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Information and communications technologies promote change in the service sector, and the banking sector is not an exception. Finding what factors impact consumer satisfaction with service and lead to loyalty is invariably important for business. However, it is important to begin with evaluating the aspects that are specific to the market and service. The purpose of this study was to investigate the factors affecting customer loyalty in Mobile Banking. Following the model designed by the researchers, consideration was given to mediator variables of satisfaction, trust, and customer service to examine how factors such as perceived risk, relative advantages, and usability affect customer loyalty. In this study, customers of an Iranian bank (Shahr Bank) based in the city of Tehran constituted the statistical population. The descriptive survey method was used on a sample that was selected through a cluster-convenience method and included 411 Shahr Bank's customers resident in the east, west, north, south, and centre of the city of Tehran. Structural Equation Modelling (SEM) test and the LISREL 8.8 software were used to analyse the data and to determine what role each factor plays on loyalty. The results showed that such factors as relative advantages, satisfaction, and trust have the most significant impact on customer loyalty. However, the effect of usability on customer service was not confirmed. Furthermore, it was identified that perceived risk has a negative impact on loyalty.

Business environment is full of ups and down and this makes companies to develop different ways of using resources. By expanding life cycle of products, these ways can be cost effective and not harmful for environment. As Reverse Logistics (RL) uses a product after end of its life, it reduces pollution, therefore it has been considered as a part of sustainable development. The core goal of current research is developing a framework by which it evaluates Third Party RL Provider (3rdPRLP) using Multi-Criteria Decision-Making (MCDM) based on Fuzzy Additive Ratio ASsessment (FARAS). Thirty-seven criteria were identified, which are classified into seven main criteria. The main criteria were ranked as follows: product lifecycle position C1, RL process function C2, organizational performance C3, organizational role of RL C4, IT system and communication C5, general company consideration C6, geographical location C7. Market coverage, destination, financial considerations, integrated system, reclaim, efficiency and quality, and growth are each group’s dominant sub-criteria. In addition, the current research helps the logistics managers to better understand the key attributes’ complex relationships in the environment of decision-making. First published online 21 January 2021

Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general purpose black-box optimization techniques. This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyperparameters (or decision variables) in a machine learning model (or a black-box function optimization problem). The developed method forms a hierarchical agent-based architecture for the distribution of the searching operations at different dimensions and employs a cooperative searching procedure based on an adaptive width-based random sampling technique to locate the optima. The behavior of the presented model, specifically against changes in its design parameters, is investigated in both machine learning and global function optimization applications, and its performance is compared with that of two randomized tuning strategies that are commonly used in practice. Moreover, we have compared the performance of the proposed approach against particle swarm optimization (PSO) and simulated annealing (SA) methods in function optimization to provide additional insights into its exploration in the search space. According to the empirical results, the proposed model outperformed the compared random-based methods in almost all tasks conducted, notably in a higher number of dimensions and in the presence of limited on-device computational resources.

Holonic multi-agent systems (HMASs) have recently attracted many researches in multi-agent systems community. Inspired from the multi-level and self-similar structures of social and biological system, holonic multi-agent systems have been widely used to model and solve complex real-world problems. The main concern in deploying HMASs is the problem of building the hierarchical holonic structure, called holarchy, and dynamically managing it during its lifetime. The way an HMAS is organized has a great impact on its applicability and performance. This paper proposes a self-organizing algorithm to build and manage the holoic structures in multi-agent systems. This algorithm is based on the local information of the agents about other agents they can communicate with. Using common social concepts, like skills, diversity, social exchange theory, and norms in definition of the algorithm, the outcomes of this research can be used in wide ranges of distributed applications. The proposed model is extensively tested in a task allocation problem; and its performance based on various design parameters is studied. Empirical results show that the proposed model properly increases the performance of the system in terms of effectiveness and efficiency.

Root rot is an important disease of field pea (Pisum sativum L.) on the Canadian Prairies. The composition and alternative crop hosts of fungal and oomycete communities colonizing pea residues from Alberta and Manitoba were studied in field trials over two years. Standing pea residues were sampled from diseased and asymptomatic patches of 17 commercial fields in 2013 and 2014 after harvest and again in early spring the following year prior to seeding. The pea residue was ground to a powder and mixed with sterile Cornell potting mix. Plants of pea, wheat, and canola were grown in this mixture under controlled conditions. The root rot severity on each plant was evaluated and sections from diseased roots were plated on agar medium. Fungal and oomycete communities isolated from symptomatic roots were identified based on culture morphology and PCR analysis. About 3000 fungal isolates, belonging to some 50 species, were identified. Canola was less susceptible to infection than pea or wheat. Fusarium avenaceum (Fr.) Sacc. was the most abundant species infecting the three hosts; F. culmorum (Wm.G.Sm.) Sacc. and Bipolaris sorokiana (Sacc.) Shoemaker were more frequently isolated from wheat, and F. solani (Mart.) Sacc., F. oxysporum Schlecht. and Aphanomyces euteiches Drechsler were more frequently isolated from pea. Pathogen communities from asymptomatic and diseased patches of fields were similar. Overall, composition of the fungal community changed over winter, but the most abundant species did not change. The results show that multiple pathogens, and particularly Fusarium spp., can survive overwinter on pea residue.

The urgent concerns for environmental issues and growing demand for green products have made companies pay much more attention to green marketing. Although, many companies invest in green marketing, but not all of them gain as much as they invest. Most of failures of investments in green marketing are rooted in the fact that customers doubt both the real green performance of these products and the real intention of companies regarding green products. This research, being quantitative in nature, attempts to investigate the impact of traditional branding constructs (perceived quality of the brand, credibility of the brand) and green branding constructs (perceived value of a green brand, the green brand image, and brand equity) on the green purchase intention of customers. The hypotheses have been developed in the form of a conceptual model to investigate the relationship of these constructs. The research focuses on consumers of certain liquid washing detergent products. All the data were collected using questionnaires and the analysis of the data was conducted utilizing LISREL 8 and SPSS 16. The results indicate that perceived brand quality has a positive impact on the perceived value of a green brand, brand credibility, and brand image. In addition, green brand value and green brand image have a positive impact on brand equity. This research can serve as validation of the constructs to fi ll the gap in the investigation of green brand dimensions. Further analysis shows that green brand equity has a meaningful impact on the green purchase intention of the customers, however the impact of brand credibility on brand equity has not been proved.

The main goal of green supply chain management (GSCM) is to reduce the negative environmental impacts in all activities and stages of a supply chain. Evaluation of suppliers in a supply chain according to environmental criteria can help us to achieve this goal of GSCM. Since this evaluation usually comprises some alternatives and some criteria, green supplier selection (GSS) could be considered as a multi-criteria decision-making (MCDM) problem. To handle the uncertainty of information in an MCDM problem, the theory of fuzzy sets is an effective tool. Interval type-2 fuzzy sets (IT2FSs), which are characterized by an interval membership function, is very flexible to model the uncertainty of the MCDM problems. In this study, a new integrated approach based on Weighted Aggregated Sum Product Assessment (WASPAS) method, is proposed to deal with multi-criteria group decision-making problems with IT2FSs. This approach is based on the operators of IT2FSs, some modifications in the classical WASPAS method and a new procedure for calculation of criteria weights. In the procedure of calculation of criteria weights, we combine the subjective weights expressed by decision-makers with objective weights resulted from an entropy method to obtain more realistic weights. To show the applicability of the proposed approach in the real-world MCDM problems, a green supplier selection problem is used. We perform a sensitivity analysis with different weights of criteria and different values of method's parameters to show the stability of the proposed approach. This analysis shows that combining the subjective and objective weights can help to increase the stability of the proposed approach with different weights of criteria. A comparison is also made between the results of the proposed approach and some existing methods for validating the proposed approach. This analysis shows that the proposed approach is efficient and well consistent with the other methods.

There are Numerous researches in the fields of social sciences and multi-agent systems that are dedicated to studying the role of diversity vs. individual capabilities in design and performance of human and artificial societies. This paper addresses more complex multi-level structures, namely holarchies, in multi-agent systems, and conducts several analyses on the effect of individuals' behavioral diversities in the constitution and performance of the whole system. The hypothesis is that diversity directly affects the structure and performance of a holonic organization. In order to check this, we first proposed a diversity-based holonic model for multi-agent systems and then carried out a set of simulations in which a holonic multi-agent system is employed in a function optimization application. In these simulations, we manipulated the behavioral diversity of the agents in various ways and carefully observed the changes that occur on the effectiveness of the system. These observations include the quality of solutions, the height and composition of the constructed systems, and the time efficiency of the model. The empirical results show that as the diversity of intra-holon agents increases, the height of holarchy is reduced while the sizes of the holons tend to increase. On the other hand, this increase in diversity results in an increase in the performance of the system in situations that diversity of the initial population is high. However, when the diversity of the initial population is low, the diversities inside the holons need to be adjusted around 60 percent in order to boost the performance.

The increasing importance of considering environmental issues as a part of the corporate social responsibility, which has environmental and social impacts as well as affects the image and competitiveness of a firm, has encouraged companies to revise their major processes of supply chain management (SCM). Since the performance of a company heavily relies on its suppliers, it is of vital importance to incorporate environmental criteria into supplier selection problem to satisfy both conventional and environmental criteria, which have only been considered by a limited number of studies. Therefore, following a brief review of green supply chain management (GSCM) and sustainable supply chain management (SSCM) concepts and investigation of supplier selection approaches, this study after focuses on a modified Delphi method that has been applied to determine supplier selection criteria. Also, the intuitionistic fuzzy value (IFV) and interval value intuitionistic fuzzy (IVIF) have been utilised for supplier evaluation regarding the subjective nature and uncertainty of judgment. By using the Choquet Integral operator and fuzzy measures, the best supplier has been selected, and the comparison between IFV and IVIF has been made. This methodology has been applied to a manufacturing company to assess the applicability of the proposed methodology. The proposed methodology can be used for real world problems that contain fuzziness or interacting decision criteria. Moreover, due to a high level of expert involvement in the decision-making process, we claimed that the knowledge of experts has been utilised constructively.

The benefit provided by chickpea ( Cicer arietinum L.) to the following durum wheat [ Triticum turgidum L. subsp. durum (Desf.) Husn.] crop is smaller than that provided by pea ( Pisum sativum L.) in the Great Plains of America. Rotation crops and the specific cropping practices associated with them may impact soil fungal communities, which in turn could affect the productivity of subsequent crops. Two separate field experiments were carried out to test the effect of pea and chickpea crops and of foliar fungicide use in chickpea on the community of fungi hosted in the roots of a subsequent durum wheat crop. The fungal communities in the healthy and diseased roots of durum wheat at maturity were characterized using plate culture and PCR-based identification. The effect of cropping practices on root-associated fungal communities depended on root type and varied with year. Functionally important fungal taxa were affected by specific cropping practices. Endophytic fungal antagonists were less abundant after chickpea than after pea and after fungicide-treated chickpea than after untreated chickpea. The chickpea cultivar CDC Vanguard increased the abundance of highly virulent pathogens in durum wheat roots in comparison with the chickpea cultivar CDC Luna and pea. We conclude that the negative impact of chickpea on durum wheat yield could be due in part to an increase in fungal pathogens resulting from the suppression of endophytic fungal antagonists by chickpea.

Interaction networks, either being implicitly or explicitly specified between the agents, play a crucial role in all multi-agent systems. These structures define and limit the ways the agents should interact with their peers, and hence help to manage the coordination problems in large-scale systems. It is widely accepted that the structure of an interaction network plays a significant role in the performance of the systems. In this article, we use the interaction network of the initial agent population to construct a holonic multi-agent system. Being based on agent interaction networks, the performance of the resulting holonic multi-agent system highly depends on the structure of the underlying agent network. Here, we study this dependency in more details. The study is carried out by applying the holonification algorithm on various network topologies and assessing the constructed holonic structure in a task environment.

The uncertain stand in terms of product and service quality invites customers to get the advantage of other customers’ experiences in order to make a purchase decision. It has been discussed that word-of-mouth (WOM) influences consumer purchase behavior and it could create a robust tie like relatives and could also tie as close friends, acquaintances, and strangers. This paper intends to define the concepts of WOM and customer equity and study the terminology of current purchase intention, customer loyalty and correlates to consumer decision making. The data was collected utilizing self-administrated questionnaires. The results are obtained using Smart PLS 2.0, then using bootstrap algorithm the t-statistics are analyzed. The results indicate that customer equity drivers increase the customer loyalty and consequently ends in WOM by the customers. Despite the literature which has investigated the influence of customer equity drivers on the loyalty and purchase intention of the customers, this study contributes the analysis of drivers on WOM. Also, it reveals that if a firm invests on the customer equity drivers, the result will be loyalty and accordingly, the easiest way of advertisement that would be WOM. This study only focuses on a unique industry that can impact the generalizability of the contexts. Also, it relates only to the positive WOM effects. Future studies could strengthen the literature by taking in negative WOM with a broader population.DOI: http://dx.doi.org/10.5755/j01.ee.29.2.17718

Frequent application of foliar fungicide is essential for chickpea production due to the susceptibility of this plant to ascochyta blight. Chlorothalonil, pyraclostrobin, and boscalid are commonly used to control the disease in Saskatchewan. While fungicides are meant to target specific fungal pathogens, they may impact non-target organisms and alter soil microbial community structure. The effects of the typical 5-time foliar fungicide application program to chickpea CDC Vanguard on the fungal communities associated with seminal and adventitious roots of the following durum wheat crop were studied in a 2 yr field experiment. Root fungal communities were characterized through analysis of the ITS1 region of root metagenomic DNA at the genus level. One hundred and seven fungal genera were detected in durum wheat roots. Fusarium was predominant in both years. A three-way interaction of fungicide application, root type and year on fungal community structure was detected. Unlike Fusarium , the relative abundances of the genera Olpidium , Alternaria , and Cryptococcus were greater in 2010, a very wet year. Fungicide application to chickpea increased the relative abundance of Fusarium in the seminal roots of a subsequent durum crop in 2009, but did not affect the relative abundance of Fusarium in 2010. We could not detect a significant impact of fungicide application to chickpea on durum wheat yield in the subsequent year. The effect of changes in root fungal communities on durum wheat grain yield is discussed.

Cooperation is often considered as one of the key and unclear concepts, which differentiates multi-agent systems from other related fields such as distributed computing. One of the popular benchmarks for the verification of the effectiveness of various cooperation algorithms is multi-agent foraging task. Different approaches have been proposed among which Markov game based ones are widely used, though they could not select consistent equilibrium for the group. In this paper, an evolutionary game based method is proposed. In this method, the interactions among the agents are modeled by snow-drift game to evolve the evolutionary stable strategy (ESS) and bring the maximal reward for the group of agents. The simulation verified the efficiency of the proposed algorithm.

This paper presents a theoretical approach for a humanoid robot performing autonomously in a golf setting. The main concentration of this research is on using vision and mathematical calculations to perform and evaluate the success of each test. Most of the problems encountered in making a humanoid perform complex actions like playing golf arise from the constraints in their features and implementations. Parts of the solution comprise linear interpolation and angle calculation. Challenges faced and solved include some that will lead to a better understanding of human–robot interaction.

Fusarium redolens [syn: F. oxysporum var. redolens] is pathogenic on a wide range of plant species. Fusarium redolens was frequently isolated from necrotic and discolored root and crown tissues of chickpea, pea, lentil and durum wheat in Saskatchewan. The fungus was identified using distance analysis of the translation elongation factor-1 alpha sequences. Comparative pathogenicity tests showed that F. redolens, like Fusarium graminearum and Cochliobolus sativus, caused lesions and/or discolorations on the root, crown and shoot of durum wheat, pea and chickpea, although it had a different host preference. While F. redolens was most virulent on pea, the other two fungi were more virulent on durum wheat. It appears that F. redolens is common in the Prairie Ecozone of Saskatchewan and it may cause yield reduction in pea, chickpea and durum crops. This warrants a closer look at the biology and ecology of F. redolens in the Canadian Prairies.

Holonic Multi-Agent Systems (HMAS) provide a convenient and relevant way to analyze, model, and simulate complex systems in which a large number of entities are interacting at different levels of abstraction. Many models have been proposed for the implementation of these systems; however, most are not general enough to cover applications other than the ones for which they are applied. In this paper, the authors introduce HoloJade, an extension to JADE platform, as a generic solution for the development of HMASs in which Holons and their assigned roles are presented as first level entities available at runtime. This includes a detailed description of the extension, in terms of its meta-model, the needed protocols for the possible interactions, and facilities for the reorganization of the holons. In this paper, the authors also present a hypothetical library example to demonstrate the steps for designing a holonic structure using this extension.

Communities are basic units of complex networks and understanding of their structure help us to understand the structure of a network. Communities are groups of nodes that have many links inside and few links outside them. Community detection in a network can be modeled as an optimization problem. We can use some measures such as Modularity and Community Score for evaluating the quality of a partition of nodes. In this paper, we present a new algorithm for detecting communities in networks based on an Estimation of Distribution Algorithm (EDA) with the assumption that the problem variables are independent. EDAs are those evolutionary algorithms that build and sample the probabilistic models of selected solutions instead of using crossover and mutation operators. In this paper, we assess our algorithm by synthetic and real data sets and compare it with other community detection algorithms.

A networked multi-agent system is a group of intelligent agents structured in a social network induced by agent interactions. As the size and diversity of the multi-agent system increase, the interactions and mutual influence of the agents in the structure become more complicated, and this makes the coordination of the system hard to deal with. Holonic models for multi-agent systems have been considered as one of promising models to manage large scale problems. This paper tackles one of the key problems in the design of holonic multi-agent systems. Here we propose a method to construct a multi-level holonic structure for a multi-agent network based on the centrality of the agents. The proposed method is applied to an urban traffic problem and the evaluation results prove its effectiveness.

A multi-agent system consists of a group of interacting autonomous agents. The key problem in such a system is coordination and cooperation, i.e. how to ensure that individual decisions of the agents result in jointly optimal decisions for the overall system. This problem becomes even more serious when the number of the agents is large. Holonic model is an effective method to manage large scale problems. In holonic approaches, the formation of the initial holons is very critical and has a great influence on their performance and effectiveness. In this paper, we use a graph based modelling approach to group a population of agents with a greedy method, driven by a very simple and effective quality measure. The proposed method is evaluated by applying it to an urban traffic problem as a case study and it is shown the proposed method produces better results.

Negotiation over limited resources, as a way for the agents to reach agreement, is one of the significant topics in Multi-Agent Systems (MASs). Most of the models proposed for negotiation suffer from different limitations in the number of the negotiation parties and issues as well as some constraining assumptions such as availability of unlimited computational resources and complete information about the participants. In this paper we make an attempt to ease the limitations specified above by means of a distributive agent based mechanism underpinned by Multi-Objective Swarm Optimization (MOPSO), as a fast and effective learning technique to handle the complexity and dynamics of the real-world negotiations. The experimental results of the proposed method reveal its effectiveness and high performance in presence of limited computational resources and tough deadlines.

Particle Swarm Optimization (PSO), a new promising evolutionary optimization technique, has a wide range of application in optimization problems including training of artificial neural networks. In this paper, an attempt is made to completely train a RBF neural network architecture including the centers, optimum spreads, and the number of hidden units. The proposed method has been evaluated on some benchmark problems: Iris, Wine, Glass, New-thyroid and its accuracy was compared with other algorithms. The results show its strong generalization ability.