No abstract

No abstract

No abstract

Production in small and medium enterprises (SMEs) makes a substantial contribution to the Gross Domestic Product directly and indirectly in developing economies including India. In the present time, applying Industry 4.0 to the SMEs will build a smart manufacturing system that will prove to be economically feasible as well as socially sustainable. The purpose of this study is to identify and prioritize major barriers of implementing Industry 4.0 in Indian SMEs. A questionnaire with 12 barriers which were identified based on the literature survey and expert discussion was made to be filled by industry experts of production, information technology, business and members of the top management in SMEs. Further, Multi-Criteria Decision Making (MCDM) methods like TOPSIS, VIKOR and PROMETHEE are used to find the rank for each barrier. The study reveals that the major implementation barriers of Industry4.0 in Indian SMEs are fear of unemployment, lack of IT training, poor IT infrastructure, etc. The ranking for each barrier will not only help to assess risks in manufacturing, supply chain or business initiative, but also to help the managers in devising risk mitigation plans. This study may be used by firms working under the manufacturing sector.

A number of biclustering algorithms have been introduced to discover local gene expression patterns in microarray data. Also, High-throughput biological techniques such as ChIP-seq have generated massive genome-wide data and offered ideal opportunities where biclustering can help unveil underlying biological mechanisms. Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) has been used to identify how transcription factors (TF) and other chromatin-associated proteins influence binding mechanisms. In the data the peaks indicate binding events with possible binding locations and their strengths. It is essential that the values associated with the peaks be as close as possible to each other in the selected biclusters. Here we present a novel framework capable of finding statistically significant biclusters on this type of real-valued datasets. The ideal biclusters should contain similar values with very low variance. We applied our algorithm on ChIP-seq datasets recently released from the ENCODE project and uncovered meaningful biclusters of genes and TFs which can be interpreted as local combinatorial regulation patterns of TFs. We also compared our proposed method to several competing biclustering algorithms to show that it outperforms others in unveiling this type of patterns.