Gender differences in vegetarian identity: How men and women construe meatless dieting

Meat is deeply associated with masculine identity. As such, it is unsurprising that women are more likely than men are to become vegetarian. Given the gendered nature of vegetarianism, might men and women who become vegetarian express distinct identities around their diets? Through two highly powered preregistered studies (Ns = 890 and 1775) of self-identified vegetarians, combining both frequentist and Bayesian approaches, I found that men and women differ along two dimensions of vegetarian identity: (1) dietary motivation and (2) dietary adherence. Compared to vegetarian men, vegetarian women reported that they are more prosocially motivated to follow their diet and adhere to their diet more strictly (i.e., are less likely to cheat and eat meat). By considering differences in how men and women construe vegetarian dieting, investigators can generate deeper insights into the gendered nature of eating behavior.     

The influence of national culture on responsible innovation: A case of CO2 utilisation in Indonesia

Responsible innovation (RI) offers a promising step towards a more responsible adoption of innovations, such as new and emerging technologies. However, how RI would be practised in different cultural contexts and settings is still less studied, especially in the context of developing countries. Taking the utilisation of CO₂ in Indonesia as an empirical case, the purpose of this paper is to explore this issue through examining the influence of key characteristics of Indonesia's national culture on core dimensions of RI: anticipation, reflexivity, deliberation, responsiveness and participation. The different RI dimensions are applied in practice in a focus group discussion (FGD) and a round of interviews with different stakeholders that explored CO₂ utilisation in the country. Hofstede's classification of national cultural dimensions is used to single out key focus points for attention in these RI processes in the Indonesian context. The results from the experiment illustrate that culture matters in the application of RI dimensions in practice. In the sense that in order for RI to work well, the governance of core RI processes such as participation and deliberation has to be adjusted to social norms of what is considered legitimate, desirable and good behaviour. These norms differ across societies. The findings further suggest ways of applying RI dimensions in practice.     

Greater than the sum: On regulating innovation in electricity distribution networks with externalities

To modernize distribution networks and enable the energy transition, we need to understand the most appropriate regulatory approach. A set of new technologies with positive externalities challenge the traditional regulatory models. We develop a decision model to assess firms' incentives to invest in new technologies under different regulatory schemes that consider externality effects. Results show that regulatory schemes under which companies retain the gains (or losses) of achieving (or not) efficiency targets more effectively promote innovation investments that reduce network costs. However, a case-by-case approach should be preferred for technologies whose benefits go mostly beyond the network activities.     

Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events

Sentiment analysis techniques are increasingly used to grasp reactions from social media users to unexpected and potentially stressful social events. This paper argues that, alongside assessments of the affective valence of social media content as negative or positive, there is a need for a deeper understanding of the context in which reactions are expressed and the specific functions that users' emotional states may reflect. To demonstrate this, we present a qualitative analysis of affective expressions on Twitter collected in Germany during the 2011 EHEC food contamination incident based on a coding scheme developed from Skinner et al.'s (2003) coping classification framework. Affective expressions of coping were found to be diverse not only in terms of valence but also in the adaptive functions they served: beyond the positive or negative tone, some people perceived the outbreak as a threat while others as a challenge to cope with. We discuss how this qualitative sentiment analysis can allow a better understanding of the way the overall situation is perceived – threat or challenge – and the resources that individuals experience having to cope with emerging demands.     

Dynamic adaptation of service compositions with variability models

Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. One way to achieve this goal is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone. In this paper, we propose a solution based on a semantically rich variability model to support the dynamic adaptation of service compositions. When a problematic event arises in the context, this model is leveraged for decision-making. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. These changes are reflected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which can be deployed at runtime. In order to reach optimum adaptations, the variability model and its possible configurations are verified at design time using Constraint Programming. An evaluation demonstrates several benefits of our approach, both at design time and at runtime.     

A framework for increasing the value of predictive data-driven models by enriching problem domain characterization with novel features

The need to leverage knowledge through data mining has driven enterprises in a demand for more data. However, there is a gap between the availability of data and the application of extracted knowledge for improving decision support. In fact, more data do not necessarily imply better predictive data-driven marketing models, since it is often the case that the problem domain requires a deeper characterization. Aiming at such characterization, we propose a framework drawn on three feature selection strategies, where the goal is to unveil novel features that can effectively increase the value of data by providing a richer characterization of the problem domain. Such strategies involve encompassing context (e.g., social and economic variables), evaluating past history, and disaggregate the main problem into smaller but interesting subproblems. The framework is evaluated through an empirical analysis for a real bank telemarketing application, with the results proving the benefits of such approach, as the area under the receiver operating characteristic curve increased with each stage, improving previous model in terms of predictive performance.

     

Home health care routing and scheduling: A review

In home health care (HHC) operations, nurses are scheduled and routed to perform various services at clients' homes. As this often requires a combination of vehicle routing and scheduling approaches, resulting optimization problems are complex and, hence, of high interest to stakeholders such as researchers, practitioners and policy-makers. With demand for HHC expected to increase substantially, future work is essential to decrease costs and to guarantee service quality. In this review, we provide a comprehensive overview of current work in the field of HHC routing and scheduling with a focus on considered problem settings. Recent advances in HHC optimization are highlighted and future research directions discussed.     

Noninvasive detection of bladder cancer using mid-infrared spectra classification

In this paper, we focus on the detection of Bladder Cancer (BC) via mid infrared spectroscopy. Two main contributions, material and methods, are presented. In terms of material, a new minimally invasive technology, combining fiber evanescent wave spectroscopy and newly patented biosensors, is used for the first time to acquire mid-infrared spectra from voided urine/bladder wash. This new machine promises practicality, cheapness and high-quality of spectrum acquisition. As for classical systems, the data acquired using the new system was highly correlated, resulting in a poor classification performance using classical methods. Therefore, the second contribution consists in developing statistical methods that alleviate the problem. Three new statistical methods based on Partial Least Square Discriminant Analysis algorithm (PLSDA) are proposed. PLSDA is a supervised classifier well-known for its ability to process correlated data. The key point is the choice of the most discriminant latent variables in the training step. In this work, we propose three new decision rules in order to select the most relevant latent variables. These decision rules give rise to three algorithms, namely bayesian, joint and best model PLSDA. A comparative study between the proposed methods and standard ones, namely SVM, K-MEANS and classical PLSDA, confirms clearly the efficiency of the former. The best performance in terms of accuracy is achieved by joint and best model PLSDA (82.35%). Besides, by embedding the proposed statistical methods in the new machine, we are able to provide a new medical device that is very promising in terms of automatic bladder cancer detection.     

Linear instrumental variables model averaging estimation

Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. The obtaining of weights for averaging across individual estimates by direct smoothing of selection criteria arising from the estimation stage is proposed. This is particularly relevant in applications in which there is a large number of candidate instruments and, therefore, a considerable number of instrument sets arising from different combinations of the available instruments. The asymptotic properties of the estimator are derived under homoskedastic and heteroskedastic errors. A simple Monte Carlo study contrasts the performance of MA procedures with existing instrument selection procedures, showing that MA estimators compare very favorably in many relevant setups. Finally, this method is illustrated with an empirical application to returns to education.     

Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval

Bag-of-visual-words (BoW) has recently become a popular representation to describe video and image content. Most existing approaches, nevertheless, neglect inter-word relatedness and measure similarity by bin-to-bin comparison of visual words in histograms. In this paper, we explore the linguistic and ontological aspects of visual words for video analysis. Two approaches, soft-weighting and constraint-based earth mover’s distance (CEMD), are proposed to model different aspects of visual word linguistics and proximity. In soft-weighting, visual words are cleverly weighted such that the linguistic meaning of words is taken into account for bin-to-bin histogram comparison. In CEMD, a cross-bin matching algorithm is formulated such that the ground distance measure considers the linguistic similarity of words. In particular, a BoW ontology which hierarchically specifies the hyponym relationship of words is constructed to assist the reasoning. We demonstrate soft-weighting and CEMD on two tasks: video semantic indexing and near-duplicate keyframe retrieval. Experimental results indicate that soft-weighting is superior to other popular weighting schemes such as term frequency (TF) weighting in large-scale video database. In addition, CEMD shows excellent performance compared to cosine similarity in near-duplicate retrieval.