Journal Of AL-Turath University College

This study aims to investigate and classify water pollution in the Groundwater in Iraq by employing a combination of penalized statistical methods and modern artificial intelligence techniques. The study is based on real environmental data provided by the Iraqi Ministry of Environment, in addition to simulated data generated using the Monte Carlo simulation approach. The research focuses on penalized logistic regression models, including LASSO, Adaptive LASSO, SCAD, and Elastic Net, alongside gradient boosting algorithms, particularly the Extreme Gradient Boosting (XGBoost) algorithm, in order to evaluate their interpretative and predictive performance when dealing with high-dimensional and highly correlated environmental data.

The Mean Squared Error (MSE) criterion was adopted to compare the performance of the proposed methods. Simulation results demonstrated that the Elastic Net method achieved the best performance among penalized logistic regression models, yielding the lowest MSE values across all sample sizes. Moreover, the results indicated that the XGBoost algorithm outperformed the traditional Gradient Boosting algorithm, showing superior predictive accuracy and stability. In the practical application to real water quality data from the Tigris River, all studied environmental variables were found to be statistically significant in both the Elastic Net model and the XGBoost algorithm. While the two approaches showed general agreement in the direction of variable effects, XGBoost exhibited a greater ability to capture nonlinear and complex relationships among variables.

The comparative analysis revealed that the Elastic Net model is more suitable for interpretative purposes and identifying the most influential environmental factors, whereas XGBoost provides higher predictive accuracy. These findings confirm that integrating penalized statistical models with artificial intelligence techniques offers a comprehensive and effective analytical framework for studying water pollution.

The recent increase in interest in Multi Pump Raman Amplifier (MPRA) has been an important field of research in terms of the surge in the demand for broadband and high-capacity optical communications in Wavelength Division Multiplexing (WDM) systems. This review aims to describe a summary of 37 recent articles published on the design, modeling and optimization of Raman amplification in current optical communication systems based on WDM in terms of strengths, limitations and gaps in knowledge. The reviewed articles fall into three major directions: (1) gain and noise figure (NF) optimization strategies, (2) machine learning-based optimization and adaptive control strategies, and (3) quantum and hybrid quantum-classical reinforcement learning systems. The architectural needs of RAs to enable the transmission of WDM systems still offer possibilities to the researchers, and it has the ability of distributing, broadband optical gain with lower noise figures (NF) and flexibility across a variety of spectral bands. Recent explored approaches to machine learning (ML) and evolutionary optimizations to rapid estimation of pump powers will result in solutions enabling the inverse design of pump powers to arbitrary shaped Raman gain profiles, and the production of adaptive pump powers in ultra-wideband (UWB) WDM systems. ML frameworks such as neural networks, particle swarm optimization (PSO), and differential evolution (DE) demonstrate minimal prediction errors with respect to an effective consumption of pump powers to operate the Raman amplifiers. Multi-band programmable amplifier designs, which act as a combination of the hybrid Raman-EDFA designs, enhance optical signal-to-noise ratio (OSNR) and transmission capacity. Recent work has focused on applying quantum reinforcement learning (QRL) methods to improve scalability and convergence of high-dimensional optimization problems. In general, the review points to a shift toward adaptive real-time control, the combination of ISRS-aware optimization, and the hybrid quantum-classical smart schemes of scalable and energy-efficient ultra-wideband optical networks.

Recent breakthroughs in generative artificial intelligence (Gen-AI) and large language models (LLMs) have improved productivity across many domains but also increased the level of phishing attack realism and targeting. This systematic review examines recent studies on (i) the application of LLMs and other Gen-AI technologies to phishing email composition and phishing URL creation or obfuscation, (ii) phishing email and URL detection, and (iii) human factors in phishing vulnerability. The review considered studies published between 2020 and 2025 and compared their performance using accuracy, recall, precision and F1-score metrics. The reviewed indicates that Machine Learning (ML), Deep Learning (DL), and ensemble learning have been widely used in phishing detection. For email phishing detection, BERT transformer models have shown the highest performance in all studies reviewed. For URL phishing detection, hybrid models that combine structural and lexical information have shown the best performance. The review also emphasizes the role of human factors, such as familiarity of the sender and the fear of missing out (FoMO), in influencing phishing vulnerability. Overall, the findings support combining high-performing detection models with user-centered warnings and training to improve real-world phishing resilience.

This study addresses the issue of medical error from the perspective of the physician’s criminal liability under Iraqi law, in light of the absence of a specific statute regulating medical errors and the judiciary’s reliance on the general provisions of the Penal Code. The research is based on the premise that applying traditional criminal texts to medical acts leads to outcomes that are inconsistent with the nature of medical practice and exposes physicians to criminal accountability that does not adequately consider the technical specificity of the profession. The study aims to analyze the concept of medical error, clarify the legal basis of criminal liability, examine the judicial trends of Iraqi criminal courts, and provide a critical evaluation of these trends. The research adopts analytical, critical, and applied approaches through the examination of legal texts and selected judicial rulings. The study concludes that the current legislative framework is insufficient to accommodate the unique nature of medical error and that there is an urgent need for specific legislative intervention that balances patient protection with the legal security of physicians. The study also briefly highlights the impact of the strict judicial approach on public health criminal policy, as excessive criminalization of medical errors may create a climate of defensive medicine, discourage professional initiative, and negatively affect the quality and availability of healthcare services.

The study investigates the role of marketing intelligence in enhancing hotel service quality within the context of an increasingly competitive hospitality industry, Reliability testing was conducted using Cronbach's Alpha test to measure the internal consistency of the study instrument (questionnaire) as well as its reliability, adopting a descriptive-analytical methodology, data were collected through a structured questionnaire administered to a sample of 235 hotel guests in Baghdad. The empirical results demonstrate a strong and statistically significant positive correlation between marketing intelligence and hotel service quality (r = 0.876). Moreover, the dimensions of marketing intelligence namely market understanding, guest understanding, product intelligence, and competitor intelligence exerted a direct influence on key service quality dimensions, including tangibility, responsiveness, and reliability. The findings highlight that the effective adoption of marketing intelligence enhances service performance, fosters guest satisfaction and loyalty, and strengthens the sustainable competitive advantage of hotels. Accordingly, the study recommends the development of advanced marketing intelligence systems and continuous staff training to optimize their effective utilization.

As the number of Wide Area Networks (WANs) grows at an unprecedented pace, supported by cloud-native applications and AI-driven workloads, routing optimization has turned into a network engineering challenge as well as a necessity of the software engineering profession. In this paper, a hybrid optimization model of Genetic Algorithms (GA) and Tabu Search (TS) is proposed with the help of strategic oscillation to create intelligent routing paths in WANs. It is a framework that is designed to be modular, scalable and maintainable, which has adaptive modules that dynamically change the mutation rates in real time depending on live network telemetry. The proposed system, which is implemented in Python and represented by the NetworkX computation graph and bespoke algorithmic libraries, was 15% more efficient in terms of path and 33% higher in terms of convergence rate than typical GA-TS implementations. In addition to its algorithmic results, it includes best-practice software engineering features, including component decoupling, algorithm reusability and distributed-system integration readiness, that allow its use in large scale and software-defined WAN(SD-WAN) applications.

Aging is a universal biological phenomenon characterize by a time-dependent, decline in organismal function, manifested by a gradual increase in risk of disease and death (leading to increased vulnerability to disease and mortality). Age-related genetic and epigenetic changes… are critically implicated in major chronic and degenerative diseases, including cancer, cardiovascular diseases, neurodegenerative conditions, and metabolic syndromes. In this narrative review, we discuss key findings related to molecular mechanisms of aging and maturation processes influenced by intrinsic and extrinsic factors, and we highlight how these interconnected pathways contribute to human disease. An extensive electronic literature search was performed (Google Scholar databases) to include English language articles from 2015 to 2025. Studies have been chosen according to their relevance, scientific value and contribution to explaining genetic mechanisms of aging. The available evidence from the studies reviewed suggests that genetic aging is caused by a still-interacting series of processes, among which are genomic instability, telomere shortening, DNA damage accumulation, epigenomic dysregulation, mitochondrial dysfunction and changes in nutrient-sensing pathways. These phenomena are highly influenced by environmental, lifestyle and social modifications which lead to cellular senescence, tissue degeneration and increased susceptibility to diseases. Furthermore, knowledge from model organisms especially the nematode Caenorhabditis elegans has provided evidence for conserved function of insulin/insulin-like growth factor-1 signaling in life and health span regulation. In Overview, genetic aging may be interpreted as a multicomponent and dynamic process combining the individual genotype with epigenetic or environmental factors. A deeper understanding of these mechanisms is essential for identifying biomarkers of biological aging and developing interventions that enhance healthy aging and reduce age associated disease burden.

This research aims to study the role of flexible recruitment in enhancing the reputation of human resources management within organizations, focusing on the relationship between innovative recruitment practices and candidate experience, and their impact on the organization's image and competitiveness as an employer. The research adopted a descriptive, analytical, and field-based approach, collecting data from a sample of (184) individuals from human resources departments and staff at the Iraqi Drilling Company.

The study results showed that implementing agile recruitment practices significantly contributes to improving the reputation of human resources management by enhancing departmental response times, improving the candidate experience, and raising the level of innovation in the recruitment process. It also demonstrated a strong positive relationship between the use of agile recruitment tools and strategies and the organization's reputation as a distinguished employer. The research concludes that adopting agile recruitment practices represents a strategic investment for organizations seeking to attract outstanding talent and maintain a positive organizational image.

Abstract
In this study, nanostructured titanium dioxide (TiO₂) thin films were synthesized on glass substrates using the Pulsed Laser Deposition (PLD) technique (utilizing a Nd: YAG laser). The impact of aluminum oxide (Al₂O₃) doping at various concentrations (0, 1, 3, 5 wt%) on the structural and optical properties was systematically investigated. X-ray Diffraction (XRD) analysis confirmed the formation of a polycrystalline tetragonal structure for all samples. It was observed that the average crystallite size decreased with higher doping levels, correlating with an increase in the Full Width at Half Maximum (FWHM) and lattice strain. Optical characterization revealed that the optical transmittance decreases as the Al₂O₃ content increases. The pure TiO₂ film exhibited the lowest absorbance in the UV region, whereas doped films showed enhanced absorption in the visible (VIS) spectrum. Notably, the optical energy gap (E g) significantly narrowed from 3.78 eV for the pure film to 2.98 eV at 5% doping, attributed to the introduction of new localized states within the band gap. Furthermore, an increase in the refractive index and both the real and imaginary parts of the dielectric constants was observed in tandem with the doping ratio in the visible range.

This research explores the role of group learning in explaining the relationship between transformational leadership and innovative performance through a field study at the General Company for Electronic Systems. The research employed a descriptive-analytical approach, utilizing a questionnaire to measure transformational leadership (its four dimensions), group learning, and innovative performance (represented by innovation culture and team innovation). The sample consisted of 100 employees randomly selected. The reliability of the instrument was verified using Cronbach's alpha coefficient, and descriptive analysis was conducted using means and relative importance to assess the trends in the respondents' answers. The descriptive results showed high levels of ideal influence, intellectual motivation, individual consideration, group learning, and innovation culture, compared to a moderate level of inspirational motivation. Regression models demonstrated a strong and significant effect of transformational leadership on innovative performance, with high explanatory power. The results also revealed that inspirational motivation and individual consideration were the most significant contributors to explaining innovative performance compared to other dimensions. These findings indicate that maximizing the return on innovation in government institutions requires transforming leadership into systematic collective learning practices and effective implementation mechanisms that translate ideas into measurable operational improvements, thereby enhancing efficiency, reducing waste, and improving service quality.

The rapid integration of Internet of Things (IoT) technologies into healthcare infrastructures has significantly enhanced real-time monitoring and clinical automation, but it has also introduced complex cybersecurity vulnerabilities. Traditional intrusion detection systems (IDS) typically network flows independently and fail to capture relational dependencies among communicating medical devices. This limitation reduces their effectiveness against coordinated and multi-stage cyberattacks in IoT healthcare environments.

This paper proposes a temporal graph attention-based intrusion detection framework that models IoT healthcare traffic as dynamic communication graphs constructed using sliding time windows. In the proposed approach, nodes represent IoT devices, while edges represent communication flows enriched with statistical network features. A Graph Attention Network (GAT) is employed to learn structural dependencies and generate graph-level embeddings for window-based attack detection. To ensure realistic evaluation and avoid temporal leakage, a strict time-based data splitting strategy is adopted.

Experimental results demonstrate that the proposed graph-based framework consistently outperforms conventional machine learning baselines, including Random Forest, XG-Boost, and Multi-Layer Perceptron, in window-level intrusion detection tasks. Ablation analysis further confirms the impact of temporal window size on detection performance. The proposed method provides a scalable and intelligent security solution suitable for real-time IoT-enabled healthcare systems.

Background: Phishing attacks grow more complex - attackers employ social engineering that targets local customs, and they build short-lived systems so that they remain hidden. This paper proposes a method that identifies phishing inside Iraq's corner of the internet. The method merges word patterns, domain records, open source intelligence plus language markers that occur in Iraq.

Materials and Methods: We assembled 18 060 URLs - gathering addresses from local Telegram channels, from worldwide threat feeds and from confirmed safe sites. Statistical inspection revealed strong differences between phishing and safe URLs (p < 0.001). The main differences appeared in URL length, in the count of special characters and in the presence of Iraqi terms. System attributes also differed - phishing links often used young domains, unusual suffixes, as well as servers located outside Iraq. Those attributes signal campaigns that aim at Iraqi users.

Results: A Random Forest model with 200 trees and a maximum depth of 20 was trained on a stratified 70 % train, 30 % test split under 10-fold cross-validation. The model reached 96.84 % accuracy, 97.18 % recall or an ROC-AUC of 0.984. An ablation test showed that the inclusion of Iraq-specific features raised recall by 2.68 % and accuracy by 1.64 %. This confirms that regional language data adds value.

Discussion: A review of feature importance ranked Iraqi keywords next to domain age as the two most informative signals. The outcome indicates that phishing detection improves when models incorporate attributes that match the target region. The benefit is largest in emerging digital markets where attackers combine local social engineering with rapid infrastructure changes.

Forecasting is considered a crucial topic in time series analysis due to its role in supporting future decision-making through the use of statistical models to predict patterns and trends. This study aimed to employ the Support Vector Regression (SVR) model to forecast nonlinear time series using five kernel functions: Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid, by generating simulated data that mimic air pollution data. This approach was chosen because such environmental time series are often nonlinear and contain outliers, making traditional models less efficient for prediction. Therefore, the performance of the five kernels within the SVR model was compared using statistical evaluation metrics: MSE, MAE, MAPE, and R². Simulation results showed the superiority of the RBF kernel over the other kernels across different sample sizes (2000, 4000, 6000, 8000), indicating its better capability to represent nonlinear relationships and improve prediction accuracy. The study concluded that the SVR model can efficiently handle nonlinear data compared to traditional models, with a recommendation to use SVR with the RBF kernel in environmental applications such as air pollutants and energy production.

Early detection of cracks on civil infrastructure is vital for implementing preventive maintenance measures. In this paper, five different ImageNet pretrained models of the residual architecture ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNetRS-101 are benchmarked for their performance in classifying crack images within the SDNET2018 dataset. Each model is trained using two different training procedures, namely training with a frozen backbone (NOFT) and training with end-to-end fine-tuning (FT). Due to the need for false positive and false negative rates to be considered in addition to the classification accuracy, a multi-threshold analysis for both metrics was conducted. The results of this study indicate that fine-tuning improves the classification performance of all five model backbones. Furthermore, the fine-tuned ResNet-34 model exhibits the best overall performance. At a threshold of 0.60 on the fixed test set of the SDNET2018 dataset (n = 8414), the model achieves an accuracy of 96.39%, precision of 87.58%, recall of 88.68%, and an F1-score of 88.13%, in addition to area under the ROC curve and area under the precision-recall curve values of 0.9805 and 0.9411, respectively. Furthermore, the model indicates 160 false positives and 144 false negatives at this threshold. Overall, then, the fine-tuned ResNet-34 model is selected as an efficient model to be utilized as a classification gate for early crack detection prior to performing crack segmentation.

Numerous inflamed cells in the joints of people with rheumatoid arthritis (RA) release a variety of inflammatory substances. Resistin and IL-17A levels in patients undergoing and not receiving treatment have been examined in this study. 120 RA patients were studied, and divided into four groups [Group I (30 patients without treatment), group II (thirty patients receiving methotrexate ), while, group III (thirty patients receiving etanercept) and Group IV (30 patients received Rituximab)]. The control consisted of 25 individuals. Blood samples were collected to measure serum levels of IL-17A and resistin by ELISA. Group I's serum levels of IL-17A were considerably greater than those of the controls, as well as those of groups III and IV. Group I also showed a large increase in resistin when compared to controls and group III, but neither group I nor groups II and IV showed any significant changes. Resistin and IL-17 showed a strong positive association. These results show that resistin and IL-17A may be associated with the pathophysiology of RA.

This paper discusses the practical roles of spatial deixis in the story, Hills Like White Elephants by Ernest Hemingway. Despite the fact that the story has been widely examined in terms of symbolism and dialogue, little attention has been paid to the role of spatial deictic expressions in meaning construction in a particular narrative context. The research aims at answering the following question: How does place deixis work outside physical reference to inform interpersonal relations and the thematic structure of the text? It is going to determine the types of spatial deixis and examine their uses in the story pragmatically. The paper is based on the qualitative descriptive method with little frequency analysis, which relies on the proposals of deixis put forward by Yule et al. and Levinson et al., the results indicate that spatial deictic expressions are not only applied to denote location but also to create psychological distance, interactional positioning and symbolic opposition. Distal forms are used more often in conflictual dialogue, which is an expression of the emotional disengagement of the characters. The analysis proves that the concept of spatial deixis is a discourse-organizing tool, which adds ambiguity and tension to a minimalist narrative style.

This study aims to identify the role of Iraqi security television programs broadcast on satellite channels in enhancing intellectual security among the Iraqi public, in addition to examining the level of security awareness formed among members of society through following these programs. Based on the importance of Iraqi security television programs and recognizing the significant role satellite channels play for audiences in obtaining information related to security issues, whether concerning internal security, terrorism, or extremist ideologies, the researcher sought to examine the role of these programs using a scientific methodology. The study aims to identify the most prominent security television programs and the types preferred by respondents, as well as patterns and motivations for viewership. It also evaluates the level of trust and credibility attributed to the security content presented in Iraqi security television programs, in addition to determining the extent to which these programs contribute to strengthening intellectual security among the Iraqi public.

The Bell regression model is a contemporary statistical framework for the analysis of count data, especially in instances of overdispersion, in contrast to the Poisson model and the Negative binomial regression model. However, the existence of multicollinearity among explanatory variables can cause instability in parameter estimations when employing the Maximum Likelihood Estimator (MLE), leading to diminished estimation accuracy.

This paper examines the issue of multicollinearity in the Bell regression model by evaluating various estimating techniques, including the Maximum Likelihood Estimator (MLE), Bell Ridge Regression (BRR), Bell Liu Regression (BLR), the K-L estimator, and the Jackknifed K-L estimator. The efficacy of these estimators was assessed utilizing the Mean Squared Error (MSE) criterion. A Monte Carlo simulation analysis was performed utilizing sample sizes (n = 50, 100, 150, 250), five explanatory variables (p = 5), and varying correlation levels (0.90, 0.95, 0.99). The simulation results indicated that the Bell Ridge regression estimator at the shrinkage parameter (k₁) achieved the lowest mean squared error (MSE) values in most instances relative to other estimators, indicating its superior performance in the presence of multicollinearity. Furthermore, real data from prostate cancer patients were analyzed. The results indicated that the Bell Ridge regression estimator outperformed the maximum likelihood estimator (MLE) and other estimators by attaining the lowest mean squared error (MSE), thereby demonstrating its capacity to deliver more stable and precise estimates, enhance the reliability of predicting disease-related outcomes, and effectively address issues of multicollinearity and overdispersion in the count data.

There is no doubt that artificial intelligence plays an important role in the functioning of investigative and criminal courts, as well as in cybersecurity. This research addresses the impact of artificial intelligence on the legal field in all its aspects, starting from investigation procedures to the issuance and enforcement of criminal decisions. The study also examines international experiences in applying artificial intelligence within judicial systems. It further discusses the impact of cybercrimes on the judicial process and their overall effect on technological and information systems, including types of cyberattacks and aspects of preventive digital security.

In addition, the research explores how artificial intelligence can be involved in committing crimes that affect the judicial domain, as well as methods for combating such crimes and detecting suspicious activities. The study also highlights the implementation of a hybrid system within criminal courts.

To address the legislative gap in the currently applicable Iraqi Penal Code, particularly the lack of explicit criminalization of cybercrimes, the research proposes a draft law that criminalizes all acts related to information security. Finally, the study presents key findings and recommendations aimed at achieving its objectives.

This research endeavor to identify the impact of social responsibility on the quality of educational services, considering the variables of social responsibility (economic, ethical, human, and legal dimensions) and the variables of educational service quality (tangible, reliable, responsive, and safe). This was achieved through a survey of a sample of faculty members at the Administrative Technical College in Baghdad. The research employed an exploratory analytical approach, utilizing a questionnaire as the data collection tool. The questionnaire was distributed to a sample of 50 faculty members at the Administrative Technical College in Baghdad. The SPSS version 26 statistical software was used to analyse the data, calculating means, standard deviations, Spearman's correlation coefficient, and simple linear regression.
The results confirmed a significant correlation between social responsibility and the quality of educational services at the college under study. Furthermore, the results demonstrated a significant impact of social responsibility on the quality of educational services, with the level of this impact increasing proportionally when both are present.
One of the most important conclusions is that the college achieves an economic return without compromising the quality of education, as well as providing comfort for the teachers, which contributes to improving the quality of the educational service provided therein.

Finding edges is an important part of many image processing tasks, such as medical imaging, recognizing biometrics, and finding objects. But there isn't a single algorithm that works best in every case. We compared the Sobel and Canny operators on four standard grayscale benchmark images (Camera, Coins, Moon, Page) using five different metrics: runtime, edge pixel count, edge density, connected components, and average component area. Sobel was faster (8.10 ms vs. 13.28 ms) and made edges denser (8.93% vs. 6.59%). However, it also made almost twice as many connected components (469.0 vs. 217.5), which is a sign of fragmented edge maps because it is sensitive to noise. Canny always made edges that were thinner and clearer. This was especially true for the picture of the Moon (105 vs. 513 parts). Sobel is better for tasks that need speed, while Canny is better for tasks that need high accuracy, noise resistance, and edge continuity in all cases, one algorithm is not better than the other.

Artificial intelligence has been recognized as a major force in revolutionizing image processing. Hence, this paper undertakes a survey of recent image processing technologies, which mainly include Artificial Intelligence techniques like machine learning and deep learning methods. Recent AI-based technologies like Convolutional Neural Networks (CNNs) and transformer-based models have significantly raised the level of performing different image tasks such as image classification, object detection, image enhancement, and pattern recognition. In addition to the High-tech in Medical Imaging Security autonomous systems, and multimedia technologies are also regarded as the main application domains of AI-driven image processing in this review.

Highlight the main issues such as very large dataset dependence, high computational cost and incomprehensibility of models. At last, we are talking about the good ways of future research development that aim at the promotion of able, scalable and interpretable AI models. In brief, this article gives a full review of AI as one of the most effective change agents in the image processing field.

The hand-lay-up mixing/casting process was used in this study to fabricate epoxy nanocomposites reinforced with SiO₂ and TiO₂ nanoparticles. The produced composites' thermal and thermogravimetric characteristics were examined using the correct characterization methods. Preparation of epoxy/ 2%wt SiO2 and epoxy/ SiO2-TiO2 hybrid nanocomposites (HNC) was studied and investigated in this paper. The HNC were processed using different TiO2 nano filler concentrations (i.e. 0, 0.3, 0.6, and 0.9) wt%. The results of DSC showed that The Glass Transition Temperature (Tg) of pure epoxy was (85.5°C) that of increases with the increase in the filler content (wt%), while the results of (TGA) curves showed that the pure (Epoxy) underwent one stages thermos gravimetric analysis process and total weight loss was (98.76%), and the weight ratios of the added SiO2-TiO2 hybrid nanocomposites showed one and two stages of thermos gravimetric analysis of the weight loss process and total weight loss were lower compared with pure Epoxy. Because of improved interfacial contact and uniform nanoparticle dispersion, the results showed that the addition of SiO₂ and TiO₂ nanoparticles considerably improved the epoxy matrix's examined characteristics. These results demonstrate how well hybrid nanoparticle reinforcement may enhance the functionality of epoxy-based nanocomposites.

This study examines the environmental dimension of the obligation of the neighborhood in Iraqi civil law as one of the fundamental legal mechanisms for achieving a balance between the individual right of ownership and the requirements of environmental protection, particularly in light of the growing environmental challenges resulting from urban expansion and industrial development. It aims to evaluate the adequacy of the legal rules governing the obligation of the neighborhood, especially Article (1051) of the Iraqi Civil Code, in addressing modern forms of environmental harm that may arise from the use or exploitation of property.
The research is based on a central question: To what extent does the obligation of the neighborhood contribute to realizing the environmental dimension within the Iraqi legal system? Moreover, does the judiciary possess sufficient authority to adapt this obligation in a manner consistent with the requirements of sustainable development? Particular attention is given to the discretionary power granted to judges in determining what constitutes abnormal harm, according to a flexible objective standard capable of accommodating emerging environmental developments.
The study adopts an analytical approach to legal texts, supported by comparative analysis and relevant juristic opinions, in order to clarify the forms of environmental harm arising from neighborhood relations, such as pollution resulting from the improper use of property and the operation of industrial or craft activities within residential areas. It also examines the legislative framework for environmental protection in Iraq and its degree of integration with tort liability rules, while highlighting the judicial role in balancing conflicting interests.
The study concludes that the obligation of the neighborhood constitutes a flexible and evolving legal mechanism that can be effectively utilized to enhance environmental protection. However, the current legislative framework still suffers from shortcomings in defining the scope of liability, particularly with regard to the possessor of property. This calls for legislative intervention to broaden the scope of the obligation and strengthen the effectiveness of legal environmental protection. The study further emphasizes the importance of adopting modern approaches aligned with the principles of sustainable development, through activating the judicial role and updating the relevant legislative framework.

Climate Change has occurred and impacted the space available for human occupation. Cascade effects of indoor environmental conditions. This research reveals an implementation gap of Higher Education Institutions to Sustainable Development Goal (SDG) No. 11, while there is a difference between institutional commitment and practice in the area of sustainability. The study aims to make an attempt to figure out the gap in sustainability governance in HEIs quantitatively. The number of actual productions and commitments is reflected in bibliometric and governance records. The kind of investigation was explained sequential mixed methods design. In Phase 1, 1,374 publications with a DOI were identified that were related to SDG 11. The Netherlands affiliated research organizations provided the baseline, the comparators, for this study. In the institutional surveys of phase 2, the institutional leaders from 12 European HEIs took part. Using our Sustainability Governance Gap score, 74 respondents created their scores. Four purposively selected institutions were used for phase 3 for analysis through semi-structured interviews and documents. The Cronbach's alpha for the validated instruments was 0.75 or higher. A minimum link strength of 15 was used in VOS viewer for the bibliometric analysis. Each level of Governance successfully produced statistically significant positive gap scores. Pedagogical co-creation had the highest implementation gap. There were negative correlations between the Longitudinal Monitoring protocols and each of the aggregate scores of IAQ. Variance in governance gaps was accounted for by the institutional type, geographic region and building age. Higher levels of the Arnstein ladder had fewer governance deficits. Fragmented knowledge production helps sustain the governance gap for sustainability. If engagement with the stakeholders is only nominal, the degree of implementation fidelity is limited. Co-creative governance structures are needed for technical solutions. The role of monitoring is distributed across groups of participants to ensure a long-term SDG-aligned practice.

In the last decade, with the expansion of digital networks, the network traffic control problem has raised the necessity of effective and accurate techniques for its management. This work aims to compare and analyze two ensemble machine learning algorithms, XGBoost and Random Forest, for network traffic congestion classification and management. The proposed approach consisted of: data preprocessing, feature selection applying the minimum redundancy maximum relevance criterion, keeping the top 10 features, and separating the training and testing data with a ratio of 80:20. We trained and tested both models in the network traffic dataset and compared them on the basis of accuracy, precision, recall and confusion matrix. The experimental results confirm that XGBoost had the best classification accuracy 98.75%, precision of 0.987 and recall of 0.986 than Random Forest having classification accuracy 96.42%, precision of 0.965 and recall of 0.961. The main contributions of this work were to offer a benchmark of two tree-based ensemble methods for digital network traffic classification and to show that XGBoost would be a promising candidate for real-time intelligent network traffic control systems, and give some indication for network operators in terms of optimized network resource allocation and congestion reduction by means of automated, data-driven decision making.

During embryogenesis, the structural organization of tissues in the human body can be essentially specified, with the use of the most coordinated cell specification, morphogenesis, and spatial patterning. The three germ layers are ectoderm, mesoderm and endoderm that make up the developmental blueprint of all tissues and organ systems. At the microscopic and macroscopic level, the tissue architecture is shaped by early molecular gradients, epigenetic programming and biomechanical forces, which are structural prerequisites for physiological specialization. This review discusses in detail the developmental basis of tissue architecture and critically examines the role of developmental patterning in shaping adult physiological function. Specific attention is given to specification of germ layer, signaling networks, epithelial such as mesenchymal interactions, organogenesis, vascularization, and neurofunctional integration. Moreover, the translational uses of developmental biology in congenital pathology, regenerative medicine, and systems physiology are addressed. Knowledge of the developmental determinants of the tissue structure provides a coherent structure that connects embryology, histology, physiology and clinical medicine.

Hydrological analysis tools successfully delineated flow paths, flow direction, and runoff accumulation patterns. Through these tools, the number of sub-basins was identified, with variations in area and spatial distribution, resulting in differences in their contribution to the main drainage channel through surface runoff. In conclusion, the results indicate that the use of these techniques provides an important scientific basis for the study and analysis of watersheds and contributes to supporting water resource management, environmental planning, and flood risk reduction.