The precise observation of thunderstorm charge motion remains challenging due to the limitations of single-modal remote sensing techniques. This paper proposes a novel multimodal remote sensing framework integrating radar echo intensity (REI) and atmospheric electric field (AEF) data for three-dimensional (3D) charge localization. Our approach tackles critical challenges in meteorological remote sensing by introducing a high-precision spatiotemporal calibration protocol to align high-frequency AEF with low-frequency radar scans, along with physics-constrained feature fusion that incorporates an AEF-REI interaction term to enhance robustness. We develop a Bayesian-optimized random forest model to improve localization accuracy under non-stationary conditions. Validated via 3D charge trajectory reconstruction, our method achieves strong consistency with radar-observed storm core movements, demonstrating its potential as a new remote sensing paradigm for thunderstorm monitoring.

Abstract Due to its open source and large user base, Android has emerged as the most popular operating system. Android's popularity and openness have made it a prime target for malicious attackers. Permissions have received great attention from researchers because of their effectiveness in restricting applications’ access to sensitive resources. However, existing malware detection methods based on permissions are easily bypassed by inter‐application resource access. To address these issues, we combine inter‐application resource access‐related intent features with permission features. Besides, we designed a customized convolutional neural network using two squeeze‐and‐excitation blocks to learn the inherent relationships between multi‐type features. The two basic SE blocks perform squeezing operations based on average pooling and max pooling, respectively, to compute channel‐wise attention from multiple perspectives. We designed a series of experiments based on real‐world samples to evaluate the efficacy of the proposed framework. Empirical results demonstrate that our framework outperforms state‐of‐the‐art methods, achieving an accuracy of 96.29%, precision of 97.52%, recall of 94.63%, F1‐score of 96.06% and MCC of 92.60%. These promising experimental results consistently demonstrate that AMERDroid is an effective approach for Android malware detection.

The continuous malicious attacks on Internet of Things devices pose a potential threat to the economic and private information security of end‐users, especially on the dominant Android devices. Combining static analysis methods with deep Learning is a promising approach to defend against that. This kind of method has two limitations: the first is that the current single‐permission mechanism is not insufficient to regulate interapplication resource acquisition; another problem is that current work on feature learning is dedicated to modifying a single network structure, which may result in a suboptimal solution. In this study, to solve the abovementioned problems, we propose a novel malware detection framework MFEMDroid, which combines multitype features analysis and ensemble modeling. The Provider feature, facilitating information requests between applications (apps) and serving as an indispensable data storage method, plays a vital role in characterizing app behavior. Hence, we extract permissions and Provider features to comprehensively characterize app behavior and probe potentially dangerous combinations between or within these features. To address oversparse datasets and reduce feature learning overhead, we employ an auto‐encoder for feature dimensionality reduction. Furthermore, we design an ensemble network based on SENet, ResNet, and the evolutionary convolutional neural network Squeeze Excitation Residual Network (SEResNet) to explore the hidden associations between different types of features from multiple perspectives. We performed extensive experiments to evaluate its method performance on real‐world samples. The evaluation results demonstrate that the proposed framework can detect malware with an accuracy of 95.38%, which is much better than state‐of‐the‐art solutions. These promising experimental results show that MFEMDroid is an effective approach to detect Android malware.

As an important factor in fine thunderstorm detections, a multi-time scale thunderstorm monitoring, warning and imaging system is proposed in this paper. The first computing phase involves a decomposition, classification, denoising and reconstruction of the atmospheric electric field signals (AEFSs), collected by a self-made three-dimensional AEF apparatus, based on autocorrelation characteristics and Fuzzy$C$-Means (FCM). Secondly, FCM classifies the equally divided AEFS components. A scale reconstruction rule is put forward and applied to obtain multi-time scale AEF branch data, according to the component temporal continuity in the same class. A corresponding scale correction strategy is then proposed. Thunderstorm point charge coordinate results are calculated by using branch data, and noise points contained in these results are removed. Finally, the curve fitting of denoised coordinate results is performed to image the point charge moving path. Empirical results confirm that the proposed system effectively warns and images thunderstorms, as well as provides a valid reference for multi-scale thunderstorm monitoring.

Cloud-native databases have become the de-facto choice for mission-critical applications on the cloud due to the need for high availability, resource elasticity, and cost efficiency. Meanwhile, driven by the increasing connectivity between data generation and analysis, users prefer a single database to efficiently process both OLTP and OLAP workloads, which enhances data freshness and reduces the complexity of data synchronization and the overall business cost. In this paper, we summarize five crucial design goals for a cloud-native HTAP database based on our experience and customers' feedback, i.e., transparency, competitive OLAP performance, minimal perturbation on OLTP workloads, high data freshness, and excellent resource elasticity. As our solution to realize these goals, we present PolarDB-IMCI, a cloud-native HTAP database system designed and deployed at Alibaba Cloud. Our evaluation results show that PolarDB-IMCI is able to handle HTAP efficiently on both experimental and production workloads; notably, it speeds up analytical queries up to ×149 on TPC-H (100GB). PolarDB-IMCI introduces low visibility delay and little performance perturbation on OLTP workloads (<5%), and resource elasticity can be achieved by scaling out in tens of seconds.

There is an ever-increasing risk of illegal access-induced Network Intrusion (NI), which calls for prompt detection of illegal network behavior through profound Network Traffic (NT) analyses. However, current intrusion detection methods are limited in accuracy due to insufficient data standardization . This paper puts forward a deoxyribonucleic acid (DNA)-Spatial Information (SI) method to overcome these limitations. A DNA encoding model is formed, which defines a mapping relationship between NT attributes and nucleobases to reconstruct NT samples expressed as DNA sequences. Then, a feature extraction algorithm is constructed that deduces a Spatial Information Feature Matrix (SIFM) to represent sequence statistical features. A Random Forest (RF) algorithm is adopted as a matching process to determine NI behaviors considering the detection efficiency. Following experiments evaluate its method performance on two datasets, NSL-KDD and UNSW-NB15. Results demonstrate that DNA-SI obtains better results than state-of-the-art works, where the accuracy, F1-score, recall, far are 95.75%, 94.41%, 94.12%, 3.26% and 92.30%, 92.78%, 89.82%, 4.66% respectively. The fact that it is insusceptible to minority intrusion samples is another point worth attention. In sum, this quick and accurate network intrusion detection points to a new orientation for safeguarding network security .