Porous pure SnO₂ nanofibers (NFs) and La₂O₃ nanoparticles (NPs)-embedded porous SnO₂ NFs were successfully synthesized via electrospinning followed by calcination. These materials were systematically evaluated as gas-sensing elements in metal-oxide-semiconductor (MOS) sensors. The La₂O₃ NPs embedded in porous SnO₂ NFs demonstrated superior gas-sensing performance compared to pure SnO₂ NFs. Specifically, the incorporation of La₂O₃ resulted in a 12-fold enhancement in gas-sensing response towards ethanol, significantly improving both sensitivity and selectivity by tuning the carrier concentration and modifying oxygen deficiencies and chemisorbed oxygen levels. Thus, La₂O₃ NPs embedded in SnO₂ NFs present a promising strategy for the development of high-performance ethanol gas sensors.

With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph’s information injunction with a correlation matrix to define similarity between graphs. A dynamic graph embedding model based on the graph similarity is proposed to cluster the graphs for anomaly detection. We implement the proposed model in vehicular edge computing for traffic incident detection. The experiments are carried out using traffic data produced by the Simulation of Urban Mobility framework. The experimental findings reveal that the proposed method achieves better results than the baselines by 14.5% and 18.1% on average with respect to F1-score and accuracy, respectively.

In this study, tin oxide (SnO₂)/polyaniline (PANI) composite nanowires (NWs) with varying amounts of PANI were synthesized for carbon dioxide (CO₂) gas sensing at room temperature (RT, 25 °C). SnO₂ NWs were fabricated via the vapor–liquid–solid (VLS) method, followed by coating with PANI. CO₂ sensing investigations revealed that the sensor with 186 μL PANI exhibited the highest response to CO₂ at RT. Additionally, the optimized sensor demonstrated excellent selectivity for CO₂, long-term stability, and reliable performance across different humidity levels. The enhanced sensing performance of the optimized sensor was attributed to the formation of SnO₂-PANI heterojunctions and the optimal PANI concentration. This study underscores the potential of SnO₂-PANI composites for CO₂ detection at RT.

Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy.

This paper presents a system, namely, the abnormal-weather monitoring and curation service (AWMC), which provides people with a better understanding of abnormal weather conditions. The service can analyze a set of multivariate weather datasets (i.e., 7 meteorological datasets from 18 cities in Korea) and show (i) which dates are mostly abnormal in a certain city, and (ii) which cities are mostly abnormal on a certain date. In particular, the dynamic graph-embedding-based anomaly detection method was employed to measure anomaly scores. We implemented the service and conducted evaluations. Regarding the results of monitoring abnormal weather, AWMC shows that the average precision was approximately 90.9%, recall was 93.2%, and F1 score was 92.1% for all the cities.

Accurate detection of rare attacks is a vital task in network intrusion detection. Although deep learning has superior feature representation capability and has become a mainstream technology in this field, existing methods still perform poorly in identifying rare attacks under long-tailed imbalanced multi-class traffic distribution, which remains a critical bottleneck in real-world deployment. This paper proposes an intrusion detection framework integrating improved parrot optimization (IPO), latent denoising diffusion implicit models (DDIM) and a hybrid CNN-Transformer. IPO balances global exploration and local exploitation via an annealing-greedy mechanism to select highly discriminative low-dimensional features, providing low-noise input for VAE-based Latent-DDIM to generate semantically consistent minority samples and alleviate class imbalance. The hybrid CNN-Transformer model captures local statistical patterns and global dependencies through multi-scale feature fusion, forming an end-to-end closed loop of “feature optimization-data augmentation-model modeling”. Experiments on NSL-KDD show that our method improves accuracy by 6.66%, F1-score by 8.01%, and recall rates of U2R and R2L attacks by 15% and 84.2% respectively compared with the best baseline. Moreover, IPO outperforms other mainstream intelligent algorithms in global optimal solution seeking with swifter convergence. On the dataset enhanced by the IPO-DDIM joint augmentation strategy, the hybrid CNN-Transformer obtains 3.93% higher accuracy and 6.24% higher F1-score, which sufficiently demonstrates the comprehensive superiority of the constructed framework.