2,681 research outputs found
Integration of Legacy Appliances into Home Energy Management Systems
The progressive installation of renewable energy sources requires the
coordination of energy consuming devices. At consumer level, this coordination
can be done by a home energy management system (HEMS). Interoperability issues
need to be solved among smart appliances as well as between smart and
non-smart, i.e., legacy devices. We expect current standardization efforts to
soon provide technologies to design smart appliances in order to cope with the
current interoperability issues. Nevertheless, common electrical devices affect
energy consumption significantly and therefore deserve consideration within
energy management applications. This paper discusses the integration of smart
and legacy devices into a generic system architecture and, subsequently,
elaborates the requirements and components which are necessary to realize such
an architecture including an application of load detection for the
identification of running loads and their integration into existing HEM
systems. We assess the feasibility of such an approach with a case study based
on a measurement campaign on real households. We show how the information of
detected appliances can be extracted in order to create device profiles
allowing for their integration and management within a HEMS
Models of Interaction as a Grounding for Peer to Peer Knowledge Sharing
Most current attempts to achieve reliable knowledge sharing on a large scale have relied on pre-engineering of content and supply services. This, like traditional knowledge engineering, does not by itself scale to large, open, peer to peer systems because the cost of being precise about the absolute semantics of services and their knowledge rises rapidly as more services participate. We describe how to break out of this deadlock by focusing on semantics related to interaction and using this to avoid dependency on a priori semantic agreement; instead making semantic commitments incrementally at run time. Our method is based on interaction models that are mobile in the sense that they may be transferred to other components, this being a mechanism for service composition and for coalition formation. By shifting the emphasis to interaction (the details of which may be hidden from users) we can obtain knowledge sharing of sufficient quality for sustainable communities of practice without the barrier of complex meta-data provision prior to community formation
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OntoKin: An Ontology for Chemical Kinetic Reaction Mechanisms.
An ontology for capturing both data and the semantics of chemical kinetic reaction mechanisms has been developed. Such mechanisms can be applied to simulate and understand the behavior of chemical processes, for example, the emission of pollutants from internal combustion engines. An ontology development methodology was used to produce the semantic model of the mechanisms, and a tool was developed to automate the assertion process. As part of the development methodology, the ontology is formally represented using a web ontology language (OWL), assessed by domain experts, and validated by applying a reasoning tool. The resulting ontology, termed OntoKin, has been used to represent example mechanisms from the literature. OntoKin and its instantiations are integrated to create a knowledge base (KB), which is deployed using the RDF4J triple store. The use of the OntoKin ontology and the KB is demonstrated for three use cases-querying across mechanisms, modeling atmospheric pollution dispersion, and as a mechanism browser tool. As part of the query use case, the OntoKin tools have been applied by a chemist to identify variations in the rate of a prompt NOx formation reaction in the combustion of ammonia as represented by four mechanisms in the literature
Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations
Recently, tremendous interest has been devoted to develop data fusion
strategies for energy efficiency in buildings, where various kinds of
information can be processed. However, applying the appropriate data fusion
strategy to design an efficient energy efficiency system is not
straightforward; it requires a priori knowledge of existing fusion strategies,
their applications and their properties. To this regard, seeking to provide the
energy research community with a better understanding of data fusion strategies
in building energy saving systems, their principles, advantages, and potential
applications, this paper proposes an extensive survey of existing data fusion
mechanisms deployed to reduce excessive consumption and promote sustainability.
We investigate their conceptualizations, advantages, challenges and drawbacks,
as well as performing a taxonomy of existing data fusion strategies and other
contributing factors. Following, a comprehensive comparison of the
state-of-the-art data fusion based energy efficiency frameworks is conducted
using various parameters, including data fusion level, data fusion techniques,
behavioral change influencer, behavioral change incentive, recorded data,
platform architecture, IoT technology and application scenario. Moreover, a
novel method for electrical appliance identification is proposed based on the
fusion of 2D local texture descriptors, where 1D power signals are transformed
into 2D space and treated as images. The empirical evaluation, conducted on
three real datasets, shows promising performance, in which up to 99.68%
accuracy and 99.52% F1 score have been attained. In addition, various open
research challenges and future orientations to improve data fusion based energy
efficiency ecosystems are explored
Computationally Linking Chemical Exposure to Molecular Effects with Complex Data: Comparing Methods to Disentangle Chemical Drivers in Environmental Mixtures and Knowledge-based Deep Learning for Predictions in Environmental Toxicology
Chemical exposures affect the environment and may lead to adverse outcomes in its organisms. Omics-based approaches, like standardised microarray experiments, have expanded the toolbox to monitor the distribution of chemicals and assess the risk to organisms in the environment. The resulting complex data have extended the scope of toxicological knowledge bases and published literature. A plethora of computational approaches have been applied in environmental toxicology considering systems biology and data integration. Still, the complexity of environmental and biological systems given in data challenges investigations of exposure-related effects. This thesis aimed at computationally linking chemical exposure to biological effects on the molecular level considering sources of complex environmental data.
The first study employed data of an omics-based exposure study considering mixture effects in a freshwater environment. We compared three data-driven analyses in their suitability to disentangle mixture effects of chemical exposures to biological effects and their reliability in attributing potentially adverse outcomes to chemical drivers with toxicological databases on gene and pathway levels. Differential gene expression analysis and a network inference approach resulted in toxicologically meaningful outcomes and uncovered individual chemical effects â stand-alone and in combination. We developed an integrative computational strategy to harvest exposure-related gene associations from environmental samples considering mixtures of lowly concentrated compounds. The applied approaches allowed assessing the hazard of chemicals more systematically with correlation-based compound groups.
This dissertation presents another achievement toward a data-driven hypothesis generation for molecular exposure effects. The approach combined text-mining and deep learning. The study was entirely data-driven and involved state-of-the-art computational methods of artificial intelligence. We employed literature-based relational data and curated toxicological knowledge to predict chemical-biomolecule interactions. A word embedding neural network with a subsequent feed-forward network was implemented. Data augmentation and recurrent neural networks were beneficial for training with curated toxicological knowledge. The trained models reached accuracies of up to 94% for unseen test data of the employed knowledge base.
However, we could not reliably confirm known chemical-gene interactions across selected data sources. Still, the predictive models might derive unknown information from toxicological knowledge sources, like literature, databases or omics-based exposure studies. Thus, the deep learning models might allow predicting hypotheses of exposure-related molecular effects.
Both achievements of this dissertation might support the prioritisation of chemicals for testing and an intelligent selection of chemicals for monitoring in future exposure studies.:Table of Contents ... I
Abstract ... V
Acknowledgements ... VII
Prelude ... IX
1 Introduction
1.1 An overview of environmental toxicology ... 2
1.1.1 Environmental toxicology ... 2
1.1.2 Chemicals in the environment ... 4
1.1.3 Systems biological perspectives in environmental toxicology ... 7
Computational toxicology ... 11
1.2.1 Omics-based approaches ... 12
1.2.2 Linking chemical exposure to transcriptional effects ... 14
1.2.3 Up-scaling from the gene level to higher biological organisation levels ... 19
1.2.4 Biomedical literature-based discovery ... 24
1.2.5 Deep learning with knowledge representation ... 27
1.3 Research question and approaches ... 29
2 Methods and Data ... 33
2.1 Linking environmental relevant mixture exposures to transcriptional effects ... 34
2.1.1 Exposure and microarray data ... 34
2.1.2 Preprocessing ... 35
2.1.3 Differential gene expression ... 37
2.1.4 Association rule mining ... 38
2.1.5 Weighted gene correlation network analysis ... 39
2.1.6 Method comparison ... 41
Predicting exposure-related effects on a molecular level ... 44
2.2.1 Input ... 44
2.2.2 Input preparation ... 47
2.2.3 Deep learning models ... 49
2.2.4 Toxicogenomic application ... 54
3 Method comparison to link complex stream water exposures to effects on
the transcriptional level ... 57
3.1 Background and motivation ... 58
3.1.1 Workflow ... 61
3.2 Results ... 62
3.2.1 Data preprocessing ... 62
3.2.2 Differential gene expression analysis ... 67
3.2.3 Association rule mining ... 71
3.2.4 Network inference ... 78
3.2.5 Method comparison ... 84
3.2.6 Application case of method integration ... 87
3.3 Discussion ... 91
3.4 Conclusion ... 99
4 Deep learning prediction of chemical-biomolecule interactions ... 101
4.1 Motivation ... 102
4.1.1Workflow ...105
4.2 Results ... 107
4.2.1 Input preparation ... 107
4.2.2 Model selection ... 110
4.2.3 Model comparison ... 118
4.2.4 Toxicogenomic application ... 121
4.2.5 Horizontal augmentation without tail-padding ...123
4.2.6 Four-class problem formulation ... 124
4.2.7 Training with CTD data ... 125
4.3 Discussion ... 129
4.3.1 Transferring biomedical knowledge towards toxicology ... 129
4.3.2 Deep learning with biomedical knowledge representation ...133
4.3.3 Data integration ...136
4.4 Conclusion ... 141
5 Conclusion and Future perspectives ... 143
5.1 Conclusion ... 143
5.1.1 Investigating complex mixtures in the environment ... 144
5.1.2 Complex knowledge from literature and curated databases predict chemical-
biomolecule interactions ... 145
5.1.3 Linking chemical exposure to biological effects by integrating CTD ... 146
5.2 Future perspectives ... 147
S1 Supplement Chapter 1 ... 153
S1.1 Example of an estrogen bioassay ... 154
S1.2 Types of mode of action ... 154
S1.3 The dogma of molecular biology ... 157
S1.4 Transcriptomics ... 159
S2 Supplement Chapter 3 ... 161
S3 Supplement Chapter 4 ... 175
S3.1 Hyperparameter tuning results ... 176
S3.2 Functional enrichment with predicted chemical-gene interactions and CTD reference pathway genesets ... 179
S3.3 Reduction of learning rate in a model with large word embedding vectors ... 183
S3.4 Horizontal augmentation without tail-padding ... 183
S3.5 Four-relationship classification ... 185
S3.6 Interpreting loss observations for SemMedDB trained models ... 187
List of Abbreviations ... i
List of Figures ... vi
List of Tables ... x
Bibliography ... xii
Curriculum scientiae ... xxxix
SelbstÀndigkeitserklÀrung ... xlii
Facilitating Knowledge Sharing and Analysis in EnergyInformatics with the Ontology for Energy Investigations (OEI)
Just as the other informatics-related domains (e.g., Bioinformatics) have discovered in recent years, the ever-growing domain of Energy Informatics (EI) can benefit from the use of ontologies, formalized, domain-specific taxonomies or vocabularies that are shared by a community of users. In this paper, an overview of the Ontology for Energy Investigations (OEI), an ontology that extends a subset of the well-conceived and heavily-researched Ontology for Biomedical Investigations (OBI), is provided as well as a motivating example demonstrating how the use of a formal ontology for the EI domain can facilitate correct and consistent knowledge sharing and the multi-level analysis of its data and scientific investigations
EBOCA: Evidences for BiOmedical Concepts Association Ontology
There is a large number of online documents data sources available nowadays.
The lack of structure and the differences between formats are the main
difficulties to automatically extract information from them, which also has a
negative impact on its use and reuse. In the biomedical domain, the DISNET
platform emerged to provide researchers with a resource to obtain information
in the scope of human disease networks by means of large-scale heterogeneous
sources. Specifically in this domain, it is critical to offer not only the
information extracted from different sources, but also the evidence that
supports it. This paper proposes EBOCA, an ontology that describes (i)
biomedical domain concepts and associations between them, and (ii) evidences
supporting these associations; with the objective of providing an schema to
improve the publication and description of evidences and biomedical
associations in this domain. The ontology has been successfully evaluated to
ensure there are no errors, modelling pitfalls and that it meets the previously
defined functional requirements. Test data coming from a subset of DISNET and
automatic association extractions from texts has been transformed according to
the proposed ontology to create a Knowledge Graph that can be used in real
scenarios, and which has also been used for the evaluation of the presented
ontology
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