Abstract
The increasing penetration of inverter-based resources (IBR) in medium voltage (MV) networks presents significant challenges for traditional overcurrent (OC) protection systems, particularly in ensuring selectivity, reliability, and fault isolation. This paper presents an adaptive protection system (APS) that dynamically adjusts protection settings based on real-time network conditions, addressing the challenges posed by distributed energy resources (DER). The methodology builds on ongoing research and development efforts, combining an offline phase, where operational scenarios are simulated using historical data, clustered with fuzzy c-means (FCM), and optimized with evolutionary particle swarm optimization (EPSO), and an online phase. To overcome the static nature of conventional schemes, a machine learning (ML)-based classifier is integrated into the APS, enabling real-time adaptation of protection settings. In the online phase, a centralized substation protection controller (CPC) leverages real-time measurements, communicated via IEC 61850 standard protocols, to classify network conditions using a support vector machine (SVM) classifier and activate the appropriate protection settings. The proposed APS has been validated on a Hardware-in-the-Loop (HIL) platform, demonstrating significant improvements in fault detection times, selectivity, and reliability compared to traditional OC protection systems. As part of a continued effort to refine and expand the system's capabilities, this work highlights the potential of integrating artificial intelligence (AI) and real-time/online decision-making to enhance the adaptability and robustness of MV network protection in scenarios with high DER penetration. © 2025 Elsevier B.V., All rights reserved.

Abstract
We present Caos: a programming framework for computer-aided design of structural operational semantics for formal models. This framework includes a set of Scala libraries and a workflow to produce visual and interactive diagrams that animate and provide insights over the structure and the semantics of a given abstract model with operational rules. Caos follows an approach where theoretical foundations and a practical tool are built together, as an alternative to foundations-first design (tool justifies theory) or tool-first design (foundations justify practice). The advantage of Caos is that the tool-under-development can immediately be used to automatically run numerous and sizeable examples in order to identify subtle mistakes, unexpected outcomes, and unforeseen limitations in the foundations-under-development, as early as possible. More concretely, Caos supports the quick creation of interactive websites that help the end-users better understand a new language, structure, or analysis. End-users can be research colleagues trying to understand a companion paper or students learning about a new simple language or operational semantics. We include a list of open-source projects with a web frontend supported by Caos that are used both in research and teaching contexts.

Abstract
Code comprehension is often supported by source code analysis tools that provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multilanguage pattern detection tool that uses the multilanguage capability of the LARA framework to support finding pattern instances in a code base. LARA provides a virtual AST, which is common to multiple OOP programming languages, and DP-LARA then performs code analysis of detecting pattern instances on this abstract representation. We evaluate the detection performance and consistency of DP-LARA with a few software projects. Results show that a multilanguage approach does not compromise detection performance, and DP-LARA is consistent across the languages we tested it for (i.e., Java and C/C++). Moreover, by providing a virtual AST as the abstract representation, we believe to have decreased the effort of extending the tool to new programming languages and maintaining existing ones.

Abstract
Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors involved and concerns about exposing real people to potentially dangerous environments. These problems impose limitations in running an emergency drill, harming the collection of valuable information for posterior analysis and decision-making. This work aims to mitigate these problems by using Agent Based Modelling (ABM) simulator to deepen the comprehension of human actions when exposed to a sudden variation in extensive crowded environmental conditions and how evacuation strategies affect evacuation performance. To assess the impact of the evacuation strategy employed, we propose a modified informed leader-flowing approach and compare it with common evacuation strategies in a simulated environment, replicating stadium benches with narrow corridors leading to different exit points. The objective is to determine the impact of each set of configurations and evacuation strategies and compare them against other established ones. Our experiments determined that agents following the crowd generally lead to a higher number of victims due to the rise of herding phenomena near the exits, which was significantly reduced when agents were guided towards the exit via knowing the exit beforehand or following leader agent with real-time information regarding exit location and exit current state, proving that relevant and controlled information in combination with Follow Leader strategies can be crucial in an emergency evacuation scenario with limited evacuation exit capabi and distribution. © The Author(s) 2024.

Abstract
Automated fruit harvesting systems rely heavily on accurate visual perception, particularly for crops such as the Arbutus tree (Arbutus unedo), which holds both ecological and economic significance. However, this species poses considerable challenges for computer vision due to its dense foliage and the morphological variability of its berries across different ripening stages. Despite its importance, the Arbutus tree remains under-explored in the context of precision agriculture and robotic harvesting. This study addresses that gap by evaluating a computer vision-based approach to detect and classify Arbutus berries into three ripeness categories: green, yellow-orange, and red. A significant contribution of this work is the release of two fully annotated open-access datasets, Arbutus Berry Detection Dataset and Arbutus Berry Ripeness Level Detection Dataset, developed through a structured manual labeling process. Additionally, we benchmarked four YOLO architectures - YOLOv8n, YOLOv9t, YOLOv10n, and YOLO11n - as well as the RT-DETR models, using these datasets. Among these, RT-DETR-L demonstrated the most consistent performance in terms of precision, recall, and generalization, outperforming the lighter YOLO models in both speed and accuracy. This highlights RT-DETR's strong potential for deployment in real-time automated harvesting systems, where robust detection and efficient inference are critical. © 2025 IEEE.

Abstract
We show that recent approaches to quantitative analysis based on non-idempotent typing systems can be extended to programming languages with effects. In particular, we consider two cases: the weak open call-by-name (CBN) and call-by-value (CBV) variants of the $\lambda$ -calculus, equipped with operations to write and read from a global state. In order to capture quantitative information with respect to time and space for both CBN and CBV, we design for each of them a quantitative type system based on a (tight) multi-type system. One key observation of this work is how CBN and CBV influence the composition of state types. That is, each type system is developed by taking into account how each language manages the global state: in CBN, the composition of state types is almost straightforward, since function application does not require evaluation of its argument; in CBV, however, the interaction between functions and arguments makes the composition of state types more subtle since only values can be passed as actual arguments. The main contribution of this paper is the design of type systems capturing quantitative information about effectful CBN and CBV programming languages. Indeed, we develop type systems that are qualitatively and quantitatively sound and complete.