Field Measurement and Bayesian Modal Identification of a Primary-Secondary Structure

AbstractThe Mong-Man-Wai Building (MMW) is a seven-storied reinforced concrete building situated on the campus of the City University of Hong Kong. On its roof a two-storied steel frame has been recently constructed to host a new wind tunnel laboratory, forming a primary-secondary structure. This paper presents field instrumentation and modal identification of the structural system. A number of setups were carried out to cover all locations of interest using a limited number of triaxial sensors. Ambient vibration measurements were obtained and modal identification was performed using a Bayesian FFT Approach. The approach views modal identification as an inference problem where probability is used as a measure for the relative plausibility of outcomes given a model of the response and measured data. The modal identification results reveal a number of interesting features about the building and the roof frame. Five modes are presented in this paper, exhibiting the dynamics of the primary and secondary structure as well as their coupling

Aqueous one-pot synthesis of epoxy-functional diblock copolymer worms from a single monomer: new anisotropic scaffolds for potential charge storage applications

Nitroxide-functional polymers have garnered considerable interest in recent years and appear to hold promise for energy storage applications. However, their synthesis can be both expensive and time-consuming. Here, we propose a highly convenient method for the preparation of TEMPO-functional diblock copolymer nanoparticles directly in water. Epoxy-functional diblock copolymer worms are synthesized from a single monomer, glycidyl methacrylate (GlyMA), using a three-step, one-pot protocol in aqueous solution via polymerization-induced self-assembly (PISA). First, an initial aqueous emulsion of GlyMA was heated at 85 °C for 9 h to afford an aqueous solution of glycerol monomethacrylate (GMA). Then reversible addition-fragmentation chain transfer (RAFT) polymerization of GMA was conducted in aqueous solution using a dicarboxylic acid-based RAFT agent to produce a water-soluble PGMA homopolymer. Finally, chain extension of this precursor block via RAFT aqueous emulsion polymerization of GlyMA at 50 °C produced amphiphilic diblock copolymer chains that self-assembled in situ to form a 15% w/w aqueous dispersion of diblock copolymer worms. These worms can be derivatized directly using 4-amino-TEMPO in aqueous solution, affording novel crosslinked anisotropic nanoparticles that contain a relatively high density of stable nitroxide radicals for potential charge storage applications

Coulomb-enhanced dynamic localization and Bell state generation in coupled quantum dots

We investigate the dynamics of two interacting electrons in coupled quantum dots driven by an AC field. We find that the two electrons can be trapped in one of the dots by the AC field, in spite of the strong Coulomb repulsion. In particular, we find that the interaction may enhance the localization effect. We also demonstrate the field excitation procedure to generate the maximally entangled Bell states. The generation time is determined by both analytic and numerical solutions of the time dependent Schrodinger equation.Comment: 12 pages, 5 figure

<i>Sneathiella chinensis</i> gen. nov., sp. nov., a novel marine alphaproteobacterium isolated from coastal sediment in Qingdao, China

The taxonomic position of strain LMG 23452T, which was isolated from coastal sediment from an aquaculture site near Qingdao, China, in 2000, was determined. Strain LMG 23452T comprised Gram-negative, non-spore-forming, motile rods and was found to be a halotolerant, aerobic, chemoheterotroph that produces catalase and oxidase. Comparative 16S rRNA gene sequence analysis revealed that strain LMG 23452T shared approximately 89 % sequence similarity with members of the genera Devosia, Hyphomonas, Ensifer and Chelatococcus, which belong to two different orders within the Alphaproteobacteria. Further phylogenetic analysis of the 16S rRNA gene sequence showed that strain LMG 23452T formed a separate branch within the order Rhizobiales, falling between the genera Devosia and Ensifer of the families Hyphomicrobiaceae and Rhizobiaceae, respectively. Strain LMG 23452T could be differentiated from its closest phylogenetic neighbours on the basis of several phenotypic features, including hydrolysis of the substrates starch and casein and assimilation of the carbohydrates d-glucose, d-mannose, mannitol, maltose and l-arabinose, and chemotaxonomically by the presence of the fatty acids C14 : 0 3-OH, C16 : 1ω11c, C16 : 1 ω5c and C18 : 1ω5c. The major fatty acids detected in strain LMG 23452T were C18 : 1 ω7c, C16 : 0, C19 : 0 cyclo ω8c, C16 : 1 ω7c and C17 : 1ω6c and the G+C content of the genomic DNA was 57.1 mol%. Therefore, the polyphasic data support the placement of strain LMG 23452T within a novel genus and species, for which the name Sneathiella chinensis gen. nov., sp. nov. is proposed. The type strain is LMG 23452T (=CBMAI 737T)

Catch me if you can: a participant-level rumor detection framework via fine-grained user representation learning

Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in the spreading chain. In this study, we propose a novel participant level rumor detection framework. It explicitly models and integrates various fine-grained user representations (i.e., user influence, susceptibility, and temporal information) of all participants from the propagation threads via deep representation learning. Experiments conducted on real world datasets demonstrate a significant accuracy improvement of our approach. Theoretically, we contribute to the effective usage of data science and analytics for social information diffusion design, particularly rumor detection. Practically, our results can be used to improve the quality of rumor detection services for social platforms.Computer Science

Modeling microscopic and macroscopic information diffusion for rumor detection

Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual efforts and are difficult to generalize to different domains. Recently, deep learning solutions have emerged as the de facto methods which detect online rumors in an end-to-end manner. However, they still fail to fully capture the dissemination patterns of rumors. In this study, we propose a novel diffusion-based rumor detection model, called Macroscopic and Microscopic-aware Rumor Detection, to explore the full-scale diffusion patterns of information. It leverages graph neural networks to learn the macroscopic diffusion of rumor propagation and capture microscopic diffusion patterns using bidirectional recurrent neural networks while taking into account the user-time series. Moreover, it leverages knowledge distillation technique to create a more informative student model and further improve the model performance. Experiments conducted on two real-world data sets demonstrate that our method achieves significant accuracy improvements over the state-of-the-art baseline models on rumor detection.Computer Science

Multi-scale graph capsule with influence attention for information cascades prediction

Information cascade size prediction is one of the primary challenges for understanding the diffusion of information. Traditional feature-based methods heavily rely on the quality of handcrafted features, requiring extensive domain knowledge and hard to generalize to new domains. Recently, inspired by the success of deep learning in computer vision and natural language processing, researchers have developed neural network-based approaches for tackling this problem. However, existing deep learning-based methods either focused on modeling the temporal characteristics of cascades but ignored the structural information or failed to take the order-scale and position-scale into consideration in modeling structures of information propagation. This paper proposed a novel graph neural network-based model, called MUCas, to learn the latent representations of cascade graphs from a multi-scale perspective, which can make full use of the direction-scale, high-order-scale, position-scale, and dynamic-scale of cascades via a newly designed MUlti-scale Graph Capsule Network (MUG-Caps) and the influence-attention mechanism. Extensive experiments conducted on two real-world data sets demonstrate that our MUCas significantly outperforms the state-of-the-art approaches.Computer Science

Surface structure and solidification morphology of aluminum nanoclusters

Classical molecular dynamics simulation with embedded atom method potential had been performed to investigate the surface structure and solidification morphology of aluminum nanoclusters Aln (n = 256, 604, 1220 and 2048). It is found that Al cluster surfaces are comprised of (111) and (001) crystal planes. (110) crystal plane is not found on Al cluster surfaces in our simulation. On the surfaces of smaller Al clusters (n = 256 and 604), (111) crystal planes are dominant. On larger Al clusters (n = 1220 and 2048), (111) planes are still dominant but (001) planes can not be neglected. Atomic density on cluster (111)/(001) surface is smaller/larger than the corresponding value on bulk surface. Computational analysis on total surface area and surface energies indicates that the total surface energy of an ideal Al nanocluster has the minimum value when (001) planes occupy 25% of the total surface area. We predict that a melted Al cluster will be a truncated octahedron after equilibrium solidification.Comment: 22 pages, 6 figures, 34 reference

Applying Raman Microspectroscopy to Evaluate the Effects of Nutrient Cations on Alkane Bioavailability to Acinetobacter baylyi ADP1

Contamination with petroleum hydrocarbons causes extensive damage to ecological systems. On oil-contaminated sites, alkanes are major components; many indigenous bacteria can access and/or degrade alkanes. However, their ability to do so is affected by external properties of the soil, including nutrient cations. This study used Raman microspectroscopy to study how nutrient cations affect alkanes' bioavailability to Acinetobacter baylyi ADP1 (a known degrader). Treated with Na, K, Mg, and Ca at 10 mM, A. baylyi was exposed to seven n-alkanes (decane, dodecane, tetradecane, hexadecane, nonadecane, eicosane, and tetracosane) and one alkane mixture (mineral oil). Raman spectral analysis indicated that bioavailability of alkanes varied with carbon chain lengths, and additional cations altered the bacterial response to n-alkanes. Sodium significantly increased the bacterial affinity toward decane and dodecane, and K and Mg enhanced the bioavailability of tetradecane and hexadecane. In contrast, the bacterial response was inhibited by Ca for all alkanes. Similar results were observed in mineral oil exposure. Our study employed Raman spectral assay to offer a deep insight into how nutrient cations affect the bioavailability of alkanes, suggesting that nutrient cations can play a key role in influencing the harmful effects of hydrocarbons and could be optimized to enhance the bioremediation strategy