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In recent years, the demand for indoor positioning systems has grown rapidly with regard to location-based services. As a cost-effective choice, WiFi-based indoor positioning has attracted great increasing research attentions because it does not require external devices installed in the target environment. Although extensive research has been conducted on WiFi fingerprint matching, the problem of automatically adapting radio maps to fresh signal space environment still exists. The traditional methods often conduct site surveys regularly to update the outdated radio maps, which is time consuming and laborious. In this work, we propose an indoor positioning system AAIMSS to automatically update the radio maps based on an enhanced transfer learning (TL) approach with altered access points (APs) identification and mapping space searching. In the system, the proposed TL approach removes the outlier features and then searches a more accurate mapping space between the original radio map and crowdsourcing data. Our lightweight solution does not rely on additional devices and inertial sensors with high-power consumption. A set of experiments has been conducted in a teaching building to evaluate AAIMSS. The results show that this AAIMSS is robust to locate users in a dynamic environment. The average positioning accuracy achieves 2.5m, which significantly outperforms the positioning strategies with the original radio map by 65.3%, the radio map by directly removing the altered APs by 19.4%, and the radio map by the traditional TL by 85.2%.

Organic contamination has been recognized as a matter of concern, and simultaneous monitoring of various organic contaminants in the complex environment has always been a part of the efforts of scientists. In the present study, settled house dust (SHD) was selected to establish HPLC fingerprint method for simultaneously evaluating organic matters in an indoor environment. The relative standard deviations (RSDs) of the relative retention time (RRT) and relative peak area (RPA) for precision were 2.46% and 0.47%, respectively, while the RSDs of RRT and RPA for repeatability were 2.51% and 0.50%, respectively, and for stability within 24 h were 2.51% and 0.49%, respectively, which indicated good reliability of the method. Then, the method was used to evaluate the trend of indoor organic matters of Yunyan district (urban) and Gangou village (rural) from 2012 to 2014. The results showed the overall organic matters of Yunyan did not change significantly within 2 years, while the organic matters of Gangou village were a little aggravated by the appearance of some suspicious matters. Our study suggests a method with the capability of simultaneously evaluating various organic matters and a possibility for detecting those that are not currently on the list of priority organic contaminants.

In recent years, Wi-Fi-based indoor positioning has attracted increasing research attention due to its ubiquitous deployment. Although extensive research has been conducted on Wi-Fi fingerprint-based positioning, especially, in complex environments and long-term deployments, the automatic adaptation of radio map has not been fully studied and the problems remain open. When the positions of some Access Points (APs) change, the traditional approach regularly conducts site surveying which is time-consuming and labor-intensive. In this paper, we propose a crowdsourcing indoor positioning approach based on ensemble learning for automatic Altered APs Identification and Fingerprints Updating, namely AAIFU. We propose an algorithm to detect and identify the altered APs in crowdsourcing data. After getting the altered APs, we use the relationship between the received signal strength values of the altered APs and the unaltered APs in the crowdsourcing data to train a prediction model used to update the radio map. We also handle the device diversity on all the processes of AAIFU. Our proposed solution is light-weight which does not rely on additional infrastructure and inertial sensors with high power consumption. The comprehensive experiments have been carried out in our teaching building to evaluate the effectiveness of AAIFU. The results show that our proposed AAIFU can effectively adapt the radio map to the movement of APs and improve positioning accuracy. Correspondingly, we achieve an average positioning accuracy of 2.6m which outperforms the fingerprinting approach with the original radio map by 63.9%.

An enduring challenge in personalized medicine lies in selecting a suitable drug for each individual patient. Here we concentrate on predicting drug responses based on a cohort of genomic, chemical structure, and target information. Therefore, a recently study such as GDSC has provided an unprecedented opportunity to infer the potential relationships between cell line and drug. While existing approach rely primarily on regression, classification or multiple kernel learning to predict drug responses. Synthetic approach indicates drug target and protein-protein interaction could have the potential to improve the prediction performance of drug response. In this study, we propose a novel heterogeneous network-based method, named as HNMDRP, to accurately predict cell line-drug associations through incorporating heterogeneity relationship among cell line, drug and target. Compared to previous study, HNMDRP can make good use of above heterogeneous information to predict drug responses. The validity of our method is verified not only by plotting the ROC curve, but also by predicting novel cell line-drug sensitive associations which have dependable literature evidences. This allows us possibly to suggest potential sensitive associations among cell lines and drugs. Matlab and R codes of HNMDRP can be found at following https://github.com/USTC-HIlab/HNMDRP.

A simple and innovative processing method for fabricating anti-static ceramics has been developed based on Fe-infiltration in sintered tetragonal polycrystalline zirconia doped with 3 mol% yttria (3Y-TZP). The 3Y-TZP samples infiltrated by Fe in a reducing atmosphere showed both excellent anti-static and mechanical properties. Effects of infiltration temperature and time on the surface resistivity and Vickers hardness of the prepared samples were investigated. The surface resistivity and hardness were found to decrease with increases in the infiltration time and temperature. For a typical sample infiltrated at 1000 °C for 4 h, the surface resistivity decreased to 10 7 Ω/□, and the Vickers hardness was 11.2 GPa. The microstructure, chemical composition, and anti-static mechanism were investigated by XRD, SEM and XPS. The results indicated that there was a phase transformation from t -ZrO 2 to m -ZrO 2 during the infiltration process. It was also found that Fe, Fe 3 O 4 and FeO were located in the infiltration layer, conferring an antistatic property to the ZrO 2 ceramics.

An innovative processing method for regulating the surface resistivity of zirconia ceramic has been developed based on solution infiltration in porous preform body. In this study, effects of infiltration solution concentration on the surface resistivity and Vickers hardness were investigated. It was shown that the surface resistivity decreased with the increase of infiltration concentration, while the surface hardness was not significantly influenced. For a typical sample infiltrated in 3 mol/L Fe(NO 3 ) 3 ·9H 2 O solution for 2 h, the surface resistivity is reduced to 10 4 Ω/□, while the Vickers hardness was kept at 13 GPa. The content of element Fe decreased gradually from the surface to the core of the samples. The status of element Fe was verified as FeO and Fe 2 O 3 . About 49.4% of the element Fe was in the status of FeO, which was the conductive composition for anti-static purpose.