Ni2O3 nanomaterial, a phase of nickel oxide, is synthesized by a simple chemical process. The pure raw materials used in the present process were nickel chloride hexahydrate NiCl2.6H2O and potassium hydroxide KOH by utilizing temperature at 250 oC for 2 hour.  The structural, morphological and optical properties of the synthesized specimens of Ni2O3 were investigated employing diverse techniques such as XRD, AFM, SEM and UV-Vis, respectively. The XRD technique confirms the presence of Ni2O3 nanomaterial with crystal size of 57.083 nm which indexing to the (2θ) of 31.82; this results revealed the Ni2O3 was a phase of nickel oxide with Nano structure. The synthesized Ni2O3 will be useful in manufacturng electrodes materials f

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

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One of the bigger problems in drinking water is disinfection by-products (DBPs) that come from chlorinated disinfection. This study’s goal was to evaluate the drinking water in Al-Yarmouk Teaching Hospital, Ibn Sina Hospital and Ibn-Al-Nafis Hospital.  Samples were collected between October 2018 and September 2019. Physical and chemical characteristics of the water were studied, including (temperature, hydrogen ion (pH), total dissolved solids (TDS), electrical conductivity (EC), turbidity, free residual chlorine, total organic carbon (TOC), total trihalomethanes (THMs), total halo acetic acid (THAAs)). Data analysis showed the highest value of study temperature, pH, TDS, EC, turbidity, free residual chlorine and TOC which was

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri