165 research outputs found
Structural optimization in steel structures, algorithms and applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better
Evolutionary robot systems offer two principal advantages: an advanced way of
developing robots through evolutionary optimization and a special research
platform to conduct what-if experiments regarding questions about evolution.
Our study sits at the intersection of these. We investigate the question ``What
if the 18th-century biologist Lamarck was not completely wrong and individual
traits learned during a lifetime could be passed on to offspring through
inheritance?'' We research this issue through simulations with an evolutionary
robot framework where morphologies (bodies) and controllers (brains) of robots
are evolvable and robots also can improve their controllers through learning
during their lifetime. Within this framework, we compare a Lamarckian system,
where learned bits of the brain are inheritable, with a Darwinian system, where
they are not. Analyzing simulations based on these systems, we obtain new
insights about Lamarckian evolution dynamics and the interaction between
evolution and learning. Specifically, we show that Lamarckism amplifies the
emergence of `morphological intelligence', the ability of a given robot body to
acquire a good brain by learning, and identify the source of this success:
`newborn' robots have a higher fitness because their inherited brains match
their bodies better than those in a Darwinian system.Comment: preprint-nature scientific report. arXiv admin note: text overlap
with arXiv:2303.1259
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a
recurrent neural network in which neurons are randomly connected. Once
initialized, the connection strengths remain unchanged. Such a simple structure
turns RC into a non-linear dynamical system that maps low-dimensional inputs
into a high-dimensional space. The model's rich dynamics, linear separability,
and memory capacity then enable a simple linear readout to generate adequate
responses for various applications. RC spans areas far beyond machine learning,
since it has been shown that the complex dynamics can be realized in various
physical hardware implementations and biological devices. This yields greater
flexibility and shorter computation time. Moreover, the neuronal responses
triggered by the model's dynamics shed light on understanding brain mechanisms
that also exploit similar dynamical processes. While the literature on RC is
vast and fragmented, here we conduct a unified review of RC's recent
developments from machine learning to physics, biology, and neuroscience. We
first review the early RC models, and then survey the state-of-the-art models
and their applications. We further introduce studies on modeling the brain's
mechanisms by RC. Finally, we offer new perspectives on RC development,
including reservoir design, coding frameworks unification, physical RC
implementations, and interaction between RC, cognitive neuroscience and
evolution.Comment: 51 pages, 19 figures, IEEE Acces
Multiscale Transport and Osmotic Tolerance in Liver Cells and Tissues
Cryopreservation enables the storage of biological samples for later use while preserving all aspects of biological interest by cooling them to a temperature where chemical reactions are sufficiently slowed. However, there have been considerable challenges in preserving complex tissues and organs due to excessive ice formation, severe thermal stress, chilling, ischemic injury, and the osmotic stress caused by highly viscous cryoprotectants (CPA). To overcome these challenges, mathematical modeling approaches have proven effective in predicting cell and tissue responses to osmotic stress and developing an optimal method for loading and unloading CPA. Predicting optimal cryopreservation protocols requires an accurate estimation of cell volume, solute concentration, and water permeability parameters. A key bottleneck in this process is the requirement of careful measurement of these parameters from the cellular to the tissue scale and the difficulty of studying these in their native three-dimensional (3D) structures: little is known about the detailed responses of individual cells and nuclei in monolayers and tissues to anisosmotic media. Over the course of four projects, my study has mainly used two approaches to overcome these barriers. It focused on real-time monitoring of cellular morphometric parameters using modern four-dimensional imaging techniques and employed mathematical models for solute and water permeability estimation. In the first project, I characterized the osmotic behavior in HepG2 cells, which serve as a model for hepatocytes, and determined the mechanism of osmoregulation within these cells. I illustrate that HepG2 cells are non-ideal osmometers by showing the difference between the expected behavior of cells in anisosmotic environments and by making predictions about their volume regulation mechanisms. Second, I compared cell volume measurement techniques for adherent cell monolayers, which included using a calcein fluorescence quenching technique to investigate the volumetric responses of HepG2 monolayers. My follow-up study uses modern 3D imaging techniques to simultaneously measure real-time cell and nuclear volume changes in adherent cells in an aniosomotic medium, including during the addition and removal of CPA. My results demonstrate that both cells and nuclei regulate their volume in response to osmotic stress. Consequently, cells and nuclear permeability to water (Lp) and CPA (Ps) are inferred during perfusion with anisosmotic and CPA solutions for adherent cell monolayers. Thirdly, I show that osmotic damage is time dependent and that the flavonoid silymarin enhances resistance to osmotic stress and may improve cryosurvival in HepG2 cells. Finally, I extend the 3D imaging technique to track and quantify three dimensional changes in cell and nuclear morphology in response to anisosmotic medium. I then estimate the volume within complex liver tissue, specifically a precision-cut liver slice (PCLS). This method allows the quantification of the expansion and contraction of the whole PCLS during CPA equilibration, as well as the tracking of nuclei and cell volume. By demonstrating the nonideality of liver cells and the complex interplay between cytoplasm and nuclear volumes, we can inform biophysical models, which may have profound implications for our understanding of cell physiology and the mechanism of osmoregulation. Furthermore, the methods described in this study can be adapted to enhance cryopreservation strategies for adherent cells, other complex tissues, and organs. Altogether, this research contributes to the development of a new cryopreservation method for liver cells and tissues and will have a broad impact on the field of tissue transplantation and biomedical research
Microcarriers with Complex Architectures Manufactured by Two-Photon Lithography for Mechanobiological Manipulation and Expansion of Mesenchymal Stem Cells
Mesenchymal stem cells (MSCs) are a powerful tool in regenerative medicine owing to their innate capacity to differentiate into a range of cell lineages and this behaviour has been utilised as a means of tissue repair and regeneration. The prevalent issue in many treatments is the vast number of cells required for therapeutic effect, but this can be addressed through expansion of cell populations in vitro to suitable levels. Microcarriers are designed to provide a high level of cell growth surface within a small volume and have become one of the most promising expansion tools to date. However, transition to approaches that integrate biomechanical cues to modulate cell responses can lead to far greater outcomes than those that can be achieved through surface area alone. Such biophysical properties that can be integrated include geometry, roughness, topography, stiffness, and porosity which can promote specific biological responses through mechanotransduction pathways. This thesis focuses on employing this approach to microcarrier technology and examining the effects of such structures on cell control and enhancement of expansion yield to facilitate MSC production for therapeutic uses.
Two-photon lithography was employed to produce microcarriers with highly complex geometry at sub-micron feature size and optimisations allowed fabrication speed to be increased by up to 423-fold at the cost of structure resolution. Biocompatibility testing identified several suitable acrylate polymers with varying characteristics but highlighted the need for further materials exploration due to suboptimal adherence in most candidates. Novel fabrication techniques allowed cell culture isolation to structures without complication by anchoring substrates which addressed a continuing issue with two-photon derived samples that has been presented in the literature. A variety of produced designs exhibited significant increase in cell proliferation and consistent interaction with structure features with observable cellular preference for certain feature types and sizes. From these selected designs further morphological analysis of cells and DNA quantification determined microcarrier designs that lead to a significant increase in expansion yield in comparison to a conventional microcarrier design. Best expansion yields were seen in Buckminsterfullerene styled structures with hollow interiors and porous outer shells and identified that expansion yield was not necessarily based on the amount of surface area alone. Analysis of stem cell phenotype changes across expansion periods indicated mixed results in the maintenance of phenotypes and requires further exploration.
This thesis demonstrated biomechanical based enhancement of expansion proficiency as well as novel techniques relating to two-photon lithography. However, for scale up of work and translation to clinical applications a significant increase in microcarrier production is necessary. Microcarriers that intelligently shape cellular proliferation and differentiation present an opportunity to act both in vitro and in vivo evolving beyond their primary function of expansion and acting as multifunctional tissue modulators
Sample-Efficient Co-Design of Robotic Agents Using Multi-fidelity Training on Universal Policy Network
Co-design involves simultaneously optimizing the controller and agents
physical design. Its inherent bi-level optimization formulation necessitates an
outer loop design optimization driven by an inner loop control optimization.
This can be challenging when the design space is large and each design
evaluation involves data-intensive reinforcement learning process for control
optimization. To improve the sample-efficiency we propose a
multi-fidelity-based design exploration strategy based on Hyperband where we
tie the controllers learnt across the design spaces through a universal policy
learner for warm-starting the subsequent controller learning problems. Further,
we recommend a particular way of traversing the Hyperband generated design
matrix that ensures that the stochasticity of the Hyperband is reduced the most
with the increasing warm starting effect of the universal policy learner as it
is strengthened with each new design evaluation. Experiments performed on a
wide range of agent design problems demonstrate the superiority of our method
compared to the baselines. Additionally, analysis of the optimized designs
shows interesting design alterations including design simplifications and
non-intuitive alterations that have emerged in the biological world.Comment: 17 pages, 10 figure
Intersection between natural and artificial swimmers: a scaling approach to underwater vehicle design.
Approximately 72% of the Earth’s surface is covered by water, yet only 20% has been mapped [1]. Autonomous Underwater Vehicles (AUVs) are one of the main tools for ocean exploration. The demand for AUVs is expected to increase rapidly in the coming years [2], so there is a need for faster and more energy efficient AUVs. A drawback to using this type of vehicle is the finite amount of energy that is stored onboard in the form of batteries. Science and roboticists have been studying nature for ways to move more efficiently. Phillips et al. [3] presents data that contradicts the idea that fish are better swimmers than conventional AUVs when comparing the energetic cost of swimming in the form of the Cost of Transport (COT). The data presented by Phillips et al. only applies to AUVs at higher length and naval displacement (mass) scales, so the question arises of whether an AUV built at different displacements and length scales is more efficient than biological animals and if current bio-inspired platforms are better than conventional AUVs.
Besides power requirements, it is also useful to compare the kinematic parameters of natural and artificial swimmers. In this case, kinematic parameters indicate how fast the swimmer travels through the water. Also, they describe how fast the propulsion mechanism must act to reach a certain swimming speed. This research adopts the approach of Gazzola et al. [4] where the Reynolds number is associated with a dimensionless number, Swim number (Sw) in this case, that has all the kinematic information. A newly developed number that extends the swim number to conventional AUVs is the Propulsion number (Jw), which demonstrates excellent agreement with the kinematics of conventional AUVs. Despite being functionally similar, Sw and Jw do not have a one-to-one relationship. Sw, Jw, COT represent key performance metrics for an AUV, herein called performance criteria, which can be used to compare existing platforms with each other and estimate the performance of non-existent designs.
The scaling laws are derived by evaluating the performance of 229 biological animals, 163 bioinspire platforms, and 109 conventional AUVs. AUVs and bio-inspired platforms have scarce data compared with biological swimmers. Only 5% of conventional and 38% of bio-inspired AUVs have kinematic data while 30% of conventional and 18% of bio-inspired AUVs have energetic data. The low amount of performance criteria data is due to the nature of most conventional AUVs as commercial products. Only recently has the COT metric been included in the performance criteria for bio-inspired AUVs. For this reason, the research here formulates everything in terms of allometric scaling laws. This type of formulation is used extensively when referring to biological systems and is defined by an exponential relationship f (x) = axb, where x is a physical parameter of the fish or vehicle, like length or displacement. Scaling laws have the added benefit of allowing comparisons with limited data, as is the case for AUVs.
The length and displacement scale (physical scale) must be established before estimating the performance criteria. Scale is primarily determined by the payload needed for a particular application. For instance, surveying the water column in deep water will require different scientific tools than taking images of an oyster bed in an estuary. There is no way to identify the size of an AUV until it is designed for that application, since these scientific instruments each have their own volume, length, and weight. A methodology for estimating physical parameters using computer vision is presented to help determine the scale for the vehicle. It allows accurate scaling of physical parameters of biological and bio-inspired swimmers with only a side and top view of the platform. A physical scale can also be determined based on the vehicle’s overall volume, which is useful when determining how much payload is needed for a particular application. Further, this can be used in conjunction with 3D modeling software to scale nonexistent platforms.
Following the establishment of a physical scale, which locomotion mode would be most appropriate? Unlike conventional AUVs that use propeller or glider locomotion, bio-inspired platforms use a variety of modes. Kinematics and energy expenditures are different for each of these modes. For bio-inspired vehicles, the focus will be on the body-caudal fin (BCF) locomotion, of which four types exist: anguilliform, carangiform, thunniform, and ostraciiform. There is ample research on anguilliform and carangiform locomotion modes, but little research on thunniform and ostraciiform modes. In order to determine which locomotion mode scales best for a bio-inspired AUV, this research examines the power output and kinematic parameters for all four BCF modes. In order to achieve this, computational fluid dynamics simulations are performed on a 2D swimmer for all four modes. Overset meshes are used in lieu of body-fitted meshes to increase stability and decrease computational time. These simulations were used to scale output power over several decades of Reynolds numbers for each locomotion mode. Carangiform locomotion was found to be the most energy efficient, followed by anguilliform, thunniform, and ostraciiform.
In order to utilize the above scaling laws in designing a novel platform, or comparing an existing one, there must be a unifying framework. The framework for choosing a suitable platform is presented with a case study of two bio-inspired vehicles and a conventional one. The framework begins by determining how the platform can be physically scaled depending on the payload. Based on the physical scale and derived scaling laws, it then determines performance criteria. It also describes a method for relative cost scaling for each vehicle, which is not covered in the literature. The cost scaling is based on the assumption that all payloads and materials are the same. The case study shows that a conventional AUV performs better on all performance criteria and would cost less to build
An Image-Analysis-Based Method for the Prediction of Recombinant Protein Fiber Tensile Strength
Silk fibers derived from the cocoon of silk moths and the wide range of silks produced by spiders exhibit an array of features, such as extraordinary tensile strength, elasticity, and adhesive properties. The functional features and mechanical properties can be derived from the structural composition and organization of the silk fibers. Artificial recombinant protein fibers based on engineered spider silk proteins have been successfully made previously and represent a promising way towards the large-scale production of fibers with predesigned features. However, for the production and use of protein fibers, there is a need for reliable objective quality control procedures that could be automated and that do not destroy the fibers in the process. Furthermore, there is still a lack of understanding the specifics of how the structural composition and organization relate to the ultimate function of silk-like fibers. In this study, we develop a new method for the categorization of protein fibers that enabled a highly accurate prediction of fiber tensile strength. Based on the use of a common light microscope equipped with polarizers together with image analysis for the precise determination of fiber morphology and optical properties, this represents an easy-to-use, objective non-destructive quality control process for protein fiber manufacturing and provides further insights into the link between the supramolecular organization and mechanical functionality of protein fibers
Geometry And Topology: Building Machine Learning Surrogate Models With Graphic Statics Method
This dissertation aims at developing a machine learning workflow in solving design-related problems, taking a data-driven structural design method with topological data using graphic statics as an example. It shows the advantages of building machine learning surrogate models for learning the design topology -- the relationship of design elements. It reveals a future tendency of the coexistence of the human designer and the machine, in which the machine learns the appearance and correlation between design data, while the human supervises the learning process.
Theoretically, with the commencement of the age of Big Data and Artificial Intelligence, the usage of machine learning in solving design problems is widely applied. The existing research mainly focuses on the machine learning of the geometric data, however, the internal logic of a design is represented as the topology, which describes the relationship between each design element. The topology can not be easily represented for the human designer to understand, however it\u27s readable and understandable by the machine, which suggests a method of using machine learning techniques to learn the intrinsic logic of a design as the topology.
Technically, we propose to use machine learning as a framework and graphic statics as a supporting method to provide training data, suggesting a new design methodology by the machine learning of the topology. Different from previous geometry-based design, in which only the design geometry is presented and considered, in this new topology-based design, the human designer employs the machine and provides training materials showing the topology of a design to train the machine. The machine finds the design rules related to the topology and applies the trained machine learning models to generate new design cases as both the geometry and the topology
Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator
Nowadays, optimization problems are solved through meta-heuristic algorithms based on stochastic search approaches borrowed from mimicking natural phenomena. Notwithstanding their successful capability to handle complex problems, the No-Free Lunch Theorem by Wolpert and Macready (1997) states that there is no ideal algorithm to deal with any kind of problem. This issue arises because of the nature of these algorithms that are not properly mathematics-based, and the convergence is not ensured. In the present study, a variant of the well-known swarm-based algorithm, the Particle Swarm Optimization (PSO), is developed to solve constrained problems with a different approach to the classical penalty function technique. State-of-art improvements and suggestions are also adopted in the current implementation (inertia weight, neighbourhood). Furthermore, a new local search operator has been implemented to help localize the feasible region in challenging optimization problems. This operator is based on hybridization with another milestone meta-heuristic algorithm, the Evolutionary Strategy (ES). The self-adaptive variant has been adopted because of its advantage of not requiring any other arbitrary parameter to be tuned. This approach automatically determines the parameters’ values that govern the Evolutionary Strategy simultaneously during the optimization process. This enhanced multi-strategy PSO is eventually tested on some benchmark constrained numerical problems from the literature. The obtained results are compared in terms of the optimal solutions with two other PSO implementations, which rely on a classic penalty function approach as a constraint-handling method
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