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Sr0.99Ce0.01Ti1-xO3 (SCT, x = 0, ± 0.0025, ± 0.0050, 0.0075) ceramics were prepared by solid state reaction methods and sintered in air atmosphere at different temperatures, with a soaking time of 2 h. The dielectric properties of all samples presented excellent temperature independence over a broad temperature range from 25 to 330 ℃ and frequency independence between 10 kHz and 1 MHz. Sr0.99Ce0.01Ti0.9925O3 (SCT0.9925) ceramics sintered in air atmosphere exhibited a high permittivity (~5400) and a low dielectric loss (~0.01) measured at room temperature and 1 kHz. XPS and complex impedance spectroscopy analysis confirmed that the high permittivity and low dielectric loss were attributed to the fully ionized oxygen vacancies and giant defect-dipoles in Ti-deficient samples. However, a higher dielectric loss of Ti-rich samples is owing to the destruction of giant defect dipoles, in which highly localized electrons were transformed into hopping electrons.

We explored the phase structure, microstructure, dielectric and energy storage properties of MgO-modified strontium barium niobate Sr0.7Ba0.3Nb2O6-xwt%MgO (SBNMx; x = 0–5) ceramics fabricated via conventional solid-state sintering precess. X-ray diffraction analysis indicates Mg2+ incorporates into lattice at x = 0.5 and secondary phases come into formation for samples at x ≥ 1, which results in the decrease of dielectric constant. There is also significant reduction of dielectric loss up to 0.002. Compared with pure SBN ceramics, the grain size of SBNMx ceramics becomes denser and more uniform, moreover, the dielectric breakdown strength shows increasing trend from 137 kV/cm to 226 kV/cm, which is in favor of the energy storage. SBNM0.5 ceramics presents the optimal energy storage performance: energy storage density of 0.93 J/cm3 and energy storage efficiency of 89.4% at 157 kV/cm, indicating that SBNM ceramics are prospective candidates for high voltage capacitor applications.

OBJECTIVES To explore the risk factors on contrast agent venous intravasation during transvaginal 4-dimensional hysterosalpingo-contrast sonography (TVS 4D-HyCoSy). METHODS The TVS 4D-HyCoSy imaging data were collected from 276 female infertile patients. The correlation between endometrial thickness, days after menstruation, intrauterine intervention history, fallopian tubal patency degree, and contrast agent venous intravasation, respectively, was analyzed. RESULTS In our study, the incidence of contrast agent venous intravasation was 13.04%. Endometrial thickness and days after menstruation were significantly associated with venous intravasation (P

3D-printed scaffolds have been developed as potential therapeutic strategies in bone tissue engineering. Mg/PCL biomaterials have been attracted much attention owing to biocompatibility, biodegradability as well as tunable mechanical properties. In this work, we developed 3D-printed customized Mg/PCL composite scaffolds with enhanced osteogenesis and biomineralization. Mg microparticles embedded in PCL-based scaffolds took a positive role in the improvement of biocompatibility, biomineralization, and biodegradable abilities. When incorporated with 3 wt% Mg, PCL-based scaffolds exhibited the optimal bone repairing ability in vitro and in vivo. The in vitro experiments indicated that 3 Mg/PCL scaffolds had improved mechanical properties, good biocompatibility, enhanced osteogenic and angiogenic activities. Besides, the in vivo studies demonstrated that Mg/PCL scaffolds promoted tissue ingrowth and new bone formation. In sum, these findings indicated that 3D-printed cell-free Mg/PCL scaffolds are promising strategies for bone healing application.

In this paper, high-Si reinforced Al matrix composites (H-Si/Al) with Si mass ratio 50–70% were successfully fabricated from the powder mixture of Si and Al by a novel route called as powder semi-solid squeeze. It is found that no Al–Si lamellar eutectic in the composites. As the Si content increases, fine Si particles in the H-Si/Al are reduced and the coarse Si particles may increase in size, or gather together to form twisted large particles and even form a space network structure. With the Si content ranging from 50% to 70%, The flexural strength varies between 248 and 179 MPa, the mean coefficient of thermal expansion from ambient temperature to 100 °C can be regulated between 11.32 and 7.55 × 10 −6 °C −1 , and the thermal conductivity varies between 140 and 120 W m −1 °C −1 , showing that H-Si/Al prepared by powder semi-solid squeeze have excellent mechanical and thermal properties.

Herein, based on G-rich lead-specific aptamer (LSA) as the recognition element of target lead ion (Pb2+), a label-free and enzyme-free electrochemical aptasensor for Pb2+ was developed by using metal-organic frameworks (MIL-101(Fe)) decorated with AgPt nanoparticles (AgPtNPs) as electrochemical probes and signal enhancers. The as-prepared AgPtNPs/MIL-101(Fe) that presented both inherent redox activity from MIL-101(Fe) and excellent electrocatalytic activity was further conjugated with single-strand DNA partially complementary to LSA (CS). In the presence of Pb2+, the G-rich LSA, being incubated onto the modified electrode surface, was specifically folded to be stable G-quadruplex structure. Through the DNA hybridization reaction between LSA and CS, the unfolded G-rich LSA captured the proposed signal probes CS-immobilized AgPtNPs/MIL-101(Fe) in the electrode surface. As a result, the detectable electrochemical signal generated by MIL-101(Fe) was dependent on Pb2+ concentration. The cooperative electrocatalysis of AgPtNPs and MIL-101(Fe) effectively enhanced the response signal and greatly improved the detection sensitivity. Thus, the developed aptasensor for Pb2+ displayed a wide linear range from 0.1 pM to 100 nM with a detection limit of 0.032 pM, as well as excellent specificity, good stability, and acceptable reproducibility. This would make the proposed label-free and enzyme-free method be promising and potential candidate for sensitive and cost-effective detection of Pb2+ in real samples.

In this work, a simple and sensitive electrochemical aptasensor for protein (thrombin - TB used as the model) was developed by using cubic Cu2O nanocages (Cu2O-NCs) loaded with Au nanoparticles (AuNPs@Cu2O-NCs) as non-enzymatic electrocatalysts and robust redox probes. Through the specific sandwich-type reaction between TB and TB aptamers (TBA), the formed AuNPs@Cu2O-NCs bound with NH2-TBA were captured onto the electrode surface modified with SH-TBA. Based on the inherent redox activity of AuNPs@Cu2O-NCs with cubic nanostructures, a detectable electrochemical signal was generated which was dependent on the analyte concentration. Meanwhile, AuNPs@Cu2O-NCs showed an efficient electrocatalytic capability in the reduction of H2O2, resulting in a significant enhancement of the response signal. Thus, the simplification of the proposed strategy and the improvement of analytical performances were easily achieved with a sub-picomolar sensitivity (the limit of detection was 0.066 pmol L-1). The applicability of the simple and sensitive aptasensor was successfully demonstrated by assaying TB in human serum samples. This non-enzymatic detection platform would be potential and promising in clinical diagnostics and protein analysis techniques.

As an important research content in cultural geography, the exploration and analysis of the laws of regional cultural differences has great significance for the discovery of distinctive cultures, protection of regional cultures and in-depth understanding of cultural differences. In recent years, with the "spatial turn" of sociology, scholars are paying more and more attention to the implicit spatial information in social media data and the various social phenomena and laws they reflect. One important aspect is to grasp the social cultural phenomena and its spatial distribution characteristics through the text. Using machine learning methods such as the popular natural language processing (NLP), this paper can not only extract hotspot cultural elements from text data but also accurately detect the spatial interaction pattern of some specific cultures and the characteristics of emotions towards non-native cultures. Taking the 6,128 answers to the question “what are the differences between South and North China that you never know” on the Zhihu Q&A Platform as an example, with the help of NLP, this paper has explored the cultural differences between South and North China in people’s mind. This paper probes into people’s feeling and cognition of the cultural differences between South and North China from three aspects, including spatial interaction patterns of hotspot cultural elements, components of hotspot culture and emotional characteristics under the influence of cultural differences between North and South. The study reveals that 1) people from North and South China have great differences in recognizing each other’s culture. 2) Food culture is the most popular among many cultural differences. 3) People tend to show negative attitude towards the food cultures different from their own. All these findings shed light upon the understanding of regional cultural differences and addressing cultural conflicts. In addition, this paper also provides an effective solution to the study from a macro perspective, which have been difficult for new cultural geography.

The interactions of opinions on the complex networks are significantly impacted by the structure of the networks. Previous studies of this kind mainly investigated the opinion dynamics on the fixed networks as a kind of synchronization. In this study, we focus on how the opinions evolving on the growing networks. We provide isolated nodes with different initial opinions at the beginning. The Achlioptas process is introduced to link the nodes eventually. The opinions of two nodes influence each other linearly if there is a link between the two nodes. We establish both random graph and scale-free networks in this paper. The finite-size scaling is discussed. We discover explosive transition of the speed for the opinions to achieve a consensus on some networks. Meanwhile, the stability of the networks to suppress the random damage is highly enhanced by the Achlioptas process to link all the nodes as a network. The encouraging results are obtained on different structures of networks.

In the era of big data, rich information about the movement behaviors of residents can be easily obtained. The movement patterns of residents can be mined from these data, which is essential for researching the correlation among zones in cities, thereby shedding light upon the understanding of the travel needs of residents. Many clustering algorithms are proposed to mine meaningful movement patterns between zones from the massive amount of origin–destination flows. However, most existing flow clustering algorithms focus only on the spatial information of the flow, but ignore the time attributes, precluding the identification of when the movement pattern occurs and how long it lasts. Nevertheless, for most existing algorithms that separately consider time attributes and cluster time and space, large amounts of redundant calculations result in high time complexity. Furthermore, only a few studies have considered flow clustering; most of people-related flows in the city are restricted by the road network, thereby causing the analyzed movement pattern between zones to not be consistent with reality. To address these problems, the present paper proposes a novel spatiotemporal similarity measure algorithm for flows. The algorithm uses flows as basic units, considers the spatiotemporal attributes, and introduces the road network constraints to make the results further realistic, in a bid to mine network-constrained movement patterns featured by spatiotemporal characteristics. To verify the feasibility and effectiveness of the proposed algorithm, we conduct experiments using artificial and real-world datasets of commuting data in Shanghai. Experiment results show that the algorithm can quickly and effectively mine movement patterns between zones with spatiotemporal attributes.

To identify potential mutations of F11 gene in a pedigree affected with hereditary coagulation factor XI (FXI) deficiency and explore its molecular pathogenesis.Prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB), coagulation factor VIII activity (FVIIIC), coagulation factor IX activity (FIXC), coagulation factor XI activity (FXIC), coagulation factor XII activity (FXIIC) and lupus anticoagulation (LA) of the proband and eight family members were determined. FXI antigen (FXIAg) was determined by enzyme-linked immunosorbent assay (ELISA). For the proband, potential mutations in the exons, flanking introns and 5'-, 3'-untranslated regions of the F11 gene were screened by direct DNA sequencing. The results were confirmed by reverse sequencing. Suspected mutations were detected in other family members. ClustalX-2.1-win and four online bioinformatic tools (PolyPhen-2, PROVEAN, SIFT, and Mutation Taster) were used to study the conservation and possible impact of the mutations. The structure of the mutational sites was processed with Swiss-PdbViewer.The propositus had prolonged APTT (69.6 s), whose FXIC and FXIAg were reduced to 6.0% and 10.7%, respectively. Her mother, elder sister, one younger sister, little brother, daughter and son showed slightly prolonged APTT and moderate FXIC and FXIAg levels. Gene sequencing revealed that the propositus carried a heterozygous nonsense mutation c.738GA (p.Trp228stop) in exon 7 and a heterozygous mutation c.1556GC (p.Trp501Ser) in exon 13. Her mother, elder sister and daughter were heterozygous for the p.Trp228stop mutation, while one younger sister and little brother and son were heterozygous for p.Trp501Ser. Her husband and the youngest sister were of the wild type. Phylogenetic analysis suggested that Trp501 was highly conserved among all homologous species. The p.Trp501Ser was predicted to be "probably damaging","deleterious", "affect protein function" and "disease causing" corresponding to PolyPhen-2, PROVEAN, SIFT and Mutation Taster. Model analysis demonstrated that the non-polar Trp501 has two benzene rings, forming a hydrogen bond with Gln512 in the wild type. Once substituted by Ser501, the side chain may form another hydrogen bond with the benzene of His396. This may affect the normal space conformation and stability of FXI protein.The compound heterozygous mutations of the F11 gene probably accounted for the low FXI concentration in this pedigree.

An electrochemical biosensor is described for highly sensitive determination of tDNA, an Alzheimer’s disease (AD)-related biomarker. Electroactive molybdophosphate anions were precipitated in-situ on a glassy carbon electrode (GCE) via catalytic hydrolysis by alkaline phosphatase (ALP). This is followed by recycling amplification of tDNA. Four DNA strands (referred to as S1, S2, S3 and S4) were designed to assemble X-shape DNA (X-DNA) building blocks. These were further extended into four directions under the action of DNA polymerase. The resultant two X-DNA motifs were polymerize. Simultaneously, ALP is encapsulated into a hydrogels network to obtain a porous material of type ALP@DNAhg. The GCE was modified with reduced graphene oxide functionalized with gold nanoparticles (Au@rGO). If ALP@DNAhg are captured via strand displacement, tDNA recycling assembly for signal amplification is initiated. This results in the immobilization of large amounts of ALP. On introduction of pyrophosphate and molybdate (MoO42−), ALP will catalyze the hydrolysis of pyrophosphate to produce phosphate. It will react with molybdate to form redox active phosphomolybdate anions (PMo12O403−). Its amperometrical signal depends on the concentration of tDNA in the 1.0 × 10−2 to 1.0 × 104 pM concentration range, and the detection limit is 3.4 × 10−3 pM.

In this study, a sensitive biosensing interface for protein was reported based on nonconductive insoluble precipitates (IPs) by the biocatalysis of manganese(III) meso-tetrakis (4-N-methylpyridiniumyl)-porphyrin (MnTMPyP), which was intercalated into formed double-strand DNA (dsDNA) scaffold triggered by hybridization chain reaction (HCR). In the proposed impedimetric aptasensor, carcinoembryonic antigen (CEA) and its aptamer were used as testing model. PtPd nanowires (PtPdNWs) with large surface area and superior conductivity were employed as nanocarriers to greatly immobilize biomolecules (e.g. CEA aptamers). Then, two DNA hairpins H1 and H2 were introduced to trigger HCR with the assistance of DNA initiator, resulting in the formation of a long dsDNA scaffold. Meanwhile, mimicking enzyme MnTMPyP molecules were embedded into the resultant dsDNA, in situ generating the complex MnTMPyP-dsDNA with peroxidase-like activity. Under the biocatalysis of MnTMPyP-dsDNA, 3,3-diaminobenzidine (DAB) was oxidized to form nonconductive IPs. As a result, the electron transfer between electrode interface and redox probe was vastly hindered, leading to the significant amplification of electrochemical impedimetric signal. So, greatly improved analytical performances of the proposed aptasensor were achieved with a detection limit as low as 0.030pgmL(-1). And the successful assay of CEA in human serum samples enabled the developed biosensing platform to have promising potential in bioanalysis.

In order to detect multiple sclerosis (MS) subjects from healthy controls (HCs) in magnetic resonance imaging, we developed a new system based on machine learning. The MS imaging data was downloaded from the eHealth laboratory at the University of Cyprus, and the HC imaging data was scanned in our local hospital with volunteers enrolled from community advertisement. Inter-scan normalization was employed to remove the gray-level difference. We adjust the misclassification costs to alleviate the effect of unbalanced class distribution to the classification performance. We utilized two-level stationary wavelet entropy (SWE) to extract features from brain images. Then, we compared three machine learning based classifiers: the decision tree, k-nearest neighbors (kNN), and support vector machine. The experimental results showed the kNN performed the best among all three classifiers. In addition, the proposed SWE+kNN approach is superior to four state-of-the-art approaches. Our proposed MS detection approach is effective.

An accurate diagnosis is important for the medical treatment of patients suffering from brain diseases. Magnetic resonance (MR) images are commonly used by technicians to assist preclinical diagnosis. The classification of MR images of normal and pathological brains poses a challenge from the technological point of view, since MR imaging generates a large information set that reflects the conditions of the brain. In this paper, we present a computer-assisted diagnosis method based on wavelet entropy (WE) of the feature space approach and a feed-forward neural network (FNN) classification method for improving the brain diagnosis accuracy by means of MR images. The most relevant image feature is selected as the WE, which is used to train an FNN classifier. The results using tenfold cross validation of 64 images show that the average accuracy attainable is 100.00%. It can be easily seen from the data that the proposed classifier can detect abnormal brains from normal controls with excellent performance, which can compete with the latest methods. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient.

The interaction between different regions normally is reflected by the form of the stream. For example, the interaction of the flow of people and flow of information between different regions can reflect the structure of cities’ network, and also can reflect how the cities function and connect to each other. Since big data has become increasingly popular, it is much easier to acquire flow data for various types of individuals. Currently, it is a hot research topic to apply the regional interaction model, which is based on the summary level of individual flow data mining. So far, previous research on spatial interaction methods focused on point-to-point and area-to-area interaction patterns. However, there are a few scholars who study the hotspot interaction pattern between two regional groups with some predefined neighborhood relationship by starting with two regions. In this paper, a method for identifying a similar hotspot interaction pattern between two regional groups has been proposed, and the Geo-Information-Tupu methods are applied to visualize the interaction patterns. For an example of an empirical analysis, we discuss China’s air traffic flow data, so this method can be used to find and analyze any hotspot interaction patterns between regional groups with adjoining relationships across China. Our research results indicate that this method is efficient in identifying hotspot interaction flow patterns between regional groups. Moreover, it can be applied to any analysis of flow space that is used to excavate regional group hotspot interaction patterns.

Copyright © 2018 Inderscience Enterprises Ltd. Understanding the heating behaviour of automotive transmissions in an important step in prototype development, it plays a significant role in achieving appropriate tolerances at the design phase and strongly influences the system’s durability in the long term. Furthermore, lubricant viscosity is strongly dependent on operating temperature, understanding this behaviour will provide insight into how transmission efficiency is affected during vehicle warm-up. In this paper, an experimental and simulation based study has been conducted to investigate the thermal behaviour of a two-speed dual clutch transmission (DCT) that has been developed for electric vehicle applications. This is achieved through the modelling of the power losses in the system that is then integrated with a compact thermal model of the transmission. Transient heating simulations for the transmission are conducted at constant speeds and compared with experimental studies, showing a strong correlation between results. Simulations are then utilised to investigate the load and speed dependencies of the transmission’s thermal behaviour.

To explore the risk factors on contrast agent venous intravasation during transvaginal 4-dimensional hysterosalpingo-contrast sonography (TVS 4D-HyCoSy).The TVS 4D-HyCoSy imaging data were collected from 276 female infertile patients. The correlation between endometrial thickness, days after menstruation, intrauterine intervention history, fallopian tubal patency degree, and contrast agent venous intravasation, respectively, was analyzed.In our study, the incidence of contrast agent venous intravasation was 13.04%. Endometrial thickness and days after menstruation were significantly associated with venous intravasation (P .05). However, there was no significance for intrauterine intervention history and fallopian tube patency degree.Contrast agent intravasation during TVS 4D-HyCoSy is not infrequent. Performing TVS 4D-HyCoSy according to endometrial thickness and menstrual period could reduce intravasation incidence to some extent.

In this work, a simple and sensitive electrochemical aptasensor for protein (thrombin - TB used as the model) was developed by using cubic Cu

The theoretical analysis of drag torques within a two-speed dual clutch transmission is presented in this article. The numerical models are developed to study the different sources of drag torques in dual clutch transmission. Simulations are performed in Matlab/Simulink platform to investigate the variation of drag torques under different operating conditions. Then an experimental investigation is conducted to evaluate the proposed model using an electric vehicle powertrain test rig. Outcomes of experimentation confirm that simulation results agree well with test data. Therefore the proposed model performs well in the prediction of drag torque for the transmission, and can be applied to assess the efficiency of the transmission. Results demonstrate that the entire drag torque is dominated by the viscous shear in the wet clutch pack and gear churning losses. This lays a theoretical foundation to future research on reducing drag torque and applications of drag torque in powertrain system efficiency optimization.

Background Omega-3 polyunsaturated fatty acids (PUFAs), including alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and docosapentaenoic acid (DPA), play critical roles in numerous biochemical reactions. Our aim is to develop a rapid and sensitive method for simultaneous determination of ALA, EPA, DHA and DPA in the plasma of hyperlipidemic and normolipidemic subjects. Methods An ultra-high-performance liquid chromatography-tandem mass spectrometric (UPLC-MS/MS) method of ALA, EPA, DHA, and DPA was developed with chlorzoxazone as the internal standard (IS). The analytes were separated on an Acquity BEH C18 column (2.1 mm × 100 mm, 1.7 μm) with gradient elution by acetonitrile and 0.1% ammonia water. ALA, EPA, DHA, DPA, and IS were determined by negative electrospray ionization (ESI-) with multiple reaction monitoring (MRM) at m/z 277.42/259.05, 301.20/257.00, 327.30/283.40, 329.24/285.32, and 168.03/132.02. A total of 80 normolipidemic subjects and 83 hyperlipidemic subjects, who underwent testing for plasma lipids, liver and kidney functions, and blood routine blood test (BRT), were enrolled. Results There was good linearity for ALA within 1–10 μg/mL, and EPA, DHA and DPA were within 0.125–10 μg/mL. The relative standard deviation (RSD) of precision was below 15%. The concentrations of ALA, EPA, DHA and DPA were 3.47 ± 2.58, 0.41 ± 0.26, 2.93 ± 1.39 and 0.25 ± 0.21 μg/mL, respectively, in normolipidemic subjects, increasing to 4.14 ± 3.71, 0.57 ± 0.46, 3.43 ± 2.13, 0.27 ± 0.25 μg/mL, respectively in hyperlipidemic subjects. Among them, only the EPA concentration was significantly different between two groups. There was a high correlation between ALA, EPA, DHA and DPA. Conclusion We developed a rapid and sensitive method for simultaneously determination of ALA, EPA, DHA and DPA in hyperlipidemic and normolipidemic subjects. In hyperlipidemic and normolipidemic subjects, concentrations of ALA were highest, followed by DHA, EPA and DPA; there were high degrees of correlation between each value.

This paper investigates the robust H ∞ dynamic output feedback control problem for networked control systems (NCSs) with quantized measurements. The measurement losses of the communicated information are considered in an unreliable communication channel. The robust H ∞ dynamic output feedback controllers are designed to handle the measurement losses and mitigate the quantization effects such that the resultant closed-loop NCS is mean-square stochastically stable with a prescribed H ∞ disturbance attenuation performance. The controller existence conditions can be derived in terms of linear matrix inequalities (LMIs). Finally, an example is provided to illustrate the effectiveness of the proposed approach.

When the single earth-return line of HVDC (High voltage direct current) system is functioning, the AC system in the same area will probably be influenced. Because two converting station at different DC potential constitute a loop via transmission line, causing certain direct current moving into the transformer neutral point and winding. The transformer DC magnetic biasing phenomenon will appear, leading increased noise and vibration of transformer. The influence of transformer DC magnetic biasing already exists and will do harm to transformers in a long term, while there's no set of effective quality inspection method. Therefore, it is necessary to establish a set of real-time current monitoring system for transformer DC magnetic biasing. In this paper, a set of automatic, high-accuracy and real-time current monitoring system is proposed. The monitoring system can display the amplitude and polarity of main transformer's neutral point direct current.

This paper first briefly introduces the basic structure of converter transformer, analyses the mechanism of vibration through simulation of the converter transformer oil-tank, designs the test scheme. This paper introduces the application of wavelet analysis method in converter transformer fault identification, then apply these methods to 24 converter transformers to obtain the characteristics parameters, finally utilize the vibro-acoustic techniques to diagnose power transformers. Through the experimental study of converter transformers in ±800kV Fengxian HVDC Substation combining with the existing criteria to evaluate existing converter transformer, and put forward the quantitative evaluation criteria.

Extracellular matrix (ECM) can promote peripheral nerve repair. In this study, a conductive fiber-film (CFF) with core-sheath structure and conductivity of ∼10Scm-1 was prepared by electrospinning of aligned poly(l-lactide acid) (PLLA) fibers and electrochemical deposition of polypyrole (PPy) nanoparticles. Then the multiple components of ECM, including laminin, fibronectin and collagen, were coated on the surface of CFF by culturing and lysing L929 cells to fabricate the bioactive scaffold of ECM-linked CFF (ECM-CFF). The electrical stimulation (ES) of 100mV/cm for 14days and 2h per day did not significantly decrease the conductivity of ECM-CFF. The results of PC12 cells test indicated that, cells adhesion rate, neurite-bearing cell rate and neurite alignment rate on ECM-CFF were ∼95%, ∼77%, ∼70%, respectively, significantly larger than the corresponding values on bare CFF (17%, 29% and 14%, respectively). The neurites length on ECM-CFF (∼79mm) was also larger than that on bare CFF (∼25mm). ES of 100mV/cm onto PC12 cells through ECM-CFF could significantly promote neurite extension in first 3days of the neurite growth. These results indicated that, the combination of ECM-CFF with ES could improve the nerve regeneration by encouraging neural-cell adhesion, neurite growth and extension.

This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal hidden neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE?+?KC-MLP?+?ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods.

In order to study the economy performance of a two-speed electric vehicle (EV) equipped with a wet dual clutch transmission (DCT), this paper presents a theoretical analysis of drag torque in wet clutches, and investigates the relationship between drag torque, transmission gear ratios, and their optimisation. As such, the application of drag torque effects in gear ratio optimisation is conducted. Through this paper mathematic models for a two-speed EV is presented and a procedure to obtain the reasonable optimal gear ratio pairs and corresponding shift schedule for a two-speed EV is described in detail, with consideration of wet clutch drag torque. Through simulation and parametric analysis using different test driving cycles, including constant speed, NEDC and UDDS, the effects of drag torque is studied in gear ratio selection and EV efficiency and economy, with result demonstrating a significant influence on overall vehicle performance, and degradation of drivetrain efficiency.

(Aim) A short-term load forecast is an arduous problem due to the nonlinear characteristics of the load series. (Method) The artificial neural network (ANN) was employed. To train the ANN, a novel hybridization of Tabu Search and Particle Swarm Optimization (TS-PSO) methods was introduced. TS-PSO is a novel and powerful global optimization method, which combined the merits of both TS and PSO, and removed the disadvantages of both. (Results) Experiments demonstrated that the proposed TS-PSO-ANN is superior to GA-ANN, PSO-ANN, and BFO-ANN with respect to a mean squared error (MSE). (Conclusion) The TS-PSO-ANN is effective in a short-term load forecast.

(Aim) Tea plays a significant role because of its high value throughout the world. Computer vision techniques were successfully employed for rapid identification of teas. (Method) In our work, we present a computer assisted discrimination system on the basis of two steps: (i) two-dimensional wavelet-entropy for feature extraction; (ii) the feedforward Neural Network (FNN) for classification. Specifically, the wavelet entropy features were fed into a FNN classifier. (Results) The 10 runs of 75 images of three categories showed that the average accuracy achieved 90.70 %. The sensitivities of green, Oolong, and black tea are 92.80 %, 84.60 %, and 96.30 %, respectively. (Conclusions) It was easily observed that the proposed classifier can distinguish tea categories with satisfying performances, which was competitive with recent existing systems.

Interaction among different regions can be illustrated in the form of a stream. For example, the interaction between the flows of people and information among different regions can reflect city network structures, as well as city functions and interconnections. The popularization of big data has facilitated the acquisition of flow data for various types of individuals. The application of the regional interaction model, which is based on the summary level of individual flow data mining, is currently a hot research topic. Thus far, however, previous research on spatial interaction methods has mainly focused on point-to-point and area-to-area interaction patterns, and investigations on the patterns of interaction hotspots between two regional groups with predefined neighborhood relationships, that being with two regions, remain scarce. In this study, a method for the identification of similar interaction hotspot patterns between two regional groups is proposed, and geo-information Tupu methods are applied to visualize interaction patterns. China’s air traffic flow data are used as an example to illustrate the performance of the proposed method to identify and analyze interaction hotspot patterns between regional groups with adjoining relationships across China. Research results indicate that the proposed method efficiently identifies the patterns of interaction flow hotspots between regional groups. Moreover, it can be applied to analyze any flow space in the excavation of the patterns of regional group interaction hotspots.

In this paper, a new method is proposed to promote the efficiency and accuracy of nonlinear interval-based programming (NIP) based on approximation models and a local-densifying method. In conventional NIP methods, searching for the response bounds of objective and constraints are required at each iteration step, which forms a nested optimization and leads to extremely low efficiency. In order to reduce the computational cost, approximation models based on radial basis functions (RBF) are used to replace the actual computational models. A local-densifying method is suggested to guarantee the accuracy of the approximation models by reconstructing them with densified samples in iterations. Thus, through a sequence of optimization processes, an optimal result with fine accuracy can be finally achieved. Two numerical examples are used to test the effectiveness of the present method, and it is then applied to a practical engineering problem.

It is important to early detect pathological brains. Traditional methods used plain support vector machine (SVM) that is vulnerable to noises and outliers. In this study, we presented a hybrid method that combined wavelet-energy (WE) and fuzzy support vector machine (FSVM). The results over a 5x5-fold cross validation showed that the proposed "WE + FSVM" produced accuracy of 93.78%, higher than "WE + KSVM" of 91.78%, "DWT + PCA + RBF-NN" of 91.33%, "WE + BP-NN" of 86.67%, and "DWT + PCA + BP-NN" of 86.22%. Therefore, this study offered a new means to solve the problem with excellent performance.

An accurate diagnosis is important for the medical treatment of patients suffered from brain disease. Nuclear magnetic resonance images are commonly used by technicians to assist the pre-clinical diagnosis, rating them by visual evaluations. The classification of NMR images of normal and pathological brains poses a challenge from technological point of view, since NMR imaging generates a large information set that reflects the conditions of the brain. In this work, we present a computer assisted diagnosis method based on a wavelet-entropy (In this paper 2D-discrete wavelet transform has been used, in that it can extract more information) of the feature space approach and a Naive Bayes classifier classification method for improving the brain diagnosis accuracy by means of NMR images. The most relevant image feature is selected as the wavelet entropy, which is used to train a Naive Bayes classifier. The results over 64 images show that the sensitivity of the classifier is as high as 94.50%, the specificity 91.70%, the overall accuracy 92.60%. It is easily observed from the data that the proposed classifier can detect abnormal brains from normal controls within excellent performance, which is competitive with latest existing methods.

(Aim) A novel and efficient method based on the quantum-behaved particle swarm was proposed to solve the cluster analysis problem. (Methods) The QPSO was utilized to detect the optimal point of the VAriance RAtio Criterion (VARAC), which was created by us as fitness function in the optimization model. The experimental dataset had 4 groups (400 data in total) with three various degrees of overlapping: non-overlapping, partial overlapping, and intensely overlapping. The proposed QPSO was compared with traditional global optimization algorithms: genetic algorithm (GA), combinatorial particle swarm optimization (CPSO), and firefly algorithm (FA) via running 20 times. (Results) The results demonstrated that QPSO could locate the best VARAC values with the least time among the four algorithms. (Conclusions) We can find that QPSO performs effectively and fast for the problem of cluster analysis.

As pure electric vehicles are considered to be a major growth trend in the automotive industry, research into and development of efficient electric powertrain systems and related control technologies have become popular research topics. The growing importance and use of multi-speed transmissions in these vehicles make shift schedule design and research a crucial aspect of the powertrain systems design of pure electric vehicles. This paper provides a gear shift schedule calculation method for pure electric vehicles, which includes a dynamic shift schedule and an economic shift schedule calculation method, demonstrating how to optimize the shift points and to produce the upshift and downshift lines based on the motor efficiency map. Through the establishment of a pure electric vehicle model, simulation results show that a properly designed shift schedule can improve the working region of the motor and can refine the dynamic performance and the economic performance of the vehicle. Finally, rig testing results are demonstrated to be comparable with simulations and indicate the correctness of the method.

© The Author(s) 2017. This article develops a systematic model to study electric vehicle powertrain system efficiency by combining a detailed model of two-speed dual-clutch transmission system efficiency losses with an electric vehicle powertrain system model. In this model, the design factors including selection of the electric machine, gear ratios' change, multi-plate wet clutch design, and gear shift schedule design are considered. Meanwhile, the application of detailed model for drag torque losses in the gearbox is discussed. Furthermore, the proposed model, developed with the MATLAB/Simulink platform, is applied to optimize/maximize the efficiency of the electric vehicle powertrain system using genetic algorithms. The optimization results demonstrate that the optimal results are different between simulations via New Europe Drive Cycle and Urban Dynamometer Driving Schedule, and comprehensive design and optimization of the powertrain system are necessary.

This paper proposes an approach to optimize the economy performance of a two-speed electric vehicle (EV) by combining gear shifting schedule design and gear ratios selection. Mathematic models for the two-speed EV subsystems are developed, including those of the battery module, electric machine, the driver, transmission and vehicle. Then a procedure for obtaining the optimal gear ratio pairs and corresponding shift schedule for the two-speed EV is presented in detail. The optimized EV powertrain parameters can not only ensure that basic requirements in dynamic performance are achieved, but realize the optimal economic performance of the EV as well. In order to investigate the effectiveness of the proposed method for EV design, simulations based on the developed powertrain model is conducted using different test driving cycles, including NEDC and constant speed. Results of these simulations validate the effectiveness of the proposed optimization method. Copyright © 2013 SAE International.

Method to improve the efficiency and the design of more efficient pure electric vehicle (PEV) system is a research hotspot for the automobile industry worldwide. Traditionally, PEV powertrains are dominated almost exclusively by single reducer structures in both concept and commercially available PEVs. This paper presents a novel two motor two speed pure electric power-train structure to study alternate methods for improving the operating efficiency of a typical PEV. This idea is derived from conventional power splitting transmissions to achieve motor efficiency optimization and ultimately lead to running range improvement. Through parameter matching and simulation, results demonstrated that the proposed two motor two speed PEV can realize better vehicle performance than single reducer PEV system; furthermore it can behave even better than a similar single motor two speed system. Copyright © 2013 SAE International.

Dual clutch transmissions (DCTs) are recognized as being suitable for electric drive applications as they can drive with high efficiency and achieve good shifting comfort. A two-speed DCT electric drivetrain is described in this paper, comprised of only two gear pairs and a final drive gear in the two-speed gearbox. The fundamental shifting control algorithm is provided. On the testing rig of University of Technology, Sydney (UTS) powertrain lab, shifting controls and some driving cycle controls were realized. The results demonstrated that the control algorithm functioned well both in transient shifting control process and in the driving cycle conditions. © 2013 Bo Zhu et al.

We call a finite graph G = (V,E) a binary network if the state set of its nodes has only two elements, say, 0 and 1, representing respectively ‘OFF’ and ‘ON’ state. A switch at node v switches both the state of v and the state of each of its neighbors. It is shown in [1] that given any initial state of a network of order n > 3, we can always reach at a consistent status, i.e., either all the nodes are ON or all are OFF. In this paper we consider a more general problem: Given a subset S ⊂ V, can we reach to a state such that the state of each node within S is 1(or 0) while the states of nodes outside S is another? We present some sufficient conditions for some specific S that satisfies this condition.

We deterministically prepare and stabilize Fock states in a superconducting cavity by real-time quantum feedback using information provided by circular Rydberg atoms to react on the field and compensate the quantum jumps due to decoherence.

Feedback loops are at the heart of most classical control procedures. A controller compares the signal measured by a sensor with the target value. It adjusts then an actuator in order to stabilize the signal towards its target. Generalizing this scheme to stabilize a micro-system's quantum state relies on quantum feedback, which must overcome a fundamental difficulty: the measurements by the sensor have a random back-action on the system. An optimal compromise employs weak measurements providing partial information with minimal perturbation. The controller should include the effect of this perturbation in the computation of the actuator's unitary operation bringing the incrementally perturbed state closer to the target. While some aspects of this scenario have been experimentally demonstrated for the control of quantum or classical micro-system variables, continuous feedback loop operations permanently stabilizing quantum systems around a target state have not yet been realized. We have implemented such a real-time stabilizing quantum feedback scheme. It prepares on demand photon number states (Fock states) of a microwave field in a superconducting cavity and subsequently reverses the effects of decoherence-induced field quantum jumps. The sensor is a beam of atoms crossing the cavity which repeatedly performs weak quantum non-demolition measurements of the photon number. The controller is implemented in a real-time computer commanding the injection, between measurements, of adjusted small classical fields in the cavity. The microwave field is a quantum oscillator usable as a quantum memory or as a quantum bus swapping information between atoms. By demonstrating that active control can generate non-classical states of this oscillator and combat their decoherence, this experiment is a significant step towards the implementation of complex quantum information operations., 12 pages, 4 figures

A scheme of information hiding for H.264 based on the prediction difference of intra_4 × 4 is proposed in this paper. According to this difference, the proposed algorithm first divided the intra_4 × 4 prediction modes (I4-modes) into two groups to form the rule of mapping between these modes and the bits to be hidden, and then modified the I4-modes by the rule of mapping to implement the hiding of information. By introducing the ratedistortion cost during the process of modulating, the proposed scheme achieved a better balance of rate-distortion, meanwhile, it had a smaller influence on the quality of video and the bit-rate of video. This scheme could detect the secret information rapidly, which meets the requirement of the real-time processing of video. Experimental results also demonstrate that it is a extremely effective scheme.