Palladium nanoparticles are produced by Polyol method. The characterization of the Pd nanoparticle has been conducted by various techniques such as SEM and AFM. The results of Pd powder showed that the particle size is directly proportional to the temperature and the reaction time. The optimum conditions for obtaining minimum nanoparticles size are 45 oC reaction temperature and 60 min reaction time and the smaller particle size achieved is equal to 25 nm. The optical limiting of smaller size nanoparticles has been studied. The palladium nanoparticles appear to be attractive candidates for optical limiting applications.

Cilnidipine is a dihydropyridine class of calcium channel blockers, it is classified as a BCS class II drug, characterized by a low oral bioavailability of 13%. Consequently, the utilization of nanoparticle preparation is anticipated to enhance its bioavailability. The objective of the research is to integrate cilnidipine nanoparticles into oral films as a means of enhancing patient adherence. The optimal polymers for producing Cilnidipine films were PVA cold and or HPMC E5 at different concentrations using a casting technique with glycerol as a plasticizer. The Nano suspension-based preparation of Cilnidipine's oral film containing the combination of polymers exhibited a significant enhancement in vitro dissolution, with a percentage excee

The research aims to identify how to enhance the quality of the human resources, focusing on four dimensions (efficiency, effectiveness, flexibility, and reliability), by adopting an adventure learning method that combines theoretical and applied aspects at the same time, when developing human resources and is applied using information technology, and that Through its dimensions, which are (cooperation, interaction, communication, and understanding), as the research problem indicated a clear deficiency in the cognitive perception of the mechanism of employing adventure learning dimensions in enhancing human resources quality, so the importance of research was to present treatments and proposals to reduce this problem. To achieve

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

Abstract
The current research aims to identify mental health and its role in promoting self-confidence and positive behavior of female university students. The researcher adopted the descriptive analytical approach in this research. The researcher depended on the availability of sources and references, literature, and previous field studies to analyze and study all aspects related to mental health and its role in promoting self-confidence and positive behavior of university students and then expand its importance and identify the areas of mental health, self-confidence, positive behavior, and university. The second chapter included the concept of mental health, the importance of the study, the most important factors of health and psyc

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par