Water navigation is crucial for the movement of people and goods in many locations, including the Amazon region. It is essential for the flow of inputs and outputs, and for certain Amazon cities, boat access is the only option. Fuel consumption accounts for over 25% of a vessel’s total operational costs. Shipping companies are therefore seeking procedures and technologies to reduce energy consumption. This research aimed to develop a fuel consumption prediction model for vessels operating in the Amazon region. Machine learning techniques such as Decision Tree, Random Forest, Extra Tree, Gradient Boosting, Extreme Gradient Boosting, and CatBoost can be used for this purpose. The input variables were based on the main design characteristics of the vessels, such as length and draft. Through metrics like mean, median, and coefficient of determination (R²), six different algorithms were assessed. CatBoost was identified as the model with the best performance and suitability for the data. Indeed, it achieved an R² value higher than 91% in predicting and optimizing fuel consumption for vessels operating in the Amazon and similar regions.

Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman’s demonstration coefficient and the coefficient of determination (R²) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of São Paulo, Brazil. The results showed that the production cost/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops.

The seasonal fluctuation of river depths is a critical factor in designing cargo capacity for river convoys and logistics processes used for grain transportation in northern Brazil. Water level variations directly impact the load capacities of pusher convoys navigating the Amazon rivers. This paper presents a machine learning model based on a multilayer perceptron artificial neural network developed with the aim of estimating the cargo capacities of river convoys one year in advance, which is essential for determining load capacities during dry periods. The prediction model was applied to the Tapajós River in the Amazon Basin, Brazil, where grain transportation is significant and relies on inland waterways. Navigability conditions were evaluated in terms of depth and geometric parameters. The results of this case study were satisfactory, validating the computational tool and enabling the assessment of capacity losses during dry periods and the identification of navigation bottlenecks. The main contributions of this work include optimizing river logistics, reducing costs, minimizing environmental impacts, and promoting the sustainable management of water resources in the Amazon. Conclusions drawn from the study indicate that the developed model is highly effective, with an R² of 0.954 and RMSE of 0.095, demonstrating its potential to significantly enhance river convoy operations and support sustainable development in the region.

Para milhões de alunos brasileiros, o transporte hidroviário escolar não é uma opção, mas o único meio para ter acesso à escola. Este estudo analisa a dinâmica do transporte escolar hidroviário em comunidades ribeirinhas do interior do estado do Amapá, com foco nas percepções dos usuários, nas fragilidades do serviço e nas possíveis melhorias para garantir acesso seguro e digno à educação e à justiça social. Diante dessa problemática, percebeu-se a necessidade de se discutir e democratizar informações que permitam o entendimento do ponto de vista dos usuários e dos órgãos responsáveis pela gestão desse transporte no estado. A metodologia pautou-se em um abordagem quali-quantitativa, com auxílio da pesquisa documental e visitas in loco, para melhor compreensão dos indicadores obtidos nas entrevistas que envolveram os alunos, pais, professores e técnicos de escolas que dependem desse tipo de transporte. Os resultados demonstram no que se refere à condução das políticas públicas voltadas ao transporte hidroviário escolar, a necessidade de estratégias descentralizadas, que considerem as especificidades socioterritoriais das diferentes regiões que depende deste transporte. Tal abordagem deve reconhecer as singularidades dos contextos locais, valorizando as capacidades institucionais de estados e municípios. Assim, o estudo reforça a urgência de políticas públicas integradas que considerem as especificidades amazônicas e assegurem a permanência escolar de crianças e adolescentes por meio de um transporte digno, seguro e regular.

O transporte fluvial urbano, de maneira geral, na Amazônia, está restrito à operação em travessias dos rios e pela orla de núcleos urbanos. Belém do Pará é um desses exemplos, que depende do transporte fluvial para o ir e vir das pessoas, principalmente, de áreas periféricas, nas viagens continente-ilhas e vice-versa. Algo que facilitaria a vida dos usuários seria o transporte integrado à rede intermodal urbana e inserido na política de transporte do município. O trabalho analisa a integração do transporte fluvial à rede intermodal urbana através do BRT Rodofluvial. Duas propostas para Belém são comparadas, com foco na inclusão social, mobilidade urbana e cidadania. A análise considera a estrutura operacional, incluindo terminais e demanda. Nos resultados obtidos, verificou-se que existe a demanda potencial, porém, há diversos desafios a serem vencidos, tais como a seleção tecnológica de embarcação e a elaboração de uma estrutura tarifária que proporcione a integração dos sistemas de transporte urbano.

Objective: This study presents a framework for assessing accessibility on a university campus, using the Federal University of Pará (UFPA) as a case study. The analysis is directed toward pedestrian mobility, aiming to identify physical and social barriers to walking and to propose inclusive interventions. Theoretical Framework: The theoretical framework encompasses the concepts of accessibility, universal design, urban mobility, and walkability, grounded in authors such as Silva & Sato (2013), Teobaldo et al. (2018), and Picharillo et al. (2023). It is also supported by national legal instruments related to mobility and inclusion, using the ABNT NBR 9050:2015 standard as a guideline for comparative analysis. Method: The methodological approach integrates principles of universal design with geoprocessing tools (ArcGIS). Data collection instruments included questionnaires administered to campus users and the completion of technical inspection forms during on-site visits, using a checklist based on the NBR 9050 standard. Based on the physical structure, the campus was divided into three sectors for analytical purposes, considering criteria such as building use typology, pedestrian circulation density, and the spatial distribution of urban equipment. This division proved convenient for organizing georeferenced data, facilitating the identification of accessibility patterns, and supporting comparisons between areas with distinct functional characteristics within the campus. Results and Discussion: The analyses revealed significant deficiencies in connectivity, signage, and regulatory compliance — with interrupted segments and discontinuous pathways in the priority routes, especially Route 2. Georeferencing pinpointed and quantified these critical areas, guiding the prioritization of actions. A targeted intervention plan is recommended, focusing on pavement rehabilitation, enhanced signage, and the installation of shelters to restore continuity and safety of pedestrian movements. Research Implications: The originality of this research lies in the combination of technical assessment, active community engagement, and the use of geospatial technologies to propose applicable and replicable solutions. The study contributes to strengthening inclusive practices within the university environment by offering a methodological model that can be adapted to other institutional contexts. Originality/Value: The combination of technical diagnosis, active community engagement, and the use of geotechnologies constitutes an innovative and replicable approach, strengthening inclusive management of university spaces.