Baghdad Science Journal

In this study, we calculated the binding energy of nuclei, which holds atomic nuclei together, by utilizing electron scattering transformations in the shell model to explore the nuclear structure for proton-rich pairs of mirror nuclei: 34Ca-34Si, 35Ca-35P, 36Ca-36S, and 37Ca-37Cl. The proton envelope that surrounds the proton-rich nucleus explains why the nucleus charge and its mirror image have different radii. The wave functions for single particles were adopted from the Skyrme-Hartree-Fock approximation and Wood-Saxon potential to compute the mirror radii, mirror charge radii, and skin thickness for protons and neutrons. It was noted that the results depend on the mass numbers of the mirror nuclei and the types of wave functions. Additionally, the displacement energy of the mirror nuclei was calculated, and a mathematical relationship was found linking it with the variance in mirror charge radii. The calculated results were compared with the available practical values, where they were found to agree with the experimental data.

Pair densities and their related correlation functions play a crucial role in capturing the complex many-body interactions within nuclear systems. This study focuses on how spin and isospin specifically influence nuclear charge density distributions. Using the folding model, the charge density is derived through an effective two-body density approach that incorporates both short-range correlations (SRC’s) and tensor correlations (TC’s), while also accounting for spin- and isospin-dependent forces. Calculations were carried out for the elastic electron scattering form factors, F(q), and the root-mean-square (RMS) charge radii, ⟨r²⟩¹ᐟ², of the ²⁰Ne and ²⁴Mg nuclei. The study also examined inelastic longitudinal electron scattering form factors for isoscalar transitions (T = 0) from the ground state to the excited states ( 0^+ 0 → 2_ ^+ 0 ) and ( 0^+ 0→ 4^+ 0) in the same nuclei. These analyses incorporated core polarization effects using the Tassie model. The results closely matched experimental observations, underscoring the reliability and precision of the theoretical approach used.

This study investigated the effects of laser energy on the phase-change mechanism, optical properties, and structure of CeO2:Al2O3 nanoparticles synthesized via pulsed laser deposition (PLD). The study employed Raman spectroscopy, UV-Vis spectroscopy, X-ray diffraction (XRD), atomic force microscopy (AFM), and field emission scanning electron microscopy (FESEM). The process techniques were compared at different laser pulse intensities (530, 730, and 930 mJ). Porous silicon (PS) substrates have been created using the photoelectrochemical etching of n-type silicon. The XRD results for CeO2:Al2O3 nanoparticles indicated a polycrystalline cubic phase. Raman spectroscopy of CeO₂:Al₂O₃ revealed weak peaks at around 615 and 1311 cm⁻¹ and major peaks at approximately 452, 519, and 979 cm⁻¹, with their intensities increasing with laser energy. The RMS surface roughness increases with increasing laser energy. The majority of nanocomposites feature spherical nanostructures that are randomly distributed and increase in size with increasing laser power. The optical bandgap of CeO₂: Al2O3 thin films was determined to be 2.36, 2.62, and 2.66 eV for the varying laser energies. The quantum confinement effect caused the energy gap to widen as the laser pulse energy grew. Cerium oxide significantly contributes to the results and is chosen over traditional materials in nanocomposites because it enhances the properties of the base materials and makes them more stable when deposited at high temperatures, as observed in this work. It is also used in optical applications and a variety of other applications.

Limb posture alters hydrostatic pressure and vascular tone through mechanical and reflex pathways, but the contribution of endothelial function remains uncertain. To determine whether vascular responses to hand elevation and dependency persist during transient endothelial dysfunction induced by ischemia–reperfusion injury (IRI), eighteen healthy adults (9 males, 9 females) underwent ECG-gated pulse recordings at three hand positions: heart level (H-0°), 90° below (H-90°), and 50° above (H+50°), before and after IRI. Primary outcomes were pulse wave velocity (PWV) and finger-to-finger pulse arrival-time difference (f–f ΔT); secondary outcomes were wrist mean blood pressure (MBP), heart rate (HR), and ΔPWV/ΔBP slope. The result at baseline, hand elevation reduced PWV by 10–13% and increased f–f ΔT by 600–727%, while dependency increased PWV by ~7% and decreased f–f ΔT by ~200%. After IRI, these postural responses persisted, with only a modest 3% PWV reduction at H-90° and H-0° (p < 0.05). MBP shifted with posture as expected, and HR rose slightly with dependency but decreased overall after IRI. The ΔPWV/ΔBP slope was steeper in elevation, consistent with enhanced compliance. In conclusion, vascular responses to hand posture remain preserved despite acute endothelial dysfunction, indicating that short-term postural adjustments in arterial stiffness are mediated predominantly by hydrostatic and reflex mechanisms rather than endothelial pathways.

As it famous the solvability of any mathematical problem play an important rule for many investigators whose interested about the numerical solution for such mathematical problems because it gives them the green light about the ability to solve such problems numerically, besides to give the green light for solving numerically many real life problems in various field of sciences; like the physics, medicine, astronomy, chemistry, biology, different branch of engineering and many other fields. For this reason, this work is devoted to studying the solvability (in infinite dimension) of a couple of nonlinear elliptic partial differential equations (CNLEPDEs) with four different types of boundary conditions (BCs): Dirichlet (DBCs), Neumann (NBCs), Robin (RBCs), and Mixed BCs (MBCs). The weak formulation (WF) for each problem is determined by the type of each B. The existence and the uniqueness of the solution for the proposed problem (CNLEPDEs) with each type of BCs is proved by employing the Minty-Browder theorem (MBTH), through using the Poincaré-Friedrichs inequality of (PFI) in the case of homogeneous BCs (HBCs) for each type, and through using the generalization of PFI (GPFI) and the trace Operator (TRO) in the case of nonhomogeneous BCs(NHBCs) for each type.

This paper deals with a new model called the power log- logistic modified Weibull distribution (PLLoGMWD) using the power transformation method, which can be regarded as an extension of the distribution introduced by Broderick O. Oluyede et al. Several significant statistical properties of the power log- logistic modified Weibull distribution are computed, including reliability, hazard rate functions, reverse hazard function, rth moment, characteristic function, Renyi entropy, conditional moments, and the reliability stress strength model. The distribution function, density function, hazard rate function and reliability of the new model have several interesting properties, which are illustrated in the graphs of each function. The estimation of model parameters is justified using the generalized method of moments, accompanied by a Monte Carlo simulation study to test the suitability of the proposed distribution. A comprehensive numerical simulation was conducted using nine different scenarios and two sample sizes,n=100 and n=500.It was observed that the performance of the estimators, especially for large samples, was less biased and more stable, ranging between -0.432 and 0.573. It also showed a significant decrease in mean square error values, ranging from 0.073 to 1.534, and mean absolute square error values, ranging from 0.298 to 1.654, offering the accuracy and robustness of the proposed model. Confidence intervals for the unknown parameters were calculated, ranging from 1.427 to 4.964. The results revealed in terms of bias, mean estimate, confidence intervals, mean absolute errors, and mean squared errors to demonstrate its future applicability in some real- life situations, especially, in the economic fields.

Decision-making problems are often characterized by complexity and uncertainty, presenting significant challenges for individuals or organizations. Traditional aggregation methods often struggle with the imprecise, inconsistent, and unclear data found in these situations. To address these issues, this study proposes a new multi-criteria decision-making method based on Multi-Valued Rough Neutrosophic Numbers. We first introduce the fundamental operational laws for multi-valued rough neutrosophic numbers, based on t-norm and t-conorm. Subsequently, we establish four new multi-valued rough neutrosophic number-based aggregation operators: the Multi-Valued Rough Neutrosophic Arithmetic Mean Operator, the Weighted Multi-Valued Rough Neutrosophic Arithmetic Mean Operator, the Multi-Valued Rough Neutrosophic Geometric Mean Operator, and the Weighted Multi-Valued Rough Neutrosophic Geometric Mean Operator. We also prove that these proposed aggregation operators satisfy desirable properties, including monotonicity, idempotency, and boundedness. To determine the optimal weights of criteria, we utilize the Shapley fuzzy measure, capturing the significance and contribution of each criterion. Following this, specific score and accuracy functions are developed to facilitate the ranking of alternatives. Finally, a numerical illustration derived from a real-world hospital selection context is presented to demonstrate the effectiveness and practical utility of the proposed approach

Chaotic systems with simple structures and controllable dynamics are highly desirable for practical applications, including secure communications, signal processing, and nonlinear control. However, most existing systems are too complex or lack flexibility in controlling signal amplitude and polarity. In this study, a simple chaotic snap system is considered that has a line of equilibria and is composed of six algebraic terms, with a single quadratic nonlinearity. In some regions of parameter space, a chaotic attractor coexists with a line equilibrium or with a limit cycle of period-2. The basin boundary of a limit cycle and chaotic attractor is smooth. Additionally, in specific regions, two limit cycles can coexist each other. A single quadratic term allows rescaling chaotic signal amplitude, while adding a constant to x controls polarity. The snap system can become self-reproducing via periodic functions, producing a 3D lattice of hidden attractors with identical Lyapunov exponents. Consequently, a chaotic signal of either polarity can be obtained by selecting different initial conditions without requiring an additional polarity converter. These properties allow the implementation of a digital circuit based on STM32 hardware to illustrate the physical feasibility of the proposed system. Finally, to validate the feasibility of the snap system for image encryption a random number generator (RNG) is implemented. The generated RNG successfully passes the National Institute of Standards and Technology (NIST) SP 800-22 statistical tests. Based on these random bits, an image encryption algorithm is developed. Security analysis confirms the system effectively encrypts and decrypts images with high performance.

This paper considers the nonlinear homogeneous fractional Burger’s equation as a type of nonlinear fractional partial differential equations (FPDE). Our goal in this paper is to show that an initial value problem (IVP) can be modified with a second initial condition when (α∈(1,2]) as the velocity of the movement, and the obtained solution agrees with the nature of the wave with space and time for the problem. The Caputo fractional derivative is used in all the fractional derivatives. Also, the algorithm of the Laplace transform decomposition method (LTDM) for fractional PDEs is presented. The approximate solution converges to the exact solution in Theorem 1. Also, a numerical simulation is made to confirm the theoretical results. In addition, the solution is displayed graphically for three values of (α) that belong to the interval (1,2] to study the effects of changing the value of the fractional order derivative on the wave solutions of the time-fractional Burger PDE. The time interval is extended in each graph to check the effect of time on the number and shape of the waves in addition to changing the fractional order. Finally, a comparison of the obtained solutions is made.

The quantity of video footage produced has skyrocketed in the last several years. The enormous increase in video content creates issues with content management. Video and image processing technologies need to receive greater attention to handle the increasing number of videos on the internet and to extract reliable information from them. Video summaries offer concise and streamlined depictions of the content of a video stream. This research aimed to build a methodology for video summarization utilizing the difference in the histogram of video frames using three stages. Frames were elicited from the video, and then SIFT detectors were utilized in the first stage to elicit interest points from each frame. Fuzzy C-means clustering was used in the second stage to collect the interest points. In the third stage, the histograms were made via the number of points for each cluster. The cluster number is represented by the x-axis, and the number of points in each cluster is represented on the y-axis. In order to elicit the summary frames, a value was manually entered, and then a query histogram was built based on these values. The Manhattan metric was used to compare the query histogram with all the histograms constructed in the third step. Experimental results displayed that the mean amount of time for clustering baby video is 3.860 with the proposed method, while the time was 9.670 to cluster the same video using all pixels; also, the precision was 94, the recall was 91, and the F1-score was 93.

Visual Question Answering (VQA) is a machine learning task that aims to create systems capable of answering natural language questions based on given images. Medical VQA systems, a domain-specific application of VQA, assist in understanding clinically relevant information from medical images. These systems leverage deep neural techniques to generate accurate answers to questions, which can be closed-ended or open-ended. This paper proposes a Cross-Attention Mechanism-based Medical VQA system. The proposed Medical VQA system achieves good performance through the integration of three-key components, each addressing critical challenges in medical visual question answering: The biomedical domain-specific pretraining of BioBERT enables extracting powerful contextual features from questions. It is highly effective in handling rare medical terminology, abbreviations and suitable for processing and grammatically complex medical questions compared to generic language models. Denoising Autoencoder model for visual features extraction enables extracting strong visual features and focusing on small object through slicing image into overlapping patches, thereby improving the localization of abnormalities within image. Finally propose a cross-attention mechanism which applied to model the hidden relationship between medical image and question and enhance the fused features vector. The attention mechanism comprises intramodal (within-modality) and intermodal (cross-modality) components, enabling the model to focus on relevant parts of image and question for answer generation. Experiments conducted on the VQA-RAD and Med-VQA 2019 datasets demonstrate that the proposed system achieves good results and accuracies was 76.5% and 78.3%, respectively, outperforming baseline models that use traditional attention mechanisms like the Bilinear attention networks (BAN) or Stacked Attention Networks (SAN).

The K-Nearest Neighbors (KNN) has been proven to be an effective method for addressing classification problems. The performance of the KNN algorithm is heavily dependent on the value of parameter K, which represents the number of nearest neighbors. Choosing an inappropriate value for K can affect the classification accuracy because a smaller chosen K can lead to overfitting and vice versa. So, the appropriate selection of the K value has a significant impact on the performance of KNN. Manually, adjusting the value of K is a very difficult process because the appropriate choices for this value depend on the status of the search. Accordingly, the need to utilize an on-line adjusting technique is still existing. One of the recent algorithms is Dragonfly Algorithm (DA), which solves several combinatorial problems. In this work, the DA is adopted to automatically determine the most appropriate value of K for the KNN algorithm. Additionally, the performance of the proposed model is enhanced via utilizing (PCA) for feature extraction. This integration produces the hybrid algorithm named (KNN-PCA-DA). Recently, diabetic retinopathy (DR), a chronic form of diabetes and the leading cause of blindness. Early and accurate diagnosis of DR is essential for early treatment and prevention of irreversible vision loss. Hence, the performance of the proposed model is evaluated using the Diabetic Retinopathy Debrecen dataset. The obtained experimental results demonstrate that the proposed model is an effective solution for the DR problem, achieving competitive results with an accuracy of 99.47% compared to other models

Visual impairment greatly restricts an individual\\'s ability for perception and interaction with the environment, making everyday activities challenging without the availability of appropriate assistive technologies. One major barrier is the inability to perceive visual scenes, which are essential for spatial awareness, wayfinding, and situational knowledge. This study aims to address this gap through the proposal of an intelligent assistive image captioning system specifically designed for visually impaired individuals. The system employs the best available computer vision and natural language processing techniques in generating context-oriented text captions for an image infused with interactive voice responses. Here, four models of Convolutional Neural Networks, namely InceptionV3, InceptionResNetV2, Xception, and DenseNet201, with LSTM-based decoders, are used each to generate the initial captions. The additional captions were generated using a transformer-based ViT-GPT2 architecture. An ensemble method is used to choose the best caption, which is done using BLEU scores. For audio, Google Text-to-Speech is used, while for real-time voice queries, YOLOv8n is used to detect humans. We have tested our system using the Flickr8k dataset, and the results show that the ensemble method outperforms CNN-LSTM and ViT-GPT2 architectures. To be precise, the ensemble method achieved a BLEU-1 score of 0.7363, a BLEU-4 score of 0.2642, a METEOR score of 0.4545, and a ROUGE-L score of 0.5107. This shows that using multiple models is helpful to obtain better captions, thus increasing interactivity, which is a significant step towards real-time, accessible, and intelligent assistance for visually impaired individuals

Digital image forgery detection has become an urgent and complex problem in an age when powerful editing tools can easily alter photographs. The familiar maxim “a picture is worth a thousand words” can no longer be taken at face value, since even subtle manipulations may conceal or fabricate critical details. Conventional detection methods frequently suffer from highly imbalanced datasets and narrow feature representations that fail to capture the diverse artifacts introduced by modern editing techniques. In response, this work presents a novel framework that casts the forgery detection task as a reinforcement learning problem, enabling an agent to learn a sequence of analysis steps rather than relying on a fixed classification pipeline. This is the first study to deploy a Deep Q‑Network (DQN) agent specifically for image forgery detection. The proposed architecture features a dual‑branch feature extractor: one branch applies Error Level Analysis to reveal compression inconsistencies. At the same time, the other performs fine‑grained noise analysis to detect manipulation traces. These features are supplied to a custom Gym environment designed with balanced sampling to mitigate class imbalance and augmented by a prioritized experience replay mechanism that biases learning toward the agent\\'s most challenging examples. Extensive experiments across multiple datasets, including testing on the latest image manipulation model, Gemini 2.0 Flash, show impressive accuracy rates between 97% and 98%. These results highlight the robustness and efficiency of the proposed approach, offering a promising new direction for digital forensic investigations

Feature reduction techniques are fundamental to enhancing machine learning (ML) algorithms by reducing the number of features in a dataset. The study here explores the impact of Principle Component Analysis (PCA) on ML algorithms within an unbalanced classification framework, in partnership with feature selection techniques like Cluster Variation Attribute Evaluator (CVAE) and Correlation Attribute Evaluator (CAE). In addition, the research introspects a comparison analysis evaluating the effectiveness of several ML methods, including Multilayer Perceptron (MLP), Decision Tree J48, k-Nearest Neighbor (k-NN) and Sequential Minimal Optimization (SMO). The informative analysis of results signifies that the MLP technique with PCA minimized the build time of the model about 50%, in the case of 5 folds’ cross validation, whilst accuracy remained at the same levels. In contrast, the J48 technique clarified a weak response to feature reduction techniques, while CVAE had a negative impact on the performance of all models. Furthermore, applying PCA with SMO promoted diagnostic accuracy from 95.56% to 95.82%. The k-NN approach realized an accuracy increase to 92.42% with PCA, up from 91.12%, and CAE notably improved the model\\'s accuracy. As a substantial point, this research employed the Weighted Average of Precision, Recall, and F-Measure to deliver a comprehensive assessment of model performance on an imbalanced dataset. The nominal-type thyroid dataset was utilized as the case study for this research

The mandible is crucial in orthodontic treatment, forensic identification, and clinical diagnosis. However, manually identifying mandibular landmarks is time-consuming and highly dependent on expert skill, necessitating a more reliable automated prediction method. Previous research has used linear and nonlinear regression methods, in which each model predicts a single landmark point, resulting in inefficiency. In addition, these methods only use the centroid of the binary image of the mandible as input. This research proposes a multi-output neural network model that is able to predict multiple mandibular landmark points simultaneously. The proposed neural network architecture in predicting mandibular landmark points is 11 neurons on the input layer, 22 neurons on the hidden layer, 32 neurons on the hidden layer, and 14 neurons on the output layer. The dataset consists of grayscale panoramic radiographs from the Dental and Oral Hospital, Faculty of Medicine, Universitas Airlangga, with 96 images for training and 6 for testing. Image features, including mean intensity, standard deviation, median, variance, skewness, kurtosis, entropy, contrast, homogeneity, energy, and correlation, were extracted and used as inputs to a multi-output neural network. The model predicted the reference points of the right condyle, left condyle, right coronoid, left coronoid, right gonion, left gonion, and menton. The results showed that the proposed model effectively predicted the reference points of the mandible, with the right condyle showing the highest accuracy. The highest prediction accuracy value, with a Successful Detection Rate (SDR) 12% and the Adam optimizer, was the right condyle point.

The Big data originates from various heterogeneous sources worldwide. Due to the diverse origins of data generation, big data involves a variety of tasks. Some require storage resources, while others need computational resources. Additionally, cloud computing provides centralized storage and computational resources for executing these tasks. However, resource optimization and efficient task scheduling remain challenging. Moreover, accurately categorizing tasks and effectively allocating resources based on their nature pose challenges for cloud computing, especially when assigning tasks to heterogeneous resources to minimize task completion time. To address these issues, this paper proposes a Performance-Aware Task Classifier (PATC) algorithm that classifies tasks and allocates resources accordingly while optimizing QoS parameters. The task classification algorithm divides tasks into two categories: data-intensive and compute-intensive. Threshold coefficients for transmission time and CPU usage are used for task classification. Furthermore, resources are allocated to compute-intensive tasks to minimize their execution time as much as possible. To evaluate system performance, various QoS parameters such as Average Response Time, Total Completion Time, Makespan, and Average Resource Utilization are analyzed. Simulation results show that our approach effectively improves these parameters compared to existing methods such as First Come First Serve (FCFS), Shortest Job First (SJF), Modified SJF, Task Scheduling Strategy (TSS), and IMFO (Improved Moth Flame Optimization).

This research evaluated activated carbon prepared from Lantana camara charred (LCC) and olive stone charred (OSC) for the adsorption of chromium (VI) and manganese (VII) ions from aqueous solutions. Analytical methods such as XRD, SEM, EDX and FTIR demonstrated that the materials have highly porous surfaces. Adsorption tests were conducted using comprehensive statistical physics models for Cr(VI) and Mn(VII) at three temperatures (25,40 and 55 °C) and at two pH levels (3and5), respectively. The adsorption capacity was determined by the Langmuir model, with monolayer adsorption peak values recorded at 55 °C for Cr (VI) and Mn(VII) for each adsorbent –metal pair: LCC/Cr, LCC/Mn, OSC/Cr and OSC/Mn. Preliminary results revealed a high adsorption capacity (11.111 mg/g) for Mn(VII) on OSC, while the desorption capacity was (1.111 mg/g), suggesting the presence of multiple active sites and adsorption mechanisms, Whilst the kinetic model of pseudo-second-order suggests chemisorption, it is important to note that chemisorption includes stronger interactions than physical adsorption, involving covalent bonds, exchange of ions, or complex formation. Nevertheless, the obscurity of evidence of the production of a chemical bond in our study suggests that adsorption may involve a combination of physical and chemical adsorption mechanisms. This dual nature is common in processes of adsorption, where an initial physical adsorption is followed by slower chemisorption. Thermodynamic analyses indicated that the adsorption process is endothermic, spontaneous, and driven by chemisorption, influenced by chemical and physical interactions. The results show that LCC and OSC are effective in removing Cr(VI) and Mn(VII) from aqueous solutions

This research aims to develop an ion Pb2+ sensor and determine the optimal conditions for testing Pb2+ ions using an MnO2/PGE modified electrode with the voltammetric method. In this study, the Pencil Graphite Electrode (PGE) was modified using MnO2 through drop-casting. Next, the optimization of the electrode was carried out by varying the supporting electrolyte type, voltammetric technique, and supporting electrolyte concentration. The results showed that the modified electrode (MnO₂/PGE) provided a better response for Pb²⁺ detection compared to the unmodified electrode (PGE). The optimal experimental conditions were found to be 0.1 M HCl as a supporting electrolyte, combined with the Square Wave Voltammetry (SWV) technique. The measurement of Pb2+ ions using MnO2/PGE under optimum conditions yielded a correlation coefficient (R2) of 0.9846 and a detection limit of 1.91 ppm, with an RSD value of ± 2.51%, indicating high sensitivity, selectivity, stability, and accuracy in the measurement of Pb2+ metal ions

The objective of the current study was to evaluate the potential of luteolin to mitigate the neurotoxic effects of OXL. Oxidative injury, inflammation, and mitochondrial dysfunction are the cardinal factors in OXL-triggered neuropathy. LUT modulates sensory and motor behavioral changes induced by OXL. Notably, LUT treatment significantly (p≤0.05) increased antioxidant activity by increasing paraoxonase-1 (PON-1) and heme oxygenase-1 (HO-1) levels, attenuating oxidative damage. Additionally, LUT inhibited neuroinflammation by repressing the levels of toll-like receptor-4 (TLR4) and intracellular adhesion molecule-1 (ICAM-1), while significantly increasing brain-derived neurotrophic factor (BDNF) and downregulating the relative gene expression level of mitogen-activated protein kinase 14 (MAPK-14). Moreover, LUT significantly reduced neuronal apoptosis (Caspase3; cysteine-aspartate-specific protease 3) and attenuated glial activation, while also restoring mitochondrial function. Furthermore, nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity in brain and sciatic nerve tissue, as well as glial fibrillary acidic protein (GFAP) immunohistochemical expression, were both markedly reduced by LUT. LUT protected neuronal structures from OXL-induced neuronal injury, as shown in histopathological examination. Lastly, both in vivo and in silico molecular docking findings confirmed the anti-inflammatory and neuroprotective effects of LUT, linked to MAPK-14 and GFAP. This study demonstrates that LUT provides a promising therapeutic effect in palliating OXL morbidity by targeting glial cells for neuropathy relief

In the present work, an environmentally friendly nano-catalyst (γ Fe2O3RHA@SO3H) was used for the preparation of 1,3-thiazolidin-4-ones analogs (T1-T9) from Schiff bases (S1-S9) by microwave irradiation. The recycled catalyst (γFe2O3RHA@SO3H) was produced from silica derived from rice husk, which was first modified with ferrite nanoparticles (Fe2O3), then with sulfuric acid. The characterization of the (γ Fe2O3RHA@SO3H)) nanocomposite and its characteristics were achieved using various techniques including, XRD, total acidity and basicity utilizing CO2/NH3-TPD, textural properties through Brunauer-Emmett-Teller (BET), functional group determination by FTIR; surface morphology through FESEM and TEM, magnetic properties by a vibrating sample magnetometer (VSM), and thermal stability via TGA. The VSM data demonstrated that the superparamagnetic catalyst (γFe2O3RHA@SO3H) can be simply separated and recovered after reaction completion employing an external magnet. The attained data revealed that the optimum reaction parameters of the catalyst γFe2O3RHA@SO3H for producing (S1-S9) were 5% (the catalyst amount), ethanol (the reaction solvent), 1h (the reaction time), and 70 °C (the reaction temperature) with a maximal yield up to 94%. The reusability study and reactivation results disclosed that the nano-bifunctional magnetic catalyst (γ Fe2O3RHA@SO3H) can preserve a high catalytic activity after at least three reuses. The findings also indicated that (γ Fe2O3RHA@SO3H) has superior magnetic characteristics, rendering it to be used as a heterogeneous catalyst in creation of 1,3-thiazolidin-4-one derivatives with high proficiency under simple reaction circumstances

Hepatocellular carcinoma (HCC) is one of the most common and aggressive subtypes of liver cancer. It has a poor prognosis, a high rate of recurrence and shows limited response to current therapies. In order to combat this condition, the use of natural products as multitarget anticancer agents is gaining momentum. In this study, we investigated the medicinal value of bisbenzylisoquinoline (BBI) alkaloids from the Stephania genus via an integrative in silico approach. Due to their improved pharmacokinetic profiles and expected low toxicity, we chose 11 BBI derivatives. Network pharmacology analysis revealed 334 overlapped targets between the compounds and HCC. Functional enrichment indicated that the PI3K-Akt signaling pathway was essential in HCC progression. In addition, protein-protein interaction (PPI) network construction and topological analysis identified AKT1 and PI3K as hub proteins. Molecular docking simulations found that isotrilobine had the highest binding affinity towards both AKT1 (−10.5 kcal/mol) and PI3K (−9.9 kcal/mol), compared to that of the reference drug sorafenib. These findings indicated a possible involvement of BBI compounds in modulating key oncogenic pathways relevant to tumor growth and metastasis. Taken together, this study unveils isotrilobine as a promising lead compound and provides a mechanistic rationale for the further exploration of Stephania-derived BBI alkaloids as potential HCC therapeutics.

Background: Colorectal cancer, the most common gastrointestinal cancer, is a significant health issue globally. Mucin 16 plays a critical role in cancer signal transduction pathways and is a potential glycoprotein target for cancer therapy. The miRNA-200 family also regulates the expression of numerous genes that play vital roles in cancer cells. This study aimed to investigate the changes in mucin 16 and miRNA-200a in patients with colorectal cancer (CRC). Subjects and Methods: Fifty-six patients with CRC, including 26 in stage 3 and 30 in stage 4, were included in this study, along with 38 healthy volunteers as a control group. Parameters such as mucin 16, miRNA-200a, total protein, albumin, globulin, and the albumin/globulin ratio were estimated in serum samples from participants. Results: The results indicated that mucin 16 concentration and miRNA-200a levels were significantly elevated (P ≤ 0.05) in the stage 4 group compared to the stage 3 and control groups. A significant increase was also observed in stage 3 compared to the control group for these two parameters. The concentrations of total protein, globulin, albumin, and the albumin/globulin ratio exhibited no significant differences between patients with stage 3 or stage 4 and the control group. The receiver operating characteristic analysis demonstrated that mucin 16 is a good diagnostic marker for CRC in late stages. Conclusions: These results identify mucin 16 as a promising marker for CRC diagnosis.

This study focuses on the synthesis, structural analysis and antibacterial properties of a composite comprising hydroxyapatite (HAp), polyethylene glycol (PEG) and chitosan (CTS). CTS was extracted from shrimp shells, and CaO for Hap was obtained from pensi shell. These composites were synthesized using the sol-gel method in situ by varying the concentrations of PEG (5, 10, 15, 20 and 25%) and CTS (1, 2, 3, 4, and 5%). The synthesized composites were characterized using FTIR, XRD, SEM-EDX, and TGA-DTA analyses, and their antimicrobial activities were also evaluated. The HAp-PEG-Chitosan composite with 5% chitosan and 10% PEG was identified as the best product as a white powder. FTIR analysis revealed the presence of specific functional groups: PO4³⁻ and OH from HAp, N-H from chitosan, and C-H and C-C from PEG, confirming the successful formation of the composite. XRD analysis showed a hexagonal structure consistent with the hydroxyapatite diffraction standard (ICSD #157481) and a reduction in crystallinity due to polymer addition. SEM analysis demonstrated that needle-like HAp derived from pensi shells underwent surface changes, resulting in a more uniform texture when combined with PEG. EDX analysis confirmed the presence of Ca, P, C, and O elements, validating the successful synthesis of the HAp-polymer composite. TG-DTA analysis showed weight loss below 200°C, indicating the thermal stability of PEG at this temperature. The antimicrobial activity of the HAp-PEG-Chitosan composite was tested against E. coli and S. aureus, with CIP antibiotics used as a positive control for comparison

Pumice is one of the raw materials that is widely available, especially in the “Middle East region and Turkey”. In this research, an ion exchanger was prepared from Syrian pumice by converting it into a low-silica ion exchanger (PNa) for the removal of Pb²⁺ ions from aqueous solutions, by several chemical and hydrothermal methods with NaOH. The prepared exchanger was also characterized using a number of distinctive techniques in this paper such as (XRD, FT-IR, BET, DTA) which demonstrated radical changes in structure after modification, the most important of which are: The surface area increased approximately 18-fold from (5 to 93) m2/g, along with an increase in relative density and a significant improvement in exchange-adsorption capacities. Maximum Pb²⁺ uptake was 4.3 mmol/g (308.15 K). Removal efficiency was 100% at low concentrations and >80% at medium/high concentrations. The adsorption kinetics followed a pseudo-first-order model, and the equilibrium data fit well to Langmuir, Freundlich, and Redlich-Peterson isotherms (R² > 0.99).

In this work, the preliminary phytochemical screening of the compounds found in the leaf extracts of Kitaibelia balansae Boiss. plant, obtained from solvents of different polarities, was investigated by LC/ESI/QTOF in the negative ion mode, and their biological activity on fungal species was studied. More than sixty compounds were observed, most of which were recognized and identified as flavonoids of all classes. Acids such as shikimic, chlorogenic, jasmonic, and its ester, methyl jasmonate, were also detected. The activity of these extracts on fungi was investigated and the results showed a dual biological effect on fungi Fusarium solani and F. equiseti, while inhibiting F. redolens and Beauveria bassiana. MIC values were also determined. However, to date, no detailed research has been conducted on the molecular constituents of the leaves of K. balansae and their antifungal activity

This study aimed to identify and classify airborne bacteria in outdoor air within the campus of the University of Baghdad, Iraq, using the passive sampling technique. Air samples were collected from ten sites and analyzed by the 16S rRNA gene sequencing technique. The results revealed significant seasonal differences in both microbial and particulate matter concentrations. Bacterial concentrations were considerably higher during the wet season, with 3238.6 CFU/m³ compared to the dry season, with 1872.6 CFU/m³. PM2.5 levels averaged 47.58 µg/m³ in the wet season and declined to 11.33 µg/m³ in the dry season, while PM10 concentrations reached up to 52.14 µg/m³ in the wet season and 10.45 µg/m³ in the dry season. The results of the basic local alignment tool (BLAST) showed that the 16S rRNA gene sequences of the isolates were related to (Enterobacter ludwigii, Moraxella osloensis, Peribacillus simplex, Neobacillus drentensis, Priestia aryabhattai, Priestia endophytica, Priestia megaterium, Exiguobacterium mexicanum, Chryseomicrobium amylolyticum, Exiguobacterium sp. and Arthrobacter luteolus). One isolate was only identified at the family level as Enterobacteriaceae. The significant findings were the identification of two pathogenic species, Enterobacter ludwigii and Moraxella osloensis, which are opportunistic bacteria that may negatively affect public health. This study provides a detailed database of airborne bacteria in the study area within the university, which may contribute to the implementation of effective interventions that reduce risks and enhance environmental safety. Nonetheless, studies that examine identification, characteristics and distribution of airborne bacteria in campuses are still rare.

Individuals with type 2 diabetes are particularly susceptible to frequent and uncontrolled dental caries. Streptococcus mutans, a main cause of dental caries, impacts millions each year and presents significant oral health challenges. Although extensively studied, the bacterium's pathogenesis remains incompletely understood, underscoring the need for further research.

This study aimed to isolate and analyze the molecular characteristics of Streptococcus mutans in patients with type 2 diabetes. It explored the bacterium's capacity to infect, survive, and endure acidic oral environments. Samples were taken from 50 individuals with both type 2 diabetes and dental caries, 50 with dental plaque, and 20 healthy donors. Bacterial identification used Gram staining and 16S rRNA primer-based analysis with primers designed via Geneious Prime software. Virulence factors dexA, ldh, and atpD were also assessed.

Thirty Streptococcus mutans samples were found, with 17 from individuals with both diabetes and caries, and 13 from those with caries alone. All isolates carried the dexA gene, 50% had the atpD gene, and 30% had the ldh gene. Four novel isolates from Baghdad, Iraq, were registered as PQ796766, PQ796767, PQ796768, and PQ796769. These findings reveal that Streptococcus mutans exhibits significant virulence and genetic diversity, with identified virulence genes providing insight into its pathogenic mechanisms and suggesting potential strategies for preventing and treating dental caries in diabetic individuals.

Vaginal candidiasis is an opportunistic fungal infection of the lower female reproductive tract caused by Candida species. This study aimed to investigate the roles of Enolase 1 and Interleukin-9 in the pathophysiology of vaginal candidiasis and examine their correlations with anthropometric and lipid profile parameters. A total of 120 participants were enrolled, including 80 patients with VC and 40 healthy controls. Patients were classified into Candida albicans and non-Candida albicans groups based on clinical and microbiological criteria. Serum and vaginal secretions levels of ENO-1 and IL-9 were measured using ELISA, while lipid profiles were assessed spectrophotometrically. Both ENO-1 and IL-9 levels were significantly higher in the serum and secretions of infected individuals compared to controls. A significant positive correlation was observed between serum IL-9 and ENO-1 in secretions within the C. albicans group. The combination of ENO-1 and IL-9 in vaginal secretions showed excellent diagnostic performance in distinguishing infected individuals from healthy ones, with AUC values approaching 1.00 in both the C. albicans and non-C. albicans groups. These findings support the potential of ENO-1 and IL-9 as dual biomarkers for the detection and monitoring of vaginal candidiasis, offering promising tools for improving diagnosis and guiding personalized treatment strategies

Mitochondrial DNA (mtDNA) is a commonly utilized genetic marker in determining maternal ancestry because of the hypervariable regions in it, as well as haplogroups, which enable scientists to establish ancestral links among populations. This research involved the analysis of the complete mitochondrial genome of 100 Faili Kurd women in different cities in Iraq using the Illumina system. The most common haplogroups were HV14 (53%), H14a (16%), M37e2 (16%), and HV2a1*1 (15%). The pair-wise AMOVA-FST analysis in terms of population revealed that there were various levels of genetic differentiation between Faili Kurds and other populations in Iraq, Iran, and surrounding areas. The Qashqai population recorded the lowest genetic distance (FST =0.2203), implying that this population is closely related in genetic terms. There was also low differentiation between Nassiriya-Iraq (FST =0.1826), Assyrians (FST =0.1764), Khuzestan (FST = 0.1623) and Tabriz (FST =0.1590), which could represent shared ancestry or recent migration. Conversely, the higher FST values were found with Damascus, Syria (0.999) and Birjand, South Khorasan, Iran (0.999), whereas moderate differentiation was found with Hama, Syria (0.8381), Armenians (0.6973) and Ilam (0.6743). Such results underscore the regional affiliation and maternal genetic patterns among the Faili Kurds, supporting their long-standing presence in the Near East, and contribute to the broader body of human genetic diversity in the Middle East.