Al-Nahrain Journal of Science

This research examines the alteration of mesomorphic behavior in azomethine chalcone compounds as liquid crystals, influenced by the linearity of molecule and alkoxy groups’position. Compounds were synthesized by the reaction of 4-substituted benzaldehydes with 4-aminoacetophenone using catalysts appropriate for the reaction type. Тhesynthesized compounds were characterized using FТ-IR, 1H NМR, and 13C-NМRtechniques.Mesomorphic characteristics were examined by differential scanning calorimetry (DSC), while the resultant textures were featured using polarized optical microscopy (POM). Compounds (R1-R3) showed an absence of mesomorphic properties due to the lack of molecular linearity, while compounds (M1-M3) exhibited nematic phases distinguished by a marble-like texture.

The investigative results from the elimination of the cadmium(II), nickel (II), and lead (II) ions into solution form utilizing p-aminoacetophenone modified poly(methylmethacrylates) are presented in the current paper. Fourier transform infrared spectroscopy(FTIR) was employed to characterize the modified polymer. The pH level in the solution, the time of the interaction, the starting concentration of a heavy metal and the dosage of adsorbent were all examined to better comprehend the adsorption procedure. Three models were utilized to examine the adsorption isotherm. Results showed that improved polymer could adsorb higher concentrations of heavy metal (II) ions. The PMMA-p-aminoacetophenone was found to have a maximum adsorption capacity of 8.9605, 15.552 and 7.2939 mg/g for cadmium(II), lead(II), nickel(II) ions respectively

In the past few decades, the cholinergic theory of Alzheimer's disease has been promoted as a crucial tool for the creation of new drugs. Cholinesterases (ChEs) play a dynamic part in the regulation of cholinergic transmission. The inhibition of ChEs is believed to be involved in the cumulative brain acetylcholine level and accordingly has been considered in the management of Alzheimer’s disease. In this study, new sinapic acid derivatives were designed by docking, synthesized, and predicted for their ADME properties. They were then tested in vitrofor their ChEs inhibitory activity. Based on the docking study, 10 new amide and Schiff base sinapic acid derivatives were synthesized and characterized using 1HNMR and 13CNMR. The in vitrotesting yielded good results at 50 and 100 M concentrations. Six new compounds showed good and potent affinity to inhibit ChEs, proving more potent than the parent compound (sinapic acid) and rivastigmine, and largely similar to that of donepezil for acetylcholinesterase (brain ChE). Compounds S4 and S9 exhibited superior activity, resembling donepezil. In vivo and animal testing studies will be needed to ascertain the resulting activity and safety of these new compounds.

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The effect of operation condition of the magnetic lens on the efficiency of the magnetic deflection system, which is consist of the magnetic lens and magnetic deflector was investigated. The relation between the value of the applied magnetic field on the magnetic lens and the properties of the out electron beam was studied. For a more accurate understanding to the relation for both of electron beam dimensions and the kinetic energy distribution were calculated for different values of applied magnetic field of lens. The results showed that the increasing of the magnetic field of the magnetic lens by 0.02T led to enhancement in the dimensions of the electron beam at image plan by 20% for ∆y and 66% for ∆z, in comparison between the applied magnetic field of B = 0.015 T and B = 0.035 T. Also the results clearly show the importance of adding a magnetic lens to the magnetic deflector to form on integrated and effective magnetic deflection system, where the beam dimensions were reduced by 40% for ∆y and 70% for ∆z, where comparing the presence of a magnetic lens with magnetic field of 0.035 T. The addition of magnetic lens to the magnetic deflector maintained a stable distribution of kinetic energy of electron beam, where the distribution was stable in the presence and absence of the lens. Also despite the change of the applied magnetic field on the lens to improve the beam dimensions. The kinetic energy distribution remained stable for all cases of change, and the loss of kinetic energy is very small and can be neglected. The results showed that the effect of the systems behavior in the presence of the lens is not affected by the characteristics of the electron beam, the characteristic of the electron beam source were changed by taking different initial angles, but the systems behavior and efficiency remained the same.

Air pollution is an escalating health problem in Iraq, particularly since dry and semi-dry conditions often exacerbate the loads of airborne dust and particles. We assess the impact of fine particulate matter PM2. with freely available Copernicus Atmosphere Monitoring Service (CAMS) products from the European Centre for Medium-Range Weather forecast (ECMWF) over Iraq.The analyses were performed on the year 2021, with a focus from January to March due to higher pollution levels within this period, it can be expected that particle concentrations are elevated. We also calculated the annual mean PM2. PM2. 5 and PM10 for 2021 are provided for a more global perspective with relevant international air-quality reference level. We used spatial methods in R to apply particulate matter fields to 20 of Iraq’s major cities, which allowed us to map geographic inconsistencies and areas with chronically high pollution. Results show that regional contrasts are considered very strong: PM2. 5 and PH10 than cities situated north or southwest. The observed patterns are in line with the synergistic influences of weather variability, recurrent dust storms, and local emissions associated with oil industry activity and urban stressors. PM2. In the most polluted areas, it was trained to 2023 data. Large portion of fine particles as implied by 5 relative to PM10.In total, mean PM concentrations were above WHO guidelines for all the cities surveyed with high levels of exposure likely resulting in health consequences. These results support the case for air pollution control and place specific interventions in Iraq.

Topological insulators are electronic systems with a large energy gap and spin-polarized surface or edge states inhabiting the band gap. As a result, they have been addressed in spintronics, fault-tolerant quantum computing, quantum information technology, and spin-based quantum networks. Furthermore, they can be manipulated into three-dimensional topological Josephson junctions, which have been proposed for nuclear vibrational spectroscopy and metrology. Importantly, the interesting properties of topological insulators depend strongly on the large-scale band structures as well as the properties of the edge or surface states. As a result, modulating the electronic properties of these gapless states becomes crucial for these applications.Defects serve as a fundamental means of changing the electronic properties of topologically protected edge or surface states. Therefore, characterizing such “intrinsic” defects is crucial for improving the use of these materials for quantum technologies. In this paper, the question of how to identify and eliminate such defects using topological elements in Josephson junctions is addressed. The analysis and investigation demonstrate that there is an urgent need for a systematic study of the relationship between defects and electronic properties. There were important results due to the use of quantum technologies that deal with free carriers on the surface of topological insulators, which seeks to compensate for the surface charges that accumulate at extrinsic (intrinsic) defects, making it difficult to characterize these defects with appropriate techniques. The collected results show that smooth surfaces and increased bulk states are desirable conditions for defect observation and can be obtained if the space charge regions within the valence and conduction band gap are increased.

This paper employs the utilization and the use of technology known as Laser-Induced Breakdown Spectroscopy (LIBS) to diagnose osteoporosis by means of nail analysis and to determine the calcium concentration in human fingernails. The primary objective of the study was to determine the calcium concentration in nails as a means of determining bone density in diagnosing osteoporosis respectively. One healthy individual and five individuals diagnosed with osteoporosis were among the individuals from Medical City Hospital that provided samples for the analysis. The findings demonstrated that nails that are in good health had higher calcium contents in comparison to nails that are afflicted by osteoporosis. The LIBS technology is a potent and accurate instrument that can be used to analyze the elemental composition of nails and may also indicate changes that are associated to diseases. This method can be adopted by medical professionals to enhance their capacity to diagnose and monitor osteoporosis, as well as to recommend the most appropriate treatment. The emission spectrum of elements plasma ranging from200 nm to 800 nm was recorded. The concentration of calcium sample that detected by LIBS were (0.85,0.65, 0.8, 0.9,0.74 and 1.1) and by XRF were(0.39,0.28, 0.34, 0.71,0.6 and 0.77).

Bone fractures are frequent injuries that compromise the structural integrity and function of bones. Detecting these fractures in X-ray images can be particularly challenging, especially when dealing with small or complex cases. Manual diagnosis is often time-consuming and error-prone due to image quality limitations and clinician fatigue. Although deep learning techniques have shown promise, their performance is often constrained by limited annotated datasets and poor generalisation across diverse imaging conditions. This paper presents a hybrid deep learning framework that integrates handcrafted texture features—extracted using the Grey-Level Co-occurrence Matrix (GLCM)—with deep features derived from six pre-trained Convolutional Neural Networks (CNNs): ResNet101, ResNet50, InceptionV3, Mobile Net, VGG16, and VGG19. Preprocessing techniques such as Gaussian filtering and Canny edge detection were employed to enhance image clarity and feature extraction. The combined feature vectors were classified using a Deep Neural Network (DNN). Experiments were conducted on a merged dataset from MURA and FracAtlas, comprising 6,466 X-ray images. The highest classification accuracy of 95.54% was achieved using VGG19 features combined with GLCM, without Canny preprocessing. Comparative analysis and cross-validation confirmed the robustness of the proposed approach. These findings highlight the effectiveness of combining handcrafted and deep features for reliable and clinically relevant bone fracture classification.

The spread of fake news within social media networks in recent years is seen as a troublesome element in the flow of public information. Malicious actors, such as some media professionals, use fabricated stories to influence vulnerable social groups. In this paper, a hybrid model is proposed to identify disinformation in Arabic language material. A pre-training algorithmic framework for detection and categorization of fake news is developed using deep learning approaches based on the Arabic Fake News Dataset (AFND) with approximately 374,000 labeled articles (80% training and 20% testing). The proposed model was compared with baseline architectures, such as CNN-BiLSTM, CNN-BiGRU, standalone recurrent networks, and pretrained language models, such as AraBERT, MARBERTv2, and AraELECTRA. The results show that the hybrid AraBERT-BiLSTM model outperforms all baseline architectures with 98.16%, 98.16%, 98.16%, and 96.58% accuracy, precision, recall, and F1-score, respectively. This is because AraBERT offers rich contextual embeddings that extract the deep semantic and syntactic nuances of Arabic text. Additionally, the model interpretability was improved by using Local Interpretable Model-agnostic Explanations (LIME) to identify the important linguistic features driving predictions.