67 research outputs found
Customer emotions in service failure and recovery encounters
Emotions play a significant role in the workplace, and considerable attention has been given to the study of employee emotions. Customers also play a central function in organizations, but much less is known about customer emotions. This chapter reviews the growing literature on customer emotions in employee–customer interfaces with a focus on service failure and recovery encounters, where emotions are heightened. It highlights emerging themes and key findings, addresses the measurement, modeling, and management of customer emotions, and identifies future research streams. Attention is given to emotional contagion, relationships between affective and cognitive processes, customer anger, customer rage, and individual differences
Universal Strategy for Designing ShapeMemory Hydrogels
Smart polymeric biomaterials have been the focusof many recent biomedical studies, especially those withadaptability to defects and potential to be implanted in thehuman body. Herein we report a versatile and straightforwardmethod to convert non-thermoresponsive hydrogels intothermoresponsive systems with shape memory ability. As aproof of concept, a thermoresponsive polyurethane mesh wasembedded within a methacrylated chitosan (CHTMA), gelatin(GELMA), laminarin (LAMMA) or hyaluronic acid (HAMA)hydrogel network, which afforded hydrogel composites withshape memory ability. With this system, we achieved good toexcellent shapefixity ratios (50-90%) and excellent shaperecovery ratios (similar to 100%, almost instantaneously) at bodytemperature (37 degrees C). Cytocompatibility tests demonstrated good viability either with cells on top or encapsulated duringall shape memory processes. This straightforward approach opens a broad range of possibilities to convey shape memoryproperties to virtually any synthetic or natural-based hydrogel for several biological and nonbiological applications
Satellite-based estimation of soil organic carbon in Portuguese grasslands
Introduction: Soil organic carbon (SOC) sequestration is one of the main
ecosystem services provided by well-managed grasslands. In the
Mediterranean region, sown biodiverse pastures (SBP) rich in legumes are a
nature-based, innovative, and economically competitive livestock production
system. As a co-benefit of increased yield, they also contribute to carbon
sequestration through SOC accumulation. However, SOC monitoring in SBP
require time-consuming and costly field work.
Methods: In this study, we propose an expedited and cost-effective indirect
method to estimate SOC content. In this study, we developed models for
estimating SOC concentration by combining remote sensing (RS) and machine
learning (ML) approaches. We used field-measured data collected from nine
different farms during four production years (between 2017 and 2021). We
utilized RS data from both Sentinel-1 and Sentinel-2, including reflectance
bands and vegetation indices. We also used other covariates such as climatic,
soil, and terrain variables, for a total of 49 inputs. To reduce multicollinearity
problems between the different variables, we performed feature selection using
the sequential feature selection approach. We then estimated SOC content using
both the complete dataset and the selected features. Multiple ML methods were
tested and compared, including multiple linear regression (MLR), random forests
(RF), extreme gradient boosting (XGB), and artificial neural networks (ANN). We
used a random cross-validation approach (with 10 folds). To find the
hyperparameters that led to the best performance, we used a Bayesian
optimization approach.
Results: Results showed that the XGB method led to higher estimation accuracy
than the other methods, and the estimation performance was not significantly
influenced by the feature selection approach. For XGB, the average root mean
square error (RMSE), measured on the test set among all folds, was 2.78 g kg−1 (r2
equal to 0.68) without feature selection, and 2.77 g kg−1 (r2 equal to 0.68) with
feature selection (average SOC content is 13 g kg−1
). The models were applied to
obtain SOC content maps for all farms.Discussion: This work demonstrated that combining RS and ML can help obtain
quick estimations of SOC content to assist with SBP management
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