Paulo Moura Oliveira

Abstract
With the serious danger to nature and humanity that forest fires are, taken into consideration, this work aims to develop an artificial intelligence model capable of accurately predicting the forest fire risk in a certain region based on four different factors: temperature, wind speed, rain and humidity. Thus, three models were created using three different approaches: Artificial Neural Networks (ANN), Random Forest (RF), and K-Nearest Neighbor (KNN), and making use of an Algerian forest fire dataset. The ANN and RF both achieved high accuracy results of 97%, while the KNN achieved a slightly lower average of 91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
AbstractWith the global population on the rise and a declining agricultural labor force, the realm of robotics research in agriculture, such as robotic manipulators, has assumed heightened significance. This article undertakes a comprehensive exploration of the latest advancements in controllers tailored for robotic manipulators. The investigation encompasses an examination of six distinct controller paradigms, complemented by the presentation of three exemplars for each category. These paradigms encompass: (i) adaptive control, (ii) sliding mode control, (iii) model predictive control, (iv) robust control, (v) fuzzy logic control and (vi) neural network control. The article further introduces and presents comparative tables for each controller category. These controllers excel in tracking trajectories and efficiently reaching reference points with rapid convergence. The key point of divergence among these controllers resides in their inherent complexity.

Abstract
Despite the ubiquitous term climate neutral cities, there is a distinct lack of quantifiable and meaningful municipal decarbonization goals in terms of the targeted energy balance and composition that collectively connect to national scenarios. In this paper we present a simple but useful allocation approach to derive municipal targets for energy demand reduction and renewable expansion based on national energy transition strategies in combination with local potential estimators. The allocation uses local and regional potential estimates for demand reduction and the expansion of renewables and differentiates resulting municipal needs of action accordingly. The resulting targets are visualized and opened as a decision support system (DSS) on a web-platform to facilitate the discussion on effort sharing and potential realization in the decarbonization of society. With the proposed framework, different national scenarios, and their implications for municipal needs for action can be compared and their implications made explicit.

Abstract
The paper presents the comparison of accuracy in the Speech Emotion Recognition task using the Hamming and Hanning windows for framing the speech and determining the spectrogram to be used as input of a convolutional neural network. The detection of between 4 and 10 emotional states was tested for both windows. The results show significant differences in accuracy between the two window types and provide valuable insights for the development of more efficient emotional state detection systems. The best accuracy between 4 and 10 emotions was 64.1% (4 emotions), 57.8% (5 emotions), 59.8% (6 emotions), 48.4% (7 emotions), 47.8% (8 emotions), 51.4% (9 emotions), and 45.9% (10 emotions). These accuracy is at the state-of-the art level.

Abstract
The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.