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The diagnosability of a system is defined as the maximum number of faulty processors that the system can guarantee to identify, which plays an important role in measuring of the reliability of multiprocessor systems. In the work of Peng et al. in 2012, they proposed a new measure for fault diagnosis of systems, namely, g-good-neighbor conditional diagnosability. It is defined as the diagnosability of a multiprocessor system under the assumption that every fault-free node contains at least g fault-free neighbors, which can measure the reliability of interconnection networks in heterogeneous environments more accurately than traditional diagnosability. The k-ary n-cube is a family of popular networks. In this study, we first investigate and determine the R g -connectivity of k-ary n-cube for 0 g n: Based on this, we determine the g-good-neighbor conditional diagnosability of k-ary n-cube under the PMC model and MM Ã model for k ! 4; n ! 3 and 0 g n: Our study shows the g-good-neighbor conditional diagnosability of k-ary n-cube is several times larger than the classical diagnosability of k-ary n-cube.

Existing parallel mining algorithms for frequent itemsets lack a mechanism that enables automatic parallelization, load balancing, data distribution, and fault tolerance on large clusters. As a solution to this problem, we design a parallel frequent itemsets mining algorithm called FiDoop using the MapReduce programming model. To achieve compressed storage and avoid building conditional pattern bases, FiDoop incorporates the frequent items ultrametric tree, rather than conventional FP trees. In FiDoop, three MapReduce jobs are implemented to complete the mining task. In the crucial third MapReduce job, the mappers independently decompose itemsets, the reducers perform combination operations by constructing small ultrametric trees, and the actual mining of these trees separately. We implement FiDoop on our in-house Hadoop cluster. We show that FiDoop on the cluster is sensitive to data distribution and dimensions, because itemsets with different lengths have different decomposition and construction costs. To improve FiDoop's performance, we develop a workload balance metric to measure load balance across the cluster's computing nodes. We develop FiDoop-HD, an extension of FiDoop, to speed up the mining performance for high-dimensional data analysis. Extensive experiments using real-world celestial spectral data demonstrate that our proposed solution is efficient and scalable.Index Terms-Frequent itemsets, frequent items ultrametric tree (FIU-tree), Hadoop cluster, load balance, MapReduce.

Oncolytic virotherapy is a promising therapeutic strategy that uses replication-competent viruses to selectively destroy malignancies. However, the therapeutic effect of certain oncolytic viruses (OVs) varies among cancer patients. Thus, it is necessary to overcome resistance to OVs through rationally designed combination strategies. Here, through an anticancer drug screening, we show that DNA-dependent protein kinase (DNA-PK) inhibition sensitizes cancer cells to OV M1 and improves therapeutic effects in refractory cancer models in vivo and in patient tumour samples. Infection of M1 virus triggers the transcription of interferons (IFNs) and the activation of the antiviral response, which can be abolished by pretreatment of DNA-PK inhibitor (DNA-PKI), resulting in selectively enhanced replication of OV M1 within malignancies. Furthermore, DNA-PK inhibition promotes the DNA damage response induced by M1 virus, leading to increased tumour cell apoptosis. Together, our study identifies the combination of DNA-PKI and OV M1 as a potential treatment for cancers.

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