Ubiquitous mobile social service allows users to interact and communicate with others at any time and place, which is different from the mobile social service in the past. It has become the mobile ...service most widely used by consumers since various mobile applications (abbreviated as “apps”) were introduced into the market. Recently, the number of elderly users of ubiquitous mobile social service aged over 60 has largely increased. However, the needs and the adoption motivations of the elderly people for this service seemed to have been neglected in the development of mobile devices or services, even in the academic research. For the purpose of understanding what factors make the elderly mobile users willing to adopt ubiquitous mobile social service, this study conducted an empirical research the integrating uses and gratification theory and the media richness theory. Totally, 226 effective questionnaires were obtained, among these questionnaires, there are 193 samples over the age of 60. The analysis results indicated that social, enjoyment and fashion motivations have influences on elderly mobile users' adoption of ubiquitous mobile social service. Furthermore, users' perceived interactive richness and apps self-efficacy also have strong influences on elderly mobile users' adoption of the service. However, the required high expenses make the elderly mobile users keep using their habitual ways to socialize with others, and therefore have a negative influence on elderly mobile users' intention to adopt ubiquitous mobile social service.
•Integrating Uses and Gratification Theory with Media Richness Theory.•Analyzing the motivations of elderly users' adoption toward mobile social service.•Except for epistemic, all the motivations have influence on elderly users' adoption.•Perceived interactive richness and self-efficacy are important for elderly users.•Users' inertia of sociality in traditional ways has negative influence on adoption.
All-aqueous printing of viscoelastic droplets (aaPVD) in yield-stress fluids is the core of an emerging voxelated bioprinting technology that enables the digital assembly of spherical bio-ink ...particles (DASP) to create functional tissue mimics. However, the mechanism of aaPVD is largely unknown. Here, by quantifying the dynamics of the whole printing process in real-time, we identify two parameters critical to aaPVD: (1) acceleration of print nozzle, and (2) droplet/nozzle diameter ratio. Moreover, we distinguish three stages associated with aaPVD: droplet generation, detachment, and relaxation. To generate a droplet of good roundness, the ink should be a highly viscous shear-thinning fluid. Using particle image velocimetry and scaling theory, we establish a universal description for the droplet displacements at various printing conditions. Along the direction of nozzle movement, the droplet displacement is determined by the detachment number, a dimensionless parameter defined as the ratio between the dragging force from the nozzle and the confinement force from the supporting matrix. Perpendicular to the direction of nozzle movement, the droplet displacement is determined by the Oldroyd number, a dimensionless parameter that describes the yielded area of the supporting matrix near the print nozzle. For a relaxed droplet, the droplet tail length is independent of droplet/nozzle diameter ratio but determined by the nozzle acceleration. We conclude that printing droplets of good fidelity requires a relatively large droplet/nozzle diameter ratio and intermediate nozzle accelerations. These ensure that the droplet is more solid-like to not flow with the nozzle to form a tadpole-like morphology and that the confinement force from the yield-stress fluid is large enough to prevent large droplet displacement. Our results provide the knowledge and tools for in situ generating and depositing highly viscoelastic droplets of good roundness at prescribed locations in 3D space, which help establish the foundational science for voxelated bioprinting.
Analogues of pixels to two-dimensional (2D) pictures, voxels – in the form of small cubes or spheres – are the basic units of three-dimensional (3D) objects. All-aqueous printing of viscoelastic droplets (aaPVD) is the core of voxelated bioprinting, an emerging technology that uses spherical bio-ink voxels as building blocks to create 3D tissue mimics. Unlike existing technologies relying on the classic Rayleigh-Plateau instability to generate droplets, aaPVD exploits previously unexplored nonlinear fluid dynamics of complex fluids to precisely manipulate viscoelastic droplets in 3D space. The developed knowledge and tools not only help advance biomanufacturing but also stimulate new research directions in soft matter and complex fluids.
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In this letter, we propose a simple yet effective unsupervised change detection approach for multitemporal synthetic aperture radar images from the perspective of clustering. This approach jointly ...exploits the robust Gabor wavelet representation and the advanced cascade clustering. First, a log-ratio image is generated from the multitemporal images. Then, to integrate contextual information in the feature extraction process, Gabor wavelets are employed to yield the representation of the log-ratio image at multiple scales and orientations, whose maximum magnitude over all orientations in each scale is concatenated to form the Gabor feature vector. Next, a cascade clustering algorithm is designed in this discriminative feature space by successively combining the first-level fuzzy c-means clustering with the second-level nearest neighbor rule. Finally, the two-level combination of the changed and unchanged results generates the final change map. Experimental results are presented to demonstrate the effectiveness of the proposed approach.
A new electrochemical intermolecular 1,2‐aminosulfonylation of alkenes with sulfinates and amines is achieved by utilizing balanced three‐component interactions and reactivity differentiation. This ...strategy can be applicable to a wide range of amines, including primary and secondary amines, thus enabling alkene aminosulfonylation for producing diverse functionalized 2‐sulfonylethan‐1‐amines without the need of additive redox catalysts, metal catalysts and chemical oxidants.
Recently, deep learning-based algorithms have been widely used for classification of hyperspectral images (HSIs) by extracting invariant and abstract features. In our conference paper presented at ...IEEE International Geoscience and Remote Sensing Symposium 2018, 1-D-capsule network (CapsNet) and 2-D-CapsNet were proposed and validated for HSI feature extraction and classification. To further improve the classification performance, the robust 3-D-CapsNet architecture is proposed in this article by following our previous work, which introduces the maximum correntropy criterion to address the noise and outliers problem, generating a robust and better generalization model. As such, discriminative features can be extracted even if some samples are corrupted more or less. In addition, a novel dual channel framework based on robust CapsNet is further proposed to fuse the hyperspectral data and light detection and ranging-derived elevation data for classification. Three widely used hyperspectral datasets are employed to demonstrate the superiority of our proposed deep learning models.
BACKGROUND AND PURPOSE—Blood-brain barrier (BBB) disruption is a critical pathological feature after stroke. MicroRNA-126 (miR-126) maintains BBB integrity by regulating endothelial cell function ...during development. However, the role of miR-126-3p and -5p in BBB integrity after stroke is unclear. Here, we investigated whether miR-126-3p and -5p overexpression regulates BBB integrity after cerebral ischemia.
METHODS—A lentivirus carrying genes encoding miR-126-3p or -5p was stereotactically injected into adult male Institute of Cancer Research mouse brains (n=36). Permanent middle cerebral artery occlusion was performed 2 weeks after virus injection. Brain infarct volume, edema volume, and modified neurological severity score were assessed at 1 and 3 days after ischemia. Immunostaining of ZO-1 (zonula occludens-1) and occludin was used to evaluate BBB integrity. IL-1β (interleukin-1β), TNF-α (tumor necrosis factor-α), VCAM-1 (vascular cell adhesion molecule-1), and E-selectin expression levels were determined by real-time polymerase chain reaction and Western blot analysis.
RESULTS—The expression of miR-126-3p and -5p decreased at 1 and 3 days after ischemia (P<0.05). Injection of lentiviral miR-126-3p or -5p reduced brain infarct volume and edema volume (P<0.05) and attenuated the decrease in ZO-1/occludin protein levels and IgG leakage at 3 days after stroke (P<0.05). Injection of lentiviral miR-126-5p improved behavioral outcomes at 3 days after stroke (P<0.05). miR-126-3p and -5p overexpression downregulated the expression of proinflammatory cytokines IL-1β and TNF-α and adhesion molecules VCAM-1 and E-selectin, as well as decreased MPO (myeloperoxidase positive) cell numbers at 3 days after ischemia (P<0.05).
CONCLUSIONS—miR-126-3p and -5p overexpression reduced the expression of proinflammatory cytokines and adhesion molecules, and attenuated BBB disruption after ischemic stroke, suggesting that miR-126-3p and -5p are new therapeutic targets in the acute stage of stroke.
We systematically investigate the effects of composition on the dynamic mechanical properties of bottlebrush polymer networks self-assembled by linear–bottlebrush–linear triblock copolymers. We fix ...the molecular architecture of the bottlebrush, which consists of 51 poly(dimethyl siloxane) (PDMS) side chains of 5 kg/mol and has a molecular weight of 255 kg/mol, and increase only the volume fraction f of the linear poly(benzyl methacrylate) (PBnMA) blocks. As f increases from 0.05 to 0.41, the network shear modulus G at room temperature increases from ∼4 to ∼100 kPa. Yet, depending on the network morphology, the relation between G and f exhibits two regimes. (i) For sphere morphology, G is nearly a constant; yet, because of a large fraction of loops, the absolute value of G is about 40% of the stiffness G m of the PDMS bottlebrush matrix. (ii) For cylinder morphology, G increases slowly with f but remains nearly 4 orders of magnitude lower than 109 Pa for the glassy cylinders formed by the end PBnMA blocks. We explain this remarkable behavior by modeling the polymer as a polycrystalline material consisting of randomly oriented grains, and each grain is a fiber-reinforced composite. We propose a modified Halpin–Tsai model to describe the shear modulus of such a polycrystalline material: G = G m(1+ζf)/(1–f), in which ζ is an adjustable parameter that describes the grain size relative to the fiber diameter. Above the glass-transition temperature of end blocks, the reinforcement to network modulus from the glassy fibers diminishes, such that G becomes a constant of the matrix stiffness. Our results not only reveal previously unexplored molecule–structure–property relations of self-assembled bottlebrush polymer networks but also provide a new class of soft, solvent-free, and reprocessable polymeric materials with a wide range of controllable stiffness.
Conventional elastomer processing requires crosslinking elastomer using specific chemical reagents and reinforcing it using filler particles. Here we report a method to simultaneously crosslink and ...reinforce styrene-butadiene rubber (SBR) using graphene oxide (GO). We find that GO not only acts as an effective reinforcing filler, but also is capable of generating free radicals upon heating, enabling covalent crosslinking of SBR. Moreover, the interaction between GO surface and SBR polymers results in an interfacial layer in which the density of crosslinks increases towards to the GO surface, thus interfacial layer shows much slower relaxation dynamics than the bulk rubber. The unique role of GO allows GO/SBR nanocomposites to have better mechanical properties than SBR crosslinked with conventional sulfur or dicumyl peroxide. The concept of using GO as both a filler and crosslinking agent may enable the discovery of polymeric nanocomposites with exceeding mechanical properties.
Graphene oxide simultaneously crosslinks and reinforces rubbers, thus acting dual roles of crosslinking agent and filler particles. Display omitted
This article proposes a multiscale spectral features graph fusion (MSFGF) method for selecting proper hyperspectral bands. The MSFGF regards that the selected bands should reflect diagnostic spectral ...information of ground objects at different scales, and it explores band selection from the aspect of multiple spatial scales. First, it adopts the multiscale low-rank decomposition (MSLRD) model to find multiscale spectral features of different ground objects. The model considers divergent spatial structures or spatial correlations of ground objects at different scales, and factorizes the hyperspectral data cube into a series of low-rank block-wise data cubes, where the blocks take spatial structures of different ground objects at increasing scales. Second, the MSFGF presents the multiscale sparse spectral clustering (MSSC) model to fuse the separate connected graphs of multiscale spectral features into a consensus graph. The consensus graph combines the complementary information of multiscale spectral features and helps to reveal the intrinsic clustering structure of all spectral bands. Finally, the MSFGF utilizes spectral clustering to find clusters from the consensus graph and selects representative bands. Experimental results on three widely used hyperspectral data prove the superiority of MSFGF in selecting bands, where it outperforms other seven state-of-the-art methods in classification with an acceptable computational cost.
Least-squares regression (LSR)-based classifiers are effective in multiclassification tasks. However, most existing methods use limited projections, resulting in loss of much discriminant ...information; furthermore, they focus only on exactly fitting samples to target matrix while ignoring overfitting issue. To solve these drawbacks, discriminative marginalized LSR (DMLSR) is proposed to learn a more discriminative projection matrix with consideration of class separability and data-reconstruction ability simultaneously. In the proposed framework, an intraclass compactness graph is employed to avoid the overfitting problem and enhance class separability, and a data-reconstruction constraint is imposed to preserve discriminant information on limited projections. Experimental results on several hyperspectral data sets demonstrate that the proposed method significantly outperforms some state-of-the-art classifiers.
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