Introdução à inteligência artificial com recurso à programação visual

Relatório da Prática de Ensino Supervisionada, Mestrado em Ensino da Matemática, Universidade de Lisboa, Instituto de Educação, 2014

Complex Interplay Between Determinants of Pacing and Performance During 20 km Cycle Time Trials

Purpose This study examined the determinants of pacing strategy and performance during self paced maximal exercise. Methods Eight well trained cyclists completed two 20 km time trials. Power output, RPE, positive and negative affect, and iEMG activity of the active musculature were recorded every 0.5km, confidence in achieving pre-exercise goals was assessed every 5 km, and blood lactate and pH were measured post-exercise. Differences in all parameters were assessed between fastest (FAST) and slowest (SLOW) trials performed. Results Mean power output was significantly higher during the initial 90% of FAST, but not the final 10%, and blood lactate concentration was significantly higher and pH significantly lower following FAST. Mean iEMG activity was significantly higher throughout SLOW. RPE was similar throughout both trials, but participants had significantly more positive affect and less negative affect throughout FAST. Participants grew less confident in their ability to achieve their goals throughout SLOW. Conclusions The results suggest that affect may be the primary psychological regulator of pacing strategy and that higher levels of positivity and lower levels of negativity may have been associated with a more aggressive strategy during FAST. Although the exact mechanisms through which affect acts to influence performance are unclear, it may determine the degree of physiological disruption that can be tolerated, or be reflective of peripheral physiological status in relation to the still to be completed exercise task.

Influence of Different Performance Levels on Pacing Strategy During the Women's World Championship Marathon Race.

Purpose To analyse pacing strategies displayed by athletes achieving differing levels of performance during an elite level marathon race. Methods Competitors in the 2009 IAAF Women’s Marathon Championship were split into Groups 1, 2, 3, and 4 comprising the first, second, third, and fourth 25% of finishers respectively. Final, intermediate, and personal best (PB) times of finishers were converted to mean speeds, and relative speed (% of PB speed) was calculated for intermediate segments. Results Mean PB speed decreased from Group 1 to 4 and speed maintained in the race was 98.5 + 1.8%, 97.4 + 3.2%, 95.0 + 3.1% and 92.4 + 4.4% of PB speed for Groups 1-4 respectively. Group 1 was fastest in all segments and differences in speed between groups increased throughout the race. Group 1 ran at lower relative speeds than other groups for the first two 5 km segments, but higher relative speeds after 35km. Significant differences (P<0.01) in the percentage of PB speed maintained were observed between Groups 1 and 4, and 2 and 4 in all segments after 20 km, and Groups 3 and 4 from 20-25 km and 30-35 km. Conclusions Group 1 athletes achieved superior finishing times relative to their PB than athletes in other Groups who selected unsustainable initial speeds resulting in subsequent significant losses of speed. It is suggested that psychological factors specific to a major competitive event influenced decision making by athletes and poor decisions resulted in final performances inferior to those expected based on PB times.

Risk Perception Influences Athletic Pacing Strategy.

PURPOSE: To examine risk-taking and risk-perception associations with perceived exertion, pacing and performance in athletes. METHODS: Two experiments were conducted in which risk-perception was assessed using the domain-specific risk-taking (DOSPERT) scale in 20 novice cyclists (Experiment 1) and 32 experienced ultra-marathon runners (Experiment 2). In Experiment 1, participants predicted their pace and then performed a 5 km maximum effort cycling time-trial on a calibrated KingCycle mounted bicycle. Split-times and perceived exertion were recorded every kilometer. In experiment 2, each participant predicted their split times before running a 100 km ultra-marathon. Split-times and perceived exertion were recorded at 7 check-points. In both experiments, higher and lower risk-perception groups were created using median split of DOSPERT scores. RESULTS: In experiment 1, pace during the first km was faster among lower compared to higher risk-perceivers, t(18)=2.0 P=0.03, and faster among higher compared lower risk-takers, t(18)=2.2 P=0.02. Actual pace was slower than predicted pace during the first km in both the higher risk perceivers, t(9)=-4.2 P=0.001, and lower risk-perceivers, t(9)=-1.8 P=0.049. In experiment 2, pace during the first 36 km was faster among lower compared to higher risk-perceivers, t(16)=2.0 P=0.03. Irrespective of risk-perception group, actual pace was slower than predicted pace during the first 18 km, t(16)=8.9 P<0.001, and from 18 to 36 km, t(16)=4.0 P<0.001. In both experiments there was no difference in performance between higher and lower risk-perception groups. CONCLUSIONS: Initial pace is associated with an individual's perception of risk, with low perceptions of risk being associated with a faster starting pace. Large differences between predicted and actual pace suggests the performance template lacks accuracy, perhaps indicating greater reliance on momentary pacing decisions rather than pre-planned strategy.

The Influence of Performance Level, Age and Gender on Pacing Strategy During a 100-km Ultramarathon

The aim of this study is to analyse the influence of performance level, age and gender on pacing during a 100-km ultramarathon. Results of a 100-km race incorporating the World Masters Championships were used to identify differences in relative speeds in each 10-km segment between participants finishing in the first, second, third and fourth quartiles of overall positions (Groups 1, 2, 3 and 4, respectively). Similar analyses were performed between the top and bottom 50% of finishers in each age category, as well as within male and female categories. Pacing varied between athletes achieving different absolute performance levels. Group 1 ran at significantly lower relative speeds than all other groups in the first three 10-km segments (all P < 0.01), and significantly higher relative speeds than Group 4 in the 6th and 10th (both P < 0.01), and Group 2 in the 8th (P = 0.04). Group 4 displayed significantly higher relative speeds than Group 2 and 3 in the first three segments (all P < 0.01). Overall strategies remained consistent across age categories, although a similar phenomenon was observed within each category whereby ‘top’ competitors displayed lower relative speeds than ‘bottom’ competitors in the early stages, but higher relative speeds in the later stages. Females showed lower relative starting speeds and higher finishing speeds than males. ‘Top’ and ‘bottom’ finishing males displayed differing strategies, but this was not the case within females. Although pacing remained consistent across age categories, it differed with level of performance within each, possibly suggesting strategies are anchored on direct competitors. Strategy differs between genders and differs depending on performance level achieved in males but not females.

Impacto da iluminação natural e artificial na disponibilidade de macro-invertebrados bentónicos para as aves limícolas

Tese de mestrado em Ecologia Marinha, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2015

Gender comparisons in non-acute cardiac symptom recognition and subsequent help-seeking decisions: a mixed methods study protocol

Introduction: Coronary heart disease (CHD) is one of the leading causes of death in both men and women worldwide. Despite the common misconception that CHD is a ‘man's disease’, it is now well accepted that women endure worse clinical outcomes than men following CHD-related events. A number of studies have explored whether or not gender differences exist in patients presenting with CHD, and specifically whether women delay seeking help for cardiac conditions. UK and overseas studies on help-seeking for emergency cardiac events are contradictory, yet suggest that women often delay help-seeking. In addition, no studies have looked at presumed cardiac symptoms outside an emergency situation. Given the lack of understanding in this area, an explorative qualitative study on the gender differences in help-seeking for a non-emergency cardiac events is needed. Methods and analysis: A purposive sample of 20–30 participants of different ethnic backgrounds and ages attending a rapid access chest pain clinic will be recruited to achieve saturation. Semistructured interviews focusing on help-seeking decision-making for apparent cardiac symptoms will be undertaken. Interview data will be analysed thematically using qualitative software (NVivo) to understand any similarities and differences between the way men and women construct help-seeking. Findings will also be used to inform the preliminary development of a cardiac help-seeking intentions questionnaire. Ethics and dissemination: Ethical approvals were sought and granted. Namely, the University of Westminster (sponsor) and St Georges NHS Trust REC, and the Trust Research and Development Office granted approval to host the study on the Queen Mary's Roehampton site. The study is low risk, with interviews being conducted on hospital premises during working hours. Investigators will disseminate findings via presentations and publications. Participants will receive a written summary of the key findings.

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.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

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.

Feasibility study on multifunctional artificial reefs for the Azores

Tese de Doutoramento, Geografia (Ordenamento do Território), 25 de Novembro de 2013, Universidade dos Açores.

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.

Controlo e optimização de sistemas AVAC recorrendo a técnicas de inteligência artificial

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

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.