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

Three essays on agricultural productivity, convergence, and causality

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

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

TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks

Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security. Recently, Generative Adversarial Networks (GAN) have gained attention for generation and anomaly detection in image domain. In this paper, we propose a novel GAN-based unsupervised method called TAnoGan for detecting anomalies in time series when a small number of data points are available. We evaluate TAnoGan with 46 real-world time series datasets that cover a variety of domains. Extensive experimental results show that TAnoGan performs better than traditional and neural network models.Comment: Made some minor changes. This is the accepted version of the paper at AusDM'2

Fuzzy Preferences in the Graph Model for Conflict Resolution

A Fuzzy Preference Framework for the Graph Model for Conflict Resolution (FGM) is developed so that real-world conflicts in which decision makers (DMs) have uncertain preferences can be modeled and analyzed mathematically in order to gain strategic insights. The graph model methodology constitutes both a formal representation of a multiple participant-multiple objective decision problem and a set of analysis procedures that provide insights into them. Because crisp or definite preference is a special case of fuzzy preference, the new framework of the graph model can include---and integrate into the analysis---both certain and uncertain information about DMs' preferences. In this sense, the FGM is an important generalization of the existing graph model for conflict resolution. One key contribution of this study is to extend the four basic graph model stability definitions to models with fuzzy preferences. Together, fuzzy Nash stability, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability provide a realistic description of human behavior under conflict in the face of uncertainty. A state is fuzzy stable for a DM if a move to any other state is not sufficiently likely to yield an outcome the DM prefers, where sufficiency is measured according to a fuzzy satisficing threshold that is characteristic of the DM. A fuzzy equilibrium, an outcome that is fuzzy stable for all DMs, therefore represents a possible resolution of the conflict. To demonstrate their applicability, the fuzzy stability definitions are applied to a generic two-DM sustainable development conflict, in which a developer plans to build or operate a project inspected by an environmental agency. This application identifies stable outcomes, and thus clarifies the necessary conditions for sustainability. The methodology is then applied to an actual dispute with more than two DMs concerning groundwater contamination that took place in Elmira, Ontario, Canada, again uncovering valuable strategic insights. To investigate how DMs with fuzzy preferences can cooperate in a strategic conflict, coalition fuzzy stability concepts are developed within FGM. In particular, coalition fuzzy Nash stability, coalition fuzzy general metarationality, coalition fuzzy symmetric metarationality, and coalition fuzzy sequential stability are defined, for both a coalition and a single DM. These concepts constitute a natural generalization of the corresponding non-cooperative fuzzy preference-based definitions for Nash stability, general metarationality, symmetric metarationality, and sequential stability, respectively. As a follow-up analysis of the non-cooperative fuzzy stability results and to demonstrate their applicability, the coalition fuzzy stability definitions are applied to the aforementioned Elmira groundwater contamination conflict. These new concepts can be conveniently utilized in the study of practical problems in order to gain strategic insights and to compare conclusions derived from both cooperative and non-cooperative stability notions. A fuzzy option prioritization technique is developed within the FGM so that uncertain preferences of DMs in strategic conflicts can be efficiently modeled as fuzzy preferences by using the fuzzy truth values they assign to preference statements about feasible states. The preference statements of a DM express desirable combinations of options or courses of action, and are listed in order of importance. A fuzzy truth value is a truth degree, expressed as a number between 0 and 1, capturing uncertainty in the truth of a preference statement at a feasible state. It is established that the output of a fuzzy preference formula, developed based on the fuzzy truth values of preference statements, is always a fuzzy preference relation. The fuzzy option prioritization methodology can also be employed when the truth values of preference statements at feasible states are formally based on Boolean logic, thereby generating a crisp preference over feasible states that is the same as would be found using the existing crisp option prioritization approach. Therefore, crisp option prioritization is a special case of fuzzy option prioritization. To demonstrate how this methodology can be used to represent fuzzy preferences in real-world problems, the new fuzzy option prioritization technique is applied to the Elmira aquifer contamination conflict. It is observed that the fuzzy preferences obtained by employing this technique are very close to those found using the rather complicated and tedious pairwise comparison approach

Increase Use of CNG as Public Transport & Reduce Emissions: A Comparative Study of the Benefits of CNG & Automobiles Fuel: Present Scenario on Bangladesh

Compressed Natural Gas (CNG) is a natural gas i.e. methane, in compressed form. Natural gas at ambient temperature and pressure has very low energy density compared to other fuels. To use natural gas as a transportation fuel, it must be compressed to increase its volumetric energy density. In recent years due to spiraling hike of fuel price, Bangladesh government had no other options but to increase in fuel price. This has led to increase in conversion of petrol/octane automobiles into CNG system, which runs on natural gas abundantly available in this country. How more and more CNG conversion workshops have developed and CNG has gained popularity and acceptability at a rapid pace. The purpose of this study is to access the popularity of CNG as an alternative automobile fuel. In many respects, the distribution of modal choices in Dhaka is unique among cities of comparable size in the Asia region. Almost 60% of the 8.5 million weekday person trips are walk trips and about 19.2% use rickshaw. For the remaining 20% trips on motorized models. The high dependence on walking and rickshaw, both slow and typically best suited for short trips on secondary roads and a low dependence on buses, in a city of 14.5 million people with an urban area about 2000 sq. km. is a symptom of inefficient and ineffective transport operations as well as uncontrollable land use (DOE 2011). The number of CNG vehicles plying on road till to date is 204243 (RPGCL, May-2012). Taking an average of 15 liter/day per vehicle, the cost of CNG use per annum amounts to tk. 170.00 crore. The same number of vehicles would spend tk. 1000.00 crore on equal amount of petrol. It was revealed that the CNG users are saving tk. 830.00 crore by using CNG instead of petrol (EAW 2006). Keywords: CNG, conversion, environment, fuel, gas, transportatio

A highly conserved WDYPKCDRA epitope in the RNA directed RNA polymerase of human coronaviruses can be used as epitope-based universal vaccine design

BACKGROUND: Coronaviruses are the diverse group of RNA virus. From 1960, six strains of human coronaviruses have emerged that includes SARS-CoV and the recent infection by deadly MERS-CoV which is now going to cause another outbreak. Prevention of these viruses is urgent and a universal vaccine for all strain could be a promising solution in this circumstance. In this study we aimed to design an epitope based vaccine against all strain of human coronavirus. RESULTS: Multiple sequence alignment (MSA) approach was employed among spike (S), membrane (M), enveloped (E) and nucleocapsid (N) protein and replicase polyprotein 1ab to identify which one is highly conserve in all coronaviruses strains. Next, we use various in silico tools to predict consensus immunogenic and conserved peptide. We found that conserved region is present only in the RNA directed RNA polymerase protein. In this protein we identified one epitope WDYPKCDRA is highly immunogenic and 100% conserved among all available human coronavirus strains. CONCLUSIONS: Here we suggest in vivo study of our identified novel peptide antigen in RNA directed RNA polymerase protein for universal vaccine – which may be the way to prevent all human coronavirus disease