Developmental and evolutionary assumptions in a study about the impact of premature birth and low income on mother–infant interaction

In order to study the impact of premature birth and low income on mother–infant interaction, four Portuguese samples were gathered: full-term, middle-class (n=99); premature, middle-class (n=63); full-term, low income (n=22); and premature, low income (n=21). Infants were filmed in a free play situation with their mothers, and the results were scored using the CARE Index. By means of multinomial regression analysis, social economic status (SES) was found to be the best predictor of maternal sensitivity and infant cooperative behavior within a set of medical and social factors. Contrary to the expectations of the cumulative risk perspective, two factors of risk (premature birth together with low SES) were as negative for mother–infant interaction as low SES solely. In this study, as previous studies have shown, maternal sensitivity and infant cooperative behavior were highly correlated, as was maternal control with infant compliance. Our results further indicate that, when maternal lack of responsiveness is high, the infant displays passive behavior, whereas when the maternal lack of responsiveness is medium, the infant displays difficult behavior. Indeed, our findings suggest that, in these cases, the link between types of maternal and infant interactive behavior is more dependent on the degree of maternal lack of responsiveness than it is on birth status or SES. The results will be discussed under a developmental and evolutionary reasoning

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Metaheuristic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Metaheuristhic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

An approach to absorptive capacity based on the Portuguese textile industry

The main purpose of this research is to identify the hidden knowledge and learning mechanisms in the organization in order to disclosure the tacit knowledge and transform it into explicit knowledge. Most firms usually tend to duplicate their efforts acquiring extra knowledge and new learning skills while forgetting to exploit the existing ones thus wasting one life time resources that could be applied to increase added value within the firm overall competitive advantage. This unique value in the shape of creation, acquisition, transformation and application of learning and knowledge is not disseminated throughout the individual, group and, ultimately, the company itself. This work is based on three variables that explain the behaviour of learning as the process of construction and acquisition of knowledge, namely internal social capital, technology and external social capital, which include the main attributes of learning and knowledge that help us to capture the essence of this symbiosis. Absorptive Capacity provides the right tool to explore this uncertainty within the firm it is possible to achieve the perfect match between learning skills and knowledge needed to support the overall strategy of the firm. This study has taken in to account a sample of the Portuguese textile industry and it is based on a multisectorial analysis that makes it possible a crossfunctional analysis to check on the validity of results in order to better understand and capture the dynamics of organizational behavior.

An approach to absorptive capacity based on the Portuguese textile industry(Final version)

The main purpose of this research is to identify the hidden knowledge and learning mechanisms in the organization in order to disclosure the tacit knowledge and transform it into explicit knowledge. Most firms usually tend to duplicate their efforts acquiring extra knowledge and new learning skills while forgetting to exploit the existing ones thus wasting one life time resources that could be applied to increase added value within the firm overall competitive advantage. This unique value in the shape of creation, acquisition, transformation and application of learning and knowledge is not disseminated throughout the individual, group and, ultimately, the company itself. This work is based on three variables that explain the behaviour of learning as the process of construction and acquisition of knowledge, namely internal social capital, technology and external social capital, which include the main attributes of learning and knowledge that help us to capture the essence of this symbiosis. Absorptive Capacity provides the right tool to explore this uncertainty within the firm it is possible to achieve the perfect match between learning skills and knowledge needed to support the overall strategy of the firm. This study has taken in to account a sample of the Portuguese textile industry and it is based on a multisectorial analysis that makes it possible a crossfunctional analysis to check on the validity of results in order to better understand and capture the dynamics of organizational behavior.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Risk Aversion in a Mixed-Integer Nonlinear Approach to Support Decision-Making for a Hydro Power Producer

In this paper, a mixed-integer nonlinear approach is proposed to support decision-making for a hydro power producer, considering a head-dependent hydro chain. The aim is to maximize the profit of the hydro power producer from selling energy into the electric market. As a new contribution to earlier studies, a risk aversion criterion is taken into account, as well as head-dependency. The volatility of the expected profit is limited through the conditional value-at-risk (CVaR). The proposed approach has been applied successfully to solve a case study based on one of the main Portuguese cascaded hydro systems.

Mixed-integer nonlinear approach for the optimal scheduling of a head-dependent hydro chain

This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain We propose a novel mixed-integer nonlinear programming (MINLP) approach, considering hydroelectric power generation as a nonlinear function of water discharge and of the head. As a new contribution to eat her studies, we model the on-off behavior of the hydro plants using integer variables, in order to avoid water discharges at forbidden areas Thus, an enhanced STHS is provided due to the more realistic modeling presented in this paper Our approach has been applied successfully to solve a test case based on one of the Portuguese cascaded hydro systems with a negligible computational time requirement.

A comparative benchmarking analysis of main Iberian container terminals: a DEA approach

Benchmarking is an important tool to organisations to improve their productivity, product quality, process efficiency or services. From Benchmarking the organisations could compare their performance with competitors and identify their strengths and weaknesses. This study intends to do a benchmarking analysis on the main Iberian Sea ports with a special focus on their container terminals efficiency. To attain this, the DEA (data envelopment analysis) is used since it is considered by several researchers as the most effective method to quantify a set of key performance indicators. In order to reach a more reliable diagnosis tool the DEA is used together with the data mining in comparing the sea ports operational data of container terminals during 2007.Taking into account that sea ports are global logistics networks the performance evaluation is essential to an effective decision making in order to improve their efficiency and, therefore, their competitiveness.

A hybrid approach to multi-agent decision-making

In the aftermath of a large-scale disaster, agents' decisions derive from self-interested (e.g. survival), common-good (e.g. victims' rescue) and teamwork (e.g. fire extinction) motivations. However, current decision-theoretic models are either purely individual or purely collective and find it difficult to deal with motivational attitudes; on the other hand, mental-state based models find it difficult to deal with uncertainty. We propose a hybrid, CvI-JI, approach that combines: i) collective 'versus' individual (CvI) decisions, founded on the Markov decision process (MDP) quantitative evaluation of joint-actions, and ii)joint-intentions (JI) formulation of teamwork, founded on the belief-desire-intention (BDI) architecture of general mental-state based reasoning. The CvI-JI evaluation explores the performance's improvement

A practical approach for profit-based unit commitment with emission limitations

This paper proposes a practical approach for profit-based unit commitment (PBUC) with emission limitations. Under deregulation, unit commitment has evolved from a minimum-cost optimisation problem to a profit-based optimisation problem. However, as a consequence of growing environmental concern, the impact of fossil-fuelled power plants must be considered, giving rise to emission limitations. The simultaneous address of the profit with the emission is taken into account in our practical approach by a multiobjective optimisation (MO) problem. Hence, trade-off Curves between profit and emission are obtained for different energy price profiles, in a way to aid decision-makers concerning emission allowance trading. Moreover, a new parameter is presented, ratio of change, and the corresponding gradient angle, enabling the proper selection of a compromise commitment for the units. A case study based on the standard IEEE 30-bus system is presented to illustrate the proficiency Of Our practical approach for the new competitive and environmentally constrained electricity supply industry.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Scheduling of head-dependent cascaded hydro systems: Mixed-integer quadratic programming approach

This paper is on the problem of short-term hydro, scheduling, particularly concerning head-dependent cascaded hydro systems. We propose a novel mixed-integer quadratic programming approach, considering not only head-dependency, but also discontinuous operating regions and discharge ramping constraints. Thus, an enhanced short-term hydro scheduling is provided due to the more realistic modeling presented in this paper. Numerical results from two case studies, based on Portuguese cascaded hydro systems, illustrate the proficiency of the proposed approach.