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An efficient immobilized denitrification bioreactor functioning under anaerobic conditions was developed by combining bacterial immobilization technology with iron-carbon (Fe–C) particles. The effects of key factors on nitrate (NO3 −–N) removal efficiency were invested, such as the carbon-nitrogen ratio (C/N), pH and hydraulic retention time (HRT). Experimental results show that 100.00% NO3 −–N removal efficiency and a low level of nitrite (NO2 −–N) accumulation less than 0.05 mg L−1 were obtained under the condition of a C/N ratio of 3, pH 7.0 and HRT of 6 h. Meteorological chromatographic analysis showed that the final product of denitrification was mainly nitrogen (N2). The main component of precipitation formed in the bioreactor was characterized as Fe3O4 by X-ray diffraction. High-throughput sequencing analysis indicated that the dominant bacterial class in the Fe–C bioreactor was Gammaproteobacteria, while the dominant genera were Zoogloea and Azospira, the relative abundances of which were as high as 23.25 and 15.43%, respectively.

An important part of the human-computer interaction process is speech emotion recognition (SER), which has been receiving more attention in recent years. However, although a wide diversity of methods has been proposed in SER, these approaches still cannot improve the performance. A key issue in the low performance of the SER system is how to effectively extract emotion-oriented features. In this paper, we propose a novel algorithm, an autoencoder with emotion embedding, to extract deep emotion features. Unlike many previous works, instance normalization, which is a common technique in the style transfer field, is introduced into our model rather than batch normalization. Furthermore, the emotion embedding path in our method can lead the autoencoder to efficiently learn a priori knowledge from the label. It can enable the model to distinguish which features are most related to human emotion. We concatenate the latent representation learned by the autoencoder and acoustic features obtained by the openSMILE toolkit. Finally, the concatenated feature vector is utilized for emotion classification. To improve the generalization of our method, a simple data augmentation approach is applied. Two publicly available and highly popular databases, IEMOCAP and EMODB, are chosen to evaluate our method. Experimental results demonstrate that the proposed model achieves significant performance improvement compared to other speech emotion recognition systems.

Catalysis plays a significant role in most processes of the chemical industry, especially in the emerging areas of sustainable energy and clean environment. A major challenge is the design of catalysts with the desired synergies in terms of activity, selectivity, stability, and cost. New insights into many fundamental questions related to the challenge have sparked a surge of interest in recent years in the area of exploring copper-based alloy catalysts. In this review, the most recent progress in the explorations of copper-alloy catalysts will be highlighted, with a focus on the structural and mechanistic characterizations of the catalysts in different catalytic reactions. The fundamental understanding of the detailed catalytic synergies of the catalysts for the targeted heterogeneous catalytic reactions depends strongly on the utilization of various analytical techniques for the characterization. Significant progress has been made in utilizing advanced techniques, both ex situ and in situ/operando characterizations, demonstrating the abilities to gain atomic/molecular level insights into the morphological, structural, electronic and catalytic properties of copper alloy catalysts, especially the dynamic surface active sites under the reaction conditions or during the catalytic processes. The focus on structural characterization in this review serves as a forum for discussions on structural and mechanistic details, which should provide useful information for identifying challenges and opportunities in future research and development of copper-alloy catalysts.

The present work reports the realisation of high-quality crystalline CdS/Mn3O4 (CM) nanocomposites by a simple cost-effective chemical method in air atmosphere. The authors have performed theoretical calculations and experimental analysis in order to understand the synthesised nanocomposites. X-ray diffraction results showed that the CM nanocomposites were cubic and orthorhombic mixed structure which is in good agreement with the theoretical studies. Field emission scanning electron microscopy images of CM confirmed the formation of well distributed nanocomposites. The outcomes of DFT calculations provide results for the bandgap calculation of pure CdS, Mn3O4 and the CM nanocomposites. Photoluminescence studies with interesting visible light absorption demonstrated the great potentiality of the as-synthesised nanocomposites towards photocatalytic applications that could be a detailed research scope for the authors’ future studies.

The development of superior catalysts for preferential oxidation of CO has always maintained challenging in heterogeneous catalysis. Herein it is found that the Pt0.05/CuO–CeO2 catalyst exhibited high activity and selectivity for preferential oxidation of CO at working temperature of proton-exchange membrane fuel cells (~80 °C). The studies indicate that the presence of platinum and cerium promote the generation of oxygen vacancy and the dissociation of O2 molecules. Meanwhile, the introduction of platinum facilitates the reduction of copper and cerium species as well as enhances the quantity of lattice oxygen. The timely update of lattice oxygen promotes CO oxidation with the help of oxygen vacancy. The finding may provide new ideas for developing the excellent ternary catalyst for preferential oxidation of CO at the proton-exchange membrane fuel cells working temperature.

With the advancement of large-scale coordinated attacks, the adversary is shifting away from traditional distributed denial of service (DDoS) attacks against servers to sophisticated DDoS attacks against Internet infrastructures. Link flooding attacks (LFAs) are such powerful attacks against Internet links. Employing network measurement techniques, the defender could detect the link under attack. However, given the large number of Internet links, the defender can only monitor a subset of the links simultaneously, whereas any link might be attacked. Therefore, it remains challenging to practically deploy detection methods. This paper addresses this challenge from a game-Theoretic perspective, and proposes a randomized approach (like security patrolling) to optimize LFA detection strategies. Specifically, we formulate the LFA detection problem as a Stackelberg security game, and design randomized detection strategies in consideration of the adversary's behavior, where best and quantal response models are leveraged to characterize the adversary's behavior. We employ a series of techniques to solve the nonlinear and nonconvex NP-hard optimization problems for finding the equilibrium. The experimental results demonstrate the necessity of handling LFAs from a game-Theoretic perspective and the effectiveness of our solutions. We believe our study is a significant step forward in formally understanding LFA detection strategies. This work is supported in part by National Natural Science Foundation (61602371, 61772411, U1736205, 61632013), CCF-NSFOCUS KunPeng Research Fund (2018002), Hong Kong ITF (No. UIM/285) and Hong Kong RGC Project No. PolyU5389/13E, PolyU152279/16E, Natural Science Basic Research Plan in Shaanxi Province (2016JQ6034), SZSTI JCYJ20170816100819428, Special Foundation of China Postdoctoral Science (2018T111065), China Postdoctoral Science Foundation (2015M582663), the Fundamental Research Funds for the Central Universities, Shaanxi Province Postdoctoral Science Foundation, of China.

The hollow inverse CeO2/CuO@SiO2 catalysts with different Ce/Cu mass ratios were synthesized by the two-step hydrothermal and incipient wetness impregnation methods, and characterized by multi-technique characterizations, such as SEM, TEM, XRD, H2-TPR, XPS and N2 adsorption-desorption techniques. It is found that the hollow shell is composed of CuO and SiO2, and CeO2 nanoparticles are coated on the surface of CuO@SiO2 support. And the CeO2/CuO@SiO2 catalyst with the Ce/Cu mass ratios of 1:1 denoted as 1CeO2/CuO@SiO2, which possesses a maximum amount of highly dispersed copper species and medium-sized CuO as well as the highest concentration of oxygen vacancies, exhibits the highest catalytic activity and widest full CO conversion window. The barrier effect of the SiO2 shell effectively prevents the reduction of CuO species, which broadens temperature window of CO total conversion and enhances CO2 selectivity above 155 °C over the 1CeO2/CuO@SiO2 catalyst in comparison with the CuO CeO2 and CeO2 CuO catalysts.

A compact fiber optic Fabry-Perot (F-P) sensor for simultaneous acoustic and temperature measurement has been proposed in the research. The sensor consists of silicon-glass-silicon sandwich structure by anodic bonding. The air F-P cavity with a circular hole and slots utilizes the deformation of the silicon membrane to realize acoustic measurement, and the silicon F-P cavity utilizes the temperature sensitivity of the refractive index of silicon to realize temperature measurement. The acoustic signal and temperature information could be recovered by acoustic and temperature demodulation method, respectively. The experimental results demonstrate that the sensor has a signal-to-noise ratio (SNR) of 53 dB and the acoustic sensitivity of 4.65 mV/Pa at 21 kHz, and the temperature sensitivity of 123.10 nm/°C at 20 °C∼120 °C. The proposed fiber optic F-P sensor has great potential for simultaneous acoustic and temperature measurement in aviation, due to its compact structure, mass production and immunity to electromagnetic interference.

A synergetic phototheranostic system, combining diagnostic photo-imaging and phototherapies [such as photothermal therapy and photodynamic therapy (PDT)], shows great potential in today's tumor precise therapy. Herein, we fabricate near-infrared (NIR) light-harvesting fullerene-based nanoparticles (DAF NPs) for photoacoustic (PA) imaging-guided synergetic tumor photothermal and PDT. The fullerene derivatives (DAF) absorbing in the NIR region have been synthesized by conjugating NIR-absorbing antenna with fullerene. In addition, DAF NPs with good biocompatibility have been fabricated via a nanoprecipitation approach. The as-prepared DAF NPs can accumulate and generate PA signals around the tumor site 6 h post injection via enhanced permeability and retention effect in vivo. More importantly, the DAF NPs exhibit better reactive oxygen species and heat generation efficacy compared with fullerene and antenna nanoparticles (DA NPs), respectively. Further in vitro and in vivo studies demonstrate that DAF NPs can effectively inhibit tumor growth through synergetic photodynamic and photothermal therapies, which provides a new sight of photosensitizer design for enhanced cancer phototheranostics.

Defect prediction assists the rational allocation of testing resources by detecting the potentially defective software modules before releasing products. When a project has no historical labeled defect data, cross project defect prediction (CPDP) is an alternative technique for this scenario. CPDP utilizes labeled defect data of an external project to construct a classification model to predict the module labels of the current project. Transfer learning based CPDP methods are the current mainstream. In general, such methods aim to minimize the distribution differences between the data of the two projects. However, previous methods mainly focus on the marginal distribution difference but ignore the conditional distribution difference, which will lead to unsatisfactory performance. In this work, we use a novel balanced distribution adaptation (BDA) based transfer learning method to narrow this gap. BDA simultaneously considers the two kinds of distribution differences and adaptively assigns different weights to them. To evaluate the effectiveness of BDA for CPDP performance, we conduct experiments on 18 projects from four datasets using six indicators (i.e., F-measure, g-means, Balance, AUC, EARecall, and EAF-measure). Compared with 12 baseline methods, BDA achieves average improvements of 23.8%, 12.5%, 11.5%, 4.7%, 34.2%, and 33.7% in terms of the six indicators respectively over four datasets.

In cognitive radio, Reinforcement Learning (RL) has been widely applied to the construction of cognition engine. However, two crucial challenges remain to be resolved: First, it takes long time to interact with the environment before reaching intelligent decision. Second, agents improve their performance through trial and error, but some applications in Cognitive Radio Networks (CRN) cannot afford the extensive latency and energy expenditure. An apprenticeship learning scheme based on expert demonstrations is adopted for solving above problems. Firstly, the reinforcement responsibility rating is introduced to enhance the efficiency of power allocation by allowing multi-level transition for the transmit power. In order to avoid the failure of expert node identification due to SU’s remote location, an adaptive radius Bregman Ball model is presented. Furthermore, the Multi-Teacher Deep Q-learning from Demonstrations (MT-DQfD) is proposed to accelerate the learning procedure by sharing demonstrations derived from multiple expert nodes. Our experiments illustrate that the proposed cross-layer routing protocol reduces the training period while improves transmission quality compared to the traditional algorithms. Moreover, the newly-joined nodes can achieve better performance than the experts.

To remove lizardite slimes from pyrite surface in pyrite-lizardite system, 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTCA) was introduced as a possible depressant for lizardite slimes. Micro-flotation test results showed that PBTCA can remove lizardite slimes from pyrite surface and the floatability of pyrite was restored. Zeta potential measurements indicated that the surface charge of lizardite transformed from positive to strongly negative values by the addition of PBTCA because of the adsorption of PBTCA at lizardite surface. As a result, the interaction of pyrite and lizardite converted from electrosteric attraction to electrosteric repulsion, and therefore the SBX adsorption at pyrite surface was promoted and the floatability of pyrite was restored.

Dorsal closure is an essential developmental process of Drosophila embryogenesis, during which the ectoderm fuses the two sides of a gap into a complete ectodermal epithelium. A defective closure may cause scar formation or even embryonic lethality. In this paper, a multiscale fracture mechanics model is established by treating the dorsal closure as a crack healing process. We investigate how the F-actin dynamics at the subcellular level and the cell-pair fusion at the cellular level are orchestrated to accomplish the tissue-level closure. A spatiotemporal cohesive law is proposed to characterize the active contractions of filopodial and lamellipodial protrusions, which involve F-actin retrogradation. The contribution of active forces to dorsal closure is evaluated in terms of the stress intensity factors at the canthi of the gap. The proposed model can well predict both the evolutionary zipping zone shape and the sealing speed during dorsal closure, and the theoretical results are in consistency with relevant experiments. This work can not only elucidate the multiscale mechanisms underlying dorsal closure, but also provide a new perspective of facture mechanics to understand some physiological and pathological processes during the development of tissues and organs.

The second-order consensus problem depends on not only the topology condition but also the coupling strength of the relative positions and velocities between neighboring agents. This paper seeks to solve the finite-time consensus problem of second-order multi-agent systems by games with special structures. Potential game and weakly acyclic game were applied for modeling the second-order consensus problem with different topologies. Furthermore, this paper introduces the event-triggered asynchronous cellular learning automata algorithm for optimizing the decision making process of the agents, which facilitates a convergence with the Nash equilibrium. Finally, numerical examples illustrate the effectiveness of the models.

In this paper, a study of control for an uncertain 2-degree of freedom &#x0028 DOF &#x0029 helicopter system is given. The 2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function &#x0028 IBLF &#x0029 is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser &#x02BC s 2-DOF helicopter.

The acidic tumor microenvironment is triggered by glycolysis in hypoxic condition, which can motivate the pH-responsive system to build certain triggers for efficiently tumor-targeted phototherapy. Additionally, the metalated porphyrin structures are widely studied in biomedical applications due to the favorable properties of high singlet oxygen quantum yield as well as strong fluorescence imaging ability. Herein, a pH-responsive zinc (II) metalated porphyrin (P-4) was designed and synthesized for amplifying cancer photodynamic/photothermal therapy with excellent fluorescence quantum yield (67.4%), superb singlet oxygen quantum yield (84.3%) and desired photothermal conversion efficiency (30.0%). In vitro , the self-assembled P-4 nanoparticles can specifically target to lysosome subcellular site and realize protonated process of dibutaneaminophenyl (DBAP) groups with high photo toxicity. Under single 660 nm laser illumination, the tumor can be ablated completely with no side effects in vivo . This work demonstrates that the pH-responsive P-4 nanoparticles provide a new avenue for highly efficient cancer combination therapy.

The microstructure evolution and strengthening mechanism of a γ′-strengthening superalloy prepared by induction-assisted laser solid forming (LSF) were clarified by an investigation of the LSFed IN-738LC alloy with different solution treatments (at 1070 °C, 1120 °C and 1160 °C). The results show that both the as-deposited and heat-treated deposits were dominated by the columnar grains with a width of 100–270 μm. The equiaxed grains mainly originated from the columnar-to-equiaxed transition (CET) during LSF while a small portion of the equiaxed grains originated from recrystallization during the heat treatment, due to the relatively low temperature gradient in the induction-assisted LSF. Fine grain strengthening was still working in the columnar-grains dominated LSFed IN-738LC deposits. However, γ′ strengthening was found to play a major role in the strengthening mechanism by comparing the microstructure and properties of different heat-treated deposits. The γ′ dissolution and coarsening kinetics were calculated and the interaction mechanism between the dislocation and γ′ phase in LSFed IN-738LC was clarified. The deformation of the coarse (d > 200 nm) near cubic γ′ was found to be beneficial to the increase of ultimate tensile strength (UTS) and elongation (EL), while a brittle continuous phase formed at the grain boundary (GB) which was rich in Ni, Cr, Al and Ti was found to be detrimental to the deposit's plasticity. The γ′ phase deformation and desquamating, carbide breaking and GB phase breaking are the predominant fracture failure mechanisms. On the basis of relatively high strength, the IN-738LC deposits with a lower yield strength (YS)/UTS ratio than the cast and SLMed ones were obtained when solution-treated at 1070 °C.

Although many properties of plantation wood are greatly enhanced by heat treatment, large quantities of volatile organic compounds (VOCs) are released from this process, resulting in potential problems for the environment and human health. Volatile byproducts were collected from Populus tomentosa wood that was treated at 160, 180, 200, and 220 °C for 2 h. The VOC gases were partly collected at the heating, holding, and cooling stages of the process and were then analyzed by gas chromatography-mass spectrometry and thermogravimetry. The results show that over 90% of the volatiles from the first and third stages were alkane compounds. The mass losses in the heating and holding stages were almost at the same level, and the liquid byproduct contained approximately 48% phenolic products at 160 °C. The volatiles at 180 °C were mainly alkanes, terpenes, and aromatic compounds, and the absolute amount of VOCs released in the holding stage was much higher than that in the heating stage. The concentrations of phenols and ketones in the liquid byproducts decreased when the treatment temperature was increased, and more complex chemicals such as N-containing compounds emerged. Ester and aromatic compounds increased with the treatment temperature. The condensate collected at 160 °C exhibited considerable biological activity in the bacterial and mildew resistance tests. These results suggest that thermally treated byproducts can be turned into wood preservatives, which would also make the wood heat treatment cleaner.

For performance and compatibility reasons, developers tend to use native code in their applications (or simply apps). This makes a bidirectional data flow through multiple contexts, i.e., the Java context and the native context, in Android apps. Unfortunately, this interaction brings serious challenges to existing dynamic analysis systems, which fail to capture the data flow across different contexts. In this paper, we first performed a large-scale study on apps using native code and reported some observations. Then, we identified several scenarios where data flow cannot be tracked by existing systems, leading to uncaught information leakage. Based on these insights, we designed and implemented NDroid , an efficient dynamic taint analysis system that could track the data flow between both Java context and native context. The evaluation of real apps demonstrated the effectiveness of NDroid in identifying information leakage with reasonable performance overhead.

Diketopyrrolopyrrole (DPP) based organic molecules have drawn significant research attention as phototheranostic agents. Herein, based on thieno[3,2-b]thienyl-DPP (TT-DPP), a near-infrared small molecule photosensitizer diethyl 3,3'-((((2,5-bis(2-decyltetradecyl)-3,6-dioxo-2,3,5,6-tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)bis(thieno[3,2-b]thiophene-5,2-diyl))bis-(4,1-phenylene))bis(7-bromo-10H-phenothiazine-10,3-diyl))(2E,2'E)-diacrylate (PDBr), with a high singlet oxygen (1O2) quantum yield of 67%, was developed. After nano-precipitation, the hydrophilic PDBr NPs present an encouraging photothermal conversion efficiency of 35.7% and excellent fluorescence/infrared-thermal imaging performance. In vitro studies disclosed the high phototoxicity but low dark cytotoxicity of PDBr NPs to tumor cells. Furthermore, PDBr NPs can effectively impede the tumor growth without noticeable side effects in living mice through imaging-guided synergistic photothermal/photodynamic therapy. Therefore, PDBr NPs could be a promising nanotheranostic agent for imaging-guided synergistic photothermal and photodynamic therapy in the clinic.

The high concentration of glutathione (GSH), a feature of solid tumors, can reduce the reactive oxygen species (ROS) damage to cancer cells, which seriously weakens the efficacy of photodynamic therapy in cancer treatment. Herein, we designed and synthesized two kinds of GSH responsive pyrrolopyrrolidone (DPP) derivatives that conjugated with 2-(diphenylmethylene) malononitrile (DPPBPh) and 2,3,3-triphenylacrylonitrile (DPPTPh), respectively. DPPTPh with two –CN groups shows higher singlet oxygen quantum yield and photothermal conversion efficiency than DPPBPh with four –CN groups. These two DPP derivatives can not only be used as colorimetric GSH probes to avoid the fluorescence quenching caused by aggregation, but also enhance the photodynamic/photothermal therapeutic efficacy of their corresponding nanoparticles (NPs). Moreover, DPPTPh NPs exhibit better therapeutic efficacy than DPPBPh NPs even at a low dose (0.2 mg kg−1). This work presents DPP derivative-based theranostic nanomedicines for GSH responsive fluorescence “turn on” imaging and enhanced photodynamic/photothermal synergistic therapy.

Deciphering the bio–chemo–mechanical mechanisms in tissue growth and deformation helps understand the morphogenesis of organs and organisms under physiological and pathological conditions. In this paper, we present a finite element method that can account for the interplay of volumetric growth, chemical transport, and mechanical deformation in soft tissues, such as tumors. The poroelastic theory and the volumetric growth model are combined to capture the essential growth and deformation traits of biological tissues. This method can not only simulate the bio–chemo–mechanical coupling processes in growing tissues, but also track their morphological instabilities and evolutions. The deformation and instability of interacting blood vessels in a growing tumor are considered as an example. The mechanisms underpinning the collapse of blood vessels observed in vascular solid tumors are revealed. This work holds promise for applications in the diagnosis and therapy of diseases such as cancer.

The architecture of catalyst particles plays an important role in determining the catalytic properties. However, the challenge is the ability to tune the architecture on a nanoscale. Herein, we report the design and construction of hollow CuO/CeO2 microspheres with controllable shells, which exhibit significantly enhanced catalytic performance. The hollow CuO/CeO2 microspheres with single and multiple shells were synthesized by a self-templating method. The catalytic activity of the catalysts for preferential CO oxidation is shown to increase with increasing the number of the spherical shells. A maximum performance is observed for the triple-shelled catalyst, reaching a CO conversion of 100% and CO2 selectivity of 91% at 95 °C. The triple-shelled catalyst also displays a broad temperature window for CO total conversion from 95 °C to 195 °C. The findings are attributed to the enhanced exposure of the active sites on the surface of the triple-shelled architecture, fine-tunable geometric and electronic interactions, and increased space inside the catalyst, which together amplify the reactant access and interaction.

Forkhead box O 6 (FOXO6), a FOX transcription factor, has been found to be involved in diabetes mellitus and related complications. However, the role of FOXO6 in diabetic nephropathy (DN) has not been fully understood. In the present study, we evaluated the functions of FOXO6 in high glucose (HG)-induced glomerular mesangial cells (MCs). The results showed that FOXO6 expression was significantly elevated in MCs after HG stimulation. Knockdown of FOXO6 by transfection with small interfering RNA (siRNA) targeting FOXO6 (siRNA-FOXO6) suppressed cell proliferation in MCs. The productions of extracellular matrix (ECM) components including collagen IV (Col IV) and fibronectin (FN) were markedly decreased after FOXO6 knockdown in MCs. Furthermore, knockdown of FOXO6 inhibited HG-induced activation of p38 MAPK signaling pathway in MCs. Collectively, these findings suggested that knockdown of FOXO6 inhibited cell proliferation and ECM accumulation in HG-induced MCs via inhibiting p38 MAPK signaling pathway. FOXO6 might be a beneficial therapeutic target for the prevention and treatment of DN.

In this paper, we propose a single-fiber bi-directional burst-mode erbium-doped fiber amplifier (EDFA) to simultaneously improve upstream and downstream loss budget in time and wavelength division multiplexed-passive optical network (TWDM-PON). Instead of additional light sources for upstream gain clamping, the downstream signal acting as saturation signal effectively suppresses the upstream burst surge, which widens the input dynamic range of upstream burst signal. For potential reflections caused by the absence of optical isolators, experimental results have demonstrated the degradation of receiver sensitivity at bit-error rate of 1 × 10-3 is negligible with a reflection power up to -14 dBm, which proves the promising applications in PONs of the proposed single-fiber bi-directional burst-mode EDFA.

Immune microenvironment amelioration and reconstruction by functional biomaterials has become a promising strategy for spinal cord injury (SCI) recovery. In this study, we evaluated the neural regeneration and immunoregulation functions of Mg/Al layered double hydroxide (Mg/Al-LDH) nanoparticles in completely transected and excised mice and revealed the immune-related mechanisms. LDH achieved significant performance in accelerating neural stem cells (NSCs) migration, neural differentiation, L-Ca2+ channel activation, and inducible action potential generation. In vivo, the behavioral and electrophysiological performance of SCI mice was significantly improved by LDH implantation, with BrdU+ endogenous NSCs and neurons clearly observed in the lesion sites. According to RNA-seq and ingenuity pathway analysis, transforming growth factor-β receptor 2 (TGFBR2) is the key gene through which LDH inhibits inflammatory responses and accelerates neural regeneration. Significant colocalization of TGFBR2 and LDH was found on the cell membranes of NSCs both in vitro and in vivo, and LDH increased the expression of TGF-β2 in NSCs and activated the proliferation of precursor neural cells. LDH decreased the expression of M1 markers and increased the expression of M2 markers in both microglia and bone marrow-derived macrophages, and these effects were reversed by a TGFBR2 inhibitor. In addition, as a carrier, LDH loaded with NT3 exhibited better recovery effects with regard to the basso mouse scale score, motor evoked potential performance, and regenerated neural cell numbers than LDH itself. Thus, we have developed Mg/Al-LDH that can be used to construct a suitable immune microenvironment for SCI recovery and have revealed the targeted receptor.

Facial makeup significantly changes the perceived appearance of the face and reduces the accuracy of face recognition. To adapt to the application of smart cities, in this study, we introduce a novel joint subspace and low-rank coding method for makeup face recognition. To exploit more discriminative information of face images, we use the feature projection technology to find proper subspace and learn a discriminative dictionary in such subspace. In addition, we use a low-rank constraint in the dictionary learning. Then, we design a joint learning framework and use the iterative optimization strategy to obtain all parameters simultaneously. Experiments on real-world dataset achieve good performance and demonstrate the validity of the proposed method.

Smartphone vendors are using multiple methods to kill processes of Android apps to reduce the battery consumption. This motivates developers to find ways to extend the liveness time of their apps, hence the name diehard apps in this paper. Although there are blogs and articles illustrating methods to achieve this purpose, there is no systematic research about them. What's more important, little is known about the prevalence of diehard apps in the wild. In this paper, we take a first step to systematically investigate diehard apps by answering the following research questions. First, why and how can they circumvent the resource-saving mechanisms of Android? Second, how prevalent are they in the wild? In particular, we conduct a semi -automated analysis to illustrate insights why existing methods to kill app processes could be evaded, and then systematically present 12 diehard methods. After that, we develop a system named DiehardDetector to detect diehard apps in a large scale. The experimental result of applying DiehardDetector to more than 80k Android apps downloaded from Google Play showed that around 21 % of apps adopt various diehard methods. Moreover, our system can achieve high precision and recall.

Track jamming can cause disorder in radar data processing process by generating false target trace points, which has low implementation difficulty in actual combat conditions and receives good interference effect. This paper analyzes the interference mechanism of track jamming, makes simulated analysis on the influence of track jamming on radar in the process of radar data processing involving links of track initiation, data correlation and tracking filtering. Experiments show that after the radar is disturbed by track jamming, a lot of resources will be consumed at the initiation and termination of the track, and there will be target data association error, making it impossible to establish or maintain accurate and stable tracking.

Electric water heaters have time-accumulating effect due to the ability to store energy, which makes them attract much attention in the field of smart homes. The load scheduling problem of an electric water heater is challenging due to the uncertainty of future electricity prices and the applicability of different types of electric water heaters. This paper formulates the problem as a Markov decision process (MDP) and uses deep reinforcement learning (DRL) to obtain the optimal scheduling policy. Specifically, Long Short-Term Memory (LSTM) is applied to forecast the future prices. Then adopt Deep Q Network (DQN) to determine the optimal strategy for this problem. LSTM can deal with the uncertainty of future prices. This model-free approach can handle the applicability of different types of electric water heaters. Simulation results indicate that the proposed algorithm can minimize the cost of electricity and dissatisfaction. In addition, the electricity cost and user dissatisfaction can be balanced by introducing a balance parameter according to user preferences.

The convolutional neural network (CNN) shows excellent performances in various applications at the cost of huge storage and calculation overheads. To compress and accelerate the CNN, conventional methods usually prune the unimportant filters under the pre-selected standards, which should be artificially and carefully designed according to the specific application scenarios. In this paper, we propose a general CNN accelerating and compressing method, i.e., the parasitic mechanism (PAM)-based filter pruning algorithm. First, the parasitic layer is constructed on the basis of the convolutional structure and the gate module. Then, the parasitic layers are attached to the original CNN, which carry out the pruning process for the filters in each layer. The above attaching and pruning process is named as the parasitic mechanism. Compared with the traditional filter pruning methods which pre-select the pruning standards artificially, the PAM method intelligently learns the pruning standard in the training process, and prunes the unimportant filters under the constraint of maintaining the performance of the original CNN. As verified by simulations, the proposed PAM algorithm has a better pruning performance than the conventional filter pruning methods.

Based on the analysis of the reactive power regulation capability of wind farm with DFIGs, this paper proposes a method of active power scheduling that maximizes the reactive power limit of wind farms. When the predicted value of the active power output of the wind farm is less than the active dispatch instruction issued by the control center station, power generation is performed according to the principle of maximum wind energy tracking; when the predicted value of the active output is greater than the dispatch instruction, the Lagrangian function method is used to prove that the average distributed active power At power, the reactive power limit of the wind farm with DFIGs is the largest. According to this principle, the active power is distributed, so as to maximize the reactive power regulation capacity of the wind farm. Finally, a simulation analysis is carried out, and the feasibility and effectiveness of the proposed method are verified by comparing with the traditional active scheduling method.

The integration of large-scale wind power into the power grid poses serious challenges to the safe and stable operation of the power grid. Wind farm participation in system frequency control is one of the important countermeasures. Based on the integrated frequency regulation control strategy of the wind turbine (WT), this paper proposes a frequency regulation control strategy for wind farms under complex wind speed conditions. Based on the total wind speed conditions, a coordinated distribution factor of deloading power is established, so that the wind farm will allocate the total deloadingpower to each WT in accordance with the established distribution factor. This method can maintain sufficient stability when the power system is disturbed, and at the same time can reduce the regulating pressure of the synchronous generator. A four-machine two-region model is established on the Digsilent/Powerfactory simulation platform. Through simulation analysis, it is concluded that the proposed control method can make good use of wind energy of different sizes in response to system changes and provide dynamic support for the power grid.

Oil-paper insulation bears AC/DC composite voltage in converter transformers. However, the AC/DC space charge behavior is still not clear, which will result in electric field distortion, material aging and even insulation failure. In this work, the space charge distribution of oil-paper under the AC/DC composite voltage with the AC/DC ratio of 1:1, 1:3, 1:5, and 1:7, is studied by pulse electroacoustic (PEA) method, respectively. It was found that as DC component increases, the accumulated charge near the electrode decreases, while the interior charge increases. It suggests that DC stress mainly influences the amount and the depth of space charge accumulation. AC component has a relatively significant impact on the injection and extraction behavior of space charge near the electrode. Furthermore, AC/DC composite voltage enhances the migration of space charge compared with that under DC voltage. This research contributes to the further understanding of the space charge characteristics of oil-paper under AC/DC composite voltage, which will benefit the safe and stable operation of converter transformers.

Due to the limitation of less information in a single image, it is very difficult to generate a high-precision 3D model based on the image. There are some problems in the generation of 3D voxel models, e.g., the information loss at the upper level of a network. To solve these problems, we design a 3D model generation network based on multi-modal data constraints and multi-level feature fusion, named as 3DMGNet. Moreover, 3DMGNet is trained by self-supervised method to achieve 3D voxel model generation from an image. The image feature extraction network (2DNet) and 3D feature extraction network (3D auxiliary network) are used to extract the features of the image and 3D voxel model. Then, feature fusion is used to integrate the low-level features into the high-level features in the 3D auxiliary network. To extract more effective features, each layer of the feature map in feature extraction network is processed by an attention network. Finally, the extracted features generate 3D models by a 3D deconvolution network. The feature extraction of 3D model and the generation of voxelization play an auxiliary role in the training of the whole network for the 3D model generation based on an image. Additionally, a multi-view contour constraint method is proposed, to enhance the effect of the 3D model generation. In the experiment, the ShapeNet dataset is adapted to prove the effect of the 3DMGNet, which verifies the robust performance of the proposed method.

The paving asphalts have long been recognized to be capable of self-healing. The objective of this study was to evaluate the healing potential of asphalt binders and investigate its relationship with molecular characteristics in terms of composition and structures. Five neat and styrene–butadiene–styrene (SBS) modified asphalt binders were characterized using the recently developed linear amplitude sweep-based healing test. The data were analyzed based on the viscoelastic continuum damage theory to establish healing master curves and determine the healing rate HR. Chemical evaluation methods included saturates, aromatics, resins, and asphaltenes fractionation, gel permeation chromatography, and nuclear magnetic resonance spectroscopy. Results indicated that the presence of more light/low-polarity fractions of saturates and aromatics or higher concentrations of small molecules promoted healing, as these molecules were expected to have higher mobility facilitating molecular diffusion across crack interfaces. Lower percentages of aromatic ring structures and more aliphatic chains corresponded to higher healing rates. The SBS-modified asphalt binders contained higher concentrations of aromatic rings, but still provided comparable healing potential with the neat asphalts.

In order to facilitate the development of product image design, the research proposes the optimized product image design integrated decision system based on Kansei Engineering experiment. The system consists of two sub-models, namely product image design qualitative decision model and quantitative decision model. Firstly, using the product image design qualitative decision model, the influential design elements for the product image are identified based on Quantification Theory Type I. Secondly, the quantitative decision model is utilized to predict the product total image. Grey Relation Analysis (GRA)&ndash, Fuzzy logic sub-models of influential design elements are built up separately. After that, utility optimization model is applied to obtain the multi-objective product image. Finally, the product image design integrated decision system is completed to optimize the product image design in the process of product design. A case study of train seat design is given to demonstrate the analysis results. The train seat image design integrated decision system is constructed to determine the product image. This shows the proposed system is effective and for predicting and evaluating the product image. The results provide meaningful improvement for product image design decision.

In the underlay cognitive radio networks, the main challenge in detecting the idle radio resources is to estimate the power spectrum maps (PSMs), where the radio propagation characteristics are hard to obtain. For this reason, we propose a novel PSMs estimation algorithm based on the generative adversarial networks (GANs). First, we constructed the PSMs estimation model as a regression model in deep learning. Then, we converted the estimation task into an image reconstruction task by image color mapping. We fulfilled the above task by designing an image generator and an image discriminator in the proposed maps&rsquo, estimation GANs (MEGANs). The generator is trained to extract the radio propagation characteristics and generate the PSMs images. However, the discriminator is trained to identify the generated images and help to improve the generator&rsquo, s performance. With the training process of MEGANs, the abilities of the generator and the discriminator are enhanced continually until reaching a balance, which means a high-accuracy PSMs estimation is achieved. The proposed MEGANs algorithm learns and utilizes accurate radio propagation features from the training process rather than making direct imprecise or biased propagation assumptions as in the traditional methods. Simulation results demonstrate that the MEGANs algorithm provides a more accurate estimation performance than the conventional methods.

In the underlay cognitive radio networks (CRNs), the power spectral density (PSD) maps play a foundational role in detecting the idle radio resources. However, it is hard to get a high-accurate PSD map estimation result because of the complicated radio environment. For this reason, we propose a novel convolutional neural network- (CNN-) based PSD map estimation algorithm named map reconstruction CNN (MRCNN). Using the CNN to estimate PSD maps for underlay CRNs has not been reported until now. First, on the basis of the proposed color mapping process, we transform the PSD map estimation task to the image reconstruction task. Then, we train the MRCNN to learn the radio environment characteristics from the training data, rather than making direct biased or imprecise wireless environment hypotheses as in the conventional methods. We utilize the extracted knowledge in the training process to reconstruct the PSD map images. As demonstrated in the simulations, the proposed MRCNN method has a better PSD map estimation performance than the conventional methods.

We introduce neural network (NN)-based equalization in high-speed passive optical networks. Data feature engineering is proposed to improve performance of NN-based equalization. Besides, an unsupervised learning scheme for NN-based equalizer is proposed to train the model without known symbols of received signal.

The desire for increased performance and functionality has introduced additional complexities to the design and fabrication of additively manufactured (AM) parts. However, addressing these needs would require improved control over local properties using in-line feedback from fast-acting low-fidelity models during the fabrication process. In this regard, differential evaporation is an inherent characteristic in metal AM processes, directly influencing local chemistry, material properties, functionality, and performance. In the present work, a differential evaporation model (DEM) is presented for laser powder bed fusion (LPBF) AM to predict and control the effect of evaporation on chemistry and properties on local and part-wide scales. The DEM model is coupled with an analytical thermal model that is calibrated against 51.2 Ni [at%] nickel titanium shape memory alloy (NiTi SMA) single-track experiments and a multi-layer model that accounts for the AM part’s multi-layer design and the inherent melt pool overlap and chemistry propagation. The combined hierarchical model, consisting of the thermal, evaporation, and multi-layer components, is used to predict location-specific chemistry for LBPF AM fabrication of Ni50.8Ti49.2 [at%] SMAs. Model predictions are validated with values obtained from multi-layer experiments on a commercial LPBF system, resulting in a root mean square error (RMSE) of 0.25 Ni [at%] for predicted Ni content. Additionally, martensitic transformation temperature, Ms, is calculated and compared with empirical data, resulting in an RMSE of 18.6 K. A practical account of the cumulative and propagative thermal-induced evaporation effect on location-specific chemistry is made through this linkage of models. Fundamentally, this model chain has also provided a solution to the forward modeling problem, enabling steps to be taken towards resolving the inverse design problem of determining processing parameters based on desired location-specific properties.

In this paper, an optimal tracking control scheme is proposed to solve the infinite-horizon linear quadratic tracking (LQT) problem using iterative adaptive dynamic programming (ADP) algorithm. The reference trajectory is assumed to be produced by a linear command generator. First, via system transformation, an augmented system composed of controlled system and command generator is constructed. Then we derive the Bellman equation in terms of the transformed system with discount factor in cost function. In order to avoid requirement for knowledge of system dynamics, the iterative ADP algorithm is introduced to solve the Bellman equation with convergence analysis. A novel approach based on controllability and observability analysis is presented to show the stability of tracking error. For facilitating the implementation of this iterative approach, three neural networks (NNs) are employed as parametric structures to identify the unknown system dynamics, approximate performance function and search control policy, respectively. Finally, a simulation example is included to verify the effectiveness of the proposed scheme.

Hierarchical porous structure plays a vital role in various energy storage systems. Herein, a wood-inspired multi-channel tubular graphene (MCTG) architecture is fabricated by template-directed chemical vapor deposition (CVD). The multi-channel template is assembled via a facile hydrothermal and freeze-drying method with Si as precursor. The unique multi-channel tubular graphene architecture reveals distinct advantages for lithium-sulfur (Li-S) batteries. The curved tubular interior space with a pore volume of 2.141 cm3 g−1 can accommodate active sulfur and anchor soluble polysulfides, while the 3D multi-channel network favors rapid mass transport and ion diffusion. Furthermore, the CVD grown few-layer graphene offers good electron conductivity, while the enlarged (002) spacing (0.35–0.41 nm) of graphene layers provide more space for sulfur storage and Li+ diffusion. Benefiting from these merits, the MCTG/S cathode (loaded with 70 wt% sulfur) achieves a high initial discharge capacity of 1390 mAh g−1 at 0.1 C rate, excellent rate capability up to 8 C and a slow capacity decay rate of 0.078% per cycle during 500 cycles. This type of MCTG will also play an important role in gas adsorption, solar steam generation, catalysis, and so on.

A new class of target link flooding attacks (LFAs) can cut off the Internet connections of a target area without being detected, because they employ legitimate flows to congest selected links. Although new mechanisms for defending against LFA have been proposed, the deployment issues limit their usage, since they require either additional modules to enhance routers or using the software-defined network to replace the traditional routers. In this paper, we propose a novel framework that employs both the end-to-end and hop-by-hop network measurement techniques to capture the abnormal path performance degradation for detecting LFA and then locate the target links or areas whenever possible, and develop a prototype of the framework named LinkScope . Although using network measurement to capture network anomaly is not new, we tackle a number of challenging issues, such as conducting large-scale Internet path monitoring via non-cooperative measurement so that users do not need to install LinkScope on every host, profiling the performance of asymmetric Internet paths and detecting LFA. The extensive evaluation in a testbed and the Internet shows that with limited bandwidth and computational overhead, LinkScope can achieve timely detection and diagnosis of LFA with high detection rate and low false positive rate.

Predicting the occurrence of landslides is important to prevent or reduce loss of lives and property. The stability of rock slopes is often dominated by one or more locked segments along a potential slip surface; these segments have relatively high strength and accumulate strain energy. Locked segments can be preliminarily classified into three categories: "rock bridge", "retaining wall" and "sustaining arch." Coupling a one-dimensional renormalization group model with the strain-softening constitutive relation of geo-materials considering the Weibull's distribution, a physical model for predicting the instability of slopes with locked segments is established. It is found that the ratio of the strain or displacement at the peak strength point to that at the volume dilation point for a locked segment is exclusively dependent on the Weibull's shape parameter m, and is approximately constant at 1.48. A corresponding accelerating displacement increment (tertiary creep) of the slope can be observed from the onset of the volume dilation of the locked segment due to its unsteady cracking. For a slope with multiple locked segments, one can predict its critical instability displacement value according to the accelerating displacement onset corresponding to the volume dilation point of the first locked segment and the number of locked segments. The back-analysis of two typical cases, the Yanchihe rockslide in China and the wedge rockslide, Libby Dam, USA, shows that their evolutionary processes, dominated respectively by one and two locked segments, follow this model, confirming the reliability of the proposed model.

The positional error of aviation drilling robot has a great influence on the strength and lives of aircrafts in the aircraft assembly. In order to improve the position accuracy of aviation drilling robot, an error compensation method based on co-kriging is proposed in this paper. The error similarity based on the kinematic of drilling robot is proposed firstly. Then, the positional errors of a set of points in the workspace are measured by using precision laser tracker. The measurement data are used to fit the cross-variogram of positional error. After the cross-variogram is obtained, the predicted positional errors of verification points can be estimated based on co-kriging. The positions after compensation are given to the drilling robot. The proposed method is carried out on an aviation drilling robot for practical compensation to verify the correctness and effectiveness of the error compensation method. The experimental results show that the average absolute positional error is reduced to 0.1150 mm from 0.7168 mm, and that the maximum absolute positional error is reduced to 0.2664 mm from 1.3073 mm. The experimental results also demonstrate that the proposed method can improve the absolute position accuracy of aviation robot and could meet the requirement of aircraft assembly.

Various type of deep convolution neural network (CNN) have been successfully applied to synthetic aperture radar (SAR) image despeckling. However, the model is limited by the length of network stru...

In this paper, we have experimentally demonstrated the C-band 4 × 25-Gb/s non-return-to-zero on - off -keying (NRZ-OOK) signals over a 160-km single-mode fiber transmission based on 10G-class directly-modulated lasers and photodiodes. The excessive negative dispersion at the receiver side is used to equalize the frequency response of bandwidth-limited directly-modulated signal, therefore enables 25-Gb/s NRZ-OOK transmission based on 10G-class optical devices. By employing a multichannel chromatic dispersion compensation module in the receiver, a single optical device can be used to equalize the frequency response of multiple channels realizing low-cost and high-capacity signal transmission. The results shown in this paper reveal that our proposed scheme would be a promising candidate for a low-cost transition to 100 Gb/s (4 × 25 Gb/s) for the point-to-point metro network applications.

The cracking mechanism, especially the initiation and propagation process of cracking, in the overlapping zone (OZ) of laser solid forming (LSF) of IN-738LC alloy was investigated. The results showed that both the solidification cracks and liquation cracks can be found in the OZ of the LSFed IN-738LC specimen. The liquation cracking can be ascribe to the local melting of γ-γ′ eutectic at grain boundaries and could further become the initiation site of solidification cracks in the OZ of LSFed IN-738LC. According to the EBSD analysis and the Rappaz's theoretical model, the cracks in OZ always propagated along the high-angle grain boundaries due to their relatively wider solidification temperature range compared with the lower angle grain boundaries. The thermal stress and residual stress (cracking driving force) of the LSFed IN-738LC deposit was estimated by Vickers micro-indentation method combing a series of microhardness and room temperature tensile testing. It was shown that the residual stress in OZ was obviously higher than that in the inner-track zones due to the different thermal history of the two zones, while the microhardness of the two zones were about at the same level. Preheating was found to be effective in suppressing cracking by reducing the residual stress in LSFed IN-738LC deposit and the crack free deposit was obtained with preheating at 700 °C.

An organic nucleating agent (NA), N,N′,N″-tricyclohexyl-1,3,5-benzenetricarboxylamide (TMC-328), was incorporated into biodegradable poly(1,4-butylene adipate) (PBA) to prepare the PBA/TMC-328 blend system to investigate the effect of the TMC-328 NA on the crystallization kinetics, polymorphism, thermal stability and biodegradation of the PBA. Due to the excellent nucleation ability, the TMC-328 enhanced the crystallization temperature, crystallization rate, crystal density and degree of crystallinity of the PBA. The TMC-328 facilitated the formation of the alpha-crystal, accelerated the beta-to-alpha-phase transition, and tailored the polymorphic crystalline structure of the PBA. The incorporated TMC-328 increased the thermal stability and decreased the enzymatic degradation rate of the PBA. The mechanisms of the tailored polymorphism, enhanced thermal stability and decreased biodegradation rate of the PBA in the presence of the TMC-328, have also been proposed and discussed. The PBA/TMC-328 blend provides us an informative model to shed more light on the correlation of hydrogen bond interaction, polymorphism and property of polymorphic polymeric material.