Abstract Biological shapes possess fascinating properties and behaviours that are the result of emergent mechanisms: they can evolve over time, dynamically adapt to changes in their environment, ...while also exhibiting interesting mechanical properties and aesthetic appeal. In this work, we bring and extend an existing biological‐inspired model of the Physarum polycephalum , aka the blob , to the field of computer graphics, in order to design porous organic‐like microstructures that resemble natural foam‐like cells or filament‐like patterns with variable local properties. In contrast to approaches based on static global optimization that provides only limited expressivity over the result, our method allows precise control over the local orientation of 3D patterns, relative cell extension and precise infill of shapes with well defined boundaries. To this end, we extend the classical agent‐based model for Physarum to fill an arbitrary domain with local anisotropic behaviour. We further provide a detailed analysis of the model parameters, contributing to the understanding of the system behaviour. The method is fast, parallelizable and scalable to large volumes and compatible with user interaction, allowing a designer to guide the structure, erase parts and observe its evolution in real‐time. Overall, our method provides a versatile and efficient means of generating intricate organic microstructures that have potential applications in fields such as additive manufacturing, design or biological representation and engineering.
Designing 3D objects from scratch is difficult, especially when the user intent is fuzzy and lacks a clear target form. We facilitate design by providing reference and inspiration from existing model ...contexts. We rethink model design as navigating through different possible combinations of part assemblies based on a large collection of pre‐segmented 3D models. We propose an interactive sketch‐to‐design system, where the user sketches prominent features of parts to combine. The sketched strokes are analysed individually, and more importantly, in context with the other parts to generate relevant shape suggestions via adesign galleryinterface. As a modelling session progresses and more parts get selected, contextual cues become increasingly dominant, and the model quickly converges to a final form. As a key enabler, we use pre‐learned part‐based contextual information to allow the user to quickly explore different combinations of parts. Our experiments demonstrate the effectiveness of our approach for efficiently designing new variations from existing shape collections.
Designing 3D objects from scratch is difficult, especially when the user intent is fuzzy and lacks a clear target form. We facilitate design by providing reference and inspiration from existing model contexts. We rethink model design as navigating through different possible combinations of part assemblies based on a large collection of pre‐segmented 3D models. We propose an interactive sketch‐to‐design system, where the user sketches prominent features of parts to combine. The sketched strokes are analyzed individually, and more importantly, in context with the other parts to generate relevant shape suggestions via a design gallery interface. As a modeling session progresses and more parts get selected, contextual cues become increasingly dominant, and the model quickly converges to a final form. As a key enabler, we use pre‐learned part‐based contextual information to allow the user to quickly explore different combinations of parts.
Quantum causal modelling Costa, Fabio; Shrapnel, Sally
New journal of physics,
06/2016, Volume:
18, Issue:
6
Journal Article
Peer reviewed
Open access
Causal modelling provides a powerful set of tools for identifying causal structure from observed correlations. It is well known that such techniques fail for quantum systems, unless one introduces ...'spooky' hidden mechanisms. Whether one can produce a genuinely quantum framework in order to discover causal structure remains an open question. Here we introduce a new framework for quantum causal modelling that allows for the discovery of causal structure. We define quantum analogues for core features of classical causal modelling techniques, including the causal Markov condition and faithfulness. Based on the process matrix formalism, this framework naturally extends to generalised structures with indefinite causal order.
Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to ...reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.
Abstract Developing modeling competence is an educational objective in many countries such as “SEP 2: Developing and Using Models” in Next Generation Science Standards (NGSS). In Vietnam, the new ...physics-education curriculum has clearly defined the key learning outcomes, including modeling-competence elements as well. Experiencing modelling cycle is an effective way to develop modelling competence for students. Our recent work studies which tool is suitable for students and how to integrate this tool in modelling activities. This paper presents the use of Coach 7 modelling software to investigate common physics phenomenon of oscillations, shows feasibility and effectiveness of these activities via tryout.
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•Archetype development, model components and potential applications for UBEM are discussed.•Stochastic and dynamic occupancy models are among the most desired improvements for ...UBEM.•UBEM could be suitable for high accuracy predictions of future climate change scenarios.•Integration of socio-economic data in UBEM is promising but not currently applied.•Assigning value year from renovation records is tempting but could entail several pitfalls.
Due to rapid urbanisation and the significant contribution of cities to worldwide energy use and greenhouse gas emissions, urban energy system planning is growing more important. Urban building energy modelling (UBEM) draws increasing attention in the energy modelling field due to its inherent capacities for modelling entire cities or building stocks, and the potential of varying data inputs, approaches and applications. This review aims to identify best practices and improvements for UBEM applications by examining previous research, with a focus on the currently least established approaches. Different archetype development procedures are analysed for common problems, six main under-developed input approaches or parameters are identified, and applications for future scenario development are surveyed. By analysing previous studies in related fields, this paper provides an overview of gaps in the published research and possible additions to future UBEM projects that can help expanding the existing modelling procedures. Comprehensive human behaviour models with additional aspects beyond occupant presence are identified as a major point of interest. Further research on socio-economic parameters, such as household income and demographics, are also suggested to further improve modelling. This study also underlines the potential for utilising UBEM as a tool for evaluating future climate change scenarios.
•Modelling approaches for the analysis of micro and small-scales ORC are reviewed.•Peculiarities of micro and small-scales ORC are considered in conducting the review.•Advantages and disadvantages of ...different modelling approaches are provided.•Off-design modelling approaches of the ORC main components are presented.•Assumption-free models for system level modelling are discussed.
Organic Rankine cycle (ORC) systems are a technology capable of producing electricity and heat from a wide range of energy sources and are particularly well-suited for medium and low-temperature sources. However, an almost infinite number of technical solutions (cycle configurations, working fluids, components, etc.) can be adopted making the full experimental characterisation of ORC operations for each application unfeasible. To overcome the limitations of extensive experimental investigations, numerical tools are often adopted, thereby supporting the design and operation of these plants. Therefore, over the last two decades, many researchers have put their efforts into developing models to elucidate the design and off-design performances of ORC systems. In this paper, the different modelling approaches for the analysis of ORC systems are discussed and a conclusive review is performed concerning the micro and small-scale ORCs. In total, more than 160 works are reviewed with many of them related to models of volumetric machines and assumption-based system modelling. Semi-empirical models of expanders show good capabilities and accuracy (with errors below 5%) while spatial resolution methods for heat exchangers are used to better capture the dynamics of the system. However, only a limited number of papers (10) deal with assumption-free models of the systems to predict their performance considering the actual boundary conditions. In summary, the present review paper provides a clear overview of the advantages and disadvantages of each modelling approach at both component and system levels to provide insights for interested readers in the advanced simulation of micro and small-scale ORC systems.
A deep transfer learning method is presented for establishing the aggregated system frequency response (SFR) model of wind-thermal hybrid power systems (HPSs). In order to deal with nonlinearities ...and non-Gaussian disturbances, the quadratic survival information potential (QSIP) of the squared identification error is employed to construct the performance index when training recurrent neural networks (RNNs). A pre-trained SFR model is then obtained by the improved RNNs using the source domain data collected from the HPS in historical scenarios. Subsequently, the maximum mean difference is utilized to test the similarity of the HPS in historical and current scenarios. After that, the pre-trained SFR model is fine-tuned by adding some nodes to the recurrent layer and a functional link to the input layer. The SFR model of the HPS operating in current scenario can then be built based on the transferred source domain pre-trained SFR model. Simulation results illustrate that the proposed data driven modelling method can obtain accurate, effective and timely SFR model for a wind-thermal HPS with different wind speeds and load disturbances.
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•Applying 3D printing as a novel printing process for deposition of polymers on fabrics.•Possible use of proposed method for developing innovative smart textiles by integrating ...functional polymers and composites.•Significant effect of different 3D printing processing parameters on the adhesion of polymers to fabrics.•High adhesion force of deposited PLA and PLA nanocomposites on PLA fabrics.
In this paper, 3D printing as a novel printing process was considered for deposition of polymers on synthetic fabrics to introduce more flexible, resource-efficient and cost effective textile functionalization processes than conventional printing process like screen and inkjet printing. The aim is to develop an integrated or tailored production process for smart and functional textiles which avoid unnecessary use of water, energy, chemicals and minimize the waste to improve ecological footprint and productivity. Adhesion of polymer and nanocomposite layers which were 3D printed directly onto the textile fabrics using fused deposition modeling (FDM) technique was investigated. Different variables which may affect the adhesion properties including 3D printing process parameters, fabric type and filler type incorporated in polymer were considered. A rectangular shape according to the peeling standard was designed as 3D computer-aided design (CAD) to find out the effect of the different variables. The polymers were printed in different series of experimental design: nylon on polyamide 66 (PA66) fabrics, polylactic acid (PLA) on PA66 fabric, PLA on PLA fabric, and finally nanosize carbon black/PLA (CB/PLA) and multi-wall carbon nanotubes/PLA (CNT/PLA) nanocomposites on PLA fabrics. The adhesion forces were quantified using the innovative sample preparing method combining with the peeling standard method. Results showed that different variables of 3D printing process like extruder temperature, platform temperature and printing speed can have significant effect on adhesion force of polymers to fabrics while direct 3D printing. A model was proposed specifically for deposition of a commercial 3D printer Nylon filament on PA66 fabrics. In the following, among the printed polymers, PLA and its composites had high adhesion force to PLA fabrics.
The maximum photosynthetic carboxylation rate (V
cmax) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross ...primary production (GPP).
Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V
cmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM).
Global GPP varied from 108.1 to 128.2 PgC yr−1, 65% of the range of a recent model inter-comparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r = 0.85–0.91) with three proxies of global GPP.
Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V
cmax variation in the field, particularly in northern latitudes.
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