Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges ...Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.展开更多
Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(...Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.展开更多
In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiologi...In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.展开更多
Carbon-based electromagnetic wave(EMW)absorbing materials attached with metal sulfides famous for good dielectric properties are favored by researchers,which can form heterogeneous interfaces and thus provide suppleme...Carbon-based electromagnetic wave(EMW)absorbing materials attached with metal sulfides famous for good dielectric properties are favored by researchers,which can form heterogeneous interfaces and thus provide supplementary loss mechanisms to make up for the deficiencies of a single material in energy attenuation.Here,Co_(9)S_(8)/Co@coral-like carbon nanofibers(CNFs)/porous carbon hybrids are successfully fabricated by hydrothermal and chemical vapor deposition.The samples have exceptional EMW absorb-ing properties,with a minimum reflection loss of-57.48 dB at a thickness of 2.94 mm and an effective absorption bandwidth of up to 6.10 GHz at only 2.20 mm.The interlocking structure formed by Co@coral-like CNFs,interfacial polarization generated by heterostructure of Co_(9)S_(8),abundant defects and large specific surface area resulted from porous properties are important factors in attaining magnetic-dielectric balance and excellent absorption performance.Different matrixes are selected instead of paraffin to investigate the effect of matrix materials on EMW absorbing capacity.Besides,the EMW attenuation potential for practical applications is also demonstrated by radar cross-section simulations,electric field intensity distribution and power loss density.This work provides a novel strategy for designing outstanding EMW absorbers with unique microstructures using facile and low-cost synthetic routes.展开更多
Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have ...Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault detection.However,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error.For this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection.YOLOv8 is a CNN-backed object detection model.Specifically,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)operator.Then,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the union.Based on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are designed.Related experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing methods.Additionally,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.展开更多
AgVO_(3)/ZIF-8 composites with enhanced photocatalytic effect were prepared by the combination of AgVO_(3)and ZIF-8.X-ray diffraction(XRD),scanning electron microscopy(SEM),high-power transmission electron microscopy(...AgVO_(3)/ZIF-8 composites with enhanced photocatalytic effect were prepared by the combination of AgVO_(3)and ZIF-8.X-ray diffraction(XRD),scanning electron microscopy(SEM),high-power transmission electron microscopy(HRTEM),X-ray photoelectron spectroscopy(XPS),ultraviolet-visible diffuse reflectance spectroscopy(UV-Vis DRS),photoluminescence(PL)spectroscopy,electron spin resonance(ESR)spectroscopy,transient photocurrent and electrochemical impedance spectroscopy(EIS)were used to characterize binary composites.Tetracycline(TC)was used as a substrate to study the performance efficiency of the degradation of photocatalysts under light conditions,and the degradation effect of TC was also evaluated under different mass concentrations and ionic contents.In addition,we further investigated the photocatalytic mechanism of the binary composite material AgVO_(3)/ZIF-8 and identified the key active components responsible for the catalytic degradation of this new photocatalyst.The experimental results show that the degradation efficiency of 10%-AZ,prepared with a molar ratio of 10%AgVO_(3)and ZIF-8 to TC,was 75.0%.This indicates that the photocatalytic activity can be maintained even under a certain ionic content,making it a suitable photocatalyst for optimal use.In addition,the photocatalytic mechanism of binary composites was further studied by the active species trapping experiment.展开更多
Porous carbons hold broad application prospects in the domains of electrochemical energy storage devices and sensors.In this study,porous carbon derived from sodium alginate-encapsulated ZIF-8(SA/ZIF-8-C)was suc-cessf...Porous carbons hold broad application prospects in the domains of electrochemical energy storage devices and sensors.In this study,porous carbon derived from sodium alginate-encapsulated ZIF-8(SA/ZIF-8-C)was suc-cessfully prepared by blending ZIF-8 particles with sodium alginate,forming hydrogel beads in the presence of divalent metal ions,and subsequently subjecting them to high-temperature pyrolysis.Various characterization techniques were employed to evaluate the properties of the prepared materials.The introduction of a carbon framework on ZIF-8-derived particles effectively enhanced the conductivity of the prepared materials.The SA/ZIF-8(1.0)-C sample heated at 800℃exhibited a specific capacitance of up to 208 F g^(-1)at a current density of 0.5 A g^(-1)and outstanding cyclic stability.Even after 10,000 charge and discharge cycles,its capacitance retention rate remained as high as 87.14%.The symmetric supercapacitor constructed with the composite demonstrated an excellent energy density of 14.58 Wh kg^(-1)at a power capacity of 403.85 W kg^(-1).The implementation of this study provides new ideas and inspiration for the development of high-performance supercapacitors.展开更多
Two-dimensional(2D)transition metal chalcogenides(TMCs)hold great promise as novel microwave absorption materials owing to their interlayer interactions and unique magnetoelectric properties.However,overcoming the imp...Two-dimensional(2D)transition metal chalcogenides(TMCs)hold great promise as novel microwave absorption materials owing to their interlayer interactions and unique magnetoelectric properties.However,overcoming the impedance mismatch at the low loading is still a challenge for TMCs due to the restricted loss pathways caused by their high-density characteristic.Here,an interface engineering based on the heterostructure of 2D Cr_(5)Te_(8) and graphite is in situ constructed via a one-step chemical vapor deposit to modulate impedance matching and introduce multiple attenuation mechanisms.Intriguingly,the Cr_(5)Te_(8)@EG(ECT)heterostructure exhibits a minimum reflection loss of up to−57.6 dB at 15.4 GHz with a thin thickness of only 1.4 mm under a low filling rate of 10%.The density functional theory calculations confirm that the splendid performance of ECT heterostructure primarily derives from charge redistribution at the abundant intimate interfaces,thereby reinforcing interfacial polarization loss.Furthermore,the ECT coating displays a remarkable radar cross section reduction of 31.9 dB m^(2),demonstrating a great radar microwave scattering ability.This work sheds light on the interfacial coupled stimulus response mechanism of TMC-based heterogeneous structures and provides a feasible strategy to manipulate high-quality TMCs for excellent microwave absorbers.展开更多
Grape crops are a great source of income for farmers.The yield and quality of grapes can be improved by preventing and treating diseases.The farmer’s yield will be dramatically impacted if diseases are found on grape...Grape crops are a great source of income for farmers.The yield and quality of grapes can be improved by preventing and treating diseases.The farmer’s yield will be dramatically impacted if diseases are found on grape leaves.Automatic detection can reduce the chances of leaf diseases affecting other healthy plants.Several studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile applications.This study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the model.The results of this study were relied on the YOLO architecture,specifically YOLOv5 and YOLOv8.After extensive experiments with different image sizes,YOLOv8 performed better than YOLOv5.YOLOv8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf diseases.The Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.展开更多
This study presents a novel approach to improving the anticorrosive performance of AZ31 Mg alloy by exploiting the role of the hydration reaction to induce interactions between Quinolin-8-ol(8HQ)molecules and the poro...This study presents a novel approach to improving the anticorrosive performance of AZ31 Mg alloy by exploiting the role of the hydration reaction to induce interactions between Quinolin-8-ol(8HQ)molecules and the porous MgO layer formed via plasma electrolytic oxidation(PEO).The AZ31 Mg alloy,initially coated with a PEO layer,underwent a dipping treatment in an ethanolic solution of 0.05 M 8HQ at 50℃ for 3 h.The results were compared with those from a different procedure where the PEO layer was subjected to a hydration reaction for 2 h at 90℃ before immersion in the 8HQ solution under the same conditions.The hydration treatment played a crucial role by converting MgO to Mg(OH)_(2),significantly enhancing the surface reactivity.This transformation introduced hydroxyl groups(−OH)on the surface,which facilitated donor-acceptor interactions with the electron-accepting sites on 8HQ molecules.The calculated binding energy(Ebinding)from DFT indicated that the interaction energy of 8HQ with Mg(OH)_(2) was lower compared to 8HQ with MgO,suggesting easier adsorption of 8HQ molecules on the hydrated surface.This,combined with the increased number of active sites and enhanced surface area,allowed for extensive surface coverage by 8HQ,leading to the formation of a stable,flake-like protective layer that sealed the majority of pores on the PEO layer.DFT calculations further suggested that the hydration treatment provided multiple active sites,enabling effective contact with 8HQ and rapid electron transfer,creating ideal conditions for charge-transfer-induced physical and chemical bonding.This study shows that hydration and 8HQ treatments significantly enhance the corrosion resistance of Mg alloys,highlighting their potential for advanced anticorrosive coatings.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
Background:Physicalfitness in childhood and adolescence is associated with a variety of health outcomes and is a powerful marker of current and future health.However,inconsistencies in tests and protocols limit interna...Background:Physicalfitness in childhood and adolescence is associated with a variety of health outcomes and is a powerful marker of current and future health.However,inconsistencies in tests and protocols limit international monitoring and surveillance.The objective of the study was to seek international consensus on a proposed,evidence-informed,Youth Fitness International Test(YFIT)battery and protocols for health monitoring and surveillance in children and adolescents aged 618 years.Methods:We conducted an international modified Delphi study to evaluate the level of agreement with a proposed,evidence-based,YFIT of core health-relatedfitness tests and protocols to be used worldwide in 6-to 18-year-olds.This proposal was based on previous European and North American projects that systematically reviewed the existing evidence to identify the most valid,reliable,health-related,safe,and feasiblefitness tests to be used in children and adolescents aged 618 years.We designed a single-panel modified Delphi study and invited 216 experts from all around the world to answer this Delphi survey,of whom one-third are from low-to-middle income countries and one-third are women.Four experts were involved in the piloting of the survey and did not participate in the main Delphi study to avoid bias.We pre-defined an agreement of 80%among the expert participants to achieve consensus.Results:We obtained a high response rate(78%)with a total of 169fitness experts from 50 countries and territories,including 63 women and 61 experts from low-or middle-income countries/territories.Consensus(>85%agreement)was achieved for all proposed tests and protocols,supporting the YFIT battery,which includes weight and height(to compute body mass index as a proxy of body size/composition),the 20-m shuttle run(cardiorespiratoryfitness),handgrip strength,and standing long jump(muscularfitness).Conclusion:This study contributes to standardizingfitness tests and protocols used for research,monitoring,and surveillance across the world,which will allow for future data pooling and the development of international and regional sex-and age-specific reference values,health-related cut-points,and a global picture offitness among children and adolescents.展开更多
Objective:To establish consensus on Chinese Herbal Medicine(CHM)for rheumatoid arthritis(RA)among 21 Singaporean experts,this study addressed the lack of CHM clinical practice guidelines(CPGs)in Singapore.Despite adva...Objective:To establish consensus on Chinese Herbal Medicine(CHM)for rheumatoid arthritis(RA)among 21 Singaporean experts,this study addressed the lack of CHM clinical practice guidelines(CPGs)in Singapore.Despite advancements in RA therapies,the disease's progressive nature and high costs of novel treatments worsen disparities in management and outcomes.The initiative aimed to bridge this gap by developing expert-backed recommendations for CHM use in RA care.Methods:The group of experts conducted two rounds of Delphi surveys containing 29 items identified from a literature review.Consensus was defined as≥75%of votes in dichotomized ratings on a fivepoint ordinal scale for recognition.Items that did not reach consensus were discussed in a focus group with four selected experts.Results:Nineteen experts completed both rounds of Delphi surveys.A consensus was reached for 27 items,which encompassed Chinese medicine rationale,pattern differentiation,management,CHM prescription,and co-effectiveness with pharmacological therapy.Collective expert opinions were formed for the two remaining items.All items received a recognition score>3.5.Conclusions:The consensus derived from this study provides a foundation for CHM CPGs for RA in Singapore.However,the findings are limited by the demographic composition of the experts and the representativeness of the patient pool.展开更多
Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially...Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.展开更多
文摘Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.
基金supported by the National Natural Science Foundation of China(Nos.81974355 and 82172524)Key Research and Development Program of Hubei Province(No.2021BEA161)+2 种基金National Innovation Platform Development Program(No.2020021105012440)Open Project Funding of the Hubei Key Laboratory of Big Data Intelligent Analysis and Application,Hubei University(No.2024BDIAA03)Free Innovation Preliminary Research Fund of Wuhan Union Hospital(No.2024XHYN047).
文摘Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.
基金supported by the Science and Technology Bureau of Xi’an project(24KGDW0049)the Key Research and Development Programof Shaanxi(2023-YBGY-264)the Key Research and Development Program of Guangxi(GK-AB20159032).
文摘In recent years,the country has spent significant workforce and material resources to prevent traffic accidents,particularly those caused by fatigued driving.The current studies mainly concentrate on driver physiological signals,driving behavior,and vehicle information.However,most of the approaches are computationally intensive and inconvenient for real-time detection.Therefore,this paper designs a network that combines precision,speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion.Specifically,the face detection model takes YOLOv8(You Only Look Once version 8)as the basic framework,and replaces its backbone network with MobileNetv3.To focus on the significant regions in the image,CPCA(Channel Prior Convolution Attention)is adopted to enhance the network’s capacity for feature extraction.Meanwhile,the network training phase employs the Focal-EIOU(Focal and Efficient Intersection Over Union)loss function,which makes the network lightweight and increases the accuracy of target detection.Ultimately,the Dlib toolkit was employed to annotate 68 facial feature points.This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition.A series of comparative experiments were carried out on the self-built dataset.The suggested method’s mAP(mean Average Precision)values for object detection and fatigue detection are 96.71%and 95.75%,respectively,as well as the detection speed is 47 FPS(Frames Per Second).This method can balance the contradiction between computational complexity and model accuracy.Furthermore,it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy.It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.
基金financially supported by the Natural Science Foundation of Shandong Province(Nos.ZR2021ME194,2022TSGC2448,and 2023TSGC0545)the Key Technology Research and Development Program of Shandong Province(No.2021ZLGX01).
文摘Carbon-based electromagnetic wave(EMW)absorbing materials attached with metal sulfides famous for good dielectric properties are favored by researchers,which can form heterogeneous interfaces and thus provide supplementary loss mechanisms to make up for the deficiencies of a single material in energy attenuation.Here,Co_(9)S_(8)/Co@coral-like carbon nanofibers(CNFs)/porous carbon hybrids are successfully fabricated by hydrothermal and chemical vapor deposition.The samples have exceptional EMW absorb-ing properties,with a minimum reflection loss of-57.48 dB at a thickness of 2.94 mm and an effective absorption bandwidth of up to 6.10 GHz at only 2.20 mm.The interlocking structure formed by Co@coral-like CNFs,interfacial polarization generated by heterostructure of Co_(9)S_(8),abundant defects and large specific surface area resulted from porous properties are important factors in attaining magnetic-dielectric balance and excellent absorption performance.Different matrixes are selected instead of paraffin to investigate the effect of matrix materials on EMW absorbing capacity.Besides,the EMW attenuation potential for practical applications is also demonstrated by radar cross-section simulations,electric field intensity distribution and power loss density.This work provides a novel strategy for designing outstanding EMW absorbers with unique microstructures using facile and low-cost synthetic routes.
基金supported by the Liaoning Province Applied Basic Research Program Project of China(Grant:2023JH2/101300065)the Liaoning Province Science and Technology Plan Joint Fund(2023-MSLH-221).
文摘Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault detection.However,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error.For this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection.YOLOv8 is a CNN-backed object detection model.Specifically,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)operator.Then,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the union.Based on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are designed.Related experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing methods.Additionally,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.
文摘AgVO_(3)/ZIF-8 composites with enhanced photocatalytic effect were prepared by the combination of AgVO_(3)and ZIF-8.X-ray diffraction(XRD),scanning electron microscopy(SEM),high-power transmission electron microscopy(HRTEM),X-ray photoelectron spectroscopy(XPS),ultraviolet-visible diffuse reflectance spectroscopy(UV-Vis DRS),photoluminescence(PL)spectroscopy,electron spin resonance(ESR)spectroscopy,transient photocurrent and electrochemical impedance spectroscopy(EIS)were used to characterize binary composites.Tetracycline(TC)was used as a substrate to study the performance efficiency of the degradation of photocatalysts under light conditions,and the degradation effect of TC was also evaluated under different mass concentrations and ionic contents.In addition,we further investigated the photocatalytic mechanism of the binary composite material AgVO_(3)/ZIF-8 and identified the key active components responsible for the catalytic degradation of this new photocatalyst.The experimental results show that the degradation efficiency of 10%-AZ,prepared with a molar ratio of 10%AgVO_(3)and ZIF-8 to TC,was 75.0%.This indicates that the photocatalytic activity can be maintained even under a certain ionic content,making it a suitable photocatalyst for optimal use.In addition,the photocatalytic mechanism of binary composites was further studied by the active species trapping experiment.
基金supports from the National Natural Science Foundation of China(22075034,22178037,and 22478047)Natural Science Foundation of Liaoning Province of China(2021-MS-303)the China Scholarship Council(CSC No 202008210171).
文摘Porous carbons hold broad application prospects in the domains of electrochemical energy storage devices and sensors.In this study,porous carbon derived from sodium alginate-encapsulated ZIF-8(SA/ZIF-8-C)was suc-cessfully prepared by blending ZIF-8 particles with sodium alginate,forming hydrogel beads in the presence of divalent metal ions,and subsequently subjecting them to high-temperature pyrolysis.Various characterization techniques were employed to evaluate the properties of the prepared materials.The introduction of a carbon framework on ZIF-8-derived particles effectively enhanced the conductivity of the prepared materials.The SA/ZIF-8(1.0)-C sample heated at 800℃exhibited a specific capacitance of up to 208 F g^(-1)at a current density of 0.5 A g^(-1)and outstanding cyclic stability.Even after 10,000 charge and discharge cycles,its capacitance retention rate remained as high as 87.14%.The symmetric supercapacitor constructed with the composite demonstrated an excellent energy density of 14.58 Wh kg^(-1)at a power capacity of 403.85 W kg^(-1).The implementation of this study provides new ideas and inspiration for the development of high-performance supercapacitors.
基金the National Natural Science Foundation of China(grant No.62174013,92265111)Central Government Guides Local Funds for Science and Technology Development(No.YDZJSX2022A021)the funding Program of BIT(grant No.3180012212214 and 3180023012204).
文摘Two-dimensional(2D)transition metal chalcogenides(TMCs)hold great promise as novel microwave absorption materials owing to their interlayer interactions and unique magnetoelectric properties.However,overcoming the impedance mismatch at the low loading is still a challenge for TMCs due to the restricted loss pathways caused by their high-density characteristic.Here,an interface engineering based on the heterostructure of 2D Cr_(5)Te_(8) and graphite is in situ constructed via a one-step chemical vapor deposit to modulate impedance matching and introduce multiple attenuation mechanisms.Intriguingly,the Cr_(5)Te_(8)@EG(ECT)heterostructure exhibits a minimum reflection loss of up to−57.6 dB at 15.4 GHz with a thin thickness of only 1.4 mm under a low filling rate of 10%.The density functional theory calculations confirm that the splendid performance of ECT heterostructure primarily derives from charge redistribution at the abundant intimate interfaces,thereby reinforcing interfacial polarization loss.Furthermore,the ECT coating displays a remarkable radar cross section reduction of 31.9 dB m^(2),demonstrating a great radar microwave scattering ability.This work sheds light on the interfacial coupled stimulus response mechanism of TMC-based heterogeneous structures and provides a feasible strategy to manipulate high-quality TMCs for excellent microwave absorbers.
文摘Grape crops are a great source of income for farmers.The yield and quality of grapes can be improved by preventing and treating diseases.The farmer’s yield will be dramatically impacted if diseases are found on grape leaves.Automatic detection can reduce the chances of leaf diseases affecting other healthy plants.Several studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile applications.This study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the model.The results of this study were relied on the YOLO architecture,specifically YOLOv5 and YOLOv8.After extensive experiments with different image sizes,YOLOv8 performed better than YOLOv5.YOLOv8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf diseases.The Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(No.2022R1A2C1006743).
文摘This study presents a novel approach to improving the anticorrosive performance of AZ31 Mg alloy by exploiting the role of the hydration reaction to induce interactions between Quinolin-8-ol(8HQ)molecules and the porous MgO layer formed via plasma electrolytic oxidation(PEO).The AZ31 Mg alloy,initially coated with a PEO layer,underwent a dipping treatment in an ethanolic solution of 0.05 M 8HQ at 50℃ for 3 h.The results were compared with those from a different procedure where the PEO layer was subjected to a hydration reaction for 2 h at 90℃ before immersion in the 8HQ solution under the same conditions.The hydration treatment played a crucial role by converting MgO to Mg(OH)_(2),significantly enhancing the surface reactivity.This transformation introduced hydroxyl groups(−OH)on the surface,which facilitated donor-acceptor interactions with the electron-accepting sites on 8HQ molecules.The calculated binding energy(Ebinding)from DFT indicated that the interaction energy of 8HQ with Mg(OH)_(2) was lower compared to 8HQ with MgO,suggesting easier adsorption of 8HQ molecules on the hydrated surface.This,combined with the increased number of active sites and enhanced surface area,allowed for extensive surface coverage by 8HQ,leading to the formation of a stable,flake-like protective layer that sealed the majority of pores on the PEO layer.DFT calculations further suggested that the hydration treatment provided multiple active sites,enabling effective contact with 8HQ and rapid electron transfer,creating ideal conditions for charge-transfer-induced physical and chemical bonding.This study shows that hydration and 8HQ treatments significantly enhance the corrosion resistance of Mg alloys,highlighting their potential for advanced anticorrosive coatings.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
基金supported by the Grant PID2020-120249RB-I00PID2023-148404OB-100funded by MCIN/AEI/10.13039/501100011033+4 种基金by the Andalusian Government(Junta de Andalucía,Plan Andaluz de Investigación,ref.P20_00124)by the Erasmus+Sport Programme of the European Union within the project FitBack4Literacy(No.101089829)Additional support is provided by the University of Granada,Plan Propio de Inves-tigación,Units of ExcellenceUnit of Excellence on Exercise,Nutrition and Health(UCEENS)by theCIBERobn Physiopa-thology of Obesity and Nutrition,and by the Spanish Network in Exercise and Health,EXERNET Network(RED2022-134800-Tand EXP_99828).
文摘Background:Physicalfitness in childhood and adolescence is associated with a variety of health outcomes and is a powerful marker of current and future health.However,inconsistencies in tests and protocols limit international monitoring and surveillance.The objective of the study was to seek international consensus on a proposed,evidence-informed,Youth Fitness International Test(YFIT)battery and protocols for health monitoring and surveillance in children and adolescents aged 618 years.Methods:We conducted an international modified Delphi study to evaluate the level of agreement with a proposed,evidence-based,YFIT of core health-relatedfitness tests and protocols to be used worldwide in 6-to 18-year-olds.This proposal was based on previous European and North American projects that systematically reviewed the existing evidence to identify the most valid,reliable,health-related,safe,and feasiblefitness tests to be used in children and adolescents aged 618 years.We designed a single-panel modified Delphi study and invited 216 experts from all around the world to answer this Delphi survey,of whom one-third are from low-to-middle income countries and one-third are women.Four experts were involved in the piloting of the survey and did not participate in the main Delphi study to avoid bias.We pre-defined an agreement of 80%among the expert participants to achieve consensus.Results:We obtained a high response rate(78%)with a total of 169fitness experts from 50 countries and territories,including 63 women and 61 experts from low-or middle-income countries/territories.Consensus(>85%agreement)was achieved for all proposed tests and protocols,supporting the YFIT battery,which includes weight and height(to compute body mass index as a proxy of body size/composition),the 20-m shuttle run(cardiorespiratoryfitness),handgrip strength,and standing long jump(muscularfitness).Conclusion:This study contributes to standardizingfitness tests and protocols used for research,monitoring,and surveillance across the world,which will allow for future data pooling and the development of international and regional sex-and age-specific reference values,health-related cut-points,and a global picture offitness among children and adolescents.
基金supported by the Nanyang Technological University Start-Up Grant(#022387‒00001).
文摘Objective:To establish consensus on Chinese Herbal Medicine(CHM)for rheumatoid arthritis(RA)among 21 Singaporean experts,this study addressed the lack of CHM clinical practice guidelines(CPGs)in Singapore.Despite advancements in RA therapies,the disease's progressive nature and high costs of novel treatments worsen disparities in management and outcomes.The initiative aimed to bridge this gap by developing expert-backed recommendations for CHM use in RA care.Methods:The group of experts conducted two rounds of Delphi surveys containing 29 items identified from a literature review.Consensus was defined as≥75%of votes in dichotomized ratings on a fivepoint ordinal scale for recognition.Items that did not reach consensus were discussed in a focus group with four selected experts.Results:Nineteen experts completed both rounds of Delphi surveys.A consensus was reached for 27 items,which encompassed Chinese medicine rationale,pattern differentiation,management,CHM prescription,and co-effectiveness with pharmacological therapy.Collective expert opinions were formed for the two remaining items.All items received a recognition score>3.5.Conclusions:The consensus derived from this study provides a foundation for CHM CPGs for RA in Singapore.However,the findings are limited by the demographic composition of the experts and the representativeness of the patient pool.
文摘Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.
