scholarly journals Building clone-consistent ecosystem models

2021 ◽  
Vol 17 (2) ◽  
pp. e1008635
Author(s):  
Gerrit Ansmann ◽  
Tobias Bollenbach
Keyword(s):  
Functional Analysis ◽  
Building Blocks ◽  
Data Driven ◽  
Ecological Studies ◽  
Ecosystem Models ◽  
Linear Combinations ◽  
Model Consistency ◽  
Wide Range ◽  
Critical Requirement ◽  
Two Populations

Many ecological studies employ general models that can feature an arbitrary number of populations. A critical requirement imposed on such models is clone consistency: If the individuals from two populations are indistinguishable, joining these populations into one shall not affect the outcome of the model. Otherwise a model produces different outcomes for the same scenario. Using functional analysis, we comprehensively characterize all clone-consistent models: We prove that they are necessarily composed from basic building blocks, namely linear combinations of parameters and abundances. These strong constraints enable a straightforward validation of model consistency. Although clone consistency can always be achieved with sufficient assumptions, we argue that it is important to explicitly name and consider the assumptions made: They may not be justified or limit the applicability of models and the generality of the results obtained with them. Moreover, our insights facilitate building new clone-consistent models, which we illustrate for a data-driven model of microbial communities. Finally, our insights point to new relevant forms of general models for theoretical ecology. Our framework thus provides a systematic way of comprehending ecological models, which can guide a wide range of studies.

scholarly journals Building clone-consistent ecosystem models

2019 ◽  
Author(s):  
Gerrit Ansmann ◽  
Tobias Bollenbach
Keyword(s):  
Functional Analysis ◽  
Building Blocks ◽  
Data Driven ◽  
Ecological Studies ◽  
Ecosystem Models ◽  
Linear Combinations ◽  
Model Consistency ◽  
Wide Range ◽  
Critical Requirement ◽  
Two Populations

Many ecological studies employ general models that can feature an arbitrary number of populations. A critical requirement imposed on such models is clone consistency: If the individuals from two populations are indistinguishable, joining these populations into one shall not affect the outcome of the model. Otherwise a model produces different outcomes for the same scenario. Using functional analysis, we comprehensively characterize all clone-consistent models: We prove that they are necessarily composed from basic building blocks, namely linear combinations of parameters and abundances. These strong constraints enable a straightforward validation of model consistency or reveal implicit assumptions required to achieve it. We show that such implicit assumptions can considerably limit the applicability of models and the generality of results obtained with them. Moreover, our insights facilitate building new clone-consistent models, which we illustrate for a data-driven model of microbial communities. Finally, our insights point to new relevant forms of general models for theoretical ecology. Our framework thus provides a systematic way of comprehending ecological models, which can guide a wide range of studies.

scholarly journals Discovering multiscale and self-similar structure with data-driven wavelets

2020 ◽  
Vol 118 (1) ◽  
pp. e2021299118
Author(s):  
Daniel Floryan ◽  
Michael D. Graham
Keyword(s):  
Multiple Scales ◽  
Spatial Scales ◽  
Building Blocks ◽  
Biological Tissues ◽  
Data Driven ◽  
Model Free ◽  
Starting Point ◽  
Wide Range ◽  
Direct Model ◽  
Self Similar

Many materials, processes, and structures in science and engineering have important features at multiple scales of time and/or space; examples include biological tissues, active matter, oceans, networks, and images. Explicitly extracting, describing, and defining such features are difficult tasks, at least in part because each system has a unique set of features. Here, we introduce an analysis method that, given a set of observations, discovers an energetic hierarchy of structures localized in scale and space. We call the resulting basis vectors a “data-driven wavelet decomposition.” We show that this decomposition reflects the inherent structure of the dataset it acts on, whether it has no structure, structure dominated by a single scale, or structure on a hierarchy of scales. In particular, when applied to turbulence—a high-dimensional, nonlinear, multiscale process—the method reveals self-similar structure over a wide range of spatial scales, providing direct, model-free evidence for a century-old phenomenological picture of turbulence. This approach is a starting point for the characterization of localized hierarchical structures in multiscale systems, which we may think of as the building blocks of these systems.

Physics Informed Machine Learning of Rainfall-Runoff Processes

2020 ◽  
Author(s):  
Vladan Babovic ◽  
Jayashree Chadalawada ◽  
Herath Mudiyanselage Viraj Vidura Herath
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Hydrological Model ◽  
Building Blocks ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Rainfall Runoff ◽  
Learning Techniques ◽  
Highly Nonlinear ◽  
Wide Range

<p>Modelling of rainfall-runoff phenomenon continues to be a challenging task at hand of hydrologists as the underlying processes are highly nonlinear, dynamic and interdependent. Numerous modelling strategies like empirical, conceptual, physically based, data driven, are used to develop rainfall-runoff models as no model type can be considered to be universally pertinent for a wide range of problems. Latest literature review emphasizes that the crucial step of hydrological model selection is often subjective and is based on legacy. As the research outcome depends on model choice, there is a necessity to automate the process of model evolution, evaluation and selection based on research objectives, temporal and spatial characteristics of available data and catchment properties. Therefore, this study proposes a novel automated model building algorithm relying on machine learning technique Genetic Programming (GP).</p><p>State of art GP applications in rainfall-runoff modelling as yet used the algorithm as a short-term forecasting tool which produces an expected future time series very much alike to neural networks application. Such simplistic applications of data driven black-box machine learning techniques may lead to development of accurate yet meaningless models which do not satisfy basic hydrological insights and may have severe difficulties with interpretation. Concurrently, it should be admitted that there is a vast amount of knowledge and understanding of physical processes that should not just be thrown away. Thus, we strongly believe that the most suitable way forward is to couple the already existing body of knowledge with machine learning techniques in a guided manner to enhance the meaningfulness and interpretability of the induced models.</p><p>In this suggested algorithm the domain knowledge is introduced through the incorporation of process knowledge by adding model building blocks from prevailing rainfall-runoff modelling frameworks into the GP function set. Presently, the function set library consists with Sugawara TANK model functions, generic components of two flexible rainfall-runoff modelling frameworks (FUSE and SUPERFLEX) and model equations of 46 existing hydrological models (MARRMoT). Nevertheless, perhaps more importantly, the algorithm is readily integratable with any other internal coherence building blocks. This approach contrasts from rest of machine learning applications in rainfall-runoff modelling as it not only produces the runoff predictions but develops a physically meaningful hydrological model which helps the hydrologist to better understand the catchment dynamics. The proposed algorithm considers the model space and automatically identifies the appropriate model configurations for a catchment of interest by optimizing user-defined learning objectives in a multi-objective optimization framework. The model induction capabilities of the proposed algorithm have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model-building algorithm are compatible with the fieldwork investigations and previously reported research findings.</p>

Factors Affecting Molecular Self-Assembly and Its Mechanism

2012 ◽  
Vol 9 (1) ◽  
pp. 43 ◽  
Author(s):  
Hueyling Tan
Keyword(s):  
Self Assembly ◽  
Building Blocks ◽  
Molecular Engineering ◽  
Molecular Structures ◽  
Polymer Science ◽  
New Approach ◽  
Factors Affecting ◽  
Dna Structures ◽  
Self Assembling ◽  
Wide Range

Molecular self-assembly is ubiquitous in nature and has emerged as a new approach to produce new materials in chemistry, engineering, nanotechnology, polymer science and materials. Molecular self-assembly has been attracting increasing interest from the scientific community in recent years due to its importance in understanding biology and a variety of diseases at the molecular level. In the last few years, considerable advances have been made in the use ofpeptides as building blocks to produce biological materials for wide range of applications, including fabricating novel supra-molecular structures and scaffolding for tissue repair. The study ofbiological self-assembly systems represents a significant advancement in molecular engineering and is a rapidly growing scientific and engineering field that crosses the boundaries ofexisting disciplines. Many self-assembling systems are rangefrom bi- andtri-block copolymers to DNA structures as well as simple and complex proteins andpeptides. The ultimate goal is to harness molecular self-assembly such that design andcontrol ofbottom-up processes is achieved thereby enabling exploitation of structures developed at the meso- and macro-scopic scale for the purposes oflife and non-life science applications. Such aspirations can be achievedthrough understanding thefundamental principles behind the selforganisation and self-synthesis processes exhibited by biological systems.

Stereospecific, Palladium-catalyzed C(sp3)–H Alkenylation and Alkynylation of a Proline Derivative Enabled by 8-Aminoquinoline as a Directing Group

2020 ◽  
Author(s):  
Aleksandra Balliu ◽  
Aaltje Roelofje Femmigje Strijker ◽  
Michael Oschmann ◽  
Monireh Pourghasemi Lati ◽  
Oscar Verho
Keyword(s):  
Carboxylic Acid ◽  
Building Blocks ◽  
Wide Range ◽  
Palladium Catalyzed ◽  
Directing Group ◽  
High Yields ◽  
Vinyl Iodides ◽  
Initial Results

<p>In this preprint, we present our initial results concerning a stereospecific Pd-catalyzed protocol for the C3 alkenylation and alkynylation of a proline derivative carrying the well utilized 8‑aminoquinoline directing group. Efficient C–H alkenylation was achieved with a wide range of vinyl iodides bearing different aliphatic, aromatic and heteroaromatic substituents, to furnish the corresponding C3 alkenylated products in good to high yields. In addition, we were able show that this protocol can also be used to install an alkynyl group into the pyrrolidine scaffold, when a TIPS-protected alkynyl bromide was used as the reaction partner. Furthermore, two different methods for the removal of the 8-aminoquinoline auxiliary are reported, which can enable access to both <i>cis</i>- and <i>trans</i>-configured carboxylic acid building blocks from the C–H alkenylation products.</p>

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

Application of Nitrogen Heterocyclic Carbenes in Organocatalysis

2020 ◽  
Vol 09 ◽  
Author(s):  
Minita Ojha ◽  
R. K. Bansal
Keyword(s):  
Asymmetric Catalysis ◽  
Building Blocks ◽  
Structural Features ◽  
Heterocyclic Carbenes ◽  
Stetter Reaction ◽  
Metal Pollutants ◽  
Synthetic Strategy ◽  
Wide Range ◽  
Benzoin Condensation ◽  
Nitrogen Heterocyclic

Background: During the last two decades, horizon of research in the field of Nitrogen Heterocyclic Carbenes (NHC) has widened remarkably. NHCs have emerged as ubiquitous species having applications in a broad range of fields, including organocatalysis and organometallic chemistry. The NHC-induced non-asymmetric catalysis has turned out to be a really fruitful area of research in recent years. Methods: By manipulating structural features and selecting appropriate substituent groups, it has been possible to control the kinetic and thermodynamic stability of a wide range of NHCs, which can be tolerant to a variety of functional groups and can be used under mild conditions. NHCs are produced by different methods, such as deprotonation of Nalkylhetrocyclic salt, transmetallation, decarboxylation and electrochemical reduction. Results: The NHCs have been used successfully as catalysts for a wide range of reactions making a large number of building blocks and other useful compounds accessible. Some of these reactions are: benzoin condensation, Stetter reaction, Michael reaction, esterification, activation of esters, activation of isocyanides, polymerization, different cycloaddition reactions, isomerization, etc. The present review includes all these examples published during the last 10 years, i.e. from 2010 till date. Conclusion: The NHCs have emerged as versatile and powerful organocatalysts in synthetic organic chemistry. They provide the synthetic strategy which does not burden the environment with metal pollutants and thus fit in the Green Chemistry.

scholarly journals MXenes for future nanophotonic device applications

Nanophotonics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1831-1853
Author(s):  
Jaeho Jeon ◽  
Yajie Yang ◽  
Haeju Choi ◽  
Jin-Hong Park ◽  
Byoung Hun Lee ◽  
...  
Keyword(s):  
Solar Cells ◽  
Transition Metal ◽  
Wide Spectrum ◽  
Building Blocks ◽  
Optoelectronic Device ◽  
High Yield ◽  
Saturable Absorption ◽  
Nanophotonic Device ◽  
Wide Range ◽  
Device Applications

AbstractTwo-dimensional (2D) layers of transition metal carbides, nitrides, or carbonitrides, collectively referred to as MXenes, are considered as the new family of 2D materials for the development of functional building blocks for optoelectronic and photonic device applications. Their advantages are based on their unique and tunable electronic and optical properties, which depend on the modulation of transition metal elements or surface functional groups. In this paper, we have presented a comprehensive review of MXenes to suggest an insightful perspective on future nanophotonic and optoelectronic device applications based on advanced synthesis processes and theoretically predicted or experimentally verified material properties. Recently developed optoelectronic and photonic devices, such as photodetectors, solar cells, fiber lasers, and light-emitting diodes are summarized in this review. Wide-spectrum photodetection with high photoresponsivity, high-yield solar cells, and effective saturable absorption were achieved by exploiting different MXenes. Further, the great potential of MXenes as an electrode material is predicted with a controllable work function in a wide range (1.6–8 eV) and high conductivity (~104 S/cm), and their potential as active channel material by generating a tunable energy bandgap is likewise shown. MXene can provide new functional building blocks for future generation nanophotonic device applications.

scholarly journals Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

2021 ◽  
pp. 204141962199349
Author(s):  
Jordan J Pannell ◽  
George Panoutsos ◽  
Sam B Cooke ◽  
Dan J Pope ◽  
Sam E Rigby
Keyword(s):  
Scaling Laws ◽  
Near Field ◽  
Specific Impulse ◽  
Gaussian Function ◽  
Data Driven ◽  
Blast Load ◽  
Structural Deformation ◽  
Load Prediction ◽  
Validation Data ◽  
Wide Range

Accurate quantification of the blast load arising from detonation of a high explosive has applications in transport security, infrastructure assessment and defence. In order to design efficient and safe protective systems in such aggressive environments, it is of critical importance to understand the magnitude and distribution of loading on a structural component located close to an explosive charge. In particular, peak specific impulse is the primary parameter that governs structural deformation under short-duration loading. Within this so-called extreme near-field region, existing semi-empirical methods are known to be inaccurate, and high-fidelity numerical schemes are generally hampered by a lack of available experimental validation data. As such, the blast protection community is not currently equipped with a satisfactory fast-running tool for load prediction in the near-field. In this article, a validated computational model is used to develop a suite of numerical near-field blast load distributions, which are shown to follow a similar normalised shape. This forms the basis of the data-driven predictive model developed herein: a Gaussian function is fit to the normalised loading distributions, and a power law is used to calculate the magnitude of the curve according to established scaling laws. The predictive method is rigorously assessed against the existing numerical dataset, and is validated against new test models and available experimental data. High levels of agreement are demonstrated throughout, with typical variations of <5% between experiment/model and prediction. The new approach presented in this article allows the analyst to rapidly compute the distribution of specific impulse across the loaded face of a wide range of target sizes and near-field scaled distances and provides a benchmark for data-driven modelling approaches to capture blast loading phenomena in more complex scenarios.

Synthesis of sp3-Enriched β-Fluoro Sulfonyl Chlorides

Synthesis ◽  
2020 ◽  
Author(s):  
Oleksandr O. Grygorenko ◽  
Rustam Gurbanov ◽  
Andriy Sokolov ◽  
Sergey Golovach ◽  
Kostiantyn Melnykov ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Double Bond ◽  
Building Blocks ◽  
Protecting Groups ◽  
Sulfonyl Chlorides ◽  
Wide Range ◽  
Cyclic Compounds

AbstractA three-step approach to the synthesis of sp3-enriched β-fluoro sulfonyl chlorides starting from alkenes is reported. The method was successfully applied to a wide range of acyclic and cyclic substrates, bearing either an exocyclic or an endocyclic double bond. The procedure worked with a wide range of substrates and tolerated a number of functional and protecting groups. Moreover, the target cyclic compounds were obtained as single cis diastereomers on a multigram scale. The title compounds are promising building blocks for drug discovery that can be used to obtain sp3-enriched β-fluoro and α,β-unsaturated sulfonamides.

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