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The main purpose of this research is to investigate finite-element analysis (FEA) on flux reversal-free stator switched reluctance motor (FRFSSRM) for industrial applications. The vibration analysis for an electrical machine is essential because of the acoustic noises. The acoustic noises originate by coincidence of natural frequencies of motor with the vibration frequencies.

The identification with the performance for FRFSRM by torque ripple, vibration. The vibration of the machine is because of unbalanced electromagnetic forces. The mutual coupled winding and a common pole between two adjacent exciting poles reduce these unbalanced forces.

The accelerometer is used to monitor the vibration amplitude in transient mode. A comparison study shows that the vibration is less in the E-core SRM than in the conventional flux reversal SRM.

The shorter flux path reduces the torque ripple and vibration content in SRM. This research article mainly focuses on the parameters such as vibration and torque ripple. The vibration of FRFSRM is identified by accelerometer; ANSYS Package predicts the simulation of the vibration measurement. The dynamic behaviors of this E-core SRM model with rated conditions the vibration had predicted.

Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production system as it affects the finished goods inventory (FGI) and backorders of the system. The purpose of this study is to compare the performance of the fixed and dynamic Kanban systems in terms of operational metrics (FGI and backorders) under the demand uncertainty.

In this paper, the system dynamics (SD) approach was used to model the performance of fixed and dynamic Kanban based production systems. SD approach has enabled the feedback mechanism and is an appropriate tool to incorporate the dynamic control during the simulation. Initially, a simple Kanban based production system was developed and then compared the performance of production systems with fixed and dynamic controlled Kanbans at the various demand scenarios.

From the present study, it is observed that the dynamic Kanban system has advantages over the fixed Kanban system and also observed that the variation in the backorders with respect to the demand uncertainty under the dynamic Kanban system is negligible.

In a just-in-time production system, the number of Kanbans is a key decision variable. The number of Kanbans is mainly depended on the demand, cycle time, safety stock factor (SSF) and container size. However, this study considered only demand uncertainty to compare the fixed and dynamic Kanban systems. This paper further recommends researchers to consider other control variables which may influence the number of Kanbans such as cycle time, SSF and container size.

This study will be useful to decision-makers and production managers in the selection of the Kanban systems in uncertain demand applications.

The purpose of this paper is to compare the influence of relative density (RD) and strain rates on failure mechanism and specific energy absorption (SEA) of polyamide lattices ranging from bending to stretch-dominated structures using selective laser sintering (SLS).

Three bending and two stretch-dominated unit cells were selected based on the Maxwell stability criterion. Lattices were designed with three RD and fabricated by SLS technique using PA12 material. Quasi-static compression tests with three strain rates were carried out using Taguchi's L9 experiments. The lattice compressive behaviour was verified with the Gibson–Ashby analytical model.

It has been observed that RD and strain rates played a vital role in lattice compressive properties by controlling failure mechanisms, resulting in distinct post-yielding responses as fluctuating and stable hardening in the plateau region. Analysis of variance (ANOVA) displayed the significant impact of RD and emphasised dissimilar influences of strain rate that vary to cell topology. Bending-dominated lattices showed better compressive properties than stretch-dominated lattices. The interesting observation is that stretch-dominated lattices with over-stiff topology exhibited less compressive properties contrary to the Maxwell stability criterion, whereas strain rate has less influence on the SEA of face-centered and body-centered cubic unit cells with vertical and horizontal struts (FBCCXYZ).

This comparative study is expected to provide new prospects for designing end-user parts that undergo various impact conditions like automotive bumpers and evolving techniques like hybrid and functionally graded lattices.

To the best of the authors' knowledge, this is the first work that relates the strain rate with compressive properties and also highlights the lattice behaviour transformation from ductile to brittle while the increase of RD and strain rate analytically using the Gibson–Ashby analytical model.

Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.

The primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.

The experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.

Data mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

This paper aims to explore the relationship between brand functionality and consumer-based brand equity.

A mixed-methods approach was adopted including a qualitative study and multiple survey-based studies. Mediation and moderated-mediation paths were tested using PROCESS and three-stage least squares simultaneous estimation models.

Study 1 finds that consumers perceive highly functional brands can enhance their self-competence to perform a task. This phenomenon is labelled brand skill and defined as the extent to which consumers perceive their own performance as emanating from their use of a particular brand. Study 2 finds that brand skill mediates the relationship between brand functionality, brand connection and consumer-based brand equity, while a post hoc study showed that these relationships are robust among private meaning brands. Study 3 demonstrates that these mediated relationships are moderated by the type of dominant benefit the brand provides (i.e. hedonic-versus utilitarian-dominant benefits).

Based on self-determination theory, brand skill is posited as the link between brand functionality, brand connection and consumer-based brand equity.

Brand managers are urged to not overlook the role of brand functionality in favor of other non-functional brand dimensions. Brand functionality enhances consumers’ perceived self-competence and fosters brand connection, especially for brands that offer superior utilitarian benefits.

This is the first study that empirically examines the process by which brand functionality leads to consumer-based brand equity and the role brand skill plays in making that connection.

It is greatly important to select the parameters for support vector machines (SVM), which is usually determined by cross-validation. However, the cross-validation is very time-consuming and complicated to create good parameters for SVM. The parameter tuning issue can be solved in the optimization framework. The paper aims to discuss these issues.

In this paper, the authors propose a novel variant of particle swarm optimization (PSO) for the selection of parameters in SVM. The proposed algorithm is denoted as PSO-TS (PSO algorithm with team-search strategy), which is with team-based local search strategy and dynamic inertia factor. The ultimate design purpose of the strategy is to realize that the algorithm can be suitable for different problems with good balance between exploration and exploitation and efficiently control the inertia of the flight. In PSO-TS, the particles accomplish the assigned tasks according to different topology and detailedly search the achieved and potential regions. The authors also theoretically analyze the behavior of PSO-TS and demonstrate they can share the different information from their neighbors to maintain diversity for efficient search.

The validation of PSO-TS is conducted over a widely used benchmark functions and applied to tuning the parameters of SVM. The experimental results demonstrate that the proposed algorithm can tune the parameters of SVM efficiently.

The developed method is original.

The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).

A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.

A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).

This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.

The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.

This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

The behavior of joints has a significant effect on the stability of water conveyance tunnel. The purpose of this paper is to study the contact and friction at the joint of the tunneling segment lining and establish its contact friction model. At the same time, the stress and deformation characteristics at the joint of the segment under hydrostatic load are analyzed.

In this study, the contact and friction in a bolted joint are examined using shear testing. The feasibility of the proposed model is verified by a numerical simulation of tests and a theoretical analysis. Accordingly, the effect of joints on the lining is explored under internal hydrostatic loading.

The results show that the openings of tunnel segments in joints gradually expand from the positions of the inner and outer edges to the location of the bolt. Moreover, the stress concentration zone is formed at the bolt. Under hydraulic loading, the opening displacement at the joint increases as the water pressure increases; nevertheless, it does not exceed engineering requirements. When the water pressure of the tunnel lining joint reaches 0.5 MPa, the opening of the joint slowly increases. When the water pressure exceeds 0.7 MPa, the opening of the joint rapidly and significantly increases.

Contact and friction in a bolted joint were examined using shear testing. A cohesive zone model of bolted joints was proposed based on test results. The influence of joint behavior on the stability of water conveyance tunnel was studied.

To facilitate innovative software development, more and more software development teams (SDTs) turn to agile methods. Such agile methods develop both extensive and efficient software responses to a client’s requirement change. However, the antecedents of successful agile software development are poorly understood. The authors goal is to propose a model of how power asymmetry and paradoxical leadership interact and affect agility in SDTs, which in turn affect their capacity to innovate. By leveraging insights from research on individuals’ cognition, the authors argue that developers with relatively higher power evaluate their contributions to their teams more ambivalently, which increases their delay or postponement of their contributions to their teams’ tasks. As a result, power asymmetry is negatively related to software teams’ response extensiveness and efficiency. Second, and drawing on leadership studies on behavioral complexity, the authors consider the moderating role of paradoxical leadership that a team receives as an important moderating factor to this effect. The authors argue that, when team leaders exhibit paradoxical leadership behaviors, high-power individuals’ ambivalence is less likely to emerge; hence, transforming power asymmetry to an asset for the enhancement of agility in the SDT.

Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA).

To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies.

The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014’s special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design.

A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.