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

Background: The present work investigated the profile and biodiversity of the pathogenic streptococci species isolated from local and imported mozzarella soft cheese in Baghdad City from October 2022 to January 2023. The study aimed to examine the molecular characterization of 16Sr RNA gene in some streptococcus species isolates from mozzarella soft cheese in Baghdad city Methods: From 50 samples, 8 isolates in all were found and identified based on the VITEK, molecular, and sequencing of the 16SrRNA gene. The eight isolates represented Lactococcus cremoris. Streptococcus alactolyticus, Streptococcus sanguinis, and Streptococcus thoraltensis. The isolates were subjected to conventional PCR and electrophoresis to detect the 16SrRNA gene usi

Nitrogen dioxide NO₂ is one of the most dangerous contaminant in the air, its toxic gas that cause disturbing respiratory effects, most of it emitted from industrial sources especially from the stack of power plants and oil refineries. In this study Gaussian equations modelled by Matlab program to state the effect of pollutant NO₂ gas on area around Durra refinery, this program also evaluate some elements such as wind and stability and its effect on stacks height. Data used in this study is the amount of fuel oil and fuel gas burn inside refinery at a year 2017. Hourly April month data chosen as a case study because it’s unsteady month. After evaluate emission rate of the all fuel and calculate exit velocity from

Background: One way to target polypharmacy and inappropriate medication in hemodialysis (HD) patients is with medication deprescribing. Objective: To assess the impact of implementing a pharmacist-led deprescribing program on medication adherence among HD patients. Method: A prospective interventional, one-group pretest-posttest-only design study was conducted at a hemodialysis center in Wasit Governorate, Iraq. Medication reconciliation followed by medication review based on the deprescribing program was done for all eligible patients, and the patients were monitored for three months for any possible complications. Results: Two hundred and seventy patients were screened for eligibility. Only one hundred and eighteen were enrolled i

The necessary optimality conditions with Lagrange multipliers  are studied and derived for a new class that includes the system of Caputo–Katugampola fractional derivatives to the optimal control problems with considering the end time free. The formula for the integral by parts has been proven for the left Caputo–Katugampola fractional derivative that contributes to the finding and deriving the necessary optimality conditions. Also, three special cases are obtained, including the study of the necessary optimality conditions when both the final time  and the final state  are fixed. According to convexity assumptions prove that necessary optimality conditions are sufficient optimality conditions.