Modelo de gestión del conocimiento para una empresa del sector de insumos químicos para la industria farmacéutica, cosmética y de alimentos: caso Disaromas S.A.

La creación de conocimiento al interior de las organizaciones es visible mediante la dirección adecuada del conocimiento de los individuos, sin embargo, cada individuo debe interactuar de tal manera que forme una red o sistema de conocimiento organizacional que consolide a largo plazo las empresas en el entorno en el que se desenvuelven. Este documento revisa elementos centrales acerca de la gestión de conocimiento visto desde varios autores y perspectivas e identifica puntos clave para diseñar un modelo de gestión de conocimiento para una empresa del sector de insumos químicos para la industria farmacéutica, cosmética y de alimentos de la ciudad de Bogotá.

Relación entre el desarrollo de sistemas de control de gestión y los recursos y capacidades caso : Teleperformance Colombia

Siguiendo un marco teórico integrado por varios autores entorno a los sistemas de control de gestión a lo largo de varias décadas, este trabajo pretende estudiar y contrastar la relación entre el desarrollo de dichos sistemas y los recursos y capacidades. Para tal fin, se desarrolló un estudio de caso en Teleperformance Colombia (TC), una empresa dedicada a prestación de servicio de tercerización de procesos o business process outsourcing. En el estudio se establecieron dos variables para evaluar el desarrollo de sistema de control de gestión: el diseño y el uso. A su vez, para cada uno de ellos, se definieron los indicadores y preguntas que permitieran realizar la observación y posterior análisis. De igual manera, se seleccionaron los recursos y capacidades más importantes para el desarrollo del negocio: innovación, aprendizaje organizacional y capital humano. Sobre estos se validó la existencia de relación con el SCG implementado en TC. La información obtenida fue analizada y contrastada a través de pruebas estadísticas ampliamente utilizadas en este tipo de estudios en las ciencias sociales. Finalmente, se analizaron seis posibles relaciones de las cuales, solamente se ratificó el relacionamiento positivo entre uso de sistema de control gestión y el recurso y capacidad capital humano. El resto de relacionamientos, refutaron los planteamientos teóricos que establecían cierta influencia de los sistemas de control de gestión sobre recursos y capacidades de innovación y aprendizaje organizacional.

The relationship between multi-species catch and effort: among fishery comparisons

The control of fishing mortality via fishing effort remains fundamental to most fisheries management strategies even at the local community or co-management level. Decisions to support such strategies require knowledge of the underlying response of the catch to changes in effort. Even under adaptive management strategies, imprecise knowledge of the response is likely to help accelerate the adaptive learning process. Data and institutional capacity requirements to employ multi-species biomass dynamics and age-structured models invariably render their use impractical particularly in less developed regions of the world. Surplus production models fitted to catch and effort data aggregated across all species offer viable alternatives. The current paper seeks models of this type that best describe the multi-species catch–effort responses in floodplain-rivers, lakes and reservoirs and reef-based fisheries based upon among fishery comparisons, building on earlier work. Three alternative surplus production models were fitted to estimates of catch per unit area (CPUA) and fisher density for 258 fisheries in Africa, Asia and South America. In all cases examined, the best or equal best fitting model was the Fox type, explaining up to 90% of the variation in CPUA. For lake and reservoir fisheries in Africa and Asia, the Schaefer and an asymptotic model fitted equally well. The Fox model estimates of fisher density (fishers km−2) at maximum yield (iMY) for floodplain-rivers, African lakes and reservoirs and reef-based fisheries are 13.7 (95% CI [11.8, 16.4]); 27.8 (95% CI [17.5, 66.7]) and 643 (95% CI [459,1075]), respectively and compare well with earlier estimates. Corresponding estimates of maximum yield are also given. The significantly higher value of iMY for reef-based fisheries compared to estimates for rivers and lakes reflects the use of a different measure of fisher density based upon human population size estimates. The models predict that maximum yield is achieved at a higher fishing intensity in Asian lakes compared to those in Africa. This may reflect the common practice in Asia of stocking lakes to augment natural recruitment. Because of the equilibrium assumptions underlying the models, all the estimates of maximum yield and corresponding levels of effort should be treated with caution.

Design, synthesis and computational modelling of aromatic tweezer-molecules as models for chain-folding polymer blends

Novel 'tweezer-type' complexes that exploit the interactions between pi-electron-rich pyrenyl groups and pi-electron deficient diimide units have been designed and synthesised. The component molecules leading to complex formation were accessed readily from commercially available starting materials through short and efficient syntheses. Analysis of the resulting complexes, using the visible charge-transfer band, revealed association constants that increased sequentially from 130 to 11,000 M-1 as increasing numbers of pi-pi-stacking interactions were introduced into the systems. Computational modelling was used to analyse the structures of these complexes, revealing low-energy chain-folded conformations for both components, which readily allow close, multiple pi-pi-stacking and hydrogen bonding to be achieved. In this paper, we give details of our initial studies of these complexes and outline how their behaviour could provide a basis for designing self-healing polymer blends for use in adaptive coating systems. (C) 2008 Elsevier Ltd. All rights reserved.

PASSCLAIM - gut health and immunity

Background The gut and immune system form a complex integrated structure that has evolved to provide effective digestion and defence against ingested toxins and pathogenic bacteria. However, great variation exists in what is considered normal healthy gut and immune function. Thus, whilst it is possible to measure many aspects of digestion and immunity, it is more difficult to interpret the benefits to individuals of variation within what is considered to be a normal range. Nevertheless, it is important to set standards for optimal function for use both by the consumer, industry and those concerned with the public health. The digestive tract is most frequently the object of functional and health claims and a large market already exists for gut-functional foods worldwide. Aim To define normal function of the gut and immune system and describe available methods of measuring it. Results We have defined normal bowel habit and transit time, identified their role as risk factors for disease and how they may be measured. Similarly, we have tried to define what is a healthy gut flora in terms of the dominant genera and their metabolism and listed the many, varied and novel methods for determining these parameters. It has proved less easy to provide boundaries for what constitutes optimal or improved gastric emptying, gut motility, nutrient and water absorption and the function of organs such as the liver, gallbladder and pancreas. The many tests of these functions are described. We have discussed gastrointestinal well being. Sensations arising from the gut can be both pleasant and unpleasant. However, the characteristics of well being are ill defined and merge imperceptibly from acceptable to unacceptable, a state that is subjective. Nevertheless, we feel this is an important area for future work and method development. The immune system is even more difficult to make quantitative judgements about. When it is defective, then clinical problems ensure, but this is an uncommon state. The innate and adaptive immune systems work synergistically together and comprise many cellular and humoral factors. The adaptive system is extremely sophisticated and between the two arms of immunity there is great redundancy, which provides robust defences. New aspects of immune function are discovered regularly. It is not clear whether immune function can be "improved". Measuring aspects of immune function is possible but there is no one test that will define either the status or functional capacity of the immune system. Human studies are often limited by the ability to sample only blood or secretions such as saliva but it should be remembered that only 2% of lymphocytes circulate at any given time, which limits interpretation of data. We recommend assessing the functional capacity of the immune system by: measuring specific cell functions ex vivo, measuring in vivo responses to challenge, e. g. change in antibody in blood or response to antigens, determining the incidence and severity of infection in target populations during naturally occurring episodes or in response to attenuated pathogens.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Sparse density estimation on the multinomial manifold

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators.

Zero attracting recursive least squares algorithms

The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the l1-norm of the parameter vector is approximated by an adaptively weighted l2-norm, in which the weighting factors are set as the inversion of the associated l1-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

Simulation and optimisation techniques for sawmill yard operation : A literature review

Increasing costs and competitive business strategies are pushing sawmill enterprises to make an effort for optimization of their process management. Organizational decisions mainly concentrate on performance and reduction of operational costs in order to maintain profit margins. Although many efforts have been made, effective utilization of resources, optimal planning and maximum productivity in sawmill are still challenging to sawmill industries. Many researchers proposed the simulation models in combination with optimization techniques to address problems of integrated logistics optimization. The combination of simulation and optimization technique identifies the optimal strategy by simulating all complex behaviours of the system under consideration including objectives and constraints. During the past decade, an enormous number of studies were conducted to simulate operational inefficiencies in order to find optimal solutions. This paper gives a review on recent developments and challenges associated with simulation and optimization techniques. It was believed that the review would provide a perfect ground to the authors in pursuing further work in optimizing sawmill yard operations.

MLCMS actual use, perceived use, and experiences of use

Mobile learning involves use of mobile devices to participate in learning activities. Most elearning activities are available to participants through learning systems such as learning content management systems (LCMS). Due to certain challenges, LCMS are not equally accessible on all mobile devices. This study investigates actual use, perceived usefulness and user experiences of LCMS use on mobile phones at Makerere University in Uganda. The study identifies challenges pertaining to use and discusses how to improve LCMS use on mobile phones. Such solutions are a cornerstone in enabling and improving mobile learning. Data was collected by means of focus group discussions, an online survey designed based on the Technology Acceptance Model (TAM), and LCMS log files of user activities. Data was collected from two courses where Moodle was used as a learning platform. The results indicate positive attitudes towards use of LCMS on phones but also huge challenges whichare content related and technical in nature.

Simulation model using meta heuristic algorithms for achieving optimal arrangement of storage bins in a sawmill yard

Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.

Universidades corporativas: transformando as estruturas de treinamento e desenvolvimento das organizações no Brasil

This work analyses the application of the so-called corporative university model in business enterprises in Brazil. What are the impacts observed in the training and development structures in companies that adopt learning systems defined within the corporative university? It aims to offer an analytical basis to those interested in the educational tendency of knowledge management as a strategy for a sustained competitive edge in business nowadays.

Fatores antecedentes da satisfação do aluno e do uso de sistemas virtuais de aprendizagem

A educação a distância tem passado por grandes transformações, principalmente após o advento da internet e das tecnologias de informação e comunicação (TICs). Inúmeras perguntas sobre qualidade e resultados de aprendizagem em ambientes virtuais foram geradas com o crescimento da modalidade. Pesquisadores têm investigado métodos de avaliação dos benefícios promovidos pelo e-learning sob um número diversificado de perspectivas. O objetivo desta pesquisa é avaliar o impacto dos construtos qualidade do sistema, qualidade da informação e qualidade do serviço na satisfação do aluno e no uso de Sistemas Virtuais de Aprendizagem em ambientes de e-learning, utilizando como base teórica o modelo de Sucesso de e-learning, adaptado do modelo de Delone e McLean por Holsapple e Lee-Post. A metodologia de pesquisa tipo survey foi administrada por meio de um curso on-line ofertado a 291 estudantes de instituições públicas e privadas de todas as regiões do Brasil. Para o tratamento e análise dos dados, utilizaram-se técnicas de modelagem de equações estruturais e análise fatorial confirmatória. Os resultados demonstram que o uso do sistema é impactado pela variação dos construtos qualidade do sistema, qualidade da informação e qualidade dos serviços, já a satisfação do aluno é antecedida pela qualidade percebida da informação e do serviço. Muitos dos benefícios gerados pela educação a distância são causados pela satisfação do aluno e pela intensidade com que este utiliza o sistema de aprendizagem. Ao identificar os indicadores que antecedem estas variáveis, os gestores educacionais podem planejar seus investimentos visando atender às demandas mais importantes, além de utilizar a informação para lidar com um dos maiores problemas em EaD: a evasão.