A Novel Drinking Category Detection Method Based on Wireless Signals and Artificial Neural Network

With the continuous improvement of people’s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and packaged drinking water. At the same time, drinking category detection can be used for vending machine self-checkout. However, the current drinking category systems rely on special equipment, which require professional operation, and also rely on signals that are not widely used, such as radar. This paper introduces a novel drinking category detection method based on wireless signals and artificial neural network (ANN). Unlike past work, our design relies on WiFi signals that are widely used in life. The intuition is that when the wireless signals propagate through the detected target, the signals arrive at the receiver through multiple paths and different drinking categories will result in distinct multipath propagation, which can be leveraged to detect the drinking category. We capture the WiFi signals of detected drinking using wireless devices; then, we calculate channel state information (CSI), perform noise removal and feature extraction, and apply ANN for drinking category detection. Results demonstrate that our design has high accuracy in detecting drinking category.     

Robustness Evaluation of the Open Source Product Community Network Considering Different Influential Nodes

With the rapid development of Internet technology, the innovative value and importance of the open source product community (OSPC) is becoming increasingly significant. Ensuring high robustness is essential to the stable development of OSPC with open characteristics. In robustness analysis, degree and betweenness are traditionally used to evaluate the importance of nodes. However, these two indexes are disabled to comprehensively evaluate the influential nodes in the community network. Furthermore, influential users have many followers. The effect of irrational following behavior on network robustness is also worth investigating. To solve these problems, we built a typical OSPC network using a complex network modeling method, analyzed its structural characteristics and proposed an improved method to identify influential nodes by integrating the network topology characteristics indexes. We then proposed a model containing a variety of relevant node loss strategies to simulate the changes in robustness of the OSPC network. The results showed that the proposed method can better distinguish the influential nodes in the network. Furthermore, the network’s robustness will be greatly damaged under the node loss strategies considering the influential node loss (i.e., structural hole node loss and opinion leader node loss), and the following effect can greatly change the network robustness. The results verified the feasibility and effectiveness of the proposed robustness analysis model and indexes.     

Measuring Uncertainty in the Negation Evidence for Multi-Source Information Fusion

Dempster–Shafer evidence theory is widely used in modeling and reasoning uncertain information in real applications. Recently, a new perspective of modeling uncertain information with the negation of evidence was proposed and has attracted a lot of attention. Both the basic probability assignment (BPA) and the negation of BPA in the evidence theory framework can model and reason uncertain information. However, how to address the uncertainty in the negation information modeled as the negation of BPA is still an open issue. Inspired by the uncertainty measures in Dempster–Shafer evidence theory, a method of measuring the uncertainty in the negation evidence is proposed. The belief entropy named Deng entropy, which has attracted a lot of attention among researchers, is adopted and improved for measuring the uncertainty of negation evidence. The proposed measure is defined based on the negation function of BPA and can quantify the uncertainty of the negation evidence. In addition, an improved method of multi-source information fusion considering uncertainty quantification in the negation evidence with the new measure is proposed. Experimental results on a numerical example and a fault diagnosis problem verify the rationality and effectiveness of the proposed method in measuring and fusing uncertain information.     

Magnetic Molecularly Imprinted Polymers for the Rapid and Selective Extraction and Detection of Methotrexatein Serum by HPLC-UV Analysis

In this work, novel selective recognition materials, namely magnetic molecularly imprinted polymers (MMIPs), were prepared. The recognition materials were used as pretreatment materials for magnetic molecularly imprinted solid-phase extraction (MSPE) to achieve the efficient adsorption, selective recognition, and rapid magnetic separation of methotrexate (MTX) in the patients’ plasma. This method was combined with high-performance liquid chromatography–ultraviolet detection (HPLC–UV) to achieve accurate and rapid detection of the plasma MTX concentration, providing a new method for the clinical detection and monitoring of the MTX concentration. The MMIPs for the selective adsorption of MTX were prepared by the sol–gel method. The materials were characterized by transmission electron microscopy, Fourier transform-infrared spectrometry, X-ray diffractometry, and X-ray photoelectron spectrometry. The MTX adsorption properties of the MMIPs were evaluated using static, dynamic, and selective adsorption experiments. On this basis, the extraction conditions were optimized systematically. The adsorption capacity of MMIPs for MTX was 39.56 mgg⁻¹, the imprinting factor was 9.40, and the adsorption equilibrium time was 60 min. The optimal extraction conditions were as follows: the amount of MMIP was 100 mg, the loading time was 120 min, the leachate was 8:2 (v/v) water–methanol, the eluent was 4:1 (v/v) methanol–acetic acid, and the elution time was 60 min. MTX was linear in the range of 0.00005–0.25 mg mL⁻¹, and the detection limit was 12.51 ng mL⁻¹. The accuracy of the MSPE–HPLC–UV method for MTX detection was excellent, and the result was consistent with that of a drug concentration analyzer.     

Identification of Protosappanoside D from Caesalpinia decapetala and Evaluation of Its Pharmacokinetic,Metabolism and Pharmacological Activity

Protosappanoside D (PTD) is a new component isolated from the extract of Caesalpinia decapetala for the first time. Its structure was identified as protosappanin B-3-O-β-D-glucoside by ¹H-NMR, ¹³C-NMR, 2D-NMR and MS techniques. To date, the pharmacological activities, metabolism or pharmacokinetics of PTD has not been reported. Therefore, this research to study the anti-inflammatory activity of PTD was investigated via the LPS-induced RAW264.7 cells model. At the same time, we also used the UHPLC/Q Exactive Plus MS and UPLC-MS/MS methods to study the metabolites and pharmacokinetics of PTD, to calculate its bioavailability for the first time. The results showed that PTD could downregulate secretion of the pro-inflammatory cytokines. In the metabolic study, four metabolites were identified, and the primary degradative pathways in vivo involved the desaturation, oxidation, methylation, alkylation, dehydration, degradation and desugarization. In the pharmacokinetic study, PTD and its main metabolite protosappanin B (PTB) were measured after oral and intravenous administration. After oral administration of PTD, its T_max was 0.49 h, t_1/2z and MRT_(0–t) were 3.47 ± 0.78 h and 3.06 ± 0.63 h, respectively. It shows that PTD was quickly absorbed into plasma and it may be eliminated quickly in the body, and its bioavailability is about 0.65%.     

Improving Catalytic Activity of “Janus” MoSSe Based on Surface Interface Regulation

The monolayer Janus MoSSe is considered to be a promising catalytic material due to its unique asymmetric structure. In order to improve its catalytic performance for hydrogen evolution reactions (HERs) and oxygen evolution reactions (OERs), many attempts have been made. In this work, a series of transition metal (TM) atoms were anchored on the Janus MoSSe surface to screen effective TM single-atom catalysts for HERs and OERs through density functional theory (DFT) calculations. Fe@MoSSe presents excellent HERs performance and Ni@MoSSe presents excellent catalytic performance for OERs with extremely low over-potential of 0.32 V. The enhanced activity is attributed to the modest energy level of the d band center of the transition metal atom, and the transition metal atom improves the conductivity of the original MoSSe and offers unoccupied states near the Fermi level. At the same time, the anchoring of transition metal atoms redistributes the charge in the MoSSe system, which effectively regulates the electronic structure of the material itself. The strain calculation shows that the activity of the catalyst can be improved by reasonably adjusting the value of the applied strain.     

H-Bonding Room Temperature Phosphorescence Materials via Facile Preparation for Water-Stimulated Photoluminescent Ink

Pure organic room-temperature phosphorescence (RTP) materials built upon noncovalent interactions have attracted much attention because of their high efficiency, long lifetime, and stimulus-responsive behavior. However, there are limited reports of noncovalent RTP materials because of the lack of specific design principles and clear mechanisms. Here, we report on a noncovalent material prepared via facile grinding that can emit fluorescence and RTP emission differing from their components’ photoluminescent behavior. Exciplex can be formed during the preparation process to act as the minimum emission unit. We found that H-bonds in the RTP system provide restriction to nonradiative transition but also enhance energy transformation and energy level degeneracy in the system. Moreover, water-stimulated photoluminescent ink is produced from the materials to achieve double-encryption application with good resolution.     

High-Throughput Preparation and Machine Learning Screening of a Blue-Phase Liquid Crystal Based on Inkjet Printing

Blue-phase liquid crystal (BPLC) is considered as the next-generation liquid crystal display material, but its practical application is seriously affected by a narrow temperature range and a long research period. In this paper, we used inkjet printing technology to prepare BPLC materials with high throughput, and try to use machine vision technology to test BPLC with high throughput. The “standard curve method” for establishing each printing channel and the “vector matching method” for searching the chromaticity value of the minimum distance were proposed to improve the accuracy of inkjet printing BPLC materials. For a large number of sample-phase images, we propose a machine learning method to identify the liquid crystal phase. In this paper, for the first time, the high-throughput preparation and high-throughput detection of 1080 BPLC samples with five common components by a comprehensive experimental method has been successfully realized. The results are helpful to improve the research efficiency of blue-phase materials and provide a theoretical basis and practical guidance for rapid screening of multi-component BPLC materials.     

Ultrasensitive Simultaneous Detection of Multiple Rare Modified Nucleosides as Promising Biomarkers in Low-Put Breast Cancer DNA Samples for Clinical Multi-Dimensional Diagnosis

Early cancer diagnosis is essential for successful treatment and prognosis, and modified nucleosides have attracted widespread attention as a promising group of cancer biomarkers. However, analyzing these modified nucleosides with an extremely low abundance is a great challenge, especially analyzing multiple modified nucleosides with a different abundance simultaneously. In this work, an ultrasensitive quantification method based on chemical labeling, coupled with LC-MS/MS analysis, was established for the simultaneous quantification of 5hmdC, 5fdC, 5hmdU and 5fdU. Additionally, the contents of 5mdC and canonical nucleosides could be obtained at the same time. Upon derivatization, the detection sensitivities of 5hmdC, 5fdC, 5hmdU and 5fdU were dramatically enhanced by several hundred times. The established method was further applied to the simultaneous detection of nine nucleosides with different abundances in about 2 μg genomic DNA of breast tissues from 20 breast cancer patients. The DNA consumption was less than other overall reported quantification methods, thereby providing an opportunity to monitor rare, modified nucleosides in precious samples and biology processes that could not be investigated before. The contents of 5hmdC, 5hmdU and 5fdU in tumor tissues and normal tissues adjacent to the tumor were significantly changed, indicating that these three modified nucleosides may play certain roles in the formation and development of tumors and be potential cancer biomarkers. While the detection rates of 5hmdC, 5hmdU and 5fdU alone as a biomarker for breast cancer samples were 95%, 75% and 85%, respectively, by detecting these three cancer biomarkers simultaneously, two of the three were 100% consistent with the overall trend. Therefore, simultaneous detection of multiple cancer biomarkers in clinical samples greatly improved the accuracy of cancer diagnosis, indicating that our method has great application potential in clinical multidimensional diagnosis.