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Go to Editorial ManagerIt 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.
This work has studied the size of the mean time between failures (MTBF) because it has a vital role in assessing reliability in manufacturing systems. Previous studies have indicated that the reliability value depends on the size of MTBF, so they indicated only 11 types of time that reliability value depends on, and they used methods of DFR and RCM to enhance the reliability level. To assess and increase reliability value, this work referred to the four main times: mean time between failures (MTBF), mean time to diagnosis (MTTD), mean time to repair (MTTR), and mean time to failure (MTTF) in more detail. Also, it designed a new arrangement of failure notification time, failure diagnosis time, downtime, failure repair, testing time, and recovery periods for ongoing operations in manufacturing systems through a new redistribution of 19 times and time intervals in detail between the four main times, so it revealed and added 8 types of other times and time intervals more than previous studies because they have vital roles in increasing reliability value. Thus, the new arrangement contains two parallel pathways and 19 types of times and time intervals. The first pathway represents 5 positions and 11 types of start and end times; the second pathway represents 4 positions and 8 time intervals. Consequently, MTBF becomes longer because the new arrangement shortens the time distances between the start of failure and repair process end, between diagnosis end and test, and between inspection end and the system's return to normal operating conditions. The motivations are to raise the reliability value, quality level, and effective maintenance and save costs. This work used the data collection and analysis method. The results showed that there is a higher reliability for manufacturing systems when the time arrangement is better, MTBF is longer, MTTD is shorter, MTTR is smaller, MTTF is longer, and the error rate is lower.
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%.
In this work, the vibrations in the rotor-bearing system are studied experimentally and theoretically using ANSYS Workbench 2020 R1 software to compute the natural frequencies and mode shapes. In the experimental part, the LABVIEW software was used to examine the signal of the frequency domain values obtained from the accelerometer sensors, based on Fast Fourier Transform (FFT) technology and dynamic response spectrum. in the theoretical part, the natural frequencies are determined based on the finite element method for analyzing the system and knowing its behavior and vibration response level. The results showed that the level of vibration becomes higher at high rotational speeds, and it becomes large when the distances between the bearings are large, according to the bearing position and type used in the system. in this work can be concluded, the system is usually affected by the dynamic response around it and is difficult to separate from it, and the vibrations in the system can be controlled by adding an external damping source, which gives the system more stable. A system operating at high speeds can give a large vibration and an unbalanced response.
The security level and robustness of memristive image encryption techniques depend on the order and dynamics complexity of the memristive system. The grid multi-double-scroll (GMDS) chaotic system (CS) offers extremely rich dynamics but the implementation of high-order chaos needs large computation time. To overcome this limitation, researchers have proposed the use of muti-lower-order CSs to assist the encryption process individually. This scenario may reduce the security level since the non-friendly user may attack each involved CS independently. This paper proposes an effective six-dimensional (6D) memristive chaotic system constructed by combining 5D, 5D, and 7D GMDS chaotic systems. Each of the six chaotic sequences is generated from three sequences corresponding to two or three of the basic CSs. The combined CS shares the same total key parameters (initial values and design parameters associated with the three basic CSs) and this leads to a key space of 22392, the highest among the reported image encryption techniques. The combined CS is used to assist the operation of a proposed color image encryption scheme consisting of four sequential stages that perform compressive sensing, scrambling, DNA encoding, and diffusion, respectively. Simulation results validate the feasibility and robust security of the proposed encryption scheme.
Providing a clean and high quality drinking water to both rural as well as urban areas is a great challenge by itself, adding to it the large volume requirements of such water at high population areas means a very high cost for such industry because mainly of the cost of expensive commercially available adsorbent used in this process. This led inhabitants of the remote and/or rural areas to use less quality water with all its risks and health challenges. In this study, a locally collected rice husk is tested to be used as an alternative adsorbent to the expensive common commercial ones. Parameters like adsorbent dosage, initial concentration of turbidity, and pH level were tested to investigate their effects on the process. Treatment of synthetic turbid water was done after changing these parameters to measure the effect of each parameter alone and the results showed a set of parameters that can be used to achieve high efficiency of turbidity removal. The study concluded that rice husk can be used as a well cheap alternative adsorbent to reduce the river water turbidity due to its availability and low cost with a decent removal efficiency approaching 95%.
Accurately identifying the kind and amount of dissolved metal salts in wastewater used in oil refining processes is an iconic feature of ultraviolet and visible absorption spectroscopy. This method relies on the dissolved metal salts' ability to absorb light at certain wavelengths after reacting with it. The experiments were conducted in a lab setting with a broadband source (200-800 nm) to measure the absorbance of dissolved element salts and precisely identify the lowest concentration up to 2 ppm. A mixture of the mineral salts from oil refining operations was prepared and diluted to different concentrations using a standard solution. This allowed us to study and compare this result with the absorbance behavior of the wastewater from the Al-Dora Refinery. The two results reinforced that we can accurately estimate the detection parameters for the lowest water contamination. These materials are lead nitrate (PbNO3), phenol, calcium carbonate (CaCO3), sodium chloride (NaCl2), sulfide (SO4), and nitrate (NO3). At wavelengths of 340, 404, and 741 nm, the concentrations (10, 20, 30, 40, 50, 60, 70, 80, 90, and 100) ppm were found, and for the concentration of 10ppm, the absorbance (0.15323, 0.15326, and 0.14685) was found, respectively. The process that has been tested with varying concentrations is considered and simulates the variation in river water concentrations caused by the river's water level and flow rate changes by the effect of rain abundance and thawing. It is fast, accurate data analysis, and a lower cost compared with the other chemical analysis and conventional methods.
The present investigation looked at whether the Bailey approach to aggregate gradation could be used to construct Superpave HMA blends. It also looked at how this approach influenced the rutting performance associated with these mixes and compared it to mixes of asphalt created by Superpave gradations. The current research included four aggregate gradations: both fine and coarse gradations for the Superpave and Bailey gradation procedures. The repeated loading test was utilized to assess the rutting performance. The findings indicated that temperature, stress level, and aggregate gradation all had a significant impact on rutting performance. In contrast to the other three gradations, the third mixture gradation exhibited the least amount of non-reversible deformation. It translates to pavement that is more resistant to rutting and less susceptible to it.
Contemporary urban discourse is paying increasing attention to the issue of urban resilience, due to the stresses, disasters and disturbances (natural and human) that the cities of the world are experiencing and facing, which confirms the need to be familiar with the concept of urban resilience, its dimensions, practices, and characteristics at different levels; In order to reach the aspects of developing the urban energy sector in them, and in a way that supports the preparedness of cities to face potential expected and unexpected disturbances in the future, as cities are usually formed from many main and sub-systems that are dynamically intertwined with each other, such as: the social and economic system, infrastructure systems, land use, and media Various transports, which have a high level of direct interactions with the natural environment; ; It is therefore necessary to understand how the city deals with the odds of threats and challenges in an integrated manner; To overcome its weaknesses and enhance its resilience of use, which aims to make cities more secure, resilient and sustainable in the future, as well as that requires rethinking the field of expanding the use of renewable energies and the general urban landscape. To become a search problem “Failure to exploit the potential of natural energies on the possibility of exploiting renewable natural energies with their components (active and passive) in the production of resilience urban formations in cities.” The aim of the research is to try to "extract an integrated theoretical framework on the characteristics of urban energy resilience from international and Arab experiences, and to diagnose its most important planning and design pillars and indicators, which can be adopted to evaluate the reality of urban energy resilience in local cities." The research hypothesized that “the exploitation of energy systems produced from renewable natural resources, for the purposes of environmental treatments for resilient cities, especially in the buildings of housing projects and their urban surroundings, reduces the consumption of fossil energies for the city, frees its sites from linking to depleted energy transmission networks, and reduces potential environmental pollution problems, which contributes to in the production of flexible energy systems and helps in the generation of flexible cities." The descriptive analysis method was adopted.
In order to avoid losing sense of sight in a large portion of the working population, Diabetic Retinopathy (DR) identification during broad examination for diabetes is crucial. To prevent blindness in the future, early illness detection and measurement of disease development are essential. DR is diagnosed through medical image analysis. After the success of Deep Learning (DL) in other applications in the real world, it is considered a vital tool for upcoming health sector applications, providing solutions with accurate results for medical image analysis. This review provides a comprehensive survey of the state-of-the-art DL models for DR detection and grading using retinal fundus photography. This review thoroughly examined and summarized 81 relevant publications that were published through IEEE Xplore, Web of Science, PubMed, and Scopus between 2018 and 2023 based on the available database with binary or multiclass CNN classification models as well as the main preprocessing techniques. According to the findings of this review, transfer learning has proven to be an excellent technique for addressing the problems of limited resources for data for DR analysis. CNN models having tens or hundreds of layers are the most frequently utilized frameworks for DR classification. The most extensively utilized datasets for DR categorization are Aptos 2019 and EyePACS. Although DL has attained or surpassed human-level DR classification accuracy, there is still more work to be done in real-world clinical procedures.
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.
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.
Recent research has focused on analysing megakaryocyte images to extract the information needed to track the progression of nervous system diseases. Segmentation is a fundamental step in describing and analysing the core contents of megakaryocytes, including the cytoplasm and nucleus. In this study, 45 megakaryocyte images were obtained. A new segmentation image technique was proposed, called the updating fuzzy c-means technique, through the intelligent selection of the centres of each cluster to separate cell components. The first step of this technique (fuzzification) was based on a knowledge analysis of the local parameters (entropy, contrast and standard deviation) that had a substantial influence on the grey-level distribution between the cytoplasm and nucleus. The second important step was the construction of fuzzy rules in terms of the variation in these local parameters to control the intelligent pick-out or update the centroid of each cluster and obtain a successful separation of the cytoplasm and nucleus. The final step was defuzzification to obtain the output images. The results revealed the superiority of the proposed method over recent technique. The accuracy of the segmented nucleus was greater than 7.46%; in the case of the cytoplasm, the accuracy was higher at 18%. These results indicated that this technique may be applied on other biomedical images.
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.
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 recent progress in integrated photonics has promoted microwave photonic filter (MPF) technologies to a supreme level to develop wireless, radar, and internet communication systems. Therefore, the specifications that distinguish the MPF chip include small size, low power, and inexpensive. The MPF that utilized these technologies has demonstrated the merit of wide frequency tuning and reconfiguring by selecting the desired spectral content and rejecting the sideband signals furthermore the immunity to electromagnetic interference. This paper reviews recent techniques involved in microwave filter design on multiple platforms, which involve cascaded micro-ring resonator (MRR), ring-assisted Mach-Zehender-Interferometer (MZI) coupler, Brillion-active waveguide, reflector-type MRR, and Bragg grating with phase shifts. In particular, we demonstrated the output characteristics of the microring integrated with a Mach–Zehnder interferometer coupler technique.
The partial shading conditions have a significant effect on the performance of Photovoltaic system and the ability of delivering energy. In this study, the impact of different partial shading on the mono crystalline (88W) PV module performance was investigated in this study. Horizontal string, vertical string, and single cell shading at different percentage of shading area have been studied. It is found that the horizontal string shading is more severe on the efficiency of the PV panel. In contrast, the efficiency of PV panel with cellular and vertical cell shading was less during the tests. The experimental results showed that the power losses were 99.8%, 66% and 56.8 % for horizontal, cellular and vertical shading respectively via applied non transparent material as shading element by 100% of shading area at 500 W/m2. Moreover, transparent material used to shade whole module horizontally, different shading area and different radiation level applied to find electrical characteristics of the module under these conditions. The results show that at 800W/m2 of irradiation levels and no shading condition the power was 68.6W, by increase shading area by 20% in each step, the power reducing by 44.94, 47.58, 49.42, 50.57 and 52.4% in compared with their initial value at no shading condition.
Cancer is a disease caused by uncontrollable cell growth and division. Surgery, chemotherapy, radiotherapy, and hormonotherapy are all cancer treatment options. In addition to noninvasive cancer ablative therapy. As an example, ultrasonic therapy, even with low-intensity pulsed ultrasound (LIPUS) or high-intensity focused ultrasound (HIFU), and Laser therapy (photo-biomodulation therapy) in low-level laser therapy (LLLT) with different wavelength ranges from ultraviolet (UV), visible and infrared (IR) that all have demonstrated different results depending on the target of treatment so previous trials therapies are being studied. This paper reviews recent studies on the in vitro treatment effect of ultrasound therapy and laser therapy on normal and cancerous cell lines with specific parameters. The effect of ultrasound results showed a decrease in cell proliferation and an increase in apoptosis in different types of cells, depending especially on sound intensity, known as Special Peak Temporal Average Intensity (ISPTA). While the laser effect is noticed on cell viability, either enhance or inhibit their viability depending upon the dose of exposure and other specific parameters like wavelength, energy density, and power density used in each treatment protocol. The previous studies conclude that each response would have a treatment method with specific parameters, even an increase or decrease in cell viability. Further studies need to be applying these methods in vivo.
Diabetic peripheral neuropathy represents one of the common long-terms complications that effect about fifty percentage?of diabetes patients. The habitual diagnosis tool based on nerve conduction study that examine the nerve damage and classify the patient status into normal and diabetic peripheral neuropathy with degree of severity without considering the effect on skeletal muscle and take on patient data. A complementary diagnostic tool proposed, in this study integrates the patient’s data including body mass index, age and duration of diabetic, average blood glucose levels, nerve conduction study that involves amplitude and latency of peroneal and tibial nerves and muscle ultrasound alongside the machine learning algorithms to facilitate the clinicians for a precise diagnosis. A group of control and diabetic patients utilized to gather the data with calculating the muscle thickness and statistical properties from the gray-level ultrasound images of six skeletal muscles. Support vector machine, naïve bayes, ensemble of bagged tree and artificial neural network supervised machine learning algorithms categorize each class with a high classification accuracy, 98.1% for tibialis anterior with naïve bayes algorithm. The outcomes of this study show a promising complementary diagnostic tool that will help the clinicians to perform an exact diagnosis and disclose the side effect on both nerves and muscles of diabetic patients.
Platinum, copper, and nickel were founded the best metals used in resistance temperature detectors RTDs. They commonly used in laboratory and industrial applications because they provide accurate and reliable measurements in a wide temperature range from (- 200 to 850 °C). They have high conductivity, sensitivity, and hardness to resist strain shock, pressure, and vibration. The accuracy level of them depends on reliability, stability, repeatability, linearity, and response to time. This study aims to determine and compare the accuracy of these three metals in regarding to their features which include stability, repeatability, and response time. The study has gathered and analyzed the data of these suitable and precise metals and compared with each other. The results showed that platinum is widely needed for RTDs due to its precision, stability, higher accuracy, and linearity output, while copper and nickel are not stable or repeatable as platinum. It was indicated that temperature coefficient of resistance TCR for nickel is bigger and for copper is medium, but for platinum is lower.
Recently, 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.
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.
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.