Green nanotechnology is a thrilling and rising place of technology and generation that braces
the ideas of inexperienced chemistry with ability advantages for sustainability, protection, and
the general protection from the race human. The inexperienced chemistry method introduces a
proper technique for the production, processing, and alertness of much less dangerous chemical
substances to lessen threats to human fitness and the environment. The technique calls for inintensity expertise of the uncooked materials, particularly in phrases in their creation into
nanomaterials and the resultant bioactivities that pose very few dangerous outcomes for people
and the environment. In the twenty-first century, nanotec

Chromene is considered a fused pyran ring with a benzene ring, which is found in many plants and is part of many important compounds such as anthocyanidins, anthocyanins, catechins, and flavanones. These compounds are included under the headings "flavonoids" and "iso-flavonoids." These compounds are well known as bioactive molecules with wide medicinal uses. According to these pharmacokinetic characteristics, many researchers are giving more attention to this type of compound and its derivatives. Many chromene derivatives have been synthesized to study their biological effects for the treatment of many diseases. Furthermore, the researcher displayed wide interest in finding new methods for synthesizing chromene derivatives. These methods

There are serious environmental problems in all countries of the world, due to the waste material such as crushed clay bricks (CCB) and in huge quantities resulting from the demolition of buildings. In order to reduce the effects of this problem as well as to preserve natural resources, it is possible to work on recycling (CCB) and to use it in the manufacture of environmentally friendly loaded building units by replacing percentages in coarse aggregate by volume. It can be used as a powder and replacing of percentages in cement by weight and study the effect on the physical and mechanical properties of the concrete and the masonry unit. Evaluation of its performance through workability, dry density, compressive strength, thermal conduct

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

    In this paper we introduce a brief review about Box-Jenkins models. The acronym ARIMA stands for “autoregressive integrated moving average”. It is a good method to forecast for stationary and non stationary time series. According to the data which obtained from Baghdad Water Authority, we are modelling two series, the first one about pure water consumption and the second about the number of participants. Then we determine an optimal model by depending on choosing minimum MSE as criterion.