Intertwined charge and pair density orders in a monolayer high-Tc iron-based superconductor

Symmetry-breaking electronic phase in unconventional high-temperature (high-Tc) superconductors is a fascinating issue in condensed-matter physics, among which the most attractive phases are charge density wave (CDW) phase with four unit-cell periodicity in cuprates and nematic phase breaking the C4 rotational symmetry in iron-based superconductors (FeSCs). Recently, pair density wave (PDW), an exotic superconducting phase with non-zero momentum Cooper pairs, has been observed in high-Tc cuprates and the monolayer FeSC. However, the interplay between the CDW, PDW and nematic phase remains to be explored. Here, using scanning tunneling microscopy/spectroscopy, we detected commensurate CDW and CDW-induced PDW orders with the same period of lambda = 4aFe (aFe is the distance between neighboring Fe atoms) in a monolayer high-Tc Fe(Te,Se) film grown on SrTiO3(001) substrate. Further analyses demonstrate the observed CDW is a smectic order, which breaks both translation and C4 rotational symmetry. Moreover, the smecticity of the CDW order is strongest near the superconducting gap but weakens near defects and in an applied magnetic field, indicating the interplay between the smectic CDW and PDW orders. Our works provide a new platform to study the intertwined orders and their interactions in high-Tc superconductors

Review of research progress on soil moisture sensor technology

Soil moisture is directly related to the amount of irrigation in agriculture and influences the yield of crops. Accordingly, a soil moisture sensor is an important tool for measuring soil moisture content. In this study, the previous research conducted in recent 2-3 decades on soil moisture sensors was reviewed and the principles of commonly used soil moisture sensor and their various applications were summarized. Furthermore, the advantages, disadvantages, and influencing factors of various measurement methods employed were compared and analyzed. The improvements were presented by several scholars have established the major applications and performance levels of soil moisture sensors, thereby setting the course for future development. These studies indicated that soil moisture sensors in the future should be developed to achieve high-precision, low-cost, non-destructive, automated, and highly integrated systems. Also, it was indicated that future studies should involve the development of specialized sensors for different applications and scenarios. This review research aimed to provide a certain reference for application departments and scientific researchers in the process of selecting soil moisture sensor products and measuring soil moisture

Survey of Automatic Labeling Methods for Topic Models

Topic models are often used in modeling unstructured corpora and discrete data to extract the latent topic. As topics are generally expressed in the form of word lists, it is usually difficult for users to understand the meanings of topics, especially when users lack knowledge in the subject area. Although manually labeling topics can generate more explanatory and easily understandable topic labels, the cost is too high for the method to be feasible. Therefore, research on automatic labeling of topic discovered provides solutions to the problem. Firstly, the currently most popular technique, latent Dirichlet allocation (LDA), is elaborated and analyzed. According to the three different representations of topic labels, based on phrases, abstracts, and pictures, the topic labeling methods are classified into three types. Then, centered on improving the interpretability of topics, with different types of generated topic labels utilized, the relevant research in recent years is sorted out, analyzed, and summarized. The applicable scenarios and usability of different labels are also discussed. Meanwhile, methods are further categorized according to their different characteristics. The focus is placed on the quantitative and qualitative analysis of the abstract topic labels generated through lexical-based, submodular optimization, and graph-based methods. The differences between separate methods with respect to the learning types, technologies used, and data sources are then compared. Finally, the existing problems and trend of development of research on automatic topic labeling are discussed. Based on deep learning, integrating with sentiment analysis, and continuously expanding the applicable scenarios of topic labeling, will be the directions of future development

Design and Analysis of Joint Group Shuffled Scheduling Decoding Algorithm for Double LDPC Codes System

In this paper, a joint group shuffled scheduling decoding (JGSSD) algorithm for a joint source-channel coding (JSCC) scheme based on double low-density parity-check (D-LDPC) codes is presented. The proposed algorithm considers the D-LDPC coding structure as a whole and applies shuffled scheduling to each group; the grouping relies on the types or the length of the variable nodes (VNs). By comparison, the conventional shuffled scheduling decoding algorithm can be regarded as a special case of this proposed algorithm. A novel joint extrinsic information transfer (JEXIT) algorithm for the D-LDPC codes system with the JGSSD algorithm is proposed, by which the source and channel decoding are calculated with different grouping strategies to analyze the effects of the grouping strategy. Simulation results and comparisons verify the superiority of the JGSSD algorithm, which can adaptively trade off the decoding performance, complexity and latency

Prediction Model and Influencing Factors of CO₂ Micro/Nanobubble Release Based on ARIMA-BPNN

The quantitative prediction of CO2 concentration in the growth environment of crops is a key technology for CO2 enrichment applications. The characteristics of micro/nanobubbles in water make CO2 micro/nanobubble water potentially useful for enriching CO2 during growth of crops. However, few studies have been conducted on the release characteristics and factors influencing CO2 micro/nanobubbles. In this paper, the factors influencing CO2 release and changes in CO2 concentration in the environment are discussed. An autoregressive integrated moving average and backpropagation neural network (ARIMA-BPNN) model that maps the nonlinear relationship between the CO2 concentration and various influencing factors within a time series is proposed to predict the released CO2 concentration in the environment. Experimental results show that the mean absolute error and root-mean-square error of the combination prediction model in the test datasets were 9.31 and 17.48, respectively. The R2 value between the predicted and measured values was 0.86. Additionally, the mean influence value (MIV) algorithm was used to evaluate the influence weights of each input influencing factor on the CO2 micro/nanobubble release concentration, which were in the order of ambient temperature > spray pressure > spray amount > ambient humidity. This study provides a new research approach for the quantitative application of CO2 micro/nanobubble water in agriculture

Web-based Agricultural Technology Evaluation Information System: Design, Application and Evaluation

This study describes the web-based agricultural technology evaluation information system, which has been shown to provide a flexible interface with which to arrange various assessment and information management function procedures. Secondly, this study examines the effects of the application of the academic misconduct detection mechanism and abnormally scoring mechanism. In an evaluative study with the two evaluation business cycle of the system, it has been found that this system is more efficient, more accurate and more rigorous than the traditional offline evaluation model. It has also been shown that cheating behaviour in agricultural technological achievements information declaration and evaluation link can be detected accurately and promptly

Pulse-Modulation Eddy Current Evaluation of Interlaminar Corrosion in Stratified Conductors: Semi-Analytical Modeling and Experiments

Interlaminar corrosion (ILC) poses a severe threat to stratified conductors which are broadly employed in engineering fields including aerospace, energy, etc. Therefore, for the pressing concern regarding the safety and integrity of stratified conductors, it is imperative to non-intrusively and quantitatively interrogate ILC via non-destructive evaluation techniques. In this paper, pulse-modulation eddy current (PMEC) for imaging and assessment of ILC is intensively investigated through theoretical simulations and experiments. A semi-analytical model of PMEC evaluation of ILC occurring at the interlayer of two conductor layers is established based on the extended truncated region eigenfunction expansion (ETREE) along with the efficient algorithm for the numerical computation of eigenvalues for reflection coefficients of the stratified conductor under inspection. Based on theoretical investigation, PMEC evaluation of ILC in testing samples are further scrutinized by using the PMEC imaging system built up for the experimental study. The theoretical and experimental results have revealed the feasibility of PMEC for imaging and evaluation of ILC in stratified conductors

Prediction Model and Influencing Factors of CO2 Micro/Nanobubble Release Based on ARIMA-BPNN

The quantitative prediction of CO2 concentration in the growth environment of crops is a key technology for CO2 enrichment applications. The characteristics of micro/nanobubbles in water make CO2 micro/nanobubble water potentially useful for enriching CO2 during growth of crops. However, few studies have been conducted on the release characteristics and factors influencing CO2 micro/nanobubbles. In this paper, the factors influencing CO2 release and changes in CO2 concentration in the environment are discussed. An autoregressive integrated moving average and backpropagation neural network (ARIMA-BPNN) model that maps the nonlinear relationship between the CO2 concentration and various influencing factors within a time series is proposed to predict the released CO2 concentration in the environment. Experimental results show that the mean absolute error and root-mean-square error of the combination prediction model in the test datasets were 9.31 and 17.48, respectively. The R2 value between the predicted and measured values was 0.86. Additionally, the mean influence value (MIV) algorithm was used to evaluate the influence weights of each input influencing factor on the CO2 micro/nanobubble release concentration, which were in the order of ambient temperature > spray pressure > spray amount > ambient humidity. This study provides a new research approach for the quantitative application of CO2 micro/nanobubble water in agriculture

Preparation and Properties of CO₂ Micro-Nanobubble Water Based on Response Surface Methodology

Carbon dioxide (CO2) enrichment in an agricultural environment has been shown to enhance the efficiency of crop photosynthesis, increasing crop yield and product quality. There is a problem of the excessive use of CO2 gas when the CO2 is enriched for crops, such as soybean and other field crops. Given the application of micro-nanobubbles (MNBs) in agricultural production, this research takes CO2 as the gas source to prepare the micro-nanobubble water by the dissolved gas release method, and the response surface methodology is used to optimize the preparation process. The results show that the optimum parameters, which are the gas–liquid ratio, generator running time, and inlet water temperature for the preparation of CO2 micro-nanobubble water, are 2.87%, 28.47 min, and 25.52 °C, respectively. The CO2 content in the MNB water prepared under the optimum parameters is 7.64 mg/L, and the pH is 4.08. Furthermore, the particle size of the bubbles is mostly 255.5 nm. With the extension of the storage time, some of the bubbles polymerize and spill out, but there is still a certain amount of nanoscale bubbles during a certain period. This research provides a new idea for using MNB technology to increase the content and lifespan of CO2 in water, which will slow the release and increase the utilization of CO2 when using CO2 enrichment in agriculture