A Multiorganisational Study of the Drivers and Barriers of Enterprise Collaboration Systems-Enabled Change

Enterprise Collaboration Systems (ECS) are emerging as the de facto technology platform for the digital workplace. This paper presents findings from an in-depth, multiorganisational study that examines the drivers and barriers of ECS-enabled change from two perspectives: i) the company initiating and driving the project and ii) key practitioners responsible for delivering the change. Data is collected from ECS using companies via a survey and face-to-face workshops, analysed using qualitative content analysis methods to identify categories of change and then synthesised to provide a rich classification and visualisation of the drivers, barriers, motivations and pain points (DBMP) to ECS-enabled change. This is followed by a discussion of the similarities and differences between drivers and barriers from both personal and company perspectives. The paper concludes by exploring the potential of the research and visualisation methods used in this work to provide the foundation for the longitudinal study of ECS-enabled change

Pathway-Dependent Post-assembly Modification of an Anthracene-Edged MII4L6 Tetrahedron

FeII4L6 tetrahedral cage 1 undergoes post-assembly modification (PAM) via a Diels–Alder cycloaddition of the anthracene panels of the cage with tetracyanoethylene (TCNE). The modified cage 2 possesses an enclosed cavity suitable for encapsulation of the fullerene C60, whereas original cage 1 forms a unique covalent adduct through a Diels–Alder cycloaddition of three of its anthracene ligands with C60. This adduct undergoes further PAM via reaction of the remaining three ligands with TCNE, enabling the isolation of two distinct products depending on the order of addition of C60 and TCNE. Modified cage 2 was also able to bind an anionic guest, [Co(C2B9H11)2]−, which was not encapsulated by the original cage, demonstrating the potential of PAM for tuning the binding properties of supramolecular hosts

Energy and Complexity in Evolving Collective Robot Bodies and Brains

The impact of the environment on evolving increasingly complex morphologies (bodies) and controllers (brains) remains an open question in evolutionary biology and has important implications for the evolutionary design of robots. This study uses evolutionary robotics as an experimental platform to evaluate relationships between environment complexity and evolving bodybrain complexity given energy costs on evolving complexity. We evolve robot body-brain designs for increasingly complex environments (difficult cooperative transport tasks) in a collective robotic gathering simulation. The impact of complexity costs on body-brain evolution is evaluated across such increasingly complex environments. Results indicate that complexity costs enable the evolution of simpler body-brain designs that are effective in simple environments but yield negligible behavior (task performance) differences in more complex environments

The Impact of Morphological Diversity in Robot Swarms

In nature, morphological diversity enhances functional diversity, however, there is little swarm (collective) robotics research on the impact of morphological and behavioral (body-brain) diversity that emerges in response to changing environments. This study investigates the impact of increasingly complex task environments on the artificial evolution of body-brain diversity in simulated robot swarms. We investigate whether increasing task environment complexity (collective behavior tasks requiring increasing degrees of cooperative behavior) mandates concurrent increases in behavioral, morphological, or coupled increases in body-brain diversity in robotic swarms. Experiments compared three variants of collective behavior evolution across increasingly complex task environments: two behavioral diversity maintenance variants and body-brain diversity maintenance. Results indicate that body-brain diversity maintenance yielded a significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment

Developing health-related indicators of climate change: Australian stakeholder perspectives

Published: 22 May 2017Climate-related health indicators are potentially useful for tracking and predicting the adverse public health effects of climate change, identifying vulnerable populations, and monitoring interventions. However, there is a need to understand stakeholders' perspectives on the identification, development, and utility of such indicators. A qualitative approach was used, comprising semi-structured interviews with key informants and service providers from government and non-government stakeholder organizations in South Australia. Stakeholders saw a need for indicators that could enable the monitoring of health impacts and time trends, vulnerability to climate change, and those which could also be used as communication tools. Four key criteria for utility were identified, namely robust and credible indicators, specificity, data availability, and being able to be spatially represented. The variability of risk factors in different regions, lack of resources, and data and methodological issues were identified as the main barriers to indicator development. This study demonstrates a high level of stakeholder awareness of the health impacts of climate change, and the need for indicators that can inform policy makers regarding interventions.Maryam Navi, Alana Hansen, Monika Nitschke, Scott Hanson-Easey and Dino Pisaniell

Stimuli-Responsive Metal-Ligand Assemblies.

The Engineering and Physical Sciences Research Council and the European Research Council are acknowledged for financial support.This is the accepted manuscript of a paper published in Chemical Reviews (McConnell AJ, Wood CS, Neelakandan PP, Nitschke JR, Chemical Reviews (2015) 115(15):7729-7793. doi:10.1021/cr500632f). The final version is available at http://dx.doi.org/10.1021/cr500632

Differentially Addressable Cavities within Metal-Organic Cage-Cross-Linked Polymeric Hydrogels

Here we report a new class of hydrogels formed by polymers that are cross-linked through subcomponent self-assembled metal–organic cages. Selective encapsulation of guest molecules within the cages creates two distinct internal phases within the hydrogel, which allows for contrasting release profiles of related molecules depending on their aptitude for encapsulation within the cages. The hydrogels were fabricated into microparticles via a droplet-based microfluidic approach and proved responsive to a variety of stimuli, including acid and competing amine or aldehyde subcomponents, allowing for the triggered release of cargo

Covalent Post-assembly Modification Triggers Multiple Structural Transformations of a Tetrazine-Edged Fe4L6 Tetrahedron

Covalent post-assembly modification (PAM) reactions are useful synthetic tools for functionalizing and stabilizing self-assembled metal-organic complexes. Recently, PAM reactions have also been explored as stimuli for triggering supramolecular structural transformations. Herein we demonstrate the use of inverse electron-demand Diels-Alder (IEDDA) PAM reactions to induce supramolecular structural transformations starting from a tetrazine-edged FeII4L6 tetrahedral precursor. Following PAM, this tetrahedron rearranged to form three different architectures depending on the addition of other stimuli: an electron-rich aniline or a templating anion. By tracing the stimulus-response relationships within the system, we deciphered a network of transformations that mapped different combinations of stimuli onto specific transformation products. Given the many functions being developed for self-assembled three-dimensional architectures, this newly established ability to control the interconversion between structures using combinations of different stimulus types may serve as the basis for switching the functions expressed within a system.D.A.R. acknowledges the Gates Cambridge Trust. B.S.P. acknowledges the Royal Commission for the Exhibition of 1851 Fellowship and Corpus Christi College, Cambridge. This work was supported by the UK Engineering and Physical Sciences Research Council (EP/M01083X/1)

Automating Network Protocol Identification

The proliferation of computer network users has, in recent years, placed a strain on network resources, such as bandwidth and number allocations. This issue is more apparent where connectivity is limited, such as in developing countries. The provisioning of services over these congested resources needs to be managed, ensuring a fair quality of experience (QoE) to consumers and producers alike. Quality of service (QoS) techniques used to manage such resources require constant revision, catering for new application protocols introduced to the network on a daily basis. This research proposes an efficient, autonomous method for distinguishing application protocols through the use of a dynamic protocol classification system (DPCS). Using this method, the burden of signature creation is reduced, while the accuracy achieved in application protocol identification increases

Machine Learning in Diagnosing Cervical Spine Injuries

Machine-learning algorithms (Artificial Intel­ ligence) have demonstrated remarkable progress in image recognition tasks, especially in the medical field. In our set­ ting, radiologist reporting on x-rays is often not available in peripheral hospitals. X-rays often need to be interpreted by junior doctors working after hours in busy emergency departments, leaving room for radiological errors. AI could prove to be the ideal diagnostic tool where swift and ac­ curate diagnosis of cervical spine injuries are required. Machine-learning networks originally developed for other tasks can be applied to skeletal x-rays with minimal in­ tervention. Machine-learning is increasingly being used in diagnosis and can be expected to gradually change clinical practice, assisting clinicians, and improving inter-rater reliability. We aimed to evaluate the diagnostic accuracy of AI in interpreting lateral cervical spine x-rays