A comparison of anhydrous ethanol production from ethylene and from corn utilizing life cycle analysis methodology

There is currently much concern with the impact on the environment due to industrial processes. One method to assess this impact is to perform a Life Cycle Analysis (LCA) on the process. Life Cycle Analysis is made up of three stages; Life Cycle Inventory Analysis, Life Cycle Impact Assessment, and Life Cycle Improvement Assessment. In this study, Life Cycle Inventory Analysis methodology was applied to two industrial processes for producing anhydrous ethanol. The first process is the fermentation of corn, and the second is the hydration of ethylene using water and a catalyst. Each process was first modeled using the BioPro Designer, a process simulator, to generate the material and energy balances associated with each process. The result of the Life Cycle Inventory Analysis on each process was an eco-vector which contained all material inputs, energy inputs, and emissions associated with each step of each of the processes

Pure Infinitely Self-Modifying Code is Realizable and Turing-complete

Although self-modifying code has been shyed away from due to its complexity and discouragement due to safety issues, it nevertheless provides for a very unique obfuscation method and a different perspective on the relationship between data and code.  The generality of the von Neumann architecture is hardly realized by today's processor models.  A code-only model is shown where every instruction merely modifies other instructions yet achieves the ability to compute and Turing machine operation is easily possible

Training Neural Networks Through the Integration of Evolution and Gradient Descent

Neural networks have achieved widespread adoption due to both their applicability to a wide range of problems and their success relative to other machine learning algorithms. The training of neural networks is achieved through any of several paradigms, most prominently gradient-based approaches (including deep learning), but also through up-and-coming approaches like neuroevolution. However, while both of these neural network training paradigms have seen major improvements over the past decade, little work has been invested in developing algorithms that incorporate the advances from both deep learning and neuroevolution. This dissertation introduces two new algorithms that are steps towards the integration of gradient descent and neuroevolution for training neural networks. The first is (1) the Limited Evaluation Evolutionary Algorithm (LEEA), which implements a novel form of evolution where individuals are partially evaluated, allowing rapid learning and enabling the evolutionary algorithm to behave more like gradient descent. This conception provides a critical stepping stone to future algorithms that more tightly couple evolutionary and gradient descent components. The second major algorithm (2) is Divergent Discriminative Feature Accumulation (DDFA), which combines a neuroevolution phase, where features are collected in an unsupervised manner, with a gradient descent phase for fine tuning of the neural network weights. The neuroevolution phase of DDFA utilizes an indirect encoding and novelty search, which are sophisticated neuroevolution components rarely incorporated into gradient descent-based systems. Further contributions of this work that build on DDFA include (3) an empirical analysis to identify an effective distance function for novelty search in high dimensions and (4) the extension of DDFA for the purpose of discovering convolutional features. The results of these DDFA experiments together show that DDFA discovers features that are effective as a starting point for gradient descent, with significant improvement over gradient descent alone. Additionally, the method of collecting features in an unsupervised manner allows DDFA to be applied to domains with abundant unlabeled data and relatively sparse labeled data. This ability is highlighted in the STL-10 domain, where DDFA is shown to make effective use of unlabeled data

Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set. Thus DDFA features are inherently discriminative from the start even though they are trained without knowledge of the ultimate classification problem. Interestingly, DDFA also continues to add new features indefinitely (so it does not depend on a hidden layer size), is not based on minimizing error, and is inherently divergent instead of convergent, thereby providing a unique direction of research for unsupervised feature learning. In this paper the quality of its learned features is demonstrated on the MNIST dataset, where its performance confirms that indeed DDFA is a viable technique for learning useful features.Comment: Corrected citation formattin

Resonant two-photon ionization spectroscopy of jet-cooled Au?

Journal ArticleA band system of jet-cooled Au, has been located in the near infrared region of the spectrum using resonant two-photon ionization spectroscopy. The origin band is located at 13 354.15 cm-? and the system extends more than 700 cm-? further to the blue. The excited state displays a radiative lifetime of approximately 28 ?s, corresponding to an absorption oscillator strength of f~.0003. Accordingly, it is thought that the transition corresponds to a spin-forbidden doublet (S = l/2) to quartet (S = 3/2) transition, which is made allowed by spin-orbit contamination, presumably in the upper state. A progression in a totally symmetric stretching vibration (? = 179.7 cm-?) is obvious in the spectrum, along with a much weaker progression in another mode, which displays an interesting pattern of splittings. Although no assignment is absolutely unambiguous, various candidates are presented. The most likely of these assigns the system as an ? ?E ? ?x ?E? transition in the D?h point group, with both the ground X?E? and excited ? ?E? states undergoing Jahn-Teller distortion. The vibronic levels of the ? ?E? state have been fitted assuming a linear Jahn-Teller effect in a system with both spin-orbit splitting and a significant anharmonicity in the Jahn-Teller active e? vibrational mode. The combined effects of anharmonicity in the Jahn-Teller active mode and spin-orbit coupling appear not to have been previously investigated, they are therefore examined in some detail

Resonant two-photon ionization spectroscopy of coinage metal trimers: Cu?Ag, Cu?Au, and CuAgAu

Journal ArticleThe jet-cooled coinage metal triatomic molecules Cu, Ag, Cu, Au, and CuAgAu have been investigated using resonant two-photon ionization spectroscopy. One band system, labeled as the ?-x system, has been observed for each species, with origin bands at 13 188, 17 217, and 17 470 cm -?, respectively. Vibrational progressions have been assigned and vibrational constants have been extracted using a linear least-squares fitting procedure. For Cu, Ag, 47 vibrational bands have been assigned within the ?-X system. The upper states of these bands derive from combinations of two symmetric (a,) and one antisymmetric (b,) mode in the C?v point group. For the ?-2 system of Cu?Au, only seven vibrational bands have been observed, all occurring within a 500 cm-? range. Lifetime measurements for the observed vibrational levels support the possibility that predissociation may be occurring in the ? excited state of Cu?Au and this may be limiting the number of vibrational levels observed within this state. Finally, in the case of CuAgAu, 92 vibrational bands have been assigned, corresponding to excitations of three totally symmetric (a?) vibrational modes in the Cg point group. For this molecule, a complete set-of vibrational frequencies (?i ) and anharmonicities (xy) have been obtained for the excited ? state. In addition, the observation of weak hot bands in the spectrum permits the three vibrational modes of the X ground state to be characterized by v? = 222.83 ? 0.29, v? = 153.27 ? 0.22, and v? = 103.90 ? 0.28 cm-? for 63Cu?07Ag?97Au (la error limits)

Spectroscopic studies of jet-cooled CuAg

Journal ArticleResonant two-photon ionization spectroscopy has been applied to jet-cooled diatomic CuAg. Four band systems have been observed, three of which have been rotationally resolved and analyzed. The ground state is X ??+ in symmetry, deriving from the 3d??cu4d??Ag?? molecular configuration. Its bond length has been determined as ro = 2.3735 ? 0.0006 ? (1? error limits). Based on an analysis of the possible separated atom limits, three of the excited states observed (A0+, A?1, and B?0+) are assigned as primarily 3d?cu4d??Ag???* in character. The observation of unusually large electronic isotope shifts in the A-X, A ?-X, and B ?-X band systems, similar in magnitude to those previously observed in the A-X and B-X systems of Cu?, and the s?d excitations in atomic copper, provides further confirmation that these excited states derive from the 3d?Cu4d??Ag ???* molecular configuration. Finally, the highest energy state observed in this work is argued to be primarily ion pair in character, and is expected to have significant contributions from both the Cu + Ag - and Cu - Ag + ion pair states