The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

A series of novel hybrid molecules containing 1,3,4-oxadiazole and 1,3,4-thiadiazole moieties bearing an ibuprofen moiety were designed, synthesized, and evaluated for their in vitro antibacterial activity against Gram-positive and Gram-negative bacteria. A carboxylic acid compound with a 1,3,4-thiadiazole unit was synthesized via the coupling reaction of 5-(1-(4-isobutylphenyl) ethyl)-1,3,4-thiadiazol-2-amine with benzoic acid. This compound was then used as the starting material to make several new 1,3,4-oxadiazole derivatives through ring closure reactions, after first converting it to its ester (methyl 4-(5-(1-(4-isobutylphenyl)ethyl)-1,3,4-thiadiazol-2-yl) benzoate) and then to the acid hydrazide (4-(5-(1-(4-isobutylphenyl) eth

This paper presents a hybrid genetic algorithm (hGA) for optimizing the maximum likelihood function ln(L(phi(1),theta(1)))of the mixed model ARMA(1,1). The presented hybrid genetic algorithm (hGA) couples two processes: the canonical genetic algorithm (cGA) composed of three main steps: selection, local recombination and mutation, with the local search algorithm represent by steepest descent algorithm (sDA) which is defined by three basic parameters: frequency, probability, and number of local search iterations. The experimental design is based on simulating the cGA, hGA, and sDA algorithms with different values of model parameters, and sample size(n). The study contains comparison among these algorithms depending on MSE value. One can conc

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

Glass fiber–reinforced polymer (GFRP) reinforcement provides an effective alternative to conventional steel in concrete structures due to its corrosion resistance. Nevertheless, the lower elastic modulus of GFRP necessitates careful consideration of serviceability behavior in GFRP-reinforced concrete members. This study presents a numerical sectional analysis model for predicting the flexural response and ultimate capacity of hybrid reinforced concrete beams incorporating embedded GFRP profiles in combination with either mild steel or GFRP reinforcement bars under monotonic static loading. The proposed model employs realistic nonlinear stress–strain relationships for concrete and steel, together with secant moduli of elasticity