Blockchain technology, originating from the Bitcoin system, is a prominent notion in both practical applications and scholarly discourse. Numerous subtopics may be seen, including the definition of blockchain, its historical significance in the evolution of currency, its durability, and its magnitude of influence within the literature. In other words, sufficient study on blockchain exists in the literature. Likewise, several studies exist about auditing, particularly concerning accounting and taxation within the setting of the Turkish economy. An examination of official declarations and legislation in Turkey reveals that the state's view on the bitcoin industry lacks definiteness. The perspectives are transitioning from negative to positive. Nevertheless, contradicting remarks have also been seen. Upon assessing the existing circumstances, the strategic plans of nations with comparable developmental stages and active cryptocurrency markets are identified. The most appropriate stance for Turkey is neither entirely liberal nor entirely restrictive. The market requires active management and oversight. This control includes accounting and taxation. Turkey should transition from a passive observation approach to one that incorporates a definitive hybrid therapy. This hybrid encryption encompasses the fundamental components of the cryptocurrency system and the corresponding regulation of pertinent regulations.
Naveed Khan, OlaOluwa S. Yaya, Xuan Vinh Vo, Hassan Zada
In this paper, we examine the volatility and time-frequency connectedness among the financial stress index (FSI), cryptocurrencies namely, Bitcoin , Ethereum, Tether, BNB, Solana, and commodities namely, Gold, Silver, Copper, Platinum, and Brent Oil, using the quantile vector autoregressive (QVAR) frequency connectedness, wavelet coherence, and hedging effectiveness techniques, for the period spanning from June 2020 to December 2023. Findings indicate that the spillover effect among FSI, cryptocurrencies, and commodities substantially varies across different volatility conditions. Also, some cryptocurrencies are net receivers of shocks during normal market conditions, while other cryptocurrencies are net transmitters during extreme market conditions. We also find that, during the bullish market, some commodities (Platinum and Brent oil) are net receivers, while other commodities are net transmitters under extreme market conditions (lower quantiles). Similarly, findings further show that, under extreme volatility conditions (higher quantiles), cryptocurrencies and commodities are net receivers of shocks, while FSI is a net transmitter during these volatility conditions. Using frequency co-movement analysis, we find strong and weak correlations between these series in the short- and long-run for shorter periods. Furthermore, findings provide important implications for policymakers and portfolio managers to pay attention to long-term dynamics and design appropriate policies that mitigate the spillover effects.
The rapid growth of Blockchain and Decentralized Finance (DeFi) has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem. This study identifies critical issues such as Transaction Order Dependence (TOD), Blockchain Extractable Value (BEV), and Transaction Importance Diversity (TID), which collectively undermine the fairness and security of DeFi systems. BEV-related activities, including Sandwich attacks, Liquidations, and Transaction Replay, have emerged as significant threats, collectively generating $540.54 million in losses over 32 months across 11,289 addresses, involving 49,691 cryptocurrencies and 60,830 on-chain markets. These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants, with Sandwich attacks being particularly impactful. Additionally, the growing adoption of Blockchain in traditional finance highlights the challenge of TID, where high transaction volumes can strain systems and compromise time-sensitive operations. To address these pressing issues, we propose a novel Distributed Transaction Sequencing Strategy (DTSS), which combines forking mechanisms and the Analytic Hierarchy Process (AHP) to enforce fair and transparent transaction ordering in a decentralized manner. Our approach is further enhanced by an optimization framework and the introduction of the Normalized Allocation Disparity Metric (NADM), which ensures optimal parameter selection for transaction prioritization. Experimental evaluations demonstrate that DTSS effectively mitigates BEV risks, enhances transaction fairness, and significantly improves the security and transparency of DeFi ecosystems. This work is essential for protecting the future of decentralized finance and promoting its integration into global financial systems.
While a plethora of machine learning (ML) models are currently available, along with their implementation on disparate platforms, there is hardly any verifiable ML code which can be executed on public blockchains. We propose a novel approach named LMST that enables conversion of the inferencing path of an ML model as well as its weights trained off-chain into Solidity code using Large Language Models (LLMs). Extensive prompt engineering is done to achieve gas cost optimization beyond mere correctness of the produced code, while taking into consideration the capabilities and limitations of the Ethereum Virtual Machine. We have also developed a proof of concept decentralized application using the code so generated for verifying the accuracy claims of the underlying ML model. An extensive set of experiments demonstrate the feasibility of deploying ML models on blockchains through automated code translation using LLMs.
This paper focuses on identifying recurring patterns among blockchain address categories using the FP-Growth algorithm, which is known for its efficiency in mining frequent itemsets within large datasets. The study provides insights into blockchain ecosystem dynamics by analyzing category associations across different blockchain networks like Ethereum and Bitcoin. Through this analysis, significant patterns were found, such as the frequent co-occurrence of categories related to smart contracts and exchanges, highlighting the central role of these categories in blockchain interactions. Additionally, the study delves into the influence of data sources on detected patterns, revealing that various data collection methods contribute to distinct biases, which affect category associations. The findings offer practical applications for blockchain analytics, such as improving classification models, anomaly detection, and enhancing regulatory compliance. This study contributes to blockchain research by showcasing how association rule mining can improve the categorization and understanding of blockchain address behaviors. The use of FP-Growth, as opposed to more traditional methods, enables faster and more comprehensive analysis, which is particularly valuable given the extensive nature of blockchain datasets. The research also points to potential directions for future work, such as integrating temporal data to observe changes over time and exploring additional blockchain networks to broaden the scope of insights. The study emphasizes the need for continuous advancements in blockchain address analysis to support security, transparency, and regulatory initiatives within this rapidly evolving digital ecosystem.
In accordance with the Imperial Constitution of 1871, the German Empire of the late nineteenth and early twentieth centuries was a federal state. The governments of the center (the Reich) and the federal states pursued a fiscal policy that had some features of “proto-competitive” federalism. Over the subsequent fifty years, however, German federalism evolved toward fiscal federalism. This transition was finally consolidated during 1919 and 1920 due to some endogenous factors and even more to exogenous ones. The article is based on statistical material as well as research from various studies, including those available from the library of the Goethe-Institut. The article compares the extent to which there were indications of proto-competitive federalism in the German budgetary system prior to Matthias Erzberger’s (finance minister of the German Empire) financial reforms (1919–1920) and indications of fiscal federalism after them. The transformation in both the distribution of power and responsibility as well as in provision of resources by various levels of the budgetary system to support those changes during the transition from proto-competitive to fiscal federalism is analyzed. The attempt to strike a balance between the fiscal interests of the center, federal states, and municipalities is explored; and equalization is singled out as a new function of the empire’s budget process. The creation of a so-called self-sufficient economy in the empire just before the First World War and its subsequent survival under pressure from sanctions and international isolation demanded a flexible balance between centralization and decentralization of spending powers along with an appropriate allocation of resources. Fiscal federalism through centralization of funds allowed Germany to begin recovery from geopolitical and socio-economic challenges, while maintaining decentralization primarily in non-tax revenues encouraged local governments to continue developing their economies. The logic derived from this historical study of the changing models of German fiscal federalism is also applicable to Russia: the reduction of revenues and growth of expenditures in the Russian Federation’s federal budget in recent years makes centralization of fiscal resources at the federal level more important, and the growth of expenditures in the regions and municipalities necessitates transfers and equalization measures.
Or Yatzkan, Reuven Cohen, Eyal Yaniv, Orit Rotem-Mindali
Urban energy efficiency and sustainability are critical challenges, as cities worldwide attempt to balance economic growth, environmental sustainability, and energy consumption. This systematic review examines the dynamics of urban energy management, focusing on how local authorities navigate energy transitions through efficiency measures, renewable energy adoption, and policy interventions. Specifically, it seeks to answer the following research question: how do local authorities implement energy-efficient practices and adopt renewable energy technologies to reduce emissions, optimize cost-effectiveness, and influence urban policy-making? The goal of this study is to assess the effectiveness of these approaches in different urban contexts. By reviewing 47 articles, this study identifies the unique characteristics of urban energy management and highlights the need for tailored, context-specific solutions, such as integrating decentralized renewable energy systems, optimizing building energy performance, and developing policy incentives that consider local socio-economic conditions. The findings reveal varying degrees of success among cities, with particular challenges in lower-income municipalities, where financial and institutional barriers hinder the implementation of sustainable energy projects. This study concludes that localized approaches and long-term strategies are essential for achieving sustainable urban energy transitions, offering a comprehensive perspective on the complexities of urban energy systems and their evolving policy landscape. Future research should focus on assessing the long-term impact of municipal energy policies, exploring innovative financing mechanisms for renewable energy integration, and examining the role of digital technologies in optimizing urban energy management.
Blockchain technology has emerged as a transformative innovation, with applications spanning diverse industries. This study provides a comprehensive comparison between public and private blockchains, focusing on six key dimensions: scalability, security, use case distribution, energy efficiency, developer ecosystem, and performance metrics. Data were collected from 30 blockchain systems, representing a wide range of consensus mechanisms and industry applications. The findings reveal significant trade-offs between the two blockchain types. Public blockchains, such as Bitcoin and Ethereum, excel in decentralization and transparency, making them ideal for open and trustless environments like cryptocurrency and decentralized finance (DeFi). However, they face limitations in scalability, high energy consumption, and slower transaction speeds. Conversely, private blockchains, such as Hyperledger Fabric and Corda, demonstrate superior scalability, energy efficiency, and privacy, making them more suitable for controlled environments like healthcare, supply chain management, and enterprise financial services. The study underscores the importance of aligning blockchain technology selection with specific application requirements. Furthermore, it highlights the potential of hybrid blockchain models to integrate the strengths of both public and private systems, addressing existing limitations. These findings provide valuable insights for organizations and developers in leveraging blockchain technologies effectively.
Social media has attracted society for decades due to its reciprocal and real-life nature. It influenced almost all societal entities, including governments, academics, industries, health, and finance. The Social Network generates unstructured information about brands, political issues, cryptocurrencies, and global pandemics. The major challenge is translating this information into reliable consumer opinion as it contains jargon, abbreviations, and reference links with previous content. Several ensemble models have been introduced to mine the enormous noisy range on social platforms. Still, these need more predictability and are the less-generalized models for social sentiment analysis. Hence, an optimized stacked-Long Short-Term Memory (LSTM)-based sentiment analysis model is proposed for cryptocurrency price prediction. The model can find the relationships of latent contextual semantic and co-occurrence statistical features between phrases in a sentence. Additionally, the proposed model comprises multiple LSTM layers, and each layer is optimized with Particle Swarm Optimization (PSO) technique to learn based on the best hyperparameters. The model's efficiency is measured in terms of confusion matrix, weighted f1-Score, weighted Precision, weighted Recall, training accuracy, and testing accuracy. Moreover, comparative results reveal that an optimized stacked LSTM outperformed. The objective of the proposed model is to introduce a benchmark sentiment analysis model for predicting cryptocurrency prices, which will be helpful for other societal sentiment predictions. A pretty significant thing for this presented model is that it can process multilingual and cross-platform social media data. This could be achieved by combining LSTMs with multilingual embeddings, fine-tuning, and effective preprocessing for providing accurate and robust sentiment analysis across diverse languages, platforms, and communication styles.
In this work, we present a simple, uniform, and elegant solution to the problem, with stunning practical effectiveness and application to virtually any Datalog-based analysis. The approach consists of leveraging the choice construct, supported natively in modern Datalog engines like Soufflé. The choice construct allows the definition of functional dependencies in a relation and has been used in the past for expressing worklist algorithms. We show a near-universal construction that allows the choice construct to flexibly limit evaluation of predicates. The technique is applicable to practically any analysis architecture imaginable, since it adaptively prunes evaluation results when a (programmer-controlled) projection of a relation exceeds a desired cardinality. We apply the technique to probably the largest, pre-existing Datalog analysis frameworks in existence: Doop (for Java bytecode) and the main client analyses from the Gigahorse framework (for Ethereum smart contracts). Without needing to understand the existing analysis logic and with minimal, local-only changes, the performance of each framework increases dramatically, by over 20x for the hardest inputs, with near-negligible sacrifice in completeness.
Margarita Capretto, Martín Ceresa, Antonio Fernández Anta, Pedro Moreno-Sánchez · 5 authors
Blockchains face a scalability challenge due to the intrinsic throughput limitations of consensus protocols and the limitation in block sizes due to decentralization. An alternative to improve the number of transactions per second is to use Layer 2 (L2) rollups. L2s perform most computations offchain using blockchains (L1) minimally under-the-hood to guarantee correctness. A sequencer receives offchain L2 transaction requests, batches them, and commits compressed or hashed batches to L1. Hashing offers much better compression but requires a data availability committee (DAC) to translate hashes back into their corresponding batches. Current L2s consist of a centralized sequencer which receives and serializes all transactions and an optional DAC. Centralized sequencers can undesirably influence L2s evolution. We propose in this paper a fully decentralized implementation of a service that combines (1) a sequencer that posts hashes to the L1 blockchain and (2) the data availability committee that reverses the hashes. We call the resulting service a (decentralized) arranger. Our decentralized arranger is based on Set Byzantine Consensus (SBC), a service where participants can propose sets of values and consensus is reached on a subset of the union of the values proposed. We extend SBC for our fully decentralized arranger. Our main contributions are (1) a formal definition of arrangers; (2) two implementations, one with a centralized sequencer and another with a fully decentralized algorithm, with their proof of correctness; and (3) empirical evidence that our solution scales by implementing all building blocks necessary to implement a correct server.
In the context of decentralized blockchains, accurately simulating the outcome of order flow auctions (OFAs) off-chain is challenging due to adversarial sequencing, encrypted bids, and frequent state changes. Existing approaches, such as deterministic sorting via consensus layer modifications (e.g., MEV taxes) (Robinson and White 2024) and BRAID (Resnick 2024) or atomic execution of aggregated bids (e.g., Atlas) (Watts et al. 2024), remain vulnerable in permissionless settings where limited throughput allows rational adversaries to submit "spoof" bids that block their competitors' access to execution. We propose a new failure cost penalty that applies only when a solution is executed but does not pay its bid or fulfill the order. Combined with an on-chain escrow system, this mechanism empowers applications to asynchronously issue their users a guaranteed minimum outcome before the execution results are finalized. It implies a direct link between blockchain throughput, censorship resistance, and the capital efficiency of auction participants (e.g., solvers), which intuitively extends to execution quality. At equilibrium, bids fully reflect the potential for price improvement between bid submission and execution, but only partially reflect the potential for price declines. This asymmetry unbounded upside for winning bids, limited downside for failed bids, and no loss for losing bids - ultimately benefits users.
Fully Homomorphic Encryption (FHE) is a cryptographic scheme that enables computations to be performed directly on encrypted data, as if the data were in plaintext. After all computations are performed on the encrypted data, it can be decrypted to reveal the result. The decrypted value matches the result that would have been obtained if the same computations were applied to the plaintext data. FHE supports basic operations such as addition and multiplication on encrypted numbers. Using these fundamental operations, more complex computations can be constructed, including subtraction, division, logic gates (e.g., AND, OR, XOR, NAND, MUX), and even advanced mathematical functions such as ReLU, sigmoid, and trigonometric functions (e.g., sin, cos). These functions can be implemented either as exact formulas or as approximations, depending on the trade-off between computational efficiency and accuracy. FHE enables privacy-preserving machine learning by allowing a server to process the client's data in its encrypted form through an ML model. With FHE, the server learns neither the plaintext version of the input features nor the inference results. Only the client, using their secret key, can decrypt and access the results at the end of the service protocol. FHE can also be applied to confidential blockchain services, ensuring that sensitive data in smart contracts remains encrypted and confidential while maintaining the transparency and integrity of the execution process. Other applications of FHE include secure outsourcing of data analytics, encrypted database queries, privacy-preserving searches, efficient multi-party computation for digital signatures, and more. A dynamic website version is available at (https://fhetextbook.github.io). Please report any bugs or errors to the Github issues board.
Afroja Akther, Ayesha Arobee, Abdullah Al Adnan, Omum Auyon · 6 authors
As artificial intelligence (AI) systems become increasingly complex and autonomous, concerns over transparency and accountability have intensified. The "black box" problem in AI decision-making limits stakeholders' ability to understand, trust, and verify outcomes, particularly in high-stakes sectors such as healthcare, finance, and autonomous systems. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a potential solution to enhance AI transparency and auditability. This paper explores the integration of blockchain with AI to improve decision traceability, data provenance, and model accountability. By leveraging blockchain as an immutable record-keeping system, AI decision-making can become more interpretable, fostering trust among users and regulatory compliance. However, challenges such as scalability, integration complexity, and computational overhead must be addressed to fully realize this synergy. This study discusses existing research, proposes a framework for blockchain-enhanced AI transparency, and highlights practical applications, benefits, and limitations. The findings suggest that blockchain could be a foundational technology for ensuring AI systems remain accountable, ethical, and aligned with regulatory standards.
The protection and management of intellectual property (IP) have become increasingly complex in the digital era. Traditional methods face significant challenges due to the ease of digital content replication and distribution. This research aims to explore the potential of blockchain technology in addressing these challenges in IP management and copyright protection. A quantitative approach using SmartPLS analysis was conducted, surveying 100 respondents from companies in Indonesia. The results indicate that IP protection, the efficiency of IP management, and transparency are critical factors in driving the adoption of blockchain technology. These factors enhance the effectiveness of IP management systems by ensuring security, reducing administrative costs, and improving overall transparency. The study findings have important implica tions for decision-makers, providing valuable insights into how blockchain can be implemented to optimize IP protection and management processes. This research highlights the need for further development of blockchain-based solutions to support IP management, offering a more efficient, transparent, and secure framework. The adoption of blockchain in this context is essential to revolutionizing IP management practices in the digital age.
Vijayan Sugumaran, E. Dinesh, R. Ramya, Elangovan Muniyandy
This research work proposes a Distributed Blockchain-Assisted Secure Data Aggregation (Block-DSD) technique for MANETs, ensuring high security and energy efficiency in disaster management scenarios. A Zone-based Clustering Approach (ZCA) is employed to segment the network into secure zones, with optimal Cluster Heads (CHs) selected using the Artificial Neuro-Fuzzy Inference System (ANFIS). Data aggregation is secured through a Two-Step Secure (STS) method and Elliptic Curve Cryptography (ECC), while optimal routing is achieved using the Improved Elephant Herd Optimization (IEHO) algorithm. Simulations using ns-3.25 demonstrate a 97% Packet Delivery Ratio (PDR), 20% lower energy consumption compared to existing methods, and minimal latency of 0.0012 s for emergency data, validating the proposed framework's efficiency and robustness in dynamic MANET environments.
Blockchain is a transformative technology with the potential to metamorphose industries, including supply chains and logistics, owing to its promise of efficiency, transparency and traceability. However, many blockchain projects have failed, requiring an analysis of the underlying reasons. This research focuses on the failure factors by studying the case of TradeLens, a supply chain platform using Blockchain to improve the visibility and coordination of international shipments. Applying Elinor Ostrom’s theory of the commons, we explored challenges related to governance, participation, interoperability, technological evolution and security. The study reveals that a lack of stakeholder engagement, unclear governance, and confidentiality concerns are major obstacles. Ostrom highlights the importance of participatory governance and a clear definition of boundaries and communities in the management of shared resources. To be successful, blockchain projects must adopt a holistic approach, with transparent governance, encourage collaboration, guarantee interoperability and invest in data security. By incorporating these recommendations and the lessons learned from past failures, future blockchain projects can improve their chances of success and make a positive contribution to the transformation of industries.
This paper examines how gold and Bitcoin have changed in terms of value and function in the context of the central banking system in the 21st century. Over the past decades, central banks have held gold as one of their primary reserve assets, given its stability, relative rarity, and traditional status as an inflation hedge and financial crisis buffer. However, with the advent of Bitcoin, central banks now have the opportunity to hold a new asset, one that has been compared to “digital gold”. On one hand, Bitcoin revolutionizes the monetary system because it is decentralized, has built in scarcity, and serves as a store of value. However, on the other hand, Bitcoin has traditionally been highly volatile, suffered from regulatory issues, and possesses a relatively short history; all of which hinder Bitcoin from becoming more accepted among central banks. Factors are discussed that affect central bank reserve management: the enduring role of gold, Bitcoin as an additional reserve, and the growing significance of central bank digital currencies. It is argued that while it remains unclear whether central banks will fully integrate Bitcoin into current reserves, its acceptance thus far may impact the decision of global monetary systems regarding incorporating digital technologies alongside more conventional assets, such as gold.
The significant progress in information technology has accelerated the rapid development of social manufacturing (SM), making performance monitoring a crucial aspect of SM management. Nonetheless, it encounters issues regarding low trust among participants and centralization in the management platform. Blockchain is a new decentralized infrastructure and distributed computing paradigm that verifies and executes business logic based on smart contracts. Although blockchain has been applied to SM to ensure credibility and decentralization, there is a lack of research on smart contracts for blockchain systems to achieve performance monitoring in SM. Therefore, in the paper, a smart contract model was designed to meet performance monitoring in SM, specifically, a manufacturing promise model was developed to define the specific composition of relative elements in the performance promise between participants, and then a state transfer rule model was established to describe state change rule for the manufacturing promise. Next, a smart contract model for performance monitoring in SM was designed based on the established models. Finally, the model is validated through a case study of SM to produce air-conditioning compressor valves, the results show that the smart contract model is efficient in monitoring the performance states in SM. The model can help the managers in SM monitor the real-time performance conditions and ensure the production plan is completed on schedule.
This article presents a novel framework for decentralized artificial intelligence model training that combines federated learning with blockchain technology in cloud environments. By integrating these cutting-edge technologies, the article addresses critical challenges in collaborative AI development, including data privacy, secure model sharing, and participant incentivization. The article framework leverages Zero Knowledge Proofs (ZKPs) for enhanced privacy guarantees while utilizing blockchain-based smart contracts to ensure transparent and automated governance of the training process. The implementation demonstrates significant improvements in data transfer efficiency, privacy preservation, system reliability, and participant diversity compared to traditional centralized approaches. The results validate the effectiveness of combining federated learning with blockchain technology for secure, scalable, and efficient distributed AI model training.
Cryptocurrency emergence poses significant challenges and opportunities for Indonesia's economy, yet comprehensive understanding of its economic impacts remains limited. This study employs a systematic literature review to analyze cryptocurrency's potential economic implications in Indonesia by examining stakeholder expectations, regulatory frameworks, and economic impacts. The research extracted and synthesized findings from ten studies using qualitative, empirical economic, and regulatory analysis methodologies. Results reveal a regulatory dichotomy between Bank Indonesia's stability-focused approach and the Ministry of Trade's growth-oriented perspective, creating market uncertainty. Cryptocurrency positively impacts banking stock prices, payment system efficiency, and investment opportunities, while raising concerns about volatility, monetary control, and cybersecurity. Stakeholder expectations vary across banking, fintech, regulatory, and investor groups, highlighting the need for balanced regulatory approaches that foster innovation while addressing systemic risks. This research contributes valuable insights for developing comprehensive cryptocurrency policies that maximize economic benefits while maintaining financial stability.
Jens Ernstberger, Jan Lauinger, Yulin Wu, Arthur Gervais · 5 authors
Transport Layer Security (TLS) is foundational for safeguarding client-server communication. However, it does not extend integrity guarantees to third-party verification of data authenticity. If a client wants to present data obtained from a server, it cannot convince any other party that the data has not been tampered with. TLS oracles ensure data authenticity beyond the client-server TLS connection, such that clients can obtain data from a server and ensure provenance to any third party, without server-side modifications. Generally, a TLS oracle involves a third party, the verifier, in a TLS session to verify that the data obtained by the client is accurate. Existing protocols for TLS oracles are communication-heavy, as they rely on interactive protocols. We present ORIGO, a TLS oracle with constant communication. Similar to prior work, ORIGO introduces a third party in a TLS session, and provides a protocol to ensure the authenticity of data transmitted in a TLS session, without forfeiting its confidentiality. Compared to prior work, we rely on intricate details specific to TLS 1.3, which allow us to prove correct key derivation, authentication and encryption within a Zero Knowledge Proof (ZKP). This, combined with optimizations for TLS 1.3, leads to an efficient protocol with constant communication in the online phase. Our work reduces online communication by 375× and online runtime by up to 4.6×, compared to prior work.
In recent years, various digital currencies have emerged, among which Bitcoin has been widely accepted as an alternative to sovereign currencies for commodity trading. However, the dramatic volatility of bitcoin prices can pose a risk to global financial markets. In this paper, we firstly construct a more comprehensive forecasting index system from seven aspects, and then construct a VMD-GRU model. This model uses the variational modal decomposition (VMD) to decompose the time series into intrinsic mode functions (IMFs) and use gated recurrent unit (GRU) to forecast different IMFs. This paper also compares the forecast results with classical machine learning models and deep learning models, and the results show that the forecast accuracy of the VMD-GRU model is more than 16% better than other models.
Francisco J. Quesada, Francisco Moya, Mercedes Rodríguez-García, Bapi Dutta
Tendering processes aim to provide transparency in the trade of services or goods but often fall short, leading to corruption and loss of trust. The emergence of Distributed Ledger Technologies (DLTs), such as blockchain, has prompted research into their application for enhancing transparency in tendering. However, adopting DLT usually incurs extra costs, network fees, and high carbon footprints. This paper conducts a Multi-Criteria Decision Making (MCDM) process to select the most suitable DLT for tendering processes. As a result, a novel tendering process based on IOTA is proposed, which improves transparency, ensures ecological sustainability, and avoids extra costs. The IOTA-based approach also fosters collaboration between human and computer capabilities in selecting the tender winner. Our method is compared with existing approaches, demonstrating the highest transparency.