The role of green libraries and information technology in promoting environmental awareness in Iraq
The research titled "The Role of Green Libraries and Information Techniques in Enhancing Environmental Awareness in Iraq" explores the significance of green libraries as vital centers for promoting environmental awareness among community members. The study employed a descriptive analytical methodology, collecting data from a sample of 120 respondents. The findings revealed a strong positive correlation between the number of visits to the library and the level of environmental awareness, with a correlation coefficient (r) of 0.95. This indicates that an increase in library visits leads to an increase in environmental awareness by an average of 0.6 points for each additional visit. Furthermore, the study demonstrated that green libraries play a pivotal role in providing information and resources related to environmental issues, thereby contributing to the enhancement of knowledge and interest in environmental matters among individuals. Several factors influencing the effectiveness of libraries in this context were identified, including the availability of educational materials, the quality of programs offered, and community engagement with libraries. In light of the findings, the study recommends the development and implementation of educational programs and workshops in libraries aimed at increasing environmental awareness, with a focus on interactive activities that encourage active participation. It is also advised to strengthen collaboration between libraries and educational and environmental institutions to expand the scope of environmental programs and initiatives. These recommendations aim to enhance the role of green libraries as educational centers that contribute to building an environmentally conscious society, thereby supporting sustainable development in Iraq.
“Strategic and Financial Management Integration in Enhancing Banks’ Competitive Advantage”
This research aims to study the relationship between the integration of strategic and financial management and its role in enhancing the competitiveness of Iraqi government banks, particularly Al-Rafidain and Al-Rasheed banks in Baghdad, within a rapidly changing and increasingly competitive banking environment. The research adopted a descriptive-analytical approach, and primary data were collected through a questionnaire specifically designed to measure the two study variables. The questionnaire was distributed to a purposive sample of (60) employees and administrators in two government banks in Baghdad: Al-Rafidain Bank and Al-Rasheed Bank. The data were analyzed using the Statistical Package for the Social Sciences (IPSS, Version 20). The strategic and financial management integration variable was measured through four main dimensions: integration between strategic and financial planning, integration in strategic and financial decision-making, integration in resource management and capital allocation, and integration in control systems and performance evaluation. The competitiveness enhancement variable was measured through the operational and financial efficiency dimensions within the government framework, the quality of banking services in the public sector, and the trust and institutional reputation of the government bank. The results of the statistical analysis, using multiple regression analysis, showed a significant impact of strategic and financial management integration, with its four dimensions combined, on enhancing the competitiveness of banks. The coefficient of determination (R²) indicated that the strategic and financial management integration variables explain 80.4% of the variance in enhancing the competitiveness of the banks studied. The research concludes that strategic and financial management integration is a fundamental framework for enhancing the competitiveness and sustainability of Iraqi government banks. It recommends adopting this integration as a comprehensive management approach in Al-Rafidain and Al-Rasheed banks to improve banking performance and support financial stability and economic development.
The Impact of Adhering to The Professional Framework Standards, According to The Guidance Manual, on Enhancing the Auditor's Reliance on Internal Audit Activities in Support of The Quality of Financial Reports
This research aims to demonstrate the impact of adhering to the professional framework standards for internal auditing, based on the guidelines issued by the Federal Board of Supreme Audit, on enhancing the external auditor's reliance on internal audit activities and outputs, and the resulting impact on the quality of financial reports in Iraq. The research's significance stems from the challenges faced by auditors due to the expanding scale and complexity of economic units' operations, and the trend towards adopting international auditing standards, particularly INTOSAI Standard 2610 and International Standard on Auditing (ISA 610) concerning the use of internal auditor activities. The research employed a descriptive-analytical methodology and was conducted through a questionnaire distributed to a sample of internal and external auditors in economic units belonging to the public and mixed sectors across several key sectors, including banking, services, industry, agriculture, hotels and tourism, education, insurance, and healthcare. A total of 209 questionnaires were deemed valid for statistical analysis. The results showed that internal audit practices focus on acquiring skills and knowledge related to detecting errors and fraud, while exhibiting a relative weakness in organizational independence and adherence to quality requirements. This limits the degree to which external auditors rely on internal audit work. The research recommends strengthening adherence to the standards of the Professional Framework for Internal Auditing and activating the provisions of the guidance manual to support integration between internal and external auditing and contribute to improving the quality and credibility of financial reports.
The Impact of Artificial Intelligence Applications on Promoting Sustainable Marketing An Analytical Field Study at Asiacell - Iraq
This study examines the role of artificial intelligence (AI) applications in enhancing sustainable marketing by analyzing the impact of artificial neural networks, expert systems, and intelligent agents on its environmental, social, and economic dimensions. A descriptive-analytical approach was adopted, and data were collected through a questionnaire from a purposive sample of (80) employees at Asiacell Telecommunications Company in Iraq. Data were analyzed using SPSS (V.25), employing descriptive statistics, Spearman correlation, and simple regression analysis.The findings revealed a significant positive relationship and effect between AI applications and sustainable marketing, with a correlation coefficient of (0.71) and a coefficient of determination (R²=0.51). The study’s originality lies in providing empirical evidence from the Iraqi telecommunications sector on the role of AI applications in supporting sustainable marketing practices.
Factors affecting the quality of accounting profits during economic crises
This research aims to study the factors affecting the quality of accounting earnings during economic crises, focusing on the impact of internal factors such as corporate governance and the quality of internal controls, and external factors such as the economic environment, legislation, and the quality of external audits, in addition to company characteristics (size, indebtedness, and sector). The research adopted a descriptive-analytical-causal approach using secondary financial data, audit reports, and questionnaires administered to financial management and accountants. The results showed a significant impact of both internal and external factors on the quality of accounting earnings. The study indicates the necessity of strengthening internal controls and effective governance, and implementing rigorous external audit mechanisms to maintain the credibility of financial statements.
Satellite Media and the Tourism Promotion System in the Arab World: Towards an Analytical Model for Measuring the Effectiveness of Television Content in Developing Tourism Awareness
Arab countries possess enormous tourism potentials, yet they remain absent from global tourism competitiveness rankings due to deficiencies in specialized tourism media. This study aims to develop an innovative Arab analytical model for measuring the effectiveness of satellite television content in developing tourism awareness, transcending traditional models limited to the promotional dimension. The study adopted a sequential explanatory mixed-methods design, combining content analysis of 135 program episodes, a questionnaire administered to 450 viewers from four Arab regions, in-depth interviews with 15 experts, and focus groups. The results revealed a structural duality in Arab tourism media; the aesthetic dimension recorded the highest mean (4.12), while the cognitive dimension ranked lowest (3.24). Structural Equation Modeling (SEM) confirmed the validity of the proposed four-dimensional model, explaining 58% of the variance in visit intention with statistically significant path coefficients (p < 0.001). The Expectation-Reality Gap Index (ERGI) revealed sharp disparities among channels, ranging from high credibility (0.17) to critical inflationary gaps (0.77). The study concluded that building sustainable tourism awareness requires an integrated model balancing aesthetic, cognitive, value-based, and behavioral dimensions while committing to credibility in portraying reality.
The Impact of Adopting Green Manufacturing Strategies on Achieving Competitive Advantage: An Analytical Study at the General Company for Food Products/Abu Ghraib Dairy Factories
This research aimed to determine the impact of adopting green manufacturing strategies (reduction, reuse, remanufacturing, and recycling) on achieving competitive advantage, as measured by its dimensions (cost, quality, flexibility, and delivery), at the General Company for Food Products/Abu Ghraib Dairy Factories. The research employed adescriptive-analytical approach, utilizing a questionnaire with a five-point Likert scale. The questionnaire was distributed to a sample of (60) managers at the Abu Ghraib Dairy Factories. Data analysis was performed using the SPSS., V.26 statistical software to calculate means, standard deviations, Spearman's correlation coefficient, and simple linear regression.The results confirmed asignificant correlation between green manufacturing and competitive advantage at the studied factories. Furthermore, the results demonstrated asignificant impact of green manufacturing on competitive advantage, with the level of this impact increasing when both are present. One of the most prominent conclusions reached by the research is that green manufacturing, with its four strategies, plays an important role in enhancing and building the reputation of the organization and contributes to achieving a competitive advantage. Despite the company under study adopting green manufacturing strategies, it suffers from weakness in implementing some strategies such as recycling, and it must work hard to provide high-quality, environmentally friendly and safe green products for customers, which will ensure that it achieves a competitive advantage and gains customer satisfaction and loyalty.
Design of Privacy-Preserving AI-Driven SDN Controller Using Federated Learning for Intrusion Detection
The rapid development of network technologies and the growing sophistication of cyber threats have emphasized the necessity of smart, scalable and privacy-sensitive security solutions. Software-Defined Networking (SDN) is a system that offers centralized control and visibility of the network globally and as such is a fitting platform on which to build complex security mechanisms. Nevertheless, SDN-based traditional intrusion detection methods usually involve a centralized amount of data collection, posing serious questions about the privacy of data and scaling. This paper suggests designing a privacy-sensitive Artificial Intelligence (AI)-controlled SDN controller with Federated Learning (FL) to detect intrusions in a distributed manner. The SDN controller in the proposed architecture is complemented with smart modules, comprising of intrusion detection system (IDS), federated learning aggregation unit, and a decision engine. The distributed controllers are trained on their own data to obtain a local model (Deep Neural Network) and only the model parameters are communicated to a central controller to be aggregated. The model is tested with the help of the NSL-KDD dataset, in which the network traffic is categorized as normal and malicious. Experimental outcomes prove that the suggested FL-based model has high detection capabilities, with the accuracy of 96% and precision of over 90, which is better than the performance of the traditional non-federated models. The suggested system is successful in terms of data privacy, lowering the communication overhead, and increasing the scalability with a high detection accuracy. These findings suggest the implementation of federated learning in an AI-based SDN controller offers a powerful and effective framework to current network security.
Age Estimation based on Dental Radiographs using Hybrid CNN
Nowadays, computer vision had been inserted on a large scale in multiple applications in several fields, such as age estimation using dental images. Age estimation based on Convolutional-Neural-Networks (CNN) is a crucial aspect of forensic dentistry, by analyzing the growth and development of teeth. Recently, different techniques based on CNN had been integrated with multiple approaches to extracting a various feature of human and used for age estimation. In this paper, a Convolutional Neural Network was developed according to two scenarios to estimate an age based on dental radiographs. In the first scenario, a CNN classifier is proposed for age estimation from dental images, in the second scenario, the CNN classifier is modified by replacing the last dense layer with a logistic regression classifier to enhance performance. The results showed that the effectiveness of the proposed method in improving the accuracy of age estimation, such that its improved to 95.15 % and the performance measures improved such that: the Precision by 1.83%, the Recall by 2.82% and the F1-score by 2.16%.
Evaluating Machine Learning Algorithms for Fault Detection in Solar Cells: Performance and Limitations
The extensive distribution, the penetration and popularity of PV have turned fault diagnostics from a general inspection into an operation necessity. With a larger PV system that becomes ever more complex, undetected fault can cause cumulative power failure with lower system performance and hence cost, especially in situations where real‑time monitoring is a prerequisite. Whilst infrared thermography, electroluminescence (EL) imaging, and current–voltage (I–V) curve analysis are common diagnostic methods, their automated implementation in industrial‑scale PV plants remains limited by practical and computational bottlenecks. We propose a multimodal fault diagnosis framework that incorporates EL image analysis with infrared thermal measurements and electrical I–V data collected by operating PV systems. Instead of depending purely on a single diagnostic method, this framework studies the performance metrics across the learning paradigms for identical experimental conditions. Based on the same dataset, a convolutional neural network (CNN) is applied and compared with established machine learning models, i.e. support vector machines (SVM) and extreme gradient boosting (XGBoost). In experimental testing, we use 2,624 real EL images. The results are as follows: CNN models have the best level of classification at 95.2%, whereas inference speed should be compatible with edge‑based processing needs. Additionally, CNN architectures are more conducive to pattern recognition (spatial fault prediction) for image data, whereas XGBoost is competitive for the structured numerical features used in predictive maintenance. The presented framework is a practical option with a high sensitivity to photovoltaic system accuracy with a low computational cost. Its results are in favor of real‑time operation and large‑scale industrial deployment, which needs both detection performance and resource constraints, respectively.
Improving Response Times in Non-Invasive Brain-Computer Interfaces (BCIs) for Spatial Computing Environments
The continuous improvement and enhancement in spatial computing devices is unquestionable, but the present use of non-intrusive Brain-Computer Interfaces (BCIs), including electroencephalography (EEG), is still substantially hindered by the problem of high latency that leaves the brain-computer interaction lagging behind perception. Experiencing more than 250 milliseconds of latency, this condition results in an incongruity of senses that lowers the quality of the user experience. This article introduces a dual processing system that merges spatially efficient, low-powered Transformer models with the Edge Computing paradigm. With the help of 50 users in a mixed-reality setting, it was established through the experiment that the suggested system cut down the overall latency time to an average of 85 ms, and at the same time, it was possible to achieve a motor imagery classification accuracy of 92%. The results here represent a crucial step towards the smooth adoption and incorporation of real-time BCIs into spatial computing systems.
Recent Trends of Developing Human Resource Management under Artificial Intelligence framework
The current paper aims to investigate the global latest movement of developing human resource management systems in parallel with the rapid transferring into the adoption of artificial intelligence in all aspects of life i.e., healthcare, education systems, business processes, logistic chain processes…etc. Since Arabian region presents around (10.2%) of the world in two continents and its papulation around (5%) of total global population which leads it to compete towards change and develop as others, so, this paper could be a good guide to perform a review of the scientific articles in Arabian libraries that related to the same terms of artificial intelligence and human resource management for the last two decades to understand the current and the coming steps that should follow artificial intelligence recently has its impact of the field of human resource management with gently growth towards positive future horizon that represented by human resource analytics. The objective of this article is to scan academic works systematically and trying to submit a comprehensive vision of the future of artificial intelligence usages in human resource management in Arabian Region.
Lightweight Privacy-Preserving AI Using Hybrid Homomorphic Encryption: Design and Evaluation of Guard AI
Artificial Intelligence (AI) has quickly revolutionized many industries, yet the growing susceptibility of AI models to adversarial attacks and privacy breaches is a significant obstacle to its proliferation. Homomorphic Encryption (HE) and other Privacy-Preserving Artificial Intelligence (PPAI) algorithms, which can compute on encrypted data, facilitate the implementation of privacy-preserving algorithms, but their implementation and application are typically limited by high computational and scaling costs, particularly on resource-limited systems. To overcome these drawbacks, this paper proposes a lightweight privacy-preserving AI model, Guard AI, on a Hybrid Homomorphic Encryption (HHE) scheme, which integrates both symmetric and homomorphic operations. The proposed design will minimize the computational complexity but ensure high data confidentiality when inferring the model. Guard AI is specially designed to support edge and low-resource devices and can classify data safely on encrypted data with no exposure of sensitive inputs or model parameters. To test the proposed framework, the given framework is applied to a practical healthcare application of health care use case on heart disease classification based on electrocardiogram (ECG) signals, which is very sensitive and highly vulnerable to privacy breaches. Through experiments, it is proven that the proposed HHE-based solution is characterized by a good trade-off between security, efficiency, and accuracy, where communication and computation overhead are low in comparison with other, HE schemes, whilst being competitive in comparison with inference without encryption. On the whole, this paper offers a scaled and effective framework of implementing privacy-sensitive AI systems in resource-constrained settings, indicating the promise of hybrid encryption methods in facilitating secure and lightweight intelligent systems.
Modeling Latent Variance in Production Line Performance Using High-Frequency Data an Application to the Iraqi State Company for Cement Industry and Its Effect on Industrial Output Stability
The current research proposes a latent variance in the performance of the production lines as the high-frequency data on operations of the Iraqi State Company of Cement Industry and assesses the impact of this factor on the stability of industrial output. Signals that are constructed on the short-interval production signals are used to construct line performance changes and modeled using GARCH(1,1) to estimate volatility clustering and persistence. The conditional variance is estimated and the result is called the Latent Variance of Line Performance (LVP) and is interpreted as a proxy of operational risk that captures the fact that there is unstable production process. Industrial Output Stability (IOS) is an index of limited stability based on dispersion in achieved output across time, and Unplanned Downtime Ratio (UDR) and Specific Energy Consumption (SEC) are added to moderate reliability shocks and energy intensity. Time-series inference is supported by unit root testing that establishes that the study variables are stationary. The results of quantile regression at τ = 0.30 indicate that LVP has significant negative impact on IOS which suggests an increase in the latent performance volatility decays stability even when the factor of downtime and energy consumption is taken into account. UDR has a huge destabilizing influence, and SEC also has a negative impact, implying that energy inefficiency is accompanied by the unstable regimes of operation. The results indicate that volatility-based indicators can be excellent monitors to signal instability episodes and preventive maintenance and energy management policies to stabilize the output of industries in the cement manufacturing industry.
Analyzing Product-Mix Change Decisions Based on Realized Demand Data and Operational Constraints an Application to the Iraqi State Company for Food Industries and Its Effect on Operational Value Maximization
The paper investigates the impacts of product-mix change decisions using the actual demand information and operational limitations on the optimization of the operational values of five food-industry firms in Iraq throughout 20152025. The analysis applies the operational indicators that are measurable to capture the intensity of the portfolio reallocation using Mix-Shift Index (PMIX_MSI), market-production mismatch using Realized Demand Gap Rate (RDG_RATE), bottleneck pressure using Constraint Utilization Rate (CU_RATE), as well as the operational quality of the chosen mix using the index of the Weighted Contribution Margin (WCM_IQDK), whereas the dependent variable is operationalized as Realized Operational Value (OV_IQD_BN). The tests of cross-section dependence point to a number of series co-moving across firms, in line with exposure to common shocks, and unit root tests of stationarity are considered based on dependent panel-appropriate unit root procedures. The paper then estimates an ARDL error-correction specification in order to capture short-run and long-run equilibrium effects. The findings show that increased bottleneck utilization and robust product-mix reallocation are linked to high long-run operational value, and continued experienced gaps in realized demand diminish value. The significant error-correction term provides a consistent long-run relationship and a systematic adjustment procedure towards equilibrium after the disturbance of demand or capacity. The study suggests building a cycle of realized-demand-driven product-mix review, the adoption of product selection and pricing based on the profits of the bottlenecks, and a small dashboard of mix, mismatch, and constraint indicators that will decrease demand gaps and enhance the results of operational value.
Emotional Marketing in Digital Space: A Domain Review of Effective Factors
This paper aims to identify a comprehensive landscape of theoretical and empirical development in the field of emotional marketing from 2010 to 2025. The methodology used is a Domain Review (Domain Review), which involves a systematic analysis of the content of 34 selected international articles sourced from prestigious academic databases. The data were extracted based on the subject criteria, methodology and Main results, and later classified in the Domain Review table. The thematic analysis identified nine macro-categories in the existing literature, the main of which are: the role of emotions in consumer behavior (29.4%) and the emotional bond/correlation between the brand and the consumer (26.5%). The results indicate that emotional marketing has escalated from a tactical level based on advertising messages to a strategic and technological one, and is now intertwined with concepts such as dynamic capabilities, organizational empathic ability, and new technologies such as artificial intelligence (AI) and virtual reality (VR). This study provides a theoretical and methodological basis for the development of new frameworks in emotional marketing.
Medical Image Compression Algorithm Using Edge-Aware Adaptive Block Partitioning
: As the world witnesses a major advance in medical staff diagnosis depending on medical images, including MRI and CT scans, it requires having compression approaches that balance between preserving essential medical information and reducing medical image data size. Such approaches that depend on local variance for the adaptive block partitioning mechanism , which is distinct form the classic method, like JPEG , in how usually experience blocking artifacts and loss of fine details. Instead of utilizing explicit edge detection, the algorithm uses both integer wavelet transform (IWT) and adaptive bit allocation to differentiate between smooth non-structural areas and structural regions containing vital edges. The algorithm shows superiority through applying the proposed algorithm on several medical images by achieving a Structural Similarity Index (SSIM) consistently exceeding 0.996 and an average Peak Signal-to-Noise Ratio (PSNR) of 48.01Db, which overcomes the performance of the JPEG standard in structural fidelity with the accomplishment of as equiponderant compression ratio (averaging 1.58) . The proposed algorithm is ideal for clinical application where accurate reconstruction in crucial, keeping diagnostically important structures and preserving tissue outlines.
Hybrid Blockchain-AI Framework for Real-Time Mitigation of Zero-Day Attacks in Software-Defined Networks
Background: Software-Defined Networking (SDN) centralises control logic, improving flexibility but exposing the controller and its interfaces to sophisticated zero-day attacks that traditional, signature-based defences struggle to detect in real time. Aim: This paper proposes and evaluates a hybrid Blockchain-AI framework designed to detect and mitigate zero-day attacks in SDNs with high accuracy and auditable, decentralised enforcement. Methodology: The framework combines an autoencoder-classifier ensemble for traffic analysis with a permissioned blockchain that records alerts and mitigation actions as tamper-evident transactions. An SDN testbed with 20 switches, 200 hosts, and 140,000 flows (50% benign, 45% known attacks, 5% synthetic zero-day) is used to compare the proposed solution against a threshold-based IDS and an AI-only baseline. All models were trained on an Intel Xeon E5-2680 v4 server (14-core, 2.40 GHz, 64 GB RAM) running Ubuntu 20.04 LTS with Python 3.9 and TensorFlow 2.11. Results: The hybrid framework achieves a zero-day detection rate of 91.8% with an F1-score of 0.896, compared to 87.1% (F1 = 0.856) for the AI-only system and 41.2% (F1 = 0.398) for the traditional IDS. Overall accuracy reaches 98.7%, with a false positive rate of 1.4% and a false negative rate of 5.8%. End-to-end security response time averages 115 ms, including blockchain confirmation, while benign throughput remains above 4,600 flows/s at 5,000 flows/s load. Cross-validation (5-fold) confirms these results, with an average zero-day F1-score of 0.891 ± 0.012 (95% CI: [0.879, 0.903]). Conclusion: The results indicate that integrating AI-based anomaly detection with blockchain-backed coordination significantly improves zero-day mitigation in SDN while preserving acceptable latency and throughput. The hybrid design offers a promising foundation for building transparent, resilient, and self-defending programmable networks.
Exploring Digital Signature Methods: A Comprehensive Review
Because of the digit development height since (2019) and the result market demand for data security investigate, it grows speedy and more than adequately with an obligation to form a huge scope of electronic mark examine. These range from classical embedded designs, through revocable and certificate-linked signatures to aggregate signatures, multi-party signatures for the wide system setting and finally privacy preserving blind signatures. It also includes post-quantum methods and proposals for blockchain environments and embedded systems, traffic, and vehicle areas. The selected and compared research works are presented in this manuscript for the years 2023 to 2025 by jointly collecting them on different features including key generation or setup cost, signature size, signature time, cycles of computation, verification time and communication cost as well as system-level load reduction. The equivalency illuminates that no one approach performs best on all measures. For aggregate and hierarchical schemes, more signature scalability is yielded at the cost of larger signatures compared to classical embedded signature schemes. We further remark that post-quantum designs are becoming more suitable and suitability-oriented through providing tech-practical trade-outs for blockchain, IoT, to a lesser extent also hardware delegates. (EdDSA) and (ECC) will still be good preferred choices for applications which require high efficiency with reasonable to strong security, while (RSA) will still stay a suitable candidate & preferred choice for legacy systems or situations were compatibility and compliance are critical driver as always.
The Formal Characteristics of Auxiliary Verbs
This paper tries to pin down the formal characteristics of auxiliaries. It introduces important grammarians who try to talk about this important grammatical issue. The paper interestingly tries to show how these auxiliaries work in sentences, perceptively dividing them into primary and modal auxiliaries where in the first kind BE contributes to aspect and voice and have to aspect only. Disengagingly, it is shown that the modal auxiliaries contribute to volition, probability and obligation. Modal Auxiliaries are referred to as signalling that a verb is about to follow. Certain verbs are isolated as being auxiliaries in “some of their uses.” Non-conjugational items are singled out as modals and the rest as non-modal operators. Insightfully, Systemic Grammar also tries to show an inviting interest in auxiliaries. Relevantly, it is maintained that the auxiliary verb stands first in a discontinuous verbal group [V]. Accordingly, Auxiliary Verbs are morphologically classified into two categories: “Non-Modal” and “Modal” and insightfully four criteria are mentioned by which auxiliaries are classified, namely (1) negation (2) inversion (3) substitution and (4) marked positive element. The paper ends by insightfully ushering us to very important understanding of auxiliaries where eleven auxiliaries are isolated as having twenty eight forms whose occurrence is cleverly realised in four syntactic structures, namely ‘negation’, ‘inversion’, ‘code’ and ‘emphatic affirmation’. The neutral ‘empty’ auxiliary do is suggested to pass all the tests. In an inviting and subtle sense, the paper also looks at these auxiliaries from the point of view of Systemic Grammar as well as of Transformational Grammar where the latter cleverly speaks of deep and surface structures. An insightful conclusion ends this work, synthesising things through bringing together the main thematic strands.
Protection of Bank Deposits through Securing Bank Capital
This research addresses the protection of bank deposits through securing bank capital, considering that bank capital represents one of the most important legal and financial mechanisms that contribute to strengthening the stability of banking institutions and ensuring their ability to fulfill obligations towards depositors. The importance of this topic has increased in light of the financial and economic risks facing the banking sector, which may affect the capacity of banks to safeguard depositors’ funds and maintain financial stability. The study analyzes the legal and regulatory framework governing bank capital and capital adequacy requirements, highlighting their role in strengthening the financial soundness of banks and reducing the risks of bank insolvency. It also examines several mechanisms that support bank capital, including capital increases, banking reserves, risk management systems, credit and investment policy controls, as well as compliance with banking governance standards and financial disclosure requirements. In addition, the research discusses the role of deposit guarantee systems in enhancing confidence in banking institutions. The study concludes that securing bank capital constitutes a fundamental pillar for the protection of bank deposits and the achievement of financial stability. Strong capital bases enhance banks’ ability to absorb financial shocks and manage risks effectively, while also strengthening depositor confidence in the banking system. The research ultimately presents several findings and recommendations aimed at improving the legal and regulatory framework governing bank capital in order to enhance depositor protection and ensure the stability of the banking system.
The Impact of Entrepreneurial Alertness on Enhancing Competitive Advantage: A Field Study of a Sample of Tourism Company Managers in Baghdad City
This study investigates the strategic nexus between Entrepreneurial Alertness (EA) and Competitive Advantage (CA) within the tourism sector in Baghdad. It establishes a comprehensive theoretical and operational framework designed to assist firms in identifying market contingencies and converting them into sustainable competitive edges amid an increasingly volatile business landscape. The research problem highlights a significant lag in the responsiveness of local tourism entities to global market transformations, primarily due to the persistence of conventional management paradigms that lack the requisite "vigilance" to capitalize on emerging opportunities. This inertia has compromised their market position against international conglomerates and digital disruptors. Adopting a descriptive-analytical approach, the study surveyed a purposive sample of (108) executive managers and decision-makers. Data were processed using SPSS (v.25) and Amos (v.25), employing Structural Equation Modeling (SEM) for hypothesis testing. The findings underscore that fostering "Entrepreneurial Intuition" is no longer optional but a strategic imperative for leadership. Specifically, the results indicate that organizations exhibiting proactive alertness toward disruptive technologies—notably Artificial Intelligence (AI) and Augmented Reality (AR)—consistently outperformed their rivals. Conversely, a critical deficit was identified in the strategic mobilization of latent resources to exploit newfound opportunities The research advocates for the institutionalization of "Entrepreneurial Vigilance" through the integration of advanced market intelligence systems and the cultivation of strategic alliances with academic and professional bodies. Furthermore, the study emphasizes the necessity of a radical digital pivot and the alignment of human capital development with global benchmarks to ensure enduring competitive excellence.
The role of digital content in enhancing internal audit procedures
This research came to demonstrate the role of digital content in enhancing internal audit procedures and its impact on improving the efficiency of internal audit in economic units, due to its importance in improving the quality of financial information. The research aims to formulate a conceptual framework about digital content and internal auditing, clarify the relationship between them, and show the effect of the absence of digital content on internal auditing. The research relied on two methodologies: the first is the deductive method to present the theoretical aspect through the use of international standards, studies, and related research, while the second is the inductive method through studying the reality of using digital content in internal auditing at the City of Medicine Department, and testing the research hypothesis. The research was based on designing a checklist to measure the availability and use of digital content in the units under study, and the extent to which this reflects on internal audit procedures. The research reached a set of conclusions, the most important of which is that the absence of digital content leads to a decline in the effectiveness of internal auditing and prevents achieving the required development in the modern auditing environment. Among the most important recommendations is the necessity to issue instructions that mandate the use of digital content
Expiration of the disbursement obligation in the trade transfer without the fulfillment of the fulfillment
This research deals with the provisions of the expiration of the disposable obligation arising from the commercial transfer in Iraqi law without the actual monetary payment being achieved, indicating the alternative paths that lead to the discharge of the debtor's debt. The research is divided into two axes: the first examines the reasons derived from the general rules in the civil law that are equivalent to the fulfillment of the obligation, such as the payment of consideration, clearing, consolidation, and renewal, and the extent of their suitability to the creditworthy nature of the commercial paper. While the second axis focuses on the pure exchange penalties leading to the expiration of the obligation without the fulfillment of the right. It is represented in the moratorium caused by the holder's neglect of the dates for submitting the hawala or the issuance of the protest, and the expiration of the statute of limitations over time. The importance of the study lies in the legislator's balance between credit protection and the stability of commercial transactions
An Advisory Computerized Rule-Based System for Defects of Joints in Rigid Pavement
Rigid pavement is preferable over other types of pavements where traffic loads are high due to its high rigidity. However, joints are unavoidable when rigid pavement is adopted, as they control thermal movements that can cause severe damage to concrete. Although they have advantages, joints are weak points, as they may be affected by traffic or other external conditions, such as environmental factors. If joint defects occur, they may reduce the driving comfort or lead to structural damage on pavements. Therefore, controlling problems in joints is highly required. However, this process requires significant expertise, which is not always available when and where required. Therefore, providing a practical alternative is greatly recommended. Hence, this study aims to provide the required alternative by constructing a computerized rule-based system capable of diagnosing defects and advising an appropriate solution. To attain the study aim, several steps were adopted. First, the domain knowledge was collected from various references through extensive extraction and from experts through several interviews and questionnaires. The collected knowledge was analyzed, classified, and represented in a rule-based form. Afterward, the represented knowledge was converted into a computer program using Visual Basic. The built system was evaluated by highway engineers with varying levels of expertise and was found to be valid to about 90%. The constructed system is available to the pavement engineer to treat defects with minimal effort and time. It can also be adopted as an archive for domain knowledge. In addition, it can be used as a foundation for future systems in similar areas.
FPGA-Based Adaptive Encryption Architecture for Secure and Low-LatencyV2X Communication in 5G Networks
With the ever-growing demand for ultra-low-latency communication in Vehicle-to-Everything (V2X), traditional software-based approaches to encryption have become an increasingly challenging subject. We propose the current adaptive encryption architecture in FPGA to enhance the real-time functionality of vehicular networks in 5G and beyond. Through the parallelism and reconfigurability properties of FPGAs, the proposed model dynamically alters cipher algorithms and key scheduling based on message priority and network situation. A modular encryption core, real-time key scheduler, and stream/block controller are developed on a Xilinx Zynq-7000 System-on-Chip (SoC)for fast and flexible cryptographic processing. Experimental performance in a simulated V2X traffic demonstrates a 42% decrease in latency and 36% increase in throughput with respect to standard microcontroller solutions, in full conformance to European Telecommunications Standards Institute Intelligent Transport Systems - G5 (ETSI ITS-G5) and 3GPP V2X. It also takes advantage of runtime cipher reconfiguration and is resistant to timing side-channel and replay attacks. These findings emphasize the feasibility of the proposed approach for secure deployment in the emerging intelligent transportation systems.
Optimal PID Controller Design for 3-DOF Robot manipulator
The most difficult task for a robotics researcher is to control the robot manipulator with adequate overall efficiency. This work presents accurate and fast techniques for optimizing the parameters of a 3-DOF robot manipulator, this kind of robot is intended for academic and educational work and it is also used in the medical field. The links of the 3- DOF are all rotary or revolute joints with serial connections. The optimization technique focuses on the Intelligent Swarm algorithms that are used for optimizing and tuning the gain of the proposed controller, which is the conventional Proportional Integral Derivative (<!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>PID</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.5pt; mso-text-raise: -5.5pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:18.75pt; height:16.5pt'> <v:imagedata src="file:///C:/Users/MAANKH~1/AppData/Local/Temp/msohtmlclip1/01/clip_image001.png" o:title="" chromakey="white"/> </v:shape><![endif]--> ) is used. The Intelligent Particle Swarm Optimization (<!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>PSO</m:r></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.5pt; mso-text-raise: -5.5pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:20.25pt;height:16.5pt'> <v:imagedata src="file:///C:/Users/MAANKH~1/AppData/Local/Temp/msohtmlclip1/01/clip_image003.png" o:title="" chromakey="white"/> </v:shape><![endif]--> ) and Social Spider Optimization (SSO) have been used for obtaining the best values for the parameters of the controller, to achieve consistency, stability, and robustness. MATLAB/Simulink is used to speed up the computing of FK, IK, and DM of the 3-DOF robot manipulator. The results show that modified PID /SSO gives the best results in terms of stable phase response, minimum Root Means Square Error with the best objective function, stable control signal and reasonable steady-state with perfect trajectory tracking for circular path.
Blockchain for Enhancing Trust in E-Government Services
This research introduces and tests a blockchain-based approach for building citizens trust in e-government services. Using the mixed-methods research design in a two-stage approach, we first conducted the expert focus groups (n=12) to generate a conceptual model of the relationship between perceived security (PS), transparency (TR), data integrity (DI), and procedural fairness (PF) with trust in service (TS), intention to adopt (AI), with governance quality (GQ) as a contextual moderator. Then we did a survey of 412 active users of public digital services and analyzed this data using PLS-SEM. The reliability and validity of the measurement model was good (alpha=.82-.91; AVE=.59-.76). The structural model accounted for 68% of TS and 61% of AI (SRMR=.056; NFI=.90). All antecedents were positively associated with TS (betaPS=.27; betaTR=.23; betaDI=.18; betaPF=.22; all p values <.05), and TS was strongly associated with AI (beta=.63, p <.001). TS mediated all the effects of PS/TR/DI/PF on AI to a full extent, and GQ significantly reinforced the connection between PS and TS (beta=.14, p<.05). We also present a four-layer framework (infrastructure, service, governance, user) to achieve a combination of auditability on-chain and the off-chain PII along with zk-compatible roadmap. The results imply that blockchain can be used to build positive influences on trust embedded in transparent, accountable, and privacy preserving governance.
GIS-Based Entropy–TOPSIS Ranking of Centralized Rainwater Harvesting Sites Using DEM-Derived Hydrological Indices: Karbala Province, Iraq
Water shortage has been a significant issue in arid and semi-arid areas owing to the uneven rainfall distribution, population explosion, and mounting pressure on the traditional water resources. Centralized rainwater harvesting (RWH) systems represent a technically feasible and sustainable approach for augmenting water resources; however, their effectiveness largely depends on the careful selection of suitable locations. Accordingly, this study proposes a GIS-driven decision-support model that integrates DEM-derived hydrological indices with an objective multi-criteria decision-making (MCDM) framework to identify suitable sites for the implementation of centralized RWH systems in Karbala Province, Iraq. Based on Shuttle Radar Topography Mission (SRTM) data, and spatial analysis, six physically meaningful criteria were obtained, namely mean slope, mean elevation, mean curvature, mean log-transformed flow accumulation, mean distance to streams, and site area. To reduce subjectivity, the Entropy Weight Method (EWM) was used to find the relative significance of each criterion objectively. Technique of Order of Preference by Similarity to an Ideal Solution (TOPSIS) was used to rank 23 candidate sites according to their proximity to the ideal solution. The findings indicate that site area and flow accumulation account for the largest share of influence on site suitability, whereas elevation and curvature contribute a comparatively smaller proportion to the overall assessment.
Authentication Approach for Sensitive Data in Block Chain –non -fungible token Environment based on Simon Encryption and Steganography Techniques
Due to the increasing growth of data sent and received between parties, and how to protect it and keep it safe from tampering and theft, this led to the necessity of using and innovating techniques and algorithms that enhance the security of this data, especially sensitive data. Blockchain technology provides a reliable option for transferring data, especially sensitive data, securely due to its decentralized properties. However, there is still a challenge in how to maintain it in the face of continuous digital development. Non-fungible tokens (NFTs), one of the growing uses of blockchain technology, have evolved as digital ownership certificates that safely store a variety of data on a distributed ledger, This study offered a strategy for protecting a valuable, sensitive image and prove that a certain person is the rightful owner of it, The image encrypted, and then uploaded to the interplanetary file system (IPFS), hidden within a carrier image for added security, and then stored on the blockchain. This strategy would mitigate the issue of counterfeit photographs and nefarious individuals attempting to mimic the rightful owners of images or sensitive data. The results regarding encryption and embedding, which were as follows: PNG images retained higher quality after encoding than JPEG images. (PSNR: inf dB, MSE:0.0).