This article introduces, analyzes, and criticizes the work of Lawrence M. Hinman, Ethics: A Pluralistic Approach to Moral Theory. Hinman is a Philosopher of Ethics in the multimedia learning. He believes that multiculturalism is important factor of nowadays so the most important responsibility of philosophers of Ethics is prevention of moral conflict. The global communication and information in 21st century needs global tolerance and respect of various nations, cultures, and moralities together because different people and societies have a common human nature and common natural rights that obligate to justice and moral relationship between different people and various societies’ beliefs throughout the world. Moral communication in multiculturalism approach is against racial, gender, national, and ethnic prejudice. Hinman believes in moral dialogue and global learning across different cultures. The book of Ethics: A Pluralistic Approach to Moral Theory has a proper category of new ethical problems, and the candid standpoint of author is considerable. He has a logical method, and in the end of every part of book there are some questions for evaluation of students learning. So this book is proper for the philosophy of ethics in universities.

With the rapid spread of technology, large volumes of unlabeled data with large dimensions needed to be processed. To reduce the dimensions, unsupervised feature selection is known as an important pre-step before machine learning tasks. In this paper, an unsupervised feature selection method is proposed. The method works dynamically and is scalable based on matrix graphs and weighted matrices. To improve the performance of this method, instead of using the Lagrange function to construct a weight matrix, a bipartite graph theory is applied. Feature selection is done on the matrix graph. This graph is constructed using k nearest neighbors, which makes the method more robust to noise. The global structure of the original data is also preserved by constructing a Reconstruction Weight Matrix with low-rank constraint. In addition, the feature score, which explicitly reflects the strength of the features, is modeled using the Frobenius norm function. The proposed method is compared with similar methods in three criteria of classification accuracy, parameter sensitivity and complexity. Experiments show that the classification accuracy of the method presented in this paper has improved by an average of 2.83%. Its complexity has also been reduced to max{O(n2d),O(nm)}, where n is the number of samples, d is the number of features and m is the number of anchor points.

Background and Aim: According to many experts and researchers expression, a supply chain with a high level of risk cannot be effective in achieving the goals of organization. The growth of environmental hazards and the destructive impact of productive activities on this area also increase the importance of addressing the issue of risk management in the supply chain, which is known as the sustainable supply chain. The main purpose of this study is to design a framework for assessing sustainable supply chain risk. Method: In this study, by studying the theoretical foundations and reviewing the research literature, a basic framework of dimensions and criteria for risk assessment of sustainable supply chain has been achieved. The case study of this research has been done on the supply chain of 5 car manufacturing companies. Then, using Best and Worst Method technique, the final hierarchical structure for risk assessment criteria is obtained and using Graph Theory and Matrix Approach technique, the results of risk assessment of supply chain of five companies from the perspective of sustainability are obtained. Findings: The research results show that the ranking of companies has been determined from the perspective of the sustainable supply chain’, s risk level that SAIPA Diesel Company has the highest rating. Conclusion: Due to the use of a comprehensive framework, this research incorporates a combination of quantitative and qualitative metrics of risk assessment of the automotive sustainable supply chain and, unlike many studies, is not limited to one stage of the supply chain and covers all stages of the supply chain. This framework can be used to evaluate different parts of the automotive supply chain (primary suppliers, manufacturing companies, distributors and retailers) and measure their risk from the sustainability perspective.

‎Graph coloring is the assignment of one color to each vertex of a graph so that two adjacent vertices are not of the same color‎. ‎The graph coloring problem (GCP) is a matter of combinatorial optimization‎, ‎and the goal of GCP is determining the chromatic number $\chi(G)$‎. ‎Since GCP is an NP-hard problem‎, ‎then in this paper‎, ‎we propose a new approximated algorithm for finding the coloring number (it is an approximation of chromatic number) by using a graph adjacency matrix to colorize or separate a graph‎. ‎To prove the correctness of the proposed algorithm‎, ‎we implement it in MATLAB software‎, ‎and for analysis in terms of solution and execution time‎, ‎we compare our algorithm with some of the best existing algorithms that are already implemented in MATLAB software‎, ‎and we present the results in tables of various graphs‎. ‎Several available algorithms used the largest degree selection strategy‎, ‎while our proposed algorithm uses the graph adjacency matrix to select the vertex that has the smallest degree for coloring‎. ‎We provide some examples to compare the performance of our algorithm to other available methods‎. ‎We make use of the Dolan-Mor\'e performance profiles to assess the performance of the numerical algorithms‎, ‎and demonstrate the efficiency of our proposed approach in comparison with some existing methods‎.

Manufacturing feature recognition is a basic component for systems that automatically generate manufacturing processes. When compared to the set of low level entities existing in a component's CAD model, features are a set of higher level entities that model the correspondence between design information and manufacturing activities. Graph-based methods, which use the part adjacency graph to recognize features, are among the most common techniques in the existing feature recognition literature. In this paper, some techniques are proposed which significantly improve the performance of traditional graph-based algorithms. The approach is more general and efficient than common graph-based approaches dealing with the problem of feature interactions. The scope of the graph-based approaches is also extended in this paper to include curved and 3D features in addition to polyhedral features, where most of the existing graph-based algorithms have focused only on the polyhedral objects.

In recent decades, consumer markets have faced the challenges of increasing brand convergence, a lack of understanding of the differences between competing offerings, and the reduction of switching costs. This has revised traditional customer-oriented practices inevitably and forces organizations to struggle to find and implement a component beyond technical requirements. Customer experience as one of the most promising management approaches is a new solution to facing these challenges. The purpose of this study is to identify factors on the cognitive, emotional, and behavioral experiences in pre-purchase touch points and determine which of the experimental strategies leads to exceeding the standards and expectations of customers. The current research is planned with a mixed approach and in the form of two consecutive processes. In the first phase, the factors affecting the pre-purchase experiences of customers have been identified through the synthesis of existing literature and studies. For this purpose, 1268 articles focusing on customer experience were reduced to 26 final studies in the screening process. In the second phase, relying on the combined approach of graph theory (matrix permanents), the governing internal relationships between the research components and measuring their importance levels were determined. To achieve this purpose, according to the value judgment of the evaluator group (selected from the statistical population of handicrafts customers), the type of governing relationships between the research components at the sub-system level has been determined and then, the hypothetical values and the degree of proximity of each factor to the limit is evaluated. The results indicate that the five main components of promotional actions, information, product, brand, and social interactions can effectively guide customers' pre-purchase experiences. The results of the analysis of the internal relations of the graphs show that among the mentioned components, social interactions (with a coefficient of 0. 482 close to the ideal hypothetical value) have the highest level of importance in the industry of traditional and handicraft arts. Introduction Over the past decades, marketing activities have primarily focused on the cognitive and physical aspects of products and services, with the belief that customers make purchasing decisions based on rational cognitive analysis and avoid emotions and feelings. However, the marketing landscape is changing, and the challenges of increasing brand similarity, homogeneous consumer markets, and digitization have made it inevitable for organizations to strive to find and implement a component beyond technical requirements. Customer experience, as one of the most promising management approaches, is a novel solution to address these challenges. However, the literature on customer experience is generally limited to a few industries (particularly banking, hospitality, and retail) and lacks a framework for customer experience formation in other industries. Craft arts, for example, are one of the fields that have not received much attention in experiential marketing studies. Today, the common meaning of craft arts (influenced by progressive concepts such as cultural freedom, individualization, and anti-commercialism) has been redefined and has taken on a symbolic and aesthetic form. The penetration of craft arts into the mainstream of the society and the increasing tendency to buy traditional products make it inevitable to adopt professional marketing strategies and approaches (including the modern customer experience-oriented approach). Based on this, the present study was conducted to identify the factors affecting cognitive, emotional, and behavioral experiences at pre-purchase touchpoints and determine which of the experiential strategies leads to surpassing customer standards and expectations. Methodology The current research was conducted with a mixed approach and in the form of two studies. In the first study, the factors affecting the pre-purchase experiences of customers have been identified through the synthesis of existing literature and studies. For this purpose, 1268 articles focusing on customer experience were reduced in the title, abstract, and content screening process. Finally, 26 articles with a Kappa coefficient of 0. 73 and a content validity ratio of more than 0. 75 have been used as the basis for extracting codes. In the second study, relying on the integrated approach of graph theory (matrix permanents, the internal relationships between the research components and the measurement of their importance levels were specified. To achieve this goal, according to the value judgment of a group of evaluators (selected from the statistical population of craft arts customers), we have determined the type of relationships between the research components at the system and subsystem level. Then, internal relationships and the degree of closeness to the ideal were determined. This means that the customer experience (system level) is ideal when all its components (subsystems) are in their best state. Findings The results of the research indicate that the five main components of promotion, information, product, brand, and social interactions can guide the pre-purchase customer experience. The results of the analysis of the internal relationships of the graphs at the system level show that among such components, social interactions (with a distance of 0. 482 close to the ideal value) are of the highest level of importance in the handicraft industry. Other components including brand (with a distance of 0. 553 close to the ideal value), product (0. 587), information (0. 602), and promotion (0. 613) are placed in the next priorities of the organization to create unique and distinguished experiences. In addition, the analysis of the internal relationships at the sub-system level shows that personalization (with an importance level of 0. 626), product pricing (0. 785), information access (0. 709), previous brand experiences (0. 641), and recommendations (0. 676) are the most preferred micro-level factors. Conclusions The research findings show that at the system level, handicraft customers are more influenced by social interactions (recommendations, suggestions, and referrals of others to buy the product, overall evaluation of opinions in networks and social media) than other activities of the organization. Moreover, the brand component is of secondary importance and the actions of the organization regarding brand awareness and recall can effectively lead to the improvement of the pre-purchase experiences of craft arts customers. Also, the analysis of the internal relationships of the product diagram shows that in the craft arts industry, product experiences are strongly influenced by the organization's pricing policies, and the effectiveness of other factors (such as the variety of product selection, labeling, and packaging) depends on the acceptance and effectiveness of price strategies. Therefore, the organization's policies regarding special event pricing, seasonal, and extraordinary discounts can lead to an acceptable level of product experience. Similarly, analysis of the internal relationships of the promotion diagram suggests that organizations with a focus on providing personalized offers and benefits (in the form of demographic filters, affinity selling, personalization of loyalty programs, etc. ) achieve a higher impact compared to other promotional activities.

Considering the major change occurred in business cells from plant to "chain" and the critical need to choose the best partners to form the supply chain for competing in today's business setting, one of the vital decisions made at the early steps of constructing a business is supplier selection. Given the fact that the early decisions are inherently strategic and therefore hard and costly to change, it's been a point of consideration for industries to select the right supplier. It's clear that different criteria must be investigated and interfered in deciding on the best partner (s) among the alternatives. Thereupon the problem might be regarded as a multiple criteria decision making (MCDM) problem. There are a variety of techniques to solve a MCDM problem. In this paper we propose a novel technique by combination of decision making trial and evaluation laboratory and graph theory and matrix approach techniques. Eventually, the results are compared to three common techniques, including SAW (current used technique), TOPSIS and VIKOR, and discussed to come to a conclusion.

Objective: The purpose of this study is to develop and improve the multilayer fuzzy cognitive maps in structuring and analysis of problems with high dimensions by providing a quantitative framework. Methods: In this study, the Self-Organizing Map method and Graph Theory and Matrix Approach has been combined in the multilayer fuzzy cognitive maps approach. Based on this approach, problem structuring is done by clustering and creating a multilayer structure for cognitive mapping. Results: The developed method in the present study has been used to analyze the problem of sustainable supply chain management achievement in the petrochemical industry. According to the results of data analysis based on the presented approach, "cooperation in the supply chain", "organizational development" and "management commitment to sustainable development" are the most effective factors in enabling sustainable supply chain management. Conclusion: Based on the method presented in the present study, the problem is modeled by clustering components and creating a multilayer structure for cognitive mapping. The method presented in the present study can model problems with a large number of intervening variables. The proposed method in this study can model problems with a high number of variables.

The purpose of the present study is to identify and prioritize the components that enable supply chain management in the petrochemical industry to achieve sustainable development goals. Accordingly, by using MetaSynthesis Method, the previous studies in the field of sustainable supply chain management investigated and with the help of experts, factors and enabling components of sustainable supply chain management were extracted. These factors are categorized into the components of "Corporate Management", "Supply Chain Management", "Supply Chain Continuity", "Supply Chain Features", "Supply Chain Partnership" and "Employees" And were prioritized using graph theory and matrix approach (GTMA). Based on the data analysis, the components related to “, supply chain continuity”,became the first priority for planning in the petrochemical industry in order to achieve sustainability. Accordingly, the development of partnerships in supply chain, knowledge sharing between partners, and increased trust between companies in the supply chain should be considered by managers.

A distribution feeder reconfiguration method has been developed. Feeder reconfiguration is an easy and in-expensive method, for distribution system loss reduction, which is done by maneuver in the network. In this work, Graph Theory approach has been adopted for analysis and optimization of distribution system. Distribution system partitioning and optimization is modeled as a linear programming problem, using this theory. The distribution system is modeled as a hyper-graph, and specific weights are assigned to every branch of the hyper-graph. Then the distribution system is partitioned to sub-graphs. Feeder reconfiguration for loss reduction can be performed in all of the sub-graphs, simultaneously. The most important feature of the presented method, is improving the performance of reconfiguration for large scale networks. Noticeable reduction in calculation time is the other advantage of network partitioning that makes it suitable for fast and real-time applications. Performance of the method has been presented on a standard distribution system. Solution algorithm and discussion is included.