Sandro Luis Freire de Castro Silva

Brazil holds a large rare-earth endowment and, since 2024, a commercial upstream operation, yet geological abundance has not translated into secure access to separated oxides, metals and magnets. This study examines how far Brazilian rare-earth capability extends along the value chain and why. We combine official mineral and trade statistics, facility disclosures, policy documents and peer-reviewed literature to score seven stages annually from 2015 to 2026 against explicit decision rules, distinguishing technological from commercial maturity and measuring how many consecutive stages from extraction operate at each level. Commercial capability now covers mining, beneficiation and the production of a mixed chemical intermediate, while separation, metallisation, magnet manufacture and circular recovery remain at research or pilot level. The record also reveals a discontinuity: pilot separation capacity documented in the mid-2010s was not sustained. Part of the recent downstream progress is disconnected from domestic feed, and the main commercial operation has been integrated into a foreign mine-to-magnet chain. We describe this configuration as a downstreaming paradox. For resource-rich economies, the findings indicate that resilience depends on connecting conversion stages in sequence, selectively and subject to explicit environmental safeguards that should be independently assessed before scale-up, rather than on extraction alone.

Reliable decision support in public organizations depends less on the sophistication of dashboards than on the governance of the data that feed them. This study examines how data-governance barriers undermine Decision Support Systems (DSS) in Brazil’s public sector. Evidence from experienced information technology managers and coordinators in federal and state organizations identifies six interconnected barriers: limited data-governance awareness, resistance to change, capability gaps and staff turnover, fragmented responsibilities and policies, legacy-system and infrastructure constraints, and difficulties operationalizing security and privacy requirements. The findings show that these barriers do not act independently. Fragmented accountability weakens standards and ownership; turnover erodes institutional memory; legacy environments intensify manual reconciliation; and weak governance awareness reduces adherence to metadata, quality, and access routines. These mechanisms propagate into inconsistent definitions, duplicated records, limited traceability, delayed access, and reduced integration, directly lowering managerial trust in DSS outputs. The analysis advances a barrier-interaction model and a three-stage capability roadmap—stabilize, standardize and integrate, and institutionalize and scale—centered on stewardship, metadata, data quality, interoperability, leadership sponsorship, and auditable access. The study shows that DSS reliability is an organizational governance outcome, not merely a technical property of information systems. For public managers, the results indicate that trustworthy decision support can be strengthened by prioritizing critical data and institutionalizing governance routines before expanding technology investments.

O termo big data, embora não tenha uma definição única, pode ser entendido como um conjunto de ferramentas que possibilitam a criação, a manipulação e o gerenciamento de grandes volumes de dados. Na saúde pública, o big data pode representar uma série de benefícios para a população, mas também apresenta desafios a serem superados, como a privacidade e a confiabilidade dos dados. Este artigo tem por objetivo apresentar a diversidade dos conceitos relacionados ao big data, discutindo a aplicabilidade dessa tecnologia no contexto da saúde pública. Para tal, foi realizado um mapeamento sistemático da literatura, que selecionou e discutiu os principais artigos sobre o tema. Por meio dos resultados, foi possível identificar um gap na literatura, em que, majoritariamente, são apresentados os benefícios dessa tecnologia, deixando implícitos os caminhos que podem viabilizar sua adoção nas instituições de saúde.

Objetivo: Analisar o fluxo de compras do IFAP à luz da Gestão por Processos e propor um guia digital de apoio à fase de planejamento, para reduzir retrabalho, ordenar responsabilidades e melhorar os prazos internos. Contexto: A Lei n. 14.133, de 2021, elevou as demandas de planejamento, definição de papéis e verificação de documentos para as compras públicas. As Diretrizes do Tribunal de Contas da União (TCU) e os modelos do IPPC reforçam a padronização e a segregação de funções, oferecendo a oportunidade de ordenar documentos mínimos e verificações por fase, apoiados por um guia digital. O Instituto Federal do Amapá (IFAP) opera sob estrutura multicampi e registra devoluções e atrasos associados a entradas incompletas de documentos em processos de compras públicas. Diagnóstico: Realizou-se a análise documental de onze processos administrativos do IFAP e o benchmarking com cinquenta e quatro instituições federais, com base em método comparativo. Identificaram-se redundâncias, retornos entre setores e dependência de conhecimento tácito, o que sustentou a priorização do controle das entradas do planejamento. Implicações práticas: O guia desenvolvido organiza os documentos mínimos do planejamento, vincula tarefas a pontos de verificação e permite consulta por fase, o que tende a reduzir devoluções e prazos. Implicações sociais: A centralização de fluxos e de normativos amplia a transparência e favorece o controle social e o acompanhamento institucional. Implicações teóricas: Demonstra a aplicabilidade da Gestão por Processos às compras públicas no contexto das instituições federais de ensino. Originalidade/Valor: O guia digital estrutura a fase de planejamento, conectando os documentos mínimos a uma sequência operacional e às verificações por fase. A comparação externa posiciona o IFAP e orienta a adoção de práticas observadas.

Background: The rapid evolution of artificial intelligence (AI) presents transformative potential for diagnostic medicine, offering opportunities to enhance diagnostic accuracy, reduce costs, and improve patient outcomes. Objective: This study aimed to assess the expected future impact of AI on diagnostic medicine by comparing global researchers’ expectations using 2 cross-sectional surveys. Methods: The surveys were conducted in September 2020 and February 2023. Each survey captured a 10-year projection horizon, gathering insights from >3700 researchers with expertise in AI and diagnostic medicine from all over the world. The survey sought to understand the perceived benefits, integration challenges, and evolving attitudes toward AI use in diagnostic settings. Results: Results indicated a strong expectation among researchers that AI will substantially influence diagnostic medicine within the next decade. Key anticipated benefits include enhanced diagnostic reliability, reduced screening costs, improved patient care, and decreased physician workload, addressing the growing demand for diagnostic services outpacing the supply of medical professionals. Specifically, x-ray diagnosis, heart rhythm interpretation, and skin malignancy detection were identified as the diagnostic tools most likely to be integrated with AI technologies due to their maturity and existing AI applications. The surveys highlighted the growing optimism regarding AI’s ability to transform traditional diagnostic pathways and enhance clinical decision-making processes. Furthermore, the study identified barriers to the integration of AI in diagnostic medicine. The primary challenges cited were the difficulties of embedding AI within existing clinical workflows, ethical and regulatory concerns, and data privacy issues. Respondents emphasized uncertainties around legal responsibility and accountability for AI-supported clinical decisions, data protection challenges, and the need for robust regulatory frameworks to ensure safe AI deployment. Ethical concerns, particularly those related to algorithmic transparency and bias, were noted as increasingly critical, reflecting a heightened awareness of the potential risks associated with AI adoption in clinical settings. Differences between the 2 survey waves indicated a growing focus on ethical and regulatory issues, suggesting an evolving recognition of these challenges over time. Conclusions: Despite these barriers, there was notable consistency in researchers’ expectations across the 2 survey periods, indicating a stable and sustained outlook on AI’s transformative potential in diagnostic medicine. The findings show the need for interdisciplinary collaboration among clinicians, AI developers, and regulators to address ethical and practical challenges while maximizing AI’s benefits. This study offers insights into the projected trajectory of AI in diagnostic medicine, guiding stakeholders, including health care providers, policy makers, and technology developers, on navigating the opportunities and challenges of AI integration.

BACKGROUND The rapid evolution of artificial intelligence (AI) presents transformative potential for diagnostic medicine, offering opportunities to enhance diagnostic accuracy, reduce costs, and improve patient outcomes. OBJECTIVE This study aimed to assess the expected future impact of AI on diagnostic medicine by comparing global researchers’ expectations using 2 cross-sectional surveys. METHODS The surveys were conducted in September 2020 and February 2023. Each survey captured a 10-year projection horizon, gathering insights from >3700 researchers with expertise in AI and diagnostic medicine from all over the world. The survey sought to understand the perceived benefits, integration challenges, and evolving attitudes toward AI use in diagnostic settings. RESULTS Results indicated a strong expectation among researchers that AI will substantially influence diagnostic medicine within the next decade. Key anticipated benefits include enhanced diagnostic reliability, reduced screening costs, improved patient care, and decreased physician workload, addressing the growing demand for diagnostic services outpacing the supply of medical professionals. Specifically, x-ray diagnosis, heart rhythm interpretation, and skin malignancy detection were identified as the diagnostic tools most likely to be integrated with AI technologies due to their maturity and existing AI applications. The surveys highlighted the growing optimism regarding AI’s ability to transform traditional diagnostic pathways and enhance clinical decision-making processes. Furthermore, the study identified barriers to the integration of AI in diagnostic medicine. The primary challenges cited were the difficulties of embedding AI within existing clinical workflows, ethical and regulatory concerns, and data privacy issues. Respondents emphasized uncertainties around legal responsibility and accountability for AI-supported clinical decisions, data protection challenges, and the need for robust regulatory frameworks to ensure safe AI deployment. Ethical concerns, particularly those related to algorithmic transparency and bias, were noted as increasingly critical, reflecting a heightened awareness of the potential risks associated with AI adoption in clinical settings. Differences between the 2 survey waves indicated a growing focus on ethical and regulatory issues, suggesting an evolving recognition of these challenges over time. CONCLUSIONS Despite these barriers, there was notable consistency in researchers’ expectations across the 2 survey periods, indicating a stable and sustained outlook on AI’s transformative potential in diagnostic medicine. The findings show the need for interdisciplinary collaboration among clinicians, AI developers, and regulators to address ethical and practical challenges while maximizing AI’s benefits. This study offers insights into the projected trajectory of AI in diagnostic medicine, guiding stakeholders, including health care providers, policy makers, and technology developers, on navigating the opportunities and challenges of AI integration.