Ciências e Tecnologia | Artigos em revistas internacionais / Papers in international journals
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- Innovating for tomorrow: industry 4.0’s role in sustainable healthcarePublication . Ferreira, Ana Rita O.; Fernandes, TiagoPurpose: It is essential for professionals to stay informed about the revolution we are witnessing and understand the related technical concepts in today’s rapidly evolving society. In addition to exploring how Industry 4.0 technologies—such as artificial intelligence (AI), the Internet of Things (IoT), big data, and blockchain—are transforming the healthcare sector, this literature review aims to address a crucial question: How can new technologies improve operational and environmental sustainability in healthcare while maintaining accessibility, safety, and equity? Methods: A comprehensive literature study was conducted to analyze current research and discussions around the integration of Industry 4.0 technology in healthcare. This paper covers the various aspects of automation, data processing, system interconnection, ethical, financial, and infrastructure issues. Among the main topics discussed is data integration, which helps to enhance resource management, predictive analytics, and decision-making. Results: Through international case studies (Mayo Clinic, Cleveland Clinic, Manifal Hospital, University College London Hospitals, Charité Hospital, Mount Sinai Hospital, Centro Hospitalar e Universitário de Coimbra) this review shows how these technologies improve patient outcomes, healthcare efficiency, and the environment. Significant problems should be warned about, including ethical issues about patient data security and privacy, unequal access to technological developments, the necessity of guaranteeing health information system interoperability, and economic viability concerns, particularly in countries or regions with limited resources. Conclusions: Healthcare 4.0 has enormous potential for global justice and sustainability, but its ethical integrity, data security, and broad accessibility must be monitored. Although the healthcare sector will benefit much from the 4th industrial revolution, this analysis cautions about the related social and economic issues and inequalities. Encouraging ethical innovation will rely much on public policies to support this technological transition being implemented in a sustainable manner in the healthcare sector.
- Comparison of two problem transformation-based methods in detecting the best performing branch-and-bound procedures for the RCPSPPublication . Guo, Weikang; Coelho, José; Vanhoucke, MarioThe branch-and-bound (B&B) procedure is one of the most frequently used methods for solving the resource constrained project scheduling problem (RCPSP) to obtain optimal solutions and has a rich history in the academic literature. Over the past decades, various variants of this procedure have been proposed, each using slightly different configurations to search for the optimal solution. While most of the configurations perform relatively well for many problem instances, there is, however, no known universal best B&B configuration that works well for all problem instances. In this work, we propose two problem transformation-based machine learning classification methods (binary relevance and classifier chains) to automatically detect the best-performing branch-and-bound configuration for the resource-constrained project scheduling problem. The proposed novel learning models aim to find the relationship between the project characteristics and the performance of a specific B&B configuration. With this obtained knowledge, the best-performing B&B configurations can be predicted, resulting in a better solution. A comprehensive computational experiment is conducted to demonstrate the effectiveness of the proposed classification models and the performance improvements over three categories of methods from the literature, including the latest branch-and-bound configurations, the state-of-the-art classification models in project scheduling, and commonly used clustering algorithms in machine learning. The results show that the proposed classification models can enhance solution quality for the RCPSP without changing the core components of existing algorithms. More specifically, the classifier chains method, when combined with the BackPropagation Neural Network algorithm, achieves the best performance, outperforming binary relevance, which demonstrates the impact of label correlation on the performance. The experiments also demonstrate the merits of the proposed model in improving the robustness of the solutions. Furthermore, these findings not only highlight the potential of the classification models in detecting best-performing B&B configurations, but also emphasize the need for future work and development to further improve the performance and applicability of these models
- The state of the art in procedural audioPublication . Menexopoulos, Dimitris; Pestana, Pedro Duarte; Reiss, Joshua D.Procedural audio may be defined as real-time sound generation according to programmatic rules and live input. It is often considered a subset of sound synthesis and is especially applicable to nonlinear media, such as video games, virtual reality experiences and interactive audiovisual installations. However, there is resistance to widespread adoption of procedural audio because there is little awareness of the state of the art, including the diversity of sounds that may be generated, the controllability of procedural audio models, and the quality of the sounds that it produces. The authors address all of these aspects in this review paper, while attempting a large-scale categorization of sounds that have been approached through procedural audio techniques. The role of recent advancements in neural audio synthesis, its current implementations, and potential future applications in the field are also discussed. Review materials are available
- The relationship between digital transformation and digital literacy: an explanatory model: systematic literature reviewPublication . Arnaud, José; São Mamede, Henrique; Branco, FredericoDigital transformation has been one of the main trends in organizations in recent years, and digital literacy is a critical factor in the success of this transformation. Digital transformation involves the use of digital technologies to improve an organization’s processes, products, and services. For this transformation to be successful, it is necessary for employees to have knowledge of and skills in digital technologies. Digital literacy allows employees to understand technologies and their applications, know how to use them efficiently and safely, evaluate and select the most appropriate digital tools for each task, and be prepared to deal with problems and challenges that arise in the digital environment. This study investigates the relationship between digital transformation and digital literacy through a Systematic Literature Review conducted in accordance with Kitchenham’s guidelines. A total of 54 articles, published from 2018, were analyzed from databases such as Scopus, Science Direct, IEEE and Springer. The results reveal that digital literacy significantly influences the success of digital transformation, particularly in areas such as employee adaptability, innovation capacity, and digital tool integration. Key mediating and moderating factors identified include organizational learning culture, leadership support, ongoing training programs, and technological infrastructure. Based on these findings, an explanatory model was developed that maps the interaction between these variables and their impact on digital transformation outcomes. The study offers practical implications for organizations seeking to enhance their digital maturity: investing in employee digital literacy development, aligning leadership strategies with digital initiatives, and fostering a supportive culture for digital adoption are crucial steps. Thus, this study is relevant because it seeks to understand how digital literacy can impact Digital Transformation in organizations and, through the construction of an explanatory model, allows the identification of variables that influence this relationship by developing strategies to improve the digital literacy of employees in organizations.
- Applying large language models to software develop-ment: enhancing requirements, design and codePublication . Santos, Gonçalo; Silveira, Clara; Santos, Vitor; Santos, Arnaldo; São Mamede, HenriqueThis paper explores the potential of Large Language Models (LLM) to optimize various stages of the software development lifecycle, including require-ments elicitation, architecture design, diagram creation, and implementation. The study is grounded in a real-world case, where development time and result quality are compared with and without LLM assistance. This research underscores the possibility of applying prompt patterns in LLM to support and enhance software development activities, focusing on a B2C digital commerce platform centered on fashion retail, designated LUNA. The methodology adopted is Design Sci-ence, which follows a practical and iterative approach. Requirements, design sug-gestions, and code samples are analyzed before and after the application of lan-guage models. The results indicate substantial advantages in the development process, such as improved task efficiency, faster identification of requirement gaps, and enhanced code readability. Nevertheless, challenges were observed in interpreting complex business logic. Future work should explore the integration of LLM with domain-specific ontologies and business rule engines to improve contextual accuracy in code and model generation. Additionally, refining prompt engineering strategies and combining LLM with interactive development envi-ronments could further enhance code quality, traceability, and explainability.
- A new proposed model to assess the digital organizational readiness to maximize the results of the digital transformation in SMEsPublication . Silva, Rui; São Mamede, Henrique; Santos, VitorScientific research in digital transformation is expanding in scope, quantity, and relevance, bringing forth diverse perspectives on which factors and specific dimensions—such as organizational structure, culture, and technological readiness—affect the success of digital transformation initiatives. Numerous studies have proposed mechanisms to assess an organization’s maturity through digital transformation across various models. Some of these models focus on external influences, others on internal factors, or both. Although these assessments provide valuable insights into a company’s transformation state, they often lack consistency, and recent research highlights key gaps. Specifically, many models primarily reflect the views of senior management on the general progress of digital transformation rather than on measurable outcomes. Moreover, these models tend to target large enterprises, overlooking small and medium enterprises (SMEs), which are crucial to economic growth yet face unique challenges, such as limited resources and expertise. Our study addresses these gaps by concentrating on SMEs and introducing a novel approach to assessing digital transformation readiness—a metric that reflects how prepared an organization is to optimize transformation outcomes. Following design science research methodology, we develop a model that centers on the perspectives of general employees, offering companies an in-depth view of their readiness across 20 dimensions. Each dimension is evaluated through behaviors indicative of the highest level of digital transformation readiness, helping companies identify areas to maximize potential benefits. Our model focuses not on technological quality but on the degree to which behaviors essential for leveraging technology and innovative business models are integrated within the organization.
- An approach to business continuity self-assessmentPublication . São Mamede, Henrique; Russo, Nelson; Reis, LeonildeBusiness Continuity Management (BCM) is critical for organizations to mitigate disruptions and maintain operations, yet many struggle with fragmented and non-standardized self-assessment tools. Existing frameworks often lack holistic integration, focusing narrowly on isolated components like cyber resilience or risk management, which limits their ability to evaluate BCM maturity comprehensively. This research addresses this gap by proposing a structured Self-Assessment System designed to unify BCM components into an adaptable, standards-aligned methodology. Grounded in Design Science Research, the system integrates a BCM Model comprising eight components and 118 activities, each evaluated through weighted questions to quantify organizational preparedness. The methodology enables organizations to conduct rapid as-is assessments using a 0–100 scoring mechanism with visual indicators (red/yellow/green), benchmark progress over time and against peers, and align with international standards (e.g., ISO 22301, ITIL) while accommodating unique organizational constraints. Demonstrated via focus groups and semi-structured interviews with 10 organizations, the system proved effective in enhancing top management commitment, prioritizing resource allocation, and streamlining BCM implementation—particularly for SMEs with limited resources. Key contributions include a reusable self-assessment tool adaptable to any BCM framework, empirical validation of its utility in identifying weaknesses and guiding continuous improvement, and a pathway from initial assessment to advanced measurement via the Plan-Do-Check-Act cycle. By bridging the gap between theoretical standards and practical application, this research offers a scalable solution for organizations to systematically evaluate and improve BCM resilience.
- Beyond algorithms: artificial intelligence driven talent identification with human insightPublication . Fernandes, Tiago Jacob; São Mamede, Henrique; Barroso, João Manuel Pereira; Santos, Vitor Manuel Pereira Duarte dosThe rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (n = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.
- Methodology for business process automation in SMEs: from requirements analysis to practical demonstrationPublication . Moreira, Sílvia; São Mamede, Henrique; Santos, Arnaldo; Ital PublicationsThis study aims to develop a methodology to assist Small and Medium Enterprises (SMEs) in effectively adopting Business Process Automation (BPA). Despite its growing importance in streamlining routine tasks and enabling employees to focus on more creative activities, numerous organizations face challenges in implementing BPA due to unclear procedures, insufficient knowledge of eligible processes, and uncertainty regarding the necessary technology. In response to these challenges, we introduce the Methodology for Business Process Automation (M4BPA), an artifact designed to guide SMEs through a structured BPA implementation process. The research follows the Design Science Research Methodology (DSRM). The requirements for the artifact came from the results of a previous Systematic Literature Review (SLR). M4BPA was demonstrated within real SME environments, providing solid evidence of its efficacy. The findings suggest that M4BPA significantly enhances SMEs' ability to implement BPA efficiently, offering a practical toolkit that facilitates the process. The novelty of this work lies in the development of a BPA methodology specifically tailored for SMEs, addressing existing gaps in current frameworks and providing a best-practice model for similar organizations. This research contributes to the intermediate results of a doctoral project, offering valuable insights for both practitioners and researchers in the field of BPA.
- Rebooting procurement processes: leveraging the synergy of RPA and BPM for optimized efficiencyPublication . Santos, Simão; São Mamede, Henrique; Santos, Vitor; MDPIEfficient procurement processes are pivotal for strategic performance in digital organizations, requiring continuous refinement driven by automation, integration, and performance monitoring. This research investigates and demonstrates the potential for synergies between RPA and BPM in procurement processes. The primary objective is to analyze and evaluate a manual procurement-intensive process to enhance efficiency, reduce time consuming interventions, and ultimately diminish costs and cycle time. Employing Design Science Research Methodology, this research yields a practical artifact designed to streamline procurement processes. An artifact was created using BPM methods and RPA tools. The RPA was developed after applying BPM Redesign Heuristics to the current process. A mixed-methods approach was employed for its evaluation, combining quantitative analysis on cycle time reduction with a qualitative Confirmatory Focus Group of department experts. The analysis revealed that the synergy between BPM and RPAs can leverage procurement processes, decreasing cycle times and workload on intensive manual tasks and allowing employees time to focus on other functions. This research contributes valuable insights for organizations seeking to harness automation technologies for enhanced procurement operations, with the findings suggesting promising enduring benefits for both efficiency and accuracy in the procurement lifecycle.