Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Capillary network models for transport in packed beds: Considerations of pore aspect ratio

The conventional analysis for the estimation of the tortuosity factor for transport in porous media is modified here to account for the effect of pore aspect ratio. Structural models of the porous medium are also constructed for calculating the aspect ratio as a function of porosity. Comparison of the model predictions with the extensive data of Currie (1960) for the effective diffusivity of hydrogen in packed beds shows good agreement with a network model of randomly oriented intersecting pores for porosities upto about 50 percent, which is the region of practical interest. The predictions based on this network model are also found to be in better agreement with the data of Currie than earlier expressions developed for unconsolidated and grainy media.

Moneyless business exchange - Practitioners' attitudes to business-to-business barter in Australia

In recent years, domestic business-to-business barter has become institutionalized as an alternative marketing exchange system in Australia, and elsewhere. This article reports the findings of a survey of 164 members of Australia's largest trade exchange, Bartercard There are few, if any, published empirical studies on this topic. This study is exploratory. Most firms surveyed are small firms in the services sectors. Although Bartercard has an extensive membership, trading within the system is limited with most members trading less than once per week and with barter transactions contributing less than 5% of their annual gross sales. The main benefits of membership include new customers and increased sales and networking opportunities. The main limitations include the limited functionality of the trade dollar limited trading opportunities, and practical trading difficulties. In selling, there appears to be no differential between the cash and trade prices, whereas trade dollars are discounted in purchasing. Participants acknowledge that business-to-business barter will remain and grow regardless of cyclical macroeconomic changes. (C) 1998 Elsevier Science Inc.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Modeling the fluid-flow-induced stress and collapse in a dendritic network

An analytical approach to the stress development in the coherent dendritic network during solidification is proposed. Under the assumption that stresses are developed in the network as a result of the friction resisting shrinkage-induced interdendritic fluid flow, the model predicts the stresses in the solid. The calculations reflect the expected effects of postponed dendrite coherency, slower solidification conditions, and variations of eutectic volume fraction and shrinkage. Comparing the calculated stresses to the measured shear strength of equiaxed mushy zones shows that it is possible for the stresses to exceed the strength, thereby resulting in reorientation or collapse of the dendritic network.

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.

Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.

Family Business: how family and ownership shapes business professionalization

This article`s main purpose consists in showing how family and ownership cultures may influence the process of making a ""well-performing"" organization, based on an empirical study in family business in Brazil. The study aimed to find critical moments of company`s history and the focus was to compare critical moments with the three-dimension model of family business development proposed by Davis et al. (1996). Through facts sequence, research was organized so as to find how the process influenced company`s professionalization. The article concludes that family and its value and culture may impact on the evolution, and the first step to organize a company is to organize the family that leads the company.

Correlation of business cycles in the euro zone

In this short article we use a simple differences-in-clifferences technique to investigate whether bilateral correlation of business cycles increased more amongst members of the European Monetary Union (EMU) after the implementation of the Euro than amidst other OECD economies. We present evidence suggesting this to be the case. (C) 2008 Elsevier B.V. All rights reserved.

GREEN MANAGEMENT, CLIMATE CHANGE AND SMALL BUSINESS IN BRAZIL: IMPLICATIONS FOR TRAINING AND EDUCATION FOR SUSTAINABLE DEVELOPMENT

This paper presents the results of a study on the analysis of training needs regarding environmental (green) management and climate change topics in micro and small enterprises (MSEs) in Brazil and its implications on education for sustainable development. It reports on an e-mail survey of Brazilian small enterprises, whose results indicate that they are indeed interested in environmental management and climate change topics in an education for sustainable development context. The study indicates that proposals for courses on environmental management and climate change should follow a systemic perspective and take sustainable development into account. By applying factor analysis, it was found that the topics of interest can be grouped into thematic modules, which can be useful in the design of training courses for the top management leaders of those companies.

The food business environment and the role of China and Brazil building a ""food bridge""

Purpose - The purpose of this paper is to discuss the economic crisis of 2008/2009 and the major impacts on developing nations and food-producing countries Within this macro-environment of food chains, there is concern that food inflation might come back sooner than expected The role of China as one of the major food consumers in the future, and Brazil, as the major food producer, is described as the food bridge, and an agenda of common development of these countries suggested. Design/methodology/approach - This paper reviews literature on muses of food inflation, production shortages, and investigation of programs to solve the problem in the future, it is also based on author`s personal insights and experience of working on this field in the last 15 years, and recent discussions in forums and interviews Findings - The major factors that jointly caused food prices increase in 2007/2008 were population growth, Income distribution, urbanization, dollar devaluations, commodity funds, social programs, production shortages, and bionic`s A list of ten policies is suggested. horizontal expansion of food production, vertical expansion, reduction in transaction costs, in protectionism and other taxes, investment in logistics, technology and better coordination, contracts, new generation of fertilizers and to use the best sources of biofuels. Originality/value - Two major outputs from this paper are the ""food demand model"" that inserts in one model the trends and muses of food inflation and the solutions, and the ""food bridge concept"" that also aligns in one box the imminent major food chain cooperation between China and Brazil