The concept of digital transformation (DT) has revolutionized different facets of society including institutions of learning, where it has continued to influence teaching, learning and research. Over time and with the continued technological advancements in biological science disciplines, it has become clear that the increased convergence between these disciplines and digital systems application will become more apparent. For example, the advent and application of next-generation sequencing (NGS), machine learning (ML), artificial intelligence (AI) and other automated biological processes have afforded unique huge data sets with high societal impact. As such, digitalization of the bioeconomy remains necessary. The digitalization of the biosciences has birthed emerging concepts like "digital biology", "engineered biology" and "bio-revolution" and involve computer-centered biosciences. These are built around the integration of scientific data, modeling at several levels, as well as networked science. Nonetheless, it is still plagued by information derivation policies and technicality issues. Although, digital biology has seen a major boost in recent years with the emergence of new and updated digital biology databases and platforms for advanced computational modelling, and connection and visualization of bio-data at a molecular level, more efforts are needed from governmental stakeholders (policy makers), industry and academia to enhance the impact of computational automation and digital transformation in bioscience activities. Hence, a steady upscale in digitalization of the biosciences would ensure bioprocess development, increase teaching, research efficiency and productivity, birth more innovative future applications for various industries, and thereby impact several aspects of life. This article presents how selected biological science disciplines have been digitally transformed through data analytics and how the concept could be further explored in advancing teaching, learning and research.

ABSTRACT The evolution towards Industry 4.0 starts from previous industrial revolutions and culminates in the convergence of advanced technologies today. Industry 4.0 uses technologies such as the internet of things, big data analysis, artificial intelligence, augmented reality, among others, to improve the efficiency and adaptability of companies; organizational productivity is focused on the modernization of processes with emerging technologies. In this context, the accounting profession is undergoing significant changes such as the automation of routine tasks allowing accountants to focus on strategic analysis. Technology impacts accounting by improving efficiency, accuracy, and data security. Accountants must acquire technological competencies, such as proficiency in accounting software and skills in data analytics, cybersecurity, and digital communication. In terms of accounting ethics, the importance of confidentiality, integrity and transparency in the digital age is highlighted. Accountants must maintain high ethical standards and adapt to technological evolution. The research concludes that the accounting profession has transformed into a more strategic role, demanding technological skills and ethics adapted to the digital era. The connection between education, industry and technology skills is essential to prepare accountants for today's challenges.

RESUMEN La evolución hacia la Industria 4.0 parte de las revoluciones industriales anteriores y culmina en la convergencia de tecnologías avanzadas en la actualidad. La Industria 4.0 emplea tecnologías como el internet de las cosas, el análisis de grandes volúmenes de datos (big data), la inteligencia artificial, la realidad aumentada, entre otras, para mejorar la eficiencia y la adaptabilidad de las empresas, la productividad organizacional se centra en la modernización de procesos con tecnologías emergentes.En este contexto, la profesión contable experimenta cambios significativos como son en la automatización de tareas rutinarias, permite a los contadores enfocarse en análisis estratégicos. La tecnología impacta la contabilidad al mejorar la eficiencia, la precisión y la seguridad de los datos. Los contadores deben adquirir competencias tecnológicas, como el dominio de software contable y habilidades en análisis de datos, ciberseguridad y comunicación digital.En cuanto a la ética contable, se destaca la importancia de la confidencialidad, integridad y transparencia en la era digital. Los contadores deben mantener altos estándares éticos y adaptarse a la evolución tecnológica. La investigación concluye que la profesión contable se ha transformado en un papel más estratégico, exigiendo habilidades tecnológicas y una ética adaptada a la era digital. La conexión entre la educación, la industria y las competencias tecnológicas es esencial para preparar a los contadores para los desafíos actuales.

Abstract Introduction: Upper endoscopy is the standard method for diagnosing early-stage gastric cancer. However, according to estimates, up to 20% of tumors are not detected, and their accuracy may be affected by the variability in their performance. In Colombia, most diagnoses take place in advanced stages, which aggravates the problem. Protocols have been proposed to ensure the complete observation of areas prone to premalignant lesions to address variability. Objective: To build and validate an automatic audit system for endoscopies using artificial intelligence techniques. Methodology: In this study, 96 patients from a teaching hospital underwent video-documented endoscopies, spanning 22 stations rearranged to minimize overlaps and improve the identification of 13 key gastric regions. An advanced convolutional network was used to process the images, extracting visual characteristics, which facilitated the training of artificial intelligence in the classification of these areas. Results: the model, called Gastro UNAL, was trained and validated with images of 67 patients (70% of cases) and tested with 29 different patients (30% of cases), which reached an average sensitivity of 85,5% and a specificity of 98,8% in detecting the 13 gastric regions. Conclusions: The effectiveness of the model suggests its potential to ensure the quality and accuracy of endoscopies. This approach could confirm the regions evaluated, alerting less experienced or trained endoscopists about blind spots in the examinations, thus, increasing the quality of these procedures.

Resumen Introducción: La endoscopia digestiva alta es el método estándar para diagnosticar el cáncer gástrico en etapas tempranas. Sin embargo, su precisión puede verse afectada por la variabilidad en su realización, y se estiman hasta 20% de tumores no detectados. En Colombia, la mayoría de los diagnósticos se realizan en etapas avanzadas, lo que agrava el problema. Para abordar la variabilidad, se han propuesto protocolos con el fin de asegurar la observación completa de áreas propensas a lesiones premalignas. Objetivo: Construir y validar un sistema de auditoría automática para endoscopias usando técnicas de inteligencia artificial. Metodología: En este estudio, 96 pacientes de un hospital universitario se sometieron a endoscopias documentadas en video, abarcando 22 estaciones reorganizadas para minimizar solapamientos y mejorar la identificación de 13 regiones gástricas clave. Se utilizó una red convolucional avanzada para procesar las imágenes, extrayendo características visuales, lo que facilitó el entrenamiento de la inteligencia artificial en la clasificación de estas áreas. Resultados: El modelo, llamado Gastro UNAL, fue entrenado y validado con imágenes de 67 pacientes (70% de los casos) y probado con 29 pacientes distintos (30% de los casos), con lo que alcanzó una sensibilidad promedio del 85,5% y una especificidad del 98,8% en la detección de las 13 regiones gástricas. Conclusiones: La eficacia del modelo sugiere su potencial para asegurar la calidad y precisión de las endoscopias. Este enfoque podría confirmar las regiones evaluadas, alertando puntos ciegos en la exploración a los endoscopistas con menos experiencia o en entrenamiento, de tal forma que se aumente la calidad de estos procedimientos.

Abstract Accurate detection and identification of weeds are essential in the implementation of Precision Agriculture (PA). In this study, we addressed the detection and classification of weeds in maize and tomato in their early growth stages by integrating images acquired from UAVs and analysis based on advanced convolutional neural networks. Subsequently, the objective of this research was the creation of geo-referenced maps that would allow localized and selective post-emergence management of weed species. Our results indicate that the combination of UAV-captured imagery and deep learning algorithm-based image analysis provides an effective solution for this purpose. The accuracy and efficiency achieved in the identification of weeds in their early stage were promising, both in the set of species present in the crop and in individual species. This advance is of great relevance in the field of PA, as it would allow a more efficient management of weeds by selecting the herbicide according to the type of weed present, reducing the use of herbicides and ultimately contributing to the sustainability and profitability of agriculture. In addition, the generation of geo-referenced maps facilitates real-time decision making. In summary, this study suggests that the combination of emerging technologies, such as drones and deep neural networks, may be applicable tools in localized weed management in the context of PA.

Resumen La detección precisa y la identificación de malas hierbas son esenciales en la implementación de la Agricultura de Precisión (AP). En este estudio, se abordó la detección y la clasificación de malas hierbas en maíz y tomate en sus etapas tempranas de crecimiento mediante la integración de imágenes adquiridas desde drones y análisis basado en avanzadas redes neuronales convolucionales. Posteriormente, el objetivo de esta investigación fue la creación de mapas georreferenciados que permitieran una gestión en post-emergencia localizada y selectiva de las especies de malas hierbas. Nuestros resultados indican que la combinación de imágenes captadas por drones y un análisis de las mismas basado en algoritmos de aprendizaje profundo proporciona una solución efectiva para este propósito. La precisión y eficiencia alcanzadas en la identificación de malas hierbas en su etapa temprana fueron prometedoras, tanto en el conjunto de especies presentes en el cultivo como en especies individuales. Este avance es de gran relevancia en el ámbito de la AP, ya que permitiría una gestión más eficiente de las malas hierbas mediante la selección del herbicida según el tipo de mala hierba presente, reduciendo el uso de herbicidas y, en última instancia, contribuyendo a la sostenibilidad y la rentabilidad de la agricultura. Además, la generación de mapas georreferenciados facilita la toma de decisiones en tiempo real. En resumen, este estudio sugiere que la combinación de tecnologías emergentes, como drones y redes neuronales profundas, pueden ser herramientas aplicables en el manejo localizado de malas hierbas en el contexto de la AP.

Abstract Technological advancement has had a significant impact on written production, especially in Additional Languages (ALs). Although technology has brought new opportunities for AL teaching, it also poses challenges, including concerns about the complexity of writing and the authenticity of students’ work. One such tool is ChatGPT, an artificial intelligence (AI) platform that has been the subject of debate since its popularization in 2022. This study analyses a corpus consisting of six tasks produced by ChatGPT in five languages (German, Spanish, French, Italian, and Portuguese), considering the proficiency levels proposed by the Common European Framework of Reference for Languages (CEFR), totalling 2991 texts and 706,401 words. The data were generated by students in a computer lab at a British university from 100 different profiles on the ChatGPT platform, following the researchers’ instructions. Data analysis employs Systemic Functional Linguistics (SFL) and the concept of lexical density ( Halliday, 1985, 1987, 1993; Halliday; Matthiessen, 2014) to investigate the complexity of the produced texts, as lexical complexity is related to proficiency in writing, where more advanced texts proportionally use more “content words” (nouns, verbs, adjectives, and some adverbs of manner). The results reveal that ChatGPT does not adhere to task instructions regarding the requested word count, thereby impacting the calculation of lexical density, nor does it produce texts that show significant differences in lexical density among additional languages and proficiency levels. production ALs. ALs . (ALs) teaching challenges work AI (AI 2022 German, German (German Spanish French Italian Portuguese, Portuguese , Portuguese) CEFR, CEFR (CEFR) 299 706401 706 401 706,40 words 10 researchers SFL (SFL Halliday 1985 1987 1993 Matthiessen 2014 content nouns, nouns (nouns verbs adjectives manner. manner manner) count (ALs 202 (CEFR 29 70640 70 40 706,4 1 198 199 201 20 2 7064 7 4 706, 19

Resumo O avanço tecnológico tem tido um grande impacto na produção escrita, especialmente em Línguas Adicionais (LAs). Embora a tecnologia tenha trazido novas oportunidades para o ensino de LAs, ela também apresenta desafios, incluindo preocupações sobre a complexidade da escrita e a autenticidade dos trabalhos dos alunos. Uma dessas ferramentas é o ChatGPT, plataforma de inteligência artificial (IA) que tem sido objeto de debates desde sua popularização em 2022. Este estudo analisa um corpus composto por seis tarefas produzidas pelo ChatGPT em cinco idiomas (alemão, espanhol, francês, italiano e português), considerando os níveis de proficiência propostos pelo Quadro Comum Europeu de Referência para Línguas (CEFR), que totalizou 2991 textos e 706,401 palavras. Os dados foram gerados por alunos em um laboratório de informática em uma universidade britânica a partir de 100 diferentes perfis na plataforma do ChatGPT, seguindo instruções dos pesquisadores. A análise dos dados utiliza a linguística sistêmico-funcional (LSF) e o conceito de densidade lexical ( Halliday, 1985, 1987, 1993; Halliday; Matthiessen, 2014) para investigar a complexidade dos textos produzidos, dado que a complexidade lexical está relacionada à proficiência na escrita, na qual textos mais avançados usam proporcionalmente mais “palavras de conteúdo” (nomes, verbos, adjetivos e alguns advérbios de modo). Os resultados revelam que o ChatGPT não segue as instruções das tarefas quanto ao número de palavras solicitadas, impactando, assim, no cálculo da densidade lexical, nem produz textos que mostram diferenças significativas da densidade lexical entre as línguas adicionais e níveis de proficiência. LAs. LAs . (LAs) desafios IA (IA 2022 alemão, alemão (alemão espanhol francês português, português , português) CEFR, CEFR (CEFR) 299 706401 706 401 706,40 10 pesquisadores sistêmicofuncional sistêmico funcional LSF (LSF Halliday 1985 1987 1993 Matthiessen 2014 produzidos conteúdo nomes, nomes (nomes verbos modo. modo modo) solicitadas impactando assim (LAs 202 (CEFR 29 70640 70 40 706,4 1 198 199 201 20 2 7064 7 4 706, 19

ABSTRACT Purpose: To explore artificial intelligence’s impact on surgical education, highlighting its advantages and challenges. Methods: A comprehensive search across databases such as PubMed, Scopus, Scientific Electronic Library Online (SciELO), Embase, Web of Science, and Google Scholar was conducted to compile relevant studies. Results: Artificial intelligence offers several advantages in surgical training. It enables highly realistic simulation environments for the safe practice of complex procedures. Artificial intelligence provides personalized real-time feedback, improving trainees’ skills. It efficiently processes clinical data, enhancing diagnostics and surgical planning. Artificial intelligence-assisted surgeries promise precision and minimally invasive procedures. Challenges include data security, resistance to artificial intelligence adoption, and ethical considerations. Conclusions: Stricter policies and regulatory compliance are needed for data privacy. Addressing surgeons’ and educators’ reluctance to embrace artificial intelligence is crucial. Integrating artificial intelligence into curricula and providing ongoing training are vital. Ethical, bioethical, and legal aspects surrounding artificial intelligence demand attention. Establishing clear ethical guidelines, ensuring transparency, and implementing supervision and accountability are essential. As artificial intelligence evolves in surgical training, research and development remain crucial. Future studies should explore artificial intelligence-driven personalized training and monitor ethical and legal regulations. In summary, artificial intelligence is shaping the future of general surgeons, offering advanced simulations, personalized feedback, and improved patient care. However, addressing data security, adoption resistance, and ethical concerns is vital. Adapting curricula and providing continuous training are essential to maximize artificial intelligence’s potential, promoting ethical and safe surgery. Purpose intelligences s education challenges Methods PubMed Scopus SciELO, SciELO , (SciELO) Embase Science Results procedures realtime real time feedback trainees skills planning intelligenceassisted assisted security considerations Conclusions privacy surgeons educators crucial vital Ethical bioethical attention guidelines transparency intelligencedriven driven regulations summary simulations care However potential surgery (SciELO

Abstract This paper presents the strategies and initial implementations of a data culture conversion process at a company in the electricity sector. Solutions are being developed within the scope of the construction management macro-process, which involves multiple sectors of the company, as well as external agents. The strategy is based on implementing Proofs of Concept as tools for converting the data culture within the company and, at the same time, supporting the essential activities of construction management. The proofs of concept aligned with the change in data culture have two components: technology and people. The first consists mainly of intelligent automation and process optimization. The second component involves sensitization, training and developing solutions together with the company's employees. The results involve a reduction in workload since some activities have been partially or totally transferred to the software. Greater assertiveness in estimating numerical data, such as prices and budgets. Increased reliability by reducing human intervention and complex calculations. Greater controllability by monitoring multiple activities and sectors, simplifying management. Finally, the integration of technology and people has shown significant advances in organizational culture, enabling employees to have a greater understanding of advanced tools and methods and to be able to identify opportunities and share solutions within the company. sector macroprocess, macroprocess macro process, macro-process agents time components optimization sensitization companys s software budgets calculations Finally

BACKGROUND: In the wake of Industry 4.0, libraries are integrating Fourth Industrial Revolution (4IR) technologies to enhance efficiency. However, this shift in practice raises ethical and privacy concerns, particularly regarding the Protection of Personal Information Act (PoPI Act OBJECTIVES: This literature review explores the ethical and privacy challenges posed by 4IR technologies in delivering information services to library patrons including the impact on PoPI Act compliance in addressing data privacy concerns METHOD: Adopting a systematic approach, this literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart and inclusion criteria focussed on 4IR in libraries and its relation to the PoPI Act. Peer-reviewed articles in English from 2013 to 2023 were sourced from scholarly databases RESULTS: The review highlights the transformation of academic library services in the 4IR era and the consequent privacy challenges. Concerns arise from advanced library systems and extensive data retention. While libraries operate non-profitably, the adoption of 4IR technologies for data acquisition mirrors commercial practices, raising ethical questions. Patron privacy issues extend beyond traditional data collection to potential misuse and improper data handling as libraries often work with vendors CONCLUSION: The integration of 4IR technologies in academic libraries necessitates a delicate balance between improved services and safeguarding user privacy and the PoPI Act serves as a crucial regulatory framework. Librarians must navigate the evolving landscape of data ethics, emphasising transparency, accountability and adherence to privacy legislation CONTRIBUTION: The introduction of artificial intelligence and 4IR technologies to library information services has unlimited possibilities including countering misinformation and disinformation, and the expansion of library services to tech-savvy users beyond the physical walls of the library building. However, the deployment of these technologies raises new questions about ethical concerns that the use of these technologies poses to information professionals in the rendition of library services. The results of this study will be useful for policy and professional practice in taking full advantage of 4IR technologies and services

ABSTRACT Objective: to examine the contemporary challenges faced by the survey method in the administration field, particularly in marketing, due to the emergence of new technologies and changes in respondent behavior. Provocations: with the rise of artificial intelligence, the traditional survey method is increasingly being questioned. Issues such as response validity, respondent fatigue, and proliferation of behavioral data obtained through automated means cast doubt on the survey’s effectiveness in capturing actual consumer behavior. Additionally, new legislation may introduce restrictions that could impact data collection via surveys. Conclusions: although not obsolete, the survey method must reinvent itself to remain relevant. Integrating new technologies, such as artificial intelligence, and combining them with qualitative methods are suggested paths to improve research effectiveness in an environment heavily influenced by technological advancements. The future of the survey depends on its ability to adapt and complement other emerging approaches. Objective field marketing behavior Provocations intelligence questioned validity fatigue surveys s Additionally Conclusions obsolete relevant advancements approaches

RESUMO Objetivo: examinar os desafios contemporâneos enfrentados pelo método survey no campo da administração, em especial no marketing, devido ao surgimento de novas tecnologias e mudanças nos comportamentos dos respondentes. Provocações: com a ascensão da inteligência artificial, o método survey tradicional começa a ser questionado. Questões como a validade das respostas, a fadiga dos respondentes e a proliferação de dados comportamentais obtidos por meios automatizados questionam a eficácia do survey na captura de comportamentos reais dos consumidores. Além disso, novas legislações podem trazer restrições capazes de afetar a coleta de dados via survey. Conclusões: apesar de não estar obsoleto, o método survey deve se reinventar para permanecer relevante. A integração com novas tecnologias, como a inteligência artificial, e a combinação com métodos qualitativos são caminhos sugeridos para melhorar a eficácia das pesquisas em um ambiente com forte avanço tecnológico. O futuro do survey depende da capacidade de adaptação e da complementaridade com outras abordagens emergentes. Objetivo administração marketing Provocações artificial questionado respostas consumidores disso Conclusões obsoleto relevante tecnológico emergentes

The quantitation of plasma cells in bone marrow (BM) is crucial for diagnosing and classifying plasma cell neoplasms. Various methods, including Romanowsky-stained BM aspirates (BMA), immunohistochemistry, flow cytometry, and radiological imaging, have been explored. However, challenges such as patchy infiltration and sample haemodilution can impact the reliability of BM plasma cell percentage estimates. Bone marrow plasma cell percentage varies across methods, with immunohistochemically stained biopsies consistently yielding higher values than Romanowsky-stained BMA or flow cytometry alone. CD138 or MUM1 immunohistochemistry and artificial intelligence image analysis on whole-slide images are emerging as promising tools for accurate plasma cell identification and quantification. Radiological imaging, particularly with advanced technologies like dual-energy computed tomography and radiomics, shows potential for multiple myeloma diagnosis, although standardisation remains a challenge. Molecular techniques, such as allele-specific oligonucleotide quantitative polymerase chain reaction and next-generation sequencing, offer insights into clonality and measurable residual disease. While no consensus exists on a gold standard method for BM plasma cell quantitation, CD138-stained biopsies are favoured for accurate estimation and play a pivotal role in diagnosing and assessing multiple myeloma treatment responses. Combining multiple methods, such as BMA, BM biopsy, and flow cytometry, enhances accuracy of diagnosis and classification of plasma cell neoplasms. The quest for a gold standard requires ongoing research and collaboration to refine existing methods. Furthermore, the rise of digital pathology is anticipated to reshape laboratory medicine and the role of pathologists in the digital era. What this study adds: This article adds a comprehensive review and comparison of different methods for plasma cell estimation in the bone marrow, highlighting their strengths and limitations. The goal is to contribute valuable insights that can guide the selection of optimal techniques for accurate plasma cell estimation.

ABSTRACT Education 4.0 is an innovative approach that seeks to leverage advanced technology, particularly artificial intelligence (AI), to enhance learning and the student experience in higher education. AI can automate tasks, personalize the learning experience, and improve efficiency in scientific research. However, its implementation poses challenges in terms of adopting new educational models, the necessary investment for implementation, and user training. In order to provide detailed information on the advantages of AI application in teaching-learning processes and scientific research in higher education, a systematic literature review was conducted that explored the fundamentals, benefits, and drawbacks of AI, which was shared with professors and students from the Universidad de Oriente, Cuba, to learn about their perceptions on the topic. The results indicate that AI can enhance the teaching-learning process and improve efficiency in scientific research, but it requires careful, ethical and responsible implementation. Furthermore, it is concluded that AI can facilitate adaptation to each student's pace and learning style, optimize time usage and efficiency in research tasks, and foster the development of professional competencies for the job market. However, it is essential to ensure equity, quality, and ethics in its use.

RESUMEN La Educación 4.0 es un enfoque innovador que busca aprovechar la tecnología avanzada, en particular la inteligencia artificial (IA), para mejorar el aprendizaje y la experiencia de los estudiantes en la educación superior. La IA puede automatizar tareas, personalizar la experiencia de aprendizaje y mejorar la eficiencia en la investigación científica. No obstante, su aplicación plantea desafíos en cuanto a la adopción de nuevos modelos educativos, la inversión necesaria para su implementación y la capacitación de los usuarios. Con el objetivo de proporcionar información detallada a estudiantes y profesores sobre las ventajas de la aplicación de la IA en los procesos de enseñanza-aprendizaje y de investigación científica en la educación superior, se realizó una revisión sistemática de la literatura que exploró los fundamentos, beneficios y desventajas de la IA, la que fue socializada con profesores y estudiantes de la Universidad de Oriente, Cuba, para conocer sus percepciones sobre el tema. Los resultados indican que la IA puede potenciar el proceso de enseñanza-aprendizaje y mejorar la eficiencia en la investigación científica, pero requiere de una implementación cuidadosa, ética y responsable. Además, se concluye que la IA puede facilitar la adaptación al ritmo y estilo de aprendizaje de cada estudiante, optimizar el uso del tiempo y la eficiencia en tareas de investigación, y favorecer el desarrollo de competencias profesionales para el mundo laboral. No obstante, es esencial garantizar la equidad, calidad y ética en su uso.

Abstract Background and Aims: Gastrointestinal (GI) endoscopy has known a great evolution in the last decades. Imaging techniques evolved from imaging with only standard white light endoscopes toward high-definition resolution endoscopes and the use of multiple color enhancement techniques, over to automated endoscopic assessment systems based on artificial intelligence. This narrative literature review aimed to provide a detailed overview on the latest evolutions within the field of advanced GI endoscopy, mainly focusing on the screening, diagnosis, and surveillance of common upper and lower GI pathology. Methods: This review comprises only lit-erature about screening, diagnosis, and surveillance strategies using advanced endoscopic imaging techniques published in (inter)national peer-reviewed journals and written in English. Studies with only adult patients included were selected. A search was performed using MESH terms: dye-based chromo-endoscopy, virtual chromoendoscopy, video enhancement technique, upper GI tract, lower GI tract, Barrett’s esophagus, esophageal squamous cell carcinoma, gastric cancer, colorectal polyps, inflammatory bowel disease, artificial intelligence. This review does not elaborate on the therapeutic application or impact of advanced GI endoscopy. Conclusions: Focusing on current and future applications and evolutions in the field of both upper and lower GI advanced endoscopy, this overview is a practical but detailed projection of the latest developments. Within this review, an active leap toward artificial intelligence and its recent developments in GI endoscopy was made. Additionally, the literature is weighted against the current international guidelines and assessed for its potential positive future impact.

Resumo Introdução/objetivos: A endoscopia digestiva conheceu uma grande evolução nas últimas décadas, tendo as técnicas de imagem evoluído de imagens com luz branca para endoscópios de alta definição com possibilidade de uso de várias técnicas de melhoramento de cores e até sistemas automatizados apoiados em inteligência artificial. Esta revisão narrativa da literatura visa fornecer uma visão detalhada das últimas evoluções no campo da endoscopia avançada, focando principalmente no rastreio, diagnóstico e vigilância. Métodos: Pesquisa da literatura sobre estratégias de rastreio, diagnóstico e vigilância utilizando técnicas avançadas de imagem endoscópica publicadas em revistas internacionais revistas por pares e escritas em inglês. Foram selecionados estudos apenas com doentes adultos e foi realizada pesquisa utilizando termos MESH: cromoendoscopia com corante, cromoendoscopia virtual, técnicas de melhoramento de vídeo, tubo digestivo superior, tubo digestivo inferior, esófago de Barrett, carcinoma de células escamosas, cancro gástrico, pólipos colorretais, doença inflamatória intestinal e inteligência artificial. Conclusões: Esta revisão avaliou de uma forma prática os últimos desenvolvimentos no cam-po da imagem avançada em endoscopia digestiva, avaliando-se também as perspetivas futuras e o potencial impacto da inteligência artificial.

RESUMO Introdução: No rápido desenvolvimento atual de ciência e tecnologia, a tecnologia de mineração de dados de rede digital desenvolve-se tão rápido quanto a expansão das fronteiras da ciência e tecnologia permitem, com um nível de aplicação muito amplo, cobrindo a maior parte do ambiente civilizado. No entanto, ainda há muito para explorar da aplicação no treinamento esportivo. Objetivo: Análise de viabilidade da mineração de dados com base na rede digital da formação esportiva, maximizar o treinamento dos atletas. Métodos: Este trabalho utiliza a análise experimental da FFT humana, combinada com a rede de inteligência artificial da BP e tecnologia de mineração profunda de dados, para projetar um novo ambiente de treinamento esportivo. O teste controlado deste modelo foi projetado para comparar modalidades avançadas de treinamento atlético com as modalidades tradicionais, comparando o poder explosivo, resistência e condição física do atleta. Resultados: Após 30 dias de treinamento físico, a força atlética dos esportistas com aptidão física avançada aumentou 15,33%, a resistência aumentou 15,85%, e o condicionamento físico aumentou 14,23%. Conclusão: O algoritmo desenhado neste artigo tem um impacto positivo na maximização do treinamento dos atletas. Pode ter um impacto favorável nos resultados do treinamento, bem como aumentar o interesse do atleta pelo esporte. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

ABSTRACT Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes’ training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes’ explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes’ training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMEN Introducción: En el rápido desarrollo actual de la ciencia y la tecnología, la tecnología de extracción de datos de redes digitales se desarrolla tan rápido como lo permiten las fronteras en expansión de la ciencia y la tecnología, con un nivel de aplicación muy amplio que abarca la mayor parte del entorno civilizado. Sin embargo, aún queda mucho por explorar de la aplicación en el entrenamiento deportivo. Objetivo: Análisis de viabilidad de la minería de datos basada en la red digital de entrenamiento deportivo, maximizar la formación de los atletas. Métodos: Este trabajo utiliza el análisis experimental de la FFT humana, combinado con la red de inteligencia artificial BP y la tecnología de minería de datos profunda, para diseñar un nuevo entorno de entrenamiento deportivo. La prueba controlada de este modelo se diseñó para comparar las modalidades de entrenamiento atlético avanzado con las modalidades tradicionales, comparando la potencia explosiva, la resistencia y la forma física del atleta. Resultados: Después de 30 días de entrenamiento físico, la fuerza atlética de los atletas con un estado físico avanzado aumentó en un 15,33%, la resistencia aumentó en un 15,85% y el estado físico aumentó en un 14,23%. Conclusión: El algoritmo diseñado en este trabajo tiene un impacto positivo en la maximización del entrenamiento de los atletas. Puede tener un impacto favorable en los resultados del entrenamiento, así como aumentar el interés del atleta por el deporte. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

ABSTRACT The Pan American Health Organization/World Health Organization (PAHO/WHO) Anti-Infodemic Virtual Center for the Americas (AIVCA) is a project led by the Department of Evidence and Intelligence for Action in Health, PAHO and the Center for Health Informatics, PAHO/WHO Collaborating Center on Information Systems for Health, at the University of Illinois, with the participation of PAHO staff and consultants across the region. Its goal is to develop a set of tools—pairing AI with human judgment—to help ministries of health and related health institutions respond to infodemics. Public health officials will learn about emerging threats detected by the center and get recommendations on how to respond. The virtual center is structured with three parallel teams: detection, evidence, and response. The detection team will employ a mixture of advanced search queries, machine learning, and other AI techniques to sift through more than 800 million new public social media posts per day to identify emerging infodemic threats in both English and Spanish. The evidence team will use the EasySearch federated search engine backed by AI, PAHO’s knowledge management team, and the Librarian Reserve Corps to identify the most relevant authoritative sources. The response team will use a design approach to communicate recommended response strategies based on behavioural science, storytelling, and information design approaches.

RESUMEN El centro virtual contra la infodemia para la Región de las Américas de la Organización Panamericana de la Salud/Organización Mundial de la Salud (OPS/OMS) es un proyecto liderado por el Departamento de Evidencia e Inteligencia para la Acción en la Salud de la OPS y el Center for Health Informatics de la Universidad de Illinois, centro colaborador de la OPS/OMS en sistemas de información para la salud, con la participación de personal y consultores de la OPS en toda la Región. Su objetivo es crear un conjunto de herramientas que combinen inteligencia artificial (IA) y los criterios humanos para apoyar a los ministerios de salud y las instituciones relacionadas con la salud en la respuesta a la infodemia. Los funcionarios de salud pública recibirán formación sobre las amenazas emergentes detectadas por el centro y recomendaciones sobre cómo abordarlas. El centro virtual está estructurado en tres equipos paralelos: detección, evidencia y respuesta. El equipo de detección empleará una combinación de consultas mediante búsqueda avanzada, aprendizaje automático y otras técnicas de IA para evaluar más de 800 millones de publicaciones nuevas en las redes sociales al día con el fin de detectar amenazas emergentes en el ámbito de la infodemia tanto en inglés como en español. El equipo de evidencia hará uso del motor de búsqueda federado EasySearch y, con el apoyo de la IA, el equipo de gestión del conocimiento de la OPS y la red Librarian Reserve Corps, determinará cuáles son las fuentes autorizadas más pertinentes. El equipo de respuesta utilizará un enfoque vinculado al diseño para difundir las estrategias recomendadas sobre la base de las ciencias del comportamiento, la narración de historias y el diseño de la información.

RESUMO O Centro Virtual Anti-Infodemia para as Américas (AIVCA, na sigla em inglês) da Organização Pan-Americana da Saúde/Organização Mundial da Saúde (OPAS/OMS) é um projeto liderado pelo Departamento de Evidência e Inteligência para a Ação em Saúde da OPAS e pelo Centro de Informática em Saúde da Universidade de Illinois, EUA (Centro Colaborador da OPAS/OMS para Sistemas de Informação para a Saúde), com a participação de funcionários e consultores da OPAS de toda a região. Seu objetivo é desenvolver um conjunto de ferramentas — combinando a inteligência artificial (IA) com o discernimento humano — para ajudar os ministérios e instituições de saúde a responder às infodemias. As autoridades de saúde pública aprenderão sobre as ameaças emergentes detectadas pelo centro e obterão recomendações sobre como responder. O centro virtual está estruturado com três equipes paralelas: detecção, evidência e resposta. A equipe de detecção utilizará consultas de pesquisa avançada, machine learning (aprendizagem de máquina) e outras técnicas de IA para filtrar mais de 800 milhões de novas postagens públicas nas redes sociais por dia, a fim de identificar ameaças infodêmicas emergentes em inglês e espanhol. A equipe de evidência usará o mecanismo de busca federada EasySearch, com apoio de IA, da equipe de gestão de conhecimento da OPAS e do Librarian Reserve Corps (LRC), para identificar as fontes abalizadas mais relevantes. A equipe de resposta usará uma abordagem de design para comunicar estratégias de resposta recomendadas com base em abordagens de ciência comportamental, narração de histórias e design da informação.

In an era where the prevalence of data is striking, the importance of adopting advanced computational techniques, such as artificial intelligence and machine learning, becomes fundamental in the training process of tomorrow’s physicists. This article introduces an innovative approach to physics education, incorporating machine learning into the classic study of the simple pendulum. Key concepts of machine learning techniques are introduced and a broad dataset is collected through accurate observation of the pendulum’s motion under different initial conditions. From these data, a regression algorithm is employed to create a model of the pendulum system with satisfactory statistical indices. This pedagogical model contrasts with traditional physics teaching methods and opens a new horizon for understandding what it means to teach physics in the information age. striking tomorrows tomorrow s physicists education pendulums conditions indices age

Em uma era onde a prevalência de dados se faz marcante, a importância da adoção de técnicas computacionais avançadas, como a inteligência artificial e o aprendizado de máquina, torna-se fundamental no processo de formação dos físicos de amanhã. Este artigo introduz uma abordagem inovadora para o ensino de física, incorporando o aprendizado de máquina ao estudo clássico do pêndulo simples. São introduzidos os conceitos das técnicas de aprendizado de máquina e coletado um conjunto amplo de dados por meio da observação acurada do movimento do pêndulo em diferentes condições iniciais. A partir desses dados, é empregado um algoritmo de regressão para criar um modelo do sistema do pêndulo com índices estatísticos satisfatórios. Este modelo pedagógico se contrasta com os métodos tradicionais de ensino de física e abre um novo horizonte para a compreensão do que significa o ensino de física na era da informação. marcante avançadas tornase torna amanhã simples iniciais satisfatórios informação