6,127 research outputs found
Assessing Destination Competitiveness: An Application to the Hot Springs Tourism Sector
This paper proposes a model to identify the factors determining the competitiveness of the hot springs tourism sector, with particular application to Taiwan. The proposed conceptual framework brings together two approaches, namely the theories of industry organization (10) and the resource-based view (RBV). The proposition underlying this framework is that destination competitiveness is achieved by the adoption of policies and strategies aligned with market opportunities, drawing upon the unique or distinctive tourism features offered by the destination. It is proposed that three major influences are evident in the case of hot springs tourism, namely Tourism Destination Resources and Attractors, Tourism Destination Strategies and Tourism Destination Environments. An evaluation is provided of the administration of a three-round Delphi survey, which was intended to validate the determinants of destination competitiveness that were derived from the literature. Drawing upon the results of the pilot study it is concluded that the development of a sector-specific model of destination competitiveness is capable of capturing the nature and characteristics of the hot springs tourism sector
Skeletal Rigidity of Phylogenetic Trees
Motivated by geometric origami and the straight skeleton construction, we
outline a map between spaces of phylogenetic trees and spaces of planar
polygons. The limitations of this map is studied through explicit examples,
culminating in proving a structural rigidity result.Comment: 17 pages, 12 figure
Mapping Topographic Structure in White Matter Pathways with Level Set Trees
Fiber tractography on diffusion imaging data offers rich potential for
describing white matter pathways in the human brain, but characterizing the
spatial organization in these large and complex data sets remains a challenge.
We show that level set trees---which provide a concise representation of the
hierarchical mode structure of probability density functions---offer a
statistically-principled framework for visualizing and analyzing topography in
fiber streamlines. Using diffusion spectrum imaging data collected on
neurologically healthy controls (N=30), we mapped white matter pathways from
the cortex into the striatum using a deterministic tractography algorithm that
estimates fiber bundles as dimensionless streamlines. Level set trees were used
for interactive exploration of patterns in the endpoint distributions of the
mapped fiber tracks and an efficient segmentation of the tracks that has
empirical accuracy comparable to standard nonparametric clustering methods. We
show that level set trees can also be generalized to model pseudo-density
functions in order to analyze a broader array of data types, including entire
fiber streamlines. Finally, resampling methods show the reliability of the
level set tree as a descriptive measure of topographic structure, illustrating
its potential as a statistical descriptor in brain imaging analysis. These
results highlight the broad applicability of level set trees for visualizing
and analyzing high-dimensional data like fiber tractography output
GECKA3D: A 3D Game Engine for Commonsense Knowledge Acquisition
Commonsense knowledge representation and reasoning is key for tasks such as
artificial intelligence and natural language understanding. Since commonsense
consists of information that humans take for granted, gathering it is an
extremely difficult task. In this paper, we introduce a novel 3D game engine
for commonsense knowledge acquisition (GECKA3D) which aims to collect
commonsense from game designers through the development of serious games.
GECKA3D integrates the potential of serious games and games with a purpose.
This provides a platform for the acquisition of re-usable and multi-purpose
knowledge, and also enables the development of games that can provide
entertainment value and teach players something meaningful about the actual
world they live in
Teaching the Basics of Reactive Oxygen Species and their Relevance to Cancer Biology: Mitochondrial Reactive Oxygen Species Detection, Redox Signaling, and Targeted Therapies
Reactive oxygen species (ROS) have been implicated in tumorigenesis (tumor initiation, tumor progression, and metastasis). Of the many cellular sources of ROS generation, the mitochondria and the NADPH oxidase family of enzymes are possibly the most prevalent intracellular sources. In this article, we discuss the methodologies to detect mitochondria-derived superoxide and hydrogen peroxide using conventional probes as well as newly developed assays and probes, and the necessity of characterizing the diagnostic marker products with HPLC and LC-MS in order to rigorously identify the oxidizing species. The redox signaling roles of mitochondrial ROS, mitochondrial thiolperoxidases, and transcription factors in response to mitochondria-targeted drugs are highlighted. ROS generation and ROS detoxification in drug-resistant cancer cells and the relationship to metabolic reprogramming are discussed. Understanding the subtle role of ROS in redox signaling and in tumor proliferation, progression, and metastasis as well as the molecular and cellular mechanisms (e.g., autophagy) could help in the development of combination therapies. The paradoxical aspects of antioxidants in cancer treatment are highlighted in relation to the ROS mechanisms in normal and cancer cells. Finally, the potential uses of newly synthesized exomarker probes for in vivo superoxide and hydrogen peroxide detection and the low-temperature electron paramagnetic resonance technique for monitoring oxidant production in tumor tissues are discussed
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