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Theoretical analysis of nuclear radius measurement using nuclear structure models and Figuretechnology-enhanced computational approaches
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The accurate determination of nuclear radius is fundamental to understanding nuclear structure and interactions. The present study conducts a comprehensive theoretical analysis of nuclear radius measurements using various nuclear structure models, including the empirical mass-number scaling model, the Hartree-Fock approach, and the relativistic mean-field (RMF) theory. These models are systematically compared against experimental nuclear radii to evaluate their predictive accuracy and assess their strengths and limitations. The study also incorporates an uncertainty analysis to quantify the reliability of theoretical predictions, employing Monte Carlo simulations and Bayesian inference techniques to refine estimations. The results reveal that while empirical models provide reasonable approximations, they lack the precision required for heavy nuclei due to the omission of interaction effects. The Hartree-Fock and RMF models incorporate nucleon-nucleon interactions and relativistic corrections, improving predictive performance, yet systematic deviations persist, particularly in neutron-rich nuclei. Comparisons with recent studies highlight the growing role of machine learning techniques in refining nuclear radius predictions, reducing uncertainty margins, and improving model accuracy. The study emphasizes the necessity for hybrid methodologies integrating empirical models, quantum mechanical calculations, and advanced computational techniques to enhance nuclear radius predictions. In addition, Figuretechnology-inspired computational techniques, including Figurescale modeling and machine learning algorithms, offer enhanced predictive capabilities by capturing complex nuclear interactions at finer scales and reducing uncertainty in nuclear radius estimation.

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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Enhanced Photocatalytic Activity of Cu2O/ZnO/GO Nanocomposites on the Methylene Blue Degradation
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This study synthesized nanocomposite photocatalyst materials from a mixture of Cu2O nanoparticles, ZnO nanoparticles, and graphene oxide (GO) through coprecipitation and hydrothermal methods. This study aims to determine the optimum composition of Cu2O/ZnO/GO nanocomposites in degrading methylene blue. The nanocomposite was synthesized in two steps:  1  the synthesis of Cu2O and ZnO nanoparticles through the coprecipitation method and the preparation of GO through the modified Hummer method.  2  The preparation of Cu2O and ZnO nanoparticles mixtures with GO through the hydrothermal method to form Cu2O/ZnO/GO nanocomposites. The adsorption-photocatalysis process of methylene blue

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Publication Date
Tue Jun 15 2021
Journal Name
Al-academy
Consistency and Consistency in Contemporary Iraqi Painting - Selected Models-: حسين شاكر قاسم العيداني
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  The tagged research is concerned with observation and investigating the concepts of consistency and harmony in contemporary Iraqi painting (selected models) in order to reveal the mechanisms and rules of these two concepts in the artistic field and their mechanisms of operation. How reflected tools Consistency and harmony in contemporary Iraqi painting? What is consistency and what are its mechanisms and principles? Is consistency a unit product quality? Are there similarities between consistency and harmony? What is harmony and its principles and rules? As for the second chapter, it included two topics that dealt with the first topic - consistency and harmony between concept and significance, while the second topic meant - histor

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Publication Date
Thu Jan 07 2016
Journal Name
International Journal Of Innovative Research In Science, Engineering And Technology
Effect Of thickness On The Structure And Electrical Conductivity Properties Of CuInSe2 Thin Films
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The influence of different thickness (500,750, and 1000) nm on the structure properties electrical conductivity and hall effect measurements have been investigated on the films of copper indium selenide CuInSe2 (CIS) the films were prepared by thermal evaporation technique on glass substrates at RT from compound alloy. The XRD pattern show that the film have poly crystalline structure a, the grain size increasing with as a function the thickness. Electrical conductivity (σ), the activation energies (Ea1,Ea2), hall mobility and the carrier concentration are investigated as function of thickness. All films contain two types of transport mechanisms of free carriers increase films thickness. The electrical conductivity increase with thickness

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Publication Date
Tue Jan 08 2019
Journal Name
Iraqi Journal Of Physics
Effect of thickness on the structure, morphology and A.C conductivity of Bi2S3 thin films
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Thin films samples of Bismuth sulfide Bi2S3 had deposited on
glass substrate using thermal evaporation method by chemical
method under vacuum of 10-5 Toor. XRD and AFM were used to
check the structure and morphology of the Bi2S3 thin films. The
results showed that the films with law thickness <700 nm were free
from any diffraction peaks refer to amorphous structure while films
with thickness≥700 nm was polycrystalline. The roughness decreases
while average grain size increases with the increase of thickness. The
A.C conductivity as function of frequency had studied in the
frequency range (50 to 5x106 Hz). The dielectric constant,
polarizability showed significant dependence upon the variation of
thic

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
The use of ARIMA, LSTM and GRU models in time series hybridization with practical application
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The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h

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Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Physics
Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration
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         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmosp

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Publication Date
Wed Mar 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
The use of ARIMA, ANN and SVR models in time series hybridization with practical application
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Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti

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Publication Date
Fri Jun 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Dumping and Its Challenges in Extent of World Trade Organization "Models for selected Agricultural Goods"
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The subject of dumping is considering today one of the subjects in which form an obstruction arise in front of the cycle of growth for some countries , such as the study of dumping is capturing a large attention by the competent because either a big  role and effect in growing the economies of nations then the subject of dumping became a field turn around its sides many measures and laws … and may be done resorting to by many states of the world to anti-dumping as approach of determent weapon delimit  the impact of dumping and gives the national agriculture sector the opportunity for rising and growing so this section of international economics is capturing a special importance and represent in same time an important

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Publication Date
Fri Jul 01 2011
Journal Name
Saudi Pharmaceutical Journal
Free radical scavenging activity of silibinin in nitrite-induced hemoglobin oxidation and membrane fragility models
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Publication Date
Tue Jun 23 2026
Journal Name
Journal Of Umm Al-qura University For Medical Science
Performance variation and implementation barriers of large language models in clinical healthcare: a systematic review
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Large language models (LLMs) are a rapidly evolving class of artificial intelligence with significant potential in clinical healthcare. Despite accelerating adoption, rigorous systematic evidence on clinical utility, patient safety, and implementation feasibility remains fragmented. To systematically review LLM applications across clinical domains, evaluate performance with appropriate contextual caveats, characterize implementation barriers, and identify ethical and regulatory considerations. Scientific databases were searched from January 2020 to January 2025. Studies evaluating transformer-based LLMs (≥10M parameters) in clinical settings were eligible. Data were independently double-extracted; quality was assessed using QUADAS-2, RE-A

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