Abstract
Olive trees play a crucial role in the global agricultural landscape, serving as a primary source of olive oil production. However, olive trees are susceptible to several diseases, which can significantly impact yield and quality. This study addresses the challenge of improving the diagnosis of diseases in olive trees, specifically focusing on aculus olearius and Olive Peacock Spot diseases. Using a novel hybrid approach that combines deep learning and machine learning methodologies, the authors aimed to optimize disease classification accuracy by analyzing images of olive leaves. The presented methodology integrates Local Binary Patterns (LBP) and an adapted ResNet50 model for feature extraction, followed by classification through optimized machine learning models, including Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrated that the hybrid model achieved a groundbreaking accuracy of 99.11%, outperforming existing models. This advancement underscores the potential of integrated technological approaches in agricultural disease management and sets a new benchmark for the early and accurate detection of foliar diseases.

Abstract
Retail transactions represent sales of consumer goods, or final goods, by consumer companies. This sector faces security challenges due to the hustle and bustle of sales, affecting employees' workload. In this context, it is essential to estimate the number of customers who will appear in the store daily so that companies can dynamically adjust employee schedules, aligning workforce capacity with expected demand. This can be achieved by forecasting transactions using past observations and forecasting algorithms. This study aims to compare the ARIMA time series algorithm with several Machine Learning algorithms to predict the number of daily transactions in different store patterns, considering data variability. The study identifies four typical store patterns based on these criteria using daily transaction data between 2019 and 2023 from all retail stores of the leading company in Portugal. Due to data variability and the results obtained, the algorithm that presents the most minor errors in predicting daily transactions is selected for each store. This study's ultimate goal is to fill the gap in forecasting daily customer transactions and present a suitable forecasting model to mitigate risks associated with transactions in retail stores.

Abstract
The automotive industry is witnessing a surge in the production of electric vehicles (EVs) driven by stringent emission regulations. Despite this growth, heavy-duty truck fleets, particularly in waste collection, remain predominantly combustion-based ones. Waste collection is critical in urban environments, presenting unique challenges due to confined operational regions. One alternative to increase EVs in waste collection is to substitute the smaller truck fleets used for waste collection in constrained environments, such as narrow streets, by EVs. In this paper, we present a new formulation for the waste collection problem that considers a truck fleet comprised of smaller EVs and regular combustion trucks. The smaller trucks are proposed for the waste collection of specific sites (i.e. dumpsters in narrow streets). Our formulation considers battery limitations of electric trucks and flexible time windows for the waste collection task. The solution was validated by comparing the emission of CO2 and collection costs of a fleet comprised solely of combustion trucks and the hybrid fleet proposed here. The results showed that using a hybrid fleet significantly reduced waste collection costs and environmental impacts.

Abstract
Cooperative robotics is exponentially gaining strength in scientific research, especially regarding the cooperation between ground mobile robots and Unmanned Aerial Vehicles (UAVs), where the remaining challenges are equipollent to its potential uses in different fields, such as agriculture and electrical tower inspections. Due to the complexity involved in the process, precision landing by UAVs on moving robotic platforms for tasks such as battery hot-swapping is a major open research question. This work explores the feasibility and accuracy of different fiducial markers to aid in the precision landing process by a UAV on a mobile robotic platform. For this purpose, a TelloUAV was used to acquire images at different positions, angles, and distances from ArUco, ARTag, and ArUco Board markers to evaluate their detection precision. The analyses demonstrate the highest reliability in the measurements performed through the ArUco marker. Future work will be devoted to using the ArUco marker to perform precision landing on a mobile robotic platform, considering the necessary adjustments to lessen the impact of errors intrinsic to detecting the fiducial marker during the landing procedure.

Abstract
Autonomous vehicles and robotic manipulators are two examples of mechanically distinct systems. Whether these areas are indoors or outside, the environment in which such vehicles will be employed will play a crucial role in how their locomotion systems develop. The speed and stability of wheeled traditional mobility on ordinary flooring are superior. Leg traction is an efficient method for navigating uneven floors, but it takes more time and uses more energy. The foundation of the hybrid configuration is the creation of a leg that enables the interchange and fusion of the two previously described locomotion methods. One advantage of the hybrid arrangement is that the robot may now be deployed in a wider variety of environments. The goal of this paper is to showcase the creation of a leg for a hybrid locomotive robot. The leg can be printed and constructed at a reasonably low-cost thanks to the design of the numerous 3D modules, which will be made accessible later. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
In the last few decades, the area of robotics has evolved immensely, creating new and improved robot mobility solutions for industrial, scientific, medical, and several other purposes. Among these solutions is the car-like robot, using wheels to move. However, there are many different options within this solution, different types of wheels and configurations on the robot that each offer key advantages for a variety of objectives. Choosing a wheel configuration for robot vehicles is extremely important for the robot’s mobility, and its purpose must be considered while studying all the options. Starting on an existing prototype with a differential configuration, other configurations were implemented to study their differences, their strong and weak points, and the trajectories they allow the robot to make. This analysis will make the choice of configuration for each scenario clearer. This paper presents three types of robot configurations and compares them according to requirements using real prototype robots that are shared with the community for many purposes, such as education, among others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.