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Simulation via direct computation of partition functions

Physical Review E 2007年第3期76卷 036708-036708页

作者： Cheng Zhang Jianpeng Ma Department of Bioengineering Rice University Houston Texas 77005 USA Verna and Marrs McLean Department of Biochemistry and Molecular Biology Baylor College of Medicine One Baylor Plaza BCM-125 Houston Texas 77030 USA

In this paper, we demonstrate the efficiency of simulations via direct computation of the partition function under various macroscopic conditions, such as different temperatures or volumes. The method can compute partition functions by flattening histograms, through, for example, the Wang-Landau recursive scheme, outside the energy space. This method offers a more general and flexible framework for handling various types of ensembles, especially ones in which computation of the density of states is not convenient. It can be easily scaled to large systems, and it is flexible in incorporating monte carlo cluster algorithms or molecular dynamics. High efficiency is shown in simulating large Ising models, in finding ground states of simple protein models, and in studying the liquid-vapor phase transition of a simple fluid. The method is very simple to implement and we expect it to be efficient in studying complex systems with rugged energy landscapes, e.g., biological macromolecules.

关键词： DENSITY-OF-STATES SPIN-GLASS SIMULATIONS monte-carlo algorithm MULTICANONICAL ENSEMBLE CANONICAL ENSEMBLE DYNAMICS LATTICE MODEL

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Growth algorithm for finding low energy configurations of simple lattice proteins

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Physical Review E 2005年第1期72卷 016704-016704页

作者： Wenqi Huang Zhipeng Lü He Shi Academy of Mathematics and Systems Science in the Chinese Academy of Science Beijing 100080 China

PERM and its new variant nPERMis have been developed to optimize the energy function of protein folding based on HP simple lattice model and were found to outperform all other previous fully blind general purpose algorithms. Using the concept of core-guiding and life-forecasting, we propose a new version of nPERMis, called nPERMh. A major difference with respect to nPERMis is that criteria for further growth of new residue are based on the species of current growing monomer and its position in the HP sequence. Seventeen sequences of length ranging from 46 to 124 residues were tested by nPERMh on the cubic lattice and our algorithm proved very efficient. It should be pointed out that our new version of nPERMis is exclusively designed for conformational search. We hope that similar methods will ultimately be useful for finding native states of more realistic protein models.

关键词： monte-carlo algorithm FOLDING SIMULATIONS GLOBULAR-PROTEINS MODEL HETEROPOLYMERS KINETICS SEARCH STATE

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Structure optimization and folding mechanisms of off-lattice protein models using statistical temperature molecular dynamics simulation: Statistical temperature annealing

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Physical Review E 2007年第1期76卷 011913-011913页

作者： Jaegil Kim John E. Straub Thomas Keyes Department of Chemistry Boston University Boston Massachusetts 02215 USA

The recently proposed statistical temperature molecular dynamics (STMD) algorithm [Kim et al., Phys. Rev. Lett. 97, 050601 (2006)] is used as the core of an optimization algorithm, statistical temperature annealing (STA), for finding low-lying energy minima of complex potential energy landscapes. Since STMD realizes a random walk in energy, the idea is simply to initiate repeated minimizations from configurations in the low-energy segments of STMD trajectories. STA is tested in structural optimization of various off-lattice AB and extended AB protein models in two and three dimensions with different chain lengths. New putative ground states were found for the two- and three-dimensional AB 55-mer, and for the three-dimensional extended AB 21-mer and 55-mer. The distinct folding features of the models are analyzed in terms of the statistical temperature and other representations of the structure of the potential energy landscape. It is shown that the characteristic behavior of the statistical temperature undergoes a qualitative change with the inclusion of a torsional potential in the extended AB model, as the more rigid backbone makes the potential energy landscape more funnel-like.

关键词： monte-carlo algorithm MULTICANONICAL ENSEMBLE PHASE-SPACE TOY MODEL DISTRIBUTIONS TRANSITIONS

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Life cycle assessment and risk assessment of liquefied natural gas vehicles promotion

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2022年 153卷

作者： Sun, Shouheng Ertz, Myriam Univ Quebec Chicoutimi UQAC LaboNFC 555 Blvd Univ Chicoutimi PQ G7H 2B1 Canada

Liquefied natural gas (LNG) is gaining traction as a substitute energy source in the transportation sector. While presented as a promising energy alternative, LNG also faces criticism for being environmentally unsustainable. This debate has sparked interest from authorities, professionals, and business developers uncertain about the stance to take toward LNG. This study seeks to offer preliminary insights into this issue by investigating the environmental and economic impact of liquefied natural gas on heavy-duty vehicles (HDVs) in Canada. In this country, LNG sources are abundant. A thorough life cycle assessment is conducted on the environmental impact of deploying LNG as an alternative fuel for heavy-duty vehicles in the city of Saguenay, Canada. In particular, an extensive risk assessment is also carried out on the impact of uncertain factors such as policies and markets on the economic benefits of stakeholders by using the monte-carlo algorithm. Although the project was eventually canceled in Saguenay and implemented by LNG Canada in Kitimat, British Columbia, Canada, the results show that promoting LNG in Saguenay may yield significant environmental benefits on almost all environmental impact categories, even in the worst possible development scenario. However, it should be noted that the engine's methane emission and energy efficiency both significantly influence LNG's global warming potential. Advances in technology will show more significant potential for environmental preservation. Additionally, appropriate incentives can significantly increase the economic benefits and reduce the investment risk of facilities and vehicles.

关键词： Liquefied natural gas Life cycle assessment Risk assessment Environmental impact Climate change monte-carlo algorithm

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SAlign-a structure aware method for global PPI network alignment

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BMC BIOINFORMATICS 2020年第1期21卷 500-500页

作者： Ayub, Umair Haider, Imran Naveed, Hammad Natl Univ Comp & Emerging Sci Dept Comp Islamabad 40100 Pakistan Computat Biol Res Lab Islamabad 40100 Pakistan

Background: High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight about healthy/disease states than studying proteins in isolation. Similarly, a comparative study of protein-protein interaction (PPI) networks of different species reveals important insights which may help in disease analysis and drug design. The study of PPI network alignment can also helps in understanding the different biological systems of different species. It can also be used in transfer of knowledge across different species. Different aligners have been introduced in the last decade but developing an accurate and scalable global alignment algorithm that can ensures the biological significance alignment is still challenging. Results: This paper presents a novel global pairwise network alignment algorithm, SAlign, which uses topological and biological information in the alignment process. The proposed algorithm incorporates sequence and structural information for computing biological scores, whereas previous algorithms only use sequence information. The alignment based on the proposed technique shows that the combined effect of structure and sequence results in significantly better pairwise alignments. We have compared SAlign with state-of-art algorithms on the basis of semantic similarity of alignment and the number of aligned nodes on multiple PPI network pairs. The results of SAlign on the network pairs which have high percentage of proteins with available structure are 3-63% semantically better than all existing techniques. Furthermore, it also aligns 5-14% more nodes of these network pairs as compared to existing aligners. The results of SAlign on other PPI network pairs are comparable or better than all existing techniques. We also introduce SAlignmc, a monte carlo based alignment algorithm, that produces multiple network alignments with si

关键词： Protein-protein interaction Global network alignment Sequence similarity Structure similarity monte-carlo algorithm

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DEVELOPMENT OF A FUZZY-BASED TIDAL VOLUME algorithm FOR PATIENTS WITH RESPIRATORY DISTRESS

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2014年第4期29卷 699-706页

作者： Guler, Hasan Ata, Fikret Firat Univ Muh Fak Elekt Elekt Munhendisligi Bolumu TR-23169 Elazig Turkey

The purpose of the present study is to develop a fuzzy-based tidal volume algorithm for patients with respiratory distress. The standard of care when ventilating patients with the acute respiratory distress syndrome is to use a tidal volume of 6 ml/kg for ideal body weight. But, this tidal volume aim is frequently exceeded for many reasons, most typically related to physician discomfort with clinical parameters of inadequate oxygenation and ventilation. Thus, an algorithm based on fuzzy logic has been developed in Matlab to prevent deviation in tidal volume. The inputs of fuzzy were positive end-expiratory pressure (PEEP), plateau pressure (Pplt) and arterial oxygen saturation (SaO(2)) and the change of PEEP. Random clinical scenarios were generated via using monte-carlo simulation and Gaussian distribution methods. The student t-test for p <0.05 was used to compare the obtained results from simulations and clinicians' decision. It was found that there is no statistical discrepancy in the value of tidal volume. It can be said that this algorithm may prove useful as means of rationalizing and standardizing departures from desired 6 ml/kg tidal volume in any given patient.

关键词： Fuzzy Logic Mechanical Ventilation monte-carlo algorithm Gaussian Distribution

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Research on Optimal Inspection Strategy for Overhead Transmission Line Based on Smart Grid

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Procedia Computer Science 2018年 130卷 1134-1139页

作者： Liu Jian Gao Feng Zhao Ming Chen Liuning Li Weiyao School of Electrical Engineering Wuhan university Wuhan 430072 China State Grid Zhengzhou Power Supply Company Henan 450052 China State Grid Nanyang Power Supply Company Henan 473005 China

Due to the urgent demand for the monitoring of overhead transmission lines in smart grid, this paper proposes an optimal inspection strategy for overhead transmission line considering time-varying failure rate. Based on the goal benefit function of inspection cost, failure electric cost and the maintaining reliability cost, modal of the inspection strategy is established with genetic algorithm. The reliability index could contribute to a dynamic and variable inspection cycle of transmission line via genetic algorithm. According to the calculation results of the middle area of China, it is presented that the algorithm can work out inspection strategy according to the actual failure rate, and get a rational and economical, regional, seasonal, dynamic inspection cycle plan. Thus, it can avoid the traditional over-inspection and lack-inspection problems. The state of transmission line can be guaranteed in more economical way, satisfying the demand of real-time capability and reliability

关键词： smart grid overhead transmission line inspection strategy time-varying failure rate monte-carlo algorithm

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Exact sequence analysis for three-dimensional hydrophobic-polar lattice proteins

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JOURNAL OF CHEMICAL PHYSICS 2005年第11期122卷 4705-4705页

作者： Schiemann, R Bachmann, M Janke, W Univ Leipzig Inst Theoret Phys D-04109 Leipzig Germany

We have exactly enumerated all sequences and conformations of hydrophobic-polar (HP) proteins with chains of up to 19 monomers on the simple cubic lattice. For two variants of the HP model, where only two types of monomers are distinguished, we determined and statistically analyzed designing sequences, i.e., sequences that have a nondegenerate ground state. Furthermore we were interested in characteristic thermodynamic properties of HP proteins with designing sequences. In order to be able to perform these exact studies, we applied an efficient enumeration method based on contact sets. (c) 2005 American Institute of Physics.

关键词： SELF-AVOIDING WALKS monte-carlo algorithm FOLDING SIMULATIONS GENETICalgorithmS CONTACT MAPS HP MODEL SEARCH HETEROPOLYMERS THERMODYNAMICS ENUMERATION

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A reinforcement learning algorithm for two-dimensional irregular packing problems 21

A reinforcement learning algorithm for two-dimensional irreg...

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Proceedings of the 2021 4th International Conference on algorithms, Computing and Artificial Intelligence

作者： Jie Fang Yunqing Rao Xiaoqiang Guo Xusheng Zhao School of Mechanical Science & Engineering Huazhong University of Science and Technology China

ISBN: (纸本)9781450385053

The two-dimensional(2D) irregular packing problem is a classical optimization problem with NP-Hard characteristics, which has high computational complexity. The traditional heuristic algorithm and meta heuristic algorithm are usually used to solve packing problems, which has low packing efficiency and high time cost. Inspired by reinforcement learning(RL) and combined with the characteristics of 2D irregular pieces packing, a novel method to solve multi-constraint packing problem based on reinforcement learning is proposed in this paper. A reinforcement learning model based on monte- carlo (MC) method and a reward mechanism based on packing height return are designed. Finally, an example is used to analyze the optimization effect of the algorithm. The experimental results show that the proposed method can quickly and efficiently realize the packing of 2D irregular pieces, and a better solution can be obtained in an acceptable time.

关键词： reinforcement learning machine learning irregular pieces monte-carlo algorithm two-dimensional packing optimization

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A reinforcement learning algorithm for two-dimensional irregular packing problems

A reinforcement learning algorithm for two-dimensional irreg...

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2021年第四届算法、计算和人工智能国际会议

作者： Jie Fang Yunqing Rao Xiaoqiang Guo Xusheng Zhao School of Mechanical Science & Engineering Huazhong University of Science and Technology

The two-dimensional(2 D) irregular packing problem is a classical optimization problem with NP-Hard characteristics, which has high computational complexity. The traditional heuristic algorithm and meta heuristic algorithm are usually used to solve packing problems, which has low packing efficiency and high time *** by reinforcement learning(RL) and combined with the characteristics of 2 D irregular pieces packing, a novel method to solve multi-constraint packing problem based on reinforcement learning is proposed in this paper. A reinforcement learning model based on monte-carlo(MC) method and a reward mechanism based on packing height return are designed. Finally, an example is used to analyze the optimization effect of the algorithm. The experimental results show that the proposed method can quickly and efficiently realize the packing of 2 D irregular pieces, and a better solution can be obtained in an acceptable time.

关键词： reinforcement learning machine learning monte-carlo algorithm two-dimensional packing optimization irregular pieces

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