This study evaluates the usability of Alfaba, a multi-sensory educational tool specifically designed to support literacy development for children and young people with Down Syndrome (DS). Alfaba integrates tactile, visual, and auditory feedback, aiming to facilitate letter recognition, phoneme association, and word construction in a manner aligned with the cognitive and motor needs of this population. A usability test was conducted with seven participants aged 7 to 17, focusing on their interactions with Alfaba to assess accessibility, engagement, and areas for improvement. The results highlight that Alfaba's tangible design and real-time feedback encourage independent learning, support error recognition, and foster motor skill development, making it a valuable literacy tool for individuals with DS. Additionally, the study identifies opportunities for enhancement, such as incorporating touchscreen capabilities, gamified elements, and customizable word groups to further engage users. Alfaba's adaptability to the Brazilian Portuguese phonetic structure also positions it as a culturally inclusive tool that addresses the specific phonological needs of Portuguese-speaking children with DS. This study contributes to the field of inclusive educational technology by offering design recommendations that can enhance Alfaba's impact on literacy and proposes directions for future research to further refine and assess its efficacy in controlled educational settings.

Summary This paper discusses the importance of feature extraction and structure similarity measurement in the analysis of complex networks. Social networks, biological systems, and transportation networks are just a few examples of the many phenomena that have been modeled using complex networks. However, analyzing these networks can be challenging due to their large size and complexity. Feature extraction techniques can help to simplify the network by identifying key nodes or substructures. Structure similarity measurement techniques can be used to compare different networks and identify similarities and differences between them. Previous research has suggested that real‐world complex networks are influenced by multiplex features and either local or global features. However, the interaction between these characteristics is not well understood. The proposed approach outperforms other graph similarity methods on publicly available datasets, with accurate estimations of overall complex network structures. Specifically, the approach based on cosine similarity outperforms as compared to existing methods. Overall, this study highlights the importance of considering various graph features–local and global features and their interactions in the analysis of complex networks.

Link prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, social theory, topology, and similarity-based. The main issue is that path-based methods ignore topological features, while structure-based methods also fail to combine the path and structured-based features. As a result, a new technique based on the shortest path and topological features’ has been developed. The method uses both local and global similarity indices to measure the similarity. Extensive experiments on real-world datasets from a variety of domains are utilized to empirically test and compare the proposed framework to many state-of-the-art prediction techniques. Over 100 iterations, the collected data showed that the proposed method improved on the other methods in terms of accuracy. SI and AA, among the existing state-of-the-art algorithms, fared best with an AUC value of 82%, while the proposed method has an AUC value of 84%.

Abstract This study seeks to answer the following research question: “What factors can explain the number of patent filing requests made by residents in Brazil at patent offices in Brazil, the United States, Europe, and triadic patent families?”. The methods used in this research are quantitative, using big data from private and public investments in Science and Technology, and about patent deposit numbers in Brazil from 2000 to 2017. A model of linear regression was performed and explains how these investments in Science and Technology influence patent deposit numbers. The results of this research study point towards the importance of universities, up and beyond the traditional training and education aspect of university activity. The importance of public and private innovation investments is also shown to be important. This study shows that the patent registrations in the different regions under analysis are affected by different factors. There is thus no single formula towards the creation of innovation output and governments would do well to continue to invest in higher education while also investing in public research and development activities. Additionally, and not least important, private entities should be continually encouraged to make innovation investments and favourable government policies need to thus exist for this to happen. Finally, the low numbers regarding patent filings in Brazil may be linked to institutional deficiencies in the country. Patent breaches may be difficult to punish, and the judicial system may be slow and untrustworthy, compared to the United States and to Europe—leading to diminished patent registrations in Brazil. A set of implications and recommendations for policy derived from this study and will be strategic for policymakers.

Abstract Pervasive computing has become a key element to build applications that use fun as a motivating component because it allows exploring new interaction schemes by making the concept of space and time ambiguous and confusing. The present research describes a pervasive social gaming experience, using as a reference SocialPG, which is a model that describes social expansion as a strategy to improve gaming experiences supported by pervasive computing. In this article a description of the model is offered, software architecture is proposed to support it and a case study related to the process of error resolution and detection of improvement opportunities in software products is developed, finally, a general idea about the final software product that will support the game experience is offered together with an evaluation performed by a set of users, where some important findings are highlighted, such as the importance of the missions as a unit of cooperative work and the spectator's participation.

The increasing difficulty in meeting the needs of the software industry and the benchmarks defined by IEEE and ACM in the adoption of programming languages in Computer Engineering courses offered by higher education institutions in Portugal presents an added difficulty. This difficulty often depends on the profiles that each institution has or intends to characterize the course offered. The objective of this article is to portray the way in which Portugal and Portuguese higher education institutions have organized themselves to respond to the needs of professionals with competences in Computer Engineering, namely in terms of offering courses and places for students, but also in terms of programming language option chosen as initial, as well as the sequence of initial languages.

Abstract As information systems and technologies grow in usage in the agri‐food industry, the same has happened to the relevance of Information Systems (IS) that allow for a parallel control, monitoring and management of the organizations' activities and business processes. As the literature proves, the benefits of implementing adequate and interoperable IS are very numerous and tend to represent a significant determinant regarding the organizations' overall success. Despite this, to the best of our knowledge there currently is no IS architecture designed to serve the specificities of the agri‐food industry. With this study a novel information systems architecture for the agri‐food sector is proposed. The artefact is composed by 12 integrated main components and a set of subcomponents aimed at supporting all the monitoring, control and management activities. In order to validate the proposed architecture a case study was implemented at a mushroom production organization. This allowed us to perceive the ability of our artefact to serve as the basis for the development of IS that address all of the organization's business and environmental needs.

The constant growth of technology leads to the development of expert systems that serve to support critical decision-making and have applications in many areas, such as healthcare, business, chemistry, financial decision-making, and engineering. These systems are computer programs derived from a computer science research branch called Artificial Intelligence (AI) and use human knowledge intensively in problem-solving. These programs combine expert knowledge and use the knowledge necessary to solve problems (Kidd, 2012). In this special issue, we present a range of papers covering some of the subareas of expert systems such as intelligent and decision support systems, ethics, computers, and security, health informatics, simulations, and big-data analytics. This special issue comprises six research papers. All manuscripts are extended versions of selected papers from WorldCIST'19 - 7th World Conference on Information Systems and Technologies, held in at La Toja Island, Galicia, Spain, April 2019. The WorldCIST conference have become a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences, and concerns in the several perspectives of Information Systems and Technologies, as well as computer science in general. The six selected papers in this special section include a Virtual Programming Lab (VPL), a model's predictions, a novel information systems architecture for the agri-food sector, various approaches for detection of malware, an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment, as well as an expert system comprising a self-aware framework for resource-efficient and accurate data transmission within a low-power lossy sensor network (LLN) deployed for indoor monitoring. Cardoso et al. (2020) present the VPL, a Moodle plugin that allows students to submit their code and get prompt feedback without the teacher's intervention. To test this concept, an experiment was performed with several classes of beginner programming students, in two editions of Algorithms and Programming course unit of the degree in Informatics Engineering lectured at the Informatics Engineering Department at the School of Engineering, Polytechnic Institute of Porto. The students were challenged to test their assignments in VPL with a set of test values previously defined by the teachers. After the experiments, the authors used surveys to gather the involved students' and teachers' opinion, and more than 70% of the students answered that they considered the VPL an added value for the teaching–learning process. The dynamics verified in the classes, the general opinion of the teachers, and the acceptance and participation of the students allow to classify the experience as positive. Almomani et al. (2020) have analysed the decision-making process underlying choice behaviour. First, neural and gaze activity were recorded experimentally from different subjects performing a choice task in a Web Interface. Second, choice models and ensembles were fitted using rational, emotional, and attentional features. The model's predictions were evaluated in terms of their accuracy and rankings were made for each user. The results show that (a) the attentional models are the best in terms of its average performance across all users, (b) each subject shows a different best model, and (c) ensembles may perform better than single choice models but an optimal building method must be found. Branco et al. (2020) have shown the information systems and technologies grow in usage in the agri-food industry, the same has happened to the relevance of Information Systems (IS) that allow for a parallel control, monitoring and management of the organizations' activities and business processes. As the literature proves, the benefits of implementing adequate and interoperable IS are very numerous and tend to represent a significant determinant regarding the organizations' overall success. In this paper the authors present a novel information systems architecture for the agri-food sector. The artefact is composed by 12 integrated main components and a set of subcomponents aimed at supporting all the monitoring, control, and management activities. To validate the proposed architecture a case study was implemented at a mushroom production organization. This allowed to perceive the ability of the artefact to serve as the basis for the development of IS that address all the organization's business and environmental needs. Jan et al. (2020) have shown that malware analysis and detection over the Android have been the focus of considerable research, during recent years, as customer adoption of Android attracted a corresponding number of malware writers. Antivirus companies commonly rely on signatures and are error-prone. Traditional machine learning techniques are based on static, dynamic, and hybrid analysis; however, for large scale Android malware analysis, these approaches are not feasible. Deep neural architectures can analyze large scale static details of the applications, but static analysis techniques can ignore many malicious behaviours of applications. The study contributes to the documentation of various approaches for detection of malware, traditional and state-of-the-art models, developed for analysis that facilitates the provision of basic insights for researchers working in malware analysis, and the study also provides a dynamic approach that employs deep neural network models for detection of malware. Moreover, the study uses Android permissions as a parameter to measure the dynamic behaviour of around 16,900 benign and intruded applications. A dataset is created which encompasses a large set of permissions-based dynamic behaviour pertaining applications, with an aim to train deep learning models for prediction of behaviour. The proposed architecture extracts representations from input sequence data with no human intervention. The state-of-the-art Deep Convolutional Generative Adversarial Network extracted deep features and accomplished a general validation accuracy of 97.08% with an F1-score of 0.973 in correctly classifying input. Furthermore, the concept of blockchain is utilized to preserve the integrity of the dataset and the results of the analysis. Oliveira et al. (2020) presents an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality-of-life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed-effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality-of-life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life. Habib et al. (2020) have developed an expert system comprising a self-aware framework for resource-efficient and accurate data transmission within a low-power LLN deployed for indoor monitoring. We derived both individual and group awareness, which could ensure the awareness of each sensor regarding its resources, neighbours and network environment. The proposed expert system facilitates decision-making under dynamic environmental conditions and employs a multi-criteria decision-making (MCDM) model to determine the selection of the best path towards the sink node with awareness of the existing network environment. The proposed system is validated by constructing a 6LoWPAN network in the Contiki Cooja simulator. MCDM is applied to generate an adaptive objective function for the IPv6 routing protocol for the LLN (RPL) and to aid in ranking the nodes to select the best available neighbouring node, while the data accuracy is ensured by the cluster head through data correlation among its associated members. The network performance is assessed by analyzing the packet delivery rate, throughput and energy consumption against varying sensors and by comparing our proposed MCDM-RPL with a standard RPL and a fuzzy-based RPL, where the results show that our framework is found to be better with gains of 13%, 25% and 13%, respectively. Álvaro Rocha holds the title of Honorary Professor, and holds a D.Sc. in Information Science, Ph.D. in Information Systems and Technologies, M.Sc. in Information Management, and BCs in Computer Science. He is a Professor of Information Systems at the University of Lisbon - ISEG, researcher at the ADVANCE (the ISEG Centre for Advanced Research in Management), and a collaborator researcher at both LIACC (Laboratory of Artificial Intelligence and Computer Science) and CINTESIS (Center for Research in Health Technologies and Information Systems). His main research interests are maturity models, information systems quality, online service quality, requirements engineering, intelligent information systems, e-Government, e-Health, and information technology in education. He is also Vice-Chair of the IEEE Portugal Section Systems, Man, and Cybernetics Society Chapter, and Editor-in-Chief of both JISEM (Journal of Information Systems Engineering & Management) and RISTI (Iberian Journal of Information Systems and Technologies). Moreover, he has served as Vice-Chair of Experts for the European Commission's Horizon 2020 program, and as an Expert at the COST - intergovernmental framework for European Cooperation in Science and Technology, at the Government of Italy's Ministry of Universities and Research, at the Government of Latvia's Ministry of Finance, at the Government of Mexico's National Council of Science and Technology, and at the Government of Polish's National Science Centre. Fernando Moreira is Head of the Science and Technology Department. He is graduated in Computer Science (1992), M.Sc. in Electronic Engineering (1997) and PhD in Electronic Engineering (2003), both at the Faculty of Engineering of the University of Porto and Habilitation (2018). He is a member of the Science and Technology Department at Portucalense University since 1992, currently as a Full Professor and a visiting professor at the University of Porto Business School. He teaches subjects related to undergraduate and post-graduate studies. He supervises several PhD and M.Sc. students. He is a (co-)author of more than 200 scientific publications with peer-review on national and international journals and conferences. He serves as a member of the Editorial Advisory Board for several journals and books. He organized several special issues from JCR journals. He has already regularly served as a member of Programme and Scientific committees of national and international conferences. He was the MSc in Computation coordinator during the last 10 years. He holds editorial experience, and he is co-editor of several books. He is associated with NSTICC, ACM and IEEE. His principal research areas are mobile computing, ICT in Higher Education, Mobile learning, Social business and Digital transformation. He was awarded Atlas Elsevier Award, April 2019. Ashwani Kumar Dubey received the AMIE (BE) degree in Electronics and Communication Engineering from the Institutions of Engineers (India) Kolkata, India, M. Tech. degree in Instrumentation and Control Engineering from Maharshi Dayanand University, Rohtak, Haryana, India, in 2007, and Ph. D degree from the Department Electrical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia (A Central Govt. University), New Delhi, India, in 2014. He served Apeejay College of Engineering (Sohna), Gurgaon, India; Applied College of Management and Engineering, Palwal, India; BDES Group of Institutions, Faridabad, Indiaq and GD Goenka World Institute-Lancaster University, UK, Sohna, India at different academic and administrative positions. Presently, he is an Associate Professor in the Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India. He has more than 20 years of experience at different positions. Dr. Dubey has filed 20 patents and regularly contributed more than 70 research papers in various reputed International and National Journals/Conferences. He has chaired many IEEE International conferences and workshops. He is a regular reviewer of IEEE Transactions on Instrument and Measurement, IEEE Transactions on Industrial Electronics, IEEE Sensors Journal, IEEE Internet of Things (IoT) Journal and Springer's Wireless Personal Communications. He is the Organizing Chair of IEEE SPIN 2019, IEEE SPIN 2020, and IEEE SPIN 2021 International Conferences. Dr Dubey has organized several International and National webinars and seminars. He has reviewed a book “Biosensors: An Introductory Textbook” published by Pan Stanford, Singapore. He edited two books namely “Microcontrollers and Embedded Systems” and “Digital Electronics” published by UDH publications, India. He is a corporate member of The Institution of Engineers (India). He had been honoured with “Shikshak Shiromani Award” by Lions Club, Delhi, India in 2011 and as an “Eminent Speaker” in “Technology based Entrepreneurship Development Programme (TEDP) 2016” sponsored by “National Science & Tech. Entrepreneurship Development Board, DST, Govt of India, New Delhi”, and “Entrepreneurship Development Institute of India, Ahmedabad, Gujarat, India in 2016”. He has developed many visual inspection systems for various industrial and agricultural applications to improve and monitor the quality and reliability of the products/systems. He has guided three PhD research scholars and presently guiding six PhD research scholars. His research works includes wireless sensor networks, digital image processing, medical image processing, food, and fruit quality inspections through computer vision, MEMS/ NEMS based smart sensors and systems, etc. As the guest editors, we are thankful to great researchers/scholars for their outstanding contributions to this special issue and reviewers for their timely and professional input. We would also take this opportunity to thank Jon Hall, Editor-in-Chief of the Wiley journal “Expert Systems”. In the end, we would extend our special gratitude and thanks to the WorldCIST'19 program committee members for their hard work and dedication, which is highly commendable, and also to the reviewers who made the reviews of the extended versions of the articles published in this special issue.

In wireless sensor networks for the Internet of Things (WSN‐IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat‐based WSN‐IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN‐IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large‐size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN‐IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN‐IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH‐set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new‐fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state‐of‐the‐art methods like MobAC (Singh and Lohani, 2019), EPSO‐C (Pathak, 2020), and PBC‐CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime.

Abstract An electronic business transaction among untrusted bodies without consulting a mutually trusted party has remained widely accepted problem. Blockchain resolves this problem by introducing peer‐to‐peer network with a consensus algorithm and trusted ledger. Blockchain originally introduced for cryptocurrency that came with proof‐of‐work consensus algorithm. Due to some performance issues, scientists brought concept of permissioned Blockchain. Hyperledger Fabric is a permissioned Blockchain targeting business‐oriented problems for industry. It is designed for efficient transaction execution over Blockchain with pluggable consensus model; however, there is limitation of rapid application development. Hyperledger introduced a new layer called Hyperledger Composer on top of the Fabric layer, which provides an abstract layer to model the business application readily and quickly. Composer provides a smart contract to extend the functionality and flexibility of Fabric layer and provides a way of communication with other systems to meet business requirements. Hyperledger Composer uses role‐based access control (RBAC) model to secure access to its valuable assets. However, RBAC is not enough because many business deals require continuous assets monitoring. Our proposed model, BlockU, covers all possible access control models required by a business. BlockU can monitor assets continuously during transactions and updates attributes accordingly. Moreover, we incorporate hooks in Hyperledger Composer to implement extended permission model that provides extensive permission management capability on an asset. Subsequently, our proposed enhanced access control model is implemented with a minimal change to existing Composer code base and is backward compatible with the current security mechanism.

Abstract At a time when communication, new media, and digitalization are transversal to the whole of society, private health care organizations have the possibility of making their business processes evolve. The objective is thus to seize the benefits associated to the active use of patients' electronic health records (EHRs) as the basis for personalized health care. In order to initially validate the health care sector acceptance of a 360° health care information system (HIS), focused on collecting patients' data to create the necessary knowledge for delivering personalized health care procedures and initiatives, a focus group involving a set of health‐related professionals was performed. Despite recognizing the immense possibilities associated to EHR and its direct incorporation on a 360° HIS, the referred professionals still highlighted their concerns relative to the maintenance of adequate security and privacy levels. With this in mind, a proposal for a 360° HIS model is presented, and its main functional blocks are described with a focus on triggering patient/customer loyalty.

Abstract The aim of this article is to study how students currently understand the conception of space through different media and how that understanding helps them to intervene in the space. Firstly, the process of teaching and learning as well as innovative supporting technologies are analysed, revealing the characteristics of the contemporary student profile and better ways of teaching according to it. Secondly, we describe the evaluation of an experiment with a virtual reality (VR) system for urban project design with students of architecture from two universities. The premise is that the technology used in VR is familiar to the current profile of students. This paper aims to study the advantages and disadvantages of this trend and to find a balance. To do so, we use a quantitative method to evaluate students' profiles and their level of satisfaction with the system. The results were obtained by a questionnaire and a survey, which show the role and use of technologies in the students' environment and the degree of satisfaction when using it in the educational processes. In line with our assumptions, the value of satisfaction in the use of an advanced visualization technology in the classroom reveals a high level of motivation, in general, with differentiation between students in their first and last phases of studies.

Virtual reality technologies have grown in complexity and functionality, increasing their relevance in various sectors of activity. Applying these innovations to tourism is advantageous for the organisations, the tourists and even the territories. They can provide multisensory immersivity, simulate actions and movements in real-time, enriching reality using interactive virtual objects. Thus, captivating visitors and showing territories from richer and livelier perspectives. This study describes a review of the adoption of virtual reality technology at the organisational level. In order to adapt the literature review to the tourism topic, the process of analysis of the literature was carried out with a constant concern to analyse research that studied the topic. Assuming the need to further develop the work done and to obtain a confirmation on the contribution resulting from the bibliographic review, a focus group was developed, which allowed several specialists to discuss collaboratively and interactively the research project topics without having to be in face-to-face contact. The results of this study allow not only to acknowledge the relevance of virtual reality for tourism but also to understand how the existing literature and experts are aligned with the identification of opportunities for disruptive development of solutions that trigger tourism destinations and organisational development.

The increasing number of mobile devices on day-to-day of the general population and particularly among youth people, leads to the emergence of new paradigms in several areas of activity, particularly in education. As an example of a new paradigm in the teaching / learning process can be invoked the mobile learning, such as technologies did not stop and progressed to become part of the Blended Mobile Learning (BML) model. In this paper and in the context of three-year courses Bologna Process European Degree structure at school, in Portugal, following the Curriculum Guidelines for Undergraduate Degree in Information Systems from ACM/AIS, we intend to test Mobile Google Docs system in our BML Context Oriented (BML-CO) model into an Information System course.