Structural Return Maximization for Reinforcement Learning

Batch Reinforcement Learning (RL) algorithms attempt to choose a policy from a designer-provided class of policies given a fixed set of training data. Choosing the policy which maximizes an estimate of return often leads to over-fitting when only limited data is available, due to the size of the policy class in relation to the amount of data available. In this work, we focus on learning policy classes that are appropriately sized to the amount of data available. We accomplish this by using the principle of Structural Risk Minimization, from Statistical Learning Theory, which uses Rademacher complexity to identify a policy class that maximizes a bound on the return of the best policy in the chosen policy class, given the available data. Unlike similar batch RL approaches, our bound on return requires only extremely weak assumptions on the true system

Simplified model for understanding natural convection driven biomass cooking stoves, A

Department Head: Allan Thomson Kirkpatrick.2010 Summer.Includes bibliographical references (pages 82-84).It is estimated that half the world's population cooks over an open biomass fire; improved biomass cooking stove programs have the potential to impact indoor air quality, deforestation, climate change, and quality of life on a global scale. The majority of these cooking stoves operate in a natural convection mode (being driven by chimney effect buoyant fluid forces). Simplified theories for understanding the behavior of this unexpectedly complex combustion system, along with practical engineering tools to inform its design are markedly lacking. A simplified model of the fundamental stove flow physics is developed for predicting bulk flow rate, temperature, and excess air ratio based on stove geometry (chimney height, chimney area, viscous and heat release losses) and the firepower (as established by the stove operator). These parameters are intended to be fundamental inputs for future work understanding and improving biomass cook stove emissions and heat transfer. Experimental validation is performed and the simplified model is shown to be both accurate and applicable to typical stove operation. Carbon monoxide and particulate matter emissions data has been recorded in conjunction with the validation data. The initial results are presented and indicate that the excess air ratio may be a promising tool for reducing carbon monoxide emissions. A dimensionless form of the simplified stove flow model is then developed. This form offers several advantages, including scale similarity and a reduction of independent experimental parameters. Plotting with dimensionless parameters, various stove configurations can be plotted concurrently, and general stove flow behavior common to all natural convection stoves is observed. With a dimensionless firepower axis, emissions trends for both carbon monoxide and particulate matter become apparent, and a region of improved combustion efficiency and lowered emissions is identified

Modelling and experimental validation of tribocharging for space resource utilisation (SRU)

Space Resource Utilisation (SRU) technology will enable further exploration and habitation of space by humankind. For example, oxygen produced \textit{in situ} can be used as the oxidiser in rocket propellant, or for life support systems. The production of oxygen on the Moon can be achieved through the thermo-chemical reduction of the lunar soil, also known as regolith. All reduction techniques require a consistent feedstock from this mix of fine mineral particles to produce oxygen reliably and consistently. The preparation of this feedstock, known as beneficiation, is a critical intermediate stage of the SRU flowsheet, however it has received little research attention relative to the preceding excavation, and the subsequent oxygen production stages. Triboelectric charging and free-fall separation are attractive technologies for mineral beneficiation as they offers low mass, low power, and low mechanical complexity compared to other approaches. Tribocharging is a process by which particles (conductors, semi-conductors, and insulators) acquire charge through frictional rubbing and subsequent separation. Previous experimental studies have tested different designs of tribocharging apparatuses for terrestrial and space applications, however charge transfer modelling methods have not been employed to optimise design parameters. Furthermore, whilst modelling of the triboelectrification process has been presented in the literature using the discrete element method (DEM), these models often depend on poorly quantified or ill-defined parameters, such as an effective work function for insulating materials. Previous studies have also been restricted to either 2D or 3D domains and have not considered the impact of this on the performance of the models. To address these knowledge and research gaps, the objectives of this thesis are as follows: \begin{enumerate} \item Develop a novel tribocharge modelling approach based on the discrete element method that de-emphasises the poorly-defined quantities found in the high-density limit approach that has been demonstrated previously; \item Determine the suitability of modelling tribocharging in 2D and 3D; \item Validate this novel tribocharge modelling method by comparing simulation outputs and experimental data; \item Present and validate a new DEM-based method for tribocharger design optimisation; and, \item Evaluate experimentally the impact of an optimised tribocharger design on the performance of an electrostatic separator using standard mineral processing criteria. \end{enumerate} A straightforward experimental method to quantify key tribocharging model parameters, namely the charge transfer limit, $\Gamma$, and the charging efficiency, $\kappa_c$, is presented herein. These parameters are then used in both 2D and 3D DEM charge transfer simulations (particle-particle and particle-wall interactions; single and multiple particles and contacts) to evaluate the suitability of faster 2D models. Both the 2D and 3D models were found to perform well against the experimental data for single-contact and single-particle, multi-contact systems, however 2D models failed to produce good agreement with multi-particle, multi-contact systems. A novel DEM-based approach for tribocharger design optimisation using particle-wall and particle-particle contact areas as proxies for charge transfer is demonstrated. This optimisation method is used to design an optimal tribocharger for use under terrestrial conditions. The novel tribocharge modelling approach was then applied to the optimised charger design. This design was then built and validated experimentally, with good agreement found between the model outputs and experimental data. The optimised terrestrial design was then employed to study the charging behaviour of pure silica and ilmenite, as well as binary mixtures of silica and ilmenite, and samples of lunar regolith simulant JSC-1. Ilmenite was used because it is a target mineral for oxygen production from the lunar regolith, and silica was used because of its position in the triboelectric series relative to ilmenite. The optimised tribocharger design affected significantly the movement of pure ilmenite in the electrostatic field, despite a negligible change in bulk charge. Experimental results from the binary mixtures indicate that ilmenite recovery is independent of initial ilmenite concentration and can be predicted from the mass distribution of pure ilmenite samples. For JSC-1, the tribocharger was found to increase the density of the material in certain collectors. This thesis presents new modelling approaches for both tribocharging and tribocharger design optimisation. These techniques will facilitate ultimately the development of beneficiation technologies for SRU. The use of these modelling methods should increase confidence in the performance of tribocharger designs proposed for future SRU missions to the Moon.Open Acces

Remodeling of the Actin Cytoskeleton and Contraction in the A7r5 Smooth Muscle Cell

https://scholarworks.moreheadstate.edu/student_scholarship_posters/1091/thumbnail.jp

Taureau: A Stock Market Movement Inference Framework Based on Twitter Sentiment Analysis

With the advent of fast-paced information dissemination and retrieval, it has become inherently important to resort to automated means of predicting stock market prices. In this paper, we propose Taureau, a framework that leverages Twitter sentiment analysis for predicting stock market movement. The aim of our research is to determine whether Twitter, which is assumed to be representative of the general public, can give insight into the public perception of a particular company and has any correlation to that company's stock price movement. We intend to utilize this correlation to predict stock price movement. We first utilize Tweepy and getOldTweets to obtain historical tweets indicating public opinions for a set of top companies during periods of major events. We filter and label the tweets using standard programming libraries. We then vectorize and generate word embedding from the obtained tweets. Afterward, we leverage TextBlob, a state-of-the-art sentiment analytics engine, to assess and quantify the users' moods based on the tweets. Next, we correlate the temporal dimensions of the obtained sentiment scores with monthly stock price movement data. Finally, we design and evaluate a predictive model to forecast stock price movement from lagged sentiment scores. We evaluate our framework using actual stock price movement data to assess its ability to predict movement direction

TINDAK PIDANA PREKURSOR NARKOTIKA DI WILAYAH NEGARA REPUBLIK INDONESIA

Penelitian ini dilakukan dengan tujuan untuk mengetahui bagaimana tindak pidana prekursor narkotika di wilayah negara Republik Indonesia dan bagaimana ketentuan pidana terhadap pelaku di wilayah negara Republik   Indonesia. Dengan menggunakan metode peneltian yuridis normatif, disimpulkan: 1. Pengaturan hukum prekursor narkotika diperlukan agar tujuan pengaturannya dapat melindungi masyarakat dari bahaya penyalahgunaan Prekursor Narkotika, mencegah dan memberantas peredaran gelap Prekursor Narkotika dan mencegah terjadinya kebocoran dan penyimpangan prekursor narkotika. Pemerintah menyusun rencana kebutuhan tahunan prekursor narkotika untuk kepentingan industri farmasi, industri nonfarmasi, dan ilmu pengetahuan dan teknologi. Rencana kebutuhan tahunan disusun berdasarkan jumlah persediaan, perkiraan kebutuhan, dan penggunaan Prekursor Narkotika secara nasional. Pengadaan Prekursor Narkotika dilakukan melalui produksi dan impor dan hanya dapat digunakan untuk tujuan industri farmasi, industri nonfarmasi, dan ilmu pengetahuan dan teknologi. 2. Pemberlakuan ketentuan pidana terhadap pelaku tindak pidana prekursor narkotika di wilayah negara Republik Indonesia yang telah memenuhi unsur-unsur tindak pidana dalam Undang-Undang Nomor 35 Tahun 2009 Tentang Narkotika, khususnya Pasal 111 sampai dengan Pasal 126, Pasal 127 ayat (1), Pasal 128 ayat (1), dan Pasal 129. Ketentuan pidana meliputi pidana mati, pidana penjara seumur hidup, pidana penjara dan pidana denda sesuai dengan perbuatan pidana yang dilakukan dan telah terbukti secara sah berdasarkan peraturan perundang-undangan yang berlaku dilakukan oleh pelaku tindak pidana.Kata kunci:  Tindak Pidana, Prekursor, Narkotika