Influential, organized groups with natural antimicrobial and anti-biofilm broad-spectrum power exist within the food chain, like a hidden dormant mimic hygienic bio life nanobodies that can terminate multiple opportunistic disease entities owing multi-stress resistant forbidden recalcitrant power, such as Candida albicans. These wonderful dynamic forces created by ALLAH Almighty are the Mycophages or fungi-eating state of fungi foodborne phages, and this project was redirected to be a dare to leap from us towards the future. Multi-stress resistant C. albicans that are resistant to different antifungal agents with their genetic tolerance plasticity to thermal pasteurization decontamination module as well as to ultraviolet irradiation

In this study, the construction of axial load-bending moment interaction diagrams for a reinforced concrete column and a steel jacket column has been presented. For a reinforced concrete column strengthened with a steel jacket consist of vertical steel sections connected by horizontal steel plates (battens), the axial load-bending moment interaction diagrams have been constructed using two analytical methods: strain compatibility method and plastic stress distribution method. In the strain compatibility method, the effects of position of the neutral axis on the stress of concrete, reinforcing bars, and steel section have been taken into consideration. In the plastic stress distribution method, the position of the neutral axis has no effect

This paper aims to study the second-order geometric nonlinearity effects of P-Delta on the dynamic response of tall reinforced concrete buildings due to a wide range of earthquake ground motion forces, including minor earthquake up to moderate and strong earthquakes. The frequency domain dynamic analysis procedure was used for response assessment. Reinforced concrete building models with different heights up to 50 stories were analyzed. The finite element software ETABS (version 16.0.3) was used to analyze reinforced concrete building models.

The study reveals that the percentage increase in buildings' sway and drift due to P-Delta effects are nearly constant for specific building height irrespective of the seism

This research suggests a robust and systematic way for Arabic Sentiment Analysis using a vast dataset of 66,666 text reviews. One of the main advantages of this study is that the dataset was perfectly balanced (33,333 positive samples and 33,333 negative samples). In machine learning, this 50/50 split is important because it eliminates class bias and enables the predictive model to treat both sentiment classes equally. As shown in the values of the metrics — overall accuracy, weighted precision, weighted recall, and F1 score — there is great similarity among them, indicating a stable and reliable assessment of the model's real potential throughout the Arabic dataset. Based on data profile, the average word count per review is 42.3

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

Regression models are among the most important tools in scientific research and data analysis. Among these models, fuzzy regression models stand out as a modern form that addresses issues of uncertain data that do not conform to the assumptions of traditional models. In this study, we present fuzzy regression models with a focus on fuzzy linear quantitative models, in addition to fuzzy support vector machine (SVM) models. Generally, linear models are considered less effective compared to non-linear models, and to address this issue, hybrid models combining both types have been introduced. The concept of hybrid models has been generalized to fuzzy models in this paper, where we introduce a hybrid model that combines both linear and non-linea

This paper shows the characteristics of temperature and adsorbed (water vapor) mass rate distribution in the adsorber unit which is the key part to any adsorption refrigeration system. The temperature profiles of adsorption/desorption phases (Dynamic Sorption) are measured experimentally under the operating conditions of 90oC hot water temperature, 30oC cooling water temperature, 35oC adsorption temperature and cycle time of 40 min. Based on the temperature profiles, The mass transfer equations for the annulus adsorbent bed are solved to obtain the distribution of adsorption velocity and adsorbate concentration using non-equilibrium
model. The relation between the adsorption velocity with time is investigated during the process of ads

The present study was conducted to evaluate the effect of variation of influent raw water turbidity, bed composition, and filtration rate on the performance of mono (sand) and dual media (sand and anthracite) rapid gravity filters in response to the effluent filtered water turbidity and headloss development. In order to evaluate each filter pe1formance, sieve analysis was made to characterize both media and to determine the effective size and uniformity coefficient. Effluent filtered water turbidity and the headloss development was recorded with time during each experiment.