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.
Green technology has gained prominence this decade since the core of green technology preserves the resources and the environment while enhancing business sustainability. However, few pieces of research present literature reviews of the application of green technology for business despite its importance in providing the map of a conceptual framework for identifying research gaps, inconsistencies in prior studies, and the “state-of-the-art” snapshot domain for future research. The research contributed to the limited literature reviews regarding the application of green technology in business research by using a novel approach, which was bibliometric analysis. The research aimed to provide evidence of collaboration between authors and the most influential countries related to applying green technology for business research using co-authorship analysis. It also analyzed the knowledge structure of this topic and determined the primary and emerging issues through co-word analysis. Furthermore, the research performed the analysis of co-citation to identify the intellectual backbone of this research domain. On top of that, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was used to provide better accuracy in identifying and extracting the data for a bibliometric review. As the result, the research finds 735 journal articles related to green technology for business research from 1995 to 2020. In addition, four clusters are found to describe the current state of green technology for business research: environmental performance, circular economy, sustainable development, and climate policy. Then, research trend is also proposed to guide potential areas for further research to ascertain that the researchers have an inclusive insight on this topic.
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%.
Greenhouses provide not only solution to problems faced by conventional farming systems but also play an important role to improve the energy efficiency and environmentally friendly awareness. To achieve benefits of greenhouse farming system in terms of energy efficiency, research related to this issue have been done by many researchers. However, resources that concern on how to practically implement the particular energy-saving technology for greenhouses need to be improved. In this research, field experiment results related to low-power communication between nodes have been reported by implementing universal prototype modules. The pros and cons of existing communication technology, the proposed architecture of network and module analysis, and the performance evaluation of the proposed module dedicated to intelligent greenhouse farming system were also discussed.
Drip irrigation can be applied in greenhouse farming system, which small amounts of water and fertilizer can be feed uniformly to the crop root zone. In this work, an IoTbased drip irrigation monitoring and controlling strategy for greenhouse farming systems is proposed. It aims to automatically control the AB-mix nutrition feeding system for the plants. Each sensor and actuator involved in this system was developed to be a single object. Then, the respective objects could be programmed accordingly to perform their own function such as controlling the pumps, controlling the valve, detecting the nutrient level, sensing the soil humidity, etc. Each object has a unique identity to allow a streamlined communication between gateway and the objects by employing a light MQTT protocol. The MQTT protocol needs two components namely MQTT Client and MQTT Broker. The MQTT broker was installed on Raspberry Pi by using Mosquitto platform whereas the MQTT Client was installed on each NodeMCU for sensing or controlling the environmental parameters involved in drip irrigation process. Finally, a realtime data acquisition from sensors and actuators can be monitored through a web-based interface.
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