Constrained Clustering Based on the Link Structure of a Directed Graph

In many segmentation applications, data objects are often clustered based purely on attribute-level similarities. This practice has neglected the useful information that resides in the link structure among data objects and the valuable expert domain knowledge about the desirable cluster assignment. Link structure can carry worthy information about the similarity between data objects (e.g. citation), and we should also incorporate the existing domain information on preferred outcome when segmenting data. In this paper, we investigate the segmentation problem combining these three sources of information, which has not been addressed in the existing literature. We propose a segmentation method for directed graphs that incorporates the attribute values, link structure and expert domain information (represented as constraints). The proposed method combines these three types of information to achieve good quality segmentation on data which can be represented as a directed graph. We conducted comprehensive experiments to evaluate various aspects of our approach and demonstrate the effectiveness of our method

A latent serotonin-1A receptor-gated spinal afferent pathway inhibiting breathing

Spinal afferents such as nociceptive afferents and group III–IV muscle afferents are known to exert an acute excitatory effect on breathing when activated. Here, we report the surprising existence of latent spinal afferents which exerted tonic inhibitory influence on breathing subliminally in anesthetized rats, an effect which was reversed upon activation of serotonin-1A receptors (5-HT[subscript 1A]Rs) in lumbar spinal cord, lesion of pontine lateral parabrachial nucleus or suppression of the adjacent Kölliker-Fuse nucleus with NMDA receptor blockade. Small-interfering RNA knockdown of 5-HT[subscript 1A]Rs in lumbar spinal cord unequivocally localized the site of 5-HT[subscript 1A]R-mediated gating of these respiratory-inhibiting interoceptive afferents to relay neurons in the spinal superficial dorsal horn at the lumbar level and not cervical spinal or supraspinal levels. Our results reveal a novel somatosensory/viscerosensory mechanism which exerts tonic inhibitory influence on homeostatic regulation of breathing independent from the classical chemoreflex excitatory pathways, and suggest a hitherto unrecognized therapeutic target in spinal dorsal horn for 5-HT[subscript 1A]R-based treatment of a variety of respiratory abnormalities.National Institutes of Health (U.S.) (Grants HL093225 and HL067966

Optimal design of sand blown wind tunnel

This work investigates the airflow driven by dual axial-flow fans in an atmospheric boundary layer (ABL) wind tunnel and the expected entrainment of sand movement together. The present study is conducted via 3D numerical simulation based on modelling the entire wind tunnel, including the power fan sections. Three configurations of dual fans in the tunnel are proposed. Simulation results show that the airflow in the tunnel with dual-fan configuration can satisfy the logarithmic distribution law for ABL flows. The airflow driven by the dual fans placed together at the tunnel outlet is highly similar to that in the tunnel with single fans. Although the boundary layer thickness is reduced, the maximum airflow velocity (53.393 m/s) and turbulence intensity (12.02%), which are respectively 1.75 and 1.49 times higher than those under the single-fan configuration, can be reached when dual fans are separately placed at the tunnel inlet and outlet. The simulation and experiment manifest that the separated arrangement of dual fans in the tunnel should be suitable for the experimental study of aeolian sand transport. Some measures, such as wind tunnel construction adjustment and optimal roughness element arrangement, are necessary to guarantee the required boundary layer thickness in the wind tunnel

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

TKSE: Trustworthy keyword search over encrypted data with two-side verifiability via blockchain

AXA Research Fund Singapor