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The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

In this chapter, we analyse the role of women in armed Basque nationalist and separatist terrorist group Euskadi Ta Askatasuna (ETA) from a gender perspective. ETA women were essential agents in the armed conflict, but their image in the mass media and society has been very different from that of their male colleagues (Rodríguez Lara, 2017). Also, their role in the terrorist gang has described a sexually based functional specialisation. In addition, this chapter seeks to contribute to the area of feminist studies on women and political violence. Women's role in one of the most important armed nationalist groups in Europe, ETA, stands as a suitable case study to understand how the mass media (press, journalism), audiovisual content, and social representations of ETA have portrayed these women. The final conclusion of this chapter is that women were not portrayed in the same way as their male colleagues. Women of ETA were doubly penalised because they were women and because they were terrorists.

The lack of geometric and dimensional accuracy of parts produced by additive manufacturing (AM) is directly related to the machine, material and process used. This paper aims to propose a method for the analysis and compensation of machine-related geometric errors applicable to any AM machine, regardless of the manufacturing process and technology used.

For this purpose, an error calculation model inspired by those used in computerized numerical control machines and coordinate measuring machines was developed. The error functions of the model were determined from the position deviations of a set of virtual points that are not sensitive to material and process errors. These points were obtained from the measurement of an ad hoc designed and manufactured master artefact. To validate the model, off-line compensation was applied to both the original designed artefact and an example part.

The geometric deviations in both cases were significantly smaller than those found before applying the geometric compensation. Dimensional enhancements were also achieved on the example part by using a correction parameter available in the three-dimensional printing software, whose value was adjusted from the measurement of the geometrically compensated master artefact.

The errors that persist in the part derive from both material and process. Compensation for these type of errors requires a detailed analysis of the influencing parameters, which will be the subject of future research.

The use of the virtual-point-based error model increases the quality of additively manufactured parts and can be used in any AM system.

The purpose of this paper is to provide a new procedure for in-plane compensation of geometric errors that often appear in the layers deposited by an additive manufacturing (AM) process when building a part, regardless of the complexity of the layer geometry.

The procedure is based on comparing the real layer contours to the nominal ones extracted from the STL model of the part. Considering alignment and form deviations, the compensation algorithm generates new compensated contours that match the nominal ones as closely as possible. To assess the compensation effectiveness, two case studies were analysed. In the first case, the parts were not manufactured, but the distortions were simulated using a predictive model. In the second example, the test part was actually manufactured, and the distortions were measured on a coordinate measuring machine.

The geometric deviations detected in both case studies, as evaluated by various quality indicators, reduced significantly after applying the compensation procedure, meaning that the compensated and nominal contours were better matched both in shape and size.

Although large contours showed deviations close to zero, dimensional overcompensation was observed when applied to small contours. The compensation procedure could be enhanced if the applied compensation factor took into account the contour size of the analysed layer and other geometric parameters that could have an influence.

The presented method of compensation is applicable to layers of any shape obtained in any AM process.

Delirium, or acute confusional syndrome, is a set of symptoms whose care involves not only psychiatry, but also many other medical specialties. Being as how the syndrome is caused by multiple factors, it is important to recognize each risk factor affecting the patient in order to anticipate and prevent it. In case of diagnosis, identifying and treating the root cause that triggered is important, given that it has a high rate of comorbidity and an elevated cost of medical care. We describe a case where a patient with hypothyroidism began suffering from delirium due to an abrupt discontinuation of levothyroxine treatment. Previously, the patient was seemingly healthy. After the medical treatment was interrupted, sensory processing and behavior were altered, and symptoms fluctuated, for a short period of time, showing disorientation and memory and language impairment.

This chapter analyses residents’ perceptions of mountain destinations. The aim is to develop a scale for assessing residents’ mountain images. An extensive literature review and insights from an empirical study of 315 residents of the Serra da Estrela in Portugal, the Alps in France, Austria and Switzerland, and the Peaks of Europe in Spain show that mountain images held by local people refer to the dimensions: mystic/sacred, historic-cultural life; health and affective image. Results were obtained by both content analysis of open-ended questions and by a quantitative approach based on scale items identified as belonging to specific dimensions in the literature review, whose relevance was confirmed through a confirmatory factor analysis using LISREL. Discussion is focused on theoretical and practical implications of findings and limitations are also presented.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.