Ternary polymer blend of chitosan/poly vinyl alcohol/ poly vinyl pyrrolidone was prepared by solution castingmethod, nanocomposite was prepared by sonication method with nano Ag and Zn. All prepared compounds have been characterizedby FT-IR, SEM, DSC, as well as Biological activity. Antimicrobialactivity related to prepared blendsand Nanocomposites againstsix types of bacteria namely, Staphylococcus aureas, E. faecalis, S.typhi, P. aeruginosa, Bacillus subtilis, Escherichia coli andC. albicans fungal were examined and evaluated. The results reveal that the prepared polymer blends and nanocompositeshavegood antimicrobial activity against all kinds of microbials.

Specialized Escherichia coli (E. coli) isolates, called uropathogenic E. coli (UPEC), cause most of urinary tract infections (UITs). Once bacteria reached the urinary tract of the host, they have to adhere to the host cell for the colonization. For this purpose, bacteria have different structures including fimbrial adhesins. Most of the UPECs contain type 1 fimbriae encoded by fim operon (fimB, E, A, I, C, D, F, G, H) which is responsible for the adhesive ability in these isolates. Ninety-four isolates of UPEC were obtained from UTI patients in Baghdad hospitals and their diagnosis were confirmed by the PCR method using 16srDNA as a housekeeping gene. The UPEC isolates were tested for their ability of adherence to the urothelial cells obtai

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

For two years, from January 1995 till December 1996, a survey on helminth parasites of aquatic birds of Bahr Al-Najaf depression, mid Iraq, was achieved. A total of 663 birds, belonging to 11 species, were captured and examined for helminth parasites. These birds were infected with seven trematodes (Notocotylus gibbus, Cyclocoelum mutabile, Echinostoma chloropodis, Patagifer parvispinosus, Psilochasmus oxyurus, Diplostomum spathaceum and Apharyngostrigea cornu), seven cestodes (Paricterotaenia porosa, Dicranotaenia tsengi, Diorchis brevis, D. inflatus, Tatria acanthorhyncha, T. decacantha and Diplophallus polymorphus) and four nematodes (Capillaria sp., Eustrongylides tubifex, Con

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

This study explores the challenges in Artificial Intelligence (AI) systems in generating image captions, a task that requires effective integration of computer vision and natural language processing techniques. A comparative analysis between traditional approaches such as retrieval- based methods and linguistic templates) and modern approaches based on deep learning such as encoder-decoder models, attention mechanisms, and transformers). Theoretical results show that modern models perform better for the accuracy and the ability to generate more complex descriptions, while traditional methods outperform speed and simplicity. The paper proposes a hybrid framework that combines the advantages of both approaches, where conventional methods prod

In this paper, fire resistance and residual capacity tests were carried out on encased pultruded glass fiber-reinforced polymer (GFRP) I-beams with high-strength concrete beams. The specimens were loaded concurrently under 25% of the ultimate load and fire exposure (an increase in temperature of 700 °C) for 70 min. Subsequently, the fire-damaged specimens were allowed to cool and then were loaded statically until failure to explore the residual behaviors. The effects of using shear connectors and web stiffeners on the residual behavior were investigated. Finite Element (FE) analysis was developed to simulate the encased pultruded GFRP I-beams under the effect of fire loading. The thermal analyses were performed using the general-pu