How to shorten the patient's clinic time and improve the service efficiency and quality has become an important problem to be solved in the emergency department.Based on Flexsim software modeling,this study simulates and optimizes the emergency department treatment process.First,the input data required by the simulation model is processed by the Kolmogorov-Smirnov goodness-of-fit test together with other statistics methods.Second,through Flexsim software modeling and simulation,the resource bottleneck of emergency department treatment process is found out.Third,an optimization and improvement scheme is proposed to eliminate the resource bottleneck.And based on the Flexsim simulation model,the rationality of the optimized scheme is verified.Through optimization and improvement,the resource bottleneck of the emergency department system was eliminated,the patient crowding was effectively reduced,the patient's clinic time was shortened,and the treatment efficiency and patient satisfaction were improved.

In recent years,triboelectric nanogenerator (TENG) has developed rapidly and is widely used in the fields of energy collection,human-computer interaction,wearable electronics medical electronics and so on.Traditional TENG is usually prepared from the nonbiodegradable and unrecyclable polymer materials and metal materials,which will definitely pollute the environment.Therefore,the exploration of green TENG has become a research hotspot.Cellulose materials promote the development of environment-friendly TENG with advantages of degradability,regeneration,easy modification and processing.In this paper,the structure,working principle and working mode of TENG are briefly introduced and the research progress and application examples of nanocelluloses film,cellulose paper and cellulose aerogel as triboelectric materials are emphatically summarized.Finally,the future development of cellulose based TENG is prospected.

Information entropy is commonly used to measure the uncertainty of random variables and plays a significant role in various fields such as physics,statistics,and information theory,aiding in our understanding and explanation of diverse phenomena.Unlike the entropy in thermodynamics,information entropy exhibits a certain degree of orderliness,which stands in stark contrast to the disorder and complexity represented by entropy.Using a binary statistical model as an example,this article explores the connection between information entropy and random events,quantitatively describes the evolution process of information entropy,and reveals the fundamental principles underlying the orderliness of information entropy.

There is a natural demand for integration between the cultural industry and the tourism industry;however,digital technology has become a driving force for the high-quality development of the cultural tourism industry.Based on the industrial characteristics of cultural industry and tourism industry and the interaction mechanism between them,index system of cultural and tourism industry was constructed from four dimensions:industrial scale,industrial reception,industrial strength and industrial efficiency in this paper.The coupling coordination model was introduced to measure the development level and coupling coordination degree of the two major industries in Shandong Province from 2010 to 2020,and the time series changes were analyzed.The results of the study showed that in addition to the impact of the epidemic in 2020,the overall development level of cultural industry and tourism industry in Shandong Province has been improving year by year,however,the development of the cultural industry generally lagged behind the development of the tourism industry;the coupling coordination degree showed a steady upward trend,increased from 0.390 8 to 0.900 6,coordination level was upgraded from mild dysregulation to excellent coordination.Based on this,from the aspects of cultivating cultural tourism industry to enhance the new momentum of integrated development of cultural tourism,building digital cultural tourism platform to improve the value co-creation mechanism,expanding the transmission channels of technology and product innovation,and training new digital cultural tourism talents,a path was put forward to promote the digital economy to empower the high-quality development of cultural tourism integration.

With the rapid development of automation and artificial intelligence,mobile robots have been widely penetrated into all walks of life,and path planning technology is the key to ensuring their autonomy and efficiency.This paper deeply analyzes several mainstream path planning algorithms,including graph search,random sampling,intelligent bionics,and deep reinforcement learning,and reveals the advantages and challenges of each algorithm in practical applications.This paper further classifies the path planning research according to the application scenarios and specifically analyzes the path planning methods and development trends of land robots,unmanned aerial vehicles,and underwater robots in their respective fields.In addition,this paper also looks forward to the possible future development directions of path planning techniques.Through this overview,this paper aims to provide valuable information and research ideas for researchers in this field.

Magnetic coal gasification slag (MCGS) was prepared by acid leaching and chemical coprecipitation process,and further characterized by scanning electron microscopy (SEM),Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD).The results showed that the coal gasification slag had a rough and porous structure,and the surface and pores were loaded with Fe₃O₄ particles with reverse spinel structure,which indicated that the magnetic coal gasification slag had been successfully prepared.Then,RhB was taken as the target pollutant to study its adsorption performance.The removal efficiency and adsorption capacity of RhB were 90.66% and 90.66 mg/g under the optimal conditions of pH 3.0,the adsorption time 200 min,the initial concentration of RhB 200 mg/L,and the dosage of magnetic coal gasification slag 2.0 g/L.Moreover,the adsorption isotherm and adsorption kinetics results demonstrated that the adsorption process of RhB by MCGS was in agreement with pseudo second-order kinetic and Langmuir isotherm,indicating that the process was a single molecular layer adsorption dominated by chemisorption.The research provides a new technique and method for the resource utilization of coal gasification slag.

Accurate power load forecasting is the basis to ensure the stable operation of the power grid,and is also an important basis for power planning.In order to improve the accuracy of power load forecasting,a new forecasting model is proposed in this paper.First,the Harris Hawk algorithm is optimized by using chaos strategy and sine and cosine perturbation strategy,and then the weight and threshold of the limit learning machine are optimized by using the improved Harris Hawk algorithm.Finally,the model is used for short-term power load forecasting.Compared with other prediction models,the prediction effect of this model is greatly improved,and it has better generalization ability and stability.

Using persimmon as raw material,Design-expert design experiment,single factor experiment and Box-Benhnken experiment were used to optimize the brewing conditions.The optimum ratio of EC yeast,Aq yeast and F33 yeast was 4∶4∶7.The optimum conditions for liquid fermentation of persimmon wine were as follows:initial sugar content 22.5%,yeast addition 0.3 g/kg,pH 3.8,fermentation temperature 20.5 ℃,fermentation for 8 d.The final sensory evaluation score was 87.8 points.The volatile components of persimmon wine fermented by mixed bacteria and single bacteria were analyzed by GC-MS.The results showed that 38 kinds of compounds were detected in persimmon wine fermented by mixed bacteria,including 7 kinds of alcohols,15 kinds of esters and 10 kinds of acids,and 6 kinds of others.Compared with single-strain fermented persimmon wine,the types of flavor substances in mixed-strain fermented persimmon wine increased,especially esters,which solved the poor flavor of persimmon wine.

In order to investigate the effect of long-term application of Gibberllin A4+7 (GA₄₊₇) on the aroma quality of pear fruits during storage,this study used headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography/time of flight mass spectrometry (GC×GC/TOFMS) for analysis.The volatile compounds,including esters,alkenes,alcohols,aldehydes,ketones,alkanes,aromatics,and sulfides were detected in control group and treatment group.Among them,esters,alcohols,aldehydes,and alkenes are the main volatile aroma components.At the beginning of storage,Gibberllin A4+7 treatment group had the highest total mass concentration and number of volatile substances,which was 1.72 times that of the control group of pears.However,after 50 days of storage,the mass concentration and number of volatile substances in the Gibberllin A4+7 treatment group decreased rapidly.Besides,principal component analysis and heat map analysis showed that there was a significant difference in aroma changes between the Gibberllin A4+7 treatment group and the control group during storage.Further aroma analysis shows that Gibberllin A4+7 treatment can increase the mass fraction of fruit aroma types on the day of fruit storage,but can reduce the fruit aroma types of pear fruits during storage.These results comprehensively elucidated the effects of Gibberllin A4+7 on the volatile components of pear fruit during storage,and provided important theoretical support for the determination of pear storage period and production management.

“Polyphenols” is a class of bioactive natural substances that widely exist in plants.Sugarcane contains a large number of polyphenols with high antioxidant activity,which play an important role in controlling blood glucose levels and regulating immunity.By optimizing the ratio of water and ethanol and realizing sugarcane polyphenols without organic residues with ultrasound-assisted extraction,the sugarcane polyphenols extraction process was optimized through one-factor experimental method and response surface design.The test results show that the best extraction process of sugarcane polyphenols is ethanol concentration 58%,liquid ratio 11∶1（mL·g^-1）,water bath temperature 49 ℃,ultrasonic power 103 W,ultrasonic time 35 min,the yield of sugarcane polyphenols 18.87 mg·g-1,and the predicted value is 19.10 mg·g^-1.The prediction accuracy of this model is 98.8%.

In order to solve the problem of low feedback accuracy of channel state information feedback method in frequency-division duplex massive multi-input multi-output system,a CSI feedback network named CDTransformer based on Transformer and convolutional decomposition (CD)is proposed.CDTransformer incorporates convolutional decomposition into an improved Transformer network architecture to improve network performance without adding computational complexity,and enables lightweight deployment by binarizing the full connectivity layer of the network.A MixedTransformer network model is proposed for the limited power of the client.The encoder adopts a single layer convolutional neural network with low computational cost and simple structure,while the decoder adopts the same structure as the CDTransformer model.The CDTransformer model incorporates the Transformer structure and convolutional decomposition to improve CSI feedback accuracy and enable lightweight deployment.In addition,the MixedTransformer model was introduced,combining the advantages of CDTransformer and convolutional neural networks to provide better performance in power limited situations.The results show that compared with the CsiTransformer network model,the normalized mean square error and cosine similarity of CDTransformer network model are improved by 37.7% and 0.2%,respectively.

With the development of deep learning,it has significance to use the knowledge that is related to deep learning to realize the informatization and intellectualization for water surface object detection.Affected by the complex environment,the instance segmentation for water surface objects is still one of the difficulties today.An improved Mask R-CNN model is proposed based on the instance segmentation for water surface objects.In the construction phase of the dataset,the images are labeled using labelme.Image enhancement is achieved by adding Gaussian noise,random brightness,etc.In the improvement of Mask R-CNN model,ResNeXt50 is used as the backbone network,and adding attention mechanism in FPN Network.In the IOU=0.5 discrimination mode,compared with the original Mask R-CNN model,the average precision of detection and segmentation is improved to 89.3%,88.5% respectively.Therefore,the improved Mask R-CNN network model is more suitable than the original Mask R-CNN network model for instance segmentation of water objects.

Paper black liquor accounts for over 90% of the pollution in the entire paper industry,which would seriously pollute environment.A large amount of organic matter in papermaking black liquor can serve as a precursor for the synthesis of fulvic acid.Many studies have shown that fulvic acid has a good adsorption effect on heavy metals.According to the method recommended by the International Humic Acid Association,the alkaline fulvic-like acid (JFLA) and ammonium fulvic-like acid (AFLA) were successfully extracted from the black liquor of two different papermaking processes.Through equilibrium complexation experiments,the adsorption effects of two types of fulvic-like acids on lead were studied in heavy metal aqueous solutions with different Pb concentrations and pH.Research has shown that AFLA has better adsorption capacity under low Pb concentration and high pH conditions,and AFLA has stronger adsorption capacity than JFLA.The results of this study indicate the possibility of black liquor fulvic-like acid to repair the Pb polluted water.

Based on strict logical reasoning,“having infinitely many prime numbers in the form of 4k-1(k∈Z+)” and “having infinitely many prime numbers in the form of 6k-1(k∈Z+)”are proved.Based on the square residue and Euler criterion,“having infinitely many prime numbers in the form of 4k+1(k∈Z+)”is proved.Based on order and Euler Theorem,“having infinitely many prime numbers in the form of 6k+1(k∈Z+)” is also proved.

In this study,chitosan/aminated graphene quantum dot composite aerogel (CSx@AGQD-HPW12) modified by phosphotungstic acid was successfully prepared by chemical cross-linking and impregnation methods.The materials were characterized by XPS,SEM,TEM,BET and UV-vis DRS.The adsorption and photocatalytic performance of CSx@AGQD-HPW12 on ciprofloxacin solution was investigated under UV-vis conditions.Compared with the other three materials,CS0.50@AGQD-HPW12 showed optimal performance with a pre-sorption photocatalytic removal rate of 98.8% for ciprofloxacin.The total organic carbon removal efficiency was about 52.1%.The regulation law of chitosan on the performance of CSx@AGQD-HPW12 was revealed,and the addition of chitosan modulated the morphology,band gap,and photoelectric properties,which in turn improved the adsorption-photocatalytic performance of the composite aerogel.

Oxygen reduction reaction (ORR) is the key cathode reaction in fuel cells.Fabricate catalyst with earth-abundant elements instead of platinum is an effective method to reduce the cost of fuel cells.In this work,nitrogen-doped TiO₂ encapsulated in nitrogen-doped carbon (N-TiO₂@N-C) was facile fabricated with hydrothermal-calcination procedures.N-TiO₂@N-C was characterized by transmission electron microscope,X-ray diffraction,X-ray photoelectron spectroscopy and electrochemical measurements.It exhibits a higher activity for oxygen reduction reaction in alkaline media compared with Pd/C and Pt/C.

Aiming at the problems of long training time and slow convergence speed of deep reinforcement learning methods in robot path planning,an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is proposed.The algorithm introduces the gravitational field and repulsive field in the artificial potential field to optimize the reward function of TD3,which guides the robot to avoid obstacles to the target point reasonably,so as to improve the convergence speed and accuracy of the algorithm;at the same time,it applies motion constraints rules to constrain the direction of the robot’s motion,which makes the trajectory smoother and more fluent.The results of simulation experiments show that in a multi-obstacle environment,the proposed algorithm can effectively make the robot avoid obstacles and plan a reasonable path,and the improved algorithm has a higher planning success rate and a shorter planning path compared with other methods.

CoCrNi medium entropy alloy was produced by laser melting deposition process,and the influence of laser power on microstructure and properties was studied.The findings show that when the power is 800~1 600 W,the metallurgical bonding between the deposition layer and the substrate is good,and the fusion line is clearly visible.The structure along the building direction of the deposition layer is planar crystal,columnar crystal and equiaxed crystal.With the increase of power,the breadth of the deposition layer increases,the microstructure coarsens,and the microhardness gradually decreases.The laser power is P=800 W,the performance is optimal,and the microhardness of the fusion zone,middle and top of the deposited layer is 393,435 and 496 HV.

Fungi can form mycelial pellets with hollow inside and dense surface under submerged cultivation.These mycelial pellets have advantages of good flocculation performance,uneasily settling at the reaction tank bottom,broad growth conditions,and easy cultivation.Researchers have widely studied on the formation mechanism and cultivation conditions of mycelial pellets.Recently,the focus of researchers has gradually shifted to their application.At present,mycelial pellets have been applied in the fields of wastewater treatment and recovery of resource and energy.This review mainly introduces the self-assembly mechanism and the influencing factors of nutrient and culture conditions on the formation fungal mycelial pellets under submerged cultivation.Also,the study of fungal mycelial pellets carrying nanoparticles,microorganisms,biochar to treat wastewater and the potential and challenges for these types of complexes were summarized.Furthermore,the cases of fungal mycelial pellets treating various kinds of sewage and the running characteristics and application of the matching reactors were introduced.The application of fungal mycelial pellets should focus on the diversified and collaborative development,coupling their own advantages with heterologous substances to play a synergistic role and to promote the improvement in wastewater treatment and environmental remediation.This review can provide guidance and reference for the application of fungal mycelial pellets in wastewater treatment.

During the plastic stress wave velocity stage of the workpiece material,brittle cutting of the workpiece can avoid problems such as difficult chip breaking,strain hardening,and tool sticking caused by the high soft viscosity of the material.In this paper,carbide tools,ceramic tools and cubic boron nitride (PCBN)tools are used to cut pure iron at high speed (linear speed up to 8 000 m/min),and the wear,cutting force and machined surface quality of the tools after cutting are observed.The results show that among the three kinds of tools,the carbide tools wear is the most serious,followed by the PCBN tools wear,ceramic tool wear is the least.Severe abrasive wear occurs on the flank face of carbide tool,and the rake face of PCBN tool shows crescent depression wear,which is full of high temperature melts and brittle fracture fragments.Ceramic tools have no obvious wear characteristics,but there is a lot of workpiece material adhesion on the rake face and flank faces.Compared with carbide and PCBN tools,ceramic tool machining workpiece machining surface has less defects,minimal lateral flow,minimum roughness and hardening layer,the ceramic tool is an ideal tool material for high-speed cutting of pure iron.

At present,multi-modal neuroimaging data is widely used in Alzheimer’s disease (AD) classification research.However,these research methods have certain limitations.Firstly,the original feature dimension is high,and it contains a large amount of redundant information,requiring a large amount of computational resources for processing and analysing.Secondly,it is difficult to obtain complete multi-modal data from the same patient,resulting in a small sample size,which is not conducive to training reliable classification models.To address these issues,this paper proposes an AD classification method based on maximum relevance-minimum redundancy (mRMR) and canonical correlation analysis (CCA).Specifically,the mRMR algorithm is first used to select discriminative features that are most relevant to the classification label from each modality data.Then,the CCA algorithm is used to explore the inherent connections among multi-modal data,and finally,classification is performed.This paper conducted experiments by using data from the Alzheimer’s Disease Neuroimaging Initiative database,and the results demonstrated the effectiveness of the proposed method in AD classification.

With the rapid development of modern intelligence and automation,mobile AGVs are beginning to play an important role.This paper is devoted to the study of trajectory tracking control method of McNamee wheel AGV (Automated Guided Vehicle),which adopts adaptive sliding mode control in order to improve the performance of its application in the field of automated logistics and manufacturing.Firstly,the McNamee wheel AGV is mathematically modelled to establish its kinematic and dynamic models while considering external disturbances.Secondly,an adaptive sliding mode trajectory tracking controller is designed on the basis of the dynamics model,which realizes the control of the system state by introducing the sliding mode surface,and then the stability of the control method is analyzed by using Lyapunov theory.Finally,the controller is verified through matlab simulation experiments to improve the trajectory accuracy as well as the effectiveness of the AGV.

Soil organic carbon (SOC),soil inorganic carbon (SIC) and soil carbon and nitrogen ratio (C/N) are important chemical properties of soil,and the study about the effects of different grazing strategies on soil chemical properties has a significant impact on maintaining the sustainable development of grassland ecosystems.To explore the effects of different grazing strategies on soil chemistry properties,this study investigated a sample of soil chemical property data under different grazing strategies in a designated area of Xilin Gol Grassland,in order to determine the impact of different soil chemical properties on the environment by using the factor analysis method in principal component analysis.The results show that the ratio of soil organic carbon,soil inorganic carbon and soil carbon nitrogen is greatly affected by the environment.In fact,the ARIMA model (integrated moving average autoregressive model)in the time series method is adopted in this paper to predict the contents of organic carbon,inorganic carbon and total nitrogen in grazing areas,and corresponding prediction models are given.The prediction model of the effects of different grazing strategies on soil organic carbon content is ARIMA (2,0,0),the prediction model of soil inorganic carbon content is ARIMA(2,1,1),and the prediction model of soil carbon-nitrogen ratio is ARIMA(3,0,0).

Levodopa (L-DOPA)can effectively treat Parkinson's disease (PD),and it is of great significance to search for novel tyrosinase (TYR) that can specifically catalyze the synthesis of L-DOPA from tyrosine.The tyrosine oxidase (RsTYR) gene from Ralstonia solanacearum was constructed into a recombinant plasmid and transferred to E.coli for heterologous expression,and the expression conditions were optimized.The results showed that in E.coli BL21(DE3),high expression of RsTYR protein (0.4 mg/mL)was achieved when the induction temperature reached 25 ℃,the concentration of the inducer IPTG was 0.5 mmol/L and the induction time was set to 12 h.Subsequently,the RsTYR protein was purified by Ni-affinity chromatography,ion exchange chromatography and gel filtration chromatography to obtain high purity recombinant protein.Subsequently,the refined RsTYR protein was used for product identification and enzymatic property characterization studies,which showed that RsTYR could specifically synthesize L-tyrosine into L-DOPA.the optimum reaction temperature of the enzyme was 45 ℃ and the optimum reaction pH was 6.5,the catalytic property of RsTYR was most stable at pH 7.0,and the optimum buffer was PBS buffer,and the enzyme molecule remained stable between pH 6.0 and 7.5.The enzyme molecules still had more than 50% residual activity between pH 6.0 and 7.5,and K⁺ was able to increase the activity of RsTYR.The enzymatic kinetic parameters of RsTYR were also determined,and the K_mand k_cat values were 0.63 μmol/L and 9.61 s^-1,respectively.Homology sequence analysis showed that RsTYR belongs to the Tyrosinase Superfamily,and the conserved catalytic active sites His81,His93,His102,His227,and His227 were predicted by homology modeling and comparative sequence analysis.This research provides a new substrate specific enzyme option for the biosynthesis of L-DOPA and provides a theoretical basis for the biocatalytic synthesis of L-DOPA.

For the problem of poor accuracy in passive location with far field and small aperture array under strong interference,a location algorithm was proposed based on direction angle lateral cross.Firstly the principle of cross lateral positioning method was analyzed to construct the improved targeting geometrical and numerical models.Then target estimate prediction equation was established by the building model and the nonlinear prediction equation was processed by linearization.The prediction equation was developed at the reference point with Taylor series and the correction equation was obtained.The goal's progressive unbiased estimate was obtained by optimizing control of iterative process to achieve target pinpoint.The main error factors of the proposed algorithm were analyzed to study the impacts on the target location accuracy of different errors.Simulation results show that the proposed algorithm can locate the target effectively and has better positioning performance than traditional location algorithms in large direction angle error and background noise.

Aiming at the problem of online measurement of flotation concentrate grade that needs to be solved urgently in the process of beneficiation production,a non-negative garrote (NNG) algorithm is embedded in an online sequential extreme learning machine (OS-ELM) and a new method is proposed based on online soft sensing algorithm for concentrate grade selected by OS-ELM and its input variables.First,the trained OS-ELM network model is obtained through parameter optimization and its excellent online learning ability is used to recursively update the network output weights by adding new samples or sample blocks in the incremental learning process;the input weights of the OS-ELM network model are sparsely optimized,redundant variables are eliminated and the prediction accuracy of the model is improved;finally,the online model is applied to the soft sensing modeling of copper concentrate grade in the flotation process of a copper mine in a mining enterprise and is combined with other advanced algorithms and comparison of the measurement results of the on-site current-carrying X-ray fluorescence grade analyzer.The experimental results show that the proposed algorithm can effectively eliminate redundant variables,improve the accuracy of concentrate grade measurement and realize real-time accurate soft measurement of flotation concentrate grade.

Blue is one of the three primary colors and is now widely used in many fields such as food,medicine and cosmetics.Most of the blue pigments currently used in food processing are synthetic blue pigments,and natural blue pigments are extremely rare in commercial applications.The study first found that Quambalaria cyanescens QY229 can metabolize to produce extracellular watersoluble blue pigment under liquid shake flask culture conditions,and the response surface optimization of its fermentation conditions was carried out.The results showed that when the peptone content was 34.61 g/L,the temperature was 31.67 ℃,and the bottling volume was 72.33 mL/250 mL,the color value of blue fermentation liquid could reach 348.2 U/mL,which was 2.1 times of that before optimization..Therefore,the production of blue pigment by liquid fermentation of Quambalaria cyanescens,QY229 as another new source of rare natural blue pigment,has a broad application prospect.

Based on the environmental data before and after the establishment of Saihanba Forest Farm,the environmental evaluation index was given and the comprehensive evaluation model of TOPSIS environmental status based on entropy weight method was established.The outstanding contribution of the construction of the ecological protection zone of Saihanba Forest Farm to the improvement of the local and surrounding environment was studied,and the quantitative evaluation model was used to analyze its role in Beijings resistance to dust storms.Then,in order to explain more accurately the importance of the construction of Saihanba Forest Farm,a TOPSIS comprehensive evaluation model of environmental conditions based on analytic hierarchy process was established.The simulation results show that the conclusions of the two models are consistent.In order to study the ecological situation in China,the environmental data of thirtyone provinces were collected,and the environmental index evaluation system was established by principal component analysis.The environmental conditions of each province were scored,and the threshold was set through the environmental conditions of typical areas.The provinces with scores below the threshold need to establish ecological protection areas.Finally,in order to put forward a more reasonable construction plan for provinces that need to establish ecological protection zones,the economic level and land area of the province should be considered,and the construction area of each ecological protection zone and its impact on national carbon neutrality are given by quantitative comparison.

To reduce the content of heavy metals of lead,cadmium and chromium in the Laminaria japonica polysaccharide,the single factor experiment was carried out with pH,silica gel dosage,temperature and time as factors,the modified silica gel as adsorbent,the removal rate of three heavy metals and the retention rate of the polysaccharide as evaluation indexes.On this basis,the removal conditions of heavy metals were optimized by orthogonal experimental design with a comprehensive evaluation index as an indicator.The results showed that the optimum process conditions for removing heavy metals from the polysaccharide were pH 8,temperature 50 ℃ and time 3.5 h.In this condition,the removal rates of lead,cadmium and chromium were 93.33%,97.27% and 69.27%,respectively,and the polysaccharide retention rate was 83.28%.After the removal,the heavy metal contents of the polysaccharide were lower than the limit value of the national standard.And the polysaccharide was retained to the greatest extent.The quality of these reprocessed raw materials was improved,providing high-quality raw materials for applying the polysaccharide in food,health food and medical food.

To monitor the hydraulic system status of a loader and conduct performance diagnostic analysis and optimization design,a multi-channel data acquisition system was implemented using MATLAB/Simulink and LabVIEW.The data acquisition program,constructed in MATLAB/Simulink,was exported to the PowerECU-57AL controller to achieve synchronized collection of multiple signals.The collected data was transmitted to the testing system via a CAN analyzer.The monitoring interface of the testing system was designed using LabVIEW,utilizing the CAN communication protocol to receive and analyze the data collected by the controller.Real-world testing results demonstrate that the system effectively synchronizes the collection of multiple analog signals and CAN bus signals,displaying them in real-time on the monitoring interface.This system aptly reflects the dynamic characteristics of various parameters during the operation of the loader’s hydraulic system,thereby meeting the stringent requirements for data collection testing of the loader's hydraulic system.

Aiming at the problems of incomplete deblurring and texture detail loss in non-uniform blind deblurring algorithms in dynamic scenes,an image deblurring method based on multi-input and multi-output codec network is proposed.Firstly,a feature extraction module is used to extract the global and local feature information from the different scale blurred images.As a result,the network can obtain richer and more comprehensive image feature information.Secondly,a feature fusion module is used to fuse multi-scale features,so that context feature information and detail information at different scales can flow in a single U-net network,which enhances the fluidity of feature information,and solves the problem of feature flow blocked due to multiple subnetworks stacking used in the traditional method.Finally,a mixed loss function consisting of L1 loss,multi-scale frequency reconstruction loss and edge loss is designed to improve the image restoration effect while preserving the texture structure and edge information better.To evaluate the deblurring performance of the network,experiments are conducted on the benchmark data sets:GoPro and HIDE.Results show that the mean values of peak signal-noise ratio and structural similarity of the restored images are 31.94 dB,29.45 dB and 0.961,0.936,respectively,which are higher than that of the compared deblurring algorithms.In terms of subjective visual effect,restored images of the proposed deblurring method is clearer in image texture structure and edges and closer to the real images.The network model proposed in this paper can obtain richer feature information,enhance the flow ability of internal features of the network,and achieve better deblurring effect.

When processing the heterogeneous data over the years in the multi-source big data fusion stage,multi-index and multi-dimensional problems are involved,and cleaning,transformation,mapping and alignment operations are needed.Related data processing tools and methods are gradually emerging,but it is still difficult to solve the problem of cross-fusion of a large number of data.Multi-source heterogeneous data fusion methods were studied based on ETL technology,the common ETL tools and data fusion techniques were analyzed,including data extraction,conversion,loading tools,and the data-processing algorithms.This paper analyzes the heterogeneity of data sources,data structure difference,difficulty of data update frequency,and studies the methods of modular extension and repeated use of ETL tools from modular design,logical and parameter separation,standardized component library,configuration file in lightweight JSON format,so as to better handle large-scale heterogeneous data.It solves the problem of cross-fusion in the multi-source big data fusion stage,which is of great significance to improve data processing efficiency,ensure data quality,and support more in-depth data analysis and decision-making.

A novel polymerized aluminum ferrosilicate flocculant (PSAFC) was prepared by using the acid leaching and alkali dissolved method based on the utilization of the natural composition of Si,Al,Fe,and Ca in coal gasification fine slag.Based upon the optimized preparation conditions,PSAFC was characterized by using X-ray fluorescence spectroscopy (XRF),X-ray diffraction (XRD),and Fourier-transform infrared spectroscopy (FT-IR).It was found that PSAFC was an amorphous,non-crystalline polymer inorganic flocculant formed by the aggregation of Si,Al,Fe,Ca elements.Subsequently,the flocculation performance of PSAFC was investigated by using kaolin suspension as the target.The results revealed that PSAFC exhibited optimal performance for kaolin suspensions with the removal efficiency more than 95% under the optimal conditions of the initial turbidity of 1 439 NTU,the mass concentration of PSAFC of 50 mg/L and pH 10.This investigation offers a novel approach and method for the comprehensive utilization of coal gasification fine slag and the preparation of polymer inorganic flocculants with high-performance.

A robust soft sensor algorithm based on gate recurrent unit (GRU)is proposed to address the issues of multi-variability,nonlinearity,time delay,and measurement outliers in modern process industrial process variables.Firstly,an initial model of GRU is trained by historical data.Secondly,an improved least absolute Lp penalized solution (ILAPPS)algorithm is proposed,which incorporates median absolute deviation into the capped mean absolute error loss,making it more resistant to outliers;and embeds an adaptive operator based on permutation importance ranking design of random forest into Lp-regularization term to improve the accuracy of input variable selection.In addition,ILAPPS is embedded in the GRU input and recurrent layers to achieve sparse optimization of the network.Finally,the proposed algorithm is applied to artificial datasets and actual industrial copper ore flotation processes,and its performance is compared with other advanced algorithms.The experimental results show that the proposed algorithm has higher prediction accuracy and robustness.

In this paper,the fuel-optimal trajectory of orbit transfer with multiple specific-direction thrusts is studied.The indirect optimization method is applied to optimize fuel consumption.To overcome the difficulty in nonsmooth integration caused by the bang-bang control,the homotopy method is adopted to solve the fuel-optimal problem from a related energy-optimal problem.An initial guess generation method is designed based on a simplified dynamical model to overcome the difficulty in providing the initial guesses for co-states.Numerical simulation is presented,the results show that the initial guess obtained by the method provided in this paper is more probable and easier to converge.The validity and efficiency of this initial guess generation method are verified.

Computing color constancy refers to the ability to eliminate the influence of scene light sources and thus reproduce the true color of an object.Currently,significant improvements in color constancy accuracy have been achieved by using deep neural networks,but most deep learning algorithms have long training time,high computational complexity,and require a large number of training samples.To address this problem,this paper proposes a simple and effective method that combines adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM).The method is divided into two phases:training and prediction.In the training phase,image features are first extracted to train two initial light source estimation models,ANFIS and LSSVM,respectively.Then the kernel function transform is used to fuse the two models,and the multivariate linear regression model of light source estimation is obtained by using the reserved training samples for further training.In the prediction phase,after extracting the features of the test image,the final color values of scene light source of the test image is predicted directly from the training data.The experimental results show that,compared with the deep learning method,the proposed method is of lower computational complexity and even with small training samples,and it also has good light source estimation performance.

The adaptive filter algorithm is the core of active control systems (ACS).To further improve the performance of the normalized variable step size filter-x least mean square (NVSS-FxLMS)algorithm,the signal interpolation concept is introduced in this research,and the algorithm combining the signal interpolation with normalized variable step size concept (INVSS-FxLMS algorithm)is proposed to improve the control performance and convergence rate of the NVSS-FxLMS algorithm,then the effect of the number of signal interpolation on the performance of the algorithm is explored.The results show that the signal interpolation can improve the control performance and convergence rate of the NVSS-FxLMS algorithm.Besides,the optimal range of the number of signal interpolation exists.When the number is about 10,the control performance and convergence rate of the algorithm is relatively better,and the total sound pressure level or acceleration level in the frequency domain is reduced by about 18%,while the computational time increases by 9.6%.The research provides a new idea for the improvement of the NVSS-FxLMS algorithm,and lays the foundation for the development of the ACS.

In aquaculture,the real-time and accuracy of water quality data play an important role in aquaculture activities.In addition to improving the accuracy of the water quality monitoring system from the physical level,the use of algorithm optimization processing for the obtained water quality data can greatly improve the accuracy of the data.This design uses the neural network-assisted Kalman filter algorithm to process the water quality monitoring data,which can reduce the average absolute error of dissolved oxygen by more than 27%,the mean square error by more than 39%,and the root mean square error by more than 22%.The mean absolute error of pH value can also be reduced by more than 20%.Accurate water quality data provides data support for farmers' scientific aquaculture.

In this study,Bacillus subtilis 168 was used as a metabolic research strain to optimize the fermentation process,including medium formulation and fermentation conditions,and the final results were obtained under the glucose 80.0 g/L,yeast extract 10.0 g/L,dried corn syrup 10.0 g/L,urea 5.0 g/L,K₂HPO₄ 10.0 g/L,MgSO₄ 1.0 g/L,MnSO₄ 0.08 g /L,fermentation temperature of 37 ℃,initial pH 6.5,inoculum density of 3%,and liquid volume of 40% of the fermentation conditions,the highest ethyl diphosgene was obtained as 32.58 g/L.The ethyl diphosgene was added to zebrafish feed to study its enticing effect on zebrafish.The results showed that adding 0.6% of ethyl diphosgene to zebrafish feed increased the attraction index of the feed from 2.11±0.09 to 3.13±0.08,which was a 48.34% increase in the attraction index,and that ethyl diphosgene at the appropriate concentration increased the antioxidant capacity of the zebrafish liver and the activity of the intestinal digestive tract.In conclusion,the use of Bacillus subtilis 168 fermentation process for the production of ethyl diphosgene was safe,reliable,with high recovery rate,and it was effective and of high application value as a zebrafish attractant.

With the continuous development of big data, the personalized recommendation of users is widely used. The existing recommendation algorithms ignore the community structure composed of various social relations between users. Relationships can be used well in recommendation systems. Based on the multi-subnet complex network model, this paper proposes a community partitioning probability matrix recommendation algorithm based on multiple social relationships by using the community structure characteristics composed of multiple social relationships. By comparing with the existing recommendation algorithms on the real data set Epinions, the accuracy evaluation indicators δ_MAE and δ_RMSE have been improved by 30% and 20% respectively. It can be proved that the community division probability matrix recommendation algorithm based on multiple social relations can effectively improve the recommended accuracy.