Abstract
When directional data fall in the positive orthant of the unit hypersphere, a folded directional distribution is preferred over a simple directional distribution for modeling the data. Since directional data, especially axial data, can be modeled using a Watson distribution, this paper considers a folded Watson distribution for such cases. We first address the parameter estimation of this distribution using maximum likelihood, which requires a numerical algorithm to solve the likelihood equations. We use the Expectation-Maximization (EM) algorithm to obtain these estimates and to analyze the properties of the concentration estimator through simulation. Next, we propose the Bayes rule for a folded Watson distribution and evaluate its performance through simulation in various scenarios, comparing it with the Bayes rule for the Watson distribution. Finally, we present examples using both simulated and real data available in the literature.

Abstract

Abstract
This study seeks to comprehensively analyze the multidimensional determinants underlying perceptions of safety, security, and comfort in transport mode choice, specifically focusing on private transport, public transport and walking. The research begins with an extensive literature review to identify and delve into the factors influencing perceptions of safety, security, and comfort across various transport modes. This inquiry is further enhanced by organizing two focused group sessions. A total of 35 key factors were identified, forming the basis for subsequent investigation. The study then progressed to the development and administration of a survey aimed at capturing responses from a diverse audience, with the goal of exploring the factors influencing perceptions related to different transport modes. A total of 302 responses were collected and meticulously analyzed to discern the factors impacting various relationships and to identify consistent perceptions across diverse transport modes. Additionally, a factor analysis was conducted to validate the findings derived from the data. The outcomes of this research constitute a significant contribution to the existing literature, offering valuable insights that pave the way for a more holistic understanding of the factors guiding transport mode choices.

Abstract

Abstract

Abstract
Monitoring the impacts of selective logging in tropical forests remains challenging due to the reliance on labor intensive field surveys. This study relies on the use of pre- and post-logging airborne LiDAR data to provide a precise and scalable method for quantifying canopy disturbances, carried out within the Sustainable Management Plan for the Jamari National Forest in Rond & ocirc;nia. The analysis of the airborne LiDAR data revealed a significant increase in canopy gaps after logging (F= 63.5,p <0.001 ), with canopy gaps corresponding to an average increase of 3.9 +/- 0.4% relative to the total plot area due to logging activities. The mean canopy gap area per felled tree was 158.29 m(2) ( +/- 35.7). A strong positive correlation was found between canopy gaps that emerged after logging and the logged AGB (18.4 +/- 1.7Mg ha(-1) ). A significant reduction in mean canopy height was also observed, decreasing from 26.26 +/- 0.40 m before logging to 24.62 +/- 0.33 m after logging (F= 9.86,p= 0.005) . The mean canopy gap area shifted from 40.68 +/- 2.30 m(2) to 77.07 +/- 2.82 m(2). Furthermore, there was an increase of 14.6% in the total number of gaps. The average Gini coefficient was 0.50 +/- 0.02 before logging and 0.64 +/- 0.01 in the post-logging areas and the average total impact on the canopy was 16.6 +/- 1.5% of the selectively logged area. The results obtained using the proposed methodology were consistent with field observations, demonstrating high accuracy of LiDAR-detected impacts when compared with inventory and GNSS data. This high detection rate highlights the sensitivity of LiDAR point cloud data in capturing small structural changes. Compared to pre-logging conditions, the observed alterations demonstrate that LiDAR provides a more precise and scalable approach for quantifying the impact of selective logging on forest structure.