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The pharmaceutical industry is a data-intensive environment and a heavily-regulated sector, where exhaustive audits and inspections are performed to ensure the safety of drugs. In this context, processing and evaluating the data generated in the manufacturing lines is a relevant challenge since it requires compliance with pharma regulations. This work combines data integrity metrics and blockchain technology to evaluate the compliance-degree of ALCOA+ principles among different levels of drug manufacturing data. We propose the DIALCOA tool, a software to assess the compliance-degree for each ALCOA+ principle, based on the assessment of data from manufacturing batch reports and its different levels of information.

Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge and classifications. It typically processes user-generated data streams to provide and refine popular services such as wikis, collaborative maps, e-commerce sites, and social networks. Nevertheless, this modus operandi raises severe concerns regarding ill-intentioned data manipulation in adversarial environments. This paper presents a simulation, modelling, and classification approach to automatically identify human and non-human (bots) as well as benign and malign contributors by using data fabrication to balance classes within experimental data sets, data stream modelling to build and update contributor profiles and, finally, autonomic data stream classification. By employing WikiVoyage – a free worldwide wiki travel guide open to contribution from the general public – as a testbed, our approach proves to significantly boost the confidence and quality of the classifier by using a class-balanced data stream, comprising both real and synthetic data. Our empirical results show that the proposed method distinguishes between benign and malign bots as well as human contributors with a classification accuracy of up to 92 %., This work has been supported by: (i) Xunta de Galicia, Spain grant ED481B-2021-118, Spain; (ii) National Funds through the FCT – Fundação para a Ciência e a Tecnologia, Portugal (Portuguese Foundation for Science and Technology) as part of project UIDB/50014/2020; (iii) CHIST-ERA, Ireland and the Irish Research Council, Ireland as part of the ‘‘Smart Pharmaceutical Manufacturing (SPuMoNI)’’ research project [Apr/2019–Dec/2022]; and (iv) University of Vigo, Spain/CISUG for open access charge.

Industrial Internet of Things (IIoT) automation should be based on a framework that guarantees flexible and energy efficient monitoring and control, without the need for frequent human intervention. The ability to analyse and process machine faults in real time is vital, however it poses many technical difficulties and challenges, mainly for industrial application environments. In our paper, we propose a novel, energy efficient, IoT and Cloud based decentralised framework for real time machine condition monitoring (MCM) and fault prediction, where computational demanding tasks are distributed across fog nodes and decision fusion rules are set and controlled by the Cloud. In particular, data acquisition phase is done by sensors distributed across machines, feature extraction and health condition classification is done by fog nodes, after receiving data and instructions as processed by the Cloud node. Our framework is based on collaboration and information flow among IoT, Fog and Cloud layers. To this purpose, we formulate a global consensus cross layer optimisation problem, concerning industrial healthy status monitoring, and we solve it in a distributed manner by applying asynchronous altering direction method of multipliers (ADMM) algorithm.

Multi-service networks aim to efficiently supply distinct goods within the same infrastructure by relying on a (typically centralised) authority to manage and coordinate their differential delivery at specific prices. In turn, final customers constantly seek to lower costs whilst maximising quality and reliability. This paper proposes a decentralised business model for multi service networks using Ethereum blockchain features – gas, transactions, and smart contracts – to execute multiple services at different prices. By employing the Ethereum cryptocurrency token, Ether, to quantify the quality of service and reliability of distinct private Ethereum networks, our model concurrently processes streams of services at different gas prices while differentially delivering reliability and service quality. This multi-service business model has been extensively tested on five concurrent Ethereum networks with various combinations of gas prices, miners, and regular nodes using a Proof of Authority consensus algorithm and throughput as the evaluation metric. It has exhibited linear scalability, providing increased throughput in high-quality Ethereum networks, i.e., composed of more validator nodes. The results also indicate that different mining prices do not impact the network performance, but networks with more miners had limited scalability and an increased level of trustworthiness and reliability. Keywords Service networks; Reliability; Quality of service; Network selection; Ethereum; Business models

Contribution: This research study deploys three serious games with various topics in an entry-level C Programming module and investigates students’ learning outcomes. The study also explores whether learners belonging to different subgroups benefit more from the use of serious games than their peers. The subgroups are formed based on learner demographics that capture learners’ attitude toward school or STEM subjects, their previous educational performance and gender. Background: Despite the latest rapid economy growth in the ICT sector, many European countries are facing the challenge of retaining students in STEM-related subjects, which could lead to unfilled vacancies in the ICT job markets in the near future. Serious games have been utilized in the classrooms of many STEM subjects to improve students’ learning experience and learning outcomes, and potentially encourage their engagement with STEM-related industries. While some prior works had assessed the effectiveness of serious games in improving students’ learning outcomes, little research has been done to investigate the impacts among students with different previous educational background and performance, attitude, and gender. Research Questions: 1) Do the proposed serious games improve students’ learning outcomes? and 2) Do students with certain previous educational performance, learning attitude, and/or gender benefit more than others from the use of serious games? Methodology: To thoroughly study students’ learning outcome, a large-scale pilot was deployed as part of the first-year undergraduate C Programming module at Dublin City University, Ireland. A multidimensional pedagogical assessment toolkit was utilized. In particular, a demographic questionnaire was carried out before the pilot began, based on which students were divided into different subgroups in terms of educational ability, initial attitude to school, attitude toward learning STEM subjects, and gender. Pre- and post-tests were conducted right before and after playing each game. For students belonging to each subgroup and for all students, the average pre- and post-test marks related to each game were compared, while paired-sample $t$ -tests were also conducted to assess the statistical significance of knowledge gain. Findings: Statistically significant knowledge gains were observed in all three games for all students. The students with good previous educational performance or strong attitude toward attending school and learning STEM subjects, tend to gain more by using serious games, as they obtained statistically significant improved learning outcomes in all three games. The students with average starting points, although performed better in the post-test than in the pretest in all games, they achieved statistically significant improvements in some cases only.

This paper provides a performance evaluation of private blockchain networks based on Ethereum, an open-source blockchain platform. Different sectors with stringent blockchain security, privacy, and auditability requirements have adopted Ethereum private networks to keep their data exclusive within a permission group. However, the concrete performance of private Ethereum networks—i.e., transactions or smart contract interactions—has received limited attention. We have set an Ethereum private network to study the following configuration parameters: (1) distinct transaction complexity; (2) different block sizes; (3) two consensus algorithms, namely Proof-of-Work and Proof-of-Authority; and (4) multiple network nodes. Our evaluation has employed time series datasets from the pharma industry, where high levels of security, privacy, and auditability are always required. However, this evaluation approach is domain-agnostic being valid for other fields. We have observed an inverse correlation between the amount of transactions per block and the block period. In this context, we have determined linear models for the simple (low gas limit) and the complex transactions (high gas limit). The model enables to calculate the block period for a specific amount of transactions to be committed in a block. We also include predictive modelling for an optimal configuration taking into account the amount of transactions to commit into a blockchain network. Keywords Blockchain · Performance · Gas limit · Ethereum · Private networks · Pharmaceutical manufacturing · Auditability

The rapid growth in the ICT sector worldwide experienced in the past decade brought about huge economic profits and many vacancies in job markets. Despite the overall relative demand, the education of STEM subjects in many countries is facing challenges as traditional teaching approaches often fail to deliver STEM knowledge in an easy-to-understand way. Recently, researchers and educators have begun to introduce various innovative teaching approaches into STEM subject education as part of the effort to address the above challenge. The NEWTON project is an EU Horizon 2020-funded project whose main objectives include building a networked platform to facilitate integration and dissemination of many technology-enhanced learning (TEL) materials and innovative learning approaches. NEWTON also investigates the impact of using various forms TEL materials, such as serious games, virtual labs, fabrication labs, augmented reality, virtual reality, and innovative learning approaches, such as problem-based learning, on students’ learning outcome and affective states. The targeted STEM subjects was deployed in various education stages. In this paper, we present the Loop game, a third-level 2D serious game developed for a Java programming module, as part of the NEWTON project Programming large-scale pilot. This game conveys the key knowledge points related to the iteration concept, including basic for-loops, for-loop with “continue” statement, and for-loop with “break” statement, deployed in an interactive undersea world scenario. To investigate the impact of the game on students’ learning outcome, the game was deployed during the H8SDEV Software Development module in the Dublin-based National College of Ireland, in 2018. In this pilot, we have attracted mostly mature students (i.e., over 25 years old) with various educational backgrounds, including non-engineering/computer science. Before the pilot started, a demographic questionnaire was conducted, based on which students were grouped into different subgroups according to their gender, prior educational ability, initial attitude towards school and initial attitude towards STEM when analyzing the results. During the pilot, pre- and post-knowledge tests were given to students before and after the game, each including questions targeting the major knowledge points covered by the game. In total, we obtained valid results from 31 students for the Loop game. Overall, there is a statistically significant improvement in learning outcomes before (average score 1.29/3) and after (2.12/3) the game. Considering different gender subgroups, the male group achieved statistically improved learning outcome, whereas the female group achieved an improvement which was not statistically significant. For different prior educational ability subgroups, statistically significant improved learning outcomes were observed in the “good mark” group of students while the “average mark” group only obtained improved learning outcome that was not statistically significant. Considering the attitude to school, the “love/like school” group of students achieved a statistically significant improved learning outcome, whereas the “school is OK” group obtained improved learning outcome that was not statistically significant. Regarding the initial attitude towards STEM, statistically significant improved learning outcomes were observed in both the “love/like learning STEM” student group and the “learning STEM is OK” group.

Production lines in pharmaceutical manufacturing generate numerous heterogeneous data sets from various embedded systems which control the multiple processes of medicine production. Such data sets should arguably ensure end-to-end traceability and data integrity in order to release a medicine batch, which is uniquely identified and tracked by its batch number/code. Consequently, auditable computerised systems are crucial on pharmaceutical production lines, since the industry is becoming increasingly regulated for product quality and patient health purposes. This paper describes the EU-funded SPuMoNI project, which aims to ensure the quality of large amounts of data produced by computerised production systems in representative pharmaceutical environments. Our initial results include significant progress in: (i) end-to-end verification taking advantage of blockchain properties and smart contracts to ensure data authenticity, transparency, and immutability; (ii) data quality assessment models to identify data behavioural patterns that can violate industry practices and/or international regulations; and (iii) intelligent agents to collect and manipulate data as well as perform smart decisions. By analysing multiple sensors in medicine production lines, manufacturing work centres, and quality control laboratories, our approach has been initially evaluated using representative industry-grade pharmaceutical manufacturing data sets generated at an IT environment with regulated processes inspected by regulatory and government agencies.

Having been designed as abstractions of common themes in object-oriented programming, patterns have been incorporated into parallel programming to allow an application programmer the freedom to generate parallel codes by parameterising a framework and adding the sequential parts. On the one hand, parallel programming patterns and their derived languages have maintained, arguably, the best adoption rate; however, they have become conglomerates of generic attributes for specific purposes, oriented towards code generation rather than the abstraction of structural attributes. On the other hand, algorithmic skeletons systematically abstract commonly-used structures of parallel computation, communication, and interaction. Although there are significant examples of relevant applications—mostly in academia—where they have been successfully deployed in an elegant manner, algorithmic skeletons have not been widely adopted as patterns have. However, the ICT industry expects graduates to be able to easily adapt to its best practices. Arguably, this entails the use of pattern-based programming, as it has been the case in sequential programming where the use of design patterns is widely considered the norm, as demonstrated by a myriad of citations to the seminal work of Gamma et al. [6] widely known as the Gang-of-Four. We contend that an algorithmic skeleton can be treated as a structural design pattern where the degree of parallelism and computational infrastructure are only defined at runtime. The purpose of this chapter is to explain how design patterns can be mapped into algorithmic skeletons. We illustrate our approach using a simple example using the visitor design pattern and the task farm algorithmic skeleton.

Cloud computing has enabled the Database-as-a-Service (DBaaS) model to manage large volumes of user-generated data using NoSQL data repositories. There are several NoSQL implementations such as document, columnar, and key-value which ensure high availability, fault tolerance and scalability to serve distinct client requirements. Nonetheless, different NoSQL data models may also introduce unnecessary heterogeneity in DBaaS, which further restricts the user to migrate the application services according to business or technology changes. In this paper, we propose a NoSQL data migration framework to foster data portability across cloud-based heterogeneous NoSQL data repositories. The proposed approach involves data standardisation and classification stages to render an efficient mapping, and translation between cloud-based different NoSQL data stores. The current implementation of the framework supports three different data models: document, columnar and graph. Moreover, the framework is meta-model driven, and therefore allows developers to extend the support for new database models. Our approach includes an online compression algorithm for data migration (document to graph) whereby a graph database requires up to 46% less space. There is also a significant reduction (37% to 55%) in the number of nodes in the compressed graph database.