KUFA JOURNAL OF ENGINEERING

Diwaniya is one of the middle Euphrates cities through which the Euphrates River passes through. The Euphrates section that passes through Diwaniya city is called (Shatt al-Diwaniya). This study deals with the river water quality for the period between 2015 and 2023 at that section. The aim of study is to assess the influence of pollution on the sustainability quality of Shatt al-Diwaniyah area using the Nemerow pollution index (NPI) based on artificial neural network (ANN) and regression analysis model (RA). To determine critical water performance parameters for the river section, investigation is required precisely to find out which characteristics and stations contribute to river pollution. Physicochemical water parameters such as Biochemical Oxygen Demand (BOD5), Total Dissolved Solid (TDS), pH, Dissolved Oxygen (DO), Nitrate (NO3), and phosphates (PO4) along the length of the river were measured by fifteen sites from Diwaniya\'s river during (2015-2023). NPI was calculated for the water samples in each station of examination. The growing NPI value indicated that the river water quality in this section is deteriorating. Where the river\'s NPI values vary between 2.14 and 3.48, indicating that its water quality is slightly polluted. Mathematical models of artificial neural networks and regression concluded that the most polluted parameter is phosphate PO4 which is concentrated in station 15 which recapitulates that river water is not suitable for use as a source of drinking water, besides, it can still be consumed to irrigate agricultural property and plants

In this study, a comparison between thermal conductivity and dynamic viscosity of a pure propylene glycol liquid as a solar thermal fluid and its nanofluid has been carried out using different concentrations of nanoparticles. Aluminum oxide nanoparticles (α phase - size 50-90 nm) were added to the pure propylene glycol liquid at three different concentrations (0.2%, 0.4%, and 0.6%). The different thermal fluids (pure and nano-propylene glycol) were tested at different temperatures (20, 60, 100, 140, 180°C) on five different days. Regarding pure liquid, as temperature increases the thermal conductivity and viscosity drop for all experiment days. The minimum value for thermal conductivity and viscosity were recorded on the first day test at 180°C temperature with (k=0.13 W/m.K and µ=13.2 centipoise). However, adding the nanoparticles has reversed the temperature effect on the resulting nanofluids thermal conductivity. Where k reached its maximum value (0.307 W/m.K) at 180 °C for 0.6% concentration nanofluid on day (60) recording an improvement of (136.14%). Viscosity in contrast kept decreasing with temperature rise for each nanoparticles concentration. Yet the nanoparticles concentration proportional directly with dynamic viscosity at constant temperature. In this contest, on day (60) test, the viscosity at 20°C for both pure PG and 0.6% nano-PG were 58 and 64 centipoises, respectively. While at 180°C, µ= 14.9 and 21 centipoises respectively. The behaviour of PG nanofluid dynamic viscosity in different concentrations and different temperatures has almost the same response to concentration and temperature for ethylene glycol based nanofluid. Finally, SEM images showed homogeneity and distribution of the nanofluid at a concentration of 0.4%, and some aggregates were detected at a concentration of 0.6%.

Functionally Graded Materials (FGMs) represent a notable advancement in materials research as they enable the smooth incorporation of distinct characteristics from various materials. This study examines the vibrational characteristics of functionally graded ceramic-metal composite beams via the use of Finite Element Analysis (FEA) in MATLAB. To capture and study the dynamic response of the replicas. The focus of this investigation is on ceramic and metallic materials that are represented by power-law distribution along their length. In order to include material variation in the global mass and stiffness matrices, the beam segments have been divided into many sections. In this regard, the objective was to calculate natural frequencies and mode shapes of these beams using eigenvalue problems. Results obtained reveal that MATLAB is a reliable tool for investigating dynamic properties of Functionally Graded Materials (FGMs) because it has proven to be user-friendly as well. The present work shows how to use functionally graded indices and porosity on vibration characteristics of composite beams.

The study demonstrates the efficiency of using recycled materials to create solar water heaters, in particular in enhancing heat transfer. The innovative heater surpasses traditional commercial models in performance, offering a sustainable solution for residential water heating. The research, conducted in Baghdad, Iraq, entailed reusing heat exchangers from an outdated air conditioner to serve as a solar water heater collector. Equipped with a thermal insulator ,and an electronic control system, the heater achieved impressive results, raising the water temperature to 55 o C in just 30 minutes with a thermal efficiency of 83.122%. While the commercial heater took an hour and a half to heat the water to 57 o C. By integrating features such as serpentine fins, black coating and foam insulation, the system increases heat absorption and reduces losses. The electronic control system enhances the efficiency of heat transfer, which makes the homemade solar heater a viable and affordable option whose manufacture costs no more than 30,000 IQD while the commercial one costs 179,000 IQD. Both are environmentally friendly for residential use. In general, the research emphasizes the possibility of using recycled materials to develop efficient and sustainable solar water heating systems.

The bond is considered one of the factors that plays an important role in the behavior of the reinforced concrete structural elements. In recent years, using the glass fiber reinforced polymer (GFRP) as an alternative to steel bars is needed to verify its performance in the structures. In this study, the bond between the concrete and GFRP bars is investigated through using two different surface enhancement techniques (sand and steel fibers). Fourteen beam specimens were cast and tested considering three different parameters, concrete compressive strength (55 and 78 MPa), bar diameter size (6mm and 10 mm) and the surface scheme (non-coated, sand-coated and steel fiber-coated). The experimental work included the recording of the bond stress and corresponding slip. For a certain value of concrete grade and 6 mm bar diameter, the bond behavior improved by 206% and 198% for the sand-coated surface and steel fiber-coated bars as compared to non-coated bars, respectively. For the same bar size (6mm), the best enhancement in bond strength was found by (61%) for the non-coated surface when the concrete compressive strength increased from 55 to 78 MPa. The use of larger bar diameter for the same concrete compressive strength, the higher bond strength improvement was 55% for the non-coated surface bar. From the bond stress-slip curves, the tests indicate that when the surface of the GFRP bars is coated by sand, it will give the best enhancement in the bond strength.

Android malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking. Therefore, understanding the unique behaviors of common Android malware categories is essential. This study utilizes machine learning techniques namely, K-Nearest Neighbor, Random Forest and Decision Tree to classify Android malware based on dynamic analysis. As feature selection and reduction techniques, Mutual Information and Principle Component Analysis are used. The research analyzes a large dataset, containing fourteen primary malware categories using the CCCS-CIC-AndMal2020 dataset. Unlike previous research, the proposed method makes a balance between the number of features and classifiers’ performance, resulting in an overall detection accuracy of 98% in the fourteen analyzed categories and excluding 78.87% of the original dataset’s features. The research, thus, introduces an efficient Android malware detection method that reduces the computational cost and improves the classification accuracy.

Evaluation of residual sludge and water quality of a treatment plant is an integrated activity through the analysis of physical and chemical properties related to health, environment, and specific water used. This study aimed to address the characteristics and properties of the raw water, treated water, and the residual sludge from Al-Wihda water treatment plant (WTP) based on two stage. The first is the historical data from 2018 to 2023 and the results showed that the maximum amount of sludge produced was 259.2 kg/1000 m3 exceeded the reported limit, while turbidity and TSS have a significant contribution to the amount of sludge produced by Al-Wihda WTP. The second, experimental tests were carried out for the residual sludge in the sedimentation tank for the duration (15 July-15 December, 2023) and the results showed that the maximum TSS in residual sludge was about 1585 m3/d, while the maximum COD was about 200 mg/L which exceeded the Iraqi standard limit (100 mg/L). The study concluded that residual sludge is dependent on the amount of TSS contained in the raw water and contains high amount of COD that could be used for the treatment of saline wastewater.

This study examines the flexural behavior of six hybrid composite castellated beams with varying castellated degrees (25%, 33.8%, and 50%). Hybrid beams are formed by utilizing plates with different strengths in the compression and tension zones. Three hybrid composite castellated specimens fabricate from half of IPE200 on the bottom and half of IPE160 on the top while the other three hybrid beam fabricate from a half of IPE200 on the upper and half of IPE160 on the bottom. Two symmetric concentrated load were delivered at the third places to the six specimens. It can be seen from the tested beams that the bearing amount decreases by (12% and 37%) with increasing the percentage castellation to (33.8% and 50%) for beams with a noticeable decrease in deflection. Strength augmentation in hybrid beams can be accomplished by incorporating low yield strength steel in the upper T-part of the beam and high yield strength steel in the lower T-part, when under load.

The experimental programs involve testing of five beams with the same length and shear connectors subjected to two concentrated loads. The main objective of this study to investigate the effect of using different types of materials (carbon and galvanized steel) for flat and corrugate web. The corrugate beams have varied the sub-panel fold of a single wave (100-150) mm. Practical results show that the galvanized steel webs have an ultimate flexural capacity lower than that for carbon steel by about (53% and 50%) respectively for flat and corrugate web. The increasing of the wave of sub-panel fold causes significant decreases in flexural capacity by about (5%). Practical results show also that the corrugate web with carbon or galvanized steel exhibited better results as compared with flat web beams in stuffiness ductility and energy absorption.

The experimental programs involve testing of five beams with the same length and shear connectors subjected to two concentrated loads. The main objective of this study to investigate the effect of using different types of materials (carbon and galvanized steel) for flat and corrugate web. The corrugate beams have varied the sub-panel fold of a single wave (100-150) mm. Practical results show that the galvanized steel webs have an ultimate flexural capacity lower than that for carbon steel by about (53% and 50%) respectively for flat and corrugate web. The increasing of the wave of sub-panel fold causes significant decreases in flexural capacity by about (5%). Practical results show also that the corrugate web with carbon or galvanized steel exhibited better results as compared with flat web beams in stuffiness ductility and energy absorption.

Deception detection is an interdisciplinary field that has researchers from psychology, criminology, and computer science. We propose the automated detection of deception based on facial micro expressions which occur spontaneously in response to the attempt to mask the inner emotion. It has received significant attention as an indicator of deceit, it reveals the genuine emotions that are concealed. In this paper, we first proposed a 3D 478 Mediapipe Face Mesh Model to extract facial landmarks that reflect facial micro expression, this is contrary to the traditional method, which relies on human judgment and the use of devices to detect facial micro expression. Second, a feature selection-based multivariate mutual information method was proposed to select facial landmarks that are most related to the deceptive cues and have critical influence on the classification task. Finally, a gated recurrent unit model was trained to predict deceptive behavior on a real-life trial dataset. The model successfully achieved 97% accuracy, outperforming other state-of-the-art methods.

Recently, most oil companies are working to reduce the cost and carbon emissions accompanied crude oil distillation process by developing suitable sustainable technologies. The present work studies the viability of retrofitting concentrated solar collector (CSC) represented by parabolic trough collector (PTC) through the crude oil distillation process in three stages; crude oil preheating in a furnace, live steam injection, and the reboiler heater. The whole oil distillation unit and the PTC unit are modeled and solved using an American software called COCO V 3.6.0.13 and SAM 2022.11.21 (System Advisor Model). Three different working fluids are investigated through the study: therminol VP-1, hitech solar salt, and pressurized water at different mass flow rates. Additionally, the proposed system was analyzed economically using levelized cost of energy (LCOE) method. The required land area for the solar unit is analyzed through the study too. It was found that hitec solar salt requires higher input flow rate than the other heat transfer fluids (HTF) where the maximum flow rate reaches about (75 kg/s) while the minimum flow rate was found for pressurized water as the flow rate value reaches about (25.7 kg/s). It was found also that the maximum required thermal energy is the energy exhausted in the furnace for crude oil preheating process, and the minimum value is the energy required for steam generation. The economic feasibility study shows that retrofitting such a solar units would be more beneficial in the years period between 2022-2050 as the LCOE of flue gas (0.0363 $/kWh) is higher than the LCOE of PTC (0.01604 $/kWh) leading to an overall cost saving reaches about 14523013 $ after year of 2021. The land area analysis shows that the required area is increasing linearly in a rate of (0.62 kW/m2). The validation with recorded data from literature shows a good agreement in compare with the current work.

With exponential growth of digital information, the need for efficient methods for automatic keyphrase extraction has become increasingly important. Key phrase candidate topic detection (KPCTD) aims to automatically identify key phrases, i.e., phrases that capture the central meaning of a text document and associate them with their corresponding topics. We have developed an innovative method that combines statistical with contextual approaches ( position and distance criteria in addition to semantic information). We present a comprehensive approach to text analysis; it enables the use of a harmonious mix of different features that allows for precise and effective extraction of relevant information. furthermore, for sifting the later extracted key phrases into condensed thematic (topic) key phrases written under (ABSTRACT) part, superiority of the various strategies is examined, such as approximate matching with key sentences at the beginning of the text, the identification of cluster foci, and the prioritization of frequent phrases. After extensive investigations on two datasets, semeval2017 and Inspec, the proposed PhraeRank approach outperforms the previous results. Quantitative metrics achieve a precision of 51.23% and a recall of 28.26% for top 5 keyphrases on the SemEval2017 dataset, and a precision of 47.89% and recall of 25.34% on the Inspec dataset. Additionally, value of a BLEU score is 0.62 on the SemEval 2017 dataset and 0.58 on the Inspec dataset. demonstrating significant improvement over existing methods. These results highlight the algorithm's ability to extract relevant information from text documents.

In this study, the environmental impacts of building exterior surface and street surface materials using the life-cycle assessment (LCA) method will be discussed. A complete understanding of these materials’ ecological implications will arise from an exploration from raw extraction to the discarding phase. The materials used in this research are asphalt, ceramics, aluminum, alucobond, artificial stone, glass, and steel which have been evaluated against various factors such as electricity consumption, greenhouse gas emissions, depletion of resources, and waste production by using OpenLCA software. From a global warming perspective, glass ceramic and artificial stone contribute less compared to scenarios where steel and aluminum are used, but for the case of depletion of resources, ceramics alone hold more than a third lesser amount than all other alloys combined. This paper proposes guidelines for designers, stakeholders, and decision-makers to take appropriate actions towards sustainable urban design.

Maximum power point tracking (MPPT) is crucial for optimizing the energy extraction from solar modules in photovoltaic (PV) systems. This paper focuses on maximizing the energy extraction from solar panels and explores the important aspects of MPPT technology in PV systems. The sliding mode learning controller (SMLC) created for MPPT provides a new way to deal with environmental factors, including radiation and temperature fluctuations. The study compares the disturbance-insensitive SMLC with linear proportional-integral-derivative (PID) controllers and conventional sliding mode controllers (CSMC). The SMLC enhances the maximum energy extraction by tracking the reference voltage signal using the perturb and observe (P&O) algorithm. Moreover, the controller can adapt to the dynamic changes in PV characteristics thanks to the learning system. The result shows that the proposed SMLC offers significant benefits, especially in challenging operating conditions. It demonstrates superior vibration-free performance, fast response (with a settling time of 24.7 ms), and smooth and precise tracking compared to other controllers.

Optimal reconfiguration is a significant alternative technique of increasing the efficacy of Radial Distribution Networks (RDNs). Reconfiguration is carried out by adjusting the status of RDN switches in such manner that the system's radiality is kept, energized wholly loads and other restrictions are fulfilled. The original version of the Dolphin Echolocation Optimization (DEO) algorithm is designed for solving continuous optimization issues only. As the reconfiguration problem is a discrete issue, the original DEO algorithm cannot deal with this problem. Fortunately, a Binary DEO (BDEO) algorithm was presented for solving discrete optimization issues which is utilized for adapting the reconfiguration issue. This approach is a powerful tool for rearranging systems by altering the status of the RDN switches in a way that minimizes power loss and enhances voltage profile. The BDEO algorithm is evaluated on an IEEE 33 bus RDN under three case studies in MATLAB to validate its performance. By comparing the simulation results with those from previously published work, it is possible to conclude that the suggested strategy is efficient in achieving the optimal outcome because it enhances the system voltage profile while minimizing losses. The comparison results showed that, in instance two, the BDEO for the test RDN greatly increased the minimum voltage from 0.9131 to 0.9431 P.U. and reduced the power loss by 34.2%, from 202.67 to 133.17 KW.

The eye is considered as one of the most complicated organs of the human body, with various ailments that can impact it. Eye diseases are any condition that disrupts its function, leading to vision problems or blindness caused by genetics and aging to injuries, infections, and environmental factors. Keratoconus is a condition where the cornea becomes thinner and bulges out, causing vision and visual impairment, and must be detected early to prevent potential complications. This paper used real data taken from TOMEY TMS-5 to build a dataset for convolutional neural networks (CNNs) GoogleNet algorithms to classify either a Keratoconus or a normal case. The suggested models provided correct decision up to 85% accuracy which is a good result compared to other approaches using raw data to avoid limiting decision to some parameters. The built dataset contains a group of patients (females and males) whose ages ranged from 14-75 years.

Aromatic compounds are considered the building block for petrochemical industries. In this study, a new pilot-scale counter-current modified spray column was designed and constructed for extracting aromatic compounds from reformate-heavy naphtha (RHN) using furfural as solvent. RHN and furfural were provided by Al-Dora Refinery in Iraq. The new design included a rotor added to the conventional spray column. The effects of solvent-feed ratio (S/F) (0.25-2) and rotor speed (0-1000 rpm) were studied. The extraction efficiency and Sauter mean diameter (d32) were investigated experimentally and modeled by dimensional analysis. The experimental results showed that the extraction efficiency varied significantly with rotor speed and S/F ratio recording 96.5% without plate, and 92.5% with plate at 0 rpm rotor speed. The experimental results also showed that d32 values ranged from 0.0025 m to 0.0055 m, corresponding with changes in efficiency. Depending on the experimental results, a correlation was derived for the efficiency and d32 based on the effective dimensionless numbers in the system. The modeling results demonstrated that Reynolds (Re) and Weber We numbers were the most effective dimensionless groups that affected the extraction efficiency and d32 values.

Disputes are major causes of poor performance in construction projects. Escalated disputes result in complicated disagreements whose resolutions are expensive, take longer, and impact relationships. Disputes management has a powerful function to ensure successful performance. Identifying the most frequent claims and disputes from past projects, their origins and causes could be a sturdy policy for effective management. This study aims to diagnose the most common main and root causes of claims and disputes in Iraqi building projects in order project participants to incorporate integrative claim management strategies before actual construction. A sample of 70 projects were selected for this purpose. Root cause analysis (RCA) using Fishbone diagram was employed to explore the relationships between the main and root causes of claims. The results can enable international and local professionals to adopt strategies prior to getting involved in projects and thus reduce the negative effects of early known problems with effective planning.

The purpose of this mixed-methods study is to explore the effectiveness of Agile methods in addressing inefficiencies within software development projects, related issues, delays, excessive defects, and concerns with improved collaboration among the teams. This paper seeks to measure the value that Agile brings to organizations and recommend the implementation of these methods as a way of addressing the challenges that organizations experience when using conventional systems in software development. No changes to the basic Agile concept were made here and thus the research is based on methods that are already implemented such as Scrum and Kanban. Measures of productivity including project completion time, number of defects, and satisfaction level of the software development teams were obtained over six months from three companies. This research is innovative in the sense that it uses both the quantitative and qualitative methods in conducting the assessment to determine whether or not the Agile methodologies improve software development effectiveness and team performance in contrast to the Waterfall model. The results of this study indicate that Agile techniques enhanced productivity combined with the quality of team relations and can be used for further expansion in the sphere of software engineering. Therefore, it is recommended that Agile practices be implemented as a way of fixing problems that are standard in software development.

The purpose of this mixed-methods study is to explore the effectiveness of Agile methods in addressing inefficiencies within software development projects, related issues, delays, excessive defects, and concerns with improved collaboration among the teams. This paper seeks to measure the value that Agile brings to organizations and recommend the implementation of these methods as a way of addressing the challenges that organizations experience when using conventional systems in software development. No changes to the basic Agile concept were made here and thus the research is based on methods that are already implemented such as Scrum and Kanban. Measures of productivity including project completion time, number of defects, and satisfaction level of the software development teams were obtained over six months from three companies. This research is innovative in the sense that it uses both the quantitative and qualitative methods in conducting the assessment to determine whether or not the Agile methodologies improve software development effectiveness and team performance in contrast to the Waterfall model. The results of this study indicate that Agile techniques enhanced productivity combined with the quality of team relations and can be used for further expansion in the sphere of software engineering. Therefore, it is recommended that Agile practices be implemented as a way of fixing problems that are standard in software development.

In the last five decades, the design of digital systems in nano-scale has attracted the attention of researchers. Quantum-dot Cellular Automata is a new method of binary representation at the nano level. Many circuits have been designed with this nanotechnology, most of them looking to reach optimality. QCA circuits, like other technologies, have evaluation metrics such as circuit area, delay, and cell count. Recently, an additional metric called circuit cost, has been proposed as a differentiation metric, and this evaluation metric has been introduced in various approaches. In this paper, the previous approaches of cost function are studied and evaluated, then new generalized form cost function is invented.

To meet the urgent need for effective cooling solutions in a manufacturing setting, the effectiveness of a vortex tube system for cooling electrical control panels is carefully investigated in this experimental study. This paper presents a novel strategy to improve cooling efficiency in industrial settings by investigating the creative use of a vortex tube for panel cooling. The main goal of this work is to extend the cooling procedure for the electronic control panel and the method of impacting different pressures (1 to 7 bar) on the system's performance. This work offers core information on the activity and capability of using the vortex tube for cooling purposes by performing some tests in Basrah / Iraq for three days during June 2023. Detecting perfect operation conditions enhances the cooling performance and reduces power consumption. The finding confirms that air pressure at the entrance has an impact on the allocate the ability of the system on the cooling. This confirms that the coefficient of performance (COP) of 0.12 is produced by 4 bar internal air pressure. Achieving solutions for high performance cooling is critical for the control panel of the manufactural field. This study helps lower energy consumption, improve equipment reliability, and lessen environmental effects by examining the operation of a vortex tube system and optimizing cooling efficiency.

Wind turbines have attracted much attention from scientists who are aiming to achieve a prominent level of performance due to the environmental changes during the year. To meet the demand for renewable energy at the lowest cost, wind energy became the target of machine learning algorithms and was employed to predict the output power of wind turbines. The aim of this study is to utilise a causal inference approach to estimate the power output of wind turbines. Initially, the outcomes derived from numerical prediction are examined using a structural causal model. After causal discovery, we identify potential confounders for the filter factors that have the most significant impact on the outcome and decide whether to include or exclude these prospective confounders in our model. This method guarantees precise predictions using the Adaptive Neuro-Fuzzy Inference System ANFIS. ANFIS is a hybrid learning approach that combines neural network and fuzzy logic system for estimating the output model for nonlinear relationships. ANFIS employed the pressure surface, wind speed and air pressure as input data while the power of the wind turbine served as the output of the predicted model. Various practical experiments were conducted using different membership functions in neuro-fuzzy training to achieve the lowest possible mean square error. The data were gathered over two months on a farm in Egypt on the Suez Gulf. Approximately 85% of the data was utilised to train the three inputs, while the rest was used to evaluate the predicted model and assess the efficiency using Akaike Information Criterion (AIC) to choose the best fit model. A further study showed that changing the ANFIS parameters could deviate from the path of efficient outcomes, compared with the neural network approach.

This study introduces an innovative approach for arrhythmia diagnosis via electrocardiogram (ECG) signals, employing a 2D Convolutional Neural Network (CNN) model fused with a Continuous Wavelet Transform (CWT) and a Selective Attention Mechanism (SAM). The SAM enhances feature focus, improving classification accuracy. The model effectively categorizes ECG signals into Normal and Abnormal classes, subcategorizing Abnormal patterns into four types. Merging deep learning with signal processing enables the correct classification of arrhythmia. The outcome yields high accuracy, such as 99.784% for multi-class and 99.94% for binary classification. The methodology proposed in this paper shows the capability of secure yielding for arrhythmia diagnosis, setting novel standards within healthcare applications.