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Go to Editorial ManagerRecently, three-dimensional models 3DM in the prosthetics field gained popularity, especially in the context of residual limb shape creation resulting from collecting medical images in Digital Imaging and Communications in Medicine DICOM format from a magnetic resonance imaging MRI after image processing accurately. In this study, a three-dimensional model of the residual limb for a patient with transtibial amputation was realized with the integration of artificial intelligence and a computer vision approach demonstrating the benefits of AI segmentation tools and artificial algorithms to generate higher accuracy three-dimensional model before prosthetic socket design or in case of comparison the 3D model generated from MRI with another 3D model generated from another technique, where a residual limb of a 23 years old male patient with amputation in the left leg wearing a prosthetic socket liner, and having 62 kg weight, 168 cm height, with high activity level. The patient was scanned using GE Medical Systems, 1,5 Tesla Signa Excite. MRI images in DICOM format were read to retrieve essential metadata such as pixel spacing and slice thickness. These images were processed to obtain a model that reflects the real shape of the residual limb using a specific algorithm, and the 3D model was extracted using AI segmentation tools. The obtained 3D model result with high resolution proves the potential of the artificial intelligence approach with deep learning to reconstruct 3D models concluding that AI has an instrumental role in medical image analysis, particularly in the areas of organ and tissue classification and segmentation., thus generating automatic and repetitive a 3D model.
Optical coherence tomography (OCT) allows for direct and immediate imaging of the morphology of retinal tissue. It has become a crucial imaging modality for diagnosing eye problems in ophthalmology. One of the most significant morphological characteristics of the retina is the structure of the retinal layers, which provides important evidence for diagnostic purposes and is related to a variety of retinal diseases. In this paper, a convolutional neural network (CNN) model is proposed that can identify the difference between a normal retina and three common macular diseases: Diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV). This proposed model was trained and tested on an open source dataset of OCT images also with professional disease classifications such as DME, CNV, Drusen, and Normal. The suggested model has achieved 98.3% overall classification accuracy, with only 7 wrong classifications out of 368 test samples. The suggested model significantly outperforms other models that made use of the identical dataset. The final results show that the suggested model is particularly adapted to the detection of retinal disorders in ophthalmology centers.
The interest in the Eye-tracking technology field dramatically grew up in the last two decades for different purposes and applications like keeping the focus of where the person is looking, how his pupils and irises are reacting for a variety of actions, etc. The resulted data can deliver an extraordinary amount of information about the user when it's interlocked through advanced data analysis systems, it may show information concerned with the user’s age, gender, biometric identity, interests, etc. This paper is concerned about eye motion tracking as an unadulterated tool for different applications in any field required. The improvements in this area of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with eye-tracking techniques allow large opportunities to develop algorithms and applications. In this paper number of models were proposed based on Convolutional neural network (CNN) have been designed, and then the most powerful and accurate model was chosen. The dataset used for the training process (for 16 screen points) consists of 2800 training images and 800 test images (with an average of 175 training images and 50 test images for each spot on the screen of the 16 spots), and it can be collected by the user of any application based on this model. The highest accuracy achieved by the best model was (91.25%) and the minimum loss was (0.23%). The best model consists of (11) layers (4 convolutions, 4 Max pooling, and 3 Dense). Python 3.7 was used to implement the algorithms, KERAS framework for the deep learning algorithms, Visual studio code as an Integrated Development Environment (IDE), and Anaconda navigator for downloading the different libraries. The model was trained with data that can be gathered using cameras of laptops or PCs and without the necessity of special and expensive equipment, also It can be trained for any single eye, depending on application requirements.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impacts cognitive functions such as memory, attention, and reasoning, ultimately affecting daily life. Early and accurate detection is crucial for timely intervention and management. Traditional diagnostic methods, including neuroimaging and cognitive assessments, can be expensive and time-consuming, necessitating more accessible and efficient alternatives. This study aims to develop an automated and efficient deep learning-based detection system that uses Electroencephalogram (EEG) signals to accurately classify AD and healthy individuals. A Convolutional Neural Network (CNN) model was designed to extract meaningful features from preprocessed EEG data. The architecture consists of convolutional layers with max pooling, dropout regularization, and fully connected layers to improve classification accuracy. The model was trained and evaluated on a comprehensive EEG dataset, using key performance metrics such as accuracy, recall, precision, and F1-score. The proposed CNN model achieved a high classification accuracy of 94.56%, a low loss of 0.2162, and an AUC value of 0.93828, demonstrating superior classification capability. The results indicate that the model effectively distinguishes between AD and healthy individuals, outperforming several state-of-the-art approaches. The findings highlight the potential of deep learning-based EEG analysis for AD detection, providing an accessible and cost-effective tool for early diagnosis. The high accuracy of the proposed CNN model suggests that it can assist medical professionals in making well-informed decisions, ultimately improving patient outcomes.
The present work is a manual that describes the practical aspects of optimizing dental precision by virtual dentoskeletal modeling, use the precises model in virtual planning, and splint design of orthognathic surgery. A single case study is used to demonstrate the stages involved in this approach, which include acquiring CBCT scan data and digital dental models, incorporating this data into developing a virtual dentoskeletal model using superimposition process to replace the unclear teeth. Utilizes virtual assessment and three-dimensional cephalometry to diagnose the maxillofacial deformity of the patient correctly. The results of the diagnosis played a crucial role in formulating a comprehensive plan for dental alignment, which involved osteotomy and correction of bone positions. The final step is to create a personalized splint. The importance of virtual tools is highlighted in our work to optimizing dental precision, diagnose and treat maxillofacial deformities. Present a virtual planning methodology for orthognathic surgeons as well as researchers.
This paper proposes robust control for three models of the linear inverted pendulum (one mass linear inverted pendulum model, two masses linear inverted pendulum model and three masses linear inverted pendulum model) which represents the upper, middle and lower body of a bipedal walking robot. The bipedal walking robot is built of light-weight and hard Aluminum sheets with 2 mm thickness. The minimum phase system and non-minimum phase system are studied and investigated for inverted pendulum models. The bipedal walking robot is programmed by Arduino microcontroller UNO. A MATLAB Simulink system is built to embrace the theoretical work. The results showed that one linear inverted pendulum is the worst performance, worst noise rejection and the worst set point tracking to the zero moment point. But two masses linear inverted pendulum models and three masses linear inverted pendulum model have a better performance, a better high-frequency noise rejection characteristic and better set-point tracking to the zero moment point.
The research proposed a developed methodology for evaluation the system performance in uncertainty associated with traditional modelling methodology is focused on either load L or resistance R variability, but not both. A two-dimensional (2D) fuzzy set (traditional model), represent with the one dimension for universe of discourse (in x-direction) and the second dimension of his membership degree (in y-direction), is not full sufficient to handle both, load and resistance variation of system performance. The theoretical principle basis of this research is based on development of the three dimensional (3D) of fuzzy set that includes system performance variability in load and resistance from two dimensional. The proposed methodology (traditional model) extends the acceptance level of partial performance of system concept to a 3D-dimantion representation. This representation allows to capturing the changing of preferences of decision makers in load and resistance. The major objective of the research is to proposed the original methodology for evaluate system performance and management that is capable of; (a) addressing uncertainty caused by load and resistance variability and ambiguity; (b) integrating objective and subjective evaluation; and (c) assisting system performance management decision making based on a more detailed certainty evaluation of load and resistance variability. The study proposed two models for fuzzy reliability performance indexes: first traditional model included (I) 2D fuzzy reliability-vulnerability Rv index, (II) 2D fuzzy robustness Ro index; the second developed model (i) 3D fuzzy reliability-vulnerability Rv index, (ii) 3D fuzzy robustness Ro index; and comparing between them. These indexes have the capability of evaluating the operational performance of complex systems. Proposed methodology is illustrated by using the Al-Wathba Water Supply System (WWSS) as a case study.
Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.
Fused deposition modeling (FDM) is a commonly used 3D printing technique that involves heating, extruding, and depositing thermoplastic polymer filaments. The quality of FDM components is greatly influenced by the chosen processing settings. In this study, the Taguchi technique and artificial neural network were employed to predict the ultimate tensile strength of FDM components and establish a mathematical model. The mechanical properties of ABS were analyzed by varying parameters such as layer thickness, printing speed, direction angle, number of parameters, and nozzle temperature at five different levels. FDM 3D printers were used to fabricate samples for testing, following the ASTM-D638 standards, using the Taguchi orthogonal array experimental design method to set the process parameters. The results indicated that the printing process factors had a significant impact on tensile strength, with test values ranging from 31 to 38 MPa. The neural network achieved a maximum error of 5.518% when predicting tensile strength values, while the analytical model exhibited an error of 19.376%.
Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of thyroid nodules ultrasound images with good performances. However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. This study proposes two deep-learning models for classifying thyroid nodule US images into two categories: benign and malignant. ResNet50 was the first model used to extract deep features from US images. The second model integrates ResNet50 and principal component analysis (PCA) for feature selection, intending to reduce dataset dimensionality while maintaining the greatest data variance possible before classification. The proposed model was created using a freely available dataset. The dataset consists of 800 images, 400 benign and 400 malignant. The suggested system was accessed based on accuracy, precision, recall, and F1 score. The classification accuracy for ResNet50 was 85%, while ReNet50-PCA was 89.16%. The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers.
The oil industry has a direct impact on the economic feasibility of other sectors and is considered to be the most important energy source used to turn the wheels of other industries. Therefore, it was necessary to pay attention and continuously develop this industry, to find the best modern techniques for designing, pre-commissioning and controlling process, to improve efficiency, preserve energy and achieve the highest production of costly components with the highest purity of the product. This study aims to provide a literary analysis of the stages of development and progress of the dynamics and control of the petroleum industry, in particular the distillation column, because it is multivariable with high interaction between control cycles, nonlinear behaviour and large gains. Control processes have undergone many developments and modernizations to achieve the best results. Various control methods have been used, ranging from simple proportional-integral-derivative controller (PID) to advanced control strategies such as model predictive control (MPC), multivariate model predictive control (MMPC), fuzzy logic control (FLC), quadratic dynamic matrix control (QDMC), artificial neural network control (ANN) and other advanced control techniques. The authors concluded from the review that the advanced control strategies superior than the conventional methods.
In this vast world after an earthquake lessons are learned; many strategies have been considered in order to achieve a proper seismic strength capacity.The aim of this paper is studying the seismic behavior of a typical reinforced concrete bridge pier in Iraq and implementing a proper technique of strengthening in order to fix any damage that had happened.Structure of a full scale three-dimensional finite element model was used in order to simulate a reinforced concrete pier via the computer software ABAQUS/CAE 2017 using concrete plasticity damage model (CDP).Under the action of Halabja earthquake, which was recorded at city of Halabja in Iraq on 12 November 2017, the behavior of model was traced, analyzed and the resulted damages were managed.The finite element analysis results indicated that the proposed configuration of carbon fiber reinforced polymers laminates substantially increases the lateral load strength and deformation capacity of the bridge pier
This study evaluates the performance and efficiency of four deep learning models—VGG-16, ResNet-50, Inception-V3, and DenseNet-121—in detecting pneumonia from chest X-rays, addressing the critical need for balanced accuracy and computational efficiency in clinical diagnostics. Methods: A dataset of 5,234 chest X-rays (3,875 pneumonia, 1,341 normal) was augmented via rotation, flipping, and zooming to mitigate class imbalance. Models were trained on an RTX 2060 GPU for 40 epochs, with performance assessed using accuracy, F1 score, sensitivity, specificity, precision, and computational metrics (training time, memory usage). Statistical significance was validated via paired t-tests (p < 0.05). Results: DenseNet-121 achieved the highest accuracy (95.2% ± 0.8), F1 score (95.1% ± 0.7), and throughput (400 images/sec) with minimal memory usage (33MB). ResNet-50 and Inception-V3 showed moderate performance, while VGG-16 exhibited overfitting tendencies. In conclusion, DenseNet-121 showed strong performance compared to other models, both in terms of accuracy and processing speed, which is essential for use in real-time clinical settings. However, the small size of the validation set and limited population diversity are important limitations that should be addressed in future studies. Moreover, more testing on larger datasets is needed to confirm the stability of the model and see how the model will work in different settings. Future work should address ethical considerations in AI-driven diagnostics and validate findings across multi-institutional datasets.
This research study the rheological properties ( plastic viscosity, yield point and apparent viscosity) of non-Newtonian fluids under the addition of different chemical additives with different concentrations, such as (xanthan gum (xc-polymer) , carboxy methyl cellulose ( high and low viscosity ) ,polyacrylamide, polyvinyl alcohol, starch, quebracho, chrome lignosulfonate, and sodium chloride (NaCl). Fann viscometer model 800 with 8-speeds was used to measure the rheological properties of these samples, that have already been prepared. All samples were subjected to Bingham plastic model. It was concluded that the plastic viscosity, yield point and apparent viscosity should be increased with increasing the concentrations of (xanthan gum (xc-polymer) , carboxy methyl cellulose ( high and low viscosity ) ,polyacrylamide, polyvinyl alcohol, starch and sodium chloride (NaCl), while the opposite is true for quebracho, chrome lignosulfonate.
Breast cancer is one of the greatest frequent tumours among females in Iraq. Medical ultrasound imaging has become a common modality for breast tumour imaging because of its ease of use, low cost, and safety. In the present study, Convolutional Neural Network (CNN) feature extraction approaches were used to classify breast ultrasound imaging. The CNN model used is composed of four-layer for breast cancer ultrasound image analysis. Two types of free datasets were used. These data were divided into groups A and B. Group A has three classes, namely benign, malignant and normal, while group B has two classes, namely, benign and malignant. The proposed technique was assessed based on its accuracy, precision, F1 score and recall. The model's classification accuracy for data A was 96%, whereas for data B was 100%.
Numerical analysis of the performance of reinforced concrete (RC) deep beam subjected to static and fixed-point pulsating loading at the midpoint has been investigated. Three-dimensional nonlinear finite element model using the Strut and Tie approach was adopted. The damage level under the influence of the applied fixed pulsating loading is higher than the static applied loading, hence early crack was observed because of the stepwise loading in the form of vibration. Although the Strut and Tie approach gave a good estimation of the resistance capacity of the beam, the beam undergo high shear damage when subjected to these two types of loading. Material strength properties, applied loadings and cross-sections adopted are some of the factors that affect the performance of the deep beam.
The solar vortex engine (SVE) has been investigated to generate power using renewable energy. The SVE was constructed from a vortex generation engine (VGE) and solar air collector (SAC). The SVE system primarily utilizes vertical air movement. However, the airflow entering the VGE experiences an obstruction. The purpose of this paper is to propose a new design for the VGE that creates a swirling updraft capable of overcoming air obstruction and reducing energy losses. A 3D numerical model of VGE was developed to visualize vortex generation. The modeling of the VGE is carried using SOLIDWORKS software and ANSYS-FLUENT 18. The improved VGE has six vertical twisted convergence blades connected to six guide vanes to direct updraft air in an anticlockwise swirl. All blades and vanes are housed in a VGE cylinder with a diameter of 20cm and a height of 30cm. The simulation results were validated by comparing with the results obtained from the present experimental model. The simulation results match with a mean difference of less than 5% with the experimental measurements. The results of the current CFD investigation indicate that there is a gradient in air temperature and pressure within the VGE, ranging from the highest values of 314 K and 3.85 Pa to the lowest values of 308 K and 2.42 Pa, respectively. The CFD visualization shows a threefold increase in axial velocity and a fivefold increase in tangential velocity within an artificial vortex. Therefore, it can be concluded that the new VGE construction is highly efficient in generating a vortex.
In the last two decades, underwater acoustic sensor networks have begun to be used for commercial and non-commercial purposes. In this paper, the focus will be on improving the monitoring performance system of oil pipelines. Linear wireless sensor networks are a model of underwater applications for which many solutions have been developed through several research studies in previous years for data collection research. In underwater environments, there are certain inherent limitations, like large propagation delays, high error rate, limited bandwidth capacity, and communication with short-range. Many deployment algorithms and routing algorithms have been used in this field. In this work a new hierarchical network model proposed with improvement to Smart Redirect or Jump algorithm (SRJ). This improved algorithm is used in an underwater linear wireless sensor network for data transfer to reduce the complexity in routing algorithm for relay nodes which boost delay in communication. This work is implemented using OMNeT++ and MATLAB based on their integration. The results obtained based on throughput, energy consumption, and end to the end delay.
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.
It is essential to review and develop a system of water control structures and canals that can be used to manage high-flow discharges and the flood control plan requirement to modify the system's capacity. Al-Ramadi Project System is considered one of the main flood control projects on the Euphrates River within Anbar Governorate, Western Iraq. This study will focus on Al-Majjarah Canal and Regulator, which is part of Al-Ramadi Project and has the function of a link canal between Al-Habbaniyah and Al-Razazza lakes, and describe the capacity of the canal under typical operating conditions and during floods. The study used HEC-RAS 6.1 software to run a numerical model to simulate this canal. According to previous research studies near the research region on the Euphrates River, for the main canal, the roughness coefficient was taken at 0.026, and for the flood plain, it was taken at 0.03. The same parameter value was applied to Al-Majjarah Canal. Due to the study region's similar geology and nature. Moreover, a sensitivity analysis was made of the roughness coefficient and its influence on the water surface elevation for the canal. The model result indicated in the current situation of Al-Majjarah Canal can pass a flow rate of 1300 m3/s when Al-Razazza Lake is at an average water level that has been approved by the Ministry of Water Resources at 32.02 m.a.m.s.l.. If the water level in Al-Razazza Lake is in the semi-filled position of 40 m.a.m.s.l., it causes floods for the canal because the water level rises above the banks of the canal at the last kilometer from the canal, even when passing a few discharges through the canal. Accordingly, it is not possible to safely pass the flow rate for a flood wave with a 500-year return period predicted by the "Study of Strategy for Water and Land Resources in Iraq (2014)", which is 2000 m3/s for this canal, without making modifications to the expansion of Al-Majjarah Regulator by adding additional gates, expanding the entrance and exit of the Regulator, reshaping and expanding some cross-sections, and raising some of the banks for the canal. The above-mentioned modification were applied for the purpose of passing the expected discharge from the canal, while maintaining a freeboard of 1 m between the water surface and the canal banks.
The goal of the study is to predict the Man in the Middle attack in the packets of Wireshark program by using Support Vector Machines (SVM).In the time of using the internet, it has become a tool targeted by attackers and hackers; it is a serious threat to the devices. A uniqueness of an attack that appears in multiple identities for legitimate agencies. It is very necessary to know the behavior attack and predict the possible actions of an attacker. In this research a detection of Man in the Middle attack by monitoring the Wireshark program and recording any changes can be recognized in packet information. The classification of packets is divided into two categories (normal and abnormal). The proposed model is designed in many stages: loading data, processing data, training data, and testing data. The detection of SVM based on abnormal network packet through movement packets in the Wireshark program that needs to deal with current packets to recognize a new attack that one does not have prior knowledge of its detection, and there is a need for an intelligent way to separate network packets that represent normal. The proposed approach achieved an accuracy of 97.34% in detecting attacks. The results show that the proposed model effectively visualizes attacker behavior from data that represents abnormal network attackers. Research achieves successful accuracy in predicting abnormalities.
Non-dispersive near-infrared technique is widely used nowadays for the detection of gases, especially in harsh environments. In this study, an optical gas cell was designed for oxygenator exhaust capnometry. A computer-based simulation was used for the analysis of air flows for model selection. ANSYS Discovery 2020 R2 was used for model simulation. The gas flow cells were tested using a custom-made gas rig to measure the fraction absorbance of carbon dioxide gas at the detector. Two gases were used, nitrogen gas as a reference gas (0%) and 9% carbon dioxide. Three gas cells with the following optical path lengths were tested: 31mm, 36mm, and 40mm. The results showed that all gas flow cells produced laminar flow and small pressure drop across the inlet and outlet of the cell (11~12 Pa). Further, the minimum velocity is obtained in the 40mm gas flow sensor and it is located at the gas outlet path away from the effective optical gas path. The simulation and experimental results indicate that the gas flow cell of 40mm optical path length is more suitable for the intended application as it offers a maximum effective absorption path compared to the stagnation areas, and as a result, it provides the maximum fraction absorbance.
The phenomenon of climate change resulting from the increase of global warming has become one of the main problems facing the world. Where researchers and specialists have worked for many years to find a solution that reduces this phenomenon and limits its risks. It is likely that clean energy is an alternative to fossil fuel sources, which are the main source of global warming. One of the clean energy sources is ocean wave energy, which is a huge and untapped energy source, despite the possibility of extracting large energy from waves. This paper focuses on the study of deep-sea turbines and their results. A study was conducted on the capture chamber. Where this paper presents an experimental model of a water tank with certain dimensions in the university laboratories to describe the dynamic behavior of the capture chamber. The Froude number scale was used to model the dimensions and depth of the water as well as the wave properties. Through experimental work and its results show, and it was found that the power generated by the motion of the wave strength is related to the height and frequency of the wave.
As internet network developed rapidly in the past ten years, and its operating environment is constantly changing along with the development of computer and communication technology, the congestion problem has become more and more serious. Since TCP is the primary protocol for transport layers on the internet, the data transmitted via the transport protocol utilizes Vegas Transmission Control Protocol (TCP) as the congestion control algorithm, where it uses increasing in delay round trip time (RTT) as a signal of network congestion. However, this congestion control algorithm will attempt to fill network buffer, which causes an increase in (RTT) determined by Vegas, thereby reducing the congestion window, and making the transmission slower, Therefore Vegas has not been widely adopted on the Internet. In this paper, an improved algorithm called TCP Vegas-A is proposed consist of two parts: the first part is sending the congestion window used by the algorithm for congestion avoidance along with the TTL (Time To Live) mechanism that limits the lifetime of a packet in the network. While the second part of the algorithm is the priority-based packet sending strategy, and jitter is used as a congestion signal indication. The combination of the two is expected to improve the efficiency of congestion detection. A mathematical model is established, and the analysis of the model shows that the algorithm has better effects on controlling congestion and improving the network throughput, decreasing packet loss rate and increasing network utilization, the simulation is done using NS-2 network simulation platform environment and the results support the theoretical analysis.
The finite element method is one of the important methods in analyzing geotechnical engineering problems; its main advantage is the ability to apply for the materials exhibiting non-linear stress-strain behavior. In this study the finite element program PLAXIS 3D 2013 is used to study the behavior of the piles under the influence of seismic waves in saturated sandy soil and the effect of adding geogrid with the pile foundation. The program has been used to facilitate the representation of the real model, input the required soil parameters and implementation of seismic data. Seismic wave, the soil geometry and the pile dimensions were fixed in all models, while dimension and depth of the geogrid used were varied to study the influence of different depth and dimension in reducing the pile displacements and the pore water pressure of soil. The results show that The reduction in settlement ratio (the difference between settlement of pile without and with using geogrid to the settlement without using geogrid) for ( ×L/2), (L×L) and (2L×2L) are 10.6%, 17% and 21.3% respectively. And the settlement ratio for geogrid at depths 8.33% and 12.5% of pile length are 9.6% and 17% respectively.
The main objective of this study was to model the left ventricle (LV) based on 2D echocardiography imaging technique to assess the cardiac mechanics for group of patients affected by heart failure. A prospective study has been made at Ibn Al-Bitar center for cardiac surgery, for 13 patients with heart failure (HF), 9 patients were males (69%) and 4 females (31%). The mean age was 54±7 years. Those patients were supposed to undergo a CRT-D (Cardiac Resynchronization Therapy Defibrillator) implant as they didn’t respond to drug therapy. Before CRT-D implantation, 2D echocardiography was performed for all the patients, to model the left ventricle and to measure indices that were used to evaluate cardiac mechanics which are LV pressure, wall stresses, global longitudinal strain, and cardiac output. After 3-months of follow-up, 2D echocardiography was re-assessed and the left ventricular mechanics has been re-measured. Post CRT-D implantation, significant improvement in the cardiac mechanics was observed in 54% of the patients which were called responders (patients that respond to CRT-D device) and the other patients were called non-responders. It has been seen that, the circumferential wall stresses were decreased in responder’s group while increased or remain unchanged in non-responders. Global longitudinal strain for the responder’s group were increased while remain unchanged in the non-responders. So, patients were divided into responders and non-responders, based on improvement of the cardiac mechanics after 3-moths of follow up. It has been concluded that the modelling of the left ventricle based on images obtained from 2D echocardiography imaging techniques, was an important computational tool that was used to enhance understanding and support the evaluation, surgical guidance and treatment management of basic biophysics underlying cardiac mechanics.
This paper discusses the development of a seven-band coherent wavelength-division multiplexing (WDM) system covering the T to U systems, aiming to enhance the capacity and system efficiency. Seven multiband systems (C+L, S+C+L, S+C+L+U, E+S+C+L, E+S+C+L+U, O+E+S+C+L+U, and T+O+E+S+C+L+U) are designed with 40 GBaud symbol rate, 50 GHz channel spacing, and dual-polarization (DP)-16QAM signaling. The analysis adopted the enhanced Gaussian noise model, considering the amplified spontaneous emission of inline optical amplifiers and nonlinear interference (NLI) from fiber nonlinear optics, including Kerr effect and stimulated Raman scattering (SRS) which it implemented using Matlab (Ver. 2020b) program. The results show that the optimal powers are -4, -5, -5, -4.5, -3.5, -6, and -4.5 dBm for the seven WDM systems, respectively. Further, with a fiber span length of 100 km, the C+L system has the longest transmission reach of 20 span. However, using S+C+L+U system gives the highest bit rate-distance product of 1619 Tbps.km. The O+E+S+C+L+U and T+O+E+S+C+L+U systems are designed with 50 km-span length to reduce the effect of NLI caused by the large numbers of channels (1060 and 1200, respectively).
The Unified Power Flow Controller (UPFC) is a most complex power electronic device, which can simultaneously control a local bus voltage and optimize power flows in the electrical power transmission system. This paper presents the effect of installing the UPFC on the Iraqi (400 kV) grid transmission system to control the active and reactive power flow by choosing the optimal location and parameters of Unified Power Flow Controllers (UPFCs), which were specified based on the Genetic Algorithm (GA) optimization method. The objectives are improving voltage profile, reducing power losses, treating power flow in overloaded transmission lines, and reducing power generation. The steady state model of UPFC has been adopted on (400 kV) Iraq transmission lines and simulated using the MATLAB programming language. The Newton-Raphson (NR) numerical analysis method has been used for solving the load flow of the system. The practical part has been solved through Power System Simulation for Engineers (PSS\E) software Version 32.0. The Comparative results between the experimental and practical parts obtained from adopting the UPFC were too close and almost the same under different loading conditions, which are (5%, 10%, 15% and 20%) of the total load.
Many researchers have applied several experiments and research studies by developing criteria's design of drainage to improve the drainage process, and to show that the filters plays an important role to improve and maintain the drainage system from being blocked due to siltation. There are several types of filters, including granular mineral materials and organic materials, the other filter that was used is made from a special fabric material such as paper, burlap, or special fabric textile material. The objective of this study is to evaluate the performance of textile filters, and if it is desirable and suitable for Iraqi soil using statistical analysis. This study was conducted in the laboratory using sand tank model and two types of filters (graded crushed gravel and textile) with two types of soil (sandy soil and loamy soil) to compare and evaluate the hydraulic performance and the efficiency of utilizing textile filter instead of graded crushed gravel filter in drainage systems using statistical analysis methods. These statistical analysis show that there was a good agreement between measured and theoretical values of entrance resistance when using the two filters in sandy soil. On the other hand, the results showed that there was a weak performance when textile filters in were used in heavy soil (loamy soil) due to the high value of root mean square error (RMSE) and low value of agreement index (d). The results of statistical analysis show that the textile filter is desirable and suitable for Iraqi soil especially for sandy soil due to low entrance resistance of flow compared to loamy soil.
A new robust control algorithm is proposed for a class of nonlinear systems represented by a Single Link Manipulator (SLM) system. This algorithm is based on new techniques and methods in order to obtain a controller for the SLM system. First of all, the system is simplified using Variable Transformation Technique (VTT) in order to fit the analysis procedure. Then, a new idea of designing a model reference for the multiple states (n=4) system is presented to correspond the control design. Next, the Lyapunov Stability Analysis (LSA) is used to figure out a proper controller that can compensate the stability and the performance of the SLM system. After that, the Most Valuable Player Algorithm (MVPA) is applied to find the optimal parameters of the proposed controller to accomplish the optimum performance improvement. Finally, it can be concluded that the proposed control algorithm has improved the stability and the performance of the SLM system. In addition, the simulation results show the remarkable effects of the proposed nonlinear controller on the SLM system.
Forced vibration has been experimentally investigated on a model consists of circular pipe with1.6m length. The pipe built in tank (1.2m length, 0.6m height and 0.6m width) horizontally at 0.4m height with two different diameters d=15mm and d=35mm. The pipe conveying laminar flow in the fully developed region, of Reynolds number equals 2000. The experimental results of span pipe conveying water at five stations of forced excitation vibration were studied. The harmonic forced vibration with two different excitation frequencies (10 Hz and 15 Hz) are imposed at all of the five locations. The distance between two stations is (0.2m). Two conditions of pipe environment have been applied, the first in air and the other was immersed in water. It is concluded that the effect of flow induced vibration due to the pipe conveying fluid increases the maximum deflection when the fluid speed increases. The water surrounds the pipes reduce the effect of excitation vibration about (33 – 46%). The effect difference between the excitation frequencies was about (4 – 7%).
Recently, there is increasing interest in using the 18 THz bandwidth offered by S+C+L band to increase the transmission capacity of fiber communication systems. This leads to the generation of ultra-wideband (UWB) wavelength-division multiplexing (WDM) optical communication systems. In these advanced systems, stimulated Raman scattering (SRS) causes a power transfer from high-frequency channels to low-frequency channels. This effect leads to an increase in the nonlinear interference (NLI) between the UWB-WDM channels. Power optimization techniques are required to balance transfer power between band channels, thus increasing the maximum transmission reach (MTR) along with increasing system capacity. In this paper, the transmission performance of S+C+L band system operating with dual-polarization 16-QAM signaling is investigated using enhanced Gaussian noise model. The transmitter and receiver for each DP channel use a -polarized laser and incorporate two identical configurations, one for x- and the other for y-state of polarization (SOP). The results are presented for two values of symbol rate, 40 and 80 GBaud, where the system carries 360 (=160+80+120) and 180 (=80+40+60) channels, respectively. The results revel that the MTR of both cases is equal to 12 100 km-spans when the channel lunch power equals to -4 and -2 dBm, respectively. This work also shows the effect of NLI components as a function of the number of spans, channel spacing, and channel launch power. The results show that the cross-phase modulation component of the NLI has high accumulated value with transmission distance, while the self-phase modulation component is almost constant.
The hydraulic characteristics of dams can be predicted with high precision and reliability of physical and numerical models depending on accurate hydraulic data. The model is operated and simulated to get a more efficient, optimized utilization of the dam. This research included a comprehensive overview and literature examination of the Makhool Dam which is considered one of the most important dams under construction in Iraq. Previous studies of the dam focused on different topics in the operation of the dam and analyses of its properties, part of which focused on the dam ability to manage flood and how it works best with other dams in critical times, and another part studied the properties of the stilling basin, velocity in the dam reservoir, pressure, seepage and other characteristics that affect the operating the dam. Despite this research and the variety of topics discussed, there is no well-established research on the operation of the bottom and emergency spillway of the dam by using computational fluid dynamics (CFD) simulation software. CFD is considered an essential tech because it has an important influence in determining the hydraulic properties of a spillway and studying its effectiveness under different operating conditions. Because the spillway is an important element in the dam body, the research highlighted the necessity of performing a simulation using appropriate CFD software for this part. This research has also reviewed previous research on CFD software and their ability to simulate previously constructed or under-construction dams to analysis of its hydraulic properties.
Artificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.
The aim of this work is to optimize EPD variables (voltage, time, and focus) using alternating current through the Taguchi Design of Experiment (DOE). Coating Nano hydroxyapatite (Nano-HA) on a Ti6Al4V substrate depends on thickness and roughness, then characterization of a coating layer to determine the optimum state. Hydroxyapatite (HAp) powder was deposited on a Ti-6Al-4V alloy substrate by electro-deposition with ethanol as a solvent under AC current, to improve the alloy surface quality based on coating thickness and maximum coating mass meeting the requirements of a biological orthopedics application. Ethanol was used as a solvent to precipitate ketazone and HAp on the base alloy. Taguchi's approach was used in order to determine the optimal conditions for EPD and subsequently to apply various criteria for depositing the biochemical coating. The surface and cross-section composition of the paint is described by characterization. Numerous tests and inspections; Zeta, XRD and SEM stability test, water contact angle and optical microscopes were used to describe the surface morphology of the HAp layer. The value of the optimum conditions for deposition of the HAp layer which is a simultaneous thickness and maximum coating mass, was predicted at a sedimentation voltage of 40 V, 2 min sedimentation time and 1 g / L for the concentration of the suspended solution at room temperature. The validity of the model resulting from the response surface methodology was assessed by comparing the expected results with the experimental results. In addition, close agreement was observed between the experimental results and the expected results. For the solution at room temperature, the results obtained with the highest value of the coating thickness of 41at the surface roughness of 0.94 and the contact angle of the alloy before coating is 67.489º reduced to. 38.132º after plating, which indicates an increase in the harmony of the metal implant and biocompatibility.
An experimental and theoretical study of free convection heat transfer for a cylinder placed in an iron test section of dimensions (0.2x0.2x0.2 m3), the test section filled with saturated porous material glass balls (5 mm), and the air is the working fluid with Raleigh number (7692.6 ? Ra ? 17654). The circular cylinder heater (D = 0.015 m, L = 0.2 m) is heated electrically, made of Copper and located in different positions (in X & Y direction). The theoretical part includes solving the free convection heat transfer using the ANSYS program (fluent). The experimental and theoretical results showed that the surface temperature values around the cylinder perimeter when changing its position within the test section are changing as moving up and down where the effect of buoyancy force appears. The maximum difference between the upper and lower position at the experimental result is 7.22%, and the average Nusselt number increases with Raleigh number and heat flux. Also, the experimental results showed that the use of porous material significantly improves the heat transfer by 48.6%. The maximum percentage change between the experimental and theoretical results is 5.46%. Moreover, experimental correlations were achieved, and a comparison was performed between the present results with the previous studies and it gives a good agreement.
This research introduces a method of an electro-optic effect and electro-refractive effect that considers very imperative for high-speed optical communication systems. In this research, it presents way by a reduction the gap between the electrodes d, and this technique achieves to solve the problem of overlap for Mach-Zehnder interferometer MZI electro-optical switch base on lithium tantalite LiTaO3, also this technique suggests a model for analysis the effect parameters on the electro-optic overlap of the electro-optic switch as the ordinary positive changing of refractive index and a length of arm switch. This study achieves a better overlap by large positive changing refractive index with a suitable small length of arm about 8µm and low driving power at least 4V/µm. Also, for lithium tantalite LiTaO3, this research achieves a better performance for system using the near infrared wavelength.
Regaining the activities of daily living after stroke and spinal cord injury requires repetitive and intensive tasks, meaning that rehabilitation therapy should be treated with a long duration. Thus, the need for rehabilitation devices based home is of most importance to increase the rehabilitation process and provide more comfortability for patients. This paper focuses on implementing and construction of a three degree of freedom (DOF) (flexion/extension, adduction/abduction, and pronation/supination), low cost, lightweight, and portable robotic exoskeleton for wrist-forearm rehabilitation. SolidWorks software program and 3D printer technology are used to model and construct the proposed robotic exoskeleton structure. In addition, the anthropometric parameters of the normal human lower arm are considered for this exoskeleton to provide a range of motion (ROM) and velocity for the links, joints, which matches with the anatomical structure of human and also to avoid the excesses motions over the normal range. The exoskeleton is constructed by a 3D printer utilizing polylactic acid (PLA) plastic material. The proposed implementing structure of the robotic exoskeleton shows comfortable, lightweight, simple and economic as well.
With the occurrence of pathological disorders in some people or aging, metabolic energy consumption begins significantly due to the weakness of the peripheral muscles and the increase in body fat with time, which aggravates the issues for this type of people, causing the rest hours extremely lengthy and consequently may produce heart or arterial diseases and elevate the mortality rate. Regarding the significance of the matter, this study examines a number of previous researches that featured several approaches to energy calculation and strategies for lowering energy consumption through the use of various external assistance devices, such as exosuits or exoskeletons, to assist people in carrying out their everyday tasks. And additionally discussed musculoskeletal simulation employs a variety of programs, especially OpenSim, which enables users to build models of musculoskeletal structures and produce dynamic movement simulations. According to the research findings, exoskeletons and other assistive technology can successfully lower the cost of metabolic energy to varying extents, depending on the device's weight, placement within the body, and whether it is active, semi-active, or inactive. In the future, the work to design and simulate a semi-active torsional ankle-foot exoskeleton with a specialized mechanism aimed to minimize metabolic energy.
The intention of this study was to explore the efficiency and feasibility of adsorption of Reactive Blue dye (H3R) used in textile industries using Raw wheat straw (RWS) and Modified wheat straw (MWS) as a low-cost adsorbent. Wheat straw was modified using cationic surfactant (CTAB) to study the improvement of dye removal. The properties of Raw and Modified wheat straw are studied by means of Fourier transform infrared (FTIR) and scanning electron microscope (SEM) analyses to determine the functional groups and the nature of their surface. Continuous experiments were done by fixed-bed column to study the characteristics of the breakthrough curve using different bed heights and flow rates. Results showed that the breakthrough time increases with increasing bed height and decreasing flow rate, in turn results into higher removal capacity. Results also showed a higher flow rate lead a lower adsorption capacity due to insufficient residence time. Bed depth service time model (BDST), Adam-Bohart and Thomas models were used to predict the breakthrough curves and to determine the adsorption capacity of the column. The highest bed capacity of 12.95 and 32.2 mg/g for MWS was obtained using 10 mg/L, 10 cm bed height at 10 mL/min and 30 mL/min respectively.
Kidney disease is a global health concern, often leading to kidney failure and impaired function. Artificial intelligence and deep learning have been extensively researched, with numerous proposed models and methods to improve kidney disease diagnosis. This work aims to enhance the efficiency and accuracy of the diagnostic system for kidney disease by using Deep Learning, thereby contributing to effective healthcare delivery. This work proposed three models: CNN, CNN-XGBoost and CNN-RF to extract features and classify kidney Ultrasound images into four categories: three abnormal cases (stones, hydronephrosis, and cysts) and one normal case. The models were tested on a real dataset of 1260 kidney ultrasound images (from 1000 patients) collected from the Lithotripsy Centre in Iraq. CNN models are often viewed as black boxes due to the challenge of understanding their learned behaviors, Visualizing Intermediate Activations (VIA) was used to address this issue. The proposed framework was assessed based on precision, recall, F1-score, and accuracy. CNN-RF is the most accurate model, with an accuracy of 99.6%. This study can potentially assist radiologists in high-volume medical facilities and enhance the accuracy of the diagnostic system for kidney disease.
In an original article, an addition was made to the well-known Taguchi’s methodical design literature by proposing how Poisson distribution may be incorporated into the Taguchi method for enhanced performance analysis in optimization. While the article is recent, it was found compelling enough to apply this novel concept of Poisson distribution to a growing area of maintenance research known as maintenance downtime analysis. Consequently, this paper contributes to the expanding research neighborhood through a Taguchi optimization method based on Poisson distribution related to the maintenance process optimization. A valuable method to optimize maintenance downtime was developed wherein the Poisson distribution was used to achieve the probability of maintenance downtime. An important foundation of the method is the Taguchi scheme. These elements were transformed into the factor-level design of the Poisson enhanced Taguchi scheme while the framework was tested using data from a process industry for validation. Interesting, the Taguchi's signal-to-noise quotient led to an enhanced set of limiting factors for better reliability of the system as G1H1I1J1K3. By interpretation, the following was found: downtime (204.61 mins), probability density function (0.00187), and cumulative density function (0.00776). The combination of these factors and levels will enhance maintenance downtime in the process industry as a result of their contributions. The outcome revealed the competence of the model to optimization schemes.
This study focused on the urban transformations resulting from the removal processes taking place in the urban fabric of sacred city centers, under the pretext of increasing urban capacity due to the density of use, which leads to the removal of important parts of the traditional urban fabric and adding them to the public urban space. To determine the amount of usage densities that the area can accommodate represented by the case study, which is the center of the holy city of Najaf: the study was based on using a quantitative measurement approach to test the hypothesis using a multivariable density measurement tool. A space matrix capable of measuring densities, accessibility, and diversity in the fabric during three time periods, a historical period 1900, 1990, and the current time 2024, to know the amount of changes that have occurred in the fabric. A qualitative measurement tool, which is a random sample questionnaire, was used to measure perceived density to find out which fabrics within the city center are more accommodating of congestion. The research has found that high and advanced accessibility through an integrated fabric with high connectivity that makes the city spaces work as one space leads to an increase in flows. It works to reduce the momentum in the city center and thus preserve the traditional urban fabric that must be emptied for pedestrians, as it represents the only fabric with The human scale at the level of the city as a whole (i.e. a fabric that is comfortable for pedestrians) also represents the identity of the area, and to accommodate the densities, the percentage of building density must be increased outside the traditional fabric.
The transportation cost problem of solid waste presents the biggest part of the budget allocated by municipalities for SWM. So, there is no comprehensive plan to address transport routes optimization problems in SWM that including the transfer of solid waste from transfer stations to final landfill sites. Therefore, the aim of the study finding a scientific method to solve the transportation problem of solid waste transport suitable Baghdad city that tries to find feasible solutions that ensure reducing total transport costs and leads to an effective solid waste management system. In this research, a new methodology has been developed to select the optimal transport routs of SWM in Baghdad city which involves determining the best-supposed scenario. the proposed methodology includes integration of Global Positioning System (GPS) technologies with Network Analysis model (NA). Therefore, this work provides an advanced framework of decision-makers for analysis and simulation of the optimal transport routs problem related to SWM. Applying these modeling tools to select the scenario that can provide economic benefits by minimizing travel time, travel distance and reduction of total transportation costs. The Results of work implementation show that all solutions that include current state S1 and suggested scenarios have been evaluated. The scenarios generated include (S2, S3) by applying the proposed technique for analyzed and identified the optimal routes. The solutions of scenario S2, specified with two landfill sites while scenarios S3 specified with four landfill sites. Finally, this work shows the Scenario S3 is the best scenario of the solution, that include applied GPS and Network Analysis for four landfill sites.
In Republic of Iraq, ready-mix concrete production plants have been adversely affected by the lack of modern and advanced technology to assess their performance in line with technological advancements. Current evaluation methods rely on traditional approaches and financial measures, yielding unrealistic performance results. To address this problem, there is a need to utilize modern models and methods for performance evaluation. The study's main objective This was achieved by employing a literature survey methodology and utilizing digital databases such as the Iraqi Scientific Journals website, virtual libraries, and scientific platforms like ScienceDirect, Springer, Google Scholar, and Gate Research between 2015 and 2023. The research study provided a comprehensive overview of performance evaluation, including its definitions, importance, and an introduction to modern models and evaluation methods. The study found that no previous studies have been conducted in Iraq to evaluate ready-mix concrete production plants. However, four studies were found in Egypt, Sudan, and India. The previous similar relevant studies discussed various topics and related studies. Firstly, they discussed the classification, advantages, and disadvantages of concrete mixing plants. Additionally, the previous studies analyzed the factors that most influence the performance of concrete production plants, including laboratory manager efficiency, work team efficiency, communication and relationships within work teams, plant operator, material transportation method, and time and courses. Furthermore, the previous research studies present a comprehensive analysis of all variable data simultaneously using the statistical package for Social Science (SPSS) input stage. The evaluation also extends to the evaluation of laboratories, encompassing plant arrangement, internal quality control systems, and final product quality. The overall evaluation results of previous studies. Indicate that 75% of the concrete production plants failed to meet the required criteria, while only 25% demonstrated satisfactory performance. The study proposed improvements to enhance the performance rate of ready-mix concrete production plants by leveraging the most influential variables, which will be considered in the study.
This study has been performed to compare the compartmental modeling of two types of extravascular routes, sustained-release (SR) oral dosage forms and intramuscular (IM) injection. Twenty healthy volunteers received a single dose of 100 mg Diclofenac Sodium (DS) sustained-release tablet, then 75 mg DS Intramuscular injection after two weeks washout period. The concentrations of DS in plasma were measured using reverse-phase high-performance liquid chromatography (HPLC). The data analyzed using compartmental modeling, with single time-variant input and output. Primary kinetic parameters for both formulations, ( , , ) and other kinetic parameters were evaluated. The result shows that the IM injection needs a shorter time to reach the maximum concentration with convergent bioavailability to SR oral dosage forms, in another hand the data of IM injection fitted to single-compartment model with a correlation coefficient of 0.93 and the data of SR tablet fitted to two-compartment models with a correlation coefficient of 0.97.
Water seepage can cause serious problems in geotechnical engineering especially for construction under the water level. Baghdad metro tunnel is one of the leading vital projects to solve the major problem of crowding roadways in a highly population increase city like Baghdad. In this study, the seepage rate that will flow toward different selected points along the tunnel section across Tigris River was calculated during the excavation process, with the consideration of three different water levels of River at maximum, moderate, and minimum water depths. A three-dimensional model of the study has been modeled using the finite element software (PLAXIS 3D V20). The water seepage was observed for six different locations on each route of the tunnel. The study showed that the change of water depth in the river has no significant effect on the seepage – time curve shape. However, increasing the water level in River from minimum to maximum leads to increase the seepage rate about 15%.
Super-hydrophobic is the tendency of a surface to spit out water droplets. Only a surface with high apparent contact angle (>1500), low contact angle hysteresis (<100), low sliding angle (<50), and strong Cassie model state stability is considered a super-hydrophobic surface. In an attempt to create highly hydrophobic synthetic surfaces suitable for a range of uses, attempts have been made to mimic the super-hydrophobicity found in natural materials (such as lotus leaves). Due to its wide range of applications including waterproof, anti-fog, anti-ice and anti-corrosion surface, the laser processing process achieved the use of process parameters which had a significant impact on the roughness factor. High roughness factor F. At constant values of p = 3 mW and ω = 10 μm, at scanning speeds of 6000 mm/s.
The aim of this study was to support surgeons to decide where to place the screws in order to achieve an optimal fracture healing and to prevent implant failure after a femoral shaft fracture So this paper focus on the analysis of bone-plate construct by using Finite element Analysis (FEA), comminuted femur fractured bone fixed with Dynamic Compression Plate (DCP) 16 holes by 4.5 Cortex screws, to investigate the effects of screws configuration on the mechanical behavior of different seven model as Interfragmentary strain which is the most important factor for femur fracture healing. The results state the relationships between the Von-Mises stress, Total deformation and Interfragmentary strain with respect to the screws configuration. The study shows the regions of maximum stress from stress distribution and also founded that we can decrease the Interfragmentary strain by increasing the number of screws.
Continuous Positive Airway Pressure (CPAP) ventilation remains a mainstay treatment for different respiratory disorders. Good pressure stability and pressure reduction during exhalation are of major importance condition to ensure the clinical efficacy and comfort of CPAP therapy. Obstructive Sleep Apnea (OSA) and today coronavirus (COVID-19) are the main two diseases mitigated by the CPAP. This paper introduced a systematic review of the CPAP design in terms of the hardware design, Simulation-based CPAP system, control algorithm, and the measured performance. The accuracy is used as measurement of performance and calculated from the pressure value. The accuracy was compared to the predefined U.S. Food and Drug Administration (FDA)-based threshold value in which it considers this value as a reference. The results related to the modern CPAP devices introduced in this study to explain the accuracy of experimental CPAP. These were compared with a commercial CPAP devices. Also, it was revealed how the results coincide with the error ratio defined by the FDA as an evaluation measurement. The FDA error ratio determines the performance of the optimized CPAP device. This work is the first review that presented the knowledge about engineering design of the CPAP system, so it will be the first in the literature.
Basrah is the richest town and the economic capital of Iraq. It suffers from lack of drinking water. This project is a dream to supply drinking water to Basrah citizens within WHO standards. Water should pass sedimentation and filtration stages before interring reverse osmosis unit. The design is carried out using lewaplus2 software. Several parameters should be selected in the design step membrane type, number of stages, number per element in each stage, and the recovery percentage. An optimization is carried out using Minitab ver. 18 for the acceptable limit of TDS and minimum cost and it was found that the optimum conditions were 52% for first stage, the numbers of vessels are 20 for both the first and second stage. In addition, results showed that the pressure and the total dissolved solid increase with increasing the recovery while parameters like the feed flow rate per vessel, the power, and the cost are decreasing with the recovery. Mathematical model described the cost was conducted and statistical study was also done to ensure the results.
The study aims to evaluate the current flood carrying capacity and its change after some cross sections developments for the 110 km reach of Tigris River and Kmait flood escape system. This reach extends from Ali Al-Gharbi station to Amarah Barrage station. The model is calibrated by using set of data at the Ali Al-Garbi gaging station, that includes flow varied between 790 to 470 m3/s during April 2019. Manning’s n coefficient value of (0.03) is selected as it has the minimum least-squares root difference of (0.148) between the measured and estimated water levels. The results show that the current capacity of Kmait flood escape and this Tigris River reach are 280 m3/s and 1100 m3/s, respectively. According to the study of strategic for water and land resources in Iraq, 2014, scenarios are conducted for some cross sections development to improve the capacity of the reach to 2750 m3/s. Results of applied development show that Tigris River can safely accommodate a flood wave of 2750 m3/s when modifying the cross-sections in different locations, and raising the banks level in three locations, 0+00, 79+00 and 95+00km. Earthworks volume of development of the reach is 247603200 million m³, with the total cost of 490 billion IQD.
Power outages are a common and persistent problem in Iraq, significantly impacting various aspects of life and business. These interruptions disrupt routine household tasks and hinder more complex technical operations in industries and services. Emphasizing the need for careful management and proactive solutions. This paper introduces a real-world time series dataset for Baghdad city, including historical outages, weather conditions (such as temperature), and power overloads, and analyzes the correlation among these parameters in different seasons. The research uses this dataset to train one-dimensional Convolutional Neural Networks (1D CNN) to find patterns and relationships that can accurately predict when power outages can happen in the long term and short term to improve the management of the Baghdad electricity grid through data-driven networks. This model was evaluated using performance metrics, and the results show that CNN is accurate in predicting outages in the short term with a Mean Absolute Error (MAE) of (0.0077), whereas, in the long term, it has achieved an MAE of (0.0775). These predictive models have the potential to facilitate the development of proactive measures aimed at reducing the impact of power outages by anticipating potential outages in advance. This research focuses on enhancing the reliability and efficiency of Baghdad's electricity supply, ultimately contributing to economic growth and stability.
Face recognition and identification have recently become the most widely employed biometric authentication technologies, especially for access to persons and other security purposes. It represents one of the most significant pattern recognition technologies that uses characteristics included in facial images or videos to detect the identity of individuals. However, most of the traditional facial algorithms have faced limitations in identification and verification accuracy. As a result, this paper presents a sophisticated system for face identification adopting a novel algorithm of deep learning, namely, You Only Look Once version 8 (YOLOv8). This system can detect the face identity of different individuals with different positions with high accuracy. The YOLOv8 model has been trained for several target face images classified as training and validation images of 1190 and 255, respectively. The experimental results show a significant improvement in face identification accuracy of 99% of mean average precision, which outperforms many state-of-the-art face identification techniques.
This study presents a numerical analysis for point contact Elasto-hydrodynamic lubrication EHL. The oils used are (0W-30 and 10W-40) as lubricants. The pressure and film-thickness profiles for point contact EHL are evaluated. The aims of this study are to estimate the effect of oil’s temperature on friction force, coefficient of friction and load carrying capacity. By using FORTRAN program, the Forward-iterative method is used, to solve two dimensional (2D) EHL problem. The viscosity is updating in the solution by using Roeland’s model. After the convergence of pressure is done, the friction force, friction power losses, and friction coefficient are calculated. The temperature used ranges from (-20 to 120 oC). The results showed the film-thickness decreases with the increasing of temperature. Though the maximum pressure is not affected, only the pressure distribution and profile are changed, inlet pressure decreases and the pressure profile tends towards a hertzian (dry contact) one. The friction force and the coefficient of friction decrease with the increasing of temperature.