Michael Schukat

The agricultural sector is facing mounting demands to enhance energy efficiency within farm enterprises, concurrent with a steady escalation in electricity costs. This paper focuses on modelling the adoption rate of photovoltaic (PV) energy within the dairy sector in Ireland. An agent-based modelling approach is introduced to estimate the adoption rate. The model considers grid energy prices, revenue, costs, and maintenance expenses to calculate the probability of PV adoption. The ABM outputs estimate that by year 2022, 2.45% of dairy farmers have installed PV. This is a 0.45% difference to the actual PV adoption rate in year 2022. This validates the proposed ABM. The paper demonstrates the increasing interest in PV systems as evidenced by the rate of adoption, shedding light on the potential advantages of PV energy adoption in agriculture. This study possesses the potential to forecast future rates of PV energy adoption among dairy farmers. It establishes a groundwork for further research on predicting and understanding the factors influencing the adoption of renewable energy.

The complexity and scale of IT systems are increasing dramatically, posing many challenges to real-world anomaly detection. Deep learning anomaly detection has emerged, aiming at feature learning and anomaly scoring, which has gained tremendous success. However, little work has been done on the thresholding problem despite it being a critical factor for the effectiveness of anomaly detection. In this paper, we model thresholding in anomaly detection as a Markov Decision Process and propose an agent-based dynamic thresholding (ADT) framework based on a deep Q-network. The proposed method can be integrated into many systems that require dynamic thresholding. An auto-encoder is utilized in this study to obtain feature representations and produce anomaly scores for complex input data. ADT can adjust thresholds adaptively by utilizing the anomaly scores from the auto-encoder and significantly improve anomaly detection performance. The properties of ADT are studied through experiments on three real-world datasets and compared with benchmarks, hence demonstrating its thresholding capability, data-efficient learning, stability, and robustness. Our study validates the effectiveness of reinforcement learning in optimal thresholding control in anomaly detection.

The practice of distributing software as open-source, which serves as a pillar for Open Science, can benefit future research in the field of fuzzy logic by enabling access and increasing the maintainability of free fuzzy logic frameworks alternatives to closed commercial solutions. Most open-source software uses a distribution license, with a wide range of restrictions and requirements on the distributions of these tools and their derived works. Therefore, understanding each license limitation and requirement is essential to ensure an ethical research process and avoid legal issues from breaching the open-source license. However, there is a lack of comparative research on the current state of open-source licenses for fuzzy modelling toolkits and frameworks. Previous studies are outdated or incomplete, missing some of the most popular open-source fuzzy frameworks or a detailed analysis of the licenses used. Our research analysed 20 of the most prominent open-source toolkits and categorised them based on their licenses, with qualitative comparisons of their characteristics and limitations. In our study, we note a concerning scenario where a potential license violation may impede the publication and dissemination of academic works derived from fuzzy toolkits using the GPL license. Additionally, we identify a gap in open-source fuzzy toolkits with support for Fuzzy Control Language (FCL) and Type-2 fuzzy sets. Finally, this study aims to support and guide future researchers in selecting the fuzzy toolkit that best matches their research problem without infringing the open-source licenses used, with an intuitive and up-to-date overview of the licenses and features of open-source fuzzy modelling frameworks available.