In the power system,power lines carry the important task of conveying electric energy,and safe and reliable power lines are of great significance to the solid development of the power system.Aiming at the problems of low accuracy and poor real-time detection of hanging objects,tower cranes and other targets in power lines using deep learning algorithms,a power line safety detection and anomaly identification method based on improved YOLOv5 is proposed.The method is based on the YOLOv5s algorithm,which introduces the ECA attention mechanism in part of the C3 module of the network to enhance the feature extraction capability of the network;adds the Fast-RFB module to enhance detection speed and accuracy after the pooling layer in the backbone network;uses the decoupled head to replace the coupled detection head of the original network to improve the detection accuracy;and finally,replaces the CIoU loss function of the original model with the Wise-IoU loss function of the original model to reduce the loss of training process.Simulation experiments show that the P_mA0.5 and P_mA0.5:0.95 of the improved YOLOv5s algorithm on the power line dataset are 92.2% and 56.5%,respectively,which exceeds the original YOLOv5 model by 10.3% and 7.3%,respectively,and the detection speed is 83 frames/s,which meets the requirements of safety detection and anomaly identification of power lines in the real environment.

Terpenoids,natural products formed from isopentenyl diphosphate (IPP)and dimethylallyl diphosphate (DMAPP)as precursors,are almost ubiquitous in all organisms,with medicinal plants being predominant.They find wide applications in pharmaceuticals,food,chemical,and energy industries.The extraction from plants and chemical synthesis are commonly employed methods to obtain terpenoids.However,both methods suffer from drawbacks such as low efficiency,unstable quality,and high costs.These limitations clearly cannot meet the increasing demand for terpenoids.With the rapid development of synthetic biology techniques,significant progress has been made in engineering microorganisms for the synthesis of terpenoids,particularly yeast cells.Yeast cells,owing to their similar structure to plant cells and mature genetic manipulation systems,have been utilized for the synthesis of various terpenoids,gradually showcasing the advantages of biosynthetic approaches.This article reviews the progress of metabolic engineering for efficient synthesis of important terpenoids using two model yeasts,Saccharomyces cerevisiae and Yarrowia lipolytica,as examples.Specifically,it summarizes various strategies for metabolic engineering,including the screening and expression of highly active terpenoid synthases,the supply and efficient utilization of key precursor acetyl-CoA,as well as strategies for enhancing the synthesis flux of direct precursors,IPP and DMAPP,while minimizing their consumption.Finally,the article concludes with a summary and outlook on the opportunities and challenges faced by yeast-based synthesis of terpenoids.

Magnetic coupling drive is a non-contact transmission technology that uses magnetic field interaction to achieve power transfer and has the advantages of soft starting,overload protection,vibration isolation and adaptability to harsh environments.This paper reviews the research status of magnetic seal drive system at home and abroad,and discusses its wide application in chemical industry,petroleum and other fields.In this paper,the torque,electromagnetic characteristics,eddy current,heat dissipation and application of magnetic coupler are discussed in detail,and it is pointed out that the magnetic seal transmission system with high torque,low eddy current,high efficiency and high temperature resistance will be the future development trend.

Real time object detection is a very important topic in computer vision and a necessary component in computer vision systems.For the problem of urban traffic vehicle detection,the YOLO model can be used to achieve intelligent road vehicle detection.To optimize the real-time detection capability of computers for vehicles,this paper proposes an improved YOLO vehicle recognition and detection algorithm based on attention mechanism.YOLOv7 is used as the main body,and attention mechanism is introduced for the Backbone and Head modules of the YOLOv7 network model to adapt to different vehicle recognition tasks.Experimental validation is conducted using a publicly available dataset of Roboflow,demonstrating notable improvements in car detection performance compared to the original YOLOv7 model.The enhanced network achieves a P_mA value of 72.2%,marking a 0.9 percentage point increase over the baseline YOLOv7 model on the same dataset.These results underscore the efficacy of the proposed algorithm in meeting the requirements of vehicle detection applications.

Using lignocellulose to produce fuel and chemicals is one of the effective ways to solve the current energy crisis.Although the pretreatment technology of lignocellulose has been widely studied,the high energy demand,high cost,and limitations of equipment corrosion resistance technology of traditional pretreatment technology have spurred the development of new pretreatment processes.The emergence of deep eutectic solvents (DES)effectively solves these problems and greatly improves the conversion efficiency of lignocellulose.At present,the application and research of binary deep eutectic solvents (BDES)is more extensive,the more efficient and targeted ternary deep eutectic solvents (TDES) are also gradually developing.In this study,TDES were systematically classified (including TDES containing alcohols and acids,TDES containing organic molecular compounds,TDES containing metal halides and TDES containing two different acids).The research progress of the above TDES in improving enzymatic hydrolysis performance after lignocellulose pretreatment was discussed;the combined pretreatment methods were reviewed.The potential application of DES in different research directions was prospected.

Most simple four-dimensional chaotic systems produce chaotic attractors;only three different phase planes have significant chaotic attractor properties.The simple four-dimensional chaotic system constructed in this study can generate six two-dimensional attractor phase diagrams and has complex chaotic properties.The phase diagram,Lyapunov exponential spectrum,bifurcation diagram,0-1 test diagram and Poincare cross section diagram are used to verify the abundant dynamic characteristics of the system.Multisim is used to simulate the system,and the results show that the circuit simulation is in agreement with the numerical analysis,which proves the physical realization of the system.Finally,a robust synchronization control scheme is designed,which proves theoretically that the driving-response system can achieve synchronization.The synchronization controller is applied to chaotic masking secure communication,which proves that the method has good security and reliability.

Spatial correlation data and distorted measurement error data are often encountered in the process of statistical modeling of environmental data in the fields of air,surface water and acoustic environment.In order to solve the problems of spatial correlation and distorted measurement error of data in actual statistical modeling,the estimation theory of partial linear spatial autoregressive model with distorted measurement error is studied.The influence of distorted measurement error is eliminated by conditional absolute mean calibration,which avoids the imposition of non-zero expectation conditions on variables.By using the calibrated variables and combining B-spline approximation technique,orthogonal projection method and two-stage least squares method,the endogeneity problem in the model is solved.The proposed method eliminates the influence of the non-parametric part on the variable selection of the parametric part,and ensures the effectiveness and consistency of the proposed estimators.Under certain conditions,the asymptotic normality of the parameter estimation vector of the linear part and the optimal convergence rate of the non-parametric function are proved.The results obtained will further improve the theoretical system of spatial data statistical model,contribute to a more accurate understanding of data patterns and relationships in practical problems,and provide a new reference method for spatial data modeling in environmental science,biomedicine,social science and other fields.

For stochastic singular uncertain time-varying delay semi-Markov switching systems,the parameters and controllers of the dynamic event-triggered control are designed,and it is proved that the minimum event interval time is constant positive.A new Lyapunov-Krasovskii functional is constructed,and the integral term weight matrix is no longer fixed and synchronized with the switching mode.The robust stochastic admissibility of closed-loop systems is analyzed in detail using conditional expectation and It process properties.The linearization of the Hessian term is solved by using the Moore-Penrose inverse formula and the full rank decomposition theorem for matrices.Finally,the effectiveness of the design scheme is verified by numerical examples and simulation images.

The interaction between cationic peptides and deoxyribonucleic acid(DNA)is of great significance to the basic research of biology and the pathogenesis of many diseases.The article employs optical microscopy to initially investigate the liquid-liquid phase separation(LLPS)of poly proline-arginine(Poly PR)in the presence of single-stranded salmon sperm DNA.It was found that an increase in peptide chain length and concentration promotes LLPS.Then,the effect of KCl concentration onthe LLPS of Poly PR-DNAsolution was observed,and it was found that the increase of KCl concentration inhibits LLPS.When the KCl concentration exceeds 800 mmol/L,the droplets disappear;at the same time,when the concentration of salt is constant,the increase of DNA concentration will promote the LLPS of the mixture.Secondly,the effect of KCl concentration of 100 mmol/L at the interaction between PolyPR and double-stranded salmon DNA in liquid phase separation(LLPS)was studied,and it was found that the longer double-stranded salmon DNA are more difficult to undergo LLPS compared with single-stranded salmon DNA.Finally,in order to quantitatively study the mechanism of DNA and Poly PR interaction,a single-molecule force microscopy(MT)was used to measure the change of criticality of Poly PR and DNA,and analyze its mechanism from the single-molecule level.The experimental results showed that the increase of Poly PR chain length and concentration also led to an increase in DNA cohesion;and the size of cohesion was measured after the addition of KCl solution,which showed that the cohesion decreased with the increase of salt concentration.The experimental results obtained by single molecule show good consistency with the findings obtained by optical microscopy,which led to the conclusion that longer polypeptide chains and higher concentrations can enhance the interaction between Poly PR and DNA and decrease the interaction between Poly PR and DNA due to salt ions.

A fuzzy relation is a generalization of a classical binary relation,which is an important tool for dealing with fuzziness related problems.Aggregation of fuzzy relations has a wide range of applications in the fields of data mining,engineering design,and decision analysis.This paper focuses on the properties of fuzzy relations (reflexivity,symmetry,transitivity,connectedness,etc.)in the aggregation process,discusses the problem of preserving the properties of fuzzy relations as well as fuzzy ordinal relations by different aggregation operators.Finally,gives an example of solving a multi-criteria decision-making problem by using fuzzy ordinal relations.

The edema around the hematoma after hemorrhagic stroke has attracted widespread attention in recent years as a marker of secondary injury after cerebral hemorrhage.Studying the occurrence and progression of edema around hematoma in patients,early identification and prediction,is of great significance for improving patient prognosis and enhancing their quality of life.Based on real clinical data such as patient personal information,imaging information,treatment plans,and prognosis,polynomial regression models and ridge regression models are used to fit the data,and Gaussian functions are used to fit the data.The best fitting method is selected based on the calculated residual results to construct a curve of edema volume progression over time for all patients.Divide the patients into four subgroups based on age,and use the obtained best fit model to analyze the age differences in the progression pattern of edema volume over time.Finally,the change in edema volume was calculated based on known data,and a random forest and multi-layer perceptron model were used for training.The model was predicted using a test set,and the impact of different treatment methods on the progression pattern of edema volume was analyzed to determine the optimal treatment plan.

To study the water migration law of cucumber slices during hot-air drying,this paper takes cucumber slices as the research object and explores the influences of different temperatures and slice thicknesses on the water ratio and drying rate of cucumber slices.Firstly,a mathematical model for describing the water migration law of cucumber slices during hot-air drying is established,and numerical simulation analysis is carried out based on the numerical simulation software COMSOL.The results show that the Page model is more suitable for describing the water change law of cucumber slices during hot-air drying.Under the experimental conditions set,the coefficient of determination R² of the Page model reaches 0.99,and the corresponding residual sum of squares RSS and chi-square χ² are less than 0.017 16 and 0.000 953 351,respectively.Meanwhile,the experimental data is fitted,and the effective diffusion coefficient of cucumber slices in the temperature range of 50～80 ℃ is calculated to be between 0.173 5×10^-9~4.189×10^-9.The higher the hot-air temperature and the greater the thickness,the larger the effective diffusion coefficient.The numerical simulation process visually describes the water migration process of cucumber slices.The water concentration of cucumber slices gradually decreases from the edge to the interior.The water concentration at the edge position decreases rapidly,while the water concentration at the core position decreases slowly.By comparing and analyzing the change processes of dry basis moisture content in the numerical simulation and experimental processes,it is found that their change trends are the same,but the dry basis moisture content decreases faster in the experimental process.This model can provide a reference for the simulation study of other materials in hot-air drying technology.

In today's world where hemorrhagic stroke has a significant impact on people's health and economy,early identification and prognosis prediction are crucial.This article focuses on the urgent onset,rapid development,and poor prognosis of hemorrhagic stroke.Based on the knowledge of random forest regression,gradient boosting machines,and multiple perceptrons,a model is constructed to predict the prognosis of patients with hemorrhagic stroke by analyzing the occurrence and evolution of hematoma expansion and edema around the hematoma in clinical patients.After reading the data,data was preprocessed,data matching and data integration were carried out according to the serial number,and then data sets were divided,and the random forest regression model and gradient elevator model were respectively used for training and to predict the 90-day mRS Scores of 160 patients.Finally,the multi-layer perceptron model is used to carry out the correlation relationship of the training feature information,obtain the influence effect among the relevant data,and put forward suggestions for the relevant clinical decision-making.The experimental results are suitable and effective,and can be applied to finance,urban planning and other fields in the future.

Pectin is a natural food additive widely present in plant cell wall,and mango peel contains a large amount of pectin.In order to enhance the utilization of mango peel pectin resources,ultrasound-assisted chelating agent acid method was used to screen and optimize the extraction conditions of mango peel pectin.The effects of ethanol volume fraction,extraction time,ultrasonication time,liquid-to-material ratio,pH of the extraction solution and extraction temperature on the extraction rate were explored by a one-way test.On this basis,the extraction process conditions were optimized using Plackett-Burman and response surface tests with mango peel pectin extraction rate as the final evaluation index.The results showed that the optimal extraction process parameters for mango peel pectin were 85% ethanol volume fraction,100 min extraction time,20 min ultrasound time,16∶1(mL·g-1) liquid to material ratio,pH 2.0 of the extracting solution,and 80 ℃ extraction temperature,which resulted in a pectin extraction rate of 17.79% and a total galacturonic acid content of 73.63%.

Bi₂Se₃ and Bi₂Te₃,which are topological insulators,show excellent thermoelectric properties.As a new class of topological Weyl semimetals,whether TaAs possesses excellent thermoelectric properties has attracted much attention.In this work,the effect of electron-phonon coupling on the electronic transport properties of TaAs was studied using first-principles calculations and Boltzmann transport theory.Compared with previous studies,when the electron-phonon coupling effect is considered,the maximum ZT value in TaAs at 900 K is found to be much lower with the value of merely 0.078.The results contribute to further understanding of the transport properties of Weyl semimetallic TaAs.

Elucidating the complex interactions that govern protein and peptide behavior in liquid-liquid phase separation (LLPS)is crucial for understanding biological functions and dysfunctions.In this study,coarse-grained molecular dynamics simulations were employed to investigate the phase separation of poly proline-arginine peptides (poly PR)with different sequences in solutions of varying concentrations and temperatures.Our findings reveal that for poly PR25 peptides with a length of 50,significant phase separation only occurs when the salt concentration reaches 2 700 mmol·L-1;below this critical concentration,no phase separation is observed.To uncover the molecular mechanisms controlling peptide phase separation,dielectric constant modifiers and hydrophobic perturbants were introduced into the peptide system.The simulation results indicate that both hydrophobic and electrostatic interactions are factors influencing the phase separation of PR25 peptides.Furthermore,the effects of temperature and variations were examined in amino acid repeat sequences on phase separation.Notably,as the temperature increases,the LLPS behavior of PR25 is significantly inhibited,and different proline-arginine sequences in poly PR result in varying degrees of phase separation.Our simulation outcomes provide novel insights for further exploring liquid-liquid phase separation.

Hydrodynamic cavitation technology is gradually gaining widespread application,and the accurate and efficient extraction of structural characteristics of cavitation clouds contributes to a better understanding of the internal mechanisms of cavitation.In this study,high-speed imaging experiments were conducted to reveal the characteristics of cavitation phenomena within a jet oscillator through grayscale value analysis.Additionally,the dynamic mode decomposition method(DMD) was employed to conduct an in-depth exploration of the transient flow field inside the oscillator,investigating the coherent structures and frequency characteristics of cavitation clouds within the oscillator.Furthermore,different dominant structures were extracted,and the overall cyclic dynamics relationship with cloud cavitation under shedding frequency was discovered.Finally,using the DMD method,this study accurately extracted the dominant frequencies and modal characteristics,revealing that the energy of the oscillator mainly concentrates in the first two modes,forming larger coherent structures,while higher-frequency modes contain less energy.These experimental results provide important experimental and theoretical foundations for further research into the mechanisms of jet cavitation flow.

The purpose of this paper is to solve the deficiency of single sweetener in taste,such as no-pure sweet taste,obvious aftertaste and bitter taste,etc.By means of single factor test and orthogonal test to stevioside Reb-A (rebaudioside A),erythritol,maltitol,L-glutamate and thaumatin with different mixing ratio optimization,a mixture sweetener with similar taste as sucrose,pure sweet taste nutrition and safety was prepared.The results showed that erythritol and maltitol could obviously modify the aftertaste and bitter taste of stevioside Reb-A and improve the taste quality.The sweet protein thaumatin increased the nutrition of the mixture,while L-glutamate could inhibit the excessive sweetness of stevioside Reb-A,improving the flavor.The optimal proportion and mass fraction of the mixed sweeteners was stevioside rebaudioside A 0.016%,erythritol 1.2%,maltitol 2.5%,thaumatin 0.003%,L-glutamate 0.2%.Compared with other mixture sweeteners in the market,the mixture sweeteners has the advantages of pure sweet taste,low calorie,similar taste as sucrose,nutrition security,etc,which could be used as a popular family-size mixture of sweeteners.

Hemorrhagic stroke is a disease with a high mortality rate,where hematoma expansion is a key factor affecting patient prognosis.Therefore,early identification and prediction of hematoma expansion are crucial for improving treatment outcomes and patient survival rates.This study aims to establish an effective model to predict hematoma expansion events in patients with hemorrhagic stroke,thereby achieving precise and personalized prognosis prediction.The research integrates the imaging characteristics of patients,using the occurrence of hematoma expansion events as the target variable,to propose an equation for accurately determining the patient's hematoma expansion status in the short term.Three methods,namely Logistic regression,support vector machines,and neural networks,are employed for modeling.The performance of these models is evaluated using leave-one-out cross-validation.Numerical experiments are conducted to simulate and validate the predictive performance of the models.The experimental results indicate that the neural network model performs best in predicting hematoma expansion events.It can effectively predict hematoma expansion events in hemorrhagic stroke patients,providing a powerful auxiliary tool for doctors to better optimize clinical treatment plans.

The haze in reality is complex and changeable,and the data-driven image dehazing algorithm cannot establish a universal mapping model by learning the data distribution of all data sets,and only use clear images as positive samples to guide the training of the dehazing network,while the key information of negative samples(hazy images)is ignored.To solve these problems,this paper proposes an image dehazing algorithm based on the global meta attention and contrastive learning.Firstly,according to the distribution characteristics of haze in the image,a dehazing network based on multi-scale feature extraction and integration is designed.Secondly,the global meta attention module is constructed to provide the global attention optimization for the multi-scale dehazing network,and the mapping model of the network can be adaptively adjusted according to the input hazy images.Finally,self-supervised contrastive learning is introduced to draw the dehazing results closer to the positive samples and away from the negative samples.A large number of experimental results show that the algorithm achieves good dehazing performance in both synthetic haze map data sets and real haze map data sets,and is superior to the existing representative image dehazing methods in both subjective and objective evaluation.

 During the liquid-liquid phase separation (LLPS)process,salt ion concentration and crowding agents have significant impacts on protein interaction,folding,and aggregation.This study employed an optical inverted microscope,dynamic light scattering,and a Q5000 UV-Vis spectrophotometer to delve into the inductive effect of yttrium(Ⅲ)chloride (YCl3)on the phase behavior of bovine serum albumin (BSA),as well as the regulatory role of polyethylene glycol (PEG)on the phase separation of the BSA-YCl3 system.The research findings indicate that YCl3 effectively induces phase separation in BSA,forming an LLPS system between the lower critical salt concentration (C*)and the higher critical salt concentration (C**).The addition of PEG further enhances the LLPS phenomenon in this system.Additionally,dynamic light scattering experimental data reveal that YCl3 primarily interacts with BSA through electrostatic interactions,while spectrophotometer data suggest that PEG does not alter the LLPS range of the original system but rather promotes LLPS.

The requirement of the flywheel energy storage device is to ensure a constant voltage of the bus capacitor during the charging and discharging processes.Aiming at the problems of non-linearity and poor robustness under traditional voltage outer loop PI control,an improved sliding mode voltage square control is proposed.The system achieves global linearization control,and improves the robustness of the system in a wide range of speed regulation.In addition,the sign function of the current sliding mode inner loop is changed to a relatively saturated function.On the basis of retaining excellent robustness,the system chattering and harmonic effects are reduced.The results show that the proposed control strategy has a faster response speed and better anti-interference ability than traditional voltage control.

In this paper,we study the test problem of a class of variable coefficient models with panel data interaction effects.Based on the empirical likelihood method,the empirical likelihood ratio test statistic of the coefficient function is constructed.Under some regular conditions,the asymptotic theory of the statistic is established,and its asymptotic distribution is analyzed theoretically,including their consistency,convergence speed and asymptotic distribution.Based on this,the rejection region at a certain confidence level is constructed.Based on the regularization test method, the test statistics are obtained by optimizing the empirical likelihood ratio function of the constraints. The whole process is robust and efficient. The asymptotic theory of the empirical likelihood test of the interactive fixed effect variable coefficient model is studied, which provides some theoretical support and method guarantee for the statistical modeling of panel data.

Animal robots refer to a novel class of robots that incorporate animals as the primary platform,complemented with information acquisition and sensing devices,and are capable of accomplishing specific tasks through human regulation.Owing to their exceptional flight and navigation capabilities,robust load-bearing adaptability,and unique homing characteristics,pigeons have emerged as an ideal animal in the field of flying animal robots,exhibiting significant research value and promising application prospects.This paper provides a comprehensive classification and summary of the relevant brain regions and neural regulatory mechanisms that control the primary motor behaviors of pigeon robots,and explores their potential implications in the design and application of pigeon robots.Furthermore,we review the technical advancements within the field of pigeon robots,including electrode implantation and outdoor flight communication technology.Additionally,we offer insights into the future trends of neural regulation and control systems,aiming to provide a reference for the theoretical research and practical enhancement of pigeon robots.

Industrial pollution is an important factor affecting ecological protection and high-quality development in the Yellow River Basin.To assess the impact of environmental regulation on promoting green industrial development,56 prefecture-level cities in the basin were selected for study.Using panel data from 2011-2020,the Super-SBM model was employed to measure green industrial development.System GMM and threshold models were applied to explore the relationship between environmental regulation and industrial green development.The results indicate:Environmental regulation has an inverted U-shaped effect on green industrial development.Green finance positively moderates this relationship;the higher the green finance level,the stronger the impact of environmental regulation.The nonlinear effect of environmental regulation shows spatial heterogeneity.

In chaotic secure communication, the core challenge lies in achieving chaotic synchronization,and time-synchronized control is of great significance for the secure transmission of confidential data.A time-synchronized control method combined with predefined time sliding mode control is proposed for the synchronization problem of chaotic systems under noise disturbance.To achieve time-synchronized control of synchronization errors,a norm-normalized sign function is introduced in the control scheme.Based on the time synchronization strategy of predefined time sliding mode control,effective control inputs and sliding mode surfaces are designed,and the stability of the synchronization error system is verified by constructing the Lyapunov function,so as to realize the time-synchronized control of chaotic systems under noise disturbance.The numerical simulation results in Matlab demonstrate the feasibility and effectiveness of the proposed method.

In the process of cultivating talents in higher education,the cultivation of college students’ innovative ability has been increasingly valued by society.In order to further optimise the cultivation mode of innovative talents and enhance the innovation ability of college students,the evaluation index system of innovation ability of college students is studied based on AHP-entropy weight method.The research adopts a combination of questionnaire survey and expert analysis to construct the evaluation index system,screens the evaluation indexes,analyses the objective research data using the entropy weight method,evaluates the weight of each index factor,and ultimately derives the framework of the evaluation index system of college students‘ innovation ability,and puts forward corresponding countermeasures and suggestions to promote the development of China’s higher education.

In modern industrial processes,accurate prediction of key performance indicators using basic data-driven models is challenging due to the system's nonlinearity,time delays,and the presence of abnormal data.This paper proposes a robust hybrid network model centered on a neural ordinary differential equation network,complemented by a gated recurrent unit structure,to enhance the dynamic modeling capabilities for continuous data and improve the analysis accuracy of time-series data.Firstly,a gated recurrent unit based on ordinary differential equations is employed as the foundational model for handling nonlinear data.Secondly,a dynamic gradient clipping method is designed and incorporated into the model training process in conjunction with weight clipping,ensuring the stability and convergence of the model training.Subsequently,a truncated Huber loss function is utilized and combined with elastic regularization to handle abnormal data.Finally,numerical simulations and industrial datasets are leveraged to validate the proposed algorithm.The results demonstrate that the algorithm can effectively improve the prediction accuracy and stability of the model.

A large number of membrane-free compartments within cells (collectively known as bio macromolecular condensates)are assembled through the phenomenon of liquid-liquid phase separation (LLPS).Phase-separated condensates participate in various biological activities,and abnormal phase separation may lead to a large number of complex human diseases,such as neurodegenerative diseases and cancer.Therefore,elucidating the mechanisms underlying the liquid-liquid phase separation of peptides and DNA may provide guidance for understanding and treating some severe human diseases.In this study,the regulation of LLPS was mainly explored related to a series of peptides (Poly KH)containing repeated lysine and histidine (KH)sequences.It was found that the KH-30 peptide produced LLPS with DNA through electrostatic interactions.At room temperature,the range of DNA-regulated phase separation was 6~14 μg/μL,and further increases in concentration led to the disappearance of the LLPS phenomenon.When PEG-1000 was added to a mixed solution of DNA and KH-30 peptides,its molecular crowding effect promoted the aggregation of small droplets into large droplets.As the concentration of PEG-1000 increased,the molecular crowding effect became more apparent,favoring the occurrence of the LLPS phenomenon.In addition,temperature is a convenient means to regulate LLPS in vitro.This study found that KH-30 exhibited entropy-driven LLPS behavior,meaning that an increase in temperature restrained the LLPS phenomenon between the KH-30 peptide and DNA.

With the continuous development and innovation in the field of road surface damage inspection,it is becoming increasingly important to assess the degree of impact of road surface damage on driving safety.Aiming at the influence of various interfering factors on the road damage detection task in practice,an improved road damage detection algorithm,Road-DETR,is proposed.The Features Reunion Pyramid is used to filter the interfering information,and the Complementary Details and Lightweight Module is adopted to achieve the lightweighting of the algorithm.The experimental results show that:Compared with the RT-DETR algorithm,the accuracy PmA0.5和PmA0.5∶0.95 of the improved Road-DETR on the public dataset RDD2022 has increased by 2.4% and 2.2% respectively,and on the self-built dataset RDI,it has increased by 2.2% and 1.9% respectively.Overall,the number of parameters has decreased significantly,and the computational load and detection speed are basically unaffected.It is indicated that the proposed improved method effectively enhances the anti-interference ability and edge detection ability of the algorithm.The effect of road surface damage detection in interference scenarios is significantly better than that of other algorithms.

With the expansion of the Industrial Internet of Things (IIoT) and the increasing connectivity among the various smart devices or systems,controlling access and fusion in complex heterogeneous networks has significantly gained importance.However,the contradiction between the reliability,scalability,and interoperability requirements of IIoT and the complex heterogeneous networks is becoming increasingly apparent.The complexity and heterogeneity of IIoT bring many challenges for unified access and fusion,real-time transmission,centralized control,and management.Software-defined networks (SDN) offer an effective way to solve these problems.To comprehensively understand the knowledge base,frontiers,current situation,and trends of the software-defined Industrial Internet of Things (SDIIoT),we apply bibliometric method to analyze the literature performance and science mapping,and propose three-layer architecture for SDIIoT.The results show that:the number of publications and citations on SDIIoT has increased over the past decade;China and India are the two countries with the largest number of publicationsand are also the countries with considerable influence in the field;the highly cited literature in earlier years discussed the standardization activities and key technologies of SDN,which led to the study of SDIIoT and divided it into four main clusters and three main research paths:security,quality of service andedge computing applicationare the main research areas of SDIIoT,while the application of new technologies such as artificial intelligence,5G,machine learning and blockchain,as well as elasticity,cost and broadband management and next-generation communication protocols are emerging topics.

This study established an efficient,accurate,and sensitive detection method for sodium azide based on liquid chromatography.This study innovatively used a chromatography column system consisting of octadecylsilane bonded silica gel column and ion exchange chromatography column connected in series through a six way valve,combined with a dual pump infusion system of gradient pump and constant flow pump,to achieve efficient separation and accurate detection of sodium azide.In addition,this study also optimized key parameters such as detection wavelength,column temperature,and valve switching time,further improving the sensitivity and accuracy of the method.The results showed that this method had a good linear relationship (R=0.999 9) in the range of 0.397 8~1.989 μg/mL under the conditions of detection wavelength of 210 nm,column temperature of 60 ℃,and valve switching time of 4 minutes and 6 minutes.The recovery rate was 81.2%～96.6%,δ_RSD≤2.8%,and the detection limit of the method was 0.039 78 μg/mL.This method has the advantages of strong specificity,high sensitivity and accuracy,good repeatability,and low detection cost,providing a reliable technical means for the detection of sodium azide in moxifloxacin sodium.

Aiming at the problem that defects in printed circuit boards (PCB) are tiny and difficult to detect,a PCB defect detection algorithm based on improved YOLOv8s is proposed.This algorithm adds a Transformer encoding unit and introduces the standard convolution of the DwConv network,realizing an improvement in the accuracy of real-time detection of PCB defects.Experimental results show that for the improved YOLOv8s model,the PmA is increased from 0.909 to 0.951,an increase of 4.2 percentage points.Compared with other mainstream object detection methods,the improved YOLOv8 algorithm shows better detection accuracy.

HBase is a popular technology in the Hadoop ecosystem,which enables real-time and random reading and writing of large-scale datasets.Filter is an important feature in HBase which is used to filter results when retrieving data.In order to distinguish the usage and characteristics of HBase Shell and Java API in operating filters,and to facilitate the selection of appropriate ways to filter data in HBase.The article adopts an experimental research method,using stock information as sample data,and implements a comparative study of operating HBase filters using HBase Shell and Java API on a pseudo distributed cluster by designing a stock data table pattern,adding stock data,designing application requirements,and writing key code.Based on this,the advantages and differences of the two methods are summarized,providing useful references for further understanding the usage of HBase filters.

In a thermal power plant,the No.1 boiler hail-side suction fan failed,and the relevant inspectors carried out macroscopic inspection on the fan blades on the spot.Three test blocks of the failure parts of the blades were selected for spectral analysis and scanning electron microscopy observation,and it was found that the content of the chemical component P exceeded the standard and there were obvious fatigue marks.The mechanical property test,hardness test and microstructure analysis of the rear baffle of the fan are not abnormal.The conclusion is that the structure of the base material of the tailgate has high concentrated stress,which is easy to generate fatigue cracks under the impact of external smoke and debris.The fatigue cracks continue to expand under the action of alternating stress,and finally lead to multi-source fatigue cracking of the impeller.

The reactor is the core equipment in the gas-liquid two-phase reaction process,and the complex flow field characteristics inside it have a crucial impact on the reaction efficiency and product quality.Due to the limitations of experimental methods in capturing and analyzing the subtle features of the gas-liquid two-phase flow within the reactor,numerical simulation methods have become an important tool for researching this field.In this paper,two different impeller models of gas-liquid two-phase reactors were established,and gas-liquid two-phase flow simulation was carried out using FLUENT software.The K-epsilon turbulence model was selected for simulating turbulent motion.The flow field characteristics and gas distribution in the reactor of two types of four-layer impeller devices were analyzed in detail.Based on the changes in gas-liquid two-phase distribution,gas uniformity,flow field velocity distribution and velocity vector distribution of the two impellers,the differences between the four wide-blade impeller and the four inclined-blade impeller were further analyzed.Finally,it was concluded that under the same reactor,the same rotational speed and the same impeller diameter,the four wide-blade impeller is superior to the four inclined-blade impeller in terms of discharge,average velocity and power consumption,and the four wide-blade impeller also helps to improve the flow uniformity in the reactor,which can further promote the mixing of gas-liquid two phases.

The uniqueness of the shape of the three-dimensional object is determined by using the phaseless far field method.The far field phase information is difficult to obtain,but the phaseless far field information is easier to measure,and it is more convenient and effective to use the phaseless far field to determine the shape of multilayer scatterers.In order to overcome the translational invariance of plane waves,three superimposed plane waves are selected as incident waves to obtain more abundant information about the object.Greens function corresponding to the model is introduced to reveal the interaction between the object and the incident wave.Finally,it is proved that the shape of the two-layer scatterer can be uniquely determined when the far field information of the phase is unknown.In addition,its material coefficient can also be uniquely determined.

We investigate the fully discrete multipoint flux mixed finite element (MFMFE) method for solving semilinear quadratic parabolic optimal control problems in this paper.The state variable is discretized using the MFMFE method,while time discretization is achieved through difference methods.The state and co-state variables are approximated using the lowest order Brezzi-Douglas-Marini (BDM1) mixed finite element spaces,and the control variable is approximated by piecewise constants.A novel numerical scheme is employed to decouple the state and co-state variables,facilitating the elimination of local flux.We derive error estimates for the control,state,and co-state variables.The results show that the proposed method is effective for the exact solution of the semilinear parabolic optimal control problem.

To address the issue of performance degradation in integrated sensing,energy and communication (ISEAC) systems under user overload conditions,a scheme is proposed to maximize the overall system rate leveraging Non-orthogonal Multiple Access (NOMA) communication.This scheme achieves optimal resource allocation while adhering to constraints such as maximum transmit power,minimum energy harvesting requirements,and minimum effective sensing power.Due to the coupling of variables,the problem is formulated as a non-convex one.To overcome the non-convexity of the rate function,semidefinite relaxation and first-order Taylor approximation methods are employed,and the successive convex approximation algorithm is utilized to solve the problem effectively for numerical solutions.Simulation results demonstrate that the proposed scheme outperforms conventional approaches,enhancing communication rates and mitigating interference among communication users.

Insulator damage in transmission lines brings many safety hazards to the power system,triggering arcs,fires and other dangers. Real-time and efficient insulator damage identification technology becomes the key to solve this problem.Based on a large number of experiments,a transmission line insulator damage identification method based on SA-YOLOv5s is proposed,which introduces the CBAM attention mechanism into the convolution module of the YOLOv5s model to improve the feature extraction capability of the model;using the GhostC3 module to replace the C3 module of the backbone network to reduce the complexity of the model;using the C2f residual module to replace the neck network's C3 module to improve detection accuracy;using MPDIoU loss function instead of CIoU localisation loss function to improve detection accuracy;and fusing the improved multi-scale SAHI slicing hyper-inference to improve the precision and accuracy of prediction results.The experimental results show that the improved SA-YOLOv5s model detects 95.2% of the P_mA0.5 value,61.9% of the P_mA0.5:0.95 value,and 98 frames/s of detection speed on the dataset,and the prediction accuracies of the insulator,insulator rupture,and surface flashover damage reach 99.2%,100%,and 100%,respectively.The improved model meets the detection needs of small and dense targets in complex environments.