Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

This paper deals with the F-compact operator defined on probabilistic Hilbert space and gives some of its main properties.

Equilibrium adsorption isotherm for the removal of trifluralin from aqueous solutions using ? –alumina clay has been studied. The result shows that the isotherms were S3 according Giels classification. The effects of various experimental parameters such as contact time, adsorbent dosage, effect of pH and temperature of trifluralin on the adsorption capacities have been investigated. The adsorption isotherms were obtained by obeying freundlich adsorption isotherm with (R2 = 0.91249-0.8149). The thermodynamic parameters have been calculated by using the adsorption process at five different temperature, the values of ?H, ?G and ?S were (_1.0625) kj. mol-1, (7.628 - 7.831) kj.mol-1 and (_2.7966 - _2.9162) kg.

In this paper, the error distribution function is estimated for the single index model by the empirical distribution function and the kernel distribution function. Refined minimum average variance estimation (RMAVE) method is used for estimating single index model. We use simulation experiments to compare the two estimation methods for error distribution function with different sample sizes, the results show that the kernel distribution function is better than the empirical distribution function.

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor

      In this paper, we introduce the concepts of Large-lifting and Large-supplemented modules as a generalization of lifting and supplemented modules.  We also give some results and properties of this new kind of modules.

The research talks about the interest and its impact on polygamy as a legitimate social psychological issue, where she talked about the concept of polygamy, and evidence of the legitimacy of polygamy, and clarify the conditions, and then touched on the great interests for which the law allowed polygamy, and mentioned some practical models in polygamy, including What is in the interest of men in pluralism, including in the interest of women in pluralism, including in the interests of both sexes in pluralism .