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This paper investigates the fault estimation problem for a class of nonlinear nonrepetitive systems subject to iteration-dependent references. Firstly, based on the high-order internal model strategy, iterative learning fault estimation scheme is proposed to track the fault signals that varies with iteration index increasing. Then, the convergence of the presented method is achieved by the norm-based approach. Further, the proposed method is also extended to the uncertain systems with varying parameter matrices, discrete-time systems with Lipschitz perturbation and time-variant coefficients. Finally, the effectiveness of the proposed iterative learning fault estimation scheme is verified by numerical simulation studies.

Eggshell membrane (ESM), a plentiful biological waste, consists of collagen-like proteins and glycosaminoglycans (GAGs) such as hyaluronic acid (HA). Here we used a keratinase (oeMtaker)-mediated system to decompose ESM. The best reaction condition was established by incubating the solution containing oeMtaker, sodium sulfite, and ESM with a weight ratio of 1:120:600. ESM enzymatic hydrolysate (ESM-EH) showed a high proportion of essential amino acids and type X collagen peptides with 963–2259 Da molecular weights. The amounts of GAGs and sulfated GAGs in ESM-EH were quantified as 6.4% and 0.7%, respectively. The precipitated polysaccharides with an average molecular weight of 1300–1700 kDa showed an immunomodulatory activity by stimulating pro-inflammatory cytokines (IL-6 and TNF-α) production. In addition, a microorganism-based system was established to hydrolyze ESM by Meiothermus taiwanensis WR-220. The amounts of GAGs and sulfated GAGs in the system were quantified as 0.9% and 0.1%, respectively. Based on our pre-pilot tests, the system shows great promise in developing into a low-cost and high-performance process. These results indicate that the keratinase-mediated system could hydrolyze ESM more efficiently and produce more bioactive substances than ever for therapeutical applications and dietary supplements.

The phytohormone cytokinin influences many aspects of plant growth and development, several of which also involve the cellular process of autophagy, including leaf senescence, nutrient re-mobilization, and developmental transitions. The Arabidopsis type-A Response Regulators (type-A ARR) are negative regulators of cytokinin signaling that are transcriptionally induced in response to cytokinin. Here, we describe a mechanistic link between cytokinin signaling and autophagy, demonstrating that plants modulate cytokinin sensitivity through autophagic regulation of type-A ARR proteins. Type-A ARR proteins were degraded by autophagy in an AUTOPHAGY-RELATED (ATG)5-dependent manner. EXO70D family members interacted with Type-A ARR proteins, likely in a phosphorylation-dependent manner, and recruited them to autophagosomes via interaction with the core autophagy protein, ATG8. Consistently, loss-of-function exo70D1,2,3 mutants compromised targeting of type-A ARRs to autophagic vesicles, have elevated levels of type-A ARR proteins, and are hyposensitive to cytokinin. Disruption of both type-A ARRs and EXO70D1,2,3 compromised survival in carbon-deficient conditions, suggesting interaction between autophagy and cytokinin responsiveness in response to stress. These results indicate that the EXO70D proteins act as selective autophagy receptors to target type-A ARR cargos for autophagic degradation, demonstrating that cytokinin signaling can be modulated by selective autophagy.

This paper deals with fault estimation problem for a class of nonlinear system with quantised measurements. In this paper, a logarithmic quantiser is introduced and an iterative learning observer scheme is constructed, meanwhile the number of quantisation levels of output signals are finite. Compared with the existing approaches of observer-based fault estimation, the proposed iterative learning observer in this paper considers both state error and fault estimation which generated by previous iteration and use them to improve the fault estimation performance in the current iteration. Simultaneously, Lyapunov stability theory is employed to achieve the stability and convergence of the designed observer. Furthermore, the extension from nominal system to system with parameter uncertainties subjecting to Bernoulli-distributed white sequences with known conditional probabilities is also addressed. Finally, an illustrative example are presented to demonstrate the theoretical results.

Additional file 1. Supplementary material.

Giant magnetostriction in insulating magnetic materials is highly required for applications but is rarely observed. Here we show that giant magnetostriction (> 1500 ppm) can be achieved in an insulating transition metal oxide CaBaCo4O7 where the ferrimagnetic ordering at TC ~ 62 K is associated with a huge change in the lattice. Moreover, because this material is pyroelectric with a non-switchable electric polarization (P), the giant magnetostriction results in a record-breaking magnetoelectric effect - a gigantic change of electric polarization (deltaP ~ 1.6 {\mu}C/cm2) in response to the applied magnetic field up to 60 T. Geometric frustration as well as the orbital instability of Co2+/Co3+ ions is believed to play a crucial role in the giant magnetostriction. Our study provides new insights on how to achieve both giant magnetostriction and pronounced magnetoelectric effect in insulating transition metal oxides., Comment: 17 pages, 4 figures

OBJECTIVE: To investigate the clinical predictors for intensive care unit (ICU) admission for patients with benzodiazepine (BZD) overdose and their clinical characteristics in the emergency department (ED). METHODS: A retrospective case-control study of acute BZD overdose patients aged ≥ 18 years presenting to the ED in our hospital from July 1, 2012 through June 30, 2015 were enrolled in this study. We collected demographic information on underlying diseases, initial presentations, causes and the classifications of BZD, complications, dispositions, and outcomes. Analyses were conducted among subgroups and were identified the possible predictive clinical factors determining ICU admission in these patients. RESULTS: A total of 140 patients were enrolled in the study, with a mean age of 51.3 ± 19.1 years (mean ± standard deviation [SD]) and female predominance with 2.59:1. The most common cause of BZD overdose was suicidal attempt. The most common underlying disease is major depression disease or bipolar disorder occupying 85.7% of all study patients. Suicide attempt accounted for 84.3% (118/140) of all study patients, among whom 41.4% (58/140) has previous history of suicide attempt. Sixty-nine point two percent (83/120) needed hospital admission, including 20 patients (14.3%) with ICU admission and a total three patients mortalities (2.1%, 3/140). Two clinical predictive factors of ICU admission were identified, including pneumonia and fl umazenil use in ED. CONCLUSION: The incidence of mortality in patients with BZD overdose is low, but all-cause mortality remains high in those admitted to ICU (15%). Emergency physicians are suggested to pay more attentions on BZD overdose patients with suicidal attempt and major depression/bipolar disorder, who have pneumonia or flumazenil use in the ED. The incorporation of hospital healthcare team resource management in dealing with the recording, intervention, and prevention of these patients was mandatory to decrease repeat overdose, enhance care quality, and improve outcomes.

Both linear and nonlinear relationships are two typical characteristics among industrial process variables, and diagnosing a process with such complicated correlations among variables is indispensable. However, individual dictionary learning or autoencoder based method is hard to extract these complicated correlations well. The parallel label consistent KSVD-stacked autoencoder (P-LCKSVD-SAE) model is proposed to integrate the linear and nonlinear feature extraction for effective industrial process fault diagnosis. First, LCKSVD and SAE methods are applied to extract the linear and nonlinear features from industrial process. Second, a matrix fusion algorithm is proposed for combining these intrinsic fault features. Then, the SVM classifier is utilized to verify the effectiveness of the proposed model for fault classification. Finally, several case studies on Tennessee Eastman process demonstrate that the proposed P-LCKSVD-SAE fault diagnosis scheme is better than the conventional LC-KSVD as well as the SAE methods at performing industrial process fault diagnosis.

This paper presents the research on small-scale wind turbine systems based on the Maximum Power Point Tracking (MPPT) algorithm. Then propose a new structure of a small-scale wind turbine system to simplify the structure of the system, making the system highly practical. This paper also presented an MPPT-Fuzzy controller design and proposed a control system using the wind speed sensor for small-scale wind turbines. Systems are simulated using Matlab/Simulink software to evaluate the feasibility of the proposed controller. As a result, the system with the MPPT-Fuzzy controller has much better quality than the traditional control system.

This paper address the remaining useful life (RUL) prediction of electronic products based on wiener degradation process model and Bayesian posterior estimation.Considering the randomness and individual difference of performance degradation process of electronic products, the degradation process can be modeled and analyzed based on the Wiener process. Combining with the historical degradation data of other similar products as the prior information, posterior parameters can be estimated by using the degradation information of the target product through Bayesian estimation through the RUL distribution of the first hitting time (FHT). It can realize real-time update of parameters. Then the RUL of target electronic product can be estimated. It can improve the accuracy of RUL prediction to a certain extent. The feasibility of the method is verified by a practical example of GaAs lasers.

Autoencoder has been popularly used as an effective feature extraction method in fault diagnosis. However, the autoencoder algorithms neglect local structure and class information that is available in the training set. To address this problem, a novel feature extraction approach based on discriminative graph regularized autoencoder is proposed for fault diagnosis task. A single-layer autoencoder with nonlinear layers is adopted to extract nonlinear features automatically from input signals. Locality relationship of original data is propagated to the feature extraction stage via a graph to learn internal representations that go beyond reconstruction and on to locality preservation. To better exploit the discriminative information, the label information of training samples is embedded to the graph to improve the fault diagnosis performance. A real industrial process are used to comparing the performance with commonly used diagnosis method, the promising experimental results validate the superiority of the proposed method.

The vibration of the tower will cause fatigue damage of the tower, reduce the bearing performance of the tower, and result partially damage of the tower. It is difficult to implement and maintenance for the traditional contact measurement method. Motivated by this fact, we propose a non-contact method to measure the vibration of the tower. We apply the video subtle motion magnification algorithm to the tower vibration observation. By processing the video sequence of the tower, we can directly observe the tiny movement of the tower using the naked eye without complicated sensor equipment. The proposed method is used Eulerian Video Magnification technology which processes the image as a whole to reduce computational cost and make the processed image real.

(1) Background: To investigate the correlation between therapeutic outcome and morphologic changes for diabetic macular edema (DME) after intravitreal injection of ranibizumab (IVIR). (2) Methods: This retrospective study included 228 eyes received IVIR for DME. Each participant was traced for two years after the initial IVIR, while the data of ophthalmic examination, optical coherence tomography (OCT) image, and systemic diseases were collected. The study population was categorized into different subgroups according to the existence of OCT morphologic change and the initial OCT morphologic pattern, including diffuse retinal thickening (DRT), cystoid macular edema (CME), serous retinal detachment (SRD), and vitreomacular interface abnormalities (VMIAs). The primary outcomes were the baseline best-corrected visual acuity (BCVA) and central macular thickness (CMT) during a two-year study period. The distribution of OCT morphologic change and its relation to primary outcome were analyzed. (3) Results: Comparing the 42 eyes (18.4%) with OCT morphological changes to another 186 eyes (81.6%) without such alteration, the former showed a poorer baseline BCVA (0.84 ± 0.39 vs. 0.71 ± 0.36, p = 0.035), worse final BCVA (0.99 ± 0.44 vs. 0.67 ± 0.30, p = 0.001), and thicker final CMT (354.21 ± 89.02 vs. 305.33 ± 83.05, p = 0.001). Moreover, the VMIA developed in 14.9% of all DME patients presenting the most common morphologic change among DRT, CME, and SRD. Besides, the presence of stroke was independently correlated to the morphologic change (adjusted odds ratio [aOR]: 6.381, 95% confidence interval (CI): 1.112–36.623, p = 0.038). (4) Conclusions: The change of OCT morphology in DME patients receiving IVIR was correlated to worse structural and visual outcome while the formation of VMIA most commonly occurred after initial treatment.

This paper deals with fault estimation problem for a class of nonlinear system with parameter uncertainties subjecting to Bernoulli-distributed white sequences with known conditional probabilities. In order to reflect the reality more closely, parameter uncertainties are considered in both the state parameter matrix and the output parameter matrix. Compared with existing observer-based fault estimation approaches, the proposed iterative learning observer considers the state error information and fault estimating information from the previous iteration to improve the fault estimation performance in the current iteration. Simultaneously, the stability and convergence of the designed observer are achieved by employing the Lyapunov stability theory. On the other hand, a novel optimal function using expectation is presented to ensure the uniform convergence of the fault estimation scheme, thus reducing the impact of randomly occurring parameter uncertainties. Finally, linear matrix inequality (LMI) is employed to obtain the solutions of sufficient condition for further improvement of iterative learning law performance. The results are suitable for the systems with time-varying uncertainties as well as constant uncertainties. Additionally, a numerical example is given to demonstrate the effectiveness of the proposed design scheme.

Nonuniform sampling can be encountered in various practical processes because of random events or poor timebase. The analysis and applications of the nonuniform sampling for deterministic signals r...

Additional file 2. Review history.

Additional file 1: Figure S1. Schematic diagram of forming the two types of byproducts. Figure S2. Sequencing chromatograms from 7 prime-edited lines harboring W542L edits. Figure S3. Sequencing chromatograms from 2 prime-edited lines harboring homozygous S621I edits. Figure S4. Prime-editing efficiency in rice protoplasts for pegRNAs based on different expression strategies. Table S1. Edits and byproducts revealed from cloned PCR fragments. Table S2. Analysis of mutations in T0 transgenic plants by NGS with a 0.5% threshold. Table S3. Edits and byproducts from the 4 additional lines. Table S4. Prime-editing efficiency in rice protoplasts analyzed by NGS. Table S5. Sequences of primers, targets, and rtT-PBS of the pegRNAs. Supplemental material. Sequences of the PE2 and pegRNA expression cassettes.

There are so many programming style variations in R. We have analyzed 94 million lines of R code and quantified the evolution in popularity of 12 style-elements from 1998 to 2018. We attribute 3 main factors that drive changes in programming style: effect of style-guides, effect of introducing new features, and effect of editors. We have identified community-specific programming style variations. For example, there are programming communities which do not use snake_case at all. A consensus in programming style is forming. We have summarised it into a _Consensus-based Style_.

Fault detection and identification could minimize unexpected degradation of system and further avoid dangerous situation. Due to the rapid development of sensor technology as well as the Internet, exponential data could be collected, resulting in that data-driven based fault diagnosis method receives increasing attention. However, most works often learned low dimensional representations so that they couldn't preserve the real local geometric structure of original data. This might degrade fault diagnosis capabilities. In this paper, a novel semi-supervised constraints propagation based approach for fault diagnosis was proposed. The key point was to spread the linking information of supervised data to its neighbors via constraints propagation. Accordingly, the propagated similarity matrix could correctly reflect the structure of the samples. Further, with the aid of propagated matrix, sample indexes were learned via singular value decomposition and support vector machine were utilized to identify the type of faults. The effectiveness of the proposed methods was demonstrated through the experimental results, compared with other popular fault diagnosis methods.

Aim: Chitosan (CS) has been extensively studied as drug delivery systems for wound healing. Results/methodology: CS nanoparticles were loaded with curcumin (Cur) and DsiRNA against prostaglandin transporter gene and they were incorporated into 20 and 25% w/v Pluronic F-127. The gels were later analyzed for their rheology, gelation temperature (Tgel), morphology, drug incorporation and in vitro drug release. The particle size was in the range of 231 ± 17–320 ± 20 nm, depending on CS concentration. The gels had Tgel of 23–28°C and exhibited sustained drug release with high accumulated amount of drugs over 48 h. Conclusion: A thermo-sensitive gel containing Cur/DsiRNA CS nanoparticles was successfully developed and has a great potential to be further developed.

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

We present the first high-resolution, simultaneous observations of the solar chromosphere in the optical and millimeter wavelength ranges, obtained with ALMA and the IBIS instrument at the Dunn Solar Telescope. In this paper we concentrate on the comparison between the brightness temperature observed in ALMA Band 3 (3 mm; 100 GHz) and the core width of the H$��$ 656.3 nm line, previously identified as a possible diagnostic of the chromospheric temperature. We find that in the area of plage, network and fibrils covered by our FOV the two diagnostics are well correlated, with similar spatial structures observed in both. The strength of the correlation is remarkable, given that the source function of the mm-radiation obeys local thermodynamic equilibrium, while the H$��$ line has a source function that deviates significantly from the local Planck function. The observed range of ALMA brightness temperatures is sensibly smaller than the temperature range that was previously invoked to explain the observed width variations in H$��$. We employ analysis from forward modeling with the RH code to argue that the strong correlation between H$��$ width and ALMA brightness temperature is caused by their shared dependence on the population number $n_2$ of the first excited level of hydrogen. This population number drives millimeter opacity through hydrogen ionization via the Balmer continuum, and H$��$ width through a curve-of-growth-like opacity effect. Ultimately, the $n_2$ population is regulated by the enhancement or lack of downward Ly$��$ flux, which coherently shifts the formation height of both diagnostics to regions with different temperature, respectively., Accepted for publication in ApJ

This paper introduced a deep learning approach to achieve fault detection with signal analysis and processing, which is based on an sparse auto-encoder and can be employed to achieve unsupervised learning to automatically extract features of complex data-sets to detect fault. This sparse auto-encoder can be employed to extract features from the unrecognized signals to achieve intelligent identification. The hidden layer of auto-encoder can be considered as an over-complete dictionary, which can be employed to reconstruct the input signals to extract data-sets features unsupervised. Furthermore, the sparse auto-encoder can be considered as the method to build up a specific architecture to describe the process industrial system, not only to avoid the requirement of large amount of data onto the training step, but also to handle the problem with small sample training data. Finally, the application of this method of Tennessee Eastman Process Chemical system can be employed to demonstrate and illustrate the effectiveness and the reliability of this proposed method, and the results have shown its excellent performance on fault detection for process industrial systems.

Purpose– Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular.Design/methodology/approach– It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications.Findings– The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works.Research limitations/implications– This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems.Originality/value– DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.

Klebsiella pneumoniae (strain 43816, K2 serotype) induces interleukin-1β (IL-1β) secretion, but neither the bacterial factor triggering the activation of these inflammasome-dependent responses nor whether they are mediated by NLRP3 or NLRC4 is known. In this study, we identified a capsular polysaccharide (K1-CPS) in K. pneumoniae (NTUH-K2044, K1 serotype), isolated from a primary pyogenic liver abscess (PLA K. pneumoniae ), as the Klebsiella factor that induces IL-1β secretion in an NLRP3-, ASC-, and caspase-1-dependent manner in macrophages. K1-CPS induced NLRP3 inflammasome activation through reactive oxygen species (ROS) generation, mitogen-activated protein kinase phosphorylation, and NF-κB activation. Inhibition of both the mitochondrial membrane permeability transition and mitochondrial ROS generation inhibited K1-CPS-mediated NLRP3 inflammasome activation. Furthermore, IL-1β secretion in macrophages infected with PLA K. pneumoniae was shown to depend on NLRP3 but also on NLRC4 and TLR4. In macrophages infected with a K1-CPS deficiency mutant, an lipopolysaccharide (LPS) deficiency mutant, or K1-CPS and LPS double mutants, IL-1β secretion levels were lower than those in cells infected with wild-type PLA K. pneumoniae . Our findings indicate that K1-CPS is one of the Klebsiella factors of PLA K. pneumoniae that induce IL-1β secretion through the NLRP3 inflammasome.

Purpose – Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues. Design/methodology/approach – Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets. Findings – The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns. Originality/value – The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

With the increasing wind capacity, the proportion of wind power in the grid is getting higher. Therefore, it is critical for the stable operation of the power grid to find out the location of the wind turbine failures. This paper proposes a fault diagnosis method of the wind turbine converter based on the deep belief network. Firstly, multiscale analysis of the signal is carried out by using wavelet transform to extract the characteristic vector of fault signal. DBN is used to obtain fault recognition models by supervised learning that uses the feature vector. Finally, the simulation results reveal that the method has a good ability to identify the converter fault.

Purpose We evaluated the efficacy and tolerability of the ketogenic diet (KD) on generalised convulsions and status epilepticus (SE) in patients with Dravet syndrome (DS). Methods Patients with DS having ≥2 generalised convulsions/month despite drug treatment were included in this study and placed on a KD for 6 months. From 3 months before (baseline) to 6 months after KD initiation, caregivers recorded patients' seizure activity, antiepileptic drug use, and adverse events. The KD efficacy was determined by examining the frequency and duration of seizures at 3 and 6 months vs. baseline. Responders were defined as individuals whose generalised convulsions decreased in frequency by ≥50% vs. baseline. Seizures lasting ≥5 min and SE were specifically evaluated. Patients' cognition was also assessed at 3 and 6 months via questionnaire. Results Twenty patients continued the KD for at least 3 months. Of the 17 responders identified at month 3, seizures decreased by 50–89% and 90–99% in nine and two patients, respectively; six patients were seizure free. The KD was ineffective in three patients, who discontinued the diet. By month 6, seizures decreased by 50–89% and 90–99% in six and one patient(s), respectively; 10 patients were seizure free. The frequency of other seizure types also improved. During all 6 months, neither generalised convulsions lasting ≥5 min nor SE was detected in the 17 responders. The KD also improved patients' cognition. Conclusion The KD is a good treatment option for medically intractable epilepsy.

Background: Astragaloside, which is one of the main components of Astragalus membranaceus, has been widely used in the treatment of congestive heart failure in China, and it can protect cardiomyocytes. Its mechanism of action remains unclear. Therefore, the present study was carried out to investigate the influence of astragaloside on rat cardiomyocytes stimulated with endothelin-1 (ET-1), and explored the underlying mechanism. Materials and Methods: ET-1 was used to stimulate primary rat cardiomyocytes and establish a cardiomyocyte hypertrophy model. Different astragaloside doses were administered in combination with ET-1. Cardiomyocyte hypertrophy and apoptosis were examined using transmission electron microscopy (TEM) and flow cytometry, respectively. The molecular mechanism was explored by analyzing the mRNA of the vitamin D receptor (VDR), cytochrome P450 family 27 subfamily B member 1(CYP27B), cytochrome P450 family 24 subfamily A member 1(CYP24A) and renin mRNA levels by quantificational real-time polymerase chain reaction(qRT-PCR). Results: Rat cardiomyocyte hypertrophy model was established successfully. Astragaloside administration significantly affected cell apoptosis and significantly inhibited ET-1-induced cardiomyocyte hypertrophy in a dose-dependent manner. Astragaloside treatment affected the expression of signaling molecules in the vitamin D axis. Conclusion: Astragaloside inhibits ET-1-induced cardiomyocyte hypertrophy. This effect can be reversed by regulating the levels of the relevant factors in the vitamin D axis.

This paper aims to design the pitch angle control based on proportional–integral–derivative (PID) controller combined with fuzzy logic for small-scale wind turbine systems. In this control system, the pitch angle is controlled by the PID controller with their parameter is tuned by the fuzzy logic controller. This control system can compensate for the nonlinear characteristic of the pitch angle and wind speed. A comparison between the fuzzy-PID-controller with the conventional PID controller is carried out. The effectiveness of the method is determined by the simulation results of a small wind turbine using a permanent magnet generator (PMSG).

Aims Our objectives were to examine the community structure of phytoplankton and the associated environmental factors in Lake Changhu in the summer of 2012,and to investigate the degree of eutrophication in the lake. Methods Biological characteristics of the alga and integrative nutritional state index were used for evaluation of eutrophication in Lake Changhu. Phytoplankton and water samples were collected at 20 sites. The water samples were fixed,precipitated and concentrated for qualitative and quantitative analysis. Variables related to water conditions such as chlorophyll a,total phosphorus,total nitrogen,total suspended solids,secchi disk depth and chemical oxygen demand were monitored.Important findings Fifty-three species(genera) of phytoplankton were identified,belonging to Chlorophyta,Cyanophyta,Bacillariophyta,Xanthophyta,Euglenophyta,Pyrrophyta and Cryptophyta,respectively. Chlorophyta(24 species),which accounted for 38.9% of the total,was the most abundant,followed by Cyanophyta(15 species,accounting for 36.0% of the total) and Bacillariophyta(7 species,accounting for 14.1% of the total). There were 10 dominant species and Oscillatoria amphibia was a common species in four areas,with maximum dominancy of 0.72. The phytoplankton density varied from 12.03 × 106 to 62.13 × 106 cell·L–1 with an average of 27.71 × 106 cells·L–1. The highest cell density occurred in the Yuanxinhu area,followed by the Haizihu area and the Mahongtai area; the Miaohu area was observed to have the lowest cell density. Biodiversity index of phytoplankton varied from 0.89 to 3.24,and evenness index varied in a range of 0.23–0.83. Based on the two methods of eutrophicationevaluation,the water was in moderately eutrophic and eutrophic state in Lake Changhu in the summer of 2012. Canonical correlation analysis suggested that the total nitrogen,total suspended solids,total phosphorus,dissolved oxygen and nitrite nitrogen were the main environmental factors affecting the spatial distribution of phytoplankton community in Lake Changhu in summer. Most of the Cyanophyta(Dactylococcopsis acicularis,Oscillatoria amphibia,Phormidium and Anabeana) had a great demand for total nitrogen. Affected by geographical feature,human activities and the hydrodynamic features,the sampling sites showed apparent regional differentiations as revealed by canonical correspondence analysis.

In this paper, an efficient robust object tracking approach based on SURF and Kalman Filter is proposed. SURF as an outstanding local invariant feature is employed. Based on the SURF feature, a SURF match method is proposed. A combination method using an ingenious method and KF is used to predict the possible region, in which the tracking object may appear. Only in this region, SURF features are extracted and matched. It can significantly reduce the computational complexity. A histogram-based re-match process is employed to dislodge failure tracking after SURF matching. To verify the performance of the proposed algorithm, several comparative experiments are conducted. The results reveal that the proposed method achieves better performance and accuracy than conventional methods.

This paper addresses a distributed model predictive control (DMPC) scheme for multiagent systems with improving control performance. In order to penalize the deviation of the computed state trajectory from the assumed state trajectory, the deviation punishment is involved in the local cost function of each agent. The closed-loop stability is guaranteed with a large weight for deviation punishment. However, this large weight leads to much loss of control performance. Hence, the time-varying compatibility constraints of each agent are designed to balance the closed-loop stability and the control performance, so that the closed-loop stability is achieved with a small weight for the deviation punishment. A numerical example is given to illustrate the effectiveness of the proposed scheme.

To achieve the robust tracking for a visual object under challenging conditions in the noisy,occlusion and the deformation,a novel visual object tracking method is proposed in this paper.By combining the Kernel Sparse Representation Classification(KSRC) and adaptive dictionary updating method under Normal Hedge framework,this method can handle tough situations like high inter-class similarities and drastically target appearance variations.Although the KSRC enhances classification performance,standard convex optimization is not fast enough for tracking in real time.Thus an efficient Kernel Random Coordinate Descent(KRCD) method is proposed to calculate the sparse coefficient vector,and the KRCD-SRC classification method is taken to calculate the loss value of each particle.In order to avoid the template drifting,the adaptive dictionary updating method is also given.At last,the states of the target are estimated by the Normal Hedge.Experiments show that the average computing frame rate of the proposed method is 14 frame/s when 50 particles are used.Extensive test results suggest that the proposed method outperforms several state-of-art tracking methods in many complex conditions.

A simple and novel design, integrating discrete commercial micro-lens and vertical illuminated optoelectronic component in a substrate with high accuracy, is presented here. Without affecting the optical performances, this integrated optical carrier also allows high-frequency radio-frequency interconnects. This feature is critical for high-speed operation. The high accuracy integrated optical carrier improves the optical coupling efficiency and helps to relax the tight circuit assembly tolerance requirement. The integrated optical carrier can be used for various photonic applications which employ vertical illuminated optoelectronic components. An integrated optical carrier prototype is designed here for the optical electrical interconnect printed circuit board (OECB). For this OECB design, the simulated results show that the integrated optical carrier helps to give an assembly misalignment tolerance of more than ±20 μm with an increase of 1 dB coupling loss. In addition, the simulated optical insertion loss from the transmitter to the receiver is less than 1.4 dB. The optical performance of the prototype integrated optical carrier is measured and compared with the simulation results to ascertain the design concept. For different OECB waveguide designs, dimensions and positions, this integrated optical carrier design is amended to give a better performance and misalignment tolerance.

Objective To analyze the constituents and metabolites of Radix Saposhnikoviae(RS)in rat plasma and urine by rapid-resolution liquid chromatography-time of flight mass spectrometry(RRLC-TOF/MS),so as to explore the active ingredients and metabolites of RS in vivo.Methods The separation was performed on a Angilent Zorbax Extend-C18(5 μm,250 mm×4.6 mm id)column,with a methanol-water mobile phase system used for gradient elution.Time-of-flight mass spectrometer(TOF/MS)was applied for qualitative analysis under positive ion mode.Based on the accurate molecular weight of TOF/MS detection and the compound list of RS established previously,the constituents and metabolites of RS in different matrix in vivo were identified.Results Six constituents of RS were identified in the plasma:sucrose,prim-O-glucosylcimifugin,cimifugin,nodakenetin,5-O-methylvisamminol,and 3'-O-i-butyrylhammaudol.Eight constituents were identified in the urine:prim-O-glucosylcimifugin,divaricatacid,cimifugin,4'-O-glucosyl-5-O-methylvisamminol,(3S)-2,2-dimethyl-3,5-dihydroxy-8-hydroxymethyl-3,4-dihydro-2H,6H-benzo-[1,2-b:5,4-b']dipyran-6-one,5-O-methylvisamminol,sec-O-β-D-glucosylhammaudol,and wogonin.Two metabolites were identified in the urine:glucuronide of cimifujin and an isomer of it.Conclusion The present method is reliable and effective for identifying compounds of RS in vivo,and it can provide a reference and evidence for the further pharmacodynamics experiments.

Objective To rapidly separate and identify the chemical components in traditional Chinese herbal medicine of Oldenlandia diffusa and its injection preparations by high performance liquid chromatography-time of flight mass spectrometry(HPLC-TOFMS).Methods An Agilent Zorbax XDB-Cl8 column(250 mm×4.6 mm,5 μm) was used for separation and identification of chemical components in Oldenlandia diffusa,with a mobile phase of 0.3% acetic acid(A) and methanol(B) in gradient elution,0-30 min,30%-90%B.The flow rate was set at 1.0 ml/min and the injection volume was 10 μl.The time-of-flight mass spectrometer was equipped with an EIS ion source.The scanning mass range was between m/z 100-1 000.Results The traditional Chinese medicine of Oldenlandia diffusa and its injection preparation were on-line separated and characterized by HPLC-TOFMS,and 11 chemical compounds were identified in Oldenlandia diffusa,6 compounds in market injection preparation,and 2 compounds in the injection prepared by ourselves.Conclusion Chromatographic demonstration of chemical compounds in Oldenlandia diffusa in one run provides a foundation for the further studying the metabolism and mechanism of Oldenlandia diffusa and its injection preparations.

To investigate the role of hTERT gene expression and AP-2alpha in n-butylidenephthalide (n-BP)-induced apoptosis in A549 lung cancer cells.Viability of A549 cells was measured by MTT assay. Protein expression was determined by Western blot. Telomerase activity was measured using the modified telomere repeat amplification protocol (TRAP) assay. Xenograft mice were used as a model system to study the cytotoxic effect of n-BP in vivo. The morphology of tumor was examined by immunohistochemical staining.The growth of A549 lung cancer cells treated with n-BP was significantly inhibited. Telomerase activity and hTERT mRNA expression were determined by telomeric repeat amplification protocol and reverse transcription-polymerase chain reaction, respectively. n-BP inhibited telomerase activity and hTERT mRNA expression in A549 cells while overexpression of hTERT could abolish BP-induced growth inhibition in the A549 cells. We also showed that hTERT promoter activity in the presence of n-BP was mediated via AP-2alpha. We saw an inhibition of tumor growth when nude mice carrying A549 subcutaneous xenograft tumors were treated with n-BP. Immunohistochemistry of this tumor tissue also showed a decrease in the expression of hTERT.The antiproliferative effects of n-BP on A549 cells in vitro and in vivo suggest a novel clinical application of this compound in the treatment of lung cancers.

We studied experimentally the high-temperature magnetoelectric $({\rm Ba}_{x}{\rm Sr}_{1-x})_3\rm Co_2Fe_{24}O_{41}$ prepared as ceramics (x = 0, 0.2) and a single crystal (x = 0.5) using inelastic neutron scattering, THz time-domain, Raman and far-infrared spectroscopies. The spectra, measured with varying temperature and magnetic field, reveal rich information about the collective spin and lattice excitations. In the ceramics, we observed an infrared-active magnon which is absent in $E^{\omega}\perp z$ polarized THz spectra of the crystal, and we assume that it is an electromagnon active in $E^{\omega} \| z$ polarized spectra. On heating from 7 to 250 K, the frequency of this electromagnon drops from 36 to 25 cm$^{-1}$ and its damping gradually increases, so it becomes overdamped at room temperature. Applying external magnetic field has a similar effect on the damping and frequency of the electromagnon, and the mode is no more observable in the THz spectra above 2 T, as the transverse-conical magnetic structure transforms into a collinear one. Raman spectra reveal another spin excitation with a slightly different frequency and much higher damping. Upon applying magnetic field higher than 3 T, in the low-frequency part of the THz spectra, a narrow excitation appears whose frequency linearly increases with magnetic field. We interpret this feature as the ferromagnetic resonance., Comment: Paper 8 pages + Suppl. Materials 3 pages

Many methods of processing epileptic EEG signals are concentrated in the classification, and most of them use the wavelet transform and SVM classification algorithm. Although these algorithms acquire the high accuracy, it is still unable to provide a good explanation of quantitative difference and physical meaning between epileptic EEG and normal EEG. This paper presents a new hybrid algorithm (CWT-SVM-HMM) to classify epileptic EEG signal. By the results of classification of HMM, we can track back abnormal signal frequency sources, through the analysis of the sources of seizures during different frequency band, we can get a seizure of accurate quantitative analysis according to clinical feature of interictal epileptiform discharges.

In this study, the electroencephalogram (EEG) signals-analysis experiments were made to classify seizures patients. Principal component analysis (PCA) and kernel principal component analysis (KPCA) were used for the data compression with the (EEG) signals. Classifiers based on support vector machine (SVM)-PCA and SVM-KPCA were designed. The classification performances of four kinds of kernel function were also compared using the same dataset. The results showed that using SVM-KPCA had higher recognition performance than SVM-PCA. Experimental results showed that the algorithm using SVM-KPCA with Gaussian-kernel had better recognition performance than the other three methods.

In this paper an activity recognition algorithm based on multi-feature fuzzy cluster is designed to find out more details of the activities so as to achieve an accurate classification among them. Firstly, it is proved that distribution of feature vectors vary from activity to activity. And then, a multi-feature extraction algorithm is designed to extract the feature vectors of each activity which makes up a standard activity class. Finally, an activity recognition algorithm based on similarity measurement is brought up and the misjudgment rate turns out to be acceptable, which proves that this algorithm is accurate and highly feasible.

It is common that multiple fault exists in actual engineering and complex systems. Due to parameters in multiple faults tightly coupled, relationship between the fault mode and known mono-fault features is non-linear. Thus, it is hard to see how distinguish in mapping set for "fault to symptom". In this case, there is no guarantee that traditional diagnosis methods for mono-fault meet the demands. With the requirement, an analysis of the traits of multiple faults is made. A summarization is given to class group distinguishing (CGD) based methods that applied in fault diagnosis. Major defects in the methods that applied in multiple fault diagnosis are analyzed. On that basis, fault modes and symptoms are taken as key points. Conceptual models for multiple fault diagnosis based on CGD are gradually explored. By the models, actual faults can be mapped to one or more known mono-faults via distinguishing analysis, and therefore multiple faults can be diagnosed. There are 4 kinds of flow chart and construction for the models are established. Each of these models presents advantages and disadvantages are separately presented at the end of the chapter.

Control schemes are needed in electric power networks to avoid unwanted outages and blackouts, otherwise non-stationary disturbance may pose stability issues in dynamic process. For improving system transient stability and avoiding voltage collapse, this paper presents a dynamic control scheme for power system by employing phase shifting transformer(PST). In particular, the control scheme is based on trajectory sensitivity analysis for realizing preventive control. The simulation results have verified the validation of the proposed method.