This paper aims at proposing a new wireless indoor localization system (ILS), called TrackCC, based on a commercial type of low-power system-on-chip (SoC), nRF24LE1. This type of chip has only l output power levels and acute fluctuation for a received minimum power level in operation, which give rise to many practical challenges for designing localization algorithms. In order to address these challenges, we exploit the Markov theory to construct a $(l+1)\times (l+1)$ -sized state transition matrix to remove the fluctuation, and then propose a priority-based pattern matching algorithm to search for the most similar match in the signal map to estimate the real position of unknown nodes. The experimental results show that, compared to two existing wireless ILSs, LANDMARC and SAIL, which have meter level positioning accuracy, the proposed TrackCC can achieve the decimeter level accuracy on average in both line-of-sight (LOS) and non-line-of-sight (NLOS) senarios. Full article

Toll-like receptor 4 (TLR4)/nuclear factor kappa B (NF-κB) signaling plays a dominant role in the pathogenesis of liver ischemia-reperfusion (IR) injury. Dexmedetomidine (Dex) protects the liver against IR injury via α₂-adrenoceptor activation, but the contribution of TLR4 signaling remains unknown. The authors aimed to examine whether pretreatment with Dex produces hepatic protection and investigate the influence of Dex on TLR4/NF-κB signaling. Dex was given via intraperitoneal injection 30 min prior to orthotopic autologous liver transplantation (OALT) in rats, and three α₂-adrenoceptor antagonists including atipamezole (a nonselective α₂ receptor blocker), ARC-239 (a specific α_2B/C blocker) and BRL-44408 (a specific α_2A blocker) were injected intraperitoneally 10 min before Dex administration. Histopathologic evaluation of the liver and the measurement of serum alanine aminotransferase activity, TLR4/NF-κB expression in the liver, and pro-inflammatory factors (serum tumor necrosis factor-α, interleukin-1β and hepatic myeloperoxidase) concentrations were performed 8 h after OALT. Dex ameliorated liver injury after OALT probably by suppressing the TLR4/NF-κB pathway and decreasing inflammatory mediator levels. The protective effects of Dex were reversed by atipamezole and BRL-44408, but not by ARC-239, suggesting that these effects were mediated in part by the α_2A subtype. In conclusion, Dex attenuates liver injury partly via the α_2A-adrenoceptor subtype, and the mechanism is due to the suppression of the TLR4/NF-κB pathway. Full article

Polylactide (PLA)/poly(butylene adipate-co-terephthalate) (PBAT)/bark flour of plane tree (PF) eco-composites were prepared via melt blending. The morphologies, mechanical properties, crystal structures and melting and crystallization behaviors of the eco-composites were investigated by means of scanning electron microscopy (SEM), mechanical tests, polarized light microscopy (PLM), wide angle X-ray diffraction (WAXD) and differential scanning calorimetry (DSC), respectively. It is shown that the interfacial adhesion between PLA matrix and PF is weak and the mechanical properties of PLA/PF eco-composites are poor. The titanate treatment improves the adhesion between the matrix and the filler and enhances the stiffness of the eco-composites. The toughness is improved by PBAT and ductile fractured surfaces can be found. The spherulitic size of PLA is decreased by the addition of PF. The α crystalline form of PLA remains in the composites. Compared with PF, T-PF (PF treated by a titanate coupling agent) and PBAT have negative effects on the crystallization of PLA. Full article

This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch’s ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches’ ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed. Full article

In this paper, a novel random facial variation modeling system for sparse representation face recognition is presented. Although recently Sparse Representation-Based Classification (SRC) has represented a breakthrough in the field of face recognition due to its good performance and robustness, there is the critical problem that SRC needs sufficiently large training samples to achieve good performance. To address these issues, we challenge the single-sample face recognition problem with intra-class differences of variation in a facial image model based on random projection and sparse representation. In this paper, we present a developed facial variation modeling systems composed only of various facial variations. We further propose a novel facial random noise dictionary learning method that is invariant to different faces. The experiment results on the AR, Yale B, Extended Yale B, MIT and FEI databases validate that our method leads to substantial improvements, particularly in single-sample face recognition problems. Full article