Journal of Al-Ma'moon College

The current research aims to analyze and interpret the relationship between leadership mindfulness and dimensions of psychological resilience and the possibility of benefiting from the cognitive and behavioral approach that was explained by the theoretical and applied research axes. In light of this, the basic research problem can be diagnosed, which is: Is there a statistically significant correlation and effect between the effectiveness of leadership mindfulness in achieving dimensions of psychological resilience? By defining the research problem and reaching the best solutions to the research problems, a hypothetical model was built that reflects the correlation and effect relationship between the research variables represented by leadership mindfulness and psychological resilience capabilities, thus reaching the best and most accurate results that can be used in finding solutions to the research problem. In order to achieve this, the most appropriate environment for the research variables was chosen, which is the General Plastics Company in Baqubah, where the intentional sample of decision-makers in the researched company was determined, amounting to 61. In order to interpret the theoretical and applied research axes, the descriptive analytical approach was chosen to reach sound and accurate results, as the main research tool represented by the research form and a set of statistical methods were used, including the arithmetic mean, standard deviation and significance. The slope of the path and others that would achieve the best results that can be used to achieve the research objectives

This research aims to demonstrate the impact of efficient supply chains, with their various dimensions, on the adoption of flexible production variables in industrial organizations. The research was launched to achieve several objectives, most notably identifying the extent to which efficient supply chains contribute to supporting the selection and adoption of flexible production that aligns with the nature of the work of the company under study. Furthermore, it aimed to offer suggestions that would enhance the decisions of senior management in the company under study, thereby improving the efficiency of production decisions. To achieve these objectives, a questionnaire was developed as the primary data collection tool. The research sample included a number of managers from production and quality departments within the Iraqi Rubber Company, totaling (72) respondents. The data was analyzed using readily available statistical software (EViews, SPSS, AMOS). The research reached several conclusions, the most prominent of which was that the dimensions of efficient supply chains constitute the fundamental structure for the company in promoting flexible production systems across their various dimensions. Senior management support for the system provides the necessary resources through decision-making, and internal integration contributes to the flow of information and coordination between company units. The research also found that building Strategic partnerships enable a company to adapt to environmental changes and easily transfer external knowledge and expertise into the organization.

This study aims to examine the impact of accounting disclosure quality and financial reporting transparency on investor confidence, and to test the mediating role of reporting transparency in the relationship between accounting disclosure quality and investor confidence within the Iraqi business environment. The research adopted a descriptive and analytical approach. A questionnaire was used as the primary data collection instrument and was distributed to a sample from the Electronic Industries Company (IELI). Data were analyzed using linear regression analysis and path analysis techniques.The descriptive findings indicated a relatively high level of accounting disclosure quality, financial reporting transparency, and investor confidence, with all variables achieving statistical significance. The statistical analysis results revealed a significant and positive effect of accounting disclosure quality on financial reporting transparency, as well as a significant positive effect of financial reporting transparency on investor confidence. Furthermore, the findings confirmed a direct effect of accounting disclosure quality on investor confidence, in addition to an indirect effect mediated by financial reporting transparency. This suggests that transparency functions as a key mediating variable that strengthens the relationship between accounting disclosure quality and investor confidence.
The study concludes that enhancing the quality of accounting disclosure and achieving higher levels of financial reporting transparency contribute to strengthening investor confidence and promoting greater stability in the financial market, particularly within the Iraqi context.

This study aims to measure the gap in the application and adoption of creative leadership dimensions within one of the formations of the Iraqi Ministry of Electricity, namely the Baghdad Al-Rusafa Electricity Distribution Company, as a case study. The theoretical problem of the study was formulated due to a knowledge gap and the scarcity of studies and research addressing the study variable and its dimensions (originality, flexibility, sensitivity to problems, perseverance and risk-taking, and fluency). The case study methodology was adopted, and a questionnaire was used, which was closest to the model of the study by Puccio et al. (2011). The dimensions mentioned above were selected as they were most frequently used by researchers in their studies, through the creation of a frequency table of the dimensions. The study population (Baghdad Al-Rusafa Electricity Distribution Company) was chosen as a representative of the Iraqi Ministry of Electricity. The sample was purposive and consisted of 25 managers from middle supervisory management (department head, division manager, unit manager). The study concluded with several findings, the most prominent of which was the existence of a gap in the application and adoption of creative leadership and its dimensions within the study population.

This research aims to study the role of green target cost technology and agile manufacturing in improving product pricing, through practical application in the battery factory-Babylon 1 plant in question, in light of the challenges facing manufacturing industries represented by high production costs, increasing environmental pressures, and intense competition in local and imported markets.
In order to achieve the goal, the battery factory – Babel 1 factory was chosen as the subject of the research, and the green target cost technology and agile manufacturing were applied, which effectively contributed to reducing the gap between the green target cost and the current cost, and then achieving the goal by pricing a green product at prices acceptable to customers.
The researcher reached several conclusions, including replacing credit materials. Replacing expensive materials (15,242) dinars with less expensive materials (1,640) dinars led to a direct reduction in the cost of materials by 13,602 dinars per unit, while maintaining the quality of the battery and its technical characteristics, which enhanced the competitiveness of the product. The study also found that the methods used in the factory are traditional in the production process and do not keep pace with changes in contemporary times, and that traditional products differ from green products in that they focus on improving the environment of the product and by replacing less expensive materials and reducing the production cycle time, and reducing labor wages as a result of improving the distribution of human resources, in addition to reducing the use of toxic materials and environmental emissions This had a positive impact on the final price of the product, increasing its competitiveness in the markets, and achieving green products characterized by better environmental performance and more sustainable production costs
The researcher recommended adopting continuous strategies to develop production equipment and mechanisms, which contributes to raising the volume of production, reducing waste, and achieving optimal use of resources.

Artificial intelligence represents one of the most significant technological transformations affecting public administration and public utilities. It has created substantial opportunities to enhance efficiency and improve the quality of public services in sectors such as health, education, transportation, and security. At the same time, it raises complex legal and administrative challenges related to legality, transparency, data protection, and equality. The study shows that international experiences—particularly European and French ones—have developed advanced regulatory frameworks based on risk classification and the imposition of strict obligations on high-risk systems. In Egypt, the Personal Data Protection Law No. 151 of 2020 and the National Artificial Intelligence Strategy constitute a general framework for the use of these technologies in public utilities. By contrast, Iraq remains at a foundational stage, lacking a comprehensive national law and relying mainly on general constitutional principles and preliminary recommendations. The study concludes that effective administrative regulation of artificial intelligence in public utilities requires a combination of general legislation, sectoral regulations, and administrative and judicial oversight, with the necessity of benefiting from international experiences in a manner consistent with local contexts.

This research discusses the practices of administrative courts in holding public administration accountable in administrative litigation, particularly since the administrative judge cannot issue explicit orders to the administration to perform or refrain from an action, except for the power to annul an administrative decision tainted by specific defects. Nor can the judge amend decisions to eliminate the defect, or replace the administration in exercising the power to issue valid administrative decisions in place of the annulled, flawed ones. However, with the development of the fundamentals of administrative law, basic principles have been adopted in administrative court procedures to ensure the administration's responsiveness in administrative litigation. These principles lie in the practices of the administrative judge, which are tailored to the specific nature of the tasks performed within the framework of the rule of law and institutions that necessitate protecting individual rights against the expansion of administrative discretion.

This research aims to analyze the legal and penal foundations adopted by the Iraqi legislator in combating the phenomenon of money laundering, considered one of the most serious crimes affecting the national economy and the state\'s financial stability. The research\'s importance stems from the fact that money laundering poses a direct challenge to economic reform efforts and the fight against corruption, in addition to its close link to money laundering and the financing of illicit activities. This makes combating it a national imperative to ensure the protection of public funds and enhance public trust in legal institutions. The research adopted a dual methodology, combining an analytical approach to present Iraqi legal texts and a comparative approach to trace the most prominent comparative legislative, jurisprudential, and judicial trends, along with a descriptive approach to demonstrate the impact of money laundering on economic security. The study yielded several results, most notably that the Iraqi legislative framework faces gaps that necessitate strengthening institutional coordination and developing financial investigation tools. It also revealed that penalties need to be more graduated and flexible to achieve both general and specific deterrence. Furthermore, international cooperation still requires more effective mechanisms to ensure the rapid exchange of information and the recovery of laundered funds. This summary outlines the research\'s general overview and its main conclusions.

Economic cooperation between Egypt and Turkey during the period 2014-2024 witnessed mixed developments influenced by political and regional factors, yet it maintained a degree of continuity and stability. Despite political tensions after 2013, trade between the two countries remained at relatively high levels, supported by the free trade agreement signed between them. Bilateral trade volume reached billions of dollars annually, with Egyptian exports concentrated in raw materials, chemicals, fertilizers, cotton, yarn, and other products, while imports consisted of machinery and manufactured goods. Turkish investments in Egypt played a significant role in supporting the industrial and commercial sectors, particularly in textiles, since 2021. With the improvement of regional conditions, political rapprochement began to positively impact economic cooperation, further strengthened by the signing of several agreements in 2023. This contributed to boosting economic growth and improving the balance of payments between the two countries.

This research aims to shed light on a family of ancient Arab origins that was interested in recording Arab-Islamic history in Andalusia, which represents Spain and Portugal at the present time, and how that family was interested in writing the history of the Muslim Arabs in that region and in that period of time. We also learn how the sons inherited this knowledge from the fathers and were interested in it, and we see what became of their works and scientific outputs, and the importance of those outputs in the science of history

This research focuses on the geographical analysis of economic deprivation indicators among the residents of Abu Ghraib district. Its aim is to reveal the nature of the economic situation of families, highlight the spatial variations in these indicators, and analyze the spatial relationships between household characteristics and economic deprivation indicators. The ultimate goal is to improve the lives of the residents through development and reform. The research employs a descriptive and analytical approach, describing and analyzing the phenomenon. To identify and interpret spatial relationships, quantitative methods are used, including Spearman\'s rank correlation coefficient and ANOVA. A field study (questionnaire) was also conducted with a random sample of 382 respondents, distributed proportionally among the administrative units of Abu Ghraib district. The results show an overall deprivation index of 32.2 in Abu Ghraib district. The percentage of deprived families in Al-Nasr wa Al-Salam sub-district was 36.8, in the district center 30.1, and in Babat Al-Salam sub-district 29.8. The ratio of deprived to better-off families was 0.8%, meaning that for every better-off family, there is 0.8% deprived families.

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The study aims to analyze the spatial dimension of the phenomenon of child labor in the city of Fallujah by examining the current status of the phenomenon and the patterns of its numerical and relative distribution among the city’s spatial units. The study is based on field data collected through a structured questionnaire supported by direct individual interviews with working children themselves. The study included a purposive probabilistic sample of (203) children within the age group (5–14) years, representing the population of working children in the city.
The research focused on analyzing the demographic, social, and economic characteristics of working children, identifying the main factors leading to their engagement in the labor market, as well as assessing the social, economic, and educational impacts resulting from this phenomenon. The study adopted the descriptive–analytical approach and the spatial geographical approach, employing appropriate statistical methods to analyze the questionnaire data. The results showed that the majority of working children have parents who live together at a rate of (44.8%) and are alive at a rate of (53.2%), indicating that child labor is not solely associated with the absence of parents, but is also influenced by other factors such as unemployment, low income, high living requirements, and large family size. In addition, a proportion of children continues their education despite working, accounting for (42.4%), compared to noticeable rates of school dropout reaching 40.4%)).
The study revealed that child labor is prevalent across several economic sectors and is mainly concentrated in markets and industrial services, at rates of (48.7%, 21.7%), respectively. Geographically, it is distributed in neighborhoods suffering from high poverty rates, with some neighborhoods in Fallujah recording higher percentages than others, particularly (Al-Mansour, Al-Risala, and Al-Ta’meem) neighborhoods, at rates of (15.8, 13.8, 12.3%), respectively. This reflects the strong association of the phenomenon with prevailing economic and social conditions. The study also presents applied recommendations that can be utilized to support policies and programs aimed at reducing the spread of the phenomenon and addressing its impacts.

This study aims to analyze the morphometric and climatic characteristics of the Birzor Valley basin in northeastern Iraq. The study employed an analytical-descriptive approach based on Geographic Information Systems (GIS) and climatological data from the Duhok station to interpret the relationships between geology, topography, climate, and river drainage. The results show that the basin is characterized by steep topography and high ruggedness, with a well-organized, dendritic river drainage network concentrated in the lower elevations, and a predominantly mountainous terrain. The results also indicate that the region experiences a semi-arid Mediterranean climate, characterized by rainy winters and hot, dry summers, which influence surface runoff intensity and erosion rates. The study revealed a clear variation in topographic and climatic characteristics that directly impacts the hydrological system, soil distribution, and land use.

Background: Suicidal behavior is a major global public health concern, and understanding its epidemiology is crucial for prevention strategies. This study aimed to estimate the prevalence and identify associated factors among patients attending Psychosocial Units (PSUs) within Iraqi Primary Health Care Centers (PHCCs).
Methods: A cross-sectional study design was employed. ( the) Data was collected from all patients attending PSUs in Iraqi PHCCs between January and October 2024. A standardized form gathered information on demographics, circumstances surrounding suicidal attempts, and past mental health history.
Results: A total of 280 patients reported suicidal attempts, representing 1% of PSU attendees and an incidence rate of 3/10,000 people. Notably, 87% were women, with a concentration in young people (60%) and young adults (34.3%). Sociodemographic characteristics revealed that 56% were unmarried, 54% had low education levels, and 68.6% were housewives. Alarmingly, 93% lacked prior psychiatric assessment, and 97.1% had no history of hospitalization. Furthermore, 82.9% reported no family history of suicide attempts. Interestingly, 51.4% reported a first-time attempt, with 94.3% occurring at home. Drug intoxication (48.6%) and self-harm (22.9%) were the most prevalent methods, with 34.3% using multiple methods. A significant finding was that 77% refused hospital admission. The study identified acute emotional distress, particularly violence, as the primary reason for attempts (63%), followed by socioeconomic and psychological factors.
Conclusion: Suicidal behavior is a concerning issue in Iraqi communities, with limited access to mental health services for those struggling. Strengthening PSUs and improving access to psychosocial interventions within PHCCs are crucial for prevention efforts.

This study aims to analyze the impact of body language of salespersons dimensions, namely: posture (physical appearance), smile, physical appearance, eye contact, and personal space, on customer satisfaction. The study employed a descriptive-analytical approach, and data were collected through an electronic questionnaire distributed to a sample of customers in Karbala, Iraq. Seventy-five questionnaires were analyzed using SPSS software. The results showed a significant impact and a positive correlation between all body language dimensions and customer satisfaction. No statistically significant differences were found based on gender or educational level, while significant differences were found based on age. The study recommends training salespersons in body language skills to enhance customer satisfaction and encourages future research in diverse settings.

Background: Melanin is heterogeneous pigment widely distributed in diverse species. The pigment exhibits a diverse spectrum of biological activities, encompassing antioxidant, anticancer, antivenin, and radiation resistant activities. Aim of the study: The present study aimed to isolation and identification of Aspergillus niger from soil, then Extraction of melanin from the isolated fungus and finally, to study antioxidant activity. Materials and methods. The fungus Aspergillus niger was extracted from the soil of a residential garden in Tikrit Governorate and identified using several culture media. The melanin pigment was isolated from the Aspergillus niger strain via the Potato Dextrose Agar (PDA) culture technique. The antioxidant properties of melanin in relation to erythrocytes were also assessed. Result: As the melanin concentration increased, the antioxidant activity of Radical Scavenging Activity (RSA) increased. Melanin had antioxidant activity of 37.5%, 43.75%, 56.25%, 62.5%, and 65% at doses of 10, 20, 40, 80, and 100 µg/ml for T1. At doses of 10, 20, 40, 80, and 100 µg/ml, melanin showed antioxidant activity of 40%, 47.5%, 52.5%, 60%, and 61.37%) for T2. While for T3, the antioxidant activity at doses of 10, 20, 40, 80, and 100 µg/ml were (35%, 40%, 50%, 56.25%, and 60%). Finally, T4 showed antioxidant activity against melanin (31.25%, 40%, 43.75%, 50%, and 56.25%) at doses of 10, 20, 40, 80, and 100 µg/ml.
Conclusion: The present study concluded that A. niger contains a high percentage of melanin, making it an important source of this pigment. In addition melanin is considered a powerful antioxidant, and showed greater antioxidant effect.

Aggressive pollution of the water bodies by the pharmaceutical emerging contaminants especially antidepressants pose a great challenge to the environment and the wellbeing of the people around the world. These compounds are resistant to the traditional treatment of wastewater and are pseudo-persistent, which has been observed to have ecotoxicological effects. The existing methods of remediation, such as the advanced oxidation and adsorption, remain rather expensive, energy consuming, or produce secondary wastes, whereas the biological methods involving single strains of microorganisms are not robust and have limited metabolic spectrums to be applied in real-life scenarios. This paper is an attempt to overcome these limitations by the purposely engineered creation of a specialized, synergistic consortium of microbes. It uses a multi-phase approach, which is selective enrichment of contaminated sites, omics-based characterization and strict optimization in batch and continuous-flow bioreactors. High removals of sertraline (88.5%), citalopram (83.9%), and fluoxetine (86.7%) are obtained with the engineered consortium in 48 hours with optimum results at pH 6.5 and 30degC. The taxonomic analysis shows that there is a stable core of eight genera with the predominant known degraders such as Pseudomonas and Sphingomonas. The presence of high catabolic genes is confirmed by metagenomic profiling that high concentrations of P450 monooxygenases (1560 RPM) and dioxygenases (1250 RPM). The consortium is shown to be very resilient with a 30-day continuous microcosm, which has a mean removal efficiency of 92.2% and 86.0% inoculum persistence. A financial analysis estimates significantly reduced operational expenses (12/1000 m3) than traditional methods, which makes it a promising sustainable scalable biotechnology to reduce pollutant levels of pharmaceuticals.

PFAS are bioaccumulative pollutants, which have been found to be of serious environmental and health concern. The proposed investigation presents a smart and interpretable model of automated PFAS-contamination identification and the classification of PFAS in landfill leachate, called PFAS-AI. The system combines the concepts of software engineering and machine learning to offer modular preprocessing, feature selection, and scalable deployment. With the help of a complete dataset of EPA leachate ( N= 200)) and the regression on order statistics (ROS) in order to correct systematic bias, the Random Forest model (verified by stratified k-fold cross-validation) and compared to baseline classifiers (SVM, XGBoost) attained 91.3% accuracy with a precision and recall of above 0.90. The rigorous enhancement of model interpretability was produced by SHAP (SHapley Additive exPlanations) with the local and global transparency applied to find the most significant predictors that rely on the PFAS transport mechanisms, including Perfluorooctane sulfonate (PFOS) and Perfluorohexane sulfonic acid (PFHxS). The classes of contamination were strictly determined according to regulatory thresholds of the environment in order to guarantee the practical applicability. PFAS-AI is a lightweight, deployable diagnostic tool by combining a rigorous explainable AI with a robust architecture to step forward with PFAS risk assessment and enable sustainable and data-driven environmental decision-making as a component of the One Health framework.

Background: Lead (Pb) is an important environmental agent to reproductive health. Purpose: The purpose of the study was to determine the relationship between the level of lead in both blood and follicular fluid (FF) and ICSI outcomes in passive smokers. Methods: A cross-sectional study was performed on 67 women (31 passive smokers, 36 non-exposed). The measurement of lead was done by Flame Atomic Absorption Spectrophotometry. Findings: Passive smokers were found to have a much higher lead level in blood (20.03 ± 3.01 µg/dL) and in FF (18.68 ± 3.92 µg/dL) than non-smokers (p < 0.05). The passive smoking group increase Metaphase I oocytes (1.23 ± 0.26) and Grade III embryos (0.65 ± 0.18) significantly (p < 0.05). There were no significant differences in MII oocytes, fertilization rates and clinical pregnancy rates. Conclusion: Secondary smoking is linked to higher levels of lead that could have a negative correlation with the maturation of the oocytes and the quality of the embryo during the ICSI cycles.

Automated emotion identification has been highly influenced by recent developments in deep learning technology, cloud computing, and video analysis. A new and innovative architecture for emotion identification in video streams involves the use of a Vision Transformers model for feature extraction and a fully connected neural network for emotion classification. The potential of cloud technology in distributed processing and scalability has been utilized for identifying five basic emotional states in a video stream. The emotional states identified in this model are joy, sorrow, anger, surprise, and neutrality.
The extraction of frames in a video stream has been done in a methodical manner using a cloud-based model. The use of a ViT model for generating discriminative and rich features involves passing each frame of the video through a pre-trained ViT model running on cloud-based GPU instances. The effectiveness of the model has been validated using a cloud-based FCNN classifier. The performance of the model has been validated using metrics such as validity, precision, recall, and F1-score.
Elastic scalability and parallel processing capabilities of the cloud-deployed framework enable it to reach competitive accuracy and computational efficiency. The connection to the cloud accelerates training, makes it more repeatable, and facilitates parallel application of emotion identification for real-time HCI systems. It also seamlessly integrates with multimedia analytics, smart healthcare, surveillance, and HCI systems. This robust approach demonstrates that ViT-based models and cloud computing have the potential to advance emotion recognition research and application.

Speech signal Processing has led to significant progress in recent years, driven by deep learning, self-perpetuated learning, and end-to-end models. This systematic review examines traditional and modern techniques for speech recognition, growth, and synthesis, and evaluates benefits, boundaries, and potential future research directions. We compare the most important algorithms, evaluate their performance in the dataset, and discuss the strengthening of new trends such as self-employed learning and adversity. This article presents a broad comparative analysis using wide tables and ensures clarity for researchers in the field.

The exponential growth of digital news material has increased the demand for strong, efficient, and scalable mining methods for large-scale media analytics and news intelligence systems. Deep learning-based language models have performed well in news topic classification, but current literature prioritizes prediction accuracy over experimental reproducibility, computational efficiency, and tough benchmarking vs. conventional machine learning benchmarks.
The present research proposes a carefully and repeatably designed news mining framework based on the lightweight DistilBERT transformer architecture.
The proposed methodology verified using the famous AG News dataset. The systems use multi-seed. Experimental evaluation to improve methodological soundness. (Seeds: 42, 7, 21), detailed metric reporting, and a fair comparative analysis against traditional classifications, specifically TF-IDF combined with Logistic Regression and Linear Support Vector Machines (SVM).
DistilBERT-based framework outperforms baselines in experiments, attaining a mean accuracy of $0.956 \pm 0.005$ and a Macro-F1 score of $0.953 \pm 0.004$. Notably, this framework is suitable for resource-constrained applications since it has less processing overhead than larger transformer structures. Complete confusion matrices and class-wise performance assessments demonstrate the approach's statistical stability and resilience across all news categories.

Heat-affected zone (HAZ) is a type of highly active zone that may be created by welding in AISI 444 stainless steel buildings, by utilizing machining modeling of an AISI 444 stainless steel alloy under complex heat operation, but the ductility and modeling techniques for HAZ in numerical simulations are unclear. The problem is resolved by combining experimental and numerical work on machining modeling of a specific alloy under complex heat operation, which is useful for predicting the general behavior of structures. The use of the Electro Discharge Machining (EDM) process machine to produce carefully defined heat treatments with high peak temperatures and heating rates using precisely defined methods also accounts for the impact of the two models, both theoretical and practical, on the definition of power supply voltage change, The overarching aim of this research is to gain a more universal comprehension of ductile machining modeling of machining-related AISI 444 stainless steel alloys that are exposed to varying voltages, complex heat operation, and are representative of machining. The EDM parameters employed had an increase in HAZ due to an increase in voltage (140, 240) V, current Ip (12, 24, 50) A, pulse on time Ton (100, 200, 400) μm, and pulse off time Toff (3, 6.5, 12) μm. Tests showed that these values had a positive impact on HAZ and an increase in HAZ at the same time. However, four parameters were tested to determine the accuracy of the results: voltage (140, 240) V, current Ip (12), voltage, current and pulse rates are gradual increases with increasing voltage and current, while the current increases gradually with increasing current and pulse.

The increasing use of solar energy systems requires a robust fully automated system for detection and mapping of solar panels using satellite imagery. In this paper, we proposed a deep learning system based on U-Net that automatically identifies the roof solar panels in satellite imagery from Google Maps. The model was built and tested on the PV03 data set that consisted of a diverse solar panel data set with high quality (HQ) satellite imagery. The experimental results indicated that the proposed approach had a high accuracy of 0.9758, with a mean Intersection over Union (IoU) of 0.9456 and a mean Dice coefficient of 0.9719, suggesting a high accuracy of segmentation of the solar panel regions. This work demonstrated that U-Net could identify diverse variation in panels such as size, orientation, location, and environment. Based on the results discussed, a deep learning approach to satellite image analysis has potential as cost-effective, reliable technology of assessing solar energy infrastructure which can be utilized for spatial land-use assessment, urban design, and renewable energy management.

Purpose; although Iraq is located in the most fertile place and has two major rivers (Tigris & Euphrates) but unfortunately and due to many global climate changes as well as global warming in addition to political issues; the situation differs and many of Iraqi fertile yards have been decertified that leads to losing most of famers their income resources especially in rural regions.
Findings; most of data showed that there is a gap between desired and actual actions especially those data related to water consumption and wasted ones; electricity supply and employment rates which all affected the dynamic wheel of developing and/ or adopting such smart village model in rural and/ or urban regions.
Research limitation- Implications; many implications are limited current paper where practical ones related limited data and information either from government or from periodic articles, essays in addition to social implications where the cultural environment limited our work to achieve its purposes.
Originality; current study could be a good reference to further researches, works; and assays to enrich both local library and regional knowledge with respect to Iraqi situation.

The nature of real-time demands, changing traffic, and unpredictable weather on the last-mile delivery systems in the urban setting represents an ever-increasing burden. The algorithms that handle routing, Dijkstra and A*, do not work well in an urban environment because they were created under the assumption of a static environment, causing inefficiencies and delays. This research paper therefore fills this gap and will suggest the use of a hybrid routing technique where the classical A* algorithm will be incorporated with the Offensive-Defensive Optimization Algorithm (ODOA), a metaheuristic approach enhanced with a context-aware learning model. The framework utilizes a decision tree regressor, developed using more than 43,000 Amazon delivery statistics, to predict delay costs based on environmental factors such as traffic intensity and weather conditions. The resulting model dynamically updates edge weights, resulting in more adaptive routing planning.
In the example of a simulation of a delivery graph, it has been shown that the proposed hybrid A*-ADOI method decreases the total route cost by up to 18.7 percent on average in comparison with the traditional A*. In addition, it positively impacts the route adaptability in fluctuating urban environments. The system further provides human-in-the-loop simulation and decision. The conclusion shows that context learning integration with hybrid optimization enhances the realistic nature and operation of logistics routing.
The work gives a new, potentially scalable method of intelligent urban delivery, which can be applicable to time-sensitive logistics and life-saving services, as well as future and smarter transportation systems.

Background: Crohns disease (CD) is a chronic inflammatory bowel disease characterized by dysregulated immune responses and elevated levels of pro-inflammatory cytokines, including interleukin-8 (IL-8). Biological therapies are utilized to control inflammation; however, their effects on circulating IL-8 levels remain inadequately defined.
Objective: This study aimed to measure serum IL-8 levels in Iraqi patients with Crohn’s disease and to compare levels among untreated patients, those undergoing biological therapy, and healthy controls.
Methods: Ninety participants aged 20 to 68 years were recruited and divided into three groups: untreated Crohn’s disease patients (n = 30), Crohn’s disease patients undergoing biological treatment (n = 30), and healthy controls (n = 30). We employed an enzyme-linked immunosorbent assay (ELISA) to assess the concentrations of IL-8 in serum. We employed appropriate parametric tests for statistical group comparisons, establishing a significance level of p &lt; 0.05.
Results: Serum IL-8 levels were significantly higher in untreated Crohn\\\\&#039;s disease patients compared to treated patients and healthy controls (p &lt; 0.01). Patients receiving biological therapy demonstrated diminished IL-8 levels in comparison to untreated individuals; however, these levels remained elevated when contrasted with controls.
Conclusion: Crohn\\\\&#039;s disease is linked to higher levels of IL-8 in the blood, and biological treatment appears to lower this inflammatory marker, suggesting that it has an effect on systemic inflammation.

Background: The Tuberculosis (TB) is induced by Mycobacterium tuberculosis (Mtb), persists as a worldwide health issue. MicroRNA-26a (miR-26a) is associated with host-pathogen interactions, nevertheless, its function in TB and its correlation with interferon-gamma (IFN-γ) expression in peripheral blood are not well understood.
Aim: To investigate the relationship between miR-26a and IFN-γ genes expression in TB patients.
Methods: In this study a with 100 persons diagnosed with active TB (ages 20–58 years) and 50 healthy controls matched for age and sex, recruited from December 2024 to May 2025. Total RNA from serum was extracted and analyzed using quantitative real-time PCR to assess miR-26a and IFN-γ expression.
Results: Circulating miR 26a was significantly elevated in TB patients (3.49 ± 0.57-fold) relative to controls, as well IFN γ expression exhibited a increase in patients (1.82 ± 0.22-fold) comparing with control. Subgroup analysis indicated elevated miR 26a levels in pulmonary TB (3.15 ± 0.26) compared to extrapulmonary TB (2.42 ± 0.18; P = 0.0498), although IFN γ levels were consistent across TB types.
Conclusions: Active TB is correlated with a substantial increase in circulating miR 26a, especially in pulmonary cases, indicating a possible regulatory function in IFN γ–mediated immunity. miR 26a may function as a new biomarker that can be used for the TB diagnose. Importantly, miR-26a may complement existing diagnostic tools, providing an additional molecular biomarker that enhances the accuracy of TB detection and disease classification. Additional research is necessary to clarify its molecular effects and therapeutic applicability.

Background: To distinguish Streptococcus mutans bacteria from other susceptible streptococcal bacteria, tooth decay is a chronic illness and one of the most prevalent diseases globally. Aim of the study: Streptococcus mutans, one of the most prevalent pathogens of tooth decay, is isolated and identified from dental caries lesions. Materials and methods: The current study involved collecting 100 samples from individuals with dental caries at the Specialized Dental Center and outpatient clinics of Tikrit Teaching Hospital. The samples were taken from both genders between July 2025 and January 2026 using Gel swabs, which were then cultured on a specific medium. Biochemical and microscopic examinations were performed, and the results showed that 100 isolates were Streptococcus mutans on Bacitracin agar salivarius (MSBA). Result: of the current study show that the most susceptible to tooth decay are in the 21-30 and 31-40 age groups, representing 32% and 31% respectively. Fifty-seven swabs were collected from males (57%) with dental caries, and 43 swabs were collected from patients with periodontal inflammation (43%). Forty-three swabs were collected from females with dental caries. Antibiotic susceptibility testing was conducted on various commonly used antibiotics. Ampicillin, Azithromicin, Cifotaxime, Cefixime, Erythromycin, Meropenem and Nitroflorantion resistance (100%) was found to be the most common antibiotic. Azithromycin, cifotaxime, cefixime, erythromycin, meropenem, and nitrofloranthin were also found to be resistant. Most bacterial isolates also showed high resistance to more than one antibiotic. The widespread and indiscriminate use of many antibiotics in treating bacterial infections has contributed to an increase in resistance rates. Isolates showed sensitivity to amikacin (80%), imipenem (89%), and augmentin (91%). Conclusion: The present study concluded that the bacteria that causes tooth decay is Streptococcus mutans. The bacterial isolates of Strep. mutans can show multidrug resistance to several drugs

Dynamic learning for streaming data has become a crucial strategy for contemporary
learning dynamic learning allows models to adjust in real time to non-stationary situations and update progressively an analysis of dynamic learning approaches for instance online and incremental learning drift detection and adaption techniques ensemble tactics active learning and ongoing learning presented in this study . The practical significance of adaptive stream learning is demonstrated by important application domains such as cybersecurity monitoring financial fraud detection smart city and others .
According to the study, real-time adaptability, ongoing knowledge acquisition, scalability, effective resource use, and resilience to idea drift are some of the main benefits of dynamic learning. Nevertheless, a number of drawbacks and restrictions are also mentioned, including model instability, catastrophic forgetting, noise sensitivity, implementation complexity, a lack of historical context, and assessment difficulties. Drift detection, high data velocity, memory limitations, label scarcity, and the stability-plasticity trade-off are important research concerns. The study comes to the conclusion that dynamic learning is essential for real-time AI systems, and that federated learning, edge intelligence, and stronger adaptable models should be the main areas of future research.