Partial Separability and Partial Additivity for Orderings of Binary Alternatives

In Multiple-Criteria Decision Analysis (MCDA), a good way to find the best alternative is to construct a value function that represents a Decision Maker’s (DM) preferences. For multidimensional alternatives, an additive value function is easiest to work with because it assesses the alternatives in a simple and transparent manner. A DM’s preferences over consequences on a subset of the set of criteria may or may not depend on consequences on the rest of the criteria. Preferences that are free from all such interdependence are said to be separable. The existence of an additive value function implies separability and, when consequences form a continuum in each dimension and preference is continuous, the converse is also true. But we concentrate on orderings of binary alternatives (only two possible consequences on each criterion), for which the converse is known to be false unless there are four or fewer criteria. On binary alternatives, the probability of a separable order arising at random decreases rapidly as the number of criteria increases. However, there are different degrees of non-separability; many combinations of separable and non-separable subsets of criteria are possible. Here, we introduce notions of partial separability and partial additivity, which could be appropriate if criteria can be grouped into two or more natural classes. We establish that partial additivity implies partial separability, but that the converse is true only when the number of criteria is less than or equal to three. We also show that, when the number of criteria is more than three, partial separability with respect to a singleton set of criteria implies partial additivity with respect to this same subset

Biogas Quality Improvement Using Water Wash and Phosphorus Recovery as Struvite in Jones Island Wwtp

Raw biogas from anaerobic digestion has a methane content of 50 to 60% and a carbon dioxide content of 40 to 50% on a molar basis (Bortoluzzi, Gatti, Sogni, & Consonni, 2014). Milwaukee Metropolitan Sewerage District (MMSD)’s Jones Island Waste Water Treatment Plant (WWTP) uses the biogas supplied from South Shore WWTP in drying Milorganite, a slow-release phosphate fertilizer. But with only 45% methane, the gas cannot be used for sophisticated purposes. To maximize its potential as energy source, the methane content must be upgraded to its market competitor natural gas. Based on simulation results from Aspen Plus software - High Pressure Water Scrubbing (HPWS) or water wash seems to be the best option. The process requires running the impure gas through pressurized water. Based on Henry’s law, CO2 is dissolved easily because of low partial pressure. The integrated process doesn’t need additional water or pressure, as it can use wastewater from WWTP and the gas is already supplied at an optimal pressure. It can also remove most of the H2S, present as a trace amount in the biogas. Furthermore, struvite, a better-quality phosphate fertilizer can be recovered with adequate aeration and adding NaOH. From simulation results, the methane content can be improved up to 98.7 % at pressures up to 150 psi

Silane Modulation of Protein Conformation and Self-Assembly

This research focused on development of nanoparticle- based therapeutics against amyloid fibrils. Amyloid fibrils are associated with various diseases such as Parkinson’s, Huntington’s, mad cow disease, Alzheimer’s, and cataracts. Amyloid fibrils develop when proteins change their shape from a native form to a pathogenic “misfolded” form. The misfolded proteins have the ability to recruit more native proteins into the pathogenic forms, which self-assemble into amyloid fibrils that are hallmarks of the various protein-misfolding diseases listed above. Amyloid fibrils are highly resistant to degradation, which may contribute to the symptoms of amyloid diseases. Synthetic drugs, natural compounds, and antibodies are widely explored for potential to stop pathogenic protein assembly or to promote fibril degradation and clearance, but to date have had little success in relieving symptoms in clinical trials. In this research, I have synthesized fluorine-containing silica nanoparticles (NPs), and tested their fibril-inhibiting activity against amyloid fibrils formed by a non-pathogenic protein, β-lactoglobulin (BLG). These fluoro-silica NPs prevented BLG amyloid formation, whereas non-fluorinated nanoparticle analogs did not inhibit fibrillation under the same reaction conditions. The fluoro-silica NPs interacted with the BLG protein in a manner that prevented the protein from adopting a form that could self-assemble into fibrils. Additional applications of the NPs were explored as small-molecule drug-delivery systems; such that multiple functionalities could be introduced into a single nano- therapeutic

Three essays on agricultural productivity, convergence, and causality

Thesis (Ph. D. in Economics)--University of Tsukuba, (A), no. 3940, 2006.3.24Includes bibliographical reference

Experimental and numerical study of feeding channel in Proton Exchange Membrane Fuel Cell

The performance of a Proton Exchange Membrane Fuel Cell (PEMFC) using different feeding configurations has been studied, with a focus on the water flooding due to electrochemical reaction. Feeding channel or Bipolar plate in Proton Exchange Fuel Cell is a dominating part. As feeding channel keeps direct contact with the gas diffusion layer, it helps efficiently supplying fuel and air into the gas diffusion layer for efficient production of electricity. Experimental data have been taken at hydrogen flow rates of 20,40,60,80,100 sccm for various bipolar plate arrangements. Three bipolar plates, namely serpentine, straight channel and interdigitated designs, were arranged in different combinations for the PEMFC anode and cathode sides. Nine combinations in total were tested under different flow rates, working temperatures and loadings. The cell voltage versus current density and the cell power density versus current density curves were obtained. Experimental results showed that for different feeding configurations, interdigitated bipolar plate in anode side and serpentine bipolar plate in cathode side had the best performance in terms of cell voltage-current density curve, power density output rate, percentage of flooded area in the feeding channels, the pattern of water flooding and the fuel utilization rate. It is found that the water patterns had a most dominating role for the cell performance. Naturally water forms due to the chemical reaction. The water could accumulate in the cell and lead to a lower cell performance. After operating the PEMFC under high current densities, the cell was split and the water flooding pattern in the feeding channels was visually inspected. Detailed studies of cell performance using a single channel bipolar plate have been performed. Experimental data for one channel were taken under a variety of flow rates. Computational simulations have been conducted for this one channel ‘cell’ and the simulation results were compared with the experimental results. Comparison shows very little difference between the experimental and the simulation work. It is expected that the outcomes of this study could help the future design of Proton Exchange Fuel Cell

Experimental and numerical analysis of fuel cells

Fuel Cells are attractive power source for use in electronic applications. Physical phenomena (water generation, saturation effect in fuel cell, poisoning, and thermal stress) are studied that governs the operation of a Proton Exchange Membrane Fuel Cell (PEMFC) and Solid Oxide Fuel cell (SOFC). Additionally, experimental studies and numerical simulations on PEMFC gas flow channel, the determination of the impact of the single channel fuel cell are presented. Furthermore, preliminary study is done for the application of APS (Air Plasma Spray) to SOFC and adhesion of anode and cathode with electrolytes for the determination of parameters involved in manufacturing the components of fuel cell. The new aspects on physical phenomena are significantly different from the currently popular relationships used in fuel cells as they are simplified from simulation and experimental results. In prior work, the physical phenomena such as water generation, saturation effect in fuel cell, poisoning, and thermal stress etc. are either assumed or used as adjustment parameters to simplify them or to achieve best fits with polarization data. In this work, physical phenomena are not assumed but determined via newly developed experimental and numerical techniques. The experimental fixtures and procedures were used to find better ways to control parameters of gas flow channel configurations for optimizing gas flow rates and performance, and gas flow channel pressure swing for CO poisoning recovery. The experimental results reveal controlling parameters for the mentioned cases and innovative design for Fuel cells. Numerical modeling were used to 2D and later 3D for simplification of single channel fuel cell model, transient localized heating to the catalyst layer for CO recovery, thermal stress that developed during SOFC fabrication by High Temperature vacuum Tube Furnace (HTVTF), and Gas Diffusion Layer and Gas Flow Channel (GDL-GFC) interfacial conditions with results based on commonly used relationships from the PEMFC literature. The modeling works reveal substantial impact on predicted GDL saturation, and consequently cause a significant impact on cell performance. Computational parametric relations and polarization curve results are compared to experimental polarization behavior which achieved a comparable relation

Silane Modulation of Protein Conformation and Self-Assembly

This research focused on development of nanoparticle- based therapeutics against amyloid fibrils. Amyloid fibrils are associated with various diseases such as Parkinson’s, Huntington’s, mad cow disease, Alzheimer’s, and cataracts. Amyloid fibrils develop when proteins change their shape from a native form to a pathogenic “misfolded” form. The misfolded proteins have the ability to recruit more native proteins into the pathogenic forms, which self-assemble into amyloid fibrils that are hallmarks of the various protein-misfolding diseases listed above. Amyloid fibrils are highly resistant to degradation, which may contribute to the symptoms of amyloid diseases. Synthetic drugs, natural compounds, and antibodies are widely explored for potential to stop pathogenic protein assembly or to promote fibril degradation and clearance, but to date have had little success in relieving symptoms in clinical trials. In this research, I have synthesized fluorine-containing silica nanoparticles (NPs), and tested their fibril-inhibiting activity against amyloid fibrils formed by a non-pathogenic protein, β-lactoglobulin (BLG). These fluoro-silica NPs prevented BLG amyloid formation, whereas non-fluorinated nanoparticle analogs did not inhibit fibrillation under the same reaction conditions. The fluoro-silica NPs interacted with the BLG protein in a manner that prevented the protein from adopting a form that could self-assemble into fibrils. Additional applications of the NPs were explored as small-molecule drug-delivery systems; such that multiple functionalities could be introduced into a single nano- therapeutic

QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi Language

We describe our top-team solution to Task 1 for Hindi in the HASOC contest organised by FIRE 2019. The task is to identify hate speech and offensive language in Hindi. More specifically, it is a binary classification problem where a system is required to classify tweets into two classes: (a) \emph{Hate and Offensive (HOF)} and (b) \emph{Not Hate or Offensive (NOT)}. In contrast to the popular idea of pretraining word vectors (a.k.a. word embedding) with a large corpus from a general domain such as Wikipedia, we used a relatively small collection of relevant tweets (i.e. random and sarcasm tweets in Hindi and Hinglish) for pretraining. We trained a Convolutional Neural Network (CNN) on top of the pretrained word vectors. This approach allowed us to be ranked first for this task out of all teams. Our approach could easily be adapted to other applications where the goal is to predict class of a text when the provided context is limited

ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN

Anomaly detection in time series data, to identify points that deviate from normal behaviour, is a common problem in various domains such as manufacturing, medical imaging, and cybersecurity. Recently, Generative Adversarial Networks (GANs) are shown to be effective in detecting anomalies in time series data. The neural network architecture of GANs (i.e. Generator and Discriminator) can significantly improve anomaly detection accuracy. In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both univariate and multivariate time series data in an unsupervised setting. We evaluate the performance of ALGAN on 46 real-world univariate time series datasets and a large multivariate dataset that spans multiple domains. Our experiments demonstrate that ALGAN outperforms traditional, neural network-based, and other GAN-based methods for anomaly detection in time series data