Green synthesis of nanoparticles (NPs) has gained attention as an eco-friendly alternative to conventional methods, reducing toxicity to the environment and living organisms. This study aims to synthesize a novel nanocomposite (Mag-Chi-Cur-AgNPs) composed of silver NPs (AgNPs) prepared using curcumin (Cur) as a reducing and stabilizing agent, along with chitosan (Chit) and magnetic iron oxide (Mag). Cur-AgNPs were synthesized by dissolving Cur in dimethyl sulfoxide and deionized water, followed by the controlled addition of AgNO3. The solution was stirred, pH-adjusted, ultrasonicated, and incubated. Chit-Cur-Ag nanocomposite was formed by dissolving nanochitosan in acetic acid, mixing it with Cur-AgNPs, then stirring, washing, centrifuging, and drying. Finally, Mag-Chit-Cur-AgNPs were obtained by coating Chit-Cur-AgNPs with magnetic iron oxide. Characterization techniques confirmed the formation of the nanocomposite. The ultraviolet-visible spectrum is used to track synthesis stages and successful formation. Fourier transform infrared spectroscopy is used to identify bond peaks, verifying molecular changes, in the field. Emission scanning electron microscope analysis measured the nanocomposite size, with particles below 100 nm. X-ray diffraction is used to determine the crystalline structure and dimensions of each component. Magnetic properties were analyzed using a vibrating sample magnetometer, demonstrating high magnetic saturation. This study successfully synthesized and characterized Mag-Chi-Cur-AgNPs using green chemistry. The results confirmed the formation and stability of the nanocomposite, showcasing its potential for future biomedical and technological applications

Community structures are fundamental in understanding the structure and functionality of complex networks. Different optimization algorithms, including both single-objective and multi-objective approaches, have been employed to address the challenge of community detection. Recently, multi-objective evolutionary algorithms (MOEAs) have attracted many researchers to identify communities in static networks. Many algorithms have been proposed to find a solution that achieves a trade-off between exploring new areas of the solution space and improving the quality of existing solutions. In this trade-off is crucial; whereas exploitation improves existing solutions, it may fail to find better solutions from insufficiently explored regions of the solution space. Therefore, mutation in evolutionary algorithms greatly impacts community detection within social networks. Conventional mutation methods usually tend to apply too much randomness, which results in convergence being less precise about finding a suitable optimum solution. This paper introduces a new mutation called community strength enhancement (CSE) to enhance the search efficiency of the Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D) and speed up the convergence of the suggested algorithm. Moreover, the proposed algorithm overcomes the limitations of traditional MOEA/D by accurately and effectively identifying communities across a wide range of social networks. The enhanced algorithm was evaluated on two groups of datasets (twenty synthetic and four real-world) using normalized mutual information (NMI) and modularity (Q) across five baseline models. Integrating the CSE mutation strategy led to significant improvements in performance, particularly under high mixing parameters and in large-scale networks, as evidenced by increased NMI and modularity scores

Our work introduces the notion of -open set. This class of - open sets is not directly comparable to the categories of soft open, -closed. We demonstrate that the category of -open sets lies strictly between the class of -open sets and -open sets. Additionally, we investigate the connections that exist between -open sets along with other kinds of soft sets. Moreover, we provide several criteria that are sufficient for determining the equivalence between -open sets and every one of -open sets and -open sets. Also, according to our findings, the family of -open sets is a supra soft topology. Furthermore, we make it clear the correlation that exists between the respective categories of -open sets in a soft topological space and in its soft topological subspace. Finally, the class of -continuous function is introduced, it is main properties are studied and derive some of the properties of these soft functions under the soft composition of soft functions.

In trendy farming, the use of Nano fertilizers is raising the productivity and strength of different crops by reducing the destructive effects of environmental changes. The current study is the first in Iraq to address anatomical characteristics and the Nanoscale effect on them. In the current study, the effect of Nano zinc oxide on the anatomy features of soybeans was tested. Concentrations of Nano zinc oxide were 0.75 and compared with control plants without spray. after doing the sections and examination, the results showed that the effect of Nano zinc oxide on the vascular bundle in the stem and midrib of leaf, also the length and width of stomata in leaf epidermis compared with control, as the stems of plants sprayed with Nano zinc were in an primary growth stage compared to the control treatment that were in secondary growth. Also, the diameter of bundles in control reached 297.3µ compared with the diameter of bundles treated with Nano zinc, which reached 116.2µ. On the other hand, the diameter of bundles in the midrib reached 135.7µ in the control and 148.1µ in the Nano zinc treatment, so the length and width of the stomata increased in the Nano zinc treatment.

In this work, the Nickel and zinc Ferrite Ni0.5Zn0.5 Fe2O4 +XCeO2 in purity and purity structure were prepared at multi–concentration X (0.0,0.05,0.10,0.15, and 0.20) using the solid state interaction method at a temperature (1000 °C) for four hours in air. The XR-diffraction shows that the prepared powder samples have a spinal structure at single cubic phase. The lattice parameters were decreasing from 8.331 to 8.3021 with increasing concentration of cerium's Ionic Ce4+, also the crystal volume for purity samples is larger than in purity samples, decreasing from (578.217 to 571.993) A°3 with increasing concentration. However, the real and imaginary dielectric constants were measured for both pure and in purity samples by Ce4+. The dielectric constant shows that up to down decreases with increasing frequency. However, the x-ray density was increasing from 6.887 g/cm3 to 6.963 g/cm3 with increasing concentration. The conductivity of alternating current for preparing the sample increased firstly with frequency and remained constant, then a constant behavior was observed at higher frequencies. The microscopic pharmaology structure using the tunneling electron microscopy, that which particles have a spherical structure.

In this study report, Cu-doped ferrite nanoparticles (CuxNi1-xFe2O4), which are normally the size of less than 100 nanometers were achieved using the sol-gel method. This method consists of using citric acid as fuel, which leads to auto combustion. In this report, the samples mentioned above will be analyzed using the following techniques (XRD, FESEM, EDX), where X-ray diffraction indicated that the specimens consist of spinel phase with face-centered cubic (FCC) structure. The results showed the lattice constant of the material increased from (8.320) Å to (8.323) Å, which indicated fact that the ionic radius of copper (72) Å has a bigger size than the ionic radius of nickel (69) Å which led to an increase in the lattice constant, the crystal size was measured using Scherrer method with the three Williamson Hall models. First, the Williamson Hall Uniform Deformation Model (UDM), second Williamson Hall Uniform Stress Model (USDM), and third Williamson Hall Uniform Deformation Energy Density Model (UDEDM). All these models adapted to define the characteristics related to stress (σ), internal tendance (ε), and deformation energy density (U). The calculated size was compared by the two methods where the crystal size ranged from (19 - 55) nm, and the phenomenon of crystal expansion due to positive stress was observed through the positive slop that appeared in the Williamson Hall models The sample taken was examined further with it morphology character using the method known field emission scanning electron microscopy This has confirmed that the particle shape to be of spherical or sub-spherical, and the size of particle to be within the nano range. The energy-dispersive X-ray spectroscopy indicated that no other new elements resulted from this compound

A time series is a sequence of observations recorded at regular intervals. Time series analysis has applications in diverse fields such as finance, stock prices, economics, environmental science, and social network data analysis. The recorded series is used to represent a measurable quantity or attribute, such as temperature readings, economic indicators, or other variables, depending on the context of the analysis. The idea of time series analysis is to identify patterns, trends, or underlying structures within the data, as well as to make predictions or forecasts about future values based on previous observations. Autoregressive (AR) models are widely used in modeling and forecasting data from time series. This work focuses on AR model parameter estimation, emphasizing the significance of the likelihood function by defining the marginal distribution of the AR process, which is getting by representing the AR process with random shocks and assuming the error terms in a time series have a normal distribution with a zero mean and variance . Some of the simulated experiments are designed to fit the model for different model orders and sample size to find model parameter estimation by likelihood function with marginal distribution. The results of Mean Squares Errors (MSE) and Mean Percentage Errors (MPE) indicate the significance and robust estimation of the AR –models parameters estimators that are computed theoretically.

When OCR systems are utilized to recognize mathematical expressions in scanned documents, they encounter numerous challenges. These challenges arise because the mathematical expressions use an extensive array of symbols, variables, numbers, and operations, each with its distinct writing style. Moreover, mathematical expressions have a hierarchical relationship and adhere to logical rules governing the grouping of their components. This paper presents a three-step approach segmentation, symbol recognition, and interpretation to overcome these issues. The segmentation process aims to identify and separate each symbol based on its spatial location. Then, various features are extracted to describe the horizontal and vertical projections of the symbol, enabling effective recognition. Two neural network-based classification methods were proposed for the recognition of the symbols. The first one achieved 96.6% recognition rates, while the second achieved 97.7%. Lastly, the paper introduces three guidelines for interpreting the mathematical meaning of the expression and accurately converting it into textual form. The study demonstrates the potential of these methods in enhancing the capabilities of OCR systems for recognizing mathematical expressions.

Type 2 diabetic mellitus (T2DM) is growing rapidly around the world, due in large part to significant changes in the modern human lifestyle. T2DM and its complications are affected by a complex interplay of genetic, life course, ecological, cultural, socioeconomic, cognitive-psychological-behavioral, and access to care factors that influence regional variations in disease burden and care quality by gender. This study aims to investigate the role of laminin subunit alpha 2 (LAMA2), mixed lineage leukemia 4 (MLL4), plexin domain containing 2 (PLXDC2), and Protein Z (PORZ) as potential biomarkers for T2DM at different durations of the disease. These biomarkers were assessed using an enzyme-linked immunosorbent assay. While biochemical parameters (FSG) and lipid profiles were measured spectrophotometrically. HbA1C using HPLC. The volunteer individuals were subdivided into five groups: group I: DM with duration of disease less than 1 year, group II: DM with duration of disease from 1-5 years, group III: DM with duration of disease from 5-10 years, group IV: DM with duration of disease more than 10 years, group V: healthy control. Results showed a significant difference among the studied groups in LAMA2 and PLXDC2. In contrast, no significant difference was observed in MLL4 and POR Z. It has been concluded that LAMA2 and PLXDC2 are involved in the duration of the disease

This paper presents the formulation and investigation for a stage-structured prey-predator system. We consider the stage- structure in both prey and predator populations, specifically dividing the population of prey into two distinct groups: immature prey and mature prey. We also divide the predator population into immature and mature groups. We assume that only immature predators are capable of attack, so they consume each immature and mature prey. Additionally, the rate of growth for immature prey based on the amount of mature prey, as immature prey does not have reproductive capability. We applied Holling Type I and Holling Type IV response functions to describe the consumption of immature and mature prey by immature predators, respectively. We conducted a mathematical analysis: boundedness of the solution, the presence of equilibrium points, and both local and global stability of the proposed system with respect to these equilibrium points. We also performed numerical simulations to verify the theoretical results