Objective : Alzheimer’s disease (AD) continues to be a major challenge because handling high-dimensional data is time-consuming and expensive due to its complexity. A large feature space often increases computational costs and reduces model interpretability. This study addresses this problem by evaluating and comparing multiple feature selection techniques to identify the most informative biomarkers for AD diagnosis.

Methods : Our study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to implement and test three feature selection a

The advancements in horizontal drilling combined with hydraulic fracturing have been historically proven as the most viable technologies in the exploitation of unconventional resources (e.g., shale and tight gas reservoirs). However, the number of fractures, well timing, and arrangement pattern can have a significant impact on the project economy. Therefore, such design and operating parameters need to be efficiently optimized for obtaining the best production performance from unconventional gas reservoirs. In this study, the process of selecting the optimal number of fractures was conducted on a section of a tight gas reservoir model (based on data from the Whicher Range (WR) tight gas field in Western Australia). Then, the optimal number

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

This work was conducted to study the oxidation of phenol in aqueous solution using copper based catalyst with zinc as promoter and different carrier, i.e. γ-Alumina and silica. These catalysts were prepared by impregnation method.
The effect of catalyst composition, pH (5.6-9), phenol to catalyst concentration ratio (2-0.5), air feed rate (30-50) ml/s, stirring speed (400-800) rpm, and temperature (80-100) °C were examined in order to find the best conditions for phenol conversion.
The best operating conditions which lead to maximum phenol conversion (73.1%) are : 7.5 pH, 4/6 phenol to catalyst concentration, 40 ml/s air feed rate, 600 rpm stirring speed, and 100 °C reaction temperature. The reaction involved an induction period

Test method was developed radioimmunotherapy to appoint in two groups of patients infected with a uterine tumor Great conditions in tumor tissue benign and malignant Ddh teacher radioactive iodine isotope

Bacteria strain H7, which produces flocculating substances, was isolated from the soil of corn field at the College of Agriculture in Abu-Ghrib/Iraq, and identified as Bacillus subtilis by its biochemical /physiological characteristics. The biochemical analysis of the partially purified bioflocculant revealed that it was a proteoglycan composed of 93.2 % carbohydrate and 6.1 % protein. The effects of bioflocculant dosage, temperature, pH, and different salts on the flocculation activity were evaluated. The maximum flocculation activity was observed at an optimum bioflocculant dosage of 0.2 mL /10 mL (49.6%). The bioflocculant had strong thermal stability within the range of 30-80 °C, and the flocculating activity was over 50 %. The biofloc

Folic acid and multivitamin tablets containing Aspergillus flavus Penicillia spp. and Cladosporia spores were prepared at a compression pressure of 148 MN/m² and stored at 35°C under different relative  humidifies (75,85, and 95)% within air tight containers, to study the effect of storage condition on them, as well as ,the estimation of the microbial level of the raw materials intended to be used in the two kinds of tablets . Result showed that some raw materials derived from natural origin were heavily contaminated with microorganism compared to that of synthetic origin ,the results also indicated the effect of relative humidity , types of fungal spore , and the hygroscopic nature of exicpient upon survival. Multivit

The present work is devoted to investigate the performance of a homemade Y-shape catalytic microreactor for degradation of dibenzothiophene (DBT), as a model of sulphur compounds including in gas oil, utilizing solar incident energy. The microchannel was coated with TiO2 nanoparticles which were used as a photocatalyst. Performance of the microreactor was investigated using different conditions (e.g., DBT concentration, LHSV, operating temperature, and (H₂O₂/DBT) ratio). Our experiments show that, in the absence of UV light, no reaction takes place. The results revealed that outlet concentration of DBT decreases as the mean residence time in the microreactor increases. Also, it was noted that operating temperature s

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m