This paper is an attempt to demonstrate the syntactic behavior of -ly adverbs and -ly adjectives. It mainly deals with -ly as an inflectional suffix that forms adverbs and adjectives It is hypothesized that there are differences between adjective-forming –ly and adverb-forming –ly.The researcher first made general and specific observations about the morphological processes of -ly adverbs and -ly adjectives. Since the study focuses on a linguistic phenomenon, its data is a set of -ly adverbs and -ly adjectives used as examples to support the hypothesis. The importance of studying the syntactic behavior of -ly stems from the fact that thousands of English adjectives and adverbs are created by adding the suffix "-ly" to their roots

Recently, environmental noise has arisen from various sources, such as those from exhaust mufflers of combustion engines found in cars, trucks, or power generators, which produce significant noise during their operation. Controlling the radiated noise from these mufflers is a major factor in improving acoustic comfort and minimizing the impact on the surrounding communities. Numerous research has been presented for this reason by modification of the internal structure of the exhaust muffler. The main objective of this work is to reduce the noise level emitted from exhaust mufflers. This can be achieved by adjusting structure parameters to attenuate the surrounding environment's radiated noise. Analysis of pressure-wave propagation h

Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T

Based on the results of standard penetration tests (SPTs) conducted in Al-Basrah governorate, this research aims to present thematic maps and equations for estimating the bearing capacity of driven piles having several lengths. The work includes drilling 135 boreholes to a depth of 10 m below the existing ground level and three standard penetration tests (SPT) at depths of 1.5, 6, and 9.5 m were conducted in each borehole. MATLAB software and corrected SPT values were used to determine the bearing capacity of driven piles in Al-Basrah. Several-order interpolation polynomials are suggested to estimate the bearing capacity of driven piles, but the first-order polynomial is considered the most straightforward. Furthermore, the root means squar

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Shallow foundations are usually used for structures with light to moderate loads where the soil underneath can carry them. In some cases, soil strength and/or other properties are not adequate and require improvement using one of the ground improvement techniques. Stone column is one of the common improvement techniques in which a column of stone is installed vertically in clayey soils. Stone columns are usually used to increase soil strength and to accelerate soil consolidation by acting as vertical drains. Many researches have been done to estimate the behavior of the improved soil. However, none of them considered the effect of stone column geometry on the behavior of the circular footing. In this research, finite ele

The aim of the study was to find out the correlations and impact between the variable of ethical leadership behavior and university performance at Sumer University. Use the descriptive analytical method by adopting the questionnaire tool to collect data. The questionnaire was distributed electronically to 113 teachers at Sumer University and the response was from 105 teachers. The research results showed that there is a correlation and effect relationship between the search variables. In addition, the responding university does not have ethically defined standards in terms of performance of the work of the cadres working there. Finally, the research presented a set of recommendations aimed at tackling problems in the ethical lead

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

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