Summary. We present the current status of the QUIJOTE (Q-U-I JOint TEnerife) CMB Experiment, a new instrument which will start operations early 2009 at Teide Observatory, with the aim of characterizing the polarization of the CMB and other processes of galactic and extragalactic emission in the frequency range 10-30 GHz and at large angular scales. QUIJOTE will be a valuable complement at low frequencies for the PLANCK mission, and will have the required sensitivity to detect a primordial gravitational-wave component if the tensor-to-scalar ratio is larger than r = 0.05.

In this work, we report the synthesis of porous flower-like Co/MnO composite microspheres assembled by plentiful lamellae for microwave absorption applications. Electron microscopy observations indicated that, in the composites, Co nanoparticles with the size of 20–30 nm are uniformly distributed in the MnO matrix. We investigated the electromagnetic (EM) wave absorption properties of the Co/MnO composites with different Co proportions. When the molar ratio between Co and MnO is 2:1, the best electromagnetic wave absorption performance is achieved. Notably, the optimal reflection loss value reaches −64.2 dB at 10.9 GHz corresponding to the thickness of 2.3 mm, and the effective absorption bandwidth (RL < −10 dB) is up to 6.0 GHz with the thickness of 2.1 mm. The prominent absorption performance of the as-prepared Co/MnO composites mainly arises from the superior impedance matching behavior and strong attenuation capacity as a consequence of the synergistic effect between Co and MnO nanoparticles as well as the hierarchical structure which leads to multiple loss pathways.

There has been increasing interest in design theranostic agents for combining diagnosis and different treatment modalities, especially for development physiological stable materials to avoid instability and dissociation in biological environment. Herein, a covalently connected silicon phthalocyanine (SiPc) and graphene oxide (GO) conjugate SiPc-GO is designed and synthesized via conjugation reaction to render stability. This novel highly water-soluble material displays intrinsically fluorescence and synchronous photothermal-photodynamic therapy (PTT/PDT) effect, along with 3-fold higher near-infrared (NIR) absorbance comparing to pristine GO. In vitro cell studies show that SiPc-GO could cause intracellular fluorescence, photothermal effect and reactive oxygen species (ROS) generation synchronously, and effective photoablation of cancer cells could be triggered by both 671 and 808 nm lasers via synergistic PTT/PDT or NIR photothermal effects, respectively. In vivo systemic administration in MCF-7 xenograft mice shows that SiPc-GO could effectively accumulate in the tumor regions and induce the inhibition of tumor growth violently after laser irradiation. This work establish SiPc-GO as a multimodality nanosized photomedicine for cancer imaging, synergistic PTT/PDT, and NIR photothermal therapy.

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Noise filtering, data predicting, and unmonitored data interpolating are important to dam deformation data analysis. However, traditional methods generally process single point monitoring data separately, without considering the spatial correlation between points. In this paper, the Space-Time Kalman Filter (STKF), a dynamic spatio-temporal filtering model, is used as a spatio-temporal data analysis method for dam deformation. There were three main steps in the method applied in this paper. The first step was to determine the Kriging spatial fields based on the characteristics of dam deformation. Next, the observation noise covariance, system noise covariance, the initial mean vector state, and its covariance were estimated using the Expectation Maximization algorithm (EM algorithm) in the second step. In the third step, we filtered the observation noise, interpolated the whole dam unmonitored data in space and time domains, and predicted the deformation for the whole dam using the Kalman filter recursion algorithm. The simulation data and Wuqiangxi dam deformation monitoring data were used to verify the STKF method. The results show that the STKF not only can filter the deformation data noise in both the temporal and spatial domain effectively, but also can interpolate and predict the deformation for the whole dam.