في هذا البحث نحاول تسليط الضوء على إحدى طرائق تقدير المعلمات الهيكلية لنماذج المعادلات الآنية الخطية والتي تزودنا بتقديرات متسقة تختلف أحيانا عن تلك التي نحصل عليها من أساليب الطرائق التقليدية الأخرى وفق الصيغة العامة لمقدرات K-CLASS. وهذه الطريقة تعرف بطريقة الإمكان الأعظم محدودة المعلومات "LIML" أو طريقة نسبة التباين الصغرى"LVR

Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

In this article, we developed a new loss function, as the simplification of linear exponential loss function (LINEX) by weighting LINEX function. We derive a scale parameter, reliability and the hazard functions in accordance with upper record values of the Lomax distribution (LD). To study a small sample behavior performance of the proposed loss function using a Monte Carlo simulation, we make a comparison among maximum likelihood estimator, Bayesian estimator by means of LINEX loss function and Bayesian estimator using square error loss (SE) function. The consequences have shown that a modified method is the finest for valuing a scale parameter, reliability and hazard functions.

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

Abstract. In this scientific work, we investigate the problem of the practical necessity of achieving the adequacy of translation activities with active translation from Russian into Arabic in various fields of translation. Based on the material of the latest suffix vocabulary, a serious attempt is made to clarify and specify the rules for the development of translator's intuition when translating from Russian into Arabic and vice versa. Based on the material collected by the latest suffix vocabulary, we try to make an attempt to reveal the role of suffix word creation in highlighting the general rules for achieving translation equivalence. The paper examines the process of creating words in multi-family languages, the difference between th

Background/objectives: To study the motion equation under all perturbations effect for Low Earth Orbit (LEO) satellite. Predicting a satellite’s orbit is an important part of mission exploration. Methodology: Using 4th order Runge–Kutta’s method this equation was integrated numerically. In this study, the accurate perturbed value of orbital elements was calculated by using sub-steps number m during one revolution, also different step numbers nnn during 400 revolutions. The predication algorithm was applied and orbital elements changing were analyzed. The satellite in LEO influences by drag more than other perturbations regardless nnn through semi-major axis and eccentricity reducing. Findings and novelty/improvement: The results demo

In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Anot