Tikrit Journal of Engineering Sciences

To enhance stability during the forming process and improve the quality of the product, it is essential to analyze and identify the influence of the process parameters on the sheet formability. Therefore, this research investigates the influence of key process parameters, including the forming fluid pressure and the blank holding force, on the formability to produce square cup parts using an AISI 1006 STEEL alloy sheet employing hydrostatic formation technology. The formability evaluation was based on two criteria: the maximum thinning in the formed parts and the minimum achievable corner radius. For this purpose, the authors manufactured an experimental setup for hydrostatic forming and verified this setup’s capability to manufacture square cups with specific dimensions. This setup provides and controls the three process pressures (forming, holding, and closing) independently, allowing greater flexibility to maintain process stability. In this regard, a process window with optimum peak fluid pressure and holding force combinations was determined. The experimental results indicated that these combinations allow for the successful formation of cups up to the entire die depth without encountering failures (wrinkling and fracture). Additionally, increasing the peak pressures for forming and holding within the process window reduced the achievable minimum corner radius, thereby enhancing dimensional accuracy. Simultaneously, it contributed to increasing the maximum percentage of thinning at the bottom corners. In particular, the thinning measurement was 23%, near the critical limit. Therefore, this process window helps select the optimal peak pressures based on the desired corner radius and the maximum allowable thinning for a given component.

Excavation deeper than about six meters is restricted by the application of sheet pile walls with anchors. This paper examined the effects of different parameters on anchored sheet pile walls under a lateral cyclic load ratio (CLR) of 10% and several cycles equal to 100. It was fixed by two anchored piles horizontally and embedded in sandy soil. The ratio of the spacing between anchored to the free head of sheet pile wall (S/H= 1/3, 2/3, and 1) and the ratio of anchored length to the free head of sheet pile wall (L/H= 1, 4/3, and 5/3) under the influence of two–way cyclic lateral load at 0.2 Hz environmental frequency. The research aims to ascertain the rod's impact spacing and length anchored on the lateral and vertical displacement of the sheet pile, as well as the vertical displacement of surcharge (line load) applied to the surface. The wale for sheet pile decreased the lateral displacement of a sheet pile to 66% compared to lateral displacement without wale. Also, the results of the length rod to the free height of the sheet pile wall (L/H= 1) increased the anchored sheet pile wall's lateral displacement by about 55% from the cantilever sheet pile wall's lateral displacement with wale because it was near the active failure zone. The ratio of length to free head of sheet pile wall (L/H= 5/3) reduced the anchor sheet pile's lateral displacement wale by about 51% compared to the cantilever sheet pile's lateral displacement with wale. The ratio of length anchored to the free head of the sheet pile wall (L/H= 4/3) decreased the anchored sheet pile's lateral displacement to 49% compared to the cantilever sheet pile's lateral displacement with wale. Both reductions for two lengths with the spacing ratio between the anchors rod to the free head of the sheet pile wall was (S/H= 1/3).

The present work elucidates the utilization of activated carbon (AC) and activated carbon loaded with silver nanoparticles (AgNPs-AC) to remove tetracycline (TC) from synthetically polluted water. The activated carbon was prepared from tea residue and loaded with silver nanoparticles. Scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and Brunauer-Emmett-Teller (BET) were used to characterize the activated carbon (AC) and silver nanoparticles-loaded activated carbon (AgNPs-AC). The impact of various parameters on the adsorption effectiveness of TC was examined. These variables were the initial adsorbate concentration (Co), solution acidity (pH), adsorption time (t), and dosage of the adsorbent. The maximum TC removal percentage was (88%) at pH = 9, time = 230 min, Co = 60 ppm, and dosage = 0.39 g/25 ml using AC as an adsorbent. Whereas the maximum TC removal percentage was (98%) at pH = 9, time = 46 min, Co = 60 ppm, and dosage = 0.0406 g/25 ml using AgNPs-AC. The isotherm models were also studied. It was found that the Langmuir isotherm model fitted well with the experimental data. The adsorption kinetics study showed that the pseudo-second-order accurately describes the experimental results. The analysis of the adsorption thermodynamics revealed that TC adsorption on TAC and AgNPs-AC was endothermic and spontaneous. The study aims to make activated carbon from tea waste and load silver nanoparticles on that activated carbon (AgNPs-AC). It also studies how two adsorbents (activated carbon and activated carbon loaded with silver nanoparticles) remove tetracycline from artificially polluted water. Then, the outcomes were compared.

Water pollution is currently one of the most serious problems facing humanity. In this regard, making this natural resource usable and unpolluted is of great social and economic importance. However, eliminating organic pollutants has received special attention to purify water waste. Chemical coagulation procedures are used by determining the optimal speed and dose of coagulants required to remove turbidity and dye from an aqueous solution while fixing pH and temperature to optimal values based on previous research. Aluminum sulfate (alum) and ferrous sulfate 〖"Fe" 〗_"2 " 〖"(SO" 〗_"4" ")" _(3 ) were used. Alum was found less efficient than 〖"Fe" 〗_"2 " 〖"(SO" 〗_"4" ")" _(3 ). It has been found that reducing turbidity was more effective than removing pigment. Maximum turbidity (NTU) and decolorization of (88% and 83%), respectively, were achieved with an optimum dose of 30 mg/L at pH 11, a settling time of 50 min, and a stirring speed of 120 rpm. Maximum turbidity (NTU) and color removal of (84% and 81%), respectively, were achieved at an optimum dose of 30 mg/L at pH 8, a settling time of 50 minutes, and a stirring speed of 120 rpm. On the other hand, physical and chemical technologies can be used as a rapid and cost-effective treatment method in terms of the availability of materials used compared to other treatment processes.

A smart grid (SG) is an interconnected system of electrical outlets that monitors and controls the energy flow from various sources to satisfy different consumers’ loads. The smart grid achieves this goal by utilizing digital and other cutting-edge technology. To increase the diversity of energy delivered, SGs can be used with renewable power sources (RES) like solar and wind. Modern technologies in computer, telecommunication, supervisors, networking, and sensor measurement are used by SGs to increase the dependability and effectiveness of the entire system. Whenever businesses are producing, customers may direct the SG to switch on their home equipment at the lowest possible cost of power. Throughout peak periods, consumers can reduce demand by shutting down certain unnecessary electrical devices. This paper provides a model of an energy storage system (ESS) linked to a photovoltaic, a wind turbine, and the SG. To fulfill the load in a range of operating situations, the SG approach is demonstrated, and the electricity flow across the RES, ESS, and network is managed using MATLAB/SIMULINK software. This study proposes an energy management system to supply the energy required to meet the loads under various operating situations, including fluctuating wind velocity and sunlight. Regarding the solar energy system, a pair of separate approaches to maximum power point tracking techniques are suggested. Although the Perturb and Observe method is suggested for wind turbines and photovoltaic systems to generate maximum power output, the Particle Swarm Optimization strategy may generate powerful efficiencies, as explained in this study.

Soil erosion negatively impacts water management, agricultural lands, amounts of sedimentation in reservoirs and rivers, water pollution, human health, countries' economies, and ecosystems. This study is important for water, soil, and environment management. In the present study, the Universal Soil Loss Equation (USLE) has been integrated with the Remote Sensing (RS) and Geographic Information System (GIS) techniques to calculate the annual rate of soil erosion of Harir Basin and to classify it on this basis and according to the soil erosion risk classes. The spatial distribution of the average rainfall erosivity factor (Average (R-Factor)), soil erodibility factor (K-Factor), slope length and slope steepness factor (LS-Factor), cropping management factor (C-Factor), and conservation practice factor (P-Factor) have been created and mapped using ArcGIS 10.8.1 software based on the average annual rainfall map from (2000 to 2021), soil map, slope map in percentage with flow accumulation map, land use and land cover map, and slope map in percentage for Harir Basin, respectively. Overall, the results of assessing Harir Basin against soil erosion have illustrated that the annual rate of soil erosion for the area of study ranges from 0.0 (ton.ha^(-1).year^(-1)) to 8.46 (ton.ha^(-1).year^(-1)). Moreover, 99.99% of the study area was under slight soil erosion, while only 0.01% was under moderate soil erosion.

Bodroon meat cans are made from a can container of mild steel and coated with an inner layer that touches the food material. The inner coated layer contains a food-grade polymer containing 0.03% tin. This paper aims to study the effect of the tin percentage on the electrochemical behavior and corrosion resistance in serum from Bodroon canned meat. The Tafel extrapolation method was used to determine the electrochemical behavior and corrosion rate. Meat serum was prepared from 10 grams of canned meat dissolved in 90 milliliters of distilled water. Different percentages of tin were chosen, i.e., 0.03, 0.3, 0.35, 0.40, 0.45, and 0.50 by weight %. The tin was added in a nano scale to the polymer to form the inner coating layer. The results proved that as the percentage of tin increased in the polymer, its corrosion resistance increased. The corrosion rate was 0.00377 mpy when adding 0.50% Sn, while it was 0.0818 mpy when adding 0.03% tin. Adding 0.03% Sn is currently used for coating the inner layer of the Bodroon meat cans. The distribution and homogeneity of the interior coat and the effects of corrosion on the interior coated layer were also studied using a scanning electron microscope SEM. The novelty of the research does not lie in using tin in the inner coating layer of 0.03% Sn, as was dealt with in previous works and based on which the current meat cans were made. The results of the present work proved that using 0.03% tin in the polymer coating layer was insufficient to prevent corrosion. The best results showed that adding 0.50% tin had the lowest corrosion rate, which is an essential point because if the corrosion leads decomposition of the coating material to spread the metal ions to the food substance, causing its contamination that harms human health.

Direct Sequence Spread Spectrum (DSSS) has high immunity against noise due to the great spreading gain of the pseudo-noise (PN) code. The Global Positioning System (GPS) satellites send navigation messages at the L1 frequency band, where coarse acquisition codes are embedded within these messages. Two scenarios are evaluated for navigation data tracking: phase tracking and code tracking processes. The coded data is recovered, and the carrier is removed using a phase tracking loop. The DLL (Delay-Locked Loop) retrieves the navigation data for code synchronization. This paper aims to model an L1 GPS signal receiver and assess its performance in both the time and frequency domains. The processing of this evaluation is regarding input dynamics such as Doppler and noise. This study emphasizes the importance of the Gold code with respect to cross-correlation and autocorrelation compared to the traditional PN coding. When it comes to wireless networks, Gold code is an interesting option since it can produce a more restricted and stable spectrum than PN code. The results demonstrate how a DLL with a narrow bandwidth may still decode data even if the system loses tracking, giving the system a higher level of reliability.

Road traffic contributes to air and noise pollution in urban areas, negatively impacting human health. Understanding exposure to air and noise pollution from road traffic is vital for epidemiological studies on human health. This paper aims to (i) summarize current modeling and assessment methods for road traffic-related air and noise pollution, (ii) emphasize the potential of existing tools and techniques for assessing combined air and noise exposure, and (iii) highlight associated challenges, research gaps, and priorities. The paper examines literature concerning air and noise pollution caused by urban road traffic, including dispersion models, Geographic Information System (GIS) tools, spatial exposure assessment scales, study locations, sample sizes, traffic data types, and building geometry information. Approximately 29% of accredited research parameters for air pollution utilized NO2, underscoring the significance of this element in the research context. Additionally, Lden was employed in nearly 34% of publication parameters for noise pollution. Deterministic modeling is the most commonly used technique for assessing short-term and long-term exposure to air and noise pollution. Among the models, more diversity is in air pollution models than in noise pollution models. Correlations between air and noise pollution vary widely and are influenced by numerous factors, such as traffic characteristics, building attributes, and meteorological conditions. Buildings serve as barriers to pollution dispersion, with a more significant reduction effect observed for noise pollution than for air pollution. Meteorology plays a greater role in influencing air pollution levels than noise pollution, although it is also essential for noise pollution assessment. There is considerable potential for developing a standardized tool to assess combined exposure to traffic-related air and noise pollution, facilitating health-related studies. With its geographic capabilities, GIS is well-established and well-suited to address air and noise pollution assessments simultaneously.

Experimental testing revealed that adding ceramic nanoparticles of alumina Al2O3 to the aluminum alloy AA2017 by a casting route significantly enhanced the mechanical and fatigue properties. Understanding the behavior of materials under cyclic loads is necessary; engineers can ensure that structures and components perform reliably throughout their intended lifespan. This work explicitly focuses on the role of these nanoparticles in the fatigue behavior of the aluminum matrix (AA2017) and the resulting (AA2017 – 0.8 wt. %, Al2O3) nanocomposite. The test influences of the stress- number of cycles S – N curves showed that cyclic stress below the endurance limit for fatigue, followed by cyclic stress above the endurance limit, decreased the fatigue life and strength. Specifically, the fatigue life was decreased more than twice for the nanocomposite and about 4% for the base metal, while the fatigue strength was lowered by (140.8 to 137.8) MPa for the matrix and (155.5 to 142.4) MPa for the final product of (AA2017 – 0.8 wt. %, Al2O3).

Squat-reinforced concrete (RC) shear walls with an aspect ratio of less than two are commonly used as lateral load-resisting buildings. It is frequently utilized in nuclear power plants and building structures due to its lateral strength and high stiffness. It is distinguished by its optimal cost and excellent performance. Nonetheless, precise assessment of the shear strength of squat shear walls is crucial for design specifications, and its computation can be exceedingly variable and intricate due to several efficient, expensive, and time-consuming constraining elements. The present study utilizes Keras deep learning techniques to develop a model for predicting the shear strength of squat RC walls to find a way to overcome these issues. The most comprehensive dataset of 1424 RC squat wall test specimens collected from the published literature to date has been used to develop the proposed deep learning model as well as three well-known machine learning models: RF, ANN, and LR. The results demonstrated that the Keras network exhibited a lower error rate and higher accuracy when predicting the shear strength of squat walls compared to earlier machine learning models, achieving 97.3% accuracy compared with the highest value in the RF algorithm, reaching 93.4%. Furthermore, parametric and sensitivity analyses were performed to verify that the algorithms can identify the most significant variables significantly influencing shear strength. The results showed that the (hw) was the most influencing factor on the peak shear strength of the squat shear wall as a ratio (6.36%), according to the results of the sensitivity analysis, followed by (lw) as a (5.10%), (tf) (4.96%), ( f´c ) (4.69%), (tw) (4.06%), (fy) of the web as a ratio (3.94%), and (ρh) (3.89%). These results and analyses were obtained using the (KNIME) analytics platform software, characterized by its vital role in precise computing operations and simple handling without the need for codes to reduce costs and time, and it was supported for Python and R languages.

The problem of sediment accumulation in dam reservoirs is considered one of the most important problems facing these large projects. The present study estimated the total amount of sediment from the Tigris and Lower Zab Rivers that will enter annually into the reservoir of the Makhoul Dam, which is currently under construction on the Tigris River in Iraq. Field and laboratory measurements were conducted to estimate the bed load of the two rivers using the Helley-Smith device, in addition to estimating this sedimentary load using five empirical equations. The filtration method was used to estimate the suspended load of the two rivers by taking samples and examining them weekly for one year. The results showed that the bed load of the two rivers using the Helley-Smith device was less than 1% of the suspended load. When calculating the bed load using the five applied equations, the results varied, as the Meyer-Peter, Schoklitsch, and Casey formula achieved unacceptable results, while the results of the Kalinske equation were good, as it estimated the bed load for the Tigris River to be 7.6% and for the Lower Zab River to be 10.9%. The results of the Einstein equation were also good, where the bottom load for the Tigris River was estimated to be 2.4% and the Lower Zab River 11.8%. The average of the results of the two equations was taken to represent the bed load and added to the suspended load. Also, the results showed that the highest value of the suspended load was during April for the Tigris and Lower Zab Rivers. The total sediment load of the Tigris River reached 2875461 tons annually, and the Lower Zab River reached 1503739 tons annually. In other words, the total sediment load of the two rivers entering the Makhoul Dam reservoir amounted to 4379200 tons annually, or the equivalent of 3368615 m3 annually.

The riverbanks are a critical component of river ecosystems that provide various ecological, social, and economic benefits. Effective riverbank management is essential for maintaining its functions while minimizing the risks associated with instability. The fluctuation of water levels in the river negatively affects riverbank stability; therefore, the present study aims to examine the instability of the riverbank caused by the variations in water flow within the Tigris River as the basis of analysis. The flow variation effects during (2022) in Salah Al-Din Governorate, which began from the Makhoul dam site (upstream) to the end of Tikrit City (downstream), were investigated by computing a series of safety factors related to outflow events for (20) cross sections along the (82) km reach of Tigris River. The BSTEM algorithm integrated into the HEC-RAS model package was utilized. The results showed that the rate of change in flow at each cross-section impacts the calculated safety factor. Also, the results showed that all cross-sections were stable, and some were conditional stable, meaning that the riverbank is not entirely stable, however, still holding up under the existing conditions. However, caution should be exercised, and it may require monitoring and evaluation to prevent future instability issues.

Iraqi engineering consultancy bureaus have significantly contributed to resolving obstacles and issues encountered in various engineering projects, leading to their successful completion within the designated timeframe. However, these bureaus need continuous performance enhancement by implementing quality control tools and techniques. Consequently, the present research focused on evaluating the work quality of the Engineering Consulting Bureau of Tikrit University, using a Strengths, Weaknesses, Opportunities, and Threats using SWOT analysis as a research model. The study aims to identify the bureau’s strengths, weaknesses, opportunities, and threats and propose strategies to improve its performance and output. The research employed a two-part methodology: a literature review and a field survey. Interviews, field visits, and data collection from former and current employees were conducted to gather information. The findings were then presented, analyzed, and discussed to shed light on the bureau's strengths and weaknesses, identify opportunities for converting weaknesses into strengths, and address potential challenges. The study provided an overview of the work conducted by the engineering consultancy bureau at Tikrit University and presented a research sample comprising strengths, weaknesses, challenges, and opportunities identified by bureau employees. The SWOT analysis revealed key findings about the Engineering Consulting Bureau. Its strengths include many professors with consultative degrees and a fixed location, distinguishing it from other bureaus. However, weaknesses were identified, such as the bureau's limited scope to Tikrit and neighboring cities. To capitalize on opportunities, the bureau should be equipped with modern devices to stay updated with industry advancements and undergo standardization and quality control to enhance result reliability. Mitigating risks involves preventing individuals from monopolizing work, involving more college competencies, and reducing high deduction rates, hindering competition with the private sector.

The quality of drinking water must adhere to the standards set by the World Health Organization, including limits on pollutants, sediments, dissolved minerals, chemical compounds, and the absence of harmful bacteria. Some developed countries have even stricter standards than those set by the World Health Organization; as a result, producing safe drinking water in these countries requires more complex and expensive treatment methods. The present study assesses the water stations performance to provide safe water for human use. The assessment focuses on the efficiency and effectiveness of the studied stations, including their ability to consistently meet required laboratory test results and infrastructure standards. The study also identifies any weaknesses in operating each station to improve them. The Strengths, Weaknesses, Opportunities, and Threats (SWOT) method is used to assess the current and future state of a particular work environment, to identify internal positive and negative factors, as well as external opportunities and threats. The water stations in Tikrit were chosen to assess the condition of their equipment, machinery, and pipelines. Monthly tests were conducted according to the Tikrit Water Department's guidelines, focusing on temperature, pH level, dissolved salts, and turbidity. Samples were gathered over four months. The results were analyzed and compared with standard specifications to identify the stations' strengths and weaknesses. Ten water treatment stations were chosen to participate in the study and gather the necessary data. The study showed that water stations in Tikrit met most water test specifications, scoring 100% in total dissolved solids, pH level, chlorine, and temperature tests. However, they lack specialized and regular personnel, as identified in inspections and examinations. The results of turbidity testing showed that the station met specifications only (17.5%) of the time and deviated from them by (82.5%). The station data showed a weak relationship between turbidity and the other tests, except for temperature, which had a simple relationship with turbidity with (-0.345) Correlation Coefficient. As temperature increased, evaporation and water consumption rose, leading to more sedimentation and increased turbidity to a certain extent.

As today’s world faces a crucial water crisis due to increased population and associated development, finding alternative and renewable resources has become necessary. Adsorption is a simple and effective process; when biowaste is applied for this purpose, a reasonable solution is proposed in terms of economical and influential sectors. In the present work, corn silks, usually considered useless, were used to remove the crystal violet dye from a simulated aqueous solution. Different experimental parameters were investigated: solution pH and temperature, adsorbent dose, mixing speed, adsorbate concentration, and contact time. The corn silks were characterized via the SEM microscope, while the chemical functional groups were analyzed using the Fourier transform infrared (FTIR) spectra analysis. The theoretical calculations of adsorption isotherm, kinetics, and thermodynamics were considered to comprehend adsorption nature. The results revealed that as the solution pH reached normality, the highest adsorption removal rate was achieved. After 70 min contact time, the adsorption capacity at a pH of 6.5 was 6.44 mg/g, and the removal rate was 95.5%. Furthermore, the temperature effect test and thermodynamic study showed that the adsorption process was endothermic. However, the adsorption removal rate increased by increasing the adsorbent dose and agitation speed to a certain contact time, and then all became analogous. The dye concentration showed identical behavior, indicating the high adsorption tendency of corn silks. Lastly, the best-fit isotherm and kinetic models were the Freundlich and Pseudo second-order models, respectively.

The mechanical properties of No-fine aggregate concrete are investigated. This paper consists of two parts: In the first part, three sizes of aggregates (9.5 mm, 12.7 mm, and 19 mm) with two water-to-cement ratios (0.4 and 0.45) were examined. In the second part, specimens made with recycled concrete aggregate with a replacement ratio of 30% by the weight of normal aggregate and a steel fiber of 35 kg/m3 were examined in two water-to-cement ratios (0.4 and 0.45), and an aggregate size of 12.7 mm. The effect of the cement-to-coarse aggregate ratio was also studied; three different ratios (1:2, 1:4, and 1:6) were examined. This study found that the increase in aggregate size and the water-to-cement ratio harmed the compressive, splitting tensile strength, and unit weight of the NO-fine aggregate concrete. The study also found that the compressive and splitting strength declined using recycled concrete aggregate; however, using steel fiber with recycled concrete aggregate improved both.

As the world's energy demand rises, crude oil and other hydrocarbon resources are required for different sectors to fulfill the global energy demand. Heavy crude oils are often difficult to transport due to their high viscosity and low mobility. Oil companies employ techniques including mixing, heating, and injection to lessen the viscosity of crude oil, as well as nanotechnology to lessen the viscosity. The present study aims to gradually reduce the viscosity of heavy crude oil by heating it using solar energy as clean energy. Solar water heater, PCM paraffin wax, heat exchangers, crude oil, and other materials were used. In this study, wax was used to store solar energy during the day and release it at night after sunset. It was observed that as the temperature increased, the viscosity fell, and vice versa. For instance, when using heavy crude oil, the viscosity rose to 8 cP at 45°C and 3 rpm and fell to 3 cP at 81°C. The viscosity was 15 cP at 45°C and 6 rpm and 4 cP at 81°C. A novel phase change material, such as wax and sand. In general, it is recommended to use solar energy systems integrated with latent thermal storage, i.e., phase change materials (organic), e.g., wax, and (inorganic), e.g., salts, or sensibly use non-phase change materials, e.g., sand ensure the provision to provide the necessary heat, to liquefy the oil (reduce viscosity), for facilitating its continuous flow through pipelines, without affecting the environment.

Speaker identification and verification are important fields contributing to smart IoT, phone banking, remote login services, E-learning, and other applications. In this work, the speaker identification and verification processes have been experimentally proven to have mutual enhancement effects if they are merged together in a proper manner. Speaker identification and verification work cooperatively so that the verifier will enhance the identifier model. The first step is to identify the speaker using context – independent speech signal, and the identifier model (ID) is trained using a classification model. The model’s outputs are then used to control the verification process as a next step. When the verification result is positive, the first process outcome is approved with high confidence. Otherwise, the negative verification will force the ID process to re-configure itself. The loop continues until both verification and ID agree on the speaker. A multiple Gaussian mixture GMM was used to efficiently model each person’s speech features (MFCC) for using expectation maximization (EM). On the other hand, the conducted experiments showed that the one-dimensional convolutional neural network (1D-CNN) proved its superiority over other models for speaker identification. A novel approach was proposed, proving that little data can be expanded with split-add-noise and train-on-the-fly procedures. In many speaker identification approaches, the specific context was used as a keyword or a password to simplify the processing, requiring big data to achieve high accuracy. It is noteworthy that a small amount of data was enough to efficiently train the proposed model, with a verification error of around 3%, i.e., an accuracy of 97%. Meanwhile, 95% and 96% identification accuracy was achieved using two different datasets. Additionally, the suggested algorithm did not imply using any keyword or password because it is a context-independent approach.

The need for a very high data rate in the next-generation wireless network is growing, and optical wireless communication has become one of the best options for addressing this important problem. The optical system endures power and bandwidth losses due to channel turbulence, particularly in the extreme channel conditions found in outdoor locations. However, suitable advanced optical modulation schemes categorized according to the appropriateness of power efficiency and suitability of bandwidth-efficient systems are used to ensure transmission reliability. In this study, orthogonal frequency-division multiplexing, generalized frequency-division multiplexing, and L-pulse position modulation are used. Utilizing the cutting-edge GFDM-Beamforming technology for the optical wireless channel was to increase the signal to noise ratio and enhance the bit error rate across the board. Gamma-Gamma distribution has been used to compute the bit error rate and spectral efficiency. An arbitrary number of light-emitting diodes and photo-detectors are used at the sources and detectors. The findings demonstrated that the optical generalized frequency-division multiplexing performs better than other modulation schemes in terms of power and bandwidth requirements, with a high peak-to-average-power-ratio, which is the primary drawback in orthogonal frequency-division multiplexing mitigated by the beamforming function.

Reusing wastewater from municipal and industrial resources has been a worldwide strategic solution to water scarcity. Reverse osmosis (RO), a well-established technology, is widely applied for wastewater treatment, producing high-quality reuse wastewater effluent. However, one of the major drawbacks of using RO technology is the volume of concentrate (known as ROC) associated with higher concentrations of constituents in wastewater feed. This drawback makes the sustainable management of ROC in terms of quality and quantity the major limitation of RO application. To address this drawback, the present review highlights and discusses the characteristics and environmental impact of ROC from municipal and industrial wastewaters, facilitating easy selection of the best applicable integrated technologies based on the concept of zero liquid discharge (ZLD) for minimizing the ROC volume produced. To achieve this objective, this paper provides an overview of various types of integrated technologies with two modes of operation (complete and partial recirculation). This paper offers critical insights into the ZLD concept and highlights future research trends by suggesting various pretreatment options for ROC. These suggestions will improve the overall recovery of water feed and minimize water pollution to meet the environmental standards for final disposal.

Five statistical distributions were used in this study to model the maximum annual rainfall at five metrological stations in and around Al-Shuwaija Marshes in Wasit governorate, Iraq. These stations are Badra, Sumar, Kut, Ilam, and Mehran. Selecting the best probability distribution is based on the test of goodness-of-fit using the Kolmogorov-Smirnov test. Data was collected from 2000 to 2020 from Iraqi and Iranian metrology authorities. This data is subdivided into two sub-samples: the first is from 2000 to 2013 for building the model, and the second is from 2014 to 2020 to validate the model. The Kolmogorov-Smirnov test results showed that for some stations, there was more than one frequency distribution that fit the data as they had the same minimum SIG value. In these cases, the t-test and F-test results were used as criteria to aid in selecting the best-fit distribution. For the Badra station, the Log-Normal frequency distribution (61.663, 0.502) was the best-fit distribution with the SIG value of 0.998. For the Sumar station, three distributions had the same SIG value (0.998) Normal, Log-Normal, and Gamma, with the best-fit for Log-Normal (69.008, 0.593) selected according to the t-test 0.146, 0.024, and 0.093 between a three generated data using each of these distributions and the observed one. The same observation was found for the Kut station, except that the best-fit distribution was the Normal distribution (55.746, 30.7323) with a SIG value of 0.879. As for the Ilam station, the results indicated that the Normal and the Gamma distributions had the same SIG value; the last was selected as the best one according to the t-test results 0.081, 0.010. and 0.263. for the Mehran station, the Normal distribution was the best one that fitted the maximum rainfall data. For each station, three series were generated using the model. Each generated series was tested against the observed using the validation data period. The Kolmogorov-Smirnov test indicated that the distribution could generate data drawn from the same population as the observed data with respect to frequency.

Rotors in various industries often encounter diverse vibrations from their supports, posing potential dangers to system integrity. The present study focuses on the effects of arbitrary stimulation brought on by random vibrations caused by supports, which necessitate statistical methods for evaluation due to their non-deterministic nature. Through white noise excitation, static ergodic random vibrations were simulated, employing mathematical modeling to derive the transfer function equation of the rotor. The investigation extended to examining the impact of copper oxide (CuO) nanoparticles in SAE10W-30 lubricant on random vibrations. Analytical solutions, facilitated by MATLAB software, were employed to solve the governing differential equations, enabling the evaluation of power spectrum density (PSD) and the standard deviation of responses. The experimental investigation of the rotor model reflected the validation of the mathematical model, especially for random vibration analysis. Reductions in standard deviation values suggested that including CuO-NPs caused a more symmetric distribution of responses in the x and y directions, which is intriguing, improving stability and reducing wear due to the effect of CuO nanoparticles on vibration characteristics. The best CuO-NPs weight was 0.0527g at a dynamic viscosity of 0.0593 Pa·s, and the PSD value reached its minimum value. This conclusion implies that using CuO-NPs can decrease the effect of random vibration on the rotor and increase the life span of the rotating machinery system in practical applications.

This study focuses on the root causes of power station problems that result in system shutdown. A power plant may experience many types of faults, some of which include, for example, line-to-line, double-line-to-ground, and single-line-to-ground. Identifying faults in the 400kV high-voltage transmission line from the Samarra Thermal Power Plant in Iraq is the primary objective of this study using artificial neural networks (ANNs). Recognizing a wide range of electrical power plant faults is an innovative use of artificial neural networks (ANNs) to speed up system recovery. The ANN can perform a wide range of tasks, including pattern recognition, classification, matching, prediction, decision-making, and control; hence, it was selected for this work. This study utilizes two neural networks trained with an input of 8 variables: 3 phases of voltage and 3 phases of current, in addition to the absolute value of the zero sequence for voltage and current. The output for the first artificial neural network (ANN) designed for fault detection will possess a single output; it is for detecting faults only, whereas the second ANN will have five outputs: four of which go to the decoding circuit and the last goes to the fault location detection circuit. Additionally, the MATLAB/Simulink 2022a software is utilized to simulate the Samarra thermal power plant model. The model represents a three-phase power system network comprising two units. The power system has four transmission lines operating at a voltage of 400kV and a frequency of 50Hz. The transmission lines have lengths of 87.10km, 145.00km, 274.00km, and 306.00km.

The Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) system is a complex, nonlinear behavior with a high uncertainty control system that equips the thermal comfort desired but consumes significant electrical energy and costs in different types of buildings, such as residential, commercial, and industrial. This paper introduces a new approach for online controlling of HVAC&R systems using model-based reinforcement learning (MB-RL) style to diminish energy usage and energy cost, maintain the occupants’ comfort levels by controlling the buildings' indoor temperature, and maintain the desired carbon dioxide levels simultaneously. For this purpose, a new model based on energy and mass conservation laws is presented to model the dynamic variations of temperature and CO2 concentration levels. The HVAC&R system control trouble is defined as a specific Markov Decision Processes (MDPs) model. The reward function balances the ability to increase energy conservation while preserving the interior comfort requirements of occupants. Employing the deterministic policy algorithm (DP), the proposed methodology can manage the dimensionality curse problem due to increased state-action space. Then, it overcomes the nonlinearity and the control system uncertainty. The MB-RL algorithm, which uses a unique DP called DP-MB-RL, can select the best decisions instead of stochastic policy to reduce the calculation time. A real case, a building in Basra City, Iraq, is simulated using MATLAB software. Devoting the MB-RL and DP-MB-RL techniques to online control of an HVAC&R system, the simulation results for both methods are provided. For instance, the parameters, like electrical power, internal comfort levels, energy consumed, and energy cost at different pricing schemes, such as fixed pricing (FP), time-of-use (TOU), and real-time pricing (RTP), are assessed. The results indicated that the suggested DP-MB-RL methodology had better indoor thermal and air quality satisfaction levels, energy-saving (more than 15%), and reduced the cost of electricity by more than 15%, 13%, and 10% for FP, TOU, and RTP pricing schemes, respectively, compared to the benchmark MB-RL style controller. The DP-MB-RL controller also performed better than the Takagi-Sugeno Fuzzy (TSF) controller for the same building, saving more than 21% energy.

Managing sediment in river basins and waterways became a key point in water management. The continuous sediment deposition from the rivers and streams flowing to the reservoirs is today’s problem. Entering sediment from the Sweedy, Crnold, and Alsalam valleys located at the right bank to the reservoir of Mosul Dam in the north of Iraq has been estimated using the ANN model. This study aims to estimate the sediment values using only available precipitation data. The challenges and difficulties in this study were that the catchments of these valleys were ungauged with very limited available data, so the ANN models were used with the assistance of the SWAT using three different models in three main steps. In the first step, the flow, concentration, and load of sediments were calculated using the SWAT model for a period (1994-2018) using the available meteorological data. Then, as a second step, based on the available rainfall data and the flow resulting from the SWAT model, the ANN model was used to obtain the flow discharge. In the last step, two separate ANN models were used to estimate the sediment concentration and sediment load entering the dam reservoir from the right-side bank. The coefficient of determination (R^2) and Nash Sutcliff Efficiency (NSE) were used to evaluate the models. Finally, the result showed the ability of ANN to use two successive models (Dual ANN) to estimate the sediment concentration and sediment load for the valleys, as mentioned earlier.

The present research examines the effect of the pyramid solar system's shape, notably the multi-bed pyramid, on the system's ability to gather water from the ambient atmosphere. In this research, a solar collector in the shape of a prism pyramid with 4 glass walls was designed and constructed. The collector stood at a height of (140 cm). The insulating layer at the pyramid's base was 15 cm thick, and its base was 100 cm × 100 cm. Thick panes of glass line the sides of the two pyramids (5 mm). The pyramid's interior included five levels of storage. Each shelf was about (20 cm) vertically apart. Each pyramidal unit's bed thickness is (10 cm). Saw wood and cloth were used as hosts in the collection process, and the calcium chloride (CaCl2) solution is used as a moisture absorber. Moisture was taken in from the atmosphere at night. During the day, solar energy evaporates the water that has been absorbed, and the water vapor condenses on the edges of the solar collector. The experimental findings demonstrated the potential to achieve an average water productivity of (1.25 L/day/m2) of surface area. The results also demonstrated that the pyramid coated with the CaCl2 solution-soaked cloth performed better than the other glass pyramid by roughly 5% on average daily in June 2022. It was deduced that this rise was caused by more CaCl2 solution being collected.

The proven optimal stacking sequence of a glass/carbon hybrid composite and the effects of longitudinal fibers orientation and the incorporation of SiO2 nanoparticles were carefully investigated in this work. For the first time, the present research explores the effects of adding silicon dioxide (SiO2) nanoparticles on the tensile and flexural strength characteristics of glass-carbon hybrid composites with varying thicknesses and stacking sequences. Three fiber orientations were considered, i.e., 0°, 45°, and 90°, and SiO2 nanoparticles as epoxy reinforcement. The infusion procedure was used to fabricate the hybrid laminates. To understand and analyze the impacts of fiber orientation and SiO2 nanoparticles, mechanical parameters, such as tensile and flexural strength, were evaluated. The results showed that increasing plate thickness and the SiO2 nanoparticles boosted tensile and flexural strengths. The bending force was increased by 80.5% when 2 wt.% SiO2 nanoparticles were added to the epoxy for composites with 8 layers. For composite with 12 layers, the bending force increased by 62%.

The displacement of battered piles is one of the most critical parameters in the design of a pile foundation. In this study, an Artificial Neural Network (ANN) algorithm was utilized to predict the displacement of piles in sandy soils subjected to pullout loading. A finite element analysis (FEA) in three dimensions, performed with the PLAXIS 3D program, was utilized to gather 2380 databases, including the length/ diameter of pile, pullout, batter angle, Poisson ratio, friction angle, dilatancy angle, relative density, and Young’s modulus as input variables, whereas the displacement of battered piles was considered an output variable. The dataset was divided into three parts: training (80%), validation (10%), and testing (10%). The performance of the Artificial Neural Network (ANN) algorithm was evaluated using the Mean Squared Error (MSE) and the Coefficient of Determination (R^2). This study applied a procedure known as a backpropagation neural network. According to the analysis of relative significance, the pullout load (Pu) and the pile length to its diameter (L/D) were the most effective characteristics among the other inputs. The R-values of the ANN model for the displacement of the battered piles dataset were 0.99 across all three phases of testing, validation, and training. The findings substantiated the viability of employing Artificial Neural Networks as a successful method for obtaining the displacement values of a single battered pile in sandy soil when subjected to pullout loading.

A low-cost and environmentally friendly biosorption material, i.e., Tigris River reed, was used to remove the Acid Blue 74 dye from synthetic industrial wastewater in a continuous flow-backed bed column. The impact of operating conditions, such as operating pressure, initial dye concentration, pH of solution, wastewater volumetric flow rate, and biomass particle size, on the dye removal efficiency and biosorption capacity of biomass was investigated. The results revealed that the dye removal efficiency in an acidic medium was higher than in an alkaline medium. The optimum pH value was 3. Moreover, the results exhibited that the dye removal efficiency and biosorption capacity were significantly improved with increased operating pressure. In addition, the increase in the initial dye concentration and wastewater flow rate led to an increase in the biosorption capacity of biomass and a decrease in dye removal efficiency. The maximum achieved values of dye removal efficiency and biosorption capacity of biomass were 99.5% and 10.4 mg g^(-1), respectively, at pressure, initial dye concentration, wastewater flow rate, and particle size of 6 bar, 150 ppm, 25 mL 〖min〗^(-1), and 500-850 µm, respectively. The encouraging dye removal efficiency (99.5%) achieved by using low-cost and environmentally friendly reed in a continuous flow process could be considered an attractive method for industrial wastewater treatment. Furthermore, batch biosorption experiments were performed to establish the sorption mechanism of AB74 dye on the Tigris River reed. Chemisorption was found to be the mechanism of biosorption.

The application of deep learning (DL) techniques to optimize signal constellations in communications using orthogonal frequency division multiplexing (OFDM) has shown potential. This novel approach utilizes network capabilities to create tailored signal constellations well suited for underwater conditions. The main objectives of this research are to enhance the effectiveness and dependability of data transmission in channels by adapting these signal constellations without adopting cyclic prefix, which can exploit the inherent bandwidth limitation effectively. The most prominent finding from this research is that even with zero cyclic prefix (CP) and a few pilot samples Np, inserted ahead of N subcarriers, the proposed signal constellation algorithm based on supervised DL attains a stable profile. Thus, it offers more bandwidth and reduces the complexity. The hegemony was depicted in the performance of the bit error rate (BER) of the proposed DL-based signal constellation prediction algorithm, which achieved 100% accuracy and a gain of 10dB and 12dB over minimum mean square error (MMSE) and least square (LS) channel estimation performance, respectively, when CP=0 at N_p=N/4. Ultimately, this work contributes to increasing the performance of communication systems for applications such as exploration, monitoring, and data collection.

The main objective of the present study is to determine the risk class of Haditha and Hemrin Dams The weighting points for various risk factors of the selected dams were substituted in Eq. (5) to find the TRF of each dam. For the studied dams, the data related to various risk factors was collected from the State Commission of Dams and Reservoirs (SCODR), Iraq. The studied dams are outside the seismic zone, and flood is considered the main hazard that makes the dams vulnerable to overtopping. Three scenarios that considered different water levels in Haditha and Hemerin Dams reservoirs were analyzed. In the worst scenario, the water levels in the reservoirs of both dams were taken more than the flood control level (152.2 m.a.s.l in the reservoir of Haditha Dam and 107.5 m.a.s.l in the reservoir of Hemrin Dam). The calculated TRFs for the worst scenario were 168 and 216 for Haditha Dam and Hemrin Dam, respectively. The rating for both TRFs showed that the risk category is high (class Ill). The modified method is a simplified method dam engineers use to easily classify a dam's total risk.

The foundations of buildings and other structures must support static and seismic loads in areas where seismic activity is common. To avoid bearing capacity failures, increase the dynamic stiffness of the structural system, and minimize dynamic oscillations during earthquakes, a transition from shallow to deep foundations is required. Although much information is available regarding pile foundations' structural response to static loads, much less is known about how they behave under dynamic loads. The present paper summarizes key findings from numerous research projects to give an extensive overview of the reaction of piling groups subjected to seismic loads. The paper covers a wide range of pile group behavior topics, such as how they dynamically respond to seismic loadings, what influences their performance, and how soil-structure interaction affects them. This research offers significant insight into the seismic reaction patterns of pile groups, which can be used to improve the dynamic performance of foundation systems. This work underscores that understanding the behavior of pile groups' underground motion is pivotal for informing and refining the design of pile foundations. By optimizing designs for improved seismic resilience and overall performance, engineers and researchers can further enhance the safety and stability of structures in seismically susceptible places.

The increasing need for fuel in our daily lives makes us think about alternative energy solutions, and finding various other sources is essential. One of the important solutions to this problem is the energy of the sun due to its many advantages. A DC/DC voltage bucks’ converter is used to control the value of the voltage generated by solar panels, as well as track the status of the panels and obtain the maximum power for power point tracking (MPPT). The solar panels were modeled to be variable in terms of temperature and radiation. A multilevel inverter was used to obtain the voltage from the inverter more accurately and to obtain an ideal alternating voltage so as not to affect the operation of the three-phase induction motor (IM) and its speed control. Simulation in MATLAB at all stages of the proposed system has proven its efficiency.

There is a gap between the industry in developing countries and developed countries. This gap entails improving the industry and increasing the management role. Generally, the challenges facing developing countries' industries are management aspects of finance, technology, and experience. This study aims to develop a management framework for industry development by investigating the aspects of construction industry management in Iraq. However, the precise objectives are finding the constructs and items of Industry Level, Obstacle, and Success Factors. In addition to the literature review, the study comprised interviews and a series of questionnaire surveys. The interviews were conducted with five professional engineers based on prepared structured questions. The sampling method of the questionnaire was simple random sampling. The questionnaire design is based on three consecutive surveys based on a closed-ended question. The questionnaires have two constructs: background information and study objectives questions. The questionnaire was distributed to 103 engineers; however, only 85 to 90 were completed. The techniques used to analyze the data were mean values and ANOVA tests. The results showed that the level of construction industries was low due to obstacles, such as lack of financial allocations, manpower, and central regulations. These obstacles significantly affected investment and development in the industry. Also, the study revealed factors to develop the industry, such as activating the private sector, implementing projects, and satisfying competition. Consequently, a clear relationship exists between the study variables, so overcoming the obstacles of finance and expert manpower satisfied by supporting the investments and private companies. The developed framework consisted of four parts: factors of industry management, aspects of industry level, Obstacle factors, and Success factors. Each one of these parts contains several factors or items. The developed framework is a roadmap for developing the industry.

Photovoltaic power from the sun's rays is becoming increasingly common in modern distribution networks due to environmental concerns. Regarding the outside environment, the photovoltaic modules' output power is not linear. This work evaluates the ideal performance for energy from renewable sources injection into distribution networks using an intelligent controller based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) technique in safety employed through communication with every Protective Devices (PDs) within the grid, as well as an alternative approach that employs communication across the PDs in the same line to maximize the performance of photovoltaic systems (PVS). The protection mechanisms were further validated utilizing scaled-grid experiments and simulations on the MATLAB platform. Additionally, the current and voltage distortion effects brought on by harmonics on the electricity distribution system's feeder network are discussed in the comparison of the characteristics of the distribution system with different levels of solar PV system penetration. The simulation results demonstrated that a large harmonic dispersal level was injected as the solar electricity system's penetrating capacity increased, implying that the solar power array should only be connected to the network's optimum carrying capacity. The main characteristics of the technical data are employed in MATLAB/SIMULINK to analyze the whole model. The model's performance was examined under various weather and partial shade conditions. The gratifying results demonstrated the great performance of the system based on an intelligent controller. The expanded grid was created for smart grids to promote the experimental behavior of protection measures for traditional and AI-based defenses.