Search results

C'est avec une certaine joie et une satisfaction toute particulière que je vous souhaite la bienvenue à ce 36e Congrès qui se tient dans mon pays natal, pays qui est en même temps le siège de notre association. Ce dernier fait n'est pas un hasard puisque c'est précisément à Lugano qu'en 1959, à l'invitation des prof. Hunziker et Krapf, différents scientifiques du tourisme se sont retrouvés afin de promouvoir la recherche touristique d'après‐guerre. Depuis que notre association existe — et l'indication “36e Congrès” précise également l'âge de l'AIEST — nous nous sommes déjà rencontrés en Suisse, en 1958 et en 1960 et je me souviens qu'en 1960 déjà nous avons eu l'occasion d'apprécier la gastronomie du Château de Chillon, ou aura lieu notre banquet de clôture.

With risk defined as the possibility of deviation in the results from the expected goals, business process reengineering (BPR) initiatives clearly involve risk taking. However, due to the high expected returns of such efforts, the acceptable risk levels of BPR will tend to be greater than those of less ambitious projects. This research reports the development of a tool to quantitatively estimate the potential risk level of a BPR effort before an organization commits its resources to that effort. The underlying research employed a survey of BPR‐experienced organizations to collect assessment information in order to build a BPR risk estimation model. The developed tool uses triangular fuzzy numbers to approximate the degree of success/failure of proposed BPR initiatives. The tool can be applied by any organization contemplating BPR, thus giving such organizations a heretofore unavailable estimate of the risk level of proposed BPR efforts. Validation was performed based upon an 18‐month BPR project conducted at the Missouri Lottery.

This paper presents the second-generation estimates for the Italian engineering industry in 1911, a year documented both by the customary demographic census, and the first industrial census. The first part of this paper uses the census data to estimate the industry’s value added, sector by sector; the second further disaggregates each sector by activity, and estimates the value added, employment, physical product, and metal consumption of each one. A third, concluding section dwells on the dependence of cross-section estimates on time-series evidence. Three appendices detail the specific algorithms that generate the present estimates; a fourth, a useful sample of firm-specific data.

The evolution of the tourist industry has also led to a change in the provision of hospitality, which was previously concentrated on reception activities, but now includes a series of complementary activities which can be defined as “post‐reception” or, in some cases “post‐tourism” activities.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.