Proceedings of the Thirty-first Annual Biochemical Engineering Symposium

The Thirty-First Annual Biochemical Engineering Symposium was held on September 7 and 8, 2001, at Kansas State University. The program included 10 oral presentations and 3 posters; however the paper by Boyack and Gilcrease was not presented because the presenter was ill and unable to come. Some of the papers describe work that is in progress while others describe completed projects. Many of the authors intend to submit their work for publication elsewhere in a more complete form. A listing of those who attended is given below. The activities began on Friday evening with an indoor picnic because of rain and wind. Contents Bioreduction of Chromium(VI) by Inactivated Medicago Sativa (Alfalfa) Biomass - Kenneth M Dokken, Jorge L. Gardea-Torresdey, Kirk J. Tiemann, Jason G. Parsons, and Gerardo Gamez TNT Transformation by Plants: Role of Hydroxylamines in the Pathway - Murali Subramanian and Jacqueline V. Shanks Plant Uptake and Transformation of Benzotriazoles - Sigifredo Castro, Lawrence C. Davis, and Larry E. Erickson Microbial Degradation of 5-Methyl Benzotriazole - Kaila Young, Larry Erickson, Lawrence Davis, and Sigifredo Castro Diaz Understanding Protein Structure-Function Relationships in Family 47 a-1,2-Mannosidases through Computational Docking of Ligands - Chandrika Mulakala and Peter J. Reilly A Mathematical Model for Carbon Bond Labeling Experiments: Analytical Solutions and Sensitivity Analysis for the Effect of Reaction Reversibilities on Estimated Fluxes - Ganesh Sriram and Jacqueline V. Shanks Platelet Derived Nitric Oxide (NO) Inhibits Thrombus Formation: The Role of Insulin - R.H. Williams and M U. Nollert Molecular Mechanics Calculations to Quantify Segmental Interactions in Bioerodible Polyanhydrides: Consequences for Drug Delivery - Matt Kipper and Balaji Narasimhan Three-Dimensional Hydrophobic Cluster Analysis: The Use of a Virtual Environment for Protein Sequence Analysis: HELIX v0.3 - Anthony D. Hill, Alain Laederach, and Peter J. Reilly Mechanical Performance of Elastin-Mimetic Hydrogels - Eder D. Oliveira, Sharon A. Hagan, and Stevin H. Gehrke Multiple Sequence Alignment and Phylogenetic Analysis of Family 1 β-Giycosidases - Anthony D. Hill, Alain Laederach, and Peter J. Reillyhttps://lib.dr.iastate.edu/bce_proceedings/1026/thumbnail.jp

Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms

The objectives of this dissertation are to: (1) review the breadth and depth of land use land cover (LUCC) issues that are being addressed by the land change science community by discussing how an existing model, Purdue\u27s Land Transformation Model (LTM), has been used to better understand these very important issues; (2) summarize the current state-of-the-art in LUCC modeling in an attempt to provide a context for the advances in LUCC modeling presented here; (3) use a variety of statistical, data mining and machine learning algorithms to model single LUCC transitions in diverse regions of the world (e.g. United States and Africa) in order to determine which tools are most effective in modeling common LUCC patterns that are nonlinear; (4) develop new techniques for modeling multiple class (MC) transitions at the same time using existing LUCC models as these models are rare and in great demand; (5) reconfigure the existing LTM for urban growth boundary (UGB) simulation because UGB modeling has been ignored by the LUCC modeling community, and (6) compare two rule based models for urban growth boundary simulation for use in UGB land use planning. The review of LTM applications during the last decade indicates that a model like the LTM has addressed a majority of land change science issues although it has not explicitly been used to study terrestrial biodiversity issues. The review of the existing LUCC models indicates that there is no unique typology to differentiate between LUCC model structures and no models exist for UGB. Simulations designed to compare multiple models show that ANN-based LTM results are similar to Multivariate Adaptive Regression Spline (MARS)-based models and both ANN and MARS-based models outperform Classification and Regression Tree (CART)-based models for modeling single LULC transition; however, for modeling MC, an ANN-based LTM-MC is similar in goodness of fit to CART and both models outperform MARS in different regions of the world. In simulations across three regions (two in United States and one in Africa), the LTM had better goodness of fit measures while the outcome of CART and MARS were more interpretable and understandable than the ANN-based LTM. Modeling MC LUCC require the examination of several class separation rules and is thus more complicated than single LULC transition modeling; more research is clearly needed in this area. One of the greatest challenges identified with MC modeling is evaluating error distributions and map accuracies for multiple classes. A modified ANN-based LTM and a simple rule based UGBM outperformed a null model in all cardinal directions. For UGBM model to be useful for planning, other factors need to be considered including a separate routine that would determine urban quantity over time

Hierarchical data-driven modelling of binary black hole mergers

[eng]Throughout history, human beings have received and interpreted information from distant stars and galaxies through electromagnetic waves (light). Until 2015 this was the dominant way for observing astrophysical events happening in our cosmos. However, on September 14'th 2015 a new window to the universe was opened thanks to the rst direct gravitational wave detection, a goal pursued for several decades by the LIGO/Virgo scienti c collaboration. Gravitational waves are tiny space-time oscillations propagating at the speed of light. They are a prediction of the Einstein theory of gravity and we need the most catastrophic astrophysical events to detect them. The rst observation of gravitational waves described the inspiral, merger and ringdown of two black holes with 36 and 29 solar masses located at 1300 billion light-years, where about the 5% of the total mass was radiated as gravitational waves and becoming the most powerful astrophysical event ever observed. The event was called GW150914, consistently with its the arrival date and was publicly announced on February 11'th 2016 by the LIGO Virgo collaboration. This has not been the only event observed during this thesis project. Relying on statistical criteria arguments, we can certify the observation of one additional event also compatible with the coalescense of a pair of black holes tagged as GW151226 plus a third one called LVT151012 likely from astrophysical origin but that did not reach the statistical signi cance required to be con rmed. The coalescense of binary black hole systems are an optimal candidate for the observation and study of gravitational waves. The current observations suggest that these kind of events could dominate the future ground based detections. Then, we need to optimise the theoretical waveform models to characterise the future observations. In this thesis we have given the rst steps towards a new upgrading of the nonprecessing gravitational waves models. These models result from the matching of the well known post-Newtonian (PN) and e ective-one-body (EOB) analytic formulations to the computationally expensive numerical solutions of the Einstein equations. They are de ned in the frequency domain and depend on the ratio of the two black hole masses (mass-ratio) and some e ective spin e that results from the combination of the components of the spins orthogonal to the orbital plane thus reducing the physical parameter space to only two dimensions. Then, although this current prescription have been demonstrated to be su cient for the searches of the gravitational waves in the data, they are not so optimal for the statistical inference of the spins of each BH, which is partially caused by the inherent degeneracy introduced by the e ective spin. The focus of this work has been the extension of the one-spin phenomenological models to its two-spin version by adding the subdominant e ects carried by the spin di erence terms = 1 � 2. To that end, we have employed the data of more than 400 simulations of binary black hole systems generated by four di erent codes (BAM, SpEC, LAZEV, MAYA), 23 of them generated throughout this thesis by means of the BAM code. This involved the di cult task of evolving, extracting the waves and the data postprocessing of each case. Then, we have rede ned the strategy for building higher than two dimensional ansaetze to add subdominant e ects and where we have also included the results of the extreme mass ratio limit. All this analysis has resulted in the prescription of new phenomenological models for the nal mass, nal spin and peak luminosity. The new models have been shown to improve the old descriptions of these quantities while they have clearly revealed the possible impact of the subdominant e ects in the near future phenomenological models

Towards simulating the emergence of environmentally responsible behavior among natural resource users : an integration of complex systems theory, machine learning and geographic information science

La gouvernance pour le développement durable comporte de nombreux défis. L'un de ces défis consiste à mieux comprendre les systèmes socio-écologiques gouvernés. Dans de tels systèmes, l'apprentissage par essais et erreurs implique le risque de conséquences inattendues, irréversibles et néfastes. De plus, en raison de la complexité des systèmes socio-écologiques, les leçons tirées d'expériences à petite échelle ne peuvent pas toujours être applicables à des problèmes à grande échelle. Un autre aspect difficile des problèmes de développement durable est que ces problèmes sont souvent multidisciplinaires et composés de composants qui sont chacun étudiés individuellement dans une discipline différente, mais il existe peu d'informations sur leur comportement ensemble. Un troisième défi de la gouvernance pour le développement durable est qu'il est souvent nécessaire d'impliquer les parties prenantes dans des actions de gestion et des mesures d'intervention coûteuses pour les individus qui y participent. De plus, dans de nombreuses situations de ce type, les incitations financières et l'application des réglementations se soldent par un échec et ne constituent donc pas des options de gouvernance. Dans cette thèse, les défis ci-dessus sont abordés dans un exemple de contrôle des perturbations forestières avec une approche intégrée. Pour éviter le problème des effets indésirables irréversibles et pour permettre des expériences répétées, une approche de simulation est utilisée. Pour relever le défi de la multidisciplinarité des problèmes des systèmes socio-écologiques, deux modèles sont développés indépendamment - portant sur les aspects sociaux et écologiques du système de l'étude - et ils sont ensuite couplés de telle sorte que la sortie de chaque modèle est utilisée comme entrée pour l'autre modèle. Pour résoudre le problème de l'engagement des parties prenantes, un plan est proposé pour la promotion d'un comportement respectueux de l'environnement. Ce plan est basé sur l'offre de reconnaissance à ceux qui adoptent volontairement le comportement responsable. Le modèle écologique de cette étude, qui simule la propagation d'une perturbation forestière, est construit à l'aide de l’apprentissage automatique supervisé. Le modèle social de cette étude, qui simule l'émergence d'une nouvelle norme de comportement, est construit à l'aide de l'apprentissage par renforcement. Les deux modèles sont testés et validés avant couplage. Le modèle couplé est ensuite utilisé comme un laboratoire virtuel, où plusieurs expériences sont réalisées dans un cadre hypothétique et selon différents scénarios. Chacune de ces expériences est une simulation. A travers ces simulations, cette étude montre qu'avec un algorithme de prise de décision approprié et avec suffisamment de temps pour l'interaction entre une entité gouvernante et la société, il est possible de créer une motivation pour un comportement responsable dans la société. En d'autres termes, il est possible d'encourager la participation volontaire des acteurs à l'action pour le développement durable, sans que l'entité gouvernante ait besoin d'utiliser des incitations financières ou d'imposer son autorité. Ces résultats peuvent être applicables à d'autres contextes où un comportement responsable des individus ou des entreprises est recherché afin d'atténuer l'impact d'une perturbation, de protéger une ressource écologique, ou de faciliter une transition sectorielle vers la durabilité.Governance for sustainable development involves many challenges. One of those challenges is to gain insight about the social-ecological systems being governned. In such systems, learning by trial and error involve the risk of unexpected, irreversible and adverse consequences. Moreover, due to complexity of social-ecological systems, lessons learned from small scale experiments may not be applicable in large-scale problems. Another challenging aspect of problems of sustainable development is that these problems are often multidisciplinary and comprised of components that are each studied individually in a different discipline, but little information exists about their behavior together as a whole. A third challenge in governance for sustainable development is that often it is necessary to involve stakeholders in management actions and intervention measures that are costly for individuals who participate in them. Moreover, in many of these situations financial incentives or enforcement of regulations result in failure, and are thus not options for governance. In this thesis, the above challenges are addressed in an example case of forest disturbance control with an integrated approach. To avoid the problem of irreversible adverse effects and to allow repeated experiments, a simulation approach is used. To tackle the challenge of multidisciplinarity of problems of social-ecological systems, two models are independently developed – pertaining to social and ecological aspects of the system of the study – and they are subsequently coupled in such a way that the output of each model served as an input for the other. To address the problem of engagement of stakeholders, a scheme is proposed for promotion of environmentally responsible behavior. This scheme is based on offering recognition to those who voluntarily perform the responsible behavior. The ecological model of this study, which simulates the spread of a forest disturbance, is built using Supervised Machine Learning. The social model of this study, which simulates the emergence of a new norm of behavior, is built using Reinforcement Learning. Both models are tested and validated before coupling. The coupled model is then used as a virtual laboratory, where several experiments are performed in a hypothetical setting and under various scenarios. Each such experiment is a simulation. Through these simulations, this study shows that with an appropriate decision-making algorithm and with sufficient time for interaction between a governing entity and the society, it is possible to create motivation for responsible behavior in the society. In other words, it is possible to encourage voluntary participation of stakeholders in action for sustainable development, without the need for the governing entity to use financial incentives or impose its authority. These results may be applicable to other contexts where responsible behavior by individuals or enterprises is sought in order to mitigate the impact of a disturbance, protect an ecological resource, or facilitate a sectoral transition towards sustainability

Optimierungsrahmen für die Verbesserung der Energieflexibilität in Wohngebäuden

Energy flexibility is balancing the supply and demand of a building according to climate conditions, user preferences, and grid constraints. Energy flexibility in households is a practical approach to achieving sustainability in the building sector. However, the diversity in flexibility potential of energy systems and climatic variability complicate the selection of envelope parameters and building energy systems (BESs). This study aimed to design a framework to improve the energy flexibility of the building. For this purpose, a single-family house and diversified BESs were simulated in a TRNSYS-Python co-simulation platform. Initially, the bi-objective optimization identified flexible building envelopes in twenty-four locations. Then, the multi-criteria assessment of BESs was conducted using life-cycle energy flexibility indicators. Lastly, the energy flexibility potential of the BES was evaluated by employing steady-state optimization and model predictive control (MPC). The findings of this work set a benchmark for flexible household envelopes. The systematic approach for selecting BES could guide the energy system design, providing insight into energy flexibility. Further, this investigation has established that the dataset of building thermal load, boundary conditions, and control disturbances can be used to develop an MPC-based dynamic control. That controller could be employed on different BESs to achieve energy flexibility.Energieflexibilität ist der Ausgleich von Versorgung und Bedarf eines Gebäudes je nach Klima, Nutzerpräferenzen und Netzbeschränkungen. Energieflexibilität ist damit ein praktischer Ansatz für Nachhaltigkeit in Gebäuden. Die Vielfalt des Flexibilitätspotenzials von Energiesystemen und die klimatischen Unterschiede erschweren jedoch die Auswahl von Hüllparametern und Gebäudeenergiesystemen (BESs). Diese Studie zielte darauf ab, einen Rahmen zur Verbesserung der energetischen Flexibilität von Gebäuden zu entwickeln. Hierzu wurden ein Einfamilienhaus und verschiedene BES in einer TRNSYS-Python Co-Simulationsplattform simuliert. Zunächst wurden über eine bi-objektive Optimierung flexible Gebäudehüllen an vierundzwanzig Standorten ermittelt. Danach erfolgte eine multikriterielle Bewertung der BES anhand von Energieflexibilitätsindikatoren über den gesamten Lebenszyklus. Schließlich wurde das Energieflexibilitätspotenzial der BES durch den Einsatz statischer Optimierung und modellprädiktiver Regelung (MPC) bewertet. Die Ergebnisse dieser Arbeit setzen einen Maßstab für flexible Gebäudehüllen. Der systematische Ansatz zur Auswahl von BES könnte als Leitfaden für die Auslegung zukünftiger Systeme dienen. Darüber hinaus hat die Untersuchung ergeben, dass Daten zu thermischer Belastung des Gebäudes, Randbedingungen und Regelungsstörungen zur Entwicklung eines MPC verwendet werden können. Dieser Regler könnte bei verschiedenen BES eingesetzt werden, um Energieflexibilität zu erreichen

Artificial intelligence for porous organic cages

Porous organic cages are a novel class of molecules with many promising applications, including in separation, sensing, catalysis and gas storage. Despite great promise, discovery of these materials is hampered by a lack of computational tools for exploring their chemical space, and significant expense associated with prediction of their properties. This results in significant synthetic effort being directed toward molecules which do not have targeted properties. This thesis presents multiple computational tools which can aid the discovery and design of these materials by increasing the number of synthetic candidates which are likely to exhibit desired, targeted properties. Firstly, a broadly applicable methodology for the construction of computational models of materials is presented. This facilitates the automated modelling and screening of materials that would otherwise have to be carried out in a more labour-intensive way. Secondly, an evolutionary algorithm is implemented and applied to the design of porous organic cages. The algorithm is capable of producing cages closely matching user-defined design criteria, and its implementation is designed to allow future applications in other fields of material design. Finally, machine learning is used to accurately predict properties of porous organic cages, orders of magnitude faster than has been possible with traditional, simulation-based approaches.Open Acces

PARSING POLYPHYLETIC PUERARIA : DELIMITING DISTINCT EVOLUTIONARY LINEAGES THROUGH PHYLOGENY

A taxon is defined as polyphyletic when it does not include the last common ancestor of all true members of the taxon, resulting in a number of subgroups not united by a common ancestor. Previous work has suggested Pueraria (Fabaceae) to be polyphyletic. Although several taxonomic treatments have recognized Pueraria as an unnatural grouping since its creation in 1825, and two have put forth taxonomic hypotheses, the polyphyly has never been resolved. The need for further biosystematic research has always been cited as the reason no changes were proposed. This project attempted to address this issue by sampling broadly across phaseoloid legumes with an initial target goal of 156 species including 15 species of Pueraria. Ultimately, 104 species across 69 genera were sampled for AS2 and 116 species across 64 genera for matK. Phylogeny reconstruction was carried out using maximum likelihood and Bayesian inference. Both analyses yielded congruent tree topologies and similar support values. Both previous taxonomic hypotheses show some congruence with the data, but discrepancies do occur. This work provides strong support for the existence of five separate clades within the genus Pueraria, requiring the resurrection of the genus Neustanthus for P. phaseoloides along with the need to create a new genus each for P. stricta, P. peduncularis, and P. wallichii.  M.S