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EN
The consensus protocol is essential in practically every blockchain application. Most of these existing blockchain consensus protocols need massive computa-tional capabilities, substantial energy consumption, and dependency on monetary stakes. These shortcomings in the mainstream consensus approach leadto their unsuitability for low-resource applications like IoT. As a result of thiswork, a lightweight consensus process referred as Delegated Proof of Accessibility (DPoAC) is implemented and evaluated. DPoAC makes use of Shamir secretsharing, Proof of Stake (PoS) with random selection, and the Inter-PlanetaryFile System (IPFS). The DPoAC operation is composed of four modules: secret generation and distribution, retrieval of secret shares, block creation and verification, and block rewards and penalty. A detailed description of DPoAChas been provided and implemented in JavaScript and experimental resultsdemonstrate that our solution meets the necessary performance and securityrequirements for a lightweight scalable protocol for IoT systems.
EN
We report a facile one-step non aqueous synthesis of oleic acid stabilized cadmium telluride (CdTe) quantum dots (QDs) with an average diameter of 3 nm to 4 nm by hot injection method. The synthesized oleic acid capped QDs observed by TEM were nearly spherical. The optical properties of QDs were characterized by UV-Vis absorption spectra and photoluminescence (PL) spectra. The structures of QDs and their surface passivation were further verified using transmission electron microscope (TEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR). The quenching effect of the CdTe QD was explored by addition of CdTe nanocrystals into a solution of rod-coil homopolymer (poly[10-(6-(9,9-diethyl-7-(pyridin-4-yl)-9H-fluoren-2-yl)naphthalen-2-yloxy) decyl methacrylate]) (PFNA) having pendent pyridine. The gradual addition of quantum dots to the solution of PFNA quenched the PL spectra of PFNA. This may be used to explore the coordination ability of pyridine containing homopolymer with CdTe quantum dots.
EN
In this paper, feature weighting is used to develop an effective computer-aided diagnosis system for breast cancer. Feature weighting is employed because it boosts the classification performance more as compared to feature subset selection. Specifically, a wrapper method utilizing the Ant Lion Optimization algorithm is presented that searches for best feature weights and parametric values of Multilayer Neural Network simultaneously. The selection of hidden neurons and backpropagation training algorithms are used as parameters of neural networks. The performance of the proposed approach is evaluated on three breast cancer datasets. The data is initially normalized using tanh method to remove the effects of dominant features and outliers. The results show that the proposed wrapper method has a better ability to attain higher accuracy as compared to the existing techniques. The obtained high classification performance validates the work which has the potential for becoming an alternative to the other well-known techniques.
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