Abstract
This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly. © 2024 The Authors. Published by ELSEVIER B.V.

Abstract
Research have been driven by the increased demand for delivery and pick-up services to develop new formulations and algorithms for solving Vehicle Routing Problems (VRP). The main objective is to create algorithms that can identify paths considering execution time in real-world scenarios. This study focused on using the Guided Local Search (GLS) metaheuristic available in OR-Tools to solve the Capacitated Vehicle Routing Problem with Time Windows using the Solomons instances. The execution time was used as a stop criterion, with short runs ranging from 1 to 10 s and a long run of 360 s for comparison. The results showed that the GLS metaheuristic from OR-Tools is applicable for achieving high performance in finding the shortest path and optimizing routes within constrained execution times. It outperformed the best-known solutions from the literature in longer execution times and even provided a close-to-optimal solution within 10 s. These findings suggest the potential application of this tool for dynamic VRP scenarios that require faster algorithms.

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Abstract
The online channel, particularly in the food retail area, has been evolving positively and exponentially in the world, including Portugal. Currently, this type of purchase is increasingly part of people's daily lives, even more so with the emergence of the Covid-19 pandemic. Consequently, in Portugal, most companies adopt a multichannel strategy, where the physical store and the online store operate independently from each other. However, it is necessary to rethink this channel integration model, which may go through an omnichannel strategy, where the physical store and the online store operate as a single store, and where several advantages are already recognized in terms of the consumer's shopping experience. The main objective of this study is to understand the strategy implemented by the company studied, Pingo Doce, through an analysis and description of its channels. To better understand the strategy of the company under study, a semi-structured exploratory interview was carried out with one of the people in charge of Pingo Doce's digital channels, to understand the strategy used by the company and thus complement the data obtained through direct observation and bibliographic research. At the end of the work developed it was possible to understand the positioning of Pingo Doce in the online food retail area and their online and offline distribution strategies.

Abstract
Almonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Tras-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.

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This study adopts an integrative review approach to explore the differences and similarities between smart cities and sustainable cities. The research starts by performing two systematic literature reviews about both paradigms and, after that, employs a thematic analysis to identify key themes, definitions, and characteristics that differentiate and connect these two urban development concepts. The findings reveal more similarities than differences between the two paradigms. Despite this, some key differences are identified. Smart cities are characterized by their use of advanced information and communication technologies to enhance urban infrastructure, improve public services, and optimize resource management. In contrast, sustainable cities focus on environmental conservation, social equity, and economic viability to ensure long-term urban resilience and quality of life. This study is important because it clarifies both concepts and highlights the potential for integrating smart and sustainable city strategies to address contemporary urban challenges more holistically. The findings also suggest a convergence towards the concept of 'smart sustainable cities', which leverage technology to achieve sustainability goals. Finally, this study concludes by identifying research gaps and proposing a future research agenda to further understand and optimize the synergy between smart and sustainable urban development paradigms.