The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compar...The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compared with those of an austenite stainless steel 1Cr18Ni9Ti. The results show that the machinability of the austenitic stainless steels with free cutting additives is much better than that of 1Cr18Ni9Ti. This is attributed to the existence of machinable additives. The inclusions might be composed of MnS. Sulfur and copper addition contributes to the improvement of the machinability of austenitic stainless steel. Bismuth is an important factor to improve the machinability of austenitic stainless steel, and it has a distinct advantage over lead. The mechanical properties of the free cutting austenitic stainless steel are similar to those of 1Cr18Ni9Ti. A new Pb-free austenitic stainless steel with high machinability as well as satisfactory mechanical properties has been developed.展开更多
Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical...Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical and chemical properties of these two machinable and electrically conductive carbides.Ti2AlC and Ti3AlC2 display superior properties such as fracture toughness,electrical and thermal conductivities,and oxidation resistance over their binary counterpart.This paper provides a comprehensive overview of the processing-microstructure-property correlations of these two carbides.Potential fields of applications for Ti2AlC and Ti3AlC2 are surveyed.In addition,we point out methods for further improving their properties in some specific applications through appropriate structural design and modification.展开更多
Tailored surface textures at the micro-or nanoscale dimensions are widely used to get required functional performances.Rotary ultrasonic texturing(RUT)technique has been proved to be capable of fabricating periodic mi...Tailored surface textures at the micro-or nanoscale dimensions are widely used to get required functional performances.Rotary ultrasonic texturing(RUT)technique has been proved to be capable of fabricating periodic micro-and nanostructures.In the present study,diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method.Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program.Textured surfaces generated by using a triangular pyramid cutting tip are constructed.Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated,which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process.Technical applications of the textured surfaces are also discussed.Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features.The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface,and anisotropic wetting properties were obtained due to the obvious directional textural features.展开更多
For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding ...For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding strength of insulators, and it is found that surface treatment of material is useful to improve the surface flashover voltage. The carburization treatment is employed to modify the surface components of newly-developed machinable ceramics （MC） materials. A series of MC samples with different glucose solution concentration （0%, 10%, 20%, 30% and 40%） are prepared by chemical reactions for surface carburization modification, and their surface fiashover characteristics are investigated under pulsed voltage in vacuum. It is found that the surface carburization treatment greatly modifies the surface resistivity of MCs and hence the flashover behaviors. Based on the reduction of surface resistivity and the secondary electron emission avalanche （SEEA） theory, the adjustment of flashover withstanding ability can be reasonably explained.展开更多
A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as...A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as the two main crystal phases. The phase separation and the crystallization of the glass have been studied. A series of tests have showed that the material is good at mechanical property and bioactivity. Espe- cially, by analysing the structure of the interface layer between the material and the bone of animal with scanning electron microscope, electron probe, etc., it has been found that the new bone hydroxya- patite is formed on the surface of the material so that the material is connected firmly with the bone.展开更多
The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and s...The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and second order etching rate, surface roughness and accuracy equations were developed using the Response Surface Method (RSM). The etching variables included etching temperature, etching duration, solution and solution concentration. The predictive models’ analyses were supported with the aid of the statistical software package – Design Expert (DE 7). The effects of the individual etching variables and interaction between these variables were also investigated. The study showed that predictive models successfully predicted the etching rate, surface roughness and accuracy readings recorded experimentally with 95% confident interval. The results obtained from the predictive models were also compared with Multilayer Perceptron Artificial Neural Network (ANN). Chemical Etching variables predictive by ANN were in good agreement with those with those obtained by RSM. This observation indicated the potential of ANN in predicting chemical etching variables thus eliminating the need for exhaustive chemical etching in optimization.展开更多
A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by...A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by adding graphite (30%, volume fraction). The Y TZP/LaPO 4 composite ceramics containing different LaPO 4 contents were obtained by infiltration and pyrolysis cycles. The machinability and mechanical properties of materials were investigated. The results show that the machinable Y TZP/LaPO 4 composite ceramics containing 2 3% to 7.5% (volume fraction) LaPO 4 has good machinability as well as outstanding mechanical properties.展开更多
The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The compos...The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The composite sol was then transformed into gel by aging under different temperatures. The gel was dried finally by super critically drying method and sintered to obtain the machinable bioactive glass-ceramics. Effect of thermal treatment on crystallization of the glass-ceramics was investigated by X-ray diffraction （ XRD ） analysis. Microstructure of the glass- ceramics was observed by Scanning Electron Microscopy （SEM） and the mechanism of machinability was discussed. Phlogopite and hydroxylapatite were identified as main crystal phases by XRD analysis under thermal treatment at 750℃ and 950℃ for 1.5 h separately. The relative bulk density could achieve 99% under 1050℃ for 4 h. Microstructure of the glass-ceramics showed that the randomly distributed phlogopite and hydroxylapatite phases were favorable to the machinability of the glass-ceramics. A mean bending strength of about 160- 180 MPa and a fracture toughness parameter KIC of aboat 2.1-2.3 were determined for the glass-ceramics.展开更多
The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model versi...Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model version 6 with a machine-learning-integrated four-mode version of the Modal Aerosol Module, we quantify global BC aging responses to emission reductions for 2011–2018 and for 2050 and 2100 under carbon neutrality. During 2011–18, global trends in BC aging degree(mass ratio of coatings to BC, R_(BC)) exhibited marked regional disparities, with a significant increase in China(5.4% yr^(-1)), which contrasts with minimal changes in the USA, Europe, and India. The divergence is attributed to opposing trends in secondary organic aerosol(SOA) and sulfate coatings, driven by regional changes in the emission ratios of corresponding coating precursors to BC(volatile organic compounds-VOCs/BC and SO_(2)/BC). Projections under carbon neutrality reveal that R_(BC) will increase globally by 47%(118%) in 2050(2100), with strong convergent increases expected across major source regions. The R_(BC) increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance the global BC mass absorption cross-section(MAC) by 11%(17%) in 2050(2100).Consequently, although the global BC burden will decline sharply by 60%(76%), the enhanced MAC partially offsets the magnitude of the decline in the BC direct radiative effect, resulting in the moderation of global BC DRE decreases to 88%(92%) of the BC burden reductions in 2050(2100). This study highlights the globally enhanced BC aging and light absorption capacity under carbon neutrality, thereby partly offsetting the impact of BC direct emission reductions on future changes in BC radiative effects globally.展开更多
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice...Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.展开更多
Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face...Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.展开更多
The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))an...The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.展开更多
Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the m...Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.展开更多
Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive di...Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models—Lasso,SVR and XGBoost—was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R^(2) from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R^(2) values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98%,with an absolute prediction error below 0.2%.Thus,rapid analysis of DCL diesel components was successfully realized.展开更多
The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for e...The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.展开更多
The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite mo...The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite molecular sieves,covalent organic frameworks(COFs)have excellent thermal and chemical stabilities and can be produced in many different forms.Using their different possible construction units,ordered structures for specific applications can be produced,giving them broad prospects in fields such as gas storage.This review analyzes the different types of COFs that have been synthesized and their different methods of CO_(2) capture.It then discusses different ways to increase CO_(2) adsorption by changing the internal structure of COFs and modifying their surfaces.The limitations of COF-derived carbon materials in CO_(2) capture are reviewed and,finally,the key role of machine learning and computational simulation in improving CO_(2) adsorption is mentioned,and the current status and future possible uses of COFs are summarized.展开更多
BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking rec...BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking recovery.OBJECTIVE:To determine whether early suspension-protected training with a personal assistant machine for stroke patients enhances walking ability and prevents muscle spasms.METHODS:Thirty-two early-stage stroke patients from Shenzhen University General Hospital and the China Rehabilitation Research Center were randomly assigned to the experimental group(n=16)and the control group(n=16).Both groups underwent 4 weeks of gait training under the suspension protection system for 30 minutes daily,5 days a week.The experimental group used the personal assistant machine during training.Three-dimensional gait analysis(using the Cortex motion capture system),Brunnstrom staging,Fugl-Meyer Assessment for lower limb motor function,Fugl-Meyer balance function,and the modified Ashworth Scale were evaluated within 1 week before the intervention and after 4 weeks of intervention.RESULTS AND CONCLUSION:After the 4-week intervention,all outcome measures showed significant changes in each group.The experimental group had a small but significant increase in the modified Ashworth Scale score(P<0.05,d=|0.15|),while the control group had a large significant increase(P<0.05,d=|1.48|).The experimental group demonstrated greater improvements in walking speed(16.5 to 38.44 cm/s,P<0.05,d=|4.01|),step frequency(46.44 to 64.94 steps/min,P<0.05,d=|2.32|),stride length(15.50 to 29.81 cm,P<0.05,d=|3.44|),and peak hip and knee flexion(d=|1.82|to|2.17|).After treatment,the experimental group showed significantly greater improvements than the control group in walking speed(38.44 vs.26.63 cm/s,P<0.05,d=|2.75|),stride length,peak hip and knee flexion(d=|1.31|to|1.45|),step frequency(64.94 vs.59.38 steps/min,P<0.05,d=|0.85|),and a reduced support phase(bilateral:24.31%vs.28.38%,P<0.05,d=|0.88|;non-paretic:66.19%vs.70.13%,P<0.05,d=|0.94|).For early hemiplegia,personal assistant machine-assisted gait training under the suspension protection system helps establish a correct gait pattern,prevents muscle spasms,and improves motor function.展开更多
of patients.In medicine and rehabilitation engineering,machine vision technology has been widely used to design intelligent prostheses to help patients restore limb function.Grip strength control is one of the key cha...of patients.In medicine and rehabilitation engineering,machine vision technology has been widely used to design intelligent prostheses to help patients restore limb function.Grip strength control is one of the key challenges in developing prosthetic hands;for example,patients need to appropriately control the grip strength of the prosthetic hand depending on the nature and size of the object to be gripped to prevent it from slipping or being damaged.This study combines machine learning and deep learning techniques to determine object grip information by analyzing images of such objects,including their type,texture,and size,so as to select the appropriate grip strength threshold.The electromyographic gesture-control mode is integrated with the visual recognition system to achieve active detection and control of the intelligent prosthetic hand.This research is also transplanted into the K210 main control board for offline recognition to achieve more efficient real-time performance.The experimental results demonstrate that the system achieves an object recognition accuracy rate of 90%,and the real-machine recognition rate is above 85%.The system successfully implements adaptive grasping for eggs(fragile items)and water bottles(rigid objects).展开更多
In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative...In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.展开更多
基金Item Sponsored by National Natural Science Foundation of China (50334010)
文摘The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compared with those of an austenite stainless steel 1Cr18Ni9Ti. The results show that the machinability of the austenitic stainless steels with free cutting additives is much better than that of 1Cr18Ni9Ti. This is attributed to the existence of machinable additives. The inclusions might be composed of MnS. Sulfur and copper addition contributes to the improvement of the machinability of austenitic stainless steel. Bismuth is an important factor to improve the machinability of austenitic stainless steel, and it has a distinct advantage over lead. The mechanical properties of the free cutting austenitic stainless steel are similar to those of 1Cr18Ni9Ti. A new Pb-free austenitic stainless steel with high machinability as well as satisfactory mechanical properties has been developed.
基金funded by the National Natural Science Foundation of China (NSFC) under Grant Nos. 50802097,50832008the IMR Innovative Research Foundation
文摘Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical and chemical properties of these two machinable and electrically conductive carbides.Ti2AlC and Ti3AlC2 display superior properties such as fracture toughness,electrical and thermal conductivities,and oxidation resistance over their binary counterpart.This paper provides a comprehensive overview of the processing-microstructure-property correlations of these two carbides.Potential fields of applications for Ti2AlC and Ti3AlC2 are surveyed.In addition,we point out methods for further improving their properties in some specific applications through appropriate structural design and modification.
基金Supported by Japan Society for the Promotion of Science(Grant Nos.14J04115,16K17990)
文摘Tailored surface textures at the micro-or nanoscale dimensions are widely used to get required functional performances.Rotary ultrasonic texturing(RUT)technique has been proved to be capable of fabricating periodic micro-and nanostructures.In the present study,diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method.Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program.Textured surfaces generated by using a triangular pyramid cutting tip are constructed.Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated,which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process.Technical applications of the textured surfaces are also discussed.Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features.The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface,and anisotropic wetting properties were obtained due to the obvious directional textural features.
基金supported in part by National Natural Science Foundation of China(Nos.50937004,50777051)NSFC-JSPS Joint Project(50911140103)
文摘For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding strength of insulators, and it is found that surface treatment of material is useful to improve the surface flashover voltage. The carburization treatment is employed to modify the surface components of newly-developed machinable ceramics （MC） materials. A series of MC samples with different glucose solution concentration （0%, 10%, 20%, 30% and 40%） are prepared by chemical reactions for surface carburization modification, and their surface fiashover characteristics are investigated under pulsed voltage in vacuum. It is found that the surface carburization treatment greatly modifies the surface resistivity of MCs and hence the flashover behaviors. Based on the reduction of surface resistivity and the secondary electron emission avalanche （SEEA） theory, the adjustment of flashover withstanding ability can be reasonably explained.
文摘A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as the two main crystal phases. The phase separation and the crystallization of the glass have been studied. A series of tests have showed that the material is good at mechanical property and bioactivity. Espe- cially, by analysing the structure of the interface layer between the material and the bone of animal with scanning electron microscope, electron probe, etc., it has been found that the new bone hydroxya- patite is formed on the surface of the material so that the material is connected firmly with the bone.
文摘The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and second order etching rate, surface roughness and accuracy equations were developed using the Response Surface Method (RSM). The etching variables included etching temperature, etching duration, solution and solution concentration. The predictive models’ analyses were supported with the aid of the statistical software package – Design Expert (DE 7). The effects of the individual etching variables and interaction between these variables were also investigated. The study showed that predictive models successfully predicted the etching rate, surface roughness and accuracy readings recorded experimentally with 95% confident interval. The results obtained from the predictive models were also compared with Multilayer Perceptron Artificial Neural Network (ANN). Chemical Etching variables predictive by ANN were in good agreement with those with those obtained by RSM. This observation indicated the potential of ANN in predicting chemical etching variables thus eliminating the need for exhaustive chemical etching in optimization.
文摘A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by adding graphite (30%, volume fraction). The Y TZP/LaPO 4 composite ceramics containing different LaPO 4 contents were obtained by infiltration and pyrolysis cycles. The machinability and mechanical properties of materials were investigated. The results show that the machinable Y TZP/LaPO 4 composite ceramics containing 2 3% to 7.5% (volume fraction) LaPO 4 has good machinability as well as outstanding mechanical properties.
文摘The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The composite sol was then transformed into gel by aging under different temperatures. The gel was dried finally by super critically drying method and sintered to obtain the machinable bioactive glass-ceramics. Effect of thermal treatment on crystallization of the glass-ceramics was investigated by X-ray diffraction （ XRD ） analysis. Microstructure of the glass- ceramics was observed by Scanning Electron Microscopy （SEM） and the mechanism of machinability was discussed. Phlogopite and hydroxylapatite were identified as main crystal phases by XRD analysis under thermal treatment at 750℃ and 950℃ for 1.5 h separately. The relative bulk density could achieve 99% under 1050℃ for 4 h. Microstructure of the glass-ceramics showed that the randomly distributed phlogopite and hydroxylapatite phases were favorable to the machinability of the glass-ceramics. A mean bending strength of about 160- 180 MPa and a fracture toughness parameter KIC of aboat 2.1-2.3 were determined for the glass-ceramics.
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
基金supported by the National Natural Science Foundation of China (42505149,41925023,U2342223,42105069,and 91744208)the China Postdoctoral Science Foundation (2025M770303)+1 种基金the Fundamental Research Funds for the Central Universities (14380230)the Jiangsu Funding Program for Excellent Postdoctoral Talent,and Jiangsu Collaborative Innovation Center of Climate Change。
文摘Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model version 6 with a machine-learning-integrated four-mode version of the Modal Aerosol Module, we quantify global BC aging responses to emission reductions for 2011–2018 and for 2050 and 2100 under carbon neutrality. During 2011–18, global trends in BC aging degree(mass ratio of coatings to BC, R_(BC)) exhibited marked regional disparities, with a significant increase in China(5.4% yr^(-1)), which contrasts with minimal changes in the USA, Europe, and India. The divergence is attributed to opposing trends in secondary organic aerosol(SOA) and sulfate coatings, driven by regional changes in the emission ratios of corresponding coating precursors to BC(volatile organic compounds-VOCs/BC and SO_(2)/BC). Projections under carbon neutrality reveal that R_(BC) will increase globally by 47%(118%) in 2050(2100), with strong convergent increases expected across major source regions. The R_(BC) increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance the global BC mass absorption cross-section(MAC) by 11%(17%) in 2050(2100).Consequently, although the global BC burden will decline sharply by 60%(76%), the enhanced MAC partially offsets the magnitude of the decline in the BC direct radiative effect, resulting in the moderation of global BC DRE decreases to 88%(92%) of the BC burden reductions in 2050(2100). This study highlights the globally enhanced BC aging and light absorption capacity under carbon neutrality, thereby partly offsetting the impact of BC direct emission reductions on future changes in BC radiative effects globally.
基金supported by the National Natural Science Foundation of China,No.82071909(to GF)the Natural Science Foundation of Liaoning Province,No.2023-MS-07(to HL)。
文摘Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.
基金Supported by CAS Basic and Interdisciplinary Frontier Scientific Research Pilot Project(XDB1190300,XDB1190302)Youth Innovation Promotion Association CAS(Y2021056)+1 种基金Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(YLU-DNL Fund 2022007)The special fund for Science and Technology Innovation Teams of Shanxi Province(202304051001007)。
文摘Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.
文摘The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.
基金Supported by the National Basic Research Program of China(2012CB025904)Zhengzhou Shengda University of Economics,Business and Management(SD-YB2025085)。
文摘Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.
基金Supported by National Natural Science Foundation of China(U24B6018,22178243)。
文摘Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models—Lasso,SVR and XGBoost—was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R^(2) from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R^(2) values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98%,with an absolute prediction error below 0.2%.Thus,rapid analysis of DCL diesel components was successfully realized.
文摘The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.
文摘The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite molecular sieves,covalent organic frameworks(COFs)have excellent thermal and chemical stabilities and can be produced in many different forms.Using their different possible construction units,ordered structures for specific applications can be produced,giving them broad prospects in fields such as gas storage.This review analyzes the different types of COFs that have been synthesized and their different methods of CO_(2) capture.It then discusses different ways to increase CO_(2) adsorption by changing the internal structure of COFs and modifying their surfaces.The limitations of COF-derived carbon materials in CO_(2) capture are reviewed and,finally,the key role of machine learning and computational simulation in improving CO_(2) adsorption is mentioned,and the current status and future possible uses of COFs are summarized.
文摘BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking recovery.OBJECTIVE:To determine whether early suspension-protected training with a personal assistant machine for stroke patients enhances walking ability and prevents muscle spasms.METHODS:Thirty-two early-stage stroke patients from Shenzhen University General Hospital and the China Rehabilitation Research Center were randomly assigned to the experimental group(n=16)and the control group(n=16).Both groups underwent 4 weeks of gait training under the suspension protection system for 30 minutes daily,5 days a week.The experimental group used the personal assistant machine during training.Three-dimensional gait analysis(using the Cortex motion capture system),Brunnstrom staging,Fugl-Meyer Assessment for lower limb motor function,Fugl-Meyer balance function,and the modified Ashworth Scale were evaluated within 1 week before the intervention and after 4 weeks of intervention.RESULTS AND CONCLUSION:After the 4-week intervention,all outcome measures showed significant changes in each group.The experimental group had a small but significant increase in the modified Ashworth Scale score(P<0.05,d=|0.15|),while the control group had a large significant increase(P<0.05,d=|1.48|).The experimental group demonstrated greater improvements in walking speed(16.5 to 38.44 cm/s,P<0.05,d=|4.01|),step frequency(46.44 to 64.94 steps/min,P<0.05,d=|2.32|),stride length(15.50 to 29.81 cm,P<0.05,d=|3.44|),and peak hip and knee flexion(d=|1.82|to|2.17|).After treatment,the experimental group showed significantly greater improvements than the control group in walking speed(38.44 vs.26.63 cm/s,P<0.05,d=|2.75|),stride length,peak hip and knee flexion(d=|1.31|to|1.45|),step frequency(64.94 vs.59.38 steps/min,P<0.05,d=|0.85|),and a reduced support phase(bilateral:24.31%vs.28.38%,P<0.05,d=|0.88|;non-paretic:66.19%vs.70.13%,P<0.05,d=|0.94|).For early hemiplegia,personal assistant machine-assisted gait training under the suspension protection system helps establish a correct gait pattern,prevents muscle spasms,and improves motor function.
文摘of patients.In medicine and rehabilitation engineering,machine vision technology has been widely used to design intelligent prostheses to help patients restore limb function.Grip strength control is one of the key challenges in developing prosthetic hands;for example,patients need to appropriately control the grip strength of the prosthetic hand depending on the nature and size of the object to be gripped to prevent it from slipping or being damaged.This study combines machine learning and deep learning techniques to determine object grip information by analyzing images of such objects,including their type,texture,and size,so as to select the appropriate grip strength threshold.The electromyographic gesture-control mode is integrated with the visual recognition system to achieve active detection and control of the intelligent prosthetic hand.This research is also transplanted into the K210 main control board for offline recognition to achieve more efficient real-time performance.The experimental results demonstrate that the system achieves an object recognition accuracy rate of 90%,and the real-machine recognition rate is above 85%.The system successfully implements adaptive grasping for eggs(fragile items)and water bottles(rigid objects).
文摘In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.
