An IoT-oriented data placement method with privacy preservation in cloud environment

© 2018 Elsevier Ltd IoT (Internet of Things) devices generate huge amount of data which require rich resources for data storage and processing. Cloud computing is one of the most popular paradigms to accommodate such IoT data. However, the privacy conflicts combined in the IoT data makes the data placement problem more complicated, and the resource manager needs to take into account the resource efficiency, the power consumption of cloud data centers, and the data access time for the IoT applications while allocating the resources for the IoT data. In view of this challenge, an IoT-oriented Data Placement method with privacy preservation, named IDP, is designed in this paper. Technically, the resource utilization, energy consumption and data access time in the cloud data center with the fat-tree topology are analyzed first. Then a corresponding data placement method, based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is designed to achieve high resource usage, energy saving and efficient data access, and meanwhile realize privacy preservation of the IoT data. Finally, extensive experimental evaluations validate the efficiency and effectiveness of our proposed method

The matched projections of idempotents on Hilbert C∗C^*-modules

The aim of this paper is to give new characterizations of some fundamental issues about idempotents. In the general setting of adjointable operators on Hilbert $C^*$-modules, a new term of quasi-projection pair is introduced. For each idempotent $Q$, a projection $m(Q)$, called the matched projection of $Q$, is constructed. It is shown that $Q$ and $m(Q)$ as idempotents are homotopic, and $\big(m(Q),Q\big)$ is a quasi-projection pair. Some formulas for $m(Q)$ are derived. Based on these formulas, representations and norm estimations associated with $m(Q)$ are dealt with.Comment: The original submission entitled "Quasi-projection pairs on Hilbert $C^*$-modules" will be divided into three parts, and this is the first par

Theoretical Investigations on Mechanisms of pd(OAC)(2)-Catalyzed Intramolecular Diaminations in the Presence of Bases and Oxidants

DFT Calculations have been carried out to study the mechanisms of Pd(OAc)(2)-catalyzed intramolecular diamination reactions in the presence of bases and oxidants. Oil the basis of the Calculation results, a mechanism involving an anti-aminopaladation/syn-C-sp3-N bond formation was proposed

Characterization of the complete chloroplast genome of Homalocladium platycladum (Polygonaceae) and its phylogenetic analysis

Homalocladium platycladum is a fascinating ornamental plant that has long been used in Chinese medicine. Here, we characterize the complete chloroplast genome sequence of this plant (GenBank: NC_062330). This circular genome has a total length of 163,202 bp containing a large single-copy region (87,820bp), a small single-copy region (13,538bp), and a pair of inverted repeat regions (30,922bp). A total of 130 predicted genes were identified, including 85 protein-coding genes, 37 transfer RNA genes, and 8 ribosomal RNA genes. Phylogenetic analysis demonstrated that H. platycladum belongs to the Polygonaceae family and is highly analogous with Homalocladium and Muehlenbeckia families

A Method for the Automatic Extraction of Support Devices in an Overhead Catenary System Based on MLS Point Clouds

A mobile laser scanning (MLS) system can acquire railway scene information quickly and provide a data foundation for regular railway inspections. The location of the catenary support device in an electrified railway system has a direct impact on the regular operation of the power supply system. However, multi-type support device data accounts for a tiny proportion of the whole railway scene, resulting in its poor characteristic expression in the scene. Therefore, using traditional point cloud filtering or point cloud segmentation methods alone makes it difficult to achieve an effective segmentation and extraction of the support device. As a result, this paper proposes an automatic extraction algorithm for complex railway support devices based on MLS point clouds. First, the algorithm obtains hierarchies of the pillar point clouds and the support device point clouds in the railway scene through high stratification and then realizes the noise that was point-cloud-filtered in the scene. Then, the center point of the pillar device is retrieved from the pillar corridor by a neighborhood search, and then the locating and initial extracting of the support device are realized based on the relatively stable spatial topological relationship between the pillar and the support device. Finally, a post-processing optimization method integrating the pillar filter and the voxelized projection filter is designed to achieve the accurate and efficient extraction of the support device based on the feature differences between the support device and other devices in the initial extraction results. Furthermore, in the experimental part, we evaluate the treatment effect of the algorithm in six types of support devices, three types of support device distribution scenes, and two types of railway units. The experimental results show that the average extraction IoU of the multi-type support device, support device distribution scenes, and railway unit were 97.20%, 94.29%, and 96.11%, respectively. In general, the proposed algorithm can achieve the accurate and efficient extraction of various support devices in different scenes, and the influence of the algorithm parameters on the extraction accuracy and efficiency is elaborated in the discussion section

Efficient Hydrogenation of Various Renewable Oils over Ru-HAP Catalyst in Water

A catalytic system over Ru-HAP catalyst is established to hydrodeoxygenate various oils to long-chain alkanes in water, for potential large-scale renewable diesel production, which has the following advantages. (i) This system is versatile to different oil sources, including Jatropha oil, palm oil, waste cooking oil, and cooking waste. (ii) Ru-HAP is highly efficient at achieving full conversion from stearic acid to alkanes at as low as 100 °C, and the isolated yield from Jatropha oil, palm oil, and waste cooking oil to long-chain alkanes reached up to 95, 96, and 87 mol % at 180 °C and 2 MPa H₂ within 4–4.5 h, respectively. (iii) The catalyst showed high stability during five runs of recycling, ICP-OES analysis, and a hydrothermal treatment. The activity decreased less than 5% after the catalyst was treated in water at 200 °C for 24 h with a stirring speed of 1000 rpm due to the strong metal and hydrothermally stable support interaction. (iv) Ru-HAP is compatible with most impurities such as various salts, sugars, and macromolecules. (v) The system required low cost for operation since no dehydration before the reaction was necessary and the alkane product can be easily separated from water. The reaction route was investigated and indicated that the coexisting hydrodehydration and hydrodecarbonylation are affected by water, temperature, and H₂ pressure. The catalyst was also characterized in detail, and its high reactivity and stability may result from the fact that highly distributed Ru nanoclusters anchored on the HAP support absorbed fatty acids by forming a metastable calcium carboxyl phosphate