Coupling reaction of 4-nitroaniline with 3-aminobenzoic acid provided the corresponding bidentate azo ligand. The prepared ligand was identified by Microelemental Analysis, 1H-NMR, FT-IR, and UV-Vis spectroscopic techniques. Treatment of the prepared ligand with Y(III) and La(III) metal ions in 1:3 M:L ratio in aqueous ethanol at optimum pH yielded a series of neutral complexes with the general formula of [M(L)3]. The prepared complexes were characterized by flame atomic absorption, Elemental Analysis (C, H, N), FT-IR, and UV-Vis spectroscopic methods, as well as conductivity measurements. The nature of the complexes formed were studied following the mole ratio and continuous variation methods; Beer's law obeyed over a concentration range of 1·10–4 – 3·10–4 M. High molar absorbtivity of the complex solutions were observed. In addition, the dyeing performance of the prepared ligand and their complexes were investigated on cotton fabric. The dyes were tested for light and detergent fastness. Biological activity of the ligand and complexes against three selected types of bacteria were also examined. Some of the complexes exhibited good bacterial activities.
In recent years, the positioning applications of Internet-of-Things (IoT) based systems have grown increasingly popular, and are found to be useful in tracking the daily activities of children, the elderly and vehicle tracking. It can be argued that the data obtained from GPS based systems may contain error, hence taking these factors into account, the proposed method for this study is based on the application of IoT-based positioning and the replacement of using IoT instead of GPS. This cannot, however, be a reason for not using the GPS, and in order to enhance the reliability, a parallel combination of the modern system and traditional methods simultaneously can be applied. Although GPS signals can only be accessed in open spaces, GP
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This paper assesses the impact of changes and fluctuations in bank deposits on the money supply in Iraq. Employing the research constructs an Error Correction Model (ECM) using monthly time series data from 2010 to 2015. The analysis begins with the Phillips-Perron unit root test to ascertain the stationarity of the time series and the Engle and Granger cointegration test to examine the existence of a long-term relationship. Nonparametric regression functions are estimated using two methods: Smoothing Spline and M-smoothing. The results indicate that the M-smoothing approach is the most effective, achieving the shortest adjustment period and the highest adjustment ratio for short-term disturbances, thereby facilitating a return
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The present study aims to present a proposed realistic and comprehensive cyber strategy for the Communications Directorate for the next five years (2022-2026) based on the extent of application and documentation of cybersecurity measures in the Directorate and the scientific bases formulating the strategy. The present study is significant in that it provides an accurate diagnosis of the capabilities of the cyber directorate in terms of strengths and weaknesses in its internal environment and the opportunities and threats that surround it in the external environment, based on the results of the assessment of the reality of cybersecurity according to the global Cybersecurity index, which provides a strong basis for building its strategic dire
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The solidification process in a multi-tube latent heat energy system is affected by the natural convection and the arrangement of heat exchanger tubes, which changes the buoyancy effect as well. In the current work, the effect of the arrangement of the tubes in a multi-tube heat exchanger was examined during the solidification process with the focus on the natural convection effects inside the phase change material (PCM). The behavior of the system was numerically analyzed using liquid fraction and energy released, as well as temperature, velocity and streamline profiles for different studied cases. The arrangement of the tubes, considering seven pipes in the symmetrical condition, are assumed at different positions in the system, i
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When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat
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