Museum libraries : how digitization can enhance the value of the museum

Es responsabilidad de la biblioteca de museo mejorar la comprensión de la colección del museo y el rol de los objetos de museo como documentos culturales. Por muchas razones, este rol solo se cumple parcialmente, si es que se cumple. Esto es debido a que en la práctica la biblioteca y el museo no están suficientemente integrados para actuar juntos de manera efectiva. Argumentamos que en un mundo digital, la biblioteca y el museo pueden y deben ser reintegrados en un único sistema de información cultural. Sin embargo, dado que en la práctica la digitalización de la biblioteca y del museo siguen diferentes caminos, los museos y sus bibliotecas tienen que cambiar su pensamiento acerca de cómo proceder

Museum libraries : how digitization can enhance the value of the museum

Es responsabilidad de la biblioteca de museo mejorar la comprensión de la colección del museo y el rol de los objetos de museo como documentos culturales. Por muchas razones, este rol solo se cumple parcialmente, si es que se cumple. Esto es debido a que en la práctica la biblioteca y el museo no están suficientemente integrados para actuar juntos de manera efectiva. Argumentamos que en un mundo digital, la biblioteca y el museo pueden y deben ser reintegrados en un único sistema de información cultural. Sin embargo, dado que en la práctica la digitalización de la biblioteca y del museo siguen diferentes caminos, los museos y sus bibliotecas tienen que cambiar su pensamiento acerca de cómo proceder

Museum libraries : how digitization can enhance the value of the museum

Es responsabilidad de la biblioteca de museo mejorar la comprensión de la colección del museo y el rol de los objetos de museo como documentos culturales. Por muchas razones, este rol solo se cumple parcialmente, si es que se cumple. Esto es debido a que en la práctica la biblioteca y el museo no están suficientemente integrados para actuar juntos de manera efectiva. Argumentamos que en un mundo digital, la biblioteca y el museo pueden y deben ser reintegrados en un único sistema de información cultural. Sin embargo, dado que en la práctica la digitalización de la biblioteca y del museo siguen diferentes caminos, los museos y sus bibliotecas tienen que cambiar su pensamiento acerca de cómo proceder

Global distributions of Phaeocystis sp. abundance and biomass - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The planktonic haptophyte Phaeocystis has been suggested to play a fundamental role in the global biogeochemical cycling of carbon and sulphur, but little is known about its global biomass distribution. We have collected global microscopy data of the genus Phaeocystis and converted abundance data to carbon biomass using species-specific carbon conversion factors. Microscopic counts of single-celled and colonial Phaeocystis were obtained both through the mining of online databases and by accepting direct submissions (both published and unpublished) from Phaeocystis specialists. We recorded abundance data from a total of 1595 depth-resolved stations sampled between 1955-2009. The quality-controlled dataset includes 5057 counts of individual Phaeocystis cells resolved to species level and information regarding life-stages from 3526 samples. 83% of stations were located in the Northern Hemisphere while 17% were located in the Southern Hemisphere. Most data were located in the latitude range of 50-70° N. While the seasonal distribution of Northern Hemisphere data was well-balanced, Southern Hemisphere data was biased towards summer months. Mean species- and form-specific cell diameters were determined from previously published studies. Cell diameters were used to calculate the cellular biovolume of Phaeocystis cells, assuming spherical geometry. Cell biomass was calculated using a carbon conversion factor for Prymnesiophytes (Menden-Deuer and Lessard, 2000). For colonies, the number of cells per colony was derived from the colony volume. Cell numbers were then converted to carbon concentrations. An estimation of colonial mucus carbon was included a posteriori, assuming a mean colony size for each species. Carbon content per cell ranged from 9 pg (single-celled Phaeocystis antarctica) to 29 pg (colonial Phaeocystis globosa). Non-zero Phaeocystis cell biomasses (without mucus carbon) range from 2.9 - 10?5 µg l-1 to 5.4 - 103 µg l-1, with a mean of 45.7 µg l-1 and a median of 3.0 µg l-1. Highest biomasses occur in the Southern Ocean below 70° S (up to 783.9 µg l-1), and in the North Atlantic around 50° N (up to 5.4 - 103 µg l-1).

On loader productivity, rock strength and explosive energy in metal mining

Twenty production blasts in two open pit mines were monitored, in rocks with medium to very high strength. Three different blasting agents (ANFO, watergel and emulsion blend) were used, with powder factors ranging between 0.88 and 1.45 kg/m3. Excavators were front loaders and rope shovels. Mechanical properties of the rock, blasting characteristics and mucking rates were carefully measured. A model for the calculation of the productivity of excavators is developed thereof, in which the production rate results as a product of an ideal, maximum, productivity rate times an operating efficiency. The maximum rate is a function of the dipper capacity and the efficiency is a function of rock density, strength, and explosive energy concentration in the rock. The model is statistically significant and explains up to 92 % of the variance of the production rate measurements.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.

Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

Facing the climate change challenges in the mining and mineral industry as providers of a sustainable manufacturing process

A sustainable manufacturing process must rely on an also sustainable raw materials and energy supply. This paper is intended to show the results of the studies developed on sustainable business models for the minerals industry as a fundamental previous part of a sustainable manufacturing process. As it has happened in other economic activities, the mining and minerals industry has come under tremendous pressure to improve its social, developmental, and environmental performance. Mining, refining, and the use and disposal of minerals have in some instances led to significant local environmental and social damage. Nowadays, like in other parts of the corporate world, companies are more routinely expected to perform to ever higher standards of behavior, going well beyond achieving the best rate of return for shareholders. They are also increasingly being asked to be more transparent and subject to third-party audit or review, especially in environmental aspects. In terms of environment, there are three inter-related areas where innovation and new business models can make the biggest difference: carbon, water and biodiversity. The focus in these three areas is for two reasons. First, the industrial and energetic minerals industry has significant footprints in each of these areas. Second, these three areas are where the potential environmental impacts go beyond local stakeholders and communities, and can even have global impacts, like in the case of carbon. So prioritizing efforts in these areas will ultimately be a strategic differentiator as the industry businesses continues to grow. Over the next forty years, world?s population is predicted to rise from 6.300 million to 9.500 million people. This will mean a huge demand of natural resources. Indeed, consumption rates are such that current demand for raw materials will probably soon exceed the planet?s capacity. As awareness of the actual situation grows, the public is demanding goods and services that are even more environmentally sustainable. This means that massive efforts are required to reduce the amount of materials we use, including freshwater, minerals and oil, biodiversity, and marine resources. It?s clear that business as usual is no longer possible. Today, companies face not only the economic fallout of the financial crisis; they face the substantial challenge of transitioning to a low-carbon economy that is constrained by dwindling natural resources easily accessible. Innovative business models offer pioneering companies an early start toward the future. They can signal to consumers how to make sustainable choices and provide reward for both the consumer and the shareholder. Climate change and carbon remain major risk discontinuities that we need to better understand and deal with. In the absence of a global carbon solution, the principal objective of any individual country should be to reduce its global carbon emissions by encouraging conservation. The mineral industry internal response is to continue to focus on reducing the energy intensity of our existing operations through energy efficiency and the progressive introduction of new technology. Planning of the new projects must ensure that their energy footprint is minimal from the start. These actions will increase the long term resilience of the business to uncertain energy and carbon markets. This focus, combined with a strong demand for skills in this strategic area for the future requires an appropriate change in initial and continuing training of engineers and technicians and their awareness of the issue of eco-design. It will also need the development of measurement tools for consistent comparisons between companies and the assessments integration of the carbon footprint of mining equipments and services in a comprehensive impact study on the sustainable development of the Economy.

Probabilistic meta-analysis of risk from the exposure to Hg in artisanal gold mining communities in Colombia

Colombia is one of the largest per capita mercury polluters in the world as a consequence of its artisanal gold mining activities. The severity of this problem in terms of potential health effects was evaluated by means of a probabilistic risk assessment carried out in the twelve departments (or provinces) in Colombia with the largest gold production. The two exposure pathways included in the risk assessment were inhalation of elemental Hg vapors and ingestion of fish contaminated with methyl mercury. Exposure parameters for the adult population (especially rates of fish consumption) were obtained from nation-wide surveys and concentrations of Hg in air and of methyl-mercury in fish were gathered from previous scientific studies. Fish consumption varied between departments and ranged from 0 to 0.3 kg d?1. Average concentrations of total mercury in fish (70 data) ranged from 0.026 to 3.3 lg g?1. A total of 550 individual measurements of Hg in workshop air (ranging from menor queDL to 1 mg m?3) and 261 measurements of Hg in outdoor air (ranging from menor queDL to 0.652 mg m?3) were used to generate the probability distributions used as concentration terms in the calculation of risk. All but two of the distributions of Hazard Quotients (HQ) associated with ingestion of Hg-contaminated fish for the twelve regions evaluated presented median values higher than the threshold value of 1 and the 95th percentiles ranged from 4 to 90. In the case of exposure to Hg vapors, minimum values of HQ for the general population exceeded 1 in all the towns included in this study, and the HQs for miner-smelters burning the amalgam is two orders of magnitude higher, reaching values of 200 for the 95th percentile. Even acknowledging the conservative assumptions included in the risk assessment and the uncertainties associated with it, its results clearly reveal the exorbitant levels of risk endured not only by miner-smelters but also by the general population of artisanal gold mining communities in Colombia.