Univesity of Thi-Qar Journal

The paper presents the Laplace homotopy analysis method (LHAM) as an efficient
and robust approach to solving systems of fractional integral differential equations
(FIDE). The combination of the Laplace transform and the homotopy analysis
method (HAM) solves the convergence and computational problems that often occur
when solving fractional systems. By transforming differential equations into
algebraic ones, LHAM increases the ease of working with partial derivatives while
maintaining flexibility and stability using auxiliary parameters. The paper
demonstrates the effectiveness of LHAM through examples, comparing its
performance with established methods such as the Adomian Decomposition Method
(ADM) and the Variational Iteration Method (VIM), showing its superior accuracy
and efficiency.

This study aims to analyze the impact of interest rates on the volume of bank deposits
in public and private banks in Iraq during the period 2010–2023. The analysis relies
on annual data derived from the reports of the Central Bank of Iraq, covering
variables such as interest rates and the average volume of deposits in both public
banks (Al-Rafidain, Al-Rasheed, and the Trade Bank of Iraq - TBI) and private banks
(Byblos, Gulf Bank, Iraqi National Bank, and International Commercial Bank). The
findings indicate a weak positive correlation between interest rates and deposit
volume in public banks, whereas the relationship is notably stronger in private banks.
Simple linear regression models were employed to examine the quantitative
relationship between the variables, providing an analytical framework for
understanding depositor behavior and their interaction with monetary policy tools.
The study concludes with a set of recommendations aimed at enhancing the
effectiveness of monetary policy instruments and improving the efficiency of the
banking sector in attracting deposits.

Content-Based Image Retrieval (CBIR) plays an important role in computer vision by allowing users
to find images using their content rather than by looking at metadata. Although there have been
advances, CBIR has issues in getting high accuracy, making its results understandable and being
generally useful in situations like scarce labeled data or complex semantics. Therefore, this paper
proposes a unified and strong CBIR approach which uses transfer learning, explainable artificial
intelligence and zero-shot retrieval. To be precise, the system connects six advanced pretrained
convolutional neural networks (CNNs) such as MobileNet, MobileNetV2, DenseNet121, VGG16,
NASNetMobile and Xception to enable the extraction of important picture features. It was Xception that
gave the highest performance, reporting a test accuracy of 98.99% on the COREL-1000 dataset. To
make the model’s decisions understandable, Grad-CAM helps us show where in the image the model
had its highest attention. The framework also uses the CLIP model which allows users to find images by
asking in natural language, even without having labeled training data. It is shown to pull up semantically
useful pictures and deliver easy and adaptable ways to use the system. The results show that combining
explainable and zero-shot approaches with deep learning leads to a reliable CBIR solution that is better
than previous systems in performance and accuracy. For this reason, the framework is ideal for use in
different areas such as e-commerce, medical analysis and smart online media searches

The present study highlights the influence of using analytical procedures through the application
of regression analysis as an analytical procedure in predicting the rate of return on banking
assets. This approach is applied to the financial statements of Mosul Bank for Development and
Investment to obtain reasonable assurance as to whether the rate of return on assets, based on the
data contained in the financial statements, will continue in the near future. The results of the
analysis demonstrate that the expected rate of return on assets for 2024 is (1.21%), compared to
the years of the study sample. This indicates that Mosul Bank for Development and Investment
does not possess a good rate of return on assets, enabling it to confront emergency and
unexpected situations resulting from future economic changes and events. Furthermore, the
phenomenon of financial failure is one of the most important phenomena requiring careful
consideration and investigation into its causes, as its consequences are now reflected on both
internal and external stakeholders. To obtain this objective, the regression model was applied to
Mosul Bank for Development and Investment, a sample of Iraqi banks listed on the Iraq Stock
Exchange, to determine the bank's ability to confront any
risks or difficulties that may arise as a result of fluctuations in the Iraqi economy.
The study recommends enhancing the use of quantitative analytical procedures in accounting and
auditing practices, with a focus on developing the skills of analysts and auditors in utilizing
analytical statistical tools, such as regression analysis, to provide more accurate and objective
assessments of financial performance. It also emphasizes the importance of integrating these methods into accounting and control systems to improve the effectiveness of financial
forecasting and strengthen the response to potential risks

This research aims to investigate the impact of digital transformation on the
accounting profession and accounting education by analyzing its implications for
accountants’ performance efficiency and the ability of educational curricula to keep
pace with technological advancements, as well as identifying the key challenges
hindering the success of this process. The study examined the extent to which digital
tools contribute to accelerating financial operations, facilitating auditing, and
enhancing financial analysis capabilities, in addition to evaluating the effectiveness
of curricula and academic staff in adapting to the digital environment. The findings
revealed that digital transformation has created a qualitative shift in the accounting
and auditing profession and contributed to improving the quality of accounting
education. However, several obstacles still limit its effectiveness, most notably
concerns related to information security, insufficiently qualified human resources,
and limited financial resources. Accordingly, the research recommends developing
accountants’ skills to adapt to digital requirements, updating measurement and
disclosure systems and accounting standards in line with rapid technological
developments, enhancing the competencies of faculty members in colleges of
economics and business administration, updating financial and regulatory legislation,
and strengthening accountants’ participation in workshops and specialized training
programs to meet the demands of the digital economy.

N-phospho methyl (glycine), another name for glyphosate, is an organophosphorus
chemical that competes with other molecules in the shikimate pathway. Both
microbes and plants use this process to produce aromatic amino acids.
Glyphosate's use in broad-spectrum herbicides like Round UpR has steadily
expanded since its 1974 launch. A variety of ecosystems, including soils, water,
plants, animals, and food supplies, are subject to glyphosate and its primary
metabolite, aminomethylphosphonic acid, monitoring. Glyphosate is often
discovered in the blood and urine of people, especially those who deal with it, and
is quickly eliminated by the human body. Glyphosate and its derivative herbicides
were originally thought to be safe for animals to eat, but they have now been
connected to a number of health issues. In 2017, glyphosate was deemed
"probably carcinogenic" to humans by the International Agency for Research on
Cancer (IARC). In spite of this, glyphosate was approved by numerous national
authorities for extended periods of time without more stringent limitations. It is
evident that there is currently a lack of international agreement about glyphosate,
as several nations have set their own maximum permissible limits

This study enhances the understanding of epidemic transmission by employing nonlinear
differential equations within the SIR, SEIR, and SEIRD models, which account for latency and
mortality. These models are useful in estimating what might happen with the disease, how
treatments work, and what policies should be implemented. Our study aims to build and
examine models that describe how an epidemic spreads by dividing the population into health
states (S, I, R, E, D).Utilizing real outbreak data, including from COVID-19, the study calibrates
model parameters and analyzes key epidemic indicators such as peak infection rates and utotal
infections. The baseline SIR model forecasts a peak infection prevalence of 13.2% and a
cumulative infection rate of 89% after 200 days. Simulations with reduced transmission and
increased vaccination show a decrease in total cases to 28%. Validation with epidemic data from
Italy and South Korea indicates high model reliability (R² > 0.96, mean absolute percentage error
< 8%). These results underscore the significance of mathematical modelling in public health
strategies aimed at addressing infectious diseases