Abstract
Pollination is critical for crop development, especially those essential for subsistence. This study addresses the pollination challenges faced by Actinidia, a dioecious plant characterized by female and male flowers on separate plants. Despite the high protein content of pollen, the absence of nectar in kiwifruit flowers poses difficulties in attracting pollinators. Consequently, there is a growing interest in using artificial intelligence and robotic solutions to enable pollination even in unfavourable conditions. These robotic solutions must be able to accurately detect flowers and discern their genders for precise pollination operations. Specifically, upon identifying female Actinidia flowers, the robotic system should approach the stigma to release pollen, while male Actinidia flowers should target the anthers to collect pollen. We identified two primary research gaps: (1) the lack of gender-based flower detection methods and (2) the underutilisation of contemporary deep learning models in this domain. To address these gaps, we evaluated the performance of four pretrained models (YOLOv8, YOLOv5, RT-DETR and DETR) in detecting and determining the gender of Actinidia flowers. We outlined a comprehensive methodology and developed a dataset of manually annotated flowers categorized into two classes based on gender. Our evaluation utilised k-fold cross-validation to rigorously test model performance across diverse subsets of the dataset, addressing the limitations of conventional data splitting methods. DETR provided the most balanced overall performance, achieving precision, recall, F1 score and mAP of 89%, 97%, 93% and 94%, respectively, highlighting its robustness in managing complex detection tasks under varying conditions. These findings underscore the potential of deep learning models for effective gender-specific detection of Actinidia flowers, paving the way for advanced robotic pollination systems.

Abstract
Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future.

Abstract
The United Nations has issued a warning over the limited time for climate disaster prevention. In the last two decades, several countries have set targets to reduce fossil fuel usage and greenhouse gas emissions. These goals are tracked through the adoption of energy systems that prioritise efficiency and low-carbon alternatives, in alignment with the Sustainable Development Goals outlined by the United Nations. In the winemaking sector, the wine produced in the European Union comprised 65 % of the worldwide total from 2014 to 2018, with vineyards making up 4.7 % of its farms in 2020. Electricity is the primary source of energy used in vineries, accounting for around 90 % of the total energy consumption. The energy consumption associated with winemaking is mostly attributed to two key processes: fermentation, which accounts for 45 % to 90 % of the entire energy consumption, and bottling and storage, which contribute around 18 % of the overall energy consumption. The aim of this article is to provide an integrated review of energy efficiency in wineries through examining 144 academic publications. The selected publications cover various aspects, including sustainable energy utilisation in the wine industry, thermal performance analysis of buildings, energy efficiency assessment of systems and technologies, and the integration of renewable energy sources. A link has been established between the geographic distribution of academic publications and wine -producing countries. In relation to European publications, it is observed that research funding is associated with the energy directives of the European Union. It can also be concluded that wine customers are pushing for environmentally friendly practices. However, not everyone in the winemaking sector is moving in the same direction or at the same pace. To identify areas for improvement, winemakers must have supporting tools to manage energy use. Systems optimisation, monitoring, and accounting can be used to decrease energy consumption in winemaking processes or equipment. Progresses on sustainable energy use through greater energy efficiency and share of renewable energies in the wineries can contribute to the reduction of greenhouse gas emissions, and consequently, brings the wine industry closer to climate neutrality.

Abstract
Fiducial markers play a fundamental role in various fields in which precise localization and tracking are paramount. In Augmented Reality, they provide a known reference point in the physical world so that AR systems can accurately identify, track, and overlay virtual objects. This accuracy is essential for creating a seamless and immersive AR experience, particularly when prompted to cope with the sub-millimeter requirements of medical and industrial applications. This research article presents a comparative analysis of four fiducial marker tracking algorithms, aiming to assess and benchmark their accuracy and precision. The proposed methodology compares the pose estimated by four algorithms running on Hololens 2 with those provided by a highly accurate ground truth system. Each fiducial marker was positioned in 25 sampling points with different distances and orientations. The proposed evaluation method is not influenced by human error, relying only on a high-frequency and accurate motion tracking system as ground truth. This research shows that it is possible to track the fiducial markers with translation and rotation errors as low as 1.36 mm and 0.015 degrees using ArUco and Vuforia, respectively.

Abstract
The design and analysis of systems that combine computational behaviour with physical processes' continuous dynamics - such as movement, velocity, and voltage - is a famous, challenging task. Several theoretical results from programming theory emerged in the last decades to tackle the issue; some of which are the basis of a proof-of-concept tool, called Lince, that aids in the analysis of such systems, by presenting simulations of their respective behaviours. However being a proof-of-concept, the tool is quite limited with respect to usability, and when attempting to apply it to a set of common, concrete problems, involving autonomous driving and others, it either simply cannot simulate them or fails to provide a satisfactory user-experience. The current work complements the aforementioned theoretical approaches with a more practical perspective, by improving Lince along several dimensions: to name a few, richer syntactic constructs, more operations, more informative plotting systems and errors messages, and a better performance overall. We illustrate our improvements via a variety of examples that involve both autonomous driving and electrical systems.

Abstract
The present study brings forward a simulation-based study of the production process of a Portuguese bakery. The main goal is to analyse different production processes and propose improvements, through the use of discrete event simulation. A relevant set of data was collected, and four productive processes were selected to be modelled using Simio software (Simulation Modelling based on Intelligent Objects). The analysis of the developed models highlighted the need for improvements and different scenarios were created to this purpose. Among the obtained results, it was found that the adoption of mixed production scenarios allowed the increase of the production level while maintaining the current existing resources. In conclusion, this study highlighted the ability of the simulation technique to analyse manufacturing processes, throughout the creation of different scenarios, providing insights on the production process optimising the companies' productive performance.