Dry-season length and runoff control annual variability in stream DOC dynamics in a small, shallowgroundwater-dominated agricultural watershed

International audienceAs a phenomenon integrating climate conditions and hydrological control of the connection betweenstreams and terrestrial dissolved organic carbon (DOC) sources, groundwater dynamics controlpatterns of stream DOC characteristics (concentrations and fluxes). Influence of intra-annualvariations in groundwater level, discharge and climatic factors on DOC concentrations and fluxeswere assessed over 13 years at the headwater watershed of Kervidy-Naizin (5 km²) in westernFrance. Four seasonal periods were delineated within each year according to groundwaterfluctuations (A: rewetting, B: high flow, C: recession, and D: drought). Annual and seasonal baseflow vs stormflow DOC concentrations were defined based on daily hydrograph readings. Highinter-annual variability of annual DOC fluxes (5.4-39.5 kg.ha-1.yr-1) indicates that several years ofdata are required to encompass variations in water flux to evaluate the actual DOC export capacity ofa watershed. Inter-annual variability of mean annual DOC concentrations was much lower (4.9-7.5mg C.l-1), with concentrations decreasing within each year from ca. 9.2 mg C.l-1 in A to ca. 3.0 mgC.l-1 in C. This indicates an intra-annual pattern of stream DOC concentrations controlled by DOCsource characteristics and groundwater dynamics very similar across years. Partial least squareregressions combined with multiple linear regressions showed that the dry season characteristics(length and drawdown) determine the mean annual DOC concentration while annual runoffdetermines the annual flux. Antagonistic mechanisms of production-accumulation and dilution depletioncombined with an unlimited DOC supply from riparian wetland soils can mitigate theresponse of stream concentrations to global changes and climatic variations

Construction of an Immigrant Integration Composite Indicator through the Partial Least Squares Structural Equation Model K-Means

Integration is a multidimensional process, which can take place in different ways and at different times in relation to each of the single economic, social, cultural, and political dimensions. Hence, examining every single dimension is important as well as building composite indexes simultaneously inclusive of all dimensions in order to obtain a complete description of a complex phenomenon and to convey a coherent set of information. In this paper, we aim at building an immigrant integration composite indicator (IICI), able to measure the different aspects related to integration such as employment, education, social inclusion, active citizenship, and on the basis of which to simultaneously classify territorial areas such as European regions. For this application, the data collected in 274 European regions from the European Social Survey (ESS), Round 8, on immigration have been used

Globally sparse PLS regression

Volume 56 ; Print ISBN : 978-1-4614-8282-6Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principle component analysis by taking into account both the independent and re- sponse variables in the dimension reduction procedure. However, PLS suffers from over-fitting problems for few samples but many variables. We formulate a new criterion for sparse PLS by adding a structured sparsity constraint to the global SIMPLS optimization. The constraint is a sparsity-inducing norm, which is useful for selecting the important variables shared among all the components. The optimization is solved by an augmented Lagrangian method to obtain the PLS components and to perform variable selection simultaneously. We propose a novel greedy algorithm to overcome the computation difficulties. Experiments demonstrate that our approach to PLS regression attains better performance with fewer selected predictor

Alternative methods to analyse the impact of HIV mutations on virological response to antiviral therapy

<p>Abstract</p> <p>Background</p> <p>Principal component analysis (PCA) and partial least square (PLS) regression may be useful to summarize the HIV genotypic information. Without pre-selection each mutation presented in at least one patient is considered with a different weight. We compared these two strategies with the construction of a usual genotypic score.</p> <p>Methods</p> <p>We used data from the ANRS-CO3 Aquitaine Cohort Zephir sub-study. We used a subset of 87 patients with a complete baseline genotype and plasma HIV-1 RNA available at baseline and at week 12. PCA and PLS components were determined with all mutations that had prevalences >0. For the genotypic score, mutations were selected in two steps: 1) p-value < 0.01 in univariable analysis and prevalences between 10% and 90% and 2) backwards selection procedure based on the Cochran-Armitage Test. The predictive performances were compared by means of the cross-validated area under the receiver operating curve (AUC).</p> <p>Results</p> <p>Virological failure was observed in 46 (53%) patients at week 12. Principal components and PLS components showed a good performance for the prediction of virological response in HIV infected patients. The cross-validated AUCs for the PCA, PLS and genotypic score were 0.880, 0.868 and 0.863, respectively. The strength of the effect of each mutation could be considered through PCA and PLS components. In contrast, each selected mutation contributes with the same weight for the calculation of the genotypic score. Furthermore, PCA and PLS regression helped to describe mutation clusters (e.g. 10, 46, 90).</p> <p>Conclusion</p> <p>In this dataset, PCA and PLS showed a good performance but their predictive ability was not clinically superior to that of the genotypic score.</p

Absorbing customer knowledge: how customer involvement enables service design success

Customers are a knowledge resource outside of the firm that can be utilized for new service success by involving them in the design process. However, existing research on the impact of customer involvement (CI) is inconclusive. Knowledge about customers’ needs and on how best to serve these needs (articulated in the service concept) is best obtained from customers themselves. However, codesign runs the risk of losing control of the service concept. This research argues that of the processes of external knowledge, acquisition (via CI), customer knowledge assimilation, and concept transformation form a capability that enables the firm to exploit customer knowledge in the form of a successful new service. Data from a survey of 126 new service projects show that the impact of CI on new service success is fully mediated by customer knowledge assimilation (the deep understanding of customers’ latent needs) and concept transformation (the modification of the service concept due to customer insights). However, its impact is more nuanced. CI exhibits an “∩”-shaped relationship with transformation, indicating there is a limit to the beneficial effect of CI. Its relationship with assimilation is “U” shaped, suggesting a problem with cognitive inertia where initial learnings are ignored. Customer knowledge assimilation directly impacts success, while concept transformation only helps success in the presence of resource slack. An evolving new service design is only beneficial if the firm has the flexibility to adapt to change

Measuring Risk Attitudes Controlling for Personality Traits*

Abstract: This study measures risk attitudes using two paid experiments: the Holt and Laury (2002) procedure and a variation of the game show Deal or No Deal. The participants also completed a series of personality questionnaires developed in the psychology literature including the risk domains of Weber, Blais, and Betz (2002). As in previous studies risk attitudes vary within subjects across elicitation methods. However, this variation can be explained by individual personality traits. Specifically, subjects behave as though the Holt and Laury task is an investment decision while the Deal or No Deal task is a gambling decision

A framework to move forward on the path to eco-innovation in the construction industry: implications to improve firms´ sustainable orientation

This paper examines key aspects in the innovative behavior of the construction firms that determine their environmental orientation while innovating. Structural equation modeling was used and data of 222 firms retrieved from the Spanish Technological Innovation Panel (PITEC) for 2010 to analyse the drivers of environmental orientation of the construction firms during the innovation process. The results show that the environmental orientation is positively affected by the product and process orientation of construction firms during the innovation process. Furthermore, the positive relation between the importance of market information sources and environmental orientation, mediated by process and product orientation, is discussed. Finally, a model that explains these relations is proposed and validated. 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Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms

[EN] The main aim of this study is to establish the effect of the Exploitation and Exploration; and the influence of these learning flows on the Innovative Outcome (IO). The Innovative Outcome refers to new products, services, processes (or improvements) that the organization has obtained as a result of an innovative process. For this purpose, a relationship model is defined, which is empirically contrasted, and can explains and predicts the cyclical dynamization of learning flows on innovative outcome in knowledge intensive firms. The quantitative test for this model use the data from entrepreneurial firms biotechnology sector. The statistical analysis applies a method based on variance using Partial Least Squares (PLS). Research results confirm the hypotheses, that is, they show a positive dynamic effect between the Exploration and the Innovative as outcomes. 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