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The self-tapping screw connectors are widely used in Cold-formed Steel (CFS) construction due to their rapid fastening with automatic power driving and ease of installation via a self-drilling process. However, there is a lack of a robust numerical method for the modelling of behaviours of screw fasteners bearing against steel sheets, where the connections can undergo different failure modes including fracture, bearing, tilting, pull-out and shear failure, particularly for CFS thin plates. A nonlinear explicit dynamic finite element (FE) model is developed in this paper to address this issue by validating the modelled load-deformation relationships and failure modes with available tests in literature. The proposed numerical model is improved in two aspects comparing to available research: firstly, a ductile fracture criterion was proposed that could address the fracture of ply metals subjected to complex stress state with less trial-and-error process. Secondly, the threads in the screw fasteners and ply sheets were carefully modelled where the inter-locking capacity between the screws and steel sheets were better taken into account, eliminating the need for an artificial friction factor. The FE modelled results were compared against the test results and showed good agreement in terms of connection strength and failure modes, including fracture, bearing, tilting, pull-out and shear failure. Finally, parametric studies were carried out to investigate the influence of steel sheet thickness and the screw outer diameter on the force–displacement relationships of the steel lap joints.

Hierarchical nitrogen-rich porous carbon (HNPC) was prepared by a simple and effective method using standard wheat straw as raw material. The as-synthesized HNPC material was found to possess high specific surface area. Because of its hierarchical porous structure, the HNPC material enables highly efficient lithium-ion transport through the electrolyte to the negative electrode, which shortens the diffusion path of the lithium-ion channel and reduces the internal resistance. The initial reversible capacity of the HNPC battery reaches 792.41 mAh·g−1 with coulombic efficiency of 88.61%. This type of HNPC material provides a potential anode for lithium-ion batteries with high specific capacity.

A new and precise control printing method was developed for preparing homogeneous microspheres. Hydroxyapatite-sodium alginate (HA-SA) mixed suspension was used as printing material and CaCl2 solution was used as solidifying liquid to prepared ceramic microspheres by pneumatic extrusion printing technology. By adjusting the positive pressure, dwell time, and hydroxyapatite concentration, the influence of each parameter on the shape of printed microspheres was studied, and the suitable preparation parameters were determined. Then, the microspheres with uniform particle size distribution were successfully prepared in large quantities by selecting appropriate printing parameters. Then, the microspheres were dried and sintered at different temperatures. During the drying and sintering process, the microspheres showed obvious shrinkage, but still keep good sphericity. The phases of sintered microspheres are hydroxyapatite and calcium oxide. The microstructure shows typical hydroxyapatite grain characteristics.

The current study involves fabrication and characterization of HA scaffolds containing different copper (Cu) content by ion exchange and 3D extrusion deposition technique. The fabricated pure HA and Cu-doped scaffolds (Cu–HA) were characterized by mechanical behavior, SEM, EDS, XRD, and FT-IR studies. The results show that the Cu-doped HA scaffolds show stronger compressive strength. In addition, the antibacterial properties and biological properties of these scaffolds were investigated in vitro. The antibacterial test results show that the addition of Cu in the HA scaffolds increased antibacterial activity, and the antibacterial properties of scaffolds significantly increased with the addition of Cu. The biocompatibility test results show that the 15Cu–HA scaffolds treated with 15% CuSO4 curing solution significantly inhibited of cell growth on day 7, while the 5Cu–HA scaffolds treated with 5% CuSO4 curing solution the cells observed good grown state on day 7. The 5Cu–HA scaffolds show stronger mechanical behavior, increased antibacterial properties and lower toxicity rat bone marrow mesenchymal stem cells (BMSCs) on day 7. So, the developed 5Cu–HA scaffolds can be used as a good biomaterial for bone tissue engineering.

Scanning radar can be used to obtain images of targets in forward-looking area, and has attracted much attention in many fields, such as ocean monitoring, air-to-ground attack, navigation, and so on. However, its azimuth resolution is extremely poor due to the limitation of the antenna size. In order to break through the limitation, many superresolution algorithms have been proposed, and iterative shrinkage-thresholding algorithm (ISTA) is one of the most famous methods because of its antinoise ability and simplicity. In the meantime, the slow convergence of iterative shrinkage-thresholding algorithm is also known to all. In this article, a second-order accelerated ISTA for scanning radar forward-looking superresolution imaging is proposed. In this algorithm, a prediction vector is constructed before each iteration by using the first and the second-order difference information of iteration vectors to reduce the number of iterations and get a faster convergence speed. In the end, simulations and experimental results are given to illustrate the effectiveness of the accelerated imaging algorithm.

We propose a method for low-concentration detection of sodium chloride based on surface plasmon resonance (SPR). A novel prism coupling structure is designed for the sodium chloride concentration sensor, where a TiO2–ZnO composite film and a MoS2–GO hybrid nanosheet are chosen to enhance the surface plasmon resonance based on an Au nanowire array because of their unique properties in metal nanowire arrays and two-dimensional nanomaterials. Using the finite element method, we analyze the thickness of each medium layer for the structure designed in order to obtain the optimum value, so that the sensitivity of the sodium chloride concentration sensor designed is substantially enhanced. The optimum thickness of each medium layer is given, and the sensitivity of the sodium chloride concentration sensor based on the surface plasmon resonance can reach 0.260°/ (g/100 ml) concentration in the detection range of the sodium chloride concentration from zero to 15 g/100 ml. We derive the relationship between the sodium chloride concentration and the surface plasmon resonance angle in the detection range of the sodium chloride concentration.

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for a small subset of inputs. To quantify the error introduced by abstraction, we provide both theoretical error bound estimation based on the theory of Bernstein polynomials and more practical sampling based error bound estimation, following a tight Lipschitz constant estimation approach based on forward reachability analysis. Compared with previous methods, our approach addresses a much broader set of neural networks, including heterogeneous neural networks that contain multiple types of activation functions. Experiment results on a variety of benchmarks show the effectiveness of our approach.

In this work, the polycaprolactone (PCL) scaffold with specific surface morphology was prepared by cryogenic printing technique. Biocompatible hydroxyapatite (HA) was added to the PCL matrix to enhance the surface properties and bioactivity of the scaffolds. The effects of HA on the mechanical strength and surface morphology were studied systematically. It was found that the extra surface morphology of filament enhanced the exposure of the HA particles, which induced a positive synergistic effect on surface properties. The wettability and bio-mineralization were significantly improved, and the protein adsorption capacity was 4.5 and 3.9 times than the ones of sample with 20%HA and smooth surface, respectively. Rat bone mesenchymal stem cells (rBMSCs) were used as the cell model to evaluate the cellular response to the specific surface of the scaffolds. The results showed that the scaffolds with 20–40% HA and extra surface had better proliferation ratio. The ALP assay also demonstrated that the synergistic effect was beneficial to cell osteogenic differentiation, and the ALP level was significantly higher than that of pure PCL and smooth PCL/HA composite scaffold samples at the 14th day. Therefore, the PCL/HA composite scaffold with extra surface morphology had better potential for bone tissue engineering.

Extrusion dripping is a facile method for the preparation of millimetre-size macrobeads. In this work, a pneumatic extrusion dripping system has been established to fabricate hydroxyapatite macrobeads with a controlled size. The size of the hydroxyapatite spherical beads could be tailored by adjusting the dripping parameters. This work is believed to provide an alternative method for the fabrication of hydroxyapatite macrobeads that can be used in bone regeneration.

In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data. Currently, the classical image classification algorithms are mostly based on RGB images or grayscale images, and fail to make good use of the depth information about objects or scenes. The depth information in the images has a strong complementary effect, which can enhance the classification accuracy significantly. In this paper, we propose an image classification technology using principal component analysis based on multi-view depth characters. In detail, firstly, the depth image of the original image is estimated; secondly, depth characters are extracted from the RGB views and the depth view separately, and then the reducing dimension operation through the PCA is implemented. Eventually, the SVM is applied to image classification. The experimental results show that the method has good performance.

In this study, a novel hybrid surface plasmon waveguide structure based on CdS nanowires and metal ridges was designed. The waveguide structure was simulated and analysed at a working wavelength of 490 nm based on the finite element method. The electric field distribution, mode characteristics, transmission length and quality factor of the waveguide were studied with geometric parameters. Results show that the comprehensive performance of the waveguide can be significantly optimised by appropriately adjusting the geometric parameters of the structure. Through the optimal parameter design, the effective mode field area reaches 0.008 λ 2 , the transmission length is 21,289 nm, and the quality factor is 702.12. This waveguide structure has great potential in the fields of optoelectronic communication and optical sensing technology.

This study presents an experimental and numerical investigation on the static behavior of headed stud shear connectors in ultra-high performance concrete (UHPC) of composite bridges. Four push-out specimens were tested. It was found that no cracking, crushing or splitting was observed on the concrete slab, indicating that UHPC slab exhibited good performance and could resist the high force transferred from the headed studs. The numerical and experimental results indicated that the shear capacity is supposed to be composed of two parts stud shank shear contribution and concrete wedge block shear contribution. The stiffness increment of a stud in UHPC was at least 60% higher than that in normal strength concrete. Even if the stud height was reduced from 6d to 2d, there was no reduction in the shear strength of a stud. Short stud shear connectors with an aspect ratio as small as 2 could develop full strength in UHPC slabs. An empirical load-slip equation taking into account stud diameter was proposed to predict the load-slip response of a stud. The reliability and accuracy of the proposed load-slip equation was verified by the experimental and numerical load-slip curves.

Fifth generation mobile networks (5G) is coming into our life and it will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer “smarter” user experience. With the commercial uses of 5G, online social networks and smart phones will spring up again. Then cross-modality data will play a more important role in the daily information dissemination. As an effective way of content analysis, topic detection has attracted much research interest, but conventional topic analysis is undergoing the limitations from the cross-modality heterogenous data. This paper proposes a sentimental image dominant graph topic model, that can detect the topic from the heterogenous data and mine the sentiment of each topic. In details, we design a topic model to transfer both the low-level visual modality and the high-level text modality into a semantic manifold, and improve the discriminative power of CNN feature by jointly optimizing the output of both convolutional layer and fully-connected layer. Furthermore, since the sentimental impact is very significant for understanding the intrinsic meaning of topics, we introduce a semantic score of subjective sentences to calculate the sentiment on the base of the contextual sentence structure. The comparison experiments on the public cross-modality benchmark show the promising performance of our model. So our method using AI technology will facilitate the intellectualization of 5G.

Porous fibers had better performance in many application fields for their larger surface area or extra nano topography than smooth ones. However, high vapor pressure solvents or selective removal of one phase from fastidious blended fibers were mostly used to fabricate porous fibers in electrospinning process, which made the process unfriendly and inconveniently. Here, a facial process on fabrication of porous fibers was developed by integrating the principles of thermally induced phase separation with the self-made cryogenic electrospinning system (CES) which can provisional solidify the polymer jet on the freezing collector. For demonstration, the polymer solution of Polycaprolactone (PCL) with solvent of glacial acetic acid was chosen. The PCL porous fibers with diameter of 3.29 ± 0.42 μm were successfully prepared at the height of 90 mm and freezing temperature of −3.6 ± 0.3℃ on collector in room environment. Furthermore, the pores on the surface of fibers exhibited two types: pits and polygon concaves. The pits were induced by phase separation at freezing temperature and the polygon concaves were due to the interaction of residual solvent with ice crystals on surface of silicon wafer or metal collector recruited from the air moisture. Generally, this process was suitable to polymer solutions with solvents of low vapor pressure but high freezing point.

In this paper, the electromagnetically induced transparency (EIT) effect of graphene nanostructures was studied by numerical methods. Numerous branch structures were added to the sides of the graphene/medium/graphene waveguide, and the Fabry-Perot resonators were formed between adjacent branches due to the phase coupling effect. The back and forth oscillation of electromagnetic waves in the cavity significantly enhanced the transmitted light in a narrow band. Thus, an output spectrum similar to the EIT effect was produced. The output spectrum can be further adjusted by the gate voltage with the unique electrically adjustable properties of graphene. The surface of plasmon exhibited stronger locality in graphene nanostructures than metals. Thus, graphene-based electro-optical devices were more compact and integrated in the deep sub-wavelength scale. The proposed graphene nanostructures facilitated the implementation of a variety of high performance electro-optical devices, including multi-channel selective filters, sensors, and modulators.

Reuse of biogas slurry (BGS) could reduce fertilizer application rate because this material is an alternative source of nutrients and irrigation that could replace some of applied fertilizer. However, the environmental impacts of BGS irrigation, such as greenhouse gas emissions (GHG) are poorly understood. We investigated the global warming potential (GWP) and greenhouse gas intensity (GHGI) of BGS irrigation by quantifying the nitrous oxide (N2O) and carbon dioxide (CO2) emissions under different BGS irrigation rates in a two-year wheat-maize rotation field in the North China Plain. We found BGS irrigation did not change the patterns and fluxes of CO2 and N2O emission during the entire wheat-maize cropping cycle. In addition, there were no significant difference between BGS treatments (BSL, BSM, and BSH) and conventional fertilizer treatment (CF) in GWP. Further, no significant difference occurred in GHGI between BSM (33% BGS and 67% groundwater were mixed and irrigated during the wintering period and joining stage of wheat and after maize sowing, 315 kg·N·hm−1), BSH (50% BGS and 50% groundwater were mixed and irrigated during the wintering period and joining stage of wheat and after maize sowing, 477 kg·N·hm−1) and CF (conventional NPK fertilizer, 420 kg·N·ha−1), despite a higher value BSL (33% BGS and 67% groundwater were mixed and irrigated during the wintering period of wheat and after maize sowing, 210 kg·N·hm−1).The BSL and BSM treatments significantly increased the nitrogen use efficiency (NUE) compared to the CF treatment (11.3% for BSL and 15.3% for BSM). Despite the lower NUE in the BSH treatment relative to CF, it was not statistically significant. Therefore, BGS reuse can increase NUE and does not cause environmental impacts. According to the results of GHGI and NUE, it can be concluded that BSM should be recommended as an improved recycling management practice for the wheat-maize rotation systems.

The bovine hydroxyapatite (BHA) was applied to prepare biological tissue engineering scaffolds by the method of extrusion freeforming. To achieve this goal, BHA were added to sodium alginate (SA) solution to form a slurry system in appropriate proportion. The resulting mixtures were fabricated to be a kind of controllable and porous scaffolds followed with cross-linking in 5% calcium chloride (CaCl2) solution for 24 h. After that, the scaffolds were sintered in air at 1 000, 1 100, 1 200 and 1 300 °C for 5 h. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) studies were performed on the scaffolds to analyze its microstructure and constituent. To explore the effect of sintering temperature on scaffolds, the compressive strength, volume shrinkage and water absorptivity of BHA-SA composite scaffolds after sintering were investigated. The research tests indicated the feasibility of applying BHA powder to 3D printing. Besides, the scaffolds sintered in a respectively lower temperature possess much more pores and performed higher water absorptivity, which means better cellular affinity. And scaffolds sintered between 1 100 and 1 200 °C presents higher compressive strength.

Osteoarthritis (OA) has been recognized as the most common chronic age-related disease. Cartilage degeneration influences OA therapy. Here we report that hematopoietic pre-B cell leukemia transcription factor-interacting protein (HPIP) is essential for OA development. Elevated HPIP levels are found in OA patients. Col2a1-CreERT2/HPIPf/f mice exhibit obvious skeletal abnormalities compared with their HPIPf/f littermates. HPIP deficiency in mice protects against developing OA. Moreover, intra-articular injection of adeno-associated virus carrying HPIP-specific short hairpin RNA in vivo attenuates OA histological signs. Notably, in vitro RNA-sequencing and chromatin immunoprecipitation sequencing profiles identify that HPIP modulates OA cartilage degeneration through transcriptional activation of Wnt target genes. Mechanistically, HPIP promotes the transcription of Wnt targets by interacting with lymphoid enhancer binding factor 1 (LEF1). Furthermore, HPIP potentiates the transcriptional activity of LEF1 and acetylates histone H3 lysine 56 in the promoters of Wnt targets, suggesting that HPIP is an attractive target in OA regulatory network., Osteoarthritis (OA) is associated with cartilage degeneration, and no effective therapy exists. Here, the authors show that the HPIP protein modulates OA progression by regulating Wnt signaling, and that its knockdown in joints via AAV-mediated gene silencing attentuates pathology.

As a novel network architecture, information-centric networking (ICN) has a good performance in facilitating content sharing among users. Although in-network cache used in ICN can effectively solve the problem of network congestion. Meanwhile, it also brings a lot of challenges such as security and privacy issues during data transformation. Some of the current solutions are based on the traditional encryption technology, but these solutions introduce significant overhead in the client side and have a high requirement to the memory and computing power of end user. In this paper, we use an efficient proxy re-encryption (PRE) scheme in ICN framework to help reduce the overhead on the user-side while guaranteeing flexible data sharing between subscribers and even their cooperator. Our proposal has the additional benefits of a non-interactivity and collusion resistance. We also prove that our scheme is secure against adaptive replayable adaptive chosen ciphertext attack (RCCA) in re-encryption and chosen ciphertext attack (CCA) secure in complete ICN encryption. Our analysis of this program also shows that the scheme has a relatively good performance in computation cost and communication complexity aspects.

The problem of low azimuth resolution has restricted the applicability for radar forward-looking imaging in practice. In this paper, a sparse with fast majorization-minimization (SFMM) superresolution algorithm was proposed to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we analyzed the azimuth signal of the radar forward-looking area and modeled the azimuth signal as a convolution of antenna pattern and targets distribution. Second, the superresolution problem was converted into an L1 regularization issue by introducing the L1 norm to represent the distribution of the targets under the regularization framework. Third, according to the principle of majorization-minimization (MM) algorithm, a simple L2 regularization issue was obtained to replace the difficult L1 one, and the real target distribution was obtained by solving the L2 regularization problem (We named it sparse with MM (SMM) superresolution algorithm for convenience). Then, in order to improve the computational efficiency of the algorithm, we adopted the second-order vector extrapolation idea to accelerate the conventional MM algorithm and solve the L2 regularization problem. The simulation and real data verified that the proposed SFMM algorithm not only improves the azimuth resolution in radar forward-looking imaging but also increases convergence speed on the basis of SMM superresolution algorithm.

Electrical treatment is a useful method for reducing viscosity of waxy crude oil. Molecular dynamics simulation was employed to study the influence of electric field on viscosity of waxy crude oil and micro properties of paraffin. The waxy crude oil model consists of light oil and 24.9% by weight of paraffin. The simulation results show that viscosity decreases when electric field is applied. The larger the electric field intensity, the greater the decreasing extent of viscosity. The electrical viscosity-reducing effect is better at lower temperatures. The radial distribution function and diffusion coefficient were analyzed for explaining the reason of viscosity decreasing. The radial distribution function results confirm that electric field promotes aggregation of paraffin molecules. Diffusion coefficient increases with the increase of electric field intensity. At the same temperature, the higher the electric field strength, the larger the increasing extent of diffusion coefficient. In a certain electric field intensity, the lower the temperature, the greater the increasing extent. The changing tendency of diffusion coefficient is consistent with the trend of viscosity reduction under electrical treatment. It is suggested that the change of diffusion coefficient is the main reason for electrical viscosity-reducing effects, the contribution of aggregation is smaller than increasing of diffusion coefficient.

In human, strontium (Sr) follows the same physiological pathway as calcium and thus could be used for improving the bioactivity and osteoconductivity of hydroxyapatite (HAp) in bone tissues. Similarly, iron (Fe) can potentially play an important role in bone remodeling due to its magnetic properties. Therefore, the current study was aimed to simultaneously co-substitute Sr2+/Fe3+ in HAp nanoparticles for various potential biomedical applications. The Sr2+/Fe3+ co-substituted HAp nanoparticles were systematically synthesized through sonication-assisted aqueous precipitation method. The as-synthesized nanoparticles were evaluated for different physicochemical and biological properties. X-ray diffraction (XRD) patterns of Sr2+/Fe3+ co-substituted HAp nanoparticles confirmed their phase purity and showed hexagonal-like structure. Scanning electron microscope (SEM) micrographs showed an agglomerated rod-like morphology of HAp nanoparticles which contained pores consisted of small spheroids. The nanoparticles displayed magnetization (Ms) reliance on the loading level of mole % (X = Fe3+) and exhibited tunable porosity and microhardness (Hv) upon heat treatment. The nanoparticles showed less than 5% hemolysis demonstrating high blood compatibility with high in vitro bioactivity performance. The multifunctional properties of synthesized nanoparticles make them a potential candidate for various biomedical applications; including bone grafting and guided bone regeneration, targeted drug delivery, magnetic resonance imaging, and hyperthermia based cancer treatment.

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.

This paper uses the linear-elastic Theory of Critical Distances (TCD) to verify and quantify the analysis of a particular central fracture mode occurring in the classical double-cracked flat plate specimen subjected to mixed mode I/mode II loading. Analytical formulae for estimating the critical loading condition for such a special central fracture mode of double-cracked flat plate specimen are derived using both standard elastic beam theory and the TCD. The influence of the crack length, specimen geometry and material parameters on the critical loading angle for such a special central fracture mode is discussed in detail. Finally, in order to verify the proposed central fracture prediction formulae, linear elastic stress analyses for two double-cracked flat plate specimens made of different materials and having different loading angles were conducted using finite element analysis. The results in this study confirm the possibility of interior fracture modes and provide insight on the mechanism of analogous interior fracture occurring in practical applications.

Surface states on TiO2 photoanodes are usually considered to hinder the photoelectrochemical (PEC) water splitting via slowing charge transport and increasing charge recombination. Here, we found that electrochemically doping the TiO2 nanotubes electrode at a potential negative than its flat band potential will induce the insertion of protons into the lattice of TiO2 via a reaction of TiIVO2 + e− + H+ = TiIIIO(OH) to form the surface states. Photoelectrochemical/electrochemical tests confirmed that the surface states (Ti-OH) are an important photogenerated electron transfer route, significantly increasing charge separation efficiency, and hence contributing to the PEC performance enhancement. In addition, we have made a self-consistent explanation on Mott-Schottky (M–S) plots of the TiO2 nanotubes electrode, demonstrating that the M–S measurement is able to accurately describe electron filling and extraction into/from surface states and not suitable to determine the flat band potential of nanostructured TiO2 electrodes. Our results offer a new insight to understanding the role of surface states during the charge separation, transfer, and injection processes of the water splitting reaction.

A novel perovskite hybrid surface plasmon waveguide structure is designed. Established its physical model and introduced its theoretical basis in detail. Based on the finite element method, the mode characteristics, quality factor, and gain threshold of the waveguide structure are analyzed with geometric parameters. The results show that the optical field constraint of the waveguide can reach a good deep sub-wavelength level under the optimal operating wavelength of 1550nm. By adjusting the waveguide design parameters, the high quality factor, low energy loss, low threshold limit and ultra small effective mode field area are obtained. Compared with the hybrid waveguide structure proposed in the current research results, this structure has stronger optical field limiting ability and microcavity binding ability. The waveguide structure can provide theoretical and technical support for the development of new efficient nano laser devices, and has a good application prospect.

Absolute localization of a flying UAV on its own in a global-navigation-satellite-system (GNSS)-denied environment is always a challenge. In this paper, we present a landmark-based approach where a UAV is automatically locked into the landmark scene shown in a georeferenced image via a feedback control loop, which is driven by the output of an aerial image registration. To pursue a real-time application, we design and implement a speeded-up-robust-features (SURF)-based image registration algorithm that focuses efficiency and robustness under a 2D geometric transformation. A linear UAV controller with signals of four degrees of freedom is derived from the estimated transformation matrix. The approach is validated in a virtual simulation environment, with experimental results demonstrating the effectiveness and robustness of the proposed UAV self-localization system.

This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.

Fiber-reinforced polymer (FRP) bar-reinforced unsaturated polyester resin concrete (UPC) can solve the problem of rebar corrosion in ordinary reinforced concrete members. However, it has not been widely used in engineering practice because there have been few studies conducted on the bond behaviors of FRP bar and UPC, and the interaction mechanisms between FRP bar and UPC have not been well understood. A series of pull-out tests are conducted on FRP bar-UPC specimens to study the bond behaviors between these two materials. Parametric studies are also carried out to investigate the effects of FRP bar diameter, fiber type, type of surface treatment, concrete cover thickness, and interfacial bond length between the two. Three failure modes of the specimens are observed from pull-out tests, i.e., FRP bar pull-out, tensile failure of FRP bar, and UPC split. A new constitutive model is, therefore, proposed to predict the bond stress of FRP bar and UPC in the residual stage, and the proposed model is finally verified by test data reported in this study.

Graft reconstruction of the mandible is an important approach that aims at improving the appearance and functionality of defected mandibles. The traditional implant materials are generally bioinert, non-degradable, and that they lack favorable pore structures for cell proliferation, which limit their clinical application. In this study, we used boron-containing bioactive glass which was combined with a three-dimensional (3D) printing technology to construct an osteoinductive implant scaffold, according to the imaging instructions of CT scan on bone defects. Here, the boron-containing bioglass scaffold (B-BGs) was prepared through sol-gel processing and a 3D print technique. Different boron content of borosilicate bioglass was prepared by incorporating B2O3 (molar: 19.4 and 38.8%) into 58S bioglass to replace parts of SiO2. For fabricated mandible implants through three-dimensional 3D printing of B-BGs (size: 8 × 2 mm; pore size: 250 μm) modified with borosilicate bioglass powder and sodium alginate. Notably, the compressive strength of the B-BGs was about 3.8 Mpa, which supported mandibular activity. Subsequently, the excellent biocompatibility of B-BGs was confirmed using cytotoxicity in vitro studies. Finally, data from in vivo experiments demonstrated that the B-BGs could promote bone regeneration and they could almost get completely degraded within 4 weeks. Our results showed that the boron-containing bioglass could repair mandibular defects.

A tunable Fano resonance based on a microring resonator is proposed. The double microring is nested in parallel in the U-shaped feedback waveguide with the graphene embedded in part of the waveguide as the active modulation region of Fano resonance. When different voltages are applied to the graphene in the feedback waveguide or microrings, the Fano resonance exhibits different and asymmetrical shapes and has a larger extinction ratio than the traditional Lorentz shape. The maximum extinction ratio of the proposed device is up to 71.50 dB. The proposed structure will have a wide application in sensors, switches, and integrated optoelectronic devices.

Future autonomous systems will employ complex sensing, computation, and communication components for their perception, planning, control, and coordination, and could operate in highly dynamic and uncertain environment with safety and security assurance. To realize this vision, we have to better understand and address the challenges from the "unknowns" - the unexpected disturbances from component faults, environmental interference, and malicious attacks, as well as the inherent uncertainties in system inputs, model inaccuracies, and machine learning techniques (particularly those based on neural networks). In this work, we will discuss these challenges, propose our approaches in addressing them, and present some of the initial results. In particular, we will introduce a cross-layer framework for modeling and mitigating execution uncertainties (e.g., timing violations, soft errors) with weakly-hard paradigm, quantitative and formal methods for ensuring safe and time-predictable application of neural networks in both perception and decision making, and safety-assured adaptation strategies in dynamic environment.

In this article, we present a layer-wise refinement method for neural network output range analysis. While approaches such as nonlinear programming (NLP) can directly model the high nonlinearity brought by neural networks in output range analysis, they are known to be difficult to solve in general. We propose to use a convex polygonal relaxation (overapproximation) of the activation functions to cope with the nonlinearity. This allows us to encode the relaxed problem into a mixed-integer linear program (MILP), and control the tightness of the relaxation by adjusting the number of segments in the polygon. Starting with a segment number of 1 for each neuron, which coincides with a linear programming (LP) relaxation, our approach selects neurons layer by layer to iteratively refine this relaxation. To tackle the increase of the number of integer variables with tighter refinement, we bridge the propagation-based method and the programming-based method by dividing and sliding the layer-wise constraints. Specifically, given a sliding number $s$ , for the neurons in layer $l$ , we only encode the constraints of the layers between $l-s$ and $l$ . We show that our overall framework is sound and provides a valid overapproximation. Experiments on deep neural networks demonstrate significant improvement on output range analysis precision using our approach compared to the state-of-the-art.

Total variation (TV) method has been utilized to realize super-resolution and preserve contour information of target in radar forward-looking imaging. However, its real-time ability is restricted to matrix inversion. In this paper, a fast TV (FTV) superresolution method is proposed to improve the real-time superresolution ability of traditional TV method. The proposed FTV method utilizes the low displacement rank features of Toplitz matrix and realizes fast matrix inversion by Gohberg-Semencul (GS) representation. It not only effectively improves the azimuth resolution and preserve the contour information of target, but also reduced the computational complexity of traditional TV method to improve its real-time superresolution ability. The superior performance of the proposed FTV method is verified by simulation and measured data processing.

Sparse regularization method has been widely used to realize super-resolution imaging in radar forward-looking imaging. However, most of existed methods directly minimize a nondifferentiable L1 regularization problem. In this paper, a Majorize-Minimization (MM) based super-resolution method is proposed to realize super-resolution for radar forward-looking imaging. According to MM principle, the proposed method converts the non-differentiable L1 regularization problem into a differentiable L2 regularization problem, and the real target distribution is obtained by solving the L2 regularization problem. Due to the introduction of the sparse prior, the proposed method can better improve the azimuth resolution of radar forward-looking imaging. In addition, the application of MM principle makes the non-differentiable L1 regularization easier to be solved. Finally, the superior performance of the proposed method is verified by simulation.

An efficient echo simulation algorithm considering topography in time domain for bistatic SAR is proposed in this paper. By using subaperture processing and equivalent scatter, this method can implement efficient echo simulation. At last, the effectiveness of this method is verified by numerical simulation.

In this paper, an improved target extraction scheme is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained firstly by deconvolution technique, and then it is used to map a patch-image. Secondly, low rank and sparse matrix decomposition is conducted on the patch image. Thirdly, constant false-alarm rate (CFAR) detection method is used to extract the targets from the sparse matrix. Finally, simulation results are given to verify the effectiveness of the proposed scheme.

Animated visualization is becoming increasingly popular as a compelling way to illustrate changes in time series data. However, maintaining the viewer’s focus throughout the entire animation is difficult because of its time-consuming nature. Viewers are likely to become bored and distracted during the ever-changing animated visualization. Informed by the role of foreshadowing that builds the expectation in film and literature, we introduce visual foreshadowing to improve the engagement of animated visualizations. In specific, we propose designs of visual foreshadowing that engage the audience while watching the animation. To demonstrate our approach, we built a proof-of-concept animated visualization authoring tool that incorporates visual foreshadowing techniques with various styles. Our user study indicates the effectiveness of our foreshadowing techniques on improving engagement for animated visualization.

We present TrojDRL, a tool for exploring and evaluating backdoor attacks on deep reinforcement learning agents. TrojDRL exploits the sequential nature of deep reinforcement learning (DRL) and considers different gradations of threat models. We show that untargeted attacks on state-of-the-art actor-critic algorithms can circumvent existing defenses built on the assumption of backdoors being targeted. We evaluated TrojDRL on a broad set of DRL benchmarks and showed that the attacks require only poisoning as little as 0.025% of the training data. Compared with existing works of backdoor attacks on classification models, TrojDRL provides a first step towards understanding the vulnerability of DRL agents.

Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverage the characteristics of specific environment and operation context for more efficient control. In this work, we develop an online intermittent-control framework that combines formal verification with model-based optimization and deep reinforcement learning to opportunistically skip certain control computation and actuation to save actuation energy and computational resources without compromising system safety. Experiments on an adaptive cruise control system demonstrate that our approach can achieve significant energy and computation savings., Accepted by Design Automation Conference (DAC) 2020

Recent successes of wireless networked systems in advancing industrial automation and in spawning the Internet of Things paradigm motivate the adoption of wireless networked systems in current and future safety-critical applications. As reliability is key in safety-critical applications, in this work we present NetDAG, a scheduler design and implementation suitable for real-time applications in the wireless setting. NetDAG is built upon the Low-Power Wireless Bus, a high-performant communication abstraction for wireless networked systems, and enables system designers to directly schedule applications under specified task-level real-time constraints. Access to real-time primitives in the scheduler permits efficient design exploration of tradeoffs between power consumption and latency. Furthermore, NetDAG provides support for weakly hard real-time applications with deterministic guarantees, in addition to heretofore considered soft real-time applications with probabilistic guarantees. We propose novel abstraction techniques for reasoning about conjunctions of weakly hard constraints and show how such abstractions can be used to handle the significant scheduling difficulties brought on by networked components with weakly hard behaviors.

In the current study, Sr/Fe co-substituted hydroxyapatite (HAp) bioceramics were prepared by the sonication-assisted aqueous chemical precipitation method followed by sintering at 1100 °C for bone tissue regeneration applications. The sintered bioceramics were analyzed for various structural and chemical properties through X-ray diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy, which confirmed the phase purity of HAp and Sr/Fe co-substitution into its lattice. The Vickers hardness measurement, high blood compatibility (less than 5% hemolysis), and ability to support the adhesion, proliferation, and osteogenic differentiation of human mesenchymal stem cells suggest the suitability of Sr/Fe:HAp bioceramics for bone implant applications. The physicochemical analysis revealed that the developed Sr/Fe:HAp bioceramics exhibited a polyphasic nature (HAp and βTCP) with almost identical structural morphology having a particle size less than 0.8 μm. The dielectric constant (ε') and dielectric loss (ε″) were potentially affected by the incorporated foreign ions together with the polyphasic nature of the material. The Sr/Fe co-substituted samples demonstrated extended drug (5-fluorouracil and amoxicillin) release profiles at the pH of physiological medium. The multifunctional properties of the developed HAp bioceramics enabled them to be an auspicious candidate for potential biomedical applications, including targeted drug-delivery applications, heating mediator in hyperthermia, and bone tissue repair implants.

We study the problem of policy repair for learning-based control policies in safety-critical settings. We consider an architecture where a high-performance learning-based control policy (e.g. one trained as a neural network) is paired with a model-based safety controller. The safety controller is endowed with the abilities to predict whether the trained policy will lead the system to an unsafe state, and take over control when necessary. While this architecture can provide added safety assurances, intermittent and frequent switching between the trained policy and the safety controller can result in undesirable behaviors and reduced performance. We propose to reduce or even eliminate control switching by ‘repairing’ the trained policy based on runtime data produced by the safety controller in a way that deviates minimally from the original policy. The key idea behind our approach is the formulation of a trajectory optimization problem that allows the joint reasoning of policy update and safety constraints. Experimental results demonstrate that our approach is effective even when the system model in the safety controller is unknown and only approximated.

We introduce ReachNN*, a tool for reachability analysis of neural-network controlled systems (NNCSs). The theoretical foundation of ReachNN* is the use of Bernstein polynomials to approximate any Lipschitz-continuous neural-network controller with different types of activation functions, with provable approximation error bounds. In addition, the sampling-based error bound estimation in ReachNN* is amenable to GPU-based parallel computing. For further improvement in runtime and error bound estimation, ReachNN* also features optional controller re-synthesis via a technique called verification-aware knowledge distillation (KD) to reduce the Lipschitz constant of the neural-network controller. Experiment results across a set of benchmarks show \(7\times \) to \(422\times \) efficiency improvement over the previous prototype. Moreover, KD enables proof of reachability of NNCSs whose verification results were previously unknown due to large overapproximation errors. An open-source implementation of ReachNN* is available at https://github.com/JmfanBU/ReachNNStar.git.

In this paper, a grating-coupled surface plasmon resonance concentration sensor employing a gold and indium tin oxide (Au/ITO) nanoparticle composite instead of metal is proposed. The structure and material parameters of the sensor are discussed and analyzed. Taking the ethylene glycol concentration as an example, the influence of the nanocomposite on the wave vector matching, the influence of the refractive index of the medium to be tested and the influence of the concentration on the refractive index were analyzed in detail. The experimental results show that when the sensor is used for the measurement of ethylene glycol concentration, the correlation coefficient between the concentration and the refractive index is as high as 0.999995. The fitting curve and data correlation are good, and the sensitivity has a good linear relationship with the sensitivity. Therefore, the sensor structure proposed in this paper can be used to accurately measure the trace concentration of liquid, and its sensing mode has certain reference value for the measurement of general trace fluid concentration.

Due to the limitation of antenna size, the azimuth resolution of radar forward-looking imaging is limited. Conventional technologies improved the azimuth resolution and obtained good effects, but they are poor in contour recovery of targets. In this paper, we employed the total variation (TV) superresolution method to improve the azimuth resolution and recover the contour of the targets in radar forward-looking imaging. Firstly, the signal model of radar forward-looking imaging was deduced. Secondly, the TV operator was employed as the penalty term under regularization framework. Thirdly, the TV regularization model was solved by split Bregman algorithm. The TV regularization method not only improved the azimuth resolution, but also better recovered the contour of the targets. The performance was verified by simulations lastly.

Neural networks are widely used in many applications ranging from classification to control. While these networks are composed of simple arithmetic operations, they are challenging to formally verify for properties such as reachability due to the presence of nonlinear activation functions. In this paper, we make the observation that Lipschitz continuity of a neural network not only can play a major role in the construction of reachable sets for neural-network controlled systems but also can be systematically controlled during training of the neural network. We build on this observation to develop a novel verification-aware knowledge distillation framework that transfers the knowledge of a trained network to a new and easier-to-verify network. Experimental results show that our method can substantially improve reachability analysis of neural-network controlled systems for several state-of-the-art tools.

Although demanding, the development of multifunctional theranostic nanoplatforms is attracting considerable worldwide interest. Herein, a theranostic nanoplatform with multifunctions based on polydopamine (PDA) nanoparticles (NPs) was developed, owning dual-imaging and dual-therapy functions for cancer theranostic applications. PDA NPs were generated using a facile polymerization method under alkaline conditions, followed by poly(ethylene glycol) (PEG) modification. Then, the obtained NPs were loaded with IR820 and Fe3+ ions to produce the final PEGylated PDA/IR820/Fe3+ (PPIF) NPs. The PPIF NPs thus generated displayed increasingly brighter photoacoustic and magnetic resonance signals with increasing NP concentration and were demonstrated to be cytocompatible and effectively taken up and internalized into HeLa cells. Under near-infrared light irradiation, PPIF NPs can produce heat and reactive oxygen species for photothermal/photodynamic combined cancer therapy. In this study, the versatility of PDA NPs was demonstrated to be promising as a multifunctional nanoplatform for potential cancer theranostic applications.

This paper discusses the evolution of the Chinese verb 断 (duàn ‘break’), exploring how the lexicon has transformed from being a substantive transitive verb in the past to the various functions it fulfils in contemporary Chinese: i.e. a resultative complement, an adjective, an adverb and a noun. Findings reveal that duàn was initially transitive and its intransitive function was derived during the Spring and Autumn period (772–476 BC). The two functions co-existed for the rest of the Pre-Qin period (before 221 BC). Duàn’s transition into a resultative complement began during the Eastern Han dynasty (AD 25–220), and its noun function was shaped in the Warring State period (475–221 BC). Finally, Duàn’s adjective and adverb functions are noted to begin in Middle Chinese (AD 420–1279). Although some of these transitions took place more or less simultaneously during overlapping periods, they developed via two separate paths: the process by which duàn developed into a resultative complement, an adverb, and an adjective is that of grammaticalization, while on the other hand, the process by which duàn developed into a noun is that of lexicalization. Essentially, it is the intransitive function that is subject to grammaticalization and it is the transitive function that is subject to lexicalization. In this paper it is therefore proposed that the above diachronic change is taken as a cline, and is put forward as follows: (a) grammaticalization and lexicalization are not distinct processes but rather happen to a lexeme more or less simultaneously; and (b) interrelationship between the two processes is orthogonal.

Earlier reports of fabricating 3D porous PCL scaffolds for tissue engineering applications were overshadowed by several limitations such as additional molds cost, relatively low efficiency, and lacking process control. In present study, combined extrusion-based cryogenic 3D printing (ECP) (−20 °C) and subsequent freeze-drying approaches were employed to facilely fabricate polycaprolactone (PCL) scaffolds, with high porosity and fidelity. Freeze-drying caused shrinkage of the scaffolds along X, Y, and Z-axes to some extent. The porosities of CP600, CP800, and CP1000 were found to be 64.0 ± 1.2%, 70.1 ± 1.3%, and 74.3 ± 0.6%, respectively. The fabricated scaffolds were characterized for various structural features and compared with the ones fabricated through traditional extrusion-based melt 3D printing (EMP). The crystallinity of PCL in ECP scaffolds was lower (57.1 ± 2.2%) than EMP scaffolds (69.8 ± 1.3%). The ECP scaffolds showed high alkaline degradation, but low compression properties. The ECP scaffolds promoted the adhesion and proliferation of MCT3T-E1 cells with well-spread morphology on the porous filaments. Together, these features justify the suitability of printed PCL scaffolds for potential TE applications. Keywords: Extrusion-based cryogenic 3D printing, Polycaprolactone, Glacial acetic acid, 3D scaffold, Tissue engineering