Effects of low-salinity and high-turbidity waters one mpirical ocean colour algorithms: an example for Southwestern Atlantic waters

The spectral reflectance of the sea surface recorded using ocean colour satellite sensors has been used to estimate chlorophyll-a concentrations for decades. However, in bio-optically complex coastal waters, these estimates are compromised by the presence of several other coloured components besides chlorophyll, especially in regions affected by low-salinity waters. The present work aims to (a) describe the influence of the freshwater plume from the La Plata River on the variability of in situ remote sensing reflectance and (b) evaluate the performance of operational ocean colour chlorophyll algorithms applied to Southwestern Atlantic waters, which receive a remarkable seasonal contribution from La Plata River discharges. Data from three oceanographic cruises are used, in addition to a historical regional bio-optical dataset. Deviations found between measured and estimated concentrations of chlorophyll-a are examined in relation to surface water salinity and turbidity gradients to investigate the source of errors in satellite estimates of pigment concentrations. We observed significant seasonal variability in surface reflectance properties that are strongly driven by La Plata River plume dynamics and arise from the presence of high levels of inorganic suspended solids and coloured dissolved materials. As expected, existing operational algorithms overestimate the concentration of chlorophyll-a, especially in waters of low salinity (S<33.5) and high turbidity (Rrs(670)>0.0012 sr−1). Additionally, an updated version of the regional algorithm is presented, which clearly improves the chlorophyll estimation in those types of coastal environment. In general, the techniques presented here allow us to directly distinguish the bio-optical types of waters to be considered in algorithm studies by the ocean colour community.

Deep chlorophyll maximum and upper ocean structure interactions: Case of the Guinea Thermal Dome

Deep Chlorophyll Maximum (DCM) modifies the upper ocean heat capture distribution and thus impacts water column temperature and stratification, as well as biogeochemical processes. This energetical role of the DCM is assessed using a 1 m-resolution 1D physical-biogeochemical model of the upper ocean, using climatological forcing conditions of the Guinea Dome (GD). This zone has been chosen among others because a strong and shallow DCM is present all year round. The results show that the DCM warms the seasonal thermocline by +2 degrees C in September/October and causes an increase of heat transfer from below into the mixed layer (ML) by vertical diffusion and entrainment, leading to a ML warming of about 0.3 degrees C in October. In the permanent thermocline, temperature decreases by up to 2 degrees C. The result is a stratification increase of the water column by 0.3 degrees C m(-1) which improves the thermocline realism when compared with observations. At the same time, the heating associated with the DCM is responsible for an increase of nitrate (+300%, 0.024 mu M), chlorophyll (+50%, 0.02 mu g l(-1)) and primary production (+45%: 10 mg C m(-2) day(-1)) in the ML during the entrainment period of October. The considered concentrations are small but this mechanism could be potentially important to give a better explanation of why there is a significant amount of nitrate in the ML. The mechanisms associated with the DCM presence, no matter which temperature or biogeochemical tracers are concerned, are likely to occur in a wide range of tropical or subpolar regions; in these zones a pronounced DCM is present at least episodically at shallow or moderate depths. These results can be generalized to other thermal dome regions where relatively similar physical and biogeochemical structures are encountered. After testing different vertical resolutions (10 m, 5 m, 2.5 m, 1 m and 0.5 m), we show that using at least a 1 to vertical resolution model is mandatory to assess the energetical importance of the DCM.

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Collaborative fuzzy clustering algorithms: some refinements and design guidelines

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

The contribution of HGAL/GCET2 in immunohistological algorithms: a comparative study in 424 cases of nodal diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma can be subclassified into at least two molecular subgroups by gene expression profiling: germinal center B-cell like and activated B-cell like diffuse large B-cell lymphoma. Several immunohistological algorithms have been proposed as surrogates to gene expression profiling at the level of protein expression, but their reliability has been an issue of controversy. Furthermore, the proportion of misclassified cases of germinal center B-cell subgroup by immunohistochemistry, in all reported algorithms, is higher compared with germinal center B-cell cases defined by gene expression profiling. We analyzed 424 cases of nodal diffuse large B-cell lymphoma with the panel of markers included in the three previously described algorithms: Hans, Choi, and Tally. To test whether the sensitivity of detecting germinal center B-cell cases could be improved, the germinal center B-cell marker HGAL/GCET2 was also added to all three algorithms. Our results show that the inclusion of HGAL/GCET2 significantly increased the detection of germinal center B-cell cases in all three algorithms (P<0.001). The proportions of germinal center B-cell cases in the original algorithms were 27%, 34%, and 19% for Hans, Choi, and Tally, respectively. In the modified algorithms, with the inclusion of HGAL/GCET2, the frequencies of germinal center B-cell cases were increased to 38%, 48%, and 35%, respectively. Therefore, HGAL/GCET2 protein expression may function as a marker for germinal center B-cell type diffuse large B-cell lymphoma. Consideration should be given to the inclusion of HGAL/GCET2 analysis in algorithms to better predict the cell of origin. These findings bear further validation, from comparison to gene expression profiles and from clinical/therapeutic data. Modern Pathology (2012) 25, 1439-1445; doi: 10.1038/modpathol.2012.119; published online 29 June 2012

Interannual variation in commercial oyster (Crassostrea gigas) farming in the sea (Florianopolis, Brazil, 27 degrees 44 ' S; 48 degrees 33 ' W) in relation to temperature, chlorophyll a and associated oceanographic conditions

Aquaculture of filter-feeding bivalve mollusks involves the fruitful conversion of marine particulate organic matter into premium protein of high nutritive value. Culture performance of bivalves is largely dependent on hydrological conditions and directly affected by e. g. temperature and chlorophyll levels. Accordingly, these parameters may be related with seasonality but also with oceanographic features combined with climate events. Yields of Pacific cupped oyster (Crassostrea gigas) reared at commercial procedures in suspended structures (long-lines) in a sheltered bay in Southern Brazil (Santa Catarina State, 27S 43'; 48 W 30') were evaluated in relation to local environmental conditions: sea surface temperature, chlorophyll a concentration, and associate effects of cold fronts events and El Nino and La Nina periods. Outputs from four consecutive commercial crop years were analyzed (2005/06, 2006/07, 2007/08, 2008/09) in terms of oyster survival and development time during the following grow-out phases of the culture cycle: seed to juvenile, juvenile to adult, adult to marketable. Since culture management and genetics were standardized significant differences verified among crop performance could be mostly related to environmental effects. Time series of temperature and chlorophyll a (remote sensing data) from crop periods displayed significant seasonal and interannual variation. As expected, performance during initial grow-out stages (seed to juvenile) was critical for final crop yield. Temperature was the main factor affecting survival in these initial stages with a trend of negative correlation, though not statistically significant. On the other hand, oyster development rate was significantly and positively affected by chlorophyll a concentration. Chlorophyll a values could be increased by upwelled cold nutrient-rich South Atlantic Central Water (SACW, related to predominant Northern winds) though further dependent on occurrence of Southern winds (cold fronts) to assist seawater penetration into the sheltered farming area. Lower salinity nutrient-rich northward drifted waters from La Plata River discharge may also result in chlorophyll a rise in the farming area. The El Nino period (July 2006 to February 2007) coincided with lower chlorophyll a levels in the farming site that may be related to both decreased number of cold fronts as well as predominance of Northern winds that retain northward spreading of La Plata River discharge waters. In contrast, the La Nina period (August 2007 to June 2008) corresponded to higher chlorophyll a values in the farming area by both upwelling of SACW and penetration of La Plata River discharge water assisted by increased occurrence of Southern winds and cold fronts. The recognition of the potentially changing climate and effects upon the environment will be an important step in planning future development of bivalve aquaculture.

Cash balance management: A comparison between genetic algorithms and particle swarm optimization

This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.

New Optimization Algorithms for Structural Reliability Analysis

Solution of structural reliability problems by the First Order method require optimization algorithms to find the smallest distance between a limit state function and the origin of standard Gaussian space. The Hassofer-Lind-Rackwitz-Fiessler (HLRF) algorithm, developed specifically for this purpose, has been shown to be efficient but not robust, as it fails to converge for a significant number of problems. On the other hand, recent developments in general (augmented Lagrangian) optimization techniques have not been tested in aplication to structural reliability problems. In the present article, three new optimization algorithms for structural reliability analysis are presented. One algorithm is based on the HLRF, but uses a new differentiable merit function with Wolfe conditions to select step length in linear search. It is shown in the article that, under certain assumptions, the proposed algorithm generates a sequence that converges to the local minimizer of the problem. Two new augmented Lagrangian methods are also presented, which use quadratic penalties to solve nonlinear problems with equality constraints. Performance and robustness of the new algorithms is compared to the classic augmented Lagrangian method, to HLRF and to the improved HLRF (iHLRF) algorithms, in the solution of 25 benchmark problems from the literature. The new proposed HLRF algorithm is shown to be more robust than HLRF or iHLRF, and as efficient as the iHLRF algorithm. The two augmented Lagrangian methods proposed herein are shown to be more robust and more efficient than the classical augmented Lagrangian method.

Ionization of chlorophyll-c(2) in liquid methanol

The ionization of chlorophyll-c(2) in liquid methanol was investigated by a sequential quantum mechanical/Monte Carlo approach. Focus was placed on the determination of the first ionization energy of chlorophyll-c(2). The results show that the first vertical ionization energy (IE) is red-shifted by 0.47 +/- 0.24 eV relative to the gas-phase value. The red-shift of the chlorophyll-c(2) IE in the liquid phase can be explained by Mg center dot center dot center dot OH hydrogen bonding and long-ranged electrostatic interactions in solution. The ionization threshold for chlorophyll-c2 in liquid methanol is close to 6 eV. (C) 2012 Elsevier B.V. All rights reserved.

Temporal variability of chlorophyll-a in the São Vicente estuary

Estuaries are extremely dynamic environments that are vulnerable to anthropogenic alterations. Thus, monitoring phytoplankton abundances and composition is an essential tool for the prediction of eutrophication and its effects on coastal ecosystems. Phytoplankton biomass, as chlorophyll-a, in the São Vicente estuary (Brazil) varies in response to tidal cycles and seasonal rainfall. Objectives. To present two datasets designed to assess the relationship between chlorophyll-a and changes in water turbidity driven by tide and rain. Methods. Weekly observations were made in the shallow embayment (February to September 2008; site 1) and observations recorded on alternate days (summer 2010, site 2). Results. At site 1, turbidity differed between high and low tides, but on most days was over 3000 RU, maintaining moderate chlorophyll-a levels (4 mg.m-3) and only two blooms developed during low turbidity. Site 2 mean turbidity was 1500 RU, nutrient level was higher during neap tides and phytoplankton blooms were mainly observed at the end of neap tides at 15-day intervals, dominated by chain-forming diatoms and occasionally flagellates and pennate diatoms. Conclusions. Taxonomic composition of the blooms was different and their frequency altered by events characterized by intense freshwater discharges from the Henry Borden Hydroelectric Dam (> 9*106.m³), inhibiting phytoplankton accumulation during neap tide periods.

Reliability algorithms applied to reinforced concrete structures durability assessment

This paper addresses the analysis of probabilistic corrosion time initiation in reinforced concrete structures exposed to ions chloride penetration. Structural durability is an important criterion which must be evaluated in every type of structure, especially when these structures are constructed in aggressive atmospheres. Considering reinforced concrete members, chloride diffusion process is widely used to evaluate the durability. Therefore, at modelling this phenomenon, corrosion of reinforcements can be better estimated and prevented. These processes begin when a threshold level of chlorides concentration is reached at the steel bars of reinforcements. Despite the robustness of several models proposed in the literature, deterministic approaches fail to predict accurately the corrosion time initiation due to the inherently randomness observed in this process. In this regard, the durability can be more realistically represented using probabilistic approaches. A probabilistic analysis of ions chloride penetration is presented in this paper. The ions chloride penetration is simulated using the Fick's second law of diffusion. This law represents the chloride diffusion process, considering time dependent effects. The probability of failure is calculated using Monte Carlo simulation and the First Order Reliability Method (FORM) with a direct coupling approach. Some examples are considered in order to study these phenomena and a simplified method is proposed to determine optimal values for concrete cover.

A contribution for developing more efficient dynamic modelling algorithms of parallel robots

Parallel kinematic structures are considered very adequate architectures for positioning and orienti ng the tools of robotic mechanisms. However, developing dynamic models for this kind of systems is sometimes a difficult task. In fact, the direct application of traditional methods of robotics, for modelling and analysing such systems, usually does not lead to efficient and systematic algorithms. This work addre sses this issue: to present a modular approach to generate the dynamic model and through some convenient modifications, how we can make these methods more applicable to parallel structures as well. Kane’s formulati on to obtain the dynamic equations is shown to be one of the easiest ways to deal with redundant coordinates and kinematic constraints, so that a suitable c hoice of a set of coordinates allows the remaining of the modelling procedure to be computer aided. The advantages of this approach are discussed in the modelling of a 3-dof parallel asymmetric mechanisms.

Effects of trophic levels (chlorophyll and phosphorous content) in three different water bodies (urban lake, reservoir and aquaculture facility) on gill morphology of Nile tilapia (Oreochromis niloticus)

Histological and ultrastructural analyses of gills were observed in Nile tilapia collected from three different waterbodies in southeast Brazil: an urban lake in a park in the city of São Paulo, a reservoir in a rural city, and a commercial aquaculture facility. These aterbodies were analyzed and classified as hypereutrophic, eutrophic, and supereutrophic, respectively, with 310.00, 94.00, 28.00 of phosphate (PO2_ 4 lg L _1) and 65.49, 24.95, 12.83 of chlorophyll (lg L _1). A significant difference in the histological alterations index (HAI) was observed only in fish from the urban lake, with the presence of cell hypertrophy, hyperplasia, aneurism, and other alterations. When compared to the other groups, a large quantity of rodlet cells was also observed in the urban group. These results demonstrate the correlation of eutrophic states of water with gill morphology. Also discussed is the premise that large amounts of organic material dissolved in water can alter the morphology of the fish gills