A Bayesian optimization algorithm for the nurse scheduling problem

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Raman Spectroscopy and DFT calculations for the Electronic Structure Characterization of Conjugated Polymers

In the last three decades, there has been a broad academic and industrial interest in conjugated polymers as semiconducting materials for organic electronics. Their applications in polymer light-emitting diodes (PLEDs), polymer solar cells (PSCs), and organic field-effect transistors (OFETs) offer opportunities for the resolution of energy issues as well as the development of display and information technologies1. Conjugated polymers provide several advantages including low cost, light weight, good flexibility, as well as solubility which make them readily processed and easily printed, removing the conventional photolithography for patterning2. A large library of polymer semiconductors have been synthesized and investigated with different building blocks, such as acenes or thiophene and derivatives, which have been employed to design new materials according to individual demands for specific applications. To design ideal conjugated polymers for specific applications, some general principles should be taken into account, including (i) side chains (ii) molecular weights, (iii) band gap and HOMO and LUMO energy levels, and (iv) suited morphology.3-6 The aim of this study is to elucidate the impact that substitution exerts on the molecular and electronic structure of π-conjugated polymers with outstanding performances in organic electronic devices. Different configurations of the π-conjugated backbones are analyzed: (i) donor-acceptor configuration, (ii) 1D lineal or 2D branched conjugated backbones, and (iii) encapsulated polymers (see Figure 1). Our combined vibrational spectroscopy and DFT study shows that small changes in the substitution pattern and in the molecular configuration have a strong impact on the electronic characteristics of these polymers. We hope this study can advance useful structure-property relationships of conjugated polymers and guide the design of new materials for organic electronic applications.

Synthetic Circuits for Feedback and Detection in Bacteria

Synthetic biology, by co-opting molecular machinery from existing organisms, can be used as a tool for building new genetic systems from scratch, for understanding natural networks through perturbation, or for hybrid circuits that piggy-back on existing cellular infrastructure. Although the toolbox for genetic circuits has greatly expanded in recent years, it is still difficult to separate the circuit function from its specific molecular implementation. In this thesis, we discuss the function-driven design of two synthetic circuit modules, and use mathematical models to understand the fundamental limits of circuit topology versus operating regimes as determined by the specific molecular implementation. First, we describe a protein concentration tracker circuit that sets the concentration of an output protein relative to the concentration of a reference protein. The functionality of this circuit relies on a single negative feedback loop that is implemented via small programmable protein scaffold domains. We build a mass-action model to understand the relevant timescales of the tracking behavior and how the input/output ratios and circuit gain might be tuned with circuit components. Second, we design an event detector circuit with permanent genetic memory that can record order and timing between two chemical events. This circuit was implemented using bacteriophage integrases that recombine specific segments of DNA in response to chemical inputs. We simulate expected population-level outcomes using a stochastic Markov-chain model, and investigate how inferences on past events can be made from differences between single-cell and population-level responses. Additionally, we present some preliminary investigations on spatial patterning using the event detector circuit as well as the design of stationary phase promoters for growth-phase dependent activation. These results advance our understanding of synthetic gene circuits, and contribute towards the use of circuit modules as building blocks for larger and more complex synthetic networks.

Sintonia automática de controladores PID: uma abordagem neuro - genética

Dissertação de dout. em Electrónica e Computação, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2004

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

The actinin family of actin crosslinking proteins: natural functions and potential applications in synthetic biology

Actinin and spectrin proteins are members of the Spectrin Family of Actin Crosslinking Proteins. The importance of these proteins in the cytoskeleton is demonstrated by the fact that they are common targets for disease causing mutations. In their most prominent roles, actinin and spectrin are responsible for stabilising and maintaining the muscle architecture during contraction, and providing shape and elasticity to the red blood cell in circulation, respectively. To carry out such roles, actinin and spectrin must possess important mechanical and physical properties. These attributes are desirable when choosing a building block for protein-based nanoconstruction. In this study, I assess the contribution of several disease-associated mutations in the actinin-1 actin binding domain that have recently been linked to a rare platelet disorder, congenital macrothrombocytopenia. I investigate the suitability of both actinin and spectrin proteins as potential building blocks for nanoscale structures, and I evaluate a fusion-based assembly strategy to bring about self-assembly of protein nanostructures. I report that the actinin-1 mutant proteins display increased actin binding compared to WT actinin-1 proteins. I find that both actinin and spectrin proteins exhibit enormous potential as nano-building blocks in terms of their stability and ability to self-assemble, and I successfully design and create homodimeric and heterodimeric bivalent building blocks using the fusion-based assembly strategy. Overall, this study has gathered helpful information that will contribute to furthering the advancement of actinin and spectrin knowledge in terms of their natural functions, and potential unnatural functions in protein nanotechnology.

Estrategias metodológicas para operacionalizar la práctica educativa transdisciplinaria, en conjunto con los actores universitarios, en las licenciaturas del Centro de Estudios Universitarios Arkos (CEUArkos) de Puerto Vallarta, Jalisco, México

Recibido 29 de noviembre de 2010 • Aceptado 09 de marzo de 2011 • Corregido 12 de abril de 2011 El artículo resume los procesos vividos y los resultados obtenidos a través de una investigación-acción con actores (profesores, estudiantes y directivos) de las distintas áreas del CEUArkos de Puerto Vallarta, Jalisco, México. Su propósito es construir y llevar a la práctica estrategias metodológicas para operacionalizar la transdisciplinariedad y la complejidad en el aprendizaje, la enseñanza y la investigación –como parte de la docencia– en las licenciaturas de la institución, a fin de sentar las bases para el paso de una formación universitaria que transite de la disciplinariedad hacia la transdisciplinariedad en todas las carreras que la Universidad oferta. Aborda, asimismo, el tema del currículo universitario.