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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Based on the First Principles,using the CASTEP module and virtual crystal approximation model,utilizing GGA+PBE functional correlation function,the influence of composition on mechanical properties parameters in AlSi7MgX (X=0.1~0.5) and AlSiXMg0.3 (X=6~9) alloys was studied.It is shown that the lattice constant decreases with the increase of the content of the alloy elements,and there exists a difference of approximately 0.265% between the maximum and minimum values;the elastic constants C11,C12,and C44 meet the structural stability conditions.The volume elastic modulus B,shear modulus G,Young’s modulus E,and Poisson's ratio u vary with Si and Mg content in A356 alloy.The change of Mg content has a more sensitive effect on the shear modulus G and Young's modulus E than the change of Si content.The comparison between the calculation results and existing experimental data shows that the calculation results are close to the experimental data,and it is feasible by using the First Principles to calculate the material parameters.

The incidence of hyperuricemia is increasing year by year,and the impact of hyperuricemia on human health has become a hot and cutting-edge issue in the field of biomedicine.Existing studies have confirmed that long-term high uric acid levels can cause chronic damage to patients′ joints and kidneys,but there are still few studies on the impact on immune homeostasis.This study successfully constructed three mouse models of hyperuricemia using different chemical inducers,and analyzed changes in uric acid,renal pathological changes,B cells in peripheral blood that are mainly responsible for humoral immunity,T cells that are responsible for cellular immunity,and innate proportion of immune cells such as NK cells and other immune cells was studied in detail.The results showed that compared with the normal control group,the proportion of B cells in the three hyperuricemia mouse models all decreased significantly,and the proportion of CD4+ T cells,CD8+ T cells and B cells in the 600 mg/kg high-dose xanthine exposure group declined particularly significant.This study reveals the important impact of hyperuricemia on the body from the perspective of immune homeostasis,provides a reference for the construction of a hyperuricemia model,and puts forward new ideas for the pathogenesis of gout and other diseases induced by hyperuricemia.

To analyze the mechanism of 4 polyacetylenes in Bidens pilosa L. on immune inflammation by network pharmacology and molecular docking,which can provide a theoretical basis for the experimental research of the compounds.The pharmacokinetic parameters of 4 polyacetylenes isolated from B.pilosa were analyzed by SwissADME database.SwissTargetPrediction database was used to predict the effective targets;GeneCards,DisGeNet,and DRUGBANK were used to screen inflammation and immune-related targets,and the intersection targets were obtained.The compound-disease-target network were constructed by Cytoscape3.7.2 software to screen the key targets of polyacetylenes in B.pilosa for intervention of immune inflammation;GO and KEGG analysis based on Metascape database to obtain the potential pathways of B.pilosa in immune inflammation.Finally,AutoDock Tool1.5.6 software was used to do molecular docking,the results were visualized using pymol software,and the activity of the compounds was verified by in vitro anti-inflammatory experiments.A total of 41 active targets were predicted and screened from 4 polyacetylenes,and 18 878 inflammatory and immune diseases related targets and 40 intersection targets of compounds and diseases were obtained.10 key targets were finally screened by PPI analysis.Molecular docking showed that the binding of most targets to compounds was stable.KEGG enrichment showed that the key targets were mainly enriched in AMPK signaling pathway,involving 40 distinct biological processes such as inflammatory response and immune regulation.The results of in vitro experiments showed that the 4 polyacetylenes had anti-inflammatory activity,which was significantly different from the model group (P<0.01).Among them,the compound GZC3 had the best effect on inhibiting NO release.Through network pharmacology and molecular docking analysis,it was found that the 4 polyacetylenes in B.pilosa can regulate STAT3,MAPK14,MMP9,KDR,EGFR and AMPK signaling pathways.Vitro experiments also confirmed that the 4 polyacetylenes have a certain regulatory effect on immune inflammation.

With the increasingly severe energy problem,the design idea of converting lignocellulose,which is abundant renewable resources in nature,into second-generation fuel ethanol provides a way to solve the current energy crisis,food crisis and environmental crisis.However,microorganisms also face a number of technical problems in the process of fermentation to produce second-generation ethanol.Among them,a variety of small molecule chemicals produced by lignocellulosic degradation will not only inhibit the growth and reproduction of microorganisms,but also reduce the fermentation efficiency of microorganisms.Acetic acid is one of the main inhibitors commonly found in many degradation products of lignocellulosic materials.In order to improve the efficiency of microbial lignocellulosic ethanol fermentation,this study selected Saccharomyces cerevisiae,an ethanol fermentation strain with the greatest potential in industrial production,as the model organism,and simulated the acetylation of histone H3K9 by mutating the ninth lysine (K) of histone H3 into glutamine (Q) to investigate the tolerance of histone H3K9Q point mutants to acetic acid,a fermentation inhibitor.The results showed that H3K9Q point mutation could significantly improve the growth performance of Saccharomyces cerevisiae under acetic acid.The growth curve experiment further showed that H3K9Q point mutation could make the strain rapidly enter the exponential phase under 3.5 g /L acetic acid stress,which was shortened from 36 h to 24 h.Moreover,after 48 h of growth in medium with acetic acid,the OD600 of H3K9Q point mutant was about 1.9 times that of the control strain.These results indicated that H3K9Q point mutation significantly improved the acetic acid resistance of Saccharomyces cerevisiae,and this study provided a useful reference for strain optimization in cellulosic ethanol production.

The material of the dimethyl carbonate pressurized tower after over-temperature was analyzed.Through on-site inspection and testing,the circumferential expansion of the cylinder joint,the wall thickness of the cylinder joint,the macroscopic damage of the internal and external parts,and the metallographic structure,hardness and size change of the over-temperature parts were analyzed.Considering the requirements of the hardness value,the allowable stress of the material subjected to over-temperature damage was determined,and the strength of the over-temperature cylinder and open-hole reinforcement were checked.Through the analysis,it is found that the dimethyl carbonate pressurized tower basically meets the strength requirements,and it is recommended to carry out the opening reinforcement treatment at the manhole of the cylinder joint,so as to ensure that the dimethyl carbonate pressurized tower can continue to serve safely.

The robust empirical likelihood inferences for varying coefficient partially linear models are studied.Using weighted composite quantile regression and empirical likelihood method,combined with orthogonal projection technology based on matrix QR decomposition,an empirical likelihood estimation method based on weighted composite fractional regression is proposed for parameter components of the model.Theoretical proof has been provided that the proposed empirical logarithmic likelihood ratio function asymptotically follows a chi square distribution,thereby constructing confidence intervals for parameter components.The orthogonal projection technique based on matrix QR decomposition is introduced in this estimation method,which ensures that the estimation of parameter components is not affected by the estimation accuracy of nonparametric components,so it has better robustness and effectiveness.

The transcription factor Ask10p of Saccharomyces cerevisiae,also known as Rgc2p,not only plays an important role in cell wall biosynthesis,but also participates in the regulation of oxidative stress as a component of the RNA polymerase II holoenzyme,and also participates in the regulation of osmotic balance as a positive regulator of the aquaglyceroporin Fps1p. In recent years,with the deepening of research,Ask10p has been confirmed to play an important regulatory role in oxidative stress,osmotic stress,acetate and toxic metal ion stress,and xylose metabolism. In this review,the biological functions and current research advances of Ask10p in Saccharomyces cerevisiae were presented,and the research directions were also prospected in order to provide reference for future research directions.

This paper establishes a half-vehicle 7 degree-of-freedom rigid-flexible coupling dynamic model and simplifies using the Euler-Bernoulli beam model.Given the limitations associated with the traditional PSO algorithm,which include susceptibility to being stuck in local optima and so on.Three methods,namely chaotic initialization,dynamic inertia weight,and adaptive learning factor,are introduced to optimize the algorithm.Accordingly,a refined variant of the PSO algorithm,namely the Adaptive Inertia Weight PSO,has been introduced.Ultimately,by using MATLAB simulation modeling,the outcomes of three scenarios are meticulously compared:the absence of optimization,optimization via the conventional PSO algorithm,and optimization through the enhanced PSO algorithm.These scenarios are evaluated based on four distinct performance aspects.The findings clearly indicate that the refined PSO algorithm surpasses the other two approaches in all metrics,thereby confirming the effectiveness of the proposed algorithm in greatly enhancing the fluidity of vehicle movement and driving stability.

Sports tourism is an indispensable part of building a sports power in the perspective of Chinese-style modernization,and strengthening urban resilience is a top priority in promoting the modernization of the national governance system and governance capacity.Based on the theory of urban resilience,sports tourism resilience is added,and the level of sports tourism resilience and the degree of coupling coordination between subsystems are assessed with the help of the entropy value method and the coupling coordination degree model of the fairyland coast city in Shandong Province from 2017 to 2021.It is found that the average value of sports tourism resilience as a whole shows a phased decline;the spatial heterogeneity of resilience at the level of sports tourism development is large,and it is an unbalanced development between regions;the average degree of coupling between sub-systems is low and the regional coupling is unbalanced,and the type of coupling is dominated by antagonistic coupling;and the degree of coupling coordination of sub-systems is in the stage of verging on dysfunctions to the stage of basic coordination.

With the wide application of action recognition technology in the fields of risk monitoring and human-computer interaction,human action recognition based on video data has gradually become a new hotspot in the field of computer vision research.Due to the continuous improvement of graph neural network methods,deep learning methods based on graph neural network have been gradually applied to human bone data in recent years to improve the efficiency of human action recognition algorithms.Therefore,this paper introduces the related methods of human action recognition based on graph neural network,analyzes and summarizes them.Firstly,the development status of human action recognition algorithm based on graph convolutional network is introduced.Then the data sets commonly used in action recognition and their characteristics are proposed.Finally,the future research direction and trend are discussed.

The corrosion resistance of EH40 steel in simulated seawater solution is studied through immersion and electrochemical methods in this paper.The soaking test results show that within 1 400 hours,as the corrosion time progresses,the number of corrosion pores on the surface of the sample continues to increase.At 360 hours,the number of corrosion pores is the highest,and after 840 hours,corrosion products gradually form on the surface,resulting in an insignificant increase in the number of corrosion pores.The electrochemical test results show that as the soaking time increases,the corrosion resistance first weakens and then enhances,and the corrosion resistance is the worst at 360 hours.The research on the corrosion behavior of EH40 steel in simulated seawater solution has important engineering practice and theoretical research value，which is helpful to promote the development and application of marine engineering materials.

Microalgae,the third generation biomass raw material,has been used to produce biodiesel for its advantages of high production capacity and high lipids content.In order to increase the production and separation efficiency of microalgae,foam flotation can be applied in the harvest of microalgae.Protein as a natural nitrogen source and a bio-surfactant can play a role of a bridge in coupling the cultivation process with the separation process.The application of the recovered whey soy protein from the soy whey wastewater is cost-effective to the production of Chlorella.The addition of whey soy protein was beneficial to the growth of Chlorella in BBM.In foam flotation,a high enrichment ration 18.7 and a high recovery percentage 96.4% of Chlorella were obtained by adding the optimal whey soy protein concentration of 0.163 g/L at pH 7.0.

Accurate wind power prediction is the basis to ensure the stable operation of wind power grid connection.In order to improve the accuracy of wind power prediction,this paper proposes a new prediction model,firstly,the chaos strategy is used to initialize the population,and the whale optimization algorithm (WOA) is optimized using the Harris Hawk strategy (HHO) with the adaptive weight strategy,and then the improved whale optimization algorithm (HHO-CAWOA) is used to optimize the long and short term memory neural network (LSTM) with the improved whale optimization algorithm (HHO-CAWOA) to optimize the number of neurons and the learning rate,and finally the model is used for wind power prediction.The model has higher prediction performance and better generalization ability and stability than other comparative models.

Self-service terminals play a key role in the modern service industry.Through the comprehensive use of face recognition,fingerprint recognition and other biometric technologies as well as various input and output devices,they can efficiently and accurately confirm customer identity and provide customers with a more efficient and convenient self-service experience.However,at present,self-service terminals generally have low face recognition accuracy,and one of the main reasons is that the shooting angle of the devices face can not adapt to customer groups of different heights,resulting in frequent face recognition failures in the process of business processing,and business processing can not be completed normally.In response to this pain point,this paper proposes an innovative solution,which combines AI technology with intelligent adjustment of camera angle,significantly improves the adaptability of self-service terminals to users of different heights,and fundamentally improves the service experience of users.Through in-depth analysis of the existing technology problems,this paper puts forward a complete set of technical solutions,including system architecture design,key technology realization and advantages and characteristics,to provide practical experience for the technical progress and innovative development of the self-service terminal industry.

In this study,copper doped tin sulfide supported graphene oxide aerogels were successfully prepared by hydrothermal freeze-drying calcination method (CuxSnSO@GO).The material was characterized by XRD,SEM,TEM,and UV-vis DRS,and the effects of different Cu doping amounts and reaction times on the production of lactic acid from photocatalytic reforming of cellulose digest were studied.The results showed that copper was successfully incorporated into the SnS2 structure,resulting in surface sulfur defects,changing the band gap of SnS2,and improving its photocatalytic properties.In addition,CuxSnSO@GO produced by the calcination process improved the separation efficiency of photo generated electrons and holes,significantly improving photocatalytic efficiency.Under visible light irradiation,CuxSnSO@GO can efficiently photocatalytic reform the cellulose hydrolysis and lactic acid can be achieved.When the Cu doping amount is 6%,the reaction time is 4 hours,CuxSnSO@GO has an optimal yield of 38.8%.The CuxSnSO@GO surface sulfur vacancies formed,high absorption,adjustable band structure,and the CuxSnS2-SnO2 heterojunction formed after calcination are all conducive to the selective conversion of lactic acid,providing a new approach for studying the mechanism of biomass photocatalytic reforming.

With the development of the economy and society,science and technology continue to progress,and human production and lifestyle have undergone tremendous changes.Many tasks have been replaced by various robots or assisted by robots to complete.Most operational robots are based on fixed bases,wheeled mobile platforms,or tracked mobile platforms,and cannot enter complex terrain environments to perform operational tasks.The quadruped robot with a single arm solves this problem effectively.This article conducts research based on the quadruped robot with a single arm.Firstly,several representative quadruped robots with a single arm,both domestically and internationally,were introduced.Then,the research status of quadruped robots with a single arm based on simulation platforms and physical experimental platforms was introduced,and the current popular research ideas were analyzed.Next,some commonly used simulation platforms were listed,and the integration and implementation of the quadruped robot system with a single arm were introduced.Finally,a summary was made and the ideas for future research work were introduced.

With the continuous promotion of China's “Ocean Power Strategy” and the rapid development of the global “blue economy”,higher requirements have been put forward for the field of maritime communication.However,at present, most research in the maritime communication mainly focuses on channel analysis and measurement,making it difficult to directly apply it to practical maritime communication.A statistical model for evaporation duct channels has been developed based on this.This model is based on the relevant data generated by the PETOOL toolbox to establish a statistical channel model for the evaporation duct in the Beyond Line of Sight (B-LOS) scenario.The constant coefficients of the variables in the model are corrected using the least squares method to reduce errors and improve the accuracy of the statistical model.Finally,through numerical analysis,the obtained evaporation duct statistical channel model has a good fitting effect.

Trametes versicolor laccase is a copper-containing polyphenol oxidase that can oxidize a variety of phenolic and aromatic compounds.Its main product is water,and it produces almost no toxic by-products.While it has good application prospects,the low yield of Trametes versicolor laccase limits its further application.This study used Atmospheric and Room Temperature Plasma (ARTP) technology to successfully mutate a new strain,TA-03,from Trametes versicolor ATCC20869.To enhance laccase production,the paper optimized fermentation conditions through single-factor shake flask experiments and response surface analysis.The results showed that under optimized conditions,with a farnesol addition of 4.48 mmol/L,copper sulfate addition of 1.90 mmol/L,and pH maintained at 4.00,the laccase production of the mutant strain reached （283.77±8.91） U/mL.To explore the reasons for the increased laccase activity of the mutant strain TA-03,resequencing and transcriptome analysis were performed.The research results show that the mutation of amino acids in the active region of laccase and the enrichment of metabolism and other aspects lead to the improvement of laccase activity.This provides a meaningful exploration of the practical application of Trametes versicolor laccase and enhances the application of laccase in the fermentation industry.

Video anomaly detection has attracted much attention due to the unpredictability of surveillance video anomaly events and the complexity of the anomaly environment. Currently, video anomaly detection often utilises unsupervised methods to acquire video information, but the lack of spatio-temporal information acquisition during feature extraction leads to the problem of inconsistent spatio-temporal features. To this end, a memory-guided two-stream spatio-temporal coding network (MSTAE) model based on memory-guidance is proposed, and a two-stream spatio-temporal feature extraction network is designed to obtain the motion features of the video in spatial streams and the temporal features in temporal streams using continuous video frame sequences and optical flow graphs as inputs, respectively, and at the same time, the attention mechanism is introduced to improve the encoder and reduce the risky error due to the data redundancy. Extensive experiments are conducted on three open standard datasets (Ped2, Avenue and ShanghaiTech datasets), and the experimental results show that the AUC accuracy of the model outperforms the state-of-the-7art methods.

A real-time fluorescent PCR method was developed for rapid qualitative detection of Streptococcus faecalis in PC drinking water barrels.The 23S rDNA gene sequence of Streptococcus faecalis was amplified and detected by using specific primers and probes.The specificity and repeatability of our method was evaluated based on the comparative use of our method and the national standard method to detect PC drinking water bucket samples.The results indicate that our method can quickly and qualitatively detect Streptococcus faecalis in PC drinking water barrels,effectively shorten detection time and reduce experimental operations,with high specificity and repeatability.

Deep learning based on image dehazing models often design and stack efficient feature extraction modules,resulting in complex models and slow inference.Knowledge distillation,which transfers knowledge from the teacher network to an efficient student network,can improve the efficiency of the model without affecting its effectiveness,and has received widespread attention.However,most existing knowledge distillation based on dehazing models focus on knowledge transfer at the same level between the teacher network and the student network,without considering whether the feature transfer is sufficient,resulting in incomplete feature distillation and poor dehazing effect.To alleviate the above issues,this article proposes the multi-scale review distillation network (MRDN),which fully transfers teacher network knowledge to different levels of student networks.Specifically,in order to ensure the ability of students and teachers to mine image hidden features and reconstruct information in the network,hybrid attention blocks (HAB) and hybrid attention block groups (HABs) are designed respectively;then,the attention fusion block (AFB) is used to review the knowledge,which integrates the current and deep level features of the student network to generate intermediate features for distillation;finally,in order to accurately transfer knowledge,the hierarchical content loss block (HCLB) is used to extract multi-scale pyramid features from intermediate features and corresponding hierarchical features of the teacher network,and calculate the losses at each level.The experimental results indicate that our model outperforms state-of-the-art methods.Specifically,MRDN performs better in removing fog on real fog images,and surpasses the best contrastive model (EPDN) in PSNR and SSIM metrics on the SOTS dataset by 9.2% and 7.8%,respectively.

A rapid prototyping technology for metal alloys is introduced.The technology is based on the principle of dynamic directional solidification.The grain refinement and porosity reduction of the alloy microstructure can be achieved by high pressure water jet dynamic and rapid cooling without adding refining agent,metamorphic agent and degassing agent,and the mechanical properties of the alloy can be improved.The condensation time of A356 aluminum alloy prepared by this technology can be shortened to 15 s.The microstructure is uniform,the primary phase (α-Al) and eutectic Si phases are obviously refined,the secondary dendrite arm spacing is significantly reduced.The porosity defects are effectively improved,the porosity can be reduced to 1.1%.The tensile strength of alloy is as high as 285 MPa.

Pulp and paper industry is faced with many problems,such as difficult pollution control,less raw material supply and high energy consumption.This paper conducts experiments to study the compounding of three biological enzymes for pulping,using a combination of biological pulping and mechanical pulping.At the same time,two enzyme preparation treatments are designed to utilize agricultural waste wheat straw and improve the pulping process after pulping into paper.The paper performance was tested and it was found that compared with simple mechanical pulping and primary enzyme treatment,the secondary biological enzyme treatment improved the paper-forming effect.Compared with untreated straw,the tensile strength is nearly doubled,the tear strength is increased by 36.5%,and the water retention value is increased by 78.8%.

This article attempts to use the proof methods of reduction to absurdity and classification discussion to provide the strict proofs for “there are infinite primes in the form of 8k+1(k∈Z)”,“there are infinite primes in the form of 8k-1(k∈Z) ”,“there are infinite primes in the form of 8k+3(k∈Z) ”,and “there are infinite primes in the form of 8k-3(k∈Z) ”.The used knowledge is the fundamental knowledge in elementary number theory,limited to some basic properties of prime number,integer division,congruence,and Legendre symbol.To prove the main conclusions,this article first derives two very useful lemmas.

This paper aims to study the numerical solution of the coupled sine-Gordon equations on an unbounded domain,which is widely applied in plasma physics.The unboundedness of the physical domain and the nonlinearity of the equations make it challenging to derive the numerical solution.Employing the artificial boundary method and the operator splitting approach to overcome the unboundedness and nonlinearity,the splitting local artificial boundary method was established for the coupled sine-Gordon equations.The Cauchy problem was reduced into an initial boundary value problem on a bounded computational domain,which can be efficiently solved by the finite difference method.The accuracy and effectiveness of the proposed method were demonstrated by some numerical results,and the propagation of solitons was simulated.

In this study,the formation process of humic substances (HS) and the variation of chemical structure were investigated by the tank high-temperature aerobic composting process with chicken manure and fungus residue as raw materials.The results showed that the chicken manure was treated by canned aerobic composting,the start-up speed of the composting was fast,the overall high temperature duration of the composting was long,the temperature above 50 ℃ lasted for more than 4 days,and the water content decreased rapidly to less than 25%;a large number of proteins,fats and sugars in the material were decomposed and utilized by microorganisms and then reconverted into macromolecular organic matter,composting to the 6th day,the C/N ratio reached 10.10±0.72,and the decomposition process was basically completed;3D-EEM spectrograms showed that fulvic acids were biologically oxidized to generate a more stable humic acid（HA）structure throughout the composting process,but after the 6th day,due to the decrease in the content of small-molecule organic matter,it resulted in the accumulation of fulvic acids.Therefore,the conversion of fulvic acid (FA) to HA can be further promoted by increasing the content of small molecule organic matter at the later stage of composting.

Lactic acid bacteria,as a globally recognized and safe microorganism,have been widely used in various fields such as food and medicine for a long time.Traditional Chinese medicine has a history of over two thousand years in the field of skincare in China.Fermentation,as a unique process,can play a role in enhancing drug efficacy and reducing toxicity by leveraging the unique metabolic pathways of microorganisms.In recent years,traditional Chinese medicine fermented by lactic acid bacteria has gradually been applied as a raw material in various skincare products.The research and production of natural skincare raw materials have gradually become a key competitive direction for domestic and foreign cosmetics companies.The article summarizes the latest research progress on the application of lactic acid bacteria fermented traditional Chinese medicine in cosmetics,and carries out an overview of its skincare effect,effective active ingredients,and skincare mechanism,in order to provide theoretical basis for further research and subsequent production practice in this field.