Stochastic algorithms
Dotaz
Zobrazit nápovědu
A stochastic differential equation describing the process of drug dissolution is presented. This approach generalizes the classical deterministic first-order model. Instead of assuming a constant fractional dissolution rate, it is considered here that the rate is corrupted by a white noise. The half-dissolution time is investigated for the model. The maximum likelihood and Bayes methods for the estimation of the parameters of the model are developed. The method is illustrated on experimental data. As expected, due to the nonlinear relationship between the fractional dissolution rate and the dissolution time, the estimates of the dissolution rate obtained from this stochastic model are systematically lower than the rate calculated from the deterministic model.
- MeSH
- algoritmy MeSH
- Bayesova věta MeSH
- kinetika MeSH
- léčivé přípravky chemie MeSH
- rozpustnost * MeSH
- statistické modely MeSH
- stochastické procesy MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- léčivé přípravky MeSH
This Rapid Communication presents a stochastic Wang tiling-based technique to compress or reconstruct disordered microstructures on the basis of given spatial statistics. Unlike the existing approaches based on a single unit cell, it utilizes a finite set of tiles assembled by a stochastic tiling algorithm, thereby allowing to accurately reproduce long-range orientation orders in a computationally efficient manner. Although the basic features of the method are demonstrated for a two-dimensional particulate suspension, the present framework is fully extensible to generic multidimensional media.
- MeSH
- algoritmy MeSH
- biofyzika metody MeSH
- komprese dat MeSH
- počítačové zpracování obrazu MeSH
- reprodukovatelnost výsledků MeSH
- statistické modely MeSH
- stochastické procesy MeSH
- testování materiálů MeSH
- zobrazování trojrozměrné metody MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Cryo Electron Tomography (cryoET) plays an essential role in Structural Biology, as it is the only technique that allows to study the structure of large macromolecular complexes in their close to native environment in situ. The reconstruction methods currently in use, such as Weighted Back Projection (WBP) or Simultaneous Iterative Reconstruction Technique (SIRT), deliver noisy and low-contrast reconstructions, which complicates the application of high-resolution protocols, such as Subtomogram Averaging (SA). We propose a Progressive Stochastic Reconstruction Technique (PSRT) - a novel iterative approach to tomographic reconstruction in cryoET based on Monte Carlo random walks guided by Metropolis-Hastings sampling strategy. We design a progressive reconstruction scheme to suit the conditions present in cryoET and apply it successfully to reconstructions of macromolecular complexes from both synthetic and experimental datasets. We show how to integrate PSRT into SA, where it provides an elegant solution to the region-of-interest problem and delivers high-contrast reconstructions that significantly improve template-based localization without any loss of high-resolution structural information. Furthermore, the locality of SA is exploited to design an importance sampling scheme which significantly speeds up the otherwise slow Monte Carlo approach. Finally, we design a new memory efficient solution for the specimen-level interior problem of cryoET, removing all associated artifacts.
- Klíčová slova
- 3D reconstruction, Cryo electron tomography, Metropolis–Hastings, Monte Carlo, Stochastic reconstruction, Subtomogram averaging,
- MeSH
- algoritmy MeSH
- elektronová kryomikroskopie metody MeSH
- makromolekulární látky chemie MeSH
- metoda Monte Carlo MeSH
- počítačové zpracování obrazu metody MeSH
- reprodukovatelnost výsledků MeSH
- ribozomy chemie MeSH
- stochastické procesy * MeSH
- tomografie elektronová metody MeSH
- zobrazování trojrozměrné metody MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- makromolekulární látky MeSH
Solving the first-passage-time problem for one-dimensional stochastic diffusion processes is a task with many applications in biomedical research. It has been noted (Musila and Lánský, Int. J. Biomed. Comput. 31, 233-245, 1992) that the first-passage time is overestimated if computed as the time when the simulated trajectory of the process crosses the threshold. It is studied in this paper how the error depends on the simulation step and on the parameters of the process. We propose an adaptive algorithm to make the simulation faster. The presented examples are related to neuronal modelling, but application in other fields is straightforward.
- MeSH
- algoritmy MeSH
- biologické modely * MeSH
- lidé MeSH
- membránové potenciály fyziologie MeSH
- modely neurologické MeSH
- neurony fyziologie MeSH
- počítačová simulace * MeSH
- refrakterní doba elektrofyziologická fyziologie MeSH
- statistické modely MeSH
- stochastické procesy * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Fractals are models of natural processes with many applications in medicine. The recent studies in medicine show that fractals can be applied for cancer detection and the description of pathological architecture of tumors. This fact is not surprising, as due to the irregular structure, cancerous cells can be interpreted as fractals. Inspired by Sierpinski carpet, we introduce a flexible parametric model of random carpets. Randomization is introduced by usage of binomial random variables. We provide an algorithm for estimation of parameters of the model and illustrate theoretical and practical issues in generation of Sierpinski gaskets and Hausdorff measure calculations. Stochastic geometry models can also serve as models for binary cancer images. Recently, a Boolean model was applied on the 200 images of mammary cancer tissue and 200 images of mastopathic tissue. Here, we describe the Quermass-interaction process, which can handle much more variations in the cancer data, and we apply it to the images. It was found out that mastopathic tissue deviates significantly stronger from Quermass-interaction process, which describes interactions among particles, than mammary cancer tissue does. The Quermass-interaction process serves as a model describing the tissue, which structure is broken to a certain level. However, random fractal model fits well for mastopathic tissue. We provide a novel discrimination method between mastopathic and mammary cancer tissue on the basis of complex wavelet-based self-similarity measure with classification rates more than 80%. Such similarity measure relates to Hurst exponent and fractional Brownian motions. The R package FractalParameterEstimation is developed and introduced in the paper.
- Klíčová slova
- Hausdorff measure, Quermass-interaction process, box-counting dimension, breast cancer, pathology,
- MeSH
- algoritmy MeSH
- diagnóza počítačová metody MeSH
- duktální karcinom prsu MeSH
- fraktály MeSH
- hodnocení rizik metody MeSH
- lidé MeSH
- nádory prsu diagnóza patologie MeSH
- patologie metody MeSH
- počítačová simulace MeSH
- stochastické procesy MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- srovnávací studie MeSH
BACKGROUND: Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model's dynamics may take a prohibitively long time. RESULTS: We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system's dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently. CONCLUSION: We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems.
- Klíčová slova
- Memoization, Population abstraction, Reaction networks, Stochastic simulation,
- MeSH
- algoritmy * MeSH
- biologické modely MeSH
- počítačová simulace * MeSH
- stochastické procesy * MeSH
- syntetická biologie metody MeSH
- Publikační typ
- časopisecké články MeSH
Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the natural behavior of pelicans during hunting. In POA, search agents are pelicans that search for food sources. The mathematical model of the POA is presented for use in solving optimization issues. The performance of POA is evaluated on twenty-three objective functions of different unimodal and multimodal types. The optimization results of unimodal functions show the high exploitation ability of POA to approach the optimal solution while the optimization results of multimodal functions indicate the high ability of POA exploration to find the main optimal area of the search space. Moreover, four engineering design issues are employed for estimating the efficacy of the POA in optimizing real-world applications. The findings of POA are compared with eight well-known metaheuristic algorithms to assess its competence in optimization. The simulation results and their analysis show that POA has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems.
- Klíčová slova
- nature inspired, optimization, optimization problem, pelican, population-based algorithm, stochastic, swarm intelligence,
- MeSH
- algoritmy * MeSH
- počítačová simulace MeSH
- teoretické modely * MeSH
- Publikační typ
- časopisecké články MeSH
Stochastic-based optimization algorithms are effective approaches to addressing optimization challenges. In this article, a new optimization algorithm called the Election-Based Optimization Algorithm (EBOA) was developed that mimics the voting process to select the leader. The fundamental inspiration of EBOA was the voting process, the selection of the leader, and the impact of the public awareness level on the selection of the leader. The EBOA population is guided by the search space under the guidance of the elected leader. EBOA's process is mathematically modeled in two phases: exploration and exploitation. The efficiency of EBOA has been investigated in solving thirty-three objective functions of a variety of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and CEC 2019 types. The implementation results of the EBOA on the objective functions show its high exploration ability in global search, its exploitation ability in local search, as well as the ability to strike the proper balance between global search and local search, which has led to the effective efficiency of the proposed EBOA approach in optimizing and providing appropriate solutions. Our analysis shows that EBOA provides an appropriate balance between exploration and exploitation and, therefore, has better and more competitive performance than the ten other algorithms to which it was compared.
In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving.
- Publikační typ
- časopisecké články MeSH
Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.
- Klíčová slova
- cat and mouse, optimization, optimization problem, population-based, stochastic,
- MeSH
- algoritmy * MeSH
- pohyb MeSH
- řešení problému MeSH
- teoretické modely * MeSH
- učení MeSH
- Publikační typ
- časopisecké články MeSH
