Univesity of Thi-Qar Journal

The development of higher oligomeric porphyrin arrays represents a promising
pathway toward next-generation molecular electronics. In this study, the
thermoelectric properties of five zinc porphyrin dimers (ZnP) were theoretically
investigated by analyzing electron transport through Zn-porphyrin dimers connected
between gold electrodes via five distinct configurations. The impact of the presence,
type, position, and number of linkers on the electrical and thermoelectric properties
was systematically examined. The results demonstrate that transitioning from direct to
linker-mediated contacts significantly alters both electrical and thermal conductance.
Moreover, variations in linker position and number strongly influence electrical
conductivity, thermal conductivity, and thermopower (S). These findings underscore
the critical role of linker engineering in controlling the electrical and thermoelectric
performance of zinc porphyrin dimers for molecular electronic applications

The present study aims to identify and measure environmental and quality costs accurately in the
various stages of product life span. It also aims to explore processes, the nature and types of
environmental and quality costs, and distribution of costs across the various activities and products.
Based upon the general financial data available in the Iraq stock exchange for the period (2018-2022),
The National Company for Chemical and Plastic Industries is the case study. In order to obtain the
objectives, the present study adopts financial data analysis. The results reveal that adopting clean
production leads to reducing pollution gradually. This gradual reduction in pollution is the outcome of
compliance with the environmental criteria imposed by state units, such as fines and penalties imposed
due to pollution, in addition to improving the relationship with society and environmental authorities.
As a result, there is an increase in the profitability and competitiveness of the commercial unit. The
resulting benefits are greater than waste management costs. The present study recommends
commercial units to measure quality and environmental costs through distinguishing them from other
costs to make use of them in the field of decision-making to serve the environment of the commercial
unit operation field.

The present study aims to carry out an analysis of the influence of integrated reporting on
earnings quality in the Iraqi commercial banks listed in the Iraqi stock exchange from 2015 until
2024. The empirical approach is adopted in the present study. A sample of (10) Iraqi commercial
banks constitute the research sample. The level of integrated reporting is measured through the
(IR Score) that is based on the International Integrated Reporting Council (IIRC) framework.
Earnings quality is measured through the Loan Loss Provisions (LLP) model. The statistical
analysis results reveal that there is a statistically significant influence of integrated reporting on
enhancing earnings quality. The results indicate that the increase in the level of integrated
reporting leads to improving earnings reliability. The results also indicate that the bank size and
return on assets have a positive influence on earnings quality. On the other hand, the results
reveal that financial leverage (debt) has a negative impact on earnings quality. In light of the
findings, the present study recommends the Iraqi commercial banks and the Iraqi Central Bank to
adopt practices of integrated financial reporting, enhance governance and internal monitoring
systems, and manage the funding structure in a more efficient manner in order to enhance the
quality of financial reports and secure investors’ confidence

The current paper is a research article that explores the potential of Accounting Information
Systems (AIS) in increasing efficiency of financial and investment planning in Iraqi
government banks. It is founded on the DeLone and McLean Information Systems Success
Model and the research on the impact of the quality of the system, quality of information,
quality of service and user training on the performance of the planning is conducted based on
the Decision Usefulness Theory. A structured questionnaire was applied to 320 AIS users of
3 large state banks in Iraq, and the quantitative and descriptive-analytical research design was
selected. The number of the valid responses entered into SPSS and SmartPLS 4 was two
hundred and eighty-seven in order to receive the structural equation modelling.
The results indicate that the AIS elements positively and significantly impact planning
efficiency, and the quality of information proves to be the most powerful factor ( = 0.32, p <
0.001). This structural model had the ability to describe 64 percent of the variation in the
planning efficiency and the user satisfaction was determined to mediate between the use of
AIS and planning results. The theoretical premise of the findings results that have been
obtained confirm the hypothesis that having the capability of the system and interaction with
its users would result in a successful financial planning and investment decision-making.
The study provides realistic suggestions to policy makers and managers in the banking
industry to improve data governance, to train the users on the continuous basis and to
introduce integrated planning tools. It is also consistent with the broader scale changes of
financial management in Iraq. The future research should consider extending the longitudinal
effects, comparing them to the private banks and applying the AIS, advanced analytics and
reporting systems. The article will contribute to the improvement of the understanding of the
capacity of the digital systems to drive the strategic financial performance in the framework
of the public banking sector.

Assess of Nursing Management of children with Leukemia. And To find -out the
association between nurses demographic data and Assessment of Nursing Management for children
with Leukemia

MicroRNA (miRNA) is a key player in regulating gene expression, It plays a
vital role in a wide range of biological processes, Including growth, cell
differentiation, and programmed cell death. This research focuses on
studying the mechanism of action of MicroRNA in controlling gene
expression and its effect on genetic diseases.
The study included a comprehensive review of the scientific literature on the
effect of MicroRNA on gene expression and its role in causing genetic
diseases such as cancer. Heart disease, neurological disorders, and others.
The results showed that changes in MicroRNA regulation may lead to
dysfunctions that contribute to the development of many diseases. The
potential therapeutic applications of MicroRNA were also highlighted,
including its potential use as a therapeutic target in personalized medicine.
The study recommends the necessity of enhance research into the role of
MicroRNA in genetic diseases and develop new technologies targeting its
regulation as a future therapeutic strategy.

High-Performance Liquid Chromatography (HPLC) plays a key role in pharmaceutical and
metabolomic analysis. It separates compounds in detail based on their physical and chemical
properties. Yet, making sense of complex chromatographic results to group compounds
remains challenging. This research suggests a machine learning approach to classify drug
compounds into multiple groups. It uses engineered features taken from HPLC
chromatograms. The team processed a selected dataset of over 1,600 chromatographic runs.
These runs showed a wide range of pharmaceutical compound types. From this data, they
extracted features based on retention. These included peak count highest absorbance, entropy,
and area under the curve. They sorted compounds into nine main groups like Amino Acids,
Drugs, Bioactives, and Inorganics. They tested several classifiers such as Random Forest,
Support Vector Machine, and deep neural networks. The Random Forest model preformed
best. It reached over 99% accuracy in training and 72% accuracy in testing across all groups.
This beat traditional models. The suggested method demonstratus that to combine HPLC
profiles with ML techniques. This allows for automatic scalable, and meaningful
classification. This work helps improve drug profiling, quality control, and compound
tracking in pharmaceutical and biomedical fields.

Climate change poses both pressing scientific and societal challenges requiring accurate
prediction of extreme events and rigorous adaptation approaches. While classical methods
such as numerical weather prediction (NWP) and general circulation (GCM) models are still
central to climate depictions, these models are computationally expensive and, therefore,
struggle at real-time applications and inherently, in their accuracy .
New advancements in artificial intelligence (AI) suggests that, at the very least, machine
learning (ML) and deep learning (DL) could provide a revolutionary and complementary
alternative to the physics-based modelling shown previously. In this review, we outline 70
peer-reviewed papers (2019-2025), we selected the studies using the literature review
according PRISMA to cover the AI application in prediction. Which highlight the risk of
using AI for climate forecasting and adaptation. Quantitative evidence indicates that
GraphCast surpasses ECMWF HRES in roughly 90% of forecasting metrics; GenCast
delivers 97% higher accuracy compared with ensemble means; and MetNet-2 extends
precipitation forecasting horizons to nine hours with improved precision. In adaptation, AI
has helped predict agricultural yield with up to 88% accuracy, alert farmers of imminent
drought two months in advance, provide early warnings of dengue fever with a reported AUC
of 0.89, and improve urban flood resilience with accuracy levels as high as 92% . This review
focus on both chances and challenges and highlight the constraints in AI applications like the
dataset and difficulty in weather explanation models.
However, despite these advances, challenges remain due to data bias, limited visibility into
deep learning models, high energy consumption, and unequal access to technology. The
review sets out an evaluation of opportunities and challenges from which AI might be
effectively applied to climate science and climate-related policy, and suggests a three-pillar
framework incorporating sustainability, transparency and equity which can enhance
responsible use within climate adaptation and resilience.

This research aims to study and evaluate the effectiveness of methods based on
partial differential equations (PDEs) in restoring binary images, which are
widely used in document applications, optical character recognition (OCR),
industrial scanning, and medical imaging. Binary images are a special case
compared to grayscale or color images, as they consist of only two values (0
and 1), making them more sensitive to noise or pixel loss. The main problem is
that many traditional PDE-based restoration methods, such as the thermal
diffusion model or the Perona–Malik equation, fail to preserve fine edges and
lead to significant geometric distortions، The research sought to achieve a set of
objectives, most notably reviewing current methods for restoring binary images
using PDEs, proposing a modified model that is compatible with the nature of
these images, and conducting practical experiments to compare its performance
with traditional models. The research also focused on adopting quantitative and
qualitative evaluation indicators appropriate for the nature of binary images,
such as PSNR, SSIM, and bit error ratio (BER), in addition to edge
preservation indicators. The research is based on fundamental hypotheses,
most notably that modified or specifically developed models for binary images
will outperform conventional models, and that the use of higher-order
equations, such as the Cahn–Hilliard equation, will contribute to improved
restoration quality and better edge preservation. The discussion demonstrated
that actual performance is significantly affected by the choice of numerical
parameters, such as the diffusion coefficient and time step، The results confirm
that binary image restoration using modified PDEs represents a promising
approach that combines theoretical accuracy with practical utility. It provides
practical solutions to challenges associated with binary images in multiple
fields, opening the way for future studies that integrate mathematical models
and deep learning algorithms.