Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Histogram-based DNA analysis for the visualization of chromosome, genome and species information

We describe a novel approach to explore DNA nucleotide sequence data, aiming to produce high-level categorical and structural information about the underlying chromosomes, genomes and species. The article starts by analyzing chromosomal data through histograms using fixed length DNA sequences. After creating the DNA-related histograms, a correlation between pairs of histograms is computed, producing a global correlation matrix. These data are then used as input to several data processing methods for information extraction and tabular/graphical output generation. A set of 18 species is processed and the extensive results reveal that the proposed method is able to generate significant and diversified outputs, in good accordance with current scientific knowledge in domains such as genomics and phylogenetics.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators’ actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

An integrated system to support electricity tariff contract definition

This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.

Implementação e validação de um método de avaliação do perfil de ácidos gordos e do teor de gordura em alimentos por cromatografia gasosa

Nos dias de hoje é necessário criar hábitos de vida mais saudáveis que contribuam para o bem-estar da população. Adoptar medidas e práticas de modo regular e disciplinado, pode diminuir o risco do aparecimento de determinadas doenças, como a obesidade, as doenças cardiovasculares, a hipertensão, a diabetes, alguns tipos de cancro e tantas outras. É também importante salientar que, uma alimentação cuidada dá saúde e aumenta a esperança média de vida. Em Portugal, nos últimos anos, os costumes alimentares da população têm vindo a alterar-se significativamente. As refeições caseiras confeccionadas com produtos frescos dão lugar à designada “cultura do fast food”. Em contrapartida, os consumidores são cada vez mais exigentes, estando em permanente alerta no que se refere ao estado dos alimentos. A rotulagem de um produto, para além da função publicitária, tem vindo a ser objecto de legislação específica de forma a fornecer informação simples e clara, correspondente à composição, qualidade, quantidade, validade ou outras características do produto. Estas informações devem ser acessíveis a qualquer tipo de público, com mais ou menos formação e de qualquer estrato social. A qualidade e segurança dos produtos deve basear-se na garantia de que todos os ingredientes, materiais de embalagem e processos produtivos são adequados à produção de produtos seguros, saudáveis e saborosos. A Silliker Portugal, S.A. é uma empresa independente de prestação de serviços para o sector agro-alimentar, líder mundial na prestação de serviços para a melhoria da qualidade e segurança alimentar. A Silliker dedica-se a ajudar as empresas a encontrar soluções para os desafios actuais do sector, oferecendo uma ampla gama de serviços, onde se inclui o serviço de análises microbiológicas, químicas e sensorial; consultadoria em segurança alimentar e desenvolvimento; auditorias; rotulagem e legislação. A actualização permanente de procedimentos na procura de uma melhoria contínua é um dos objectivos da empresa. Para responder a um dos desafios colocados à Silliker, surgiu este trabalho, que consistiu no desenvolvimento de um novo método para determinação de ácidos gordos e da gordura total em diferentes tipos de alimentos e comparação dos resultados, com os obtidos com o método analítico até então adoptado. Se a gordura é um elemento de grande importância na alimentação, devido às suas propriedades nutricionais e organoléticas, recentemente, os investigadores têm focado a sua atenção nos mais diversos ácidos gordos (saturados, monoinsaturados e polinsaturados), em particular nos ácidos gordos essenciais e nos isómeros do ácido linoleico conjugado (CLA), uma mistura de isómeros posicionais e geométricos do ácido linoleico com actividade biológica importante. A técnica usada nas determinações foi a cromatografia gasosa com ionização de chama, GC-FID, tendo as amostras sido previamente tratadas e extraídas de acordo com o tipo de matriz. A metodologia analítica desenvolvida permitiu a correcta avaliação do perfil em ácidos gordos, tendo-se para isso usado uma mistura de 37 ésteres metílicos, em que o ácido gordo C13:0 foi usado como padrão interno. A identificação baseou-se nos tempos de retenção de cada ácido gordo da mistura e para a quantificação usaram-se os factores de resposta. A validação do método implementado foi baseada nos resultados obtidos no estudo de três matrizes relativas a materiais certificados pela BIPEA (Bureau Interprofessionnel des Etudes Analytiques), para o que foram efectuadas doze réplicas de cada matriz. Para cada réplica efectuada foi calculado o teor de matéria gorda, sendo posteriormente o resultado comparado com o emitido pela entidade certificada. Após análise de cada constituinte foi também possível calcular o teor em ácidos gordos saturados, monoinsaturados e polinsaturados. A determinação do perfil em ácidos gordos dos materiais certificados foi aceitável atendendo aos valores obtidos, os quais se encontravam no intervalo de valores admissíveis indicados nos relatórios. A quantificação da matéria gorda no que se refere à matriz de “Paté à Tartinier” apresentou um z-score de 4,3, o que de acordo com as exigências internas da Silliker, não é válido. Para as outras duas matrizes (“Mélange Nutritif” e “Plat cuisiné à base de viande”) os valores de z-score foram, respectivamente, 0,7 e -1,0, o que permite concluir a validade do método. Para que o método possa vir a ser adoptado como método alternativo é necessário um estudo mais alargado relativamente a amostras com diferentes composições. O método foi aplicado na análise de amostras de fiambre, leite gordo, queijo, ovo com ómega 3, amendoim e óleo de girassol, e os resultados foram comparados com os obtidos pelo método até então adoptado.

Molecularly-imprinted materials for potentiometric transduction: application to the antibiotic enrofloxacin

Enrofloxacin (ENR) is an antimicrobial used both in humans and in food producing species. Its control is required in farmed species and their surroundings in order to reduce the prevalence of antibiotic resistant bacteria. Thus, a new biomimetic sensor enrofloxacin is presented. An artificial host was imprinted in specific polymers. These were dispersed in 2-nitrophenyloctyl ether and entrapped in a poly(vinyl chloride) matrix. The potentiometric sensors exhibited a near-Nernstian response. Slopes expressing mVΔlog([ENR]/M) varied within 48–63. The detection limits ranged from 0.28 to 1.01 µg mL 1. Sensors were independent from the pH of test solutions within 4–7. Good selectivity was observed toward potassium, calcium, barium, magnesium, glycine, ascorbic acid, creatinine, norfloxacin, ciprofloxacin, and tetracycline. In flowing media, the biomimetic sensors presented good reproducibility (RSD of ±0.7%), fast response, good sensitivity (47 mV/Dlog([ENR]/ M), wide linear range (1.0×10-5–1.0×10-3 M), low detection limit (0.9 µg mL-1), and a stable baseline for a 5×10-2 M acetate buffer (pH 4.7) carrier. The sensors were used to analyze fish samples. The method offered the advantages of simplicity, accuracy, and automation feasibility. The sensing membrane may contribute to the development of small devices allowing in vivo measurements of enrofloxacin or parent-drugs.

Biomimetic sensors of molecularly-imprinted polymers for chlorpromazine determination

A new man-tailored biomimetic sensor for Chlorpromazine host-guest interactions and potentiometric transduction is presented. The artificial host was imprinted within methacrylic acid, 2-vinyl pyridine and 2-acrylamido-2-methyl-1-propanesulfonic acid based polymers. Molecularly imprinted particles were dispersed in 2-nitrophenyloctyl ether and entrapped in a poly(vinyl chloride) matrix. Slopes and detection limits ranged 51–67 mV/decade and 0.46–3.9 μg/mL, respectively, in steady state conditions. Sensors were independent fromthe pHof test solutionswithin 2.0–5.5.Good selectivitywas observed towards oxytetracycline, doxytetracycline, ciprofloxacin, enrofloxacin, nalidixic acid, sulfadiazine, trimethoprim, glycine, hydroxylamine, cysteine and creatinine. Analytical features in flowing media were evaluated on a double-channel manifold, with a carrier solution of 5.0×10−2 mol/L phosphate buffer. Near-Nernstian response was observed over the concentration range 1.0×10−4 to 1.0×10−2 mol/L. Average slopes were about 48 mV/decade. The sensors were successfully applied to field monitoring of CPZ in fish samples, offering the advantages of simplicity, accuracy, automation feasibility and applicability to complex samples.

Quantification of endocrine disruptors and pesticides in water by gas chromatography–tandem mass spectrometry. Method validation using weighted linear regression schemes

Amulti-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.

Man-tailored biomimetic sensor of molecularly imprinted materials for the potentiometric measurement of oxytetracycline

A novel biomimetic sensor for the potentiometric transduction of oxytetracycline is presented. The artificial host was imprinted in methacrylic acid and/or acrylamide based polymers. Different amounts of molecularly imprinted and non-imprinted polymers were dispersed in different plasticizing solvents and entrapped in a poly(vinyl chloride) matrix. Only molecularly imprinted based sensors allowed a potentiometric transduction, suggesting the existence of host–guest interactions. These sensors exhibited a near-Nernstian response in steady state evaluations; slopes and detection limits ranged 42–63 mV/decade and 2.5–31.3 µg/mL, respectively. Sensors were independent from the pH of test solutions within 2–5. Good selectivity was observed towards glycine, ciprofloxacin, creatinine, acid nalidixic, sulfadiazine, cysteine, hydroxylamine and lactose. In flowing media, the biomimetic sensors presented good reproducibility (RSD of ±0.7%), fast response, good sensitivity (65 mV/decade), wide linear range (5.0×10−5 to 1.0×10−2 mol/L), low detection limit (19.8 µg/mL), and a stable baseline for a 5×10−3M citrate buffer (pH 2.5) carrier. The sensors were successfully applied to the analysis of drugs and urine. This work confirms the possibility of using molecularly imprinted polymers as ionophores for organic ion recognition in potentiometric transduction.

Cardiorespiratory fitness is negatively associated with metabolic risk factors independently of the adherence to a healthy dietary pattern

Background and aim: Cardiorespiratory fitness (CRF) and diet have been involved as significant factors towards the prevention of cardio-metabolic diseases. This study aimed to assess the impact of the combined associations of CRF and adherence to the Southern European Atlantic Diet (SEADiet) on the clustering of metabolic risk factors in adolescents. Methods and Results: A cross-sectional school-based study was conducted on 468 adolescents aged 15-18, from the Azorean Islands, Portugal. We measured fasting glucose, insulin, total cholesterol (TC), HDL-cholesterol, triglycerides, systolic blood pressure, waits circumference and height. HOMA, TC/HDL-C ratio and waist-to-height ratio were calculated. For each of these variables, a Z-score was computed by age and sex. A metabolic risk score (MRS) was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had 1SD of this score. CRF was measured with the 20 m-Shuttle-Run- Test. Adherence to SEADiet was assessed with a semi-quantitative food frequency questionnaire. Logistic regression showed that, after adjusting for potential confounders, unfit adolescents with low adherence to SEADiet had the highest odds of having MRS (OR Z 9.4; 95%CI:2.6e33.3) followed by the unfit ones with high adherence to the SEADiet (OR Z 6.6; 95% CI: 1.9e22.5) when compared to those who were fit and had higher adherence to SEADiet.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Using test infrastructures for (remote) online evaluation of the sensitivity to SEUs of FPGAs

This paper proposes an online mechanism that can evaluate the sensitivity of single event upsets (SEUs) of field programmable gate arrays (FPGAs). The online detection mechanism cyclically reads and compares the values form the external and internal configuration memories, taking into account the mask information. This remote detection method also signals any mismatch as a result of a SEU that affects both used and not-used FPGA parts, which maximizes the monitored area. By utilizing an external, Web-accessible controller that is connected to the test infrastructure, the possibility of running the same operation in a remote manner is enabled. Moreover, the need for a local memory to store the mask values is also eliminated.