Browsing by Subject "Model"

An integrated biotrickling filter (BTF)-Anammox bioreactor system was established for the complete treatment of ammonia. Shortcut nitrification process was successfully achieved in the biotrickling filter through free ammonia and free nitrous acid inhibition of nitrite oxidizing bacteria. During transients, while increasing nitrogen loading, free ammonia was the main factor that inhibited the activity of ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB). During steady state operation, free nitrous acid was mainly responsible for inhibition of NOB due to the accumulation of nitrite at relatively low pH. Ammonia removal by the BTF reached up to 50 gN m-3 h-1 with 100% removal at an inlet concentration of 403 ppm and a gas residence time of 20.8 s. Average removal of ammonia during stable operation was 95%. The anammox bioreactor could remove 75% of total nitrogen discharged by the BTF when the two reactors were connected. The possibility of operating in complete closed loop mode for the liquid was investigated. However, due to the limited activity of the Anammox bioreactor or the fact that this reactor was undersized, recycling the Anammox effluent back to BTF caused accumulation of nitrite in the system which further inhibited activity of Anammox and progressively caused failure of the system.

A conceptual model of both bioreactors was also developed to optimize the integrated system. The model was developed by including mass balances of nitrogen in the system and inhibition factors in microbial kinetics. Parameters such as hydraulic residence time (HRT), empty bed residence time (EBRT) and pH had significant impact on the partial nitritation process in the BTF. Model simulations also indicated that implementing a recycle for the Anammox bioreactor was needed to reduce the inhibitory effect of nitrite on the performance of the system.

Ammonia is an odorous gaseous compound emitted by a variety of industrial facilities. This study aimed to address the feasibility of ammonia gas removal using a biotrickling filter (BTF) coupled with an anammox bioreactor. In the BTF, the influent ammonia gas partitioned into the trickling water and was converted to nitrite via partial nitrification. The effluent liquid from the BTF, containing nitrite and ammonium concentrations, was fed into the anammox reactor where autotrophic denitrifying bacteria converted the ammonium and nitrite to dinitrogen gas. For the anammox reactor to operate efficiently, the influent ammonium and nitrite concentrations must be in a 1 to 1 molar ratio. To evaluate the feasibility of this system, a lab scale BTF and anammox reactor were constructed and operated and a conceptual model for this system was developed. To obtain a nitrite to ammonium ratio close to 1, it was found that the effluent pH from the BTF must be maintained below 7, and the loading rate could not exceed 8.7 g N/m3h. At this loading rate, complete ammonia gas removal occurred. A recycle rate of 1.4 times that of the influent was implemented in the BTF to increase performance and improve the nitrite to ammonium ratio. The addition of the recycle line achieved a nitrite of ammonium ratio of 0.97 at a pH value of 7.67. The anammox reactor achieved 88% removal of ammonium and nitrite at a loading rate of 10.5 g N /m3h. The fact that the BTF was able to achieve a 1 to 1 nitrite to ammonium ratio indicated that coupling of a BTF with the anammox reactor should be feasible. The mathematical model underpredicted effluent ammonium and nitrite concentrations in the BTF and greatly overpredicted the effluent concentrations from the anammox reactor. To improve the BTF model inhibition factors and oxygen supply need to be accounted for. Further development of the growth kinetics in the annamox model are necessary as well.

Purpose: Accurately quantifying the extent vessel coverage in the pancreas is essential for determining the feasibility of surgery. The aim of this study is to train a segmentation model specialized for Pancreatic Ductal Adenocarcinoma (PDAC) imaging, focusing on delineating pancreas, tumor, arteries (celiac artery, superior mesenteric artery, common hepatic artery), and veins (portal vein, superior mesenteric vein) using an improved Attention Unet CNN approach.Methods: Data from 100 PDAC patients treated at the Ruijin Hospital between 2020 and 2022 were utilized. Using Synapse 3D software, masks of the tumor, arteries (including the celiac artery, superior mesenteric artery, and hepatic artery), and veins (including the superior mesenteric vein and portal vein) were generated semi-automatically and reviewed by radiologists. Standard image processing techniques, including adjustment of window level to 60 and width to 350 and histogram equalization were subsequently applied. Two types of CNN-based Attention Unet segmentation models were developed: (1) Unified Unet Model that segments all four components simultaneously, and (2) four Individual Unet Models that segments pancreas, tumor, veins, and arteries separately. The train-validation-test data assignment was set to 7:2:1. The segmentation efficacy was assessed using Dice similarity coefficient, with the Adam optimizer utilized for optimization. Results: The individual segmentation models achieve notable performance: pancreas (Accuracy: 0.84, IoU: 0.81, Dice: 0.76), tumor (Accuracy: 0.78, IoU: 0.77, Dice: 0.68), vein (Accuracy: 0.88, IoU: 0.86, Dice: 0.80), and artery (Accuracy: 0.91, IoU: 0.93, Dice: 0.93). However, the unified model demonstrates inferior performance with accuracy, IoU, and Dice coefficient scores of 0.61, 0.50, and 0.45, respectively. Conclusion: Accurate segmentation models have been developed for pancreas, pancreatic tumors, arteries, and veins in PDAC patients. This will enable the efficient quantification of vessel coverage in the pancreas, thereby enhancing the decision-making process regarding the feasibility of surgery for PDAC patients. The findings also demonstrate that Individual Models outperform the Unified Model in segmentation accuracy, highlighting the importance of tailored segmentation strategies for different anatomical structures in PDAC imaging.

Scientific models are used to investigate reality. Here “model” refers to a representation which is created by an agent for a particular inferential purpose. These purposes include but are not limited to explanation, prediction, exploration, classification, and measurement. Through modeling, scientists become capable of understanding the composition and structure of natural systems and social systems in a systematic manner constitutive of scientific research. This process of understanding is underwritten by a logical structure distinctive to scientific modeling.

Throughout this dissertation, I articulate, justify, and defend a specific account of the logical structure of scientific modeling. In order to do so, I detail economic models, which I contend are representative of general scientific modeling. Broadly, my account of scientific modeling can be decomposed into three distinct claims. First, I argue for understanding scientific modeling in terms representation. Following others, I then conceptualize representation in terms of purpose and relevant similarity. However, against this conceptualization are numerous counterarguments, which I proceed to detail and then disarm.

Second, I argue that the ideal scientific model is a useful model. Connectedly, I contend that in order for a scientific model to be useful, it must first be idealized. In order to demonstrate the necessity of idealizations for scientific modeling, I begin by detailing a number of idealization strategies and demonstrate how they are integral to the use of scientific models across the natural and social sciences. But in order to demonstrate that idealized models are not only useful but are ideal, I dismantle the putative ideal of completeness which holds that the ideal model completely represents reality in all its detail and complexity. However, as I demonstrate, completeness is neither achievable nor a legitimate aspiration for working scientist.

Third, I argue that in order to use scientific models, it is often necessary for scientists to alter them in order to better fit particular target systems. In order to explain the alteration process, I detail the representational continuum found across the sciences which stretches from highly concrete data models to highly abstract principles. Between these extremes are theoretical models and empirical models. In order to construct such models, scientists must engage in an exploratory process by which possibilities are mapped and relative likelihoods estimated. In this way, scientists can construct highly specialized models which can allow them to better pursue specific inferential purposes. All of this results in a division of inferential labor and associated efficiency gains which, I argue, are constitutive of scientific progress.

An increasing number of experimental studies attempt to maximize biomass production of trees in plantations by removing nutrient and water limitations. The results from these studies begin to inform operational managers. We investigated a Populus trichocarpa Torr. x P. deltoides Bartr. & Marsh plantation with a combined irrigation and nutrient supply system designed to optimize biomass production. Sap flux density was measured continuously over four of the six growing season months, supplemented with periodic measurements of leaf gas exchange and water potential. Measurements of tree diameter and height were used to estimate leaf area and biomass production using allometric relations. Sap flux was converted to canopy conductance, and analyzed based on an empirical model to isolate the effects of water limitation. Actual and soil water-unlimited potential CO2 uptakes were estimated using a Canopy Conductance Constrained Carbon Assimilation (4C-A) scheme, which couples actual or potential canopy conductance with vertical gradients of light distribution, leaf-level conductance, maximum Rubisco capacity (Vcmax) and maximum electron transport (Jmax). Net primary production (NPP) was ~0.43 of gross primary production (GPP); when estimated for individual trees, this ratio was independent of tree size. Based on the same ratio, we found that current irrigation reduced growth by ~18 % compare to growth with no water limitation. To achieve this maximum growth, however, would require 70% more water for transpiration, and would reduce water use efficiency by 27 %, from 1.57 to 1.15 g stem wood C kg-1 water. Given the economic and social values of water, plantation managers appear to have optimized water use.

The release of ChatGPT-4 has led to the prevalent use of a new term in the field of artificial intelligence (AI): generative AI. This paper aims to understand generative AI more thoroughly and place it within a broader framework of models and their relationship with knowledge. By closely examining AI’s historical development, this paper will first introduce the concept of emergence to distinguish generative AI from other forms of AI. Second, by theorizing generative AI as models, this paper will evaluate their significance in human knowledge production. Third, by classifying generative AI specifically as generative models, this paper will demonstrate their unique potential, especially for art creation.

Supercritical water oxidation (SCWO) had been investigated as an advanced technology for the removal of inert and stable organics found in wide range of wastes. Ammonia and nitrous oxide are confirmed in outlet of SCWO system treating municipal sludge. In this study, a mathematical model was established to simulate nitrogen reaction, in order to explore the kinetics of ammonia reaction and reduce the nitrous oxide generation. This developed mathematical model was trained by data from Duke Sanitation Solution group where a pilot-scale supercritical water oxidation facility is invested to treat municipal sludge. The final model was validated by practical data obtained from this facility, and give instruction on SCWO operation.

The mammalian olfactory system uses a large family of odorant receptors to detect and discriminate amongst a myriad of volatile odor molecules. The odorant receptors are similar in protein sequence, but their ligand selectivities dramatically differ. It is not clear how the protein sequences determine the responsiveness of odorant receptors. In this study, I attempt to establish the link between the protein sequences of odorant receptors and their ligand selectivity.

Starting from the response profiles of hundreds of mouse odorant receptors to an odorant generated from my previous work, I used machine learning and variable selection methods to identify properties of amino acid residues that predict receptor response. This leads to protein sequence-based models for odorant receptor response prediction. The models trained with mouse odorant receptor data can predict human odorant receptor responses.

Global climate is predicted to have significant impacts on the chemical, biological, and physical characteristics of wetlands and the watersheds in which they are contained. In particular, climate prediction models suggest a significant increase in extreme precipitation events - both more frequent and more intense flood and drought occurrences. A wetland model that incorporates surfacewater-groundwater interactions (WETSAND2.0) was used to investigate the potential impacts of these stochastically generated extreme events on wetland flow regimes in an urban watershed. The results predict increases in streamflow and flooding as well as drought conditions on a near yearly basis. However, the model also shows that the impact on the Sandy Creek-Duke University watershed will not be as extreme as many suggest. Although flooding will occur, it will be relatively minor and comparable to historic flows. And although droughts are also predicted, the balance of wet and dry in this wetland watershed can actually be a positive for the environment. Therefore watersheds, no matter the spatial scale, must be analyzed individually. Although some comparisons can be made between similar regions, the effects of extreme precipitation events vary greatly depending on watershed characteristics.