Abstract
Methane (CH4) detection plays a crucial role in atmospheric monitoring, industrial safety, global climate assessments, and environmental sensing. Laser absorption spectroscopy techniques have become the gold standard for achieving fast, selective, and highly sensitive CH4 measurements across a wide range of conditions. This review summarizes the main spectroscopic methods used for methane detection, their operating principles, performance characteristics, and practical implementation considerations. Emphasis is placed on tunable diode laser absorption spectroscopy (TDLAS), wavelength modulation spectroscopy (WMS), Dual-Comb Spectroscopy (DCS), cavity-enhanced methods such as CRDS, photoacoustic and photothermal techniques including QEPAS and hollow-core fiber photothermal interferometry, and emerging MIR/quantum-cascade–based approaches. © 2001-2012 IEEE.

Abstract
Wikipedia is the largest and most globally well-known online encyclopedia, but its collaborative nature leads to a significant disparity in article quality. In this work, we explore real-time and automatic quality assessment within Wikipedia through machine-learning. We first constructed a dataset of 36,000 English articles and 145 features, then compared the performance of multiple classification and regression algorithms and studied how the number of classes and features affects the model's performance. The six-class experiments achieved a classifier accuracy of 64% and a mean absolute error of 0.09 in regression methods, which matches or beats most state-of-the-art approaches. Our model produces similar results on some non-English Wikipedias, but the error is slightly higher on other versions. We have also determined that the features measuring the article's content and revision history bring the largest performance boost.

Abstract
This paper proposes a vision-based framework for the intelligent control of mobile Open Radio Access Network (O-RAN) base stations (gNBs) operating in dynamic wireless environments. The framework comprises three innovative components. The first is the introduction of novel Service Models (SMs) within a vision-enabled O-RAN architecture, termed VisionRAN. These SMs extend state-of-the-art O-RAN-based architectures by enabling the transmission of vision-based sensing data and gNB positioning control messages. The second is an O-RAN xApp, VisionApp, which fuses vision and radio data, and uses this information to control the position of a mobile gNB, using a Deep Q-Network (DQN). The third is a digital twin environment, VisionTwin, which incorporates vision data and can emulate realistic wireless scenarios; this digital twin was used to train the DQN running in VisionApp and validate the overall system. Experimental results, obtained using real vision data and an emulated radio, demonstrate that the proposed approach reduces the duration of Line-of-Sight (LoS) blockages by up to 75% compared to a static gNB. These findings confirm the viability of integrating multimodal perception and learning-based control within RANs. © 2026 IEEE.

Abstract
Este artigo investiga as interseções entre place branding, comunicação estratégica, cidadania e turismo. Exploramos a relação dinâmica entre esses conceitos, considerando particularmente o tensionamento entre políticas públicas voltadas para a melhoria do bem-estar dos cidadãos em cidades brasileiras e a tematização de demandas que ainda permanecem sem respostas. Argumentamos que as escolhas estratégicas de comunicação feitas pelas administrações municipais priorizam a promoção das cidades como bens de consumo, distanciando-se de práticas discursivas voltadas para a comunicação pública que prioriza os interesses coletivos. Pensar a marca de lugar enquanto comunicação estratégica envolve promover as cidades como destinos turísticos e, sobretudo, construir processos cooperativos de comunicação pública que priorizem o reconhecimento da legitimidade das demandas populares e o desenvolvimento geral do território, sem desconsiderar a polifonia do diálogo e sem tentar universalizar aquilo que dificilmente pode ser generalizado.

Abstract
Synthetic datasets are essential for the development and benchmarking of machine learning methods in biomedicine, as they help overcome the pervasive data scarcity in biomedical research. In fields such as immunomics, genomics and proteomics, they enable the development of prediction algorithms, including methods for immune receptor-antigen binding prediction. When generated with transparent and fully specified parameters, synthetic datasets serve as rule-based systems for reproducible and interpretable model testing, an essential step towards digital twins that emulate biological systems for diagnosis and therapy design. A key obstacle, however, is the 'simulation to reality' (sim2real) gap, which describes the uncertainty about whether performance on synthetic data is predictive of performance on experimental data. Divergent statistical and biological properties may erode generalizability and clinical relevance. The lack of standardized sim2real benchmarks impedes validation and widespread adoption. We argue that multilayered validation frameworks, incorporating techniques such as domain adaptation and hybrid validation, and grounded in biological realism, are essential to ensuring that synthetic datasets faithfully capture biological complexity. Closing the sim2real gap will unlock the full translational potential of synthetic data, accelerating diagnostic and therapeutic discovery, guiding clinical decision-making, and advancing the development of predictive digital twins.

Abstract
The stochastic dynamic inventory-routing problem (SDIRP) is a fundamental problem within supply chain operations that integrates inventory management and vehicle routing while handling the stochastic and dynamic nature of exogenous factors unveiled over time, such as customer demands, inventory supply and travel times. While practical applications require dynamic and stochastic decision-making, research in this field has only recently experienced significant growth, with most inventory-routing literature focusing on static variants. This paper reviews the current state of research on SDIRPs, identifying critical gaps and highlighting emerging trends in problem settings and decision policies. We extend the existing inventory-routing taxonomies by incorporating additional problem characteristics to better align models with real-world contexts. As a result, we highlight the need to account for further sources of uncertainty, multiple-supplier networks, perishability, multiple objectives, and pickup and delivery operations. We further categorize each study based on its policy design, investigating how different problem aspects shape decision policies. To conclude, we emphasize that large-scale and real-time problems require more attention and can benefit from decomposition approaches and learning-based methods.