A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs lengths and their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy. The optimization carried out is subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studio software was used to analyze 1200 different cases. For each case the
length of protection (L) and volume of structure (V) required to satisfy the safety factors mentioned previously were estimated for the input values, namely, the upstream cutoff depth (S1), the downstream cutoff depth (S2), the foundation width (B), the angle of inclination of the upstream cutoff (Ɵ1) and the angle of inclination of the downstream cutoff (Ɵ2), H (differencehead), kr (degree of anisotropy) and D (depth of impervious layer). An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the crossover probability, the mutation probability and level,
the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate
that the most factors that affects. the optimum solution is the $ number of population required. The minimum value that gives stable global optimum solution of this parameter is (30000) while other variables have little effect on the optimum solution.
Regression models are one of the most important models used in modern studies, especially research and health studies because of the important results they achieve. Two regression models were used: Poisson Regression Model and Conway-Max Well- Poisson), where this study aimed to make a comparison between the two models and choose the best one between them using the simulation method and at different sample sizes (n = 25,50,100) and with repetitions (r = 1000). The Matlab program was adopted.) to conduct a simulation experiment, where the results showed the superiority of the Poisson model through the mean square error criterion (MSE) and also through the Akaiki criterion (AIC) for the same distribution.
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... Show MoreThe green production of iron oxide nanoparticles (FeONPs) due to its numerous biotechnological uses has attracted a lot of attention and clean and eco-friendly approaches in the medical field.
The objectives of this study are to demonstrate the biogenic creation of FeONPs. The search for alternative antimicrobial medicines has been prompted by growing worries about multidrug resistance.
The study included the investigation of fungi which associated with heavy animal's leather (Cows and Buffalos) and light (Sheep’s and Goats )through different processing stages (raw hides ,dehairing ,pickling,chrome tanned and stainning or finished stages)there were 10 genera and 25 species in addition to sterile fungi associated with animal leathers which included Alternaria ,Aspergillus,Cladosporium,Fusarium, Mucor , Penicillium , Rhizopus , and Trichoderma .Aspergillus and Penicillium have observed in all leather samples and different processing stages, and that the first time isolate two genera Helminthosporium , Stemphylium form leather for staining stage.
Complexes of some metal ions with 2-thiotolylurea were prepared in ethanolic medium using (1:1) (Metal : Ligand) ratio yielded series of neutral complexes as the general formula [M(L)Cl2]. The prepared complexes were identified by atomic absorption FT.IR, UV-Visble spectra, molar conductivity and magnetic properties. From the above data the tetrahedral structure was suggested for all complexes.
Six species of aquatic snails were sorted from three sites, the irrigation canal of Baghdad University campus (S1), River Tigris at Al-Za'afaraniah district / Baghdad(S2) , and River Euphrates at Al-Haideriah district Al-Najaf province(S2). The species included Melanopsis nodosa ;Melanoides tuberculata ; Thaiodaxsas jordani ; Lymnaea auricularia ; Physa acuta and Bellamya bengalensis. The first specis recorded the highest total number and was found in high density in the R. Euphrates site (S3), while the last species was the most widely distributed species, and found in all study sites. The last three species were found in Tigris river (S2) , while the first and last species were collected from the irrigation canal (S1).The result reveal
... Show MoreHierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil
... Show MoreIn the present investigation, 24 adult dipteran species with forensic importance belonging to 13 genera and 8 families that were collected from different localities of Iraq. The specimens were identified by different taxonomical keys; in addition the date and localities of collecting specimens were recorded.