With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.Th...With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.This paper proposes an innovative fingerprint template protection scheme,which generates key streams through an improved fourdimensional superchaotic system(4CSCS),uses the space-filling property of Hilbert curves to achieve pixel scrambling,and introduces dynamic DNA encoding to improve encryption.Experimental results show that this scheme has a large key space 2^(528),encrypts image information entropy of more than 7.9970,and shows excellent performance in defending against statistical attacks and differential attacks.Compared with existing methods,this scheme has significant advantages in terms of encryption performance and security,and provides a reliable protection mechanism for fingerprint authentication systems in the Internet of things environment.展开更多
Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal viscer...Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal visceral organs and to assess the feasibility of 4D flow MRI(4D-f-MRI)in this population.The researchers performed 4D-f-MRI on 9 pediatric patients with a history or suspi-cion of bowel pathology.Flow velocities were measured in the abdominal aorta and superior and inferior mesenteric arteries.The quality of the 4D-f-MRI images was evaluated,and the agreement between the measured flow velocities and those obtained from Duplex ultrasound was established.However,due to the specific limitations of this work,future studies should address the issues of small sample size and the specific age group design.展开更多
Addressing the core weaknesses in the innovation and entrepreneurship capabilities of vocational college graduates,such as market insight and risk tolerance,as well as issues with the existing training model,including...Addressing the core weaknesses in the innovation and entrepreneurship capabilities of vocational college graduates,such as market insight and risk tolerance,as well as issues with the existing training model,including courses that are disconnected from industry,a lack of systematic practical training,and superficial school-enterprise cooperation,this paper constructs a“three-dimensional,four-dimensional”training system.The“three-dimensional”foundational framework encompasses three pillars:curriculum,general education layer,professional integration layer,practical application layer,practice as in three stages:introductory,simulated,and practical,and support including dual mentors,policies,and platforms.The“four-dimensional”differentiated strategies include four implementation pathways:professional differentiation,stage differentiation,addressing capability shortcomings,and school-government-industry collaboration.This system is grounded in theories such as multiple intelligences theory and systems theory,forming a closed-loop process of“theoretical input—practical application—support mechanisms”.Based on the practices of Guangdong Vocational Institute of Public Administration,the paper proposes a competency development pathway tailored by major and stage,which can effectively enhance the innovative and entrepreneurial core competencies of vocational college graduates.This provides a replicable systematic solution for vocational college innovative and entrepreneurial education,supporting vocational education reform and regional economic development.展开更多
As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique ...As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
BACKGROUND Four-dimensional(4D)flow magnetic resonance imaging(MRI)is used as a noninvasive modality for assessing hemodynamic information with neurovascular and body applications.The application of 4D flow MRI for as...BACKGROUND Four-dimensional(4D)flow magnetic resonance imaging(MRI)is used as a noninvasive modality for assessing hemodynamic information with neurovascular and body applications.The application of 4D flow MRI for assessment of bowel disease in children has not been previously described.AIM To determine feasibility of superior mesenteric venous and arterial flow quantitation in pediatric patients using 4D flow MRI.METHODS Nine pediatric patients(7-14 years old,5 male and 4 female)with history or suspicion of bowel pathology,who underwent magnetic resonance(MR)enterography with 4D flow MR protocol from November 2022 to October 2023.Field strength/sequence:3T MRI using 4D flow MR protocol.Flow velocity and peak speed measurements were performed by two diagnostic radiologists placing the region of interest in perpendicular plane to blood flow on each cross section of superior mesenteric artery(SMA)and superior mesenteric vein(SMV)at three predetermined levels.Bland-Altman analysis,showed good agreement of flow velocity and peak speed measurements of SMV and SMA between two readers.RESULTS Mean SMV flow velocity increased from proximal to mid to distal(0.14 L/minute,0.17 L/minute,0.22 L/minute respectively).Mean SMA flow velocity decreased from proximal to mid to distal(0.35 L/minute,0.27 L/minute,0.21 L/minute respectively).Observed agreement was good for flow velocity measurements of SMV(mean bias-0.01 L/minute and 95%limits of agreement,-0.09 to 0.08 L/minute)and SMA(mean bias-0.03 L/minute and 95%limits of agreement,-0.23 to 0.17 L/minute)between two readers.Good agreement for peak speed measurements of SMV(mean bias-1.2 cm/second and 95%limits of agreement,-9.4 to 7.0 cm/second)and SMA(mean bias-3.2 cm/second and 95%limits of agreement,-31.4 to 24.9 cm/second).CONCLUSION Flow quantitation using 4D Flow is feasible to provide hemodynamic information for SMV and SMA in children.展开更多
The article"Assessment of superior mesenteric vascular flow quantitation in children using four-dimensional flow magnetic resonance imaging"suggests to use of four-dimensional(4D)flow magnetic resonance imag...The article"Assessment of superior mesenteric vascular flow quantitation in children using four-dimensional flow magnetic resonance imaging"suggests to use of four-dimensional(4D)flow magnetic resonance imaging(MRI)which is also to measure the blood flow in the superior mesenteric vein(SMV)in pediatric patients over the traditional method.The study focuses on assessing the potential of SMV and superior mesenteric artery(SMA)flow quantification in children utilizing 4D flow MRI.It included 9 pediatric patients aged 18 years and below where 5 were male and 4 were female patients,on whom magnetic resonance enterorrhaphy(MRE)with 4D flow MRI protocol was used.Statistical analysis was performed using MedCalc.Measurements of SMV and SMA between two readers were calculated using Bland-Altman analysis.The results stated that six patients showed no MRE evidence of active inflammatory bowel disease,two patients showed unmarkable bowel appearance on MRI and one patient showed normal MRE without endoscopy performed at the same timeframe.The study utilized available 4D flow MRI sequences in this study aiming to show the feasibility of 4D flow quantitation of SMA and SMV flow in pediatric patients.The study also discovered good agreement for both peak velocity and peak speed measurements of SMA and SMV.展开更多
A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5w...A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.展开更多
Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combini...Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.展开更多
The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-e...The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-electron lasers to solid-state near-infrared lasers.Laser fields were shown to enhance the fusion yields,and the mechanism of this enhancement was explained.Low-frequency lasers are more efficient in enhancing fusion than high-frequency lasers.The calculation results show enhancements of fusion yields by orders of magnitude with currently available intense low-frequency laser fields.The temperature requirement for controlled nuclear fusion may be reduced with the aid of intense laser fields.展开更多
Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collect...Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.展开更多
Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learni...Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.展开更多
Background Fusion genes play a crucial role in the pathogenesis of acute myeloid leukemia(AML).This study investigated the utility of targeted next-generation sequencing(NGS)of RNA for detecting rare and unknown fusio...Background Fusion genes play a crucial role in the pathogenesis of acute myeloid leukemia(AML).This study investigated the utility of targeted next-generation sequencing(NGS)of RNA for detecting rare and unknown fusion genes in patients with AML.Methods A total of 85 adult AML samples previously identified as fusion gene-negative by multiplex nested reverse transcription-polymerase chain reaction(RT-PCR)were subjected to NGS analysis.Results Fusion genes were detected in 21 of 72(29.2%)patients.Among the 26 primary refractory patients,11(42.3%)exhibited fusion genes,whereas among the 18 relapsed patients,fusion genes were identified in five(27.8%).Notably,lysine methyltransferase 2A(KMT2A)and nucleoporin 98(NUP98)rearrangements were enriched in refractory/relapsed patients.Additionally,recurrent fusion transcripts involving eukaryotic translation initiation factor 4A1(EIF4A1)were identified.The identification of additional fusion genes resulted in an approximate 20.8%(11/53)reclassification of medium-risk karyotypes to the high-risk category,thereby enhancing diagnostic accuracy.Conclusions Targeted NGS may complement conventional methods for identifying novel fusions in refractory/relapsed AML;however,its prognostic value requires validation in prospective controlled trials.展开更多
A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughn...A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughness poses a challenge for the future development of high-field superconducting magnets in fusion reactors.The yield strength of the International Thermonuclear Experimental Reactor developed for low-temperature structural materials at 4.2K is below 1100MPa,which fails to meet the demand for structural components with yield strengths exceeding 1500MPa at 4.2K in the future fusion reactors.CHSN01(formerly N50H),which is a low-temperature structural material developed in China,exhibits exceptional strength and toughness,thereby making it highly promising for practical applications.Recently,a 30 t jacket measuring approximately 5000m in total length was produced.Its low-temperature mechanical properties were tested using a sampling method to ensure compliance with application requirements.This paper presents the experimental data of the CHSN01 jacket and tests of the physical properties of the material in the temperature range of 4–300 K.The physical properties were unaffected by magnetic field.Furthermore,this paper discusses the feasibility of employing CHSN01 as a cryogenic structural material capable of withstanding high magnetic fields in next-generation fusion reactors.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
文摘With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.This paper proposes an innovative fingerprint template protection scheme,which generates key streams through an improved fourdimensional superchaotic system(4CSCS),uses the space-filling property of Hilbert curves to achieve pixel scrambling,and introduces dynamic DNA encoding to improve encryption.Experimental results show that this scheme has a large key space 2^(528),encrypts image information entropy of more than 7.9970,and shows excellent performance in defending against statistical attacks and differential attacks.Compared with existing methods,this scheme has significant advantages in terms of encryption performance and security,and provides a reliable protection mechanism for fingerprint authentication systems in the Internet of things environment.
文摘Leesmidt et al present a comprehensive analysis of abdominal vascular flow in children using four-dimensional(4D)flow magnetic resonance imaging(MRI),aim to establish normal hemodynamic values for the abdominal visceral organs and to assess the feasibility of 4D flow MRI(4D-f-MRI)in this population.The researchers performed 4D-f-MRI on 9 pediatric patients with a history or suspi-cion of bowel pathology.Flow velocities were measured in the abdominal aorta and superior and inferior mesenteric arteries.The quality of the 4D-f-MRI images was evaluated,and the agreement between the measured flow velocities and those obtained from Duplex ultrasound was established.However,due to the specific limitations of this work,future studies should address the issues of small sample size and the specific age group design.
基金2024 University-level Innovation and Entrepreneurship Educational Reform Project,“Research on the Innovation and Entrepreneurship Education Model of Higher Vocational Colleges Based on the Theory of Technological Innovation Diffusion”(Project No.:CYJG202414)Academic Year Higher Education Institution Graduate Employment and Entrepreneurship Research Project,“Research on Strategies for Cultivating Innovation and Entrepreneurship Abilities Among Graduates of Higher Vocational Colleges”(Project No.:GJXY2024N083)2024 Guangdong Province General Higher Education Institution Specialized Innovation Project,“Research on a Specialized-Entrepreneurial Integration Talent Development System Guided by Core Competencies in the Era of Artificial Intelligence”(Project No.:2024WTSCX339)。
文摘Addressing the core weaknesses in the innovation and entrepreneurship capabilities of vocational college graduates,such as market insight and risk tolerance,as well as issues with the existing training model,including courses that are disconnected from industry,a lack of systematic practical training,and superficial school-enterprise cooperation,this paper constructs a“three-dimensional,four-dimensional”training system.The“three-dimensional”foundational framework encompasses three pillars:curriculum,general education layer,professional integration layer,practical application layer,practice as in three stages:introductory,simulated,and practical,and support including dual mentors,policies,and platforms.The“four-dimensional”differentiated strategies include four implementation pathways:professional differentiation,stage differentiation,addressing capability shortcomings,and school-government-industry collaboration.This system is grounded in theories such as multiple intelligences theory and systems theory,forming a closed-loop process of“theoretical input—practical application—support mechanisms”.Based on the practices of Guangdong Vocational Institute of Public Administration,the paper proposes a competency development pathway tailored by major and stage,which can effectively enhance the innovative and entrepreneurial core competencies of vocational college graduates.This provides a replicable systematic solution for vocational college innovative and entrepreneurial education,supporting vocational education reform and regional economic development.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22A20598 and 52104107)the"Qinglan Project"of Jiangsu Colleges and Universities,Young Elite Scientists Sponsorship Program of Jiangsu Province(Grant No.TJ-2023-086).
文摘As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
文摘BACKGROUND Four-dimensional(4D)flow magnetic resonance imaging(MRI)is used as a noninvasive modality for assessing hemodynamic information with neurovascular and body applications.The application of 4D flow MRI for assessment of bowel disease in children has not been previously described.AIM To determine feasibility of superior mesenteric venous and arterial flow quantitation in pediatric patients using 4D flow MRI.METHODS Nine pediatric patients(7-14 years old,5 male and 4 female)with history or suspicion of bowel pathology,who underwent magnetic resonance(MR)enterography with 4D flow MR protocol from November 2022 to October 2023.Field strength/sequence:3T MRI using 4D flow MR protocol.Flow velocity and peak speed measurements were performed by two diagnostic radiologists placing the region of interest in perpendicular plane to blood flow on each cross section of superior mesenteric artery(SMA)and superior mesenteric vein(SMV)at three predetermined levels.Bland-Altman analysis,showed good agreement of flow velocity and peak speed measurements of SMV and SMA between two readers.RESULTS Mean SMV flow velocity increased from proximal to mid to distal(0.14 L/minute,0.17 L/minute,0.22 L/minute respectively).Mean SMA flow velocity decreased from proximal to mid to distal(0.35 L/minute,0.27 L/minute,0.21 L/minute respectively).Observed agreement was good for flow velocity measurements of SMV(mean bias-0.01 L/minute and 95%limits of agreement,-0.09 to 0.08 L/minute)and SMA(mean bias-0.03 L/minute and 95%limits of agreement,-0.23 to 0.17 L/minute)between two readers.Good agreement for peak speed measurements of SMV(mean bias-1.2 cm/second and 95%limits of agreement,-9.4 to 7.0 cm/second)and SMA(mean bias-3.2 cm/second and 95%limits of agreement,-31.4 to 24.9 cm/second).CONCLUSION Flow quantitation using 4D Flow is feasible to provide hemodynamic information for SMV and SMA in children.
文摘The article"Assessment of superior mesenteric vascular flow quantitation in children using four-dimensional flow magnetic resonance imaging"suggests to use of four-dimensional(4D)flow magnetic resonance imaging(MRI)which is also to measure the blood flow in the superior mesenteric vein(SMV)in pediatric patients over the traditional method.The study focuses on assessing the potential of SMV and superior mesenteric artery(SMA)flow quantification in children utilizing 4D flow MRI.It included 9 pediatric patients aged 18 years and below where 5 were male and 4 were female patients,on whom magnetic resonance enterorrhaphy(MRE)with 4D flow MRI protocol was used.Statistical analysis was performed using MedCalc.Measurements of SMV and SMA between two readers were calculated using Bland-Altman analysis.The results stated that six patients showed no MRE evidence of active inflammatory bowel disease,two patients showed unmarkable bowel appearance on MRI and one patient showed normal MRE without endoscopy performed at the same timeframe.The study utilized available 4D flow MRI sequences in this study aiming to show the feasibility of 4D flow quantitation of SMA and SMV flow in pediatric patients.The study also discovered good agreement for both peak velocity and peak speed measurements of SMA and SMV.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+8 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)2024 Scientific Research Project of Shaanxi National Defense Industry Vocational and Technical College(Gfy24-07)Shaanxi Vocational and Technical Education Association 2024 Vocational Education Teaching Reform Research Topic(2024SZX354)National Natural Science Foundation of China(U24A20115)2024 Shaanxi Provincial Education Department Service Local Special Scientific Research Program Project-Industrialization Cultivation Project(24JC005,24JC063)Shaanxi Province“14th Five-Year Plan”Education Science Plan,2024 Project(SGH24Y3181)National Key Research and Development Program of China(2023YFB4606400)Longmen Laboratory Frontier Exploration Topics Project(LMQYTSKT003)。
文摘A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.
基金funded by the Natural Science Foundation of Chongqing Municipality,grant number CSTB2022NSCQ-MSX0503.
文摘Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.
基金supported by the National Natural Science Foundation of China(Nos.12405288,12374241,12474484,U2330401,12088101)the Natural Science Foundation of Top Talent of SZTU(No.GDRC202526)。
文摘The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-electron lasers to solid-state near-infrared lasers.Laser fields were shown to enhance the fusion yields,and the mechanism of this enhancement was explained.Low-frequency lasers are more efficient in enhancing fusion than high-frequency lasers.The calculation results show enhancements of fusion yields by orders of magnitude with currently available intense low-frequency laser fields.The temperature requirement for controlled nuclear fusion may be reduced with the aid of intense laser fields.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC.
文摘Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.
基金supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2025/03/32440).
文摘Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.
基金supported by the National Natural Science Foundation of China(No.82100164,82302692)the Capital Medical University Research Cultivation Fund(No.PYZ22099)the Guangdong Provincial Medical Science and Technology Research Fund Project(No.A2024190).
文摘Background Fusion genes play a crucial role in the pathogenesis of acute myeloid leukemia(AML).This study investigated the utility of targeted next-generation sequencing(NGS)of RNA for detecting rare and unknown fusion genes in patients with AML.Methods A total of 85 adult AML samples previously identified as fusion gene-negative by multiplex nested reverse transcription-polymerase chain reaction(RT-PCR)were subjected to NGS analysis.Results Fusion genes were detected in 21 of 72(29.2%)patients.Among the 26 primary refractory patients,11(42.3%)exhibited fusion genes,whereas among the 18 relapsed patients,fusion genes were identified in five(27.8%).Notably,lysine methyltransferase 2A(KMT2A)and nucleoporin 98(NUP98)rearrangements were enriched in refractory/relapsed patients.Additionally,recurrent fusion transcripts involving eukaryotic translation initiation factor 4A1(EIF4A1)were identified.The identification of additional fusion genes resulted in an approximate 20.8%(11/53)reclassification of medium-risk karyotypes to the high-risk category,thereby enhancing diagnostic accuracy.Conclusions Targeted NGS may complement conventional methods for identifying novel fusions in refractory/relapsed AML;however,its prognostic value requires validation in prospective controlled trials.
基金supported in part by the National Natural Science Foundation of China(No.12305196)Anhui Provincial Natural Science Foundation(No.2308085QA23)+1 种基金Open Fund of Magnetic confinement Fusion Laboratory of Anhui Province(No.2023AMF03003)Science Foundation of Institute of Plasma Physics,Chinese Academy of Sciences(No.DSJJ-2024-10).
文摘A low-temperature-resistant and high-strength stainless-steel jacket is a key component in the superconducting magnet of a fusion reactor.The development of cryogenic structural materials with high strength and toughness poses a challenge for the future development of high-field superconducting magnets in fusion reactors.The yield strength of the International Thermonuclear Experimental Reactor developed for low-temperature structural materials at 4.2K is below 1100MPa,which fails to meet the demand for structural components with yield strengths exceeding 1500MPa at 4.2K in the future fusion reactors.CHSN01(formerly N50H),which is a low-temperature structural material developed in China,exhibits exceptional strength and toughness,thereby making it highly promising for practical applications.Recently,a 30 t jacket measuring approximately 5000m in total length was produced.Its low-temperature mechanical properties were tested using a sampling method to ensure compliance with application requirements.This paper presents the experimental data of the CHSN01 jacket and tests of the physical properties of the material in the temperature range of 4–300 K.The physical properties were unaffected by magnetic field.Furthermore,this paper discusses the feasibility of employing CHSN01 as a cryogenic structural material capable of withstanding high magnetic fields in next-generation fusion reactors.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
