Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its ...infancy. This paper introduces an approach to sensor data analytics by using the OpenIoT 1 middleware; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as an interface for novel analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms to enable analytics on data streams. It can be regarded as a valuable tool to understand complex phenomena, e.g., air pollution dynamics and its impact on human health.
Injection moulding is a polymer processing method of choice for making plastic parts on industrial scale, but its traditional mould is made from tooling steel with time-consuming and costly ...production. Additive manufacturing technologies arise as an alternative for creating mould inserts at lower costs and shorter lead times. In this context, this study describes a series of stereolithography (SLA)-printed injection mould inserts fabricated from two photopolymer resins, utilised to mould standard tensile specimens of a commercial-grade polypropylene, aiming to evaluate effects on the polymer’s thermal and mechanical properties. Our results demonstrated that the glass fibre-filled resin inserts withstood more moulding cycles before failure, had superior mechanical properties, higher Tg and greater thermal conductivity. Calorimetric data revealed that PP thermal properties and degree of crystallinity were little affected, while mechanical testing suggests a significant effect in the elongation at break. Thus, these findings highlight the importance of adequate heat extraction during injection moulding and endorse further application of SLA mould inserts for the manufacturing of injection-moulded plastic parts in the case of prototypes or small batches, provided suitable cooling is made available, contributing to the feasibility and affordability of employing this approach for an industrial setting.
Abstract We update the record of cloud opacity observations conducted by the Mars Science Laboratory (MSL) Curiosity rover to cover the first five Mars Years (MYs) of the mission ( L s = 160° of MY ...31 to L s = 160° of MY 36). Over the three MY period that we add to the previously analyzed two MY record, we achieve good diurnal coverage between 07:00 and 17:00 with nearly 1200 new observations. We derive a new scattering phase function for the clouds of the Aphelion Cloud Belt (ACB) using results from the Zenith and Suprahorizon movie data sets. Our phase function is generally smooth and featureless, which is consistent with the overall lack of atmospheric optical phenomena on Mars aside from a single instance of an observed halo. Applying our new phase function to the data, we find that there is very minimal variability in the ACB's opacity, either diurnally, intraseasonally, or interannually, noting that our observations are only sensitive to ice clouds and cannot detect any ice hazes that may be present over Gale. This contrasts with previous results, which observed a 57% difference in the opacity of morning and afternoon clouds in MY 33. The MY 33 results now appear to be an outlier, not replicated at any point during the MSL mission. We conclude that the higher morning opacities in MY 33 were a consequence of an incomplete understanding of the nature of the scattering phase function close to the Sun.
Abstract We examine 3 yr of phase-function observations of water-ice clouds taken during the Aphelion Cloud Belt season by the Mars Science Laboratory (MSL). We derive lower-bound single-scattering ...phase functions for Mars years (MYs) 34, 35, and 36, over a range of scattering angles from 45° to 155°, expanding on the MY 34 phase function previously derived from MSL observations using the same method. We also modify the procedure used for MY 34 to make use of cloud opacity values derived from other MSL observations, often taken in conjunction with the phase-function observations. From these, we see little variability, both interannually and diurnally in the phase function at Gale Crater. We use our derived phase functions to attempt to constrain a dominant ice-crystal geometry by fitting a two-term Henyey–Greenstein function. In comparing to HG functions of Martian dust and modeled water-ice crystals, we see agreement especially with droxtal water-ice crystals, dust at Gale crater, and irregular volcanic glasses. This could be indicative of crystals composed of some irregular shape.
Microblogging social media focuses on fast open real-time communication using short messages between users and their followers. These platforms generate large amounts of content, and community ...finding techniques are a suitable alternative for organising it. However, there is no clear agreement in the literature for a definition of
user community
for the microblogging use case, leading to unreliable ground-truth data and evaluation. In this work, we differentiate between
functional
and
structural
definitions of communities for microblogging. A functional community groups its users by a common independent social function, e.g. fans of the same football team, while in a structural community the members exclusively depend on their connectivity in a network, e.g. modularity. We build and characterise eight types of functional communities to be used as user-labelled ground-truth and five types of user interactions networks from Twitter. We then evaluate—in static and dynamic scenarios—thirteen popular structural community definitions using five different Twitter datasets, exploring their goodness and robustness for detecting the functional ground-truth under different perturbation strategies. Our results show that definitions based on internal connectivity, e.g. Triangle Participation Ratio, Fraction Over Median Degree or Conductance work best for the Twitter use case and are very robust. On the other hand, other scores such as Modularity are limited and do not perform well due to the sparsity and noise of microblogging. Furthermore, using user activity as basis to separate communities into active
hotspots
further improves the performance of community detection in microblogging.
Abstract
In many real-world scenarios, the utility of a user is derived from a single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected utility of the ...returns must be optimised. Various scenarios exist where a user’s preferences over objectives (also known as the utility function) are unknown or difficult to specify. In such scenarios, a set of optimal policies must be learned. However, settings where the expected utility must be maximised have been largely overlooked by the multi-objective reinforcement learning community and, as a consequence, a set of optimal solutions has yet to be defined. In this work, we propose first-order stochastic dominance as a criterion to build solution sets to maximise expected utility. We also define a new dominance criterion, known as expected scalarised returns (ESR) dominance, that extends first-order stochastic dominance to allow a set of optimal policies to be learned in practice. Additionally, we define a new solution concept called the ESR set, which is a set of policies that are ESR dominant. Finally, we present a new multi-objective tabular distributional reinforcement learning (MOTDRL) algorithm to learn the ESR set in multi-objective multi-armed bandit settings.
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision-making in the context of epidemic mitigation is multi-dimensional hence complex, ...reinforcement learning in combination with complex epidemic models provides a methodology to design refined prevention strategies. Current research focuses on optimizing policies with respect to a single objective, such as the pathogen’s attack rate. However, as the mitigation of epidemics involves distinct, and possibly conflicting, criteria (i.a., mortality, morbidity, economic cost, well-being), a multi-objective decision approach is warranted to obtain balanced policies. To enhance future decision-making, we propose a deep multi-objective reinforcement learning approach by building upon a state-of-the-art algorithm called Pareto Conditioned Networks (PCN) to obtain a set of solutions for distinct outcomes of the decision problem. We consider different deconfinement strategies after the first Belgian lockdown within the COVID-19 pandemic and aim to minimize both COVID-19 cases (i.e., infections and hospitalizations) and the societal burden induced by the mitigation measures. As such, we connected a multi-objective Markov decision process with a stochastic compartment model designed to approximate the Belgian COVID-19 waves and explore reactive strategies. As these social mitigation measures are implemented in a continuous action space that modulates the contact matrix of the age-structured epidemic model, we extend PCN to this setting. We evaluate the solution set that PCN returns, and observe that it explored the whole range of possible social restrictions, leading to high-quality trade-offs, as it captured the problem dynamics. In this work, we demonstrate that multi-objective reinforcement learning adds value to epidemiological modeling and provides essential insights to balance mitigation policies.
•Epidemic mitigation involves multiple criteria (mortality, economic cost, well-being).•Multi-objective RL (MORL) to explore the Pareto front of deconfinement strategies.•We investigate deconfinement strategies after the first Belgian lockdown of COVID-19.•Minimize both COVID-19 cases and societal burden; leads to many distinct trade-offs.•MORL brings essential insights to balance mitigation policies and help policy makers.
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