Adapter module for self-learning production systems

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica, Sistemas e Computadores

Optimization of magnetoelectric composites based on electroactive polymers for energy harvesting and sensor application

Tese de Doutoramento em Engenharia de Materiais.

Viral self-assembly as a thermodynamic process

The protein shells, or capsids, of nearly all spherelike viruses adopt icosahedral symmetry. In the present Letter, we propose a statistical thermodynamic model for viral self-assembly. We find that icosahedral symmetry is not expected for viral capsids constructed from structurally identical protein subunits and that this symmetry requires (at least) two internal switching configurations of the protein. Our results indicate that icosahedral symmetry is not a generic consequence of free energy minimization but requires optimization of internal structural parameters of the capsid proteins

Viral self-assembly as a thermodynamic process

The protein shells, or capsids, of nearly all spherelike viruses adopt icosahedral symmetry. In the present Letter, we propose a statistical thermodynamic model for viral self-assembly. We find that icosahedral symmetry is not expected for viral capsids constructed from structurally identical protein subunits and that this symmetry requires (at least) two internal switching configurations of the protein. Our results indicate that icosahedral symmetry is not a generic consequence of free energy minimization but requires optimization of internal structural parameters of the capsid proteins

Design and optimization of selfnanoemulsifying drug delivery systems for enhanced dissolution of gemfibrozil

Self-nanoemulsifying drug delivery systems of gemfibrozil were developed under Quality by Design approach for improvement of dissolution and oral absorption. Preliminary screening was performed to select proper components combination. BoxBehnken experimental design was employed as statistical tool to optimize the formulation variables, X1 (Cremophor® EL), X2 (Capmul® MCM-C8), and X3 (lemon essential oil). Systems were assessed for visual characteristics (emulsification efficacy), turbidity, droplet size, polydispersity index and drug release. Different pH media were also assayed for optimization. Following optimization, the values of formulation components (X1, X2, and X3) were 32.43%, 29.73% and 21.62%, respectively (16.22% of gemfibrozil). Transmission electron microscopy demonstrated spherical droplet morphology. SNEEDS release study was compared to commercial tablets. Optimized SNEDDS formulation of gemfibrozil showed a significant increase in dissolution rate compared to conventional tablets. Both formulations followed Weibull mathematical model release with a significant difference in td parameter in favor of the SNEDDS. Equally amodelistic parameters were calculated being the dissolution efficiency significantly higher for SNEDDS, confirming that the developed SNEDDS formulation was superior to commercial formulation with respect to in vitro dissolution profile. This paper provides an overview of the SNEDDS of the gemfibrozil as a promising alternative to improve oral absorption.

Defects in Self Assembled Colloidal Crystals

Colloidal self assembly is an efficient method for making 3-D ordered nanostructures suitable for materials such as photonic crystals and macroscopic solids for catalysis and sensor applications. Colloidal crystals grown by convective methods exhibit defects on two different scales. Macro defects such as cracks and void bands originate from the dynamics of meniscus motion during colloidal crystal growth while micro defects like vacancies, dislocation and stacking faults are indigenous to the colloidal crystalline structure. This paper analyses the crystallography and energetics of the microscopic defects from the point of view of classical thermodynamics and discusses the strategy for the control of the macroscopic defects through optimization of the liquid-vapor interface.

Self-assembled healable materials through a combination of pi-pi stacking and hydrogen bonding

The discovery of polymers with stimuli responsive physical properties is a rapidly expanding area of research. At the forefront of the field are self-healing polymers, which, when fractured can regain the mechanical properties of the material either autonomically, or in response to a stimulus. It has long been known that it is possible to promote healing in conventional thermoplastics by heating the fracture zone above the Tg of the polymer under pressure. This process requires reptation and subsequent re-entanglement of macromolecules across the fracture void, which serves to bridge, and ‘heal’ the crack. The timescale for this mechanism is highly dependent on the molecular weight of the polymer being studied. This process is in contrast to that required to affect healing in supramolecular polymers such as the plasticised, hydrogen bonded elastomer reported by Leibler et al. The disparity in bond energies between the non-covalent and covalent bonds within supramolecular polymers results in fractures propagating through scission of the comparatively weak supramolecular interactions, rather than through breaking the stronger, covalent bonds. Thus, during the healing process the macromolecules surrounding the fracture site only need sufficient energy to re-engage their supramolecular interactions in order to regenerate the strength of the pristine material. Herein we describe the design, synthesis and optimization of a new class of supramolecular polymer blends that harness the reversible nature of pi-pi stacking and hydrogen bonding interactions to produce self-supporting films with facile healable characteristics.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Optimization of incubation time of protein Tc85 in the construction of biosensor: Is the EIS a good tool?

Electrochemical impedance spectroscopy (EIS) in pH 6.9 phosphate buffer solution was used to investigate each step of the procedure employed to modify a screen-printed electrode (SPE). The SPE was modified with self-assembled monolayers (SAMs) of cystamine (CYS, deposited from 20 mM solution), followed by glutaraldehyde (GA, 0.3 M solution). The Trypanosoma cruzi antigen was immobilized using different deposition times. The influence of incubation time (2-18 h) of protein was also investigated. The topography of modified electrode with this protein was investigated by atomic force microscopy (AFM). Interpretation of impedance data was based on physical and chemical adsorption, and degradation of the layer at high and meddle frequencies, and charge transfer reaction involving mainly the reduction of oxygen at low frequencies. EIS studies on modified electrodes with Tc85 protein immobilized for different incubation times indicated that the optimum incubation time was 6-8 h. It was demonstrated that EIS is a good technique to evaluate the different steps and the integrity of the surface modifications, and to optimize the incubation time of protein in the development of biosensors. (C) 2010 Elsevier B.V. All rights reserved.

Self-reinforced composites obtained by the partial oxypropylation of cellulose fibers. 1. Characterization of the materials obtained with different types of fibers

The in-depth oxypropylation of different types of cellulose fibers, namely Avicel, Rayon, Kraft, and Filter Paper, was investigated. New biphasic mono-component materials were obtained, which could be hot-pressed to form films of cellulose fibers dispersed into a thermoplastic matrix. The success of this chemical modification was assessed by FTIR spectroscopy, X-ray diffraction, scanning electron microscopy. differential scanning calorimetry, thermogravimetric analysis and contact angle measurements. The optimization of this process led to the establishment of the optimal molar ratio between the cellulose CH groups and propylene oxide, which varied as a function of the specific morphology of the fibers. (C) 2008 Elsevier Ltd. All rights reserved.

Optimization of incubation time of protein Tc85 in the construction of biosensor: Is the EIS a good tool?

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Optimization of orthodontic treatment using the Centrex System to retract anterior teeth

INTRODUÇÃO: a Ortodontia passa, atualmente, por um momento de importantes inovações e grande efervescência criativa. Somente para citar algumas mudanças introduzidas ou aprimoradas nos últimos anos, nós podemos relembrar a popularização dos braquetes autoligáveis e o surgimento da ancoragem absoluta com a utilização de implantes ortodônticos. No final da década de 1990, a adoção dos mini-implantes como ancoragem permitiu uma mudança de paradigma que tem influenciado até mesmo a forma de pensar a mecânica ortodôntica. A imbricação das especialidades de Ortodontia e Implantodontia, cujo início se deu com os preparos ortodônticos para posterior inserção de implantes protéticos, floresceu com o uso de implantes palatinos e, posteriormente, com a introdução de mini-implantes. O aprimoramento da técnica de inserção de mini-implantes com a introdução de parafusos autoperfurantes tem permitido, inclusive, o requinte do ortodontista concentrar em suas mãos o planejamento e a colocação dessa preciosa peça de ancoragem. Levando em consideração a versatilidade de posicionamento desses pequenos parafusos, foi desenvolvido um conceito que possibilita a construção de linhas de ação de força que buscam otimizar o planejamento e a previsibilidade da movimentação ortodôntica. OBJETIVO: apresentar alguns resultados clínicos de tratamentos conduzidos com o uso de um sistema de tratamento ortodôntico, o Centrex System, que aproxima a linha de ação da força do centro de resistência das unidades a serem movimentadas. O caminho trilhado até o seu desenvolvimento, cuja teoria mecânica foi apresentada anteriormente nesse periódico, será detalhado para uma melhor compreensão de seu funcionamento.

Green-Markov models - new optimization strategies: a case study for user allocation in co-channel macro/femto networks

ABSTRACT: The femtocell concept aims to combine fixed-line broadband access with mobile telephony using the deployment of low-cost, low-power third and fourth generation base stations in the subscribers' homes. While the self-configuration of femtocells is a plus, it can limit the quality of service (QoS) for the users and reduce the efficiency of the network, based on outdated allocation parameters such as signal power level. To this end, this paper presents a proposal for optimized allocation of users on a co-channel macro-femto network, that enable self-configuration and public access, aiming to maximize the quality of service of applications and using more efficiently the available energy, seeking the concept of Green networking. Thus, when the user needs to connect to make a voice or a data call, the mobile phone has to decide which network to connect, using the information of number of connections, the QoS parameters (packet loss and throughput) and the signal power level of each network. For this purpose, the system is modeled as a Markov Decision Process, which is formulated to obtain an optimal policy that can be applied on the mobile phone. The policy created is flexible, allowing different analyzes, and adaptive to the specific characteristics defined by the telephone company. The results show that compared to traditional QoS approaches, the policy proposed here can improve energy efficiency by up to 10%.