Spectral and magnetic properties of phenylazo-6-aminouracil complexes

Complexes of Co(II), Ni(II) and Cu(II) with substituted phenylazo-6-aminouracils containing (-H, p-OH, p-CH(3), p-OCH(3) p-COOH) have been synthesized and characterized by elemental analysis, magnetic measurements and spectral measurements (IR, UV-Vis, ESR). Infrared spectra assigned the fundamental bands of the major groups, O-H, N-H, C-H, C=O, C=N, N=N, C-N and C-O (nu, delta and gamma modes of vibrations). The absence of nu(OH) and the appearance of nu(C=O) in the infrared spectra of the free ligands of 5-(p-tolyl and p-anisylazo)-6-aminouracil, assigned the keto structure, whereas in cases of 5-(phenyl, p-hydroxyphenyl and p-carboxyphenylazo)-6-aminouracil ligands, the data showed strong nu(OH) and nu(C=O) bands to assign keto-enol tautomerisms. The modes of interactions between the ligands and the metals were discussed, where oxygen and nitrogen atoms (of amino-amide groups) are involved in chelation. The azo group was not involved in chelation for all the prepared complexes except those of copper complexes derived from 5-(phenyl, p-tolyl, p-hydroxyphenyl and p-carboxyphenylazo)-6-aminouracils. The room temperature effective magnetic moment values, the Nujol mull spectra and ESR proved that all the prepared complexes were of octahedral geometry, except the nickel complex derived from 5-(phenylazo)-6-aminouracil and cobalt complex derived from 5-(p-carboxy-phenylazo)-6-aminouracil were square planar.     

KMo5O13 and KNb1.76Sb3.24O13

The title compounds, potassium pentamolybdenum oxide, KMo₅O₁₃, and potassium niobate antimonate or potassium niobium antimony oxide (1/1.76/3.24), KNb_1.76Sb_3.24O₁₃, were synthesized by solid‐state reactions and are isomorphous in space group Cmcm. The structure of the Mo complex has three unique Mo atoms and consists of MoO₆ octahedra sharing edges to form Mo₂O₆ pairs and Mo₃O₉ triplets, which, in turn, form layers by sharing corners. These layers are linked together in the [100] direction, yielding a three‐dimensional network similar to that of KSb₅O₁₃. This framework delimits interconnected tunnels, running approximately along the [110] and [10] directions, in which K⁺ ions are located. In the isomorphous KNb_1.76Sb_3.24O₁₃ structure, one of the Mo sites in KMo₅O₁₃ is replaced by Sb and the other two Mo sites have been replaced by Nb/Sb.     

Mathematical Analysis and Performance Evaluation of the Cell-Free mMIMO Networks Based on Cognitive Relays

Wireless Personal Communications - The cell-Free massive multiple input multiple output “mMIMO” networks can provide a satisfied performance for the fifth generation “5G”...     

2D and 3D image localization,compression and reconstruction using new hybrid moments

In this article, we will present a new set of hybrid polynomials and their corresponding moments, with a view to using them for the localization, compression and reconstruction of 2D and 3D images. These polynomials are formed from the Hahn and Krawtchouk polynomials. The process of calculating these is successfully stabilized using the modified recurrence relations with respect to the n order, the variable x and the symmetry property. The hybrid polynomial generation process is carried out in two forms: the first form contains the separable discrete orthogonal polynomials of Krawtchouk–Hahn (DKHP) and Hahn–Krawtchouk (DHKP). The latter are generated as the product of the discrete orthogonal Hahn and Krawtchouk polynomials, while the second form is the square equivalent of the first form, it consists of discrete squared Krawtchouk–Hahn polynomials (SKHP) and discrete polynomials of Hahn–Krawtchouk squared (SHKP). The experimental results clearly show the efficiency of hybrid moments based on hybrid polynomials in terms of localization property and computation time of 2D and 3D images compared to other types of moments; on the other hand, encouraging results have also been shown in terms of reconstruction quality and compression despite the superiority of classical polynomials.

     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Schedule-Based Cooperative Multi-agent Reinforcement Learning for Multi-channel Communication in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become an important component in the Internet of things (IoT) field. In WSNs, multi-channel protocols have been developed to overcome some limitations related to the throughput and delivery rate which have become necessary for many IoT applications that require sufficient bandwidth to transmit a large amount of data. However, the requirement of frequent negotiation for channel assignment in distributed multi-channel protocols incurs an extra-large communication overhead which results in a reduction of the network lifetime. To deal with this requirement in an energy-efficient way is a challenging task. Hence, the Reinforcement Learning (RL) approach for channel assignment is used to overcome this problem. Nevertheless, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Self-schedule based Cooperative multi-agent Reinforcement Learning for Channel Assignment (SCRL CA) approach is proposed to improve the network lifetime and performance. The proposal addresses both regular traffic scheduling and assignment of the available orthogonal channels in an energy-efficient way. We solve the cooperation between the RL agents problem by using the self-schedule method to accelerate the RL iterations, reduce the communication overhead and balance the energy consumption in the route selection process. Therefore, two algorithms are proposed, the first one is for the Static channel assignment (SSCRL CA) while the second one is for the Dynamic channel assignment (DSCRL CA). The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance through the two algorithms.

     

Architectural Design,Improvement, and Challenges of Distributed Software-Defined Wireless Sensor Networks

Wireless Personal Communications - Software-defined networking (SDN) is widely perceived to simplify network management and monitoring. The introduction of the SDN model into wireless sensor...     

ESHARP: Energy-Efficient and Smart Hierarchical Routing Protocol Based on Smart Slumber for Wireless Sensor Networks

Wireless Personal Communications - This paper proposes a new energy management function intended to be integrated in the Hierarchical Smart Routing Protocol (HSRP) mainly proposed for Wireless...     

Numerical modeling of ZnSnO/CZTS based solar cells

Numerical simulation has been performed to improve the performance of Cu2ZnSnS4 (CZTS) solar cells by replacing CdS with Zn1–xSnxO buffer layer. The influences of thickness, donor concentration and defect density of buffer layers on the performance of CZTS solar cells were investigated. It has been found that Zn1–xSnxO buffer layer for Sn content of 0.20 is better for CZTS solar cell. A higher efficiency can be achieved with thinner buffer layer. The optimized solar cell demonstrated a maximum power conversion efficiency of 13%.     

Vacancies in the solids of low molecular weight organic compounds observed by positron annihilation

PAL were measured for several low molecular organic compounds, normal and cyclic-hydrocarbons and their perfluorinated ones, from room temperature down to about 40 K, and information about the vacancies in them has been extracted from ₃ andI ₃. Normally the size of vacancies were larger in the solids comprising larger molecules, showing thato-Ps can represent the vacancy size. In a special case of perfluorocyclohexane whose solid had to be prepared by sublimation the vacancy size was larger than expected. In all the solid molecules studied twoo-Ps states could be observed and their relative importance could easily change showing a hysteresis-like behavior. At further lower temperatures of around 40 K theo-Ps state with longer lifetime and larger intensity became overwhelming for all molecules. Also ₃ andI ₃ were not very sensitive to most of the phase transitions, including the melting points.