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 perio...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 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
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al...In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.展开更多
Antimicrobial resistance is a global public health threat,and the World Health Organization(WHO)has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed.The ...Antimicrobial resistance is a global public health threat,and the World Health Organization(WHO)has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed.The discovery and introduction of novel antibiotics are time-consuming and expensive.According to WHO’s report of antibacterial agents in clinical development,only 18 novel antibiotics have been approved since 2014.Therefore,novel antibiotics are critically needed.Artificial intelligence(AI)has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics.Here,we first summarized recently marketed novel antibiotics,and antibiotic candidates in clinical development.In addition,we systematically reviewed the involvement of AI in antibacterial drug development and utilization,including small molecules,antimicrobial peptides,phage therapy,essential oils,as well as resistance mechanism prediction,and antibiotic stewardship.展开更多
Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext...Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.展开更多
The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical r...The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.展开更多
Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie...Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesi...Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesian optimization model(B-RF)and the optimal model(Stacking model).These models are applied to a data set comprising 438 observations with five input variables,with the aim of predicting the compressive strength of reclaimed concrete.Furthermore,we evaluate the performance of the optimized models in comparison to traditional machine learning models,such as support vector regression(SVR),decision tree(DT),and random forest(RF).The results reveal that the Stacking model exhibits superior predictive performance,with evaluation indices including R2=0.825,MAE=2.818 and MSE=14.265,surpassing the traditional models.Moreover,we also performed a characteristic importance analysis on the input variables,and we concluded that cement had the greatest influence on the compressive strength of reclaimed concrete,followed by water.Therefore,the Stacking model can be recommended as a compressive strength prediction tool to partially replace laboratory compressive strength testing,resulting in time and cost savings.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ...Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.展开更多
In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive streng...In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.展开更多
In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
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 the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.
文摘In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.
基金supported by the National Natural Science Foundation of China(32300157)the Shanghai Municipal Science and Technology Commission(19411964900)+1 种基金the Major Research and Development Project of Innovative Drugs,Ministry of Science and Technology of China(2017ZX09304005)the Wellcome Trust.
文摘Antimicrobial resistance is a global public health threat,and the World Health Organization(WHO)has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed.The discovery and introduction of novel antibiotics are time-consuming and expensive.According to WHO’s report of antibacterial agents in clinical development,only 18 novel antibiotics have been approved since 2014.Therefore,novel antibiotics are critically needed.Artificial intelligence(AI)has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics.Here,we first summarized recently marketed novel antibiotics,and antibiotic candidates in clinical development.In addition,we systematically reviewed the involvement of AI in antibacterial drug development and utilization,including small molecules,antimicrobial peptides,phage therapy,essential oils,as well as resistance mechanism prediction,and antibiotic stewardship.
基金the University of Transport Technology under grant number DTTD2022-12.
文摘Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.
文摘The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
基金Funded by China National Key Research and Development Program for Application and Verification of Typical Groundwater Contaminated Sites(No.2019YFC1804805)Shenyang Key Laboratory of Safety Evaluation and Disaster Prevention of Engineering Structures(No.S230184)the Funding Project of Northeast Geological S&T Innovation Center of China Geological Survey(No.QCJJ2023-39)。
文摘Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesian optimization model(B-RF)and the optimal model(Stacking model).These models are applied to a data set comprising 438 observations with five input variables,with the aim of predicting the compressive strength of reclaimed concrete.Furthermore,we evaluate the performance of the optimized models in comparison to traditional machine learning models,such as support vector regression(SVR),decision tree(DT),and random forest(RF).The results reveal that the Stacking model exhibits superior predictive performance,with evaluation indices including R2=0.825,MAE=2.818 and MSE=14.265,surpassing the traditional models.Moreover,we also performed a characteristic importance analysis on the input variables,and we concluded that cement had the greatest influence on the compressive strength of reclaimed concrete,followed by water.Therefore,the Stacking model can be recommended as a compressive strength prediction tool to partially replace laboratory compressive strength testing,resulting in time and cost savings.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
基金Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1445)。
文摘Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.
基金Funded by the Natural Science Foundation of China(No.52109168)。
文摘In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
