2,605 research outputs found
Complete RNA inverse folding: computational design of functional hammerhead ribozymes
Nanotechnology and synthetic biology currently constitute one of the most
innovative, interdisciplinary fields of research, poised to radically transform
society in the 21st century. This paper concerns the synthetic design of
ribonucleic acid molecules, using our recent algorithm, RNAiFold, which can
determine all RNA sequences whose minimum free energy secondary structure is a
user-specified target structure. Using RNAiFold, we design ten cis-cleaving
hammerhead ribozymes, all of which are shown to be functional by a cleavage
assay. We additionally use RNAiFold to design a functional cis-cleaving
hammerhead as a modular unit of a synthetic larger RNA. Analysis of kinetics on
this small set of hammerheads suggests that cleavage rate of computationally
designed ribozymes may be correlated with positional entropy, ensemble defect,
structural flexibility/rigidity and related measures. Artificial ribozymes have
been designed in the past either manually or by SELEX (Systematic Evolution of
Ligands by Exponential Enrichment); however, this appears to be the first
purely computational design and experimental validation of novel functional
ribozymes. RNAiFold is available at
http://bioinformatics.bc.edu/clotelab/RNAiFold/.Comment: 17 pages, 2 tables, 7 figures, final version to appear in Nucleic
Acids Researc
Application of Surrogate Based Optimisation in the Design of Automotive Body Structures
The rapid development of automotive industry requires manufacturers to continuously reduce the development cost and time and to enhance the product quality. Thus, modern automotive design pays more attention to using CAE analysis based optimisation techniques to drive the entire design flow. This thesis focuses on the optimisation design to improve the automotive crashworthiness and fatigue performances, aiming to enhance the optimisation efficiency, accuracy, reliability, and robustness etc. The detailed contents are as follows: (1) To excavate the potential of crash energy absorbers, the concept of functionally graded structure was introduced and multiobjective designs were implemented to this novel type of structures. First, note that the severe deformation takes place in the tubal corners, multi-cell tubes with a lateral thickness gradient were proposed to better enhance the crashworthiness. The results of crashworthiness analyses and optimisation showed that these functionally graded multi-cell tubes are preferable to a uniform multi-cell tube. Then, functionally graded foam filled tubes with different gradient patterns were analyzed and optimized subject to lateral impact and the results demonstrated that these structures can still behave better than uniform foam filled structures under lateral loading, which will broaden the application scope of functionally graded structures. Finally, dual functionally graded structures, i.e. functionally graded foam filled tubes with functionally graded thickness walls, were proposed and different combinations of gradients were compared. The results indicated that placing more material to tubal corners and the maximum density to the outmost layer are beneficial to achieve the best performance. (2) To make full use of training data, multiple ensembles of surrogate models were proposed to maximize the fatigue life of a truck cab, while the panel thicknesses were taken as design variables and the structural mass the constraint. Meanwhile, particle swarm optimisation was integrated with sequential quadratic programming to avoid the premature convergence. The results illustrated that the hybrid particle swarm optimisation and ensembles of surrogates enable to attain a more competent solution for fatigue optimisation. (3) As the conventional surrogate based optimisation largely depends on the number of initial sample data, sequential surrogate modeling was proposed to practical applications in automotive industry. (a) To maximize the fatigue life of spot-welded joints, an expected improvement based sequential surrogate modeling method was utilized. The results showed that by using this method the performance can be significantly improved with only a relatively small number of finite element analyses. (c) A multiojective sequential surrogate modeling method was proposed to address a multiobjective optimisation of a foam-filled double cylindrical structure. By adding the sequential points and updating the Kriging model adaptively, more accurate Pareto solutions are generated. (4) While various uncertainties are inevitably present in real-life optimisations, conventional deterministic optimisations could probably lead to the violation of constraints and the instability of performances. Therefore, nondeterministic optimisation methods were introduced to solve the automotive design problems. (a) A multiobjective reliability-based optimisation for design of a door was investigated. Based on analysis and design responses surface models, the structural mass was minimized and the vertical sag stiffness was maximized subjected to the probabilistic constraint. The results revealed that the Pareto frontier is divided into the sensitive region and insensitive region with respect to uncertainties, and the decision maker is recommended to select a solution from the insensitive region. Furthermore, the reduction of uncertainties can help improve the reliability but will increase the manufacturing cost, and the tradeoff between the reliability target and performance should be made. (b) A multiobjective uncertain optimisation of the foam-filled double cylindrical structure was conducted by considering randomness in the foam density and wall thicknesses. Multiobjective particle swarm optimisation and Monte Carlo simulation were integrated into the optimisation. The results proved that while the performances of the objectives are sacrificed slightly, the nondeterministic optimisation can enhance the robustness of the objectives and maintain the reliability of the constraint. (c) A multiobjective robust optimisation of the truck cab was performed by considering the uncertainty in material properties. The general version of dual response surface model, namely dual surrogate model, was proposed to approximate the means and standard deviations of the performances. Then, the multiobjective particle optimisation was used to generate the well-distributed Pareto frontier. Finally, a hybrid multi-criteria decision making model was proposed to select the best compromise solution considering both the fatigue performance and its robustness. During this PhD study, the following ideas are considered innovative: (1) Surrogate modeling and multiobjective optimisation were integrated to address the design problems of novel functionally graded structures, aiming to develop more advanced automotive energy absorbers. (2) The ensembles of surrogates and hybrid particle swarm optimisation were proposed for the design of a truck cab, which could make full use of training points and has a strong searching capacity. (3) Sequential surrogate modeling methods were introduced to several optimisation problems in the automotive industry so that the optimisations are less dependent on the number of initial training points and both the efficiency and accuracy are improved. (4) The surrogate based optimisation method was implemented to address various uncertainties in real life applications. Furthermore, a hybrid multi-criteria decision making model was proposed to make the best compromise between the performance and robustness
A Multi-Label Active Learning Framework for Microcontroller Performance Screening
In safety-critical applications, microcontrollers have to be tested to satisfy strict quality and performances constraints. It has been demonstrated that on-chip ring oscillators can be be used as speed monitors to reliably predict the performances. However, any machine-learning model is likely to be inaccurate if trained on an inadequate dataset, and labeling data for training is quite a costly process. In this paper, we present a methodology based on active learning to select the best samples to be included in the training set, significantly reducing the time and cost required. Moreover, since different speed measurements are available, we designed a multi-label technique to take advantage of their correlations. Experimental results demonstrate that the approach halves the training-set size, with respect to a random labelling, while it increases the predictive accuracy, with respect to standard single-label machine-learning models
Universal thermodynamic bounds on nonequilibrium response with biochemical applications
Diverse physical systems are characterized by their response to small
perturbations. Near thermodynamic equilibrium, the fluctuation-dissipation
theorem provides a powerful theoretical and experimental tool to determine the
nature of response by observing spontaneous equilibrium fluctuations. In this
spirit, we derive here a collection of equalities and inequalities valid
arbitrarily far from equilibrium that constrain the response of nonequilibrium
steady states in terms of the strength of nonequilibrium driving. Our work
opens new avenues for characterizing nonequilibrium response. As illustrations,
we show how our results rationalize the energetic requirements of two common
biochemical motifs.Comment: 21 pages, 15 figure
Semi-Supervised Deep Learning for Microcontroller Performance Screening
In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of F_max (the maximum operating frequency). Data extracted from on-chip ring oscillators (ROs) can model the F_max of integrated circuits using machine learning models. Those models are suitable for the performance screening process. Acquiring data from the ROs is a fast process that leads to many unlabeled data. Contrarily, the labeling phase (i.e., acquiring F_max) is a time-consuming and costly task, that leads to a small set of labeled data.
This paper presents deep-learning-based methodologies to cope with the low number of labeled data in microcontroller performance screening. We propose a method that takes advantage of the high number of unlabeled samples in a semi-supervised learning fashion. We derive deep feature extractor models that project data into higher dimensional spaces and use the data feature embedding to face the performance prediction problem with simple linear regression. Experiments showed that the proposed models outperformed state-of-the-art methodologies in terms of prediction error and permitted us to use a significantly smaller number of devices to be characterized, thus reducing the time needed to build ML models by a factor of six with respect to baseline approaches
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