Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: Application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K

The conformational space annealing (CSA) method for global optimization has been applied to the 10-55 fragment of the B-domain of staphylococcal protein A (protein A) and to a 75-residue protein, apo calbindin D9K (PDB ID code 1CLB), by using the UNRES off-lattice united-residue force field. Although the potential was not calibrated with these two proteins, the native-like structures were found among the low-energy conformations, without the use of threading or secondary-structure predictions. This is because the CSA method can find many distinct families of low-energy conformations. Starting from random conformations, the CSA method found that there are two families of low-energy conformations for each of the two proteins, the native-like fold and its mirror image. The CSA method converged to the same low-energy folds in all cases studied, as opposed to other optimization methods. It appears that the CSA method with the UNRES force field, which is based on the thermodynamic hypothesis, can be used in prediction of protein structures in real time.

On the nature of the galactic early-B hypergiants

Aims. Despite their importance to a number of astrophysical fields, the lifecycles of very massive stars are still poorly defined. In order to address this shortcoming, we present a detailed quantitative study of the physical properties of four early-B hypergiants (BHGs) of spectral type B1-4 Ia+; Cyg OB2 #12, ζ1 Sco, HD 190603 and BP Cru. These are combined with an analysis of their long-term spectroscopic and photometric behaviour in order to determine their evolutionary status. Methods. Quantitative analysis of UV–radio photometric and spectroscopic datasets was undertaken with a non-LTE model atmosphere code in order to derive physical parameters for comparison with apparently closely related objects, such as B supergiants (BSGs) and luminous blue variables (LBVs), and theoretical evolutionary predictions. Results. The long-term photospheric and spectroscopic datasets compiled for the early-B HGs revealed that they are remarkably stable over long periods ( ≥ 40 yrs), with the possible exception of ζ1 Sco prior to the 20th century; in contrast to the typical excursions that characterise LBVs. Quantitative analysis of ζ1 Sco, HD 190603 and BP Cru yielded physical properties intermediate between BSGs and LBVs; we therefore suggest that BHGs are the immediate descendants and progenitors (respectively) of such stars, for initial masses in the range ~30−60 M⊙. Comparison of the properties of ζ1 Sco with the stellar population of its host cluster/association NGC 6231/Sco OB1 provides further support for such an evolutionary scenario. In contrast, while the wind properties of Cyg OB2 #12 are consistent with this hypothesis, the combination of extreme luminosity and spectroscopic mass (~110 M⊙) and comparatively low temperature means it cannot be accommodated in such a scheme. Likewise, despite its co-location with several LBVs above the Humphreys-Davidson (HD) limit, the lack of long term variability and its unevolved chemistry apparently excludes such an identification. Since such massive stars are not expected to evolve to such cool temperatures, instead traversing an O4-6Ia → O4-6Ia+ → WN7-9ha pathway, the properties of Cyg OB2 #12 are therefore difficult to understand under current evolutionary paradigms. Finally, we note that as with AG Car in its cool phase, despite exceeding the HD limit, the properties of Cyg OB2 #12 imply that it lies below the Eddington limit – thus we conclude that the HD limit does not define a region of the HR diagram inherently inimical to the presence of massive stars.

Purification of phosphoric acid by solvent extraction

This work follows a feasibility study (187) which suggested that a process for purifying wet-process phosphoric acid by solvent extraction should be economically viable. The work was divided into two main areas, (i) chemical and physical measurements on the three-phase system, with or without impurities; (ii) process simulation and optimization. The object was to test the process technically and economically and to optimise the type of solvent. The chemical equilibria and distribution curves for the system water - phosphoric acid - solvent for the solvents n-amyl alcohol, tri-n-butyl phosphate, di-isopropyl ether and methyl isobutyl ketone have been determined. Both pure phosphoric acid and acid containing known amounts of naturally occurring impurities (Fe P0 4 , A1P0 4 , Ca3(P04)Z and Mg 3(P0 4 )Z) were examined. The hydrodynamic characteristics of the systems were also studied. The experimental results obtained for drop size distribution were compared with those obtainable from Hinze's equation (32) and it was found that they deviated by an amount related to the turbulence. A comprehensive literature survey on the purification of wet-process phosphoric acid by organic solvents has been made. The literature regarding solvent extraction fundamentals and equipment and optimization methods for the envisaged process was also reviewed. A modified form of the Kremser-Brown and Souders equation to calculate the number of contact stages was derived. The modification takes into account the special nature of phosphoric acid distribution curves in the studied systems. The process flow-sheet was developed and simulated. Powell's direct search optimization method was selected in conjunction with the linear search algorithm of Davies, Swann and Campey. The objective function was defined as the total annual manufacturing cost and the program was employed to find the optimum operating conditions for anyone of the chosen solvents. The final results demonstrated the following order of feasibility to purify wet-process acid: di-isopropyl ether, methylisobutyl ketone, n-amyl alcohol and tri-n-butyl phosphate.

Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms

Fermentation processes as objects of modelling and high-quality control are characterized with interdependence and time-varying of process variables that lead to non-linear models with a very complex structure. This is why the conventional optimization methods cannot lead to a satisfied solution. As an alternative, genetic algorithms, like the stochastic global optimization method, can be applied to overcome these limitations. The application of genetic algorithms is a precondition for robustness and reaching of a global minimum that makes them eligible and more workable for parameter identification of fermentation models. Different types of genetic algorithms, namely simple, modified and multi-population ones, have been applied and compared for estimation of nonlinear dynamic model parameters of fed-batch cultivation of S. cerevisiae.

An efficient, approximate path-following algorithm for elastic net based nonlinear spike enhancement

Unwanted spike noise in a digital signal is a common problem in digital filtering. However, sometimes the spikes are wanted and other, superimposed, signals are unwanted, and linear, time invariant (LTI) filtering is ineffective because the spikes are wideband - overlapping with independent noise in the frequency domain. So, no LTI filter can separate them, necessitating nonlinear filtering. However, there are applications in which the noise includes drift or smooth signals for which LTI filters are ideal. We describe a nonlinear filter formulated as the solution to an elastic net regularization problem, which attenuates band-limited signals and independent noise, while enhancing superimposed spikes. Making use of known analytic solutions a novel, approximate path-following algorithm is given that provides a good, filtered output with reduced computational effort by comparison to standard convex optimization methods. Accurate performance is shown on real, noisy electrophysiological recordings of neural spikes.

Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.

Novas abordagens para configurações automáticas dos parâmetros de controle em comitês de classificadores

Significant advances have emerged in research related to the topic of Classifier Committees. The models that receive the most attention in the literature are those of the static nature, also known as ensembles. The algorithms that are part of this class, we highlight the methods that using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The choice of the architecture and base components to be recruited is not a trivial task and has motivated new proposals in an attempt to build such models automatically, and many of them are based on optimization methods. Many of these contributions have not shown satisfactory results when applied to more complex problems with different nature. In contrast, the thesis presented here, proposes three new hybrid approaches for automatic construction for ensembles: Increment of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems for automatic configuration of parameters for models of ensemble. In the first one approach, we propose a solution that combines different diversity techniques in a single conceptual framework, in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance of such systems. In the second one approach, using a genetic algorithm for automatic design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively to evolve a better distribution of the feature space to be presented for the components of ensemble. Finally, the last one approach, which proposes new techniques for recommendation of architecture and based components on ensemble, by techniques of traditional meta-learning and multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis that hybrid tools are a powerful solution in building effective ensembles for pattern classification problems.

Caracterização de superfícies seletivas de frequência de periódicas e não-periódicas

The past years have seen a great interest in the use of frequency selective surfaces (FSS), as spatial filters, in many microwave applications. Among these, we highlight applications in telecommunication systems (such as satellite communications and radar), high gain antennas (combined with planar antennas) and (home and industrial) microwave ovens. The FSS is usually composed of two-dimensional periodic arrays, with equally spaced elements, which may be metallic patches (printed on dielectric substrates) or aperture (holes in thin metal surfaces). Using periodic arrays, the FSS have been able to meet the demands of the telecommunications industry. However, new demands are finding technological limitations. In this context, adverse filtering requirements have forced designers to use FSS optimization methods to find specific formats of FSS elements. Another alternative that has been used to increase the selectivity of the FSS is the cascaded FSS, a simple technique that has as main drawback the increased dimensions of the structure, as well as its weight. This work proposes the development of a new class of selective surfaces frequency (FSS) composed of quasi-periodic (or non-periodic) arrangements. The proposed FSS have no array periodicity, in relation with the spatial position of their elements. The frequency responses of these structures were simulated using commercial softwares that implement full-wave methods. For the purpose of validation of this study, FSS prototypes were built and measured, being possible to observe a good agreement between simulated and measured results. The main conclusions of this work are presented, as well as suggestions for future works.

Otimização do controle do diagrama de radiação de radares de varredura para rastreio de foguetes usando o método GAMMC para o Caso Planar (GAMMC-P)

Launching centers are designed for scientific and commercial activities with aerospace vehicles. Rockets Tracking Systems (RTS) are part of the infrastructure of these centers and they are responsible for collecting and processing the data trajectory of vehicles. Generally, Parabolic Reflector Radars (PRRs) are used in RTS. However, it is possible to use radars with antenna arrays, or Phased Arrays (PAs), so called Phased Arrays Radars (PARs). Thus, the excitation signal of each radiating element of the array can be adjusted to perform electronic control of the radiation pattern in order to improve functionality and maintenance of the system. Therefore, in the implementation and reuse projects of PARs, modeling is subject to various combinations of excitation signals, producing a complex optimization problem due to the large number of available solutions. In this case, it is possible to use offline optimization methods, such as Genetic Algorithms (GAs), to calculate the problem solutions, which are stored for online applications. Hence, the Genetic Algorithm with Maximum-Minimum Crossover (GAMMC) optimization method was used to develop the GAMMC-P algorithm that optimizes the modeling step of radiation pattern control from planar PAs. Compared with a conventional crossover GA, the GAMMC has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, the GAMMC prevents premature convergence, increases population fitness and reduces the processing time. Therefore, the GAMMC-P uses a reconfigurable algorithm with multiple objectives, different coding and genetic operator MMC. The test results show that GAMMC-P reached the proposed requirements for different operating conditions of a planar RAV.

Parallel Problem Solving from Nature – PPSN XIV:14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.

Otimização dos parâmetros de maquinagem no processo de fresagem

Cada vez mais, os principais objetivos na indústria é a produção a baixo custo, com a máxima qualidade e com o tempo de fabrico o mais curto possível. Para atingir esta meta, a indústria recorre, frequentemente, às máquinas de comando numérico (CNC), uma vez que com esta tecnologia torna se capaz alcançar uma elevada precisão e um tempo de processamento mais baixo. As máquinas ferramentas CNC podem ser aplicadas em diferentes processos de maquinagem, tais como: torneamento, fresagem, furação, entre outros. De todos estes processos, o mais utilizado é a fresagem devido à sua versatilidade. Utiliza-se normalmente este processo para maquinar materiais metálicos como é o caso do aço e dos ferros fundidos. Neste trabalho, são analisados os efeitos da variação de quatro parâmetros no processo de fresagem (velocidade de corte, velocidade de avanço, penetração radial e penetração axial), individualmente e a interação entre alguns deles, na variação da rugosidade num aço endurecido (aço 12738). Para essa análise são utilizados dois métodos de otimização: o método de Taguchi e o método das superfícies. O primeiro método foi utilizado para diminuir o número de combinações possíveis e, consequentemente, o número de ensaios a realizar é denominado por método de Taguchi. O método das superfícies ou método das superfícies de resposta (RSM) foi utilizado com o intuito de comparar os resultados obtidos com o método de Taguchi, de acordo com alguns trabalhos referidos na bibliografia especializada, o RSM converge mais rapidamente para um valor ótimo. O método de Taguchi é muito conhecido no setor industrial onde é utilizado para o controlo de qualidade. Apresenta conceitos interessantes, tais como robustez e perda de qualidade, sendo bastante útil para identificar variações do sistema de produção, durante o processo industrial, quantificando a variação e permitindo eliminar os fatores indesejáveis. Com este método foi vi construída uma matriz ortogonal L16 e para cada parâmetro foram definidos dois níveis diferentes e realizados dezasseis ensaios. Após cada ensaio, faz-se a medição superficial da rugosidade da peça. Com base nos resultados obtidos das medições da rugosidade é feito um tratamento estatístico dos dados através da análise de variância (Anova) a fim de determinar a influência de cada um dos parâmetros na rugosidade superficial. Verificou-se que a rugosidade mínima medida foi de 1,05m. Neste estudo foi também determinada a contribuição de cada um dos parâmetros de maquinagem e a sua interação. A análise dos valores de “F-ratio” (Anova) revela que os fatores mais importantes são a profundidade de corte radial e da interação entre profundidade de corte radial e profundidade de corte axial para minimizar a rugosidade da superfície. Estes têm contribuições de cerca de 30% e 24%, respetivamente. Numa segunda etapa este mesmo estudo foi realizado pelo método das superfícies, a fim de comparar os resultados por estes dois métodos e verificar qual o melhor método de otimização para minimizar a rugosidade. A metodologia das superfícies de resposta é baseada num conjunto de técnicas matemáticas e estatísticas úteis para modelar e analisar problemas em que a resposta de interesse é influenciada por diversas variáveis e cujo objetivo é otimizar essa resposta. Para este método apenas foram realizados cinco ensaios, ao contrário de Taguchi, uma vez que apenas em cinco ensaios consegue-se valores de rugosidade mais baixos do que a média da rugosidade no método de Taguchi. O valor mais baixo por este método foi de 1,03μm. Assim, conclui-se que RSM é um método de otimização mais adequado do que Taguchi para os ensaios realizados. Foram obtidos melhores resultados num menor número de ensaios, o que implica menos desgaste da ferramenta, menor tempo de processamento e uma redução significativa do material utilizado.

Intertemporal and spatial location of disposal facilities

The optimal capacities and locations of a sequence of landfills are studied, and the interactions between these characteristics are considered. Deciding the capacity of a landfill has some spatial implications since it affects the feasible region for the remaining landfills, and some temporal implications because the capacity determines the lifetime of the landfill and hence the moment of time when the next landfills should be constructed. Some general mathematical properties of the solution are provided and interpreted from an economic point of view. The resulting problem turns out to be non-convex and, therefore, it cannot be solved by conventional optimization techniques. Some global optimization methods are used to solve the problem in a particular case in order to illustrate how the solution depends on the parameter values.

Geological realism in hydrogeological and geophysical inverse modeling: A review

Scientific curiosity, exploration of georesources and environmental concerns are pushing the geoscientific research community toward subsurface investigations of ever-increasing complexity. This review explores various approaches to formulate and solve inverse problems in ways that effectively integrate geological concepts with geophysical and hydrogeological data. Modern geostatistical simulation algorithms can produce multiple subsurface realizations that are in agreement with conceptual geological models and statistical rock physics can be used to map these realizations into physical properties that are sensed by the geophysical or hydrogeological data. The inverse problem consists of finding one or an ensemble of such subsurface realizations that are in agreement with the data. The most general inversion frameworks are presently often computationally intractable when applied to large-scale problems and it is necessary to better understand the implications of simplifying (1) the conceptual geological model (e.g., using model compression); (2) the physical forward problem (e.g., using proxy models); and (3) the algorithm used to solve the inverse problem (e.g., Markov chain Monte Carlo or local optimization methods) to reach practical and robust solutions given today's computer resources and knowledge. We also highlight the need to not only use geophysical and hydrogeological data for parameter estimation purposes, but also to use them to falsify or corroborate alternative geological scenarios.

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

Multi-mode communications in near field exploiting OAM properties

This dissertation analyzes the exploitation of the orbital angular momentum (OAM) of the electromagnetic waves with large intelligent surfaces in the near-field region and line-of-sight conditions, in light of the holographic MIMO communication concept. Firstly, a characterization of the OAM-based communication problem is presented, and the relationship between OAM-carrying waves and communication modes is discussed. Then, practicable strategies for OAM detection using large intelligent surfaces and optimization methods based on beam focusing are proposed. Numerical results characterize the effectiveness of OAM with respect to other strategies, also including the proposed detection and optimization methods. It is shown that OAM waves constitute a particular choice of communication modes, i.e., an alternative basis set, which is sub-optimum with respect to optimal basis functions that can be derived by solving eigenfunction problems. Moreover, even the joint utilization of OAM waves with focusing strategies led to the conclusion that no channel capacity achievements can be obtained with these transmission techniques.