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.

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.

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.

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

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.

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.

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.

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 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.

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.

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.

Tomato fruit is rich in lycopene,vitamin C,polyphenols and other active ingredients,which have both nutritional and health values.In this paper,the content,physiological function,synergistic effect and lycopene extraction technology of these components were reviewed.Lycopene can resist oxidation through conjugated double bond structure,reducing the risk of cardiovascular and cerebrovascular diseases and cancer;Vitamin C participates in immune regulation and collagen synthesis;Polyphenols regulate inflammatory pathways and induce cancer cell apoptosis.These components can synergistically enhance the physiological functions of tomato,such as antioxidation and anti-inflammation.For the extraction technology of lycopene,the traditional organic solvent extraction method is mature,but the solvent residue is obvious.Enzymatic hydrolysis is suitable for high value-added products,but the cost is high.Modern technologies,such as supercritical CO₂ extraction and membrane separation,are efficient and environmentally friendly.Emerging microbial fermentation and nano-embedding technologies have potential applications,but the problems of safety and scale need to be solved.At present,the optimization of energy consumption and cost of collaborative mechanism analysis and extraction technology still needs to be broken through,and future research can focus on collaborative target mining,industrial adaptation of new technologies and functional product development.This study provides theoretical support for efficient utilization of tomato resources.

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 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 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.

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.

Oxidative stress is the root cause of chronic diseases.Soybean protein can be hydrolyzed or fermented to obtain antioxidant peptides (SAPs),which can scavenge free radicals and hold great potential in the functional food industry.Research has elucidated their structure-activity relationships and some in vivo and in vitro mechanisms,but large-scale production and safety evaluation still require further exploration.This study aimed to utilize probiotic liquid fermentation of soybean prote in to prepare antioxidant peptides.Two stepwise fermentation methods were adopted:(1)inoculation with Bacillus subtilis natto(37 ℃,24 h),followed by inoculation with Bacillus coagulans (47 ℃,42 h);(2)on the basis of method (1),sequential inoculation with Lactobacillus rhamnosus (37 ℃,36 h).The two fermentation broths were ultrafiltered to obtain fractions with molecular weight <1 kDa (SPAP1,SPAP2),and their in vitro antioxidant activities and in vivo efficacy in Caenorhabditis elegans were evaluated.Results showed that the IC50 values for ABTS radical scavenging activity were (0.509±0.016) and (0.591±0.028) mg/mL,respectively,and the FRAP values were (0.427±0.02) and (0.314±0.019)mmol/L,respectively.The C.elegans assayindicated that SPAP2 was superior to SPAP1 in delaying aging,reducing reactive oxygen species (ROS) levels,improving swallowing ability,and prolonging boths tress resistance and natural lifespan.RP-HPLC-MS/MS analysis identified a total of 175 potential antioxidant peptides in SPAP2.In conclusion,this study explored a probiotic combination and its stepwise fermentation conditions for the preparation of soybean antioxidant peptides,verified their efficacy through in vitro and in vivo experiments,and identified their peptide composition by mass spectrometry,providing technical support for the development and application of soybean bioactive peptides.

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.

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.

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.

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.

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.

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.

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.

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.

Ceramic coatings were synthesized on the surface of 2195 Al-Li alloy by micro arc oxidation(MAO),and the structure and high temperature friction wear performance of the coatings were studied.The phase and elemental composition were investigated by XRD and SEM.The surface,cross-section and abrasion morphology and elemental composition of the coatings were observed by SEM and EDS.The 3D morphology of abrasion tracks were observed by laser confocal microscopy(CLSM).The frictional wear performance of the coating was investigated at 200 ℃using an HT-1000 high-temperature friction and wear tester.The results show that the coatings consist of α-Al2O3 phase.The surfaces of the coatings are rough and porous,showing a "lotus leaf" like pile-up shape,with a surface roughness of about 14.3 μm.The thickness of the coating is about 7.1 μm.The average friction coefficient of the coating is about 0.68.The volume wear rate and mass loss of the coating are much lower than that of the Al-Li alloy substrate,with the mass loss being about one-ninth of that of the substrate and the wear rate being about one-tenth of that of the substrate.This indicates that the micro arc oxide coating has desirable wear resistance under high temperature conditions.Analysis of the wear track morphology and composition reveals that the wear mechanism of the Al-Li alloy substrate at 200 ℃ is abrasive wear,fatigue wear and oxidation wear,and the abrasive wear is the main one,while the MAO coating is mainly adhesive wear and also accompanied by oxidation wear.The researches have shown that micro-arc oxidation coatings exhibit good wear resistance under high-temperature conditions.

This study utilizes data from Chinese A-share listed companies from 2007 to 2019 to empirically analyze the impact of urban agglomeration population concentration on firms’ new quality productivity and its heterogeneous characteristics.It also examines the mediating roles of knowledge exchange and industrial structure optimization.The findings are as follows:(1) Urban agglomeration population concentration has a significant impact on the level of firms’ new quality productivity;(2) Knowledge exchange and industrial structure optimization play mediating roles in the relationship between urban agglomeration population concentration and firms’ new quality productivity;(3) Population concentration in cross-regional urban agglomerations has a positive impact on firms’ new quality productivity,with this promoting effect being more remarkable in central and western regions and areas with lower marketization levels.

To further reveal the influence of grain size and morphology on the mechanical properties of metal additive manufacturing materials,this paper adopts the crystal plasticity finite element method(CPFEM) and takes the AlSi10Mg alloy formed by selective laser melting(SLM) as the research object to establish representative volume element(RVE) models with different average grain sizes.It explores the impact of grain size on its mesoscopic tensile mechanical behavior.The results show that as the grain size decreases,the distribution of stress and strain becomes more uniform,and the yield strength and tensile strength of the AlSi10Mg alloy increase.When the average grain size decreases from 20 μm to 6 μm,the yield strength and tensile strength of the material increase by approximately 37.89% and 17.91% respectively,which is consistent with the Hall-Petch relationship.In addition,stress and strain are concentrated at the grain boundaries,indicating that grain boundaries can hinder the movement of dislocations.The distribution of strain bands is affected by crystal orientation and forms a 45° angle with the loading direction,which is in line with Schmid's slip law.

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.

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.

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.

This study developed a whisky-making process using tartary buckwheat as the raw material and optimized the process.Optimization of tartary buckwheat gelatinization and liquefaction process:Through single-factor and orthogonal experimental designs,the optimal conditions for gelatinization and liquefaction were determined:thermostable amylase 16 U/g,thermostable cellulase 4 U/g,solid-to-water ratio 1∶6(g/mL),temperature 96 ℃,time 30 min.Under these conditions,the dextrose equivalent(ED) value of the liquefied mash reached 18.3%.The optimized saccharification conditions were:α-amylase 75 U/g,pullulanase 60 U/g,protease 50 U/g,tartary buckwheat addition ratio 30%,saccharification time 60 min.The resulting wort contained 11.85% reducing sugars and 203.5 mg/L α-amino nitrogen. Using Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry(HS-SPME-GC-MS),46 volatile compounds were detected in the heart cut of the tartary buckwheat whisky.Among these,12 compounds had odor activity values(VOA)>1,such as ethyl decanoate(VOA=251),isoamyl octanoate(VOA=36),and ethyl hexanoate(VOA=34).After aging for 2 months in pottery jars with oak chips,the spirit acquired a light caramel hue,retained the aroma of tartary buckwheat ,and its aroma exhibited increased complexity and a more layered profile.

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.

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.

In practical engineering applications,factors such as modeling errors and parameter perturbations can lead to model uncertainties,while external disturbances may significantly degrade the tracking accuracy and dynamic performance of the controlled system,even causing instability.To address these critical challenges,this paper proposes a control strategy based on disturbance estimation for linear systems subject to both model uncertainties and external disturbances,achieving system stabilization.First,a stabilizing controller is designed for model uncertainties using Lyapunov stability theory.Subsequently,for external disturbances,a disturbance observer with asymptotic estimation capability is constructed by selecting an appropriate filter,enabling asymptotic rejection of the disturbances.Theoretical analysis demonstrates that the proposed control method not only effectively mitigates the impact of parameter uncertainties but also achieves complete cancellation of external disturbances,ensuring global asymptotic stability of the closed-loop system.Numerical simulations further validate the superior performance of the control scheme in terms of dynamic response and robustness.

G-quadruplex (G4) is a non-classical secondary structure formed by guanine-rich nucleic acid sequences,which is widely distributed in the promoter region of oncogenes and telomeres and other key regions of the genome,and is involved in a variety of disease processes.Histone H1,as an important regulator of chromatin high level structure,forms dynamic condensates with G4 by liquid-liquid phase separation (LLPS).Among them,the H1 carboxy-terminal structural domain (CTD) plays a key role in driving LLPS due to its long sequence and high charge density properties.However,there is still a research gap on the minimum length threshold required for CTD-triggered phase separation and its role in regulating the topological conformation of G4.In this study,we systematically investigated the effect of different lengths of histone H1 truncated peptide chains on the LLPS of G4-short peptide system by comprehensively utilizing various experimental techniques.The experimental results showed that LLPS formation was significantly inhibited when the peptide chain length was below a critical threshold;further studies revealed that the LLPS behavior of G4-short peptides was related to the short peptide-induced enhancement of folding and compression of the G4 structure.These results support that multivalent interactions are one of the important driving forces mediating phase separation.

To investigate the immunomodulatory effects of porcine skin collagen peptides on RAW264.7 cells,this study prepared Porcine skin collagen peptides via enzymatic hydrolysis and analyzed their amino acid composition and molecular weight distribution.The effects of porcine collagen peptides on RAW264.7 cells were systematically evaluated using a range of methods,including the MTT assay,neutral red phagocytosis assay,cell morphological observation,cytokine content measurement,and real-time quantitative PCR.These analyses assessed cell proliferation activity,phagocytic function,morphological changes,the production levels of NO,TNF-α,IL-2,and IL-12,as well as the mRNA expression of related immune factors(iNOS,TNF-α,IL-2,and IL-12).The results demonstrated that Porcine skin collagen peptides are primarily composed of glycine,proline,alanine,glutamic acid,aspartic acid,and arginine,with molecular weights mainly distributed between 500 and 5 000 Da.Porcine skin collagen peptides significantly promoted RAW264.7 cell proliferation and phagocytosis,induced morphological changes,enhanced NO,TNF-α,IL-2,and IL-12 production,and upregulated the gene expression of iNOS,TNF-α,IL-2,and IL-12.In conclusion,Porcine skin collagen peptides exhibit potential in activating macrophages and enhancing non-specific immune functions.

Rosa roxburghii pomace is an abundant yet underutilized byproduct of the fruit juice industry,which has attracted extensive research interest due to its rich nutritional composition.In this study,a combined milling and acidic pretreatment was applied to enhance its glucan conversion potential.The effects of pretreatment were systematically investigated using SEM,FTIR,TGA,and XRD.Results demonstrated that the pretreatment significantly improved enzymatic hydrolysis efficiency,increasing cellulose conversion by 30.39%.Compositional analysis revealed an elevated relative cellulose content after pretreatment.SEM and swelling capacity analysis indicated that the pretreatment disrupted the compact structure of the pomace,creating visible pores and a looser,more fragmented morphology,thereby increasing the specific surface area.FTIR,XRD,and TGA analyses confirmed the effective removal of non-cellulosic components,leading to higher cellulose exposure,enrichment,and crystallinity.These structural modifications enhanced cellulose accessibility to enzymes,ultimately improving hydrolysis efficiency.