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This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios.

Communication is essential to negotiating safer sex, yet power dynamics make condom use difficult to manage. Guided by Dyadic Power Theory and the chilling effect, we predicted that men’s sexual re...

Aiming at the problem of that the standard salp swarm algorithm has low result precision and slow convergence velocity in the evolutionary process, an improved salp swarm algorithm optimization based on memory mechanism and boundary collision processing is proposed. Firstly, a Chebyshev chaotic map was used to initialize the salps to make them distribute more evenly in search space. Secondly, adding the memory mechanism introduces the individual history optimum of salps into the optimizing strategy of individual salps, which accelerates the convergence speed of the algorithm. Finally, the boundary collision rebound mechanism is introduced to ensure the effectiveness and diversity of the population. The improved algorithm is simulated on 12 types of benchmark test functions in this paper, and compared with other intelligent optimization algorithms under the same conditions. The results show that the improved algorithm has not only a obvious improvement in convergence speed and convergence accuracy, but also has better optimization performance.

The purpose of the aspect-level sentiment analysis task is to analyze the sentiment orientation expressed by different aspects in the text. It has more fine-grained sentiment evaluation objects and is more in line with actual needs. Therefore, it has received extensive attention in recent years. At present, the model that combines the attention mechanism with the Recurrent Neural Network and its variants has gradually become the main method to solve aspect level sentiment analysis tasks. However, this type of method is limited by the deficiencies of the RNN itself, the training time is too long and the dependence between words decreases with the increase of distance. Even if the attention mechanism is added, the above problems will still exist. Therefore, this paper introduces the multi-head attention mechanism into the GCAE model (Gated Convolutional network with Aspect Embedding), and then proposes the GCAE-MHA model (Improved GCAE model based on Multi-Head Attention mechanism). This model models the context and specific aspects of the text sequence at the same time, and learns the interactive relationship between them. And finally uses the two together as the basis for sentiment classification. At the same time, in order to make up for the problem that the convolutional neural network can only extract the dependencies between local words, the GCAE-MHA model also uses the Dilated Convolutional Neural Network to replace it, which can extract the semantic information between long-distance words, and by setting different dilation rate to obtain a richer semantic feature. Finally, experiments are carried out on the SemEval2014 dataset and Twitter dataset. The experimental results show that the GCAE-MHA model can effectively improve the effect of aspect level sentiment analysis while ensuring the simplicity of the model network structure.

We propose a compact cascaded meta-surface system (CCMS) to produce well converged orbital angular momentum (OAM) vortex waves with tailored spin angular momentum (SAM) by integrating a meta-surface lens (ML) with an assistant meta-mirror (AM). Specifically, the co-linearly polarized (LP) waves from the feed would be reflected by the ML firstly and then twisted into the cross-LP counterparts by the AM to penetrate the ML for the perfect synthesis of the OAM vortex beams while performing the linear-to-circular polarization conversion. Especially, the CCMS can pack the ML and the AM closely together with a quarter of the ML focal length when we apply proper phase distributions on the AM. In addition, the proposed CCMS can readily be extended to the generation of multiple circularly polarized OAM vortex waves with different modes. Our design should thus pave the way for building up more efficient wireless communication systems with expanded channel capacity.

Chaotic-based random stepped frequency signal is applied in the multiple-input-multiple-output through-the-wall detection radar (MIMO-TWDR)recently. When the frequency modulation (FM) sequence of transmission signal is controlled by the chaotic signal, the single-frequency interference such as the power harmonics sneaking into the phase detector becomes periodical and therefore can be filtered in frequency domain. However, the target echo signal becomes random after chaotic modulation, where the matched filter usually is unable to be realised by Fourier transform and consequently the envelope of direct wave after the phase detector varies stochastically and is difficult to be eliminated by an analogue filter. The FM sequence of random disorganising cannot meet the demand of filtering out the single-frequency interference and direct wave simultaneously. Therefore, a new method of FM sequence optimisation of chaotic-based random stepped frequency signal based on genetic algorithm is proposed to solve these problems in this study. Simulations show that the optimised stepped frequency signal possesses the advantages of both chaotic-based random and linear stepped frequency signal. The proposed scheme achieves excellent performance on direct wave and single-frequency interference suppression and target detection. Moreover, it can avoid the interference between transmission antennas of MIMO radar.

This paper investigates a new and special imaging mode, bistatic forward-looking high-speed maneuvering platform synthetic aperture radar, in which both platforms travel curvilinear trajectories with high velocities and accelerations. A thorough understanding of its new properties of motion, 2-D resolution, and echo signal coupling is given. Applicability of gradient method neglecting time dependence of gradient to this special configuration is addressed. Moreover, 2-D coupling characteristic of echo signal at different imaging distances is investigated.

Chemotherapy resistance remains a serious impediment to successful antitumor therapy around the world. However, existing chemotherapeutic approaches are difficult to cope with the notorious multidrug resistance in clinical treatment. Herein, we developed tumor-specific multiple stimuli-activated dendrimeric nanoassemblies with a metabolic blockade to completely combat both physiological barriers and cellular factors of multidrug resistance. With a sophisticated molecular and supramolecular engineering, this type of tumor-specific multiple stimuli-activated nanoassembly based on dendrimeric prodrugs can hierarchically break through the sequential physiological barriers of drug resistance, including stealthy dendritic PEGylated corona to optimize blood transportation, robust nanostructures for efficient tumor passive targeting and accumulation, enzyme-activated tumor microenvironment targeted to deepen tumor penetration and facilitate cellular uptake, cytoplasmic redox-sensitive disintegration for sufficient release of encapsulated agents, and lysosome acid-triggered nucleus delivery of antitumor drugs. In the meantime, we proposed a versatile tactic of a tumor-specific metabolism blockade for provoking several pathways (ATP restriction, apoptotic activation, and anti-apoptotic inhibition) to restrain multiple cellular factors of drug resistance. The highly efficient antitumor activity to drug-resistant MCF-7R tumor in vitro and in vivo supports this design and strongly defeats both physiological barriers and cellular factors of chemotherapy resistance. This work sets up an innovative dendrimeric nanosystem to surmount multidrug resistance, contributing to the development of a comprehensive nanoparticulate strategy for future clinical applications.

[Image: see text] Monoclonal antibody therapy has offered treatment benefits. Nonetheless, a lack of efficacy still exists, partially because monovalent binding of antibodies to specific receptors fails to translate into an active response. Here, we report a pretargeting–postassembly approach that exploits the selective Watson–Crick base pairing properties of oligonucleotides and multivalently tethers receptor-prebound antibodies to albumin at the cell surface. We demonstrate that this two-step self-assembling strategy allows sequential actions of receptor binding and clustering that broadens and strengthens the functions of antibodies. We show that anti-CD20 obinutuzumab (OBN) modified with one morpholino oligonucleotide (OBN-MORF1) maintains the feature of naked OBN antibody upon CD20 binding, and results in actin redistribution, homotypic adhesion, and lysosome-mediated cell death. Consecutive treatment with albumin grafted with multiple copies of a complementary morpholino oligonucleotide (HSA-(MORF2)(x)) hybridizes with surface-attached OBN-MORF1, manipulates CD20 clustering, and engages additional signals to induce calcium influx and caspase-related apoptosis. With the two types of different mechanisms collaborating in one system, the simple design exerted a notable survival extension of mice bearing disseminated B-cell lymphomas.

In order to reconstruct the linear frequency modulated (LFM) signal, such as radar signal due to the complexity. A novel parameter estimation method based on a modified convolution kernel function (MCKF) is proposed for multi-component LFM signal in this paper. The method has fewer external cross-terms and light computational burden because of non-searching operations. Moreover, it is robust against additive noise. Finally, simulated and real data results confirm the proposed method.

African American women are vulnerable for sexual health risk; thus, condom use is essential. Guided by research linking goals to communicative content, this study explored women's use of I-, you-, we-, and hedging language during condom negotiation. Female participants (N = 193) engaged in a condom negotiation role play with male confederates, where language use measures were coded. I-language was used the most frequently. Language use differed as a function of men's tactics, such that women primarily used I-language in response to verbal attacks, you-language in response to seduction, and I- and we-language in response to information seeking. Women who engaged in more recent condom use were more likely to use you-language, and when confederates attacked, they responded with more you-language and less hedging.

This chapter explores why and how lesbian, gay, and bisexual (LGB) individuals deceive friends about their sexual orientation. Quantitative and qualitative data from 87 participants showed that individual and friend characteristics (e.g., age, gender, sexual orientation, attitude toward homosexuality) and cognitive appraisals of coming out conversations (e.g., fear of negative consequences and communication efficacy) appeared to determine LGB individuals’ decision to deceive. Sexual minority individuals mostly used three deception strategies: passive (i.e., keeping silent and letting friends assume heterosexuality), active (i.e., talking about heterosexual topics around friends), and interactive (i.e., directly interacting with persons of the opposite sex). LGB individuals in same-sex romantic relationships also utilized avoidance or denial (e.g., avoiding social events where romantic partners are invited, denying that they are in a relationship) and naming (e.g., referring to a romantic partner as a “roommate”) to deceive friends about their sexual orientations. Deception motivations and strategies are discussed in light of past studies on relational development and maintenance. Directions for future research on sexual orientation deception are also discussed.

Small and medium-sized users with distributed generators or flexible demand response resources desire to participate in market transactions solely, rather than aggregated by aggregators. The commonly used decentralized bookkeeping mode, blockchain-based trading schemes can now realize uniform clearing of decentralized transactions with the help of smart contracts. However, there are still risks of excessive disclosure of information and privacy leakage caused by incomplete decentralization. To solve this problem, we propose a decentralized optimization-based market clearing model, which can be applied on blockchain. The model has three advantages: ① limited access to the consumers’ or generators’ private data, ② convenient calculation methods that can be deployed on the smart contract and ③ pure decentralization to realize transparent on-chain market clearing. Moreover, considering the insufficient throughput of the existing blockchain, a blockchain network, which can process the quotation calculation in parallel with various sub-chains, is also designed to support the proposed market clearing model. Finally, the effectiveness of proposed models is validated and tested on the Ethereum private blockchain.

In recent years, the proposal of neural network has provided new idea for solving natural language processing, and at the same time, neural machine translation has become the frontier method of machine translation. In low-resource languages, due to the sparse bilingual data, the model needs more high-quality data, and the translation quality fails to achieve the desired effect. In this paper, experiments on neural network machine translation based on attention are conducted on Tibetan-Chinese language pairs, and transfer learning method combined with back translation method is used to alleviate the problem of insufficient Tibetan-Chinese parallel corpus. Experimental results show that the proposed transfer learning combined with back translation method is simple and effective. Compared with traditional translation methods, the translation effect is significantly improved. From the analysis of translation, it can be seen that the citation of Tibetan-Chinese neural machine translation is smoother, which is greatly improved compared to the translation without back translation. At the same time, there are common deficiencies in neural machine translation such as inadequate translation and low translation loyalty.

We research ship 6 DOF inertial measurement method and displacement reconstruction form acceleration signal to establish the strap-down inertial navigation measurement system through sensors configuration technology. The inertial measurement system consists of three groups of vertically mounted angular rate and acceleration sensors. Through designing an FIR integration filter and studying the ship displacement signal reconstruction from the acceleration, the filter parameters design method and implementation way were determined. By means of the beam vibration to analog ship heave motion reconstructs linear displacement form acceleration signals. The root mean square error of the reconstructed line displacement is 0.004 mm, and the mean value of the displacement peak value is 5.92%, which is close to the design precision of the filter. The test shows that the measurement system and the FIR filter to achieve the predetermined precision with noise suppression.

As a basic tool for signal processing in Synthetic Aperture Radar (SAR), raw data simulation plays a significant role in those complicated configurations in which the real raw data is difficult to obtain. For the new and special configuration in Bistatic Forward-looking High-speed Maneuvering-platform SAR (BFHM-SAR), the double square roots and high-order terms in range history caused by high velocities and accelerations from both platforms make it difficult to gain the corresponding 2-dimensional frequency spectrum (2-D FS) effectively. In addition, space variances in phase terms of 2-D FS owing to curvilinear trajectories and different traveling directions of two platforms influence the accuracy of echo data significantly. In this paper, a fast and effective scene raw data simulator is developed based on the effectual access of 2-D FS with high precision and quantificational analysis and correction of phase space variance of it. As the inverse process of imaging, raw signal can be reconstructed and generated by this method more efficiently than traditional approaches nearly without sacrificing accuracy. Plenty of simulation results for scattering targets and real scene verify its validity and effectiveness. Moreover, our method can be applied for handling the simulation in the conventional airborne platform of bistatic SAR besides BFHM-SAR. Bistatic Forward-looking High-speed Maneuvering platform SAR is introduced.There exist double square roots and high order terms in the range history.Space variance should be considered and corrected to design raw data simulator.An effective approach is achieved from the precise access of frequency spectrum.The accurate raw data is generated more efficiently without sacrificing precision.

This paper investigates a new imaging algorithm for high-maneuverability synthetic aperture radar (HM-SAR) travels with high velocities and time-variant accelerations. To be specific, a more precise acceleration-separation-based equivalent range model (ASERM) is established, which can separate time-variant acceleration errors from the ideal linear range model. After 2-D spectral interpolation, a joint compensation method based on inverse-mapping and local filtering is constructed for compensating the space-variant motion error (ME) and image distortions caused by the time-variant acceleration.

Monoclonal antibodies (mAbs) against B cell antigens have been extensively used in the treatment of rheumatoid arthritis (RA). The B cell depletion therapy prevents RA symptoms and/or alleviates existing inflammation. We have applied the previously established two-step drug-free macromolecular therapeutics (DFMT) in the treatment of collagen-induced rheumatoid arthritis in a collagen-induced rheumatoid arthritis mouse model. DFMT is a B cell depletion strategy utilizing Fab’ fragment of anti-CD20 mAb for biorecognition and receptor crosslinking to induce B cell apoptosis. DFMT is composed from two nanoconjugates: 1) bispecific engager, Fab’-MORF1 (anti-CD20 Fab’ fragment conjugated with morpholino oligonucleotide MORF1), and 2) a crosslinking (effector) component P-(MORF2)(X) (N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer grafted with multiple copies of complementary morpholino oligonucleotide MORF2). The absence of Fc fragment has the potential to avoid development of resistance and infusion-related reactions. DFMT produced B cell depletion, kept the RA score low for more than 100 days, and showed minimal cartilage and bone erosion and inflammatory cell infiltration. Further improvements will be explored to optimize DFMT strategy in autoimmune disease treatment.

Tibetan-Chinese neural machine translation (NMT) is facing serious resource scarcity problem. This paper compares the application effect of multiple neural network optimization algorithms under the condition of resource scarcity, and proposes an optimization method which is suitable for resource scarcity languages. Then, by which it improving the performance of the Tibetan-Chinese NMT. Experimental results show that when choosing a suitable optimization algorithm, the Tibetan-Chinese NMT can still exceed the traditional statistical machine translation (SMT) and achieve better translation performance.

Bi-functional meta-surfaces capable of simultaneously controlling polarization states and wave-fronts of electromagnetic fields are introduced into the design of Cassegrain system for the synthesis of multi-beams. More specifically, electromagnetic fields reflected by the secondary meta-surface with tailored diverged wave-fronts would be collimated by the primary meta-surface into multi-beams with transformed polarization states that can directly go through the secondary meta-surface without any blockage. Especially, we show that such a polarization-twisting meta-surface Cassegrain system can possess much more compact configuration by properly devising the phase distribution over the secondary meta-surface, and can also achieve high-purity polarized multiple radiations when we enlarge the secondary meta-surface as a radome. The present approach of integrating two bi-functional meta-surfaces into the design of Cassegrain system for the generation of multi-beams should pave the way for building up more advanced meta-surface based architectures with specific characteristics.

Checkpoint blockade immunotherapies harness the host’s own immune system to fight cancer, but only work against tumors infiltrated by swarms of pre-existing T cells. Unfortunately, most cancers to date are immune-deserted. Here, we report a polymer-assisted combination of immunogenic chemotherapy and PD-L1 degradation for efficacious treatment in originally non-immunogenic cancer. “Priming” tumors with backbone-degradable polymer-epirubicin conjugates elicits immunogenic cell death and fosters tumor-specific CD8+ T cell response. Sequential treatment with a multivalent polymer-peptide antagonist to PD-L1 overcomes adaptive PD-L1 enrichment following chemotherapy, biases the recycling of PD-L1 to lysosome degradation via surface receptor crosslinking, and produces prolonged elimination of PD-L1 rather than the transient blocking afforded by standard anti-PD-L1 antibodies. Together, these findings established the polymer-facilitated tumor targeting of immunogenic drugs and surface crosslinking of PD-L1 as a potential new therapeutic strategy to propagate a long-term antitumor immunity, which might broaden the application of immunotherapy to immunosuppressive cancers.

In [1] , the result of Fig. 13(b) was incorrectly provided. Now, we provide the corrected result, as shown in Fig. 1 .

With the rapid development of distributed renewable energy (DRE), demand response (DR) programs, and the proposal of the energy internet, the current centralized trading of the electricity market model is unable to meet the trading needs of distributed energy. As a decentralized and distributed accounting mode, blockchain technology fits the requirements of distributed energy to participate in the energy market. Corresponding to the transaction principle, a blockchain-based integrated energy transaction mechanism is proposed, which divides the trading process into two stages: the call auction stage and the continues auction stage. The transactions among the electricity and heat market participants were used as examples to explain the details of the trading process. Finally, the smart contracts of the transactions were designed and deployed on the Ethereum private blockchain site to demonstrate the validity of the proposed transaction scheme.

Discovering new strategies for combating drug-resistant tumors becomes a worldwide challenge. Thereinto, stubborn drug-resistant tumor membrane is a leading obstacle on chemotherapy. Herein, we report a novel tumor-activatable amphipathic peptide-dendronized compound, which could form nanoaggregates in aqueous solutions, for perturbing tumor plasma/organelle membrane and reversing multidrug resistance. Distinguished from classical linear amphipathic peptide drugs for membrane disturbance, dendritic lysine-based architecture is designed as a multivalent scaffold to amplify the supramolecular interactions of cationic compound with drug-resistant tumor membrane. Moreover, arginine-rich residues as terminal groups are hopeful to generate multiple hydrogen bonding and electrostatic interactions with tumor membrane. On the other hand, antitumor molecule (doxorubicin) is devised as a hydrophobic moiety to intensify the membrane-inserting ability owing to the prominent interactions with hydrophobic domains of drug-resistant tumor membrane. As expected, these amphipathic peptide-dendronized compounds within the nanoaggregates could severely disturb both the structures and functions of tumor plasma/organelle membrane system, thereby resulting in the rapid leakage of many critical biomolecules, highly efficient apoptotic activation and antiapoptotic inhibition. This strategy on tumor membrane perturbation demonstrates a bran-new antitumor activity with high contributions to cell cycle arrest (at the S phase), strong apoptosis-inducing ability and satisfying cytotoxicity to a variety of drug-resistant tumor cell lines.

Inverse synthetic aperture radar (ISAR) imaging plays an important role in modern radar systems. It is a powerful tool to surveil the air and space targets under all-weather and day-and-night conditions. With increasing demands for space surveillance and target recognition, real-time ISAR imaging is of great urgency. Based on field programmable gate arrays and digital signal processors, a new design of real-time processing of ISAR imaging is presented in this paper. First, a thorough description of the imaging algorithm is presented. The algorithm includes pulse compression, envelope alignment, phase adjustment, and cross range focusing. For the pulse compression, two different methods are adopted to process the “Dechirp” data and the directly sampled wideband data, respectively. Second, a hardware design of real-time imaging processing is given. Each step of the algorithm is mapped onto the hardware, and the computing resources and latency are analyzed. Finally, the realization is verified by both the simulated and measured data, and the quality of imaging result is evaluated qualitatively.

In this paper, we propose a new way to generate videos via recurrent convolutional generative adversarial networks (CRGAN). The video tasks involving spatio-temporal series are more difficult than image tasks. In order to deal with spatio-temporal series tasks, we use a method that combines convolutional neural networks (CNN), which are used to deal with spatio relationships of videos, with Long Short Term Memory (LSTM), which is a variant of recurrent neural networks and used to deal with temporal relationships of videos, called convolutional recurrent neural networks (CRNN) to process the video inputs.Generative adversarial networks (GAN) is a method of unsupervised learning and has attained great improvements in image generation. In our paper, we combine CRNN with GAN and use unsupervised learning to generate videos. In the end, we will present some videos generated by our methods.

We propose to utilize the $^{1}\mathrm{\ensuremath{\Sigma}}{\ensuremath{-}}^{1}\mathrm{\ensuremath{\Sigma}}$ electronic transition system for direct laser cooling of heteronuclear diatomic molecules. AgH, as well as its deuterium isotopolog AgD, is used as an example to illustrate the cooling schemes. Potential-energy curves and relevant molecular parameters of both AgH and AgD, including the spin-orbit constants and the electronic transition dipole moments, are determined in internally contracted multiconfiguration-reference configuration interaction calculations. The highly diagonal Franck-Condon matrices of the $A{\phantom{\rule{0.16em}{0ex}}}^{1}{\mathrm{\ensuremath{\Sigma}}}^{+}\ensuremath{-}X{\phantom{\rule{0.16em}{0ex}}}^{1}{\mathrm{\ensuremath{\Sigma}}}^{+}$ transitions predicted by the calculations suggest the existence of quasi-closed-cycle transitions, which renders these molecules suitable for direct laser cooling. By solving rate equations numerically, we demonstrated that both AgH and AgD molecules can be cooled from 25 K to 2 mK temperature in approximately 20 ms. Our investigation elucidates and supports the hypothesis that molecules in the simplest $^{1}\mathrm{\ensuremath{\Sigma}}{\ensuremath{-}}^{1}\mathrm{\ensuremath{\Sigma}}$ system can serve as favorable candidates for direct laser cooling.

For the purpose of target parameter estimation of the orthogonal frequency-division multiplexing (OFDM) radar, a high-resolution method of joint estimation on range and direction of arrival (DOA) based on OFDM array radar is proposed in this paper. It begins with the design and analysis of an echo model of OFDM array radar. Since there is no coupling between range and angle parameter estimation for a narrow-band signal, a method which exploits the data of one snapshot to estimate the range and angle of the target by means of multiple signal classification (MUSIC) based on virtual two-dimensional spatial smoothing in range and angle dimensions is devised. The proposed method is capable of joint estimating the range and DOA of the target in a high resolution under a single snapshot circumstance. Simulation experiments demonstrate the validity of the proposal.

In airborne case, synthetic aperture radar images generally suffer from the deterioration because of the unknown phase error caused by unstable platform and atmosphere perturbation. To obtain the phase error, a novel autofocus algorithm referred to as blind homomorphic deconvolution autofocus algorithm is proposed in this study. In this method, a wavelet scaling function is used to construct a smooth subspace that is orthogonal to noise subspace. Then, the phase error can be separated and reconstructed by the generated smooth subspace based on the differences of the smoothness properties between phase error and image reflectivity. Compared with the traditional autofocus methods, the proposed method does not require an iterative estimation. Thus, the computational complexity can be significantly reduced. Simulation and real data processing results validate the effectiveness of the proposed method.

Herein, multifunctional mimics of viral architectures and infections self-assembled from tailor-made dendritic lipopeptides for programmed targeted drug delivery are reported. These viral mimics not only have virus-like components and nanostructures, but also possess virus-like infections to solid tumor and tumor cells. Encouragingly, the viral mimics provide the following distinguished features for tumor-specific systemic delivery: i) stealthy surface to resist protein interactions and prolong circulation time in blood, ii) well-defined nanostructure for passive targeting to solid tumor site, iii) charge-tunable shielding for tumor extracellular pH targeting, iv) receptor-mediated targeting to enhance tumor-specific uptake, and v) supramolecular lysine-rich architectures mimicking viral subcellular targeting for efficient endosomal escape and nuclear delivery. This bioinspired design make in vivo tumor suppression by drug-loaded viral mimics against BALB/c mice bearing 4T1 tumor greatly exceed the positive control group (more than three times). More importantly, viral mimics hold great potentials to reduce side effects and decrease tumor metastasis after systemic administration.

Bioinspired tryptophan-rich peptide dendrimers (TRPDs) are designed as a new type of dendritic peptide drugs for efficient tumor therapy. The TRPDs feature a precise molecular structure and excellent water solubility and are obtained in a facile process. Based on the unique features of peptide dendrimers, including highly branched structures, abundant terminal groups, and globular-protein-like architectures, the therapeutic dendrimers show significant supramolecular interactions with DNA through the tryptophan residues (indole rings and amino groups). Further experimental results indicate that TRPDs are efficient antitumor agents both in vitro and in vivo.

Dendrimer-based nanoplatforms have exhibited wide prospects in the field of nanomedicine for drug delivery, without great success due to many predicaments of cytotoxicity, high cost, and low yield. In this work, we report a feasible strategy on dynamic cross-linkings of low-generation peptide dendrimers into bioreducible nanogels for efficient drug controlled release. With a facile fabrication, the disulfide cross-linking of biocompatible peptide dendrimers successfully possess well-defined and stable nanostructures with abundant expanded voids for efficient molecular encapsulation. More importantly, high reducing condition is capable of triggering the cleavage of disulfide bonds, the disintegration of peptide-dendrimeric nanogels, and stimuli-responsive release of guest molecules. The bioreducible nanogels improve antitumor drug internalization, contribute to endosomal escape, and realize intracellular drug controlled release. The doxorubicin-loaded nanogels afford high antitumor efficiency and reduce the side effects to BALB/c mice bearing 4T1 tumor. Therefore, dynamic cross-linkings of low-generation dendrimers into smart nanogels will be an alternative and promising strategy to resolve the dilemmas of current dendrimer-based nanocarriers as well as develop innovative nanoplatforms.

The data communication always needs to be implemented in application of the Wireless Power Transfer (WPT) technology. However, there are also some problems need to be dealt with in practical applications. For example, the interference between power transfer and communication is serious, when the power and data are transmitted through the same coupling coil. A simple design is presented to implement bidirectional communication through the power transmission channel in this paper. The main idea is that the high-frequency signal can be sent via a single antenna. The power is transferred in the frequency range of 20 KHz to 100 KHz, the data is transferred by employing carrier at around 10MHz. The interference between the two function is analyzed. In order to demonstrate the effectiveness of the proposal, a hardware experiment prototype platform is built up in the project. In the experiment, the transmission gap distance is 10mm, the power transfer is reached 30W, and the data transmission rate is capable of up to 80 Kbps.

Buck-IPT system is composed of a front power regulation buck circuit and a behind inductive power transfer circuit (IPT), which is higher-order, multi-operating-mode, compound nonlinear system. Aiming at dynamic problems such as large inductor current and slow response in the start-up progress, this paper proposes a dynamic characteristic optimization control method. Firstly, this paper proposes a reduced second-order time-varying model considering the inverter's equivalent load characteristic. And then, Based on this model, a sliding-mode control using the exponential reaching law is applied to optimize the start-up process of the Buck-IPT system. Finally, the simulation and experiment results have verified that the proposed method can realize good dynamic performance with fast response, limited-current of front inductor, no overshoot of front capacitor voltage.

Attention-based neural machine translation (NMT) employs an attention network to capture structural correspondences between the source and target language at the word level. Unfortunately, alignments between source and target equivalents are complicated, which makes word-level attention not adequate to model these relations (e.g., alignments between a source idiom and its target translation). In order to handle this issue, we propose a phrase-level attention mechanism to complement the word-level attention network in this paper. The proposed phrasal attention framework is simple yet effective, keeping the strength of phrase-based statistical machine translation (SMT) on the source side. Experiments on Chinese-to-English translation task demonstrate that the proposed method is able to statistically improve word-level attention-based NMT.

IMM (Interacting Multiple Model) algorithm is widely used in target tracking, and its basic principle is described in detail at first. However, the IMM algorithm fails to obtain the prior probability of model conversion quickly and accurately when tracking for target. In this paper, an improved IMM algorithm based on ANFIS (the adaptive neural fuzzy inference system) is proposed. The improved algorithm can update the value of system noise covariance in real-time by ANFIS module through observing the coefficient of system noise covariance. Consequently, the probability of model conversion can be obtained more quickly and accurately. Then, the comparison and analysis of the experiment results between the original IMM algorithm and the improved one have been carried out. The experiment results show that the reaction rate for maneuvering target tracking is significantly boosted and tracking error is obviously reduced because the improved algorithm can update the value of system noise covariance in real-time and improve the system adaptability.

BackgroundCigarette pictorial warning labels (PWLs) could produce stronger quit intentions than text-only warning labels (TWLs) due to greater emotional arousal. Yet, it remains unclear whether PWLs that elicit different levels of emotions produce different outcomes. To better understand the role of negative emotions in the effects of PWLs, this study developed two sets of PWLs arousing different emotional levels (high vs low) but equally high on informativeness and compared them to each other and to the current TWLs.MethodsAdult US smokers (n=1503) were randomised to view nine high-emotion-arousing or low-emotion-arousing PWLs or TWLs. After each label, participants reported the negative emotions they felt while looking at the label. After seeing all the labels, participants reported their intentions to quit smoking. Mediation analyses tested whether message condition influenced quit intentions indirectly through negative emotions.ResultsCompared with TWLs, PWLs produced higher levels of negative emotions (b=0.27, SE=0.04, p=0.24, SE=0.07, pConclusionsThe results provide additional evidence for negative emotions as the mechanism through which PWLs motivate smokers to consider quitting. The findings call on the Food and Drug Administration to design and implement high-emotion-arousing cigarette warning labels.

In airborne/stationary bistatic-synthetic-aperture-radar imaging, translational invariance was no longer valid. After range cell migration correction, the range-compressed signal under the same range gate exhibited azimuth-dependent FM rates that made the motion-induced phase error difficult to separate from the echoes. To solve this problem, an azimuth-dependent phase gradient autofocus (PGA) algorithm was proposed. Different from the conventional PGA, the residual quadratic phase arising from the azimuth-dependent FM rates was additionally estimated and compensated. As the influence of the azimuth-dependent FM rates was greatly reduced, a phase gradient estimator was subsequently applied for accurate phase error retrieval. Acquired raw data were analyzed to verify the proposed algorithm.