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Loopholes exist in nearly every regime, which leads to invalidity and instability in the running process of a regime. In this paper, we propose a new framework based on agent method and game theory to find loopholes in a regime, which contains two players with their clear payoffs. Following the principle of "winners stay, losers change", agents make their own choices among strategies, and their choices can affect the payoffs of other players, resulting in a dynamic equilibrium that has obvious features in decision path, which can be easily found in the final figures after simulation. We further exploit typical cases, such as prisoner's dilemma, anti-coordination game, coordination game and harmony, of which the results have been mathematical proved, to illustrate the validity of our method. Finally, we make some regimes in this paper based on the classical case, "Boxed Pigs", and find the loopholes in these regimes. By utilizing the framework proposed in this paper, managers are able to detect possible problems in advance at the time when they make regimes, which helps reduce the loss of costs caused by management and improve team relations. [ABSTRACT FROM AUTHOR]

In the process of k-coverage of the target node, there will be a lot of data redundancy forcing the phenomenon of congestion which reduces network communication capability and coverage, and accelerates network energy consumption. Therefore, this paper proposes a novel energy balanced k-coverage control algorithm based on probability model (EBKCCA). The algorithm constructs the coverage network model by using the positional relationship between the nodes. By analyzing the network model, the coverage expected value of nodes and the minimum number of nodes in the monitoring area are given. In terms of energy consumption, this paper gives the proportion of energy conversion functions between working nodes and neighboring nodes. By using the function proportional to schedule low energy nodes, we achieve the energy balance of the whole network and optimizing network resources. The last simulation experiments indicate that this algorithm can not only improve the quality of network coverage, but also completely inhibit the rapid energy consumption of node, and extend the network lifetime. [ABSTRACT FROM AUTHOR]

Multi-node and multi-feature fusion is an important approach for digital modulations signal recognition in modern communication field. Information obtained from multi-node and multi-feature needs to be fused because of incompleteness of single feature and uncertainty of single node. As a powerful method for data fusion and conclusion reasoning in uncertain environments, evidence theory is widely used. Establishing reliable BPA is the prerequisite for evidence fusion. In this paper, in order to improve the coincidence of basic probability assignment (BPA) with real probability, the notion of bell function (Bell-F) based similarity evaluation model (SEM) is introduced. Through comparative experiments, it is proved that the new method based on Bell-F is effective for BPA acquisition. Furthermore, a new information fused based digital signal modulation recognition scheme is described. Finally, a case study is given to illustrate the performance of the proposed model. Through test and calculations under the digital modulation signal data set, under 0 ~ 10dB, the recognition rate based on the Bell-F fusion method is above 90%, which is 20% higher than methods without fusion. Under 0dB or less, the integrated recognition rate of the Bell-F is 40% higher than the Gray relation method. [ABSTRACT FROM AUTHOR]

Delirium and dementia represent an emerging global crisis in healthcare. Attempts have been made to identify the pathognomonic feature that would make delirium stand out from dementia but unfortunately the global neural dysfunction of both disorders has made the establishment of a direct measurement difficult. Modern conceptualisations of delirium have been influenced by the assessment tools used to assess, detect, and analyse its complex and transient nature. Recent publication of the DSM-V criteria for delirium has marginally altered the previous DSM-IV criteria with a focus upon inattention with vague terms such as consciousness downplayed. Such an alteration has been found to be restrictive and thus impact upon delirium case identification. Although these findings are approximating the empirical state of delirium as measured by validated instruments, a more refined neuroscientifically informed phenomenological framework is required in order to enhance the theoretical understanding of delirium assessment and resolve these challenges. One such application is the predictive coding (PC) model, also known as the hierarchical Bayesian inference model, to interpreting delirium pathophysiology. Therefore, the aims of this paper are to 1) propose the hypothesis that delirium pathophysiology can be explained in terms of the PC model, 2) support this hypothesis by applying this model to current methods of assessing delirium phenomenology, particularly attention, and 3) outline a future programme of research to test many of the parameters of this application. [ABSTRACT FROM AUTHOR]

Most existing algorithms used for constructing virtual backbones are based on the ideal deterministic network model (DNM) in which any pair of nodes is either fully connected or completely disconnected. Different from DNM, the probabilistic network model (PNM), which presumes that there is a probability to connect and communicate between any pair of nodes, is more suitable to the practice in many real applications. In this paper, we propose a new algorithm to construct reliable virtual backbone in probabilistic wireless sensor networks. In the algorithm, we firstly introduce Effective Degree of Delivery Probability (EDDP) to indicate the reliable degree of nodes to transfer data successfully, and then exclude those nodes with zero EDDP from the candidate dominator set to construct a reliable connected dominating set (CDS). Moreover, each dominatee selects the neighbor dominator with the maximum delivery probability to transfer data. Through simulations, we demonstrate that our proposed algorithm can remarkably prolong the network lifetime compared with existing typical algorithms. [ABSTRACT FROM AUTHOR]

Kohomen's model as the self-organizing model for the neural network can be considered as a kind of adaptive vector quantization algorithm. Numerous reports have been presented on the application of the model to practical problems. Although some results have been presented for the theoretical properties of Kohonen's model, many properties remain to be clarified. Among various properties of Kohonen's model as an adaptive vector quantization algorithm, this paper considers the problem of how the reference vectors are placed according to the probability distribution of the input signal. Considering the limit where the number of reference vectors is increased to infinity, this problem can be discussed theoretically as the distribution of the reference vectors. Due to the effect of the "learning by neighborhood," which is the feature of Kohonen's model, the property of the Kohonen model differs quantitatively from the property of the ordinary vector quantization algorithm. This paper discusses quantitatively the properties of Kohonen's model using the average learning equation. [ABSTRACT FROM AUTHOR]

For a fixed but arbitrary infinite ordinal a and a prime p, we define the classes of a-simply presented and strongly a-simply presented p-primary abelian groups as well as we explore their characteristic properties. Certain important characterizations, some of which generalize corresponding results from Keef and Danchev's paper 'On n-simply presented primary abelian groups' ([10]), and some of which contrast with them, are also established. [ABSTRACT FROM AUTHOR]

Let N = pq be the product of two large primes. Consider Chinese remainder theorem-Rivest, Shamir, Adleman (CRT-RSA) with the public encryption exponent e and private decryption exponents dp, dq . It is well known that given any one of dp or dq (or both) one can factorise N in probabilistic poly(log N) time with success probability almost equal to 1. Though this serves all the practical purposes, from theoretical point of view, this is not a deterministic polynomial time algorithm. In this paper, we present a lattice-based deterministic poly(log N) time algorithm that uses both dp, dq (in addition to the public information e, N) to factorise N for certain ranges of dp, dq. We like to stress that proving the equivalence for all the values of dp, dq may be a nontrivial task. [ABSTRACT FROM AUTHOR]