Neural Mechanisms of Exercise: Anti-Depression, Neurogenesis, and Serotonin Signaling

Depression is associated with decreased serotonin metabolism and functioning in the central nervous system, evidenced by both animal models of depression and clinical patient studies. Depression is also accompanied by decreased hippocampal neurogenesis in diverse animal models. Neurogenesis is mainly defined in dentate gyrus of hippocampus as well as subventricular zone. Moreover, hypothalamus, amygdala, olfactory tubercle, and piriform cortex are reported with evidences of adult neurogenesis. Physical exercise is found to modulate adult neurogenesis significantly, and results in mood improvement. The cellular mechanism such as adult neurogenesis upregulation was considered as one major mood regulator following exercise. The recent advances in molecular mechanisms underlying exercise-regulated neurogenesis have widen our understanding in brain plasticity in physiological and pathological conditions, and therefore better management of different psychiatric disorders.

Challenges for e-learning and adult students in higher education

This article examines Lifelong Learning, from the perspective of the adult learner in higher education, by presenting some of the results of a project, funded by the European Commission's Socrates Programme, LIHE, Learning in Higher Education. It is structured as follows: first, the background of the project is described, then the experiences of the adult student, concerning their induction and tuition, are presented. Some future trends concerning adults in higher education and lifelong learning are outlined and conclusions drawn.

Educational characteristics of adult students in portuguese technological schools

Despite a massive expansion of education in Portugal, since the 1970’s, educational attainment of the adult population in the country remains low. The numbers of working-age people in some form of continuing education are among the lowest, according to the OECD and EU-27 statistics. Technological Schools(TS), initially created in the 1990’s, under the umbrella of the Ministry of Economy in partnership with industry and industrial associations, aimed to prepare qualified staff for industries and services in the country, particularly in the engineering sector, through the provision of post secondary non-university programmes of studies, the CET (Technological Specialization Courses). Successful CET students are awarded a DET(Diploma of Technological Specialization), which corresponds to Vocational Qualification level IV of the EU, according to the latest alteration (2005) of the Education Systems Act (introduced in 1986). In this, CET’s are also clearly defined as one of the routes for access to Higher Education (HE), in Portugal. The PRILHE (Promoting Reflective and Independent Learning in Higher Education) multinational project, funded by the European Socrates Grundtvig Programme, aimed to identify the learning processes which enable adult students in higher education to become autonomous reflective learners and search best practices to support these learning processes. During this research, both quantitative and qualitative methods were used to determine how students organise their studies and develop their learning skills. The Portuguese partner in the project’ consortium used a two case studies approach, one with students of Higher Education Institutions and other with students of TS. This paper only applies to students of TS, as these have a predominant bias towards engineering. Results show that student motivation and professional teaching support contribute equally to the development of an autonomous and reflective approach to learning in adult students; this is essential for success in a knowledge economy, where lifelong learning is the key to continuous employment.

Adult learners in higher education

Copyright © 2005, Idea Group Inc., distributing in print or electronic forms without written permission of IGI is prohibited.

Building a knowledge and learning society in Portugal: adult students in technological schools and higher education institutions

Lifelong learning (LLL) has received increasing attention in recent years. It implies that learning should take place at all stages of the “life cycle and it should be life-wide, that is embedded in all life contexts from the school to the work place, the home and the community” (Green, 2002, p.613). The ‘learning society’, is the vision of a society where there are recognized opportunities for learning for every person, wherever they are and however old they happen to be. Globalization and the rise of new information technologies are some of the driving forces that cause depreciation of specialised competences. This happens very quickly in terms of economic value; consequently, workers of all skills levels, during their working life, must have the opportunity to update “their technical skills and enhance general skills to keep pace with continuous technological change and new job requirements” (Fahr, 2005, p. 75). It is in this context that LLL tops the policy agenda of international bodies, national governments and non-governmental organizations, in the field of education and training, to justify the need for LLL opportunities for the population as they face contemporary employability challenges. It is in this context that the requirement and interest to analyse the behaviour patterns of adult learners has developed over the last few years

Lifelong learning: case study of adult students in portuguese technological schools and higher education institutions

Paper to be presented in ESREA 2007 ADC Network Seminar - Changing Relationships between the State, Civil Society and the Citizen: Implications for adult education and adult learning, 14 -16 June 2007 - University of Minho - Campus de Gualtar, Braga (Portugal).

Learning in higher education: strategies to overcome challenges faced by adult students - lessons drawn from two studies in portugal

Int’l J. of Information and Communication Technology Education, 3(2), 1-14, April-June 2007

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.

Differences in Mismatch Negativity (MMN) response to Pure-tone and Speech sounds in normal subjects: an additional explanation

The relation of automatic auditory discrimination, measured with MMN, with the type of stimuli has not been well established in the literature, despite its importance as an electrophysiological measure of central sound representation. In this study, MMN response was elicited by pure-tone and speech binaurally passive auditory oddball paradigm in a group of 8 normal young adult subjects at the same intensity level (75 dB SPL). The frequency difference in pure-tone oddball was 100 Hz (standard = 1 000 Hz; deviant = 1 100 Hz; same duration = 100 ms), in speech oddball (standard /ba/; deviant /pa/; same duration = 175 ms) the Portuguese phonemes are both plosive bi-labial in order to maintain a narrow frequency band. Differences were found across electrode location between speech and pure-tone stimuli. Larger MMN amplitude, duration and higher latency to speech were verified compared to pure-tone in Cz and Fz as well as significance differences in latency and amplitude between mastoids. Results suggest that speech may be processed differently than non-speech; also it may occur in a later stage due to overlapping processes since more neural resources are required to speech processing.

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.

Neural networks for condition monitoring of wind turbines gearbox

Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.

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.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.