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In this study, the rocks of the Fatha Formation in the Zurbatiyah district were studied from a mineralogical standpoint, through the extraction of calcium from its ores were represented by hydrated calcium sulfate (gypsum rocks) and calcium carbonate (limestone) by leaching process in HCl presence. Twenty experiment tests were conducted for both gypsum and limestone samples. The effects of particle size distribution and acid concentration on extraction efficiency were studied. Different particles sizes (225, 125, 75, 63, and 53) μm, and two concentrations of hydrochloric acid (2, 4) M were used for extraction. X-ray diffraction results showed that the gypsum rocks are of high purity, and the carbonate rocks are dolomitic limestone. Electron microscopic examination confirmed that the gypsum rocks are high-purity crystals of multiple shapes, while the limestone contains crystals of calcite and dolomite, as well as small percentages of gypsum. The results of the experiments work showed the efficiency of hydrochloric acid in extracting calcium\, but it was more effective in extraction calcium from gypsum rocks compared with limestone rocks. The percentage of extracted calcium from gypsum rocks was up to (90.6%), while the extracted calcium to (38.2%) in limestone rocks. It was also found that increasing the concentration of the acid solution led to a significant increase in the efficiency of the process in gypsum rocks. Also, new correlations with high reliability have been created between particles size and leaching percentage for both types of rocks. [ABSTRACT FROM AUTHOR]

Presently water treatment plants are among of the most important infrastructure facilities for the sustainability of most service, agricultural and industrial development projects. Conducted rapid in it mixing operations are on it. furthermore, coagulation, flocculation then sedimentation, and finally, chlorination. consequently, pumped through networks to the consumers. The present study aims to evaluate the efficiency of sedimentation basin units in the old Safi water project located in the metropolis of Karbala on the Husseiniya River, one of the branches of the Euphrates River, where the amount of raw water entering it is 1300 m3/hr. and after conducted analytical calculations and compared them with the required design, the efficiency of removal of the sedimentation basin was founded to 62.29%, depending on turbidity of the raw and treated water. While it was designed to exceed 70% as the lowest percentage, and this is due to the quality of the alum, the accumulation of mud and the lack of continuous cleaning. In addition to that, the service over flow rate, (SOR=19.15 m3/day.m2), which is one of the most important design criteria, was less than the acceptable limits and also the detention time (6.26 hrs.) exceeded limit. In conclusion, it was required to add flowrate 1200 m3/hr for adjust the value of SOR and detention time to be them within the allowable design criteria. [ABSTRACT FROM AUTHOR]

Architecture represents silent assets that can show the forces of place, as it is a language, and technology is a means to show that language. The architectural effect varies on the human perception between darkness and light, its psychological impact, and the place's definition. Architecture goes through stages similar to the stages of human growth, from being a child to becoming an adult, constantly needing technology to adapt to the factors of time, and this technology is urban lighting, passing through slow mutations without radical changes in its structural structure, where the architecture of the place can increase the value of the place. The absence of indicators and strategies related to architectural lighting technology and its role in enhancing the sustainability of the architecture of place in historical and cultural sites). In light of this, the research hypothesis was determined: (lighting is a key strategy in the sustainability of the historical and tourist architecture of the place); because it enhances employment during the night hours, activates the economic aspects, and creates active and effective social hotspots, which are the basic principles of sustainability. Contemporary technology can also enhance that architecture. Urban lighting is an issue that consists of several aspects: standards, city enrichment and aesthetics, place-making, urban environment formation, architectural and urban structure accentuation, and revitalization of urban spaces through lighting and communication technology without making big changes and simplifying the control of essential functions and Promoting economic; promoting economic and cultural sustainability by revitalizing the selling hours of ancient handicrafts in the region. This research paper aims to shed light on (The role of lighting technology as a basic strategy for enhancing architectural sustainability, enriching the architecture of the place, and increasing urban attractiveness). The research found that urban lighting technology is a positive generator of the values and forces of the place, enhances the attractive values of the recipient, and works to create sustainable place architecture. [ABSTRACT FROM AUTHOR]

Current life shows an increase in the urbanization and population, this increment showed an increase in the different types of resources consumption and wastes generated due to using of these resources. As a sustainable solution, many researches try to find solutions to reduce the effect of these wastes on the environment. Replacement of water and wastewater sludge with cement in the concrete mix considered to be an environmentally friendly solution. Water and wastewater sludge were collected and dried at 105 ° C for 24 hours then burnt in an electrical furnace at temperatures ranging from 700 °C for two hours. The produced ash is sieved and replaced with cement in the concrete mix at percentages of 10%, 15% and 20%. The tests of compressive strength and flexural strength show that replacement of water sludge with percentage of 15% was the best. [ABSTRACT FROM AUTHOR]

Replacement of the contents of concrete mixture with different types of solid waste is considered to be a good solution for sludge disposal. Replacement cement with pulverized brick wastes is a sustainable eco-friendly solution, which has a pozzolanic specification on the concrete mixture. In this study two types of brick powders were used as a partial replacement for cement; Pulverized clay brick waste (PCBW) and pulverized burnt clay brick waste (PBCBW). Brick was ground to produce (PCBW) and burnt to 700°C to produce (PBCBW) with different particle shapes and agglomeration and study its effect on the concrete mixture. The percentage of replacement was 10% for both PCBW and PBCBW, according to many researches this percentage is considered to be the ideal value for the replacement. Then samples are molded in 150 mm cubic, cured for 7 days, and others for 28 days, these periods are expected to be enough to give an indication of the suitability of the brick wastes replacement. Chemical composition, SEM, and EDX were analyzed, furthermore, compressive strength results showed that replacement with PBCBW at 28 days of curing was greater than PCBW, and the higher Ca/Si ratio gives PBCBW more cementitious properties. Furthermore, PBCBW contains a higher ratio of less reactive crystalline silica (SiO2) than highly reactive amorphous silica, which increased the compressive strength of PBCBW at 28 days. [ABSTRACT FROM AUTHOR]

The current research aims to study the sustainable dashboard strategy and explain the reasons for the shift from traditional production to sustainable production and its role in evaluating the performance of economic units, by adapting this tool to the principles of sustainable development, so that its role moves from just measuring the company's financial performance to measuring the comprehensive sustainable performance in its dimensions The three: (Economic, Social. Environmental) The research has been summarized by a set of conclusions, the most important of which are: The traditional methods of evaluating and improving performance suffer from many shortcomings due to their reliance on financial indicators only without taking into account other non-financial indicators as they focus on results and lack consideration. Pay attention to the causes. Therefore, there was a need to use a coherent set of financial and non-financial measures in the process of evaluating performance [ABSTRACT FROM AUTHOR]

The research aims to analyze the benefits and costs in light of the shift to the application of cloud computing technology at Najaf Al-Ashraf International Airport and to demonstrate the benefit and costs to rationalize the decision to switch to cloud computing technology. Study the risks and downsides of adopting cloud-computing technology. To achieve this goal, the researchers relied on the data of Najaf Ashraf International Airport by conducting personal interviews with airport staff and field meetings with department managers for applying computing technology in light of value engineering. The study concluded through its results that the decision-making process to replace the existing system with cloud computing necessitates the study of the benefits and total costs as a result of the replacement process, And, showed that the study of the total costs must include the cost of bearing the risks of information security and design and that the appropriate decision may vary accordingly. [ABSTRACT FROM AUTHOR]

Because of laminated composites exceptional properties including high strength relative to weight ratio, strongly electrical insulation, and capacity to transmit load, they are being seriously considered to replace traditional materials for structural applications. In a previous study, the effect of layer arrangement on the mechanical properties of multilayered composites was studied and it turned out the setup (GF, KF, and CF) to be the best arrangement. The current study investigates at the way silicon carbide hybridization influences the tension and compression of the epoxy resin composites for the best arrangement. The composites were made using a hand lay-up technique at seven different degrees of reinforcement (2, 4, 6, 8, 10, 12, and 14 percentages of weight (wt%)). The extracted results showed that the tensile load, as well as yield stress for many composites containing silicon carbide nanoparticles increase linearly with particle loading. As particle loading increases, both increase the tension and compression strengths. There are a few abnormalities related to aggregating of nanoparticles with increased particle loading and the creation of micro voids. Even with some nanoparticle percentages (8-10%), notable advancements were achieved in composite mechanical characteristics, higher percentages (>10%) contributed in a lesser amount improvement. A number of preset criteria, involving strain, stress, and displacement distributions have been met by the convergent process of finite element analysis with ABAQUS software. [ABSTRACT FROM AUTHOR]

The current work focuses on the manner in which silicon carbide hybridization affects the impact, bending, and hardness testing of silicon carbide particle-reinforced hybrid epoxy composites. Hand lay-up approach was used to create the composites at seven various levels of reinforcement (2, 4, 6, 8, 10, 12, and 14 weight percentage (wt %). The extracted results showed the weight percentage (10 %) has higher impact strength than the other percentages and so it means absorb more energy than the other recipe due to high impact strength (241.61 N). In flexural test, the weight percentage (10%) of the ideal configurations (GF, KF, and CF) have higher flexural strengths (1.56 KN) compared to other percentages. The results from the experiments and the finite element models coincided well, and the simulations accurately predicted the failure processes in the specimens that were tested under flexural stress and impact. Due to its ability to absorb more energy than they allow a body to transmit, silicon carbide nanoparticles are suitable for body armor and vests used by military and police personnel. [ABSTRACT FROM AUTHOR]

For polymer composites modified polypropylene, polymer poor conduction is evaluated as the most significant parameter and a drawback. To increase its electrical and thermal conductivity, MWCNT is being developed. One of the most effective reinforcing materials for improved electrical and thermal properties is still carbon nanotubes (CNTs). This study examined the effects of fillers made of multiwalled carbon nanotubes (MWCNTs) on the structural, electrical, thermal, and physical properties of polypropylene PP/MWCNTs composites. Solution casting was used to create the PP/MWCNTs composites, which had different MWCNT concentrations of 0.5, 1, 2, and 3 weight percent. According to the electrical conductivity data, the best ratio of electrical conductivity at 3% was 7.8×10-4 with Ea = 0.92ev, and the addition of MWCNT increased electrical conductivity. Investigations into the thermal conduction of PP/MWCNT nanocomposites with varying MWNT concentrations revealed. Modified materials including high correlation between the TC value and the MWCNT content, kind, and structure in the composite. TC value between 0.12 and 0.33 W/m.K. The inclusion of MWCNT results in a rise in the onset (Tc,o) and peak endset (Tc,e) tem. of crystallization. The addition of 3 weight percent of MWCNT resulted in an increase of 36.9◦C to the Tc,o of PP nanocomposites and 52.8◦C to the endset crystallization temperature Tc,e. The Tm of PP and its nanocomposites was found to be between 112.1◦C and 168.1◦C. As the weight fraction of MWCNT grew, so did the contact angle, roughness, and hardness; the optimal ratio was 3% Wt., the contact angle was 76.40, the roughness ranged from 3.22 to 4.6 µm, and the hardness ranged from 60 to 77. [ABSTRACT FROM AUTHOR]

The study assesses the YSZ nanoceramic-coated on Ti-13Zr-13Nb alloy's corrosion and tape testing results. By employing dip coating and electrophoretic deposition processes, a thin adhesive covering was produced using a Taguchi design of experiments (DOE) methodology. The impact of dip coating & electrophoretic deposition parameters, including duration, temperature, concentration, voltage, of YSZ, and level of substrate pulverization in Ti alloy, on deposition yield was investigated using a L9-type orthogonal Taguchi array. After thickness and adhesion studies were utilized to optimize the parameters of both processes, the Ti alloy, were coated used the best settings for the electrophoretic deposition and dip coating techniques, as previously reported. For electrophoretic deposition, 400 degrees of grinding, 7 minutes, 20% concentration, and 60 volts were the ideal settings. The ideal parameters for dip coating were, 60 °C, 10% concentration, 10 seconds, and 400 °G grinding. The coated metals' (FE-SEM) images were used for the examination. Optical microscopy (AFM) was also used to assess the coated surfaces' thickness and microstructure. Using electrochemical techniques including cyclic polarization and polarizing, the corrosion resistance of the best coated Ti alloys was investigated in simulated body fluid (SBF) (Tafel). The corrosion-resistant values shown below were used to compare the coated Ti-13Zr-13Nb alloys: The resistance to corrosion in Ringer's solution at 37°C was enhanced by both coating alloys; however, the Ti-13Zr-13Nb alloy coated by electrophoretic deposition was corroded more slowly than the Ti-13Zr-13Nb alloy coated through dip coating (1.888×10-3 against 5.417×10-3, respectively). [ABSTRACT FROM AUTHOR]

Sandwich panels have uses in the transportation industry, civil engineering, ships, and aircraft. The characteristics of the sandwich components directly affect the mechanical characteristics of these composites. The paper presents Nano composites and its impact on the mechanical characteristics of polymer composites with sandwich structures. The sandwiches include core and two thin sheets of AL6061-T6 lightweight shell, nSiO2, nZrO2 with grain size has been measured by a laser light diffraction instrument, it is found 35nm were added at the equal amount (1%wt.) for separated condition and 0.5%wt for hybrid sandwich composite using a mixture of epoxy resin and chopped S-glass fibre at a weight of 10% to construct the sandwich's backbone. To make reinforcement mixes manually, use mechanical mixing and ultrasonic stirring. Mechanical testing is done on produced sandwich composites to determine their qualities, such as their tensile strength and ductility, impact strength, and hardness. The result demonstrates that composites with filler material exhibit greater strength in comparison to composites without filler material, the percentage of improvements for tensile strength are 71% for nSiO2, 65% for nZrO2, and 64.8% for hybrid, while for impact strength and hardness they are 75% for SiO2, 74% for ZrO2, and 61% for hybrid. The scanning electron microscopy (SEM) results reveal that the ZrO2 and SiO2 nanoparticles in the chopped S-glass fibre composite are uniformly dispersed and do not mix. Science involving water absorption, X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). A complete oxidation of the composite was shown by the FTIR data, which showed the existence of several functional oxygen groups in the sample. [ABSTRACT FROM AUTHOR]

A Three-dimensional numerical simulation is conducted to evaluate the performance of integrating two heat transfer passive techniques including pin fins and secondary channels in a minichannel heat sink (MCHS). The assessment process of the current design adopted three models namely; the conventional model of MCHS, MCHS with circular pin fins and secondary channels, and MCHS with diamond pin fins and secondary channels denoted by models A, B, and C respectively. For models (B, and C), the improvements in heat transfer, pressure drop, and performance factor are compared with the conventional MCHS model (A)) mini channel heat sink) as the basic model. The study found that using a combination of pin fins and secondary channels can significantly improve the thermal performance of a minichannel heat sink. Besides, the outcomes revealed that pin fin shapes have a considerable influence on the flow behavior and thermal performance of MCHS. The study showed that model C with a diamond fin pin resulted in the highest enhancement in Nusselt number with a percentage of 270%, while model B with a circular pin fin achieved a ratio of 250% in comparison with the conventional minichannel heat sink of model A. Based on the performance factors, model C was identified as the most efficient which yielded a value of 2.33 compared to model B which achieved a value of 2.03. [ABSTRACT FROM AUTHOR]

One of the methods that can improve the mechanical properties of polymers is blending, for this purpose polyvinlydenefloride (PVDF) was blended into polyurethane PU with different weight fractions (10,30,70.100) % using solution casting method and then PU/ PVDF blends were compression molding at 200 °C. Fourier transform infrared spectroscopy (FTIR), Atomic force microscope (AFM), Differential scanning calorimetry (DSC), and mechanical properties (hardness and impact test) have been used for polymeric blends characterization. The results of FTIR tests showed that there is no change occurring when PU blended with PVDF. Moreover, results of fracture toughness show that the impact strength of PU/PVDF blends was increased by (5.25 %) as the amount of PVDF increased, while the shore D hardness decreased by 59.3%. The AFM results showed that the addition of PVDF has lower the surface roughness of blends. DSC analysis illustrates that the melting temperature of the blends PU/PVDF slightly increased when the amount of PVDF increased from (10 to 70) %, while the melting enthalpy decreased for TPU/PVDF blends consequently the crystallinity decreased as PVDF content increased. [ABSTRACT FROM AUTHOR]

During work hours in all workstations such as (industrial facilities, office rooms... etc.), it is not possible to avoid the impact of change in the interior air environment in terms of relative humidity, velocity, temperature,...etc., resulting from turning on or off the different heat sources, which can often cause a danger threat to human health. In this research, a numerical study of an office room with a mixed ventilation system under an Iraqi hot and dry climate in the summer season for Hilla City was presented using computational fluid dynamics (CFD) and using the AIRPAK3.0.16 software to know the effects resulting from adding a secondary heat source and changing its location to know its effects on the indoor air parameters. In the research papers that study the effect of adding a heat source and changing its location, observed that most types of ventilation systems used were displacement and natural ventilation systems. Also, some researchers studied these effects using a mixing ventilation system with exhaust vents located at the same level as the supply diffuser at the top of the wall. Therefore, the current study used a mixed ventilation system with exhaust vents located under the supply diffuser in the same wall. The 3D steady Reynolds Averaged Navier Stokes (RANS) equations are solved in conjunction with the energy equation and the turbulence (RNG k-Ɛ) model. The computational fluid dynamics were confirmed firstly using experimental data from the previous studies, and the results were acceptable. The results of the tests found that the indicators of thermal comfort predicted percentage dissatisfaction and the predicted mean vote was better before the secondary heat source was turned on. The results also showed that the optimal location for the secondary heat source at the position (x=0.3, z= 0.8 m) and (y=0.4 m) height from the floor according to the indicators of thermal comfort and indoor air quality. [ABSTRACT FROM AUTHOR]

This research was conducted at the University of Mosul - College of Education for Pure Sciences - Department of Biology, in order to investigate the effect of spraying the plant's bean (Vicia faba L.) in soil that is contaminated with nickel and lead as well as an additional treatment that involves using ascorbic acid. All of the treatments were compared to a control that did not utilize the chemical. The results indicated that adding heavy metals (nickel and lead) to the soil increased the concentration of heavy metals in bean grains significantly. Treatment with nickel at a concentration of 60 milligram /kilogram of soil and with lead at a concentration of 600 milligram /kilogram of soil resulted in a significant increase in nickel and lead concentrations in bean grains, which were 1.788 and 1,900 milligram /kilogram, respectively. While a large decline in Ca and Na concentrations was observed in bean beans when the soil was processed with nickel at a concentration of 60 milligram /kilogram and lead at a concentration of 600 milligram /kilogram of soil, which was approximately 1.40 mg/g for calcium. Sodium The concentrations are 0.040 and 0.058 mg/g respectively. The results demonstrated that spraying the vegetative component twice the concentration of nickel in leguminous beans to 0.168 mg/kg. When ascorbic acid was employed with a concentration of 250 mg/L, the lead content decreased as the concentration reached 0.098 mg/kg. While it was observed that the calcium content of the grains in the plants that were sprayed for two times was significantly higher than the control treatment, this amounted to 2.80 mg / g instead of 1.60 mg / g. [ABSTRACT FROM AUTHOR]

The well-timed recognition of cancer and understanding the genetic paths that fuel its development are crucial for enhancing treatment efficacy and survival rates, with significant social and financial insinuations. Over the past two decades, researchers have explored the use of machine learning (ML) and deep learning (DL) methods to classify different types of cancer. These techniques hold promise for improving cancer diagnosis and treatment outcomes, ultimately contributing to better health outcomes for individuals artificial by cancer. In this paper is about the use of machine learning and deep learning techniques to classify DNA cancer data. We have two systems for classifying cancerous or normal DNA by K Nearest Neighbors (KNN), Probabilistic Neural Network (PNN), Long Short Term Memory (LSTM), and Convolution Neural Network (CNN) methods. These ways are improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) in order to select the best features from the dataset towards an increase in diagnosis accuracy. The study assesses and appraises answers of the most correct algorithm used during classification by utilizing confusion matrices. Additionally, the paper considers processes of handling data such as deleting alike columns, normalizing, filling in nulls, etc., using missing value techniques. The research offers valuable insights into evolutionary optimized deep learning approaches for classifying DNA cancer data that may help improve outcomes of diagnosis and treatment for patients with cancer. [ABSTRACT FROM AUTHOR]

The swift development of artificial intelligence has revolutionized the world of self-driving cars by integrating intricate models and algorithms Autonomous vehicles are regarded as significant developments in computer technology and artificial intelligence. Using sophisticated and powerful algorithms in these vehicles will effectively address a range of driving obstacles, especially for those with disabilities or advanced age. The objective of our study is to make self-driving automobiles travel from point A to point B on the first track without deviating from it and to move on the second track without having to undergo any training on it. The current study introduces a computer vision method that extracts information from a dataset. Study tasks involve processes including behavioral cloning, data augmentation, image processing, and the improvement of the CNN model. The proposed model attained a mean square error of 0.0250 for the validation value and 0.0281 for the training value, which was deemed an outstanding outcome. [ABSTRACT FROM AUTHOR]

Reduced graphene oxide (rGO) film sensors were fabricated by thermally reduced graphene oxide (GO) at various temperatures. According to the morphological analysis, it was found that rGO synthesized at 200 °C exhibited a crumpled plate-like structure with a much larger surface area within the range of 195.82 m2 g−1, above that of GO. The structural analysis demonstrated the presence of a significant concentration of oxygen functional groups on a typical GO surface, which decreased after the reduction process. Additionally, the peak corresponding to the (001) plane shifted from 13.7° to 25.65° upon increasing the temperature to 200 °C. The rGO films were utilized as a sensor for NH3 gas at varying operational temperatures. The sensitivity, response, and recovery times of the rGO and GO sensors were investigated. The rGO sensor synthesis at 200 °C was found to have a high response of 11% when operated at a working temperature of 200 °C. [ABSTRACT FROM AUTHOR]

The urban road transport network is vital for regional growth and has a considerable impact on the spatial organization of the city. An advanced and effective transportation network in urban areas is comparable to the circulation of blood via the veins within a human being. The purpose of this study is to determine if the connectedness and intensity of the road network in Kerbala City's corporate area reflect variations in road network centrality. Furthermore, it looks at the connections between nearby intersections to understand the features of road network patterns. The road network features of the main cities in Kerbala are assessed using the Comparative Neighborhood Graph (RNG), a proximity graph, as a model for building new networks. Road networks with regular road patterns are compared using the crossing factor (CF) and the grid-tree proportion (GTP) index. Road links are compared with each link in the proximity graphs, which are created utilizing the point data of intersections on a digital map. Our objective is to analyze and appraise the road transportation system at the municipal level in a growing country such as Iraq. The analysis demonstrates that the city possesses a highly interconnected system of roadways in its central areas, with the level of connection and coverage of the network decreasing as the distance from the central core increases. The model result offers a decision-making mechanism to assist local planners in monitoring and interpreting the process of urban form evolution in Kerbala City. The outcome reveals that the RNG connections encompass the majority of the grid roads; however, many prominent streets cannot be well represented by proximity graphs. As the GTP index increases, the correlation between roads also increases. [ABSTRACT FROM AUTHOR]

Several factors influence the energy production of photovoltaic (PV) installations. The widely used ground albedo value of 0.2 in utilizing the PV system modelling; a PV array should be angled at the proper angle to optimize the system's performance. Most designers do not consider albedo to get the optimum tilted angle. This study aims to improve previous research that applied a 31°optimum tilt angle for a stand-alone PV system with an albedo value of 0.7, located at Technical College/Al-Mussaib (TCM) in the Babylon Governorate, Iraq. The improvement will be taking advantage of albedo to get the maximum amount of irradiation reached PV panel by increasing the module area and then testing the PV system by increasing the tilt angle until reaching a maximum value of, a global incident in the collector plane, and effective irradiation on collectors and energy on collectors. Using PV syst. and software Perez module in simulation, the results show that the optimum tilt angle at this location with albedo 0.7 will be 38o instead of 31o. [ABSTRACT FROM AUTHOR]

Nowadays, BLDC motors have become important motors for their many applications. Some of these applications require precise position and speed control, such as paper and textile mills, robots and CNC machines, etc. These and other applications require an accurate tracking trajectory of the movement. In this paper, design a cascade P-PI controller to control the position of the BLDC motor. This cascade controller consists of three controllers, one for current control, one for speed control, and the last for position control. In addition, the DC-link current can be used in block current control. Parameter values of these three controllers are calculated and extracted using the two new methods which are: SSO and GWO. The simulation result proves the power of the cascade P-PI controllers to reject disturbances, in addition, the results showed SSO Algorithm is better than the GWO algorithm. [ABSTRACT FROM AUTHOR]

Wireless power transmission (WPT) revolutionizes convenience by eliminating physical connections, enabling seamless charging for various devices. WPT faces challenges related to efficiency loss over distance, as energy transmission weakens with increased separation between transmitter and receiver. This paper aims to design and practically implement a WPT-based magnetic resonator coupling (MRC) technique to transfer energy from a transmitter coil to a receiver coil for supplying temperature and humidity environmental sensors by power using a zero voltage switching (ZVS) model. The system comprises three main circuits: receiver, transmitter, and monitoring environment. The transmitter circuit operates at a frequency of 17 kHz using the ZVS model. The receiver and transmitter coils are hand-twisted and made of 15 American Wire Gauges (AWG). Built-in environmental circuitry with a DHT11 sensor and Arduino Uno microcontroller makes it easy to monitor temperature and humidity under environmental conditions. Different impedance loads of 50, 100, 150, and 200 ohms were tested in the system at 1 and 10 cm distances between the receiver and transmitter coils. The results have shown that the system worked fairly when using 50-ohm resistors spaced between 1 and 4 cm between the transmitter and receiver coils, providing a voltage of over 4 volts and a current of over 70 mA. As a result, these parameters enable suitable environment monitoring circuits comprising the Arduino Uno microcontroller and the DHT11 sensor. Furthermore, the system attained a notable efficiency of 95% based on real measurements when the distance between the two coils was 1 cm and the resistive load value was 50 ohms. Moreover, this system shows exceptional performance compared to previous related research and shows notable improvement. [ABSTRACT FROM AUTHOR]

Electric energy demand has increased recently, power networks have become larger and more advanced, hence power systems have become a mature technology, they operate near their limits, these factors increase the probability of power system blackout, the operator in the control center must develop a comprehensive restoration strategy, with a particular focus on minimizing the time required for recovery. This study presents the development of an expert plan that incorporates a novel strategy, serving as a comprehensive framework to facilitate recovery methods using a modified depth-first search (MDFS) technique to find optimum paths. Optimal allocation of distributed generators (DG) can enhance the voltage profile of the system and reduce total power losses. This work introduces a technique for efficiently determining the best positioning and size of distributed generation (DG) units inside a grid through the utilization of particle swarm optimization (PSO). The analysis focuses on the impact of DG in reducing loss during restoration, which is found high based on the results. Priority is given to restoring DGs with strong black start capability within the constraints of the network and the DGs themselves. The efficacy of the suggested restoration method is confirmed using the IEEE 39-bus system by MATLAB simulation. [ABSTRACT FROM AUTHOR]

Water is an essential prerequisite for the sustenance of life on Earth, and its accessibility is imperative for survival. Numerous scholars have embraced the pursuit of viable remedies for the eradication of water scarcity, with solar distillation emerging as the most uncomplicated solution, particularly in regions with ample sunlight during the early hours of the day. The employment of solar-powered distillation devices represents the most rudimentary method employed to obtain potable water. For this, three identical single slope solar stills are built up; the first is a conventional solar still (CSS), the second is a conventional solar still integrated with copper pipe solar collector (CSS-CPSC) and the third is a conventional solar still integrated with finned tube solar collector (CSS-FTSC). Which acts as a preheating system for evaporation process augmentation. The results show that the CSS-FTSC operates with temperatures higher than those of CSS-CPSC. Also, the yields of the CSS, CSS-CPSC and CSS-FTSC are 2.886, 4.372 and 4.766 L/m2/day respectively. Thus, the productivity enhanced by 51.50% and 65.12% when using FTSC and CPSC, respectively, as compared to that of CSS. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is a life-threatening medical condition caused by high blood sugar, high blood pressure, alcohol, smoking, and congenital abnormalities. There are several ways to diagnose CVD, such as stethoscopes, electrocardiography (ECG), phonocardiography (PCG), and computed tomography (CT). Some of these devices are inaccurate and require medical expertise. In stethoscopes, the doctor's skill determines heart sound interpretation and external disturbances can affect their effectiveness. Additionally, ECG and PCG cannot provide complete heart health information. CT scans, however, emit radiation that can harm patients. This study examines various CVD early detection and prediction methods to find the best one for patients and doctors. Previous research has identified methods, including artificial intelligence (AI) algorithms and wireless sensors. The study also seeks to identify the best model and examine CVD detection challenges. Comparing previous research, the modified scalp swarm optimization method and adaptive fuzzy neurological system (MSSO-ANFIS) had the highest rating accuracy, 97.45%. Researchers' CVD detection difficulties were also highlighted in the paper. In conclusion, the use of echocardiography devices provides comprehensive and accurate information about the heart, including details such as the shape of the heart, the thickness of the heart muscle, and heart sounds. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA), a prevalent degenerative joint disease, triggers significant global impairment. Timely intervention relies on accurate early diagnosis. Recent advancements employ deep learning (DL) models that integrate patient clinical data with Imaging techniques like X-rays, MRI, and CT scans, enhancing OA detection and assessment. This review surveys recent research on DL techniques for OA detection, highlighting the development of convolutional neural networks (CNNs) and innovative architectures. CNNs analyze medical images, automatically extracting features indicative of OA progression. Models combining patient demographic information, clinical history, symptoms, joint biomechanics, and imaging data show improved OA onset and progression prediction compared to imaging alone. Transfer learning fine-tuning CNNs pre-trained on datasets like ImageNet enhances feature extraction and classification accuracy. Hybrid models, merging CNNs with traditional machine learning (ML) methods like SVM, capitalize on the strengths of both approaches. Despite progress, challenges include reliance on training data volume and quality, class imbalance, and limited model generalization across diverse datasets. DL holds promise for automated and objective OA diagnosis, severity grading, treatment planning, and prognostication. However, further research with multi-modal datasets and optimized model architectures is essential to realize its clinical utility and generalizability for OA management fully. This review synthesizes the field's current state, outlining future directions in this evolving application at the intersection of artificial intelligence and musculoskeletal health. [ABSTRACT FROM AUTHOR]

Chronic kidney failure is a life-threatening disease that many people are suffering from. According to the last survey by the International Society of Nephrology, about 850 million people suffer from chronic kidney disease. The ideal treatment other than kidney transplant is the Hemo-dialysis sessions, the procedure for many patients is considered costly and uncomfortable since the patient has to go to the hospital three or four times a week undergoing the risk of contamination. Therefore, it was essential to find an alternative means to help these patients by developing an embedded electrical portable dialysis machine consisting of a filtering system (ultrafiltration 8000 filter and dialyzer filter), a safety system consisting of many flow sensors, and blood leak detection. It also includes an alarm system that ends the dialysis procedure by clamping the venous line if any safety system parts are out of the normal range. Ten patients' waste solution samples were taken, and they went under study and filtered using our proposed system. Where it was noticed that the filtration efficiency of the conventional center-based dialysis machine deteriorates over the years since the total dissolved solids increased over the years due to the progression of kidney failure and the accumulation of toxins and large molecules in the blood vessels, which demands an alternative method to overcome this problem. [ABSTRACT FROM AUTHOR]

Using no-core optical fiber (NCF) and conventional single-mode fiber (SMF), this research needs to increase the sensitivity of temperature and relative humidity measurements for radiology device applications. Operating on the Mach-tender Interferometer (MZI) principle, the sensors make use of surface plasmon resonance. Traditional sensing technologies sometimes confront difficulties in reaching high sensitivity and accuracy, especially in demanding environments like radiology facilities. In order to increase the sensitivity of four relative humidity sensors constructed from SMF, TNCF, and SMF, a 2 cm NCF segment was incorporated. To enhance sensitivity, the sensors have been coated with a variety of substances, such as gold nanoparticles (AuNPs), silica nanoparticles (PVN/PVP), and a layer-by-layer technique. The sensor coated with AuNPS exhibited the greatest sensitivity with respect to temperature, attaining 0.486 db/°C. The fiber optic sensors that have been developed exhibit great potential for utilization in biomedical and environmental domains. Experimental tests are carried out to assess the performance of the proposed sensing system, including sensitivity, accuracy, and reaction time. [ABSTRACT FROM AUTHOR]

Cardiovascular Diseases (CVD) have become increasingly common around the world in recent times. Diagnosing these diseases is difficult due to traditional diagnostic methods such as stethoscopes and auscultation. This study aimed to detect and predict these diseases before the patient's condition worsens. Machine learning (ML) techniques were used, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Decision Tree (DT). A Kaggle dataset of 1,000 samples and 14 clinical features was trained and tested. Furthermore, the performance metrics of these algorithms were examined. According to the results, RF performs better in terms of F1-score, accuracy, sensitivity, specificity, precision, training time, and testing time, with scores of 97.8%, 98%, 98.2%, 97.6%, 97.8%, 0.182 sec, and 0.030 sec, respectively. In conclusion, it was noted that modern diagnostic methods provide comprehensive and exact information about the heart. [ABSTRACT FROM AUTHOR]

In recent years, the importance of text detection and recognition in images has grown due to the huge number of applications developed for mobile devices and computer vision. Text detection becomes difficult when backgrounds are more complex, or when the capture conditions are not controlled or when it contains complex textual content. In this work, a robust multilanguage text detection and recognition method is based on a pre-trained CRAFT (Character-Region Awareness For Text Detection) algorithm with OCR (Optical Character Recognition system) for text recognition. The preprocessing techniques include advanced image pre-processing methods, which include channel modifications, grayscale conversion, and normalization to enhance text visibility and detectability. Then, our algorithm effectively analyzes text from a variety of backgrounds and languages by combining a VGG16-BN backbone network with extra Up-Conv blocks. Finally, using the modified OCR system for text recognition. Experimental results show that the proposed text detection and recognition method is robust to distortions in geometry, noise reduction, lighting, and resolution (high brightness, shadows, inconsistent illumination, low contrast). Furthermore, the proposed method often performs better than state-of-the-art methods when tested on natural scene datasets, as seen by its excellent recall and precision rates. Moreover, it achieved a precision rate of 97.3% and a harmonic mean score of 93.5% on the ICDAR2015 dataset, indicating its dependability and efficiency in text detection for practical uses. [ABSTRACT FROM AUTHOR]

This review paper finely analyzes eye-to-text communication and explores the development and evaluation of an advanced eye-controlled communication system. The study focuses on the algorithms for eye tracking and gaze estimation, the Artificial Neural Networks (ANNs), and the training process for converting eye movements into textual output. The article highlights Computer Vision (CV) algorithms utilized for real-time eye tracking and gaze estimation, emphasizing the robustness and accuracy of the algorithms. The authors detail the techniques employed, including pupil detection, iris segmentation, and gaze estimation, highlighting their effectiveness in capturing and analyzing eye movements. Moreover, the article discusses challenges faced during training that provide insights into potential improvements for future work. The review paper presents comprehensive experimental results, including ANN comparisons with existing methods such as gaze estimation error and user satisfaction, thoroughly assessing the system's capabilities. The system enables users to express themselves and interact with digital devices more independently, enhancing the Quality of Life (QoL) for individuals with limited motor abilities. This review aims to pinpoint the deficiencies left unaddressed by researchers, including issues such as head motion, low illumination, low-resolution cameras, and user fatigue. These identified shortcomings emphasize the pressing demand for eye-to-text communication systems. [ABSTRACT FROM AUTHOR]

Osteoarthritis is a degenerative joint disease that commonly affects the elderly population. The Kellgren-Lawrence grading system serves as the benchmark for evaluating OA severity through the analysis of radiographs. However, this method relies heavily on physician expertise, resulting in variability. To address this issue, machine Learning methods have been explored for automated OA diagnosis and grading. This study compares different Machine Learning models (Neural Network, Random Forest, and Logistic Regression) were deployed for classifying osteoarthritic knee radiographs. The models were trained and tested on a dataset of normal and osteoarthritic knee X-ray images from Kaggle. The results demonstrate that Logistic Regression achieved the highest classification accuracy of 88.6%, sensitivity of 84%, precision of 88.6%, specificity of 88.6%, and AUC of 95.2%, compared to the Neural Network and Random Forest models. The study validates the efficacy of the Logistic Regression model through comparison with previous works. The key contributions include the implementation of multiple Machine Learning algorithms, determining Logistic Regression as the optimal method for early OA detection, and superior accuracy compared to prior research models. The study indicates that Logistic Regression is a promising approach for automated classification and grading of knee osteoarthritis severity. [ABSTRACT FROM AUTHOR]

The task of recognizing and categorizing vehicles in videos and images as objects poses a considerable challenge in terms of appearance-based representation. However, it holds great importance in the practical implementation of Intelligent Transportation Systems (ITS), particularly in real-time applications. The fast advancement of deep learning has resulted in an increasing need within the computer vision field for the development of efficient, robust, and outstanding services across diverse domains. This paper provides an extensive analysis of various methodologies for vehicle detection and classification, along with their utilization in real-time targets, estimating the density of traffic, and related domains through the implementation of Deep Learning techniques. The major findings of our survey highlight crucial insights obtained from an extensive analysis of existing literature, shedding light on the current state-of-the-art techniques. Through a comprehensive review of deep learning methodologies, performance metrics, benchmark datasets, and a comprehensive exploration of the challenges encountered, our survey offers valuable contributions to the field. By synthesizing and presenting the collective knowledge in this domain, our paper serves as a key resource for researchers and practitioners alike, providing a holistic understanding of the advancements and challenges in vehicle identification and categorization within deep learning architectures. [ABSTRACT FROM AUTHOR]

Biomedical signal processing is essential for diagnosing and monitoring various health disorders. Conventional signal processing methods frequently struggle to effectively deal with biomedical data's intricate, nonlinear, and non-stationary characteristics, such as electroencephalograms (EEG). Time-frequency analysis (TFA) approaches provide valuable insights into the temporal variations of signal components, offering a possible way to address these difficulties. This study aimed to present time-frequency approaches, their applications across various biomedical signals, challenges encountered, and potential future directions. To highlight the proposed concept, Epilepsy (mental disturbance) was selected. The epileptic datasets, which included 3570 pairs of electrooculography (EEG) signals, were utilized. These signals were categorized into focal and non-focal signals. By applying the proposed approach that involves a scalogram-short time Fourier transform as a feature enhancement then fed to the convolution neural network CNN, the model achieved accuracy, precision, recall, and f1-score for the proposed model 76%,78%,75%, and 76% respectively. The results depict that integrating multiple time-frequency analysis techniques enhances biomedical signal processing capabilities, offering significant improvements in diagnosing and monitoring health conditions. [ABSTRACT FROM AUTHOR]

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details provided by the X-ray images dataset, the study showed that the using of X-ray data set in our deep learning algorithm could provide promising results by getting accuracy of validation for both Convolution Neural Network and SequeezeNet models 93%, 76%, respectively while the validation loss in both models Convolution Neural Network and SequeezeNet 34%, 30% respectively, these promise results will make the physician give a swift decision in diagnosis of lung cancer and keeping the patients away from exposing to unnecessary extra radiation dose during the Computed Tomograph exam as well as the low cost of X-ray examination comparing with Computed Tomograph exam. [ABSTRACT FROM AUTHOR]

High blood sugar levels for an extended time lead to the development of diabetic foot, which is a major medical problem among people with diabetes. Continuous care and management are essential to avoid potential physical complications. Nerve and blood vessel dysfunction caused by diabetic foot disease can lead to skin problems and ulcers, necessitating medical intervention. Diabetes increases the risk of foot-related complications, requiring prompt identification and resolution to prevent serious complications. The goal is to create a system consisting of a wireless sensor network or a system based on the use of thermal imaging for real-time monitoring of individuals with diabetes. This network will collect important physiological data, such as temperature, pressure, and humidity, using machine learning algorithms to predict diabetic foot complications. Automation will enhance forecasting accuracy and efficiency. An in-depth review of prior research on early detection methods for diabetic foot was undertaken to identify the most effective approach. Through rigorous comparative analysis, the proposed machine learning model demonstrates the superiority of the convolutional neural network (CNN) over other deep learning classifiers, achieving an accuracy rate of 98.27%. Different methodologies of ML, including regression (LR) and random forest (RF) classifiers, have also shown predictive solid performance, reaching up to 97% accuracy in forecasting diabetic foot disease. In conclusion, there is an urgent need for an advanced diabetic foot health monitoring system. The review highlights the need for additional research to overcome persistent challenges and unleash the full potential of machine learning models in enhancing diabetic foot disease prediction and treatment. [ABSTRACT FROM AUTHOR]

Diabetic foot is a severe medical problem that occurs as a result of high blood sugar levels. It is a common complication in diabetics. Diabetes can lead to complications, especially in the form of diabetic foot problems. If these problems are not detected and treated promptly, they can worsen, leading to severe consequences. Screening methods for the disease can be conventional and do not predict diabetic foot in the early stages. These prompted researchers to find an alternative solution to detect diabetic foot early and non-surgically. Researchers have sought other non-invasive methods to diagnose and predict diabetic feet using image processing techniques and machine learning algorithms. This study presents a comparative performance between six machine learning algorithms (Neural Network, Random Forest, Adaboost, Naïve Bayes) based on a dataset of images of normal and diabetic feet. The results show that Neural Network has an accuracy of 95.6%, the highest performance among other algorithms used in this study. [ABSTRACT FROM AUTHOR]

Pre- and post-Earthquake warning system are essential for saving living, reducing and preventing the damage of landslides. WSNs are a high-potential innovation that has been successfully implemented in many real-time applications, especially landslide monitoring. This paper proposes a low-cost earthquake warning system that can detect earthquake events and monitor the status of buildings in real-time using LoRa (long-range) technology in addition to monitoring the reliability of the communication system. The vibration sensor (sw-420) and accelerometer sensor (ADXL 345) are connected to Arduino UNO, and the data collected by Arduino are sent through the LoRa transmitter module. Then, it was received through another Arduino UNO that was equipped with a LoRa receiver module. Several cases are adopted on the Electrical Engineering Technical College site with different parameters and communication paths (LOS & NLOS) to analyze the communication reliability between transmitter and receiver sides in addition to the packet received ratio. The results showed that the system is more reliable for detecting earthquakes, especially with an LOS communication system with RSSI less than -148dBm. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a degenerative condition that greatly affects the population, especially the elderly. The main cause is the gradual death of melanocytes in the brain, which produces dopamine and transmit nerve signals during motor and cognitive activities. The disease has various symptoms, including motor and non-motor, which appear after the level of dopamine in the melanocytes reaches less than 40% of its normal levels. Early detection of the disease is crucial to prevent patients from reaching catastrophic stages that affect their quality of life. Artificial intelligence technologies, such as machine learning, deep learning, and the Internet of Things (IOT), provide important benefits in predicting, detecting, and tracking disease progression. This review examines previous literature on AI techniques and their use in finding successful solutions to reduce disease progression. Researchers focused on prediction and early detection using medical technologies such as brain MRI scans. In contrast, others focused on clinical symptoms such as acoustic measurements, gait analysis, and electroencephalography, in addition to exploiting IOT technologies to track disease progression. All studies used approved criteria to test the efficiency of the models. proposed classifications, such as classification accuracy, sensitivity, and specificity, to determine their effectiveness in controlling the disease. The review findings highlight the status of intelligent systems and models proposed in the literature and identify their advantages, disadvantages, difficulties, and potential paths forward. These cutting-edge monitoring and prediction systems have the potential to transform the treatment of Parkinson's disease and provide invaluable assistance to patients and medical professionals, if technological advances continue. [ABSTRACT FROM AUTHOR]

The smart, automated car washing device, which is primarily based on Internet of Things (IoT) technology, is a new approach to wash cars more efficiently. This device was carried out using the Mega Arduino board and a few Arduino-like-minded components, including relays, gearboxes, pumps, and sensors. Each element has a relay connected to it to govern turning the part on and off, and the related relay is controlled via the Arduino, which is programmed using the incorporated improvement surroundings, where the synchronization approach is used for the three washing degrees and in accordance with the size of the car to be washed. In particular, the car passes through the three gates of the three degrees. In the primary gate, soap liquid is pumped and unique brushes are moved to clean the car, and in the second gate, water is pumped to put off the liquid soap using pumps. In the third gate, the car is dried utilizing three fans. Thus, the showering process is completed, as the car is eliminated from the three ranges through the transmission. This entire process is controlled by IoT generation via the Hypertext Transfer Protocol (HTTP) browser via customers' mobile gadgets, and the procedure is described. The method of washing and the length of time it takes are determined by the client and in line with his/her preferences. This device includes a civil defense system to provide more security to the website and those found on it. A fireplace sensor was used to detect infrared rays and lighting, and an alarm bell became extensively utilized to alert those within the website. All the system parts are connected to communique networks to send a text message to the worried government, which is involved with reporting the fire situation through the Global System for Mobile Communications (GSM) generation. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a neurological disease that affects the nervous system of the human body. Parkinson's disease is considered a silent disease that does not show symptoms at the beginning of the patient's infection with the disease. Therefore, the diagnosis of the disease is very late, perhaps after months or even years. Hence, treating and controlling the disease is a challenge, so early detection is crucial to improving the quality of life and treatment of patients on the one hand and the possibility of recovery on the other hand. This research proposes a model based on Machine Learning (ML) algorithms. This model uses peoples' voice measurements collected and obtained from the Kaggle dataset (for, where 195 audio recordings were examined, to determine whether they have the disease or not. The proposed model was used to classify 1,000 important patient features. Neural Networks, Multilayer Perceptron (MLP), Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and Decision Trees (DT) were among the machine learning methods used. With a classification accuracy of 95.5%, the MLP algorithm outperformed other methods that achieved lower classification accuracy, as the RF algorithm achieved a classification accuracy of 88.4%, LR achieved 87.7%, GB 91.5%, KNN 86%, and DT achieved classification accuracy, reached 86.8%, according to the data. This work provides the option of creating a model by comparing several ML algorithms, selecting the most accurate one for classification, and using acoustic measurement analysis to identify PD. [ABSTRACT FROM AUTHOR]

Wireless communication systems have become increasingly important in recent decades. Antennas are crucial components of these systems, acting as transducers that serve as the system's eyes and ears. Multiple Input Multiple Output (MIMO) antennas are now widely used in mobile handsets to reduce channel fading and optimize channel capacities. This research paper presents a MIMO antenna design for 5G C-band mobile handsets. The suggested design is characterized by its straightforward and uncomplicated construction. Our eight-element antenna array resonates at 3.7 GHz, providing a potential bandwidth of 400 MHz Each antenna element has a slot-dipole shape, which provides a spherical radiation pattern. Our design incorporates a Defected Ground Structure (DGS) and a dual-polarized structure, significantly reducing mutual coupling, resulting in an isolation value Sij ≤ -18 dB between ports. We manufacture the proposed design structure and measure the parameters of the reflection coefficient (CC). The system offers a significant radiation efficiency of 71%. Moreover, we analyzed the Envelope Correlation Coefficient (ECC < 0.03) and Diversity Gain (DG ≤ 9.9 dB) to demonstrate the MIMO performance of the antenna. We also studied how the system performance is affected during talk mode by the presence of the human head. Our proposed design has dimensions of (140 x 70 x 1.6) mm³ and is compared to previous literature studies. Furthermore, it is placed on an FR-4 substrate with a dielectric constant 4.3. The process of simulating is executed using CSTMW Studio. [ABSTRACT FROM AUTHOR]

Rainwater harvesting is an essential technique that collects and stores rainfall runoff for various purposes, such as agriculture, livestock, home usage, industrial, groundwater recharge, and flood control. This study was conducted in Shuwaijah Watershed in Kut Governorate-Iraq. Geographic Information System (GIS) and remote sensing data were utilized to address this issue and to choose the best locations for rainwater harvesting through DEM (Digital Elevation Model), drainage density, land use/cover, slope, precipitation data, and soil texture. The Weighted Linear Combination (WLC) approach integrated these diverse factors and comprehensively evaluated the potential harvesting sites. The study revealed that approximately 42.713% of the ability of the research region for water harvesting is being assessed, with 27.681% classified as having medium suitability and 29.606% as low or very low suitability. Based on the findings, the study recommends the construction of water reservoirs in the Shuwaijah area to enhance water availability and management. Finally, the study contributes valuable insights into sustainable water resource management strategies in response to climate change-induced drought in the region. [ABSTRACT FROM AUTHOR]

This study objective to evaluate the operational efficiency of IFAS wastewater treatment plant and the compatibility of the water quality after treatment with the Iraqi limitations for disposal it into water resource. IFAS wastewater treatment plant is considered as the study area with surface area of about 1.8Ha, which is located in the southeast of Najaf Governorate within Al-Barrakiha plants, on shatt Al-kufa, a branch of the Euphrates River the parameters that study concentrated on were biochemical oxygen demand (BOD5), total suspension solids (TSS), chemical oxygen demand (COD), chloride (cl), hydrogen ion concentration (pH), sulfate (SO4), ammonia (NH3) and phosphate PO4 during the period of (2022-2023). The results showed that the flow entering the plant is classified a medium to strong in strength with BOD5/COD ratio in the range 0.19 and 0.87, which appears that the wastewater is moderately biodegradable and therefore might be subject to biological treatment. It was found that most of the parameters of the quality of the effluent are within Iraqi standards for disposal to River. "Regression equation was carryout to evaluation the removal ratio of BOD5, COD, TSS, SO4, PO4, CL and NH3 that the plant should perform by to achieve the disposal limitations". The percentage of removal for parameters were BOD5, COD, TSS, PO4, SO4, CL and NH3 were 88%, 85%, 76%,48%,10%,20% and 44% respectively. [ABSTRACT FROM AUTHOR]

The presence of toxic heavy metals in wastewater is a growing environmental threat. Removing Cd2+ from wastewater has been a global priority for decades due to the fact that It's one of the most dangerous metals since even trace amounts may trigger health problems. Adsorption is one of the ways for that purpose; the study's objective is to produce absorbent from pomegranate peel charcoal (P.P.C). The (P.P.C.) is characterised by FTIR and SEM tests under different conditions (pH, Contact time, Agitation speed, Temperature, and Doses of adsorbent). The P.P.C. powder (less than 1mm) is directly used in different adsorbent/metal ion ratios. The optimal conditions for this study were pH 8, 60 minutes contact time, 200 rpm, 25°C, and the adsorbent dosage was fixed at 0.5g/100ml. The results show an increase in Cd2+ removed with the increase in the weight of the adsorbent to demonstrate the phenomenon of adsorbate-adsorbent interaction. Equilibrium experimental data was validated for the Langmuir and Freundlich isotherm models. The Cd2+ ions on the adsorbing surface of the P.P.C. followed the Langmuir model with a high correlation value (R2=0.99985) and a maximum adsorption capacity (qmax) of 10.88 mg. g−1, as reported in the study. [ABSTRACT FROM AUTHOR]

Offshore structures include fixed, floating platforms and port decks. It is well known that more than 25% of worldwide oil and gas comes from offshore facilities. So, the stability of offshore construction is the main priority to be designed and well executed, and a long and permanent maintenance program should be scheduled. One of the offshore structures is a jacket substructure of platforms. This Jacket is a rigid steel structure that supports the deck and topsides of a continuous offshore platform. The loads subject to the offshore Jacket are dead and live loads in addition to the environmental loads. The environmental loading is mainly due to wind, current waves, and seismic activity. Environmental loads affect a percentage exceeding 35% of the total loads on the structure, the largest percentage of which is from the effect of waves. Offshore the rigs. The stability and response of the maritime structures should be studied under the effect of these types of environmental loadings. The analytical and numerical methods are used to analyze environmental forces subjected to jacket offshore structure platforms. Incremental Dynamic Analysis (IDA) was employed to analyze the linear and nonlinear dynamic response of jacket-type offshore platforms under seismic loading. The effect of the environmental loadings experienced by the jacket structures will be reviewed in this study. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of supplementary cementitious materials (SCMs) on the properties of high-strength concrete (HSC) reinforced with basalt fiber and composed of dune sand. The study aims to enhance the sustainability and performance of concrete by using SCMs such as fly ash and slag to replace silica fume, while the cement amount is constant for all concrete mixtures. Various mix proportions incorporating different types and proportions of SCMs are examined to evaluate their effects on the compressive strength, flexural strength, and splitting tensile strength of the concrete. The basalt fiber (BF) was added in constant amount of 1% of binder weight to enhance flexural and tensile strengths. Fly ash and GGBS was used as SCMs at amounts of 0%, 30%, 70%, and 100% of binder weight to replace the silica fume. The results indicate that the inclusion of GGBS and fly ash to replace silica fume reduced the 28 days-compressive strength of concrete from 53.5 MPa to 22.3 MPa for HSC containing 100% GGBS and 19.4 MPa for HSC containing 100% fly ash. The results obtained contribute to the understanding of the use of SCMs in HSC production, mostly in areas where dune sand is abundantly available. [ABSTRACT FROM AUTHOR]

This research explains using steel bars in the near-surface mounted technique to repair reinforced concrete deep beams containing two types of openings, rectangular and circular. Fifteen specimens with dimensions (200*450*1600) mm³ were tested and these specimens were divided into two groups (A & B). Group (A) consists of eight repaired specimens with rectangular openings measuring (140*144) mm² in addition to one control specimen which was loaded to failure without any repairing process. Group (B) included four specimens with circular openings whose size was equivalent to the size of the openings in group (A), in addition to one control specimen. In addition to the above, a solid specimen without openings was tested and considered a control specimen for specimens in both mentioned groups. Twelve specimens were repaired after being preloaded with two damage ratios (40% & 65%) of the ultimate load of the control specimens. The studied parameters were the repairing schemes around the openings (vertical and inclined) in both groups and reducing the number of steel bars or the diameter of the bars exclusively in group (A). The results showed that adopting the inclined repairing scheme while changing the shape of the opening from rectangular to circular after preloading with a damage ratio of (40%) led to an increase in the ultimate load by (28.048%). On the other hand, under the same mentioned conditions of the circular shape of the openings, preload by (40%) and the inclined repairing scheme, the highest increase in stiffness was obtained for the specimens by (149.35%) compared to the specimens with rectangular openings. As for the ratio of ductility, an increase of (6.2%) was obtained in the specimen with circular openings based on the inclined repairing scheme after preloading with a damage ratio of (40%) compared to the specimen that is similar to it but with rectangular openings. It was found that reducing the number of bars by half when adopting the vertical scheme led to a higher reduction in the ultimate load by (24.24%) and an increase in the ductility and stiffness of the beams by (9.968% and 70.441%) respectively after preloading with two damage ratios (40% and 65%) compared to the control beam. In addition to the above, increasing the diameter of the bars from (10 mm) to (12 mm) and following the inclined scheme led to an improvement in the ultimate load and stiffness by (1.219% & 53.617%), respectively. [ABSTRACT FROM AUTHOR]

Deep beams have spread recently and have been widely used in various concrete structures because they bear greater loads than normal beams. This spread was accompanied by several problems, including the obstruction of electrical, mechanical and other services, especially in high-rise buildings. To solve the problems above, they resorted to making openings of various shapes inside these beams to conduct what is related to these services, taking into account the treatment of the region surrounding the openings to avoid early failure. Therefore, this research explains one of the techniques used: the near-surface mounted repair technique using steel bars through which deep reinforced concrete beams containing openings were repaired. The experimental work included testing nine beams with cross-section (200*450) mm² and length (1600 mm). Eight of these nine beams were repaired after pre-loading with a damage ratio (40% & 65%) of their ultimate capacity, while one beam was left unrepaired as a control beam. All nine of these beams contain rectangular openings (140*144) mm² in the middle of the shear spans. The considered parameters are the repairing bars orientation (vertical and inclined), reinforcement quantity, changing the steel bars number or diameter bars and initial damage ratio (40% & 65%). The specimens were tested under two loading points. The test results showed that the highest ratio of restored shear capacity was (139.39%) (which means the capacity ratio with respect to the specimen un-strengthened one having the same openings size) when using the inclined repairing scheme, while the lowest restored percentage was (75.75%) when using the vertical repairing scheme. It was found that reducing the number of bars by half when adopting the vertical scheme led to a higher decrease in the ultimate load by (24.24%) and a higher increase in the ductility and stiffness of the beams that were (9.968% & 70.441%), respectively after pre-loading them with two damage ratios (40% & 65%) compared with control beam. In addition to the above, enlarging the bars diameter from (10 mm) to (12 mm) and adopting the inclined scheme led to improve the ultimate load and stiffness by (25.75% & 110.598%) respectively, compared with the control beam. An increase in stiffness by (53.617%) was obtained in the specimen, repaired by inclined steel bars of (12 mm) in diameter and damage ratio of (40%). A slight decrease in ductility occurred when the diameter of the repairing bar was increased from (10 mm) to (12 mm) when adopting the inclined repairing scheme for specimen having damage ratio of (65%). Finally, in general, specimens with inclined repairing schemes showed an increase in ductility and stiffness more than those with vertical repairing schemes. [ABSTRACT FROM AUTHOR]