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.

The result of the present electricity price prediction as a key signal in the power market,for the normal operation of the power system,plays an important role.In this paper,a prediction model of the present electricity price is proposed based on the self-attention mechanism with the long-nosed raccoon optimization algorithm of the convolutional neural network and the bi-directional gated recurrent unit network.The model fully considers many factors such as the boundary conditions of the power market and the external environment that affect the price of electricity,and firstly uses the Pearson correlation coefficient method to correlate the disclosure data of the power market in Shandong Province,and comes up with the key factors that affect the price of electricity.Then the data are input into a CNN-BiGRU model based on self-attention mechanism and long-nosed raccoon optimization algorithm for training.The experimental results show that the three evaluation indexes of the model,Mean Absolute Error (δ_MAE),Mean Absolute Percentage Error (δ_MAPE),and Coefficient of Determination (R-square,R²),are 10.481, 3.23%, respectively, 0.954,the three indicators are obviously better than other models,with higher prediction accuracy and stability,fully verifying the feasibility of the model in the prediction of the present electricity price.

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%.

To address the low detection accuracy caused by occluded pest targets and camouflage effects (where pest body colors blend with the environment) in crop pest detection,this study proposes an RT-DETR-based algorithm named RT-DETR-SDIC.First,the original backbone network’s early stages (S2,S3) are augmented with DBRB.By integrating multi-branch topological structures and heterogeneous paths of varying scales and complexities,the DBRB enriches the feature space.The later stages (S4,S5) of the backbone network are enhanced with IRMB_CGA.This module mitigates the lack of direct long-range semantic interaction in the original architecture while improving discrimination capability for environmental features.Second,in the feature fusion network,a parameter-free attention mechanism— SPA—is introduced to capture fine-grained spatial information.Additionally,CGFM is proposed for the feature fusion layer,orchestrating multi-scale feature integration.Experimental results demonstrate that RT-DETR-SDIC achieves a 19.6% reduction in parameters,a 9.9% decrease in computational load,a 6.2% improvement in P_mA,0.5 (average precision at IoU=50%),and a 2.6% improvement in P_mA,50:95 (mean average precision across IoU thresholds from 50% to 95%).

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.

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.

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.

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.

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.

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.

To address the issues of weakened target information and loss of fine details in fused images,this paper proposes a progressive multi-scale feature extraction and fusion method for infrared and visible image fusion.A structurally symmetric,parameter-independent dual-branch generation network is constructed.The original images and their enhanced versions are first fed into dilated convolution modules to extract contextual features at multiple scales,effectively capturing multi-scale information.Then,a multi-attention complementary residual aggregation module is introduced to enhance feature selectivity by emphasizing salient features and suppressing redundant ones,enabling progressive interaction and complementary fusion across scales.In the discriminator design,a dual-discriminator architecture is adopted to separately model the distributions of infrared and visible images,mitigating contrast shift and detail attenuation problems commonly encountered in multi-modal adversarial learning.Experimental results demonstrate that the proposed method outperforms most state-of-the-art algorithms in both objective metrics and subjective visual quality,retaining more texture details and achieving superior fusion performance.

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 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.

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.

To enhance vehicle ride comfort,an active suspension system based on the inverse piezoelectric effect with piezoelectric stacks is proposed.According to Lagrange’s theorem,a six-degree-of-freedom active suspension dynamics model is established.Six performance metrics including vertical cabin acceleration are selected to formulate a fitness function,and an LQR (Linear Quadratic Regulator) controller is designed by integrating a genetic algorithm.Numerical simulations demonstrate that the piezoelectric effect-based active suspension outperforms the passive suspension in performance.
Key words:piezoelectric actuators;vehicle-road coupling;genetic algorithms;ride comfort

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.

In this study,CuS/BiOI heterostructures were successfully synthesized via a hydrothermal method.The materials were characterized using Zeta potential analysis,XRD,SEM,TEM,XPS,UV-DRS,which collectively confirmed the successful construction of the CuS/BiOI heterojunction.Under visible-light irradiation,the photocatalytic degradation performance of the CuS/BiOI heterojunction toward tetracycline (TC) was studied.The optimized CuS/BiOI-2 sample exhibited a degradation efficiency of 81.46% and a degradation rate constant of 0.014 3 min^-1 representing a 68.55% improvement and a 2.64-fold enhancement compared to pristine BiOI,respectively.By analyzing the band structures of CuS and BiOI,combined with photoelectrochemical analyses and radical scavenging experiments,it was demonstrated that the S-scheme heterojunction formed between p-type CuS (with localized surface plasmon resonance,LSPR properties) and n-type BiOI significantly enhanced the light absorption capacity of BiOI and promoted the efficient separation of photogenerated charge carriers.These synergistic effects ultimately endowed the CuS/BiOI-2 composite with exceptional photocatalytic performance.

In this research,a strain was screened from the rotten kelp and was identified and named as Vibrio furnissii C1.Two alginate lyase genes alg792 and alg796 from V.furnissii C1 were heterologously expressed in Escherichia coli.The recombinant alginate lyases Alg792 and Alg796 were purified,and their properties were analyzed.The specific activities of Alg792 and Alg796 were 950 and 116 U/mg,respectively.The optimal temperature for Alg792 and Alg796 were 40 ℃ and 25 ℃,respectively.When incubated at 30 ℃,Alg792 possessed better enzyme viability stability.When the temperature was above 40 ℃,more than 80% of both enzymes activities were lost in 30 min.Both Alg792 and Alg796 possessed an optimum pH of 7.0.Alg796 possessed a wider pH applicable range,while Alg792 had better pH stability.Cu²⁺,Zn²⁺ and EDTA completely inhibited the activities of the two recombinases.Mg²⁺ and Na⁺ promoted the enzyme activities observably,while Ca²⁺,Mn²⁺ and K⁺ had opposite effects on the two recombinases.Both recombinant enzymes were bifunctional,Alg792 was endonuclease and Alg796 was exonuclease.These two alginate lyases have good application potential in the development of high value-added products of alginate.

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.

To extract and integrate various factors influencing wind power generation,and to reduce the prediction error caused by fluctuations in single factors,this paper proposes a model that combines historical wind speed and power data based on Graph Convolutional Networks (GCN).The method treats wind turbines as nodes and constructs a graph structure among turbines to extract the features of both wind speed and power.The model captures the complex interactions between wind speed and power generation,as well as the synergistic effects between different turbines within a wind farm.By effectively extracting both local and global features,the model adapts to the nonlinear and time-varying characteristics of wind power.Experimental results on real wind farm data show that,compared with traditional methods,the GCN-based model achieves higher accuracy and stability in short-term wind power forecasting.This provides a reliable reference for wind farm scheduling and operations,ensuring greater stability under varying environmental conditions.

Fire monitoring is crucial for reducing the loss of life and property.However,traditional methods suffer from insufficient real-time performance and accuracy in complex environments.This paper proposes a lightweight fire image detection algorithm based on improved YOLOv5s,which optimizes the monitoring system by integrating edge computing technology.By introducing the Convolutional Block Attention Module (CBAM) to enhance feature learning capabilities,employing Atrous Spatial Pyramid Pooling (ASPP) to expand the model’s receptive field,and utilizing the EIoU Loss function to accelerate convergence and improve regression accuracy,the improved model’s fire recognition rate is increased to 94%,with precision and recall rates reaching 94.2% and 92.4% respectively.By deploying the system on a modular AI module to directly process video data,cloud transmission latency is avoided,and detection real-time performance is significantly enhanced.This method provides an efficient solution for fire monitoring in complex scenarios and is of great significance for improving emergency response capabilities.

Waste denim fabric,due to its resistance to degradation and low recycling efficiency,presents significant obstacles to effective resource reutilization,particularly as traditional recycling methods are often technologically complex and prone to causing deterioration in fiber performance,thereby limiting their reuse potential.In this study,waste denim was subjected to various pretreatment strategies and utilized as a reinforcing phase within a polylactic acid (PLA) matrix to prepare fabric-reinforced composites through a composite processing technique.The effects of different pretreatments,including alkali treatment and cyclic tensile conditioning,on the composites’ microstructure,mechanical behavior,and thermal properties were comprehensively investigated.The findings revealed that the combination of alkali treatment and cyclic tensile conditioning led to a marked enhancement in the overall mechanical performance of the composites.This research not only offers a novel route for the high-value reutilization of waste textiles but also contributes to the development of sustainable composite materials with promising application prospects in construction,automotive interiors,and aerospace engineering.

With the continuous development of medical imaging technology,chest CT images play a crucial role in the early diagnosis and treatment of lung diseases.Computer-aided detection systems can provide valuable references for clinicians,thereby reducing diagnostic errors caused by human factors.To address the challenge of varying feature channel importance in lung cancer segmentation from chest CT images,TransUnet-SE is proposed,this net is an enhanced Transformer-based U-Net architecture incorporating residual-aware mechanisms for pulmonary lesion segmentation.The SENet attention mechanism is embedded into the decoder’s upsampling process to accurately mitigate multi-channel feature differences through a three-step process of ‘squeeze,excite,and scale’.To validate the model’s generalizability,EMPIRICAL TEST was firstly conducted on the public Synapse multi-organ CT dataset,followed by fine-tuning and evaluation on lung cancer-specific CT images from the Lung-PET-CT-Dx dataset.Comparative experiments with state-of-the-art models demonstrate that our method achieves a Dice Similarity Coefficient of 86.05%.Furthermore,a user-friendly lung cancer segmentation assistant system was developed using PyQt5 for clinical implementation.This system invokes the weight parameters of the TransUnet-SE model to implement the segmentation function,thereby providing support for clinical diagnosis.

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.

The correct disposal of monitoring and alarm information in substation is very important to ensure the safe operation of substation and equipment maintenance.The premise of correct disposal is to extract the power entities in the monitoring alarm information quickly and accurately.Because of the existing power entity extraction methods,it is difficult to meet the dual requirements of accuracy and speed in practical application.In order to solve the above problems,a semi-supervised-ERNIE-GP power entity extraction method based on enhanced representation through knowledge integration (ERNIE) and Global Pointer (GP) and using semi-supervised learning strategy is proposed.This method is based on ERNIE-GP to improve the accuracy and speed of power entity extraction,and introduces semi-supervised learning idea to mine entity extraction knowledge from unlabeled data.In order to verify the effectiveness of the proposed method,the monitoring and alarm information of substation is used to construct a data set and conduct a series of experiments.Comparative experiments show that,compared with the better baseline model BERT-Bi-LSTM-CRF,the Semi-Supervised-ERNIE-GP adopted in this thesis improves the accuracy,recall and F1 score by 4.90%,2.50% and 3.71% respectively.Through curve analysis,the superiority of the extraction speed of this method in large-scale data application scenarios is further confirmed.

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.

In this paper,based on dynamic characteristics of quadcopter UAV,Proportional-Derivative (PD) control method is used to simulate the position tracking and formation control on the AirSim platform.The stability of UAV’s speed control is ensured by adjusting the real time UAV’s position through the PD controller.Compared with Proportional-Integral-Derivative (PID) control that includes integral term,PD control has the advantages of fast response speed and simple structure in UAV tasks,which can effectively avoid cumulative error problem.Based on this,the UAV formation control algorithm is further developed by adopting a leader-follower strategy,and formation control for two UAVs is implemented using the Flask framework and requests library.Compared with traditional MATLAB/Simulink simulation,the AirSim platform can combine the influence of real environments to provide simulation effects that are closer to practical applications,which verifies the effectiveness of the proposed method in complex scenarios.

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.

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.

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.

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.

In order to optimize the preparation process of porous noodles fermented by yeast.Through a single-factor experimental design,the influence of multiple process parameters such as raw material ratio and processing conditions on the mature and broken strip rate and sensory score of porous hanging noodles was systematically studied.Then,a significance analysis of four factors and three levels was conducted through the response surface optimization test,with the response values being the sensory score and the rate of cooked and broken noodles.The optimal combination of process parameters was obtained:Taking 100 g of flour as the benchmark,3 g of yeast,34 g of water,1.5 g of edible salt were added,and the fermentation time was 60 min.The sensory score of the porous noodles prepared by this process formula was the highest at 93.2,and the rate of cooked and broken strips was the lowest at 2%.Finally,the quality characteristics of the porous noodles were analyzed.The optimal cooking time was 260 seconds,the rate of cooked broken strips was 3%,the cooking loss rate was 6.52%,the mass fraction of protein was 8.61%,the mass fraction of reducing sugar was 8.75%,the mass fraction of ash was 2.13%,the mass fraction of fat was 0.58%,and the pH was 6.51,which significantly improved the quality characteristics of the porous noodles.The research provides a theoretical basis for the technological production of porous noodles.

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.

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.

Circulant graphs are Cayley graphs over a cyclic group.A graph is integral if all its eigenvalues are integers.The Sombor energy of a graph is the sum of absolute values of the eigenvalues for its Sombor matrix.In this paper,using matrix theory,and the relationship between the Sombor energy of a graph and its general energy,several formulas for calculating Sombor energy of integral circulant graphs were obtained.

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.

The problem of stabilizing highly nonlinear stochastic time-delay systems using aperiodic intermittent control based on discrete time-delay observation is studied.An aperiodic intermittent controller with time-delay discrete observation is designed.The designed controller can make the controlled stochastic system mean square exponentially stable.Different from the ordinary intermittent controller,the intermittent controller is based on time-delay discrete observation.By using Lyapunov function,Itô formula,M matrix and intermittent control strategy,the sufficient conditions for the stability of the controlled system are established,and the exponential stability,almost definite exponential stability and almost definite asymptotic stability of the controlled system are proved.Finally,a numerical example is calculated to illustrate the effectiveness of the theoretical results,and numerical simulation is carried out.

This paper is concerned with social optimality for a class of mean-field linear quadratic (Linear Quadratic,LQ) control systems with Markov jump parameters,where the individual diffusion coefficient can depend on both the state and the control of the agent.All agents cooperate with each other to minimize the social objective.First,with the aid of person-by-person optimality principle,one arrives at an auxiliary LQ control problem.A decentralized strategy is then obtained by a mean-field forward-backward stochastic differential equation (Forward-backward Stochastic Differential Equation,FBSDE) consistency condition.Finally,by some estimates of FBSDEs,the obtained decentralized strategy is proved to be asymptotic social optimality.

This study investigates the influence of nickel content on the microstructure evolution and mechanical properties of low-temperature ductile iron (QT400-18L) through the synergistic control of nickel microalloying and annealing heat treatment processes.Based on the research path of composition design-process optimization-performance characterization,a composition range with a nickel gradient of 0~1% was designed,combined with heat treatment processes.The performance of the samples was tested through tensile and impact tests,and the microstructure before and after heat treatment was characterized to analyze the effects of nickel on the matrix structure and properties.The experimental results show that the as-cast structure of the samples obtained by adding different amounts of nickel to ductile iron is mainly composed of spheroidal graphite, ferrite, and a small amount of pearlite. Nickel has a slight graphite-forming effect and promotes pearlite formation.To improve the impact toughness of the samples,heat treatment was used to regulate the microstructure, significantly reducing pearlite content.The microstructure of the heat-treated samples consists of spheroidal graphite and ferrite. When the nickel mass fraction is 0.4%,the heat-treated sample exhibits optimal strength and plasticity,with a tensile strength of 395 MPa, an elongation of 17.5%, a hardness of 139 HBW,and a -40 ℃low-temperature impact absorption work of 14.7 J, which represents an approximately 22.5% improvement in impact toughness compared to the QT400-18L standard.

In order to accurately predict the subsidence of urban areas,a deep belief network (DBN) prediction model for urban subsidence based on the optimization algorithm of dung beetle is proposed.Firstly,to address the difficulty of parameter adjustment in deep belief networks,the Dung Beetle Optimizer (DBO) algorithm is introduced to optimize the parameter settings;Secondly,the Small Baseline Subset InSAR (SBAS InSAR) technique was used to obtain the surface subsidence of the Huainan feature area as the original sequence for prediction and calculation.K-fold cross validation was employed to avoid overfitting risks,and the prediction results of backpropagation neural network (BP),DBN,and DBO-DBN models were compared and analyzed.The results showed that:(1) the accuracy of the DBO-DBN model prediction was 96.30%,with a root mean square error of 0.840 mm and a value of 0.992 6.Compared with the BP neural network and DBN model,the improved DBO-DBN model improved the prediction accuracy,and the absolute error trend between the predicted surface subsidence value and the true value was the best.(2) the prediction of the settlement amount of the two feature points P1 and P2 in the next 12 months shows that the future settlement amount of point P1 fluctuates within a certain range,while the future settlement amount of point P2 is basically stable,and neither of them shows a significant settlement trend.