Showing papers by "Dalian University of Technology published in 2016"

Nickel–vanadium monolayer double hydroxide for efficient electrochemical water oxidation

Abstract: Highly active and low-cost electrocatalysts for water oxidation are required due to the demands on sustainable solar fuels; however, developing highly efficient catalysts to meet industrial require ...

The Visual Object Tracking VOT2016 Challenge Results

Abstract: The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).

Recent Development of Chemosensors Based on Cyanine Platforms

Abstract: The cyanine platforms including cyanine, hemicyanine, and squaraine are good candidates for developing chemosensors because of their excellent photophysical properties, outstanding biocompatibility, and low toxicity to living systems. A huge amount of research work involving chemosensors based on the cyanine platforms has emerged in recent years. This review focuses on the development from 2000 to 2015, in which cyanine, hemicyanine, and squaraine sensors will be separately summarized. In each section, a systematization according to the type of detection mechanism is established. The basic principles about the design of the chemosensors and their applications as bioimaging agents are clearly discussed. In addition, we emphasize the advances that have been made in improving the detection performance through incorporation of the chemosensors into nanoparticles.

Blind Image Deblurring Using Dark Channel Prior

Abstract: We present a simple and effective blind image deblurring method based on the dark channel prior. Our work is inspired by the interesting observation that the dark channel of blurred images is less sparse. While most image patches in the clean image contain some dark pixels, these pixels are not dark when averaged with neighboring highintensity pixels during the blur process. This change in the sparsity of the dark channel is an inherent property of the blur process, which we both prove mathematically and validate using training data. Therefore, enforcing the sparsity of the dark channel helps blind deblurring on various scenarios, including natural, face, text, and low-illumination images. However, sparsity of the dark channel introduces a non-convex non-linear optimization problem. We introduce a linear approximation of the min operator to compute the dark channel. Our look-up-table-based method converges fast in practice and can be directly extended to non-uniform deblurring. Extensive experiments show that our method achieves state-of-the-art results on deblurring natural images and compares favorably methods that are well-engineered for specific scenarios.

Fluorescent Probes for Sensing and Imaging within Specific Cellular Organelles.

Abstract: Fluorescent probes have become powerful tools in biosensing and bioimaging because of their high sensitivity, specificity, fast response, and technical simplicity. In the last decades, researchers have made remarkable progress in developing fluorescent probes that respond to changes in microenvironments (e.g., pH, viscosity, and polarity) or quantities of biomolecules of interest (e.g., ions, reactive oxygen species, and enzymes). All of these analytes are specialized to carry out vital functions and are linked to serious disorders in distinct subcellular organelles. Each of these organelles plays a specific and indispensable role in cellular processes. For example, the nucleus regulates gene expression, mitochondria are responsible for aerobic metabolism, and lysosomes digest macromolecules for cell recycling. A certain organelle requires specific biological species and the appropriate microenvironment to perform its cellular functions, while breakdown of the homeostasis of biomolecules or microenvironmental mutations leads to organelle malfunctions, which further cause disorders or diseases. Fluorescent probes that can be targeted to both specific organelles and biochemicals/microenvironmental factors are capable of reporting localized bioinformation and are potentially useful for gaining insight into the contributions of analytes to both healthy and diseased states. In this Account, we review our recent work on the development of fluorescent probes for sensing and imaging within specific organelles. We present an overview of the design, photophysical properties, and biological applications of the probes, which can localize to mitochondria, lysosomes, the nucleus, the Golgi apparatus, and the endoplasmic reticulum. Although a diversity of organelle-specific fluorescent stains have been commercially available, our efforts place an emphasis on improvements in terms of low cytotoxicity, high photostability, near-infrared (NIR) emission, two-photon excitation, and long fluorescence lifetimes, which are crucial for long-time tracking of biological processes, tissue and body imaging with deep penetration and low autofluorescence, and time-resolved fluorescence imaging. Research on fluorescent probes with both analyte responsiveness and organelle targetability is a new and emerging area that has attracted increasing attention over the past few years. We have extended the diversity by developing organelle-specific responsive probes capable of detecting changes in biomolecular levels (reactive oxygen species, fluoride ion, hydrogen sulfide, zinc cation, thiol-containing amino acids, and cyclooxygenase-2) and the microenvironment (viscosity, polarity, and pH). Future research should give more considerations of the "low-concern" organelles, such as the Golgi apparatus, the endoplasmic reticulum, and ribosomes. In addition, given the tiny sizes of subcellular organelles (20-1000 nm), we anticipate that clearer visulization of the cellular events within specific organelles will rely on super-resolution optical microscopy with nanoscopic-scale resolution.

Implant-derived magnesium induces local neuronal production of CGRP to improve bone-fracture healing in rats

Abstract: Orthopedic implants containing biodegradable magnesium have been used for fracture repair with considerable efficacy; however, the underlying mechanisms by which these implants improve fracture healing remain elusive. Here we show the formation of abundant new bone at peripheral cortical sites after intramedullary implantation of a pin containing ultrapure magnesium into the intact distal femur in rats. This response was accompanied by substantial increases of neuronal calcitonin gene-related polypeptide-α (CGRP) in both the peripheral cortex of the femur and the ipsilateral dorsal root ganglia (DRG). Surgical removal of the periosteum, capsaicin denervation of sensory nerves or knockdown in vivo of the CGRP-receptor-encoding genes Calcrl or Ramp1 substantially reversed the magnesium-induced osteogenesis that we observed in this model. Overexpression of these genes, however, enhanced magnesium-induced osteogenesis. We further found that an elevation of extracellular magnesium induces magnesium transporter 1 (MAGT1)-dependent and transient receptor potential cation channel, subfamily M, member 7 (TRPM7)-dependent magnesium entry, as well as an increase in intracellular adenosine triphosphate (ATP) and the accumulation of terminal synaptic vesicles in isolated rat DRG neurons. In isolated rat periosteum-derived stem cells, CGRP induces CALCRL- and RAMP1-dependent activation of cAMP-responsive element binding protein 1 (CREB1) and SP7 (also known as osterix), and thus enhances osteogenic differentiation of these stem cells. Furthermore, we have developed an innovative, magnesium-containing intramedullary nail that facilitates femur fracture repair in rats with ovariectomy-induced osteoporosis. Taken together, these findings reveal a previously undefined role of magnesium in promoting CGRP-mediated osteogenic differentiation, which suggests the therapeutic potential of this ion in orthopedics.

Electroactive edge site-enriched nickel–cobalt sulfide into graphene frameworks for high-performance asymmetric supercapacitors

Abstract: Tailor-made edge site-enriched inorganics coupled graphene hybrids hold a promising platform material for high-performance supercapacitors. Herein, we report a simple strategy for fabricating edge site-enriched nickel–cobalt sulfide (Ni–Co–S) nanoparticles decorated on graphene frameworks to form integrated hybrid architectures (Ni–Co–S/G) via an in situ chemically converted method. The Kirkendall effect-involved anion exchange reaction, e.g. the etching-like effort of the S2− ions, plays a crucial role for the formation of the edge site-enriched nanostructure. Density functional theory (DFT) calculations reveal that the Ni–Co–S edge sites have a high electrochemical activity and strong affinity for OH− in the electrolyte, which are responsible for the enhanced electrochemical performance. Benefiting from the integrated structures of Ni–Co–S nanoparticles and conductive graphene substrates, the resultant Ni–Co–S/G hybrid electrodes exhibit a high specific capacitance of 1492 F g−1 at the current density of 1 A g−1, a superior rate capability of 96% when the current density is increased to 50 A g−1, and excellent electrochemical stabilities. An asymmetric supercapacitor fabricated using the edge site-enriched Ni–Co–S/G hybrids as the positive electrode and porous carbon nanosheets (PCNS) as negative electrodes shows a high energy density of 43.3 W h kg−1 at a power density of 0.8 kW kg−1, and an energy density of 28.4 W h kg−1 can be retained even at a high power density of 22.1 kW kg−1.

Sustainable Synthesis and Assembly of Biomass-Derived B/N Co-Doped Carbon Nanosheets with Ultrahigh Aspect Ratio for High-Performance Supercapacitors

Abstract: The practical application of graphene has still been hindered by high cost and scarcity in supply. It boosts great interest in seeking for low-cost substitute of graphene for upcoming usage where extremely physical properties are not absolutely critical. The conversion of renewable biomass offers a great opportunity for sustainable and economic fabrication of 2D carbon nanostructures. However, large-scale production of carbon nanosheets with ultrahigh aspect ratio, satisfied electronic properties, and the capability of organized assembly like graphene has been rarely reported. In this work, a facile yet efficient approach for mass production of flexible boric/nitrogen co-doped carbon nanosheets with very thin thickness of 5–8 nm and ultrahigh aspect ratio of over 6000–10 000 is demonstrated by assembling the biomass molecule in long-range order on 2D hard template and subsequent annealing. The advantage of these doped carbon nanosheets over conventional products lies in that they can be readily assembled to multilevel architectures such as freestanding flexible thin film and ultralight aerogels with better electrical properties, which exhibit exceptional capacitive performance for supercapacitor application. The recyclability of boric acid template further reduces the discharge of the waste and processing cost, rendering high cost-effectiveness and environmental benignity for scalable production.

Saliency Detection with Recurrent Fully Convolutional Networks

Abstract: Deep networks have been proved to encode high level semantic features and delivered superior performance in saliency detection. In this paper, we go one step further by developing a new saliency model using recurrent fully convolutional networks (RFCNs). Compared with existing deep network based methods, the proposed network is able to incorporate saliency prior knowledge for more accurate inference. In addition, the recurrent architecture enables our method to automatically learn to refine the saliency map by correcting its previous errors. To train such a network with numerous parameters, we propose a pre-training strategy using semantic segmentation data, which simultaneously leverages the strong supervision of segmentation tasks for better training and enables the network to capture generic representations of objects for saliency detection. Through extensive experimental evaluations, we demonstrate that the proposed method compares favorably against state-of-the-art approaches, and that the proposed recurrent deep model as well as the pre-training method can significantly improve performance.

An open access database for the evaluation of heart sound algorithms.

Abstract: In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Metal–organic-framework-engaged formation of Co nanoparticle-embedded carbon@Co9S8 double-shelled nanocages for efficient oxygen reduction

Abstract: Hollow nanostructures with a complex interior and superb structural tenability offer great advantages for constructing advanced catalysts. Herein, we report the designed synthesis of novel Co nanoparticle-embedded carbon@Co9S8 double-shelled nanocages (Co-C@Co9S8 DSNCs) by a metal–organic-framework-engaged strategy. Uniform zeolitic imidazolate framework (ZIF-67)@amorphous CoS yolk–shelled structures are first fabricated and then converted to Co-C@Co9S8 DSNCs by thermal annealing in N2 flow. The Co-C nanocages inside Co9S8 shells function as the active centers for the oxygen reduction reaction (ORR). The Co9S8 shells prevent the Co-C active centers from aggregation while acting as nanoreactors. As a result, the Co-C@Co9S8 DSNCs exhibit excellent performance for the ORR in terms of low over-potential, high current density, excellent stability and methanol tolerance capability.

Metal–Organic Frameworks: Versatile Materials for Heterogeneous Photocatalysis

Abstract: Photocatalysis is one of the most important chemical methods to mitigate the energy and environmental crisis via converting inexhaustible solar energy into clean chemical potential. The general history of the development of photocatalysis based on porous metal–organic frameworks (MOFs) is simply divided into three branches with a focus placed on the distinct structural role of the photocatalytic center: the inorganic cluster nodes, the organic linkers, and the guests in the pores of MOFs. In each branch, these photocatalytic centers are considered to be monodispersed within the crystal lattices with the other two structure roles regularly distributed to isolate the active centers and sometimes to provide more functions other than photoactivity. This distinctive nature has rendered MOFs as promising candidates for photocatalysis not only because they combine the benefits of heterogeneous catalysis and homogeneous catalysis but also because they facilitate the possibility of merging multifunctional catalyti...

A new topology optimization approach based on Moving Morphable Components (MMC) and the ersatz material model

Abstract: This paper presents a new topology optimization approach based on the so-called Moving Morphable Components (MMC) solution framework. The proposed method improves several weaknesses of the previous approach (e.g., Guo et al. in J Appl Mech 81:081009, 2014a) in the sense that it can not only allow for components with variable thicknesses but also enhance the numerical solution efficiency substantially. This is achieved by constructing the topological description functions of the components appropriately, and utilizing the ersatz material model through projecting the topological description functions of the components. Numerical examples demonstrate the effectiveness of the proposed approach. In order to help readers understand the essential features of this approach, a 188 line Matlab implementation of this approach is also provided.

Dye-Sensitized Core/Active Shell Upconversion Nanoparticles for Optogenetics and Bioimaging Applications.

Abstract: Near-infrared (NIR) dye-sensitized upconversion nanoparticles (UCNPs) can broaden the absorption range and boost upconversion efficiency of UCNPs. Here, we achieved significantly enhanced upconversion luminescence in dye-sensitized core/active shell UCNPs via the doping of ytterbium ions (Yb3+) in the UCNP shell, which bridged the energy transfer from the dye to the UCNP core. As a result, we synergized the two most practical upconversion booster effectors (dye-sensitizing and core/shell enhancement) to amplify upconversion efficiency. We demonstrated two biomedical applications using these UCNPs. By using dye-sensitized core/active shell UCNP embedded poly(methyl methacrylate) polymer implantable systems, we successfully shifted the optogenetic neuron excitation window to a biocompatible and deep tissue penetrable 800 nm wavelength. Furthermore, UCNPs were water-solubilized with Pluronic F127 with high upconversion efficiency and can be imaged in a mouse model.

Facet Dependence of CO2 Reduction Paths on Cu Electrodes

Abstract: Experimental results have shown that CO2 electroreduction is sensitive to the surface morphology of Cu electrodes. We used density functional theory (DFT) to evaluate the thermodynamics and kinetics of CO2 reduction pathways on Cu(100) and Cu(111) with the aim of understanding the experimentally reported differences in CO2 reduction products. Results suggest that the hydrogenation of CO* to hydroxymethylidyne (COH*) or formyl (CHO*) is a key selective step. Cu(111) favors COH* formation, through which methane and ethylene are produced via a common CH2 species under high overpotential (<−0.8 V vs RHE). On Cu(100), formation of CHO* is preferred and ethylene formation goes through C–C coupling of two CHO* species followed by a series of reduction steps of the C2 intermediates, under relatively lower overpotential (−0.4 to −0.6 V vs RHE). Further reduction of these C2 intermediates, however, require larger potentials (∼−1.0 V vs RHE) and conflicts with the experimentally observed low potential pathway to C2 ...

A low-cost spiro[fluorene-9,9′-xanthene]-based hole transport material for highly efficient solid-state dye-sensitized solar cells and perovskite solar cells

Abstract: A low-cost spiro[fluorene-9,9′-xanthene] (SFX) based organic hole transport material (HTM) termed X60 was designed and synthesized using a two-step synthetic route. Devices with X60 as HTM showed high power conversion efficiencies (PCEs) amounting to 7.30% in solid-state dye-sensitized solar cells (ssDSCs) and 19.84% in perovskite solar cells (PSCs), under 100 mW cm−2 AM1.5G solar illumination. To the best of our knowledge, this is the first example of an easily synthesized spiro-structured HTM that shows comparable performance with respect to the well-known HTM Spiro-OMeTAD in both ssDSCs and PSCs. Furthermore, the facile synthesis of X60 from commercially available starting materials makes this HTM very promising for large-scale industrial production in the future.

Uncovering the Key Role of the Fermi Level of the Electron Mediator in a Z-Scheme Photocatalyst by Detecting the Charge Transfer Process of WO3-metal-gC3N4 (Metal = Cu, Ag, Au).

Abstract: Z-scheme photocatalytic system shows superiority in degradation of refractory pollutants and water splitting due to the high redox capacities caused by its unique charge transfer behaviors. As a key component of Z-scheme system, the electron mediator plays an important role in charge carrier migration. According to the energy band theory, we believe the interfacial energy band bendings facilitate the electron transfer via Z-scheme mechanism when the Fermi level of electron mediator is between the Fermi levels of Photosystem II (PS II) and Photosystem I (PS I), whereas charge transfer is inhibited in other cases as energy band barriers would form at the semiconductor-metal interfaces. Here, this inference was verified by the increased hydroxyl radical generation and improved photocurrent on WO3-Cu-gC3N4 (with the desired Fermi level structure), which were not observed on either WO3-Ag-gC3N4 or WO3-Au-gC3N4. Finally, photocatalytic degradation rate of 4-nonylphenol on WO3-Cu-gC3N4 was proved to be as high a...

Delay-Dependent Stability of Single-Loop Controlled Grid-Connected Inverters with LCL Filters

Abstract: LCL filters have been widely used for grid-connected inverters. However, the problem that how time delay affects the stability of digitally controlled grid-connected inverters with LCL filters has not been fully studied. In this paper, a systematic study is carried out on the relationship between the time delay and stability of single-loop controlled grid-connected inverters that employ inverter current feedback (ICF) or grid current feedback (GCF). The ranges of time delay for system stability are analyzed and deduced in the continuous s -domain and discrete z -domain. It is shown that in the optimal range, the existence of time delay weakens the stability of the ICF loop, whereas a proper time delay is required for the GCF loop. The present work explains, for the first time, why different conclusions on the stability of ICF loop and GCF loop have been drawn in previous studies. To improve system stability, a linear predictor-based time delay reduction method is proposed for ICF, while a time delay addition method is used for GCF. A controller design method is then presented that guarantees adequate stability margins. The delay-dependent stability study is verified by simulation and experiment.

STCT: Sequentially Training Convolutional Networks for Visual Tracking

Abstract: Due to the limited amount of training samples, finetuning pre-trained deep models online is prone to overfitting. In this paper, we propose a sequential training method for convolutional neural networks (CNNs) to effectively transfer pre-trained deep features for online applications. We regard a CNN as an ensemble with each channel of the output feature map as an individual base learner. Each base learner is trained using different loss criterions to reduce correlation and avoid over-training. To achieve the best ensemble online, all the base learners are sequentially sampled into the ensemble via important sampling. To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features. The proposed online training method is applied to visual tracking problem by transferring deep features trained on massive annotated visual data and is shown to significantly improve tracking performance. Extensive experiments are conducted on two challenging benchmark data set and demonstrate that our tracking algorithm can outperform state-of-the-art methods with a considerable margin.

Ultrafine-Grained AlCoCrFeNi2.1 Eutectic High-Entropy Alloy

Abstract: The development of microstructure and mechanical properties was investigated in a heavily cold-rolled and annealed AlCoCrFeNi2.1 high-entropy alloy. The as-cast alloy having a eutectic morphology consisting of alternate bands of ordered L1(2) and B2 phases was 90% cold-rolled. The deformed microstructure showed profuse shear banding and disordering of the L12, but no transformation of the B2 phase. A duplex microstructure consisting of ultrafine equiaxed grains (similar to 0.60 mu m) of disordered face centered cubic and B2 was observed after annealing at 800 degrees C. The annealed material showed remarkable strength-ductility combination having ultimate tensile strength similar to 1.2 GPa and elongation to failure similar to 12%.

Ultralow-Power Near Infrared Lamp Light Operable Targeted Organic Nanoparticle Photodynamic Therapy

Abstract: Tissue penetration depth is a major challenge in practical photodynamic therapy (PDT) A biocompatible and highly effective near infrared (NIR)-light-absorbing carbazole-substituted BODIPY (Car-BDP) molecule is reported as a class of imaging-guidable deep-tissue activatable photosensitizers for PDT Car-BDP possesses an intense, broad NIR absorption band (600–800 nm) with a remarkably high singlet oxygen quantum yield (ΦΔ = 67%) After being encapsulated with biodegradable PLA–PEG-FA polymers, Car-BDP can form uniform and small organic nanoparticles that are water-soluble and tumor-targetable Rather than using laser light, such nanoparticles offer an unprecedented deep-tissue, tumor targeting photodynamic therapeutic effect by using an exceptionally low-power-density and cost-effective lamp light (12 mW cm–2) In addition, these nanoparticles can be simultaneously traced in vivo due to their excellent NIR fluorescence This study signals a major step forward in photodynamic therapy by developing a new cl

Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning

Abstract: To improve the efficiency of big data feature learning, the paper proposes a privacy preserving deep computation model by offloading the expensive operations to the cloud. Privacy concerns become evident because there are a large number of private data by various applications in the smart city, such as sensitive data of governments or proprietary information of enterprises. To protect the private data, the proposed model uses the BGV encryption scheme to encrypt the private data and employs cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently for deep computation model training. Furthermore, the proposed scheme approximates the Sigmoid function as a polynomial function to support the secure computation of the activation function with the BGV encryption. In our scheme, only the encryption operations and the decryption operations are performed by the client while all the computation tasks are performed on the cloud. Experimental results show that our scheme is improved by approximately 2.5 times in the training efficiency compared to the conventional deep computation model without disclosing the private data using the cloud computing including ten nodes. More importantly, our scheme is highly scalable by employing more cloud servers, which is particularly suitable for big data.

Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder

Abstract: Deep learning, which represents data by a hierarchical network, has proven to be efficient in computer vision. To investigate the effect of deep features in hyperspectral image (HSI) classification, this paper focuses on how to extract and utilize deep features in HSI classification framework. First, in order to extract spectral–spatial information, an improved deep network, spatial updated deep auto-encoder (SDAE), is proposed. SDAE, which is an improved deep auto-encoder (DAE), considers sample similarity by adding a regularization term in the energy function, and updates features by integrating contextual information. Second, in order to deal with the small training set using deep features, a collaborative representation-based classification is applied. Moreover, in order to suppress salt-and-pepper noise and smooth the result, we compute the residual of collaborative representation of all samples as a residual matrix, which can be effectively used in a graph-cut-based spatial regularization. The proposed method inherits the advantages of deep learning and has solutions to add spatial information of HSI in the learning network. Using collaborative representation-based classification with deep features makes the proposed classifier extremely robust under a small training set. Extensive experiments demonstrate that the proposed method provides encouraging results compared with some related techniques.

Naturally Dried Graphene Aerogels with Superelasticity and Tunable Poisson's Ratio.

Abstract: A novel natural drying (ND) strategy for low-cost and simple fabrication of graphene aerogels (GAs) is highlighted. The as-formed NDGAs exhibit ultralarge reversible compressibility (99%) and tunable Poisson's ratio behaviors (-0.30 < ν < 0.46), which suggests promising applications in soft actuators, soft robots, sensors, deformable electronic devices, drug release, thermal insulator, and protective materials.