An efficient architecture for the integration of sensor and actuator networks into the future internet

In the future, sensors will enable a large variety of new services in different domains. Important application areas are service adaptations in fixed and mobile environments, ambient assisted living, home automation, traffic management, as well as management of smart grids. All these applications will share a common property, the usage of networked sensors and actuators. To ensure an efficient deployment of such sensor-actuator networks, concepts and frameworks for managing and distributing sensor data as well as for triggering actuators need to be developed. In this paper, we present an architecture for integrating sensors and actuators into the future Internet. In our concept, all sensors and actuators are connected via gateways to the Internet, that will be used as comprehensive transport medium. Additionally, an entity is needed for registering all sensors and actuators, and managing sensor data requests. We decided to use a hierarchical structure, comparable to the Domain Name Service. This approach realizes a cost-efficient architecture disposing of "plug and play" capabilities and accounting for privacy issues

Context-based user grouping for multi-casting in heterogeneous radio networks

Along with the rise of sophisticated smartphones and smart spaces, the availability of both static and dynamic context information has steadily been increasing in recent years. Due to the popularity of social networks, these data are complemented by profile information about individual users. Making use of this information by classifying users in wireless networks enables targeted content and advertisement delivery as well as optimizing network resources, in particular bandwidth utilization, by facilitating group-based multi-casting. In this paper, we present the design and implementation of a web service for advanced user classification based on user, network, and environmental context information. The service employs simple and advanced clustering algorithms for forming classes of users. Available service functionalities include group formation, context-aware adaptation, and deletion as well as the exposure of group characteristics. Moreover, the results of a performance evaluation, where the service has been integrated in a simulator modeling user behavior in heterogeneous wireless systems, are presented

The subcortical and neurochemical organization of the Ventral and Dorsal Attention Networks

Attention is a core cognitive function that filters and selects behaviourally relevant information in the environment. The cortical mapping of attentional systems identified two segregated networks that mediate stimulus-driven and goal-driven processes, the Ventral and the Dorsal Attention Networks (VAN, DAN). Deep brain electrophysiological recordings, behavioral data from phylogenetic distant species, and observations from human brain pathologies challenge purely corticocentric models. Here, we used advanced methods of functional alignment applied to resting-state functional connectivity analyses to map the subcortical architecture of the Ventral and Dorsal Attention Networks. Our investigations revealed the involvement of the pulvinar, the superior colliculi, the head of caudate nuclei, and a cluster of brainstem nuclei relevant to both networks. These nuclei are densely connected structural network hubs, as revealed by diffusion-weighted imaging tractography. Their projections establish interrelations with the acetylcholine nicotinic receptor as well as dopamine and serotonin transporters, as demonstrated in a spatial correlation analysis with a normative atlas of neurotransmitter systems. This convergence of functional, structural, and neurochemical evidence provides a comprehensive framework to understand the neural basis of attention across different species and brain diseases

From Ligand to Phosphor: Rapid, Machine-Assisted Synthesis of Substituted Iridium(III) Pyrazolate Complexes with Tuneable Luminescence

A first generation machine-assisted approach towards the preparation of hybrid ligand/metal materials has been explored. A comparison of synthetic approaches demonstrates that incorporation of both flow chemistry and microwave heating, can be successfully applied to the rapid synthesis of a range of new phenyl-1H-pyrazoles (ppz) substituted with electron withdrawing groups (-F, -CF3, -OCF3, -SF5), and these, in turn, can be translated in to heteroleptic complexes, [Ir(ppz)2(bipy)]BF4 (bipy = 2,2’-bipyridine). Microwave-assisted syntheses for the IrIII complexes allows isolation of spectroscopically pure species in less than 1 hour of reaction time from IrCl3. All new complexes have been explored photophysically (including nanosecond time-resolved transient absorption spectroscopy), electrochemically and by TD-DFT studies which show that the complexes possess ligand-dependent, and thus, tuneable green-yellow luminescence (500-560 nm), with quantum yields in the range 5-15 %

Compressed representation of brain genetic transcription

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. Established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorization (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility with respect to signalling, microstructural, and metabolic targets. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.Comment: 21 pages, 5 main figures, 1 supplementary figur

Stroke risk in patients with device-detected atrial high-rate episodes

Cardiovascular implantable electronic devices (CIEDs) can detect atrial arrhythmias, i.e. atrial high-rate episodes (AHRE). The thrombo-embolic risk in patients showing AHRE appears to be lower than in patients with clinical atrial fibrillation (AF) and it is unclear whether the former will benefit from oral anticoagulants. Based on currently available evidence, it seems reasonable to consider antithrombotic therapy in patients without documented AF showing AHRE >24 hours and a CHA(2)DS(2)-VASc score (congestive heart failure, hypertension, age >= 75 years [doubled], diabetes mellitus, prior stroke [doubled], vascular disease, age 65-74 years and female sex) >= 1, awaiting definite answers from ongoing randomised clinical trials. In patients with AHR

Stationary and Recurrent Properties of Atrial Fibrillation Conduction Patterns in Goat

Introduction. Electrical mapping of the atria is used to assess the substrate of atrial fibrillation (AF). Targeted ablation of the AF substrate assumes spatiotemporal stationarity. In this study we analyzed long AF recordings of AF using high-density contact mapping.Methods. In 12 goats with stable AF 10 successive 60s files were recorded, within a single AF episode. AF cycle length, fractionation index (FI), lateral dissociation, conduction velocity, breakthroughs and preferentiality of conduction (Prefi were assessed to construct AF-property maps. The Pearson correlation coefficient (PCC) between AF-property maps of consecutive recordings was calculated. Recurrence plots and recurrence quantification analysis were used to identify recurrent patterns.Results Spatiotemporal stationarity for the 6 properties were high, PCC ranged from 0.66 +/- 0.11 for Pref to 0.98 +/- 0.01 for FI. The PCC is not affected by the time delay between files. Yet, highly dynamic patterns were found. Recurrence plots revealed few (1.6 +/- 0.7) recurrent patterns in individual animals.Conclusions AF properties were stationary in stable AF. This cannot be attributed to stable recurrent conduction patterns. during This suggests that spatial properties of the atrium determine AF properties

The European Network for Translational Research in Atrial Fibrillation (EUTRAF): objectives and initial results.

Atrial fibrillation (AF) is the most common sustained arrhythmia in the general population. As an age-related arrhythmia AF is becoming a huge socio-economic burden for European healthcare systems. Despite significant progress in our understanding of the pathophysiology of AF, therapeutic strategies for AF have not changed substantially and the major challenges in the management of AF are still unmet. This lack of progress may be related to the multifactorial pathogenesis of atrial remodelling and AF that hampers the identification of causative pathophysiological alterations in individual patients. Also, again new mechanisms have been identified and the relative contribution of these mechanisms still has to be established. In November 2010, the European Union launched the large collaborative project EUTRAF (European Network of Translational Research in Atrial Fibrillation) to address these challenges. The main aims of EUTRAF are to study the main mechanisms of initiation and perpetuation of AF, to identify the molecular alterations underlying atrial remodelling, to develop markers allowing to monitor this processes, and suggest strategies to treat AF based on insights in newly defined disease mechanisms. This article reports on the objectives, the structure, and initial results of this network