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
A metal-organic framework/inorganic composite(ZIF-8@AMP)was synthesized by the in situ introduction of the active component ammonium phosphomolybdate(AMP)during the ambient solution-phase synthesis of the metal-organi...A metal-organic framework/inorganic composite(ZIF-8@AMP)was synthesized by the in situ introduction of the active component ammonium phosphomolybdate(AMP)during the ambient solution-phase synthesis of the metal-organic framework(ZIF-8).The structure and properties of the composite were characterized using scanning electron microscopy(SEM),X-ray powder diffraction(XRD),X-ray photoelectron spectroscopy(XPS),thermogravimetric analysis(TGA),and Fourier transform infrared spectroscopy(FTIR).Its adsorption performance for Rb^(+)and Cs^(+)in water was investigated.Results indicate that ZIF-8@AMP exhibited adsorption efficiencies of 93.5%and 95.6%for Rb^(+)and Cs^(+)within 30 min,with maximum adsorption capacities of 92.7 and 104.5 mg·g^(-1),respectively.After five adsorption-desorption cycles,it maintained high adsorption capacity and achieved over 84.9%adsorption efficiency for Rb^(+)and Cs^(+)in actual brine samples.The adsorption of ZIF-8@AMP for Rb^(+)and Cs^(+)follows pseudosecond-order kinetics and the Langmuir adsorption isotherm,indicating an endothermic,entropy-increasing,and spontaneous process.The adsorption mechanism involves electrostatic attraction and ion exchange between ZIF-8@AMP and Rb^(+)and Cs^(+).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
OBJECTIVE:To develop an expert consensus on kidney deficiency syndrome(KDS)in pregnant women and construct a validated self-reported KDS Patient-Reported Measures Pregnancy Scale(KDS-PRMs-Pregnancy Scale)for early ide...OBJECTIVE:To develop an expert consensus on kidney deficiency syndrome(KDS)in pregnant women and construct a validated self-reported KDS Patient-Reported Measures Pregnancy Scale(KDS-PRMs-Pregnancy Scale)for early identification and management.METHODS:The study was conducted in three phases.First,a comprehensive review of Traditional Chinese Medicine(TCM)literature and diagnostic criteria was performed,generating initial KDS symptoms for pregnancy.Second,a two-round Delphi survey,involving 21 experts from TCM,obstetrics,and gynaecology,assessed importance,relevance,and appropriateness of the items.Third,a psychometric evaluation was conducted,including exploratory factor analysis and internal consistency assessment.RESULTS:In the first Delphi round,19 items were flagged for revision or removal due to expert variability,with 12 items deemed irrelevant.In the second round,consensus was reached,resulting in a 25-item scale.After psychometric evaluation,seven items were removed due to poor factor loadings,leaving an 18-item scale.Three factors—physiological discomfort,fatigue&weakness,and excretion abnormalities—accounted for 78.4%of the variance.The final scale demonstrated excellent internal consistency(Cronbach's alpha=0.959).CONCLUSION:The validated 18-item KDS-PRMsPregnancy Scale is a reliable tool for assessing KDS in pregnant women.Future research should focus on validation in diverse populations and exploring its predictive validity for pregnancy outcomes.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
Metal-organic framework(MOF)-derived porous carbon has attracted particular attention in the electrochemical energy storage field,of which the key is the design and preparation of electrode materials with adjustable p...Metal-organic framework(MOF)-derived porous carbon has attracted particular attention in the electrochemical energy storage field,of which the key is the design and preparation of electrode materials with adjustable porosity and defects for supercapacitors.Here,a novel strategy of coating ZIF-8 with coal tar pitch(CTP)is presented to tailor the porosity and defects of derived porous carbon,by which the inward contraction of ZIF-8 is prevented to enlarge the ultra-micropores,and the defects of ZIF-8-derived carbon are repaired to form a continuous conjugated network.The tradeoff between porosity and electrical conductivity endows this novel hard/soft carbon electrode with fast ion/electron diffusion,achieving high yet balanced capacitance and rate performance of a top-level specific area-normalized capacitance(40μF cm^(-2))and a capacitance retention of 52.1%at a 1000-fold increased current density.Meanwhile,the novel electrode realizes a high capacitance of 704 F g^(-1)at 1 A g^(-1)and capacitance retention of 91.9%after 50000 cycles in KOH+PPD electrolyte.This study provides an effective approach to designing novel hard/soft carbon with tuned porosity and carbon defects from MOFs and CTP for supercapacitors and other metal-ion batteries.展开更多
Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the ...Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.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
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.
文摘A metal-organic framework/inorganic composite(ZIF-8@AMP)was synthesized by the in situ introduction of the active component ammonium phosphomolybdate(AMP)during the ambient solution-phase synthesis of the metal-organic framework(ZIF-8).The structure and properties of the composite were characterized using scanning electron microscopy(SEM),X-ray powder diffraction(XRD),X-ray photoelectron spectroscopy(XPS),thermogravimetric analysis(TGA),and Fourier transform infrared spectroscopy(FTIR).Its adsorption performance for Rb^(+)and Cs^(+)in water was investigated.Results indicate that ZIF-8@AMP exhibited adsorption efficiencies of 93.5%and 95.6%for Rb^(+)and Cs^(+)within 30 min,with maximum adsorption capacities of 92.7 and 104.5 mg·g^(-1),respectively.After five adsorption-desorption cycles,it maintained high adsorption capacity and achieved over 84.9%adsorption efficiency for Rb^(+)and Cs^(+)in actual brine samples.The adsorption of ZIF-8@AMP for Rb^(+)and Cs^(+)follows pseudosecond-order kinetics and the Langmuir adsorption isotherm,indicating an endothermic,entropy-increasing,and spontaneous process.The adsorption mechanism involves electrostatic attraction and ion exchange between ZIF-8@AMP and Rb^(+)and Cs^(+).
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 the National Natural Science Foundation of China:to Explore the Intergenerational Effects of Bu-Shen-Tian-Jing Therapeutic Principle on the Offspring of Hyper-Androgenic Polycystic Ovary Syndrome Based on Regulating Rhythmic Iron Death in the Ovarian Granulosa Cells Mediated by Fos Proto-OncogeneRetinoic Acid Receptor-Related Orphan Receptor A-Solute Carrier Family 7 Member 11(No.82274564)the National Natural Science Foundation of China:the Underlying Mechanism of Bu-Shen-Jian-Pi Therapeutic Principle in Regulating Ovarian Granulosa Cells Autophagy Mediated by Short-chain Fatty Acids-forkhead Box O1 Pathway and its Effects on the Development of Offspring of Polycystic Ovary Syndrome(No.82074476)the Open Fund Project of Zhejiang Key Laboratory of Maternal and Infant Health,Women’s Hospital,School of Medicine,Zhejiang University:Mediating Role of Kidney Deficiency in the Relationship between Fear of Childbirth and Delivery Modes:an Exploratory Investigation Grounded in the Classic Traditional Chinese Medicine Theories of“Fear Injuring Kidney”and“Kidney Storing Essence”(No.ZDFY2024-MI-2)。
文摘OBJECTIVE:To develop an expert consensus on kidney deficiency syndrome(KDS)in pregnant women and construct a validated self-reported KDS Patient-Reported Measures Pregnancy Scale(KDS-PRMs-Pregnancy Scale)for early identification and management.METHODS:The study was conducted in three phases.First,a comprehensive review of Traditional Chinese Medicine(TCM)literature and diagnostic criteria was performed,generating initial KDS symptoms for pregnancy.Second,a two-round Delphi survey,involving 21 experts from TCM,obstetrics,and gynaecology,assessed importance,relevance,and appropriateness of the items.Third,a psychometric evaluation was conducted,including exploratory factor analysis and internal consistency assessment.RESULTS:In the first Delphi round,19 items were flagged for revision or removal due to expert variability,with 12 items deemed irrelevant.In the second round,consensus was reached,resulting in a 25-item scale.After psychometric evaluation,seven items were removed due to poor factor loadings,leaving an 18-item scale.Three factors—physiological discomfort,fatigue&weakness,and excretion abnormalities—accounted for 78.4%of the variance.The final scale demonstrated excellent internal consistency(Cronbach's alpha=0.959).CONCLUSION:The validated 18-item KDS-PRMsPregnancy Scale is a reliable tool for assessing KDS in pregnant women.Future research should focus on validation in diverse populations and exploring its predictive validity for pregnancy outcomes.
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.
基金funded by the National Natural Science Foundation of China (No. 52372037)the Natural Science Foundation of Anhui Province (Nos. 2408085MB032)+1 种基金the Outstanding Scientific Research and Innovation Team Program of Higher Education Institutions of Anhui Province (No. 2023AH010015)support from the Anhui International Research Center of Energy Materials Green Manufacturing and Biotechnology
文摘Metal-organic framework(MOF)-derived porous carbon has attracted particular attention in the electrochemical energy storage field,of which the key is the design and preparation of electrode materials with adjustable porosity and defects for supercapacitors.Here,a novel strategy of coating ZIF-8 with coal tar pitch(CTP)is presented to tailor the porosity and defects of derived porous carbon,by which the inward contraction of ZIF-8 is prevented to enlarge the ultra-micropores,and the defects of ZIF-8-derived carbon are repaired to form a continuous conjugated network.The tradeoff between porosity and electrical conductivity endows this novel hard/soft carbon electrode with fast ion/electron diffusion,achieving high yet balanced capacitance and rate performance of a top-level specific area-normalized capacitance(40μF cm^(-2))and a capacitance retention of 52.1%at a 1000-fold increased current density.Meanwhile,the novel electrode realizes a high capacitance of 704 F g^(-1)at 1 A g^(-1)and capacitance retention of 91.9%after 50000 cycles in KOH+PPD electrolyte.This study provides an effective approach to designing novel hard/soft carbon with tuned porosity and carbon defects from MOFs and CTP for supercapacitors and other metal-ion batteries.
基金funded by the Fundamental Research Funds for the Central Universities(Grant No.106-YDZX2025022)the Startup Foundation of New Professor at Nanjing Agricultural University(Grant No.106-804005)the“Qing Lan Project”of Jiangsu Higher Education Institutions.
文摘Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.
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。
