Optical Properties of Heavily Fluorinated Lanthanide Tris β-Diketonate Phosphine Oxide Adducts

The construction of lanthanide(III) chelates that exhibit superior photophysical properties holds great importance in biological and materials science. One strategy to increase the luminescence properties of lanthanide(III) chelates is to hinder competitive non-radiative decay processes through perfluorination of the chelating ligands. Here, the synthesis of two families of heavily fluorinated lanthanide(III) β-diketonate complexes bearing monodentate perfluorinated tris phenyl phosphine oxide ligands have been prepared through a facile one pot reaction [Ln(hfac)3{(ArF)3PO}(H2O)] and [Ln(F7-acac)3{(ArF)3PO}2] (where Ln = Sm3+, Eu3+, Tb3+, Er3+ and Yb3+). Single crystal X-ray diffraction analysis in combination with photophysical studies have been performed to investigate the factors responsible for the differences in the luminescence lifetimes and intrinsic quantum yields of the complexes. Replacement of both bound H2O and C–H oscillators in the ligand backbone has a dramatic effect on the photophysical properties of the complexes, particularly for the near infra-red emitting ion Yb3+, where a five fold increase in luminescence lifetime and quantum yield is observed. The complexes [Sm(hfac)3{(ArF)3PO}(H2O)] (1), [Yb(hfac)3{(ArF)3PO}(H2O)] (5), [Sm(F7-acac)3{(ArF)3PO}2] (6) and [Yb(F7-acac)3{(ArF)3PO}2] (10) exhibit unusually long luminescence lifetimes and attractive intrinsic quantum yields of emission in fluid solution (ΦLn = 3.4% (1); 1.4% (10)) and in the solid state (ΦLn = 8.5% (1); 2.0% (5); 26% (6); 11% (10)), which are amongst the largest values for this class of compounds to date

Cryptocurrency ecosystems and social media environments: An empirical analysis through Hawkes’ models and natural language processing

Copyright © 2021 The Author(s). We analyse, using a mixture of statistical models and natural language process techniques, what happened in social media from June 2019 onwards to understand the relationships between Cryptocurrencies’ prices and social media, focusing on the rise of the Bitcoin and Ethereum prices. In particular, we identify and model the relationship between the cryptocurrencies market price changes, and sentiment and topic discussion occurrences on social media, using Hawkes’ Model. We find that some topics occurrences and rise of sentiment in social media precedes certain types of price movements. Specifically, discussions concerning governments, trading, and Ethereum cryptocurrency as an exchange currency appear to negatively affect Bitcoin and Ethereum prices. Those concerning investments, appear to explain price rises, whilst discussions related to new decentralized realities and technological applications explain price falls. Finally, we validate our model using a real case study: the already famous case of ”Wallstreetbet and GameStop”1 that took place in January 2021.Funding: No funding was received for this work

A Longitudinal Study of Anti Micro Patterns in 113 Versions of Tomcat

Background: Micro patterns represent design decisions in code. They are similar to design patterns and can be detected automatically. These micro structures can be helpful in identifying portions of code which should be improved (anti-micro patterns), or other well-designed parts which need to be preserved. The concepts expressed in these design decisions are defined at class-level; therefore the primary goal is to detect and provide information related to a specific granularity level. Aim: this paper aims to present preliminary results about a longitudinal study performed on anti-micro pattern distributions over 113 versions of Tomcat. Method: we first extracted the micro patterns from the 113 versions of Tomcat, then found the percentage of classes matching each of the six anti-micro pattern considered for this analysis, and studied correlations among the obtained time series after testing for stationarity, randomness and seasonality. Results: results show that the time series are stationary, not random (except for Function Pointer), and that additional studied are needed for studying seasonality. Regarding correlations, only the Pool and Record time series presented a correlation of 0.69, while moderate correlation has been found between Function Pointer and Function Object (0.58) and between Cobol Like and Pool (0.44)

The Butterfly “Affect”: Impact of Development Practices on Cryptocurrency Prices

The network of developers in distributed ledgers and blockchains open source projects is essential to maintaining the platform: understanding the structure of their exchanges, analysing their activity and its quality (e.g. issues resolution times, politeness in comments) is important to determine how "healthy" and efficient a project is. The quality of a project affects the trust in the platform, and therefore the value of the digital tokens exchanged over it. In this paper, we investigate whether developers' emotions can effectively provide insights that can improve the prediction of the price of tokens. We consider developers' comments and activity for two major blockchain projects, namely Ethereum and Bitcoin, extracted from Github. We measure sentiment and emotions (joy, love, anger, etc.) of the developers' comments over time, and test the corresponding time series (i.e. the affect time series) for correlations and causality with the Bitcoin/Ethereum time series of prices. Our analysis shows the existence of a Granger-causality between the time series of developers' emotions and Bitcoin/Ethereum price. Moreover, using an artificial recurrent neural network (LSTM), we can show that the Root Mean Square Error (RMSE) - associated with the prediction of the prices of cryptocurrencies - significantly decreases when including the affect time series.UCL Centre for Blockchain Technologie

MindTheDApp: A Toolchain for Complex Network-Driven Structural Analysis of Ethereum-based Decentralised Applications

This paper presents MindTheDApp, a toolchain designed specifically for the structural analysis of Ethereum-based Decentralized Applications (DApps), with a distinct focus on a complex network-driven approach. Unlike existing tools, our toolchain combines the power of ANTLR4 and Abstract Syntax Tree (AST) traversal techniques to transform the architecture and interactions within smart contracts into a specialized bipartite graph. This enables advanced network analytics to highlight operational efficiencies within the DApp’s architecture. The bipartite graph generated by the proposed tool comprises two sets of nodes: one representing smart contracts, interfaces, and libraries, and the other including functions, events, and modifiers. Edges in the graph connect functions to smart contracts they interact with, offering a granular view of interdependencies and execution flow within the DApp. This network-centric approach allows researchers and practitioners to apply complex network theory in understanding the robustness, adaptability, and intricacies of decentralized systems. Our work contributes to the enhancement of security in smart contracts by allowing the visualisation of the network, and it provides a deep understanding of the architecture and operational logic within DApps. Given the growing importance of smart contracts in the blockchain ecosystem and the emerging application of complex network theory in technology, our toolchain offers a timely contribution to both academic research and practical applications in the field of blockchain technology.10.13039/501100015595-Ethereum Foundation (Grant Number: FY23-1048