E-business impacts for urban freight: results from an Australian study

E-Business is expected to dramatically change the way business is conducted internationally, nationally, within states and at the local area level. Moreover, these changes are very likely to happen well within the planning time frames required for provision of transport infrastructure and services. E-business is defined as including e-commerce, either between Businesses to Business (B2B) or Business to Customers (B2C), and the adoption of electronic technology within businesses. This paper presents some results from a study commissioned by the Australian National Transport Secretariat (NTS) to assist Australian business and government pro-actively address the transport issues arising from e-business. The resulting working papers will be used to establish a research framework for identifying policy and planning levers to maximize benefits to Australia from national and global e-business activity. The study sought to investigate three principal questions on e-business impacts: how will the transport task change; what will be affected; and how can the transport system respond? Current literature suggests that growth in e-business stems from the combined existence of market demand, suitable enabling technology, and skills and familiarity in management/users/ industry/government. The results of the study suggest that e-business will have implications for urban freight including higher levels of demand for goods and services, increased requirements for logistics distribution, changes in location preferences and improved transport network performance

Intelligent packaging in meat industry : An overview of existing solutions

Traditional packaging systems are refused since these systems do not provide any information about the quality of food products to the consumers and manufacturers at any stage of supply chain. The essence of a new technology to monitor the food spoilage from farm to fork is emerged to reduce hazards such as food borne diseases. Moreover, the food quality monitoring systems clarify the main factors in food wastage during supply chain. Intelligent packaging is employed to provide information about the history of food handling and storage to enhance food products quality and meet consumer satisfactions. Meat is one of the most perishable foods which causes sever illnesses in the case of spoilage. Variety of indicators and sensors have been proposed to warn about meat spoilage in meat industry. In this paper an overview of proposed approaches as well as commercial technologies to monitor the quality of meat during storage and transportation is presented. Furthermore, the existing technologies are compared in the sense of advantages and disadvantages in meat packaging applications

Developing an Adaptive Building Evacuation Simulation and Decision Support Framework using Cognitive Agent-Based Modelling

Preparing for an unprecedented event involving the movement of populations could take up large amounts of resources if done conventionally. The main motivation of this study is the behavioural modification approach which is an underexplored potential in evacuation dynamics, offering new possibilities in terms of practicality and ease of implementation. This paper tackles an adaptive building evacuation simulation and decision support framework that will serve as a guide to evaluate and propose evacuation strategies for disaster management researchers and decision-making authorities. The framework mainly involves the formulation of the cognitive agent model, the evacuation simulation, and the decision support. The timeliness in the Philippine context of the long-overdue “Big One” earthquake, the vulnerability of the case study, and the capability of the framework to be a standard guide where components can be customized by users based on the disaster type and site-specific requirements make this research a significant undertaking

Activity Patterns and Pollution Exposure

In recent times there has been increasing interest in modelling policies to limit impacts of air pollution due to motor vehicles. Impacts of air pollution on human health and comfort depend on the relationship between the distribution of pollutants and the spatial distribution of the urban population. As emissions, weather conditions and the location of the population vary with time of day, day of month and season of the year, the problem is complex. Travel demand models with activity-based approaches and a focus on the overall structure of activity/travel relations, not only spatially, but temporally can make a valuable contribution. They are often used to estimate emissions due to the travel patterns of city populations but may equally be used to provide distributions of urban populations during the day. A case study for Melbourne, Australia demonstrates the use of activity data in the estimation of population exposure. Additionally the study shows some marked differences in activity between seasons and even greater the differences in effect of that activity on exposure to air pollution. Numbers of cities will have seasonal pollutant patterns similar to Melbourne and others will benefit from exploring such patterns

Function approximation using neural networks : a simulation study

Thesis (Ph. D.)--University of Hawaii at Manoa, 1992.Includes bibliographical references (leaves 144-148)Microfiche.xiii, 148 leaves, bound ill. 29 cmThis dissertation analyzes the effect of different characteristics of data on the training and estimation accuracy of neural networks. The literature on the universal approximation property of neural networks is reviewed. An examination of the relationship of the neural network approach to traditional statistical methods of approximation brought about proposed enhancements to the neural network training procedure. The study generated data samples characterized by different functional forms, levels of random noise, number and magnitude of outliers, and strength of multicollinearity. These samples were then used to train a neural network. The accuracy of the neural network estimate was tested and compared with the accuracy of the estimates obtained from the true model and those from Specht's GRNN model. Statistics on the length of training and the complexity of the neural network estimate were also collected and analyzed

Mechanism of Carbon Finance’s Influence on Radical Low-Carbon Innovation with Evidence from China

Radical low-carbon innovations have considerable technological and revolutionary influences. These key technologies considerably reduce carbon dioxide emissions. This study examines the role of carbon finance development in China’s radical low-carbon innovations. The paper identifies the key entities involved, constructs a network model of the interaction between carbon finance and radical low-carbon innovation, and uses multi-agent simulation modeling to analyze the associated influence mechanism. The results demonstrate that the carbon market can promote radical low-carbon innovation by (1) regulating the number of enterprises participating subject to carbon emission regulations, (2) regulating the number of market intermediaries, (3) establishing the market regulation level, and (4) setting the carbon intensity reduction level. The paper concludes that the Chinese government can formulate novel carbon market-related policies and regulations that, in a timely manner, influence the relationship between the carbon market and participating entities to promote the development of radical low-carbon technologies