Valuing Estuarine Resource Services Using Economic and Ecological Models: The Peconic Estuary System Study

This article summarizes four integrated economic studies undertaken to contribute to resource preservation and restoration decisions for the Peconic Estuary System of Suffolk County, NY. Completed as part of the National Estuary Program, the studies apply distinct resource valuation methods to a wide range of resource issues. The principal goals of this article are to highlight different methodologies that may be used to assess nonmarket economic values in a coastal management context, and characterize differences in the results that one may expect from each approach. We also emphasize potential relationships among values estimated by different nonmarket methodologies, and comment on the implications of these relationships for the interpretation and use of economic value estimates.     

The role of individual characteristics in car ownership shortly after relationship dissolution

The dissolution of a relationship is a life event that often coincides with many other changes in life, such as a decline in income level or household size or a change in place of residence. This study aims to provide more insight into the ways in which circumstances shortly following the disruption of a relationship affect travel behaviour. Register data that combines information on the Dutch population, income and vehicle registration are used to understand how personal situations that are closely related to relationship disruption affect car ownership. The study shows that several characteristics of singles and single partners shortly after a breakup negatively affect car ownership. For instance, a relatively low income level, unemployment, living in a city or a residential move all affect car ownership negatively. This study focuses on the role of circumstances shortly after relationship disruption, demonstrating the importance of such an event.     

Traffic accident risk perception among drivers: a latent variable approach

ABSTRACT

Governments require decision tools to deal with road traffic accidents, a pandemic resulting in millions of deaths around the world. Evidence shows that human factors are one of the major causes of road accidents, and there is much interest in identifying variables that may have an impact on drivers’ perception of risk. To this aim, we design a stated choice experiment with eight hypothetical driving scenarios considering attributes that have been strongly associated with increased accident risks: (i) driving speed, (ii) driving the wrong way in a one-way street, (iii) overtaking on a bend, and (iv) driving under the influence of alcohol or drugs. Data from a sample of survey respondents are used to estimate a hybrid discrete choice model incorporating two latent variables, Driver Concentration and Safe Driving. Our results may contribute to the design of public policies geared to prevent accidents by encouraging safer driving behaviour.     

Addressing endogeneity in strategic urban mode choice models

Transportation - Endogeneity is a potential anomaly in econometric models, which may cause inconsistent parameter estimates. Transport models are prone to this problem and applications that...     

Logistics and supply chain management

In this paper an introduction to the principles and methods used in logistics and supply chain management is presented. It begins by a discussion on fundamentals and explains the relevant terms. Next policy and practice associated with logistics and freight services are regarded with a focus on the EU policy for the sector which greatly influences the development of logistics chains and services. Mathematical formulation of typical transport and logistics-related problems is also presented followed by a discussion on the concept of sustainability.     

Numeric-based XML labeling schema by generalized dynamic method

Most efficient indeces and query techniques over XML (extensible markup language) data are based on a certain labeling scheme, which can quickly determine ancestor-descendant and parent-child relationship between two nodes. The current basic labeling schemes such as containment scheme and prefix scheme cannot avoid relabeling when XML documents are updated. After analyzing the essence of existing dynamic XML labels such as compact dynamic binary string (CDBS) and vector encoding, this paper gives a common unifying framework for the numeric-based generalized dynamic label, which can be implemented into a variety of dynamic labels according to the different user-defined value comparison methods. This paper also proposes a novel dynamic labeling scheme called radical sign label. Extensive experiments show that the radical sign label performs well for the initialization, insertion and query operations, and especially for skewed insertion where the storage cost of the radical sign label is better than that of former methods.     

Skin detection method based on cascaded AdaBoost classifier

Skin detection has been considered as the principal step in many machine vision systems, such as face detection and adult image filtering. Among all these techniques, skin color is the most welcome cue because of its robustness. However, traditional color-based approaches poorly perform on the classification of skin-like pixels. In this paper, we propose a new skin detection method based on the cascaded adaptive boosting (AdaBoost) classifier, which consists of minimum-risk based Bayesian classifier and models in different color spaces such as HSV (hue-saturation-value), YCgCb (brightness-green-blue) and YCgCr (brightness-green-red). In addition, we have constructed our own database that is larger and more suitable for training and testing on filtering adult images than the Compaq data set. Experimental results show that our method behaves better than the state-of-the-art pixel-based skin detection techniques on processing images with skin-like background.     

Degradation of selected indoor air pollutants: Comparison study of photocatalytic,ozone-assisted photocatalytic and amine adsorption processes

Indoor air pollutants impact human health, comfort and productivity. The method of photocatalysis has been applied mainly in flow reactors and little information is available on indoor air pollutant removal in airtight reactors. In the paper, experiments were carried out to remove formaldehyde (HCHO), ammonia (NH₃) and volatile organic compounds (VOCs) in the airtight and ventilated chambers. Results demonstrated that 90.4% of HCHO, 92.3% of NH₃ and 57.9% of VOCs were removed in the amine adsorption process, while 67.5% (hereinbefore, these are the mass fraction) of HCHO, 60.0% of NH₃, and 61.2% of VOCs were removed in the photocatalytic process. However, ozone-assisted photocatalytic process showed great potential to degrade indoor air pollutants in the ventilated chamber. Factors and mechanisms of the photocatalytic degradation of HCHO, NH₃ and VOCs were also discussed.     

Simultaneous localization and mapping of autonomous underwater vehicle using looking forward sonar

A method of underwater simultaneous localization and mapping (SLAM) based on on-board looking forward sonar is proposed. The real-time data flow is obtained to form the underwater acoustic images and these images are pre-processed and positions of objects are extracted for SLAM. Extended Kalman filter (EKF) is selected as the kernel approach to enable the underwater vehicle to construct a feature map, and the EKF can locate the underwater vehicle through the map. In order to improve the association efficiency, a novel association method based on ant colony algorithm is introduced. Results obtained on simulation data and real acoustic vision data in tank are displayed and discussed. The proposed method maintains better association efficiency and reduces navigation error, and is effective and feasible.