide more realistic and powerful tools to describe interactions among agents.

Bridge condition assessment using D numbers.

Bridge condition assessment is a complex problem influenced by many factors. The uncertain environment increases more its complexity. Due to the uncertainty in the process of assessment, one of the key problems is the representation of assessment results. Though there exists many methods that can deal with uncertain information, however, they have more or less deficiencies. In this paper, a new representation of uncertain information, called D numbers, is presented. It extends the Dempster-Shafer theory. By using D numbers, a new method is developed for the bridge condition assessment. Compared to these existing methods, the proposed method is simpler and more effective. An illustrative case is given to show the effectiveness of the new method.

TOPPER: topology prediction of transmembrane protein based on evidential reasoning.

The topology prediction of transmembrane protein is a hot research field in bioinformatics and molecular biology. It is a typical pattern recognition problem. Various prediction algorithms are developed to predict the transmembrane protein topology since the experimental techniques have been restricted by many stringent conditions. Usually, these individual prediction algorithms depend on various principles such as the hydrophobicity or charges of residues. In this paper, an evidential topology prediction method for transmembrane protein is proposed based on evidential reasoning, which is called TOPPER (topology prediction of transmembrane protein based on evidential reasoning). In the proposed method, the prediction results of multiple individual prediction algorithms can be transformed into BPAs (basic probability assignments) according to the confusion matrix. Then, the final prediction result can be obtained by the combination of each individual prediction base on Dempster's rule of combination. The experimental results show that the proposed method is superior to the individual prediction algorithms, which illustrates the effectiveness of the proposed method.