Predicting Coupled Electron and Phonon Transport Using Steepest-Entropy-Ascent Quantum Thermodynamics

The current state of the art for determining thermoelectric properties is limited to the investigation of electrons or phonons without including the inherent electron-phonon coupling that is in all materials. This gives rise to limitations in accurately calculating base material properties that are in good agreement with experimental data. Steepest-entropy-ascent quantum thermodynamics is a general non-equilibrium thermodynamic ensemble framework that provides a general equation of motion for non-equilibrium system state evolution. This framework utilizes the electron and phonon density of states as input to compute material properties, while taking into account the electron-phonon coupling. It is able to span across multiple spatial and temporal scales in a single analysis. Any system's thermoelectric properties can, therefore, be attained provided the accurately determined density of states is available.Comment: Supplementary Materials Section is the last two pages of the manuscrip

A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach

Framework for Interior Design Work Development Plan (IDWDP) in Malaysian context / Arniatul Aiza Mustapha

The works of an interior designer in managing interior design projects have been recognized as similar as an architect, as well as the work processes. The interior designer takes part on theme an ideal space, and the architect addressing the theme for the buildings as well as general spatial layout. Particularly, the scope and process in interior project delivery conducted by interior designer has frequently been argued on lacking of standard of scope of work, which was raised disputes amongst the team players, on the work coordination, process and flow. Initially, in searching to address this issue, this research was undertaken to confirm the arguments and to develop a management framework for interior designer in managing interior design projects. The argument was on to what extent is the existence of standard work development plan, and what are the definite needs of scope of work for interior design project delivery, the processes, flow, gaps, and work elements of each work stages. A qualitative research methodology was employing for the research. A preliminary investigation has been executed amongst the interior design project team players including the interior designers themselves. A scrupulous content analysis from a written documents collected was executed verbatim, to set up for a data base. Sets of interviews amongst twelve key respondents for twenty current ongoing and completed projects to record the work flow, processes and scopes. The interior design framework was employed and tested to a selected interor design work development process framework to identify the gaps of the work flow and process of interior design project delivery. The findings found significant gaps in the current interior design project delivery practices which this is closely related to the issue stated, the non existence of appropriate written standard documents of plan of work for interior design project delivery practice. Significant of new knowledge related to the interior design projects delivery was identified through this research, where with this framework, the interior projects delivery process can be improvised

BIM semantic-enrichment for built heritage representation

In the built heritage context, BIM has shown difficulties in representing and managing the large and complex knowledge related to non-geometrical aspects of the heritage. Within this scope, this paper focuses on a domain-specific semantic-enrichment of BIM methodology, aimed at fulfilling semantic representation requirements of built heritage through Semantic Web technologies. To develop this semantic-enriched BIM approach, this research relies on the integration of a BIM environment with a knowledge base created through information ontologies. The result is knowledge base system - and a prototypal platform - that enhances semantic representation capabilities of BIM application to architectural heritage processes. It solves the issue of knowledge formalization in cultural heritage informative models, favouring a deeper comprehension and interpretation of all the building aspects. Its open structure allows future research to customize, scale and adapt the knowledge base different typologies of artefacts and heritage activities

Knowledge-based systems and geological survey

This personal and pragmatic review of the philosophy underpinning methods of geological surveying suggests that important influences of information technology have yet to make their impact. Early approaches took existing systems as metaphors, retaining the separation of maps, map explanations and information archives, organised around map sheets of fixed boundaries, scale and content. But system design should look ahead: a computer-based knowledge system for the same purpose can be built around hierarchies of spatial objects and their relationships, with maps as one means of visualisation, and information types linked as hypermedia and integrated in mark-up languages. The system framework and ontology, derived from the general geoscience model, could support consistent representation of the underlying concepts and maintain reference information on object classes and their behaviour. Models of processes and historical configurations could clarify the reasoning at any level of object detail and introduce new concepts such as complex systems. The up-to-date interpretation might centre on spatial models, constructed with explicit geological reasoning and evaluation of uncertainties. Assuming (at a future time) full computer support, the field survey results could be collected in real time as a multimedia stream, hyperlinked to and interacting with the other parts of the system as appropriate. Throughout, the knowledge is seen as human knowledge, with interactive computer support for recording and storing the information and processing it by such means as interpolating, correlating, browsing, selecting, retrieving, manipulating, calculating, analysing, generalising, filtering, visualising and delivering the results. Responsibilities may have to be reconsidered for various aspects of the system, such as: field surveying; spatial models and interpretation; geological processes, past configurations and reasoning; standard setting, system framework and ontology maintenance; training; storage, preservation, and dissemination of digital records

Assessing, valuing and protecting our environment- is there a statistical challenge to be answered?

This short article describes some of the evolution in environmental regulation, management and monitoring and the information needs, closely aligned to the statistical challenges to deliver the evidence base for change and effect

Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI Data

University of Minnesota Ph.D. dissertation. June 2019. Major: Biostatistics. Advisors: Joseph Koopmeiners, Lin Zhang. 1 computer file (PDF); xiv, 134 pages.As a continuously developing tool for the diagnosis and prognosis of prostate cancer, multi-parametric magnetic resonance imaging (mpMRI) has been widely used in a variety of prostate cancer-related topics. While current research has shown the great potential of mpMRI in detecting prostate cancer, further investigation is needed for modeling some specific features of mpMRI, including the anatomic difference between different regions of a prostate, the spatial correlation between voxels within each prostate image, and the difference in the distribution of the observed mpMRI parameters between patients. This dissertation focuses on novel statistical methods for the voxel-wise classification of prostate cancer using mpMRI data. Systematic modeling frameworks will be proposed to improve cancer classification by incorporating the aforementioned features of mpMRI. Three topics are discussed in depth: (1) development of a general Bayesian modeling framework that can incorporate the various mpMRI features; (2) how to model the mpMRI features in the proposed Bayesian framework, preferably in a computationally efficient manner; (3) development of an alternative approach to accounting for the mpMRI features, which uses a multi-resolution modeling technique to account for the regional heterogeneity, and is flexible to be extended to more complex classification problems for prostate cancer. The solutions are presented in the following order. In Chapter 2, we propose a Bayesian hierarchical modeling framework that allows complex distributional assumptions for the various data components. Based on the modeling framework, two approaches will be proposed for modeling the heterogeneity between regions of the prostate, which can be combined with a spatial Gaussian kernel smoother to account for residual spatial correlation and reduce random noise in the data. In Chapter 3, we add additional layers in the hierarchical model to model the spatial correlation structure and between-patient heterogeneity. Modeling the spatial correlation structure is computationally challenging and even infeasible for our mpMRI data set, due to the large number of voxels within each image. Three scalable spatial modeling approaches are then proposed for the correlation between voxels. In Chapter 4, we develop an alternative, machine learning-based method to account for the mpMRI features: a super learner with an ensemble learning technique is utilized to combine base learners trained in multi-resolution sub-regions. Specific algorithms will be introduced for both the classification of binary cancer status, and a more complex problem: classification of an ordinal outcome that indicates the clinical significance of prostate cancer. Method performance will be illustrated by simulation studies and applications to in vivo data that motivated the method's development

Investigation of an integrated regional environmental watershed methodology

Recent public awareness of the environment has placed increased emphasis on the health and current state of the regional watershed. The watershed has been defined as that area in which water flowing across and beneath a given land surface drains into a specific stream or river, ultimately flowing through a single point or outlet on that stream or river. Since the processes involved are many and are analyzed in the literature on an individual basis, the current investigation attempts a more holistic approach by suggesting a methodology that integrates all elements of the hydrologic cycle. The investigation utilizes the area topography in the form of a digital elevation model (DEM) as the base for analysis. Basic to any watershed model is a characterization of the water flow in streams by a mathematical function expressed through the hydrograph. The investigation explores the hydrograph and proposes that it can be constructed from hydrological components in a feedback concept with precipitation as input and the volume of flow as output. Feedback, for example, is represented as ground water and infiltration. An approach is presented to develop the watershed hydrograph from a Taylor series expansion using the derivatives of measured flow as parameters. The expansion result is transformed through LaPlace techniques into a representation of the hydrograph. Once done, the resulting time function can be transformed by the Fourier operator and a unique spectral signature of the stream obtained. It is further asserted that the national network of stream gages can be a useful source of data for this construct. Included in the research is an investigation of the framework needed to package the information describing the watershed model. The Geographic Information System (GIS) is suggested as the ideal method to organize and provide clarity to the watershed model. Particularly important is the structured relational database required in this approach. Added to this are spatial geographic capabilities, which did not exist in the past. Lastly, an investigation into the project management tasks necessary for the successful pursuit of a watershed-monitoring program is outlined. Emphasis here is placed on the inclusion of all the interested parties in the care taking of the watershed. The analysis and modeling of watersheds are gaining increasing attention as managers and custodians become more acutely aware of the interactions of human activity and the environmental health of the watershed. Government investment in the streamgaging networks will contribute to this process by providing improved physical data to be used as input into the modeling efforts. The future holds greater promise to manage our natural resources through more comprehensive models of the environment