Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation using Wi-Fi Fingerprinting Based on Deep Neural Networks

One of key technologies for future large-scale location-aware services in access is a scalable indoor localization technique. In this paper, we report preliminary results from our investigation on the use of deep neural networks (DNNs) for hierarchical building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting, which we carried out as part of a feasibility study project on Xi'an Jiaotong-Liverpool University (XJTLU) Campus Information and Visitor Service System. To take into account the hierarchical nature of the building/floor classification problem, we propose a new DNN architecture based on a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification with argmax functions to convert multi-label classification results into multi-class classification ones. We also describe the demonstration of a prototype DNN-based indoor localization system for floor-level location estimation using real received signal strength (RSS) data collected at one of the buildings on the XJTLU campus. The preliminary results for both building/floor classification and floor-level location estimation clearly show the strengths of DNN-based approaches, which can provide near state-of-the-art performance with less parameter tuning and higher scalability.Comment: 5 pages, 6 figures, FOAN 2017 (Munich, Germany, Oct. 2017

Application of Electrochemical Sensor Based on the Reaction of Potassium Ferricyanide and Uric Acid in Uric Acid Detection

At present, the number of patients with hyperuricemia and gout in China is increasing year by year, and the demand for uric acid detection is increasing. In the current situation, a uric acid detection chip was designed based on the redox reaction between potassium ferricyanide and uric acid and the principle of electrochemical sensor, and the uric acid content was measured by the electrochemical detection chip and the software PSTrace5.8, and the error of the result was within ±10%. The method is low cost and high accuracy, which can provide a new technical means for uric acid detection

Microbial responses to inorganic nutrient amendment overridden by warming: Consequences on soil carbon stability.

Eutrophication and climate warming, induced by anthropogenic activities, are simultaneously occurring worldwide and jointly affecting soil carbon stability. Therefore, it is of great interest to examine whether and how they interactively affect soil microbial community, a major soil carbon driver. Here, we showed that climate warming, simulated by southward transferring Mollisol soil in agricultural ecosystems from the cold temperate climate zone (N) to warm temperate climate (C) and subtropical climate zone (S), decreased soil organic matter (SOM) by 6%-12%. In contrast, amendment with nitrogen, phosphorus and potassium enhanced plant biomass by 97% and SOM by 6% at the N site, thus stimulating copiotrophic taxa but reducing oligotrophic taxa in relative abundance. However, microbial responses to nutrient amendment were overridden by soil transfer in that nutrient amendment had little effect at the C site but increased recalcitrant carbon-degrading fungal Agaricomycetes and Microbotryomycetes taxa derived from Basidiomycota by 4-17 folds and recalcitrant carbon-degrading genes by 23%-40% at the S site, implying a possible priming effect. Consequently, SOM at the S site was not increased by nutrient amendment despite increased plant biomass by 108%. Collectively, we demonstrate that soil transfer to warmer regions overrides microbial responses to nutrient amendment and weakens soil carbon sequestration

Low-mass dark matter search results from full exposure of PandaX-I experiment

We report the results of a weakly-interacting massive particle (WIMP) dark matter search using the full 80.1\;live-day exposure of the first stage of the PandaX experiment (PandaX-I) located in the China Jin-Ping Underground Laboratory. The PandaX-I detector has been optimized for detecting low-mass WIMPs, achieving a photon detection efficiency of 9.6\%. With a fiducial liquid xenon target mass of 54.0\,kg, no significant excess event were found above the expected background. A profile likelihood analysis confirms our earlier finding that the PandaX-I data disfavor all positive low-mass WIMP signals reported in the literature under standard assumptions. A stringent bound on the low mass WIMP is set at WIMP mass below 10\,GeV/c$^2$, demonstrating that liquid xenon detectors can be competitive for low-mass WIMP searches.Comment: v3 as accepted by PRD. Minor update in the text in response to referee comments. Separating Fig. 11(a) and (b) into Fig. 11 and Fig. 12. Legend tweak in Fig. 9(b) and 9(c) as suggested by referee, as well as a missing legend for CRESST-II legend in Fig. 12 (now Fig. 13). Same version as submitted to PR

Public sentiment analysis and topic modeling regarding ChatGPT in mental health on Reddit: Negative sentiments increase over time

In order to uncover users' attitudes towards ChatGPT in mental health, this study examines public opinions about ChatGPT in mental health discussions on Reddit. Researchers used the bert-base-multilingual-uncased-sentiment techniques for sentiment analysis and the BERTopic model for topic modeling. It was found that overall, negative sentiments prevail, followed by positive ones, with neutral sentiments being the least common. The prevalence of negative emotions has increased over time. Negative emotions encompass discussions on ChatGPT providing bad mental health advice, debates on machine vs. human value, the fear of AI, and concerns about Universal Basic Income (UBI). In contrast, positive emotions highlight ChatGPT's effectiveness in counseling, with mentions of keywords like "time" and "wallet." Neutral discussions center around private data concerns. These findings shed light on public attitudes toward ChatGPT in mental health, potentially contributing to the development of trustworthy AI in mental health from the public perspective.Comment: 11 pages.8 figures, 2 table