Selective determination of urea using urease immobilized on ZnO nanowires

Well-aligned zinc oxide (ZnO) nanowire arrays were fabricated on gold-coated plastic substrates using a low-temperature aqueous chemical growth (ACG) method. The ZnO nanowire arrays with 50–130 nm diameters and ∼1 μm in lengths were used in an enzyme-based urea sensor through immobilization of the enzyme urease that was found to be sensitive to urea concentrations from 0.1 mM to 100 mM. Two linear sensitivity regions were observed when the electrochemical responses (EMF) of the sensors were plotted vs. the logarithmic concentration range of urea from 0.1 mM to 100 mM. The proposed sensor showed a sensitivity of 52.8 mV/decade for 0.1–40 mM urea and a fast response time less than 4 s was achieved with good selectivity, reproducibility and negligible response to common interferents such as ascorbic acid and uric acid, glucose, K⁺ and Na⁺ ions.     

Linear systems,and ARMA- and Fliess models

Linear (dynamical) systems are central objects of study (in linear system theory), and ARMA- and Fliess models are two very important classes of models that are used to represent them. This article is concerned with the question of what is a relation between them (in case of higher dimensions). It is shown that the category of linear systems, the ‘weak’ category of ARMA-models and the category of Fliess models are equivalent to each other.     

Rice-like CuO nanostructures for sensitive electrochemical sensing of hydrazine

In this work, a low temperature aqueous chemical growth methodology was used for the fabrication of CuO nanostructures. The as-synthesised nanostructures were then elaborately characterised by number of analytical techniques such as scanning electron microscopy (SEM) and X-ray powder diffraction (XRD). The obtained nanostructures were observed to possess interlaced rice-shaped structural features with the length and width of individual rice determined to be in the range of 200–300 nm and 50–100 nm respectively. The unique nanostructures when utilised as electrode material exhibited excellent electro-catalytic potential towards oxidation of hydrazine in alkaline media. The excellent conductive of CuO added by the high surface area of obtained nanorice-like structures enabled development of highly sensitive (3087 µA mM⁻¹ cm⁻²), selective and stable electrochemical sensor for hydrazine. In addition, the successfully application of the developed sensor in spiked tap, bottled and industrial water samples for the detection of hydrazine suggested its feasibility for practical environmental application.

     

Siamese convolutional neural network and fusion of the best overlapping blocks for kinship verification

Multimedia Tools and Applications - Analysis of facial images decoding familial features has been attracting the attention of researchers to develop a computerized system interested in determining...     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.     

Assessment and Improvement of IDF Generation Algorithms Used in the IDF_CC Tool

Water Resources Management - Climate change will modify the spatio-temporal variation of hydrological variables worldwide, potentially leading to more extreme events and hydraulic infrastructure...     

Nanoheterostructure by Liquid Metal Sandwich-Based Interfacial Galvanic Replacement for Cancer Targeted Theranostics

Nanoheterostructures with exquisite interface and heterostructure design find numerous applications in catalysis, plasmonics, electronics, and biomedicine. In the current study, series core–shell metal or metal oxide-based heterogeneous nanocomposite have been successfully fabricated by employing sandwiched liquid metal (LM) layer (i.e., LM oxide/LM/LM oxide) as interfacial galvanic replacement reaction environment. A self-limiting thin oxide layer, which would naturally occur at the metal–air interface under ambient conditions, could be readily delaminated onto the surface of core metal (Fe, Bi, carbonyl iron, Zn, Mo) or metal oxide (Fe₃O₄, Fe₂O₃, MoO₃, ZrO₂, TiO₂) nano- or micro-particles by van der Waals (vdW) exfoliation. Further on, the sandwiched LM layer could be formed immediately and acted as the reaction site of galvanic replacement where metals (Au, Ag, and Cu) or metal oxide (MnO₂) with higher reduction potential could be deposited as shell structure. Such strategy provides facile and versatile approaches to design and fabricate nanoheterostructures. As a model, nanocomposite of Fe@Sandwiched-GaIn-Au (Fe@SW-GaIn-Au) is constructed and their application in targeted magnetic resonance imaging (MRI) guided photothermal tumor ablation and chemodynamic therapy (CDT), as well as the enhanced radiotherapy (RT) against tumors, have been systematically investigated.