Human-agent collectives

We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented

Introduction

Book introductio

Eosinophilic esophagitis—established facts and new horizons

Despite dramatic advances in our understanding of the pathogenesis and course of disease in the relatively short timeframe since the discovery and first description of eosinophilic esophagitis (EoE) less than three decades ago, many open questions remain to be elucidated. For instance, we will need to better characterize atypical clinical presentations of EoE and other forms of esophageal inflammatory conditions with often similar clinical presentations, nut fulfilling current diagnostic criteria for EoE and to determine their significance and interrelationship with genuine EoE. In addition, the interrelationship of EoE with other immune-mediated diseases remains to be clarified. Hopefully, a closer look at the role of environmental factors and their interaction with genetic susceptibility often in context of atopic predisposition may enable identifying the candidate substances/agents/allergens and potentially earlier (childhood) events to trigger the condition. It appears plausible to assume that in the end—comparable to current concepts in other immune-mediated chronic diseases, such as for instance inflammatory bowel disease or asthma bronchiale—we will not be rewarded with the identification of a “one-and-only” underlying pathogenetic trigger factor, with causal responsibility for the disease in each and every EoE patient. Rather, the relative contribution and importance of intrinsic susceptibility, i.e., patient-driven factors (genetics, aberrant immune response) and external trigger factors, such as food (or aero-) allergens as well as early childhood events (e.g., infection and exposure to antibiotics and other drugs) may substantially differ among given individuals with EoE. Accordingly, selection and treatment duration of medical therapy, success rates and extent of required restriction in dietary treatment, and the need for mechanical treatment to address strictures and stenosis require an individualized approach, tailored to each patient. With the advances of emerging treatment options, the importance of such an individualized and patient-centered assessment will increase even further

Discrete fracture network based drift stability at the Éléonore mine

Photogrammetry tools were used to characterise the rock mass structural regime at selected mining drifts at the Éléonore underground mine in Canada. This information was used to provide the input data for generating a series of discrete fracture networks (DFN) models. The generated DFN models were subsequently used to investigate the creation of rock wedges along the drifts that may impact the stability of the excavations. The impact of the choice of employed DFN model on the analysis was investigated with reference to the stability of excavations. A series of parametric analyses demonstrated the sensitivity of the model to variations in the properties of the structural regime. The benefits of using stochastic modelling to capture the inherent variability are reviewed

Comparison of Nonoriented and Grain-Oriented Material in an Axial Flux Permanent-Magnet Machine

The performance and iron losses of an axial flux permanent-magnet synchronous machine (AFPMSM) using nonoriented (NO) steel are compared with the performance and iron losses of an AFPMSM using grain-oriented (GO) material. The machine is modeled by several 2-D finite element models in circumferential direction, at different radii. The material model for the GO material is an anhysteretic anisotropic model based on the magnetic energy. The magnetic energy is computed by using several measured quasi-static -loops on an Epstein frame in seven directions starting from the rolling direction to the transverse direction. The losses are calculated a posteriori, based on the principles of loss separation and dynamic loop measurements. A loss model was made for each of the seven directions, assuming unidirectional fields. In comparison with the more usual NO material, both the saturation induction and the torque are higher with GO material. The magnetic field in the GO material is lower than for NO material in the major part of the iron, but higher in the tooth tips where the field is not in the rolling direction. The stator iron losses are about 7 times lower for the considered GO compared to the NO material