Modern scientific methods and their potential in wastewater science and technology

Application of novel analytical and investigative methods such as fluorescence in situ hybridization, confocal laser scanning microscopy (CLSM), microelectrodes and advanced numerical simulation has led to new insights into micro-and macroscopic processes in bioreactors. However, the question is still open whether or not these new findings and the subsequent gain of knowledge are of significant practical relevance and if so, where and how. To find suitable answers it is necessary for engineers to know what can be expected by applying these modern analytical tools. Similarly, scientists could benefit significantly from an intensive dialogue with engineers in order to find out about practical problems and conditions existing in wastewater treatment systems. In this paper, an attempt is made to help bridge the gap between science and engineering in biological wastewater treatment. We provide an overview of recently developed methods in microbiology and in mathematical modeling and numerical simulation. A questionnaire is presented which may help generate a platform from which further technical and scientific developments can be accomplished. Both the paper and the questionnaire are aimed at encouraging scientists and engineers to enter into an intensive, mutually beneficial dialogue. (C) 2002 Elsevier Science Ltd. All rights reserved.

Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: A non-parametric approach

Applying programming techniques to detailed data for 406 rice farms in 21 villages, for 1997, produces inefficiency measures, which differ substantially from the results of simple yield and unit cost measures. For the Boro (dry) season, mean technical efficiency was efficiency was 56.2 per cent and 69.4 per cent, allocative efficiency was 81.3 per cent, cost efficiency was 56.2 per cent and scale efficiency 94.9 per cent. The Aman (wet) season results are similar, but a few points lower. Allocative inefficiency is due to overuse of labour, suggesting population pressure, and of fertiliser, where recommended rates may warrant revision. Second-stage regressions show that large families are more inefficient, whereas farmers with better access to input markets, and those who do less off-farm work, tend to be more efficient. The information on the sources of inter-farm performance differentials could be used by the extension agents to help inefficient farmers. There is little excuse for such sub-optimal use of survey data, which are often collected at substantial costs.

Clinical assessment of HIV-associated lipodystrophy syndrome: bioelectrical impedance analysis, anthropometry and clinical scores

Background Diagnosis of the HIV-associated lipodystrophy syndrome is based on clinical assessment, in lack of a consensus about case definition and reference methods. Three bedside methods were compared in their diagnostic value for lipodystrophy. Patients and Methods. Consecutive HIV-infected outpatients (n = 278) were investigated, 128 of which also had data from 1997 available. Segmental bioelectrical impedance analysis (BIA) and waist, hip and thigh circumferences were performed. Changes in seven body regions were rated by physicians and patients using linear analogue scale assessment (LASA). Diagnostic cut-off values were searched by receiver operator characteristics. Results. Lipodystrophy was diagnosed in 85 patients (31%). BIA demonstrated higher fat-free mass in patients with lipodystrophy but not after controlling for body mass index and sex. Segmental BIA was not superior to whole body BIA in detecting lipodystrophy. Fat-free mass increased from 1997 to 1999 independent from lipodystrophy. Waist-hip and waist-thigh ratios were higher in patients with lipodystrophy. BIA, anthropometry and LASA did not provide sufficient diagnostic cut-off values for lipodystrophy. Agreement between methods, and between patient and physician rating, was poor. Conclusion: These methods do not fulfil the urgent need for quantitative diagnostic tools for lipodystrophy. BIA estimates of fat free mass may be biased by lipodystrophy, indicating a need for re-calibration in HIV infected populations. (C) 2001 Harcourt Publishers Ltd.

Learning dependent sources using mixtures of Dirichlet: applications on hyperspectral unmixing

This paper is an elaboration of the DECA algorithm [1] to blindly unmix hyperspectral data. The underlying mixing model is linear, meaning that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. The proposed method, as DECA, is tailored to highly mixed mixtures in which the geometric based approaches fail to identify the simplex of minimum volume enclosing the observed spectral vectors. We resort then to a statitistical framework, where the abundance fractions are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. With respect to DECA, we introduce two improvements: 1) the number of Dirichlet modes are inferred based on the minimum description length (MDL) principle; 2) The generalized expectation maximization (GEM) algorithm we adopt to infer the model parameters is improved by using alternating minimization and augmented Lagrangian methods to compute the mixing matrix. The effectiveness of the proposed algorithm is illustrated with simulated and read data.

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Effects of health decentralization, financing and governance in Mexico

OBJECTIVE: To identify the effects of decentralization on health financing and governance policies in Mexico from the perspective of users and providers. METHODS: A cross-sectional study was carried out in four states that were selected according to geopolitical and administrative criteria. Four indicators were assessed: changes and effects on governance, financing sources and funds, the final destination of resources, and fund allocation mechanisms. Data collection was performed using in-depth interviews with health system key personnel and community leaders, consensus techniques and document analyses. The interviews were transcribed and analyzed by thematic segmentation. RESULTS: The results show different effectiveness levels for the four states regarding changes in financing policies and community participation. Effects on health financing after decentralization were identified in each state, including: greater participation of municipal and state governments in health expenditure, increased financial participation of households, greater community participation in low-income states, duality and confusion in the new mechanisms for coordination among the three government levels, absence of an accountability system, lack of human resources and technical skills to implement, monitor and evaluate changes in financing. CONCLUSIONS: In general, positive and negative effects of decentralization on health financing and governance were identified. The effects mentioned by health service providers and users were related to a diversification of financing sources, a greater margin for decisions around the use and final destination of financial resources and normative development for the use of resources. At the community level, direct financial contributions were mentioned, as well as in-kind contributions, particularly in the form of community work.

Analysing organochlorine pesticides in strawberry jams using GC-ECD, GC-MS/MS and QuEChERS sample preparation

This paper describes a comparison of adaptations of the QuEChERS (quick, easy, cheap, effective, rugged and safe) approach for the determination of 14 organochlorine pesticide (OCP) residues in strawberry jam by concurrent use of gas chromatography (GC) coupled to electron capture detector (ECD) and GC tandem mass spectrometry (GC-MS/MS). Three versions were tested based on the original QuEChERS method. The results were good (overall average of 89% recoveries with 15% RSD) using the ultrasonic bath at five spiked levels. Performance characteristics, such as accuracy, precision, linear range, limits of detection (LOD) and quantification (LOQ), were determined for each pesticide. LOD ranged from 0.8 to 8.9 microg kg-1 ; LOQ was in the range of 2.5–29.8 microg kg- 1; and calibration curves were linear (r2>0.9970) in the whole range of the explored concentrations (5–100 microg kg- 1). The LODs of these pesticides were much lower than the maximum residue levels (MRLs) allowed in Europe for strawberries. The method was successfully applied to the quantification of OCP in commercially available jams. The OCPs were detected lower than the LOD.

Monitorization of alveolar deposited surface area of nanoparticles and ultrafine particles in different environments

Nanotechnology is an important emerging industry with a projected annual market of around one trillion dollars by 2015. It involves the control of atoms and molecules to create new materials with a variety of useful functions. Although there are advantages on the utilization of these nano-scale materials, questions related with its impact over the environment and human health must be addressed too, so that potential risks can be limited at early stages of development. At this time, occupational health risks associated with manufacturing and use of nanoparticles are not yet clearly understood. However, workers may be exposed to nanoparticles through inhalation at levels that can greatly exceed ambient concentrations. Current workplace exposure limits are based on particle mass, but this criteria could not be adequate in this case as nanoparticles are characterized by very large surface area, which has been pointed out as the distinctive characteristic that could even turn out an inert substance into another substance exhibiting very different interactions with biological fluids and cells. Therefore, it seems that, when assessing human exposure based on the mass concentration of particles, which is widely adopted for particles over 1 μm, would not work in this particular case. In fact, nanoparticles have far more surface area for the equivalent mass of larger particles, which increases the chance they may react with body tissues. Thus, it has been claimed that surface area should be used for nanoparticle exposure and dosing. As a result, assessing exposure based on the measurement of particle surface area is of increasing interest. It is well known that lung deposition is the most efficient way for airborne particles to enter the body and cause adverse health effects. If nanoparticles can deposit in the lung and remain there, have an active surface chemistry and interact with the body, then, there is potential for exposure. It was showed that surface area plays an important role in the toxicity of nanoparticles and this is the metric that best correlates with particle-induced adverse health effects. The potential for adverse health effects seems to be directly proportional to particle surface area. The objective of the study is to identify and validate methods and tools for measuring nanoparticles during production, manipulation and use of nanomaterials.

Strabismus, visual acuity, and uncorrected refractive error in Portuguese children aged 6 to 11 years

Introduction: Visual anomalies that affect school-age children represent an important public health problem. Data on the prevalence are lacking in Portugal but is needed for planning vision services. This study was conducted to determine the prevalence of strabismus, decreased visual acuity, and uncorrected refractive error in Portuguese children aged 6 to 11 years. Methods and materials: A cross-sectional study was carried out on a sample of 672 school-age children (7.69 ± 1.19 years). Children received an orthoptic assessment (visual acuity, ocular alignment, and ocular movements) and non-cycloplegic autorefraction. Results: After orthoptic assessment, 13.8% of children were considered abnormal (n = 93). Manifest strabismus was found in 4% of the children. Rates of esotropia (2.1%) were slightly higher than exotropia (1.8%). Strabismus rates were not statistically significant different per sex (p = 0.681) and grade (p = 0.228). Decreased visual acuity at distance was present in 11.3% of children. Visual acuity ≤20/66 (0.5 logMAR) was found in 1.3% of the children. We also found that 10.3% of children had an uncorrected refractive error. Conclusions: Strabismus affects a small proportion of the Portuguese school-age children. Decreased visual acuity and uncorrected refractive error affected a significant proportion of school-age children. New policies need to be developed to address this public health problem.

Multiple sequence alignment correction using constraints

Trabalho apresentado no âmbito do European Master in Computational Logics, como requisito parcial para obtenção do grau de Mestre em Computational Logics

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia

Structured Light System Calibration for Perception in Underwater Tanks

The process of visually exploring underwater environments is still a complex problem. Underwater vision systems require complementary means of sensor information to help overcome water disturbances. This work proposes the development of calibration methods for a structured light based system consisting on a camera and a laser with a line beam. Two different calibration procedures that require only two images from different viewpoints were developed and tested in dry and underwater environments. Results obtained show, an accurate calibration for the camera/projector pair with errors close to 1 mm even in the presence of a small stereos baseline.

Validation of QuEChERS method for organochlorine pesticides analysis in tamarind (Tamarindus indica) products: Peel, fruit and commercial pulp

In this study a citrate-buffered version of QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method for determination of 14 organochlorine pesticides (OCPs) residues in tamarind peel, fruit and commercial pulp was optimized using gas chromatography (GC) coupled with electron-capture detector (ECD) and confirmation by GC tandem mass spectrometry (GC–MS/MS). Five procedures were tested based on the original QuEChERS method. The best one was achieved with increased time in ultrasonic bath. For the extract clean-up, primary secondary amine (PSA), octadecyl-bonded silica (C18) and magnesium sulphate (MgSO4) were used as sorbents for tamarind fruit and commercial pulp and for peel was also added graphitized carbon black (GCB). The samples mass was optimized according to the best recoveries (1.0 g for peel and fruit; 0.5 g for pulp). The method results showed the matrix-matched calibration curve linearity was r2 > 0.99 for all target analytes in all samples. The overall average recoveries (spiked at 20, 40 and 60 μg kg−1) have been considered satisfactory presenting values between 70 and 115% with RSD of 2–15 % (n = 3) for all analytes, with the exception of HCB (in peel sample). The ranges of limits of detection (LOD) and quantification (LOQ) for OCPs were for peel (LOD: 8.0–21 μg kg−1; LOQ: 27–98 μg kg−1); for fruit (LOD: 4–10 μg kg−1; LOQ: 15–49 μg kg−1) and for commercial pulp (LOD: 2–5 μg kg−1; LOQ: 7–27 μg kg−1). The method was successfully applied in tamarind samples being considered a rapid, sensitive and reliable procedure.