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To generate a high-spatial-resolution hyperspectral (HHS) image from a high-spatial-resolution multispectral (HMS) image, both spatial information and spectral information should be considered simultaneously if we want to build a more accurate mapping from HMS to HHS. To this end, a spectral and spatial jointed spectral super-resolution method is proposed in this letter using an end-to-end learning strategy for each subspace with the cluster-based multibranch backpropagation neural network (BPNN). More specifically, in addition to the spectra similarity, a modified superpixel segmentation is introduced to jointly take spatial contextual information into account, and a new framework with it is given. Comparisons on the Columbia University Automated Vision Environment (CAVE) data set show that our proposed method outperforms other relative state-of-the-art methods more than 0.3 in the root mean squared error (RMSE) and more than 1.0 in the spectral angle mapper (SAM) index. Especially, an exemplary application is demonstrated using the synchronized observation data collected by the multispectral and hyperspectral sensors mounted on the HJ-1A satellite at the same time.

Anomalies in remote sensing images are generally reflected in two aspects of spatial and spectral ones, as for the anomaly detection of sea surface using multispectral or hyperspectral images, spectral information is more important. To this end, a novel spectral anomaly detection method based on dictionary optimization is proposed in this letter. More specifically, the normal scene is first defined to distinguish the anomaly. Then, without any assumption about the distribution of anomaly, a spectral dictionary is formulated and derived theoretically with optimization to express the normal scenes. Using the sparse and low-rank constraints, the alternating direction method of multiplier (ADMM) is employed to solve the above optimization in the spectral domain. Finally, for a given sea-surface image to be detected, the error matrix that cannot be fully expressed by the optimized spectral dictionary is regarded as anomalies. It shows certain generality for various kinds of spectral anomalies on the sea surface. Taking multispectral images obtained by the HY-1C satellite as an example, comparisons with related state-of-the-art methods demonstrate that our proposed method achieves the best anomaly detection performance not only for oil-spill pollution but also for algae pollution.

Purpose: To investigate the effect of miR-486 on rats with acute myocardial infarction (AMI), and its mechanism of action.Methods: A rat model of AMI was established. They were randomly divided into 4 groups, namely, sham, model, agomiR-486 and antagomiR-486 groups, respectively. Rats in these different groups were treated with agomiR-21 (5 μL, 40 nmol/mL), antagomiR-21 (5 μL, 40 nmol/mL) or agomiR-NC (5 μL, 40 nmol/mL), respectively. Then, key miRNAs were sorted out using gene-chip assay and verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay. Luciferase reporter gene assay was conducted to determine the interaction between miR-486 and gene of PTEN. After intraperitoneal injection of agomiR-486 and antagomiR-486, hemodynamics was measured to determine the effect of miR-486 on myocardial function of the rats. The effect of miR-486 expression level on the expression of myocardial enzymes in serum, the morphology of myocardial tissues, and the apoptosis of myocardial tissues in rats, were investigated. Additionally, the effect of miR-486 expression level on PTEN/AKT signaling pathway in the rats was determined by Western blotting.Results: The results of gene-chip and qRT-PCR assays revealed that there were 8 differentially expressed genes in rat myocardial tissues in the model group when compared with the sham group. MiR-486 improved the cardiac function of rats and the morphology of myocardial tissues, but reduced AMI-induced apoptosis of myocardial cells and the expression of myocardial enzymes (markers of myocardial injury) in a dose-dependent manner (p < 0.05). The results of luciferase reporter gene assay showed that PTEN was a direct target of miR-486. In rat models of AMI, a raised expression of miR-486 remarkably suppressed the protein expression level of PTEN and up-regulated that of p-AKT/AKT (p < 0.05).Conclusion: MiR-486 protects against AMI in rats probably by targeting PTEN and activating the AKT signaling pathway. The results of the current study may provide new insights for the treatment of AMI.

In this work, iridium(III)bis(4-(tert-butyl)-2,6-diuoro-2,3-bipyridinne)acetylacetonate (FK306) was utilized as sensitizer and incorporated into both light-emitting and electron transport layers to constitute a double-sensitized system because of its low-lying energy levels and well matched triplet energy. Experimental results confirmed that the incorporated FK306 molecules were helpful in effectively modulating the transportation of electrons due to sensitizing effect, which caused decreased electron accumulation, improved carrier balance and thus enhanced carrier recombination probability. Furthermore, the presence of FK306 molecules within light-emitting and electron transport layers helped to broaden the recombination zone and suppress exciton quenching, thus leading to higher efficiency and slower efficiency roll-off. Finally, high performance green OLED with a double-sensitized structure obtained the maximum current efficiency of 95.86 cd A−1, external quantum efficiency of 28.6%, power efficiency of 94.06 lm W−1 and brightness of 102 924 cd m−2. And, our experimental results demonstrated that this device design strategy was also efficient in enhancing the performance of red OLEDs.

With the accelerated development of the global economy, the scale of UHV (Ultra High Voltage) large-capacity transmission lines has increased dramatically. Nevertheless, existing transmission line inspection methods rely on human inspectors detecting abnormal facilities from ground level or helicopters. Those methods not only require extremely high labor and equipment costs, but also are time-consuming. In this paper, a thin object 3D reconstruction method is proposed for the extraction of 3D structures of power lines from sequential oblique UAV images. The proposed method recovers the 3D structure of the power lines from three sequential images that depict the structure of power lines in 2D. Proposed method was tested in a typical transmission line corridor and the reconstructed results show the effectiveness of the proposed method.

Purpose Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. Methods The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand-crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. Results The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. Conclusions The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system.

Hyperspectralimages (HSIs) can provide powerful spectral discriminative information for the land-covers, thus is widely used in classification and target detection. However, HSIs always suffer from...

Compressed sensing provides the possibility of efficient compression of massive hyperspectral data. However, the existing methods often use the organization pattern of image blocks in sparse repres...

In this study, an efficient device design strategy in improving the performances of blue fluorescent electroluminescence was demonstrated by co-doping 9-(4-tert-butylphenyl)-3,6-bis(triphenylsilyl)-9H-carbazole (CzSi) into a light-emitting layer (EML) as an electron transport ladder. 9,10-Bis[4-(6-methylbenzothiazol-2-yl)phenyl]anthracene (DBzA) and 4,4′,4′′-tris(carbazol-9-yl)triphenylamine (TcTa) were utilized as the emitter and the main host material, respectively. Compared with the device without CzSi, the performances of the co-doped device were improved effectively because of the well-matched energy levels and excellent electron transport ability of CzSi, which facilitated the injection of electrons and thus balanced the distribution of carriers. The feasibility of this device design strategy was demonstrated by investigating single-carrier devices, transient electroluminescent (EL) spectra and normalized EL spectra. Finally, the optimal pure blue fluorescent device realized a maximum brightness, current efficiency, power efficiency and external quantum efficiency (EQE) of up to 28 009 cd m−2, 7.51 cd A−1, 7.60 lm W−1 and 6.8%, respectively. Even at a high brightness of 1000 cd m−2, current efficiency and EQE as high as 6.21 cd A−1 and 5.6%, respectively, can still be retained by the same device.

Two-step anodization has been widely used because it can produce highly self-organized anodic TiO2 nanotubes, but the differences in morphology and current-time curve of one-step anodization and two-step anodization are rarely reported. Here, one-step anodization and two-step anodization were conducted at different voltages. By comparing the FESEM image of anodic TiO2 nanotubes fabricated by one-step anodization and two-step anodization, it was found that the variation of morphology characteristics is same with voltage. The distinction of morphology and current-time curve between one-step anodization and two-step anodization at the same voltage were analyzed: the nanotube average growth rate and porosity of two-step anodization are greater than that of one-step anodization. In the current-time curve, the duration of stage I and stage II in two-step anodization are significantly shorter than one-step anodization. The traditional field-assisted dissolution theory cannot explain the three stages of the current-time curves and their physics meaning under different voltages in the same fluoride electrolyte. Here, the distinction between one-step anodization and two-step anodization was clarified successfully by the theories of ionic current and electronic current and oxygen bubble mould.

Background and Purpose: Inflammation and immune response play an important role in hemorrhage transformation after acute ischemic stroke. According to previous studies, systemic immune-inflammation index is associated with severity of stroke. We aimed to evaluate the association between systemic immune-inflammation index and hemorrhage transformation in anterior circulation acute ischemic stroke due to large-artery atherosclerosis.Methods: This was a retrospective analysis of patients with anterior circulation acute ischemic stroke due to large-artery atherosclerosis. The laboratory data were collected within 24 h after admission. Hemorrhage transformation was defined on follow-up magnetic resonance imaging or Computed Tomography. The univariate analysis and multivariate logistic regression were performed to assess the association of systemic immune-inflammation index with hemorrhage transformation. Then the relationship between systemic immune-inflammation index and hemorrhage transformation in different stroke subtypes was further studied.Results: We included 310 Chinese anterior circulation acute ischemic stroke patients due to large-artery atherosclerosis (mean age 65 ± 11.4 years; 72.6% male). Hemorrhage transformation occurred in 41 patients (13.2%). After multivariate regression analyses, systemic immune-inflammation index (odds ratio [OR] 1.109, 95% Confidence Interval [CI] 1.054-1.167, p＜0.001) was independently associated with hemorrhage transformation. Systemic immune-inflammation index was found to be significantly related to hemorrhagic transformation in artery-to-artery embolization (OR 1.111, 95% CI 1.029-1.210, p＜0.001) and In-situ thrombosis (OR 1.059, 95% CI 1.011-1.194, p = 0.045).Conclusions: Higher systemic immune-inflammation index is associated with greater risk of hemorrhagic transformation in patients with anterior circulation acute ischemic stroke due to large-artery atherosclerosis, especially in artery-to-artery embolization and In-situ thrombosis.

Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32.

Currently, the global healthcare market is increasing at high speed with the impendent global aging issue. Healthcare Industry 4.0 has emerged as an efficient solution towards the aging issue since it was integrated with ubiquitous medical sensors, big health processing platform, high bandwidth, speed technologies, and medical services, etc. It is believed to fulfil the requirement of the tremendously growing health market. The acquisition of medical data acts as the dominant precondition to implement the Healthcare Industry 4.0. In the same way, the widely available smartphone could serve as the best biomedical information collect station. In this study, a smartphone-powered photochemical dongle is demonstrated to precisely estimate blood creatinine from the fingertip blood, which works as a highly compact reflectance spectral analyzer with an enzymatically dry chemical test strip. Comparing with conventional laboratory facility for the evaluation and treatment of chronic kidney disease (CKD), it implemented the platform of point care with agreed accuracy. In order to estimate the efficiency of treatment and recovery of the CKD, the proposed photochemical dongle would provide a flexible and rapid platform for point of care. Furthermore, the proposed measured technology is very promising method for remote CKD management.

High-spatial hyperspectral (HH) image reconstruction using both high-spatial multispectral (HM) image and low-spatial hyperspectral (LH) image over the same scene is widely used in many real applications. Nevertheless, the pair of HM image and LH image over the same scene is hard to obtain. To solve this problem, a new HH image reconstruction method using spectral library-based dictionary learning (named as HIRSL) is proposed in this paper, only from one HM image. The above reconstruction problem is formulated in the framework of sparse representation, as an estimation of the band matching matrix, the spectral dictionary, and the sparse coefficients. More specifically, a band matching method is proposed for mapping the common spectral library to a specific spectral library corresponding to the reconstructed HH image in spectral domain. Then, an efficient spectral dictionary learning method is proposed for the construction of spectral dictionary using the matched specific spectral library, which avoids the dependence of the LH image over the same scene. Finally, the sparse coefficients of the HM image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers without nonnegative constraint. Comparison results on simulated and real data sets with the relative state-of-the-art methods demonstrate that even only using one HM image, our proposed method achieves a comparable reconstruction quality of high-spatial hyperspectral image both in spatial and spectral domains.

In recent days, discrimination diagrams have been widely used for tracing the tectonic settings and origins of basalts from orogenic belts. However, conventional discrimination diagrams are not accurate enough. Here, we reported six new discrimination diagrams obtained from the global database using data mining methods. For most individual diagrams, island arc basalt can be nearly 100% was identified, whereas ocean island basalt and mid-ocean ridge basalt can be discriminated from each other with less than 10% of overlap, under a confidence coefficient of 85%. Using the six new discrimination diagrams together, basalts of different origins can be efficiently identified.

Surface soil moisture (SM) retrieval over agricultural areas from polarimetric synthetic aperture radar (PolSAR) has long been restricted by vegetation attenuation, simplified polarimetric scattering modelling, and limited SAR measurements. This study proposes a modified polarimetric decomposition framework to retrieve SM from multi-incidence and multitemporal PolSAR observations. The framework is constructed by combining the X-Bragg model, the extended double Fresnel scattering model and the generalised volume scattering model (GVSM). Compared with traditional decomposition models, the proposed framework considers the depolarisation of dihedral scattering and the diverse vegetation contribution. Under the assumption that SM is invariant for the PolSAR observations at two different incidence angles and that vegetation scattering does not change between two consecutive measurements, analytical parameter solutions, including the dielectric constant of soil and crop stem, can be obtained by solving multivariable nonlinear equations. The proposed framework is applied to the time series of L-band uninhabited aerial vehicle synthetic aperture radar data acquired during the Soil Moisture Active Passive Validation Experiment in 2012. In this study, we assess retrieval performance by comparing the inversion results with in-situ measurements over bean, canola, corn, soybean, wheat and winter wheat areas and comparing the different performance of SM retrieval between the GVSM and Yamaguchi volume scattering models. Given that SM estimation is inherently influenced by crop phenology and empirical parameters which are introduced in the scattering models, we also investigate the influence of surface depolarisation angle and co-pol phase difference on SM estimation. Results show that the proposed retrieval framework provides an inversion accuracy of RMSE

Objective: This study aims to analyze the effect of lyophilized recombinant human brain natriuretic peptide on the endothelial function of patients with acute myocardial infarction. Methods: One hundred and thirty-six patients with acute myocardial infarction in our hospital were randomly divided into a control group and an experimental group (68 cases each). The patients in the control group were treated by conventional treatment. The patients in the experimental group were treated with lyophilized recombinant human brain natriuretic peptide besides the conventional treatment. The levels of flow-mediated dilatation (FMD), serum nitric oxide (NO), and endothelin-1 were compared between the two groups before and after treatment. Results: Before treatment, there was no significant difference between the two groups in the level of FMD (P>0.05); after treatment, the level of FMD in the experimental group was higher than that in the control group, and the difference was statistically significant (P0.05); after treatment, the levels of serum NO and endothelin-1 in the experimental group significantly improved, which were better than those in the control group (P

Background: Ankle fusion is the primary treatment for advanced ankle arthritis. With the advancement of arthroscopy technology, ankle arthroscopy fusion has shown many advantages over traditional surgery. However, there are few related studies globally, and evidence-based medicine is needed to verify the reliability and feasibility of ankle arthroscopy fusion. Objective: To compare the clinical efficacy and safety of arthroscopic ankle arthrodesis and open ankle arthrodesis. Methods: We searched the databases of PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure [CNKI], Wanfang Database, and VIP Database for published prospective or retrospective controlled studies of arthroscopic-assisted ankle fusion in the treatment of advanced ankle arthritis. The dates were limited from the construction of the library to June 30, 2019. Literature was included based on the principles and methods of evidence-based medicine. Literature retrieval, data extraction, and quality assessment were performed by 2 independent reviewers using the Cochrane 5.1 risk bias assessment tool. The methodological bias of the literature was evaluated, and a meta-analysis was using by RevMan 5.3 software. Results: A total of 18 studies and 1102 patients were included in the study, including 551 in the arthroscopic surgery group and 551 in the open surgery group. Arthroscopy-assisted surgery for advanced ankle arthritis was more effective than open surgery in terms of fusion rate (odd ratio[OR] = 3.32, 95% confidence interval[CI]:2.16, 5.10), fusion time (mean difference[MD] = −2.31, 95% CI:−4.63, −2.21), intraoperative blood loss (MD = −43.37, 95%CI: −48.49, −38.25), hospital stay (MD = −1.80, 95%CI: −2.28, −1.33), and visual analog scale score (MD = −1.75, 95%CI: −2.04, −1.46). In addition, rate of complications (OR = 0.33, 95%CI: 0.21, 0.52) was superior to open ankle fusion (P

The current mainstream point cloud instance segmentation methods are mainly divided into two steps. Firstly, the points of each instance are aggregated in the feature space by means of metric learning to make the features of the same instance are as similar as possible, and then the aggregated vector clusters are segmented to construct the proposal of each instance. Much of the previous work has focused on the aggregation of vectors and ignored how the instance is divided after the vector aggregation. In this paper, we propose a seed point selection network. The seed point selection network selects a better seed point generation proposal by judging the "seedness" of each point, and completes the instance-level segmentation of all points. In addition, the speed of instance segmentation effectively improved by the fast processing of the generated instance points and the low "seedness" points. In the experiment, we graft the seed point selection network onto different instance segmentation networks, and the accuracy and efficiency of segmentation are improved in different degrees.

Time series analysis (TSA) based on multi-temporal polarimetric synthetic aperture radar (PolSAR) images can deeply mine the scattering characteristics of objects in different stages and improve the interpretation effect, or help to extract the range of surface changes. However, as far as classification is concerned, it is difficult to directly generate the classification map for a new temporal image, by the use of conventional TSA or change detection methods. Once some labeled samples exist in historical temporal images, semi-supervised domain adaptation (DA) is able to use historical label information to infer the categories of pixels in the new image, which is a potential solution to the above problem. In this paper, a novel semi-supervised DA algorithm is proposed, which inherits the merits of maximum margin criterion and principal component analysis in the DA learning scenario. Using a kernel mapping function established on the statistical distribution of PolSAR data, the proposed algorithm aims to find an optimal subspace for eliminating domain influence and keeping the key information of bi-temporal images. Experiments on both UAVSAR and Radarsat-2 multi-temporal datasets show that, superior classification results with the average accuracy of about 80% can be obtained by a simple classifier trained with historical labeled samples in the learned low- dimensional subspaces.

A new approach in polarimetric synthetic aperture radar (PolSAR) speckle filtering is proposed in this article. The proposed method preserves both point targets and dominant scattering mechanisms. The point targets are detected based on the span image, and they are then neither filtered nor involved in the other pixels’ filtering. To achieve the protection of the dominant scattering mechanism of each pixel, only pixels of the same dominant scattering mechanism as the centre pixel are included in the selection of the homogeneous pixels. Both point targets not being filtered and fact that only pixels of the same dominant scattering mechanism are included in the selection of the homogeneous pixels, which greatly improves the filtering efficiency. A likelihood-ratio test statistic based on the PolSAR covariance matrices is applied to determine the homogeneous pixels. Finally, the speckle filtering is processed using the weighted minimum mean square error estimator on the homogeneous pixels. We demonst...

The exciton formation zone has been studied in phosphorescent organic light emitting diodes (OLEDs). It is found that the width (W) of the exciton formation zone can be increased by reducing the transit time for holes (electrons) from the anode (cathode) into the emissive layer (EML) by decreasing the thickness of the hole (electron) transport layer. The increase in the thickness of the EML enables the exciton formation zone to be widened. The efficiency roll-off of the device is relieved when W increases. The device stability is found to be directly proportional to the product of W and the thickness of the electron transport layer. A thicker electron transport layer better suppresses diffusion of the metal cathode into the EML, increasing device stability but simultaneously decreasing the transit time for electrons from the cathode into the EML, and thereby W, so decreasing device stability. We also investigated the effect of charge carrier mobilities in the EML, modified by dye concentration, on W. The current research provides novel insights into guiding the delicate designs of OLEDs, hopefully pushing OLED technology towards high-luminance applications.

Land-cover classification in polarimetric synthetic aperture radar images is a vital technique that has been developed for years. The Wishart distribution, which the polarimetric coherence matrix obeys, has been researched to design the well-known Wishart classifier. This model is appropriate for homogeneous scenes, but it usually fails in reality when a category consists of several subcategories or clusters. Therefore, a simple but powerful sample-merging strategy is proposed to generate representative subcenters, based on a dissimilarity measure. In addition, a weighted likelihood-ratio criterion is also proposed to further improve the performance of the Wishart distribution-based classification, based on the Wishart test statistic. Two experiments on EMISAR and UAVSAR data sets confirm that combining the proposed strategies can achieve better results than can the Wishart classifier and the other existing methods.

Cloud and snow detection has significant remote sensing applications, while they share similar low-level features due to their consistent color distributions and similar local texture patterns. Thus, accurately distinguishing cloud from snow in pixel level from satellite images is always a challenging task with traditional approaches. To solve this shortcoming, in this letter, we proposed a deep learning system to classify cloud and snow with fully convolutional neural networks in pixel level. Specifically, a specially designed fully convolutional network was introduced to learn deep patterns for cloud and snow detection from the multispectrum satellite images. Then, a multiscale prediction strategy was introduced to integrate the low-level spatial information and high-level semantic information simultaneously. Finally, a new and challenging cloud and snow data set was labeled manually to train and further evaluate the proposed method. Extensive experiments demonstrate that the proposed deep model outperforms the state-of-the-art methods greatly both in quantitative and qualitative performances.

Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can achieve a much better detection performance than traditional methods, it is difficult to achieve satisfying performance for small-sized ships nearshore due to the weak scattering caused by their material and simple structure. Another difficulty is that a huge amount of data needs to be manually labeled to obtain a reliable CNN model. Manual labeling each datum not only takes too much time but also requires a high degree of professional knowledge. In addition, the land and island with high backscattering often cause high false alarms for ship detection in the nearshore area. In this study, a novel method based on candidate target detection, boundary box optimization, and convolutional neural network (CNN) embedded with active learning strategy is proposed to improve the accuracy and efficiency of ship detection in nearshore areas. The candidate target detection results are obtained by global threshold segmentation. Then, the strategy of boundary box optimization is defined and applied to reduce the noise and false alarms caused by island and land targets as well as by sidelobe interference. Finally, a lightweight CNN embedded with active learning scheme is used to classify the ships using only a small labeled training set. Experimental results show that the performance of the proposed method for small-sized ship detection can achieve 97.78% accuracy and 0.96 F1-score with Sentinel-1 images in complex nearshore areas.

With the rapid advancement of database theory, this paper uses mathematical algorithm modeling methods, and derives the mathematical XXX derivation. In combination with Android system optimization, the performance is improved through the use of Java language and MySQL database, and XXX empirical formulas are used. Finally, after AP ’ s continuous dynamic interactive communication and tracking statistical comparative analysis, it proves that the physical fitness database algorithm has significant effects. It shows that the real-time health information and exercise effect of the college student’s physical fitness database can help college students develop long-term exercise habits and health. Plays a positive role.

At present, in China’s higher education institutions, with the rapid progress of software engineering theory, this paper adopts the signal monitoring system method and derives the mathematical analog signal strength monitoring system. It is optimized in combination with the software engineering wearable system. By using the heart rate acquisition module to improve the performance, using the temperature acquisition module empirical formula, and finally through the software engineering statistical comparison analysis, it is proved that the student basic signal monitoring system has a significant effect and can play a role in preventing students from fainting or sudden death. positive effect.

With the rapid development of artificial intelligence technology and the rapid progress of big data technology, based on the theory of artificial intelligence fusion systems based on the lives of college students, and the use of advanced technology, university courses have also expanded in multiple directions. A reasonable and scientific development model of electronic technology design. Derived by using electronic data as a carrier to optimize the derivative tools of university courses and improve performance in many aspects such as learning and sports. Using the empirical formula of the Big Data Fourier algorithm, the manual verification was verified through the statistical comparison and analysis of the competitive auxiliary review system. The application of intelligent systems in the fusion system of university curriculum derivatives and artificial intelligence algorithms based on Internet big data play an active role in the fusion system of university curriculum derivatives.

Hyperspectral images (HSIs) usually contain hundreds of spectral bands. When they are used for classification tasks, HSIs may suffer from the curse of high dimensionality. To address this problem, the essential procedures of dimension reduction and feature extraction (FE) are employed. In this letter, we propose an FE method for HSIs using low-rank representation with neighborhood preserving regularization (LRR_NP). The proposed method can simultaneously employ locally spatial similarity and the spectral space structure, which comprises a union of multiple low-rank subspaces. The framework of LRR can structurally represent the union structure of a spectral space. Because spatial neighbor pixels always share high similarity in a feature space, an NP regularization item is introduced into the framework of LRR to consider the locally spatial correlation. Classification experiments are conducted on real HSI data sets; the results demonstrate that the features that are extracted by LRR_NP are more discriminative than the state-of-art methods, including both unsupervised methods and supervised methods.

Due to an increasing number of nuclear reactors in operation, the radiocarbon (14C) released from nuclear power plants (NPPs) has become an important anthropogenic source of 14C. The examination of seasonal Δ14C and monthly Sr/Ca, Mg/Ca variations in a coral in Daya Bay (China) shows that NPPs located there have an impact on the Δ14C level and sea surface temperature (SST). The Mg/Ca variation was in good correlation with the Pacific Decadal Oscillation (PDO) before the operation of Ling’ao NPP in 2002, but this correlation became weak due to an abnormally higher SST after 2002. As illustrated by the Δ14C variation in the coral, there were two relative increases of Δ14C values in 1994 and 2002 when Daya Bay NPP and Ling’ao NPP began operations, respectively. The 14C released from NPPs, instead of oceanic circulation, is probably the primary factor on the Δ14C variation in Daya Bay during the NPPs’ operation. The relative increase in Δ14C value was ~80‰, which equals to ~18 Bq/kgC in specific activity. The seasonal variability in Δ14C value usually peaked in summer, the real reason of which was unknown. This study sheds light on how the NPPs influence the 14C content and SST in surrounding marine environment.

Objective: To investigate the effects and safety of recombinant human brain natriuretic peptide (rhBNP) in the treatment of elderly acute myocardial infarction induced cardiac failure. Methods: One hundred and forty-six patients who were diagnosed as elderly acute myocardial infarction induced cardiac failure in the hospital from July 2014 to July 2015 were selected. They were divided into a test group and a control group, 73 each. Patients in both groups were given conventional treatment such as stabilization of atherosclerotic plaques, anti-platelet and remodeling and reversion of myocardium. The curative effects and the incidence of adverse reactions of the two groups were observed. Results: The overall efficacy of the test group and the control group was 87.7% and 65.8% respectively, and the difference had statistical significance (P

A series of deep learning algorithms have recently shown excellent performances in many different fields. Deep models are usually generated by stacking similar modules, e.g., restricted Boltzmann machines (RBMs), and they are especially suitable for discriminating complex objects through the use of ‘big data’. However, object-oriented classification (OOC) for polarimetric synthetic aperture radar (PolSAR) imagery is based on homogeneous regions instead of pixels, which results in a degraded performance for the deep models, as the data volume is inadequate. To solve this problem, we adopt an RBM as the module, and use it to construct an adaptive boosting (AdaBoost) model instead of a stacked deep model, to carry out OOC for PolSAR imagery. The experimental results demonstrate that the proposed model is superior to the stacked RBM model and the other common methods for OOC.

Hyperspectral images (HSIs) have been used in a wide range of fields, such as agriculture, food safety, mineralogy, and environment monitoring, but being corrupted by various kinds of noise limits its efficacy. Low-rank representation (LRR) has proved its effectiveness in the denoising of HSIs. However, it just employs local information for denoising, which results in ineffectiveness when local noise is heavy. In this paper, we propose an approach of group low-rank representation (GLRR) for the HSI denoising. In our GLRR, a corrupted HSI is divided into overlapping patches, the similar patches are combined into a group, and the group is reconstructed as a whole using LRR. The proposed method enables the exploitation of both the local similarity within a patch and the nonlocal similarity across the patches in a group simultaneously. The additional nonlocally similar patches can bring in extra structural information to the corrupted patches, facilitating the detection of noise as outliers. LRR is applied to the group of patches, as the uncorrupted patches enjoy intrinsic low-rank structure. The effectiveness of the proposed GLRR method is demonstrated qualitatively and quantitatively by using both simulated and real-world data in experiments.

The quick and accurate extraction of water bodies from images is imperative for land resources management, ecological protection, and flood disaster prevention. However, the prevalent methods for water body extraction of SAR imagery have the major issues of depending on expert experience, the poor retention of water-land boundaries, and a high false alarm rate. In this paper, focusing on these issues, we study the effectiveness of water body extraction using only simple polarimetric decomposition components and commonly used machine learning classifiers for Gaofen-3(GF-3) image. We test it using different sizes of training samples and compare it with the previous methods. The experimental results showed that the combination of polarimetric decomposition components and machine learning classifiers can achieve satisfactory accuracies in water body extraction of GF-3 image, and has strong robustness, reliability and high application value.

The combination of transfer learning and remote sensing image processing technology can effectively improve the automation level of image information extraction from a remote sensing time series. However, in the processing of polarimetric synthetic aperture radar (PolSAR) time-series images, the existing transfer learning methods often cannot make full use of the time-series information of the images, relying too much on the labeled samples in the target domain. Furthermore, the speckle noise inherent in synthetic aperture radar (SAR) imagery aggravates the difficulty of the manual selection of labeled samples, so these methods have difficulty in meeting the processing requirements of large data volumes and high efficiency. In lieu of these problems and the spatio-temporal relational knowledge of objects in time-series images, this paper introduces the theory of time-series clustering and proposes a new three-phase time-series clustering algorithm. Due to the full use of the inherent characteristics of the PolSAR images, this algorithm can accurately transfer the labels of the source domain samples to those samples that have not changed in the whole time series without relying on the target domain labeled samples, so as to realize transductive sample label transfer for PolSAR time-series images. Experiments were carried out using three different sets of PolSAR time-series images and the proposed method was compared with two of the existing methods. The experimental results showed that the transfer precision of the proposed method reaches a high level with different data and different objects and it performs significantly better than the existing methods. With strong reliability and practicability, the proposed method can provide a new solution for the rapid information extraction of remote sensing image time series.

BACKGROUND: Current management of patients with atrial fibrillation (AF) is limited by low detection of AF, nonadherence to guidelines and lack of consideration of patient's preferences, thus highlighting the need for a holistic and integrated approach to AF management. The present study aims to determine whether a mHealth technology-supported AF integrated management strategy will reduce AF-related adverse events.METHODS/DESIGN: The mAFA II trial is a prospective, cluster randomized controlled trial. The 40 sites will be randomized to mAFA-integrated care intervention or usual care arms. Prior to randomization, study sites will be paired to be matched in size and the proportion of study eligible patients. All AF patients aged over 18 years old with CHA2 DS2 -VASc score ≥2 will be enrolled. Assuming a composite adverse event rate of 10% pre-intervention, reduced to 5% after intervention, we aim to recruit 3660 patients assuming a 10% loss to follow-up. The primary study endpoint is a composite of stroke/thromboembolism, all-cause death, and rehospitalization. Ancillary analyses would determine patient-related outcome measures, health economics and cost effectiveness, as well as an embedded qualitative study.DISCUSSION: The mAFA II trial will provide evidence for an integrated care approach to holistic AF care, supported by mobile health technology to improve screening, patient involvement and optimization of management. This article is protected by copyright. All rights reserved.

Thyroid tumor is a common clinical disease. Fully automated computer-aided diagnosis of thyroid nodules has important clinical significance. However, most of the previous works focused on the classification of nodules, benign or malignant, but how to locate the nodules in the ultrasound images is rarely studied. This paper focuses on the problem of thyroid nodule detection, aiming to achieve a fully automated method for delineating the nodule bounding box from the thyroid ultrasound image. We propose a nodule detection algorithm based on the convolutional neural network for this task. We explore the performance of nodule detection in three aspects: multi-scale prediction architecture design, loss function design and postprocessing method. This method is evaluated on clinical data and compared to the ground truth labeled by doctors. The experimental results show that this proposed method can achieve 88.08 % AP with 90.08 % overall recall.

Hyperspectral images are of high spectral resolution and have been widely used in many applications, but the imaging process to achieve high spectral resolution is at the expense of spatial resolution. This paper aims to construct a high-spatial-resolution hyperspectral (HHS) image from a high-spatial-resolution RGB image, by proposing a novel class-based spectral super-resolution method. With the help of a set of RGB and HHS image-pairs, our proposed method learns nonlinear spectral mappings between RGB and HHS image-pairs using class-based back propagation neural networks (BPNNs). In the training stage, unsupervised clustering is used to divide an RGB image into several classes according to spectral correlation, and the spectrum-pairs from the classified RGB images and the corresponding HHS images are used to train the BPNNs, to establish the nonlinear spectral mapping for each class. In the spectral super-resolution stage, a supervised classification is used to classify the given RGB image into the classes determined during the training stage, and the final HHS image is reconstructed from the classified given RGB image using the trained BPNNs. Comparisons on three standard datasets, ICVL, CAVE and NUS, demonstrate that, our proposed method achieves a better spectral super-resolution quality than related state-of-the-art methods.

Objective Proper participation in physical exercise can not only benefit people physically, but also promote people's psychological benefits. However, people are often only aware of the benefits of physical exercise, such as physical fitness, body shape and function, and ignore the psychological benefits caused by physical exercise. In recent years, more and more people pay attention to the positive psychological benefits of physical exercise on cognition. As the hope of the future of the country, the problem of physical health is widely concerned. To study the effect of physical exercise on children's cognitive function, and to analyze the possible biological mechanism of physical exercise affecting cognitive ability, and to provide a reference for promoting the good development of children's physique. Methods Through the retrieval of Chinese knowledge network, Wanfang Data knowledge service platform, VVP cube knowledge discovery system, 100 chain database collection sports related research literature, collating literature information, in-depth integration of literature, analysis of the impact and mechanism of physical exercise on children's cognition. Results (1) Physical exercise can improve the function of the brain, improve the efficiency of the brain, and enhance memory. Animal experiments have shown that physical exercise can enhance short-term memory by improving hippocampal function in rats. (2) Regular physical exercise can improve children's memory and executive function, and promote their cognitive function and academic performance. (3) The effect of physical exercise on specific cognitive function is in turn motor function, motor skills, academic performance, reasoning, reaction time and executive function. Physical exercise promotes cognitive performance by improving the goal of the brain pointing to the maintenance of processing, but physical exercise does not improve or slow down processing inhibition, and physical exercise is not related to processing inhibition. 30 minutes of acute physical exercise plays a significant role in maintaining children's attention. Acute high intensity physical exercise can improve selective attention and short-term memory tasks. In the experiment of the effect of one-off acute physical exercise on cognitive function, moderate intensity of heart rate variables and increased cognitive performance were related. For children with attention deficit hyperactivity disorder, physical exercise can improve their performance of executive function. (4) Physical exercise may affect the possible biological mechanisms of cognitive function: Firstly, exercise can improve the synthesis and secretion of neurotransmitters such as acetylcholine and dopamine, activate conduction pathways, improve synaptic transmission efficiency, and promote the development of learning and memory function. Secondly, exercise can increase brain derived neurotrophic factor (BDN F), release of nerve growth factors such as insulin-like growth factor --1 (IGF--1) and so on, thus promoting the development of learning and memory function. Again, physical exercise may also improve learning and memory performance by affecting the release of glucocorticoids. Then, physical exercise can enhance the gene expression in the brain area related to learning and memory. Finally, physical exercise may maintain and improve cognitive ability by improving the antioxidant capacity of brain tissue. Conclusions Physical exercise can promote the improvement of children's cognitive ability. The basic cognitive ability of children can be developed through physical exercise. The mechanism of the exercise can be explained by the changes of the brain nerve mechanism, such as increasing the brain capacity and increasing the flow velocity of the brain. The specific biological mechanism still needs to be studied.

Objective Aging is an inevitable rule in the process of life. When human growth and development mature, with the increase of age, the organs and organs of the body will have functional and morphological degenerative changes. China has now entered an aging society, and the aging related diseases are increasing, and the aging of the brain and related diseases are one of the main reasons for the decline of the quality of life of the elderly. It is particularly important to study methods of delaying brain aging. The development of physical exercise to promote the development of brain health has become a hot spot. Appropriate exercise can effectively improve the normal physiological function of the hippocampus neurons, delay the aging process of the central nervous system, and explore the effect of physical exercise on the brain senescent patients and its mechanism of action is very necessary. Methods Through the retrieval of Chinese knowledge network, Wanfang Data knowledge service platform, VVP cube knowledge discovery system, 100 chain database collection sports related research literature on brain senescence, collating literature information, in-depth integration of literature, analysis of the effect of physical exercise on brain aging and mechanism. Results (1) The brain is the most advanced part of the central nervous system, and the structure and function of the brain are very complex, and each brain region bears the corresponding function, in which the hippocampus is considered to be related to the learning and memory function. The aging of the central nervous system plays a leading role in the aging of the body, and its pathological changes are mainly found in the cerebral cortex and hippocampus. After the growth of the mature period, the myelin development in the brain is damaged, the brain myelin development is damaged, the nerve cells atrophy, the intracellular lipofuscin accumulates, the synaptic connections and the neurotransmitters decrease, and the ability to accept and transmit information is reduced, and the specific expression is reaction retardation, intelligence, learning, memory, analysis, judgment and push. The attenuation of the ability to be reasonable. Brain senescence is often accompanied by changes in brain weight, brain nerve cells, nerve fiber conduction velocity and brain cell dysfunction. It is a complex process, and any single factor can not explain its mechanism. In recent years, with the rapid development and promotion of molecular biology and cell biology, great progress has been made in the study of aging, and some theories about aging are put forward. The mechanism of aging of brain mainly includes the following theories: the free radical damage theory, the theory of genetic mutation, the theory of end grain loss, the theory of mitochondria, and the protein. Qualitative change theory, waste accumulation theory, aging network theory and so on. At present, free radical damage and telomere theory are widely recognized. In recent years, nonenzymatic glycosylation has also made great progress. (2) The hippocampus, the marginal system in the nervous system, is an important brain area for learning and memory. It plays a key role in the memory of various new knowledge, and the brain senescence is closely related to it. Aging related memory changes were positively correlated with activity changes in the two brain regions of the prefrontal cortex and hippocampus. In the hippocampus, dentate gyrus is the most serious subregion affected by aging. The hippocampus and prefrontal cortex are highly susceptible to aging. Therefore, the ability of brain control tasks will decrease correspondingly with the increase of age. The cerebral cortex is the high pole center of the human body's functional activities, is the area where neurons are concentrated, and is the material basis of people's thinking activities. The cerebral cortex analyses and synthesizes various stimuli from the internal and external environment of the body, produces sensations, thoughts, and establishes language and governing actions. The cerebral cortex, as part of the brain tissue, also enters the old age with aging. (3) Appropriate exercise can improve the state of brain senescence to a certain extent, and even reverse the decrease of the number of peripheral nerve terminal branches caused by aging. The effect of exercise intensity and duration on the hippocampus is very important. The exercise that has protective effects on the hippocampus is usually a small intensity and longer duration of exercise. Long term exercise can reverse the loss of the cholinergic fibers in the senescence process and promote the lateral shoots, and the content of synaptophysin in the hippocampus follows the age of age. Decline, long term exercise can delay the decrease of synaptophysin, promote the increase of neurons and the proliferation of dendrites, clarify the mechanism of motion retarding the aging effect of brain, and postpone brain senescence to promote the compensatory effect of the structure of central nervous system (CNS). (4) Exercise is one of the means to interfere with the aging of the brain. It is observed by electrical stimulation that some regions of the cerebral cortex have a close relationship with physical movement. Electrical stimulation of some regions of the cerebral cortex can cause some muscle contraction in certain parts of the cerebral cortex, and exercise can increase the thickness of the cerebral cortex. Increase the nerve cell dendrites of the brain. Exercise can promote the dynamic balance of free radical metabolism, enhance the ability to clear free radicals in the body, enhance the antioxidant capacity of the cerebral cortex, and improve the antioxidant capacity of brain cells. (5) Reasonable exercise can promote metabolism, enhance vitality, improve cardiovascular function and enhance immunity. Exercise can also protect animals from premature death, thereby prolonging the survival time of animals. The study of the effect of exercise on the peripheral nervous system shows that long term exercise can delay the morphological changes of the senescence related neuromuscular junction (NMJ) in rodents, promote the release of neurotransmitters in the end of exercise, and even reverse the decline of the age related peripheral nerve terminal branches. Exercise can improve psychomotor dysfunction with age by delaying the decline of age-related dopamine system function. Long-term moderate exercise can reduce the loss of neurons in the anterior horn of the spinal cord in the process of aging, and motor has a protective effect on neurons. Moreover, exercise can improve spatial learning behavior, and clearly shows the positive role of exercise in the prevention and treatment of brain aging. Exercise with different loads has a certain effect on scavenging free radicals in the brain and reducing lipid peroxidation level in the brain, thus delaying brain aging. Conclusions Hippocampus and prefrontal cortex are particularly susceptible in many regions of aging. Appropriate exercise can improve the state of brain senescence to a certain extent, and even reverse the decrease in the number of peripheral nerve terminal branches caused by aging. Looking at the history of the study of the action of the motion to the organism, it can be seen that there are two obvious trends in the study. One is the function of the movement to the system of the circulation system, the immune system of the motion system, and the two is the whole organ and molecule level of the movement. Therefore, only after a comprehensive understanding of the molecular mechanism of the action of the movement on the central nervous system, it is possible to fully and accurately elucidate the mechanism of action, and only in this way can we establish the position of motion in delaying brain aging.

The warm white organic light-emitting diodes (WOLEDs) consisting of four emissive layers (EMLs) as well as pure WOLEDs consisting of three EMLs have been fabricated by using three primary color phosphors as emitters. Experimental results demonstrated that energy transfer between dopants, direct carriers' trapping and main recombination zone are all important factors in determining the performances of obtained devices. By precisely managing dopants' concentrations and emitting layers' thicknesses, we realized the optimized warm WOLED with a Commission Internationale de l’Eclairage coordinates (CIEx,y) of (0.388, 0.383), giving the highest current efficiency (ηc) of 69.00 cd/A, power efficiency (ηp) of 60.19 lm/W and external quantum efficiency (EQE) of 20.63%. Additionally, by increasing the proportion of blue emission, we also realized the pure WOLED (CIEx,y value is (0.331, 0.385)) giving 60.48 cd/A (EQE = 16.90%, CRI = 72.03) and 51.32 lm/W in highest ηc and ηp, respectively. Especially, the optimal warm and pure WOLEDs exhibited the superior color stability with small CIE discrepancies of (0.020, 0.003) and (0.022, 0.004) in the range of 1–10 mA/cm2, respectively.

In this work, we demonstrated an efficient device design strategy in improving the electroluminescent performances of red phosphorescent emitter iridium(III) bis(2-phenylquinoly-N,C2′)dipivaloylmethane (PQ2Ir(dpm)) by constructing predominant and supplementary light-emitting layers (EMLs). 4,4′,4″-tris(carbazole-9-yl) triphenylamine (TcTa) and 4,7-dicarbazol-9-yl- [1,10]-phenanthroline (BUPH1) were selected as host materials of EMLs. Due to the well-designed double-EMLs device structure and the well-matched energy levels of selected materials, improved carriers’ balance and broadening recombination zone caused the low operation voltage and high device performances. Finally, the 4 wt% double-EMLs device obtained the highest brightness, external quantum efficiency, current efficiency, and power efficiency up to 29870 cd m−2, 12.18%, 19.74 cd A−1, and 25.84 lm W−1, respectively. At very low operation voltage of 3.4 V, this device realized the certain brightness of 1000 cd m−2 with power efficiency of 14.68 lm W−1.

Super-resolution (SR) reconstruction is one of the effective ways to improve the spatial resolution of remote sensing images, but the blur kernel estimation without any additional prior information is the key to the quality of SR reconstruction. Facing the above problem, a blind super-resolution method via sparse representation and transformed self-similarity is proposed in this paper. In this method, the blur kernel as well as the reconstructed high-resolution image are estimated at the same time using the structural self-similarity from a single image, a down-sampled version of the observed image is used as the training samples for the dictionary learning, and the internal patch search space is expanded using the transformed self-similarity to improve the quality of estimation. Experiment results show that, our method performs well both in quality of kernel estimation and SR reconstruction.

Converter transformers are key facilities in HVDC systems. The operation condition of converter transformers is more complex than that of conventional AC transformers. Because of the superposition of the voltage harmonics, the valve voltage wave is more serrated than a pure AC voltage. As a result, the insulation system withstands severe stress. Several tasks are accomplished in this study to obtain a better understanding of the electrical field distribution in converter transformers as well as analyze the effects of voltage harmonics on the main insulation of converter transformers. First, an HVDC system with a voltage of ±800 kV is simulated and the voltage harmonics at the valve side are obtained. The simulation is verified based on the measurement in a real ±800 kV HVDC substation. The harmonics in valve winding are mainly characterized by components with frequencies of 6k, 12k, 12k ± 1, and 6k ± 1. Then, a full-scaled 3D converter transformer model is proposed. The electric field of the proposed model is calculated under AC, DC, and polarity reversal voltages. Compared with conventional 2D models, the inhomogeneity of the electric field distribution is obtained, and the difference can reach 30%. Finally, the valve voltage waveform is applied on the 3D model to calculate the electric field using finite element method. Results show that the voltage harmonics in valve side further deepen the inhomogeneity of electric field in insulation structures. Moreover, a high value of voltage changing rate (dV/dt) with value 2.96 kV/μs is observed in simulation. The influence of defects on partial discharge within insulation materials under the voltage harmonics is also analyzed.

Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign and malignant nodules have heterogeneous appearances. Computer-aided diagnosis (CAD) methods could potentially provide objective suggestions to assist radiologists. However, the performance of existing learning-based approaches is still limited, for direct application of general learning models often ignores critical domain knowledge related to the specific nodule diagnosis. In this study, we propose a novel deep-learning-based CAD system, guided by task-specific prior knowledge, for automated nodule detection and classification in ultrasound images. Our proposed CAD system consists of two stages. First, a multi-scale region-based detection network is designed to learn pyramidal features for detecting nodules at different feature scales. The region proposals are constrained by the prior knowledge about size and shape distributions of real nodules. Then, a multi-branch classification network is proposed to integrate multi-view diagnosis-oriented features, in which each network branch captures and enhances one specific group of characteristics that were generally used by radiologists. We evaluated and compared our method with the state-of-the-art CAD methods and experienced radiologists on two datasets, i.e. Dataset I and Dataset II. The detection and diagnostic accuracy on Dataset I were 97.5% and 97.1%, respectively. Besides, our CAD system also achieved better performance than experienced radiologists on Dataset II, with improvements of accuracy for 8%. The experimental results demonstrate that our proposed method is effective in the discrimination of thyroid nodules.

Matrix factorization with non-negative constrains is widely used in hyperspectral image fusion. Nevertheless, the non-negative restriction on the sparse coefficients limits the efficiency of dictionary representation. To solve this problem, a new hyperspectral image fusion method based on non-factorization sparse representation and error matrix estimation is proposed in this paper, for the fusion of remotely sensed high-spatial multi-bands image with low-spatial hyperspectral image in the same scene. Firstly, an efficient spectral dictionary learning method is specifically adopted for the construction of the spectral dictionary, which avoids the procedure of matrix factorization. Then, the sparse codes of the high-spatial multi-bands image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers (ADMM) without non-negative constrains. For improving the quality of final fusion result, an error matrix estimation method is also proposed, exploiting the spatial structure information after non-factorization sparse representation. Experimental results both on simulated and real datasets demonstrate that, compared with the related state-of-the-art methods, our proposed method achieves the highest quality of hyperspectral image fusion, which can improve PSNR over 2.5844 and SAM over 0.3758.

Purpose: To explore the outcomes and safety of atorvastatin/trimetazidine combination therapy in patients with chronic cardiac failure.Methods: A total of 144 patients with chronic cardiac failure were divided into test group (n = 72) and control group (n = 72). In addition to conventional anti-heart failure treatment, all patients in the two groups received atorvastatin, and those in the test group received, in addition, trimetazidine, for 28 days. The clinical outcomes and safety profiles of the two groups were determined and compared.Results: Compared with pre-treatment stage, the left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), left ventricular early diastolic peak velocity (E), as well as E to left ventricular end diastolic peak velocity (A) ratio (E/A ratio) for both groups improved significantly after treatment, while A and serum brain natriuretic peptide (BNP) level decreased significantly (all p < 0.05). Moreover, compared with control group, the increases in LVEF, LVFS, E, and E/A ratio of the test group were greater (42.81 ± 3.04 vs 47.97 ± 4.22 %; 31.01 ± 3.19 vs 36.02 ± 3.31 %; 57.44 ± 5.18 vs 61.93 ± 5.42 cm/s; 1.02 ± 0.06 vs 1.19 ± 0.11, respectively), while the decreases in A and BNP level were greater (both p < 0.05) (57.34 ± 4.70 vs 52.37 ± 3.17 cm/s; 589.73 ± 41.19 vs 498.65 ± 30.89 pg/mL, respectively). Therapeutic outcomes were significantly better in the test group than in control group (p < 0.05). Blood pressure, heart rate and serum levels of alanine transaminase (ALT) and creatinine did not differ significantly between the two groups (p > 0.05), but serum potassium and aspartate amino transferase levels were lower in the test group than in the control group (p < 0.05).Conclusion: Atorvastatin combined with trimetazidine effectively reduces BNP level and improves cardiac function in patients with cardiac failure. The safety profile of the combined therapy is good.Keywords: Atorvastatin, Trimetazidine, Chronic cardiac failure, Combination therapy, Biochemical profile

Non-negative Matrix Factorization is the most typical model for hyperspectral image super-resolution. However, the non-negative restriction on the coefficients limited the efficiency of dictionary expression. Facing this problem, a new hyperspectral image super-resolution method based on non-factorization sparse representation and dictionary learning (called NFSRDL) is proposed in this paper. Firstly, an efficient spectral dictionary learning method is specifically adopted for the construction of spectral dictionary using some low spatial resolution hyperspectral images in the same or similar areas. Then, the sparse codes of the high-resolution multi-bands image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers (ADMM) without non-negative constrains. Experimental results on different datasets demonstrate that, compared with the related state-of-the-art methods, our method can improve PSNR over 1.3282 and SAM over 0.0476 in the same scene, and PSNR over 3.1207 and SAM over 0.4344 in the similar scenes.