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Variants of Hybrid Genetic Algorithms for Optimizing Likelihood ARMA Model Function and Many of Problems
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Optimization is essentially the art, science and mathematics of choosing the best among a given set of finite or infinite alternatives. Though currently optimization is an interdisciplinary subject cutting through the boundaries of mathematics, economics, engineering, natural sciences, and many other fields of human Endeavour it had its root in antiquity. In modern day language the problem mathematically is as follows - Among all closed curves of a given length find the one that closes maximum area. This is called the Isoperimetric problem. This problem is now mentioned in a regular fashion in any course in the Calculus of Variations. However, most problems of antiquity came from geometry and since there were no general methods to solve such problems, each one of them was solved by very different approaches.

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Publication Date
Wed Sep 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Application
Suggested methods for prediction using semiparametric regression function
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Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

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Scopus
Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Improve the Performance of PID Controller by Two Algorithms for Controlling the DC Servo Motor
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The paper uses the Direct Synthesis (DS) method for tuning the Proportional Integral Derivative (PID) controller for controlling the DC servo motor. Two algorithms are presented for enhancing the performance of the suggested PID controller. These algorithms are Back-Propagation Neural Network and Particle Swarm Optimization (PSO). The performance and characteristics of DC servo motor are explained. The simulation results that obtained by using Matlab program show that the steady state error is eliminated with shorter adjusted time when using these algorithms with PID controller. A comparative between the two algorithms are described in this paper to show their effectiveness, which is found that the PSO algorithm gives be

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Publication Date
Wed Jul 01 2026
Journal Name
Nature Communications
Structural basis for independent pore function of Vpb4 from Bacillus thuringiensis
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Abstract<p> The Bacterial_Exotoxin_B family constitutes translocating pore-forming proteins that function as the binding (B) component in the binary AB Toxin mechanism. While the two-component system of the family is consistent among well-characterised members, the single-component Vpb4 subclass from entomopathogenic <italic>Bacillus thuringiensis</italic> challenges this dogma. Here, through single-particle cryo-electron microscopy, we elucidate the inserted pore structure of a Vpb4 member, Vpb4Aa2, at 2.2 Å resolution. The structure reveals distinguishing features from other family members: missing molecular bottleneck</p> ... Show More
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Publication Date
Wed Jan 02 2019
Journal Name
Pramana
The description of quantum dielectric function for insulators over Bethe surface
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Publication Date
Sat Jun 30 2001
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Optimizing Viscous Flow in Pipes Through Improved Flow Conditions and Chemical Injections
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Publication Date
Mon Jan 01 2024
Journal Name
Fusion: Practice And Applications
Optimizing Task Scheduling and Resource Allocation in Computing Environments using Metaheuristic Methods
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Optimizing system performance in dynamic and heterogeneous environments and the efficient management of computational tasks are crucial. This paper therefore looks at task scheduling and resource allocation algorithms in some depth. The work evaluates five algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FA) and Simulated Annealing (SA) across various workloads achieved by varying the task-to-node ratio. The paper identifies Finish Time and Deadline as two key performance metrics for gauging the efficacy of an algorithm, and a comprehensive investigation of the behaviors of these algorithms across different workloads was carried out. Results from the experiment

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Publication Date
Thu Oct 01 2015
Journal Name
Engineering And Technology Journal
Genetic Based Optimization Models for Enhancing Multi- Document Text Summarization
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Publication Date
Fri Apr 01 2011
Journal Name
Al-mustansiriyah Journal Of Science
A Genetic Algorithm Based Approach For Generating Unit Maintenance Scheduling
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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
A Parallel Adaptive Genetic Algorithm for Job Shop Scheduling Problem
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Publication Date
Tue Feb 17 2026
Journal Name
Pharmacia
Therapeutic drug monitoring for optimizing ustekinumab therapy in Iraqi patients with psoriasis: A real-world study
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Psoriasis is a complex autoimmune disorder characterized by skin inflammation with keratinocyte proliferation, and 85% of cases present as plaque-type psoriasis. Biologics such as ustekinumab (UST) are highly successful therapies for psoriasis. To achieve maximum clinical efficacy while minimizing undesirable effects, therapeutic drug monitoring (TDM) for biologics has emerged. This study aims to measure the UST trough level (TL) and anti-drug antibodies (ADAs) and to evaluate clinical response in Iraqi patients with moderate-to-severe psoriasis in order to make appropriate recommendations for modifying therapy. This cross-sectional study enrolled 75 patients, divided into two groups: Group 1, patients who achieved the UST target TL (≥0.6

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