The increasing popularity of cryptocurrencies as a means of financial inclusion for investment and trade has become a major concern for individuals seeking to benefit from the cryptocurrency market. This study aims to provide insights for cryptocurrency investors, financial sector professionals, and academics by utilizing machine learning techniques such as ARIMA and LSTM to compare the accuracy of modeling performance on datasets predicting the prices of five cryptocurrencies, namely Bitcoin, Ethereum, Binance Coin, Tether, and Cardano. Data was obtained by downloading from the Yahoo Finance website using Jupyter notebook. The LSTM method outperformed the ARIMA method, achieving a lower MAPE value of less than 10 percent and effectively capturing price movements, providing valuable information for decision-making.
Danda Prudhvi Krishna, R. Ramaguru, K. Praveen, M. Sethumadhavan · 7 authors
OAuth2.0 is a Single Sign-On approach that helps to authorize users to log into multiple applications without re-entering the credentials. Here, the OAuth service provider controls the central repository where data is stored, which may lead to third-party fraud and identity theft. To circumvent this problem, we need a distributed framework to authenticate and authorize the user without third-party involvement. This paper proposes a distributed authentication and authorization framework using a secret-sharing mechanism that comprises a blockchain-based decentralized identifier and a private distributed storage via an interplanetary file system. We implemented our proposed framework in Hyperledger Fabric (permissioned blockchain) and Ethereum TestNet (permissionless blockchain). Our performance analysis indicates that secret sharing-based authentication takes negligible time for generation and a combination of shares for verification. Moreover, security analysis shows that our model is robust, end-to-end secure, and compliant with the Universal Composability Framework.
Large time-varying graphs are increasingly common in financial, social and biological settings. Feature extraction that efficiently encodes the complex structure of sparse, multi-layered, dynamic graphs presents computational and methodological challenges. In the past decade, a persistence diagram (PD) from topological data analysis (TDA) has become a popular descriptor of shape of data with a well-defined distance between points. However, applications of TDA to graphs, where there is no intrinsic concept of distance between the nodes, remain largely unexplored. This paper addresses this gap in the literature by introducing a computationally efficient framework to extract shape information from graph data. Our framework has two main steps: first, we compute a PD using the so-called lower-star filtration which utilizes quantitative node attributes, and then vectorize it by averaging the associated Betti function over successive scale values on a one-dimensional grid. Our approach avoids embedding a graph into a metric space and has stability properties against input noise. In simulation studies, we show that the proposed vector summary leads to improved change point detection rate in time-varying graphs. In a real data application, our approach provides up to 22% gain in anomalous price prediction for the Ethereum cryptocurrency transaction networks.
This paper presents a systematic and comprehensive survey on blockchain interoperability, where interoperability is defined as the ability of blockchains to flexibly transfer assets, share data, and invoke smart contracts across a mix of public, private, and consortium blockchains without any changes to the underlying blockchain systems. Analyzing the vast landscape of both research papers and industry projects, we classify the existing works into five categories, namely, (1) sidechains, (2) notary schemes, (3) hashed time lock contracts (HTLC), (4) relays, and (5) blockchain agnostic protocols. We analyze the existing works under a taxonomy that consists of system and safety characteristics, such as decentralization, direction of communication, locking mechanism, verification mechanism, trust, safety, liveness, and atomicity. Different from other surveys, we are the first to evaluate the performance of some representative interoperability approaches between Bitcoin and Ethereum covering sidechains, notary schemes, and HTLCs. Even though the performance of cross-chain transactions is low (typically fewer than 10 transactions per second), the main reason is the underlying blockchain (e.g., Bitcoin and Ethereum) and not the interoperability approach. Finally, we discuss existing challenges and possible research directions in blockchain interoperability. For example, we identify challenges in interoperability across permissioned and permissionless blockchains, in interacting with scripting blockchains, in security and privacy.
To ensure the success of energy transition and achieve the target of reducing the carbon footprint of energy systems, the management of energy systems needs to be decentralized. Public blockchains offer favorable features to support energy sector democratization and reinforce citizens' trust, such as tamper-proof energy data registration and sharing, decentralization, transparency, and support for peer-to-peer (P2P) energy trading. However, in blockchain-based P2P energy markets, transactional data are public and accessible, which raises privacy concerns related to prosumers' energy profiles while lacking scalability and featuring high transactional costs. In this paper, we employ secure multi-party computation (MPC) to assure privacy on a P2P energy flexibility market implementation in Ethereum by combining the prosumers' flexibility orders data and storing it safely on the chain. We provide an encoding mechanism for orders on the energy market to obfuscate the amount of energy traded by creating groups of prosumers, by splitting the amount of energy from bids and offers, and by creating group-level orders. The solution wraps around the smart contracts-based implementation of an energy flexibility marketplace, assuring privacy features on all market operations such as order submission, matching bids and offers, and commitment in trading and settlement. The experimental results show that the proposed solution is effective in supporting P2P energy flexibility trading, reducing the number of transactions, and gas consumption with a limited computational time overhead.
Abstract: The decentralized asset marketplace is a cutting-edge platform that simplifies the buying and selling of non-fungible tokens (NFTs) by providing a secure and transparent peer-to-peer trading platform. The marketplace is built on the Ethereum blockchain, ensuring that transactions are immutable, secure, and transparent. It allows users to buy, sell and trade NFTs using cryptocurrencies such as Ethereum and Bitcoin. The smart contract that powers the platform defines a MarketItem struct that represents an NFT on sale and has several functions to support trading. The platform is designed to provide a seamless user experience with fast and efficient transactions, and performance enhancements such as using IPFS to store NFT metadata. The platform's benefits include increased security, transparency, and cost-effectiveness, making it a significant breakthrough in the world of NFT trading. Overall, the decentralized asset marketplace provides a safe, secure, and efficient way for users to trade NFTs without intermediaries or complex transaction processes.
The concept of digital cash has the potential to completely change how people think about money. Digital currency has emerged as a possible alternative for exchanging currency and traditional payment systems, in addition to a popular investment option due to its potential for high returns. One of the three main varieties of digital currency is cryptocurrency that is secured by blockchain technology. Bitcoin, Ethereum, and many other cryptocurrencies exist in crypto markets. Investing in cryptocurrencies still carries risks and uncertainties due to the price volatility. It is thus important to approach such investments with caution and thoroughly research the market and its risks before making investment decisions. This paper presents an application of AI technology for learning the price movement of Ethereum (ETH) which is second only to Bitcoin in market capitalization. Based on the Technical factor, the XGBoost model is constructed for classification of return on Ethereum close price. The technical indicators such as moving averages and relative strength index, together with the Bitcoin price trend are chosen to determine influence on Ethereum price further used for computing the short-term return separate into 3 classes: downtrend, sideway, and uptrend. The model performance is measured by multiclass ROC-AUC, achieving the micro-average ROC-AUC of 0.66 saying the model is reasonably good at predicting the overall trend of ETH price.
Distributed ledger technologies (DLT) usage is currently limited to a single platform as they do not have design-based interoperability capabilities. In general, it's challenging for a DLT to communicate with another one. Although several DLT solutions have been proposed and applied in specific application areas, building a general-purpose interoperability solution for any DLT remains a challenge. In previous work, we proposed a tailor-made interoperability solution between Hyperledger Fabric and Corda. This paper extends that work to enable interoperability between Hyperledger Fabric and Ethereum. The main contribution of this paper is to provide a new approach to enable interoperability between these two DLT. The approach follows a new request-response interaction model and future payments to enable the payments of services. A prototype was developed and evaluated through a case scenario, performance tests and cost analysis. Performance tests showed bottlenecks under heavy load scenarios due to Ethereum's design. Costs analysis showed that the approach is suitable for purchasing high-priced services. These promising results constitute a step forward in developing a general-purpose solution for DLT interoperability.
Abstract Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum (ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theil’s U1, and $${R}_{OOS}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mi>OOS</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> was used for out-of-sample. The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day-of-the-week anomaly for ETH and ADA was found.
A smart contract is a special form of computer program that runs on a blockchain and provides a new way to implement financial and business transactions in a conflict-free and transparent environment. In blockchain systems such as Ethereum, smart contracts can handle and autonomously transfer assets of considerable value to other parties. Hence, it is particularly important to ensure that smart contracts function as intended since bugs or vulnerabilities may lead, and indeed have led, to substantial economic losses and erosion of trust for blockchain. While a number of approaches and tools have been developed to find vulnerabilities, formal methods present the highest level of confidence in the security of smart contracts. In this paper, we propose a formal solution to model a smart contract based on colored Petri nets (CPNs). Herein, we focus on the most common type of security bugs in smart contract, i.e., reentrancy bugs, which led to a serious financial loss of around USD 34 million for the Cream Finance project in 2021. We present a hierarchical CPN modelling method to analyze potential security vulnerabilities at the contract’s source code level. Then, modeling analysis methods such as correlation matrix, state space report and state space graph generated via CPN Tools simulation are exploited for formal analysis of smart contracts. The example shows the full state space and wrong path in accordance with our expected results. Finally, the conclusion was verified on the Ethereum network based on the Remix platform.
Ala Abdulsalam Alarood, Adamu Abubakar, Abdulrahman Alzahrani, Faisal S. Alsubaei
The adoption of recycling and proper disposal techniques for electronic waste is crucial in advancing sustainable development. The traditional approaches of motivating people for collection of electronic waste are often insufficient due to inadequate methods reaching out, given incentives, calculating appropriate incentives associate to the task of gathering the electronic waste and a lack of transparency in the entire process. Insufficient collection of electronic waste could result in the release of numerous harmful substances into the environment. Prior research studies have been carried out to tackle the issue of electronic waste management in a broad sense. The findings of those studies indicated diverse strategies, each of which is relevant solely to a restricted range of electronic waste reprocessing circumstances. The current study has presented a proposed technique for incentivizing tasks and activities associated to collection of electronic waste through the adoption of vector space technique. Blockchain smart contact technology, has been used to implemented the strategy. The method used involves various well defined mapping of tasks, nature of activities, and their magnitude in order to derived an incentive. Some scenarios for the calculations of incentives are presented, and the findings reveals the based case is by utilizing weighting scale scheme where each tasks and activities are mapped to its associated inventive rather than providing fixed incentive values. Ethereum was used as digital token for each unit of incentive. This concept has contributed in encouraging personal accountability in the management of e-waste collection in order to cultivate sustainable behaviors for a long term solution.
Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions involved in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction dynamics of the network after this major fork. As such, this paper provides an update on work previous to 2019 on the link between EthUSD/BitUSD and gas price. For forecasting, we compare a novel combination of machine learning methods such as Direct-Recursive Hybrid LSTM, CNN-LSTM, and Attention-LSTM. These are combined with wavelet threshold denoising and matrix profile data processing toward the forecasting of block minimum gas price, on a 5-min timescale, over multiple lookaheads. As the first application of the matrix profile being applied to gas price data and forecasting that we are aware of, this study demonstrates that matrix profile data can enhance attention-based models; however, given the hardware constraints, hybrid models outperformed attention and CNN-LSTM models. The wavelet coherence of inputs demonstrates correlation in multiple variables on a 1-day timescale, which is a deviation of base free from gas price. A Direct-Recursive Hybrid LSTM strategy is found to outperform other models, with an average RMSE of 26.08 and R2 of 0.54 over a 50-min lookahead window compared to an RMSE of 26.78 and R2 of 0.452 in the best-performing attention model. Hybrid models are shown to have favorable performance up to a 20-min lookahead with performance being comparable to attention models when forecasting 25–50-min ahead. Forecasts over a range of lookaheads allow users to make an informed decision on gas price selection and the optimal window to submit their transaction in without fear of their transaction being rejected. This, in turn, gives more detailed insight into gas price dynamics than existing recommenders, oracles and forecasting approaches, which provide simple heuristics or limited lookahead horizons.
Jaime Fúster de la Fuente, Álvaro Pendás-Recondo, Leon Wong, Paul Harvey
Operation and management of telecommunication networks are increasingly difficult with the demands and behaviors of users exceeding the capacity of network engineers to keep pace. This has led to increased automation of the network, enabled by various forms of intelligent software. One such proposal from the ITU-T Focus Group on Autonomous Networks (standardization group) is an architecture to achieve self-driven automation (i.e. autonomy) of network operation, whereby technology from different operators and third parties is self-assembled and deployed in production networks. This raises questions and challenges regarding transparency, auditability, and trust while maintaining interoperability.This work presents an initial study of a distributed and decentralized marketplace to bring transparent and auditable trust to the proposed architecture without sacrificing interoperable functionality. We demonstrated this by our proof of concept implementation of both the proposed architecture and marketplace based on the combination of Ethereum and IPFS.
The maintenance of any confidential records pertaining to education, health, or finance has seen a dramatic rise in the use of advanced technologies.However, because of their uncertainties, current sophisticated technologies have some problems.In terms of anonymity, attack-free security, transparency, dependability, and flexibility, these technologies have some shortcomings.Consequently, a blockchain application can be created to handle the events of a department or college.Decentralization improves the usability and upkeep of this application without adding to the costs incurred by hosting a centralised application on a leased third-party server.This programme does not rely on any third-party storage software because it stores the documents effectively using the IPFS file system.This method allows for seamless communication between students and teachers without the interference of outside programmes like WhatsApp or Google Classroom.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Abstract: The public and private sectors are both in control of a wide range of security-related issues, and Blockchain is a promising technology that is gaining popularity for doing so. Charitable organizations are growing more and more interested in Blockchain technology. Due to the lack of transparency in the transactions involving donations, which hinders donors from understanding whether their contributions are being used properly, people have lost faith in charity. A decentralized Blockchainbased donation tracking system is proposed in the article, which will provide total transparency,accountability, and direct access to the intended receivers. It will be used in Ethereum Blockchain implementation.
Abstract: NGOs in developing nations get financing from donor agencies for various goals, including disaster assistance, education advancement, women’s empowerment, and economic development. However, due to reports of fund misuse and record irregularities, some donor agencies have lost faith in these NGOs.For example, in education funding, some students appear onthe records of multiple NGOs as beneficiaries, even though only one NGO should be paying for a specific student, so here blockchain plays an important role, The lack of transparencyin charities has resulted in a decrease in social investment and a decrease in donor confidence, as donors are frequently uninformed of how their donations are being used. Corruption exacerbates this skepticism. To address these challenges, the Charity-Chain decentralized network has been suggested, which will use the Ethereum blockchain and smart contract-based incentives to validate the impact of social organizations. This improved transparency would help funders, such as charitable organizations, impact investors, and small donors, to more readily monitor their transactions and re-establish trust in these social organizations.
This paper aims to analyze cryptocurrency volatility by examining the effect of Gold, Dollar Index, and Composite Stock Price Index (IHSG) as independent variables and on Bitcoin and Ethereum as dependent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum, which have the largest market capitalization. The data in this study used the period January 1, 2018, to December 31, 2021. This study used GARCH analysis. This study's results indicate that Bitcoin's volatility is influenced by the price of Bitcoin itself, gold, and the stock exchange index, and Ethereum and the stock exchange index influence Ethereum. This shows that the cryptocurrency market is inefficient as the prices are also affected by past prices.
Runtime-verification of smart contracts ensures the absence of exploitations within a transaction during execution. It is a crucial security aspect that is often omitted due to its high onchain overhead. The lack of runtime-verification in public blockchains allowed attackers to compromise vulnerable contracts and cause significant monetary losses. Although several runtime protection solutions have been proposed, they do not discuss the onchain overhead limitation, which may hinder their deployment and undermine their effectiveness. To address this problem, we propose an efficient Smart contract Runtime Protection framework, called SRP, that minimizes the onchain burden of runtime-verification by integrating an off-chain mechanism with onchain contract execution. The proposed hybrid architecture is designed to protect already-deployed smart contracts from attacks in real-time while maintaining the throughput of the underlying blockchain. We first present SRP from a design perspective proposing a protocol customized for off-chain runtime-verification interoperability. Then, we evaluate our approach empirically and demonstrate the applicability of SRP using a proof-of-concept implementation on a local instance of the Ethereum network. Our empirical and experimental results indicate the feasibility and efficiency of our approach, where SRP outperforms the onchain-only mechanism in terms of service time and throughput, for increasing workloads.
Blockchains have caught the attention of governments, businesses, and researchers due to their potential to revolutionize the way data is stored and consumed. Despite the current interest in blockchains globally, there has been unsatisfactory progress of this promising technology in developing countries, including Tanzania. One factor hindering the advancement of blockchain in the country is the lack of knowledge of the technology itself. Challenged by the situation, this study implemented a blockchain lab to provide a platform for users to explore and practice various concepts of blockchain. This online lab, called Dinari, was implemented using Ethereum blockchain and was hosted on a cloud server. Experiments were conducted to verify the performance of the developed lab. Results from the experiment show that the platform gives an average transaction processing time of 15.17 seconds. The processing times were within an acceptable range of performance when compared with other online platforms, such as Mainnet and Kovan that provide average processing times of between 8.8 seconds and 18.3 seconds. The study can be scaled up in future to deploy more use cases, including health, in addition to the existing payment use case.
Blockchain technology, which serves as the backbone of Bitcoin, has gained tremendous attention in recent years due to its potential to revolutionize various industries.At its core, blockchain functions as an immutable ledger that enables decentralized transactions, making it a highly secure and transparent way of conducting business.This has led to the emergence of blockchain-based applications in several industries, including finance, healthcare, manufacturing, and education.One of the key features of blockchain technology is its ability to provide a unique set of benefits, including increased transparency, security, and credibility.By leveraging the power of cryptography and distributed consensus algorithms, blockchain technology eliminates the need for intermediaries, reducing costs, and streamlining processes.This makes it an ideal solution for industries that require high levels of security and transparency, such as finance and healthcare.However, despite its many benefits, blockchain technology also faces several challenges that must be addressed for it to reach its full potential.One of the biggest challenges facing blockchain technology is scalability, as the technology's current infrastructure can only support a limited number of transactions per second.This has led to long processing times and high transaction fees, which limit the technology's adoption in industries that require high transaction volumes.Another significant challenge facing blockchain technology is security.While blockchain technology is highly secure, it is not immune to attacks.In recent years, several high-profile hacks and security breaches have occurred, highlighting the need for more robust security measures to be implemented.Despite these challenges, the potential benefits of blockchain technology are too great to ignore.As a result, several industries are actively exploring ways to leverage blockchain technology to streamline processes and enhance their offerings.In the finance industry, blockchain technology is being used to create more efficient payment systems and improve the transparency of financial transactions.Similarly, the healthcare industry is exploring the use of blockchain technology to improve the security and accessibility of medical records.One of the most exciting developments in blockchain technology is the emergence of decentralized applications (Dapps).Dapps are applications that run on a decentralized network, such as a blockchain, and are not controlled by any single entity.This makes them highly transparent and resistant to censorship, making them ideal for use cases that require high levels of security and transparency.There are several different types of blockchain, each with its own unique set of features and benefits.The most popular blockchain platforms are Bitcoin, Ethereum, and Ripple, each of which has its own use cases and applications.For example, Bitcoin is primarily used as a digital currency, while Ethereum is used to build decentralized applications and smart contracts.In conclusion, blockchain technology has the potential to revolutionize several industries by providing increased transparency, security, and credibility.Despite facing several challenges, including scalability and security issues, blockchain technology is being actively explored by several industries, including finance, healthcare, manufacturing, and education.The emergence of decentralized applications is another exciting development in blockchain technology, enabling the creation of highly transparent and secure applications.As blockchain technology continues to mature and evolve, it is likely to play an increasingly important role in shaping the future of several industries.
Aidin Rasti, Amal Ahmed Anda, Sofana Alfuhaid, Alireza Parvizimosaed · 8 authors
Complementary materials for the paper that extends the conference paper : "Symboleo2SC: From Legal Contract Specifications to Smart Contracts" <code>symboleo-js-core</code> includes the implementation of the ontology of Symboleo. <code>Symboleo2SC-demo</code> includes the five evaluated Symboleo contracts, their generated smart contracts, and their unit tests.
Cryptocurrency merupakan mata uang digital yang dapat digunakan untuk transaksi atau investasi. Investasi aset cryptocurrency saat ini semakin banyak diminati oleh masyarakat. Investasi ini memiliki resiko yang tinggi dikarenakan harganya dapat turun ataupun naik dalam waktu yang singkat. Karena keadaan naik turunnya harga cryptocurrency yang begitu drastis inilah membuat para investor yang berharap ingin mendapatkan keuntungan justru mengalami kerugian. Oleh karena itu, diperlukan sebuah sistem prediksi yang dapat membantu memberikan pertimbangan kepada investor dalam pembelian aset cryptocurrency. Pada penelitian ini menggunakan metode GRU untuk memprediksi harga cryptocurrency, yaitu bitcoin dan ethereum dari tahun 2018 sampai 2021. Data dilakukan pelatihan menggunakan varian nilai window size untuk mendapatkan model dengan window size yang optimal dari nilai error terkecil dengan perhitungan Mean Absolute Percentage Error (MAPE). Berdasarkan hasil pengujian, dengan menggunakan nilai window size sebanyak 2, sistem mendapatkan hasil error yang paling kecil. Perhitungan akurasi prediksi untuk 1, 6, dan 12 bulan berikutnya pada data uji bitcoin masing-masing sebesar 90.26%, 77.74%, dan 75.98%, sedangkan pada data uji ethereum masing-masing sebesar 90.15%, 76,88%, dan 66.09%. Dapat dikategorikan sistem prediksi harga cryptocurrency ini tergolong sangat baik untuk memprediksi 1 bulan berikutnya dan dikategorikan cukup untuk memprediksi 6 dan 12 bulan berikutnya.
In the world of online marketplaces, creating online auctions is an intriguing notion, and Ethereum auctions are among the most important e-business applications.By giving buyers and sellers a forum to trade goods using cryptocurrencies, these auctions increase the safety and security of the payment system.By supplying asset addresses for their products, registered users of the Ethereum Auction program can suggest new auctions, allowing other users to make bitcoin purchases or submit bids.Because it provides a dependable, simple, and quick way to buy and sell a variety of goods and services, this system is gaining popularity.The Ethereum Auction System is made to simplify and safeguard the payment process, giving users a hassle-free experience.The system is as simple to use as visiting a webpage, so even non-technical people may do so with ease.The ability to sell goods from the convenience of their homes is a major advantage of this application.
Blockchain enables peer-to-peer transactions in cyberspace without a trusted third party. The rapid growth of Ethereum and smart contract blockchains generally calls for well-designed Transaction Fee Mechanisms (TFMs) to allocate limited storage and computation resources. However, existing research on TFMs must consider the waiting time for transactions, which is essential for computer security and economic efficiency. Integrating data from the Ethereum blockchain and memory pool (mempool), we explore how two types of events affect transaction latency. First, we apply regression discontinuity design (RDD) to study the causal inference of the Merge, the most recent significant upgrade of Ethereum. Our results show that the Merge significantly reduces the long waiting time, network loads, and market congestion. In addition, we verify our results' robustness by inspecting other compounding factors, such as censorship and unobserved delays of transactions via private changes. Second, examining three major protocol changes during the merge, we identify block interval shortening as the most plausible cause for our empirical results. Furthermore, in a mathematical model, we show block interval as a unique mechanism design choice for EIP1559 TFM to achieve better security and efficiency, generally applicable to the market congestion caused by demand surges. Finally, we apply time series analysis to research the interaction of Non-Fungible token (NFT) drops and market congestion using Facebook Prophet, an open-source algorithm for generating time-series models. Our study identified NFT drops as a unique source of market congestion -- holiday effects -- beyond trend and season effects. Finally, we envision three future research directions of TFM.