Abstract
Multi-label image classification models often inevitably learn on partially labeled datasets, where a considerable proportion of labels are missing. However, the popular PyTorch deep learning ecosystem is less compatible with training on partially labeled datasets, as many built-in functions like loss functions and metrics do not work correctly or raise errors when unknown labels are present. To this end, we present an original and easy-to-install Python package called mlcpl, which expands the PyTorch ecosystem to offer a friendly environment for learning with partially labeled datasets. The package provides a series of multi-label loss functions and metrics that are compatible with unknown labels. Seven recently proposed approaches are also implemented for the convenient use of cutting-edge techniques. In addition, eleven dataset loading functions, followed by three partial label simulation schemes, expedite the development of experiments. Furthermore, these functions are simple to use, have a PyTorch-like interface, and can collaborate well with other PyTorch components. Several examples of experiments with mlcpl are also provided for demonstration. We wish the release of this package could facilitate relevant academic research and real-world applications. The source code is available at https://github.com/ maxium0526/mlcpl.

Abstract
Solar photovoltaic (PV) power generation has become one of the most widely adopted forms of clean energy worldwide. In large-scale PV farm operation and maintenance, unmanned aerial vehicles equipped with thermal infrared (TIR) cameras are increasingly used to enable automated fault detection and classification. However, the long imaging distance and the inherently low resolution of TIR images often lead to fault patterns appearing with low contrast, making subtle discriminative features difficult to extract and posing significant challenges to achieving highly accurate fault identification and classification. To address these challenges, we propose GEPFNet, a network that exploits Group Equivariant Convolutions to explicitly model the geometric structures of faults, incorporates multi-scale processing with unified local-global contextual representations, and adopts a parallel feature fusion strategy to integrate multi-level features and enhance contextual utilization effectively. The design of feature extraction and fusion mechanisms ensures the proposed GEPFNet achieves strong robustness and generalization under complex operational conditions. The effectiveness of GEPFNet was validated on two public datasets with distinct resolutions, class distributions, and feature characteristics: PVF-10 and the Infrared Solar Module (ISM) dataset. Extensive experiments and statistical analyses demonstrate that the proposed GEPFNet achieves state-of-the-art performance on the PVF-10 dataset, obtaining an accuracy of 96.05 %+/- 0.42 for the 2-Class task and 94.64 %+/- 0.35 for the 10-Class task. On the ISM dataset, GEPFNet achieves an improvement of approximately 5 % over the baseline models. Moreover, under highly imbalanced data distributions, the proposed GEPFNet achieves average accuracy improvements of 5.83% and 3.82% on PVF-10 and ISM, respectively, further demonstrating its capability to enhance class-wise performance. With only 9.51 GFLOPs, GEPFNet also exhibits notable computational efficiency, making it well suited for PV fault classification in TIR imagery.

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.