A identificação e desenvolvimento de um medicamento que seja seguro, selectivo e eficaz, é um processo intrincado que envolve um plano de investigação estimulante, porque o entendimento da fisiopatologia humana é complexo e altamente variável entre indivíduos. A metabolómica permite a descoberta de biomarcadores que esclarecem as bases moleculares dos processos metabólicos, e que podem ser utilizados para diagnóstico, para avaliação do prognóstico e eficácia/segurança de um tratamento. A metabolómica constitui um componente importante do processo de descoberta de medicamentos, sendo uma ferramenta crucial para medicina de precisão. Da farmacologia à exploração espacial e caracterização de organismos, a metabolómica tem um papel central na resolução de diversas questões biológicas, biomédicas e ambientais. Com os avanços tecnológicos em espectrometria de massa e plataformas de espectroscopia que geram conjuntos de dados complexos e ricos em informações, conhecidos como big-data, a análise de dados evoluiu para acompanhar o ritmo da instrumentação analítica. Ferramentas de software, recursos, bancos de dados e soluções de processamento dos mesmos, contribuem para extrair conclusões e eventual sucesso de tradução da informação. Este trabalho pretende contribuir para um melhor entendimento de conceitos nas áreas emergentes da metabolómica e inteligência artificial (AI). Pretende-se igualmente discutir as possíveis aplicações em ciências farmacêuticas e descoberta de medicamentos, salientando o potencial impacto dessas tecnologias. A AI e, especificamente, o deep learning (DL), uma área emergente de investigação nas áreas de machine learning (ML) e AI, registaram avanços significativos nos últimos anos. As técnicas de DL estão a ser aplicadas de forma a ajudar os profissionais de saúde e investigadores a melhorar o diagnóstico clínico, à prevenção de doenças e à otimização do longo processo de descoberta de medicamentos cuja taxa de insucesso é geralmente muito elevada. Espera-se que o DL ajude a obter conhecimento fundamentado a partir de "big-data", incluindo dados metabolómicos, em áreas emergentes das ciências farmacêuticas.

A evolução da Inteligência Artificial (IA) tem impulsionado mudanças significativas em diversos setores da sociedade civil, incluindo a pesquisa e desenvolvimento de novas tecnologias. Motivada pela ideia de analisar as novas tecnologias e como os países estão a reagir com a Inteligência Artificial e seus regulamentos, o desenvolvimento do presente trabalho foi dedicado ao estudo do sistema de patentes com o intuito de pretender demonstrar a situação atual da proteção às obras e criações geradas por Inteligência Artificial, bem como a necessidade de adequação, pelo Direito Industrial, nomeadamente pelo Direito de Patente. Reconhecemos que as patentes desempenham um papel fundamental na proteção dos avanços tecnológicos e na promoção da inovação. É nesse contexto que trazemos uma análise do impacto da IA no sistema de patentes, explorando as intersecções entre essas duas áreas. Discute-se como a IA está sendo aplicada em várias obras e criações, a análise também aborda questões relacionadas à legislação internacional de patentes e como as diferentes jurisdições estão respondendo aos desafios propostos no uso da IA. O estudo destaca a possibilidade de harmonização das políticas de patentes para garantir uma proteção adequada à inovação impulsionada pela IA. A troca de conhecimento e a criação de políticas eficazes são essenciais para garantir que a IA continue a estimular os avanços tecnológicos. Ao mesmo tempo verificaremos os níveis de proteção adequadamente coligados aos direitos da propriedade intelectual. Por último, foram apresentadas as conclusões do trabalho, onde se reuniram as principais ideias desenvolvidas ao longo dos diversos capítulos. Além disso, apesar das incertezas do futuro, os robôs, algoritmos e máquinas têm demonstrado uma crescente autonomia, complexidade e aprimoramento resultando em produtos que se assemelham cada vez mais às criações do intelecto humano. Nesse cenário, emergem as questões centrais que norteiam esta pesquisa ao vislumbrar respostas ao principal questionamento: as máquinas podem ser enquadradas como autoras na criação de novas invenções pelo sistema de patentes?.

Background: Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients. Methods: Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEFNov) and by experts (LVEFExp) were compared with LVEF reference measurements (LVEFRef) taken manually by echo experts. Results: LVEFRef ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEFRef < 50% (left ventricular systolic dysfunction). Real-time LVEFExp and LVEFNov measurements ranged from 31 to 68% (mean 54 ± 10%) and from 28 to 70% (mean 54 ± 9%), respectively. The reproducibility of measurements was comparable for LVEFExp (5 ± 4%) and for LVEFNov (6 ± 5%) and significantly better than for reference measurements (p < 0.001). We observed a strong relationship between LVEFRef and both real-time LVEFExp (r = 0.86, p < 0.001) and LVEFNov (r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEFExp and 0 ± 7% for LVEFNov. The sensitivity to detect systolic dysfunction was 70% for real-time LVEFExp and 73% for LVEFNov. The specificity to detect systolic dysfunction was 98% both for LVEFExp and LVEFNov. Conclusion: Machine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved.

Presents the results of a study of the impact of artificial intelligence on early career researchers (ECRs). An important group to study because their millennial mindset may render them especially open to AI. We provide empirical data and a validity check of the numerous publications providing forecasts and prognostications. This interview-based study—part of the Harbingers project on ECRs—covers a convenience sample of 91 ECRs from all fields and seven countries using both qualitative and quantitative data to view the AI experience, engagement, utility, attitudes and representativeness of ECRs. We find that: (1) ECRs exhibit mostly limited or moderate levels of experience; (2) in regard to engagement and usage there is a divide with some ECRs exhibiting little or none and others enthusiastically using AI; (3) ECRs do not think they are unrepresentative when compared to their colleagues; (4) ECRs who score highly on these measures tend to be computer scientists, but not exclusively so; (5) the main concerns regarding AI were around authenticity, especially plagiarism; (6) a major attraction of AI is the automation of ‘wordsmithing’; the process and technique of composition and writing.

More than 99% of cybercriminal attacks exploit human characteristics and behaviour (people’s vulner- abilities) rather than vulnerabilities in computer systems. When it comes to cybersecurity, it is crucial to consider human behaviour on the part of the user. Cognitive aspects such as attention, memory, and reasoning can be affected by conditions such as fatigue, illness, or injury and are pointed out as human characteristics that are at the origin of several incidents. Users’ stress and anxiety may be correlated with making mistakes that compromise cybersecurity. Thus, behavioural psychology should be used to develop effective educational strategies and encourage safe and conscious behaviour in relation to cybersecurity. By understanding how users interact and respond to AI, more effective and aware cyber- security systems can be created. This chapter’s overall objective is to map the literature regarding the psychology of behaviour towards AI in cybersecurity, using a scoping review methodology to provide input into the behavioural psychology aspects regarding AI and cybersecurity.

The growing presence of AI-based tools in teaching practices presents new challenges for training institutions, requiring future teachers to be prepared not only to use these technologies but also to critically understand their implications. This study aims to investigate future teachers’ perceptions of the main challenges of AI in teacher training, focusing on three central dimensions: technological, pedagogical, and ethical challenges. The research employed a quantitative–descriptive approach, utilizing a questionnaire-based method with a sample of 72 participants. The respondents were students enrolled in the Bachelor’s degree in Basic Education and the professional Master’s degree in Teaching at a higher education institution located in northern Portugal. The results not only reveal a widespread perception of the relevance of AI for pedagogical practice but also point to significant limitations. On the technological level, difficulties related to the available infrastructure and the digital training of teachers in training stand out. Pedagogical challenges are associated with the effective integration of AI tools into teaching strategies, as well as the redefinition of the teacher’s role in a technology-mediated setting. Finally, ethical challenges include concerns about data privacy, algorithm transparency, and the risks of dehumanization of the educational process. It is concluded that teacher training should incorporate a critical and interdisciplinary approach to AI, promoting technical, pedagogical, and ethical skills that enable future educators to act responsibly and conscientiously in an increasingly digital and automated educational setting.

This study is dedicated to critically analysing research addressing human resource management (HRM) and the adoption of artificial intelligence (AI) with the purpose of driving development in the field of human resources (HR) at the strategic and managerial level. Design/methodology/approach – A systematic literature review (SLR) was conducted using the Scopus database, which gathered 61 articles between 2002 and 2022. The SLR process has the potential to, in addition to generating knowledge and theories, support and guide policy development and practice in many disciplines. Findings – The results of this study allowed the author to identify three main conclusions: (a) there are four thematic clusters – (i) Strategic HR and AI, (ii) Recruitment and AI, (iii) Training and AI and (iv) Future of work; (b) there is a growing academic interest in studying the implementation of AI to develop the HR sector and (c) the application of AI stands out in the strategic HR and AI cluster as a means of achieving profit maximisation and the overall development of the organisation. Originality/value – This study is the first SLR to present a strategic and managerial view on AI applications associated with specificHRMdimensions. The study is also the first SLR to identify key trends in the literature, drivers and obstacles to the development of AI in HRM and then place them within the landscape of positive and negative approaches in a framework. Also, as a contribution, the study has practical implications for HR managers and practitioners in adopting AI as a decision support in the area’s processes.

The use of artificial intelligence has emerged as an important tool for teacher training, providing new approaches to the development of pedagogical skills, classroom management, and the personalization of teaching. This research aims to identify the most relevant AI tools for teacher training and to examine the benefits and challenges of integrating AI into teacher training, with a focus on the advantages of personalizing teaching and the difficulties associated with adopting new technologies in the educational context. The methodology used is a literature review, in which studies, scientific articles, and reports on the application of AI in teacher training are analyzed to identify the technological tools available for teaching. The results suggest that AI has enormous potential to transform teacher training, offering tools that make it possible to personalize the training process, adapting it to teachers' needs and learning styles. However, the implementation of these tools faces challenges such as the need for ongoing teacher training, resistance to adopting new technologies, and the lack of technological infrastructure in some educational institutions.

As the world's population continues to grow, environmental challenges become more pressing, the agribusiness sector, which includes all operations related to producing and distributing food, plays a crucial role in addressing climate change, resource scarcity, and sustainability. The research focuses on Georgia, a nation with a rich agricultural history and immense potential, aiming to analyse the role of artificial intelligence (AI) and agricultural technologies in the agroindustry. To accomplish this objective, a quantitative research methodology was utilised by gathering farmers’ opinions regarding the implementation of AI in their businesses. Additionally, measuring farmers' attitudes towards AI and agricultural technologies was the main element in determining the significance of these technologies in the agribusiness sector of Georgia. Based on theoretical and empirical research in this field, the study evaluates the role of AI in agribusiness, assesses how agricultural technologies affects production, and the benefits and methods of utilizing AI. Furthermore, the research explores Georgia’s opportunities and challenges in advancing AI and agricultural technologies within the agribusiness sector. According to the research and the obtained results, one of the significant obstacles faced by farmers in adopting modern agricultural technologies and AI applications is their lack of skills and knowledge in using them. Despite not having technological skills, the assessments’ findings indicate that most respondents hold positive attitudes towards the impact of AI applications and modern technologies on production yield and the realisation process in agribusiness. Moreover, the research highlights the importance of considering gender differences when examining farmers' attitudes towards agricultural technologies. Results provide evidence that there are significant differences in the attitude towards agricultural technologies based on the age of farmers, younger farmers display a more positive attitude towards embracing AI and agricultural technologies. Furthermore, the educational level of farmers influences their perception of the importance of AI and agricultural technologies. Finally, this thesis aims to contribute to the development of a sustainable and prosperous agro-industrial sector in Georgia by reinforcing the adoption of AI and agricultural technologies.

In contemporary society, certain physical attributes are celebrated, while others are stigmatized, leading to barriers in the social inclusion of individuals who do not conform to these idealized standards. People with disabilities (PwD) often face societal prejudices, further exacerbated by the absence of adaptive tools, pushing them away from a conventional life. Approximately 15% of the global population has some form of disability, with a significant portion experiencing physical disabilities related to upper limbs. Among these, many undergo amputation, a transformative process that affects both their physical and psychological well-being. Prostheses, while beneficial, have limitations in replicating the full range of limb movements and are often financially inaccessible to many. This research proposes an innovative system that leverages the retained ability of amputees to generate electromyographic (EMG) signals post-amputation. The system aims to control electronic devices directly through these signals, bypassing the need for prosthetics. Potential applications include replacing traditional computer mice and controlling gaming platforms. The core design is a compact bracelet equipped with non-invasive EMG sensors, an accelerometer, and a gyroscope. Data from these sensors are processed using artificial intelligence techniques to generate device-specific commands. The overarching goal is to enhance the autonomy and social integration of amputees, while also contributing to technological advancements in the field.

Future industrial systems can be realized using the cyber-physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system's functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.

The world population, on average, has seen its life expectancy increase. This phenomenon is causing the elderly population to grow. This reality has put pressure on the development of solutions - across a multidisciplinary spectrum - that are capable of helping the elderly in their daily lives. This assistance is especially important for elderly people who live alone and isolated - a common reality in many rural areas where there is a low population density. This work presents an architecture based on the use of digital image acquisition and processing associated with using Artificial Intelligence. This way, we can create solutions with easy deployment that do not disrupt the elderly’s normal day-to-day life and ensure that third parties accompany them. Even though the proposed architecture can be complemented with sensory aspects linked to the elderly person themselves, this component, as it is independent and easily incorporated, was not the object of study.

This research aims to explore the potentialities and challenges of AI in education in Cape Verde, in order to understand how AI can be used in the teaching–learning context. To carry out the research, a qualitative methodology was adopted supported by a literature review. With support from the documents found, the benefits and potentialities were analyzed, reflecting on the ethical implications of the application of AI in education, with emphasis on privacy, data security and ethics. We discussed the findings related to the potential and challenges of applying AI and how it can play a key role in analyzing data on learners’ performance and preferences, providing immediate feedback, personalized recommendations and complementary activities. Research suggests that AI in education has the potential to improve the equity, quality and reach of education, however, it is necessary to face challenges, such as ensuring the ethical use of AI, accessibility for all students, data protection and privacy. In Cape Verde, AI can be a powerful tool to mitigate socio-economic and geographical challenges, providing a more balanced and equal education across all islands. However, the challenges of access to high-speed internet and the quality of services provided by utilities can be real challenges to overcome.

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

The 3rd International Congress on Blockchain and Applications 2021 will be held in Salamanca from 6 to 8 of October. This annual congress will reunite blockchain and artificial intelligence (AI) researchers, who will share ideas, projects, lectures, and advances associated with those technologies and their application domains. Among the scientific community, blockchain and AI are seen as a promising combination that will transform the production and manufacturing industry, media, finance, insurance, e-government, etc. Nevertheless, there is no consensus with schemes or best practices that would specify how blockchain and AI should be used together. Combining blockchain mechanisms and artificial intelligence is still a particularly challenging task.

Business intelligence (BI) and analytics are a set of techniques, methodologies, and tools used in the analysis of business data, which allow users (decision makers) to have a clearer view of the market, leveraging the decision-making process, allowing timely business decisions. Usually BI refers to Extract Transform and Load processes (ETL), Data Warehouse (DW), Data mining (DM), online analytical processing (OLAP), visualization tools, and reports. In turn, the analytics generally uses advanced techniques, providing BI users with Artificial Intelligence (AI) and Machine Learning (ML) techniques. In this context, Gartner introduced the term “augmented analytics” in 2017, making the line between BI and advanced analytics clear. The main objective of this work is to explore the area of Augmented Analytics in the context of BI, through the use of ML and Natural Language Processing (NLP) resources and capabilities, as an innovative paradigm of augmented analytics in the decision-making process.

Digital twins are increasingly used, as they allow the creation of detailed virtual representations of physical products and systems. They face, however, significant challenges such as heterogeneous data integration and high costs. This article presents an innovative methodology that uses Large Language Models to unify information and automate the generation of Digital Twin models. The proposal comprises several modules, covering the stages of data collection, semantic processing, modular construction and validation of the Digital Twin. In this way, the proposed model guarantees interoperability, efficiency and scalability for various domains.

Data analytics and Artificial Intelligence (AI) have emerged as essential tools in manufacturing over recent years, providing better insight into production systems. Their importance can be highlighted by the way it can transform quality control, from prescriptive to proactive. Data analytics combined with AI can identify abnormal trends and patterns in huge amounts of data, that could uncover potential defects and allow pre-emptive action to minimize or even prevent these from happening. A direct effect of this is the contribution to waste reduction, as well as saving time and resources. While data in a digital factory is ample and the resources for developing artificial intelligence applications are accessible, the implementation of accurate, robust, standard, and economically viable quality monitoring and assessment approaches is not straightforward. This is also strengthened by the scarce skillset in today’s manufacturing companies in this area. In this study, the capabilities and potential of data analytics combined with AI are reviewed with a focus on manufacturing. The implementation challenges posed for a practitioner, as well as the benefits of implementing a solution for a manufacturer using data analytics and AI for quality assessment are discussed, based on real-world experiences from existing production environments. Lastly, a learning approach utilizing a high-fidelity digital twin at its core is presented which a practitioner can utilize to create, test and continuously improve a predictive analytics model.

In the last decade there has been a substantial increase in references to Artificial Intelligence (AI). AI has entered people’s lives, without them often realizing it. AI is increasingly present in our lives and in our daily lives, from our homes, transport, travel, industry, etc. It is inseparable to talk about AI without talking about chatbots. Therefore, the main objective of this study is to study the potential of chatbots in the tourism industry. For this purpose, a questionnaire was carried out among Higher Education students in Portugal, as it is understood that it is a good universe of study, since these technologies are still more associated with young audiences. On the other hand, also as a result of programs such as Erasmus, these young people travel more and are more aware of the potential of chatbots in tourism. The results of the questionnaire are presented in light of the literature and the responses collected, as well as the limitations of the present study. Future work that may mitigate the limitations of this research work are also presented.

The 12th Edition of the International Conference on Videogame Sciences and Arts, Videojogos2020, is a joint organization of the School of Public Management, Communication and Tourism – Polytechnic Institute of Bragança (EsACT – IPB) and the Portuguese Society of Videogames Sciences (SPCV). This year, due to the pandemic context, activities were conducted online.