The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Myriophyllum spicatum distribution in Al-Burgga marsh, Hor Al-Hammar was described in relation to some of the physical-chemical properties for its habitat (water depth, light penetration, water temperature, water salinity, pH, dissolved oxygen, Ca+2, Mg+2, reactive NO2=, reactive NO3-1, and reactive PO4-3) during 2011, seasonally. CANOCO ordination program (CCA) was used to analyse the data. Its vegetation cover percentage was with its peak at summer, its value was 90 %, while the lowest value was 20 % in winter. Statistically, Positive relationships for WT, sal., Ca+2, Mg+2, reactive NO2=, reactive NO3-1, and reactive PO4-3 with the vegetation cover percentage were observed. While, negative relationships for WD, pH, and DO with the ve

Bearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us

Visible-light photodetectors constructed Fe2O3 were manufactured effectively concluded chemical precipitation technique, films deposited on glass substrate and Si wafer below diverse dopant (0,2,4,6)% of Cl, enhancement in intensity with X-ray diffraction analysis was showed through favored orientation along the (110) plane, the optical measurement presented direct allowed with reduced band gap energies thru variation doping ratio , current–voltage characteristics Fe2O3 /p-Si heterojunction revealed respectable correcting performance in dark, amplified by way of intensity of incident light, moreover good photodetector properties with enhancement in responsivity occurred at wavelength between 400 nm and 470 nm.

Abstract

 The problem of missing data represents a major obstacle before researchers in the process of data analysis in different fields since , this problem is a recurrent one in all fields of study including social , medical , astronomical and clinical experiments .

The presence of such a problem within the data to be studied may influence negatively on the analysis and it may lead to misleading conclusions , together with the fact that these conclusions that result from a great bias caused by that problem in spite of the efficiency of wavelet methods but they are also affected by the missing of data , in addition to the impact of the problem of miss of accuracy estimation

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.