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Networked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the citation relationships in scholarly publications where a paper is cited by others not because they have the same content, but because they share one or multiple subject matters. To date, while many network embedding methods exist to take the node content into consideration, they all consider node content as simple flat word/attribute set and nodes sharing connections are assumed to have dependency with respect to all words or attributes. In this paper, we argue that considering topic-level semantic interactions between nodes is crucial to learn discriminative node embedding vectors. In order to model pairwise topic relevance between linked text nodes, we propose topical network embedding, where interactions between nodes are built on the shared latent topics. Accordingly, we propose a unified optimization framework to simultaneously learn topic and node representations from the network text contents and structures, respectively. Meanwhile, the structure modeling takes the learned topic representations as conditional context under the principle that two nodes can infer each other contingent on the shared latent topics. Experiments on three real-world datasets demonstrate that our approach can learn significantly better network representations, i.e., 4.1% improvement over the state-of-the-art methods in terms of Micro-F1 on Cora dataset. (The source code of the proposed method is available through the github link: https://github.com/codeshareabc/TopicalNE.)

Biological carbon pumping (BCP) is a key process in which dissolved inorganic carbon in terrestrial aquatic ecosystems is utilized by aquatic autotrophs for photosynthesis and transformed into autochthonous organic matter (AOC). However, the mechanisms underlying BCP and the amount of generated AOC deposited effectively, are still poorly understood. Therefore, we conducted a systematic study combining modern hydrochemical monitoring and a sediment trap experiment in Fuxian Lake (Yunnan, SW China), the second-deepest plateau, oligotrophic freshwater lake in China. Temperature, pH, EC (electrical conductivity), DO (dissolved O2), [HCO3−], [Ca2+], SIc, partial CO2 (pCO2) pressure, and carbon isotopic compositions of HCO3− (δ13CDIC) in water from Fuxian Lake all displayed distinct seasonal and vertical variations. This was especially apparent in an inverse correlation between pCO2 and DO, indicating that variations of hydrochemistry in the lake water were mainly controlled by the metabolism of the aquatic phototrophs. Furthermore, the lowest C/N ratios and highest δ13Corg were recorded in the trap sediments. Analyses of the C/N ratio demonstrated that the proportions of AOC ranged from 30% to 100% of all OC, indicating that AOC was an important contributor to sedimentary organic matter (OC). It was calculated that the AOC flux in Fuxian Lake was 20.43 t C km−2 in 2017. Therefore, AOC produced by carbonate weathering and aquatic photosynthesis could potentially be a significant carbon sink and may have an important contribution to solving the lack of carbon sinks in the global carbon cycle.

Cu2+ is a bio-accumulative and toxic environmental pollutant, so its sensitive and selective detection is of great importance. In this work, gold nanoparticles were electrochemically deposited on fluorine-doped tin oxide and characterized by scanning electron microscope and cyclic voltammetry. 4-Mercaptobenzoic acid (4-MBA), which contained carboxyl chelator, was self-assembled on the surface of gold nanoparticles through S–Au bond. The strong chelation of Cu2+ with 4-MBA formed a stable Cu2+-4-MBA complex, which was confirmed by energy-dispersive X-ray spectroscopy. Square wave voltammetry was applied to determine the concentration of Cu2+. Under optimized condition, the oxidation peak current was proportional to the concentration of Cu2+ in the range of 10–1500 nM with limit detection of 8 nM. The proposed electrochemical sensor showed excellent selectivity towards Cu2+. In addition,the applicability of the developed sensor was evaluated by determining the concentrations of Cu2+ in river water samples, which were consistent with the results of inductively coupled plasma mass spectroscopy.

BDS-3 currently has 28 operational satellites in orbit, of which 27 IGSO/MEO satellites provide open services on five frequencies simultaneously. In particular, the linear combinations of the BDS-3 B1C/B1I/B2a signals have significant benefits in reducing the influence of ionospheric delay error as well as improving ambiguity estimation and positioning accuracy. The presented optimal ionosphere-free combination (242, 218, − 345) and ionosphere-reduced combination (2, 2, − 3) can improve the measurement accuracy by about 20% compared to the BDS-3 B1C/B2a or GPS L1/L5 dual-frequency combination. The ionosphere-reduced combination (2, 2, − 3) with a wavelength of 10.9 cm is almost immune to the ionospheric delay error and has a smaller noise amplification factor compared to the existing dual-frequency combinations. Therefore, its combined ambiguities can be fixed directly even in the case of a long baseline, which can simplify the traditional precise positioning process based on the ionosphere-free combination. The numerical results of BDS-3 real data show that the triple-frequency ionosphere-free or ionosphere-reduced combinations can improve the single-point positioning accuracy by 16–20% and the phase differential positioning accuracy by 7–9%, respectively. The ambiguity resolution of the ionosphere-reduced combination (2, 2, − 3) is achieved with a fixing rate of 88.4% over long baseline up to 1600 km. The presented ionosphere-free and ionosphere-reduced combinations are also very promising to be applied in current PPP applications to simplify the ambiguity fixing process as well as improve positioning accuracy and shorten convergence time.

A triangular gold nanoplate (AuNPL)–based colorimetric assay is presented for ultrasensitive determination of cupric ions (Cu2+) and mercuric ions (Hg2+) in sequence. AuNPLs were found to be etched efficiently when producing triiodide ions (I3−) by a redox reaction between Cu2+ and iodide ions (I−), leading to a change of the shape of AuNPLs from triangular to sphere along with a color change from blue to pink. In the presence of Hg2+ the etching of AuNPLs was suppressed due to the consumption of I− by the formation of HgI2. With an increase of the concentration of the Hg2+ a transformation from sphere to triangular in the shape of AuNPLs occurred with a color change from pink to blue. The evolution of AuNPLs from etching to anti-etching state by sequential addition of Cu2+ and Hg2+ was accompanied with color variations and band shifts of localized surface plasmon resonance (LSPR), allowing for visual and spectroscopic determination of Cu2+ and Hg2+ successively within 15 min. In the range 0.01–1.5 μM for Cu2+ and 0.02–3.0 μM for Hg2+, the linear relationship between the band shift values and the target ions concentration was found good (R2 > 0.996). The limit of detections (3S/k) was 19 nM for Cu2+ and 9 nM for Hg2+, respectively. The lowest visual estimation concentration was 80 nM for both Cu2+ and Hg2+ through the distinguishable color changes. This system exhibited desirable selectivity for Cu2+ and Hg2+ over other common ions tested. The method has been successfully applied to sequential determination of Cu2+ and Hg2+ in real water and food samples.

The aim of this study was to elucidate the gastroprotective activity and possible mechanism of involvement of araloside A (ARA) against ethanol- and aspirin-induced gastric ulcer in mice. The experimental mice were randomly divided into control, model, omeprazole (20 mg/kg, orally) and ARA (10, 20 and 40 mg/kg, orally). Gastric ulcer in mice was induced by intragastric administration of 80% ethanol (10 mL/kg) containing 15 mg/mL aspirin 4 h after drug administration on day 7. The results indicated that ARA could significantly raise gastric juice volume and acidity; ameliorate gastric mucosal blood flow, gastric binding mucus volume, ulcer index and ulcer inhibition rate; suppress H+/K+-ATPase activity, which was confirmed by computer-aided docking simulations; inhibit the release of mitochondrial cytochrome c into the cytoplasm; inhibit caspase-9 and caspase-3 activities and down-regulate mRNA expression levels; down-regulate the mRNA and protein expressions of apoptosis protease-activating factor-1 and protein expression of cleaved poly(ADP ribose) polymerase-1; and up-regulate Bcl-2 mRNA and protein expressions and down-regulate Bax mRNA and protein expressions, thus elevating the Bcl-2/Bax ratio in a dose-dependent manner. Histopathological observations further provided supportive evidence for the aforementioned results. The results demonstrated that ARA exerted beneficial gastroprotective effects on alcohol- and aspirin-induced gastric ulcer in mice, which was related to suppressing H+/K+-ATPase activity as well as pro-apoptotic protein expression, and promoting anti-apoptotic protein expression, thus alleviating gastric mucosal injury and cell death.

Excessive activation of macrophages has been implicated in various types of inflammatory injury. Suppression of macrophage activation would have therapeutic benefits, leading to the alleviation of the progression of inflammatory diseases. Eburicoic acid (EA) is one of main bioactive components isolated from Laetiporus sulphureus (Bull.:Fr.) Murrill. In our previous study, we found that EA possessed anti-inflammatory activities. However, the cellular and molecular mechanisms underlying its anti-inflammatory activities remain to be poorly understood. The present study aimed to further evaluate its effect on lipopolysaccharide (LPS)-induced inflammatory responses in RAW264.7 macrophage cells. We investigated the anti-inflammatory effect by modulating LPS-induced activation of phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR)/nuclear transcription factor-κB (NF-κB) pathway in RAW264.7 cells. The results showed that EA caused no obvious cytotoxicity, and its suitable concentrations on RAW264.7 cells were in the range from 0.02 to 0.08 μM. EA significantly inhibited the releases of inflammatory mediators, nitrite oxide (NO) and prostaglandin E2 (PGE2); suppressed mRNA and protein expression levels of inducible nitrite oxide synthase (iNOS) and cyclooxygenase-2 COX-2 and pro-inflammatory cytokine TNF-α, IL-6, and IL-1β; and reduced levels of phosphorylated PI3K, Akt, mTOR, and NF-κBp65 in LPS-induced RAW264.7 cells in dose- and time-dependent manners. These aforementioned results indicated that EA executed anti-inflammatory effect on LPS-induced RAW264.7 cells, and this effect might be achieved via suppressing the PI3K/Akt/mTOR/NF-κB signaling pathway and inhibiting the LPS-induced productions of inflammatory mediators and pro-inflammatory cytokines.

This study presents an application of nickel affinity solid-phase extraction (SPE) for the quantitative monitoring of carbendazim and thiabendazole residues in some fruits and vegetables. The nickel oxide nanoparticle was deposited on the silica to obtain an affinity adsorbent (SiO2@NiO) for the selective extraction of the compounds containing imidazole groups from complex matrix. Coupling with high-performance liquid chromatography with a fluorescence detector, the extraction conditions of the SiO2@NiO adsorbent for the carbendazim and thiabendazole were optimized, and a sensitive quantitative method with good linearities (R 2 > 0.998) in the range of 10–1000 ng g−1 was developed. The detection limits for the two analytes were in the range of 2.9–7.5 ng g−1, which are lower than the maximum residue limits established for these compounds. Relative standard deviations (RSDs) of intra-day and inter-day precisions were less than 8.8%, which showed perfect repeatability. The proposed method has been successfully applied to analyze carbendazim and thiabendazole in apples, pears, cucumbers, potatoes, cabbages, and eggplants. Good recoveries of carbendazim and thiabendazole spiked in fruits and vegetables were found to range from 80.0 to 115.4%, demonstrating that the proposed method was reliable in monitoring these two benzimidazoles in fruits and vegetables.

The linear combinations of multi-frequency carrier-phase measurements for Global Navigation Satellite System (GNSS) are greatly beneficial to improving the performance of ambiguity resolution (AR), cycle slip correction as well as precise positioning. In this contribution, the existing definitions of the carrier-phase linear combination are reviewed and the integer property of the resulting ambiguity of the phase linear combinations is examined. The general analytical method for solving the optimal integer linear combinations for all triple-frequency GNSS is presented. Three refined triple-frequency integer combinations solely determined by the frequency values are introduced, which are the ionosphere-free (IF) combination that the Sum of its integer coefficients equal to 0 (IFS0), the geometry-free (GF) combination that the Sum of its integer coefficients equal to 0 (GFS0) and the geometry-free and ionosphere-free (GFIF) combination. Besides, the optimal GF, IF, extra-wide lane and ionosphere-reduced integer combinations for GPS and BDS are solved exhaustively by the presented method. Their potential applications in cycle slip detection, AR as well as precise positioning are discussed. At last, a more straightforward GF and IF AR scheme than the existing method is presented based on the GFIF integer combination.

In this paper, we study the synchronization problem for switched complex networks with delayed coupling via semi-periodically intermittent control technique and a mode-dependent average dwell time method. The mathematical model of switched complex networks is established, and the networks switch among a finite number of subnetworks in accordance with a switching rule. By applying multiple Lyapunov function method and the mode-dependent average dwell time (MDADT) approach, less conservative synchronization criteria are derived based on a sequence of solvable linear matrix inequalities. Particularly, the conventional synchronization conditions are improved by using semi-periodically intermittent control and MDADT approach. Moreover, delayed coupling may affect the stability of the switched complex networks. In this paper, novel synchronization criteria are derived to deal with the relations between time delay and other parameters appropriately. Finally, a numerical example is provided to demonstrate the effectiveness of the derived theoretical results.

Approximate policy iteration (API) is studied to solve undiscounted optimal control problems in this paper. A discrete-time system with the continuous-state space and the finite-action set is considered. As approximation technique is used for the continuous-state space, approximation errors exist in the calculation and disturb the convergence of the original policy iteration. In our research, we analyze and prove the convergence of API for undiscounted optimal control. We use an iterative method to implement approximate policy evaluation and demonstrate that the error between approximate and exact value functions is bounded. Then, with the finite-action set, the greedy policy in policy improvement is generated directly. Our main theorem proves that if a sufficiently accurate approximator is used, API converges to the optimal policy. For implementation, we introduce a fuzzy approximator and verify the performance on the puddle world problem.

Intelligent diagnosis of operation states of distribution grid is a prerequisite to the self-healing ability of a smart grid. In this paper, an improved support vector machine (SVM)-based cognitive diagnosis algorithm is proposed to cognize the current operation state of distribution grid by classifying the disturbance into different operation states. Based on the current measurement in distribution grid, wavelet-packet time entropy is developed to extract features of the operation states. Considering the rejection recognition of multi-class classification, an improved SVM multi-class classifier based on a kernel metric is constructed. To investigate the performance of the proposed cognitive diagnosis algorithm, simulations of real distribution grid cases are carried out in PSCAD–EMTDC. Compared with wavelet-packet energy and Fuzzy C-means, the simulation results demonstrate that the proposed cognitive diagnosis algorithm can achieve higher accuracy and more robust performance on different grids and fault conditions.

The chelator ethylene diaminetetraacetate (EDTA) has been intercalated into layered double hydroxides by the anion exchange method. The resulting composites were characterized by powder X-ray diffraction, FTIR spectroscopy, thermogravimetry and X-ray photoelectron spectrometry. They were applied to modify a carbon paste electrode for the stripping voltammetric determination of lead (II) ions at ng L−1 levels. Stripping currents are linearly related to the logarithm of Pb (II) concentrations from 2 ng L−1 to 33 μg L−1. The detection limit (3σ) is as low as 0.95 ng L−1. The method was successfully applied to the determination of Pb (II) in spiked tap water without any pretreatment.

Sparse-representation-based classification (SRC), which classifies data based on the sparse reconstruction error, has been a new technique in pattern recognition. However, the computation cost for sparse coding is heavy in real applications. In this paper, various dimension reduction methods are studied in the context of SRC to improve classification accuracy as well as reduce computational cost. A feature extraction method, i.e., principal component analysis, and feature selection methods, i.e., Laplacian score and Pearson correlation coefficient, are applied to the data preparation step to preserve the structure of data in the lower-dimensional space. Classification performance of SRC with structure-preserving dimension reduction (SRC–SPDR) is compared to classical classifiers such as k-nearest neighbors and support vector machines. Experimental tests with the UCI and face data sets demonstrate that SRC–SPDR is effective with relatively low computation cost

The regional constellation of BeiDou navigation satellite system (BDS) has been officially in operation since December 27, 2012, and real-time kinematic positioning using BDS and GPS multi-frequency observations is feasible. A heavy computational problem arises when resolving ambiguities in the case of multi-system with multi-frequency observations. A multi-carrier fast partial ambiguity resolution strategy is developed with the property that the extra-wide-lane and wide-lane ambiguities in the multi-frequency case can be resolved reliably in advance. Consequently, the technique resolves ambiguities sequentially instead of the usual batch ambiguity resolution (AR) mode so as to improve the computational efficiency of AR significantly. The strategy is demonstrated with real BDS/GPS dual- and triple-frequency observations. The results have shown that the probability of correct AR by the proposed method is comparable to that of the batch AR. Experimentally, the new method is about 2.5 times as fast as the batch AR in the dual-frequency case, 3 times in the mixed dual- and triple-frequency case and 3.5 times in the triple-frequency case.

BeiDou regional navigation satellite system (BDS) also called BeiDou-2 has been in full operation since December 27, 2012. It consists of 14 satellites, including 5 satellites in Geostationary Orbit (GEO), 5 satellites in Inclined Geosynchronous Orbit (IGSO), and 4 satellites in Medium Earth Orbit (MEO). In this paper, its basic navigation and positioning performance are evaluated preliminarily by the real data collected in Beijing, including satellite visibility, Position Dilution of Precision (PDOP) value, the precision of code and carrier phase measurements, the accuracy of single point positioning and differential positioning and ambiguity resolution (AR) performance, which are also compared with those of GPS. It is shown that the precision of BDS code and carrier phase measurements are about 33 cm and 2 mm, respectively, which are comparable to those of GPS, and the accuracy of BDS single point positioning has satisfied the design requirement. The real-time kinematic positioning is also feasible by BDS alone in the opening condition, since its fixed rate and reliability of single-epoch dual-frequency AR is comparable to those of GPS. The accuracy of BDS carrier phase differential positioning is better than 1 cm for a very short baseline of 4.2 m and 3 cm for a short baseline of 8.2 km, which is on the same level with that of GPS. For the combined BDS and GPS, the fixed rate and reliability of single-epoch AR and the positioning accuracy are improved significantly. The accuracy of BDS/GPS carrier phase differential positioning is about 35 and 20 % better than that of GPS for two short baseline tests in this study. The accuracy of BDS code differential positioning is better than 2.5 m. However it is worse than that of GPS, which may result from large code multipath errors of BDS GEO satellite measurements.

The first results of the short baseline single-epoch kinematic positioning based on dual-frequency real BeiDou/GPS data are presented. The performance of the BeiDou/GPS single-epoch positioning is demonstrated in both static and kinematic modes and compared with corresponding GPS-only performance. It is shown that the availability and reliability of the single-frequency BeiDou/GPS and dual-frequency BeiDou single-epoch kinematic positioning are comparable to those of the dual-frequency GPS. The fixed rate and reliability of ambiguity resolution for the single- and dual-frequency BeiDou/GPS are remarkably improved as compared to that of GPS-only, especially in case of high cutoff elevations. For positioning accuracy with fixed ambiguities, the BeiDou/GPS single-epoch solutions are improved by 23 and 4 % relative to the GPS-only case for two short baseline tests of 8 km, respectively. These results reveal that dual-frequency BeiDou real-time kinematic (RTK) is already applicable in Asia–Pacific areas and that single-frequency BeiDou/GPS RTK is also achievable but only with initialization of several seconds. More promisingly, the dual-frequency BeiDou/GPS RTK can overcome the difficulties with GPS-only RTK under the challenging conditions assuming, of course, that the additional BeiDou satellites are visible.

In this paper, we analyze an internal goal structure based on heuristic dynamic programming, named GrHDP, to tackle the 2-D maze navigation problem. Classical reinforcement learning approaches have been introduced to solve this problem in literature, yet no intermediate reward has been assigned before reaching the final goal. In this paper, we integrated one additional network, namely goal network, into the traditional heuristic dynamic programming (HDP) design to provide the internal reward/goal representation. The architecture of our proposed approach is presented, followed by the simulation of 2-D maze navigation (10*10) problem. For fair comparison, we conduct the same simulation environment settings for the traditional HDP approach. Simulation results show that our proposed GrHDP can obtain faster convergent speed with respect to the sum of square error, and also achieve lower error eventually.

The integrity requirement should be satisfied when GNSS is used in aviation. There are now two ways for integrity monitoring, that is, receiver autonomous integrity monitoring (RAIM) and GNSS integrity channel (GIC) based on augmentation system. The rapid development of the Beidou satellite navigation system (COMPASS) will significantly improve the satellite geometry. Besides, the effects of first order ionosphere delay will be mitigated by the combination of GNSS multi-frequency signals. The availability of RAIM will be enhanced, which makes it possible to provide a worldwide seamless integrity service for aviation by using RAIM. The contribution of COMPASS to the availability of RAIM is analyzed by simulation; and the integrity requirement of which aviation approaching phrase can be satisfied when using COMPASS single system and its combination with other satellite navigation system is analyzed. Moreover, the influence of user range accuracy (URA) of COMPASS on integrity performance is discussed.

As one of the four global satellite navigation systems, Compass not only enhances satellite visibility and availability for positioning, navigation and timing (PNT) for users in China and the surrounding areas, but also improves PNT precision for global users. The improvements in satellite visibility and the dilution of precision are analyzed under GNSS compatibility and interoperation conditions. The contribution of the Compass satellite navigation system to global users, especially the benefits that users can acquire from the combination of Compass, GPS, GLONASS, and Galileo navigation systems, is analyzed using simulation data.

In this paper, we aim to develop computational intelligence approaches for wind profile prediction. Specifically, we focus on two aspects in this work. First, we investigate the missing value recovery for wind data. Due to the complexity of data collection in such processes, wind data normally include missing values. Therefore, how to effectively recover such missing values for learning and prediction is an important aspect for wind profile prediction. Second, we develop an ensemble learning approach based on multiple neural network models. Our proposed method uses a new strategy based on the temporal information to assign the weights for each model dedicated for wind profile prediction to achieve better prediction performance. Various simulation studies and statistical testing demonstrate the effectiveness of our approach.

Difficulties of learning from nonstationary data stream are generally twofold. First, dynamically structured learning framework is required to catch up with the evolution of unstable class concepts, i.e., concept drifts. Second, imbalanced class distribution over data stream demands a mechanism to intensify the underrepresented class concepts for improved overall performance. To alleviate the challenges brought by these issues, we propose the recursive ensemble approach (REA) in this paper. To battle against the imbalanced learning problem in training data chunk received at any timestamp t, i.e., $${{\mathcal{S}}_t,}$$ REA adaptively pushes into $${{\mathcal{S}}_t}$$ part of minority class examples received within [0, t − 1] to balance its skewed class distribution. Hypotheses are then progressively developed over time for all balanced training data chunks and combined together as an ensemble classifier in a dynamically weighted manner, which therefore addresses the concept drifts issue in time. Theoretical analysis proves that REA can provide less erroneous prediction results than a comparative algorithm. Besides that, empirical study on both synthetic benchmarks and real-world data set is also applied to validate effectiveness of REA as compared with other algorithms in terms of evaluation metrics consisting of overall prediction accuracy and ROC curve.

Depolymerization of nylon 6 to produce e-caprolactam using an environmentally friendly heteropoly acid catalyst was studied at temperatures between 553 and 603 K in water. The products of depolymerization were analyzed qualitatively and quantitatively by means of mass spectrometry and high-performance liquid chromatography. The results showed that the depolymerized product was mainly e-caprolactam with a little 6-aminocaproic acid and oligomers. The phosphotungstic heteropoly acid used as a catalyst can improve the hydrolysis rate and yield of e-caprolactam. The optimum hydrolysis conditions for e-caprolactam yield were as follows: phosphotungstic heteropoly acid content, 3%; reaction temperature, 573 K; and reaction time, 85 min. Under these conditions, the yield of e-caprolactam was 77.96%. In the temperature range 553–603 K, the activation energy of 3% phosphotungstic heteropoly acid-catalyzed depolymerization was evaluated as 77.38 kJ/mol, which is lower than the 86.64 kJ/mol value for no catalyst.

The potential of biopartitioning micellar chromatography (BMC) for screening and analysis of traditional Chinese medicines (TCM) for permeable compounds has been investigated by chromatography of methanolic extracts of Ligusticum chuanxiong, as example. Six other ordinary TCM were also studied. Results showed that more than ten components of Ligusticum chuanxiong extract are significantly retained in BMC. According to relationships between retention in BMC and oral drug absorption, cell-membrane permeability, and blood–brain barrier permeability, the cell-membrane permeability and absorption properties of the permeable components of Ligusticum chuanxiong extract could be predicted from their retention in BMC. Effects on the chromatography of components of Ligusticum chuanxiong methanolic extracts of column temperature, mobile phase pH, and concentration of Brij 35 in the mobile phase are also discussed.

The aim of this study was to compare the cardioprotective effects of salvianolic acid B (Sal B) and the angiotension-converting enzyme inhibitor, benazepril, in rats with chronic myocardial infarction (MI) that resulted from a coronary artery ligation for 4 weeks. The rats were divided into four groups: those undergoing a sham operation; a MI group; a MI+SalB group (100 mg/kg by a gavage, once a day for 4 weeks); a MI+benazepril group (10 mg/kg by a gavage, once a day for 4 weeks). The following parameters were measured: echocardiographic, hemodynamic and hemorheological changes, angiogenesis, infarct size and cardiac remodeling and the messenger ribonucleic acid (mRNA) of vascular endothelium growth factor (VEGF). Rats treated with SalB or benazepril manifested the following: (1) marked improvements in echocardiographic, hemodynamic and hemorheological parameters; (2) significant reduction of infarct size; (3) significantly attenuated heart, kidney and lung hypertrophies, left ventricular (LV) dilatation and fibrosis. The unique effects of SalB were angiogenesis and augmented VEGF expression in the border and remote noninfarcted left ventricular area. These results suggest that both SalB and benazepril exerted beneficial cardioprotective effects in our experimental system, but that the modality of Sal B was different from that of benazepril. The additional beneficial effects of Sal B relative to benazpril, augmenting VEGF expression and promoting angiogenesis, may result in improved myocardial microcirculation.

The differencing technique is useful in global positioning system (GPS) positioning when two or more GPS receivers collect simultaneous observables from common satellites at each epoch, and all carrier-phase observables have the same normal distribution. An analytical probability distribution of the single-, double-, triple-, and multi-difference GPS observables is obtained. This analytical model, called ISO2002, has a good matrix structure, in which I indicates the number of receivers, S indicates the number of observed satellites, and O indicates the number of epochs. The variance–covariance matrix can be expressed as the Kronecker product of several small matrices, so its inverse is equal to the Kronecker product of the inverses of those sub-matrices. Moreover, the small matrices are circulant or symmetric diagonal Toeplitz matrices, so their inverses have analytical solutions. The analytical model ISO2002 proposed to compute the inverse variance–covariance matrix is shown to be very effective.

This special issue includes seven highly selected papers from the Fourth International Workshop on Advanced Computational Intelligence (IWACI 2011), held from October 19 to 21, 2011, in Wuhan, China. The IWACI 2011 was a great success and provided a high-level international forum for scientists and engineers to present the latest research in the filed of computational intelligence and applications. To highlight the success of this conference, we edited this special issue for the Cognitive Computation. We chose seven papers from 412 papers submitted to the IWACI’11, as these papers reflect the high quality of the presentations at the conference while capturing the spirit of our theme, ‘‘Computational Intelligence and Applications,’’ for this special issue. Over the past decades, we have witnessed tremendous efforts and developments from all aspects of computational intelligence research, ranging from theoretical foundations, principles, algorithms, to practical applications in different domains. To reflect a flavor of such recent research activities in the community, we carefully selected these seven papers for this special issue, with a focus on different computational intelligence techniques and real-world applications. The first group of papers is focused on the computational intelligence techniques for image processing. As computer vision and image processing has become a critical issue for many of today’s data-intensive applications, ranging from civilian applications such as video processing to surveillance and security-related applications, we selected three papers on this topic. The paper by Zhao et al. discussed an important method for non-blind image de-blurring from a single image. In order to overcome the limitations of the traditional maximum a posteriori (MAP) estimation for nonblind image de-blurring, the authors proposed a Bayesian minimum mean squared error (MMSE) estimation to perform de-blurring. This method is based on an effective statistical framework to model prior knowledge of natural images with an efficient Gibbs sampling algorithm. Various simulations and comparative studies have been developed to demonstrate the effectiveness of this approach. In the paper by Ding et al., a method based on neural network and selforganizing map (SOM) was proposed to connect a sequence of images acquired by a rotating camera. This is a very interesting problem with broader practical applications. A detailed algorithm with a system-level implementation has been presented in this paper. Image classification was studied in the paper by Bian et al. Specifically, the authors presented a method based on modified k-means support vector machines (SVMs) with hybrid sampling method for image classification. This method can reduce convex hulls of clustering structure as well as the overtraining caused by active sampling approaches. Simulation results based on three challenging remote sensing images with comparative study of random sampling (RS) and margin sampling (MS) demonstrated the effectiveness of this approach. The second group includes two theoretically focused papers. In the paper by Feng et al., an interesting problem of stochastic suppression and stabilization of nonlinear Z. Zeng Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China e-mail: zgzeng@gmail.com

The Seventh International Symposium on Neural Networks (ISNN 2010) was held on June 6–9, 2010 in Shanghai, China. The ISNN 2010 was a great success and provided a high-level international forum for scientists and engineers to present the latest research in neural networks, computational intelligence, cognitive systems, and related fields. To highlight the success of this conference, we edited this special issue for Cognitive Computation. We chose nine papers from over 591 papers submitted to ISNN 2010, which reflect the high quality of the presentations at the conference while capturing the spirit of our theme, ‘‘Advances in Computational Intelligence and Applications,’’ for this special issue. Our goal for this special issue is to present the latest research developments in computational intelligence with a focus on cognitive computation and their applications across a wide range of domains. Over the past decades, we have witnessed tremendous interest and developments in all aspects of computational intelligence research, ranging from both theoretical foundations, principles, to practical applications in different domains. To reflect a flavor of recent research activities in the community, we carefully selected these nine papers for this special issue. The selected papers can be organized into the following four coherent sections. The first section is directly related to neural network models and applications. The paper by Deng presents a new control strategy for the chaotic neural network, in which the refractoriness is tuned by using feedback control based on online averaging of network states. Simulation results demonstrate that the proposed model can achieve favorable performance in handling noisy, incomplete, and composite patterns, while also achieving either enhanced or comparable memory capacity compared with the classic Hopfield net and other chaotic neural network models. In the other paper, Gluge et al. discuss recurrent networks for implicit learning of temporal sequences, a very important area to understand high-level cognitive intelligence. The authors propose a recurrent network with backpropagation training algorithm that is adapted to the reinforcement learning scheme. The simulation results fit quantitatively as well as qualitatively to the behavioral results, suggesting the role of temporal context in associative learning scenarios. The second section is related to language processing and understanding, an important topic in cognitive science and computational intelligence. The paper by Murata et al. investigates the maximum entropy (ME) approach for natural language processing (NLP) problems including machine translation and information extraction. Comparative studies of the proposed approach with the existing techniques are illustrated in this paper. In related work to this field, the paper by Kaznatcheev investigates connectionist models on the interplay of nouns and pronouns in personal pronoun acquisition, a very interesting and important problem in psychology. For instance, the author discusses the learning in the shifting reference situation for children. It is reported that learning of two different noun-and-pronoun addressee patterns is consistent with Z. Zeng (&) Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China e-mail: zgzeng@gmail.com

The aim of this study was to compare the cardioprotective effects of salvianolic acid B (Sal B) and the angiotension-converting enzyme inhibitor, benazepril, in rats with chronic myocardial infarction (MI) that resulted from a coronary artery ligation for 4 weeks. The rats were divided into four groups: those undergoing a sham operation; a MI group; a MI+SalB group (100 mg/kg by a gavage, once a day for 4 weeks); a MI+benazepril group (10 mg/kg by a gavage, once a day for 4 weeks). The following parameters were measured: echocardiographic, hemodynamic and hemorheological changes, angiogenesis, infarct size and cardiac remodeling and the messenger ribonucleic acid (mRNA) of vascular endothelium growth factor (VEGF). Rats treated with SalB or benazepril manifested the following: (1) marked improvements in echocardiographic, hemodynamic and hemorheological parameters; (2) significant reduction of infarct size; (3) significantly attenuated heart, kidney and lung hypertrophies, left ventricular (LV) dilatation and fibrosis. The unique effects of SalB were angiogenesis and augmented VEGF expression in the border and remote noninfarcted left ventricular area. These results suggest that both SalB and benazepril exerted beneficial cardioprotective effects in our experimental system, but that the modality of Sal B was different from that of benazepril. The additional beneficial effects of Sal B relative to benazpril, augmenting VEGF expression and promoting angiogenesis, may result in improved myocardial microcirculation.