Combinatorial Design of the MAUT and PAMSSEM II Methods for Multiple Attributes Group Decision Making with Probabilistic Linguistic Information

This paper considers the application of probabilistic linguistic term sets (PLTS) in multiple-attribute group decision-making (MAGDM) when the weights can’t be determined. First, as an improvement of the PROMETHEE method, the PAMSSEM method can not only handle missing evaluations, but also proposes a rejection threshold to calculate the overall consistency of the plan, so as to rank the plan more reasonably. At the same time, the MAUT uses the marginal utility function to reallocate the attribute values of the alternatives in the interval , and then calculate the total utility to sort them. Because the utility function is beneficial in expressing consumer satisfaction, we combine the MAUT method and PAMSSEM II method and apply it to solve decision-making problems under probabilistic linguistic environment. Secondly the coefficient of variation method, entropy method and analytic hierarchy process are used to calculate the weights in a combination. In the process of data processing, we use the transfer function to convert the PLTS into the hesitant probabilistic fuzzy set (HPFS) for calculation. Finally, the PL-MAUT-PAMSSEM II method, PROMRTHE method, TOPSIS method and ARAS method are compared with each other.

Study of Dielectric, Polarization Properties of Cyclopentanol and Their Solutions in Cyclopentane by a New Variation Method

Results of calculating the theoretical principles of the variational method for measuring the dielectric parameters of polar liquids: cyclopentanol and its solutions in cyclopentane have been shown in the paper. Their dielectric constant ξ' and dielectric losses ξ'' are calculated. Solutions to the equations were found and a graphical solution method and an automated method for calculating ξ' and ξ'' were developed on the basis of this method. Comparison with the results of other methods revealed that these indicators are at the same time minimal within 1.5–2.0%.

Study on College English Online Teaching Model in Mixed Context Based on Genetic Algorithm and Neural Network Algorithm

College English classroom teaching evaluation is an important basis for understanding teaching level and improving teaching quality. The traditional college English classroom teaching evaluation is mainly carried out through questionnaires and scales, but this method is time-consuming and laborious, inevitably introduces subjective errors, and reduces the accuracy and credibility of the evaluation results. In recent years, the rise and development of wisdom education not only provides a more convenient and efficient modern education form but also brings new ideas for classroom teaching evaluation. A subjective and objective fusion statistical evaluation model based on multidirectional genetic variation method and optimized neural network is proposed. The algorithm avoids subjective errors and improves the accuracy and reliability of the evaluation results, and a comprehensive evaluation model is constructed. Finally, according to different evaluation indexes, a systematic visualization scheme is designed to generate students’ classroom learning evaluation report and teachers' classroom teaching evaluation report, respectively, and visualize them on the web.

A data-driven approach to approximate the correlation functions in cluster variation method

The cluster variation method (CVM) is one of the thermodynamic models used to calculate phase diagrams considering short range order (SRO). This method predicts the SRO values through internal variables referred to as correlation functions (CFs), accurately up to the cluster chosen in modeling the system. Determination of these CFs at each thermodynamic state of the system requires solving a set of nonlinear equations using numerical methods. In this communication, a neural network model is proposed to predict the values of the CFs. This network is trained for the BCC phase under tetrahedron approximation for both ordering and phase separating systems. The results show that the network can predict the values of the CFs accurately and thereby Helmholtz energy and the phase diagram with significantly less computational burden than that of conventional methods used.

Why Should Different City Characteristics Customize the Rate of Pollution Charge? Evidence of Factors Affecting Willingness-to-pay for Wastewater Management in Thailand

A challenge for wastewater management is enhancing the efficiency of resource allocation. Polluter Pays Principle is a wastewater charge collection approach that could help support the financial burden faced by many countries. Tailor-made policy implementation based on city characteristics is critical to the success of implementing a national policy to fit well in different local contexts. This study examined gaps for improving municipal wastewater management systems in Thailand through in-depth interviews and a questionnaire survey to identify the factors influencing households’ perception and willingness to pay in three different cities. A contingent variation method technique was used to evaluate the preferences of residents. The study revealed that different cities have different factors that influence willingness-to-pay decisions as well as pay-out levels. A single common factor positively affecting willingness-to-pay preference in all three cities is perception and engagement of wastewater treatment service. Citizens with a positive perception of receiving wastewater service are not only more likely to pay the wastewater charge, but also at a higher amount.

Theoretical and Experimental Substractions of Device Temperature Determination Utilizing I-V Characterization Applied on AlGaN/GaN HEMT

A differential analysis of electrical attributes, including the temperature profile and trapping phenomena is introduced using a device analytical spatial electrical model. The resultant current difference caused by the applied voltage variation is divided into isothermal and thermal sections, corresponding to the instantaneous time- or temperature-dependent change. The average temperature relevance is explained in the theoretical section with respect to the thermal profile and major parameters of the device at the operating point. An ambient temperature variation method has been used to determine device average temperature under quasi-static state and pulse operation, was compared with respect to the threshold voltage shift of a high-electron-mobility transistor (HEMT). The experimental sections presents theoretical subtractions of average channel temperature determination including trapping phenomena adapted for the AlGaN/GaN HEMT. The theoretical results found using the analytical model, allow for the consolidation of specific methodologies for further research to determine the device temperature based on spatially distributed and averaged parameters.

Novel Classification of IoT Devices Based on Traffic Flow Features

The concept of IoT (Internet of Things) assumes a continuous increase in the number of devices, which raises the problem of classifying them for different purposes. Based on their semantic characteristics, meaning, functionality or domain of usage, the system classes have been identified so far. This research purpose is to identify devices classes based on traffic flow characteristics such as the coefficient of variation of the received and sent data ratio. Such specified classes can combine devices based on behavior predictability and can serve as the basis for the creation of network management or network anomaly detection classification models. Four generic classes of IoT devices where defined by using the classification of the coefficient of variation method.

Novel Classification of IoT Devices Based on Traffic Flow Features

The concept of IoT (Internet of Things) assumes a continuous increase in the number of devices, which raises the problem of classifying them for different purposes. Based on their semantic characteristics, meaning, functionality or domain of usage, the system classes have been identified so far. This research purpose is to identify devices classes based on traffic flow characteristics such as the coefficient of variation of the received and sent data ratio. Such specified classes can combine devices based on behavior predictability and can serve as the basis for the creation of network management or network anomaly detection classification models. Four generic classes of IoT devices where defined by using the classification of the coefficient of variation method.