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In recent decades, fault diagnosis of photovoltaic (PV) arrays has become more and more important for the power generation of PV systems. Many traditional artificial intelligence methods have been successfully applied to use fault data samples to build fault diagnosis models, but most rely on manual feature extraction or expert knowledge to build diagnosis models, which is inefficient and may ignore some potentially useful features. Therefore, this paper proposes an SE-ResNet PV array fault diagnosis algorithm based on Residual Network (ResNet) and Squeeze-and-Excitation Network (SENet), and uses Bayesian Optimization (BO) optimize the parameters. In order to verify the effectiveness of the proposed fault diagnosis algorithm, a comprehensive fault experiment was carried out on the PV platform. Experimental results show that the model has achieved high overall performance in terms of accuracy, generalization performance, reliability and training efficiency, and has high practicability.

To safeguard the interests of transacting parties, non-repudiation mechanisms need to assure fairness and timeliness. The non-repudiation service currently implemented usually does not consider the requirement of fairness and the fair non-repudiation protocols to date can not be suitably applied in real environment due to its complex interaction. This paper discusses the transaction-oriented non-repudiation requirement for Web services transaction, analyzes the constraints of the traditional model for the available fair non-repudiation protocols and designs a new Online-TTP fair non-repudiation protocol. The new protocol provides a fair non-repudiation solution to secure Web services transactions and can be embedded into a single Web service call. The protocol adopts evidence chained to decreasing the overhead of evidence verification and management and alleviates the overhead of certificate revocation checking and time-stamp generation for signatures. The protocol has strong fairness, timeliness, efficiency and practicability.

Although classified as benign, giant cell tumors of the bone (GCTB) may be aggressive, recur and even metastasize to the lungs. In addition, the pathogenesis and histogenesis remain unclear; thus, the driving factors behind the strong tumor growth capacity of GCTB require investigation. In the present study, the expression levels of hypoxia-inducible factor (HIF)-1α and vascular endothelial growth factor (VEGF), which are promoted by hypoxic conditions, were determined in 22 sacral GCTB samples using immunohistochemistry and western blot analysis. Furthermore, CD34 expression was analyzed using these methods. The correlation between HIF-1α or VEGF expression and the tumor microvessel density (MVD) was then determined. The results demonstrated that HIF-1α, VEGF and CD34 were overexpressed in the 22 sacral GCTB specimens, and overexpression of HIF-1α and VEGF correlated with the tumor MVD. Thus, the present study has provided novel indicators for the tumor growth capacity of GCTBs.

Mix-net is a cryptographic technique that is used to hide the origin of messages in computer network communications. The optimistic mix-net proposed by Golle et al. is a very practical and efficient construction which has a very fast mixing process when all players behave honestly. A new type of relation attack on Golle's optimistic mix-net and its modified editions is presented in this paper. In particular a malicious mix server can break any given user's anonymity in previous mix- sessions by using current session as a decryption oracle at the cost of being accused of cheating. The attack suggests that the optimist mix-net must use different keys in each mix-session and this demerit would deliver a significant blow to its practical value.

A mix network is a cryptographic construction for anonymous communications. In addition to anonymity, a reputable mix network first defined by Golle offers a reputation property: the mix-net can prove that every message it outputs corresponds to an input submitted by a user without revealing which input. This property can shield the mix-net from liability in the event that an output message is objectionable or illegal. In this work we analyze two reputable ElGamal based mix-net schemes proposed by Golle and present two active attacks for them. Our attacks rely on the homomorphism properties of RSA signature and ElGamal cryptosystem and can break the reputation properties of those schemes. We also show how to counter our attacks by using secure hash functions.

Detecting community structure is a powerful approach to understanding complex networks. Recently, modularity function Q has been widely used as a measure to identify communities in complex networks. However, optimising Q function has some resolution limitations. In this paper, we present a new quantitative function DQ (degree modularity) that detects community structure based on local connectivity of communities. We first prove that the function DQ can improve the resolution limitations of modularity Q. Furthermore, we experimentally evaluate the performance of the new quantitative function using a variety of real and computer-generated networks and find communities of widely differing sizes can be detected with higher sensitivity and reliability. Also, even in large-scale biological networks, such as protein-protein interaction (PPI) networks, we can obtain higher matching rate between the predicted protein modules and the known protein complexes. All the experimental results support the usefulness of the new quantitative function DQ as the measure for community structure detection.