This paper aims at reviewing and systematically mapping research on blockchain potentials in improving supply chain performance. Articles were retrieved from several prominent databases, selected, reviewed, grouped into several themes and synthesized. This paper suggests that applying blockchain in the supply chain could improve its performance in terms of transparency, traceability, sustainability, trust, and cost-efficiency. As a cutting-edge technology, blockchain has not been widely implemented in supply chain industries. Research on blockchain application in the supply chain is also relatively limited. This paper contributes to the literature by offering a comprehensive map of research on blockchain potentials in improving supply chain performance. The findings of this study will also be beneficial for managers who seek for a comprehensive understanding of how blockchain technology affects their companies particularly in supply chain management.

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Abstract. As biological image databases are growing rapidly, automated species identification based on digital data becomes of great interest for accelerating biodiversity assessment, research and monitoring. This research applied high performance computing (HPC) to a medicinal plant identification system. A parallel technique for medicinal plant image processing using Fuzzy Local Binary Pattern (FLBP) is proposed. The FLBP method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic to represent texture images. The main goal of this research was to measure the efficiency of using the proposed parallel technique for medicinal plant image processing and evaluation in order to find out whether this approach is reasonable for handling large data sets. The parallel processing technique was designed in a message-sending model. 30 species of Indonesian medical plants were analyzed. Each species was represented by 48 leaf images. Performance evaluation was measured using the speed-up, efficiency, and isoefficiency of the parallel computing technique. Preliminary results show that HPC worked well in reducing the execution time of medical plant identification. In this work, parallel processing of training images was 7.64 times faster than with sequential processing, with efficiency values greater than 0.9. Parallel processing of testing images was 6.73 times faster than with sequential processing, with efficiency values over 0.9. The system was able to identify images with an accuracy of 68.89%.