In this workshop, participants engaged with and reflected on authentic artifacts from data modeling projects related to the solar system and to deforestation that were completed by elementary students in grade 5 (average age 11). These authentic examples were used to ground a discussion of using a data modeling approach to help elementary students make sense of and meaningful integrated use of mathematics and science concepts and tools. School-based ways of helping teachers understand this approach in order to be able to use it in their classrooms were also discussed.

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Background: With the rapid progress in the world, it is still a drawback that data modeling have to be developed and maintained separately each time when enhanced work is carried out. The technology we have now is not good enough to automate itself and keep itself up to date. It needs regularly to get help from people to keep working. In addition, a lot of storage space is needed to hold the new design and data because a single source cannot manage it. Every organization has to deal with this problem. The majority of large, dispersed organizations are most affected. Companies have a lot of data to handle and keep track of, so the data modeling process should be quick, error-free, and not risky in any way. Objectives: To get around these problems, there should be a way to automate the data modeling. The aim of this thesis is to give a way to solve the problem through automation. The outcome of the research not only helps to automate the data modeling, but it also keeps the data in a way that doesn’t waste memory on unnecessary data. Methods: This research employs a case study and a literature review. The case study was done at Jeppesen on the Dave modeling language. A literature review was undertaken in order to employ a specific approach for extending the data model. A survey is carried out to identify Dave’s limitations and considerations for improvement. The replies are supplied by Jeppesen employees (developers and users of Dave teams). Research was conducted and the findings were implemented as a program to upgrade the Dave modeling language to meet new object-oriented demands. Results: The findings identify some limitations of the existing Dave language and present an approach for automating the data modeling abilities by incorporating new features such as abstraction and inheritance so that it can keep up with the real time environments. It creates and automates the data model to provide rapid and right standard. The implementation strategy is drawn from the findings of a literature review. Conclusions: The existing data model requires extensive manual labour. By adding abstraction and inheritance to the data model, the new data model automates the process, reduces staffing needs, and runs with fewer risks.

Indoor Localization Systems (ILS), also known as Indoor Positioning Systems (IPS), has been created to determine the position of individuals and other assets inside facilities. Indoor Localization Systems have been implemented for monitoring individuals and objects in a variety of sectors. In addition, ILS could be used for energy and sustainability purposes. Energy management is a complex and important challenge in the Built Environment. The indoor localization market is expected to increase by 33.8 billion in the next 5 years based on the 2016 global survey report (Marketsandmarkets.com). Therefore, this thesis focused on exploring and investigating "depth sensors" application in detecting occupants' indoor positions to be used for smarter management of energy consumption in buildings. An interconnected passive depth-sensor-based system of occupants' positioning was investigated for human-building interaction applications. This research investigates the fundamental requirements for depth-sensing technology to detect, identify and track subjects as they move across different spaces. This depth-based approach is capable of sensing and identifying individuals by accounting for the privacy concerns of users in an indoor environment. The proposed system relies on a fixed depth sensor that detects the skeleton, measures the depth, and further extracts multiple features from the characteristics of the human body to identify them through a classifier. An example application of such a system is to capture an individuals' thermal preferences in an environment and deliver services (targeted air conditioning) accordingly while they move in the building. The outcome of this study will enable the application of cost-effective depth sensors for identification and tracking purposes in indoor environments. This research will contribute to the feasibility of accurate detection of individuals and smarter energy management using depth sensing technologies by proposing new features and creating combinations with typical biometric features. The addition of features such as the area and volume of human body surface was shown to increase the accuracy of the identification of individuals. Depth-sensing imaging could be combined with different ILS approaches and provide reliable information for service delivery in building spaces. The proposed sensing technology could enable the inference of people location and thermal preferences across different indoor spaces, as well as, sustainable operations by detecting unoccupied rooms in buildings. / Master of Science / Although Global Positioning System (GPS) has a satisfactory performance navigating outdoors, it fails in indoor environments due to the line of sight requirements. Physical obstacles such as walls, overhead floors, and roofs weaken GPS functionality in closed environments. This limitation has opened a new direction of studies, technologies, and research efforts to create indoor location sensing capabilities. In this study, we have explored the feasibility of using an indoor positioning system that seeks to detect occupants’ location and preferences accurately without raising privacy concerns. Context-aware systems were created to learn dynamics of interactions between human and buildings, examples are sensing, localizing, and distinguishing individuals. An example application is to enable a responsive air-conditioning system to adapt to personalized thermal preferences of occupants in an indoor environment as they move across spaces. To this end, we have proposed to leverage depth sensing technology, such as Microsoft Kinect sensor, that could provide information on human activities and unique skeletal attributes for identification. The proposed sensing technology could enable the inference of people location and preferences at any time and their activity levels across different indoor spaces. This system could be used for sustainable operations in buildings by detecting unoccupied rooms in buildings to save energy and reduce the cost of heating, lighting or air conditioning equipment by delivering air conditioning according to the preferences of occupants. This thesis has explored the feasibility and challenges of using depth-sensing technology for the aforementioned objectives. In doing so, we have conducted experimental studies, as well as data analyses, using different scenarios for human-environment interactions. The results have shown that we could achieve an acceptable level of accuracy in detecting individuals across different spaces for different actions.

In many US homes, 15% of the energy that can be saved is hidden beneath complex mathematical calculations. Hidden energy savings can be revealed by converting mathematical calculations to data visualizations, creating a story for residents to see how they are consuming energy. Cloud-based data visualization platforms offer the ability to appropriately communicate complex building energy data to a broad set of stakeholders. Unfortunately, proprietary solutions are too expensive and open-source options lack standardization for cloud-based energy monitoring. This study aims to create a comprehensive energy dashboard display to increase residents' energy awareness of how energy is consumed throughout their homes. But before energy dashboards can be created, a content analysis of current visualization chart types used on utility bills and energy monitoring devices were discovered to see how energy data has been visualized in the energy domain. Next, a literature review was conducted to reveal other visualization chart types outside of the energy domain that could be used to visualize energy data. The content analysis results identified eight visualization chart types that are used on utility bills and energy monitoring devices. In addition, the literature review uncovered eight additional visualization chart types that have the functionality to visualize energy data. Next, the visualization chart types were combined with data modeling design techniques to create prototype energy dashboard displays to communicate energy insights to residents. Soon utility companies will begin to provide data visualizations for the majority of their customers. The insights from this study can help to inform and lead the development of commercially used data visualizations. In addition, this research can provide utility companies with a blueprint on how to share energy consumption data with customers. / Master of Science / For residents to live an energy-efficient lifestyle, they must first begin by learning about one's energy consumption behaviors in the home. Unfortunately, utility bills miss out on communicating energy insights to customers based on how the energy data appears on the utility bill. Graphs on utility bills that display aggregate monthly energy consumption do not provide enough information for residents to comprehend how energy is consumed through their homes or provide information on how to lower energy consumption. There are commercial energy consumption devices on the market such as CURB and eGauge that provide an energy dashboard display, but the visuals are too complex to draw conclusions. This study aims to create an energy dashboard display that allows residents to see how energy is consumed throughout their homes. But before energy dashboards can be created, a content analysis of current visualization chart types used on utility bills and energy monitoring devices were discovered to see how energy data has been visualized in the energy domain. Next, a literature review was conducted to reveal other visualization chart types outside of the energy domain that could be used to visualize energy data. The content analysis results identified eight chart types used of utility bills and energy monitoring devices. In addition, the literature review results uncovered eight additional chart types not used on utility bills and energy monitoring devices that have the potential to visualize energy data. Next, the identified and uncovered chart types were combined with data modeling design techniques to create example energy dashboard displays. Changing the way energy data is displayed to residents, can educate residents on how energy is consumed throughout their home. In addition, the insights from this study can provide utility companies with a model for displaying energy data to increase their customers' energy awareness. Living an energy-efficient lifestyle, first began by understanding how energy is consumed throughout one's home.

Commercial buildings consume more than 40% of the total energy consumption in the United States. Almost 90% of these buildings are small- and medium-sized buildings that do not have a Building Energy Management (BEM) system. The reasons behind this are – lack of awareness, unavailability of inexpensive packaged solutions, and disincentive to invest in a BEM system if the tenant is not the owner. Several open source tools and technologies have emerged recently that can be used for building automation and energy management. However, none of these systems is turnkey and deployment ready. They also lack consistent and intuitive navigation, security, and performance required for a BEM system. The overall project - of which this thesis research is a part - addresses the design and implementation of an open source secure web based user platform to monitor, schedule, control, and perform functions needed for a BEM system serving small and medium-size buildings. The focus of this work are: principles of intuitive graphical user interface design, abstracting device functions into a comprehensive data model, identifying threats and vulnerabilities, and implementing a security framework for the web platform. Monitor and control solutions for devices such as load controllers and sensors are abstracted and their decentralized control strategies are proposed and implemented using an open source robust scalable user platform accessible locally and remotely. The user platform is open-source, scalable, provides role-based access, dynamic, and modular in design. The comprehensive data model includes a user management model, device model, session model, and a scheduling model. The data model is designed to be flexible, robust and can be extended for any new device type. Security risks are analyzed using a threat model to identify security goals. The proposed security framework includes user authentication, device approval, role-based access, secure information exchange protocols, and web platform security. Performance of the user interface platform is evaluated for responsiveness in different screen sizes, page response times, throughput, and the performance of client side entities. / Master of Science

Sustainable demolition waste management is rarely practiced within the construction industry. This is mainly due to the fact that the decision-making process for sustainable demolition waste management is a very resource-demanding and time-consuming task in terms of data collection and data management. The decision-making process includes multiple analyses of possible demolition waste management alternatives from economic, environmental, and social perspectives. Such analyses require waste managers to capture and manage huge amounts of data scattered within fragmented data sources at the end-of-life of a building. The process of capturing and managing this information for the building end-of-life would be time-consuming and costly. Therefore, the waste managers are reluctant to pursue sustainable demolition waste management practices in order to prevent potential delays and incurred costs. This research identified information that is required to conduct sustainable demolition waste management analyses. The identified information was then classified based on information sources. An object-oriented data model (OODM) was proposed to allow the waste managers to more efficiently store and manage the information at the end-of-life phase. Furthermore, a sustainable demolition waste management prototype application was developed to demonstrate how the required information is captured from different sources of data, stored within OODM classes, and retrieved from the integrated database. Finally, the proposed OODM was verified in terms of its scope, flexibility, and implementability. The goal of the research is to offer a method for storing and managing end-of-life information in an efficient and effective manner to support sustainable demolition waste management decision making. To achieve the goal, this dissertation outlines the objectives of the research, the methodologies used in developing the object-oriented data model, conclusions, limitations, and potential future research work. / Ph. D.

O presente trabalho é uma tentativa de validar uma metodologia para estimar a produção de sedimentos para todo território Brasileiro. Foram utilizadas ferramentas de sistemas de informação geográfi ca (GIS), estatística espacial, modelagem e gerenciamento de bancos de dados aplicados a conservação de solos, permitindo combinar a equação Universal de Perda do Solo (USLE) com diferentes modelos de taxa de transferência de sedimentos (SDR). A metodologia utilizou como base de teste dados da rede sedimentométrica brasileira composta de 201 bacias. As estimativas foram analisadas por regressão linear múltipla obtendo valores de R2 de até de 46% entre dados observados e modelados. Observou-se a pouca sensibilidade do modelo USLE em relação ao fator de erosividade (fator R) quando duas observações por métodos diferentes diferem espacialmente em seus valores máximos entre 18000 MJ.mm.ha-1.h-1.ano-1 e 28000 MJ.mm.ha-1.h-1.ano-1 . Por outro lado o modelo mostrou-se sensível ao fator de cobertura do solo (Fator C da USLE) afetando as taxas máximas estimadas de erosão entre 160 Mg.ha-1.ano-1 ate 460 Mg.ha-1.ano-1. Nesse sentido a metodologia sugerida pode ser utilizada para dar indicativos sobre mudanças de uso da terra em escalas regionais e subsidiar tomadas de decisões quanto ao planejamento e gestão territorial. / This work is an attempt to validate a methodology for estimating sediment production for the whole Brazilian territory. Tools were used geographic information systems (GIS), spatial statistics, modeling and database management applied to soil conservation, allowing combine the Universal Soil Loss Equation (USLE) with different models of sediment transfer rate (SDR). The methodology used as test data of Brazilian sedimentometric network composed of 201 basins. The estimates were analysed by multiple linear regression getting values of R2 to 46% between observed and modelled data. Noted the low sensitivity of USLE model in relation to the erosivity factor (R factor) when two observations by different methods differ in their maximum values and spatial distribution of 18000 MJ.mm.ha-1.h-1.year-1 and 28000 MJ.mm.ha-1.h-1.year-1. the other aspects the model proved to be sensitive to soil coverage factor (factor C of USLE) affecting the estimated maximum rates of erosion between 160 Mg.ha-1.year-1 up to 460 Mg.ha-1.year-1.

<p></p><p></p><p>Peatlands are the most efficient natural carbon sink on the planet. They are the most carbon-intensive storages than any other vegetation types. However, recent studies indicate that global peatlands can potentially release 6% of the global soil carbon into the atmosphere when they are drained or deforested. They cover only about 3% of the total global land area, but sequester over 30% of the Earth’s soil organic carbon. Peatlands in northern mid-to-high latitudes (45°-90°N) occupy ~90% of the global peatland area and account for ~80% of the total global peat organic carbon stock. Those peatlands are mainly located in Canada, Russia, and the USA. Peatlands in tropical regions cover ~10% of the global peatlands area and store 15-19% of the global peat organic carbon. They are mainly distributed in Southeast Asia and South and Central America. The temperature at the global scale has been rising since the middle of the last century and has accelerated during the last 40 years and the warming will continue in this century. The large storage of soil organic carbon within the peatlands can significantly respond to the changing climate by varying the roles between their carbon sink (from atmosphere to soil) and source (from soil to atmosphere) activities. This dissertation focuses on quantifying the soil organic carbon dynamics in North America and South America using mechanistically-based biogeochemistry models. </p><p></p><p>Peatlands in Alaska occupy 40 million hectares and account for ~10% of the total peatland area in northern mid-to-high latitudes. The regional soil organic carbon dynamics and its response to climate are still with large uncertainty. Most of the studies on peatlands to date are based on short-term site-level observation. This dissertation first used an integrated modeling framework that coupled the dynamics of hydrology, soil thermal regime, and ecosystem carbon and nitrogen to quantify the long-term peat carbon accumulation in Alaska during the Holocene. Modeled hydrology, soil thermal regime, carbon pools and fluxes and methane emissions were evaluated using long-term observation data at several peatland sites in Minnesota, Alaska, and Canada. The model was then applied for a 10,000-year (15 ka to 5 ka; 1 ka = 1000 cal yr before present) simulation at four peatland sites. The model simulations matched the observed carbon accumulation rates at fen sites during the Holocene (R^2= 0.88, 0.87, 0.38 and -0.05 for four sites respectively using comparisons in 500-year bins from 15 ka to 5 ka). The simulated (2.04 m) and observed peat depths (on average 1.98 m) also compared well (R^2 = 0.91). The early Holocene carbon accumulation rates, especially during the Holocene thermal maximum (HTM) (35.9 g 〖C m〗^(-2) yr^(-1)), were estimated up to 6-times higher than the rest of the Holocene (6.5 g 〖C m〗^(-2) yr^(-1)). It suggested that high summer temperature and the lengthened growing season resulted from the elevated insolation seasonality, along with wetter-than-before conditions might be major factors causing the rapid carbon accumulation in Alaska during the HTM. The sensitivity tests indicated that, apart from climate, initial water-table depth and vegetation canopy were major drivers to the estimated peat carbon accumulation. </p><p></p><p>To further quantify the regional long-term soil organic carbon accumulation rates and the current carbon stocks in Alaska, the second part of my research focused on quantifying the soil organic carbon accumulation in multiple Alaskan terrestrial ecosystems over the last 15,000 years for both peatland and non-peatland ecosystems. Comparable with the previous estimates of 25-70 Pg carbon (C) in peatlands and 13-22 Pg C in non-peatland soils within 1-m depth in Alaska using peat core data, our model estimated a total SOC of 36-63 Pg C at present, including 27-48 Pg C in peatland soils and 9-15 Pg C in non-peatland soils. Current living vegetation stored 2.5-3.7 Pg C in Alaska with 0.3-0.6 Pg C in peatlands and 2.2-3.1 Pg C in non-peatlands. The simulated average rate of peat soil C accumulation was 2.3 Tg C yr^(-1) with a peak value of 5.1 Tg C yr^(-1) during the Holocene Thermal Maximum (HTM) in the early Holocene, four folds higher than the average rate of 1.4 Tg C yr^(-1) over the rest of the Holocene. The accumulation slowed down, or even ceased, during the neo-glacial climate cooling after the mid-Holocene, but increased again in the 20th century. The model-estimated peat depths ranged from 1.1 to 2.7 m, similar to the field-based estimate of 2.29 m for the region. The changes in vegetation and their distributions were the main factors to determine the spatial variations of SOC accumulation during different time periods. Warmer summer temperature and stronger radiation seasonality, along with higher precipitation in the HTM and the 20th century might have resulted in the extensive peatland expansion and carbon accumulation. </p><p>Most studies on the role of tropical peatlands have focused on Indonesian peatlands. Few have focused on the Amazon basin, where peatlands remain intact and have been a long-term carbon sink. To address the problem, my third study quantified the carbon accumulation for peatland and non-peatland ecosystems in the Pastaza-Marañon foreland basin (PMFB), the most extensive peatland complex in the Amazon basin from 12,000 years before present to 2100 AD. Model simulations indicated that warming accelerated peat carbon loss while increasing precipitation accelerated peat carbon accumulation at millennial time scales. The uncertain parameters and spatial variation of climate were significant sources of uncertainty to modeled peat carbon accumulation. Under warmer and presumably wetter conditions over the 21st century, the warming effect on increasing peat carbon loss might overwhelm the wetter effect on increasing peat carbon accumulation. Peat soil carbon accumulation rate in the PMFB slowed down to 7.9 (4.3~12.2) g C m^(-2) yr^(-1) from the current rate of 16.1 (9.1~23.7) g C m^(-2) yr^(-1) and the region might turn into a carbon source to the atmosphere at -53.3 (-66.8~-41.2) g C m^(-2) yr^(-1) (negative indicates source), depending on the level of warming. Peatland ecosystems showed a higher vulnerability than non-peatland ecosystems as indicated by the ratio of their soil carbon density changes (change of soil carbon/existing soil carbon stock) ranging from 3.9 to 5.8). This was primarily due to larger peatlands carbon stocks and more dramatic responses of their aerobic and anaerobic decompositions in comparison with non-peatland ecosystems under future climate conditions. Peatland and non-peatland soils in the PMFB might lose up to 0.4 (0.32~0.52) Pg C by 2100 AD with the largest loss from palm swamp. The carbon-dense Amazonian peatland might switch from a current carbon sink into a source in the 21st century.</p><p>Peatlands are important sources and sinks for greenhouse gases (carbon dioxide and methane). Their carbon (C) balance between soil and atmosphere remains unquantified due to the large data gaps and uncertainties in regional peat carbon estimation. My final study was to quantify the C accumulation rates and C stocks within North America peatlands over the last 12,000 years. I find that 85-174 Pg C have been accumulated in North American peatlands over these years including 0.37-0.76 Pg C in subtropical peatlands in this region. During the 10- 8 ka period, the warmer and wetter conditions might have played an important role in stimulating peat C accumulation by enhancing plant photosynthesis. The enhanced peat decomposition due to warming through the Holocene slows down carbon accumulation in the region.</p><div><br></div><p><br></p>