Maximum Utility Value Research Articles

Using Game Theory to Analyze the Interaction between Teachers and Students in the Teaching of Civics in College Courses

Abstract In this paper, firstly, on the basis of Marx’s theory of intersubjectivity, we use the concept of teachers and students as the main body of each other to construct the interactive teaching mode and mechanism of teachers and students in Civics courses and actively explore the path and prospect of interactive teaching of Civics in courses in the new era. Then we use the pure strategy game in game theory to determine the performance production function, and then we use Nash equilibrium to obtain the teacher utility function and student utility function on the basis of the performance production function so as to further construct the game model of teacher-student behavior in the teaching process of Civics courses, and analyze the teacher-student interaction in the Civics of higher education courses by using the game model. The results show that in the pure strategy game analysis of teacher-student behavior, 0.3872 teachers will choose to reduce teaching inputs, while the other 0.6128 teachers will choose to reduce teaching inputs to “almost no” or “very little”, i.e., the optimal choice strategy is “no” or “very little”, i.e., the optimal choice strategy is “no” or “very little”. The optimal choice strategy is “no reduction in instructional inputs”. In the Nash equilibrium analysis of teacher-student behavior, the equilibrium value of the teacher-student behavior game is (42.22% and 64.59%), and teachers and students need to continuously improve their behavioral strategy choices in order to achieve the maximum utility value of each other. This study improves the quality of teaching and ultimately achieves the objective of Civic Education.

Present status and prioritization of rural road accessibility: A case study of Sunamganj districts of Bangladesh

This study was conducted to examine the Rural Transport Accessibility Index and improve the connectivity of the rural road network for agricultural development in the Sunamganj district, Bangladesh, and facilitate the distribution of agricultural products as well as its accessibility to people. The demographic, socioeconomic, and agricultural data of eleven Upazila’s were obtained from the Bangladesh Bureau of Statistics (BBS) in order to fulfill the goals of this study. The administrative area boundary and transport network data were collected from the Local Government Engineering Department (LGED) and Roads and Highways Department (RHD), respectively. Rural Transport Accessibility was estimated using ArcGIS-Arc Map. Analyzes were performed using the Microsoft Excel Analysis Tool pack. To determine the priority ranking, Data Envelopment Analysis (DEA) and correlation analysis were used. The outcome of this method helped in ranking the Dharamapasha, Derai, Jagannathpur, Sullah, Tahirpur, Dakshin Sunamganj, Jamalganj, Chhatak, Dowarabazar, Bishwamvarpur, and Sunamganj Sadar Upazilas respectively inspect accessibility level. Improving rural road network considering agricultural productivity per kilometer and prioritizing the road ranking was prepared from the maximum habitations within the desired distance. These 121 links (Upazila Distance Matrix) were calculated. From Bishwamvarpur to Sunamganj Sadar, the maximum utility value was 12281.0. This link will be upgraded as the priority, and Jagannathpur to Sullah links will be upgraded as the least priority because of its minimum value of 149.9.

Dynamic Actor Network Analysis Approach In Management of Mass Public Transport Services

Taking into account the progress of community mobility, increased motorization of private vehicles, congestion, and poor urban transportation services, the Bandung City Government seeks to provide mass public transport services Trans Metro Bandung (TMB). What policies should be established to achieve the successful implementation of the TMB program?. This study aims to provide recommendations to TMB management to achieve the success of services that prioritize reliability, comfort, and humanity. The analysis was carried out on stakeholder opinion using actor analysis method software, namely Dynamic Actor-Network Analysis (DANA). The involvement of all stakeholders is needed to get their views, perceptions, and opinions about the implementation of the TMB program. From the interview processing, the results obtained from the perception diagram and identified 19 different factors in one arena. The factors for reducing traffic congestion and improving services for mass public transport are defined as “goals”. The analysis of relevance and centrality identifies the limitations of infrastructure, facilities, and financing which are the main causes of the problem so that TMB services cannot be implemented optimally. In the strategy analysis, four actions were analyzed. The most ideal solution strategy that has the maximum utility value, the highest satisfaction, and lowest frustration score is the formation of a consortium and socialization.

Hybrid Taguchi-PCA-utility approach for simultaneous optimisation of multiple responses in turning alloy steel

ABSTRACT This study focussed on the multi-response optimisation of turning process using an easier and accurate hybrid approach namely Taguchi-PCA-Utility approach. Turning of alloy steel under dry and LN2-assisted cryogenic cooling with two different geometry-shaped uncoated carbide inserts was carried out based on suitable design of experiments (Taguchi L16 orthogonal array). Five vital responses in turning such as temperature, roughness, feed force, cutting force and MRR were measured by varying the input factors (speed, feed, cutting condition and cutting inserts type). Principal component analysis (PCA) for relative weight calculation of responses and utility concept for deriving out the unified process index were followed. The optimum parameter settings for maximum utility value were found at 134 m/min speed with 0.12 mm/rev feed rate under cryogenic cooling using SNMG 120408-type cutting insert. Finally, in order to verifying the significance of proposed approach validation tests were conducted.

Integrated quality of user experience and quality of service approach to service selection in internet of services

Discovery and retrieval of an ideal service in an SOA-like infrastructure comprising a resource-constrained hosting platform is really a challenging job. In this paper, we propose the simple service selection component for IoS, which selects the services that have maximum utility value over Quality of Services (QoS) and Quality of User Experience (QoUE) attributes. The novelty behind the proposed middleware development is in achieving high-degree configuration and customisability by selecting a subset of middleware functionality depending on the need. The conventional service selection approaches do not consider QoUE and QoS as a whole; they expect the user to provide a set of QoS constraints. The selection approach is based on the MAUT (Multi-Attribute Utility Theory) decision-making method. The experimental results are encouraging and the middleware performs well when compared to the constraint model and linear programming models.

Active Learning With Optimal Instance Subset Selection

Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assess individual instances and label the ones with the maximum values for training. In this paper, we argue that such approaches cannot produce good labeling subsets mainly because instances are evaluated independently without considering their interactions, and individuals with maximal ability do not necessarily form an optimal instance subset for learning. Alternatively, we propose to achieve AL with optimal subset selection (ALOSS), where the key is to find an instance subset with a maximum utility value. To achieve the goal, ALOSS simultaneously considers the following: 1) the importance of individual instances and 2) the disparity between instances, to build an instance-correlation matrix. As a result, AL is transformed to a semidefinite programming problem to select a k-instance subset with a maximum utility value. Experimental results demonstrate that ALOSS outperforms state-of-the-art approaches for AL.

Optimal Subset Selection for Active Learning

Active learning traditionally relies on instance based utility measures to rank and select instances for labeling, which may result in labeling redundancy. To address this issue, we explore instance utility from two dimensions: individual uncertainty and instance disparity, using a correlation matrix. The active learning is transformed to a semi-definite programming problem to select an optimal subset with maximum utility value. Experiments demonstrate the algorithm performance in comparison with baseline approaches.

Target Exploration for Disconnected Feasible Regions in Enterprise-Driven Multilevel Product Design

Enterprise-level business decisions are linked with engineering product decisions by integrating enterprise utility optimization and engineering design optimization under a hierarchical, multilevel, decision-based design framework. The enterprise problem sets attribute targets, that is, specifications, for engineering product development, which then optimizes product performance within the feasible design space to match the targets with minimum deviations. When the feasible domain imposed by engineering product development is disconnected in the space of attribute targets, an engineering design with the minimum deviation from the targets may not correspond to the design with the maximum utility value, even though the design is a converged solution from the multilevel optimization. To address this issue, a new algorithm is developed, which systematically explores the target space to lead the engineering product development to a feasible and optimal design in the enterprise context. Analytical examples and an automotive suspension design case study are presented to demonstrate the effectiveness of the proposed methodology.