Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language models (LLMs) have demonstrated strong performance in phishing-related classification tasks, but their operational costs and reliance on external providers limit their practical adoption in many business environments. This paper presents a detection pipeline for malicious websites and investigates the feasibility of Small Language Models (SLMs) using raw HTML code and URLs. A key advantage of these models is that they can be deployed on local infrastructure, providing organisations with greater control over data and operations. We systematically evaluate 15 commonly used SLMs, ranging from 1 billion to 70 billion parameters, benchmarking their classification accuracy, computational requirements, and cost-efficiency. Our results highlight the trade-offs between detection performance and resource consumption. While SLMs underperform compared to state-of-the-art proprietary LLMs, the gap is moderate: the best SLM achieves an F1-score of 0.893 (Llama3.3:70B), compared to 0.929 for GPT-5.2, indicating that open-source models can provide a viable and scalable alternative to external LLM services.

We present a supervised learning method to estimate two local descriptors of time-series dynamics, the mean-reversion rate $\theta$ and a heavy-tail estimate $\alpha$, from short windows of data. These parameters summarize recovery behavior and tail heaviness and are useful for interpreting stochastic signals in sensing applications. The method is trained on synthetic, dimensionless Ornstein–Uhlenbeck processes with $\alpha$-stable noise, ensuring robustness for non-Gaussian and heavy-tailed inputs. Gradient-boosted tree models (CatBoost) map window-level statistical features to discrete $\alpha$ and $\theta$ categories with high accuracy and predominantly adjacent-class confusion. Using the same trained models, we analyze daily financial returns, daily sunspot numbers, and NASA POWER climate fields for Austria. The method detects changes in local dynamics, including shifts in the financial tail structure after 2010, weaker and more irregular solar cycles after 2005, and a redistribution in clear-sky shortwave irradiance around 2000. Because it relies only on short windows and requires no domain-specific tuning, the framework provides a compact diagnostic tool for signal processing, supporting the characterization of local variability, detection of regime changes, and decision making in settings where long-term stationarity is not guaranteed.

Digital Twins’ design frequently incorporates machine learning and, more recently, deep reinforcement learning techniques in order to interpret data and forecast future outcomes based on incoming data. However, because neural networks are typically considered a ”black box” model, doubts over the reliability of the output from deep learning models continue. In our work, we developed crop rotation policies using explainable tabular reinforcement learning techniques. We created five-step rotations, or a succession of five successive crops, to compare these policies to those produced by a deep Q-learning technique. The purpose of the rotations is to maximize crop yields while upholding prescribed planting guidelines and preserving a healthy amount of nitrogen in the soil. We incorporated phenomena related to weather and price fluctuations into the reward signal to account for the potential impact of external factors on crop production. The deployed explainable tabular reinforcement learning methods outperform the deep Q-learning approach regarding collected rewards when the rewards are not perturbed. For the perturbed case, robust tabular reinforcement learning methods outperform the deep learning approach even more while maintaining human interpretable policies. By consulting with farmers and crop rotation experts, we demonstrate that the derived policies are reasonable to use and more resilient towards external perturbations. Furthermore, the use of interpretable and explainable reinforcement learning techniques increases confidence in resulting policies, thereby increasing the likelihood that farmers will adopt the suggested policies. Digital Twins are often intertwined with machine learning and, more recently, deep reinforcement learning methods in their architecture to process data and predict future outcomes based on input data. However, concerns about the trustworthiness of the output from deep learning models persist due to neural networks generally being regarded as a black box model. In our work, we developed crop rotation policies using explainable tabular reinforcement learning techniques. We compared these policies to those generated by a deep Q-learning approach by generating five-step rotations, i.e. producing a series of five consecutive crops. The aim of the rotations is to maximise crop yields while maintaining a healthy nitrogen level in the soil and adhering to established planting rules. Crop yields may vary due to external factors such as weather patterns or changes in market prices, so perturbations have been added to the reward signal to account for those influences. The deployed explainable tabular reinforcement learning methods collect, on average, at least as much reward over 100 crop rotation plans when randomly starting with any crop compared to the deep learning model. For the perturbed case, robust tabular reinforcement learning methods collect similar amounts of reward across 100 crop rotation plans compared to the non-random reward setting, whereas the deep reinforcement learning agent collects even fewer rewards compared to learning on non-perturbed rewards. By consulting with farmers and crop rotation experts, we demonstrate that the derived policies are reasonable to use and more resilient towards external perturbations. Furthermore, the use of interpretable and explainable reinforcement learning techniques increases confidence in resulting policies, thereby increasing the likelihood that farmers will adopt the suggested policies.