Conventional flexible pavements are released to different types of failure in the initial phases of their service life due to high traffic density, high speeds, heavy loads, and harsh climates. To eliminate pavement damage and failure early, the present search investigates the impact of adding glass, steel, and basalt fibers in the asphalt mixtures. Also, the study evaluates these materials characteristics compared to the mixtures without fibers. The Marshall test and tensile strength ratio test (TSR) were utilized to evaluate the asphalt mixture's performance. A set of specimens were produced by incorporating glass fiber (GF), steel fiber (SF), and basalt fiber (BF) at (0.10%, 0.15%, 0.20%), (0.25%, 0.35%, 0.45%), and (0.15%, 0.35%

There are many aims of this book: The first aim is to develop a model equation that describes the spread of contamination through soils which can be used to determine the rate of environmental contamination by estimate the concentration of heavy metals (HMs) in soil. The developed model equation can be considered as a good representation for a problem of environmental contamination. The second aim of this work is to design two feed forward neural networks (FFNN) as an alternative accurate technique to determine the rate of environmental contamination which can be used to solve the model equation. The first network is to simulate the soil parameters which can be used as input data in the second suggested network, while the second network sim

Density Functional Theory (DFT) with B3LYP hybrid exchange-correlation functional and 3-21G basis set and semi-empirical methods (PM3) were used to calculate the energies (total energy, binding energy (Eb), molecular orbital energy (EHOMO-ELUMO), heat of formation (?Hf)) and vibrational spectra for some Tellurium (IV) compounds containing cycloctadienyl group which can use as ligands with some transition metals or essential metals of periodic table at optimized geometrical structures.

Abstract

   The extremes effects in parameters readings which are BOD (Biological Oxygen Demands) and DO(Dissolved Oxygen) can caused error estimating of the model’s parameters which used to determine the ratio of de oxygenation and re oxygenation of the dissolved oxygen(DO),then that will caused launch big amounts of the sewage pollution  water to the rivers and it’s turn is effect in negative form on the ecosystem life and the different types of the water wealth.

   As result of what mention before this research came to employees Streeter-Phleps model parameters estimation which are (K_d,K_r) the de oxygenation and re oxygenation ratios on respect

This study used a continuous photo-Fenton-like method to remediate textile effluent containing azo dyes especially direct blue 15 dye (DB15). A Eucalyptus leaf extract was used to create iron/copper nanoparticles supported on bentonite for use as catalysts (E@B-Fe/Cu-NPs). Two fixed-bed configurations were studied and compared. The first one involved mixing granular bentonite with E@B-Fe/Cu-NPs (GB- E@B-Fe/Cu-NPs), and the other examined the mixing of E@B-Fe/Cu-NPs with glass beads (glass beads-E@B-Fe/Cu-NPs) and filled to the fixed-bed column. Scanning electron microscopy (SEM), zeta potential, and atomic forces spectroscopy (AFM) techniques were used to characterize the obtained particles (NPs). The effect of flow rate and DB15 concent

Light is an important factor that influences the growth and photosynthetic efficiency of microalgae; however, little is known about how light intensity together with the wavelength affect the photosynthetic capacity and growth of marine microalgae. In the present study, the growth of the marine green microalga Dunaliella parva was studied and optimized under different light intensities (25 ~ 70 μmol m^-2 s^-1) and qualities (blue, green, and red) in comparison with white light at 40 μmol m^-2 s^-1 as a control. The growth was monitored by counting the cell number, pigment content, Chl a, Chl b, and carotenoids concentrations. The optimal growth and highest photosyntheti

The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T