Demonstration of a stream reasoning platform on low-end devices to enable personalized real-time cycling feedback

During amateur cycling training, analyzing sensor data in real-time would allow riders to receive immediate feedback on how they are performing, and adapt their training accordingly. In this paper, a solution with Semantic Web technologies is presented that gives such real-time personalized feedback, by integrating the data streams with domain knowledge, rider profiles {\&} other context data. This solution consists of a stream reasoning engine running on a low-end Raspberry Pi device, and a tablet app showing feedback based on the continuous query results. To demonstrate this in a static environment, a virtual training app is presented, allowing a user to simulate an amateur cycling training

Towards a cascading reasoning framework to support responsive ambient-intelligent healthcare interventions

In hospitals and smart nursing homes, ambient-intelligent care rooms are equipped with many sensors. They can monitor environmental and body parameters, and detect wearable devices of patients and nurses. Hence, they continuously produce data streams. This offers the opportunity to collect, integrate and interpret this data in a context-aware manner, with a focus on reactivity and autonomy. However, doing this in real time on huge data streams is a challenging task. In this context, cascading reasoning is an emerging research approach that exploits the trade-off between reasoning complexity and data velocity by constructing a processing hierarchy of reasoners. Therefore, a cascading reasoning framework is proposed in this paper. A generic architecture is presented allowing to create a pipeline of reasoning components hosted locally, in the edge of the network, and in the cloud. The architecture is implemented on a pervasive health use case, where medically diagnosed patients are constantly monitored, and alarming situations can be detected and reacted upon in a context-aware manner. A performance evaluation shows that the total system latency is mostly lower than 5 s, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed

DIVIDE : adaptive context-aware query derivation for IoT data streams

In the Internet of Things, it is a challenging task to inte-grate & analyze high velocity sensor data with domain knowledge &context information in real-time. Semantic IoT platforms typically con-sist of stream processing components that use Semantic Web technologiesto run a set of fixed queries processing the IoT data streams. Configur-ing these queries is still a manual task. To deal with changes in contextinformation, which happen regularly in IoT domains, queries typicallyrequire reasoning on all sensor data in real-time to derive relevant sen-sors & events. This can be an issue in real-time, as expressive reasoningis required to deal with the complexity of many IoT domains. To solvethese issues, this paper presents DIVIDE. DIVIDE automatically derivesqueries for stream processing components in an adaptive, context-awareway. When the context changes, it derives through reasoning which sen-sors & observations to filter, given the context & a use case goal, withoutrequiring any more reasoning in real-time. This paper presents the detailsof DIVIDE, and performs evaluations on a healthcare example showinghow it can reduce real-time processing times, scale better when there aremore sensors & observations, and can run efficiently on low-end devices

Topological superconducting phases in disordered quantum wires with strong spin-orbit coupling

Zeeman fields can drive semiconductor quantum wires with strong spin-orbit coupling and in proximity to s-wave superconductors into a topological phase which supports end Majorana fermions and offers an attractive platform for realizing topological quantum information processing. Here, we investigate how potential disorder affects the topological phase by a combination of analytical and numerical approaches. Most prominently, we find that the robustness of the topological phase against disorder depends sensitively and non-monotonously on the Zeeman field applied to the wire.Comment: 6 pages, 3 figures; published versio

Continuous athlete monitoring in challenging cycling environments using IoT technologiesis

Internet of Things (IoT)-based solutions for sport analytics aim to improve performance, coaching, and strategic insights. These factors are especially relevant in cycling, where real-time data should be available anytime, anywhere, even in remote areas where there are no infrastructure-based communication technologies (e.g., LTE and Wi-Fi). In this article, we present an experience report on the use of state-of-the-art IoT technologies in cycling, where a group of cyclists can form a reliable and energy efficient mesh network to collect and process sensor data in real-time, such as heart rate, speed, and location. This data is analyzed in real-time to estimate the performance of each rider and derive instantaneous feedback. Our solution is the first to combine a local body area network to gather the sensor data from the cyclist and a 6TiSCH network to form a multihop long-range wireless sensor network in order to provide each bicycle with connectivity to the sink (e.g., a moving car following the cyclists). In this article, we present a detailed technical description of this solution, describing its requirements, options, and technical challenges. In order to assess such a deployment, we present a large publicly available data-set from different real-world cycling scenarios (mountain road cycle racing and cyclo-cross) which characterizes the performance of the approach, demonstrating its feasibility and evidencing its relevance and promising possibilities in a cycling context for providing low-power communication with reliable performance

Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P < 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = -0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10-6, 1.7 × 10-9, 3.5 × 10-12 and 1.0 × 10-4, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to non-carriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes