• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.278-287
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• 2013

Modern information technologies continue to provide industries with new and improved methods. With the rapid development of Machine to Machine (M2M) communication, a smart container supply chain management is formed based on high performance sensors, computer vision, Global Positioning System (GPS) satellites, and Globle System for Mobile (GSM) communication. Existing supply chain management has limitation to real time container tracking. This paper focuses on the studies and implementation of real time container chain management with the development of the container identification system and automatic alert system for interrupts and for normal periodical alerts. The concept and methods of smart container modeling are introduced together with the structure explained prior to the implementation of smart container tracking alert system. Firstly, the paper introduces the container code identification and recognition algorithm implemented in visual studio 2010 with Opencv (computer vision library) and Tesseract (OCR engine) for real time operation. Secondly it discusses the current automatic alert system provided for real time container tracking and the limitations of those systems. Finally the paper summarizes the challenges and the possibilities for the future work for real time container tracking solutions with the ubiquitous mobile and satellite network together with the high performance sensors and computer vision. All of those components combine to provide an excellent delivery of supply chain management with outstanding operation and security.

• 한국균학회소식:학술대회논문집
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• 2015.05a
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• pp.59-59
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• 2015

Cryptococcus neoformans causes life-threatening meningoencephalitis in humans, but the treatment of cryptococcosis remains challenging. To develop novel therapeutic targets and approaches, signaling cascades controlling pathogenicity of C. neoformans have been extensively studied but the underlying biological regulatory circuits remain elusive, particularly due to the presence of an evolutionarily divergent set of transcription factors (TFs) in this basidiomycetous fungus. In this study, we constructed a high-quality of 322 signature-tagged gene deletion strains for 155 putative TF genes, which were previously predicted using the DNA-binding domain TF database (http://www.transcriptionfactor.org/). We tested in vivo and in vitro phenotypic traits under 32 distinct growth conditions using 322 TF gene deletion strains. At least one phenotypic trait was exhibited by 145 out of 155 TF mutants (93%) and approximately 85% of the TFs (132/155) have been functionally characterized for the first time in this study. Through high-coverage phenome analysis, we discovered myriad novel TFs that play critical roles in growth, differentiation, virulence-factor (melanin, capsule, and urease) formation, stress responses, antifungal drug resistance, and virulence. Large-scale virulence and infectivity assays in insect (Galleria mellonella) and mouse host models identified 34 novel TFs that are critical for pathogenicity. The genotypic and phenotypic data for each TF are available in the C. neoformans TF phenome database (http://tf.cryptococcus.org). In conclusion, our phenome-based functional analysis of the C. neoformans TF mutant library provides key insights into transcriptional networks of basidiomycetous fungi and ubiquitous human fungal pathogens.