Abstract
Climate change has driven agriculture to alter farming methods for food production. This paper presents a new concept for monitoring, acquisition, management, analysis, and synthesis of ecological data, which captures the environmental determinants and direct gradients suited to a particular requirement for specific plant cultivation and sustainable agriculture. The purpose of this study is to investigate a smart seablite cultivation system. A novel digital agricultural method was developed and applied to digitised seablite cultivation. Machine learning was used to predict the future growth conditions of plants (seablites). The study identified the illustrative maps of seablite origins, a conceptual seablite smart farming model, essential factors for growing seablite, a digital circuit for cultivating seablite, and digital data of seablite growth phases comprised the digital data. The findings indicate that: (1) An indicator of soil salinity is a quantity of sodium chloride extracted from a seablite sample indicating its origin of environmental determinants. (2) Saline soil, saline water, pH, moisture, temperature, and sunlight are essential factors for seablite development. These factors are dependent on climate change and were measured using a smart seablite cultivation system. (3) Digital circuits of seablite cultivation provide a better understanding of the relationship between the essential factors for seablite growth and seablite growth phases. (4) Deep neural networks outperformed vector machines, with 86% accuracy at predicting future growth of seablites. Therefore, this finding showed that the essential seablite development factors can be manipulated as key controllers for agriculture in response to climate change and agriculture can be planned. Basic digitisation of specific plants aids plant migration. Digital agriculture is an important practice for agroecosystems.

Abstract
Future electrical grids, particularly the distribution networks, may face more severe voltage rises/drops, and in general, more power quality problems in the presence of new loads such as electric vehicle chargers and renewable energy generation units like photovoltaic systems. This necessitates investing in additional high-cost infrastructure to increase the capability of the feeder in hosting higher levels of loads and generation units while the existing capacity is not utilized effectively. In the stated condition, effective voltage stabilization strategies in electrical distribution networks can contribute to hosting capacity improvement and the better utilization of the existing infrastructure. Accordingly, in this paper, the application of Open-UPQC in voltage profile improvement and hosting capacity enhancement is evaluated in low-voltage distribution networks. Furthermore, a dynamic reference voltage adjustment strategy is applied to the device to improve its capabilities in power quality improvement and hosting capacity enhancement. Simulation studies have been implemented to evaluate the capability of Open-UPQC either with static reference voltage or the dynamically-adjusted one in low-voltage networks with real measured data while different cases are assessed regarding the topology and the length of the feeder. The simulation results approved the capability of Open-UPQC especially with the dynamic reference voltage in hosting capacity enhancement while providing the highest level of voltage profile improvement among all the assessed custom power devices in the studied low-voltage networks.

Abstract
The wireless capsule endoscopy is a non-invasive imaging method that allows observation of the inner lumen of the small intestine, but with the cost of a longer duration to process its resulting videos. Therefore, the scientific community has developed several machine learning strategies to help reduce that duration. Such strategies are typically trained and evaluated on small sets of images, ultimately not proving to be efficient when applied to full videos. Labelling full Capsule Endoscopy videos requires significant effort, leading to a lack of data on this medical area. Active learning strategies allow intelligent selection of datasets from a vast set of unlabelled data, maximizing learning and reducing annotation costs. In this experiment, we have explored active learning methods to reduce capsule endoscopy videos' annotation effort by compiling smaller datasets capable of representing their content.

Abstract
Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households' electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

Abstract

Abstract
This paper describes the development of a complete controller for the FANUC S-420FD 6-axis industrial robot. The original controller of the robot presented failures that made it impossible to operate and that negatively impacted the academic and research activities. To solve this problem, it was proposed the development of a new open-technology controller and also the design of an intuitive and functional graphical interface, allowing the programming, control and monitoring of the robot parameters. The developed interface offers advanced features such as trajectory programming, custom parameter configuration, and real-time visualization of the robot's state. This work highlights the importance of efficient and affordable solutions for the maintenance of industrial robots in university environments, encouraging scientific and technological advancement in these areas of study.