Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyviny...Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyvinyl chloride(PVC)pipe waste polymers into nanofibers(NFs)optimized for TENG applications.We focused on optimizing the morphology of recycled PVC polymer to NFs and enhancing their piezoelectric properties by incorporating ZnO nanoparticles(NPs).The optimized PVC/0.5 wt%ZnO NFs were tested with Nylon-6 NFs,and copper(Cu)electrodes.The Nylon-6 NFs exhibited a power density of 726.3μWcm^(-2)—1.13 times higher than Cu and maintained 90%stability after 172800 cycles,successfully powering various colored LEDs.Additionally,a 3D-designed device was developed to harvest energy from biomechanical movements such as finger tapping,hand tapping,and foot pressing,making it suitable for wearable energy harvesting,automatic switches,and invisible sensors in surveillance systems.This study demonstrates that recycling polymers for TENG devices can effectively address energy,sensor,and environmental challenges.展开更多
This study explores the mechanical properties of a novel composite material,blending polylactic acid(PLA)with sea shells,through a comprehensive tensile test analysis.The tensile test results offer valuable insights i...This study explores the mechanical properties of a novel composite material,blending polylactic acid(PLA)with sea shells,through a comprehensive tensile test analysis.The tensile test results offer valuable insights into the material’s behavior under axial loading,shedding light on its strength,stiffness,and deformation characteristics.The results suggest that the incorporation of sea shells decrease the tensile strength of 14.55%and increase the modulus of 27.44%for 15 wt%SSP(sea shell powder)into PLA,emphasizing the reinforcing potential of the mineral-rich sea shell particles.However,a potential trade-off between decreased strength and reduced ductility is noted,highlighting the need for a delicate balance in material composition.The study underscores the importance of uniform sea shell particle distribution within the PLA matrix for consistent mechanical performance.These results offer a basis for additional PLA-sea shell blend optimization,directing future efforts to balance strength,flexibility,and other critical attributes for a range of applications,including biomedical devices and sustainable packaging.This investigation opens the door to more sustainable and mechanically strong materials in the field of additive manufacturing by demonstrating the positive synergy between nature-inspired materials and cutting-edge testing techniques.展开更多
This paper endeavours to bridge the existing gap in muscular actuator design for ligament-skeletal-inspired robots,thereby fostering the evolution of these robotic systems.We introduce two novel compliant actuators,na...This paper endeavours to bridge the existing gap in muscular actuator design for ligament-skeletal-inspired robots,thereby fostering the evolution of these robotic systems.We introduce two novel compliant actuators,namely the Internal Torsion Spring Compliant Actuator(ICA)and the External Spring Compliant Actuator(ECA),and present a comparative analysis against the previously conceived Magnet Integrated Soft Actuator(MISA)through computational and experimental results.These actuators,employing a motor-tendon system,emulate biological muscle-like forms,enhancing artificial muscle technology.Then,applications of the proposed actuators in a robotic arm inspired by the human musculoskeletal system are presented.Experiments demonstrate satisfactory power in tasks like lifting dumbbells(peak power:36 W),playing table tennis(end-effector speed:3.2 m/s),and door opening,without compromising biomimetic aesthetics.Compared to other linear stiffness serial elastic actuators(SEAs),ECA and ICA exhibit high power-to-volume(361×10^(3)W/m^(3))and power-to-mass(111.6 W/kg)ratios respectively,endorsing the biomimetic design’s promise in robotic development.展开更多
Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnos...Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.展开更多
Artificial intelligence(AI)has evolved at an unprecedented pace in recent years.This rapid advancement includes algorithmic breakthroughs,cross-disciplinary integration,and diverse applications—driven by growing comp...Artificial intelligence(AI)has evolved at an unprecedented pace in recent years.This rapid advancement includes algorithmic breakthroughs,cross-disciplinary integration,and diverse applications—driven by growing computational power,massive datasets,and collaborative global research.This special issue of Emerging Artificial Intelligence Technologies and Applications was conceived to provide a platformfor cuttingedge AI research communication,developing novel methodologies,cross-domain applications,and critical advancements in addressing real-world challenges.Over the past months,we have witnessed a remarkable diversity of submissions,reflecting the global trend of AI innovation.Below,we synthesize the key insights from these works,highlighting their collective contribution to advancing AI’s theoretical frontiers and practical applications.展开更多
Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-...Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective.To address these challenges,this research proposes a computer-aided diagnosis(CAD)approach using Artificial Intelligence(AI)techniques for binary and multiclass classification of glaucoma stages.An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network(ConvNet)models–ResNet-50,VGG-16,and InceptionV3 is utilized in this paper.This fusion technique enhances diagnostic accuracy and robustness by ensemble-averaging the predictions from individual models,leveraging their complementary strengths.The objective of this work is to assess the model’s capability for early-stage glaucoma diagnosis.Classification is performed on a dataset collected from the Harvard Dataverse repository.With the proposed technique,for Normal vs.Advanced glaucoma classification,a validation accuracy of 98.04%and testing accuracy of 98.03%is achieved,with a specificity of 100%which outperforms stateof-the-art methods.For multiclass classification,the suggested ensemble approach achieved a precision and sensitivity of 97%,specificity,and testing accuracy of 98.57%and 96.82%,respectively.The proposed E-GlauNet model has significant potential in assisting ophthalmologists in the screening and fast diagnosis of glaucoma,leading to more reliable,efficient,and timely diagnosis,particularly for early-stage detection and staging of the disease.While the proposed method demonstrates high accuracy and robustness,the study is limited by the evaluation of a single dataset.Future work will focus on external validation across diverse datasets and enhancing interpretability using explainable AI techniques.展开更多
The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their functionality.Through introducing innovative solutions for energy harvesting and autonomous sensing,this...The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their functionality.Through introducing innovative solutions for energy harvesting and autonomous sensing,this research presents a cost-effective strategy to enhance the performance of triboelectric nanogenerators(TENGs).The TENG was fabricated from polyvinylidene fluoride(PVDF)and N,N'-poly(methyl methacrylate)(PMMA)blend with a porous structure via a novel optimized quenching method.The developed approach results in a highβ-phase content(85.7%)PVDF/3wt.%PMMA porous blend,known for its superior piezoelectric properties.PVDF/3wt.%PMMA modified porous TENG demonstrates remarkable electrical output,with a dielectric constant of 40 and an open-circuit voltage of approximately 600 V.The porous matrix notably increases durability,enduring over 36000 operational cycles without performance degradation.Moreover,practical applications were explored in this research,including powering LEDs and pacemakers with a maximum power output of 750mWm^(-2).Also,TENG served as a self-powered tactile sensor for robotic applications in various temperature conditions.The work highlights the potential of the PVDF/PMMA porous blend to utilize the next-generation self-powered sensors and power small electronic devices.展开更多
This work is focused on developing AA2124/4 wt.%B4 C nano-composite coatings on Ti-6 A1-4 V using friction surfacing to improve the wear resistance. The composite was produced using conventional stir casting method an...This work is focused on developing AA2124/4 wt.%B4 C nano-composite coatings on Ti-6 A1-4 V using friction surfacing to improve the wear resistance. The composite was produced using conventional stir casting method and coatings were laid using an indigenously-developed friction surfacing machine. The rotational speed of the mechtrode was varied. The microstructure of the composite coating was observed using conventional and advanced microscopic techniques. The sliding wear behavior was evaluated using a pin-on-disc apparatus. The coating geometry(thickness and width) increased with increased rotational speed. The interface was straight without thick intermetallic layer. Homogenous distribution of nano B4C particles and extremely fine grains was observed in the composite coating. The interfacial bonding between the aluminum matrix and B4C particles was excellent. The composite coating improved the wear resistance of the titanium alloy substrate due to the reduction in effective contact area,lower coefficient of friction and excellent interfacial bonding.展开更多
The objective of this study is to investigate the improvement possibilities of the floatability of galena with ultrasonic application in the presence of potassium ethyl xanthate(KEX). For this purpose, micro-flotation...The objective of this study is to investigate the improvement possibilities of the floatability of galena with ultrasonic application in the presence of potassium ethyl xanthate(KEX). For this purpose, micro-flotation experiments were carried out in addition to surface chemistry studies including zeta potential, contact angle, and bubble-particle attachment time measurements at various ultrasonic power levels and conditioning time. The results showed that, the maximum micro-flotation recovery of 77.5% was obtained with 30 W ultrasound power and 2 min conditioning time. In addition, more negative zeta potential values were obtained with ultrasound as well as higher contact angle and lower bubble-particle attachment time, which indicated the increased hydrophobicity of galena with ultrasound.展开更多
A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel fiat surfaces, is derived, together with non-holonomic constraints. B...A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel fiat surfaces, is derived, together with non-holonomic constraints. By referring to the fact that humans grasp an object in the form of precision prehension, dynamically and stably by opposable forces, between the thumb and another finger （index or middle finger）, a simple control signal constructed from finger-thumb opposition is proposed, and shown to realize stable grasping in a dynamic sense without using object information or external sensing （this is called ＂blind grasp＂ in this paper）. The stability of grasping with force/torque balance under non-holonomic constraints is analyzed on the basis of a new concept named ＂stability on a manifold＂. Preliminary simulation results are shown to verify the validity of the theoretical results.展开更多
Casting defects that are closely related to entrapped air bubbles and metallic oxides occur very frequently in the sand mold casting process. Many researchers have shown that these defects can be reduced by adopting a...Casting defects that are closely related to entrapped air bubbles and metallic oxides occur very frequently in the sand mold casting process. Many researchers have shown that these defects can be reduced by adopting an appropriate gating system design. However, it is difficult for field engineers to identify a specific gating system that is more appropriate for their products. In this study, we tried to draw a comparison between two gating system designs with and without a ceramic foam filter. The ceramic foam filter was added to the horizontal runner just after the down sprue to prevent air bubble generation and reduce turbulence without a change of gating system design. The water modeling experiment was conducted with four different amounts of the initial volumes of water in the reservoir to verify the effects of initial pouring velocity. The results of the experiment applying the filter showed remarkably changed flow characteristics. The use of the filter was found to convert the flow pattern of water in the desired direction. The ceramic foam filter performed well to reduce flow velocity and stabilize the water stream.The flow pattern without a filter can be improved significantly even with the the use of just a 10 PPI irregular filter. Although the study confirmed that use of the filter may change the flow characteristics, it needs to be noted that the use of the ceramic filter alone cannot solve all the problems caused by a poorly designed gating system.展开更多
Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches...Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for CPDP.This paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source code.The proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive models.The process involves graph construction,feature learning through graph embedding and LSTM,and defect prediction.Experimental evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.展开更多
The purpose of this research is a quantitative analysis of movement patterns of dance, which cannot be analyzed with a motion capture system alone, using simultaneous measurement of body motion and biophysical informa...The purpose of this research is a quantitative analysis of movement patterns of dance, which cannot be analyzed with a motion capture system alone, using simultaneous measurement of body motion and biophysical information. In this research, two kinds of same leg movement are captured by simultaneous measurement; one is a leg movement with given strength, the other is a leg movement without strength on condition of basic experiment using optical motion capture and electromyography （EMG） equipment in order to quantitatively analyze characteristics of leg movement. Also, we measured the motion of the traditional Japanese dance using the constructed system. We can visualize leg movement of Japanese dance by displaying a 3D CG character animation with motion data and EMG data. In addition, we expect that our research will help dancers and researchers on dance through giving new information on dance movement which cannot be analyzed with only motion capture.展开更多
In this study, high velocity impact behaviour of friction stir welded AA7075-T651 25 mm thick plates were investigated using a 7.62 mm × 51 mm lead core and 7.62 mm × 39 mm steel core projectiles. Prior to b...In this study, high velocity impact behaviour of friction stir welded AA7075-T651 25 mm thick plates were investigated using a 7.62 mm × 51 mm lead core and 7.62 mm × 39 mm steel core projectiles. Prior to ballistic trails, mechanical and metallurgical properties of friction stir welded AA 7075-T651 25 mm thick plates were studied. Microstructural and hardness studies revealed that friction stir welds constituted three distinct regions namely Weld Nugget(WN), Thermo-Mechanically Affected Zone(TMAZ) and Heat Affected Zone(HAZ). Base Material(BM) and all three weld regions were ballistically tested as per military standard NIJ.0108.01 using lead and steel core bullets at maximum permissible velocities of 830 ± 20 and 700 ± 30 m/s, respectively. It has been found that base material(AA7075-T651)and all three weld regions of 25 mm thick plates were able to resist perforation by both types of projectiles used. However depth of penetration has been found to increase from BM to WN, HAZ and TMAZ for both types of projectiles. In all cases steel core projectiles caused higher depth of penetration compared to those caused by lead core projectiles. TMAZs of the friction stir welds were found to be the weakest zone. The fracture that occurred in the base material was spall fragmentation indicating brittle failure, whereas all zones of friction stir welded AA7075-T651 targets with a front petalling, indicating ductile failure. The post-ballistic tested samples showed no significant change in the microstructure of the BM and WN. On the other hand, TMAZ and HAZ showed severe grain deformation in the direction of projectile penetration, and the formation of adiabatic shear bands(ASB). This work showed that 25 mm thick friction stir welded AA7075-T651 joints responded well to ballistic impact loads, making them a good choice for light combat vehicles.展开更多
Marvi et al(Science,2014,vol.346,p.224)concluded a sidewinder rattlesnake increases the body contact length with the sand when granular incline angle increases.They also claimed the same principle should work on robot...Marvi et al(Science,2014,vol.346,p.224)concluded a sidewinder rattlesnake increases the body contact length with the sand when granular incline angle increases.They also claimed the same principle should work on robotic snake too.We have evidence to prove that this conclusion is only partial in describing the snake body-sand interaction.There should be three phases that fully represent the snake locomotion behaviors during ascent of sandy slopes,namely lifting,descending,and ceasing.The snake body-sand interaction during the descending and ceasing phases helps with the climbing while such interaction during the lifting phase in fact contributes resistance.展开更多
Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widel...Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.展开更多
OBJECTIVE:To identify the optimal intensity and duration of cupping that would minimize mechanical skin deformation.METHODS:We developed an optical measurement probe and system for measuring skin color values in real ...OBJECTIVE:To identify the optimal intensity and duration of cupping that would minimize mechanical skin deformation.METHODS:We developed an optical measurement probe and system for measuring skin color values in real time.We applied cupping at the following five Bladder Meridian acupoints.To investigate optimal intensity,negative pressure intensity was increased every 2 min up to 80 k Pa.To investigate optimal time,10 measurement sites were selected and negative pressure intensities of 30,60,and 80 k Pa were applied for 5 min each.Skin color information was analyzed by the following skin color values:red color saturation,erythema index,and melanin index.RESULTS:The red color saturation and erythema index increased steadily up to 60 kpa negative pressure intensity,then decreased between 60 and 80 k Pa.Therefore,maximal values were observed at 60 k Pa.The melanin index consistently increased with increasing negative pressure intensity.The red color saturation and erythema index did not change after 20 s at 60 k Pa negative pressure intensity.For negative pressure intensities below 80 k Pa,significant changes in melanin index were not observed after 20 s.At 80 k Pa negative pressure intensity,the melanin index exhibited an increasing pattern for200 s,then showed no changes.CONCLUSIONS:To minimize skin deformation,60 k Pa and 20 s were the appropriate intensity and duration when using red color saturation and erythema index as diagnostic indexes.Because of the increasing pattern up to 80 k Pa negative pressure intensity,the optimal intensity of melanin index could not be determined.When applying 80 k Pa negative pressure intensity and using melanin index as the diagnostic index,we recommend a duration of 200 s.展开更多
The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applica...The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artificial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchfields that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.展开更多
In clinical practice,brain death is the irreversible end of all brain activity.Compared to current statistical methods for the determination of brain death,we focus on the approach of complex networks for real-world e...In clinical practice,brain death is the irreversible end of all brain activity.Compared to current statistical methods for the determination of brain death,we focus on the approach of complex networks for real-world electroencephalography in its determination.Brain functional networks constructed by correlation analysis are derived,and statistical network quantities used for distinguishing the patients in coma or brain death state,such as average strength,clustering coefficient and average path length,are calculated.Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state.Our findings might provide valuable insights on the determination of brain death.展开更多
基金supported by the research projects AP23486880 from the Ministry of Higher EducationScience of the Republic of Kazakhstan and 111024CRP2010,20122022FD4135 from Nazarbayev University.
文摘Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyvinyl chloride(PVC)pipe waste polymers into nanofibers(NFs)optimized for TENG applications.We focused on optimizing the morphology of recycled PVC polymer to NFs and enhancing their piezoelectric properties by incorporating ZnO nanoparticles(NPs).The optimized PVC/0.5 wt%ZnO NFs were tested with Nylon-6 NFs,and copper(Cu)electrodes.The Nylon-6 NFs exhibited a power density of 726.3μWcm^(-2)—1.13 times higher than Cu and maintained 90%stability after 172800 cycles,successfully powering various colored LEDs.Additionally,a 3D-designed device was developed to harvest energy from biomechanical movements such as finger tapping,hand tapping,and foot pressing,making it suitable for wearable energy harvesting,automatic switches,and invisible sensors in surveillance systems.This study demonstrates that recycling polymers for TENG devices can effectively address energy,sensor,and environmental challenges.
文摘This study explores the mechanical properties of a novel composite material,blending polylactic acid(PLA)with sea shells,through a comprehensive tensile test analysis.The tensile test results offer valuable insights into the material’s behavior under axial loading,shedding light on its strength,stiffness,and deformation characteristics.The results suggest that the incorporation of sea shells decrease the tensile strength of 14.55%and increase the modulus of 27.44%for 15 wt%SSP(sea shell powder)into PLA,emphasizing the reinforcing potential of the mineral-rich sea shell particles.However,a potential trade-off between decreased strength and reduced ductility is noted,highlighting the need for a delicate balance in material composition.The study underscores the importance of uniform sea shell particle distribution within the PLA matrix for consistent mechanical performance.These results offer a basis for additional PLA-sea shell blend optimization,directing future efforts to balance strength,flexibility,and other critical attributes for a range of applications,including biomedical devices and sustainable packaging.This investigation opens the door to more sustainable and mechanically strong materials in the field of additive manufacturing by demonstrating the positive synergy between nature-inspired materials and cutting-edge testing techniques.
基金research project funded by the National Natural Science Foundation of China(NSFC)under Grant 91948302 and Grant 52021003Research England fund at NERIC.
文摘This paper endeavours to bridge the existing gap in muscular actuator design for ligament-skeletal-inspired robots,thereby fostering the evolution of these robotic systems.We introduce two novel compliant actuators,namely the Internal Torsion Spring Compliant Actuator(ICA)and the External Spring Compliant Actuator(ECA),and present a comparative analysis against the previously conceived Magnet Integrated Soft Actuator(MISA)through computational and experimental results.These actuators,employing a motor-tendon system,emulate biological muscle-like forms,enhancing artificial muscle technology.Then,applications of the proposed actuators in a robotic arm inspired by the human musculoskeletal system are presented.Experiments demonstrate satisfactory power in tasks like lifting dumbbells(peak power:36 W),playing table tennis(end-effector speed:3.2 m/s),and door opening,without compromising biomimetic aesthetics.Compared to other linear stiffness serial elastic actuators(SEAs),ECA and ICA exhibit high power-to-volume(361×10^(3)W/m^(3))and power-to-mass(111.6 W/kg)ratios respectively,endorsing the biomimetic design’s promise in robotic development.
基金funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan,grant numbers AP14969403 and AP23485820.
文摘Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.
文摘Artificial intelligence(AI)has evolved at an unprecedented pace in recent years.This rapid advancement includes algorithmic breakthroughs,cross-disciplinary integration,and diverse applications—driven by growing computational power,massive datasets,and collaborative global research.This special issue of Emerging Artificial Intelligence Technologies and Applications was conceived to provide a platformfor cuttingedge AI research communication,developing novel methodologies,cross-domain applications,and critical advancements in addressing real-world challenges.Over the past months,we have witnessed a remarkable diversity of submissions,reflecting the global trend of AI innovation.Below,we synthesize the key insights from these works,highlighting their collective contribution to advancing AI’s theoretical frontiers and practical applications.
基金funded by Department of Robotics and Mechatronics Engineering,Kennesaw State University,Marietta,GA 30060,USA.
文摘Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective.To address these challenges,this research proposes a computer-aided diagnosis(CAD)approach using Artificial Intelligence(AI)techniques for binary and multiclass classification of glaucoma stages.An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network(ConvNet)models–ResNet-50,VGG-16,and InceptionV3 is utilized in this paper.This fusion technique enhances diagnostic accuracy and robustness by ensemble-averaging the predictions from individual models,leveraging their complementary strengths.The objective of this work is to assess the model’s capability for early-stage glaucoma diagnosis.Classification is performed on a dataset collected from the Harvard Dataverse repository.With the proposed technique,for Normal vs.Advanced glaucoma classification,a validation accuracy of 98.04%and testing accuracy of 98.03%is achieved,with a specificity of 100%which outperforms stateof-the-art methods.For multiclass classification,the suggested ensemble approach achieved a precision and sensitivity of 97%,specificity,and testing accuracy of 98.57%and 96.82%,respectively.The proposed E-GlauNet model has significant potential in assisting ophthalmologists in the screening and fast diagnosis of glaucoma,leading to more reliable,efficient,and timely diagnosis,particularly for early-stage detection and staging of the disease.While the proposed method demonstrates high accuracy and robustness,the study is limited by the evaluation of a single dataset.Future work will focus on external validation across diverse datasets and enhancing interpretability using explainable AI techniques.
基金supported by the research projects AP14869428 from the Ministry of Science and Higher Education of the Republic of Kazakhstan20122022FD4135 from Nazarbayev University.
文摘The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their functionality.Through introducing innovative solutions for energy harvesting and autonomous sensing,this research presents a cost-effective strategy to enhance the performance of triboelectric nanogenerators(TENGs).The TENG was fabricated from polyvinylidene fluoride(PVDF)and N,N'-poly(methyl methacrylate)(PMMA)blend with a porous structure via a novel optimized quenching method.The developed approach results in a highβ-phase content(85.7%)PVDF/3wt.%PMMA porous blend,known for its superior piezoelectric properties.PVDF/3wt.%PMMA modified porous TENG demonstrates remarkable electrical output,with a dielectric constant of 40 and an open-circuit voltage of approximately 600 V.The porous matrix notably increases durability,enduring over 36000 operational cycles without performance degradation.Moreover,practical applications were explored in this research,including powering LEDs and pacemakers with a maximum power output of 750mWm^(-2).Also,TENG served as a self-powered tactile sensor for robotic applications in various temperature conditions.The work highlights the potential of the PVDF/PMMA porous blend to utilize the next-generation self-powered sensors and power small electronic devices.
基金Department of Science and Technology [DST-WOS-A, No.SR/WOS-A/ET-1093/2015 (G)] for funding the project
文摘This work is focused on developing AA2124/4 wt.%B4 C nano-composite coatings on Ti-6 A1-4 V using friction surfacing to improve the wear resistance. The composite was produced using conventional stir casting method and coatings were laid using an indigenously-developed friction surfacing machine. The rotational speed of the mechtrode was varied. The microstructure of the composite coating was observed using conventional and advanced microscopic techniques. The sliding wear behavior was evaluated using a pin-on-disc apparatus. The coating geometry(thickness and width) increased with increased rotational speed. The interface was straight without thick intermetallic layer. Homogenous distribution of nano B4C particles and extremely fine grains was observed in the composite coating. The interfacial bonding between the aluminum matrix and B4C particles was excellent. The composite coating improved the wear resistance of the titanium alloy substrate due to the reduction in effective contact area,lower coefficient of friction and excellent interfacial bonding.
基金the Research Fund of Istanbul University under grant FAB-2017-25658.
文摘The objective of this study is to investigate the improvement possibilities of the floatability of galena with ultrasonic application in the presence of potassium ethyl xanthate(KEX). For this purpose, micro-flotation experiments were carried out in addition to surface chemistry studies including zeta potential, contact angle, and bubble-particle attachment time measurements at various ultrasonic power levels and conditioning time. The results showed that, the maximum micro-flotation recovery of 77.5% was obtained with 30 W ultrasound power and 2 min conditioning time. In addition, more negative zeta potential values were obtained with ultrasound as well as higher contact angle and lower bubble-particle attachment time, which indicated the increased hydrophobicity of galena with ultrasound.
基金This work was supported in part by the Grant-in-Aid for Exploratory Research of the JSPS (No. 16656085).
文摘A mathematical model expressing the motion of a pair of multi-DOF robot fingers with hemi-spherical ends, grasping a 3-D rigid object with parallel fiat surfaces, is derived, together with non-holonomic constraints. By referring to the fact that humans grasp an object in the form of precision prehension, dynamically and stably by opposable forces, between the thumb and another finger （index or middle finger）, a simple control signal constructed from finger-thumb opposition is proposed, and shown to realize stable grasping in a dynamic sense without using object information or external sensing （this is called ＂blind grasp＂ in this paper）. The stability of grasping with force/torque balance under non-holonomic constraints is analyzed on the basis of a new concept named ＂stability on a manifold＂. Preliminary simulation results are shown to verify the validity of the theoretical results.
文摘Casting defects that are closely related to entrapped air bubbles and metallic oxides occur very frequently in the sand mold casting process. Many researchers have shown that these defects can be reduced by adopting an appropriate gating system design. However, it is difficult for field engineers to identify a specific gating system that is more appropriate for their products. In this study, we tried to draw a comparison between two gating system designs with and without a ceramic foam filter. The ceramic foam filter was added to the horizontal runner just after the down sprue to prevent air bubble generation and reduce turbulence without a change of gating system design. The water modeling experiment was conducted with four different amounts of the initial volumes of water in the reservoir to verify the effects of initial pouring velocity. The results of the experiment applying the filter showed remarkably changed flow characteristics. The use of the filter was found to convert the flow pattern of water in the desired direction. The ceramic foam filter performed well to reduce flow velocity and stabilize the water stream.The flow pattern without a filter can be improved significantly even with the the use of just a 10 PPI irregular filter. Although the study confirmed that use of the filter may change the flow characteristics, it needs to be noted that the use of the ceramic filter alone cannot solve all the problems caused by a poorly designed gating system.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2022-00155885).
文摘Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for CPDP.This paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source code.The proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive models.The process involves graph construction,feature learning through graph embedding and LSTM,and defect prediction.Experimental evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.
基金This work was partly supported by the"21st Century COE program",the"Open Research Center program"the"Grantin-in-Aid for Scientific Research"of the Ministry of Education,Science,Sports and Culture(No.(B)16300035).
文摘The purpose of this research is a quantitative analysis of movement patterns of dance, which cannot be analyzed with a motion capture system alone, using simultaneous measurement of body motion and biophysical information. In this research, two kinds of same leg movement are captured by simultaneous measurement; one is a leg movement with given strength, the other is a leg movement without strength on condition of basic experiment using optical motion capture and electromyography （EMG） equipment in order to quantitatively analyze characteristics of leg movement. Also, we measured the motion of the traditional Japanese dance using the constructed system. We can visualize leg movement of Japanese dance by displaying a 3D CG character animation with motion data and EMG data. In addition, we expect that our research will help dancers and researchers on dance through giving new information on dance movement which cannot be analyzed with only motion capture.
基金funding from the Armament Research Board(ARMREB),Defence Research and Development Organization(DRDO),Ministry of Defence,Government of India (Grant no.:ARMREB/MAA/2018/200)。
文摘In this study, high velocity impact behaviour of friction stir welded AA7075-T651 25 mm thick plates were investigated using a 7.62 mm × 51 mm lead core and 7.62 mm × 39 mm steel core projectiles. Prior to ballistic trails, mechanical and metallurgical properties of friction stir welded AA 7075-T651 25 mm thick plates were studied. Microstructural and hardness studies revealed that friction stir welds constituted three distinct regions namely Weld Nugget(WN), Thermo-Mechanically Affected Zone(TMAZ) and Heat Affected Zone(HAZ). Base Material(BM) and all three weld regions were ballistically tested as per military standard NIJ.0108.01 using lead and steel core bullets at maximum permissible velocities of 830 ± 20 and 700 ± 30 m/s, respectively. It has been found that base material(AA7075-T651)and all three weld regions of 25 mm thick plates were able to resist perforation by both types of projectiles used. However depth of penetration has been found to increase from BM to WN, HAZ and TMAZ for both types of projectiles. In all cases steel core projectiles caused higher depth of penetration compared to those caused by lead core projectiles. TMAZs of the friction stir welds were found to be the weakest zone. The fracture that occurred in the base material was spall fragmentation indicating brittle failure, whereas all zones of friction stir welded AA7075-T651 targets with a front petalling, indicating ductile failure. The post-ballistic tested samples showed no significant change in the microstructure of the BM and WN. On the other hand, TMAZ and HAZ showed severe grain deformation in the direction of projectile penetration, and the formation of adiabatic shear bands(ASB). This work showed that 25 mm thick friction stir welded AA7075-T651 joints responded well to ballistic impact loads, making them a good choice for light combat vehicles.
基金the Natural Science Foundation of China(51175494,61128008)Newton Research Collaboration Programme(NRCP/1415/89)
文摘Marvi et al(Science,2014,vol.346,p.224)concluded a sidewinder rattlesnake increases the body contact length with the sand when granular incline angle increases.They also claimed the same principle should work on robotic snake too.We have evidence to prove that this conclusion is only partial in describing the snake body-sand interaction.There should be three phases that fully represent the snake locomotion behaviors during ascent of sandy slopes,namely lifting,descending,and ceasing.The snake body-sand interaction during the descending and ceasing phases helps with the climbing while such interaction during the lifting phase in fact contributes resistance.
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and granted financial resources from the Ministry of Trade,Industry,and Energy,Republic of Korea(No.20204010600090)The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups.Project under grant number(R.G.P.1/257/43).
文摘Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.
基金Supported by the Industrial Technology Innovation Program(Development of Safe and Comfortable Human augmentation Hybrid Robot Suit)Funded by the Ministry of Trade,Industry&Energy,No.20007058,MOTIE,Korea)。
文摘OBJECTIVE:To identify the optimal intensity and duration of cupping that would minimize mechanical skin deformation.METHODS:We developed an optical measurement probe and system for measuring skin color values in real time.We applied cupping at the following five Bladder Meridian acupoints.To investigate optimal intensity,negative pressure intensity was increased every 2 min up to 80 k Pa.To investigate optimal time,10 measurement sites were selected and negative pressure intensities of 30,60,and 80 k Pa were applied for 5 min each.Skin color information was analyzed by the following skin color values:red color saturation,erythema index,and melanin index.RESULTS:The red color saturation and erythema index increased steadily up to 60 kpa negative pressure intensity,then decreased between 60 and 80 k Pa.Therefore,maximal values were observed at 60 k Pa.The melanin index consistently increased with increasing negative pressure intensity.The red color saturation and erythema index did not change after 20 s at 60 k Pa negative pressure intensity.For negative pressure intensities below 80 k Pa,significant changes in melanin index were not observed after 20 s.At 80 k Pa negative pressure intensity,the melanin index exhibited an increasing pattern for200 s,then showed no changes.CONCLUSIONS:To minimize skin deformation,60 k Pa and 20 s were the appropriate intensity and duration when using red color saturation and erythema index as diagnostic indexes.Because of the increasing pattern up to 80 k Pa negative pressure intensity,the optimal intensity of melanin index could not be determined.When applying 80 k Pa negative pressure intensity and using melanin index as the diagnostic index,we recommend a duration of 200 s.
基金funded and supported by the Taif University Researchers,Taif University,Taif,Saudi Arabia,under Project TURSP-2020/147.
文摘The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artificial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchfields that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.
基金by the National Natural Science Foundation of China under Grant Nos 10672057 and 10872068the Fundamental Research Funds for the Central Universities and Japan Society for the Promotion of Science(22560425).
文摘In clinical practice,brain death is the irreversible end of all brain activity.Compared to current statistical methods for the determination of brain death,we focus on the approach of complex networks for real-world electroencephalography in its determination.Brain functional networks constructed by correlation analysis are derived,and statistical network quantities used for distinguishing the patients in coma or brain death state,such as average strength,clustering coefficient and average path length,are calculated.Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state.Our findings might provide valuable insights on the determination of brain death.
