Nanotechnology is a continually expanding field for its uses and applications in multiple areas i.e. medicine, science, and engineering. Biosynthesis is straightforward, less-toxicity, and cost-effective technology. TiO2 NPs biosynthesis has attained consideration in recent decades. In this study, probiotic bacteria were isolated from cow’s raw milk samples, and then were identified by using the Vitek2 system; as Leuconostoc spp. included Leuconostoc mesenteroides subsp. mesenteroides (Leu.1), Leuconostoc mesenteroides subsp. cremoris (Leu.4), and Leuconostoc pseudomesenteroides (Leu.14). All Leuconostoc spp. isolates showed an ability for TiO2 NPs bio-production, after being incubated at anaerobic conditions (30 o C/ 24 h) in DeM

In this study, successive electrocoagulation (EC) and electro-oxidation (EO) processes were used to minimize some of the major pollutants in real wastewater, such as organics (detected by chemical oxygen demand (COD)), and turbidity. The wastewater utilized in the present study was collected from the Midland Refinery Company in Baghdad-Iraq. The performance of the successive batch EC-EO processes was studied by utilizing Graphite and Aluminum (Al) as monopolar anode electrodes and stainless steel (st.st.) as the cathode. The Taguchi experimental design approach was used to attain the best experimental conditions for COD reduction as a major response. Starting from chemical oxygen demand COD of (600 ppm), the effects of current density (C

Symmetric cryptography forms the backbone of secure data communication and storage by relying on the strength and randomness of cryptographic keys. This increases complexity, enhances cryptographic systems' overall robustness, and is immune to various attacks. The present work proposes a hybrid model based on the Latin square matrix (LSM) and subtractive random number generator (SRNG) algorithms for producing random keys. The hybrid model enhances the security of the cipher key against different attacks and increases the degree of diffusion. Different key lengths can also be generated based on the algorithm without compromising security. It comprises two phases. The first phase generates a seed value that depends on producing a rand

Developing smart city planning requires integrating various techniques, including geospatial techniques, building information models (BIM), information and communication technology (ICT), and artificial intelligence, for instance, three-dimensional (3D) building models, in enabling smart city applications. This study aims to comprehensively analyze the role and significance of geospatial techniques in smart city planning and implementation. The literature review encompasses (74) studies from diverse databases, examining relevant solutions and prototypes related to smart city planning. The focus highlights the requirements and preparation of geospatial techniques to support the transition to a smart city. The paper explores various aspects,

Diazotization reaction between 1-(2,4,6-Trihydroxy-phenyl)-ethanone and diazonium salts was carried out resulting in ligand 4-(3-Acetyl-2,4,6-trihydroxy-phenylazo)-N-(5-methyl-isoxazol-3-yl)-benzenesulfonamide, this in turn reacted with the next metal ions (V4+ , Cr3+ , Mn2+ and Cu2+) forming stable complexes with unique geometries such as (Octahedral for both Cr3+ , Mn2+ and Cu2+ ,squar pyramidal for V4+). The creation of such complexes was detected by employing spectroscopic means involving ultraviolet-visible which proved the obtained geometries, fourier transfer proved the formation of azo group and and the coordination with metal ion through it. Pyrolysis (TGA & DSC) studies proved the coordination of water residues with me

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod