The symbolic costs of advice: how social situations explain the occurrence of unplanned knowledge sharing interactions

PurposeInformal knowledge sharing interactions (IKSI) are of particular value for innovation projects. This is especially true for unplanned IKSI, because they are even more likely to provide non-redundant knowledge and new perspectives than planned IKSI. Seminal studies have shown that the formation of unplanned IKSI can be explained on the basis of spatial structures. Strictly speaking, however, these studies only explain unplanned encounters. Whether unplanned IKSI result from these unplanned encounters, though, cannot be satisfactorily explained on the basis of spatial configurations alone. The purpose of this paper is to tackle this explanatory gap by unraveling the fundamental social processes by application of the symbolic interaction theory.Design/methodology/approachFor this purpose, the formation of 132 IKSI on innovation projects from three research and development departments of large companies was recorded in detail using a combination of diaries and interviews. The data were analyzed using qualitative content analysis.FindingsThe analysis reveals that IKSI cause symbolic costs (image damages), and that these costs vary between types of social situations. Because actors anticipate situation-specific costs, their propensity to initiate IKSI can be explained in terms of the situations in which they encounter one another. Furthermore, the analysis reveals six particularly relevant characteristics of situations and further elaborates the basic argument by analyzing their functioning.Originality/valueThe paper complements previous explanations of unplanned IKSI by opening up the social processes underlying their formation.

Delineating the Spatial Boundaries of Megaregions in China: A City Network Perspective

Globalization and informatization have significantly reshaped the map of the global economy. Mega cities and regions have become the battlegrounds in the interplay between globalization and localization, with megaregions becoming the most globally significant spatial configurations in this regard. However, academics and government departments disagree on how to define the spatial boundaries of megaregions. In this study, on the basis of highway traffic flow data between cities, we integrate the community detection and core-periphery profile algorithms to characterize the city networks in China and identify the city groups and delineate the core structures of city groups, which are the underlying megaregional structures in China. Based on this, we identify 21 megaregions among city groups in China, including the Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Hebei, and Chengdu-Chongqing megaregions, and preliminarily delineate their spatial boundaries. On the whole, there are spatial differences among China’s megaregions to a certain extent. Central and eastern China have numerous, large, and a high density of megaregions, while the western region has relatively few megaregions. The latter also differs notably from mature megaregions in terms of rank sizes, urban systems, and functional divisions of labor. Generally, this study develops a novel analytical framework for identifying the functional regions of megaregional space in China from a perspective of relational geography, with methodological implications for other fields of inquiry.

A real-time predictive software prototype for simulating urban-scale energy consumption based on surrogate models

This paper discusses the development of an experimental software prototype that uses surrogate models for predicting the monthly energy consumption of urban-scale community design scenarios in real time. The surrogate models were prepared by training artificial neural networks on datasets of urban form and monthly energy consumption values of all zip codes in San Diego county. The surrogate models were then used as the simulation engine of a generative urban design tool, which generates hypothetical communities in San Diego following the county's existing urban typologies and then estimates the monthly energy consumption value of each generated design option. This paper and developed software prototype is part of a larger research project that evaluates the energy performance of community microgrids via their urban spatial configurations. This prototype takes the first step in introducing a new set of tools for architects and urban designers with the goal of engaging them in the development process of community microgrids.

A Novel 3D Ring-Based Flapper Valve for Soft Robotic Applications

In this paper, the design and testing of a novel valve for the intuitive spatial control of soft or continuum manipulators are presented. The design of the valve is based on the style of a hydraulic flapper valve, but with simultaneous control of three pressure feed points, which can be used to drive three antagonistically arranged hydraulic actuators for positioning soft robots. The variable control orifices are arranged in a rotationally symmetric radial pattern to allow for an inline mounting configuration of the valve within the body of a manipulator. Positioning the valve ring at various 3D configurations results in different pressurizations of the actuators and corresponding spatial configurations of the manipulator. The design of the valve is suitable for miniaturization and use in applications with size constraints such as small soft manipulators and surgical robotics. Experimental validation showed that the performance of the valve can be reasonably modeled and can effectively drive an antagonistic arrangement of three actuators for soft manipulator control.

Where Are They Going? Predicting Human Behaviors in Crowded Scenes

In this article, we propose a framework for crowd behavior prediction in complicated scenarios. The fundamental framework is designed using the standard encoder-decoder scheme, which is built upon the long short-term memory module to capture the temporal evolution of crowd behaviors. To model interactions among humans and environments, we embed both the social and the physical attention mechanisms into the long short-term memory. The social attention component can model the interactions among different pedestrians, whereas the physical attention component helps to understand the spatial configurations of the scene. Since pedestrians’ behaviors demonstrate multi-modal properties, we use the generative model to produce multiple acceptable future paths. The proposed framework not only predicts an individual’s trajectory accurately but also forecasts the ongoing group behaviors by leveraging on the coherent filtering approach. Experiments are carried out on the standard crowd benchmarks (namely, the ETH, the UCY, the CUHK crowd, and the CrowdFlow datasets), which demonstrate that the proposed framework is effective in forecasting crowd behaviors in complex scenarios.

MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images

Precisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of the 3D objects, as opposed to typical multi-spectral imaging scenarios where all image bands observe the same scene. Thus, we refer to this special multi-spectral scenario as multi-layer. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR localisation (segmentation and detection) where different image bands (and physical locations) have their own set of results. Furthermore, to address the difficulty of producing dense AR annotations for training supervised machine learning (ML) algorithms, we adapt a training strategy based on weak labels (i.e. bounding boxes) in a recursive manner. We compare our detection and segmentation stages against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs) and state-of-the-art deep learning methods (Faster RCNN, U-Net). Additionally, both detection and segmentation stages are quantitatively validated on artificially created data of similar spatial configurations made from annotated multi-modal magnetic resonance images. Our framework achieves an average of 0.72 IoU (segmentation) and 0.90 F1 score (detection) across all modalities, comparing to the best performing baseline methods with scores of 0.53 and 0.58, respectively, on the artificial dataset, and 0.84 F1 score in the AR detection task comparing to baseline of 0.82 F1 score. Our segmentation results are qualitatively validated by an expert on real ARs.

Dance

The study of dance can be summed up as the four Ps: patterning, partnering, pacing, and person. Patterning is about the intra- and interpersonal processes used in creating complex movement patterns in space and time. Partnering in dance involves the coordinated movement of multiple dancers, generally in defined spatial configurations, sometimes occurring through direct body contact. Next, pacing in dance refers to the synchronization of movement patterns with both musical beats and interaction partners. Finally, the person aspect of dance deals with how dancers are able to engage in acting by portraying characters in narrative forms of dance and to tell stories with their bodies in a wordless manner using iconic and affective gestures.

Twist-to-Bend Ratios and Safety Factors of Petioles Having Various Geometries, Sizes and Shapes

From a mechanical viewpoint, petioles of foliage leaves are subject to contradictory mechanical requirements. High flexural rigidity guarantees support of the lamina and low torsional rigidity ensures streamlining of the leaves in wind. This mechanical trade-off between flexural and torsional rigidity is described by the twist-to-bend ratio. The safety factor describes the maximum load capacity. We selected four herbaceous species with different body plans (monocotyledonous, dicotyledonous) and spatial configurations of petiole and lamina (2-dimensional, 3-dimensional) and carried out morphological-anatomical studies, two-point bending tests and torsional tests on the petioles to analyze the influence of geometry, size and shape on their twist-to-bend ratio and safety factor. The monocotyledons studied had significantly higher twist-to-bend ratios (23.7 and 39.2) than the dicotyledons (11.5 and 13.3). High twist-to-bend ratios can be geometry-based, which is true for the U-profile of Hosta x tardiana with a ratio of axial second moment of area to torsion constant of over 1.0. High twist-to-bend ratios can also be material-based, as found for the petioles of Caladium bicolor with a ratio of bending elastic modulus and torsional modulus of 64. The safety factors range between 1.7 and 2.9, meaning that each petiole can support about double to triple the leaf’s weight.

Into the Space of the Digital Museum

This chapter explores the space of the digital museum, by which I refer to the space generated by digital art and the hybrid space produced in the experience of encountering collections through technology. I will showcase a number of artworks and digital platforms showing that digital museums spaces tend to be augmented, performative and relational, operating as microscopes, by bringing visitors closer or even inside artworks, and/or as telescopes, making it possible for visitors to experience remote artworks or heritage sites. These new spaces, I will explain, form deep spaces that can be encountered both inside and outside the museum, constantly renegotiating the visitor’s continuous repositioning of their own presence across different temporalities and spatial configurations.