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Presents the thoughts on decision processes of Chester I. Barnard, one of the century’s greatest management theorists. Includes his classic article, “Mind in everyday affairs”; his unpublished book, “The Significance of Decisive Behaviour in Social Action”; his correspondence with Herbert Simon, and significant comments found in his personal papers.

This chapter proposes a conceptualization of joyful leadership as a unique leadership style and identifies a future research agenda to further explore the concept. While the concept of joyful leadership appears repeatedly in the nonacademic literature, including in blogs, vlogs, and podcasts, there is limited reference to joyful leadership in the academic literature highlighting a lack of academic rigor around the concept. Joyful leadership is proposed as a unique leadership style with specific patterns of behavior demonstrated by the leader. This research draws on understandings of emotion, positive affect, and leadership in the academic literature to develop a conceptualization of joyful leadership.

The proposed conceptualization is based on an extensive literature review drawing from both the leadership field and the study of emotions including various theoretical perspectives from these diverse fields.

Based on discrete emotion theory a conceptualization of joyful leadership as a unique leadership style is presented, identifying key patterns of behavior associated with joyful leadership including discrete autonomic patterns, actions, nonverbal signals, and identified feelings.

This research outlines a conceptual model to provide an understanding of the concept of joyful leadership as a unique leadership style. It draws on the current study of emotion, positive affect, and leadership and more specifically examines the concept of joyful leadership aligned to discrete emotion theory. This particular theory of emotion, when examined in relation to leadership, provides a basis for the concept of joyful leadership as a leadership style and the basis for its proposed characteristics and outcomes.

The first step toward developing an automated construction workers performance monitoring system is to initially establish a complete and competent activity recognition solution, which is still lacking. This study aims to propose a novel approach of using labor physiological data collected through wearable sensors as means of remote and automatic activity recognition.

A pilot study is conducted against three pre-fabrication stone construction workers throughout three full working shifts to test the ability of automatically recognizing the type of activities they perform in-site through their lively measured physiological signals (i.e. blood volume pulse, respiration rate, heart rate, galvanic skin response and skin temperature). The physiological data are broadcasted from wearable sensors to a tablet application developed for this particular purpose, and are therefore used to train and assess the performance of various machine-learning classifiers.

A promising result of up to 88% accuracy level for activity recognition was achieved by using an artificial neural network classifier. Nonetheless, special care needs to be taken for some activities that evoke similar physiological patterns. It is expected that blending this method with other currently developed camera-based or kinetic-based methods would yield higher activity recognition accuracy levels.

The proposed method complements previously proposed labor tracking methods that focused on monitoring labor trajectories and postures, by using additional rich source of information from labors physiology, for real-time and remote activity recognition. Ultimately, this paves for an automated and comprehensive solution with which construction managers could monitor, control and collect rich real-time data about workers performance remotely.

After a vigorous debate in the late 1970s, the sociology of emotion put aside most discussion of whether or not the physiological arousal associated with emotion labels is differentiated. Since this early period, scholars have made great progress on two fronts. First, theories about the interrelationship of identity, action and emotion have specified a family of new concepts related to emotion. Second, a large corpus of research on the physiological correlates of emotional experience emerged. In this chapter, we review the well-developed control theories of identity and emotion, and focus on the key concepts that might relate to different physiological states. We then review the general classes of physiological measures, discussing their reliability, intrusiveness and other features that might determine their usefulness for tracking responses to social interaction. We then offer a highly provisional mapping of physiological measures onto the concepts that they might potentially measure, given past research about how these physiological processes relate to environmental stimuli. While any linkage between concepts and measures must be speculative at this point, we hope that this review will serve as a stimulus to theoretically guided research that begins to assess the validity of these new measures for sociological use.

A detailed analysis on the characteristics of laminar flow over a bell‐shaped stenosis for a physiological pulsatile flow is presented in this study. In order to have a good understanding of the physiological pulsatile flow, a comparison of the numerical solutions to three types of pulsatile flows, including a physiological flow, an equivalent pulsatile flow and a pure sinusoidal flow, are made in this work. The comparison shows that the flow behavior cannot be properly estimated if the equivalent or simple pulsatile inlet flow is used in the study of flow fields through stenosed arteries instead of actual physiological one. Then the physiological pulsatile flow is further studied by considering the effect of constriction ratio of stenosis, Womersley number and Reynolds number on the flow behavior through stenosed arteries. The analysis shows that the variation of these flow parameters puts significant impacts on the pulsatile flow field for the physiological flow.

The Guilty Knowledge Test (GKT) is a psychophysiological questioning technique that can be used as part of a polygraph examination which purports to assess whether suspects conceal “guilty knowledge” by measuring their physiological responses while responding to a series of multiple choice questions. The present study sets out to consider a number of key issues in relation to the GKT paradigm. Specifically, the following questions were considered: Does response mode matter? Does motivation influence outcome? Are combined physiological measures better than single ones? Does gender have an effect on physiological responsivity during a polygraph examination? Results demonstrated real variations between the physiological measures used. Gender differences were also observed in polygraph response patterns. These findings are discussed in relation to the validity of the Guilty Knowledge Test.

Effective communication is crucial in the medical field where different stakeholders use various terminologies to describe and classify healthcare concepts such as ICD, SNOMED CT, UMLS and MeSH, but the problem of polysemy can make natural language processing difficult. This study explores the contextual meanings of the term “pattern” in the biomedical literature, compares them to existing definitions, annotates a corpus for use in machine learning and proposes new definitions of terms such as “Syndrome, feature” and “pattern recognition.”

Entrez API was used to retrieve articles form PubMed for the study which assembled a corpus of 398 articles using a search query for the ambiguous term “pattern” in the titles or abstracts. The python NLTK library was used to extract the terms and their contexts, and an expert check was carried out. To understand the various meanings of the term, the contextual environment was analyzed by extracting the surrounding words of the term. The expert determined the appropriate size of the context for analysis to gain a more nuanced understanding of the different meanings of the term pattern.

The study found that the categories of meanings of the term “pattern” are broader in biomedical publications than in common definitions, and new categories have been emerging from the term's use in the biomedical field. The study highlights the importance of annotated corpora in advancing natural language processing techniques and provides valuable insights into the nuances of biomedical language.

The study's findings demonstrate the importance of exploring contextual meanings and proposing new definitions of terms in the biomedical field to improve natural language processing techniques.

This chapter proposes a more holistic approach to understanding work stress by incorporating eustress, the positive response to stressors. We begin by casting the study of eustress as part of a contemporary movement in both psychology and organizational behavior that accentuates the positive aspects of human adaptation and functioning. We discuss the development of the concept of eustress, and provide extensive evidence, both psychological and physiological, for the purpose of developing an explicit construct definition. An exploratory study of hospital nurses is presented as an initial test of our holistic model of stress. We conclude by asserting that there must exist a complement to coping with distress such that rather than preventing or resolving the negative side of stress, individuals savor the positive side of stress.

As drones are rapidly transforming tasks such as mapping and surveying, safety inspection and progress monitoring, human operators continue to play a critical role in ensuring safe drone missions in compliance with safety regulations and standard operating procedures. Research shows that operator's stress and fatigue are leading causes of drone accidents. Building upon the authors’ past work, this study presents a systematic approach to predicting impending drone accidents using data that capture the drone operator's physiological state preceding the accident.

The authors collect physiological data from 25 participants in real-world and virtual reality flight experiments to design a feedforward neural network (FNN) with back propagation. Four time series signals, namely electrodermal activity (EDA), skin temperature (ST), electrocardiogram (ECG) and heart rate (HR), are selected, filtered for noise and used to extract 92 time- and frequency-domain features. The FNN is trained with data from a window of length t = 3…8 s to predict accidents in the next p = 3…8 s.

Analysis of model performance in all 36 combinations of analysis window (t) and prediction horizon (p) combinations reveals that the FNN trained with 8 s of physiological signal (i.e. t = 8) to predict drone accidents in the next 6 s (i.e. p = 6) achieved the highest F1-score of 0.81 and AP of 0.71 after feature selection and data balancing.

The safety and integrity of collaborative human–machine systems (e.g. remotely operated drones) rely on not only the attributes of the human operator or the machinery but also how one perceives the other and adopts to the evolving nature of the operational environment. This study is a first systematic attempt at objective prediction of potential drone accident events from operator's physiological data in (near-) real time. Findings will lay the foundation for creating automated intervention systems for drone operations, ultimately leading to safer jobsites.

The purpose of this paper is to investigate the role of emerging technologies in the promotion of health and well-being at the urban, domestic and bodily scale, through the systematic examination of technologies such as physical sensing systems and physiological data monitoring, that are currently explored as drivers for achieving sustainable healthcare within a multi-scalar approach.

A comprehensive study of the various technologies associated with smart healthcare is provided, first investigating smart cities, physical sensing systems and geospatial data as potential enablers of public health. Then the discourse shifts towards exploring Smart Home technologies for healthcare, first reviewing strategies of enhancing the home environment with multisensory components, and then discussing the emergence of physiological monitoring devices and their interconnection with the domestic and urban environment.

While the implementation of Internet of Things, physical sensing systems and geospatial analytics in extracting and analyzing the multiple information layers of the urban, the domestic and the bodily environment, has been widely explored, there is little consideration on the transition from the domestic to the urban level, and while within each of the different scales, the need for a multi-componential approach is addressed, there is minimal effort towards its materialization.

The major contribution of this study therefore lies in laying the ground for further research towards a multi-scalar relational approach that views smart healthcare as a trajectory, binding the bodily, to the domestic and the urban fabric.