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
The preparation of immobilized enzyme with excellent performance is one of the difficulties that restrict the application of enzyme catalysis technology.Here,Candida rugosa lipase(CRL)was firstly adsorbed on the surfa...The preparation of immobilized enzyme with excellent performance is one of the difficulties that restrict the application of enzyme catalysis technology.Here,Candida rugosa lipase(CRL)was firstly adsorbed on the surface of magnetic zeolitic imidazolate framework-8(ZIF-8)nanospheres,which was further encapsulated with a mesoporous SiO_(2)nano-membrane formed by tetraethyl orthosilicate(TEOS)polycondensation.Consequently,lipase could be firmly immobilized on carrier surface by physical binding rather than chemical binding,which did not damage the active conformation of enzyme.There were mesopores on the silica nano-membrane,which could improve the accessibility of enzyme and its apparent catalytic activity.Moreover,silica membrane encapsulation could also improve the stability of enzyme,suggesting an effective enzyme immobilization strategy.It showed that TEOS amount and the encapsulation time had significant effects on the thickness of silica membrane and the enzyme activity.The analysis in enzyme activity and protein secondary structure showed that lipase encapsulated in silica membrane retained the active conformation to the greatest extent.Compared with the adsorbed lipase,the encapsulated lipase increased its thermostability by 3 times and resistance to chemical denaturants by 7 times.The relative enzyme activity remained around 80%after 8 repetitions,while the adsorbed lipase only remained at7.3%.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
BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an e...BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.展开更多
Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosupp...Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.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
电子元器件种类繁多且没有一致的细粒度分类标准,为快速满足元器件在不同粒度下的分类需求,提出一种基于深度学习的YOLOR-ECA(YOLOv8 and ResNet50 with efficient channel attention)电子元器件检测算法。首先采用YOLOv8网络定位元器...电子元器件种类繁多且没有一致的细粒度分类标准,为快速满足元器件在不同粒度下的分类需求,提出一种基于深度学习的YOLOR-ECA(YOLOv8 and ResNet50 with efficient channel attention)电子元器件检测算法。首先采用YOLOv8网络定位元器件位置,然后采用ResNet50网络对定位获取的元器件进行识别分类,通过元器件种类的增减满足不同细粒度的分类标准。为提升模型对尺寸小、特征相似元器件的细节特征提取能力,分类网络引入ECA注意力机制,并对残差结构的捷径连接部分进行改进;为避免神经元失活,采用GELU(Gaussian Error Linear Units)激活函数。实验结果表明,改进的YOLOR-ECA模型的检测准确率为96.6%,并且对于小尺寸元器件的识别精度最高可达100%,对于具有特征相似性元器件的误检率最低可降到0.01%,能实现电子元器件在不同细粒度分类标准下的高效检测。展开更多
The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framewo...The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framework based on YOLOv8,with the aim of enhancing detection accuracy and the ability to recognize multi-scale targets in blurry and complex underwater environments.A streamlined Vision Transformer(ViT)model is used as the feature extraction backbone,which retains global self-attention feature extraction and accelerates training efficiency.In addition,a detection head named Dynamic Head(DyHead)is introduced,which enhances the efficiency of processing various target sizes through multi-scale feature fusion and adaptive attention modules.Furthermore,a dynamic loss function adjustment method called SlideLoss is employed.This method utilizes sliding window technology to adaptively adjust parameters,which optimizes the detection of challenging targets.The experimental results on the RUOD dataset show that the proposed improved model not only significantly enhances the accuracy of target detection but also increases the efficiency of target detection.展开更多
Photocatalytic fuel cells provide promising opportunities for sustainable wastewater treatment and energy conversion.However,their applications are challenged by the sluggish oxygen reducton reaction(ORR)kinetics at c...Photocatalytic fuel cells provide promising opportunities for sustainable wastewater treatment and energy conversion.However,their applications are challenged by the sluggish oxygen reducton reaction(ORR)kinetics at cathodes owning to the low O_(2) solubility and diffusion rate.Herein,we proposed a photobiocatalytic fuel cell(PBFC) with a novel hybrid biocathode based on artificially engineered algal cells coated by ZIF-8 confined carbon dots/bilirubin oxidase(ZIF-8/CDs/BOD@algae).Microalgae absorbed CO_(2) and provided O_(2) in situ for BOD catalysts.Due to effective absorption of O_(2) by imidazole and confinement of hydrophobic porous ZIF-8,oxygen diffusion has been accelerated in MOF/enzyme systems.Importantly,the introduction of CDs alleviated the poor conductivity of ZIF-8 and improved the electron transfer rate of BOD.Thus,the biocathode exhibited a high current density of 1767 μA/cm^(2),a 2.26-fold increase compared with that of CDs/BOD/algae biocathode.Also,it displayed enduring operational stability for up to 60 h since the firmly wrapped ZIF-8 shells could encapsulate proteins and protect algae from the external stimulation.When coupled with Mo:BiVO_(4) photoanodes,the PBFC exhibited a remarkable power output of 131.8 μW/cm^(2) using tetracycline hydrochloride(TCH) as a fuel and an increased degradation rate of TCH.Therefore,this work not only establishs an effective confinement strategy for enzyme to enrich oxygen,but also unveils new possibilities for modified microalgal cells aiding photoelectrocatalytic systems to recover energy from wastewater treatment.展开更多
To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target...To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target Detection YOLO)model for anti-drone object detection,based on the YOLOv8 architecture.To overcome the limitations of existing methods in detecting small objects within complex backgrounds,we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set,aiming to reduce the model’s computational complexity.To improve multi-scale feature fusion,we construct a Multi-Branch Feature Pyramid Network(MB-FPN)that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects.Additionally,a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle(UAV)targets,thereby improving detection performance across different scales.Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks.On the Det-Fly dataset,it improves precision by 3%,recall by 5.6%,and mAP50 by 4.5%compared with the baseline,while reducing parameters by 21.2%.Cross-validation on the VisDrone dataset further validates its robustness,yielding additional gains of 3.2%in precision,6.1%in recall,and 4.8%in mAP50 over the original YOLOv8.These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios.展开更多
Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due...Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due to the complex topography,variable climate,and challenges in field surveys in desert regions,this paper proposes YOLO-Desert-Shrub(YOLO-DS),a detection method for identifying desert shrubs in UAV remote sensing images based on an enhanced YOLOv8n framework.This method accurately identifying shrub species,locations,and coverage.To address the issue of small individual plants dominating the dataset,the SPDconv convolution module is introduced in the Backbone and Neck layers of the YOLOv8n model,replacing conventional convolutions.This structural optimization mitigates information degradation in fine-grained data while strengthening discriminative feature capture across spatial scales within desert shrub datasets.Furthermore,a structured state-space model is integrated into the main network,and the MambaLayer is designed to dynamically extract and refine shrub-specific features from remote sensing images,effectively filtering out background noise and irrelevant interference to enhance feature representation.Benchmark evaluations reveal the YOLO-DS framework attains 79.56%mAP40weight,demonstrating 2.2%absolute gain versus the baseline YOLOv8n architecture,with statistically significant advantages over contemporary detectors in cross-validation trials.The predicted plant coverage exhibits strong consistency with manually measured coverage,with a coefficient of determination(R^(2))of 0.9148 and a Root Mean Square Error(RMSE)of1.8266%.The proposed UAV-based remote sensing method utilizing the YOLO-DS effectively identify and locate desert shrubs,monitor canopy sizes and distribution,and provide technical support for automated desert shrub monitoring.展开更多
The field of nanomedicine has been revolutionized by the concept of immunogenic cell death(ICD)-enhanced cancer therapy,which holds immense promise for the efficient treatment of cancer.However,precise delivery of ICD...The field of nanomedicine has been revolutionized by the concept of immunogenic cell death(ICD)-enhanced cancer therapy,which holds immense promise for the efficient treatment of cancer.However,precise delivery of ICD inducer is severely hindered by complex biological barriers.How to design and build intelligent nanoplatform for adaptive and dynamic cancer therapy remains a big challenge.Herein,this article presents the design and preparation of CD44-targeting and ZIF-8 gated gold nanocage(Au@ZH) for programmed delivery of the 1,2-diaminocyclohexane-Pt(Ⅱ)(DACHPt) as ICD inducer.After actively targeting the CD44 on the surface of 4T1 tumor cell,this Pt-Au@ZH can be effectively endocytosed by the 4T1 cell and release the DACHPt in tumor acidic environment,resulting in ICD effect and superior antitumor efficacy both in vitro and in vivo in the presence of mild 808 nm laser irradiation.By integration of internal and external stimuli intelligently,this programmed nanoplatform is poised to become a cornerstone in the pursuit of effective and targeted cancer therapy in the foreseeable future.展开更多
Magnesium-based anode materials have attracted significant attention in the energy storage domain because of their high theoretical capacities and low electrochemical potentials.However,in conventional electrolyte sys...Magnesium-based anode materials have attracted significant attention in the energy storage domain because of their high theoretical capacities and low electrochemical potentials.However,in conventional electrolyte systems,magnesium metal electrodes dynamically generate an ion-blocking surface layer,resulting in prominent voltage polarization,which severely limits their practical applications.In this study,ZIF-8/carbon nanotubes(CNTs)coatings were used to modify the anodes of magnesium batteries.Compared with the unaltered magnesium battery,the voltage lag time of the ZIF-8/CNTs coating was shortened from 4 s before modification to 0.26 s,and the battery impedance was lowered by two orders of magnitude.The duration of the discharge platform was increased from 4 h before modification to 6-10 h,the anode utilization rate was more than doubled,and the specific energy density was significantly enhanced compared with the battery before modification.The mechanism indicates that the ZIF-8/CNTs coating can limit the infiltration of corrosive substances,extend their transmission path,and offer more effective protection to the magnesium anode.The incorporation of CNTs improves the conductivity of the battery,and it significantly improves the electrochemical performance of the magnesium battery.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the financial supports from the National Natural Science Foundation of China(Nos.22378093,21878065)Natural Science Foundation of Hebei Province,China(No.E2022201100)+2 种基金the Science and Technology Support Plan of Baoding City(No.2241ZF111)the Medical Science Foundation of Hebei University(No.2021A09)the Foundation of Affiliated Hospital of Hebei University(No.2021Z003)。
文摘The preparation of immobilized enzyme with excellent performance is one of the difficulties that restrict the application of enzyme catalysis technology.Here,Candida rugosa lipase(CRL)was firstly adsorbed on the surface of magnetic zeolitic imidazolate framework-8(ZIF-8)nanospheres,which was further encapsulated with a mesoporous SiO_(2)nano-membrane formed by tetraethyl orthosilicate(TEOS)polycondensation.Consequently,lipase could be firmly immobilized on carrier surface by physical binding rather than chemical binding,which did not damage the active conformation of enzyme.There were mesopores on the silica nano-membrane,which could improve the accessibility of enzyme and its apparent catalytic activity.Moreover,silica membrane encapsulation could also improve the stability of enzyme,suggesting an effective enzyme immobilization strategy.It showed that TEOS amount and the encapsulation time had significant effects on the thickness of silica membrane and the enzyme activity.The analysis in enzyme activity and protein secondary structure showed that lipase encapsulated in silica membrane retained the active conformation to the greatest extent.Compared with the adsorbed lipase,the encapsulated lipase increased its thermostability by 3 times and resistance to chemical denaturants by 7 times.The relative enzyme activity remained around 80%after 8 repetitions,while the adsorbed lipase only remained at7.3%.
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Supported by Inter Disciplinary Direction Cultivation Project of Hunan University of Chinese Medicine,No.2025JC01032025 Hunan Province Science and Technology Innovation Plan Project,No.2025RC9012+2 种基金2022"Unveiling and Leading"Project of Discipline Construction at Hunan University of Chinese Medicine,No.22JBZ044Changsha Municipal Natural Science Foundation,No.kq2402174Hunan Provincial Science Popularization Fund Project,No.2025ZK4223.
文摘BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.
文摘Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.
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.
文摘电子元器件种类繁多且没有一致的细粒度分类标准,为快速满足元器件在不同粒度下的分类需求,提出一种基于深度学习的YOLOR-ECA(YOLOv8 and ResNet50 with efficient channel attention)电子元器件检测算法。首先采用YOLOv8网络定位元器件位置,然后采用ResNet50网络对定位获取的元器件进行识别分类,通过元器件种类的增减满足不同细粒度的分类标准。为提升模型对尺寸小、特征相似元器件的细节特征提取能力,分类网络引入ECA注意力机制,并对残差结构的捷径连接部分进行改进;为避免神经元失活,采用GELU(Gaussian Error Linear Units)激活函数。实验结果表明,改进的YOLOR-ECA模型的检测准确率为96.6%,并且对于小尺寸元器件的识别精度最高可达100%,对于具有特征相似性元器件的误检率最低可降到0.01%,能实现电子元器件在不同细粒度分类标准下的高效检测。
基金supported by the National Natural Science Foundation of China(No.52106080)the Jilin City Science and Technology Innovation Development Plan Project(No.20240302014)+2 种基金the Jilin Provincial Department of Education Science and Technology Research Project(No.JJKH20230135K)the Jilin Province Science and Technology Development Plan Project(No.YDZJ202401640ZYTS)the Northeast Electric Power University Teaching Reform Research Project(No.J2427)。
文摘The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framework based on YOLOv8,with the aim of enhancing detection accuracy and the ability to recognize multi-scale targets in blurry and complex underwater environments.A streamlined Vision Transformer(ViT)model is used as the feature extraction backbone,which retains global self-attention feature extraction and accelerates training efficiency.In addition,a detection head named Dynamic Head(DyHead)is introduced,which enhances the efficiency of processing various target sizes through multi-scale feature fusion and adaptive attention modules.Furthermore,a dynamic loss function adjustment method called SlideLoss is employed.This method utilizes sliding window technology to adaptively adjust parameters,which optimizes the detection of challenging targets.The experimental results on the RUOD dataset show that the proposed improved model not only significantly enhances the accuracy of target detection but also increases the efficiency of target detection.
基金support from National Natural Science Foundation of China (Nos.22176086,52100014)Natural Science Foundation of Jiangsu Province (No.BK20210189)+7 种基金State Key laboratory of Pollution Control and Resource Reuse,the Fundamental Research Funds for the Central Universities (Nos.021114380183,021114380189,021114380199)the Research Funds from Frontiers Science Center for Critical Earth Material Cycling of Nanjing UniversityResearch Funds for Jiangsu Distinguished ProfessorCarbon Peaking and Carbon Neutrality Technological Innovation Foundation of Jiangsu Province (No.BE2022861)the Central Universities - Cemac “Geo X” Interdisciplinary Program (No.021114380217)Frontiers Science Center for Critical Earth Material Cycling of Nanjing University (No.2024QNXZ07)Postdoctoral Fellowship Program of CPSF (No.GZC20231105)the Jiangsu Funding Program for Excellent Postdoctoral Talent (No.2023ZB226)。
文摘Photocatalytic fuel cells provide promising opportunities for sustainable wastewater treatment and energy conversion.However,their applications are challenged by the sluggish oxygen reducton reaction(ORR)kinetics at cathodes owning to the low O_(2) solubility and diffusion rate.Herein,we proposed a photobiocatalytic fuel cell(PBFC) with a novel hybrid biocathode based on artificially engineered algal cells coated by ZIF-8 confined carbon dots/bilirubin oxidase(ZIF-8/CDs/BOD@algae).Microalgae absorbed CO_(2) and provided O_(2) in situ for BOD catalysts.Due to effective absorption of O_(2) by imidazole and confinement of hydrophobic porous ZIF-8,oxygen diffusion has been accelerated in MOF/enzyme systems.Importantly,the introduction of CDs alleviated the poor conductivity of ZIF-8 and improved the electron transfer rate of BOD.Thus,the biocathode exhibited a high current density of 1767 μA/cm^(2),a 2.26-fold increase compared with that of CDs/BOD/algae biocathode.Also,it displayed enduring operational stability for up to 60 h since the firmly wrapped ZIF-8 shells could encapsulate proteins and protect algae from the external stimulation.When coupled with Mo:BiVO_(4) photoanodes,the PBFC exhibited a remarkable power output of 131.8 μW/cm^(2) using tetracycline hydrochloride(TCH) as a fuel and an increased degradation rate of TCH.Therefore,this work not only establishs an effective confinement strategy for enzyme to enrich oxygen,but also unveils new possibilities for modified microalgal cells aiding photoelectrocatalytic systems to recover energy from wastewater treatment.
基金supported by the Key R&D Programof Xianyang City,Shaanxi Province(L2024-ZDYF-ZDYF-GY-0043).
文摘To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target Detection YOLO)model for anti-drone object detection,based on the YOLOv8 architecture.To overcome the limitations of existing methods in detecting small objects within complex backgrounds,we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set,aiming to reduce the model’s computational complexity.To improve multi-scale feature fusion,we construct a Multi-Branch Feature Pyramid Network(MB-FPN)that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects.Additionally,a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle(UAV)targets,thereby improving detection performance across different scales.Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks.On the Det-Fly dataset,it improves precision by 3%,recall by 5.6%,and mAP50 by 4.5%compared with the baseline,while reducing parameters by 21.2%.Cross-validation on the VisDrone dataset further validates its robustness,yielding additional gains of 3.2%in precision,6.1%in recall,and 4.8%in mAP50 over the original YOLOv8.These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios.
基金supported by the National Public Welfare Forest Desert Shrubbery Monitoring Project。
文摘Desert shrubs are indispensable in maintaining ecological stability by reducing soil erosion,enhancing water retention,and boosting soil fertility,which are critical factors in mitigating desertification processes.Due to the complex topography,variable climate,and challenges in field surveys in desert regions,this paper proposes YOLO-Desert-Shrub(YOLO-DS),a detection method for identifying desert shrubs in UAV remote sensing images based on an enhanced YOLOv8n framework.This method accurately identifying shrub species,locations,and coverage.To address the issue of small individual plants dominating the dataset,the SPDconv convolution module is introduced in the Backbone and Neck layers of the YOLOv8n model,replacing conventional convolutions.This structural optimization mitigates information degradation in fine-grained data while strengthening discriminative feature capture across spatial scales within desert shrub datasets.Furthermore,a structured state-space model is integrated into the main network,and the MambaLayer is designed to dynamically extract and refine shrub-specific features from remote sensing images,effectively filtering out background noise and irrelevant interference to enhance feature representation.Benchmark evaluations reveal the YOLO-DS framework attains 79.56%mAP40weight,demonstrating 2.2%absolute gain versus the baseline YOLOv8n architecture,with statistically significant advantages over contemporary detectors in cross-validation trials.The predicted plant coverage exhibits strong consistency with manually measured coverage,with a coefficient of determination(R^(2))of 0.9148 and a Root Mean Square Error(RMSE)of1.8266%.The proposed UAV-based remote sensing method utilizing the YOLO-DS effectively identify and locate desert shrubs,monitor canopy sizes and distribution,and provide technical support for automated desert shrub monitoring.
基金financially supported by the Natural Science Foundation of Jiangsu Province (No.BK20200709)the Natural Science Foundation of China (Nos.62288102,32201127 and 82270113)+2 种基金the Natural Science Foundation of Guangdong Province (No.2023A1515011386)the Natural Science Foundation of the Jiangsu Higher Education Institutes (No.20KJB430031)the startup fund from Nanjing Tech University,and Disciplinary Fund of School of Pharmaceutical Sciences (2024)。
文摘The field of nanomedicine has been revolutionized by the concept of immunogenic cell death(ICD)-enhanced cancer therapy,which holds immense promise for the efficient treatment of cancer.However,precise delivery of ICD inducer is severely hindered by complex biological barriers.How to design and build intelligent nanoplatform for adaptive and dynamic cancer therapy remains a big challenge.Herein,this article presents the design and preparation of CD44-targeting and ZIF-8 gated gold nanocage(Au@ZH) for programmed delivery of the 1,2-diaminocyclohexane-Pt(Ⅱ)(DACHPt) as ICD inducer.After actively targeting the CD44 on the surface of 4T1 tumor cell,this Pt-Au@ZH can be effectively endocytosed by the 4T1 cell and release the DACHPt in tumor acidic environment,resulting in ICD effect and superior antitumor efficacy both in vitro and in vivo in the presence of mild 808 nm laser irradiation.By integration of internal and external stimuli intelligently,this programmed nanoplatform is poised to become a cornerstone in the pursuit of effective and targeted cancer therapy in the foreseeable future.
基金supported by the Guangxi Natural Science Foundation,China(No.2020GXNSFAA 159011)the National Natural Science Foundation of China(No.51664011).
文摘Magnesium-based anode materials have attracted significant attention in the energy storage domain because of their high theoretical capacities and low electrochemical potentials.However,in conventional electrolyte systems,magnesium metal electrodes dynamically generate an ion-blocking surface layer,resulting in prominent voltage polarization,which severely limits their practical applications.In this study,ZIF-8/carbon nanotubes(CNTs)coatings were used to modify the anodes of magnesium batteries.Compared with the unaltered magnesium battery,the voltage lag time of the ZIF-8/CNTs coating was shortened from 4 s before modification to 0.26 s,and the battery impedance was lowered by two orders of magnitude.The duration of the discharge platform was increased from 4 h before modification to 6-10 h,the anode utilization rate was more than doubled,and the specific energy density was significantly enhanced compared with the battery before modification.The mechanism indicates that the ZIF-8/CNTs coating can limit the infiltration of corrosive substances,extend their transmission path,and offer more effective protection to the magnesium anode.The incorporation of CNTs improves the conductivity of the battery,and it significantly improves the electrochemical performance of the magnesium battery.
