Bearings are broadly applied in various types of industrial systems. Fault diagnosis, as a promising way for reliability of modern industrial internet of thing applications, has attracted increasing attention from both academia and industry fields. Being ideal modeling and inference tool in uncertainty situations, Bayesian network (BN) is becoming increasingly popular in many systems. However, in practical uncertain and complicated engineering surroundings, it’s difficult or expensive to collect massive labeled fault data for the sake of fault diagnosis model learning. To address the issue of BN parameter learning under small data set conditions, this paper proposes a Varying Coefficient Transfer Learning (VCTL) algorithm based on aggregation and transfer learning, that considers both knowledge from the resource domain and the resource relevance contributions. The balancing weight function is designed to determine whether the learning task in the resource domain is activated. Relevance weight factors are proposed to measure the relevance of resource and target parameters quantitatively, by combing parameter information from resource domains with those obtained from the target domain, using maximum a posterior (MAP) or maximum likelihood estimation (MLE). Finally, the target parameters are aggregated with both the target initial parameters and the parameter knowledge from the resource domain. Based on VCTL, a bearing fault diagnosis approach is proposed and verified. The experimental results show that, under the condition of the small data set, learning accuracy of VCTL algorithm with varying coefficient aggregation is better than MLE algorithm, MAP algorithm or state-of-the-art parameter transfer method, local linear pooling transfer learning (LoLP) algorithm. Under the condition of sufficient data set, learning accuracy of VCTL algorithm approaches the classical MLE or MAP, and the correctness of the proposed algorithm is verified. Moreover, we illustrate the successful application to real-world bearing fault diagnosis case with VCTL, where we had access to expert-provided resource knowledge and real fault diagnosis data.

In the Big Data era, data provenance has become an important concern for enhancing the trustworthiness of key data that are rapidly generated and shared across organizations. Prevailing solutions employ authoritative centers to efficiently manage and share massive data. They are not suitable for secure and trustworthy decentralized data provenance sharing due to the inevitable dishonesty or failure of trusted centers. With the advent of the blockchain technology, embedding data provenance in immutable blocks is believed to be a promising solution. However, a provenance file, usually a directed acyclic graph, cannot be embedded in blocks as a whole because its size may exceed the limit of a block, and may include various sensitive information that can be legally accessed by different users. To this end, this paper proposed the BSTProv, a blockchain-based system for secure and trustworthy decentralized data provenance sharing. It enables secure and trustworthy provenance sharing by partitioning a large provenance graph into multiple small subgraphs and embedding the encrypted subgraphs instead of raw subgraphs or their hash values into immutable blocks of a consortium blockchain; it enables decentralized and flexible authorization by allowing each peer to define appropriate permissions for selectively sharing some sets of subgraphs to specific requesters; and it enables efficient cross-domain provenance composition and tracing by maintaining a high-level dependency structure among provenance graphs from different domains in smart contracts, and by locally storing, decrypting, and composing subgraphs obtained from the blockchain. Finally, a prototype is implemented on top of an Ethereum-based consortium blockchain and experiment results show the advantages of our approach.

Studying the impact of terrain undulation on low frequency (LF) sky-wave propagation delay is of great signifi-cance for exploring the potential applications in low-ionospheric detection, remote navigation, and timing service. This letter combines gradient ionospheric parameters and uses the finite-difference time-domain (FDTD) method to calculate the reflected signal of LF radio waves from the ionosphere. Using the sky- and ground-wave separation technology in time domain, the variation law of LF sky-wave propagation delay with terrain changes is given in both daytime and nighttime models. At the same time, this letter also analyze the influence of different ionospheres (in the presence or absence of natural magnetic field) on LF one-hop sky-wave propagation delay, providing some reference significance for studying the propagation characteristics of LF sky waves.

Since the original signature model did not account for the possible threat of the forging signature attack, the majority of existing multivariate polynomial public key based signature schemes are at risk of forging signatures by equivalent key attacks. In this research, an enhanced signature model, Triple Unlocking Mechanism (TUM), is provided to resist all the possibility that the signature generated by the equivalent keys. The additional public keys are generated in the sake of enhancing the security by verifying internal unit information in the scheme after three designed unlocking operations. As a result, the signature can only be generated by the user who has the real legal key and the threat of the key recovery attack can be eliminated. The security of TUM signature based on random oracle model is proved. Experimental results demonstrate that, using an MI system coupled with TUM as an example, the advantages of the enhanced signature model are more secure than the original one at the expense of taking a little more time for signing. Moreover, influences of different parameters in multivariate polynomial public key signatures are investigated. Based on the original state-of-art model, the presented model is generic construction and applicable for existing multivariate signature scheme’s construction.

Background: Schwannomas are benign tumors that arise from Schwann cells. Rare cases are shown to arise from the olfactory nerve. The genesis of Olfactory groove schwannoma (OGSs) is still puzzling. Yusda et al hypothesized that olfactory ensheathing cell tumors (OECTs) might be the origin of OGSs. Clinical Presentation: Here, the authors report the case of a 59-year-old woman who presented with a paroxysmal headache for 1 year. The tumor appeared as hypointensity on T1-weighted images, hyperintensity on T2-weighted, and exhibited strong, heterogeneous enhancement. The tumor was removed through a lateral supraorbital approach. The final pathologic diagnosis was schwannoma. The postoperative period was uneventful after 4 months, and the headache disappeared. Disscussion and Conclusion: OGSs and OECTs are extremely rare. There are many similarities in clinical manifestations, images, and pathologic findings. OGSs are difficult to distinguish from OECTs.