12 resultados para conjugated gradient descent algorithm

em Universidade do Minho


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The monitoring data collected during tunnel excavation can be used in inverse analysis procedures in order to identify more realistic geomechanical parameters that can increase the knowledge about the interested formations. These more realistic parameters can be used in real time to adapt the project to the real structure in situ behaviour. However, monitoring plans are normally designed for safety assessment and not especially for the purpose of inverse analysis. In fact, there is a lack of knowledge about what types and quantity of measurements are needed to succeed in identifying the parameters of interest. Also, the optimisation algorithm chosen for the identification procedure may be important for this matter. In this work, this problem is addressed using a theoretical case with which a thorough parametric study was carried out using two optimisation algorithms based on different calculation paradigms, namely a conventional gradient-based algorithm and an evolution strategy algorithm. Calculations were carried for different sets of parameters to identify several combinations of types and amount of monitoring data. The results clearly show the high importance of the available monitoring data and the chosen algorithm for the success rate of the inverse analysis process.

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Tissue-to-tissue interfaces are commonly present in all tissues exhibiting structural, biological and chemical gradients serving a wide range of physiological functions. These interfaces are responsible for mediation of load transfer between two adjacent tissues. They are also important structures in sustaining the cellular communications to retain tissueâ s functional integration and homeostasis. [1] All cells have the capacity to sense and respond to physical and chemical stimulus and when cultured in three-dimensional (3D) environments they tend to perform their function better than in two-dimensional (2D) environments. Spatial and temporal 3D gradient hydrogels better resemble the natural environment of cells in mimicking their extracellular matrix. [2] In this study we hypothesize that differential functional properties can be engineered by modulation of macromolecule gradients in a cell seeded threedimensional hydrogel system. Specifically, differential paracrine secretory profiles can be engineered using human Bone Marrow Stem Cells (hBMSCâ s). Hence, the specific objectives of this study are to: assemble the macromolecular gradient hydrogels to evaluate the suitablity for hBMSCâ s encapsulation by cellular viability and biofunctionality by assessing the paracrine secretion of hBMSCâ s over time. The gradient hydrogels solutions were prepared by blend of macromolecules in one solution such as hyaluronic (HA) acid and collagen (Col) at different ratios. The gradient hydrogels were fabricated into cylindrical silicon moulds with higher ratio solutions assembled at the bottom of the mould and adding the two solutions consecutively on top of each other. The labelling of the macromolecules was performed to confirm the gradient through fluorescence microscopy. Additionally, AFM was conducted to assess the gradient hydrogels stiffness. Gradient hydrogels characterization was performed by HA and Col degradation assay, degree of crosslinking and stability. hBMSCâ s at P3 were encapsulated into each batch solution at 106 cells/ml solution and gradient hydrogels were produced as previously described. The hBMSCâ s were observed under confocal microscopy to assess viability by Live/Dead® staining. Cellular behaviour concerning proliferation and matrix deposition was also performed. Secretory cytokine measurement for pro-inflammatory and angiogenesis factors was carried out using ELISA. At genomic level, qPCR was carried out. The 3D gradient hydrogels platform made of different macromolecules showed to be a suitable environment for hBMSCâ s. The hBMSCâ s gradient hydrogels supported high cell survival and exhibited biofunctionality. Besides, the 3D gradient hydrogels demonstrated differentially secretion of pro-inflammatory and angiogenic factors by the encapsulated hBMSCâ s. References: 1. Mikos, AG. et al., Engineering complex tissues. Tissue Engineering 12,3307, 2006 2. Phillips, JE. et al., Proc Natl Acad Sci USA, 26:12170-5, 2008

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The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version.

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Promoting the use of non-motorized modes of transport, such as cycling, is an important contribution to the improvement of mobility, accessibility and equity in cities. Cycling offers a fast and cheap transportation option for short distances, helping to lower pollutant emissions and contributing to a healthier way of life. In order to make the cycling mode more competitive in relation to motorized traffic, it is necessary to evaluate the potential of alternatives from the perspective of the physical effort. One way to do so consists of assessing the suitability of locations for implementing cycling infrastructures. In this work, four tools to determine the gradient along potential cycling paths are compared. Furthermore, an evaluation of the reliability of some low-cost tools to measure this parameter was conducted, by comparison with standard measurements using cartographic plans, on a field case study applied to the city of Braga, Portugal. These tools revealed a good level of accuracy for the planning stage, but proved to be less reliable for use in design.

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The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

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The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.

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In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

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This paper presents a single-phase Series Active Power Filter (Series APF) for mitigation of the load voltage harmonic content, while maintaining the voltage on the DC side regulated without the support of a voltage source. The proposed series active power filter control algorithm eliminates the additional voltage source to regulate the DC voltage, and with the adopted topology it is not used a coupling transformer to interface the series active power filter with the electrical power grid. The paper describes the control strategy which encapsulates the grid synchronization scheme, the compensation voltage calculation, the damping algorithm and the dead-time compensation. The topology and control strategy of the series active power filter have been evaluated in simulation software and simulations results are presented. Experimental results, obtained with a developed laboratorial prototype, validate the theoretical assumptions, and are within the harmonic spectrum limits imposed by the international recommendations of the IEEE-519 Standard.

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Objective. To evaluate the degree of axial elongation with soft radial refractive gradient (SRRG) contact lenses, orthokeratology (OK), and single vision (SV) spectacle lenses (control) during a period of 1 year before treatment and 2 years after treatment. Methods. This was a prospective, longitudinal, nonrandomized study. The study groups consisted of 30, 29, and 41 children, respectively. The axial length (AL) was measured during 2 years after recruitment and lens fitting. Results. The baseline refractive sphere was correlated significantly (r 2 = 0.542; P < 0.0001) with the amount of myopia progression before baseline. After 2 years, the mean myopia progression values for the SRRG, OK, and SV groups were −0.56 ± 0.51, −0.32 ± 0.53, and −0.98 ± 0.58 diopter, respectively. The results represent reductions in myopic progression of 43% and 67% for the SRRG and OK groups, respectively, compared to the SV group. The AL increased more in the SV group compared to the SRRG and OK groups, with 27% and 38% lower axial elongation, respectively, compared to the SV group at the 2-year visit (P < 0.05). SRRG and OK showed no differences (P = 0.430). Conclusion. The SRRG lens significantly decreased AL elongation compared to the SV control group. The SRRG lens was similarly effective to OK in preventing myopia progression in myopic children and adolescent.

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Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.

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 The success of synthetic bone implants requires good interface between the material and the host tissue. To study the biological relevance of fi bronectin (FN) density on the osteogenic commitment of human bone marrow mesenchymal stem cells (hBMMSCs), human FN was adsorbed in a linear density gradient on the surface of PCL. The evolution of the osteogenic markers alkaline phosphatase and collagen 1 alpha 1 was monitored by immunohistochemistry, and the cytoskeletal organization and the cell-derived FN were assessed. The functional analysis of the gradient revealed that the lower FN-density elicited stronger osteogenic expression and higher cytoskeleton spreading, hallmarks of the stem cell commitment to the osteoblastic lineage. The identifi cation of the optimal FN density regime for the osteogenic commitment of hBM-MSCs presents a simple and versatile strategy to signifi cantly enhance the surface properties of polycaprolactone as a paradigm for other synthetic polymers intended for bone-related applications.

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The use of biomaterials to direct osteogenic differentiation of human mesenchymal stem cells (hMSCs) in the absence of osteogenic supplements is thought to be part of the next generation of orthopedic implants. We previously engineered surface-roughness gradients of average roughness (Ra) varying from the sub-micron to the micrometer range ( 0.5–4.7 lm), and mean distance between peaks (RSm) gradually varying from 214 lm to 33 lm. Here we have screened the ability of such surface-gradients of polycaprolactone to influence the expression of alkaline phosphatase (ALP), collagen type 1 (COL1) and mineralization by hMSCs cultured in dexamethasone (Dex)-deprived osteogenic induction medium (OIM) and in basal growth medium (BGM). Ra 1.53 lm/RSm 79 lm in Dex-deprived OI medium, and Ra 0.93 lm/RSm 135 lm in BGM consistently showed higher effectiveness at supporting the expression of the osteogenic markers ALP, COL1 and mineralization, compared to the tissue culture polystyrene (TCP) control in complete OIM. The superior effectiveness of specific surface-roughness revealed that this strategy may be used as a compelling alternative to soluble osteogenic inducers in orthopedic applications featuring the clinically relevant biodegradable polymer polycaprolactone.