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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.

To avoid the structural defect, early crack detection is oneof the important aspects in the recent area of research. The purpose of this paper is to detect the crack before its failure by means of its position and severity.

This paper uses two trees based regressors, namely, decision tree (DT) regressor and random forest (RF) regressor for their capabilities to adopt different types of parameter and generate simple rules by which the method can predict the crack parameters with better accuracy, making it possible to effectively predict the crack parameters such as its location and depth before failure of the beam.

The predicted parameters can be achieved, if the relationship between vibration and crack parameters can be attained. The relationship yields the results of beam natural frequencies, which is used as the input value for the regression techniques. It is observed that the RF regressor predicts the parameters with better accuracy as compared to DT regressor.

The idea is used the developed regression techniques to identify the crack parameters which are more effective as compared to other developed methods because the alternate name of prediction is called regression. The authors have used DT regressor and RF regressor to achieve the target. In this paper care has been given to the generalization of the model, so that the adaptability of the model can be ensured. The robustness of proposed methods has been verified in support of numerical and experimental analysis.

The circular economy is a production and consumption model that encourages people to share, lease, reuse, repair, refurbish and recycle existing materials and products for as long as possible. The blockchain-based circular economy is being used in many industries worldwide, but Indian electronic MSMEs face many problems in adopting a blockchain-based circular economy. The research aims to discover the barriers the electronic MSMEs face in adopting a blockchain-based circular economy and pull back from achieving environmental sustainability in their operations.

Fifteen barriers are identified from the literature review and finalized with experts' opinions. These barriers are evaluated by using interpretive structural modeling (ISM), MICMAC analysis and fuzzy TOPSIS method.

Lack of support from distribution channels, lack of traceability mechanism and customer attitudes toward purchasing remanufactured goods are identified as the most critical barriers.

The study will benchmark the electronic MSMEs in achieving environmental sustainability in the blockchain-based circular economy.

It is a study that not only establishes a hierarchical relationship among the barriers of blockchain adoption in Indian electronic MSMEs but also verifies the results with fuzzy TOPSIS method.

The purpose of this paper is to identify the crack in beam-like structures before the complete failure or damage occurs to the structure. The beam-like structure plays an important role in modern architecture; hence, the safety of this structure is much dependent on the safety of the beam. Hence, predicting the cracks is much more important for the safety of the overall structure.

In the present work, the regression analysis has been carried out through LASSO and Ridge regression models. Both the statistical models have been well implemented in the detection of crack depth and crack location. A cantilever beam-like structure has been taken for the analysis in which the first three natural frequencies have been considered as the independent variable and crack location and depth is used as the dependent variable. The first three natural frequencies, f₁, f₂ and f₃ are used as an independent variable. The crack location and crack depth are estimated though the regressor models and the accuracy are compared, to verify the correctness of the estimation.

As stated in the purpose of work, the main aim of the present work is to identify the crack parameters using an inverse technique, which will be more effective and will provide the results with less time. The data used for regression analysis are obtained from theoretical analysis and later the theoretical results are also verified through experimental analysis. The regression model developed is tested for its Bias Variance Trade-off (“Bias” – Overfitting, “variance” – generalization). The regression results have been compared with the theoretical results to check the robustness in the subsequent result section.

The idea is an amalgamation of existing and well-established technologies, that is aimed to achieve better performance for the given task. A regressor is trained from the data obtained through numerical simulation. The model is developed taking bias variance trade-off into consideration. This generalized model gives us very much acceptable performance.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

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 objective of this study is to investigate how country risk, different political actions from the government and bureaucratic behavior influence the activities in industry supply chains (SCs) in emerging markets. The main objective of this study is to investigate the influence of these external stakeholders’ elements to the demand-side and supply-side drivers and barriers for improving competitiveness of Ready-Made Garment (RMG) industry in the way of analyzing supply chain. Considering the phenomenon of recent change in the RMG business environment and the competitiveness issues this study uses the principles of stakeholder and resource dependence theory and aims to find out some factors which influence to make an efficient supply chain for improving competitiveness. The RMG industry of Bangladesh is the case application of this study. Following a positivist paradigm, this study adopts a two phase sequential mixed-method research design consisting of qualitative and quantitative approaches. A tentative research model is developed first based on extensive literature review. Qualitative field study is then carried out to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. A survey is carried out with sample of top and middle level executives of different garment companies of Dhaka city in Bangladesh and the collected quantitative data are analyzed by partial least square-based structural equation modeling. The findings support eight hypotheses. From the analysis the external stakeholders’ elements like bureaucratic behavior and country risk have significant influence to the barriers. From the internal stakeholders’ point of view the manufacturers’ and buyers’ drivers have significant influence on the competitiveness. Therefore, stakeholders need to take proper action to reduce the barriers and increase the drivers, as the drivers have positive influence to improve competitiveness.

This study has both theoretical and practical contributions. This study represents an important contribution to the theory by integrating two theoretical perceptions to identify factors of the RMG industry’s SC that affect the competitiveness of the RMG industry. This research study contributes to the understanding of both external and internal stakeholders of national and international perspectives in the RMG (textile and clothing) business. It combines the insights of stakeholder and resource dependence theories along with the concept of the SC in improving effectiveness. In a practical sense, this study certainly contributes to the Bangladeshi RMG industry. In accordance with the desire of the RMG manufacturers, the research has shown that some influential constructs of the RMG industry’s SC affect the competitiveness of the RMG industry. The outcome of the study is useful for various stakeholders of the Bangladeshi RMG industry sector ranging from the government to various private organizations. The applications of this study are extendable through further adaptation in other industries and various geographic contexts.