Bibliometric analysis of soil phosphate solubilizing microorganisms research using VOSviewer

Phosphorus-solubilizing microbes play key roles in improving phosphorus availability and in alleviating phosphorus nutrient limitation in soils. However, we did not have a comprehensive understanding of the overall research progress and development trend of phosphorus solubilizing microorganisms. In this study, we obtain documents from the Web of Science (WOS) core collection between 2002 and 2022, and a comprehensive review of the progress of global research on soil phosphate solubilizing microorganisms was conducted by using the VOSviewer bibliometric analysis tool. The results showed an increasing trend in the number of published articles from 2002 to 2022. India, accounting for 28% of the total number of published articles, became the most productive country. However, Canada was the country with the highest average citation frequency of articles. Chinese Academy of Sciences (CAS) was the greatest contributor with the most publications. Among the published journals, Frontiers in Microbiology, Applied Soil Ecology and Plant and Soil were the top three core journals in this field. Based on the keyword analysis, the assessment of the mechanisms between phosphorus solubilizing microbes and the soil carbon cycles with the different management practices became the new research trend among the scientific communities. These findings would provide an important reference value for future in-depth research on soil phosphate solubilizing microorganisms.

Formation environment and hydrocarbon potential of the Paleogene Enping Formation coal measures in the ZhuⅠDepression of northern South China Sea

The coal-measure source rock in the Chinese sea area plays a significant role as a hydrocarbon source rock,with its genetic environment,development and distribution,and hydrocarbon generation potential serving as essential factors for the exploration of coal-type oil and gas fields.This study focuses on the coal-measure source rock of the Paleogene Enping Formation in the ZhuⅠDepression,located in the northern South China Sea.The main geological insights obtained are as follows.The coal measures of the Enping Formation are developed in a warm and wet tropical-subtropical climate.The development environment of the coal-measure source rock in the Enping Formation includes the braided river delta upper plain peat swamp,characterized by dry forest swamp coal facies with relatively thick coal seams and a small number of layers.The braided river delta lower plain swamp-interdistributary bay of braided river delta front represents a forest edge-wetland herbaceous swamp coal facies with numerous layers of thin coal seams and poor stability.The shore swamp corresponds to an open water swamp coal facies with multiple layers of thin coal seams and poor stability.The organic matter abundance in the braided river delta upper plain is the highest,followed by the braided river delta lower plain-braided river delta front,and the shore-shallow lake.The organic matter type is predominantly typeⅡ1.Thermal evolution analysis suggests that the organic matter has progressed into a substantial oil generation stage.The hydrocarbon generation potential of the coal-measure source rock in the Enping Formation is the highest in the braided river delta upper plain,followed by the braided river delta lower plain-braided river delta front and the shore-shallow lake.Overall,this study proposes three organic facies in the coal-measure source rock of the Enping Formation:upper-plain swamp-dry forest swamp facies,lower plain-interdistributary bay-forest-herbaceous swamp facies,and lake swamp-herbaceous swamp facies.

Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

Research on trend prediction of component stock in fuzzy time series based on deep forest

With the continuous development of machine learning and the increasing complexity of financial data analysis,it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry.To improve the effectiveness of stock trend prediction and solve the problems in time series data processing,this paper combines the fuzzy affiliation function with stock-related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index.Meanwhile,in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge,this paper combines the deep forest model to train the stock data separately.The experimental results show that(1)the accuracy of the extreme random forest and the accuracy of the multi-grain cascade forest are both higher than that of the gated recurrent unit(GRU)model when the un-fuzzy index-adjusted dataset is used as features for input,(2)the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index-adjusted dataset as features for input,(3)the accuracy of the fuzzy index-adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un-fuzzy index-adjusted dataset as features for inputting the extreme random forest and(4)the average accuracy of the fuzzy index-adjusted dataset as features for inputting multi-grain cascade forest increased by 5.67%.

Research on DCC Strategy for Grid-Connected Inverter of D-PMSG under Unbalanced Network Voltage Conditions

According to performance analysis of a three-phase grid-connected inverter mathematical model of a directly-driven wind turbine with a permanent magnet synchronous generator (D-PMSG) under unbalanced network voltage conditions, a dual current-loop control strategy (DCC) oriented on positive voltage and negative current is proposed to inhibit the DC voltage fluctuation. Meanwhile, a notch filter is introduced into the conventional control strategy of a phase-locked loop to complete the low voltage ride through (LVRT) ability of the wind generator. A 1.5-MW D-PMSG with a back-to-back IGBT frequency converter was simulated in the PSCAD/EMTDC environment, and simulation results showed that: the maximum wind power tracking was achieved in this system and the proposed DCC strategy could successfully inhibit the rising aging of DC voltage and enhance the ride-through capability of D-PMSG wind generation system under unbalanced network voltage conditions.

Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time series of process variables may have an important indication about the process operation.For example,in a batch process,the change points can correspond to the operations and phases defined by the batch recipe.Hence identifying change points can assist labelling the time series data.Various unsupervised algorithms have been developed for change point detection,including the optimisation approachwhich minimises a cost functionwith certain penalties to search for the change points.The Bayesian approach is another,which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point.The paper investigates how the two approaches for change point detection can be applied to process data analytics.In addition,a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data.The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data.The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions,which will be useful for other machine learning tasks.

Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.

Kaempferol improves glucose uptake in skeletal muscle via an AMPK-dependent mechanism

Insulin resistance is a hallmark of type-2 diabetes(T2D)pathogenesis.Because skeletal muscle(SkM)is the major tissue for insulin-mediated glucose disposal,insulin resistance in SkM is considered a major risk factor for developing T2D.Thus,the identifi cation of compounds that enhance the ability of SkM to take up glucose is a promising strategy for preventing T2D.Our previous work showed that kaempferol,a fl avonol present in many foods,improves insulin sensitivity in obese mice,however,the mechanism underlying this beneficial action remains unclear.Here,we show that kaempferol directly stimulates glucose uptake and prevents lipotoxicity-impaired glucose uptake in primary human SkM.Kaempferol stimulates Akt phosphorylation in a time-dependent manner in human SkM cells.The effect of kaempferol on glucose uptake was blunted by inhibition of glucose transporter 4,phosphoinositide 3-kinase(PI3K),or AMPK.In addition,kaempferol induced AMPK phosphorylation,and inhibition of AMPK prevented kaempferol-stimulated Akt phosphorylation.In vivo,kaempferol administration induced rapid glucose disposal accompanied with increased Akt and AMPK phosphorylation in SkM tissue of the mice.Taken together,these fi ndings suggest that kaempferol stimulates glucose uptake in SkM via an AMPK/Akt dependent mechanism,and it may be a viable therapeutic agent for insulin resistance.

Introduction to the Special Issue on Advances on Modeling and State Estimation for Industrial Processes

In the past few years,significant progress has been made in modeling and state estimation for industrial processes to improve control performance,reliable monitoring,quick and accurate fault detection,diagnosis,high product quality,fule and resource consumption,etc.However,with the fast development of information technology,numerous essential issues are faced in modeling and state estimation,which generates the new need for novel modeling and or state estimation methodologies and in-depth studies of them.Therefore,this special issue is dedicated to innovative modeling and state estimation from applicability,computational efficiency,and effectiveness.

VAT Neutrality and Corporate Cash Holdings—Based on the Research of Uncredited VAT Refund Policy

This paper regards the uncredited value-added tax(VAT)refund policy promulgated in 2018 as an exogenous shock to improve the neutrality for VAT.Based on the statistics of listed companies in the non-financial sectors from 2013 to 2020,this paper applies the difference-in-differences(DID)approach to study the impact of the uncredited VAT refund policy on corporate cash holdings.The study finds that the policy has eased the impact of non-tax-neutral impact of non-refundable tax credits and lowered the level of corporate cash holdings.Furthermore,the analysis of capital sources verified that the policy has optimized the corporate capital conditions.These findings have proved to be robust after a series of tests.At the level of cross-sectional differences,the uncredited VAT refund policy exerts both"resource effect"and"signal effect",enabling companies to reduce cash holdings in the dimensions of both tax endowment and market expectation.At the level of policy effects,the policy improved the corporate behavior of high cash holdings for the production and operation cycle,with more notable impact on enterprises with higher tax credit rating.At the level of economic consequences,the adjustment of corporate cash holdings induced by uncredited VAT refund policy has significantly improved operating performance and reduced performance volatility,with a greater impact on enterprises with high capital intensity.The research findings evaluate the uncredited VAT refund policy from the perspective of tax neutrality,laying a theoretical foundation for further optimizing the uncredited VAT refund policy and the modern fiscal,tax and financial systems.

Intelligent reflecting surface-assisted federated learning in multi-platoon collaborative networks

Inspired by mobile edge computing(MEC),edge learning has gained a momentum by directly performing model training at network edge without sending massive data to a centralized data center.However,the quality of model training will be affected by the limited communication and computing resources of network edge.In this paper,how to improve the training performance of a federated learning system aided by intelligent reflecting surface(IRS)over vehicle platooning networks is studied,where multiple platoons train a shared federated learning model.Multi-platoon cooperation can alleviate the pressure of data processing caused by the limited computing resources of single platoon.Meanwhile,IRS can enhance the inter-platoon communication in a cost-effective and energy-efficient manner.Firstly,the federated learning optimization problem of maximizing the learning accuracy is formulated by jointing platoon scheduling,bandwidth allocation and phase shifts at the IRS to maximize the number of scheduled platoon.Specif-ically,in the proposed learning architecture each platoon updates the learning model with its own data and uploads it to the global model through IRS-based wireless networks.Then,a method based on sequential optimization algorithm(SOA)and a group-based optimization method are analyzed for single IRS aided and large-scale IRS aided commu-nication,respectively.Finally,a platoon scheduling scheme is designed based on the communication reliability and computing reliability of platoons.Simulation results demonstrate that large-scale IRS assisted communication can effectively improve the reliability of multi-user communication networks.The scheduling scheme based on learning reliability balances the communication performance and computing performance of platoons.

循环水通道阻塞效应对自主水下航行器水动力系数估算的影响

The Reynolds-averaged Navier–Stokes(RANS)equation was solved using computational fluid dynamics to study the effect of the circulating tank wall on the hydrodynamic coefficient of an autonomous underwater vehicle(AUV).Numerical results were compared with the experimental results in the circulating water tank of Harbin Engineering University.The numerical results of the model with different scale ratios under the same water in the flume were studied to investigate the effect of blockage on the hydrodynamic performance of AUV in the circulating flume model test.The results show that the hydrodynamic coefficient is stable with the scale reduction of the model.The influence of blocking effect on AUV is given by combining theoretical calculation with experiment.

Physical characteristics of high concentrated gas hydrate reservoir in the Shenhu production test area,South China Sea

High concentrated and heterogeneous distribution of gas hydrates have been identified in the gas hydrate production test region in the Shenhu area,South China Sea.The gas hydrate-bearing sediments with high saturation locate at two ridges of submarine canyon with different thickness and saturations just above the bottom simulating reflection.The crossplots of gamma ray,acoustic impedance(P-impedance)and porosity at four sites show that the sediments can be divided into the upper and lower layers at different depths,indicating different geotechnical reservoir properties.Therefore,the depositional environments and physical properties at two ridges are analyzed and compared to show the different characteristics of hydrate reservoir.High porosity,high P-wave velocity,and coarse grain size indicate better reservoir quality and higher energy depositional environment for gas hydrate at Sites W18 and W19 than those at Sites W11 and W17.Our interpretation is that the base of canyon deposits at Sites W18 and W19 characterized by upward-coarsening units may be turbidity sand layers,thus significantly improving the reservoir quality with increasing gas hydrate saturation.The shelf and slope sliding deposits compose of the fine-grained sediments at Sites W11 and W17.The gas hydrate production test sites were conducted at the ridge of W11 and W17,mainly because of the thicker and larger area of gas hydrate-bearing reservoirs than those at Sites W18 and W19.All the results provide useful insights for assessing reservoir quality in the Shenhu area.

Inoculation of Chlorella and Food Waste Improves the Physio-Morphological Features of Red Pepper by Regulating Activating Antioxidant Defense System

Food waste is recognized as a valuable source for potential agricultural applications to supply organic matter and nutrients to arable soil.However,the information on the combined application of food waste and the plant growth-promoting bacterial strain,Chlorella,related to plant metabolic features and sodium chloride content in arable soil is limited.The present study was conducted to investigate the exogenous application of food waste along with Chlorella,which improved the physio-morphological features of red pepper.Our results revealed that this combination enhanced the organic matter in the soil,ultimately improving the fertility rate of the soil,and the physio-morphological features,such as chlorophyll a content(24.5±0.7),root(7.8±0.7)cm and shoot length(12.1±0.7)cm,fresh weight(2.1±0.05)g,dry weight(0.19±0.05)g,mineral contents,and hormonal concentration(ABA by up to 2 folds).The combined treatment also minimized free radicals via the activation of the intrinsic antioxidant series cascade and electrolyte leakage.Our findings showed that adding Chlorella and food wastes improved growth characteristics and can be used as a green bio-fertilizer for sustainable agriculture.

用无声放电转化甲烷和二氧化碳同时制备合成气与烃(英文)

在低温常压条件下 ,研究了在无声放电反应器中以A型分子筛为催化剂从甲烷和二氧化碳合成烃和合成气 ,实现了在无声放电反应器中同时合成烃和合成气。实验在原料气流量 2 0 0～ 6 0 0ml/min、原料气甲烷和二氧化碳摩尔比 1/1～ 3/1及输入功率 10 0～ 5 0 0W的范围内进行。研究结果表明 ,低原料气流量有利于甲烷和二氧化碳的转化 ,而高原料气流量有利于烃的生成 ;原料气甲烷和二氧化碳摩尔比对制得合成气的H2 /CO摩尔比的影响最显著 ;甲烷和二氧化碳转化率及合成气和烃的产率均随输入功率的增加而提高。而所研究的范围内 ,当原料气流量为 2 0 0ml/min、甲烷和二氧化碳摩尔比为 1/1、输入功率为 5 0 0W时 ,甲烷和二氧化碳转化率达到最高值 ,分别为6 4%和 39%。以此法制备的合成气的H2 /CO摩尔比可以在很宽的范围内变化 ,本研究合成气H2 /CO摩尔比的变化范围是 0 .7～ 3 .1。

无声放电和电晕放电转化温室气体比较研究

在无声放电和电晕放电条件下 ,研究了温室气体甲烷和二氧化碳的转化特性 .实验结果表明 ,甲烷和二氧化碳在不同放电形式下反应得到不同的产物 .甲烷、二氧化碳电晕放电反应的主要产物是合成气 ,无声放电反应的产物包括合成气、烃、含氧化物和高聚物等 ,类似于Fischer- Tropsch合成的产物 ,但分布不符合 Schulz- Flory方程 .在实验条件下 ,电晕放电反应体系的能量产率是 2 .2 6 mol/ (k W· h) ,无声放电反应体系的为 0 .34 mol/ (k W· h) .输入功率对等离子体反应影响较显著 ,甲烷和二氧化碳转化率随体系输入功率的提高而很快增加 .将 13X分子筛催化剂引入无声放电反应 ,提高了烃类产物选择性 ,抑制了炭黑和高聚物的生成 ,但甲烷和二氧化碳转化率分别由 6 4.3%和 5 5 .4%降低至 5 1.6 %和 41.7% ,合成气的H2 / CO比由 1.7降至 1.

华南湿地松EMCC制浆过程中木素结构的变化(英文)

以华南湿地松为原料制备卡伯值为 16～ 32的常规硫酸盐浆和深化脱木素的改良硫酸盐 (EMCC)浆 .从木材和纸浆中分离木素 ,利用13C_NMR和31P_NMR技术表征木素样品的结构 ,研究制浆过程中木素结构的变化 .结果表明 ,从木材和纸浆中分离出来的木素结构是不同的 ;制浆方法影响纸浆残余木素的结构特征 ,包括脂肪羟基、酚羟基和羧基含量 ,β_O_4结构和缩合结构含量 .对常规硫酸盐浆和改良硫酸盐浆 ,卡伯值较低的纸浆残余木素脂肪羟基和 β_O_4结构含量较低 ,酚羟基、羧基和缩合结构含量较高 .当卡伯值相同时 ,EMCC木素的脂肪羟基和酚羟基含量较低 ,β_O_4结构和羧基含量较高 。

Preparation,characterization and catalytic properties of S_2O_8^(2-)/ZrO_2-CeO_2 solid superacid catalyst

A novel solid superacid catalyst S2O8^2-/ZrO2-CeO2 was prepared by a coprecipitation method and characterized by means of XRD FTIR, BET, TEM and DSC/TG analysis methods. The results indicated that incorporation of appropriate amounts of Ce into the catalyst was beneficial to the formation of sole tetragonal ZrO2 and effectively prevented from the formation of monoclinic ZrO〉 and restrained the loss of sulfated species. XRD revealed the presence of tetragonal Ce0.16Zr0.84O2phase in the case of S2O8^2-/ZrO2-CeO2 calcined above 500 ℃. Catalytic activities of S2O8^2-/ZrO2-CeO2 for the esterification of lactic acid with n-butanol was studied. The results showed that the optimum conditions were as follows： calcination temperature of the catalyst 600 ℃, n（lactic acid）：n（n-butyl alcohol）=1.0：3.0, w（S2O8^2-/ZrO2- CeO2）=12.0%, reaction temperature 145 ℃, and reaction time 2 h. The esterification efficiency of lactic acid was about 96.6%.

Influence of Pore Size,Salinity and Gas Composition upon the Hydrate Formation Conditions

An experimental device was set up to study the hydrate formation conditions.Effects of pore size,salinity,and gas composition on the formation and dissociation of hydrates were investigated.The result indicates that the induction time for the formation of hydrates in porous media is shorter than that in pure water.The decrease in pore size,by decreasing the size of glass beads,increases the equilibrium pressure when the salinity and temperature are kept constant.In addition,higher salinity causes higher equilibrium pressure when the pore size and temperature are kept constant.It is found that the effects of pore size and salinity on the hydrate equilibrium are quite different.At lower methane concentration,the hydrate equilibrium is achieved at lower pressure and higher temperature.