Modelling nucleation in wet granulation

Nucleation is the first stage in any granulation process where binder liquid first comes into contact with the powder. This paper investigates the nucleation process where binder liquid is added to a fine powder with a spray nozzle. The dimensionless spray flux approach of Hapgood et al. (Powder Technol. 141 (2004) 20) is extended to account for nonuniform spray patterns and allow for overlap of nuclei granules rather than spray drops. A dimensionless nuclei distribution function which describes the effects of the design and operating parameters of the nucleation process (binder spray characteristics, the nucleation area ratio between droplets and nuclei and the powder bed velocity) on the fractional surface area coverage of nuclei on a moving powder bed is developed. From this starting point, a Monte Carlo nucleation model that simulates full nuclei size distributions as a function of the design and operating parameters that were implemented in the dimensionless nuclei distribution function is developed. The nucleation model was then used to investigate the effects of the design and operating parameters on the formed nuclei size distributions and to correlate these effects to changes of the dimensionless nuclei distribution function. Model simulations also showed that it is possible to predict nuclei size distributions beyond the drop controlled nucleation regime in Hapgood's nucleation regime map. Qualitative comparison of model simulations and experimental nucleation data showed similar shapes of the nuclei size distributions. In its current form, the nucleation model can replace the nucleation term in one-dimensional population balance models describing wet granulation processes. Implementation of more sophisticated nucleation kinetics can make the model applicable to multi-dimensional population balance models.

Change in woolgrower profits from adopting staple strength-enhancing management strategies

Wool tenderness is a significant problem in Australia, especially in areas where sheep graze under highly seasonal conditions. In this study, a profit function model is specified, estimated and simulated to assess the economic impact of staple strength-enhancing research on the profits of Australian woolgrowers. The model is based on a number of fundamental characteristics of the Australian wool industry and the staple-strength enhancing technology being assessed. The model consists of a system of demand and supply equations that are specified in terms of effective, rather than actual, prices. The interrelationships between the inputs and outputs are allowed for in the model in a manner that is consistent with theoretical restrictions. The adoption of the new feed management strategy results in a 4.4% increase in the expected profits of Australian wool producers in the short-run, and a 2.2% increase in expected profits in the long-run.

Vertical and horizontal scope economies in the regulated US electricity power industry

This paper investigates vertical economies between generation and distribution of electric power, and horizontal economies between different types of power generation in the U.S. electric utility industry. Our quadratic cost function model includes three generation output measures (hydro, nuclear and fossil fuels), which allows us to analyze the effect that generation mix has on vertical economies. Our results provide (sample mean) estimates of vertical economies of 8.1% and horizontal economies of 5.4%. An extensive sensitivity analysis is used to show how the scope measures vary across alternative model specifications and firm types. © 2012 Blackwell Publishing Ltd and the Editorial Board of The Journal of Industrial Economics.

The relationship of student and teacher variables on academic achievement in Florida schools

Since 1995, Florida has been one of the leading states in the country initiating a high-stakes school accountability system. Public schools in Florida receive letter grades based on their performance on the Florida Comprehensive Assessment Test (FCAT). These school grades have significant effects on schools' reputations and funding. Consequently, the plan has been criticized for grading all schools in the same manner, without taking into account such variables as student poverty and mobility rates which are beyond the control of the school. ^ The purpose of this study was to examine the relationship of student variables (poverty and mobility rates) and teacher variables (average years of teacher experience and attained degree level) on FCAT math and reading performance. This research utilized an education production function model to examine which set of inputs (student or teacher) has a stronger influence on student academic output as measured by the FCAT. ^ The data collected for this study was from over 1500 public elementary schools in Florida that listed all pertinent information for 2 school years (1998/1999 & 1999/2000) on the Florida Department of Education's website. ^ It was concluded that student poverty, teacher average years of experience and student mobility taken together, provide a strong predictive measure of FCAT reading and math performance. However, the set of student inputs was significantly stronger than the teacher inputs. High student poverty was highly correlated with low FCAT scores. Teacher experience and student mobility rates were not nearly as strongly related to FCAT scores as was student poverty. The results of this study provide evidence for educators and other school stakeholders of the relative degree to which student and teacher variables are related to student academic achievement. The underlying reasons for these relationships will require further examination in future studies. These results raise questions for Florida's school policymakers about the educational equity of the state's accountability system and its implementation. ^

Projected Reorganization of Florida Bay Seagrass Communities in Response to the Increased Freshwater Inflow of Everglades Restoration

Historic changes in water-use management in the Florida Everglades have caused the quantity of freshwater inflow to Florida Bay to decline by approximately 60% while altering its timing and spatial distribution. Two consequences have been (1) increased salinity throughout the bay, including occurrences of hypersalinity, coupled with a decrease in salinity variability, and (2) change in benthic habitat structure. Restoration goals have been proposed to return the salinity climates (salinity and its variability) of Florida Bay to more estuarine conditions through changes in upstream water management, thereby returning seagrass species cover to a more historic state. To assess the potential for meeting those goals, we used two modeling approaches and long-term monitoring data. First, we applied the hydrological mass balance model FATHOM to predict salinity climate changes in sub-basins throughout the bay in response to a broad range of freshwater inflow from the Everglades. Second, because seagrass species exhibit different sensitivities to salinity climates, we used the FATHOM-modeled salinity climates as input to a statistical discriminant function model that associates eight seagrass community types with water quality variables including salinity, salinity variability, total organic carbon, total phosphorus, nitrate, and ammonium, as well as sediment depth and light reaching the benthos. Salinity climates in the western sub-basins bordering the Gulf of Mexico were insensitive to even the largest (5-fold) modeled increases in freshwater inflow. However, the north, northeastern, and eastern sub-basins were highly sensitive to freshwater inflow and responded to comparatively small increases with decreased salinity and increased salinity variability. The discriminant function model predicted increased occurrences ofHalodule wrightii communities and decreased occurrences of Thalassia testudinum communities in response to the more estuarine salinity climates. The shift in community composition represents a return to the historically observed state and suggests that restoration goals for Florida Bay can be achieved through restoration of freshwater inflow from the Everglades.

Benthic Macrophyte Distribution and Abundance in Estuarine Mangrove Lakes and Estuaries: Relationships to Environmental Variables

Annual mean salinity, light availability, and sediment depth to bedrock structured the submerged aquatic vegetation (SAV) communities in subtropical mangrove-lined estuaries. Three distinct SAV communities (i.e., Chara group, Halodule group, and Low SAV coverage group) were identified along the Everglades–Florida Bay ecotone and related to water quality using a discriminant function model that predicted the type of plant community at a given site from salinity, light availability, and sediment depth to bedrock. Mean salinity alone was able to correctly classify 78% of the sites and reliably separated the Chara group from the Halodule group. The addition of light availability and sediment depth to bedrock increased model accuracy to 90% and further distinguished the Chara group from the Halodule group. Light availability was uniquely valuable in separating the Chara group from the Low SAV coverage group. Regression analyses identified significant relationships between phosphorus concentration, phytoplankton abundance, and light availability and suggest that a decline in water transparency, associated with increasing salinity, may have also contributed to the historical decline of Chara communities in the region. This investigation applies relationships between environmental variables and SAV distribution and provides a case study into the application of these general principals to ecosystem management.

Forecasting Responses of Seagrass Distributions to Changing Water Quality Using Monitoring Data

Extensive data sets on water quality and seagrass distributions in Florida Bay have been assembled under complementary, but independent, monitoring programs. This paper presents the landscape-scale results from these monitoring programs and outlines a method for exploring the relationships between two such data sets. Seagrass species occurrence and abundance data were used to define eight benthic habitat classes from 677 sampling locations in Florida Bay. Water quality data from 28 monitoring stations spread across the Bay were used to construct a discriminant function model that assigned a probability of a given benthic habitat class occurring for a given combination of water quality variables. Mean salinity, salinity variability, the amount of light reaching the benthos, sediment depth, and mean nutrient concentrations were important predictor variables in the discriminant function model. Using a cross-validated classification scheme, this discriminant function identified the most likely benthic habitat type as the actual habitat type in most cases. The model predicted that the distribution of benthic habitat types in Florida Bay would likely change if water quality and water delivery were changed by human engineering of freshwater discharge from the Everglades. Specifically, an increase in the seasonal delivery of freshwater to Florida Bay should cause an expansion of seagrass beds dominated by Ruppia maritima and Halodule wrightii at the expense of the Thalassia testudinum-dominated community that now occurs in northeast Florida Bay. These statistical techniques should prove useful for predicting landscape-scale changes in community composition in diverse systems where communities are in quasi-equilibrium with environmental drivers.

Modelling the size distribution of cosmic voids

The Standard Cosmological Model is generally accepted by the scientific community, there are still an amount of unresolved issues. From the observable characteristics of the structures in the Universe,it should be possible to impose constraints on the cosmological parameters. Cosmic Voids (CV) are a major component of the LSS and have been shown to possess great potential for constraining DE and testing theories of gravity. But a gap between CV observations and theory still persists. A theoretical model for void statistical distribution as a function of size exists (SvdW) However, the SvdW model has been unsuccesful in reproducing the results obtained from cosmological simulations. This undermines the possibility of using voids as cosmological probes. The goal of our thesis work is to cover the gap between theoretical predictions and measured distributions of cosmic voids. We develop an algorithm to identify voids in simulations,consistently with theory. We inspecting the possibilities offered by a recently proposed refinement of the SvdW (the Vdn model, Jennings et al., 2013). Comparing void catalogues to theory, we validate the Vdn model, finding that it is reliable over a large range of radii, at all the redshifts considered and for all the cosmological models inspected. We have then searched for a size function model for voids identified in a distribution of biased tracers. We find that, naively applying the same procedure used for the unbiased tracers to a halo mock distribution does not provide success- full results, suggesting that the Vdn model requires to be reconsidered when dealing with biased samples. Thus, we test two alternative exten- sions of the model and find that two scaling relations exist: both the Dark Matter void radii and the underlying Dark Matter density contrast scale with the halo-defined void radii. We use these findings to develop a semi-analytical model which gives promising results.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Intercity Bus Route Evaluation for Statewide Planning, 1981

The goal of this research was to evaluate the needs of the intercity common carrier bus service in Iowa. Within the framework of the overall goal, the objectives were to: (1) Examine the detailed operating cost and revenue data of the intercity carriers in Iowa; (2) Develop a model or models to estimate demand in cities and corridors served by the bus industry; (3) Develop a cost function model for estimating a carrier's operating costs; (4) Establish the criteria to be used in assessing the need for changes in bus service; (5) Outline the procedures for estimating route operating costs and revenues and develop a matrix of community and social factors to be considered in evaluation; and (6) Present a case study to demonstrate the methodology. The results of the research are presented in the following chapters: (1) Introduction; (2) Intercity Bus Research and Development; (3) Operating Characteristics of Intercity Carriers in Iowa; (4) Commuter Carriers; (5) Passenger and Revenue Forecasting Models; (6) Operating Cost Relationships; (7) Social and General Welfare Aspects of Intercity Bus Service; (8) Case Study Analysis; and (9) Additional Service Considerations and Recommendations.

An upgrade on the rabbit model of anthracycline-induced cardiomyopathy: shorter protocol, reduced mortality, and higher incidence of overt dilated cardiomyopathy

Current protocols of anthracycline-induced cardiomyopathy in rabbits present with high premature mortality and nephrotoxicity, thus rendering them unsuitable for studies requiring long-term functional evaluation of myocardial function (e.g., stem cell therapy). We compared two previously described protocols to an in-house developed protocol in three groups: Group DOX2 received doxorubicin 2 mg/kg/week (8 weeks); Group DAU3 received daunorubicin 3 mg/kg/week (10 weeks); and Group DAU4 received daunorubicin 4 mg/kg/week (6 weeks). A cohort of rabbits received saline (control). Results of blood tests, cardiac troponin I, echocardiography, and histopathology were analysed. Whilst DOX2 and DAU3 rabbits showed high premature mortality (50% and 33%, resp.), DAU4 rabbits showed 7.6% premature mortality. None of DOX2 rabbits developed overt dilated cardiomyopathy; 66% of DAU3 rabbits developed overt dilated cardiomyopathy and quickly progressed to severe congestive heart failure. Interestingly, 92% of DAU4 rabbits showed overt dilated cardiomyopathy and 67% developed congestive heart failure exhibiting stable disease. DOX2 and DAU3 rabbits showed alterations of renal function, with DAU3 also exhibiting hepatic function compromise. Thus, a shortened protocol of anthracycline-induced cardiomyopathy as in DAU4 group results in high incidence of overt dilated cardiomyopathy, which insidiously progressed to congestive heart failure, associated to reduced systemic compromise and very low premature mortality.

Re-conceptualising IS function's support performance : a preliminary model

While IS function has gained widespread attention for over two decades, there is little consensus among information systems (IS) researchers and practitioners on how best to evaluate IS function's support performance. This paper reports on preliminary findings of a larger research effort proceeds from a central interest in the importance of evaluating IS function's support in organisations. This study is the first that attempts to re-conceptualise and conceive evaluate IS function's support as a multi- dimensional formative construct. We argue that a holistic measure for evaluating evaluate IS function's support should consist of dimensions that together assess the variety of the support functions and the quality of the support services provided to end-users. Thus, the proposed model consists of two halves, "Variety" and "Quality" within which resides seven dimensions. The Variety half includes five dimensions: Training; Documentation; Data- related Support, Software-related Support; and Hardware-related Support. The Quality half includes two dimensions: IS Support Staff and Support Services Performance. The proposed model is derived using a directed content analysis of 83 studies; from top IS outlets, employing the characteristics of the analytic theory and consistent with formative construct development procedures.

A prospective surveillance model for rehabilitation for women with breast cancer

Background: The current model of care for breast cancer is focused on disease treatment followed by ongoing recurrence surveillance. This approach lacks attention to the patients’ physical and functional well-being. Breast cancer treatment sequelae can lead to physical impairments and functional limitations. Common impairments include pain, fatigue, upper extremity dysfunction, lymphedema, weakness, joint arthralgia, neuropathy, weight gain, cardiovascular effects, and osteoporosis. Evidence supports prospective surveillance for early identification and treatment as a means to prevent or mitigate many of these concerns. Purpose: This paper proposes a prospective surveillance model for physical rehabilitation and exercise that can be integrated with disease treatment to create a more comprehensive approach to survivorship health care. The goals of the model are to promote surveillance for common physical impairments and functional limitations associated with breast cancer treatment, to provide education to facilitate early identification of impairments, to introduce rehabilitation and exercise intervention when physical impairments are identified and to promote and support physical activity and exercise behaviors through the trajectory of disease treatment and survivorship. Methods: The model is the result of a multi-disciplinary meeting of research and clinical experts in breast cancer survivorship and representatives of relevant professional and advocacy organizations. Outcomes: The proposed model identifies time points during breast cancer care for assessment of and education about physical impairments. Ultimately, implementation of the model may influence incidence and severity of breast cancer treatment related physical impairments. As such, the model seeks to optimize function during and following treatment and positively influence a growing survivorship community.