903 resultados para Unconstrained and convex optimization
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By manipulation of applied pressure or voltage, pressurized flow capillary electrochromatography (P-CEC) permits unique control of selectivity for ionic solutes. A simple mathematical model has been developed to describe the quantitative relationship between the electrochromatographic retention factor (k(*)) of charged solutes and the applied voltage and pressure. The validity of the model was verified experimentally with hydrophilic interaction mode CEC (HI-CEC). On the basis of the model developed, it was found that the value of k(*) could be predicted accurately using only a limited number of data points from the initial experiments at different voltages or pressures. Correlation between the experimentally measured and calculated k(*) was excellent, with a correlation coefficient greater than 0.999. Optimization for the separation of peptides by P-CEC was also performed successfully on the basis of the proposed model.
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A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.
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In this report, gold nanoparticles (AuNPs) labeled by Raman reporters (AuNPs-R6G) were assembled on glass and used as the seeds to in situ grow silver-coated nanostructures based on silver enhancer solution, forming the nanostructures of AuNPs-R6G@Ag, which were characterized by scanning electron microscopy (SEM) and UV-visible spectroscopy. More importantly, the obtained silver-coated nanostructures can be used as a surface enhancement Raman scattering (SERS) substrate. The different SERS activities can be controlled by the silver deposition time and assembly time of AuNPs-R6G on glass. The results indicate that the maximum SERS activity could be obtained on AuNPs-R6G when these nanostructures were assembled on glass for 2 h with silver deposition for 2 min.
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Target transformation factor analysis was used to correct spectral interference in inductively coupled plasma atomic emission spectrometry (ICP-BES) for the determination of rare earth impurities in high purity thulium oxide. Data matrix was constructed with pure and mixture vectors and background vector. A method based on an error evaluation function was proposed to optimize the peak position, so the influence of the peak position shift in spectral scans on the determination was eliminated or reduced. Satisfactory results were obtained using factor analysis and the proposed peak position optimization method.
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Gracilaria lemaneiformis Bory is an economically important alga that is primarily used for agar production. Although tetraspores are ideal seeds for the cultivation of G. lemaneiformis, the most popular culture method is currently based on vegetative fragments, which is labor-intensive and time-consuming. In this study, we optimized the conditions for tetraspore release and evaluated the photosynthetic activities of different colonies formed from the branches of G. lemaneiformis using a PAM (pulse-amplitude-modulated) measuring system. The results showed that variations in temperature and salinityhad significant effects on tetraspore yield. However, variations in the photon flux density (from 15 mu mol m(-2) s(-1) to 480 mu mol m(-2) s(-1)) had no apparent effect on tetraspore yield. Moreover, the PAM-parameters Y(I), Y(II), ETR(I), ETR(II) and F (v)/F (m) of colonies formed from different branches showed the same trend: parameter values of first generation branches > second generation branches > third generation branches. These results suggest that the photosynthetic activities of different colonies of branches changed with the same trend. Furthermore, photosynthesis in G. lemaneiformis was found to be involved in vegetative reproduction and tetraspore formation. Finally, the first generation branches grew slowly, but accumulated organic compounds to form large numbers of tetraspores. Taken together, these results showed that the first generation branches are ideal materials for the release of tetraspores.
Resumo:
Orthogonal design and uniform design were used for the optimization of separation of enantiomers using 2,6-di-O-methyl-beta-cyclodextrin (DM-beta-CD) as a chiral selector by capillary zone electrophoresis, The concentration of DM-beta-CD, buffer pH, running voltage, and capillary temperature were selected as variable parameters, their different effects on peak resolution were studied by the design methods. It was concluded that orthogonal design offers a rapid and efficient means for testing the importance of individual parameters and for determining the optimum operating conditions. However, for a large number of both factors and levels, uniform design is more efficient, The effect of addition of methanol and citric acid buffer on the separation of enantiomers was also examined.
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A model is developed for predicting the resolution of interested component pair and calculating the optimum temperature programming condition in the comprehensive two-dimensional gas chromatography (GC x GC). Based on at least three isothermal runs, retention times and the peak widths at half-height on both dimensions are predicted for any kind of linear temperature-programmed run on the first dimension and isothermal runs on the second dimension. The calculation of the optimum temperature programming condition is based on the prediction of the resolution of "difficult-to-separate components" in a given mixture. The resolution of all the neighboring peaks on the first dimension is obtained by the predicted retention time and peak width on the first dimension, the resolution on the second dimension is calculated only for the adjacent components with un-enough resolution on the first dimension and eluted within a same modulation period on the second dimension. The optimum temperature programming condition is acquired when the resolutions of all components of interest by GC x GC separation meet the analytical requirement and the analysis time is the shortest. The validity of the model has been proven by using it to predict and optimize GC x GC temperature programming condition of an alkylpyridine mixture. (c) 2005 Elsevier B.V. All rights reserved.
Resumo:
X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen. Feature Selection based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters, vol. 28, no. 4, pp. 459-471, 2007.
Resumo:
The influence of process variables (pea starch, guar gum and glycerol) on the viscosity (V), solubility (SOL), moisture content (MC), transparency (TR), Hunter parameters (L, a, and b), total color difference (ΔE), yellowness index (YI), and whiteness index (WI) of the pea starch based edible films was studied using three factors with three level Box–Behnken response surface design. The individual linear effect of pea starch, guar and glycerol was significant (p < 0.05) on all the responses. However, a value was only significantly (p < 0.05) affected by pea starch and guar gum in a positive and negative linear term, respectively. The effect of interaction of starch × glycerol was also significant (p < 0.05) on TR of edible films. Interaction between independent variables starch × guar gum had a significant impact on the b and YI values. The quadratic regression coefficient of pea starch showed a significant effect (p < 0.05) on V, MC, L, b, ΔE, YI, and WI; glycerol level on ΔE and WI; and guar gum on ΔE and SOL value. The results were analyzed by Pareto analysis of variance (ANOVA) and the second order polynomial models were developed from the experimental design with reliable and satisfactory fit with the corresponding experimental data and high coefficient of determination (R2) values (>0.93). Three-dimensional response surface plots were established to investigate the relationship between process variables and the responses. The optimized conditions with the goal of maximizing TR and minimizing SOL, YI and MC were 2.5 g pea starch, 25% glycerol and 0.3 g guar gum. Results revealed that pea starch/guar gum edible films with appropriate physical and optical characteristics can be effectively produced and successfully applied in the food packaging industry.
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TCP performance degrades when end-to-end connections extend over wireless connections-links which are characterized by high bit error rate and intermittent connectivity. Such link characteristics can significantly degrade TCP performance as the TCP sender assumes wireless losses to be congestion losses resulting in unnecessary congestion control actions. Link errors can be reduced by increasing transmission power, code redundancy (FEC) or number of retransmissions (ARQ). But increasing power costs resources, increasing code redundancy reduces available channel bandwidth and increasing persistency increases end-to-end delay. The paper proposes a TCP optimization through proper tuning of power management, FEC and ARQ in wireless environments (WLAN and WWAN). In particular, we conduct analytical and numerical analysis taking into "wireless-aware" TCP) performance under different settings. Our results show that increasing power, redundancy and/or retransmission levels always improves TCP performance by reducing link-layer losses. However, such improvements are often associated with cost and arbitrary improvement cannot be realized without paying a lot in return. It is therefore important to consider some kind of net utility function that should be optimized, thus maximizing throughput at the least possible cost.
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A comparison study was carried out between a wireless sensor node with a bare die flip-chip mounted and its reference board with a BGA packaged transceiver chip. The main focus is the return loss (S parameter S11) at the antenna connector, which was highly depended on the impedance mismatch. Modeling including the different interconnect technologies, substrate properties and passive components, was performed to simulate the system in Ansoft Designer software. Statistical methods, such as the use of standard derivation and regression, were applied to the RF performance analysis, to see the impacts of the different parameters on the return loss. Extreme value search, following on the previous analysis, can provide the parameters' values for the minimum return loss. Measurements fit the analysis and simulation well and showed a great improvement of the return loss from -5dB to -25dB for the target wireless sensor node.
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Wireless sensor networks (WSN) are becoming widely adopted for many applications including complicated tasks like building energy management. However, one major concern for WSN technologies is the short lifetime and high maintenance cost due to the limited battery energy. One of the solutions is to scavenge ambient energy, which is then rectified to power the WSN. The objective of this thesis was to investigate the feasibility of an ultra-low energy consumption power management system suitable for harvesting sub-mW photovoltaic and thermoelectric energy to power WSNs. To achieve this goal, energy harvesting system architectures have been analyzed. Detailed analysis of energy storage units (ESU) have led to an innovative ESU solution for the target applications. Battery-less, long-lifetime ESU and its associated power management circuitry, including fast-charge circuit, self-start circuit, output voltage regulation circuit and hybrid ESU, using a combination of super-capacitor and thin film battery, were developed to achieve continuous operation of energy harvester. Low start-up voltage DC/DC converters have been developed for 1mW level thermoelectric energy harvesting. The novel method of altering thermoelectric generator (TEG) configuration in order to match impedance has been verified in this work. Novel maximum power point tracking (MPPT) circuits, exploring the fractional open circuit voltage method, were particularly developed to suit the sub-1mW photovoltaic energy harvesting applications. The MPPT energy model has been developed and verified against both SPICE simulation and implemented prototypes. Both indoor light and thermoelectric energy harvesting methods proposed in this thesis have been implemented into prototype devices. The improved indoor light energy harvester prototype demonstrates 81% MPPT conversion efficiency with 0.5mW input power. This important improvement makes light energy harvesting from small energy sources (i.e. credit card size solar panel in 500lux indoor lighting conditions) a feasible approach. The 50mm × 54mm thermoelectric energy harvester prototype generates 0.95mW when placed on a 60oC heat source with 28% conversion efficiency. Both prototypes can be used to continuously power WSN for building energy management applications in typical office building environment. In addition to the hardware development, a comprehensive system energy model has been developed. This system energy model not only can be used to predict the available and consumed energy based on real-world ambient conditions, but also can be employed to optimize the system design and configuration. This energy model has been verified by indoor photovoltaic energy harvesting system prototypes in long-term deployed experiments.
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Introduction: Older individuals are particularly vulnerable to potentially inappropriate prescribing (PIP), drug related problems (DRPs) and adverse drug reactions (ADRs). A number of different interventions have been proposed to address these issues. However to-date there is a paucity of well-designed trials examining the impact of such interventions. Therefore the aims of this work were to: (i) establish a baseline PIP prevalence both nationally and internationally using the STOPP, Beers and PRISCUS criteria, (ii) identify the most comprehensive method of assessing PIP in older individuals, (iii) develop a structured pharmacist intervention supported by a computer decisions support system (CDSS) and (iv) examine the impact of this intervention on prescribing and incidence of ADRs. Results: This work identified high rates of PIP across all three healthcare settings in Ireland, 84.7% in the long term care, 70.7% in secondary care and 43.3% in primary care being reported. This work identified that for a comprehensive assessment of prescribing to be undertaken, an amalgamation of all three criteria should be deployed simultaneously. High prevalences of DRPs and PIP in older hospitalised individuals were identified. With 82.0% and 76.3% of patients reported to have at least one DRP or PIP instance respectively. The structured pharmacist intervention demonstrated a positive impact on prescribing, with a significant reduction MAI scores being reported. It also resulted in the intervention patients’ having a reduced risk of experiencing an ADR when compared to the control patients (absolute risk reduction of 6.8 (95% CI 1.5% - 12.3%)) and the number needed to treat = 15 (95% CI 8 - 68). However the intervention was found to have no significant effect on length of stay or mortality rate. Conclusion: This work shows that PIP is highly prevalent in older individuals across three healthcare settings in Ireland. This work also demonstrates that a structured pharmacist intervention support by a dedicated CDSS can significantly improve the appropriateness of prescribing and reduce the incidence of ADRs in older acutely ill hospitalised individuals.
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In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.