A complete VARMA modelling methodology based on scalar components

Abstract. This article proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.     

Bioprinting a cell-laden matrix for bone regeneration: A focused review

Although many efforts have been made to regenerate the bone lesions, existing challenges can be mitigated through the development of tissue engineering scaffolds. However, the weak control on the microstructure of constructs, limitation in preparation of patient-specific and multilayered scaffolds, restriction in the fabrication of cell-laden matrixes, and challenges in preserving the drug/growth factors' efficacy in conventional methods have led to the development of bioprinting technology for regeneration of bone defects. So in this review, conventional 3D printers are classified, then the priority of the different types of bioprinting technologies for the preparation of the cell/growth factor-laden matrixes are focused. Besides, the bio-ink compositions, including polymeric/hybrid hydrogels and cell-based bio-inks are classified according to fundamental and recent studies. Herein, different effective parameters, such as viscosity, rheological properties, cross-linking methods, biodegradation biocompatibility, are considered. Finally, different types of cells and growth factors that can encapsulate in the bio-inks to promote bone repair are discussed, and both in vitro and in vivo achievement are considered. This review provides current and future perspectives of cell-laden bioprinting technologies. The restrictions and challenges are identified, and proper strategies for the development of cell-laden matrixes and high-performance printable bio-inks are proposed.     

AN INTERACTIVE SEARCH ASSISTANT ARCHITECTURE BASED ON INTRINSIC QUERY STREAM CHARACTERISTICS

Search engine query log mining has evolved over time to more like data stream mining due to the endless and continuous sequence of queries known as query stream. In this paper, we propose an online frequent sequence discovery (OFSD) algorithm to extract frequent phrases from within query streams, based on a new frequency rate metric, which is suitable for query stream mining. OFSD is an online, single pass, and real-time frequent sequence miner appropriate for data streams. The frequent phrases extracted by the OFSD algorithm are used to guide novice Web search engine users to complete their search queries more efficiently. YourEye, our online phrase recommender is then introduced. The advantages of YourEye compared with Google Suggest, a service powered by Google for phrase suggestion, is also described. Various characteristics of two specific Web search engine query logs are analyzed and then the query logs are used to evaluate YourEye. The experimental results confirm the significant benefit of monitoring frequent phrases within the queries instead of the whole queries because none-separable items. The number of the monitored elements substantially decreases, which results in smaller memory consumption as well as better performance. Re-ranking the retrieved pages based on past users clicks for each frequent phrase extracted by OFSD is also introduced. The preliminary results show the advantages of the proposed method compared to the similar work reported in Smyth et al.     

Fullerene: biomedical engineers get to revisit an old friend

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The synthesis of nano-sized ZSM-5 zeolite by dry gel conversion method and investigating the effects of experimental parameters by Taguchi experimental design

In this study ZSM-5 Nano particles with high crystallinity were synthesized using a dry gel conversion technique. An L9 orthogonal array of the Taguchi method was applied to investigate the effect of synthesis parameters, such as crystallization time, gel drying temperature, molar composition of template (TPAOH) and water content in the crystallization stage on crystallinity and particle size of the ZSM-5 catalyst. The products were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Beside short crystallization time, the particle sizes were considerably smaller in comparison with those prepared using the hydrothermal method. The results showed that the particle size and crystallinity increased with increasing water content and crystallization time. The effects of gel drying temperature and molar composition of template were found to be more complex, however. Comparing to hydrothermal method, ZSM-5 samples synthesized with the dry gel conversion exhibited higher selectivity to gasoline than other hydrocarbons.     

UML-CI: A reference model for profiling critical infrastructure systems

The study of critical infrastructure systems organization and behavior has drawn great attention in the recent years. This is in part due to their great influence on the ordinary life of every citizen. In this paper, we study critical infrastructures’ characteristics and propose a reference model based on the Unified Modeling Language (UML). This reference model attempts to provide suitable means for the task of modeling an infrastructure system through offering five major metamodels. We introduce each of these metamodels and explain how it is possible to integrate them into a unique representation to characterize various aspects of an infrastructure system. Based on the metamodels of UML-CI, infrastructure system knowledge bases can be built to aid the process of infrastructure system modeling, profiling, and management.     

A new feature selection algorithm and composite neural network for electricity price forecasting

In a competitive electricity market, the forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. In this paper, a new forecast strategy is proposed for day ahead prediction of electricity price, which is a complex signal with nonlinear, volatile and time dependent behavior. Our forecast strategy includes a new two stage feature selection algorithm, a composite neural network (CNN) and a few auxiliary predictors. The feature selection algorithm has two filtering stages to remove irrelevant and redundant candidate inputs, respectively. This algorithm is based on mutual information (MI) criterion and selects the input variables of the CNN among a large set of candidate inputs. The CNN is composed of a few neural networks (NN) with a new data flow among its building blocks. The CNN is the forecast engine of the proposed strategy. A kind of cross-validation technique is also presented to fine-tune the adjustable parameters of the feature selection algorithm and CNN. Moreover, the proposed price forecast strategy is equipped with a few auxiliary predictors to enrich the candidate set of inputs of the forecast engine. The whole proposed strategy is examined on the PJM, Spanish and Californian electricity markets and compared with some of the most recent price forecast methods.     

Nano levels detection of beryllium by a novel beryllium PVC-based membrane sensor based on 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy

A novel poly(vinyl chloride)-based 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy (PBC) with sodium tetraphenyl borate (NaTPB) as an anion excluder, benzyl acetate (BA), acetophenon (AP) and o-nitrophenyloctyl ether (NPOE) as plasticizing solvent mediators was prepared and investigated as a beryllium selective sensor. The best performance was observed with the membrane having the PVC–NaTPB–NPOE–PBC composition 30%:3%:62%:5%, which worked well over a very wide concentration range (1.0×10⁻⁷ M to 1.0×10⁻¹ M). The sensor exhibits a Nernstian slope of 29.9 mV per decade of Be²⁺ activity. The detection limit of the sensor is 7.0×10⁻⁸ M (630 ppt). The proposed electrode shows excellent discriminating ability toward Be²⁺ ion with regard to alkali, alkaline earth, transition and heavy metal ions. It was successfully applied to the determination of beryllium in a mineral sample.     

Cylindrical functionally graded shell model based on the first order shear deformation nonlocal strain gradient elasticity theory

Microsystem Technologies - In this article, a cylindrical functionally graded shell model is developed in the framework of nonlocal strain gradient theory for the first time. For this purpose, the...     

Fabrication and characterization of highly efficient three component CuBTC/graphene oxide/PSF membrane for gas separation application

Metal organic frameworks (MOFs) with marvelous properties have aroused enormous attention for different application especially gas adsorption and separation. In this regard, fabrication of MOF hybrids with carbon based materials is new strategy to upgrade MOF performance. In this study CuBTC (Copper benzene-1,3,5-tricarboxylic acid)/graphene oxide (GO) composite was synthesized and characterized by BET, SEM, TGA, XRD and FT-IR techniques. Then CuBTC and CuBTC/GO composite were incorporated into polysulfone (PSF) polymer to construct mixed matrix membranes (MMMs). The obtained membranes were characterized by SEM, TGA, XRD and tensile tests and their gas permeability was measured. The results were compared to those of CuBTC/PSF MMMs. It was revealed that CuBTC/GO composite as filler showed superior performance relative to CuBTC. For instance, 15 wt% loading of CuBTC/GO in PSF represented outstanding gas separation behavior while the same loading of CuBTC in PSF deteriorated performance of MMM. Well particle dispersion and favorable polymer-filler interaction were responsible for such observed difference. A high H₂/CH₄ and H₂/N₂ selectivity of 80.03 and 70.46 were recorded for CuBTC/GO in PSF (15 wt%) compared to 44.56 and 40.92 for CuBTC in PSF (15 wt%).