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Go to Editorial ManagerThis research deals with the extent to which corrosion affects the behavior of buckling for 6061-T4 aluminum alloy under increasing compressive dynamic loads. Two types of columns, long, and intermediate were used.1% of the length column is the allowable lateral deflection. This is called the critical buckling of the columns. For the purpose of calculating the critical deflection, a digital dial gauge was used and set at a distance of 0.7 of column length from the fixed end condition for the column. The experimental analysis revealed that the corrosion time negatively affects the mechanical properties of materials such as the corroded specimens of 60 days (The least time to observe the corrosion of aluminum in the soil) which have approximately 2.7 % reduction in ultimate strength compared with the non-corroded specimen. Increasing the corrosion time reduces the critical load such as the maximum reduction will be 4.24% in critical buckling load for 60 days’ corrosion time. The results obtained were experimentally compared with the theoretical formulas of the Perry-Robertson and Euler-Johnson formula with the results of the ANSYS. It was found that the Perry-Robertson formula has a good agreement with the experimental results with a safety factor of 1.2, while the Euler-Johnson formula agreed with the experimental results taking a safety factor of 1.5. The ANSYS results showed a good agreement between the measured and calculated values by taking 1.1 factor of safety.
The effects of the repeated solution heat treatment on hardness, tensile strength and microstructure of aluminum were investigated. For this purpose, an alloy of AA6061-T6 was undergo to cyclic solution heat treatment process which is composed of repeated period (10 min) held at 520 °C for 1, 4, 8 and 12 cycles. The hardness was tested for five aging times (as quenching, one week, three weeks, one month and five months) to all cycles (1, 4, 8 and 12) firstly and it is found that the hardness of five months as aging time for all cycles has the best results (90Hv) as compared with others (as quenching, one week, three weeks, and one month), so it was adopted for all cycles to implement the tensile test and the microstructure. Hardness results were improved to Vickers hardness of (90Hv) with increasing of cycles up to 8 cycles then decreasing after that to (45Hv). Tensile results were showed an increment (34%) also for the same group of 8 cycles compared with (17%) and (9%) for 4 and 12 cycles, respectively. Microstructure is revealed that whenever cycles are increased, the precipitate phase in alloy is increased also, thus, it is improved the hardness and tensile strength.
The aim of this laboratory study is to estimate the best initial pH of purging solution for cadmium clean-up from an artificially contaminated soil using electro-kinetic cell. An efficiency enhancement scheme was employed involving pH control and injection wells as a part of the investigative program. Seven tests were performed at different pH controlled in the anode, cathode and injection wells start from 2 to 8. Sandy loam soil was contaminated with cadmium concentration equal to 2000 mg/kg and an initial moisture content equal to 30%. The duration of remediation was seven days with a potential gradient of 1.2 V/cm. The experimental results showed that the best removal efficiency was 62.8% at pH=3._x000D_ Keywords: , , , ,
This paper proposes robust control for three models of the linear inverted pendulum (one mass linear inverted pendulum model, two masses linear inverted pendulum model and three masses linear inverted pendulum model) which represents the upper, middle and lower body of a bipedal walking robot. The bipedal walking robot is built of light-weight and hard Aluminum sheets with 2 mm thickness. The minimum phase system and non-minimum phase system are studied and investigated for inverted pendulum models. The bipedal walking robot is programmed by Arduino microcontroller UNO. A MATLAB Simulink system is built to embrace the theoretical work. The results showed that one linear inverted pendulum is the worst performance, worst noise rejection and the worst set point tracking to the zero moment point. But two masses linear inverted pendulum models and three masses linear inverted pendulum model have a better performance, a better high-frequency noise rejection characteristic and better set-point tracking to the zero moment point.
Open-graded-fraction-course (OGFC), is a hot asphalt mixture usually utilized as a private purpose wearing course, because of open graded asphalt mixture and aggregates skeleton (stone-on-stone) contact, it contain a relatively high air voids’ percentage, after compaction which are permeable to water. In this research one type of gradation was used (12.5 mm) NMAS, to preparing the OGFC asphalt mixtures, penetration grade 40/50, crushed aggregate, asphalt content prepared with 4 % and up to 6 % by weight of mixture with 0.5 % increments. Optimum asphalt content (OAC) was selected based on these criteria, air voids content, asphalt draindown, permeability, and abrasion resistance (aged and un-aged) condition. The mix performance had been investigated by indirect tensile strength and moisture susceptibility (sensitivity) measured according to the (AASHTO T283-14). Results illustrate that the increasing of asphalt binder content leads to a decrease of the air voids content, abrasion loss and permeability values, while draindown increase, conversely, the indirect tensile strength (ITS) had been significantly increased for both conditions and this is a gaod suggestion to resistance alongside moisture susceptibility. It can be decided that the increasing of asphalt binder percent in OGFC asphalt mixture, leads to an increase in the thickness of binder coating around the aggregates. On the other hand, the influence of modifier that prepared with 4% styrene-butadiene-styrene (SBS) on OGFC asphalt mixture tends to improve the mix properties and exhibit higher (TSR) as compared with original asphalt by (31, 27.7 and 24.4) % at asphalt percent (4.8, 5.3 and 5.8) %, respectively. The SBS improved the adhesion between aggregate and asphalt which leads to reduce stripping of HMA, horizontal deformation, and increased the tensile stiffness modulus value.
Many mobile applications use infrared (IR) and Ultrasonic sensors for distance measurements. In this paper, these two types of sensors have been used in building obstacle detection system and the attributes of each sensor has been tested, the system consists of transmitter and receiver circuit, furthermore, Arduino UNO card has been used for transmitting and receiving signal for each type of sensor based on the Arduino software. The test was performed through distributing these sensors on the road then analyze the reflected signal. Neural network trained and used for monitoring the street and producing the number of cars in each line of street and the total number of cars in the same street.
The adsorption characteristics of Nickel (II) onto Iraqi Bentonite clay from aqueous solution have been investigated with respect to changes in pH of solution, adsorbent dosage, contact time and temperature of the solution. The maximum removal efficiency of Nickel (II) ions is 96% at pH=6.5 and exposure to 100 g/L adsorbent. For the adsorption of Nickel (II) ions, the Freundlich isotherm model fitted the equilibrium data better than the Langmuir isotherm model. Experimental data are also evaluated in terms of kinetic characteristics of adsorption and it was found that the adsorption process for Ni+2 ions follows well pseudo-second-order kinetics. Thermodynamic functions, the change of free energy (?G°), enthalpy (?H°) and entropy (?S°) of adsorption are also calculated for Nickel (II) ions. The results show that the adsorption of the Nickel (II) ions on Iraqi Bentonite is feasible and exothermic at (20-50) °C.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impacts cognitive functions such as memory, attention, and reasoning, ultimately affecting daily life. Early and accurate detection is crucial for timely intervention and management. Traditional diagnostic methods, including neuroimaging and cognitive assessments, can be expensive and time-consuming, necessitating more accessible and efficient alternatives. This study aims to develop an automated and efficient deep learning-based detection system that uses Electroencephalogram (EEG) signals to accurately classify AD and healthy individuals. A Convolutional Neural Network (CNN) model was designed to extract meaningful features from preprocessed EEG data. The architecture consists of convolutional layers with max pooling, dropout regularization, and fully connected layers to improve classification accuracy. The model was trained and evaluated on a comprehensive EEG dataset, using key performance metrics such as accuracy, recall, precision, and F1-score. The proposed CNN model achieved a high classification accuracy of 94.56%, a low loss of 0.2162, and an AUC value of 0.93828, demonstrating superior classification capability. The results indicate that the model effectively distinguishes between AD and healthy individuals, outperforming several state-of-the-art approaches. The findings highlight the potential of deep learning-based EEG analysis for AD detection, providing an accessible and cost-effective tool for early diagnosis. The high accuracy of the proposed CNN model suggests that it can assist medical professionals in making well-informed decisions, ultimately improving patient outcomes.
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.
The research aims to make a comparison between two highly used aluminum alloy though studding the effects left by the microwave furnace wavelengths by (middle dry and amid aqueous solutions) on the mechanical properties and estimated fatigue life of highly resistant widely use aluminum alloy AA 7075-T6 and AA 2024-T3. Since the microwave effect differ from other heating methods through its effects (Heat Transfer) r heating methods effects on the surface of the alloy, which might change some of its properties as well as resistance to fatigue, also to see how this effect changes from alloy to another through this study. The results show some great effects on both mechanical properties and estimated fatigue life for both alloys but with different levels. This new technique is differing from other traditional heat treating ones that is simple, cheap and fast accurate method than the other techniques.There is a common misconception about the use of minerals in microwave ovens and the concept is unscientific and based on false grounds and simplest proof of that is that most of these ovens are built from the inside metal fully, how dangerous this is consistent? This research aims to focus on and remove those problematic and misconceptions.
One of the most common causes of mortality worldwide is Lung cancer, an early diagnosis crucial for a patient’s survival and recovery. Automated segmentation of lung lesions in chest CT has become a pre-eminent focal point for research, particularly with the development of hybrid methods combining traditional image processing with advanced deep learning methods such as CNN. These hybrid approaches aim to minimize individual methods limitations by controlling their merge strengths to enhance segmentation efficiency, precision, and clinical utility. This review comprehensively analyzes different hybrid techniques, such as deep learning improved by rule-based systems, multi-scale feature extraction, and ensemble learning. As well as inspect their clinical effect, particularly in improving diagnostic accuracy and optimizing treatment procedures. Despite their possibility, these approaches still face significant challenges, such as computational complexity, data requirements, and the requirement for explainable AI (XAI). Upcoming advancements in lung lesion segmentation will focus on refining these models to achieve faster processing, improved accuracy, and integration with diagnostic tools to protect transparency and ethical considerations.