Abstract
Este artigo investiga as interseções entre place branding, comunicação estratégica, cidadania e turismo. Exploramos a relação dinâmica entre esses conceitos, considerando particularmente o tensionamento entre políticas públicas voltadas para a melhoria do bem-estar dos cidadãos em cidades brasileiras e a tematização de demandas que ainda permanecem sem respostas. Argumentamos que as escolhas estratégicas de comunicação feitas pelas administrações municipais priorizam a promoção das cidades como bens de consumo, distanciando-se de práticas discursivas voltadas para a comunicação pública que prioriza os interesses coletivos. Pensar a marca de lugar enquanto comunicação estratégica envolve promover as cidades como destinos turísticos e, sobretudo, construir processos cooperativos de comunicação pública que priorizem o reconhecimento da legitimidade das demandas populares e o desenvolvimento geral do território, sem desconsiderar a polifonia do diálogo e sem tentar universalizar aquilo que dificilmente pode ser generalizado.

Abstract
Synthetic datasets are essential for the development and benchmarking of machine learning methods in biomedicine, as they help overcome the pervasive data scarcity in biomedical research. In fields such as immunomics, genomics and proteomics, they enable the development of prediction algorithms, including methods for immune receptor-antigen binding prediction. When generated with transparent and fully specified parameters, synthetic datasets serve as rule-based systems for reproducible and interpretable model testing, an essential step towards digital twins that emulate biological systems for diagnosis and therapy design. A key obstacle, however, is the 'simulation to reality' (sim2real) gap, which describes the uncertainty about whether performance on synthetic data is predictive of performance on experimental data. Divergent statistical and biological properties may erode generalizability and clinical relevance. The lack of standardized sim2real benchmarks impedes validation and widespread adoption. We argue that multilayered validation frameworks, incorporating techniques such as domain adaptation and hybrid validation, and grounded in biological realism, are essential to ensuring that synthetic datasets faithfully capture biological complexity. Closing the sim2real gap will unlock the full translational potential of synthetic data, accelerating diagnostic and therapeutic discovery, guiding clinical decision-making, and advancing the development of predictive digital twins.

Abstract
The stochastic dynamic inventory-routing problem (SDIRP) is a fundamental problem within supply chain operations that integrates inventory management and vehicle routing while handling the stochastic and dynamic nature of exogenous factors unveiled over time, such as customer demands, inventory supply and travel times. While practical applications require dynamic and stochastic decision-making, research in this field has only recently experienced significant growth, with most inventory-routing literature focusing on static variants. This paper reviews the current state of research on SDIRPs, identifying critical gaps and highlighting emerging trends in problem settings and decision policies. We extend the existing inventory-routing taxonomies by incorporating additional problem characteristics to better align models with real-world contexts. As a result, we highlight the need to account for further sources of uncertainty, multiple-supplier networks, perishability, multiple objectives, and pickup and delivery operations. We further categorize each study based on its policy design, investigating how different problem aspects shape decision policies. To conclude, we emphasize that large-scale and real-time problems require more attention and can benefit from decomposition approaches and learning-based methods.

Abstract
Measuring water motion is essential for oceanography, coastal engineering, and marine environmental monitoring. A wide range of sensing technologies is used to quantify water velocity, wave motion, and flow dynamics, each suited to specific spatial and temporal scales. This paper presents a comprehensive review of modern sensor technologies for marine flow measurement, covering mechanical, electromagnetic, pressure-based, acoustic, optical, MEMS-based, inertial, Lagrangian, and remote-sensing approaches. The operating principles, strengths, and limitations of each technology are examined alongside their suitability for different environments and deployment platforms, including moorings, buoys, vessels, autonomous underwater vehicles, and drifters. Special attention is given to rapidly advancing fields such as MEMS flow sensors, multi-sensor fusion, and hybrid systems that combine inertial, acoustic, and optical data. Applications range from high-resolution turbulence measurements to large-scale current mapping and wave characterization. Remaining challenges include biofouling, performance degradation in energetic shallow waters, uncertainties in indirect velocity estimation, and long-term calibration stability. By synthesizing the state of the art across sensing modalities, this review provides a unified perspective on current technological capabilities and identifies key trends shaping the future of marine flow measurement.

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