A fast and low-cost spray method for prototyping and depositing surface-enhanced Raman scattering arrays on microfluidic paper based device

In this study, a fast, low-cost, and facile spray method was proposed. This method deposits highly sensitive surface-enhanced Raman scattering (SERS) silver nanoparticles (AgNPs) on the paper-microfluidic scheme. The procedures for substrate preparation were studied including different strategies to synthesize AgNPs and the optimization of spray cycles. In addition, the morphologies of the different kinds of paper substrates were characterized by SEM and investigated by their SERS signals. The established method was found to be favorable for obtaining good sensitivity and reproducible results. The RSDs of Raman intensity of randomly analyzing 20 spots on the same paper or different filter papers depositing AgNPs are both below 15%. The SERS enhancement factor is approximately 2 x 10(7). The whole fabrication is very rapid, robust, and does not require specific instruments. Furthermore, the total cost for 1000 pieces of chip is less than $20. These advantages demonstrated the potential for growing SERS applications in the area of environmental monitoring, food safety, and bioanalysis in the future

SPACE-TIME GRAPH-BASED CONVOLUTIONAL NEURAL NETWORKS OF STUDY ON MOVEMENT RECOGNITION OF FOOTBALL PLAYERS

Behaviour recognition technology is an interdisciplinary technology, integrating many research achievements in computer vision, deep learning, pattern recognition and other fields. The key information of bone data on human behavior can not only accurately describe the motion posture of the human body in three-dimensional space, but also its rigid connection structure is robust to various external interference factors. However, the behavioral recognition algorithm is influenced by different factors such as background, light and environment, which is easy to lead to unstable recognition accuracy and limited application scenarios. To address this problem, in this paper, we propose a noise filtering algorithm based on data correlation and skeleton energy model filtering, construct a set of football player data sets, using the ST-GCN algorithm to train the skeleton characteristics of football players, and construct a behavior recognition system applied to football players. Finally, by comparing the accuracy of Deep LSTM, 2s-AGCN and the algorithm in this paper, the accuracy of TOP1 and TOP5 is 39.97% and 66.34%, respectively, which are significantly higher than the other two algorithms. It can realize the statistics of athletes and analyze the technical and tactical movements of players on the football field

A Quantitative LC-MS/MS Method for Determination of a Small Molecule Agonist of EphA2 in Mouse Plasma and Brain Tissue

Compound 27 {1, 12‐bis[4‐(4‐amino‐6,7‐dimethoxyquinazolin‐2‐yl)piperazin‐1‐yl]dodecane‐1,12‐dione} is a novel small molecule agonist of EphA2 receptor tyrosine kinase. It showed much improved activity for the activation of EphA2 receptor compared with the parental compound doxazosin. To support further pharmacological and toxicological studies of the compound, a method using liquid chromatography and electrospray ionization tandem mass spectrometry (LC–MS/MS) has been developed for the quantification of this compound. Liquid–liquid extraction was used to extract the compound from mouse plasma and brain tissue homogenate. Reverse‐phase chromatography with gradient elution was performed to separate compound 27 from the endogenous molecules in the matrix, followed by MS detection using positive ion multiple reaction monitoring mode. Multiple reaction monitoring transitions m/z 387.3 → 290.1 and m/z 384.1 → 247.1 were selected for monitoring compound 27 and internal standard prazosin, respectively. The linear calibration range was 2–200 ng/mL with the intra‐ and inter‐day precision and accuracy within the acceptable range. This method was successfully applied to the quantitative analysis of compound 27 in mouse plasma and brain tissue with different drug administration routes

Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction

The remarkable achievements and rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in quantitative investment. Traders can effectively leverage these LLMs to analyze financial news and predict stock returns accurately. However, integrating LLMs into existing quantitative models presents two primary challenges: the insufficient utilization of semantic information embedded within LLMs and the difficulties in aligning the latent information within LLMs with pre-existing quantitative stock features. We propose a novel framework consisting of two components to surmount these challenges. The first component, the Local-Global (LG) model, introduces three distinct strategies for modeling global information. These approaches are grounded respectively on stock features, the capabilities of LLMs, and a hybrid method combining the two paradigms. The second component, Self-Correlated Reinforcement Learning (SCRL), focuses on aligning the embeddings of financial news generated by LLMs with stock features within the same semantic space. By implementing our framework, we have demonstrated superior performance in Rank Information Coefficient and returns, particularly compared to models relying only on stock features in the China A-share market.Comment: 8 pages, International Joint Conferences on Artificial Intelligenc

Self-reductive synthesis of MXene/Na0.55Mn1.4Ti0.6O4 hybrids for high-performance symmetric lithium ion batteries.

Increasing environmental problems and energy challenges have created an urgent demand for the development of green and efficient energy-storage systems. The search for new materials that could improve the performance of Li-ion batteries (LIBs) is one of today's most challenging tasks. Herein, a stable symmetric LIB based on the bipolar material-MXene/Na0.55Mn1.4Ti0.6O4 was developed. This bipolar hybrid material showed a typical MXene-type layered structure with high conductivity, containing two electrochemically active redox couples, namely, Mn4+/Mn3+ (3.06 V) and Mn2+/Mn (0.25 V). This MXene/Na0.55Mn2O4-based symmetric full cell exhibited the highest energy density of 393.4 W h kg−1 among all symmetric full cells reported so far, wherein it is bestowed with a high average voltage of 2.81 V and a reversible capacity of 140 mA h g−1 at a current density of 100 mA g−1. In addition, it offers a capacity retention of 79.4% after 200 cycles at a current density of 500 mA g−1. This symmetric lithium ion full battery will stimulate further research on new LIBs using the same active materials with improved safety, lower costs and a long life-span

Contrasting patterns of community-weighted mean traits and functional diversity in driving grassland productivity changes under N and P addition

Fertilization could influence ecosystem structure and functioning through species turnover (ST) and intraspecific trait variation (ITV), especially in nutrient limited ecosystems. To quantify the relative importance of ITV and ST in driving community functional structure and productivity changes under nitrogen (N) and phosphorous (P) addition in semiarid grasslands. In this regard, we conducted a four-year fertilizer addition experiment in a semiarid grassland on the Loess Plateau, China. We examined how fertilization affects species-level leaf and root trait plasticity to evaluate the ability of plants to manifest different levels of traits in response to different N and P addition. Also, we assessed how ITV or ST dominated community-weighted mean (CWM) traits and functional diversity variations and evaluated their effects on grassland productivity. The results showed that the patterns of plasticity varied greatly among different plant species, and leaf and root traits showed coordinated variations following fertilization. Increasing the level of N and P increased CWM_specific leaf area (CWM_SLA), CWM_leaf N concentration (CWM_LN) and CWM_maximum plant height (CWM_Hmax) and ITV predominate these CWM traits variations. As a results, increased CWM_Hmax, CWM_LN and CWM_SLA positively influenced grassland productivity. In contrast, functional divergence decreased with increasing N and P and showed negative relationships with grassland productivity. Our results emphasized that CWM traits and functional diversity contrastingly drive changes in grassland productivity under N and P addition

Nucleons pair shell model in M-scheme

The nucleon pair shell model (NPSM) is casted into the so-called M-scheme for the cases with isospin symmetry and without isospin symmetry. The odd system and even system are treated on the same foot. The uncoupled commutators for nucleon-pairs, which are suitable for M-scheme, are given. Explicit formula of matrix elements in M-scheme for overlap, one-body operators, two-body operators are obtained. It is found that the $cpu$ time used in calculating the matrix elements in M-scheme is much shorter than that in the J-scheme of NPSM.Comment: 38 pages, 2 figure