New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

Engineering proteins, subcloning and hyperexpressing oxidoreductase genes

A very efficient system for subcloning and studying proteinsequences, combining previously established elements for hyperexpression,replication and screening, was used to hyperproduce and characterizeseven different products. It expedited the cloning of genes,in a multipurpose recombinant DNA construct, for all the requirementsto study and engineer proteins with a strain of Escherichiacoli. Genes encoding six heme proteins and a flavoprotein havebeen subcloned and expressed to 13–30% of the total cellprotein, greatly facilitating purification and analyses. Threeof the heme proteins and the flavoprotein incorporated prostheticgroups in E.coli, and exhibited the expected activities. Fourof the enzymes have been purified to homogeneity and two ofthese crystallized for X-ray diffraction analysis. A rapid muta-genesisprotocol, based on polymerase chain reactions, was successfullyapplied to clone derivatives of one of these enzymes, cytochromec peroxidase. Thus, this system fulfills all criteria for engineeringproteins in an efficient and concerted manner.     

Biochemical and physiological studies of certain ticks (Ixodoidea): Isolation and partial identification of a new fatty acid in eggs ofDermacentor andersoni Stiles (Ixodidae)

Gas liquid chromatographic analysis of the fatty acid methyl esters from eggs ofDermacentor andersoni Stiles (Ixodidae) revealed the presence of significant quantities (15% total fatty acids) of an unidentified component with a retention time between C_18∶3−C_22∶0 fatty acids. Smaller amounts of the unidentified component (ca. 5% total fatty acid) also were detected in host rabbit serum. Purified, the unidentified component's methyl ester collected from the tick eggs by preparative gas liquid chromatography was partially identified and characterized by chemical and spectroscopic analyses. The evidence suggests that the unidentified component is a methyl branched C₁₅ tricarboxylic acid containing two vicinal carboxylic acid groups. Biosynthesis of the unidentified component by the tick is under investigation.     

Anomalous codeposition of Co–Ni: alloys from gluconate baths

Thin films of cobalt–nickel alloys were galvanostatically deposited onto steel substrates from gluconate baths. Cathodic polarization curves were determined for the parent metals and Co–Ni alloy. The effects of bath composition, current density and temperature on cathodic current efficiency (CCE) and alloy composition were studied. The deposition of Co–Ni alloy is of anomalous type, in which the less noble metal (Co) is preferentially deposited. The CCE of codeposition is high and increases with increase in temperature and current density, but it decreases as the [Co²⁺]/[Ni²⁺] ratio in the bath increases. The percentage of Co in the deposit increases with increasing cathodic current density, temperature and increasing Co²⁺ ion concentration. The structure and surface morphology of the deposit were studied by XRD, ALSV and SEM. The results showed that the alloys consisted of a single solid solution phase with a hexagonal close packed structure.     

A cooperative recurrent neural network for solving L(1) estimation problems with general linear constraints

The constrained L(1) estimation is an attractive alternative to both the unconstrained L(1) estimation and the least square estimation. In this letter, we propose a cooperative recurrent neural network (CRNN) for solving L(1) estimation problems with general linear constraints. The proposed CRNN model combines four individual neural network models automatically and is suitable for parallel implementation. As a special case, the proposed CRNN includes two existing neural networks for solving unconstrained and constrained L(1) estimation problems, respectively. Unlike existing neural networks, with penalty parameters, for solving the constrained L(1) estimation problem, the proposed CRNN is guaranteed to converge globally to the exact optimal solution without any additional condition. Compared with conventional numerical algorithms, the proposed CRNN has a low computational complexity and can deal with the L(1) estimation problem with degeneracy. Several applied examples show that the proposed CRNN can obtain more accurate estimates than several existing algorithms.     

Hierarchically Distributed Peer-to-Peer Document Clustering and Cluster Summarization

In distributed data mining, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a Hierarchically-distributed Peer-to-Peer (HP2PC) architecture and clustering algorithm. The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Within a certain level of the hierarchy, peers cooperate within their respective neighborhoods to perform P2P clustering. Using this model, we can partition the clustering problem in a modular way across neighborhoods, solve each part individually using a distributed K-means variant, then successively combine clusterings up the hierarchy where increasingly more global solutions are computed. In addition, for document clustering applications, we summarize the distributed document clusters using a distributed keyphrase extraction algorithm, thus providing interpretation of the clusters. Results show decent speedup, reaching 165 times faster than centralized clustering for a 250-node simulated network, with comparable clustering quality to the centralized approach. We also provide comparison to the P2P K-means algorithm and show that HP2PC accuracy is better for typical hierarchy heights. Results for distributed cluster summarization match those of their centralized counterparts with up to 88% accuracy.     

Investigating the influence of the counter Si-cell on the optoelectronic performance of high-efficiency monolithically perovskites/silicon tandem cells under various optical sources

Nowadays, tandem structures have become a valuable competitor to conventional silicon solar cells, especially for perovskite over silicon, as metal halides surpassed Si with tunable bandgaps, high absorption coefficient, low deposition, and preparation costs. This led to a remarkable enhancement in the overall efficiency of the whole cell and its characteristics. Consequently, this expands the usage of photovoltaic technology in various fields of applications not only under conventional light source spectrum in outdoor areas, i.e., AM1.5G, but also under artificial light sources found indoors with broadband intensity values, such as Internet of things (IoTs) applications to name a few. We introduce a numerical model to analyze perovskite/Si tandem cells (PSSTCs) using both crystalline silicon (c-Si) and hydrogenated amorphous silicon (a-Si:H) experimentally validated as base cells. All proposed layers have been studied with J-V characteristics and energy band diagrams under AM1.5G by using SCAPS-1D software version 3.7.7. Thereupon, the proposed architectures were tested under various artificial lighting spectra. The proposed structures of Li4Ti5O12/CsPbCl3/MAPbBr3/CH3NH3PbI3/Si recorded a maximum power conversion efficiency (PCE) of 25.25% for c-Si and 17.02% for a-Si:H, with nearly 7% enhancement concerning the Si bare cell in both cases.     

Chemical sensor based on a novel capacitive microwave flexible transducer with polymer nanocomposite-carbon nanotube sensitive film

Microsystem Technologies - This study presents the results on the feasibility of a resonant planar chemical capacitive sensor in the microwave frequency range suitable for gas detection and...     

Rheological characteristics of modified calcium grease with graphene nanosheets

The effect of graphene nanosheets (GNS) on the rheological characteristics and thermal conductivity of calcium grease used in marine applications, and water pumps for lower demanding applications, has been experimentally evaluated in this paper. Various volume fractions of GNS (0.5, 1, 2, 3, and 4% wt.) have been immersed in the grease aiming at finding the better percentages that improve the properties of nanogrease. The rheological characteristics and thermal conductivity were evaluated with a Brookfield Rheometer DV-III ULTRA and KD2 thermal analyzer, respectively. The results of the modified calcium grease indicated that the best concentration of GNS is 3% wt. The rheological characteristics of the grease with a different volume fraction of GNS indicated a non-Newtonian behavior. Thermal conductivity of nanogreas also increases with an increase of GNS volume fraction. Moreover, the apparent viscosity, shear stress and dropping point increase by 59%, 52%, and 65%, respectively, with increasing GNS volume fraction.     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.