An investigation into equations for estimating water requirements and the development of new equations for predicting total water intake

The primary purpose of this study was to investigate agreement among five equations by which clinicians estimate water requirements (EWR) and to determine how well these equations predict total water intake (TWI). The Institute of Medicine has used TWI as a measure of water requirements. A secondary goal of this study was to develop practical equations to predict TWI. These equations could then be considered accurate predictors of an individual’s water requirement. ^ Regressions were performed to determine agreement between the five equations and between the five equations and TWI using NHANES 1999–2004. The criteria for agreement was (1) strong correlation coefficients between all comparisons and (2) regression line that was not significantly different when compared to the line of equality (x=y) i.e., the 95% CI of the slope and intercept must include one and zero, respectively. Correlations were performed to determine association between fat-free mass (FFM) and TWI. Clinically significant variables were selected to build equations for predicting TWI. All analyses were performed with SAS software and were weighted to account for the complex survey design and for oversampling. ^ Results showed that the five EWR equations were strongly correlated but did not agree with each other. Further, the EWR equations were all weakly associated to TWI and lacked agreement with TWI. The strongest agreement between the NRC equation and TWI explained only 8.1% of the variability of TWI. Fat-free mass was positively correlated to TWI. Two models were created to predict TWI. Both models included the variables, race/ethnicity, kcals, age, and height, but one model also included FFM and gender. The other model included BMI and osmolality. Neither model accounted for more than 28% of the variability of TWI. These results provide evidence that estimates of water requirements would vary depending upon which EWR equation was selected by the clinician. None of the existing EWR equations predicted TWI, nor could a prediction equation be created which explained a satisfactory amount of variance in TWI. A good estimate of water requirements may not be predicted by TWI. Future research should focus on using more valid measures to predict water requirements.^

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

Lattice Boltzmann method for flow and heat transfer in microgeometries

Recent technological developments have made it possible to design various microdevices where fluid flow and heat transfer are involved. For the proper design of such systems, the governing physics needs to be investigated. Due to the difficulty to study complex geometries in micro scales using experimental techniques, computational tools are developed to analyze and simulate flow and heat transfer in microgeometries. However, conventional numerical methods using the Navier-Stokes equations fail to predict some aspects of microflows such as nonlinear pressure distribution, increase mass flow rate, slip flow and temperature jump at the solid boundaries. This necessitates the development of new computational methods which depend on the kinetic theory that are both accurate and computationally efficient. In this study, lattice Boltzmann method (LBM) was used to investigate the flow and heat transfer in micro sized geometries. The LBM depends on the Boltzmann equation which is valid in the whole rarefaction regime that can be observed in micro flows. Results were obtained for isothermal channel flows at Knudsen numbers higher than 0.01 at different pressure ratios. LBM solutions for micro-Couette and micro-Poiseuille flow were found to be in good agreement with the analytical solutions valid in the slip flow regime (0.01 < Kn < 0.1) and direct simulation Monte Carlo solutions that are valid in the transition regime (0.1 < Kn < 10) for pressure distribution and velocity field. The isothermal LBM was further extended to simulate flows including heat transfer. The method was first validated for continuum channel flows with and without constrictions by comparing the thermal LBM results against accurate solutions obtained from analytical equations and finite element method. Finally, the capability of thermal LBM was improved by adding the effect of rarefaction and the method was used to analyze the behavior of gas flow in microchannels. The major finding of this research is that, the newly developed particle-based method described here can be used as an alternative numerical tool in order to study non-continuum effects observed in micro-electro-mechanical-systems (MEMS).

How people resolve dilemmas: An elicitation method for subjective decision factors

In human society, people encounter various deontic conflicts every day. Deontic decisions are those that include moral, ethical, and normative aspects. Here, the concern is with deontic conflicts: decisions where all the alternatives lead to the violation of some norms. People think critically about these kinds of decisions. But, just ‘what’ they think about is not always clear. ^ People use certain estimating factors/criteria to balance the tradeoffs when they encounter deontic conflicts. It is unclear what subjective factors people use to make a deontic decision. An elicitation approach called the Open Factor Conjoint System is proposed, which applies an online elicitation methodology which is a combination of two well-know research methodologies: repertory grid and conjoint analysis. This new methodology is extended to be a web based application. It seeks to elicit additional relevant (subjective) factors from people, which affect deontic decisions. The relative importance and utility values are used for the development of a decision model to predict people’s decisions. ^ Fundamentally, this methodology was developed and intended to be applicable for a wide range of elicitation applications with minimal experimenter bias. Comparing with the traditional method, this online survey method reduces the limitation of time and space in data collection and this methodology can be applied in many fields. Two possible applications were addressed: robotic vehicles and the choice of medical treatment. In addition, this method can be applied to many research related disciplines in cross-cultural research due to its online ability with global capacity. ^

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Improved Annual Average Daily Traffic (AADT) estimation for local roads using parcel-level travel demand modeling

Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. ^ This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. ^ To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.^

Development and assessment of a method to estimate meal intake of nursing home residents

The objective was to develop and validate a method for estimating food intake of nursing home residents. The study was conducted with certified nursing assistants (CNAs) at a 180-bed nursing facility. CNAs assisted in the development of the new method by providing feedback on existing estimation methods. Four simulated resident trays were used to estimate both food intake and overall meal intake. Twelve CNAs' intake estimates for 34 simulated food items (n=384 estimates) were compared to weighed values. Eightyfive percent of the 384 intake estimates for the simulated food items were correct; Cohen's kappa was 0.80, p

Lattice Boltzmann Method for Flow and Heat Transfer in Microgeometries

Recent technological developments have made it possible to design various microdevices where fluid flow and heat transfer are involved. For the proper design of such systems, the governing physics needs to be investigated. Due to the difficulty to study complex geometries in micro scales using experimental techniques, computational tools are developed to analyze and simulate flow and heat transfer in microgeometries. However, conventional numerical methods using the Navier-Stokes equations fail to predict some aspects of microflows such as nonlinear pressure distribution, increase mass flow rate, slip flow and temperature jump at the solid boundaries. This necessitates the development of new computational methods which depend on the kinetic theory that are both accurate and computationally efficient. In this study, lattice Boltzmann method (LBM) was used to investigate the flow and heat transfer in micro sized geometries. The LBM depends on the Boltzmann equation which is valid in the whole rarefaction regime that can be observed in micro flows. Results were obtained for isothermal channel flows at Knudsen numbers higher than 0.01 at different pressure ratios. LBM solutions for micro-Couette and micro-Poiseuille flow were found to be in good agreement with the analytical solutions valid in the slip flow regime (0.01 < Kn < 0.1) and direct simulation Monte Carlo solutions that are valid in the transition regime (0.1 < Kn < 10) for pressure distribution and velocity field. The isothermal LBM was further extended to simulate flows including heat transfer. The method was first validated for continuum channel flows with and without constrictions by comparing the thermal LBM results against accurate solutions obtained from analytical equations and finite element method. Finally, the capability of thermal LBM was improved by adding the effect of rarefaction and the method was used to analyze the behavior of gas flow in microchannels. The major finding of this research is that, the newly developed particle-based method described here can be used as an alternative numerical tool in order to study non-continuum effects observed in micro-electro-mechanical-systems (MEMS).

How People Resolve Dilemmas: An Elicitation Method for Subjective Decision Factors

In human society, people encounter various deontic conflicts every day. Deontic decisions are those that include moral, ethical, and normative aspects. Here, the concern is with deontic conflicts: decisions where all the alternatives lead to the violation of some norms. People think critically about these kinds of decisions. But, just ‘what’ they think about is not always clear. People use certain estimating factors/criteria to balance the tradeoffs when they encounter deontic conflicts. It is unclear what subjective factors people use to make a deontic decision. An elicitation approach called the Open Factor Conjoint System is proposed, which applies an online elicitation methodology which is a combination of two well-know research methodologies: repertory grid and conjoint analysis. This new methodology is extended to be a web based application. It seeks to elicit additional relevant (subjective) factors from people, which affect deontic decisions. The relative importance and utility values are used for the development of a decision model to predict people’s decisions. Fundamentally, this methodology was developed and intended to be applicable for a wide range of elicitation applications with minimal experimenter bias. Comparing with the traditional method, this online survey method reduces the limitation of time and space in data collection and this methodology can be applied in many fields. Two possible applications were addressed: robotic vehicles and the choice of medical treatment. In addition, this method can be applied to many research related disciplines in cross-cultural research due to its online ability with global capacity.

Analysis of Land Use Change as a Method of Predicting Water Demands in an Urbanizing Environment: Redland, Miami-Dade County, Florida

The fluctuation in water demand in the Redland community of Miami-Dade County was examined using land use data from 2001 and 2011 and water estimation techniques provided by local and state agencies. The data was converted to 30 m mosaicked raster grids that indicated land use change, and associated water demand measured in gallons per day per acre. The results indicate that, first, despite an increase in population, water demand decreased overall in Redland from 2001 to 2011. Second, conversion of agricultural lands to residential lands actually caused a decrease in water demand in most cases while acquisition of farmland by public agencies also caused a sharp decline. Third, conversion of row crops and groves to nurseries was substantial and resulted in a significant increase in water demand in all such areas converted. Finally, estimating water demand based on land use, rather than population, is a more accurate approach.