Lacing reinforcement plays a critical role in the design and performance of reinforced concrete (RC) slabs by distributing the applied loads more evenly across the slab, ensuring that no specific area of the slab is overloaded. In this study, nine slabs, divided into three groups according to the investigated parameters, were meticulously designed and evaluated to study the interplay between the lacing reinforcement and other key parameters. Each slab was crafted for simple support and was subjected to both static and repeated two-point load tests. The lacing reinforcement had an angle of 45° with various tension and lacing steel. The repeated-tested specimens with lacing reinforcement experienced smaller ductility than those of s

Lacing reinforcement plays a critical role in the design and performance of reinforced concrete (RC) slabs by distributing the applied loads more evenly across the slab, ensuring that no specific area of the slab is overloaded. In this study, nine slabs, divided into three groups according to the investigated parameters, were meticulously designed and evaluated to study the interplay between the lacing reinforcement and other key parameters. Each slab was crafted for simple support and was subjected to both static and repeated two-point load tests. The lacing reinforcement had an angle of 45° with various tension and lacing steel. The repeated-tested specimens with lacing reinforcement experienced smaller ductility than those of s

A new, accurate, precise and economic two spectrophotometric methods for determination of Paracetamol (Par), Ibuprofen (Ibu), and Caffeine (Caf) were suggested. Those methods were the first and second ratio derivative spectrum using a double devisor. Par, Ibu, and Caf showed many useful peaks for their quantified determination. The validity of all analysis modes for determination of the three compounds, peak to baseline, peak area and peak to peak were according to ICH. The linearity of two methods was between 5 µg/ml as a lower concentration and 50 µg/ml as the highest concentration for three compounds. Recovery percentage was around 100% and relative standard deviation was less than 2.6%. The methods were applied successfully in the

The object of research is studying Raman scattering technique, photoluminescence and some optical properties of silver nanoparticles created by eco-friendly technique which independent on a long time, effort, energy and high temperatures, and with the highest adsorption capacity in order to achieve a high inhibition to paralyze the activity of the bacterial wall, by achieving the highest surface plasmon resonance (SRR). Silver nanoparticles were prepared using Matricaria Flower extract. Characterization of silver nanoparticles and detection of their effectiveness against microbial using two types of bacteria (Escherichia Coli and Staphylococcus aureus ), these nanoparticles were measured using a number of measurements, X-ray diffrac

In this study, method for experimentally determining the electron density (ne) and the electron temperature (Te) in the atmospheric Argon plasma jet is used; it is based on optical emission spectroscopy (OES). Boltzmann plot method used to calculate these parameters measured for different values of gas flow rate. The results show that the electron temperature decreasing with the increase of gas flow rate also indicates an increasing in the electron density of plasma jet with increasing of gas flow rate.

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

The two most popular models inwell-known count regression models are Poisson and negative binomial regression models. Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. Negative binomial regression is similar to regular multiple regression except that the dependent (Y) variables an observed count that follows the negative binomial distribution. This research studies some factors affecting divorce using Poisson and negative binomial regression models. The factors are unemplo