199 research outputs found
Report of the In Situ Resources Utilization Workshop
The results of a workshop of 50 representatives from the public and private sector which investigated the potential joint development of the key technologies and mechanisms that will enable the permanent habitation of space are presented. The workshop is an initial step to develop a joint public/private assessment of new technology requirements of future space options, to share knowledge on required technologies that may exist in the private sector, and to investigate potential joint technology development opportunities. The majority of the material was produced in 5 working groups: (1) Construction, Assembly, Automation and Robotics; (2) Prospecting, Mining, and Surface Transportation; (3) Biosystems and Life Support; (4) Materials Processing; and (5) Innovative Ventures. In addition to the results of the working groups, preliminary technology development recommendations to assist in near-term development priority decisions are presented. Finally, steps are outlined for potential new future activities and relationships among the public, private, and academic sectors
Livro de atas do XVI Congresso da Associação Portuguesa de Investigação Operacional
Fundação para a Ciência e Tecnologia - FC
Historical archaeology of Alaskan placer gold mining settlements: Evaluating process-pattern relationships
Thesis (Ph.D.) University of Alaska Fairbanks, 1998The objective of this research is to explicate appropriate methods for investigating relationships between past historical processes and variables, and resulting contemporary patterns in archaeological and historical data sets. Turn-of-the-twentieth century placer gold mining in interior Alaska is used as a case study to evaluate these relationships. By linking observable patterns in historical data sets with the variables and processes that in part create and shape them, a more-complete, context-specific explanation of past events and actions emerges when the data are evaluated in specific historical settings. The methodological approach used here is to just formulate explicit "expectations," and then to evaluate them against independent Alaskan historical and archaeological data sets. The expectations derive from independent comparative historical geographical, and archaeological research. One series of nine expectations evaluates attributes of artifacts relating to site and feature abandonment processes relating to curation and scavenging, including specific traits of artifacts in curated and scavenged deposits; the changing effects of continued curation and scavenging on an artifactual assemblage through time; and spatial characteristics of artifacts within curated and scavenged foundations. Four types of data are used evaluate the expectations, including the size of artifacts, whether they are still functional or usable, their spatial provenience within excavated structures, and a feature's data range. Seven of these expectations are corroborated, one is falsified, and one requires further data for a full evaluation. A second series of seven expectations examines aspects of placer gold mining settlement and transportation systems, including the core-peripheral relationship between Alaska and the United States; the nature of expansion of gold mining settlements into new areas; locational, demographic, and physical layout characteristics of settlement systems; the mining settlement hierarchy and its changing components through time; and characteristics of the supporting transportation supply system. These expectations, while also corroborated by the Alaskan data, lend themselves more to historical context-specific understanding and interpretation, as opposed to the strict corroboration-falsification dichotomy of the abandonment analyses
Codebook and explanatory note on the EurOccupations dataset about the job content of 150 occupations
Feature selection strategies for improving data-driven decision support in bank telemarketing
The usage of data mining techniques to unveil previously undiscovered knowledge has
been applied in past years to a wide number of domains, including banking and marketing. Raw
data is the basic ingredient for successfully detecting interesting patterns. A key aspect of raw
data manipulation is feature engineering and it is related with the correct characterization or
selection of relevant features (or variables) that conceal relations with the target goal.
This study is particularly focused on feature engineering, aiming at the unfolding
features that best characterize the problem of selling long-term bank deposits through
telemarketing campaigns. For the experimental setup, a case-study from a Portuguese bank,
ranging the 2008-2013 year period and encompassing the recent global financial crisis, was
addressed. To assess the relevance of such problem, a novel literature analysis using text
mining and the latent Dirichlet allocation algorithm was conducted, confirming the existence of a
research gap for bank telemarketing.
Starting from a dataset containing typical telemarketing contacts and client information,
research followed three different and complementary strategies: first, by enriching the dataset
with social and economic context features; then, by including customer lifetime value related
features; finally, by applying a divide and conquer strategy for splitting the problem in smaller
fractions, leading to optimized sub-problems. Each of the three approaches improved previous
results in terms of model metrics related to prediction performance. The relevance of the
proposed features was evaluated, confirming the obtained models as credible and valuable for
telemarketing campaign managers.A utilização de técnicas de data mining para a descoberta de conhecimento tem sido
aplicada nos últimos anos a uma grande variedade de domínios, incluindo banca e marketing.
Os dados no seu estado primitivo constituem o ingrediente básico para a deteção de padrões
de informação. Um aspeto chave da manipulação de dados em bruto consiste na "engenharia
de atributos", que compreende uma correta definição e seleção de atributos relevantes (ou
variáveis) que se relacionem com o alvo da descoberta de conhecimento.
Este trabalho foca-se numa abordagem de "engenharia de atributos" para definir as
variáveis que melhor caraterizam o problema de vender depósitos bancários a prazo através de
campanhas de telemarketing. Sendo um estudo empírico, foi utilizado um caso de estudo de
um banco português, abrangendo o período 2008-2013, que inclui os efeitos da crise financeira
internacional. Para aferir da importância deste problema, foi realizada uma inovadora análise
da literatura recorrendo a text mining e ao algoritmo latent Dirichlet allocation, confirmando a
existência de uma lacuna nesta matéria.
Utilizando como base um conjunto de dados de contactos de telemarketing e
informação sobre os clientes, três estratégias diferentes e complementares foram propostas:
primeiro, os dados foram enriquecidos com atributos socioeconómicos; posteriormente, foram
adicionadas características associadas ao valor do cliente ao longo do seu tempo de vida;
finalmente, o problema foi dividido em problemas mais específicos, permitindo abordagens
otimizadas a cada subproblema. Cada abordagem melhorou as métricas associadas à
capacidade preditiva do modelo. Adicionalmente, a relevância dos atributos foi avaliada,
confirmando os modelos obtidos como credíveis e valiosos para gestores de campanhas de telemarketing
Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure
© 2013 IEEE. Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients
Business analytics adoption and technological intensity: An efficiency analysis
Despite the overwhelming popularity of business analytics (BA) as an evidence-based decision support mechanism, the impact of its adoption on organizational performance has received scant attention from the research community. This study aims to unfold the adoption efficiencies of BA and its applications by proposing a data envelopment analysis (DEA) methodology to holistically assess the underlying factors with respect to the level of achievement regarding organizational performance, operational performance, and financial performance. Furthermore, the study unveils the firm-level and sectoral-level discrepancies in BA adoption efficiency in different industry settings. Relying on survey data obtained from 204 executives in various industries, this study provides empirical support for the cross-industry differences in BA adoption efficiencies. The results show that the firms in low-tech industries seem to achieve the highest efficiency from adopting BA regarding its influence on firm performance
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