This article describes the tailored product innovation processes used in a partnership between Add Latent Ltd., an asset integrity and maintenance management consulting services provider in the energy sector and University of Salford. The challenge faced by the company is to make their inhouse expertise more readily available to a worldwide audience. A longitudinal embedded case study has been used to investigate how installable desktop software applications have been redesigned to create a new set of cloud hosted software services. The innovation team adapted an agile scrum process to include exploratory prototyping and manage the geographical distribution of the team members. A minimum viable product was developed that integrated functional elements of previous software tools into an end-to-end data collection, analysis and visualisation product called AimHi which uses a cloud-hosted web services approach. Field trials were conducted using the software at the Uniper, Isle of Grain power station in Kent, UK. Enhancements were made to the AimHi product which was adopted for use at the Uniper site. The product emerged from a Knowledge Transfer Partnership which was evaluated on completion by Innovate UK and awarded the highest possible "outstanding" grade. Extended periods of evaluation and reflection, prototyping and requirement refinement were combined with periods of incremental feature development using sprints. The AimHi product emerged from a technology transfer and innovation project that has successfully reconciled conflicting demands from customers, universities, partner companies and project staff members.
Social cues, such as eye gaze and pointing fingers, can increase the prioritisation of specific locations for cognitive processing. A previous study using a manual reaching task showed that, although both gaze and pointing cues altered target prioritisation (reaction times [RTs]), only pointing cues affected action execution (trajectory deviations). These differential effects of gaze and pointing cues on action execution could be because the gaze cue was conveyed through a disembodied head; hence, the model lacked the potential for a body part (i.e., hands) to interact with the target. In the present study, the image of a male gaze model, whose gaze direction coincided with two potential target locations, was centrally presented. The model either had his arms and hands extended underneath the potential target locations, indicating the potential to act on the targets (Experiment 1), or had his arms crossed in front of his chest, indicating the absence of potential to act (Experiment 2). Participants reached to a target that followed a nonpredictive gaze cue at one of three stimulus onset asynchronies. RTs and reach trajectories of the movements to cued and uncued targets were analysed. RTs showed a facilitation effect for both experiments, whereas trajectory analysis revealed facilitatory and inhibitory effects, but only in Experiment 1 when the model could potentially act on the targets. The results of this study suggested that when the gaze model had the potential to interact with the cued target location, the model's gaze affected not only target prioritisation but also movement execution.
Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.
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