Integrity monitoring is a key criterion for achieving robust and safe navigation systems. This work explores two integrity frameworks: the classical methods and their respective evolution towards the road vehicle urban scenario, and the artificial intelligence-based methods, where the monitoring process is accomplished by data analysis and learning techniques. In most cases, machine learning outperforms traditional models, which are often observed under controlled, non-real-time conditions, by employing simple algorithms that may have limited success in real-world applications. An overview is provided on how these algorithms have been used, including a comparison of their characteristics and performances, offering insights into how they can evolve and possible future directions to achieve more reliable solutions.

Industry 4.0 has driven a transformation in industrial environments through the adoption of technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and real-time Big Data analytics. In this context, efficient decision-making becomes essential to address process complexity and operational variability. This work proposes the development of an AI-based system to support informed and adaptive decision-making in industrial processes. By identifying efficiency indicators and applying advanced analytical techniques, the proposed system monitors industrial production processes and continuously adapts to real operating conditions. Adopting the Adaptive Business Intelligence (ABI) paradigm, this work integrates forecasting methods with energy efficiency metrics, pursuing a more effective and dynamic approach to operational management. This project aims to contribute to the advancement of decision support solutions in the Industry 4.0 landscape, promoting a more intelligent and responsive integration of analytical systems into industrial operations.

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.

Purpose: Chemodynamic therapy (CDT) has emerged as a promising cancer treatment strategy leveraging tumor microenvironment conditions to generate reactive oxygen species (ROS) through Fenton-type reactions. This study reports the synthesis, in-depth characterization, and biological evaluation of novel manganese-based zeolitic imidazolate framework (ZIF) nanoparticles, ie, Mn-rods, as a carrier-free potential CDT platform with exceptionally high manganese loading. Methods: Mn-rods were synthesized through coordination of Mn2+ ions with 2-methylimidazolate and characterized using transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), and inductively coupled plasma optical emission spectroscopy (ICP-OES). Two human non-small cell lung cancer lines (A549 and Calu-3) were used to evaluate nanoparticle internalization and therapeutic response was assessed using cell viability assays, ROS generation measurements, and rescue experiments with pathway-specific inhibitors. Results: The synthesized Mn-rods exhibited a rod-shaped morphology (226 ± 93 nm length x 26.5 ± 9.5 nm width) with an exceptional Mn2+ loading of 50 wt.%, surpassing existing manganese-based systems. Both A549 and Calu-3 cells internalized Mn-rods, however, only A549 cells exhibited marked dose-dependent cell viability reduction, highlighting the influence of cellular phenotype on therapeutic response. Mechanistic studies suggest that Mn-rods induce ferroptosis-like cell death in A549 cells through lipid peroxidation and redox imbalance, independent of apoptosis, necroptosis and iron-mediated pathways. Rescue experiments with ferroptosis inhibitors (ferrostatin-1 and liproxstatin-1) confirmed the lipid ROS-driven mechanism, further supported by increased intracellular ROS levels and progressive membrane damage. Conclusion: These findings establish Mn-rods as potent CDT agents whose efficacy is dictated by tumor cell oxidative vulnerability. Understanding such cell-specific responses is critical for optimizing nanoparticle design and tailoring therapeutic strategies in heterogeneous tumor environments. Future studies should extend these investigations across diverse cancer models to refine their translational potential. © 2026 Ivasiv et al.

Realistic Global Navigation Satellite System (GNSS) synthetic data is essential for the research and development of vehicular applications, such as Advanced Driver Assistance Systems (ADAS), autonomous driving, and solutions or scenarios that are difficult and expensive to test in the real world, such as vehicular cooperative positioning. However, generating GNSS synthetic data is complex due to satellite dynamics, signal characteristics, and various noise and error sources. This complexity increases in automotive contexts by vehicle movement and environmental factors influencing signal propagation, with multipath effects being particularly challenging to simulate accurately. This paper introduces a novel pipeline that leverages a 3D virtual environment to produce more realistic GNSS synthetic data for automotive applications. The pipeline integrates the CARLA Simulator and GPSoft's SatNav Toolbox for Matlab, with custom-developed modules that generate raw GNSS measurements incorporating environment- and location-specific multipath effects. Our contributions include a tailored simulation pipeline for automotive applications, with integration of GNSS satellite orbits within CARLA, a dynamic multipath model reflecting obstacles in the simulated environment, and a synthetic dataset generated by this approach available to the community. Evaluation on CARLA's Town03 map showed that while standard multipath models result in unrealistic uniform effects, our dynamic model produces effects that correlate with the vehicle's surroundings, accurately reflecting real-world conditions such as increased errors in urban areas and lack of signals in tunnels. This approach can support the research, development, and validation of GNSS positioning algorithms and Artificial Intelligence (AI) model training, with potential applications extending also beyond the automotive context.

There is an increasing demand for efficient and robust systems able to cope with today's global needs for intensive data dissemination, e.g., media content or news feeds. Unfortunately, traditional approaches tend to focus on one end of the efficiency/robustness design spectrum, by either leveraging rigid structures such as trees to achieve efficient distribution, or using loosely-coupled epidemic protocols to obtain robustness. In this paper we present BRISA, a hybrid approach combining the robustness of epidemic-based dissemination with the effi- ciency of tree-based structured approaches. This is achieved by having dissemination structures such as trees implicitly emerge from an underlying epidemic substrate by a judicious selection of links. These links are chosen with local knowledge only and in such a way that the completeness of data dissemination is not compromised, i.e., the resulting structure covers all nodes. Failures are treated as an integral part of the system as the dissemination structures can be promptly compensated and repaired thanks to the underlying epidemic substrate. Besides presenting the protocol design, we conduct an extensive evaluation in a real environment, analyzing the effectiveness of the structure creation mechanism and its robustness under faults and churn. Results confirm BRISA as an efficient and robust approach to data dissemination in the large scale.

O crescente volume de dados em Big Data (BD) e as limitações computacionais para o treino de modelos de Machine Learning (ML) tornam fundamental o desenvolvimento de técnicas que otimizem o equilíbrio entre o esforço computacional e o desempenho preditivo. Esta dissertação investiga como a seleção inteligente de dados pode reduzir o custo computacional do treino sem comprometer o desempenho preditivo dos modelos de ML. Foram comparados sete métodos de seleção de dados, aplicados a cinco conjuntos de dados heterogéneos com taxas de amostragem de 1%, 5%, 10% e 20%. A abordagem Baseline, que utiliza o conjunto completo de dados, foi contrastada com técnicas de subsampling (Random, Diversity Subsampling (DS) e Coreset Sampling) e quatro variações de Importance Sampling (IS) (Importance Sampling Basic (ISB), Importance Sampling Random (ISR), Importance Sampling Hybrid (ISH) e Importance Sampling Stratified (ISS)), utilizando uma rede neuronal como modelo de ML. O desempenho preditivo foi avaliado por métricas como Area Under the Curve (AUC), precisão e recall, enquanto o esforço computacional foi medido pelo tempo total de treino e pelo tempo médio por época, com trade-offs analisados via gráficos de Pareto. Os resultados mostram que o Coreset Sampling alcança AUC próxima da Baseline com reduções significativas no tempo de treino por época (68% menos que a Baseline), sendo ideal para conjuntos de dados de dimensionalidade pequena e balanceados. O ISS destaca-se em conjuntos de dados desbalanceados, enquanto o Random é eficiente em conjuntos de dados de grande dimensionalidade. Este trabalho contribui com uma análise detalhada do impacto da amostragem no desempenho de modelos de ML, oferecendo orientações práticas para cenários com restrições computacionais e uma base teórica para futuras investigações sobre escalabilidade e generalização. As técnicas de redução de dados demonstram-se viáveis, promovendo soluções mais eficientes e sustentáveis para aplicações de ML.

Monitoring the condition of road surfaces can bring benefits to drivers, ranging from increased safety and comfort, to cost reduction. In this context, accelerometer-based approaches offer a cheap and robust solution to the mitigation of common issues that arise from other types of sensors, like cameras, namely computational cost concerns and inability in low visibility scenarios. Nonetheless, as more applications compete for resources in modern vehicles, it becomes imperative to design such applications considering power efficiency constraints (TinyML): to this end, we propose a lightweight machine learning model to detect road irregularities (speed bumps, potholes, and manholes, including the side of impact) using 4 Inertial Measurement Unit (IMU) mounted near each vehicle wheel. Furthermore, to have a real-time monitoring system, we deploy an XGBoost model on a Field-Programmable Gate Array (FPGA) through the use of the Conifer framework, to translate the model using High Level Synthesis (HLS) language. Our solution achieves an accuracy of 88.1%, with 750 microseconds of inference time, showcasing a robust performance in varied scenarios.

Multipath interference poses substantial challenges to global navigation satellite system (GNSS) receivers, leading to inaccuracies in the time of arrival (TOA) measurement of the line of sight (LOS) signal. Therefore, to mitigate the impact of multipath on receivers, the problem has been approached at several system development stages—signal design, reception, and processing. While efforts and advancements have been achieved over the years at each stage seeking navigation robustness, this article focuses on the signal processing stage by presenting a review of advanced multipath mitigation techniques using adaptive channel parameter estimation at the correlation level. The multipath mitigation literature often resorts to optimistic assumptions—high signal-to-noise ratio (SNR), static multipath channel, single fading channel distribution, and so on—while in real-world scenarios noise is prominent, the number of paths and their states vary at different rates, channels are nonstationary, along with other nonideal conditions. Moreover, it is important to directly compare different techniques to characterize their applicability and limitations. An analysis of adaptive algorithms is conducted for multipath mitigation applications.