This paper aimed to investigate the effect of the height-to-length ratio of unreinforced masonry (URM) walls when loaded by a vertical load. The finite element (FE) method was implemented for modeling and analysis of URM wall. In this paper, ABAQUS, FE software with implicit solver was used to model and analysis URM walls subjected to a vertical load. In order to ensure the validity of Detailed Micro Model (DMM) in predicting the behavior of URM walls under vertical load, the results of the proposed model are compared with experimental results. Load-displacement relationship of the proposed numerical model is found of a good agreement with that of the published experimental results. Evidence shows that load-displacement curve obtained fro

The objective of this article is to delve into the intricate dynamics of marriage relationships, exploring the impact of emotions such as fear, love, financial considerations and likability. In our investigation, we adopt a perspective that acknowledges the nonlinear nature of interactions among individuals. Diverging from certain prior studies, we propose that the fear element within the context of marriage is not a singular, isolated factor but rather a manifestation resulting from the amalgamation of numerous social issues. This, in turn, contributes to the emergence of strained and unsuccessful relationships. Unlike conventional approaches, we extensively examine the conditions essential for the existence of all socially signifi

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

As a result of the significance of image compression in reducing the volume of data, the requirement for this compression permanently necessary; therefore, will be transferred more quickly using the communication channels and kept in less space in memory. In this study, an efficient compression system is suggested; it depends on using transform coding (Discrete Cosine Transform or bi-orthogonal (tap-9/7) wavelet transform) and LZW compression technique. The suggested scheme was applied to color and gray models then the transform coding is applied to decompose each color and gray sub-band individually. The quantization process is performed followed by LZW coding to compress the images. The suggested system was applied on a set of seven stand

Non-thermal atmospheric pressure plasma has emerged as a
new promising tool in medicine and biology. In this work, A DBD
system was built as a source of atmospheric pressure non-thermal
Plasma suitable for clinical and biological applications. E. coli and
staphylococcus spp bacteria were exposed to the DBD plasma for a
period of time as inactivation (sterilization) process. A series of
experiments were achieved under different operating conditions. The
results showed that the inactivation, of the two kinds of bacteria, was
affected (increasing or decreasing) according to operation conditions
because they affects, as expected, the produced plasma properties
according to those conditions.

The aim of this study is to look at  the potential of a  local sustainable energy network in  a pre-existing context to develop a novel design beneficial to the environment. Nowadays, the concept of smart cities is still in the developmental phase/stage   andwe are currently residing in a transitional period, therefore it is very important to discover new solutions that show direct benefits the people may get from  transforming their city from a traditional to a smart city. Using experience and knowledge of successful projects in various European and non-European smart cities, this study attempts to demonstrate the practical potential of gradually moving existing cities to t

This work involves hard photon rate production from quark -gluon plasma QGP interaction in heavy ion collision. Using a quantum chromodynamic model to investigate and calculation of photons rate in 𝑐𝑔 → 𝑠𝑔𝛾 system due to strength coupling, photons rate, temperature of system, flavor number and critical. The photons rate production computed using the perturbative strength models for QGP interactions. The strength coupling was function of temperature of system, flavor number and critical temperature. Its influenced by force with temperature of system, its increased with decreased the temperature and vice versa. The strength coupling has used to examine the confinement and deconfinement of quarks in QGP properties and inf