The Role of Non-Market Valuation in Forest Management and Recreation Policy

Forests, and particularly those where native and mixed species are gown, provide a variety of non-wood values, important among which are recreation and environmental services. Substantial progress has been made in recent years in estimating economic values on these services. A considerable amount of research on forest values has been carried out recently in tropical and sub-tropical eastern Australia, some of which is reported in the following papers. The need for estimates of non-wood forest benefits is apparent, and it is clear that further development of techniques and a greater understanding of the way these values can be integrated into public-sector decision making is required.

Future contributions of crop modelling - from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be major contributions of crop modelling in the future. Two major opportunities are envisioned for increased modelling activity in the future. One opportunity is in a continuing central, heuristic role to support scientific investigation, to facilitate decision making by crop managers, and to aid in education. Heuristic activities will also extend to the broader system-level issues of environmental and ecological aspects of crop production. The second opportunity is projected as a prime contributor in understanding and advancing the genetic regulation of plant performance and plant improvement. Physiological dissection and modelling of traits provides an avenue by which crop modelling could contribute to enhancing integration of molecular genetic technologies in crop improvement. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Segmenting the market of West Australian senior tourists using an artificial neural network

Measuring perceptions of customers can be a major problem for marketers of tourism and travel services. Much of the problem is to determine which attributes carry most weight in the purchasing decision. Older travellers weigh many travel features before making their travel decisions. This paper presents a descriptive analysis of neural network methodology and provides a research technique that assesses the weighting of different attributes and uses an unsupervised neural network model to describe a consumer-product relationship. The development of this rich class of models was inspired by the neural architecture of the human brain. These models mathematically emulate the neurophysical structure and decision making of the human brain, and, from a statistical perspective, are closely related to generalised linear models. Artificial neural networks or neural networks are, however, nonlinear and do not require the same restrictive assumptions about the relationship between the independent variables and dependent variables. Using neural networks is one way to determine what trade-offs older travellers make as they decide their travel plans. The sample of this study is from a syndicated data source of 200 valid cases from Western Australia. From senior groups, active learner, relaxed family body, careful participants and elementary vacation were identified and discussed. (C) 2003 Published by Elsevier Science Ltd.

The over-production of US sports and the new International Division of Cultural Labor

This article focuses on how US professional sports utilize the New International Division of Cultural Labor to supplement an overly costly local labor pool and over-supplied local market. We argue that while the classic problem of over-production is slowly eroding the sealed-off nature of US culture, the forces of its hyper-protectionist capitalism continue to characterize sports, precluding equal exchange.

Production and photosynthetic activity of Mimosa Verde and Mimosa Roxa lettuce in two farming systems

Lettuce (Lactuca sativa L.) is the most commonly consumed leaf vegetable in the Brazilian diet, and it is a good source of vitamins and minerals. It is widely grown in the conventional farming system. However, the hydroponic farming system has been gaining importance in the market, wining confidence from consumers, who are becoming increasingly more demanding on food quality. The objective of this study was to evaluate the performance of two lettuce cultivars on hydroponic and conventional farming systems for the production of fresh mass (FM) and dry mass (DM), photosynthesis, contents of chlorophyll and anthocyanin. The following two experiments were carried out: hydroponics farming (HF) and conventional farming (CF), performed in protect and unprotect environments, respectively, in Florianópolis, SC. Mimosa Verde cultivar (MV) showed greater fresh mass than Mimosa Roxa (MR), in both farming systems and the two cultivars presented better performance in the hydroponic system (287.7 g MV and 139.1 g MR) than the conventional system (129.7 g MV and 111.8 g MR). Mimosa Verde cultivar presented lower average contents of total chlorophyll (7.7 mg g-¹ FM) than Mimosa Roxa (11.8 mg g-¹FM), and both cultivars displayed higher means for this variable in the hydroponic farming system. Mimosa Roxa presented higher contents of anthocyanin in the conventional system (88.24 mg g-¹ FM) than the ones in the hydroponic system (36.89 mg g-¹ FM). The best results for CO2 net assimilation rate regarded to photosyntheticaly active photon flux density were found in the hydroponic system, for both lettuce cultivars. Variation in the contents of chlorophyll were also found. Those variations were higher in the protected system than in the hydroponic system and contents of anthocyanin were higher in the conventional system.

Economic viability of the third milking in systems of production using closed-circuit mechanical milking

This study aimed to analyze the economic viability of the third milking in production systems using mechanical milking in a closed circuit, aiming to provide technicians and farmers with information to assist them in decision-making. Specifically, it intended: (a) to estimate the cost of one milking; (b) to estimate the cost of the third milking; (c) to develop a mathematical equation to estimate the minimum amount of milk produced with two milkings, from which it would be economically feasible to do the third milking. Data were collected from three dairy farms, from November 2010 to March 2011, keeping a twice-a-day milking frequency, with three data collections in each farm, totalizing nine collections. Considering the average data, it would be feasible to do the third milking if the average milk yield per day of lactating cows in a twice-a-day milking frequency was greater than or equal to 24.43 kg of milk.

The portuguese insurance industry: basis for strategic process decision

Nowadays, the Portuguese insurance industry operates in a market with a much more aggressive structure than a few decades ago. Markets and the economy have become globalised since the last decade of the 20th century. Market forces have gradually shifted – power is now mainly on the demand side. In order to meet the new requirements, the insurance industry must develop a strong strategic ability to respond to constant changes of the new international economic order.One of the basic aspects of this strategic development will focus on the ability to predict the future. We introduce the subject by briefly describing the sector, its organisational structure in the Portuguese market, and challenges arising from the development of the European Union. We then analyse the economic and financial structure of the sector. From this point of view, we aim at the possibility of designing models that could explain the demand for insurance, claims and technical reserves evolution. Such models, (even if based on the past), would resolve, at least partly, one of the greatest difficulties experienced by insurance companies when estimating the budget. Thus, we examine the existence of variables that explain the previous points, which are capable of forming a basis for designing models that are simple but efficient, and can be used for strategic planning.

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.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Mobi_System: A Personal Travel Assistance for Electrical Vehicles in Smart Cities

In this work it is proposed the design of a mobile system to assist car drivers in a smart city environment oriented to the upcoming reality of Electric Vehicles (EV). Taking into account the new reality of smart cites, EV introduction, Smart Grids (SG), Electrical Markets (EM), with deregulation of electricity production and use, drivers will need more information for decision and mobility purposes. A mobile application to recommend useful related information will help drivers to deal with this new reality, giving guidance towards traffic, batteries charging process, and city mobility infrastructures (e. g. public transportation information, parking places availability and car & bike sharing systems). Since this is an upcoming reality with possible process changes, development must be based on agile process approaches (Web services).

Balancing market integration in MASCEM electricity market simulator

With the restructuring of the energy sector in industrialized countries there is an increased complexity in market players’ interactions along with emerging problems and new issues to be addressed. Decision support tools that facilitate the study and understanding of these markets are extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent simulator for competitive electricity markets. It is essential to reinforce MASCEM with the ability to recreate electricity markets reality in the fullest possible extent, making it able to simulate as many types of markets models and players as possible. This paper presents the development of the Balancing Market in MASCEM. A key module to the study of competitive electricity markets, as it has well defined and distinct characteristics previously implemented.

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).

Agent-based simulation of electronic marketplaces with decision support

This paper presents a Multi-Agent Market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agents reactions.

Decision support system with incomplete and domain incoherent information management

Many of the most common human functions such as temporal and non-monotonic reasoning have not yet been fully mapped in developed systems, even though some theoretical breakthroughs have already been accomplished. This is mainly due to the inherent computational complexity of the theoretical approaches. In the particular area of fault diagnosis in power systems however, some systems which tried to solve the problem, have been deployed using methodologies such as production rule based expert systems, neural networks, recognition of chronicles, fuzzy expert systems, etc. SPARSE (from the Portuguese acronym, which means expert system for incident analysis and restoration support) was one of the developed systems and, in the sequence of its development, came the need to cope with incomplete and/or incorrect information as well as the traditional problems for power systems fault diagnosis based on SCADA (supervisory control and data acquisition) information retrieval, namely real-time operation, huge amounts of information, etc. This paper presents an architecture for a decision support system, which can solve the presented problems, using a symbiosis of the event calculus and the default reasoning rule based system paradigms, insuring soft real-time operation with incomplete, incorrect or domain incoherent information handling ability. A prototype implementation of this system is already at work in the control centre of the Portuguese Transmission Network.

Virtual power producers market strategies

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper detail some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study.