Journal of Al-Farabi for Engineering Sciences
AI-Enabled Hybrid Optical–Acoustic Communication for UUVs Using WDM-OOK: Design and Real-Time Performance Evaluation
One of the key challenges is reliable communication for Unmanned Underwater Vehicles (UUVs) since underwater environments are dynamic and challenging for signal transmission. The traditional acoustic communication systems allow long-distance communication but are limited by the low bandwidth, high latency, and doppler effect. Optical wireless, on the other hand, provides high data rates with water absorption and scattering occurring within the transmission distance, but has a short range of communication. To overcome these drawbacks, a smart hybrid optical-acoustic communication system is proposed that is a combination of Wavelength Division Multiplexing and On-Off Keying (WDM-OOK) to transmit a high-speed signal over optical and acoustic channels to achieve high reliability of long-range communication. The design of an intelligent hybrid optical–acoustic communications network to improve the continuity, reliability, and throughput of the communication between a UUV system is the focus of this research. It is a combination of a simple model of Multilayer Perceptron (MLP), a real-time communication mode switch, an optical subsystem (Wavelength Division Multiplexing On–Off Keying (WDM-OOK)), and an acoustic communication subsystem. The system has been modelled and simulated in MATLAB/Simulink and synthesized in HDL Coder as it will be implemented on an FPGA (Kintex-7 325T). The results of the simulations validate the superiority of the proposed hybrid method (hybrid with AI) over the purely optical method, the purely acoustic method, and the hybrid method without AI. The minimum BER recorded on this platform was (10-7), the average throughput was 152 Mbps, and the end-to-end latency time was 12.7ms with good water clarity. The FPGA synthesis results also show that the model was appropriate for real-time implementation with only 18.1% of the LUTs, 10.5% of the flip-flops, 23.6% of the BRAMs, and 17.4% of the DSPs utilized for the Kintex-7 325T FPGA. The results verified the feasibility of the proposed hybrid optical–acoustic communication system, where the high-speed feature of using the optical communication WDM-OOK system and the robustness of the acoustic communication system are provided by the intelligent adaptive switching. The proposed framework is feasible and is scalable for underwater reliable real-time communication between unmanned optical vehicles (UOVs) in underwater unmanned vehicle networks (UUVnets) in the near future.
Wavelength Division Multiplexing On–Off Keying
Leakage-Safe Borrower Segmentation Using Deep Clustering and Machine Learning in Large-Scale Lending Data
Large-scale lending data often combine pre-origination borrower information with post-origination servicing and outcome fields, which can create data leakage and misleading model performance. This study proposes a leakage-safe borrower-segmentation framework for LendingClub data. The framework constructs an ex-ante analytical matrix, compares direct K-Means/Gaussian Mixture Model (GMM) baselines with deep latent-space clustering using Deep Embedded Clustering (DEC), Improved Deep Embedded Clustering (IDEC), and Deep Clustering Network (DCN), and evaluates supervised operationalization using XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) . The final matrix contained 2,168,488 observations and 89 features. Conventional clustering showed weak separation, with the best Silhouette (SIL) value of 0.120454, whereas DEC achieved SIL = 0.851837, Davies-Bouldin index (DBI) = 0.179926, and Calinski-Harabasz index (CH) = 1.349271 × 10^6. XGBoost achieved the strongest recoverability performance for the DEC-derived labels. The results support leakage-safe preparation followed by latent deep clustering, while supervised learning is interpreted as operational recoverability of cluster-derived labels rather than direct default prediction.
Deep Reinforcement Learning for Adaptive Intrusion Detection in Software-Defined Networks
Cybersecurity threats against Software-Defined Networks (SDN) are continuously emerging, demanding improved intrusion detection capabilities that can quickly adapt to new threats. In this paper, we design a Deep Reinforcement Learning-based Intrusion Detection System (DRL-IDS) that leverages reinforcement learning to model network intrusion detection as a Markov decision process. We utilize Dueling Double Deep Q-Network (D3QN) with Prioritized Experience Replay to enable our IDS agent to perform online traffic classification in SDNs. Our proposed approach is evaluated on the comprehensive InSDN dataset in both binary and multi-class settings. We achieved 99.99% accuracy, precision, recall, and F1-score on binary detection tasks, with an AUC-ROC of 0.9988. In a multi-classification setting with seven different attack types (DDoS, DoS, Probe, BFA, BOTNET, Web-Attack, and U2R), we achieve an accuracy of 99.83% and F1-score of 99.97%. Our approach is compared with Random Forest, Decision Tree, K-Nearest Neighbors, and Multi-Layer Perceptron algorithms and exhibits comparable or better performance in all evaluation metrics. Adaptive reward with bonus rewards for detecting attacks and prioritized experience sampling allows tackling class imbalance problems. Our method can be implemented for intrusion detection systems (IDS) in software-defined networking (SDN) environments that demand real-time detection with low false-positive rates.
Review Study on the Effect of the End Sill Geometry on the Downstream Scour
Excessive downstream scour and inefficient energy dissipation in hydraulic structures remain critical challenges that can compromise structural stability and channel safety. The aim of this study is to evaluate the performance of various end sill geometries in reducing scour depth and improving energy dissipation efficiency. In terms of methodology, a comprehensive review was conducted based on 52 peer-reviewed studies published between 2000 and 2025, covering five primary end sill geometries: rectangular, inclined (sloped), semi-circular, convex, and perforated. The collected data were systematically analyzed to compare hydraulic performance under a wide range of flow conditions. The results indicate that inclined end sills with slopes between 45° and 60° provide significant scour reduction, typically ranging from 50% to 95%. Perforated end sills with porosity ratios of 40–50% demonstrate the highest energy dissipation efficiency, reaching 74% to 94%. Convex geometries are more effective in protecting the channel centerline, whereas concave configurations offer improved bank protection. Additionally, recent studies incorporating computational fluid dynamics (CFD) and machine learning models report high predictive accuracy, with coefficients of determination exceeding 0.98. In conclusion, despite these advancements, several research gaps remain, particularly regarding long-term scour evolution, the effects of sediment gradation, and the interaction between multiple energy dissipation devices. Addressing these gaps is essential for optimizing end sill design under varying hydraulic conditions, including different Froude numbers, sediment characteristics, and structural configurations.
Integration of Deep Learning Techniques with Hu Moment Invariants for Automated Cancer Stage Determination
: Accurate determination of breast cancer staging from mammographic imagery remains a clinically demanding challenge, owing to substantial inter-lesion variability in morphological appearance, inconsistencies in image acquisition quality, and the inherent limitations of conventional deep learning architectures in capturing diagnostically relevant structural characteristics. Predominant existing methodologies disproportionately exploit texture and intensity-based representations while systematically neglecting rotation- and scale-invariant shape descriptors that carry considerable discriminative value in tumor characterization and malignancy grading. This study seeks to develop a robust, automated breast cancer staging framework that synergistically integrates deep semantic representations with invariant morphological descriptors, with the dual objective of enhancing multi-class classification performance and enabling a more holistic and clinically interpretable characterization of mammographic lesions across BI-RADS categories. A hybrid computer-aided diagnosis (CAD) framework is proposed and evaluated on mammographic images drawn from the King Abdulaziz University (KAU) benchmark dataset. The pipeline is initialized with an autoencoder-based preprocessing stage that performs noise attenuation and perceptual image reconstruction, thereby ensuring high-fidelity input representations for downstream processing. Subsequently, high-level semantic features are extracted via the pretrained EfficientNet-B7 convolutional architecture through transfer learning, while complementary morphological representations are encoded by computing the seven Hu Moment invariant descriptors to capture affine-invariant shape properties of breast lesions. The resultant feature sets are integrated through a principled feature-level fusion strategy, yielding a unified discriminative representation for multi-stage BI-RADS classification. Comprehensive model assessment is conducted employing five standardized performance metrics: Accuracy, Precision, Recall, F1-score, and Specificity. Empirical evaluation on the KAU dataset demonstrated that the proposed hybrid framework attained an overall classification accuracy of 96.0%, accompanied by a precision of 95.5%, a recall of 97.3%, an F1-score of 96.3%, and a specificity of 99.4%. These results consistently surpass those achieved by standalone deep learning architectures and conventional handcrafted-feature methodologies, substantiating the complementary nature of fusing semantic deep representations with invariant morphological descriptors for improved lesion discrimination. The proposed hybrid CAD framework effectively harnesses the representational strengths of EfficientNet-B7 and the geometric invariance of Hu Moment descriptors to advance automated breast cancer stage determination from mammographic images. The empirical findings underscore the diagnostic value of incorporating morphological shape information into deep learning-driven classification pipelines and affirm the clinical translational potential of the proposed methodology as a decision-support instrument for radiologists engaged in BI-RADS-based breast cancer screening and diagnosis.
الحقوق الفكرية (c) 2026 Journal of Al-Farabi for Engineering Sciences
Deep Learning Framework for Intellectual Property Authentication of Digital Artworks
The fast growth of production and dissemination of digital art via websites and artificial intelligence technology has posed serious issues in the field of protecting intellectual property and authentication verification. Current methods sometimes have difficulty telling actual artworks apart from real derivative works and copyright-infringing material, especially with complex digital adjustments or AI-generated changes. This research work aims to propose an intelligent framework for the verification and copyright protection of digital artwork. The proposed framework combines deep feature extraction, semantics graph construction, a Graph-Manifold invariant representational learning and classification based on machine learning to accurately classify original, derivative and infringing artworks. The proposed framework includes of several sequential stages such as image preprocessing and normalization, salient region detection, a deep the extraction of features, semantic graph structure, Graph-Manifold based invariant feature learning, feature similarity measurement performed by cosine similarity and final classification by Support Vector Machine (SVM). The benchmark dataset consisted of 9,000 digital artworks comprising original artworks, lawful derivatives and copyright infringing pictures acquired via sophisticated modification and AI based approaches. The experimental results showed that the suggested framework performed better than the current techniques. The semantic Graph-Manifold framework achieved a general accuracy in classification of 98.1%, F1 score of 0.967 and AUC value of 0.982. Unaffected by image edits, stylistic shifts, or AI-generated adjustments, the system could distinguish between legitimate derivative works and those that infringed on copyrights. Semantic graphing, graph-based representational learning, and support vector machine classification all work together to provide a solid strategy for ensuring the legitimacy of digital artworks and safeguarding intellectual property. In modern AI-driven creative environments, the proposed approach offers a practical answer to the problems of copyright management and through the internet asset protection while simultaneously improving semantic understanding, classification accuracy, and misclassification rates.
Enhanced Cobra Optimization for Lightweight Intrusion Detection in Edge-IoT Networks
The rapid proliferation of Internet of Things (IoT) ecosystems has led to the generation of massive, heterogeneous, and noisy data streams from distributed sensors, posing significant challenges for intrusion detection. Conventional machine learning models, such as Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), rely on handcrafted and static feature representations, limiting their ability to capture complex and dynamic IoT attack patterns. To address these limitations, this paper proposes an Enhanced Cobra Optimization Algorithm (E-COA) integrated within a hybrid edge–cloud deployment framework for efficient and real-time IoT intrusion detection. The proposed E-COA introduces a non-linear adaptive hunting probability to dynamically balance exploration and exploitation, along with a composite fitness function that jointly optimizes classification accuracy, false positive rate (FPR), and feature sparsity. Experiments are conducted on the MQTT-IoT-IDS2020 dataset, which consists of 71,341 labeled network traffic samples across five classes. The proposed model achieves a classification accuracy of 97.2%, a macro-averaged F1-score of 0.971, and a false positive rate of 2.2%. It also demonstrates superior performance compared to SVM, Random Forest, 1D-CNN, PSO, and standard COA models. Furthermore, the optimized model maintains a compact size of approximately 75 KB and achieves an average inference latency of 4.3 ms on edge-simulated hardware. These results indicate that the proposed framework effectively balances detection accuracy, computational efficiency, and suitability for deployment in resource-constrained edge-IoT environments.
Machine Learning-Based Prediction of Smartphone Addiction Using Behavioral Usage Features
The rapid growth of smartphone usage has raised serious concerns regarding problematic and addictive usage behaviors, which may negatively affect sleep quality, productivity, and psychological well-being. Despite increasing research attention, existing studies often rely on self-reported scales or limited analytical approaches, reducing objectivity and generalizability. This study aims to develop a data-driven framework for predicting smartphone addiction using behavioral usage features and to identify the most influential predictors of addictive behavior. A publicly available dataset consisting of 500 users (Kaggle, 2024) was analyzed, encompassing demographic and behavioral variables including age, daily screen time, social media notification frequency, sleep duration, and number of installed applications. Four supervised machine learning classifiers—Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT)—were trained and evaluated under identical conditions using an 80/20 stratified split with 10-fold cross-validation. Class imbalance was addressed using SMOTE applied exclusively to the training set. Results indicate that daily screen time, notification frequency, and sleep duration are the strongest predictors of smartphone addiction. The Random Forest model achieved the highest performance (Accuracy = 88.4%, F1-score = 0.898, AUC = 0.923). Correlation analysis confirmed strong associations between addiction status and screen time (r = 0.721, p < 0.01), notification frequency (r = 0.653, p < 0.01), and a significant negative association with sleep duration (r = −0.611, p < 0.01). Approximately 58.2% of users in the sample were classified as addicted. The findings demonstrate that behavioral usage data can effectively support automated prediction of smartphone addiction and contribute to the development of real-time digital health monitoring tools. This study provides a reproducible comparative machine learning framework and highlights key behavioral indicators suitable for early detection and clinical screening applications.
EMBER-Based Static Malware Detection: A Critical Review of Accuracy, Explainability, and Temporal Robustness Trade-offs
Static analysis-based malware detection of Portable Executable (PE) files has evolved remarkably since the release of the EMBER dataset in 2018. Yet evaluation methodology and model explainability continue to suffer from critical challenges that limit real-world implementation. This literature review explores five thematic clusters: Traditional Machine Learning, Deep Learning, Ensemble and Hybrid Architectures, Explainable AI (XAI), and Zero-day Detection with Concept Drift. The review analyzes 27 primary studies and 15 supporting references for a total of 42 studies published between 2018 and early 2026. The review shows that gradient boosted decision tree steadily offers better baseline performance. In comparison, ensemble and hybrid architectures show the highest accuracy overall. That being said, this comes with the cost of reduced explainability and an increase in computational overhead. Deep Learning methods make the performance gap thinner but bring up transparency and resource concerns. And the emerging Large Language Model (LLM)-based approaches remaining premature and unverified. Across all of the five clusters, six intersecting gaps are identified, the most notable being the near-universal dependence on random instead of temporal train/test splits. Other gaps include the lack of sufficient false positive rate reporting at operational thresholds, and the consistent separation between explainability and detection performance. Critically, no reviewed study achieved a successful integration of ensemble level accuracy, embedded explainability, and temporal oriented evaluation within a single framework. It’s a gap that this review specifically recognizes and highlights as the most crucial priority of the research in this field. The gaps explored can be addressed with the seven future research directions presented later in this review. The most critical one of them is the incorporation of ensemble accuracy, explainability and temporal evaluation in a unified framework. This is a combination that no reviewed study has achieved yet.
Leakage-Free Segmentation-Based ANN for Crack Localization and Depth Estimation in Cantilever Beams
This study develops a controlled and leakage-free learning framework for vibration-based crack characterization in a metallic cantilever beam using compact relative frequency shift (RFS) features. A fully numerical methodology is implemented in MATLAB based on a one-dimensional Euler–Bernoulli finite-element model, in which the cracked beam element is represented through a local stiffness matrix and statically condensed into an equivalent element for global assembly. A database of 1200 labelled damaged scenarios is generated by varying crack location over 100 beam elements and crack depth over 12 levels from 0.20 to 2.40 mm. Each scenario is represented by a six-dimensional RFS vector computed from the first six bending natural frequencies, while the outputs are the normalized crack location and crack-depth ratio. All models are trained and evaluated under a single leakage-free Master Split (70/15/15) to ensure fair and reproducible comparison. The baseline artificial neural network achieves test-set mean absolute errors of 2.992 mm for crack location and 0.0251 mm for crack depth. Under the same split, a segmentation-based coarse-to-fine strategy reduces these errors to 1.245 mm and 0.0211 mm, respectively. The main contribution of this work is a reproducible leakage-free segmentation-based ANN framework that improves RFS-based crack localization and depth estimation within a controlled numerical setting.
A Suite of Interpretable Hybrid Systems with a Voting Mechanism for Detecting Financial Fraud in Digital Forensics
Financial fraud has become a critical challenge in modern digital payment systems as financial transactions increasingly rely on online platforms. Despite advancements in payment technologies, detecting fraudulent activities remains difficult due to evolving attack strategies and system vulnerabilities. Traditional fraud detection methods suffer from high false positive rates, inability to adapt to new fraud patterns, and limited interpretability, especially when using complex models that function as black boxes. This study proposes a multilayered fraud detection framework that integrates Decision Tree (DT) and Fuzzy Inference System (FIS) within a unified architecture. Initially, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address class imbalance in the dataset, followed by constructing a Decision Tree model to extract clear and interpretable decision rules. These rules are then transformed into fuzzy logic rules within the Fuzzy Inference System to handle uncertainty and borderline cases more effectively. Finally, a hard voting mechanism is employed to combine the outputs of both models and produce a robust final decision. The proposed model was evaluated using real-world financial transaction data. Experimental results demonstrated an accuracy of 96.75% ± 0.12, outperforming several baseline models such as Random Forest, CNN-SVM, and rule-based systems. The findings indicate that the proposed hybrid framework enhances detection accuracy, reduces false alarms, and improves interpretability, making it a reliable solution for real-time financial fraud detection.
Process Mining and Business Intelligence for the Detection of Suspicious Transactions
Digital forensic investigations still have a hard time finding suspicious activity in huge financial datasets. When it comes to finding complicated behavioral patterns and little abnormalities in big transactional databases, traditional database forensic methods have their limits. This is because they typically depend on human analysis or searches that have already been set up. This work presents a data forensic system grounded on process mining to assist in identifying suspicious activity inside financial systems. The framework has three main parts: process discovery, which uses Petri net representations to make diagrams of processes to event logs; conformance checking, which at first compares the data observed with the uncovered method framework; and clustering and behavior analysis, which puts suspicious cases at the top of the list based on unusual traces. We evaluated the suggested method on an actual-world financial database and put it through its paces utilizing the ProM mining framework. The trials showed that the strategy works to find strange patterns of transactions while making it easier to find any fraudulent activity. The suggested process mining-based structure is better than existing methods since it is organized, scalable, and automated.
An Intelligent Deep Residual Network for Automated Lung Cancer Detection Using ResNet50
Lung cancer accounts for one of the leading causes of cancer death in the world; patients' prognosis can be greatly improved when diagnosed and predicted accurately as early as possible. The proposed intelligent deep learning models will enable automatic identification and classification of lung cancer from histopathological images. The architecture used was a deep residual network based on ResNet-50, trained on approximately 15,000 lung histopathology images in three classes: adenocarcinoma (ACA), squamous cell carcinoma (SCC), and normal tissue (N). Different testing protocols were performed to objectively evaluate the proposed model robustness and generalization capabilities, including prospective tests using images from a single dataset and external tests using images from other sources. In the experimental results, the overall accuracy for classification using this proposed model was 99.96%, implying a strong classification ability. Additional evaluation with a confusion matrix and ROC curve confirmed the discriminative power of the model, and external testing demonstrated that the model generalized well when tested on unseen samples from different sources. The findings indicate that the effective ResNet50-based deep learning technique may be employed as a pioneering tool for automatic lung cancer diagnosis, while also supporting pathologists in improving precision and efficiency.
Classification of ECG Signals Using CNNs: An Improved Model for Cardiac Disease Diagnosis
The analysis of ECG signals poses significant challenges as a result of the complex electrical patterns of the heart and issues related to the imbalanced data. Addressing such challenges requires having advanced methods and models for the purpose of ensuring accurate diagnoses and improving the results of the treatments. The combination of the ECG analysis with the approaches of artificial intelligence (AI) became a priority for the health-care improvement, especially as the prevalence of heart diseases keeps rising worldwide. In the present study, Convolutional Neural Networks (CNNs) have been used for creating a classification model for the ECG signals, tested across the balanced as well as the imbalanced data structures from MIT-BIH Arrhythmia data-sets. Results have shown consistently high accuracy of classification, which exceeds 98% in all of the cases. For example, the model has been able to achieve 98.36% accuracy with the unbalanced data after 20 cycles (epochs) of training with the use of the early stopping. When the number of the cycles has been increased to 104, with extended patience setting of 25, accuracy has been increased to 98.76%. Balanced data had produced outcomes that are slightly better, with the model reaching an accuracy level of 98.88% in 25 cycles and 98.45% in 20 cycles. These findings have highlighted the importance of the training cycle count as well as the data balance in increasing the accuracy of the model. Moreover, early stopping proved beneficial in maintaining high performance and training efficiency in ECG signal classification.
A close look at the H-C phase Diagram and Hydrocarbon Reservoir Classification and Behavior
H-C phase diagram of reservoir fluids is an important tool used in explaining reservoir fluids behavior during the life period of any reservoir. In this paper, detailed analysis of certain terms of the Pressure–Temperature Diagram (P-T diagram) are presented and discussed. Some of these terms are redefined since the already existing definitions does not fit all cases and conditions of reservoir pressure and temperature, and hence, new definitions are given to such terms so as to make them fit all possible conditions that might exist in the reservoir. Special attention was given to explain and analyze the phase behavior inside the phase envelope (two phase region) of the P-T diagram. Among such terms for example, new definitions and analysis are given to the quality lines to give them much more interest and importance in the P-T diagram as they should be given than just representing the percentages of both liquid and gas phases. The new given definition will open new interpretations and add more interesting and valuable importance to the quality lines. Another new definition is given herein to the Cricondenbar Pressure to make it fit all possible conditions on the P-T diagram since the existing definition does not fit all pressure and temperature conditions. This paper also include some explanations regarding the retrograde gas condensation process and region on the P-T diagram. Finally, the herein mentioned factors and definitions will add more important and wider interpretations and applications for the P-T Diagram for a better understanding of H-C reservoirs. Such interpretations might be applied, also to the fluid flow control inside the reservoir and in the choosing of the best EOR method to apply whenever and wherever it is necessary.
Bubble Point Pressure (Saturation Pressure)
Investigation of Wear Rate of Passenger Airplanes Tyres During Landing
A real, functioning copy of one main landing gear wheel is used to show the landing impact of an aircraft. By minimizing abrasive sliding between aircraft tires and runway surfaces right after touchdown, this study seeks to identify possible tire-life improvements. An experimental investigation of size 22 X 5.5, type VII airplane tyres was conducted to evaluate the wear and related properties of temperature and friction caused while braking. One way to evaluate the tire's performance on surfaces with uniform slip ratios, including asphalt, concrete, and slurry seal, was to gear it up to the driving wheel of a ground vehicle. The range of slip ratios often attributed to an airplane's braking system was covered by the data gathered during dry runway operations. Our results show that cumulative tire wear is affected by runway surface features, increases with increasing slip ratio, and changes linearly with miles. The differing wear rates associated with the various surfaces may be used to rank runways based on tire wear.
The Additives Impact the Gel Strength of the Water Based Drilling Mud
One of the most important functions of drilling mud is its ability to suspend the rock cuttings during drilling cease, this ability is related mainly to gel strength. Gel strength is caused by the attractive forces between the active particles of bentonite (clay) during the static period. Sodium-based bentonite concentration has a direct effect on the gel strength. In addition, cement contaminants may encounter drilling mud as a result of casing string cementation which in turn also leads to a noticeable impact on gel strength. As consequence, it is vital to observe drilling mud gel strength continuously to identify efficient drilling mud functions. In this paper, an experimental study was performed via a shearometer, the aim of this study is to investigate the effect of the variety of the weights of sodium-based bentonite and cement slurries on the gel strength of water based mud. To analyze the influence of bentonite additives, four drilling mud samples were prepared with bentonite weights; 40, 60, 70, and 80 gm respectively, and gel strength was measured at each weight. While cement contamination had been examined by preparing three drilling mud samples contaminated with 2 gm, 3 gm, and 5 gm of class G cement. The results showed that an increase in bentonite quantities leads to rising in gel strength directly. The findings also showed that gel strength increased as the level of cement slurries raised. Furtherer increase appeared from 20.2 to 42 lb/100 ft2 when cement weight increased from 2 to 5 gm respectively.
Employing traditional heritage elements in shaping contemporary architecture
The architecture of Baghdad suffers from a lot of problems, ranging from the application of materials and form to the expression in contemporary buildings that not only change the traditional architectural identity but also the performance of the buildings that meet the needs of the local population. This unfortunately increases the damage related to cultural values and environmental sustainability regardless of adaptable solutions that can improve the energy efficiency of building performance. The traditional elements of architecture in the city of Baghdad represent a source of inspiration for contemporary architecture and are exploited to enhance its identity and spatial affiliation. The process of exploiting these elements takes place on several levels, including those that achieve environmental, symbolic, or formal aspects. Hence, the research problem crystallizes in the lack of clarity on the levels of benefiting from the elements of traditional local architecture in enhancing the identity of contemporary architecture and its spatial affiliation. The research aims to shed light on how to analyze the components of the performance and value of traditional architecture in the city of Baghdad, which is rich in cultural heritage and which can be translated into contemporary architecture that creates continuity in traditional cultural identity. The study uses the theoretical descriptive approach through which data is collected on the vocabulary of local traditional architecture of the city of Baghdad and its roles at the environmental, formal, and aesthetic levels to arrive at a main vocabulary for the theoretical framework, which is represented in four basic vocabulary represented in the aspects of form formation, dependence on the environment, and the interface. , aesthetic aspect, and cultural value. These vocabulary were applied to two recently elected projects in the city of Baghdad, through which the four indicators and the role of each of them in enhancing the identity of contemporary architecture and its spatial affiliation were analyzed.
Dynamic Characteristics of Continuous Stirred Tank Reactor (CSTR) Part I: Adiabatic Reactor
CSTR is a very common reactor type which is widely used in the chemical process industries. It is well-known that its dynamics are nonlinear with a multi-mode behavior. This aspect was and still is an active subject of research with the investigative approach being either mathematical or experimental involving specific reaction systems. In this work a different approach is adopted in which the variation of the damping coefficient value of the linearized reactor’s transfer function is investigated over chosen values/ranges of relevant parameters. Our results indicate that an adiabatic type of such a reactor in which a liquid-phase, 1st-order, irreversible, exothermic chemical reaction takes place can be unstable or stable. If stable, it is either critically damped or overdamped with the latter mode being overwhelming; however, it is never underdamped. Furthermore, even when stable its dynamic behavior can be peculiar depending on certain values of some parameters.
Pollution of Water’s direct effect in Iraq on the Public Health & Safety
The devastating effects of pollution of Water are significant, affecting human life, animal life and vegetation at the same time. Recently, Iraq has experienced the effects of pollution of Water, destroying an abundance of plants and many animals. Pollution of Water is one of the mostly deadly and severe problems facing humankind currently. This study analysed the effects of heavy metals Pb, like Cr, Zn and Fe in water for drinking. The findings revealed an upsurge in the number of cholesterol, urea, and white blood cells among people in Basra, Baghdad, and other urban areas. It was also found that these individuals were more likely to have fever, diarrhea, kidneys, and respiratory problems. Recent studies have revealed that pollution of Water in Iraq is caused by a variety of factors, i.e., solid pollutants, chemicals, and microorganisms. Unfortunately, due to the current state of war, there is not much concentration on water treatment plants (WTPs) resulting in a decrease in the quality of the supplied water. Due to the lack of wastewater treatment facilities in most Iraqi cities, sewage is simply discharged into the river. In addition, supply of water and sanitation systems in some major cities have been damaged and the two networks are jointly linked.
A Review on Common Approaches Used for Graphene Characterization
Evaluating Urban Thermal Comfort through a Holistic Micro-Climate Model: Baghdad as a Case Study
Urbanization greatly affects the change in local climate conditions. To address this, urban vegetation needs to be expanded to help dissipate excess heat by promoting evaporation. This study aims to reconnect the urban corridors and spaces of the Al-Mustansiriya region in Baghdad city through a network of green infrastructure (GI), employing the ENVI-MET V4 simulation program, which is a comprehensive micro climate modeling system used to measure and enhance pedestrian comfort levels in an urban environment. The results of the analysis show the significant effect of urban intervention strategies in reducing pedestrian heat stress. Where the air temperature drops by 3-4°C. Furthermore, an association was detected between leaf area density and PET levels. In a broader sense, this research supports the use of ecological urbanization along with integrated urban micro-environment modeling as a catalyst for urban quality improvement.
الحقوق الفكرية (c) 2023 Journal of AL-Farabi for Engineering Sciences
Improvement Airport Security System with Face Recognition Using Neural Network Based on the Arduino Uno Microcontroller
The aim of this study is to implement an algorithm for face recognition, based on the Arduino uno microcontroller. This paper presents it as an airport security system to detect passenger’s face and compare the result with the database of unwanted people. The system combines laser trigger circuit for image capturing and artificial neural network for image recognition. The laser trigger circuit has many benefits such as avoiding camera shakes or taking a picture without a timer. The captured image will be analyzed and processed in MATLAB using an artificial neural network to recognize the passenger’s face from the real captured images after the training phase. Many experiments have been conducted on our face databases with various numbers of iterations. The recognitions’ accuracy and efficiency of the proposed model are 93.33% and 2.67 respectively with 0.696530 seconds as execution time. The result shows the robustness of the developed model in terms of mean squared error, execution time, recognitions’ efficiency and accuracy. The smallest obtained mean squared error is 9.9991e-04 for the training data set and 0.1764 for the testing data set when they are recorded for a modified neural network which makes the developed system more reliable. Finally, the artificial neural network demonstrates the ability to detect the unseen relationship between features belong to the same face.
Reconfigurable Compact Quad-port MIMO Antennas for sub-6 GHz Applications
This paper presents compact quad-port frequency reconfigurable multi-input multi-output (MIMO) antennas for 5 G applications that operate at sub-6 GHz. The proposed design provides more isolation (>13 dB) and pattern diversity using four orthogonal radiating elements. C-shaped metal is used to extend the antenna's radiating elements by inserting one positive intrinsic negative diode (PIN) in the metal. The C-shaped metal and matching stub achieve frequency reconfigurability with a consistent radiation pattern. The PIN diode's switching characteristics allow the frequency to be shifted between two communication bands. One mode (diode ON) covers 2.5 and 5 GHz, while the second mode (diode OFF) covers another dual band of 3.5 and 5.7 GHz. Substrate dimensions are only 50 x 50 x1.6 mm3, making the proposed design compact. Antenna peaks at a gain of 4.18 dB and radiation efficiencies of 80 and 94% in the four frequency bands. The antenna design is appropriate for multi-functional wireless systems and cognitive radio applications since it spans frequency bands below 6 GHz and can be reconfigurable between wide and narrow bands.
The Ecological Balance Restoration in Existing Cities
Over time, the environmental issues that cities face, have grown as a result of a variety of causes, including population growth, increased densities and built-up areas, and changes in numerous land uses. Because of these difficulties, our cities have lost many of their natural qualities and ecological balance. As a result, finding solutions to these urban problems and implementing measures to restore the lost ecological balance in existing cities becomes vital. In order to restore the ecological balance and reduce its negative effects on city life, a number of ways have been used. One of these ways is "Protecting forest and green areas". So green areas are one of the most important environmental treatments in urban spaces. And because this importance is more evident in cities than in the countryside. Balancing the green areas leads directly and indirectly to the balance of the rest of the elements that affect the ecological balance within the city.
Void space and cavity measurement of the prepared zeolite y using X-ray diffracting modelling
Zeolite was synthesized using a solitary gel technique with Iraqi kaoline. Zeolite 's specific structural nature makes it difficult to use X-ray diffraction (XRD) to evaluate a single crystalline characteristic. Structural analysis to estimation and visualization of one crystal geometric properties by CrystalMaker software was carried out using XRD and the simulation of a crystal pattern using a Rietveld refinement technique using Maud software. This paper describes the Crystal Maker software suite, which includes the visualization, modelling and diffraction simulation of modular programs for crystal and molecular structures. The fundamental reasoning for the program is explored and a variety of applications are given for teaching and research in crystal chemistry and molecular dynamics.
A Comprehensive Evaluation Study for the Maintenance Management System of Roadways: A Review
Pavement infrastructure is essential and must be protected with limited resources. For decades, industrialized nations have used Pavement Management Systems (PMS) and pavement distress assessment to examine network and project-level pavement conditions. Pavement condition models can anticipate pavement degradation, schedule maintenance, and create multi-year rehabilitation plans based on historical data. Pavement condition surveys are done annually or biannually to calibrate pavement condition models and reduce network maintenance costs. This study highlights road system deficiencies to meet traffic demand, improper systematic methods of maintaining networks, budget constraints for decision makers, deficiencies in road geometrics, poor construction and maintenance practices, the need for proper planning and resource management, and improper planning. The technique comprises a theoretical element of PMS and a review of performance evaluation studies. A PMS lets users export data in an easy-to-understand manner, helping them manage their roadways better. Manual pavement distress surveys need certified raters.
Space Syntax & Regression Analysis: Evaluating the Impacts of Urban form and Socio-Economic on Walkability in some of the Neighborhoods Dora, Baghdad.
Even though it has been found that walking around a neighborhood has many benefits, including health benefits, these factors must be taken into account when planning. Walkability is hard to measure and can't be boiled down to a single number because it depends on both the cultural and social aspects of a community. Using space syntax theory, the layout of streets in different kinds of communities was looked at, and regression analysis was used to measure the socioeconomic factor. This study looks at the syntactical properties of the city's organized and unorganized (agricultural) areas in relation to people's walking patterns; examines the socioeconomic characteristics of the population; and looks into people's walking patterns and the variables that influence walkability in the study area. By conducting observations of the areas and interviews with residents. Some of the results of the research are the variation and difference in the variables that affect both areas of the analysis or field observation. The findings are then used to recommend approaches that planners, designers, and decision-makers could take to create transit- and pedestrian-oriented developments.
Determination of Water Saturation Based on Well Logging in Mishrif Carbonate Reservoir
Water saturation is calculated by many models that developed over the years based on known physical principles or on empirically derived relationships. The accurate determination of water saturation in complex carbonate reservoirs is the most challenging aspects of well log analysis. The complex formation under study is Mishrif carbonate formation which is one of the deepest hydrocarbons bearing zone in the Nasiriya oilfield in south of Iraq. The available scanned copes of well logs are digitalized by using Neurolog software. Schlumberger charts 2005 had been used for environmental corrections. These correction charts are supplied in the Interactive Petrophysics software. Simple Archie equation, Dual water model, and Indonesia model have been used to calculate water saturation of Mishrif carbonate formations. After making the environmental corrections, the porosity interpretation shows that the logging tools have a good quality of data reading. From Indonesian model, the water saturation for Mishrif formation is ranged from 0.25 to 0.41. This study is provided water saturation values with depth that should use to calculate original oil in place and detect the perforation zones in the pay zone using Interactive Petrophysics software.
Urban Space and Vitality of traditional city
Sustainability was the worst banderole assemble in prior events, prowl discussing relationship to different life branches. In our cessation, we presume this reprove is strenuous, and accordingly involved to pliant more round environmental, natural and manmade elements. Accordingly we buttocks worth peasant-like technology okay and attainmentfriendly live with surrounding environment. As a smooth product we destine b apply to pin plant on stretch shrug off dismiss deportment, activate green area, and recycling. Impede commerce we discussing close by, our routine cities ordeal non-native neglecting sustainability on parish presence, causing by Sisyphean covenant for city frankly spaces, and resulting negative characteristic on achieving urban fabric sustainability for these cities. Inspect conclude was by activation guileless crack arrangement in the age-old cities reaching us to set out sustainable these centers and traditional cities, commensurate with explain, our target to succeed assorted strategies be capable execution reaching to reasonable venerable cities centers, function by investing cities sustainability elements, and make it as main tool for revitalization village life. The banderole meagre for this corroborate by formidable to administer strategies, stranger conspectus break down, on accustomed borough center, to reorder urban forthright aperture for reaching to create sustainable city and saving cultural and traditional values.