Our study empirically predicts the bubble of Non-Fungible Tokens (NFTs): transferrable and unique digital assets on public blockchains.is subject is important because, despite their strong market growth in 2021, NFTs have not been studied in terms of bubble prediction. To achieve the purpose, we applied Logarithmic Periodic Power Law (LPPL) model to the time-series price data of major NFT projects, retrieved from nonfungible.com. Results implied that, as of December 20, 2021, (i) NFTs in general are in a small bubble (predicting price decline), (ii) Decentraland project is in a medium bubble (predicting price decline), and (iii) Ethereum Name Service and ArtBlocks projects are in a small negative bubble (predicting price increase). Future works are to re ne the prediction by considering heterogeneity of NFTs, comparing other methods, and using more enriched data.

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Many types of cryptocurrencies, which predominantly utilize blockchain technology, have emerged worldwide. Several issuers plan to circulate their original cryptocurrencies for monetary use. This study investigates whether issuers can stimulate cryptocurrencies to attain a monetary function. We use a multi-agent model, referred to as the Yasutomi model, which simulates the emergence of money. We analyze two scenarios that may result from the actions taken by the issuer. These scenarios focus on increases in the number of stores that accept cryptocurrency payments and situations whereby the cryptocurrency issuer designs the cryptocurrency to be attractive to people and conducts an airdrop. We find that a cryptocurrency can attain a monetary function in two cases. One such case occurs when 20% of all agents accept the cryptocurrency for payment and 50% of the agents are aware of this fact. The second case occurs when the issuer continuously airdrops a cryptocurrency to a specific person while maintaining the total volume of the cryptocurrency within a range that prevents it from losing its attractiveness.

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When considering the electrification of a particular region in developing country, the electricity consumption in that region must be estimated. In sub-Saharan Africa, which is one of the areas with the lowest electrification rates in the world, the villages of minority groups are scattered over a vast area of land, so electrification using distributed generators is being actively studied. Specifically, constructing a microgrid or introducing a solar system to each household is being considered. In this case, the electricity consumption of each area needs to be estimated, then a system with enough capacity could be introduced. In this study, we propose a household income electricity consumption model to estimate the electricity consumption of a specific area. We first estimate the electricity consumption of each household based on income and the electricity consumption of a specific area can be derived by adding up them in that area. Through a case study in Tanzania, electricity consumption derived using this model was compared with electricity consumption published by TANESCO, and the validity of the model was verified. We forecasted the electricity consumption in each region using the household income electricity consumption model, and the average forecast accuracy was 74%. The accuracy was 87% when the electricity consumption in Tanzania mainland was forecasted by adding the predicted values.