AIM: Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict ...species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo. LOCATION: Borneo, Southeast Asia. METHODS: We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range‐restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north‐eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. RESULTS: Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. MAIN CONCLUSIONS: We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.
As an art form between drawing and sculpture, relief has been widely used in a variety of media for signs, narratives, decorations and other purposes. Traditional relief creation relies on both ...professional skills and artistic expertise, which is extremely time‐consuming. Recently, automatic or semi‐automatic relief modelling from a 3D object or a 2D image has been a subject of interest in computer graphics. Various methods have been proposed to generate reliefs with few user interactions or minor human efforts, while preserving or enhancing the appearance of the input. This survey provides a comprehensive review of the advances in computer‐assisted relief modelling during the past decade. First, we provide an overview of relief types and their art characteristics. Then, we introduce the key techniques of object‐space methods and image‐space methods respectively. Advantages and limitations of each category are discussed in details. We conclude the report by discussing directions for possible future research.
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.
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.
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.
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.
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.
•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.
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