Nutrient Utilization from Anaerobic Digester Effluent Through Algae Cultivation

Nutrients present in digested animal waste can be utilized for algae cultivation under suitable conditions. Algal growth, however, depends on the chemical forms and speciation of these nutrients. In this study a chemical equilibrium model was first used to describe nutrient speciation and predict conditions that enhance the solubility of nutrients in anaerobic digester effluent. Dilution with water and separation of large particulates greatly improved nutrient availability and light penetration - conditions favorable for algal cultivation. Algae growth was tested using three strains - Scenedesmus dimorphous (UTEX # 417), Chlorella vulgaris (UTEX# 265), and an algal isolate (designated as LLAI and later identified to be closely related to Chlorella vulgaris) from the wastewater treatment lagoons in Logan, UT. All tested strains could be adapted to the effluent to enhance the utilization of native nutrients present in both organic and inorganic forms. There was a marked improvement in growth rates (up to 4.8-fold) and biomass production (up to 8.7-fold) of algal cultures after they adapted to the effluent. Also, effluent-adapted strains were able to switch from phototrophy to heterotrophy to prolong the growth when light availability became limited. However, an increase in irradiance levels in light-limited cultures led to resumption of phototrophic growth. It was found that this approach of light supplementation prolonged growth and increased biomass production (up to 2.7-fold) in algal cultures. Of all the strains tested, the isolate from the wastewater treating lagoons grew to highest culture densities and produced the highest concentration of intracellular triacylglycerides (TAG). This culture also grew best in non-sterile, native effluent and could reach biomass concentration of up to 4.5g/L with TAG content of approximately 10% (w/w). Culture densities were lower when this organism was grown in sterilized effluent or in sterile artificial media, suggesting that this organism symbiotically associated with other microbes in digested animal waste. Findings of this research study suggest that microalgae can be grown efficiently on inexpensive natural substrates in non-sterile growth conditions. When commercially implemented, biodiesel production from such systems could be more cost effective and sustainable

Bid distributions of competing agents in simple models of auctions

Models of auctions or tendering processes are introduced. In every round of bidding the players select their bid from a probability distribution and whenever a bid is unsuccessful, it is discarded and replaced. For simple models, the probability distributions evolve to a stationary power law with the exponent dependent only on the number of players. For most situations, the system converges towards a state where all players are identical. A number of variations of this model are introduced and the application of these models to the dynamics of market makers is discussed. The effect of price uncertainty on bid distributions is presented. An underlying market structure generates heterogenous agents which do not have power law bid distribution in general

Do Institution Investors Exacerbate Managerial Myopia?

This study analyzes corporate expenditures for property, plant and equipment (PP&E) and research and development (R&D) for over 2,500 firms from 1987 to 1994. We document a positive relation between expenditures for PP&E and R&D and institutional share ownership. This relation is robust to a variety of specifications. We examine the link between firm-level expenditures and institutional ownership by using lead-lag structures and changes in institutional ownership. The data do not support the contention that institutional investors cause corporate managers to behave myopically. Indeed, the data indicate that the presence of institutional shareholders allows managers to invest more in PP&E and R&D than would individual shareholders.

Novel algorithms for Uncertainty Quantification in large scale systems

Uncertainty Quantification (UQ) algorithms are of increasing significance in science and engineering. The process of modeling physical reality on computers is rife with uncertainties. These uncertainties get propagated through the computer model, leading to uncertain outputs. As decision-makers from every facet of society come to increasingly rely on computer predictions, the need to characterize this uncertainty has never been greater. However, doing so efficiently remains challenging. This is primarily because computer models are often time consuming to run and because their inputs live in high-dimensional spaces that are difficult to explore. In this thesis, we seek to address this challenge in the context of two UQ problems. In the first UQ problem, we study rare-event simulation: given a smooth non-linear map with uncertain inputs, what is the probability that the output evaluates inside a specified interval? Standard statistical approaches for computing this probability, such as the Monte Carlo method, become computationally inefficient as the event under consideration becomes rare. To address this inefficiency, we present two Importance Sampling (IS) algorithms. Our first algorithm, called the Bayesian Inverse Monte Carlo (BIMC) method, relies on solving a fictitious Bayesian inverse problem. The solution of the inverse problem yields a posterior PDF, a local Gaussian approximation to which serves as the importance sampling density. We subject BIMC to rigorous theoretical and experimental analysis, which establishes that BIMC can lead to speedups of several orders-of-magnitude (over the Monte Carlo method) when the forward map is nearly affine, or weakly non-linear. When these conditions are violated, that is, when the forward map is significantly nonlinear, BIMC leads to a poor-quality IS distribution. Motivated by these limitations, we propose modifications to BIMC. The modified algorithm, which we term Adaptive-BIMC (A-BIMC), proceeds in two stages. The first stage roughly identifies those regions in input space that trigger a rare event. The second stage then refines the approximation from the first stage of the algorithm. We study A-BIMC’s performance on synthetic problems and demonstrate that its performance doesn’t depend on how small the target probability is. Rather it depends on the nonlinearity of the input-output map. Through these experiments, we also find that A-BIMC’s performance deteriorates with increasing ambient dimensionality of the problem. To address this issue, we lay the foundation for a general dimension reduction strategy for rare-event probability estimation. The second UQ problem concerns the statistical calibration of model inputs from observed data, with the ultimate aim of issuing uncertainty-equipped predictions of a Quantity-of- Interest (QoI). The physical system that we study here is a hydrocarbon reservoir containing geological faults. Operational decisions concerning the reservoir rely on predictions of financial summaries of the reservoir, such as its Net Present Value. These summaries depend on the nature of fluid flow within the reservoir, which is itself controlled by the extent to which an individual fault inhibits or facilitates flow. This fault property, known as the fault transmissibility, isn’t directly measurable and must be calibrated using production data. Here, we design and analyze a complete data-to-prediction workflow to quantify post-calibration uncertainties. We also discuss how these uncertainties change under different reservoir conditions.Computational Science, Engineering, and Mathematic

Spinoffs, Ex Ante.

Cusatis, Miles and Woolridge (1993) report large positive excess stock returns following spinoffs for the parent firms that undertake the spinoffs and for the spun off subsidiaries themselves. They examine the period 1965 through 1988 and consider returns for up to 36 months following the spinoff. We investigate whether a trading strategy based on this ex post analysis earns positive excess returns on an ex ante basis using a holdout sample of spinoffs that occurred over the period 1989 through 1995. We find that, at best, such a strategy produced break-even results when compared with a size-and industry-matched firm benchmark. Tests of “beat-the-market” strategies based on long-run post-event returns are often used to argue against the semi-strong form of the efficient market hypothesis. We do not know whether U.S. stock markets are semi-strong form inefficient, but our results indicate that post-spinoff returns cannot be used to make that argument.

High-frequency quoting, trading, and the efficiency of prices

We examine the relation between high frequency quotation and the behavior of stock prices between 2009 and 2011 for the full cross-section of securities in the U.S. On average, higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading. We also explore market resiliency during periods of exceptionally high low-latency trading: large liquidity drawdowns in which, within the same millisecond, trading algorithms systematically sweep large volume across multiple trading venues. Although such large drawdowns incur trading costs, they do not appear to degrade the subsequent price formation process or increase the subsequent cost of trading. In an out-of-sample analysis, we investigate an exogenous technological change to the trading environment on the Tokyo Stock Exchange that dramatically reduces latency and allows co-location of servers. This shock also results in prices more closely resembling a random walk, and a sharp decline in the cost of trading

Agency Conflicts in Closed-End Funds: The Case of Rights Offerings

We study 120 rights offerings by closed-end funds over 1988-1998. On average, rights offerings are announced when funds trade at a premium. This premium turns into a discount over the course of the offering. The premium decline is more severe when the increases in investment advisor’s compensation are larger and when the fund uses affiliated broker-dealers to solicit subscriptions to the offer. A clinical analysis shows that rights offerings allow investment advisors to sidestep fee rebates and increase pecuniary benefits to affiliated entities. Overall, our results suggest the presence of significant conflicts of interests in rights offerings by closed-end funds

En la trastienda de la inteligencia artificial. Una investigación sobre las plataformas de micro-trabajo en francia

Micro-work internet services allocate small, standardized tasks of data generation and annotation to crowds of providers. The outputs are mainly used to produce artificial intelligence solutions. It is an exemplary instance of the ?platformization? of the economy, and of the transformations of labour that digital technologies bring about. To uncover the conditions under which micro-work is performed, and its broader societal implications, we conducted an extensive empirical study in France. In this paper, we use the data collected to present the socio-economic backgrounds of the people who engage in micro-work, their motivations and needs, and their everyday practices. We show that, while the micro-working population is very diverse, a significant portion (including working women with children and with higher education degrees) face pressing financial needs and rely on this activity to make ends meet. Micro-workers are also exposed to distinctive psycho-social risks, without adequate protection and with only limited opportunities to voice issues or seek advice. Especially as the current boom of artificial intelligence raises demand for their services, their working conditions call for attention from policy-makers, unions, and the general public. This article is a summarized version of the report ?Le Micro-travail en France. Derrière l?automatisation de nouvelles précarités au travail?