PHUIMUS: A Potential High Utility Itemsets Mining Algorithm Based on Stream Data with Uncertainty

High utility itemsets (HUIs) mining has been a hot topic recently, which can be used to mine the profitable itemsets by considering both the quantity and profit factors. Up to now, researches on HUIs mining over uncertain datasets and data stream had been studied respectively. However, to the best of our knowledge, the issue of HUIs mining over uncertain data stream is seldom studied. In this paper, PHUIMUS (potential high utility itemsets mining over uncertain data stream) algorithm is proposed to mine potential high utility itemsets (PHUIs) that represent the itemsets with high utilities and high existential probabilities over uncertain data stream based on sliding windows. To realize the algorithm, potential utility list over uncertain data stream (PUS-list) is designed to mine PHUIs without rescanning the analyzed uncertain data stream. And transaction weighted probability and utility tree (TWPUS-tree) over uncertain data stream is also designed to decrease the number of candidate itemsets generated by the PHUIMUS algorithm. Substantial experiments are conducted in terms of run-time, number of discovered PHUIs, memory consumption, and scalability on real-life and synthetic databases. The results show that our proposed algorithm is reasonable and acceptable for mining meaningful PHUIs from uncertain data streams

Modeling and Analyzing Operational Decision-Making Synchronization of C2 Organization in Complex Environment

In order to improve capability of operational decision-making synchronization (ODMS) in command and control (C2) organization, the paper puts forward that ODMS is the negotiation process of situation cognition with three phases about “situation cognition, situation interaction and decision-making synchronization” in complex environment, and then the model and strategies of ODMS are given in quantity. Firstly, measure indexes of three steps above are given in the paper based on the time consumed in negotiation, and three patterns are proposed for negotiating timely in high quality during situation interaction. Secondly, the ODMS model with two stages in continuous changing situation is put forward in the paper, and ODMS strategies are analyzed within environment influence and time restriction. Thirdly, simulation cases are given to validate the process of ODMS under different continuous changing situations the results of this model are better than the other previous models to fulfill the actual restrictions, and the process of ODMS can be adjusted more reasonable for improving the capability of ODMS. Then we discuss the case and summarize the influence factors of ODMS in the C2 organization as organization structure, shared information resources, negotiation patterns, and allocation of decision rights

Performance Analysis and Optimal Allocation of Layered Defense M/M/N Queueing Systems

One important mission of strategic defense is to develop an integrated layered Ballistic Missile Defense System (BMDS). Motivated by the queueing theory, we presented a work for the representation, modeling, performance simulation, and channels optimal allocation of the layered BMDS M/M/N queueing systems. Firstly, in order to simulate the process of defense and to study the Defense Effectiveness (DE), we modeled and simulated the M/M/N queueing system of layered BMDS. Specifically, we proposed the M/M/N/N and M/M/N/C queueing model for short defense depth and long defense depth, respectively; single target channel and multiple target channels were distinguished in each model. Secondly, we considered the problem of assigning limited target channels to incoming targets, we illustrated how to allocate channels for achieving the best DE, and we also proposed a novel and robust search algorithm for obtaining the minimum channel requirements across a set of neighborhoods. Simultaneously, we presented examples of optimal allocation problems under different constraints. Thirdly, several simulation examples verified the effectiveness of the proposed queueing models. This work may help to understand the rules of queueing process and to provide optimal configuration suggestions for defense decision-making

Co-expression of apoptin (VP3) and antibacterial peptide cecropin B mutant (ABPS1) genes induce higher rate of apoptosis in HepG2 and A375 cell lines

The antibacterial peptide cecropin B mutant (ABPS1) gene has a broad range of antibacterial and  antiproliferative properties. Apoptin (VP3), a chicken anaemia virus-encoded protein is known to induce  apoptosis in human transformed cells. To explore drug combination in human tumor cells, apoptin and ABPS1 eukaryotic expression vector pIRES2-EGFP-apoptin and pIRES2-EGFP-ABPS1 were constructed and their expression effect individually and in combinations were studied in HepG2 and A375 cells. The vector pIRES2-EGFP-ABPS1 and pIRES2-EGFP-apoptin were transfected into tumor cells HepG2 and A375 by the  lipofectamine-mediated DNA transfection procedure. At 48 h post transfection, the apoptotic rate obtained by flow cytometry and the morphological changes under light and scanning electron microscope of tumor cells  were significant. In contrast, the microvilli on the surface of the control cells were disrupted, decreased and even disappeared. The cell membrane was injured and intracellular substances leaked out. Furthermore, our  results indicate that the apoptotic rates of apoptin (27.32% in HepG2 and 9.34% in A375 cells), were higher  than ABPS1 (23.79% in HepG2 and 8.33% in A375 cells). Moreover, the co-expression of Apoptin and ABPS1  showed higher apoptotic rates which were 27.66 and 10.33% in HepG2 and A375 cells respectively. However, the apoptotic rates obtained in HepG2 cells treated with apoptin and apoptin and ABPS1 together were closely  similar, but, not in A375 cells. Therefore, the results of the present study showed that the combination of  Apoptin and ABPS1 has synergistic effect in HepG2 and A375 cell lines.Keys words: Apoptin, ABPS1, apoptosis, co-expression, HepG2, A375

Diverse phylogeny and morphology of magnetite biomineralized by magnetotactic cocci

Magnetotactic bacteria (MTB) are diverse prokaryotes that produce magnetic nanocrystals within intracellular membranes (magnetosomes). Here, we present a large‐scale analysis of diversity and magnetosome biomineralization in modern magnetotactic cocci, which are the most abundant MTB morphotypes in nature. Nineteen novel magnetotactic cocci species are identified phylogenetically and structurally at the single‐cell level. Phylogenetic analysis demonstrates that the cocci cluster into an independent branch from other Alphaproteobacteria MTB, that is, within the Etaproteobacteria class in the Proteobacteria phylum. Statistical analysis reveals species‐specific biomineralization of magnetosomal magnetite morphologies. This further confirms that magnetosome biomineralization is controlled strictly by the MTB cell and differs among species or strains. The post‐mortem remains of MTB are often preserved as magnetofossils within sediments or sedimentary rocks, yet paleobiological and geological interpretation of their fossil record remains challenging. Our results indicate that magnetofossil morphology could be a promising proxy for retrieving paleobiological information about ancient MTB.This study was supported financially by the National Natural Science Foundation of China (grants 41920104009, 41890843 and 41621004), The Senior User Project of RVKEXUE2019GZ06 (Centre for Ocean Mega-Science, Chinese Academy of Sciences), The Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology (grant MGQNLM201704) and the Australian Research Council (grants DP140104544 and DP200100765)

Dense Connectivity Based Two-Stream Deep Feature Fusion Framework for Aerial Scene Classification

Aerial scene classification is an active and challenging problem in high-resolution remote sensing imagery understanding. Deep learning models, especially convolutional neural networks (CNNs), have achieved prominent performance in this field. The extraction of deep features from the layers of a CNN model is widely used in these CNN-based methods. Although the CNN-based approaches have obtained great success, there is still plenty of room to further increase the classification accuracy. As a matter of fact, the fusion with other features has great potential for leading to the better performance of aerial scene classification. Therefore, we propose two effective architectures based on the idea of feature-level fusion. The first architecture, i.e., texture coded two-stream deep architecture, uses the raw RGB network stream and the mapped local binary patterns (LBP) coded network stream to extract two different sets of features and fuses them using a novel deep feature fusion model. In the second architecture, i.e., saliency coded two-stream deep architecture, we employ the saliency coded network stream as the second stream and fuse it with the raw RGB network stream using the same feature fusion model. For sake of validation and comparison, our proposed architectures are evaluated via comprehensive experiments with three publicly available remote sensing scene datasets. The classification accuracies of saliency coded two-stream architecture with our feature fusion model achieve 97.79%, 98.90%, 94.09%, 95.99%, 85.02%, and 87.01% on the UC-Merced dataset (50% and 80% training samples), the Aerial Image Dataset (AID) (20% and 50% training samples), and the NWPU-RESISC45 dataset (10% and 20% training samples), respectively, overwhelming state-of-the-art methods

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references