Bacterial enzymes and multi-enzymatic systems for cleaning-up dyes from the environment

Synthetic dyes are xenobiotic compounds that are being increasingly used in several industries, with special emphasis in the paper, textile and leather industries. Over 100,000 commercial dyes exist today and more than 7 × 105 tons of dyestuff is produced annually, of which 1–1.5 × 105 tons is released into the wastewaters (Rai et al in Crit Rev Environ Sci Tecnhol 35:219–238, 2005). Among these, azo dyes, characterized by the presence of one or more azo groups (–N=N–), and anthraquinonic dyes represent the largest and most versatile groups.

Bacterial agents isolated from cerebrospinal fluid of patients with Acquired Immunodeficiency Syndrome (AIDS) and neurological complications

Cerebrospinal fluid (CSF) samples from 2083 patients with acquired immunodeficiency syndrome (AIDS) and neurological complications were bacteriologically examined during a period of 7 years (1984-1990). The percentage of patients who had at least one bacterial agent cultured from the CSF was 6.2%. Mycobacterium tuberculosis was the most frequently isolated agent (4.3%), followed by Mycobacterium avium complex or MAC (0.7%), Pseudomonas spp (0.5%), Enterobacter spp (0.4%), and Staphylococcus aureus (0.3%). Among 130 culture positive patients, 89 (68.5%) had M. tuberculosis and 15 (11.6%) had MAC. The frequency of bacterial isolations increased from 1988 (5.2%) to 1990 (7.2%), partly due to the increase in MAC isolations. Bacterial agents were more frequently isolated from patients in the age group 21-30 years and from women (p<0.05).

Dot-enzyme-linked immunosorbent assay (Dot-ELISA) for detection of pneumococcal polysaccharide antigens in pleural fluid effusion samples.: Comparison with bacterial culture, counterimmunoelectrophoresis and latex agglutination

A dot-enzyme-linked immunosorbent assay (Dot-ELISA) for pneumococcal antigen detection was standardized in view of the need for a rapid and accurate immunodiagnosis of acute pneumococcal pneumonia. A total of 442 pleural fluid effusion samples (PFES) from children with clinical and laboratory diagnoses of acute bacterial pneumonia, plus 38 control PFES from tuberculosis patients and 20 negative control serum samples from healthy children were evaluated by Dot-ELISA. The samples were previously treated with 0.1 M EDTA pH 7.5 at 90°C for 10 min and dotted on nitrocellulose membrane. Pneumococcal omniserum diluted at 1:200 was employed in this assay for antigen detection. When compared with standard bacterial culture, counterimmunoelectrophoresis and latex agglutination techniques, the Dot-ELISA results showed relative indices of 0.940 to sensitivity, 0.830 to specificity and 0.760 to agreement. Pneumococcal omniserum proved to be an optimal polyvalent antiserum for the detection of pneumococcal antigen by Dot-ELISA. Dot-ELISA proved to be a practical alternative technique for the diagnosis of pneumococcal pneumonia.

O papel dos sistemas de Non-Intrusive Load Monitoring para a eficiência energética do setor residencial

A sustentabilidade energética do planeta é uma preocupação corrente e, neste sentido, a eficiência energética afigura-se como sendo essencial para a redução do consumo em todos os setores de atividade. No que diz respeito ao setor residencial, o indevido comportamento dos utilizadores aliado ao desconhecimento do consumo dos diversos aparelhos, são factores impeditivos para a redução do consumo energético. Uma ferramenta importante, neste sentido, é a monitorização de consumos nomeadamente a monitorização não intrusiva, que apresenta vantagens económicas relativamente à monitorização intrusiva, embora levante alguns desafios na desagregação de cargas. Abordou-se então, neste documento, a temática da monitorização não intrusiva onde se desenvolveu uma ferramenta de desagregação de cargas residenciais, sobretudo de aparelhos que apresentavam elevados consumos. Para isso, monitorizaram-se os consumos agregados de energia elétrica, água e gás de seis habitações do município de Vila Nova de Gaia. Através da incorporação dos vetores de água e gás, a acrescentar ao da energia elétrica, provou-se que a performance do algoritmo de desagregação de aparelhos poderá aumentar, no caso de aparelhos que utilizem simultaneamente energia elétrica e água ou energia elétrica e gás. A eficiência energética é também parte constituinte deste trabalho e, para tal, implementaram-se medidas de eficiência energética para uma das habitações em estudo, de forma a concluir as que exibiam maior potencial de poupança, assim como rápidos períodos de retorno de investimento. De um modo geral, os objetivos propostos foram alcançados e espera-se que num futuro próximo, a monitorização de consumos não intrusiva se apresente como uma solução de referência no que respeita à sustentabilidade energética do setor residencial.

Bacterial antigen detection in cerebrospinal fluid by the latex agglutination test

Eighty purulent cerebrospinal fluid (CSF) samples from patients with clinical evidence of meningitis were studied using the Directigen latex agglutination (LA) kit to determine the presence of bacterial antigen in CSF. The results showed a better diagnostic performance of the LA test than bacterioscopy by Gram stain, culture and counterimmunoelectrophoresis (CIE), as far as Neisseria meningitidis groups B and C, and Haemophilus influenzae type b are concerned, and a better performance than bacterioscopy and culture considering Streptococcus pneumoniae. Comparison of the results with those of culture showed that the LA test had the highest sensitivity for the Neisseria meningitidis group C. Comparing the results with those of CIE, the highest levels of sensitivity were detected for N. meningitidis groups B and C. Regarding specificity, fair values were obtained for all organisms tested. The degree of K agreement when the LA test was compared with CIE exhibited better K indices of agreement for N. meningitidis groups B and C.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Contextual Intelligent Load Management Considering Real-Time Pricing in a Smart Grid Environment

The use of demand response programs enables the adequate use of resources of small and medium players, bringing high benefits to the smart grid, and increasing its efficiency. One of the difficulties to proceed with this paradigm is the lack of intelligence in the management of small and medium size players. In order to make demand response programs a feasible solution, it is essential that small and medium players have an efficient energy management and a fair optimization mechanism to decrease the consumption without heavy loss of comfort, making it acceptable for the users. This paper addresses the application of real-time pricing in a house that uses an intelligent optimization module involving artificial neural networks.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Stochastic Short-term Incentive-based Demand Response Scheduling of Load-serving Entities

In competitive electricity markets it is necessary for a profit-seeking load-serving entity (LSE) to optimally adjust the financial incentives offering the end users that buy electricity at regulated rates to reduce the consumption during high market prices. The LSE in this model manages the demand response (DR) by offering financial incentives to retail customers, in order to maximize its expected profit and reduce the risk of market power experience. The stochastic formulation is implemented into a test system where a number of loads are supplied through LSEs.

Determining the Adjustment Baseline Parameters to Define an Accurate Customer Baseline Load

Demand response is an energy resource that has gained increasing importance in the context of competitive electricity markets and of smart grids. New business models and methods designed to integrate demand response in electricity markets and of smart grids have been published, reporting the need of additional work in this field. In order to adequately remunerate the participation of the consumers in demand response programs, improved consumers’ performance evaluation methods are needed. The methodology proposed in the present paper determines the characterization of the baseline approach that better fits the consumer historic consumption, in order to determine the expected consumption in absent of participation in a demand response event and then determine the actual consumption reduction. The defined baseline can then be used to better determine the remuneration of the consumer. The paper includes a case study with real data to illustrate the application of the proposed methodology.

Voltage Magnitude Analysis Considering Load Curtailment Minimization in Sub-Transmission Networks with Distributed Generators

Most of distribution generation and smart grid research works are dedicated to the study of network operation parameters, reliability among others. However, many of this research works usually uses traditional test systems such as IEEE test systems. This work proposes a voltage magnitude study in presence of fault conditions considering the realistic specifications found in countries like Brazil. The methodology considers a hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzyprobabilistic models and a remedial action algorithm which is based on optimal power flow. To illustrate the application of the proposed method, the paper includes a case study that considers a real 12 bus sub-transmission network.

An automatic tool to Extract, Transform and Load data from real electricity markets

The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.