Abstract
Modulation instability (MI) of the continuous wave (cw) has been associated with the onset of stable solitons in conservative and dissipative systems. The cubic complex Ginzburg-Landau equation (CGLE) is a prototype of a damped, driven, nonlinear, and dispersive system. The inclusion of nonlinear gradients is essential to stabilize pulses whether stationary or oscillatory. The soliton solutions of this model have been reasonably studied; however, its cw solution characteristics and stability have not been reported yet. Here, we obtain the cw solutions of the cubic CGLE with nonlinear gradient terms and study its short- and long-term evolution under the effect of small perturbations. We have found that, for each admissible amplitude, there are two branches of cw solutions, and all of them are unstable. Then, through direct integration of the evolution equation, we study the evolution of those cw solutions, observing the emergence of plain and oscillatory solitons. Depending on whether the cw and/or its perturbation are sinusoidal, we can obtain a train of a finite number of pulses or bound states.

Abstract
In this study, we demonstrate the measurement of electric power using an optical ground wire ( OPGW). The tests were conducted on an OPGW cable from a high-voltage transmission line in Sines, Portugal, operating at 400 kV. A buried fiber position, free of 50 Hz and 100 Hz frequency interference, was selected to confirm that the 50 Hz frequency is not due to mechanical perturbation or electronic noise. Additionally, two suspended fiber positions (at 2500 m and 8500 m), where these frequencies were clearly observed, were analyzed. This study also examined the positioning of poles and splice detection between cables.

Abstract
The Renewable Energy Communities (RECs) and self-consumption frameworks defined in Directive (EU) 2023/2413 and Directive (EU) 2024/1711 are currently being integrated into national regulations across EU member states, adapting legislation to incorporate these new entities. These regulations establish key principles for individual and collective self-consumption, outlining operational rules such as proximity constraints, electricity sharing mechanisms, surplus electricity management, grid tariffs, and various organizational aspects, including asset sizing, licensing, metering, data exchange, and role definitions. This study introduces a model tailored to optimize investment and energy-sharing decisions within RECs, enabling multiple members to invest in solar photovoltaic (PV) and wind generation assets. The model determines the optimal generation capacity each REC member should install for each technology and calculates the energy shared between members in each period, considering site-specific constraints on renewable deployment. A case study with a four-member REC is used to showcase the model's functionality, with simulation results underscoring the benefits of CSC over ISC.

Abstract
Log analysis is fundamental to modern software observability systems, playing a key role in improving system reliability. Recently, there has been a growing adoption of Large Language Models (LLMs) for log anomaly detection, due to their ability to learn complex patterns. In this work, we propose a model-agnostic framework that allows seamless plug-and-play integration of different LLMs, making it easy to experiment with and select the model that fits specific needs. These models are first fine-tuned on normal log data, learning their patterns. During inference, the model predicts the most probable next tokens based on the preceding context in each sequence. Anomaly detection is performed using Top-K predictions, where sequences are flagged as anomalous if the actual log entry does not appear among the K most probable next tokens, with K determined using the validation dataset. The proposed framework is evaluated on three widely-used benchmark datasets—HDFS, BGL, and Thunderbird—where it consistently achieves competitive results, outperforming state-of-the-art methods in multiple scenarios. These results highlight the effectiveness of LLM-based log analysis and the importance of flexibility when selecting models for specific operational contexts. © 2025 Elsevier B.V., All rights reserved.

Abstract

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.