Multiple Matrices Research Articles

Fact-based nutrition for infants and lactating mothers-The NUTRISHIELD study.

Human milk (HM) is the ideal source of nutrients for infants. Its composition is highly variable according to the infant's needs. When not enough own mother's milk (OMM) is available, the administration of pasteurized donor human milk (DHM) is considered a suitable alternative for preterm infants. This study protocol describes the NUTRISHIELD clinical study. The main objective of this study is to compare the % weight gain/month in preterm and term infants exclusively receiving either OMM or DHM. Other secondary aims comprise the evaluation of the influence of diet, lifestyle habits, psychological stress, and pasteurization on the milk composition, and how it modulates infant's growth, health, and development. NUTRISHIELD is a prospective mother-infant birth cohort in the Spanish-Mediterranean area including three groups: preterm infants <32 weeks of gestation (i) exclusively receiving (i.e., >80% of total intake) OMM, and (ii) exclusively receiving DHM, and (iii) term infants exclusively receiving OMM, as well as their mothers. Biological samples and nutritional, clinical, and anthropometric characteristics are collected at six time points covering the period from birth and until six months of infant's age. The genotype, metabolome, and microbiota as well as the HM composition are characterized. Portable sensor prototypes for the analysis of HM and urine are benchmarked. Additionally, maternal psychosocial status is measured at the beginning of the study and at month six. Mother-infant postpartum bonding and parental stress are also examined. At six months, infant neurodevelopment scales are applied. Mother's concerns and attitudes to breastfeeding are registered through a specific questionnaire. NUTRISHIELD provides an in-depth longitudinal study of the mother-infant-microbiota triad combining multiple biological matrices, newly developed analytical methods, and ad-hoc designed sensor prototypes with a wide range of clinical outcome measures. Data obtained from this study will be used to train a machine-learning algorithm for providing dietary advice to lactating mothers and will be implemented in a user-friendly platform based on a combination of user-provided information and biomarker analysis. A better understanding of the factors affecting milk's composition, together with the health implications for infants plays an important role in developing improved strategies of nutraceutical management in infant care. https://register.clinicaltrials.gov, identifier: NCT05646940.

Enhanced Gastric/Lung Arsenic Bioaccessibility from Lignite Fly Ashes: Comparing Bioaccessibility Rates with Multiple Environmental Matrices.

Inorganic arsenic (As), a carcinogenic element to humans, is among the most dangerous and flammable substances that coal-burning plants could release. When coal is burned, large portions of arsenic are captured on fly-ash (FA) particles, but it could also contribute significantly to stack emissions of fine fly-ash particles. The aim of this study was to evaluate the oral and respiratory bioaccessibility of arsenic in lignite fly-ash (LFA) samples, and their contribution to total As exposure. Arsenic bioaccessibility fractions via ingestion and inhalation showed significant differences, suggesting the presence of highly soluble As-bearing phases in the studied LFA samples. The bioaccessible As fractions (BAF%) in the simulated gastric fluids (UBM protocol, ISO 17924:2018) showed a range of 45-73%, while the pulmonary bioaccessibility rates in the simulated lung fluid (artificial lung fluid (ALF)) exhibited significantly enhanced levels ranging from 86% to 95%. The obtained arsenic bioaccessibility rates were compared with previous data for multiple environmental matrices such as soil and dust-related materials, revealing that LFA exhibited significantly higher bioaccessibility (%) for the inhalation pathway.

Generalized Decay Synchronization of Directly Coupled Stochastic Dynamic Networks With Multiple Time Delays

This article is dedicated to addressing the generalized decay synchronization problem of directly coupled stochastic dynamic networks with multiple time delays. First, by means of the presentations of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\psi $ </tex-math></inline-formula> -type function and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\psi $ </tex-math></inline-formula> -type stability, the definitions of decay synchronization <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\psi $ </tex-math></inline-formula> -type synchronization) are introduced. After that, by constructing appropriate feedback controllers and the Lyapunov functional method, we study the decay synchronization for multiple coupled dynamic networks. Moreover, for different coupling matrices, their zero eigenvalues are correspondingly denoted by different normalized left eigenvectors (NLEVec), so it is difficult to establish the functional based on these NLEVec. With the help of the weighted combination of NLEVec for multiple coupling matrices, under the assumption that the Chebyshev gap among NLEVec is less than an allowable deviation interval, some generalized decay (anti-) synchronization conditions are established. Furthermore, we discuss exponential synchronization and polynomial synchronization as special cases, and more relevant criteria are acquired. Examples are provided to verify the effectiveness of those obtained conditions.

Flexible Multiview Spectral Clustering With Self-Adaptation.

Multiview spectral clustering (MVSC) has achieved state-of-the-art clustering performance on multiview data. Most existing approaches first simply concatenate multiview features or combine multiple view-specific graphs to construct a unified fusion graph and then perform spectral embedding and cluster label discretization with k -means to obtain the final clustering results. They suffer from an important drawback: all views are treated as fixed when fusing multiple graphs and equal when handling the out-of-sample extension. They cannot adaptively differentiate the discriminative capabilities of multiview features. To alleviate these problems, we propose a flexible MVSC with self-adaptation (FMSCS) method in this article. A self-adaptive learning scheme is designed for structured graph construction, multiview graph fusion, and out-of-sample extension. Specifically, we learn a fusion graph with a desirable clustering structure by adaptively exploiting the complementarity of different view features under the guidance of a proper rank constraint. Meanwhile, we flexibly learn multiple projection matrices to handle the out-of-sample extension by adaptively adjusting the view combination weights according to the specific contents of unseen data. Finally, we derive an alternate optimization strategy that guarantees desirable convergence to iteratively solve the formulated unified learning model. Extensive experiments demonstrate the superiority of our proposed method compared with state-of-the-art MVSC approaches. For the purpose of reproducibility, we provide the code and testing datasets at https://github.com/shidan0122/FMICS.

Approximate Fuzzy Inverse Matrix Calculation Method using Scenario-based Inverses and Bisection

In this paper, we introduce a numerical method to construct the inverse of a square matrix whose elements are trapezoidal or triangular fuzzy numbers (FNs). A set of fuzzy linear equations is required to be solved in order to determine the fuzzy inverse matrix. The proposed technique first iteratively searches the possible solution intervals and then narrows those too-wide estimated intervals via bisection. Using interval arithmetic in left and right matrix multiplication, we aim to approximate the identity matrix as a result of product operations. The dissimilarity of the endpoints of intervals belonging to multiplication matrices with the identity matrix is considered to be an error function to be minimized. In this way, even if the entries of a matrix are uncertain, the fuzzy inverse matrix containing all inverse matrices can be found quickly with the use of computer technology. The method is explained, and comparisons are drawn with inverse stable examples from the literature.

Evaluation and Monitoring of the Natural Toxin Ptaquiloside in Bracken Fern, Meat, and Dairy Products

Ptaquiloside, a naturally occurring cancer-causing substance in bracken fern, has been detected in the meat and milk of cows fed a diet containing bracken fern. A rapid and sensitive method for the quantitative analysis of ptaquiloside in bracken fern, meat, and dairy products was developed using the QuEChERS method and liquid chromatography-tandem mass spectrometry. The method was validated according to the Association of Official Analytical Chemists guidelines and met the criteria. A single matrix-matched calibration method with bracken fern has been proposed, which is a novel strategy that uses one calibration for multiple matrices. The calibration curve ranged from 0.1 to 50 µg/kg and showed good linearity (r2 > 0.99). The limits of detection and quantification were 0.03 and 0.09 µg/kg, respectively. The intraday and interday accuracies were 83.5-98.5%, and the precision was <9.0%. This method was used for the monitoring and exposure assessment of ptaquiloside in all routes of exposure. A total of 0.1 µg/kg of ptaquiloside was detected in free-range beef, and the daily dietary exposure of South Koreans to ptaquiloside was estimated at up to 3.0 × 10-5 µg/kg b.w./day. The significance of this study is to evaluate commercially available products in which ptaquiloside may be present, to monitor consumer safety.

CuFe2O4/diatomite actuates peroxymonosulfate activation process: Mechanism for active species transformation and pesticide degradation

Peroxymonosulfate (PMS) activation is a promising technology for water purification, but the removal performance of multiple pollutant matrices and the mechanism for reactive species transformation in the heterogeneous catalytic system remain ambiguous. Herein, a novel CuFe2O4/diatomite was fabricated for PMS activation to achieve efficient removal of typical pesticides. Uniform distribution of CuFe2O4 on diatomite efficiently alleviated the agglomeration of CuFe2O4 and increased specific surface area (57.20 m2 g−1, 3.8-fold larger than CuFe2O4). CuFe2O4/5% diatomite (5-CFD)/PMS system showed nearly 100% removal efficiency for mixed pesticide solution within 10 min (0.10 g L−1 5-CFD and 0.40 g L−1 PMS) and excellent anti-interference performance towards various coexisting substances (≥90% removal efficiency). The electrochemical measurements confirmed that the lower charge transfer resistance of 5-CFD significantly enhanced the electron-transfer capacity between 5-CFD and PMS, accelerating the reactions among Fe(III)/Fe(II), Cu(II)/Cu(I), and PMS, further generating •OH (261.3 μM), 1O2 (138.8 μM), SO4•– (11.8 μM), and O2•–. The O in reactive oxygen species didn't originate from dissolved oxygen (DO) but PMS, independent of the low solubility of DO and slow diffusion rate of O2 in water. Furthermore, the production of 1O2 went through the process: PMS → O2•– → 1O2, and SO4•– could rapidly convert into •OH. The degradation pathways and the evolution of intermediates were proposed by HPLC-QTOF-MS/MS and DFT calculations. QSAR analysis illustrated that the toxicity became lower with the reaction process. This study provides novel insights into the mechanism for pesticide degradation and active species transformation and the anti-interference capability of systems.

Evaluation of automated clean-up for large scope pesticide multiresidue analysis by liquid chromatography coupled to mass spectrometry

Clean-up step is essential during the multiresidue sample preparation process to remove undesired matrix components that may cause analytical interferences or suppression effect. However, its application generally by specific sorbents entails time-consuming work producing low recoveries for some compounds. Moreover, it usually needs to be adapted to the different co-extractives from the matrix present in the samples by using different chemical sorbents increasing the number of validation procedures. Therefore, the development of a more efficient and automated and unified clean-up procedure means a significant time reduction and laboratory work with improved performance.In this study, extracts from different matrices (tomato, orange, rice, avocado and black tea) were purified by manual dispersive clean-up (different procedures according to the matrix group) in parallel with an automated µSPE clean-up workflow, in both cases based on QuEChERS extraction. The latter procedure employed clean-up cartridges containing a mixture of sorbent materials (anhydrous MgSO4/PSA/C18/CarbonX) suitable for multiple matrices. All the samples were analysed by liquid chromatography mass spectrometry and the results obtained from both procedures have been compared in terms of the extract cleanness, performance, interferences, and sample workflow. At the levels studied, similar recoveries were achieved by both techniques (manual and automated) except for reactive compounds when PSA was used as the sorbent material producing low recoveries. However, the µSPE recoveries were between 70–120%. Furthermore, closer calibration line slopes were provided when µSPE was applied to the different matrix groups studied. It is important to note that up to 30% more samples per day can be analysed using an automated µSPE compared to the manual method (which requires shaking, centrifuging, then taking the supernatant and adding formic acid in ACN); it also provides good repeatability - an RSD (%) < 10%. Consequently, this technique is a very useful option for routine analyses, greatly simplifying the work of muti-residue methods.

Covariance-Free Variational Bayesian Learning for Correlated Block Sparse Signals

We consider the problem of estimating channel in massive machine type communication (mMTC) systems. The sparse device activity in a mMTC system makes the channel block-sparse, with intra-block correlation. Block-sparse Bayesian learning (B-SBL) is a powerful framework for estimating such signals. The existing B-SBL algorithms become computationally expensive for high-dimensional problems, which is common in mMTC systems. This is because of large number of devices in a mMTC system, they invert a large-dimensional matrix to calculate the covariance matrix. To address this problem, we exploit variational Bayesian inference, and design a novel covariance-free variational B-SBL algorithm which inverts multiple small-sized block matrices, instead of inverting a complete big-sized matrix. The complexity is further reduced by avoiding explicit computation of the covariance matrix. The proposed algorithm, instead of performing costly matrix inversions, solves multiple linear systems to calculate an unbiased estimate of the posterior statistics. The proposed algorithm is numerically shown to estimate the mMTC channel with a much lesser complexity, and that too without compromising the reconstruction performance.

Liquid Crystal Monomer: A Potential PPARγ Antagonist.

Liquid crystal monomers (LCMs) are a large family of artificial ingredients that have been widely used in global liquid crystal display (LCD) industries. As a major constituent in LCDs as well as the end products of e-waste dismantling, LCMs are of growing research interest with regard to their environmental occurrences and biochemical consequences. Many studies have analyzed LCMs in multiple environmental matrices, yet limited research has investigated the toxic effects upon exposure to them. In this study, we combined in silico simulation and in vitro assay validation along with omics integration analysis to achieve a comprehensive toxicity elucidation as well as a systematic mechanism interpretation of LCMs for the first time. Briefly, the high-throughput virtual screen and reporter gene assay revealed that peroxisome proliferator-activated receptor gamma (PPARγ) was significantly antagonized by certain LCMs. Besides, LCMs induced global metabolome and transcriptome dysregulation in HK2 cells. Notably, fatty acid β-oxidation was conspicuously dysregulated, which might be mediated through multiple pathways (IL-17, TNF, and NF-kB), whereas the activation of AMPK and ligand-dependent PPARγ antagonism may play particularly important parts. This study illustrated LCMs as a potential PPARγ antagonist and explored their toxicological mode of action on the trans-omics level, which provided an insightful overview in future chemical risk assessment.

A deep multiple kernel learning-based higher-order fuzzy inference system for identifying DNA N4-methylcytosine sites

N4-methylcytosine (4mC), as a DNA modification, plays a crucial role in epigenetic regulation. However, the existing experimentation methods for accurately identifying 4mC sites are inefficient and highly consumable, making them difficult to implement. Although a variety of new identification methods are continuously being proposed, existing techniques are not yet fully mature. Compared to traditional 4mC site predictors, based on support vector machine or convolutional neural network, we present an alternative computational approach. In this study, we propose a method based on a kernelized higher-order fuzzy inference system (KHFIS) and deep multiple kernel learning, called DMKL-HFIS, to improve the accuracy of 4mC site identification DNA sequences. We use PSTNP to process the benchmark datasets, and then apply KHFIS to obtain multiple fuzzy kernel matrices. A deep neural network is used to fuse multiple fuzzy kernel matrices. Finally, the predicted value is derived from the fused matrix. Our approach was compared with existing mainstream computational methods. On the benchmark datasets (G. subterraneus, D. melanogaster, E. coli, A. thaliana, and C. elegans), the accuracy of our approach exceeded that of a state-of-the-art method by 0.4%, 0.44%, 1.51%, 0.55%, and 0.25%, respectively. Compared to mainstream methods, our approach exhibits a higher level of accuracy and can therefore be considered an effective prediction tool.

Selective and efficient removal of emerging contaminants by sponge-like manganese ferrite synthesized using a solvent-free method: Crucial role of the three-dimensional porous structure

Ubiquitous macromolecular natural organic matter (NOM) in wastewater seriously influences the removal of emerging small-molecule contaminants via heterogeneous advanced oxidation processes because this material covers active sites and quenches reactive oxygen species. Here, sponge-like magnetic manganese ferrite (MnFe2O4-S) with a three-dimensional hierarchical porous structure was prepared via a facile solvent-free molten method. Compared with the particle-like structure of MnFe2O4-P, the sponge-like structure of MnFe2O4-S presents an enlarged specific surface area (112.14 m2·g−1 vs. 58.73 m2·g−1) and a smaller macropore diameter (68.2–77.2 nm vs. 946.5 nm). Enlarging the specific surface area increases the exposure of active sites, and adjusting the pore size helps sieve NOM and emerging contaminants. These changes are expected to effectively improve the degradation activity and overcome interference. To confirm the superiority of the sponge-like structure, MnFe2O4-S was used to activate peroxymonosulfate (PMS) for the degradation of multiple emerging contaminants, and its ability to degrade bisphenol A with and without humic acid (HA) was compared with that of MnFe2O4-P. The degradation activity of MnFe2O4-S was 1.6 times greater than that of MnFe2O4-P. Moreover, 20 mg·L−1 HA inhibited the degradation activity of MnFe2O4-S by only 7.1%, which was much lower than that obtained for MnFe2O4-P (53.4%). In addition, the excellent performance was maintained in multiple water matrices. Notably, under lake water matrices, the degradation activity of MnFe2O4-P was inhibited by 35.6% while that of MnFe2O4-S was hardly inhibited. More importantly, the MnFe2O4-S/PMS system was also applicable to the treatment of actual wastewater and 73.0% and 90.1% of total organic carbon and chemical oxygen demand was removed from bio-treated coking wastewater containing non-biodegradable contaminants and NOM. This study provides an alternative route for the green production of high-activity porous spinel ferrites with environmental anti-interference properties.

Matrix Chain Multiplication and Equivalent Reduced-Order Parallel Calculation Method for a Robotic Arm

Intelligence development has put forward increasing requirements of real-time planning and dynamic feedback in controlling robotic arms. It has become essential in engineering applications to complete the kinematics calculation of complex manipulators in real time. This paper proposes a matrix cascading multiplication equivalent reduced-order parallel computing method for the multiplication of homogeneous matrices in the process of forward and inverse kinematics solutions, which reduces the order of the matrix according to the distribution of zero vectors in the homogeneous matrix calculations. The method removes the unnecessary multiplication in joint homogeneous matrixes containing zero vectors. It obtains the optimal calculation order of the cascade matrix multiplication through dynamic planning searches, improving the efficiency of effective dot product calculation by parallel computation. Calculation processes and specific examples are presented in this paper. Compared with the previous algorithms, the proposed algorithm reduces the calculation cycle by 90%, effectively improving the real-time calculation efficiency in the complex control process.

Multilabel Recognition Algorithm With Multigraph Structure

In multi-label recognition tasks, convolutional neural networks (CNNs) can extract rich features of images in Euclidean space. However, CNNs struggle to handle dependencies between multi-labels in non-Euclidean space. Graph Convolutional Networks (GCNs) have natural advantages in processing the data in non-Euclidean space. Therefore, many excellent algorithms used GCN to capture the deep dependencies among multi-labels. However, these algorithms directly applied GCN for label feature fusion, leading to nodes with background features affecting the quality of the representation of their neighbor nodes. For example, during label feature smoothing, the representation of nodes with weak edge information tends to converge to nodes with background features. We propose a multi-label image recognition algorithm with a multi-graph structure to address the impact of background features on recognition performance. In brief, when smoothing the dependencies among labels using GCN, we transform the shared adjacency matrix into a combination of multiple adjacency matrices. Unlike node features sharing an adjacency matrix, the multi-graph structure can calculate the performance of each feature carried by the nodes separately during feature fusion and convey more appropriate information rather than in a generalized manner. We have conducted extensive experiments on two multi-label image recognition datasets and have achieved the state-of-the-art.

Refined Admissible Analysis and Design Conditions for Discrete Fuzzy Singular Systems with Multiple Difference Matrices

ABSTRACT This study mainly discusses extended admissibility and fuzzy parallel distributed compensation (PDC) control issues for discrete singular fuzzy systems with multiple difference matrices existing in the rules. By the overall system associated with the discrete singular models with multiple difference matrices, we first propose an extended admissibility analysis criteria, where the new results not only involve some slack matrices but also have a less number of linear matrix inequalities’ (LMIs) constraints. Furthermore, by hiring the fuzzy PDC, explicit design criteria are further developed for the regarded system. Noticeably, the new design method can cope with controller synthesis of the admissibility and D-admissibility issues. Due to all the presented criteria are formed by the strict LMIs, we can readily evaluate them via some existing LMI solvers. Finally, two numerical examples are involved to demonstrate the applicability and the feasibility of the developed results.

The Law of Multiplication of Large Random Matrices Revisited

The Law of Multiplication of Large Random Matrices Revisited

Interrelationship between trade and environment: a bibliometric analysis of published articles from the last two decades.

Extant literature indicates that the concepts of international trade and the environment are intertwined. There is a plethora of theoretical and empirical research studies that explore the relationship between the two concepts. However, no bibliometric attempts have been made to analyze these publications to understand the current research trends in the trade and environment regulation/protection/policy intersection. Hence, the present study conducted a bibliometric analysis of 1390 research articles collected from the Scopus database from 2000 to 2021. The study accomplished performance and science mapping analysis using Bibliometrix and VOSViewer software. The study shows an increasing publication trend. The most productive country was the USA (259 publications with 8400 citations), and the most productive institution was the University of California (33 publications). The study found that Cole MA was the most relevant author in this area by considering multiple matrices. By using keywords, conceptual structure, and bibliographic coupling analysis, the study suggests that future studies can be conducted on climate change, carbon leakage, climate policy, environmental protection, air pollution, economic growth, carbon dioxide emission, emission trading, abatement cost, environmental performance, green supply chain management, composition effect, carbon footprints, and multi-regional input-output model. The current study provides scholars and practitioners interested in trade and environmental interlinkages with a comprehensive overview of the domain by presenting readers with significant studies, authors, universities, concepts, and sources. Further, the study results will be helpful for scholars to get insights into the current research development trends and research themes in the trade and environmental regulation field.

Studies on spray dried topical ophthalmic emulsions containing cyclosporin A (0.05% w/w): systematic optimization, in vitro preclinical toxicity and in vivo assessments.

Cyclosporin A (CsA, 0.05% w/w)-loaded positively charged emulsions were prepared based on castor oil, chitosan, poloxamer 188, glycerin and double-distilled water. To augment the shelf/storage-stability of original emulsions, the solid-dry powder for reconstitution was made by spray drying technique. The screening (Taguchi OA) and optimization (face-centered central composite) designs produced the optimized conditions for spray drying: 40 Nm3/h aspirator flow rate, 15ml/min feed rate, 115°C inlet temperature, 10% mannitol and 1.25% trehalose. The % drug entrapment efficiency values of original and reconstituted emulsions ranged from 73.20 ± 0.13 to 71.55 ± 1.25%. At 20min post-dissolution, two times higher CsA release was seen from reconstituted emulsions than the original emulsions (85.78 ± 1.14 vs. 42.25 ± 1.84%) in simulated tear fluid. Using MTT assay, the reconstituted emulsions with or without CsA produced 94.512 ± 2.12 to 99.941 ± 1.89% cell viability values in HCE-2 cells. No appreciable change in capillary integrity was visualized in HET CAM following reconstituted emulsions treatment. At equivalent 15µg drug, the in vitro protein denaturation assay showed augmented inhibition value (~ 85%) for tested CsA emulsions compared to diclofenac reference (68.30 ± 2.05) indicating enhanced anti-inflammatory activity. The CsA concentrations in multiple ocular matrices of rabbit eyes determined by the UPLC-MS/MS method attained the therapeutic drug level of 50-300ng/ml even at 90min post-topical instillation of both emulsions. Overall, the CsA emulsion eyedrops can be supplied as a spray dried storable intermediate product for reconstitution.

Single-spoke binning: Reducing motion artifacts in abdominal radial stack-of-stars imaging.

To increase the effectiveness of respiratory gating in radial stack-of-stars MRI, particularly when imaging at high spatial resolutions or with multiple echoes. Free induction decay (FID) navigators were integrated into a three-dimensional gradient echo radial stack-of-stars pulse sequence. These navigators provided a motion signal with a high temporal resolution, which allowed single-spoke binning (SSB): each spoke at each phase encode step was sorted individually to the corresponding motion state of the respiratory signal. SSB was compared with spoke-angle binning (SAB), in which all phase encode steps of one projection angle were sorted without the use of additional navigator data. To illustrate the benefit of SSB over SAB, images of a motion phantom and of six free-breathing volunteers were reconstructed after motion-gating using either method. Image sharpness was quantitatively compared using image gradient entropies. The proposed method resulted in sharper images of the motion phantom and free-breathing volunteers. Differences in gradient entropy were statistically significant (p = 0.03) in favor of SSB. The increased accuracy of motion-gating led to a decrease of streaking artifacts in motion-gated four-dimensional reconstructions. To consistently estimate respiratory signals from the FID-navigator data, specific types of gradient spoiler waveforms were required. SSB allowed high-resolution motion-corrected MR imaging, even when acquiring multiple gradient echo signals or large acquisition matrices, without sacrificing accuracy of motion-gating. SSB thus relieves restrictions on the choice of pulse sequence parameters, enabling the use of motion-gated radial stack-of-stars MRI in a broader domain of clinical applications.

An Improved Unknown Input Observer Design for Disrete-Time Fuzzy Bilinear Systems

Design methods of an observer for a discrete-time Takagi-Sugeno fuzzy bilinear system with unknown inputs is proposed. Takagi-Sugeno fuzzy bilinear system describes a large class of nonlinear systems and this system with unknown inputs even represents a realistic model for most practical systems. An unknown input observer that estimates the state of Takagi-Sugeno fuzzy bilinear system with unknown input is proposed based on multiple Lyapunov matrices. For fuzzy systems, a parallel distributed observer (PDO), which is constructed with of local linear observers and the appropriate grade of the membership functions, has been used, but its design conditions are eventually conservative. To reduce such conservativeness in observer design conditions, non-PDO with multiple Lyapunov matrices technique is proposed in this paper. Such a design method is based on a multiple Lyapunov function with double sum of the membership functions. In order to show the effectiveness of our proposed approach, a simple example is given. Finally, concluding remarks are given.