Al-Nahrain Journal of Science

Several metal ion complexes of manganese (II), cobalt (II), nickel (II), copper (II) and zinc(II) were derived from (E)-2-(2-hydroxyphenyl)-2-((4-(phenyldiazenyl)phenyl)amino)acetonitrile ligand (L) and have been characterized using conventional techniques such as1H and 13C-NMR, FT-IR and UV-VIS spectroscopy, elemental analysis, thermal analysis, flam atomic absorption and molar conductivity. The five new complexes were suggested to be of octahedral geometry and were observed to be electrolytic. The coordination pattern of the ligand was bidentateof NO-type through amine and hydroxyl groups forming stable 6-membered ring releasing nitrile group free. [ML2(H2O)2]Cl2.XH2O is the suggested general formula for the complexes that M: manganese(II), cobalt(II),nickel(II), copper(II) or zinc(II)], X = 1, 4, 2, 4 or 1 respectively, hence that each metal interacted with two ligands and two aqua molecules. All the complex compounds and the ligand were investigated for anti-bacterial activity against four bacterial genera including: Pseudomonasspp., Escherichia coli, Proteusspp. and Staphylococcus aureus. The antibacterial activity results were promising, that they ranged from 11mm to 21mm,especially the L against Escherichia coli and Staphylococcus aureus bacteria, meanwhile, the complexes showed very good inhibition zones against the gram positive bacteria only, except Ni(II) and Zn(II) complexes showed no activity against Escherichia coli. Generally, the activity was suggested due to taking in account the presence of nitrile moiety, beside complex form that possesses metal ion. The aim of this work is to design a chelating α-aminonitrile ligand to interact with some transition element salts to produce the corresponding complexes and to investigate their activities against selected bacteria.

Doxorubicin (DOX), a potent anthracycline antibiotic widely used in cancer therapy, is notorious for inducing off-target multi-organ toxicities. These toxicities arise through mechanisms such as DNA intercalation, topoisomerase II poisoning, oxidative stress via free radical generation, and membrane disruption caused by altered sphingolipid metabolism. Mitigating these adverse effects is essential to improving the therapeutic index of DOX. This study investigates the protective potential of a diosmin-hesperidin conjugate using in silico and in vitro methods. The diosmin-hesperidin conjugate, derived from natural flavonoids, was assessed for its anti-apoptosis and antioxidant potential to alleviate DOX-induced toxicities. In silico analyses, including molecular docking and dynamic simulations, revealed high binding affinities of the conjugate to key oxidative stress-related targets, such as catalase, glutathione reductase, and apoptosis-related proteins (e.g., caspase-3, Hsp70, and Hsp90β). In vitro assays, including DPPH, NO scavenging, FRAP, H₂O₂ scavenging, lipid peroxidation inhibition, and total antioxidant capacity, validated the conjugate’s robust antioxidant activity. The computational and in vitro studies showed that the conjugate stabilized antioxidant enzymes and inhibited apoptotic pathways. Overall, by leveraging the synergistic effects of this flavonoid conjugate, this study highlighted the diosmin-hesperidin conjugate as a promising natural adjunct therapy to mitigate DOX’s off-target oxidative stress-and apoptosis-mediated toxicities. Thus, providing a foundation for further preclinical and clinical evaluations, and contributing to the advancement of natural product-based cancer therapy.

The accuracy of DNA profile identification is significantly enhanced by increasing the number of STR markers that are examined. Forensic markers' usefulness depends on the population, necessitating regional studies. This study aims to provide a better understanding of the effectiveness of autosomal STR loci as a forensic tool by comparing the genetic factors found in Arabs living in Voronezh, Russia, to published datasets from other Arab groups. Blood samples were collected from 98 unrelated Arab volunteers. Using a 3130XL Genetic Analyzer, DNA typing was conducted for 19 polymorphic STR loci and the Amelogenin locus with the use of the GOrDIS Plus kit. The analysis revealed a high degree of polymorphism, with the number of alleles per locus ranging from 6 to 36. The highest allele frequency observed was 0.5816 at the TPOX locus. The observed heterozygosity (Ho) and expected heterozygosity (He) values were generally consistent, indicating that the population is largely in Hardy–Weinberg equilibrium (HWE). Theforensic parameters demonstrated high discriminatory power: the Power of Discrimination (PD) ranged from 0.813 to 0.962, the Polymorphism Information Content (PIC) from 0.58 to 0.86, and the Power of Exclusion (PE) from 0.388 to 0.689. High Ho was observed at FGA and D21S11 loci (87.76% and 87.8%, respectively), while TPOX showed the lowest Ho (63.27%).In conclusion, the Analysis of Molecular Variance (AMOVA) and pairwise FST analysis indicated that Arabs residing in Voronezh, Russia, show strong genetic similarities with Arab populations in Europe and Arab countries. These findings underscore the robustness of the selected STR markers for forensic applications within this population and contribute valuable data to the global forensic genetic database.

The increasing rates of multidrug-resistant (MDR) organisms among patients with chronic suppurative otitis media (CSOM) have caused treatment procedures to become more complex, raising healthcare costs and significantly increasing the time of recovery. Since medicinal plants are the natural repositories for bio-active and secondary metabolites, they are an excellent alternative to antibiotics to combat MDR bacterial strains. This study aims to assess the antibacterial activity and the presence of phytochemicals in the aqueous and methanol leaf extracts of the Indian deciduous tree Pterygota alata against 5 MDR strains of CSOM-causing bacteria: Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Proteus mirabilis, and Klebsiella pneumoniae. Fresh P. alataleaves were harvested, dried, and extracted with methanol and water. Qualitative and quantitative assessments demonstrated the detection of primary and secondary metabolites. The antibacterial activities were determined using the agar well diffusion method, including minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), dose-response, and time-kill studies against CSOM bacterial isolates. Most phytochemicals, except proteins, were present in moderate to high concentrations in aqueous and methanol extracts. The methanol extracts had higher levels of phenolics (85.6mg/g), flavonoids (42.3mg/g), tannins (40.8mg/g), and saponins (36.9mg/g). Steroids were minimal in both extracts, while alkaloids were moderately present. All extracts showed significant antibacterial activity, particularly the methanol extracts against S. aureus and P. mirabilis (ANOVA, p ≤ 0.05).The effect was concentration-dependent, with rapid bactericidal action observed at higher concentrations. It was further confirmed with one-way ANOVA followed post-hoc Tukey test, there were significant differences among different treatments (p ≤ 0.05), supporting the reliability of the results.Therefore, it can be concluded from this study that methanolic leaf extracts of P. alatacan be used as an alternative or complementary antibacterial agent against MDR CSOM infections.

Breast cancer liver metastasis (BCLM) is a multi-step process that characterized by the spread of breast cancer cells to the liver. The majority of breast cancer-related deaths are due to metastatic rather than primary breast tumors. CXCL12 is one of the chemokines, small proteins capable of migrating various types of immune cells to the site of inflammation. CXCL12 can affect several cell biological activities, such as migration and proliferation, when it binds to its receptors. The aim of this study is to investigate the predictive value of plasma CXCL12, CA15-3, and some liver-related parameter levels in breast cancer patients with liver metastasis. This study involves 94 women,25 healthy controls and 69 patients with breast cancer, divided into three subgroups: 26 newly diagnosed women with primary breast cancer, 28 women with non-metastatic breast cancer undergoing chemotherapy, and 15 women with liver metastatic breast cancer. Plasma levels of CXCL12, CA15-3, albumin, ALP, ALT, and AST were measured using absorbance photometry and ELISA assays. The results of the study reveals that the level distributions of CXCL12 (p>0.9999) and CA15-3 (p>0.9999) showed no significant difference between breast cancer patients with and without liver metastasis. Albumin levels were significantly lower (p=0.0089) in women with liver metastatic breast cancer compared to those without metastasis. Levels of ALP(p=0.0006 ,)pALT )=0.0015), and AST (p<0.0001) were significantly elevated in breast cancer liver metastatic patients compared to patients without metastasis. CXCL12 (AUC, 0.5840, p=0.3788)and CA15-3(AUC, 0.5787, p=0.4099)could not serve as reliable discriminatory parameters for liver metastasis. Albumin (AUC, 0.7267, p=0.0176) showed a moderate ability to distinguish between BC patients with and without BCLM.ALP (AUC, 0.8693, p=0.0001) and AST (AUC, 0.9453, p<0.0001) exhibited the highest diagnostic accuracies for liver metastasis. Together, none of the study parameters was identified as an independent risk factor for liver metastasis in breast cancer patients.

This paper is about the application of Steroid treatment during pregnancy as practiced in common in practice, in rheumatological and autoimmune diseases, its prolonged use has an adverse effect on fetal growth. The study aimed to assess the effect of prenatal dexamethasone and hydrocortisone treatment on rats' offspring concerning growth parameters, congenital malformation, and survival rate. Sixtypregnant rats were allocated into 12 groups, and administered intraperitoneally either dexamethasone (1mg/kg/day) or hydrocortisone (9 mg/kg/day), and a control group that received normal saline. These were administered in the first week or last week of gestation. Neonatal body weight, length, and gross morphological anomalies were evaluated postnatal at 1 and 15 days. Prenatal dexamethasone treatment in late pregnancy resulted in 27 % neonatal mortality on day 3. Dexamethasone and hydrocortisone significantly reduced body weight and length of neonates compared to the control group, which is more prominent in dexamethasone treatment. However, both glucocorticoids resulted in profound developmental deficits when given in late pregnancy, especially dexamethasone, which causes profound growth retardation and teratogenic effects in neonates. At the same time hydrocortisone has a less severe impact on postnatal malformations and growth retardation. This sheds more light on the timing of steroid administration during the prenatal period, which should be taken into consideration for risk-benefit assessment.

Unitary Correlation Operator Method (UCOM) has been used to inspect the ground state properties of 24Mg and 28Si nuclei, such as, charge densities, the root mean square (????????????) radii and elastic electron scattering form factors. Inelastic longitudinal electron scattering form factors have been computed for iso scalar or transition ∆T=0 of the (0+0→21+0) and (0+0→41+0transitions) for the 24Mg and 28Si nuclei. The results of the computations have been discussed, and a comparison of those results with the experimental data has occurred. It was assured that the unitary correlation operator method is suitable for studying the nuclear structure.

The widespread adoption of innovative cementitious composites requires proper attention to the controlled performance of structures by using these functional materials. This paper delves deeper into this concept, utilizing a conventional Engineered Cementitious Composites (ECC45) matrix that has been embedded with conductive fillers, enabling it to detect damage in a variety of loading scenarios. As functional fillers, graphene nanoplatelets (GNPs) combined with polyvinyl alcohol (PVA) and nylon (NF) fibers were used at different curing ages of 28, 56, 90, and 180 days. In an attemptto build on previous results and address some knowledge gaps, the simple matrix was incorporated with graphene nanoplatelets at a fixed concentration of 2 % by wt. of cementitious materials along with one of the reinforcement fibers (PVA) or (NF) at 2% volume fraction, which remained constant throughout the experiment. The matrices were tested for compressive strength, splitting strength, three-point flexural strength, and direct tensile strength to evaluate the innovative matrices' behavior and ability to sense damage under these types of stresses. results demonstrated a unique behavior of the samples due to the excellent behavior of GNPs, particularly during the late curing ages, and the synergistic behavior it exhibits with the reinforcement fibers, which enables entire additives to work in a balanced manner in developing the self-sensing ability in conjunction with the high efficiency of the mechanical properties against the loading conditions described in this paper.

This work presents a new model for electrical conductivity versus grain size derived from a new theory called the time of events theory. The time of events (TE) model was applied to experimental data of electrical conductivity versus grain sizes of two metals, copper Cu and Nickel Ni due to their industrial importance in transferring electricity and electrical circuits. In addition, a comparative analysis of two other models was applied to the experimental data. The Maydas-Shatzkes model and the Andrews model. The newly derived model showed the best agreement with experimental data of Cu and Ni among all models. It uses mainly three factors: the first one is for extremely tiny grain sizes, while the second is for small grainsizes, and the third is for relatively large grain sizes. From comparing the fitting data with the Anderws models, it was noticed that the third parameter values were close to the Anderws model parameter. Therefore, Andrews' model is well applied to large grain sizes, but it deviates at extremely tiny grain sizes. This new model also predicted that the Ni data have minima and maxima, while other models did not have such a prediction

The efficiency of the magnetic deflector for variation operation condition was investigated. The design appears that the possibility of finding the constant characteristics of the deflector which lead to stability in the properties of the electron beam at the image plane despite the change in the properties of the electron source as: the initial beam divergence and the energy. The relation between the value of applied magnetic field on the magnetic deflector and the properties of the electron beam was studied. also the distribution of different value of the energy of the electron beam was studied. The calculation was carried out for different values of the divergence angle. The result showed that the increases in divergence angle dose not result change in the nature of the distribution of energy of the electron beam at image plane, and one can find constant dimensions of the electron beam at image plane even though the electron beams energy was altered, and this results can be achieved by choosing the appropriative values of magnetic field to be applied to the deflector. The calculations showed that the non-linear relation between the amount of the magnetic field and values of the kinetic energy electron beam.

This work includes preparation of CdO nanoparticles by laser ablation in water at two energies (360 mJ and 560 mJ). Various techniques (XRD, SEM, and AFM) were employed to ascertain the structural and morphological properties. X-ray diffraction of CdO films at different energies (360 mJ and 560 mJ) demonstrated their polycrystalline nature with a cubic phase. The crystalline size was 28.30 and 40.16 nm at energies 360 mJ and 560 mJ respectively. AFM measurements indicate that the average diameter has risen from 82.99 nm to 117 nm, while the surface roughness has escalated from 1.18 nm to 2.68 nm as laser energy rose from 360 mJ to 560 mJ. The SEM scans indicate that the substrate surfaces are uniformly coated with interconnected particles, exhibiting smallest diameter 13.94 nm and 21.78 nm at energies of 360 mJ and 560 mJ, respectively. (UV, and FTIR) techniques were employed to ascertain the optical and chemical properties, where the energy bandgap for CdO is documented as 3.8 eV and 3.6 eV at energies 360 mJ and 560 mJ respectively. FTIR spectra identified the range 400-1000 cm-1as the characteristic bands for the CdO mode at 400-1000 cm-1. Increased inhibition of bacteria and fungi was observed with increasing laser energy concentration, where the inhibition zone of Staphylococcus aureus and candida albicans was 12 mm and 19 mm at 360 mJ, while it records 14 mm and 20 mm at 560 mJ

Anisole, also known as methoxy benzene, is an organic compound with the formula \(CH_{3}OC_{6}H_{5}\). It is a colorless liquid with a sweet, aromatic odor similar to anise seeds and is used as an intermediate in the synthesis of pharmaceuticals, agro-chemicals, and fragrances. This study is about the quantum chemical insight into the Electronic, vibrational, and thermodynamic properties. The study first analyzed the optimized molecular structures and identified different physical properties. The Non-Co-valent Interactions (NCI)-Reduced Density Gradient (RDG) reveal weak hydrogen bonding and strong repulsive interactions. Molecular electrostatic potential mapping shows higher reactivity in chlorine-substituted anisole, with key reactive sites at oxygen, chlorine, and carbon atoms. Anisole has an excitation energy of 5.770 eV and a lower electronegativity of 3.2850 eV, indicating kinetic stability. Para-chloroanisole (PCA) exhibits the lowest energy gap at 5.481 eV and chemical hardness of 2.7405 eV, suggesting high reactivity. The quantum energy levels are observed in the density of States (DOS) spectrum. Due to their electronegative nature, chlorine atoms show positive charges while oxygen atoms, owing to their electron-withdrawing properties, show negative Mulliken charges, respectively, and serve as key reactive sites. In addition to that, Infrared (IR), Raman, and UV-visible spectra reveal different vibration modes and spectral intensities. Thermodynamic functions such as constant pressure (Cp), enthalpy (H), and entropy (S) for all studied molecules increase with temperature.

Laser ablation techniques employing photo/thermosensitive materials were used to synthesize and characterizeCdTe/ZnTe core/shell nanocrystals (NCs) or quantum dots (QDs).Upon excitation by a laser pulse (Nd: YAG laser with 150 pulses and 600 mJ of power), a broad signal indicating ground state bleach in the core/shell nanocrystals was observed in the transient absorption spectrum, which was compatible with the absorption spectra. These photoelectrodes were pre-sensitized by CdTe/ZnTe core/shell NCs that were deposited utilising an FTO/TPD/CdTe-ZnTe/PEDOT: PSS/Ag scaffold of a certain thickness on a transparent glass-substrateFTO (Fluoride tin oxide). According to PL measurements, the energygap (Eg) of CdTe/ZnTe core/shell NCs is around 2 and 2.5eV for CdTe-core and ZnTe-shell respectivelyfrom Taucrelation. A current-voltage (I-V) characteristic, in addition to lighting at 3V, verifies an ideal environment and formation. A laser ablation-produced CdTe/ZnTe core/shell NCs improved the photoelectrodes' performance by improving the carrier's charge mobility and, therefore, recombination processes inside the NCs along with TPD and PEDOT: PSS polymer ions. The development of the photoelectrode device's increasing interaction with PEDOT: PSS and TPD ions regarding core/shell NCs to use limited voltages and provide desirable light output may cause a bias in forward current flow.

n the present work improvement of the solar-to-thermal conversion in aluminum substrates coated with heat-resistant black paint and enhanced by iron (Fe) and chromium (Cr) powders was attempted. Aluminum sheets were cleaned and coated with a commercial black paint. On the top of the primary paint Fe and Cr powders (both individually and combined) were applied using an air-spraying method. The morphology and uniformity of coatings were examined using optical and scanning electron microscopes. The coating elemental makeup was investigated using energy-dispersive X-ray spectrometer (EDS). The capability of the photo-thermal conversion was tested by exposing the coated substrates to solar radiation in collector setup. The substrate temperature was monitored over time. The reflectance measurements were conducted using a pyrheliometer-like device. The spectral absorptance was determined and analyzed via UV-Vis spectroscopy measurements. The elemental analysis confirmed the effective and homogeneous incorporation of the iron and chromium powders into the coatings. Aminor cross-contamination was attributed to the processing of the coatings. Results show that by adding Fe and Cr powders a notable change in the surface morphology was observed. This has led to increase in the roughness and complexity of the coating surface. This roughness was a key parameter for light trapping and absorption. Among the coatings investigated, the Fe-modified sample showed the highest solar absorptance (96.23%) and greatest temperature increase under irradiation, surpassing both the Cr-modified and combined Fe+Cr coatings. The UV-Vis analysis revealed that Fe-containing coatings possessed the greatest integrated absorbance, especially within the visible spectrum (500–800 nm), aligning with their superior photo-thermal performance. These results highlight that integrating Fe powder into heat-resistant paint can be an effective and practical approach to optimize the solar-to-thermal energy conversion. Such approach offers promising application in solar thermal collectors.

Explainable Artificial Intelligence (XAI) is a branch of Artificial Intelligence (AI) that focuses on developing tools, methodologies, and algorithms capable of delivering interpretable, intuitively understandable insights and rationales for human users. The growing need for XAI in medicine and other fields stems from the increasing demand for equitable and ethical decision-making. It has been established that AI systems largely depends on historical data, therefore, any existing bias or behavior will be perpetuated. As such, deep examination and interpretation are required in this process. Since these black-box models lack transparency and interpretability, several XAI models are developed for the respective domains, ranging from healthcare, military, energy, finance, and industry. The highly sensitive areas, such as healthcare, require knowing the underlying principles of model predictions. Emphasizing feature importance, XAI has improved machine learning models to identify the most critical variables that help improve accuracy and efficiency. With the use of appropriate XAI techniques, actionable insights can be derived that would support informed decisions. In the healthcare sector, the primary objective of XAI is to provide clinicians with tools to effectively evaluate AI-generated data for better patient care. This survey covers Explainable AI, its methods, and their applications in the medical domain.

The Deep Neural Networks (DNNs) are increasingly leveraged in medical image segmentation, particularly for complex skin conditions like Monkey pox. Due to the distinctive visual features of Monkey pox, such as rashes, blisters, and scabs, distinguishing these features from those of Smallpox and Chickenpox may be difficult, thereby complicating diagnosis. We propose a comparison study with Graph Neural Networks (GNNs) integrated with both Felzenszwalb's and Quick Shift segment algorithms for Monkey pox blister segmentation. Our GNN and Felzenszwalb's algorithm combination reached a Mean Structural Similarity Index Metric (SSIM) of 0.85 ± 0.091 and an accuracy of 85.03%, outperforming the other traditional methods. Most distinctly, the combination of Felzenszwalb and GNN were more accurate in segmentation than Quick Shift, which achieved a lower performance SSIM of 0.63 ± 0.080.Correspondingly, GNN using Felzenszwalb's algorithm reached a lower average test loss of 0.0082 compared to the other approaches. This further establishes that GNNs combined with traditional segmentation methods may greatly enhance accuracy and speed of Monkey pox blister segmentation for improved diagnostic applications. It supports automated lesion analysis for early Monkey pox screening in digital dermatoscopy and teledermatology systems.

The appearance of the sixth generation (6G) networks introduces new realities of demand towards real-time, scalable and secure communication infrastructures. To respond to such challenges, the overlap of three major technologies is required, namely Cognitive Networks (CN),Artificial Intelligence (AI), and Software-Defined Networking (SDN). Nevertheless, the issue of integration is not simple as the environments of 6G will be heterogeneous, and threats to security will be constantly changing. This paper will suggest a combination of hybrid layered architecture which integrates AI-based analytics, cognitive flexibility and SDN programmability to optimize dynamic network control and advanced security in 6G environments. This architecture styles use of reinforcement learning (RL) and convolutional neural networks (CNN) to enable autonomous decision creation, resource optimization, and detection of threats. The simulation results (obtained with synthetic traffic and attack scenarios) show the improvements compared to the baseline systems are high: 30 percent less latency, 25 percent higher throughput, 20 percent higher energy efficiency, 95 percent DDoS attack detection and 98 percent malware propagation detection accuracies. These results confirm the capabilities of the framework in providing mission critical 6G applications in areas like in healthcare, industries, and smart cities.

Correct and discernible segmentation of an image is an important part of biomedical imaging, especially when anatomical structures and pathological regions are identified. Although deep learning architectures, like U-Net and its variants, have performed well, their lack of interpretability, transparency and contextual reasoning has limited their clinical adoption. This research helps to overcome the most important issue of high-performance medical segmentation by introducing a new explainable framework that combines convolutional encoders with transformer-based decoders and dual attention mechanisms. There are three aspects of this work, based on the following objectives. (1) To boost the performance of segmentation by means of hybrid local-global feature modelling; (2) To bring about clarity through visual explanation tools; and (3) To conduct clinical viability checks through expert assessments. The architecture proposed includes CBAM for fine spatial and channel attention and combines Grad-CAM++ and SHAP for local and global explainability. Such modules make it possible for clinicians to view the decision paths of models and establish trust in automated outputs. Experiments in two datasets, which were publicly available, were performed: ISIC 2018 for skin lesion segmentation and BraTS 2021 for brain tumor segmentation. The quantitative results show that the suggested method is better than several strong baseline models, such as U-Net, Attention U-Net, and TransbUNet, delivering Dice scores of 0.902 and 0.895 on ISIC and BraTS datasets, respectively. Visual comparisons and expert confirmation validate the clinical plausibility of the predicted masks and demonstrate the potential worth of the explainability in high-stakes healthcare conditions. On the whole, this work provides a prudent, precise and explainable deep learning approach toward medical image segmentation and offers ample room for scientific integration and trust-conscious implementation in diagnostics workflows

This study proposes a new version of the Autoregressive Integrated Moving Average (ARIMA) model using Artificial Neural Networks (ANNs) denoted by ARIMA-NN. The new model incorporates a multi-layer perceptron with matrix multiplication within a feed-forward network. The logistic, hyperbolic tangent (tanh), and sigmoid activation functions are used for weight updates in ARIMA-NN. A new forecasting algorithm is proposed, and one-step and multiple-steps forecasting procedures are rigorously analyzed. The proposed model was evaluated against existing forecasting model using performance metrics such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to assess its effectiveness. The U.S. Census Bureau (www.census.gov) provides a data set of monthly drug sales spanning ten years (2014-2024), which is utilized in the study. The ARIMA-NN model is applied to generate forecasts for drug sales in the U.S. for the next four years to demonstrate the models' utility and efficacy. All the computations and visualizations are performed using various R packages in version 4.3.2.

This paper estimates three parameters for a new model called “IDAL Distribution”. This model expands the exponential Weibull distribution by adding a third parameter and studying its characteristics. Several estimation methods, such as maximum likelihood estimation, ordinary least squares estimation, and proposed ordinary least squares methods, have been used to investigate the unknown parameters of the IDAL distribution. The goodness of fit of the proposed distribution is ultimately compared, and the results of the applied approaches are evaluated based on MSE.

This research aims to provide a comprehensive view of the separation mechanisms of HILIC stationary phases in various applications. This study was conducted to understand the retention behavior of nalidixic acid and enoxacin, to show the effect of alkyl chain length on their selectivity, and to develop a simple and sensitive HILIC method for their determination in pharmaceutical formulations. Two lab-made columns (ZIC-HILIC-1 and ZIC-HILIC-3) were used to examine the retention behavior of nalidixic acid and Enoxacin. The retention mechanism in this research was studied by investigating the effect of parameters that affect the selectivity of the separation, which include (acetonitrile content, pH, and buffer concentration). The zwitterionic columns are characterized by their different alkyl spacer chain length, which have been employed to examine the retention behavior of select drugs. The eluent consists of acetonitrile:10mM acetate buffer (90:10 v/v, pH 4.75) equipped with a UV detector set at 220nm, at a flow rate of 0.75 mL/min. This research developed a ZIC-HILIC method to determine nalidixic acid and enoxacin in pharmaceutical formulations. The technique exhibited linearity in the concentration range (0.1–6 μg/mL) and 0.1-12 μg/mL for nalidixic acid and Enoxacin, respectively. LOD is 0.070 and 0.04 μg/mL for nalidixic acid, and LOD is 0.080 and 0.060 μg/mL for enoxacinin ZIC-HILIC-1 and ZIC-HILIC-3 columns, respectively.