Prochloperazine maleate (PCM) is one of the most prescribed phenothiazine. The purpose of the present research was to develop fast dissolving tablets of PCM with β-cyclodextrin inclusion complex. Tablets prepared  by wet granulation with sublimation and by using  different superdisintegrants type [ low-hydroxypropylcellulose LH21 (L-HPC LH21), carboxymethylcellulose calcium (ECG505), crospovidone (CP)], and different type of subliming agents (urea and ammonium bicarbonate (AB)). Tablets evaluated for its % friability, disintegration time, wetting time, hardness, content uniformity, weight variation, in vitro dissolution studies. For further enhancement of disintegration and dissolution, PCM orodispersible tablet were formula

Wind energy is considered one of the most important sources of renewable energy in the world, because it contributes to reducing the negative effects on the environment. The most important types of wind turbines are horizontal and vertical axis wind turbines. This work presents the full details of design for vertical axis wind turbine (VAWT) and how to find the optimal values of necessary factors. Additionally, the results shed light on the efficiency and performance of the VAWT under different working conditions. It was taken into consideration the variety of surrounding environmental conditions (such as density and viscosity of fluid, number of elements of the blade, etc.) to simulate the working of vertical wind turbines under di

The present work provides to treat real oily saline wastewater released from drilling oil sites by the use of electrocoagulation technique. Aluminum tubes were utilized as electrodes in a concentric manner to minimize the concentrations of 113400 mg TDS/L, 65623 mg TSS/L, and the ions of 477 mg HCO3/L, 102000 mg Cl/L and 5600 mg Ca/L presented in real oily wastewater under the effect of the operational parameters (the applied current and reaction time) by making use of the central composite rotatable design. The final concentrations of TDS, TSS, HCO3, Cl, and Ca that obtained were 93555 ppm (17.50%), 11011 ppm (83.22%), 189ppm (60.38%), 80000ppm (22%), and 4200 ppm (25%), respectively, under the optimum values of the operational parameters

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

There is an assumption implicit but fundamental theory behind the decline by the time series used in the estimate, namely that the time series has a sleep feature Stationary or the language of Engle Gernger chains are integrated level zero, which indicated by I (0). It is well known, for example, tables of t-statistic is designed primarily to deal with the results of the regression that uses static strings. This assumption has been previously treated as an axiom the mid-seventies, where researchers are conducting studies of applied without taking into account the properties of time series used prior to the assessment, was to accept the results of these tests Bmanueh and delivery capabilities based on the applicability of the theo

In this study, we focused on the random coefficient estimation of the general regression and Swamy models of panel data. By using this type of data, the data give a better chance of obtaining a better method and better indicators. Entropy's methods have been used to estimate random coefficients for the general regression and Swamy of the panel data which were presented in two ways: the first represents the maximum dual Entropy and the second is general maximum Entropy in which a comparison between them have been done by using simulation to choose the optimal methods.

The results have been compared by using mean squares error and mean absolute percentage error to different cases in term of correlation valu

Large language models (LLMs) are a rapidly evolving class of artificial intelligence with significant potential in clinical healthcare. Despite accelerating adoption, rigorous systematic evidence on clinical utility, patient safety, and implementation feasibility remains fragmented. To systematically review LLM applications across clinical domains, evaluate performance with appropriate contextual caveats, characterize implementation barriers, and identify ethical and regulatory considerations. Scientific databases were searched from January 2020 to January 2025. Studies evaluating transformer-based LLMs (≥10M parameters) in clinical settings were eligible. Data were independently double-extracted; quality was assessed using QUADAS-2, RE-A

Image compression is a suitable technique to reduce the storage space of an image, increase the area of storage in the device, and speed up the transmission process. In this paper, a new idea for image compression is proposed to improve the performance of the Absolute Moment Block Truncation Coding (AMBTC) method depending on Weber's law condition to distinguish uniform blocks (i.e., low and constant details blocks) from non-uniform blocks in original images. Then, all elements in the bitmap of each uniform block are represented by zero. After that, the lossless method, which is Run Length method, is used for compressing the bits more, which represent the bitmap of these uniform blocks. Via this simple idea, the result is improving