Spectrophotometric methods were developed for the determination of rantidine-HCl in pharmaceutical tablets. These methods were based on the reaction of DDQ and p-chloranil with rantidine-HCl, resulting in the formation of an orange-red and purple colored products which are quantified spectrophotometrically at 460 and 540nm in DDQ and p-chloranil, respectively. A graph of absorbance versus concentration show that Beer’s law is obeyed in a concentration ranges of 20-160 and (30-120)g/ml with molar absorptivities of 2.631 x 103 and 1.052 x 103 The optimum conditions for color development are described and the proposed methods were applied satisfactory to pharmaceutical preparations

A simple, rapid and sensitive spectrophotometirc method for the determination of trace amounts of promethazine hydrochloride in the aqueous solution is described. The method is based on the complexation of promethazine hydrochloride with In (III) in the presence of sodium hydroxide to form an soluble product with maximum absorption at 304nm. Beer’s law is obeyed over the concentration range of (2- 20μg/ml) with molar absorptivity of (1.92× 103 L.mol-1 .cm -1 ). The optimum conditions for all development are described and the proposed method has been successfully applied for the determination of promethazine hydrochloride in bulk drug.

a simple accurate and sensitive spectrophotometric method for the determination of promethazine HCI has been developed the method is based on the oxidative coupling reaction of promethazine

Modern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan

Target tracking is a significant application of wireless sensor networks (WSNs) in which deployment of self-organizing and energy efficient algorithms is required. The tracking accuracy increases as more sensor nodes are activated around the target but more energy is consumed. Thus, in this study, we focus on limiting the number of sensors by forming an ad-hoc network that operates autonomously. This will reduce the energy consumption and prolong the sensor network lifetime. In this paper, we propose a fully distributed algorithm, an Endocrine inspired Sensor Activation Mechanism for multi target-tracking (ESAM) which reflecting the properties of real life sensor activation system based on the information circulating principle in the endocr

The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages