Informatica

The Hamy mean (HM) operator, as a convenient mathematical aggregation tool, can deal with the interrelationship among multiple input parameters, and the power average (PA) operator can relieve the influence of awkward assessment values in the decision consequences. The interval neutrosophic sets (INSs) are a more powerful mathematical tool to handle insufficient, indeterminate and vague information that exists in real life problems. Yet, in some complicated decision-making situations, we require to consider the correlation between multi-input arguments and remove the influence of awkward data at the same time. To deal with such situations, in this paper, we combine the conventional HM operator to the traditional PA operator in interval neutrosophic settings and present two novel interval neutrosophic aggregation operators, that is, the interval neutrosophic power Hamy mean (INPHM) operator and the weighted interval neutrosophic power Hamy mean (WINPHM) operators. Then, some preferable properties of the developed aggregation operators are discussed. Moreover, based on these developed aggregation operators, we propose a new method for multiple attribute group decision making (MAGDM) under the INSs. Lastly, some examples are given to show the effectiveness of the developed method by comparing it with other existing methods.

As the tourism and mobile internet develop, car sharing is becoming more and more popular. How to select an appropriate car sharing platform is an important issue to tourists. The car sharing platform selection can be regarded as a kind of multi-attribute group decision making (MAGDM) problems. The probabilistic linguistic term set (PLTS) is a powerful tool to express tourists’ evaluations in the car sharing platform selection. This paper develops a probabilistic linguistic group decision making method for selecting a suitable car sharing platform. First, two aggregation operators of PLTSs are proposed. Subsequently, a fuzzy entropy and a hesitancy entropy of a PLTS are developed to measure the fuzziness and hesitancy of a PLTS, respectively. Combining the fuzzy entropy and hesitancy entropy, a total entropy of a PLTS is generated. Furthermore, a cross entropy between PLTSs is proposed as well. Using the total entropy and cross entropy, DMs’ weights and attribute weights are determined, respectively. By defining preference functions with PLTSs, an improved PL-PROMETHEE approach is developed to rank alternatives. Thereby, a novel method is proposed for solving MAGDM with PLTSs. A car sharing platform selection is examined at length to show the application and superiority of the proposed method.

Neutrosophic linguistic numbers (NLNs) can depict the uncertain and imperfect information by linguistic variables (LVs). As the classical aggregation operator, the Maclaurin symmetric mean (MSM) operator has its prominent characteristic that reflects the interactions among multiple attributes. Considering such circumstance: there are interrelationship among the attributes which take the forms of NLNs and the attribute weights are fully unknown in multiple attribute group decision making (MAGDM) problems, we propose a novel MAGDM methods with NLNs. Firstly, the MSM is extended to NLNs, that is, aggregating neutrosophic linguistic information by two new operators – the NLN Maclaurin symmetric mean (NLNMSM) operator and the weighted NLN Maclaurin symmetric mean (WNLNMSM) operator. Then, we discuss some characteristics and detail some special examples of the developed operators. Further, we develop an information entropy measure under NLNs to assign the objective weights of the attributes. Based on the entropy weights and the proposed operators, an approach to MAGDM problems with NLNs is introduced. Finally, a manufacturing industry example is given to demonstrate the effectiveness and superiority of the proposed method.

Generating sequences of random numbers or bits is a necessity in many situations (cryptography, modeling, simulations, etc…). Those sequences must be random in the sense that their behavior should be unpredictable. For example, the security of many cryptographic systems depends on the generation of unpredictable values to be used as keys. Since randomness is related to the unpredictable property, it can be described in probabilistic terms, studying the randomness of a sequence by means of a hypothesis test. A new statistical test for randomness of bit sequences is proposed in the paper. The created test is focused on determining the number of different fixed length patterns that appear along the binary sequence. When ‘few’ distinct patterns appear in the sequence, the hypothesis of randomness is rejected. On the contrary, when ‘many’ different patterns appear in the sequence, the hypothesis of randomness is accepted.

In this paper, the main measure, an amount of information, of the information theory is analyzed and corrected. The three conceptions of the theory on the microstate, dissipation pathways, and self‐organization levels with a tight connection to the statistical physics are discussed. The concepts of restricted information were introduced as well as the proof of uniqueness of the entropy function, when the probabilities are rational numbers, is presented.