Letter from Thomas Beach & Committee to James B. Finley

An invitation for Finley to address the Greenfield Division #316, Sons of Temperance. Abstract Number - 1193https://digitalcommons.owu.edu/finley-letters/2173/thumbnail.jp

Letter from Thomas Beach & Committee to James B. Finley

Thomas Beach replies to Rev. Finley concerning the latter\u27s acceptance of an invitation to be with the Greenfield Division #316, Sons of Temperance. He regrets that he omitted the date of the celebration -- July 4th. Abstract Number - 1194https://digitalcommons.owu.edu/finley-letters/2174/thumbnail.jp

Integrating building and urban semantics to empower smart water solutions

Current urban water research involves intelligent sensing, systems integration, proactive users and data-driven management through advanced analytics. The convergence of building information modeling with the smart water field provides an opportunity to transcend existing operational barriers. Such research would pave the way for demand-side management, active consumers, and demand-optimized networks, through interoperability and a system of systems approach. This paper presents a semantic knowledge management service and domain ontology which support a novel cloud-edge solution, by unifying domestic socio-technical water systems with clean and waste networks at an urban scale, to deliver value-added services for consumers and network operators. The web service integrates state of the art sensing, data analytics and middleware components. We propose an ontology for the domain which describes smart homes, smart metering, telemetry, and geographic information systems, alongside social concepts. This integrates previously isolated systems as well as supply and demand-side interventions, to improve system performance. A use case of demand-optimized management is introduced, and smart home application interoperability is demonstrated, before the performance of the semantic web service is presented and compared to alternatives. Our findings suggest that semantic web technologies and IoT can merge to bring together large data models with dynamic data streams, to support powerful applications in the operational phase of built environment systems

Global Implications of U.S. Biofuels Policies in an Integrated Partial and General Equilibrium Framework

With the increasing research interests in biofuels, global implications of biofuels production have been generally examined either in a partial equilibrium (PE) or general equilibrium (GE) frameworks. Though both of these approaches have unique strengths, they also suffer from many limitations due to complexity of addressing all the relevant aspects of biofuels. In this paper we have exploited the strengths of both PE and GE approaches for analyzing the economic and environmental implications of the U.S. policies on corn-ethanol and biodiesel production. In this study, we utilize the Forest and Agricultural Sector Optimization Model (FASOMGHG: Adams et al. 1996, 2005; Beach et al. 2009), a non-linear programming, PE model for the United States. We also use the GTAP-BIO model (Birur et al. 2008), a multi-region, multi-sector CGE model for global-scale assessment of biofuels policies. Following Britz and Hertel (2009), we link the GTAP-BIO model through a static, quadratic restricted revenue function obtained from perturbing crop prices from the FASOMGHG model. With this linkage we implement the U.S. Corn ethanol and biodiesel scenarios in the GTAP-BIO model and obtain the FASOMGHG-consistent, global land use changes. The resulting crop price changes from the GE model are fed back into the FASOMGHG model to obtain the disaggregated impacts in the U.S.Biofuels, Indirect land use change, Land use emissions, Partial Equilibrium, Computable General Equilibrium, Land Economics/Use, Resource /Energy Economics and Policy,

Efficient least angle regression for identification of linear-in-the-parameters models

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm

Automated model construction for combined sewer overflow prediction based on efficient LASSO algorithm

The prediction of combined sewer overflow (CSO) operation in urban environments presents a challenging task for water utilities. The operation of CSOs (most often in heavy rainfall conditions) prevents houses and businesses from flooding. However, sometimes, CSOs do not operate as they should, potentially bringing environmental pollution risks. Therefore, CSOs should be appropriately managed by water utilities, highlighting the need for adapted decision support systems. This paper proposes an automated CSO predictive model construction methodology using field monitoring data, as a substitute for the commonly established hydrological-hydraulic modeling approach for time-series prediction of CSO statuses. It is a systematic methodology factoring in all monitored field variables to construct time-series dependencies for CSO statuses. The model construction process is largely automated with little human intervention, and the pertinent variables together with their associated time lags for every CSO are holistically and automatically generated. A fast least absolute shrinkage and selection operator solution generating scheme is proposed to expedite the model construction process, where matrix inversions are effectively eliminated. The whole algorithm works in a stepwise manner, invoking either an incremental or decremental movement for including or excluding one model regressor into, or from, the predictive model at every step. The computational complexity is thereby analyzed with the pseudo code provided. Actual experimental results from both single-step ahead (i.e., 15 min) and multistep ahead predictions are finally produced and analyzed on a U.K. pilot area with various types of monitoring data made available, demonstrating the efficiency and effectiveness of the proposed approach

Contextualising water use in residential settings: a survey of non-intrusive techniques and approaches

Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included

Management of collaborative BIM data by the Federatinon of Distributed Models

The architecture engineering and construction sector is currently undergoing a significant period of change and modernization. In the United Kingdom in particular this is driven by the government’s objective of reducing the cost of construction projects. This is to be achieved by requiring all publicly funded projects to utilize fully collaborative building information modeling by 2016. A common goal in increasing building information model (BIM) adoption by the industry is the movement toward the realization of a BIM as either a single data model or a series of tightly coupled federated models. However, there are key obstacles to be overcome, including uncertainty over data ownership, concerns relating to the security/privacy of data, and reluctance to “outsource” data storage. This paper proposes a framework that is able to provide a solution for managing collaboration in the architecture engineering and construction (AEC) sector. The solution presented in this paper provides an overlay that automatically federates and governs distributed BIM data. The use of this overlay provides an integrated BIM model that is physically distributed across the stakeholders in a construction project. The key research question addressed by this paper is whether such an overlay can, by providing dynamic federation and governance of BIM data, overcome some key obstacles to BIM adoption, including questions over data ownership, the security/privacy of data, and reluctance to share data. More specifically, this paper provides the following contributions: (1) presentation of a vision for the implementation and governance of a federated distributed BIM data model; (2) description of the BIM process and governance model that underpins the approach; (3) provision of a validation case study using real construction data from a U.K. highways project, demonstrating that both the federated BIM overlay and the process and governance model are fit for purpose. - See more at: http://ascelibrary.org/doi/full/10.1061/(ASCE)CP.1943-5487.0000657#sthash.jIj574Lh.dpu

Performance analysis of multi-institutional data sharing in the Clouds4Coordination system

Cloud computing is used extensively in Architecture/ Engineering/ Construction projects for storing data and running simulations on building models (e.g. energy efficiency/environmental impact). With the emergence of multi-Clouds it has become possible to link such systems and create a distributed cloud environment. A multi-Cloud environment enables each organisation involved in a collaborative project to maintain its own computational infrastructure/ system (with the associated data), and not have to migrate to a single cloud environment. Such infrastructure becomes efficacious when multiple individuals and organisations work collaboratively, enabling each individual/ organisation to select a computational infrastructure that most closely matches its requirements. We describe the “Clouds-for-Coordination” system, and provide a use case to demonstrate how such a system can be used in practice. A performance analysis is carried out to demonstrate how effective such a multi-Cloud system can be, reporting “aggregated-time-to-complete” metric over a number of different scenarios