No abstract

No abstract

In the recent years, the rapid growth of the tourism industry has risen to prominence as a global concern. Tourism empowers communities and uplifts the economy. However, it poses social and environmental challenges, which in turn draws attention to tourism patterns. Sustainable tourism promises protection of the environment and the social-cultural elements of any given destination. Hence, this study aims to understand the complex relationship between sustainability policy, management, and tourist behavior. Thus, we examined the relationships between sustainable tourism policy and destination management, destination social responsibility, and tourist value orientation with sustainable tourism development. We recruited participants at managerial level coming from 163 Malaysian companies and conducted a cross-sectional quantitative study, using partial least square structural equation modeling. We propose that sustainable tourism policy and destination management and destination social responsibility significantly impact sustainable tourism development. Moreover, destination social responsibility partially mediates the relationship between sustainable tourism policy destination management and sustainable tourism development.

Abstract. Management of higher education have a problem in producing 100% of graduates who can meet the needs of industry while industry is also facing the problem of finding skilled graduates who suit their needs partly due to the lack of an effective method in assessing problem solving skills as well as weaknesses in the assessment of problem-solving skills. The purpose of this paper is to propose a suitable classification model that can be used in making prediction and assessment of the attributes of the student's dataset to meet the selection criteria of work demanded by the industry of the graduates in the academic field. Supervised and unsupervised Machine Learning Algorithms were used in this research where; K-Nearest Neighbor, Naïve Bayes, Decision Tree, Neural Network, Logistic Regression and Support Vector Machine. The proposed model will help the university management to make a better long-term plans for producing graduates who are skilled, knowledgeable and fulfill the industry needs as well.

International audienceThe automated computation of appropriate viewpoints in complex 3D scenes is a key problem in a number of computer graphics applications. In particular, crowd simulations create visually complex environments with many simultaneous events for which the computation of relevant viewpoints remains an open issue. In this paper, we propose a system which enables the conveyance of events occurring in complex crowd simulations. The system relies on Reynolds' model of steering behaviors to control and locally coordinate a collection of camera agents similar to a group of reporters. In our approach, camera agents are either in a scouting mode, searching for relevant events to convey, or in a tracking mode following one or more unfolding events. The key benefit, in addition to the simplicity of the steering rules, holds in the capacity of the system to adapt to the evolving complexity of crowd simulations by self-organizing the camera agents to track interesting events.Le placement automatique de caméra dans une scène 3D est un problème important en informatique graphique. En particulier, les simulations de foules produisent des scènes complexes pour lesquelles le choix du point de vue est un problème non résolu. Dans ce travail, nous présentons une approche permettant de déterminer le placement et le cadrage de plusieurs caméras évoluant dans une simulation de foule, de facon à montrer aux mieux les événements qui se déroulent dans la simulation. Outre sa simplicité, notre méthode présente l'avantage d'adapter automatiquement le comportement des caméras à la complexité de la scène