The OSIRIS detector is a subsystem of the liquid scintillator filling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $$10^{-16}\hbox { g/g}$$ 10 - 16 g/g of $$^{238}\hbox {U}$$ 238 U and $$^{232}\hbox {Th}$$ 232 Th requires a large ($$\sim 20\,\hbox {m}^3$$ ∼ 20 m 3 ) detection volume and ultralow background levels. The present paper reports on the design and major components of the OSIRIS detector, the detector simulation as well as the measuring strategies foreseen and the sensitivity levels to U/Th that can be reached in this setup.

No abstract

Nowadays, an increasing number of patients are seriously affected by lung cancer. Si Jun Zi Tang (SJZ), a four-herb Chinese medicine formula first described approximately one thousand years ago, is often prescribed for cancer patients as a complementary therapy. But the research on the effective materials for treating cancer using SJZ was rarely reported. To solve this problem, we evaluate the inhibitory effect of 10 samples of SJZ from different origins on PC9 cells. Ultraperformance liquid chromatography (UPLC) and hierarchical cluster analysis (HCA) along with canonical correlation analysis (CCA) and bioactivity validation were used to investigate the underlying correlation between the chemical ingredients and the antiproliferative effect of SJZ on PC9 cells. The evaluation indicated that 10 batches of SJZ could inhibit proliferation of PC9 cells and there was a notable difference in pharmacological activity between the different SJZ samples. The results of CCA and multivariate statistical analysis indicated that ginsenoside Ro and ginsenoside Rg1 might be active constituents of the antiproliferative effect as determined by spectrum-effect relationships. The results showed that bioassay and spectrum-effect relationships are suitable to associate sample quality with the active ingredient associated with clinical efficacy. And our finding would provide foundation and further understanding of the quality evaluation of traditional Chinese medicine decoction.

A wide-band and high gain circularly polarized (CP) graphene-based reflectarray operating in the THz regime is proposed and theoretically investigated in this paper. The proposed reflectarray consists of a THz CP source and several graphene-based unit-cells. Taking advantages of the Pancharatnam Berry (PB) phase principle, the graphene-based unit-cell is capable of realizing a tunable phase range of 360° in a wide-band (1.4–1.7 THz) by unit-cell rotating, overcoming the restriction of intrinsic narrow-band resonance in graphene. Therefore, this graphene-based unit-cell exhibits superior bandwidth and phase tunability to its previous counterparts. To demonstrate this, a wide-band (1.4–1.7 THz) focusing metasurface based on the proposed unit-cell that exhibits excellent focusing effect was designed. Then, according to the reversibility of the optical path, a CP reflectarray was realized by placing a wide-band CP THz source at the focal point of the metasurface. Numerical simulation demonstrates that this reflectarray can achieve a stable high gain up to 15 dBic and an axial ratio around 2.1 dB over the 1.4–1.7 THz band. The good radiation performance of the proposed CP reflectarray, as demonstrated, underlines its suitability for the THz communication applications. Moreover, the design principle of this graphene-based reflectarray with a full 360° phase range tunable unit-cells provides a new pathway to design high-performance CP reflectarray in the THz regime.

In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool University, Suzhou, China. We also provide preliminary results of localization and trajectory estimation based on convolutional neural network (CNN) and long short-term memory (LSTM) network with this database. For localization, we map RSS data for a reference point to an image-like, two-dimensional array and then apply CNN which is popular in image and video analysis and recognition. For trajectory estimation, we use a modified random way point model to efficiently generate continuous step traces imitating human walking and train a stacked twolayer LSTM network with the generated data to remember the changing pattern of geomagnetic field intensity against (x, y) coordinates. Experimental results demonstrate the usefulness of our new database and the feasibility of the CNN and LSTMbased localization and trajectory estimation with the database.