Affaf Shahid, Umair Sarfraz, Muhammad Waseem Malik, Muhammad Sohaib Iftikhar · 6 authors
Supply chains are evolving into automated and highly complex networks and are becoming an important source of potential benefits in the modern world. However, it is challenging to track the provenance of data and maintain traceability throughout the network. The traditional supply chains are centralized and dependent on third party for trading. Centralized systems lack transparency, accountability and auditability. In our proposed solution, we have presented a blockchain-based reputation system in Agriculture and Food (Agri-Food) supply chain. It leverages the key features of blockchain and smart contract deployed over ethereum blockchain network. Although blockchain provides an immutable network for supply chain events, it still fails to solve the problem of trust among entities. Therefore, a reputation system is required that logs reviews of sellers and maintains the trust between trading entities.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Ferdınando Chıacchıo, Lucio Compagno, Diego D’Urso, Luca Velardita · 5 authors
With the growth of a global market, new regulations aiming at preventing the fraud of counterfeiting drugs have been released all over the world. In the pharma industry, an innovative technology named serialization is becoming more and more popular as it allows to implement a packaging process based on a hierarchical aggregation. This guarantees that each box entering in the distribution network is marked with a unique identifier for an easy traceability by a central regulatory in charge to follow the life cycle of the product until given to the final patient. In this scenario, the blockchain might offer a breakthrough proposing a decentralized and immutable traceability mechanism able to increase the security of the data and to reduce the success of a fraud attempt. To demonstrate the effectiveness of this technology, in this paper, a DAPP based on the Ethereum blockchain has been coded and tested as prototype in a pharma industry.
Open access
Blockchain Technology Applications and Security
Innovative Microfluidic and Catalytic Techniques Innovation
Physical Unclonable Functions (PUFs) and Hardware Security
Bitcoin merupakan mata uang digital yang sekarang paling banyak digunakan. Perubahan harga yang sewaktu-waktu dapat berubah membuat pengguna bitcoin harus teliti ketika melakukan penukaran. Kepopuleran bitcoin terus meningkat dan menjadi aset untuk investasi bagi para penggunanya. Untuk mengatasi perubahan harga yang tidak menentu maka dibutuhkan sebuah aplikasi prediksi harga bitcoin untuk membantu para penggunanya dalam memprediksi harga bitcoin kedepannya. Prediksi dilakukan dengan menggunakan metode Autoregressive Integrated Moving Average (ARIMA) yang mampu menghasilkan tingkat akurasi tinggi dalam prediksi jangka pendek. Metode ini mengabaikan variabel independen dalam membuat prediksi, sehingga cocok untuk data statistik saling terhubung serta memiliki beberapa asumsi yang harus dipenuhi seperti autokorelasi, trend, maupun musiman. Evaluasi hasil prediksi menggunakan Mean Absolute Percentage Error (MAPE). Hasil pengujian menujukkan bahwa model ARIMA (3,1,3) menghasilkan prediksi dengan nilai MAPE terkecil daripada kandidat model lainnya. Rata-rata nilai MAPE yang dihasilkan adalah sebesar 0,84 dan rentang nilai 1,34 untuk prediksi hari pertama dan 0,98 untuk prediksi hari ketujuh. Dengan demikian model ARIMA (3,1,3) mampu menghasilkan prediksi dengan akurasi yang baik dan layak untuk digunakan sebagai metode prediksi bitcoin untuk satu sampai tujuh hari kedepan.
This paper analyses co-movement between Bitcoin exchanges in 34 major countries around the world and the US (the global benchmark) over the period January 24, 2011 - January 7, 2019. More specifically, we run IV regressions to investigate the importance of cultural factors (such as tightness, individualism, trust and risk-taking) following an earlier study by Eun et al. (2015) which had shed light on their importance to explain stock co-movement within individual countries. The results suggest that markets in tighter, more individualistic, trustful and risk-taking societies are more tightly linked to the US one. Further, it appears that culturally looser, collectivistic, trustful and risk-taking countries are more likely to shut down their Bitcoin exchanges compared to other countries. These findings confirm our priors.
An Braeken, Madhusanka Liyanage, Salil S. Kanhere, S. K. Dixit
This theme issue will elaborate on the opportunities, challenges, and solutions to be offered by combining blockchain and cyberphysical systems for different application domains. Cyberphysical systems (CPSs) combine physical objects or systems with integrated computing facilities and data storage. Such CPSs can be interconnected in networks, within which they can exchange and share data and information with other objects and systems. The Internet of Things (IoT) in general is making rapid progress by providing connectivity to consumer devices. Blockchain is another promising technology in the information and communications technology domain. Blockchain is a decentralized digital database (ledger), which stores the transactions committed by users. The authenticity of such transactions is verified by the connected community (miners) before adding them to the ledger. Thus, blockchain employs a distributed trust model by eliminating a third -party centralized trust model. In the blockchain, each block bundles an array of transaction records and their cryptographic chain links.
The purpose of this paper is to identify the impact and benefits of the latest information technologies on Accounting Information Systems (AIS). Taking into account the numerous papers related to new technologies and their application in the accounting profession within Industry 4.0, and conducted survey about perception of practitioners in Republic of North Macedonia, this paper summarizes the characteristics and key benefits of some of the new technologies for the functioning of AIS in the digital age. First of all, the evolution of AIS is elaborated, based on theoretical and empirical analysis of the accounting process from the appearance of the first AIS up to nowadays’ services and techniques available for supporting the accounting function. The first technology to be elaborated is Big Data and its potential to change the business landscape, especially in the field of automating operation processes, customer engagements, and predictive decision-making process. Secondly, the Blockchain Technology as an example of Distributed Ledger Technology (DLT), which adoption brings new possibilities in eliminating or redefining the role of entities external to the company. Cloud Computing i.e. Cloud Accounting is the third technology which is elaborated in this paper through the services it offers on the cloud, especially the way AIS process, store and backup the sensitive and confidential data. Last but not least, Artificial Intelligence (AI), a technology that could change the professional services, the need, and opportunities that are provided for a solution to the current accounting issues. In summary, taking into account the relevant literature and the perception of the respondents-practitioners, increased use of these technologies is necessary because their application reduces costs; increases transparency and confidence in information; flexibility, i.e. no time and space restrictions on their use, etc., which is especially useful in the current state of Pandemic, caused by the virus COVID-19.
Blockchain is a distributed open (Public) ledger that is used to record the transaction across many computers. Blockchain technology can be applied in any domain such as banking, healthcare, real estate, travel, food, and supply chain. In supply chain management to train the self-driving vehicle in blockchain technology also integrate the Artificial Intelligence (AI) and Machine Learning (ML) Algorithms. In this paper we have proposed Reinforcement learning integrated heuristic search method (RLIH) for self-driving vehicle using blockchain in supply chain management by combining the advantage of reinforcement learning and heuristic search method. RLIH is developed using Decentralized app and result shows that proposed method outperform the existing heuristic search method in term of service time and data traffic.
Aiming at the problem of privacy leakage during data sharing in the Internet of Things, a blockchain based secure data sharing platform with fine-grained access control(BSDS-FA) is proposed. First, this paper proposes a new hierarchical attribute-based encryption algorithm, which uses hierarchical attribute structure and multi-level authorization center. The algorithm implements flexible and fine-grained access control by distributing different user attributes to different authorization centers. Then, it combined with the Fabric blockchain technology to solve the problem of huge decryption cost for users in the Internet of things. Smart contract in blockchain executes high-complexity partial decryption algorithm to reduce the users' decryption overhead. Blockchain can also realize the traceability of historical operations to meet the security requirements of data restriction open and transparent supervision. Finally, the hierarchical attribute-based encryption algorithm is proved to be CPA-safe. The theoretical analysis and experimental results show that BDSS-FA provides more secure and reliable data sharing services for users in the Internet of Things.
Building trust relationships between different decentralized entities in the IoT ecosystem is essential. Hereof, the combination of blockchain technology and trust evaluation techniques is recently considered as an efficient measure. However, both technologies within the IoT are still facing some limitations which are addressed in this research. First, this publication reviews various blockchain-based trust approaches and depicts their strengths and limitations regarding their usage in decentralized IoT communities. Then, an optimized trust model with a multi-layer adaptive and trust-based weighting system is proposed. Additionally, different trust metric parameters and their mathematical models used for trust evaluation are presented. Moreover, this publication presents a novel approach for incentivization processes in the IoT marketplace using control loops and smart contracts. Thereby, participants are motivated to continuously improve their behavior. Finally, the proposed trust model is proved to be reliable. The experimental results conducted from different scenarios show that the presented approach provides more resiliency against various attacks than existing ones.
shows that transformers can act as "soft theorem provers" by answering questions over explicitly provided knowledge in natural language. In our work, we take a step closer to emulating formal theorem provers, by proposing PROVER, an interpretable transformer-based model that jointly answers binary questions over rule-bases and generates the corresponding proofs. Our model learns to predict nodes and edges corresponding to proof graphs in an efficient constrained training paradigm. During inference, a valid proof, satisfying a set of global constraints is generated. We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation, with strong generalization performance. First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation). Second, when trained on questions requiring lower depths of reasoning, it generalizes significantly better to higher depths (up to 15% improvement). Third, PROVER obtains near perfect QA accuracy of 98% using only 40% of the training data. However, generating proofs for questions requiring higher depths of reasoning becomes challenging, and the accuracy drops to 65% for "depth 5", indicating significant scope for future work.
The article deals with the problems of cryptographic protection of data processing algorithms and techniques. They are novel techniques allowing to process private information without disclosing it to persons engaged in processing. One of the main applications of such security tools is the creation of personalized information services, which opens up new opportunities for business and reduces the risks of unauthorized access to personal data. We review important building blocks for cryptographic protection of data processing, such as zero-knowledge proofs, secure multi-party computations, and homomorphic encryption. Often, personalized information services are based on data mining and machine learning, so privacy-preserved machine learning is a very important building block for them. We analyze the concept of differential privacy which serves as the basis for privacy-preserving machine learning and some other cryptographic schemes. At the end of the paper, we forecast the perspectives of encrypted data processing.
A blockchain is an ordered, decentralized, immutable ledger that allows a recording of transactions in a network. Over recent years, blockchain has emerged to be a technology that can be applied in various sectors. It has promised to be the technology that will allow transactions simply, effectively, safely, and cheaply. The main aim of this thesis was to study the possible impacts of blockchain in the banking industry along with its challenges and limitations. The outcome would include a good explanation of blockchain technology, how it works, future bank implementation, and its challenges. \n \n To answer the research questions, a literature review was conducted. The articles were selected from evidence-based scientific databases such as ABI Inform, Academic Search Elite, Emerald, Sage Premier, ScienceDirect, Springer Open, and Google Scholar. Six scholarly articles were selected by using inclusion and exclusion criteria and screening the relevant articles. \n Data were systematically extracted and grouped into five main categories. \n \n The result shows that blockchain technology could enhance the efficiency of various sectors of the banking industry. It has the potential to upgrade and transform the cross-border payment, trade finance, capital markets, and financial reporting and compliance. It also makes the process of knowing your customer straightforward. So, the implementation of blockchain is projected to disrupt the banking and finance system by facilitating a new way of payment, faster trade execution, secured ledger, smart contracts, and many other innovations. However, there are some obstacles such as regulation and technological challenges to be solved for efficient implementation in the banking sector.
AbuYusuf Aminu-Ibrahim, John Chinemerem Ogbete, Kazeem Babatunde Ambali
Expanding diagnostic access across underserved and rural healthcare regions remains a critical determinant of health equity, early disease detection, and system-wide resilience. Infrastructure-driven approaches offer a pragmatic pathway to closing persistent diagnostic gaps caused by geographic isolation, workforce shortages, fragmented referral networks, and underinvestment in health facilities. This paper examines how strategic development of physical, digital, and organizational infrastructure can enable scalable, sustainable diagnostic services in low-resource and rural settings. It synthesizes evidence from health systems strengthening, rural health planning, and diagnostic network design to identify core infrastructure enablers that improve access, quality, and continuity of care. Key infrastructure components include decentralized laboratory hubs, modular and prefabricated diagnostic facilities, reliable power and water systems, cold-chain and specimen transport logistics, and interoperable health information systems. When combined with digital connectivity, telepathology, and point-of-care diagnostics, these assets reduce turnaround times, minimize patient travel burdens, and support timely clinical decision-making. The paper further highlights the role of workforce-aligned infrastructure, emphasizing training-centered facility design, task-shifting support spaces, and remote supervision platforms that extend specialist expertise into rural contexts. From a policy and financing perspective, infrastructure-driven expansion requires coordinated investment models that align capital planning with service delivery objectives. Public–private partnerships, performance-based financing, and regional diagnostic networks are discussed as mechanisms to de-risk infrastructure investment while ensuring affordability and long-term operability. Governance frameworks that integrate maintenance planning, quality assurance, biosafety, and regulatory compliance are identified as essential to preventing infrastructure decay and service fragmentation. The papaer concludes that infrastructure is not merely a physical input but a systems-level enabler of equitable diagnostic access. By embedding diagnostics within resilient infrastructure ecosystems that integrate technology, logistics, workforce capacity, and governance, health systems can extend high-quality diagnostic services to underserved and rural populations. Such infrastructure-driven strategies are foundational to universal health coverage, pandemic preparedness, and the reduction of avoidable morbidity and mortality in marginalized regions. Importantly, infrastructure planning must be context-sensitive, data-informed, and community-engaged, ensuring that diagnostic expansion aligns with local disease burdens, cultural practices, referral pathways, and sustainability constraints while promoting trust, utilization, and long-term health system integration across diverse rural geographies globally and fragile health markets.
The article investigates some of the most relevant legal issues that emerge in connection with blockchain technology and smart contracts by addressing them from a public policy perspective.In particular, it focuses on some under-investigated problems connected to some possible legal hurdles to their widespread adoption in the legal practice of business at the national and international levels.The legal analysis of blockchain and smart contracts is then employed to explore the more general question of how much the law needs to change in order to accommodate new technologies, or how much it is instead preferable to believe that the existing law is already capable of accommodating innovation, however radical it may be.* Assistant Professor of Comparative Public Law, University of Turin.I would like to express my debt to the participants at the conference on Blockchain e diritto
One of the major current topics and one of the major innovations in the contract law, as well as in insurance law is the invention of the smart contracts. The author is basing her research on use of smart contract in insurance law and what are the main legal issues arising from the use of smart contract. In her paper, the author points out that the implementation of the smart contract in insurance law will greatly affect all participants in insurance contract and a significant step forward in improving the level of protection of insurance users (consumers), although it takes time and readiness of European and domestic legislators to create a special regulatory framework so that smart contract can reach its potential.
Finansal zaman serilerinde görülen değişen varyans sorununun (ARCH etkisi) sonucu olarak otoregresif koşullu değişen varyans modelleri bulunmuştur. Çalışmamızda, piyasa değeri en yüksek üç kripto paranın [Bitcoin (BTC), Ethereum (ETH) ve Ripple (XRP)] getirileri incelenmiş ve söz konusu getirilerde finansal zaman serilerine benzer şekilde ARCH etkisi bulunmuştur. Söz konusu üç kripto paranın volatiliteleri için en iyi modelin hesaplanmasında altı GARCH modelini karşılaştırılmıştır. Bu modeller sırasıyla GARCH (1,1), EGARCH (1,1), TGARCH (1,1), APARCH (1,1), CGARCH (1,1) ve ACGARCH (1,1) modellerinden oluşmaktadır. Çalışma kapsamında 01.10.2015 - 01.10.2018 tarihleri arasında Bitcoin (BTC), Ethereum (ETH) ve Ripple (XRP) kripto paralarının günlük kapanış verilerinden elde edilen getiriler kullanılmıştır. Volatilite tahminlerinde Bitcoin (BTC) ve Ethereum (ETH) için en iyi model EGARCH (1,1), Ripple (XRP) için ise APARCH (1,1) modeli bulunmuştur. Çalışma kapsamında bu modeller kullanılarak volatiliteler üzerinde negatif şokların pozitif şoklardan daha fazla etkisinin bulunduğunu gösteren kaldıraç etkisi incelenmiştir. Bitcoin (BTC) ve Ethereum (ETH) modellerinde kaldıraç etkisi bulunmamış, bununla birlikte pozitif şoklar negatif şoklara göre daha fazla volatiliteye neden olmuştur. Ancak, Ripple (XRP) volatilite modelinde kaldıraç etkisi belirlenmiştir.
Bitcoin, the first decentralized cryptocurrency, has become popular not only because a growing size of merchants accepts it in transactions, but also because people buy it as an investment. This study focuses on the Bitcoin price forecast using Hidden Markov Model and two machine learning methods, LSTM and GRU Recurrent networks. Evaluated by MAPE and RMSE, the results indicate that the Hidden Markov Model with the Gaussian Mixture Models has the best performance among all methods. The GRU model outperforms the LSTM model, though sometimes it might have a more extreme result. When the price remains constant or changes steadily, the predictions are more precise than the fluctuation period.