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Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result in the waste of energy and storage resources. As a result, the demand for data deletion possibilities in blockchain has risen over the last decade. Although several prior studies have introduced methods to address data modification features in blockchain, most of the proposed systems need shorter deletion delays and security requirements. This study proposes a novel blockchain architecture called Unlichain that provides data-modification features within public blockchain architecture. To achieve this goal, Unlichain employed a new indexing technique that defines the deletion time for predefined lifetime data. The indexing technique also enables the deletion possibility for unknown lifetime data. Unlichain employs a new metadata verification consensus among full and meta nodes to avoid delays and extra storage usage. Moreover, Unlichain motivates network nodes to include more transactions in a new block, which motivates nodes to scan for expired data during block mining. The evaluations proved that Unlichain architecture successfully enables instant data deletion while the existing solutions suffer from block dependency issues. Additionally, storage usage is reduced by up to 10%. [ABSTRACT FROM AUTHOR]

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model and Long-Short Term Memory autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states—two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of the Gaussian Mixture Model and Long Short-Term Memory in fault detection. Gaussian Mixture Models are deployed for initial fault classification, leveraging their clustering capabilities, while Long-Short Term Memory autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the Gaussian Mixture Model and the reconstruction error distribution of the Long-Short Term Memory autoencoder model. [ABSTRACT FROM AUTHOR]

This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics. [ABSTRACT FROM AUTHOR]