BCOA: Blockchain Open Architecture

Reference architectures for big data and machine learning include not only interconnected building blocks but important considerations (among others) for scalability, manageability and usability issues as well. Leveraging on such reference architectures, the automated deployment of distributed toolsets and frameworks on various clouds is still challenging due to the diversity of technologies and protocols. The paper focuses particularly on the widespread Apache Spark cluster with Jupyter as the particularly addressed framework, and the Occopus cloud‐agnostic orchestrator tool for automating its deployment and maintenance stages. The presented approach has been demonstrated and validated with a new, promising text classification application on the Hungarian academic research infrastructure, the OpenStack‐based MTA Cloud. The paper explains the concept, the applied components, and illustrates their usage with real use‐case measurements.

There are great similarities between blockchain and supply chain, and the application of blockchain can continuously promote innovation in the development mode of supply chain finance. Blockchain, with its unique technological features, has formed an organic integration with supply chain finance business, effectively solving many pain points in traditional supply chain finance, such as bill fraud, "information silos," inability to transmit credit, and high performance risk. Based on this, this article first elaborates on the concept of supply chain finance, digital economy supply chain finance, and foreign supply chain finance. Based on the analysis of the characteristics and advantages of blockchain technology, it provides a detailed explanation of the design of blockchain based supply chain finance security solutions from three aspects: building a visual data system, implementing digital voucher splitting and multi-level credit transmission, and standardizing the use of smart contract technology, Finally, corresponding strategies were proposed based on the current problems faced by blockchain based supply chain finance, providing new ideas and solutions for risk prevention in supply chain finance.

4 - Blockchain applications for the healthcare sector: Uses beyond Bitcoin

Blockchain technologies have seen a steady growth in interest from industries as the technology is gaining maturity. It is offering a novel way to establish trust amongst multiple stakeholders without relying or trusting centralised authorities. While its use as a decentralised store of value has been validated through the emergence of cryptocurrencies, its use case in industrial applications with multiple stakeholder ecosystems such as industrial supply chain management, is still at an early stage of design and experimentation where private blockchains are used as opposed to public blockchains. Many enterprise blockchain projects failed to gain traction after initial launches, due to inefficient design, lack of incentives to all stakeholders or simply because the use of blockchain was not really necessary in the first place. There has been a need for a framework that allows blockchain designers and researchers to evaluate scenarios when a blockchain solution is useful and design the key configurations for an enterprise blockchain solution. Literature on blockchain architectures are sparse and only applicable to specific use cases or functionalities. This paper proposes a comprehensive Enterprise Blockchain Design Framework (EBDF), that not only identifies the relevant use cases when a blockchain must be utilised, but also details all the characteristics and configurations for designing an enterprise blockchain ecosystem, applicable to multiple industries. To validate the EBDF, we apply the same to the Vortal e-Procurement ecosystem allowing for multiple platforms to interoperate with greater transparency and accountability over the proposed blockchain framework. In this use case, many vendors bid for procurement procedures, often for publicly managed funds where it is extremely vital that full transparency and accountability is ensured in the entire process. Ensuring that certain digital certification functions, such as timestamps are independent from e-Procurement platform owners has been a challenge. Blockchain technology has emerged as a promising solution for not only ensuring transparency and immutability of records, but also providing for interoperability across different platforms by acting as a trusted third-party. The applied framework is used to design a Hyperledger based blockchain solution with some of the key architectural elements that could fulfil these needs while presenting the advantages of such a solution.

Artificial intelligence (AI) has rapidly expanded across multiple industries and technologies, driving economic growth and offering innovative solutions to structural challenges. However, its environmental impact remains contested. While firms investing in AI aim to lower its carbon footprint, its widespread use continues to generate significant emissions. This study examines the environmental effects of AI investments, particularly on carbon emissions, while also accounting for human and economic development indicators. The analysis employs the Panel ARDL-PMG approach using data from 2012–2023 for nine technologically advanced economies characterized by extensive use of robotics (South Korea, Japan, Germany, the United States, China, Singapore, Sweden, Italy, and France). The findings reveal the existence of a stable long-run equilibrium among the variables. The negative and significant ECT indicates that about 32% of short-term imbalances are corrected each year, suggesting that the system steadily moves toward its long-run equilibrium. In the long run, per capita GDP and renewable energy consumption reduce carbon emissions, whereas AI investments (AIINV), Foreign Direct Investment (FDI), and the Human Development Index (HDI) increase them. The results show that AIINV and FDI do not contribute to reducing carbon emissions. In this context, the findings suggest that investments in the energy sector are not directed toward encouraging the transformation of energy sources. These results highlight the environmental risks posed by the growing prevalence of AI. However, AIINV and FDI have the potential to help reduce carbon emissions if they are aligned with the transformation of energy sources. Thus, aligning AI with green innovation and sustainable environmental policies is essential. This study emphasizes the importance of enabling the energy transition to reduce carbon emissions arising from AIINV and FDI in the sector. Promoting eco-efficient technologies and sustainable innovation processes can help mitigate the carbon-intensive effects of digital transformation.

Wave load cycles, wet-deck slamming events, accelerations and motion comfort are important considerations for high- speed catamarans operating in moderate to large waves. This paper provides an overview of data analytics methods and cloud computing resources for remotely monitoring motions and structural responses of a 111 m high-speed catamaran. To satisfy the data processing requirements, MATLAB Reference Architectures on Amazon Web Services (AWS) were used. Such combination enabled fast parallel computing and advanced feature engineering in a time-efficient manner. A MATLAB Production Server on AWS has been set up for near real-time analytics and execution of functions developed according to the class guidelines. A case study using Long Short-Term Memory (LSTM) networks for ship speed and Motion Sickness Incidence (MSI) is provided and discussed. Such data architecture provides a flexible and scalable solution, leading to deeper insights through big data processing and machine learning, which supports hull monitoring functions as a service.

The manufacturing industry is an ecosystem full of changes and variations where production conditions are never the same. As an example, the raw materials received from suppliers differ from one another, though within tolerances. And similar variations appear in all areas of manufacturing, such as tool wearing, the statuses of production machines, and even operator decisions.Improvements to production must factor in technical considerations and economic ones. Several points of view must be included to determine the best solution. Even when applying artificial intelligence (AI) in the manufacturing process, the situation should be similar: Several agents with different goals should interact to determine the most holistic solution.This paper presents the ontology, semantics, and architecture that facilitates multiagent interaction. The Reference Architecture Model Industry 4.0, or RAMI 4.0 (RAMI4.0 – 2018 – DE (plattform-i40.de)), has been selected as the basis for this approach.Given that most of the time, the operator’s decision is based on intuition and experience, not based on data analysis, this paper also analyses which data architecture will permit the data analysis of raw materials, finished products, tooling characteristics and statuses, machine parameters, and external conditions, to minimize the influence of intuition and personal bias on decision-making in manufacturing.

The increasing network connectivity of embedded devices in the IoT era hasexposed them to the hostile threats that pullulate in the cyberspace. Multiple reported cyberattacks on these devices supporting critical infrastructure, medical devices, or automobiles reflect the harmful effects of this exposition and highlight the paramount need to maintain their security. The cyberattacks usually result from the exploitation of bugs and vulnerabilities rooted in the firmware of these devices. These firmware defects are not different from those affecting the software of regular computers. However, they are more challenging to detect and mitigate. The main reasons for this particular difficulty are the unique characteristics of the embedded devices, including diversity of hardware and firmware architectures, strong coupling between firmware and peripherals, and constrained computing, power consumption and timing capabilities. In this thesis, we present three novel solutions that holistically tackle the uniquecharacteristics of embedded devices to detect and mitigate firmware defects. First, to protect and isolate DMA operations, we present D-BOX, the reference architecture and systematic method that holistically enable secure DMA operations, providing strong security properties, a practical definition of a capability-based security policy, high-performance, and low power consumption. Second, to holistically test firmware, we present DICE, a drop-in solution for firmware analyzers to emulate and manipulate DMA input channels, which current firmware analyzers ignore regardless of their profound implications on the security, power, and performance of embedded devices. Finally, to test firmware as a whole with the highest fidelity, we present SHIFT, a semi-hosted fuzz testing framework that runs firmware natively in an MCU, supporting peripherals with zero-emulation, real-time and DMA operations, and state-of-the-art instrumentation for fault detection.--Author's abstract

Although the continuous construction of smart grid and power Internet of Things has brought massive data to grid enterprises, the cost of data storage, operation and maintenance is gradually increasing, on the contrary, the value of data has not been effectively mined and utilized, the main reason is that the current data asset management of State Grid Corporation of China is still an infancy stage, and still faces the problems of data aggregation quality, safety and compliance control, shared application scope, and management mechanism efficiency and so on. In order to solve the above problems, Big Data Center of State Grid Corporation of China takes advantage of the blockchain with distributed consensus autonomy and data storage, non-tampering and traceability, and business intelligence contract script, explores the application of blockchain in power grid data asset management, and then, tries to construct the architecture of power grid data asset management based on blockchain, which is to promote security compliance and open sharing of power grid data operation, and realize the efficient management and operation of power grid data assets.

The rise of data-sharing platforms, driven by public demand for open data and legislative mandates, has raised several pertinent issues. These encompass uncertainties over data accuracy, provenance and lineage, privacy concerns, consent management, and the lack of equitable incentives for data providers. The advanced nature of blockchain makes it well suited to address these concerns. Yet, the limitations of blockchains, particularly their restricted performance, scalability, and high cost, make them less adept at managing the four “Vs” of big data—volume, variety, velocity, and veracity. As the body of work proposing blockchain-based data-sharing solutions grows, so does the confusion in selecting between these platforms, particularly in terms of sharing mechanisms, services, quality of services, and applications. In this article, we aim to fill this knowledge gap through an in-depth survey of blockchain-based data-sharing architectures and applications. We first identify the key challenges of existing data-sharing techniques and lay out the foundations of blockchains. Our focus then shifts to the intersection of blockchain and data sharing, wherein we aim to clarify the existing landscape and propose a reference architecture for blockchain-based data sharing. Subsequently, we explore various industrial applications of blockchain-based data sharing, spanning healthcare, smart grids, transportation, and decarbonization. For each application, we draw from real-world deployments to present key lessons learned in the implementation of blockchain-based data sharing. Lastly, we shed light on current research challenges and open avenues for further study in this space. This article aims to serve as a comprehensive resource for researchers/practitioners looking to navigate the complex terrain of blockchain-based data-sharing solutions.

The architecture of enterprise data at AI-first government and higher education organizations will have to balance AI ambitious agendas with data infrastructure that is stable and responsible. In this paper, I will suggest a layered reference architecture, which combines data mesh and lakehouse designs with a cloud-native platform based on the ISO/IEC 25012, DAMA-DMBOK, and the NIST AI Risk Management Framework. Methodologically, the work follows a design-science approach, synthesizing recent industry and policy literature, deriving a multi-stakeholder requirements matrix, and mapping representative public-sector and higher-education use cases such as fraud and risk detection, student success analytics, and smart-campus operations onto the proposed design. It is an ingestion and integration architecture, a governance and security architecture, metadata and semantic modeling, an analytical data store warehouse, lakehouse, vector stores, and a common AI/ML platform including MLOps and LLMOps, which consumes value through APIs, dashboards, and apps with AI infused in them. According to illustrative evaluation founded on 2023-2024 benchmark ranges, an AI-first EDA is capable of achieving: Reducing tens of seconds to low tens of seconds of analytical query latency, improving data completeness and timeliness to the mid-90s range, and risk and fraud detection model AUC to 0.84-0.88 plus higher concurrency and lowering the cost of infrastructure by 20-30%. Meanwhile, the paper identifies the most pertinent risks related to privacy, ethical AI, security risks, and skill gaps, asserting that the zero-trust securities, as well as long-term change management are the complements to the technical modernization. The blueprint suggested is therefore a workable, governance-consistent way of governments and universities shifting out of their secluded AI experiments into credible, systemic AI-empowered business processes.

Nowadays, there are more than 43 billion devices connected to the Internet, generating, collecting and sharing data [1]. From them, 15.14 billion are connected IoT devices, and this number is expected to double by 2030 [2]. Although IoT devices are mainly used in industry, consumer segment is pushing the fastest growth of this market representing the 60% of the total [3]. Moving computation to the origin of the data has arose concepts such as far edge, where data is analysed closer to it. However, processing still needs to be shifted from the original device to others with higher computation capabilities [4]. This paper analyses current trends and proposed solutions setting the path for the development of an European Reference Architecture and a common Open Source Stack aiming to position key players in the worldwide arena.

Performance analysis of high performance systems is a difficult task. Current tools have proven successful in analysis tasks but their implementation is limited in several respects. Closed architectures, predefined analysis and views, and specific platforms account for these limitations. Embedded systems are particularly affected by these concerns. This paper presents an open architecture for performance data mining that addresses these limitations. Comparisons of the architecture with current tools show its capabilities address a wider range of system phases and environments.

Heterogeneous system design, modeling and testing requires that concepts and ideas from the diverse disciplines of Distributed Artificial Intelligence, Multi Agent Systems and Generalized System Theory be brought together under a common framework. Multi layer, hybrid, open architectures need be designed to manage distributed objects, services and modules between system components and support overall system functionality. Issues that need be addressed include, among others, individual component control, component coordination and communication, as well as overall system functionality in the presence of communication failures. This presentation considers the case of heterogeneous teams of unmanned ground - aerial robots operating in uncertain / hostile environments. It discusses aspects of distributed system architecture; it addresses fundamental control and communication problems and recommended solutions to distributed task allocation using unmanned ground - aerial vehicles; it examines asset recruitment with and without communication failures. Experimental studies and demos related to demining applications and traffic monitoring and emergency response have been conducted and they will be discussed.

With increasing demand for collaborative work on healthcare data for research and effective healthcare management, the need for a distributed mechanism for resource sharing is inevitable. Common implementation for integrated healthcare data management usually rely upon a centralized data processing and repository, in which data is extracted as needed. However, such implementation is unscalable and prone to security and privacy issues, specifically when dealing with private and confidential health data. In this paper, we propose a preliminary design of permissioned blockchain application for integrated health data management. Our proposed scheme utilizes distributed ledger mechanism for data storage and verification, which allows participating entities to access and verify healthcare data in a distributed manner without depending on centralized facility. The proposed design reflected a simulated scenario involving medical institutions and military service in healthcare data management in national service recruitment.