scholarly journals Planning and management of aircraft maintenance using a genetic algorithm

2021 ◽  
Vol 23 (1) ◽  
pp. 143-153
Author(s):  
Mirosław Kowalski ◽  
Mariusz Izdebski ◽  
Jolanta Żak ◽  
Paweł Gołda ◽  
Jerzy Manerowski
Keyword(s):  
Genetic Algorithm ◽  
Real Data ◽  
Maintenance Management ◽  
Aircraft Maintenance ◽  
Maintenance Planning ◽  
Technical Documentation ◽  
New Approach ◽  
Planning And Management ◽  
The Individual ◽  
Algorithm Construction

The aim of the article was to develop a tool to support the process of planning and managing aircraft (ac) maintenance. Aircraft maintenance management has been presented for scheduled technical inspections resulting from manufacturers’ technical documentation for ac. The authors defined the problem under investigation in the form of a four-phase decisionmaking process taking into account assignment of aircraft to airports and maintenance stations, assignment of crew to maintenance points, setting the schedules, i.e. working days on which aircraft are directed to maintenance facilities. This approach to the planning and management of aircraft maintenance is a new approach, unprecedented in the literature. The authors have developed a mathematical model for aircraft maintenance planning and management in a multi-criteria approach and an optimisation tool based on the operation of a genetic algorithm. To solve the problem, a genetic algorithm was proposed. The individual steps of the algorithm construction were discussed and its effectiveness was verified using real data.

Fast converging elitist genetic algorithm for knot adjustment in B-spline curve approximation

2019 ◽  
Vol 13 (4) ◽  
pp. 317-328
Author(s):  
Johannes Bureick ◽  
Hamza Alkhatib ◽  
Ingo Neumann
Keyword(s):  
Genetic Algorithm ◽  
Real Data ◽  
Adjustment Problem ◽  
Data Set ◽  
New Approach ◽  
B Spline ◽  
Spline Curve ◽  
Curve Approximation ◽  
Data Gaps

Abstract B-spline curve approximation is a crucial task in many applications and disciplines. The most challenging part of B-spline curve approximation is the determination of a suitable knot vector. The finding of a solution for this multimodal and multivariate continuous nonlinear optimization problem, known as knot adjustment problem, gets even more complicated when data gaps occur. We present a new approach in this paper called an elitist genetic algorithm, which solves the knot adjustment problem in a faster and more precise manner than existing approaches. We demonstrate the performance of our elitist genetic algorithm by applying it to two challenging test functions and a real data set. We demonstrate that our algorithm is more efficient and robust against data gaps than existing approaches.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

In silico modeling of cellular probabilistic nanoparticle radiosensitization in head and neck cancers

Nanomedicine ◽  
2020 ◽  
Vol 15 (29) ◽  
pp. 2837-2850
Author(s):  
Myxuan Huynh ◽  
Ivan Kempson ◽  
Eva Bezak ◽  
Wendy Phillips
Keyword(s):  
Head And Neck ◽  
Patient Outcomes ◽  
Head And Neck Cancers ◽  
New Approach ◽  
Treatment Regimens ◽  
Therapeutic Benefits ◽  
In Silico Modeling ◽  
The Individual ◽  
Dose Deposition ◽  
Therapy Treatment

Background: The use of gold nanoparticles (AuNPs) as radiosensitizers may offer a new approach in the treatment of head and neck cancers; minimizing treatment-associated toxicities and improving patient outcomes. AuNPs promote localized dose deposition; permitting improved local control and/or dose reduction. Aim: This work aimed to address the theoretical optimization of radiation doses, fractionation and nanoparticle injection schedules to maximize therapeutic benefits. Materials & methods: Probabilistic nanoparticle sensitization factors were incorporated into the individual cell-based HYP-RT computer model of tumor growth and radiotherapy. Results: Total dose outcomes across all radiation therapy treatment regimens were found to be significantly reduced with the presence of AuNPs, with bi-weekly injections showing the most decrease. Conclusion: Outcomes suggest the need for regular AuNP administration to permit effective radiosensitization.

scholarly journals New Method for Generating New Families of Distributions

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 726
Author(s):  
Lamya A. Baharith ◽  
Wedad H. Aljuhani
Keyword(s):  
Exponential Distribution ◽  
Real Data ◽  
Rate Function ◽  
New Method ◽  
Likelihood Method ◽  
Alpha Power ◽  
Unknown Parameters ◽  
Failure Rate Function ◽  
New Approach ◽  
Numerical Studies

This article presents a new method for generating distributions. This method combines two techniques—the transformed—transformer and alpha power transformation approaches—allowing for tremendous flexibility in the resulting distributions. The new approach is applied to introduce the alpha power Weibull—exponential distribution. The density of this distribution can take asymmetric and near-symmetric shapes. Various asymmetric shapes, such as decreasing, increasing, L-shaped, near-symmetrical, and right-skewed shapes, are observed for the related failure rate function, making it more tractable for many modeling applications. Some significant mathematical features of the suggested distribution are determined. Estimates of the unknown parameters of the proposed distribution are obtained using the maximum likelihood method. Furthermore, some numerical studies were carried out, in order to evaluate the estimation performance. Three practical datasets are considered to analyze the usefulness and flexibility of the introduced distribution. The proposed alpha power Weibull–exponential distribution can outperform other well-known distributions, showing its great adaptability in the context of real data analysis.

scholarly journals Sustainable Geographical Changes in Rural Areas: Key Paths, Orientations and Limits

2021 ◽  
Vol 13 (4) ◽  
pp. 2059
Author(s):  
Angel Paniagua
Keyword(s):  
Rural Areas ◽  
Anglo Saxon ◽  
Rural Studies ◽  
New Approach ◽  
Rural Restructuring ◽  
Theoretical Approaches ◽  
Methodological Approaches ◽  
Social Environmental ◽  
The Individual ◽  
New Rurality

Rural differentiation processes have formed the backbone of rural studies. Owing to the strength of rural–urban and local–global relationships, the theoretical approaches to rural restructuring in the Anglo-Saxon world and new rurality in Latin America only have a limited capacity to explain contemporary global phenomena of rural spaces. Due to this, transverse theoretical and methodological approaches have emerged to explain social, environmental and spatial (rural) processes. Here, a new approach is proposed called the individual–global field, based on the individual–global binary category to substitute the traditional relevance of the locality–community–globality association This new approach tries to reinvigorate rural geography in a more flexible way, based on minor theory, to adapt to all the phenomena that can occur globally. In any case, various spatial planes are proposed, dominated by specific socioeconomic processes on which the rural individual would move.

scholarly journals Individualized Hemodynamic Management in Sepsis

2021 ◽  
Vol 11 (2) ◽  
pp. 157
Author(s):  
Marcell Virág ◽  
Tamas Leiner ◽  
Mate Rottler ◽  
Klementina Ocskay ◽  
Zsolt Molnar
Keyword(s):  
Fluid Overload ◽  
Fluid Management ◽  
Multiple Organ ◽  
Treatment Protocols ◽  
Hemodynamic Management ◽  
New Approach ◽  
Hemodynamic Optimization ◽  
The Individual ◽  
Sepsis And Septic Shock ◽  
Insight Into

Hemodynamic optimization remains the cornerstone of resuscitation in the treatment of sepsis and septic shock. Delay or inadequate management will inevitably lead to hypoperfusion, tissue hypoxia or edema, and fluid overload, leading eventually to multiple organ failure, seriously affecting outcomes. According to a large international survey (FENICE study), physicians frequently use inadequate indices to guide fluid management in intensive care units. Goal-directed and “restrictive” infusion strategies have been recommended by guidelines over “liberal” approaches for several years. Unfortunately, these “fixed regimen” treatment protocols neglect the patient’s individual needs, and what is shown to be beneficial for a given population may not be so for the individual patient. However, applying multimodal, contextualized, and personalized management could potentially overcome this problem. The aim of this review was to give an insight into the pathophysiological rationale and clinical application of this relatively new approach in the hemodynamic management of septic patients.

scholarly journals A new catalogue of multiple galaxies in the Local Supercluster

2000 ◽  
Vol 174 ◽  
pp. 40-45
Author(s):  
D. I. Makarov ◽  
I. D. Karachentsev
Keyword(s):  
New Approach ◽  
Galaxy Groups ◽  
Closed Orbits ◽  
Crossing Time ◽  
Local Supercluster ◽  
Virial Mass ◽  
Nearby Galaxies ◽  
The Individual ◽  
Group Member ◽  
Harmonic Radius

AbstractA new approach is suggested which makes use of the individual properties of galaxies, for the identification of small galaxy groups in the Local Supercluster. The criterion is based on the assumption of closed orbits of the companions around the dominating group member within a zero velocity sphere.The criterion is applied to a sample of 6321 nearby galaxies with radial velocities V0 ≤ 3000 km s−1. These 3472 galaxies have been assigned to 839 groups that include 55% of the sample considered. For the groups identified by the new algorithm (with k ≥ 5 members) the median velocity dispersion is 86 km s−1, the median harmonic radius is 247 kpc, the median crossing time is 0.08(1/H), and the median virial-mass-to-light ratio is 56 M⊙/L⊙.

scholarly journals Sparse Functional Linear Discriminant Analysis

Biometrika ◽  
2021 ◽  
Author(s):  
Juhyun Park ◽  
Jeongyoun Ahn ◽  
Yongho Jeon
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Real Data ◽  
New Approach ◽  
Linear Discriminant ◽  
Finite Samples ◽  
Regularization Problem ◽  
Bayes Error ◽  
Functional Setting ◽  
Regularized Solution

Abstract Functional linear discriminant analysis offers a simple yet efficient method for classification, with the possibility of achieving a perfect classification. Several methods are proposed in the literature that mostly address the dimensionality of the problem. On the other hand, there is a growing interest in interpretability of the analysis, which favors a simple and sparse solution. In this work, we propose a new approach that incorporates a type of sparsity that identifies nonzero sub-domains in the functional setting, offering a solution that is easier to interpret without compromising performance. With the need to embed additional constraints in the solution, we reformulate the functional linear discriminant analysis as a regularization problem with an appropriate penalty. Inspired by the success of ℓ1-type regularization at inducing zero coefficients for scalar variables, we develop a new regularization method for functional linear discriminant analysis that incorporates an L1-type penalty, ∫ |f|, to induce zero regions. We demonstrate that our formulation has a well-defined solution that contains zero regions, achieving a functional sparsity in the sense of domain selection. In addition, the misclassification probability of the regularized solution is shown to converge to the Bayes error if the data are Gaussian. Our method does not presume that the underlying function has zero regions in the domain, but produces a sparse estimator that consistently estimates the true function whether or not the latter is sparse. Numerical comparisons with existing methods demonstrate this property in finite samples with both simulated and real data examples.

scholarly journals Supervised dimensionality reduction for big data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua T. Vogelstein ◽  
Eric W. Bridgeford ◽  
Minh Tang ◽  
Da Zheng ◽  
Christopher Douville ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Data Science ◽  
Real Data ◽  
Low Rank ◽  
Conditional Moment ◽  
Desktop Computer ◽  
Reduction Techniques ◽  
Reduction Methods ◽  
The Individual ◽  
Low Dimensional

AbstractTo solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

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