Three new hydrazone derivatives of Etodolac were synthesized and evaluated for their anti-inflammatory activity by using egg white induced paw edema method. All the synthesized target compounds were characterized by CHN- microanalysis, FT-IR spectroscopy, and ¹HNMR analysis. The synthesis of the target (P1-P3) compounds was accomplished following multistep reaction procedures. The synthesized target compounds were found to be active in reducing paw edema thickness and their anti-inflammatory effect was comparable to that of the standard (Etodolac).

In this work, pure and copper mixed oxide PAni nanofiber thin films are successfully synthesized on silicon substrates by hydrothermal method and spin coating technique at room temperature with thickness of about 325 nm. The structural, surface morphological, optical and photoconductivity properties have been investigated. The XRD results showed that PAni films have crystalline nature, CuO and PAni/CuO nanostructure composites are monoclinic polycrystalline structure. The FESEM images of PAni clearly indicate that it has nanofiber-like structure, whereas the CuO film has spongelike shape. The surface morphology analysis of PAni/CuO composite shows that nanofiber caped with inorganic material which is CuO is a core-shell structure. Op

Over the last few decades the mean field approach using selfconsistent
Haretree-Fock (HF) calculations with Skyrme effective
interactions have been found very satisfactory in reproducing
nuclear properties for both stable and unstable nuclei. They are
based on effective energy-density functional, often formulated in
terms of effective density-dependent nucleon–nucleon interactions.
In the present research, the SkM, SkM*, SI, SIII, SIV, T3, SLy4,
Skxs15, Skxs20 and Skxs25 Skyrme parameterizations have been
used within HF method to investigate some static and dynamic
nuclear ground state proprieties of ^84-108Mo isotopes. In particular,
the binding energy, proton, neutron, mass and charge densities

Buildings such as malls, offices, airports and hospitals nowadays have become very complicated which increases the need for a solution that helps people to find their locations in these buildings. GPS or cell signals are commonly used for positioning in an outdoor environment and are not accurate in indoor environment. Smartphones are becoming a common presence in our daily life, also the existing infrastructure, the Wi-Fi access points, which is commonly available in most buildings, has motivated this work to build hybrid mechanism that combines the APs fingerprint together with smartphone barometer sensor readings, to accurately determine the user position inside building floor relative to well-known lan

This paper presents on the design of L-Band Multiwavelength laser for Hybrid Time Division Multiplexing/ Wavelength Division Multiplexing (TDM/WDM) Passive Optical Network (PON) application. In this design, an L-band Mulltiwavelength Laser is designed as the downstream signals for TDM/WDM PON. The downstream signals ranging from 1569.865 nm to 1581.973 nm with 100GHz spacing. The multiwavelength laser is designed using OptiSystem software and it is integrated into a TDM/WDM PON that is also designed using OptiSystem simulation software. By adapting multiwavelength fiber laser into a TDM/WDM network, a simple and low-cost downstream signal is proposed. From the simulation design, it is found that the proposed design is suitable to be used

The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

Productivity estimating of ready mixed concrete batch plant is an essential tool for the successful completion of the construction process. It is defined as the output of the system per unit of time. Usually, the actual productivity values of construction equipment in the site are not consistent with the nominal ones. Therefore, it is necessary to make a comprehensive evaluation of the nominal productivity of equipment concerning the effected factors and then re-evaluate them according to the actual values.

In this paper, the forecasting system was employed is an Artificial Intelligence technique (AI). It is represented by Artificial Neural Network (ANN) to establish the predicted model to estimate wet ready mixe