III. 4JNET’s four guaranteesMSB license: 4JNET has secured the U.S. MSB license for encrypted tokens and is allowed to lawfully engage in initial coin offerings (ICOs), initial exchange offerings (IEOs) and encrypted token transactions.Transparency: All business logics of 4JNET are achieved through smart contracts. All of the contract’s addresses and codes are open sourced.Contract audit: 4JNET’s smart contract has been audited by the blockchain security company CertiK, and its security and fairness are further guaranteed.Fairness: 4JNET’s unique mechanism effectively curbs the token oligarchs and centralized holding of tokens.IV. Conclusions
One key point to note about Safemoon crypto is that the developers haven’t really described any real-world use cases yet. The Safemoon protocol was hyped hugely by celebrities back in May, causing retail investors to jump on the bandwagon and push price higher. Following this, the price fell dramatically, leading many to claim that Safemoon was a ‘pump and dump’ scheme designed to make certain investors rich.

One example is the negative correlation that occurs between Ethereum and its associated factors around June 2016 (left facing arrows at the top and just left of the horizontal middle of the Ethereum scalograms). During this time interval, one of the most well-known applications at the time, the DAO, built on top of the Ethereum environment, was hacked. It can be seen that all factors are negatively correlated in the short term with the price during this time interval. As a result of the uncertainty generated by the hack, price dropped sharply, but activity on social media and interest increased (causing the negative correlation). The negative relationship can be seen during the 2–4 day band for all factors.
In order to provide a methodology to detect bubbles in time series, Phillips, Wu, and Yu [23] proposed the supremum augmented Dickey-Fuller (SADF) test. This applies a series of right-tailed unit root tests to expanding windows of a time series (with a fixed start date), defined by where r2 is the final data point to be considered in each window, starting at r0 which is a fraction representing the smallest allowed window size and expanding to 1(the complete data set).