Baking Dalarna's biking cake : Collaboration as a means for destination competitiveness, a case study of “Biking Dalarna"

In the contemporary tourism industry, the competitive game is between destinations. Tourism operations struggle to remain competitive on the international market and their success depends to a large extent on other complementary and competing tourism organizations at the destination. It is the sum of the total tourism offerings at the destination which determines its attractiveness. This research explores tourism collaboration process as a means of generating destination competitiveness. The focus of the research is on the enhancing factors which contribute to the success of the collaboration and to the development of quality tourism products. The research studies the case of Biking Dalarna, a collaboration of different organizations at five biking destinations in Dalarna, Sweden. Its purpose is to develop biking tourism in the region and to make Dalarna into Sweden’s leading biking destination. It is a qualitative research; the empirical data was collected through in depth interviews with representatives of six Biking Dalarna member organizations. The qualitative data collected from the participants provides inside look into the members reflections and experience of collaborating. The findings of this research demonstrate how collaboration has improved the biking product in Dalarna and promoted solutions to development problems. The research finds the good relationship between the collaborating actors and the involvement and leadership of the regional tourism management organization as the most contributing factors to the success of Biking Dalarna. The research also suggests that a third desired outcome of collaboration, improved marketing attributes was yet to be achieved in the case of Biking Dalarna.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

Real-Time Control Of Floods Along The Demer River, Belgium, By Means Of MPC In Combination With GA And A Fast Conceptual River Model

Climate model projections show that climate change will further increase the risk of flooding in many regions of the world. There is a need for climate adaptation, but building new infrastructure or additional retention basins has its limits, especially in densely populated areas where open spaces are limited. Another solution is the more efficient use of the existing infrastructure. This research investigates a method for real-time flood control by means of existing gated weirs and retention basins. The method was tested for the specific study area of the Demer basin in Belgium but is generally applicable. Today, retention basins along the Demer River are controlled by means of adjustable gated weirs based on fixed logic rules. However, because of the high complexity of the system, only suboptimal results are achieved by these rules. By making use of precipitation forecasts and combined hydrological-hydraulic river models, the state of the river network can be predicted. To fasten the calculation speed, a conceptual river model was used. The conceptual model was combined with a Model Predictive Control (MPC) algorithm and a Genetic Algorithm (GA). The MPC algorithm predicts the state of the river network depending on the positions of the adjustable weirs in the basin. The GA generates these positions in a semi-random way. Cost functions, based on water levels, were introduced to evaluate the efficiency of each generation, based on flood damage minimization. In the final phase of this research the influence of the most important MPC and GA parameters was investigated by means of a sensitivity study. The results show that the MPC-GA algorithm manages to reduce the total flood volume during the historical event of September 1998 by 46% in comparison with the current regulation. Based on the MPC-GA results, some recommendations could be formulated to improve the logic rules.

Operations strategy in perspective: the means for achieving superior performance

Manufacturing strategy has been widely studied and it is increasingly gaining attention. It has a fundamental role that is to translate the business strategy to the operations by developing the capabilities that are needed by the company in order to accomplish the desired performance. More precisely, manufacturing strategy comprises the decisions that managers take during a certain period of time in order to achieve a desire result. These decisions are related to which operational practices and resources are implemented. Our goal was to identify the relationship between these two decisions with operational performance. We based our arguments on the resource-based view for identifying sources of competitive advantage. Hence, we argued that operational practices and resources affect positively the operational performances. Additionally, we proposed that in the presence of some resources the implementation of operational practices would lead to a greater performance. We used previous scales for measuring operational practices and performance, and developed new constructs for resources. The data used is part of the High Performance Manufacturing project and the sample is composed by 291 plants. Through confirmatory factor analysis and multiple regressions we found that operational practices to a certain extant are positively related to operational performance. More specifically, the results show that JIT and customer orientation practices have a positive relationship with quality, delivery, flexibility, and cost performances. Moreover, we found that resources like technology and people explain a great variance of operational performance.

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

In situ differentiation of labile/inert metal species in Brazilian tropical rivers by means of a time-controlled batch-procedure based on TEPHA resin

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimation of genetic parameters for partial egg production periods by means of random regression models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analysis of Factor VIII polymorphic markers as a means for carrier detection in Brazilian families with haemophilia A

Haemophilia A is an X-linked, recessively inherited bleeding disorder of varying severity, which results from the deficiency of procoagulant factor VIII f(8). Linkage diagnosis using polymorphic markers in the f8 gene is widely used to detect carriers. The objective of this study was to verify the informativeness of three polymorphic markers in the Brazilian population, to evaluate the usefulness of such markers in carrier detection procedures. Sixty-three unrelated healthy volunteers and 10 haemophilic families were studied. Two microsatellite repeats and one HindIII RFLP markers were used. Carrier and non-carrier status could be determined in 80% of females investigated. Intron 13 markers presented the highest heterozygosity rate (79%) followed by intron 22 (68%) and intron 19 (57%). When all three markers were used together, linkage analysis informativeness increased significantly. We conclude that these markers are suitable for carrier detection in the Brazilian population and we recommend their use in combination to maximize diagnostic efficiency.

Chemical and microbiological evaluation of the internal surfaces of aluminum tubes both unlined and lined with epoxi resin by means of the stereoscope and scanning electron microscope

A superfície interna das bisnagas fabricadas com alumínio não revestido e revestido com resina epóxi, utilizadas para acondicionar cremes, pomadas, géis, etc., foram avaliadas quimicamente e por métodos microbiológicos correlacionados com a aderência de microrganismos. A prova da porosidade e da resistência à remoção da resina foi observada por meio do microscópio eletrônico de varredura (Topcon FM300) e estereoscópio Leica (MZ12) acoplado a Sistema de Digitalização de Imagens. Para avaliar a ação dos microrganismos foram utilizados corpos-de-prova esterilizados (discos de 10mm de diâmetro), imersos em caldo Mueller Hinton (Difco) e colocados em tubos de polipropileno com tampa de rosca (Corning). Foram inoculados tubos com meio de cultura para cada uma das suspensões bacterianas (10(9)UFC/mL) de Streptococcus agalactiae, Staphylococcus aureus, Acinetobacter lwoffii e Candida albicans, incubados a 37°C, sob agitação constante durante 12 dias. O meio de cultura era trocado a cada 3 dias. Após esse período, os corpos-de prova foram removidos, processados e observados em microscópio eletrônico de varredura JEOL-JSM (T330A). A observação por meio do microscopio eletrônico de varredura mostrou a aderência e a formação de biofilme sobre a superfície de alumínio não revestido e revestido com resina epóxi.

Two-dimensional modeling of material failure in reinforced concrete by means of a continuum strong discontinuity approach

The paper presents a new methodology to model material failure, in two-dimensional reinforced concrete members, using the Continuum Strong Discontinuity Approach (CSDA). The mixture theory is used as the methodological approach to model reinforced concrete as a composite material, constituted by a plain concrete matrix reinforced with two embedded orthogonal long fiber bundles (rebars). Matrix failure is modeled on the basis of a continuum damage model, equipped with strain softening, whereas the rebars effects are modeled by means of phenomenological constitutive models devised to reproduce the axial non-linear behavior, as well as the bondslip and dowel effects. The proposed methodology extends the fundamental ingredients of the standard Strong Discontinuity Approach, and the embedded discontinuity finite element formulations, in homogeneous materials, to matrix/fiber composite materials, as reinforced concrete. The specific aspects of the material failure modeling for those composites are also addressed. A number of available experimental tests are reproduced in order to illustrate the feasibility of the proposed methodology. (c) 2007 Elsevier B.V. All rights reserved.

Uma nova forma de calcular os centros dos Clusters em algoritmos de agrupamento tipo fuzzy c-means

Clustering data is a very important task in data mining, image processing and pattern recognition problems. One of the most popular clustering algorithms is the Fuzzy C-Means (FCM). This thesis proposes to implement a new way of calculating the cluster centers in the procedure of FCM algorithm which are called ckMeans, and in some variants of FCM, in particular, here we apply it for those variants that use other distances. The goal of this change is to reduce the number of iterations and processing time of these algorithms without affecting the quality of the partition, or even to improve the number of correct classifications in some cases. Also, we developed an algorithm based on ckMeans to manipulate interval data considering interval membership degrees. This algorithm allows the representation of data without converting interval data into punctual ones, as it happens to other extensions of FCM that deal with interval data. In order to validate the proposed methodologies it was made a comparison between a clustering for ckMeans, K-Means and FCM algorithms (since the algorithm proposed in this paper to calculate the centers is similar to the K-Means) considering three different distances. We used several known databases. In this case, the results of Interval ckMeans were compared with the results of other clustering algorithms when applied to an interval database with minimum and maximum temperature of the month for a given year, referring to 37 cities distributed across continents

Novos métodos determinísticos para gerar centros iniciais dos grupos no algoritmo fuzzy C-Means e variantes

Data clustering is applied to various fields such as data mining, image processing and pattern recognition technique. Clustering algorithms splits a data set into clusters such that elements within the same cluster have a high degree of similarity, while elements belonging to different clusters have a high degree of dissimilarity. The Fuzzy C-Means Algorithm (FCM) is a fuzzy clustering algorithm most used and discussed in the literature. The performance of the FCM is strongly affected by the selection of the initial centers of the clusters. Therefore, the choice of a good set of initial cluster centers is very important for the performance of the algorithm. However, in FCM, the choice of initial centers is made randomly, making it difficult to find a good set. This paper proposes three new methods to obtain initial cluster centers, deterministically, the FCM algorithm, and can also be used in variants of the FCM. In this work these initialization methods were applied in variant ckMeans.With the proposed methods, we intend to obtain a set of initial centers which are close to the real cluster centers. With these new approaches startup if you want to reduce the number of iterations to converge these algorithms and processing time without affecting the quality of the cluster or even improve the quality in some cases. Accordingly, cluster validation indices were used to measure the quality of the clusters obtained by the modified FCM and ckMeans algorithms with the proposed initialization methods when applied to various data sets