Search

In this paper, on the basis of multifunctional reconfigurable pixel antenna (RPA) elements, a novel linear sparse array with an attractive compound reconfigurability is presented. It has the potential advantages of its beam scanning with low gain fluctuation, low sidelobe in two orthogonal planes, and polarization reconfigurable performance. Specifically, an RPA with simultaneous polarization and pattern reconstruction capabilities, consisting of the driven patch and the parasitic pixels on the same layer of dielectric substrate, is firstly designed, which can work in several operation modes corresponding to steerable beam directions θ=0°;θxoz=25°, 45°;θyoz=15° with two circular polarizations in X-band. Cross-slot coupling feed is used to improve polarization reconstruction capability and reduce the complexity of hybrid reconstruction topology optimization. Then, those RPAs are integrated into the 1×8 linear sparse array to realize the reconfiguration of two circular polarizations and beam steering in xoz- and yoz-plane. Simulation results show that the gain fluctuation and sidelobe level of the array during beam scanning have significant advantages over the previous phased array, and the generation of antenna grating lobes is avoided. Moreover, both RPA element and RPA array prototypes have been fabricated and measured to testify the efficiency. The measured results agree well with the simulated ones, which indicates the application potential in the field of modern wireless communication system of the proposed linear sparse array.

Disassembly is a crucial process of remanufacturing end-of-life products. It can be done by either humans or robots. Manual disassembly has a high cost and low disassembly efficiency, while a robot is not flexible to perform different dynamic, complicated tasks. The combination of them can improve the disassembly efficiency. Because it is hard to know the detailed information about the wear and tear of the used products, there is a risk of disassembly failure. In this work, disassembly failure risks, constraints of disassembly priority, cycle time, and actual cost are taken into full consideration based on an AND/OR graph of used products. Besides, a mathematical model is constructed with the objectives of maximizing the profit and minimizing consumption, disassembly difficulties, and the number of workstations. Then, a new multi-objective artificial bee colony algorithm is proposed, which is combined with stochastic simulation. The effectiveness and feasibility of the proposed algorithm are verified and experimental results show that the proposed algorithm outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimization algorithm.

Ferroptosis is an iron-dependent pathway of regulated cell death. But the exact mechanism of ferroptosis in ischemic stroke remains unclear. We hypothesize that conventional protein kinase cγ (cPKCγ) can attenuate neuronal death by regulating ferroptosis. In this study, primary cultured cortical neurons were used to establish 1 h oxygen-glucose deprivation (OGD) and reoxygenation (R) 0-12 h (i.e., 1 h OGD/R 0-12 h) as in vitro models of cell ischemia. After 1 h OGD/R 0-12 h, cyclooxygenase 2 (COX2) and acyl-CoA synthetase long-chain family member 4 (ACSL4) levels increased, and glutathione peroxidase 4 (GPx4) levels decreased significantly. Concurrently, GPx4 activity decreases, and iron levels increased. The inhibition of ferroptosis by Liproxstatin-1 ameliorated OGD-induced neuronal injury. Liproxstatin-1 administration prominently induced GPx4 expression and suppressed COX2 expression. Additionally, Liproxstatin-1 administration substantially reduced iron accumulation and rescued GPx4 activity, accompanying by prominent changes in lipid peroxidation indicators. cPKCγ knockdown significantly aggravated neuronal death, and increased GPx4 depletion and COX2 and ACSL4 levels, thus dramatically increasing iron accumulation and GPx4 inactivation. Changes in lipid peroxidation indicators were also significantly increased. Ferroptosis is closely associated with OGD-induced ischemic injury, and cPKCγ can attenuate ischemic injury after OGD via ferroptosis suppression.

Red mud is a solid waste in the production of alumina from bauxite. Red mud iron alloy (RMIA) is prepared from the red mud and laterite nickel ore via the high-temperature carbothermal reduction smelting and refining. Herein, by adjusting the contents of chromium (Cr) and titanium (Ti) in RMIA, low alloy cast iron with high hardness and wear resistance was obtained. Optical emission spectrometer, optical microscope, scanning electron microscopy, and hardness and two-body wear test were used for characterization. The results show that the addition of either Cr or Ti element can lead to carbide formation and refining of graphite. With an increase in the content of Cr or Ti, the hardness and the wear resistance of the alloy were enhanced, and the effect of Cr element was better than that of Ti element. The optimal microstructure and properties of the alloy were obtained at 4.8%Cr0.36%Ti, at which stage, the network eutectic carbide structure disappeared and evenly distributed in the matrix, and meanwhile, the graphite morphology was refined. The hardness of the alloy was higher than high chromium cast iron (HCCI), and the wear resistance of the modified alloy was better than HCCI under low load. The aim of this work is to provide a reference for the preparation of wear-resistant ferroalloys from the red mud directly via high-temperature carbothermal reduction.

The circular economy follows the principle of reducing resource usage and energy consumption, reusing usable resources including subassemblies and components in discarded or used products, and recycling usable materials. It is guided by saving resources, improving the utilization rate of resources, reducing pollution, and protecting an ecological environment. Effective product disassembly planning methods can improve recovery efficiency and promote the circular economy. However, the existing studies pay little attention to sequential dependency disassembly, which makes it difficult to implement the existing planning methods under the constraints of limited disassembly methods and tools. In this paper, a single-objective sequence-dependent disassembly line balancing problem (SDLB) is studied. This problem requires that disassembly tasks are assigned to a group of orderly disassembly workstations to obtain the near optimal solution while meeting a disassembly priority constraint. Because solution complexity increases with the number of parts in a product, an improved artificial bee colony method (IABC) is proposed to solve the problem. Through experiments and compared with a genetic algorithm, the effectiveness of the proposed algorithm is verified.

This paper proposes a trifrequency reconfigurable antenna (FRA), which can work in the X-band, Ku-band, and Ka-band, by controlling only two RF MEMS switches. The antenna element has a frequency ratio beyond 3 : 1 and provides a good candidate for the frequency reconfigurable antenna array, since the size of the antenna is reduced by loading multiple metal shorting holes between the antenna radiating surface and the ground plate, and the overall size is only 0.14λX × 0.35λX (λX is the free-space wavelength at 8.6 GHz). Based on the proposed FRA element, a 1 × 16 linear irregular frequency reconfigurable antenna array (FRAA) with beam deflection ability is designed, which effectively addresses the element spacing problem in the optimization of the array. In addition, the close-coupling in X-band and the grating lobe caused by the long distance of array element spacing in Ka-band are comprehensively considered. With uniform amplitude feeding network, the sidelobe level is below −15 dB under beam deflection. Moreover, both FRA elements and FRAA prototypes have been fabricated and measured to verify their superiority. Good agreements are obtained between simulated and measured results, which indicates that the antenna has potential application in the future multifrequency wireless communication and intelligent radar anti-interference fields.

Moving target detection is widely used in the field of intelligent monitoring. This paper proposes an Improved Frame Difference Method Fusion Horn-Schunck Optical Flow Method (IFHS) algorithm to detect moving targets in video surveillance. First, a three-frame difference method is performed on the image to obtain a difference image. Second, the differential image is tracked according to the HS optical flow method to obtain an optical flow vector. Finally, the moving target is obtained by fusing the optical flow vector information of the two difference results. The problem of incomplete contours of moving targets detected in video surveillance by the separate optical flow method and the three-frame difference method is solved. The experimental simulation and experimental applications in different scenarios prove the robustness and accuracy of the method.

It is known that after segmentation and morphological operations on scanned topographic maps, gaps occur in contour lines. It is also well known that filling these gaps and reconstruction of contour lines with high accuracy and completeness is not an easy problem. In this paper, a novel method is proposed dedicated in automatic or semiautomatic filling up caps and reconstructing broken contour lines in binary images. The key part of end points’ auto-matching and reconnecting is deeply discussed after introducing the procedure of reconstruction, in which some key algorithms and mechanisms are presented and realized, including multiple incremental backing trace to get weighted average direction angle of end points, the max constraint angle control mechanism based on the multiple gradient ranks, combination of weighted Euclidean distance and deviation angle to determine the optimum matching end point, bidirectional parabola control, etc. Lastly, experimental comparisons based on typically samples are complemented between proposed method and the other representative method, the results indicate that the former holds higher accuracy and completeness, better stability and applicability.

Owing to the limitation of DNA-specific recognition, improving the sensitivity of bacteria detection without additional culture poses a considerable challenge. Herein, a sensitive, novel, and high-specificity fluorescence detection method was developed using transcription activator-like effectors (TALEs) as the specifically recognized element. Based on the property of TALEs for recognizing double-stranded DNA, the detection method was first proposed. Its sensitivity was further improved by isothermal amplification of RPA. The "humanoid" molecular beacon (H-MB) was designed to achieve signal output. The steric hindrance of the protein opens the stem, enhancing the fluorescence intensity of an attached FAM/BHQ-1 pair. This method can be used to detect Escherichia coli O157:H7 with single-base mismatch specificity and high sensitivity (detection limit of 3 cfu mL-1) and can be applied in actual sample detection by boiling the sample for 10 min. TALEs can be assembled according to the target, so the method can be used to detect other foodborne pathogens and viruses in the food safety and clinical fields.

Traffic prediction is the key to intelligent traffic guidance, management and control. Aiming at the spatiotemporal characteristics of urban traffic and the complexity of nonlinear variation of traffic parameters, a long short-term memory network (LSTM) urban traffic condition prediction model (SA-LSTM) based on self-attention mechanism is proposed. SA-LSTM uses the self-attention mechanism to assign different weights to traffic state information in different space and time, reflecting the spatio-temporal correlation of traffic data prediction. It can also avoid the vanishing gradient problem faced by traditional RNN and improve the defect that LSTM cannot accurately express the different importance and spacio-temporal characteristic of traffic information. Based on SA-LSTM, experiments were conducted on the Shenzhen road network data and floating car data. LSTM and SA-LSTM were selected for Comparative verification experiments, and the results confirmed that SA-LSTM is superior to LSTM in multiple evaluation indicators. Moreover, the road spatio-temporal correlations obtained by traffic data analysis and obtained by model learning are highly consistent, which proves that SA-LSTM can precisely learn and express the spatio-temporal characteristics and changing trend of the traffic.

To develop nanocrystalline soft magnetic composites (NSMCs) with good combination of soft magnetic properties, the core-shell structured FeSiBNbCu@Fe3O4 powders were prepared by nitric acid (HNO3) oxidation of the nanocrystalline FeSiBNbCu alloy. The NSMCs were then fabricated by binding and pressing with resin followed by annealing treatment. The formation mechanism of the Fe3O4 layer on the FeSiBNbCu powders was discussed and the effects of HNO3 concentration and annealing temperature on the magnetic properties of the NSMCs were investigated. It was found that the sharp edges of the initial powders disappeared after the powder was oxidized with 30 wt% HNO3. A complete and uniformly distributed Fe3O4 layer formed on the powder surface. For the NSMCs annealed at 500 °C, the formation of Fe3O4 layer by HNO3 oxidation treatment significantly reduced the core loss but also decreased the permeability. After annealing at 550 °C and 600 °C, the permeability of the NSMCs can be greatly improved. The formed Fe3O4 layer can resist the high annealing temperature up to 600 °C, which is beneficial to effectively releasing the residual stress. The core loss shows a significant increase as the annealing temperature over 600 °C, which is mainly attributed to the decomposition of resin binders. The excellent magnetic properties with permeability μe = 80 and core loss Ps = 269 kW/m3 at 50 mT and 100 kHz have been achieved in the present work.

Adenosine is a potent physiological and pharmacological regulator, and its abnormal level is closely related to disease development. The sensitive and specific detection of adenosine is crucial for health evaluation and disease diagnosis. In this work, a target triggered proximity combination-based fluorescence sensing strategy is developed for the sensitive and specific detection of adenosine. A difunctional probe showing target recognition and signal amplification is designed, by integration of DNA linker-connected split aptamer fragments with a fragment-elongated polymerase/nicking template. The presence of adenosine would glue the split aptamers, which triggers the two distal aptamer fragments to combine with each other into proximity. The approaching aptamer fragment ends then initiate the strand displacement amplification (SDA) reaction, generating numerous DNA primers. The DNA primers further hybridize with a padlock probe and initiate the rolling circle amplification (RCA) reaction, producing numerous G-quadruplex sequences. The G-quadruplex sequences finally bind with Thioflavin T to obtain enhanced fluorescence signals. The method exhibits a linear correlation within the adenosine concentration range from 5.0 × 10-7 M to 2.0 × 10-5 M (R = 0.999) with a detection limit of 8.4 × 10-8 M, and a good selectivity to distinguish adenosine from its analogues. The recoveries of adenosine in human serum are from 91% to 94%, demonstrating that the system works well in biological fluids. The proposed sensing strategy is anticipated to hold promise in biochemical research, clinical diagnosis and disease treatment.

Herkinorin is an opiate analgesic with limited adverse effects, functioning as a primary selective atypical opioid µ agonist. The present study aimed to identify whether herkinorin has a positive effect on ischemic/reperfusion (I/R) injury. Adult male C57BL/6 mice were randomly divided into five groups: i) Naive, ii) sham, iii) I/R, iv) I/R with dimethyl sulfoxide (I/R+D) and v) I/R with herkinorin (I/R+H). The I/R injury model was induced by occluding the middle cerebral artery for 1 h followed by 24 h or 7 days of reperfusion. Neurobehavioral scores and sensorimotor functions were examined 24 h and 7 days following reperfusion. In addition, infarct volumes were examined at these time points using a 2,3,5‑triphenyltetrazolium chloride assay. Herkinorin treatment improved neurobehavioral and sensorimotor functional recovery from I/R‑induced brain injury. There was a significant decrease in infarct volume in the I/R+H group at 24 h or 7 days following reperfusion compared with the I/R and I/R+D groups. Western blotting suggested that the decrease in conventional protein kinase C γ (cPKCγ) membrane translocation in the peri‑infarct region may be attenuated by herkinorin pretreatment. These results indicated that herkinorin may be beneficial in I/R‑induced mouse brain injury, and this may be attributed to the membrane translocation of cPKCγ following activation.

Multiplex DNA methylation and glycosylation are ubiquitous in the human body to ensure the normal function and stability of the genome. The methyltransferases and glycosylases rely on varied enzymes with different action mechanism, which still remain challenges for multiple detection. Herein, we developed a tri-functional dsDNA probe mediated exponential amplification strategy for sensitive detection of human DNA (cytosine-5) methyltransferase 1 (Dnmt1) and uracil-DNA glycosylase (UDG) activities. The tri-functional dsDNA probe was rationally designed with M-DNA and U-DNA. M-DNA contains the 5′-GCmGCGC-3′ site for Dnmt1 recognition. U-DNA possesses one uracil as the substrate of UDG and a primer sequence for initiating the amplification reaction. M-DNA was complementary to partial sequence of U-DNA. In the presence of Dnmt1 and UDG, BssHⅡ and Endo Ⅳ were used to nick the 5′-GCGCGC-3′ and AP sites respectively, resulting in the release of single-stranded DNA sequence (primer sequence), respectively. After magnetic separation, the released primer sequence hybridizes with padlock DNA (P-DNA), initiating exponential rolling circle amplification to produce numerous G-quadruplexes for recordable signals. The strategy exhibited the limit of detection as low as 0.009 U mL−1 and 0.003 U mL−1 for Dnmt1 and UDG, respectively. Meanwhile, this strategy was successfully applied to detect Dnmt1 and UDG activities in living cell samples at single-cell level and assay the inhibitors of Dnmt1 and UDG. Therefore, the strategy provided a potential method to detect Dnmt1 and UDG activities in biological samples for early clinic diagnosis and therapeutics.

Objectives The sequential reactivation of mechanistic target of rapamycin (mTOR) inhibited autophagic flux in neurons exposed to oxygen-glucose deprivation/reperfusion (OGD/R), which was characterized by reduction of autophagosome formation and restriction of autolysosome degradation. However, its detailed molecular mechanism was still unknown. In this study, we further explore the existing form of mTOR and its suppression on the transcriptional levels of related mRNA from neurons exposed to ischemia-reperfusion injury. Methods The OGD/R or middle cerebral artery occlusion/reperfusion (MCAO/R)-treated neurons was used to simulate ischemia/reperfusion injury . Autophagy flux was monitored by means of microtubule-associated protein 1 light chain 3 (LC3) and p62. The reactivation of mTOR was determined by phosphorylation of ribosomal protein S6 kinase 1 (S6K1). Then the inhibitors of mTOR were used to confirm its existence form. Finally, the mRNA transcription levels were analyzed to observe the negative regulation of mTOR. Results The sequential phosphorylation of mTOR contributed to the neuronal autophagy flux blocking. mTOR was re-phosphorylated and existed as mTOR complex 1 (mTORC1), which was supported by phosphorylation of S6K1 at Thr 389 in neurons. In addition, the phosphorylation of S6K1 was decreased roughly by applying mTORC1 inhibitors, rapamycin and torin 1. However, the administration of mTORC1/2 inhibitor PP242 could recover the phosphorylation of S6K1, which suggested that mTORC2 was involved in the regulation of mTORC1 activity. In paralleling with reactivation of mTORC1, related mRNA transcription was repressed in neurons under ischemia-reperfusion exposure in vivo and in vitro. The mRNA expression levels of LC3, Stx17, Vamp8, Snap29, Lamp2a, and Lamp2b were decreased in neurons after reperfusion, comparing with ischemia-treated neurons. Conclusions The reactivated mTORC1 could suppress the transcription levels of related mRNA, such as LC3, Stx17, Vamp8, Snap29, Lamp2a, and Lamp2b. The research will expand the horizons that mTOR would negatively regulate autophagy at transcription and post-translation levels in neurons suffering ischemia-reperfusion injury.

In order to improve the recognition rate of multi-face image, this paper proposes a face image recognition method based on haar-like and Euclidean distance. First of all, the initial features of Haar are moved in the image and gradually enlarged, the pixel sum of the feature area is obtained quickly by using the integral graph. The Haar feature is obtained by calculating the difference between black and white pixels in the feature region, and the threshold of weak classifier is calculated. Then, the extracted facial feature data is trained and classified to form a cascade classifier for face detection. Finally, the face of the image to be detected is compared with the training samples by using Euclidean distance. The experimental results show that the time of one attendance is 5-10 seconds and the success rate of face recognition is 91.1%, which verifies the advantage of the algorithm in improving the efficiency of attendance.

The train wheel rim thickness is always manually measured by using handheld devices or in a non-contact way by using laser and image techniques. To reduce the operating costs, this work studied some soft measurement methods with classical machine learning algorithms including neural network (NN), locally weighted linear regression (LWLR), and support vector machine (SVM). By analyzing the correlation between features, it can improve the efficiency of the model. Some approaches were used to optimize the number of hidden layer neurons in NN, parameters in LWLR and SVM. Experiments on real data are conducted, and the results show that the proposed methods can ensure high precision and accuracy, while NN has the highest accuracy.

The mercuric ion is a highly toxic contaminant and causes severe harm to the environment and human health. Herein, a T–Hg2+–T metallo-base pair-mediated dual amplification fluorescent strategy was proposed for the selective and sensitive detection of Hg2+ based on a target cycle and DNAzyme cycle. First, Hg2+ selectively bound with T–T mismatches in H-DNA and A-DNA to form stable T–Hg2+–T metallo-base pairs. This initiated the strand displacement between H-DNA and A-DNA to obtain the Hg2+-mediated partial double-stranded structure, with a blunt 3′-terminus of the opened H-DNA (donated as the Hg-complex). Next, under the action of Exo III, the Hg-complex was digested to release DNAzyme, A-DNA and Hg2+. The released Hg2+ could bind with another A-DNA and H-DNA, and the target cycle started anew, eventually generating numerous DNAzymes. DNAzymes then catalyzed the cleavage of a molecular beacon (MB) to generate free fluorophores. Upon cleavage, DNAzymes were released and continuously hybridized with another MB to trigger a second DNAzyme cycle. Finally, numerous fluorophores were liberated, resulting in a significantly amplified signal. The strategy showed a good linear relationship in the range from 2.0 × 10−10 mol L−1 to 1.0 × 10−8 mol L−1, with a detection limit of 7.2 × 10−11 mol L−1. The proposed strategy exhibited remarkable selectivity towards Hg2+ against other metal ions. Furthermore, this strategy was successfully applied to detect Hg2+ in real water samples. The proposed strategy provided a reliably quantitative candidate for potential application in environmental monitoring and biotoxicity analysis.

The sensitive detection of clinically significant DNA is of critical importance in early clinical diagnostics and medical research. Herein, we developed a sensitive fluorescent method for the detection of DNA fragments from the breast cancer 1 gene based on Au nanoparticles (AuNPs) fluorescence switch-mediated target recycling amplification. First, the designed FAM-labeled single-stranded DNA recognition probes at the 5′-terminus (donated as F-DNA) were adsorbed on the surface of AuNPs, followed by the substantial fluorescence quenching of the FAM. Then, the F-DNA specifically hybridized with the complementary region of the target DNA (T-DNA) and desorbed from the AuNPs surface, leading to the recovery of the partially quenched fluorescence. Finally, under the action of Exo III, T-DNA was released and hybridized with F-DNA for the target recycling; thereby large amounts of fluorescent probe fragments were obtained, resulting in significant fluorescent amplification for T-DNA detection. The proposed strategy exhibited a detection limit of 1.0 × 10−11 mol L−1 and good selectivity towards the mismatched T-DNA, which was better than or comparable to the existing nanomaterial-based fluorescent methods. The method possessed perfect recoveries in the human serum and cell lysate. Therefore, the proposed strategy would offer a new potential for quantification of specific DNA sequences in early clinical diagnostics and medical research.

The current TFIDF (Term Frequency and Inverted Document Frequency) algorithm cannot effectively reflect the relationship between the importance of a word and its distribution. This paper proposes a Class Variance-Term Frequency and Inverted Document Frequency algorithm. This algorithm improves the TFIDF algorithm based on three distribution factors: category, inter-class and variance. In order to measure the optimization effect of this method, three algorithms were compared using the original algorithm, improved algorithm and TFIDF algorithm based on dual parallel calculation model. Experiments show that the improved algorithm has significantly improved recall, accuracy, and F metric values, comparing with the original algorithm, and has improved compared with the TFIDF algorithm based on dual parallel calculation model. Therefore, the improved algorithm can well adapt to the demand for feature word extraction and has better text classification performance.

The traditional dark channel priori has been successfully applied to the single image deblurring problem. According to the characteristics of the dark channel priori, clear images are recovered. However, when the fuzzy image has significant noise pollution, the dark channel prior can not play a role in the fuzzy kernel estimation. The new image denoising method based on the rational order differential inherits the advantages of the total variation denoising method which greatly improves the high frequency part of the image and the fractional order differential denoising method which can well retain the texture details of the image. In this paper, the theory of rational order differential calculation is combined with the dark channel priori of fuzzy image, and an image deblurring method based on the improved dark channel priori is proposed. The specific work is as follows: combining the maximum posterior estimation algorithm and the rational order dark channel prior, a fuzzy image model is constructed; furthermore, the model is solved by using the semi quadratic splitting method. Finally, the multi-scale iterative framework is used to estimate the fuzzy kernel of the accurate image, and then the new non blind image deblurring algorithm can be used to solve the clear image. Experimental results show that the method is effective.

The algorithm based on kernel correlation filtering shows excellent performance. Aiming at the problem that traditional correlation filtering algorithms are sensitive to deformation and cannot solve the tracking failure caused by scale changes, this paper proposes a scale-adaptive correlation filtering algorithm combining statistical color feature. Firstly, predict and train the HOG features of the correlation filter and the color histogram features to obtain the respective response values, namely the template response and the histogram response. Then adopt the block method, and perform scale prediction based on the position of each response point. Finally, the final fusion response is calculated. The experimental results demonstrate that compared with traditional KCF, when the target scale has large scale change and deformation, the improved algorithm in this paper performs better, and can still track stably.

Moving target tracking is one of the core issues in the fields of automobile assisted driving and autonomous driving. This algorithm proposes a target tracking algorithm suitable for fisheye cameras in vehicle environment. A background point removal method based on median ordering is proposed to optimize the optical flow method to solve the problem of target scale change in fisheye camera videos. Multi-scale scaling processing for frames, tracking algorithm based on cyclic matrix structure (CSK), target tracking for multiple images after zooming, using fitting to determine the maximum corresponding value, reducing the error caused by target deformation on tracking results. The tracking test results of multiple fisheye camera videos in the vehicle environment show that the algorithm has high real-time tracking accuracy.

Target tracking algorithm based on deep learning generally adopts offline trained deep network to extract features, but with unsatisfactory separation effect of the target and the background. A target tracking algorithm based on deep optimization features is proposed to solve this problem. First, the Conv3-1 and Conv4-3 layers are selected from the offline trained convolutional neural network model VGGNet16 for basic feature extraction. Then the loss function is used to quantitatively describe the effectiveness of different filter positioning target in the basic feature layer. Finally, the selected high-efficiency filters are fused to extract deep optimization features and achieve effective tracking under the correlation filtering framework. The experimental results show that compared with other related algorithms, this algorithm can better recognize the target and present better results.

This paper proposes a shortest path planning of agent in an environment based on reinforcement learning. This method adopts the Q-learning algorithm, which has gained increasingly using in agent path planning recently. This algorithm can be fully designed to a reasonable environment model and applied to other professional fields. Yet, reinforcement learning in path planning still needs to improve the rate of convergence. The learning process takes several iterations and spends a long time to find the final path. A local optimal problem in search process deals with other explorations that provided shortest path for agent to take optimal actions. For the agent, the reward value of inclined movement is introduced to find the shortest path. The autonomous obstacle avoidance is utilized to obtain the optimal path in the process. Finally, the model of intelligent agent movement environment has established in matlab. The effectiveness of the algorithm has proved by simulation results.

Focusing on the issue that the kernel correlation filter tracking method can not accurately adapt to the target scale change, a scale estimation method is added under the kernel correlation filter tracking frame, and a scale adaptive target tracking algorithm is proposed. The algorithm firstly uses the kernel function to obtain the kernel correlation filter, and estimates the position of the target for the candidate sample detection; then uses the scale estimation method to detect the target scale to further determine the optimal scale of the target to be tracked in the current frame; Finally, according to the occlusion judgment mechanism, the parameters of the target performance model and the scale model are updated online. The experimental results show that compared with kernel correlation filtering algorithm, the proposed algorithm can accurately target tracking process to adapt to changes in the target scale, and the center position error reduced by an average of 4.39 pixels.

For network control systems with long delay and packet loss, firstly, it is based on the effectiveness of model-based control in network systems with long delay, and it also establish a feedback model that simulates delay as a system component. Considering the possible modeling errors of the feedback model, a design of the traditional linear system is proposed to design the controller by using the communication interference observer to compensate for the delay characteristics. Then, the steady-state error of the characteristic equation of the system is obtained and prove the feasibility and robustness of the method, and carried out the equivalent analysis of packet loss. Finally, the effectiveness of the controller is demonstrated by the simulation of communication interference data from comparing with the Smith predictor.

Objectives To assess the efficacy and safety of istradefylline as an adjunct to levodopa in patients with Parkinson's Disease (PD). Methods In this study, we searched the Cochrane Library, MEDLINE, Embase, China Academic Journal Full-text Database (CNKI), China Biomedical Literature Database (CBM), Chinese Scientific Journals Database (VIP), and Wanfang Database. The quality of included studies was strictly evaluated. Data analyses were performed by the Cochrane Collaboration's RevMan5.0 software. Results Five randomized controlled trials (RCTs) were included. The result showed a significant reduction of the awake time per day spent in the OFF state and improvement of the Unified Parkinson's Disease Rating Scale (UPDRS) Part III in the ON state when receiving istradefylline compared with patients receiving placebo. There was no significant difference between the istradefylline 20 mg and the istradefylline 40 mg groups in the UPDRS Part III in the ON state (WMD = 1.27, 95% CI [− 0.40, 2.95]). The results showed significant differences in dyskinesia (RR = 1.63, 95% CI [1.16, 2.29]) compared to istradefylline 40 mg with placebo. There was no significant statistical difference with regard to other adverse events. Conclusions The present study showed that istradefylline is safe and effective as an adjunct to levodopa in patients with PD. Future large-scale, higher-quality, long-treatment, and placebo-controlled trials are needed.

Here, we explored a modular strategy for rational design of nuclease-responsive three-way junctions (TWJs) and fabricated a dynamic DNA device in a "plug-and-play" fashion. First, inactivated TWJs were designed, which contained three functional domains: the inaccessible toehold and branch migration domains, the specific sites of nucleases, and the auxiliary complementary sequence. The actions of different nucleases on their specific sites in TWJs caused the close proximity of the same toehold and branch migration domains, resulting in the activation of the TWJs and the formation of a universal trigger for the subsequent dynamic assembly. Second, two hairpins (H1 and H2) were introduced, which could coexist in a metastable state, initially to act as the components for the dynamic assembly. Once the trigger initiated the opening of H1 via TWJs-driven strand displacement, the cascade hybridization of hairpins immediately switched on, resulting in the formation of the concatemers of H1/H2 complex appending numerous integrated G-quadruplexes, which were used to obtain label-free signal readout. The inherent modularity of this design allowed us to fabricate a flexible DNA dynamic device and detect multiple nucleases through altering the recognition pattern slightly. Taking uracil-DNA glycosylase and CpG methyltransferase M.SssI as models, we successfully realized the butt joint between the uracil-DNA glycosylase and M.SssI recognition events and the dynamic assembly process. Furthermore, we achieved ultrasensitive assay of nuclease activity and the inhibitor screening. The DNA device proposed here will offer an adaptive and flexible tool for clinical diagnosis and anticancer drug discovery.

We have reported that neuron-specific conventional protein kinase C (cPKC)γ is involved in the development of cerebral hypoxic preconditioning (HPC) and the neuroprotection against ischemic injuries, but its molecular mechanism is unclear. In this study, the adult and postnatal 24 h C57BL/6J wild-type (cPKCγ+/+) and cPKCγ knockout (cPKCγ−/−) mice were respectively used to establish the models of middle cerebral artery occlusion (MCAO)-induced ischemic stroke in vivo and oxygen-glucose deprivation (OGD)-treated primarily cultured cortical neurons as cell ischemia in vitro. The results showed that cPKCγ knockout could increase the infarct volume and neuronal cell loss in the peri-infarct region, and enhance the neurological deficits, the impaired coordination, and the reduced muscle strength of mice following 1 h MCAO/1–7 days reperfusion. Meanwhile, cPKCγ knockout significantly increased the conversion of LC3-I to LC3-II and beclin-1 protein expression, and resulted in more reductions in P-Akt, P-mTOR, and P-S6 phosphorylation levels in the peri-infarct region of mice with ischemic stroke. The autophagy inhibitor BafA1 could enhance or reduce neuronal cell loss in the peri-infarct region of cPKCγ+/+ and cPKCγ−/− mice after ischemic stroke. In addition, cPKCγ knockout and restoration could aggravate or alleviate OGD-induced neuronal ischemic injury in vitro through Akt-mTOR pathway-mediated autophagy. These results suggested that cPKCγ-modulated neuron-specific autophagy improves the neurological outcome of mice following ischemic stroke through the Akt-mTOR pathway, providing a potential therapeutic target for ischemic stroke.

A fast multi-baseline Interferometric synthetic aperture radar (InSAR) phase ambiguity resolving method is presented. The ambiguity solution vector of the shortest baseline is first computed and used as a reference, and then the ambiguity solutions of all the other baselines are selected with logical judgment of phase difference between the measured phase and the wrapped phase, which is obtained by rewrapping the product of the referenced phase and the ratios of the other baselines to the referenced baseline. 1-dimensional searching is then executed on the same index of the reserved ambiguity solutions vectors. Furthermore, the wrong estimates affected by noise are eliminated by taking the largest frequency of the ambiguity numbers counted in a window. Compared to the conventional Chinese remainder theory (CRT), the proposed method is more robust and has much less computational complexity. The validity is investigated with simulated results. Index Terms - multi-baseline interferometric synthetic aperture radar (InSAR), phase ambiguity resolving, Chinese remainder theory (CRT)

The prefrontal cortex is crucial for memory encoding and processing, in which the lateral prefrontal cortex (LPFC) is more involved in semantic and episodic memory, whereas the medial prefrontal cortex (MPFC) is more related to the associative information processing and social cognition. Social statistical information is a kind of typical associative information with sociality. However, the role of the prefrontal cortex in comprehending the social statistical information remains unknown yet.This study focused on the brain activities of 36 normal subjects in the prefrontal cortex using fMRI while they viewed the social statistical information presented in either visual form as a graph or textual form as a verbal description of the information in the graph. The results showed that the graph and textual tasks consistently activated the anterior and posterior portions of ventrolateral prefrontal cortex (VLPFC), the dorsal and ventral MPFC. The results suggest that the VLPFC and the MPFC commonly contribute to the social statistical information processing.

To assess the efficacy and safety of istradefylline as an adjunct to levodopa in patients with Parkinson's Disease (PD).In this study, we searched the Cochrane Library, MEDLINE, Embase, China Academic Journal Full-text Database (CNKI), China Biomedical Literature Database (CBM), Chinese Scientific Journals Database (VIP), and Wanfang Database. The quality of included studies was strictly evaluated. Data analyses were performed by the Cochrane Collaboration's RevMan5.0 software.Five randomized controlled trials (RCTs) were included. The result showed a significant reduction of the awake time per day spent in the OFF state and improvement of the Unified Parkinson's Disease Rating Scale (UPDRS) Part III in the ON state when receiving istradefylline compared with patients receiving placebo. There was no significant difference between the istradefylline 20mg and the istradefylline 40 mg groups in the UPDRS Part III in the ON state (WMD=1.27, 95% CI [-0.40, 2.95]). The results showed significant differences in dyskinesia (RR=1.63, 95% CI [1.16, 2.29]) compared to istradefylline 40 mg with placebo. There was no significant statistical difference with regard to other adverse events.The present study showed that istradefylline is safe and effective as an adjunct to levodopa in patients with PD. Future large-scale, higher-quality, long-treatment, and placebo-controlled trials are needed.

This paper analyses the primary problems which have existed in urban evacuation, after researching the status of urban evacuation home and abroad, discusses the key technology of evacuation. This paper designs a frame of urban evacuation based on GIS and Intelligent Traffic Technology, with dynamic optimization theory as its foundation. This frame includes several modules as following: electronic map operating module, GIS spatial analysis module, GIS visualization module, command and communication module, evacuation scheme forming module, dynamic traffic flow information catching in real time module, evacuation information distributing module. With the help of Intelligent Traffic Technology, the decision-maker gets the dynamic traffic flow information of evacuation area in real time, modulates the evacuation scheme in real time. Achieving the aim that the shortest evacuate time, the most evacuate people, the highest evacuation safety.

In this paper, an algorithm for hyperspectral image compression is presented. It carries DCT (Discrete Cosine Transform) on spectral bands to exploit the spectral correlation and then DWT (Discrete Wavelet Transform) on every eigen image to exploit the spatial correlation. After that, 3D-SPIHT (three-dimensional Set Partitioning in Hierarchical Trees) is performed for encoding. Experiments were done on the OMIS-I (Operational Modular Imaging Spectrometer) image and the performance of this algorithm was compared with that of 2D-SPIHT. The results show that the performance of 3D-SPIHT based on DCT and DWT is much better than that of 2D-SPIHT and the quality of the reconstructed images is satisfying.

v-Support vector machine(v-SVM) is one of the most widely used support vector machines for providing a way to control the classification precision of training. However, its different classification precision of the two training classes with different sizes and neglect of the important level of training samples prevent it from being applied to some applications in which the training classes' sizes are uneven and the importance of samples is different from each other. In this paper, a class and sample weighted v-SVM is proposed. It introduces class weights and sample weights into the error penalty part of the objective function. Theoretical analysis shows that class weights can be used to control the classification precision of each class, and sample weights can be used to increase the probability of correct classification of some samples.