Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve ...Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.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
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
BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery...BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery speed and quality of life.Effective prevention of anxiety and depression in elderly patients has become an urgent problem.AIM To investigate the trajectory of anxiety and depression levels in elderly patients after LIF,and the influencing factors.METHODS Random sampling was used to select 239 elderly patients who underwent LIF from January 2020 to December 2024 in Shenzhen Pingle Orthopedic Hospital.General information and surgery-related indices were recorded,and participants completed measures of psychological status,lumbar spine dysfunction,and quality of life.A latent class growth model was used to analyze the post-LIF trajectory of anxiety and depression levels,and unordered multi-categorical logistic regression was used to analyze the influencing factors.RESULTS Three trajectories of change in anxiety level were identified:Increasing anxiety(n=26,10.88%),decreasing anxiety(n=27,11.30%),and stable anxiety(n=186,77.82%).Likewise,three trajectories of change in depression level were identified:Increasing depression(n=30,12.55%),decreasing depression(n=26,10.88%),and stable depression(n=183,76.57%).Regression analysis showed that having no partner,female sex,elevated Oswestry dysfunction index(ODI)scores,and reduced 36-Item Short Form Health Survey scores all contributed to increased anxiety levels,whereas female sex,postoperative opioid use,and elevated ODI scores all contributed to increased depression levels.CONCLUSION During clinical observation,combining factors to predict anxiety and depression in post-LIF elderly patients enables timely intervention,quickens recovery,and enhances quality of life.展开更多
BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is ...BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is often recommended.We present a surgical technique that was successfully used on four such patients to convert them to a knee fusion(KF)using a cephalomedullary nail.CASE SUMMARY Four patients with infected long stem revision knee replacements that refused AKA had a single stage removal of their infected revision total knee followed by a KF.They were all treated with a statically locked antegrade cephalomedullary fusion nail,augmented with antibiotic impregnated bone cement.All patients had successful limb salvage and were ambulatory with assistive devices at the time of last follow-up.All were infection free at an average follow-up of 25.5 months(range 16-31).CONCLUSION Single stage cephalomedullary nailing can result in a successful KF in patients with infected long stem revision total knees.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
As a virtual representation of a specific physical asset,the digital twin has great potential for realizing the life cycle maintenance management of a dynamic system.Nevertheless,the dynamic stress concentration is ge...As a virtual representation of a specific physical asset,the digital twin has great potential for realizing the life cycle maintenance management of a dynamic system.Nevertheless,the dynamic stress concentration is generated since the state of the dynamic system changes over time.This generation of dynamic stress concentration has hindered the exploitation of the digital twin to reflect the dynamic behaviors of systems in practical engineering applications.In this context,this paper is interested in achieving real-time performance prediction of dynamic systems by developing a new digital twin framework that includes simulation data,measuring data,multi-level fusion modeling(M-LFM),visualization techniques,and fatigue analysis.To leverage its capacity,the M-LFM method combines the advantages of different surrogate models and integrates simulation and measured data,which can improve the prediction accuracy of dynamic stress concentration.A telescopic boom crane is used as an example to verify the proposed framework for stress prediction and fatigue analysis of the complex dynamic system.The results show that the M-LFM method has better performance in the computational efficiency and calculation accuracy of the stress prediction compared with the polynomial response surface method and the kriging method.In other words,the proposed framework can leverage the advantages of digital twins in a dynamic system:damage monitoring,safety assessment,and other aspects and then promote the development of digital twins in industrial fields.展开更多
At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multi...At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multicomponent information fusion method for big data based on radar map is proposed in this paper.The data model of perceptual digital image is constructed by using the linear regression analysis method.The ID tag of the collected image data as Transactin Identification(TID)is compared.If the TID of two data is the same,the repeated data detection is carried out.After the test,the data set is processed many times in accordance with the method process to improve the precision of data cleaning and reduce the omission.Based on the radar images,hierarchical visualization of processed multi-level information fusion is realized.The experiments show that the method can clean the redundant data accurately and achieve the efficient fusion of multi-level information of big data in the digital image.展开更多
The task of multimodal sentiment classification aims to associate multimodal information, such as images and texts with appropriate sentiment polarities. There are various levels that can affect human sentiment in vis...The task of multimodal sentiment classification aims to associate multimodal information, such as images and texts with appropriate sentiment polarities. There are various levels that can affect human sentiment in visual and textual modalities. However, most existing methods treat various levels of features independently without having effective method for feature fusion. In this paper, we propose a multi-level fusion classification(MFC) model to predict the sentiment polarity based on the fusing features from different levels by exploiting the dependency among them. The proposed architecture leverages convolutional neural networks(CNNs) with multiple layers to extract levels of features in image and text modalities. Considering the dependencies within the low-level and high-level features, a bi-directional(Bi) recurrent neural network(RNN) is adopted to integrate the learned features from different layers in CNNs. In addition, a conflict detection module is incorporated to address the conflict between modalities. Experiments on the Flickr dataset demonstrate that the MFC method achieves comparable performance compared with strong baseline methods.展开更多
Gelugpa is the most influential extant religious sect of Tibetan Buddhism,which is the spiritual prop for Tibetans,with thousands of monasteries and followers in Tibetan areas of China.Studies on the spatial diffusion...Gelugpa is the most influential extant religious sect of Tibetan Buddhism,which is the spiritual prop for Tibetans,with thousands of monasteries and followers in Tibetan areas of China.Studies on the spatial diffusion processes of Gelugpa can not only reveal its historical geographical development but also lay the foundation for anticipating its future development trend.However,existing studies on Gelugpa lack geographical perspective,making it difficult to explore the spatial characteristics.Furthermore,the prevailing macro-perspective overlooks spatiotemporal heterogeneity in diffusion processes.Therefore,taking monastery as the carrier,this study establishes a multi-level diffusion model to reconstruct the diffusion networks of Gelugpa monasteries,as well as a framework to explore the detailed features in the spatial diffusion processes of Gelugpa in Tibetan areas of China based on a geodatabase of Gelugpa monastery.The results show that the multi-level diffusion model has a considerable applicability in the reconstruction of the diffusion networks of Gelugpa monasteries.Gelugpa monasteries in the Three Tibetan Inhabited Areas present disparate spatial diffusion processes with diverse diffusion bases,speeds,stages,as well as diffusion regions and centers.A powerful single-center diffusion-centered Gandan Monastery was rapidly formed in U-Tsang.Kham experienced a slower and more varied spatial diffusion process with multiple diffusion systems far apart from each other.The spatial diffusion process of Amdo was the most complex,with the highest diffusion intensity.Amdo possessed the most influential diffusion centers,with different diffusion shapes and diffusion ranges crossing and overlapping with each other.Multiple natural and human factors may contribute to the formation of Gelugpa monasteries.This study contributes to the understanding of the geography of Gelugpa and provides reference to studies on religion diffusion.展开更多
To enhance the mechanical properties of Mo alloys prepared through laser powder bed fusion(LPBF),a hot isostatic pressing(HIP)treatment was used.Results show that following HIP treatment,the porosity decreases from 0....To enhance the mechanical properties of Mo alloys prepared through laser powder bed fusion(LPBF),a hot isostatic pressing(HIP)treatment was used.Results show that following HIP treatment,the porosity decreases from 0.27%to 0.22%,enabling the elements Mo and Ti to diffuse fully and to distribute more uniformly,and to forming a substantial number of low-angle grain boundaries.The tensile strength soars from 286±32 MPa to 598±22 MPa,while the elongation increases from 0.08%±0.02%to 0.18%±0.02%,without notable alterations in grain morphology during the tensile deformation.HIP treatment eliminates the molten pool boundaries,which are the primary source for premature failure in LPBFed Mo alloys.Consequently,HIP treatment emerges as a novel and effective approach for strengthening the mechanical properties of LPBFed Mo alloys,offering a fresh perspective on producing high-performance Mo-based alloys.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.There...Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.Therefore,it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations.This paper introduces a new algorithm,called thunderstorm wind gust identification network(TGNet).It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events.The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations,which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons.Then,the encoder,structured upon the U-shaped network(U-Net)and incorporating recurrent residual convolutional blocks(R2U-Net),is employed to extract the corresponding spatial convective characteristics of satellite,radar,and lightning observations.Finally,by using the multimodal deep fusion module based on multi-head cross-attention,the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts.TGNet products have high accuracy,with a critical success index reaching 0.77.Compared with those of U-Net and R2U-Net,the false alarm rate of TGNet products decreases by 31.28%and 24.15%,respectively.The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°,updated every 10minutes.The results are finer and more accurate,thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.展开更多
In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Ela...In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Elatty E.Abd Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–1630.DOI:10.32604/cmc.2022.018621,URL:https://www.techscience.com/cmc/v70n1/44361,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.展开更多
In this editorial,the authors of this paper comment on the article by Bokov et al published in the recent issue of World Journal of Orthopedics.We reviewed a general overview of oblique lumbar interbody fusions(OLIF)a...In this editorial,the authors of this paper comment on the article by Bokov et al published in the recent issue of World Journal of Orthopedics.We reviewed a general overview of oblique lumbar interbody fusions(OLIF)and lateral lumbar interbody fusions(LLIF),their indications and complications as an increasingly popular minimally invasive technique to address several lumbar pathologies.This editorial thoroughly discusses and reviews the literature regarding factors affecting outcomes of indirect decompression utilized through OLIF and LLIF procedures.Several parameters play a critical role in patient outcomes including restoration of disc height,foraminal height,central canal squared,and foraminal area.The indirect decompression allows for unbuckling of the ligamentum flavum which can significantly decompress the neural elements as well as aid in reduction of spondylolisthesis.However,the authors further highlight the limitations of indirect decompression and factors that may predict unsuccessful outcomes including bony foraminal stenosis,severe central canal stenosis,and osteoporosis.As a result,failure of indirect decompression can lead to persistent pain,radiculopathy and unsatisfied patients.Spinal surgeons may be left to reimage patients and consider additional procedures with direct decompression.展开更多
基金This research received funding from Duhok Polytechnic University.
文摘Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.
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 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 by the Scientific Research Projects of the Health System in Pingshan District,No.2023122.
文摘BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery speed and quality of life.Effective prevention of anxiety and depression in elderly patients has become an urgent problem.AIM To investigate the trajectory of anxiety and depression levels in elderly patients after LIF,and the influencing factors.METHODS Random sampling was used to select 239 elderly patients who underwent LIF from January 2020 to December 2024 in Shenzhen Pingle Orthopedic Hospital.General information and surgery-related indices were recorded,and participants completed measures of psychological status,lumbar spine dysfunction,and quality of life.A latent class growth model was used to analyze the post-LIF trajectory of anxiety and depression levels,and unordered multi-categorical logistic regression was used to analyze the influencing factors.RESULTS Three trajectories of change in anxiety level were identified:Increasing anxiety(n=26,10.88%),decreasing anxiety(n=27,11.30%),and stable anxiety(n=186,77.82%).Likewise,three trajectories of change in depression level were identified:Increasing depression(n=30,12.55%),decreasing depression(n=26,10.88%),and stable depression(n=183,76.57%).Regression analysis showed that having no partner,female sex,elevated Oswestry dysfunction index(ODI)scores,and reduced 36-Item Short Form Health Survey scores all contributed to increased anxiety levels,whereas female sex,postoperative opioid use,and elevated ODI scores all contributed to increased depression levels.CONCLUSION During clinical observation,combining factors to predict anxiety and depression in post-LIF elderly patients enables timely intervention,quickens recovery,and enhances quality of life.
文摘BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is often recommended.We present a surgical technique that was successfully used on four such patients to convert them to a knee fusion(KF)using a cephalomedullary nail.CASE SUMMARY Four patients with infected long stem revision knee replacements that refused AKA had a single stage removal of their infected revision total knee followed by a KF.They were all treated with a statically locked antegrade cephalomedullary fusion nail,augmented with antibiotic impregnated bone cement.All patients had successful limb salvage and were ambulatory with assistive devices at the time of last follow-up.All were infection free at an average follow-up of 25.5 months(range 16-31).CONCLUSION Single stage cephalomedullary nailing can result in a successful KF in patients with infected long stem revision total knees.
基金the National Key Research and Development Program of China(Grant No.2022YFF0711400)which provided valuable financial support and resources for my research and made it possible for me to deeply explore the unknown mysteries in the field of lunar geologythe National Space Science Data Center Youth Open Project(Grant No.NSSDC2302001),which has not only facilitated the smooth progress of my research,but has also built a platform for me to communicate and cooperate with experts in the field.
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金supported by the National Key R&D Program of China(Grant No.2018YFB1700704)the National Natural Science Foundation of China(Grant No.52075068).
文摘As a virtual representation of a specific physical asset,the digital twin has great potential for realizing the life cycle maintenance management of a dynamic system.Nevertheless,the dynamic stress concentration is generated since the state of the dynamic system changes over time.This generation of dynamic stress concentration has hindered the exploitation of the digital twin to reflect the dynamic behaviors of systems in practical engineering applications.In this context,this paper is interested in achieving real-time performance prediction of dynamic systems by developing a new digital twin framework that includes simulation data,measuring data,multi-level fusion modeling(M-LFM),visualization techniques,and fatigue analysis.To leverage its capacity,the M-LFM method combines the advantages of different surrogate models and integrates simulation and measured data,which can improve the prediction accuracy of dynamic stress concentration.A telescopic boom crane is used as an example to verify the proposed framework for stress prediction and fatigue analysis of the complex dynamic system.The results show that the M-LFM method has better performance in the computational efficiency and calculation accuracy of the stress prediction compared with the polynomial response surface method and the kriging method.In other words,the proposed framework can leverage the advantages of digital twins in a dynamic system:damage monitoring,safety assessment,and other aspects and then promote the development of digital twins in industrial fields.
基金2018 National Grade Innovation and Entrepreneurship Training Program for College Students,China(No.201811562005)Research Project of Gansu University,China(No.2016A-105)Innovation and Entrepreneurship Education Project of Gansu Province in 2019,China(No.2019024)。
文摘At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multicomponent information fusion method for big data based on radar map is proposed in this paper.The data model of perceptual digital image is constructed by using the linear regression analysis method.The ID tag of the collected image data as Transactin Identification(TID)is compared.If the TID of two data is the same,the repeated data detection is carried out.After the test,the data set is processed many times in accordance with the method process to improve the precision of data cleaning and reduce the omission.Based on the radar images,hierarchical visualization of processed multi-level information fusion is realized.The experiments show that the method can clean the redundant data accurately and achieve the efficient fusion of multi-level information of big data in the digital image.
基金supported in part by the National Key Research and Development(R&D)Program of China(2018YFB1403003)。
文摘The task of multimodal sentiment classification aims to associate multimodal information, such as images and texts with appropriate sentiment polarities. There are various levels that can affect human sentiment in visual and textual modalities. However, most existing methods treat various levels of features independently without having effective method for feature fusion. In this paper, we propose a multi-level fusion classification(MFC) model to predict the sentiment polarity based on the fusing features from different levels by exploiting the dependency among them. The proposed architecture leverages convolutional neural networks(CNNs) with multiple layers to extract levels of features in image and text modalities. Considering the dependencies within the low-level and high-level features, a bi-directional(Bi) recurrent neural network(RNN) is adopted to integrate the learned features from different layers in CNNs. In addition, a conflict detection module is incorporated to address the conflict between modalities. Experiments on the Flickr dataset demonstrate that the MFC method achieves comparable performance compared with strong baseline methods.
基金supported by the Humanities and Social Sciences Foundation of the Ministry of Education of China(Grant No.18YJAZH140).
文摘Gelugpa is the most influential extant religious sect of Tibetan Buddhism,which is the spiritual prop for Tibetans,with thousands of monasteries and followers in Tibetan areas of China.Studies on the spatial diffusion processes of Gelugpa can not only reveal its historical geographical development but also lay the foundation for anticipating its future development trend.However,existing studies on Gelugpa lack geographical perspective,making it difficult to explore the spatial characteristics.Furthermore,the prevailing macro-perspective overlooks spatiotemporal heterogeneity in diffusion processes.Therefore,taking monastery as the carrier,this study establishes a multi-level diffusion model to reconstruct the diffusion networks of Gelugpa monasteries,as well as a framework to explore the detailed features in the spatial diffusion processes of Gelugpa in Tibetan areas of China based on a geodatabase of Gelugpa monastery.The results show that the multi-level diffusion model has a considerable applicability in the reconstruction of the diffusion networks of Gelugpa monasteries.Gelugpa monasteries in the Three Tibetan Inhabited Areas present disparate spatial diffusion processes with diverse diffusion bases,speeds,stages,as well as diffusion regions and centers.A powerful single-center diffusion-centered Gandan Monastery was rapidly formed in U-Tsang.Kham experienced a slower and more varied spatial diffusion process with multiple diffusion systems far apart from each other.The spatial diffusion process of Amdo was the most complex,with the highest diffusion intensity.Amdo possessed the most influential diffusion centers,with different diffusion shapes and diffusion ranges crossing and overlapping with each other.Multiple natural and human factors may contribute to the formation of Gelugpa monasteries.This study contributes to the understanding of the geography of Gelugpa and provides reference to studies on religion diffusion.
基金National Natural Science Foundation of China(52105385)Stable Support Plan Program of Shenzhen Natural Science Fund(20220810132537001)+2 种基金Guangdong Basic and Applied Basic Research Foundation(2022A1515010781)Joint Fund of Henan Province Science and Technology R&D Program(225200810002)Fundamental Research Funds of Henan Academy of Sciences(240621041)。
文摘To enhance the mechanical properties of Mo alloys prepared through laser powder bed fusion(LPBF),a hot isostatic pressing(HIP)treatment was used.Results show that following HIP treatment,the porosity decreases from 0.27%to 0.22%,enabling the elements Mo and Ti to diffuse fully and to distribute more uniformly,and to forming a substantial number of low-angle grain boundaries.The tensile strength soars from 286±32 MPa to 598±22 MPa,while the elongation increases from 0.08%±0.02%to 0.18%±0.02%,without notable alterations in grain morphology during the tensile deformation.HIP treatment eliminates the molten pool boundaries,which are the primary source for premature failure in LPBFed Mo alloys.Consequently,HIP treatment emerges as a novel and effective approach for strengthening the mechanical properties of LPBFed Mo alloys,offering a fresh perspective on producing high-performance Mo-based alloys.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3004104)the National Natural Science Foundation of China(Grant No.U2342204)+4 种基金the Innovation and Development Program of the China Meteorological Administration(Grant No.CXFZ2024J001)the Open Research Project of the Key Open Laboratory of Hydrology and Meteorology of the China Meteorological Administration(Grant No.23SWQXZ010)the Science and Technology Plan Project of Zhejiang Province(Grant No.2022C03150)the Open Research Fund Project of Anyang National Climate Observatory(Grant No.AYNCOF202401)the Open Bidding for Selecting the Best Candidates Program(Grant No.CMAJBGS202318)。
文摘Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.Therefore,it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations.This paper introduces a new algorithm,called thunderstorm wind gust identification network(TGNet).It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events.The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations,which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons.Then,the encoder,structured upon the U-shaped network(U-Net)and incorporating recurrent residual convolutional blocks(R2U-Net),is employed to extract the corresponding spatial convective characteristics of satellite,radar,and lightning observations.Finally,by using the multimodal deep fusion module based on multi-head cross-attention,the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts.TGNet products have high accuracy,with a critical success index reaching 0.77.Compared with those of U-Net and R2U-Net,the false alarm rate of TGNet products decreases by 31.28%and 24.15%,respectively.The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°,updated every 10minutes.The results are finer and more accurate,thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.
文摘In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Elatty E.Abd Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–1630.DOI:10.32604/cmc.2022.018621,URL:https://www.techscience.com/cmc/v70n1/44361,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.
文摘In this editorial,the authors of this paper comment on the article by Bokov et al published in the recent issue of World Journal of Orthopedics.We reviewed a general overview of oblique lumbar interbody fusions(OLIF)and lateral lumbar interbody fusions(LLIF),their indications and complications as an increasingly popular minimally invasive technique to address several lumbar pathologies.This editorial thoroughly discusses and reviews the literature regarding factors affecting outcomes of indirect decompression utilized through OLIF and LLIF procedures.Several parameters play a critical role in patient outcomes including restoration of disc height,foraminal height,central canal squared,and foraminal area.The indirect decompression allows for unbuckling of the ligamentum flavum which can significantly decompress the neural elements as well as aid in reduction of spondylolisthesis.However,the authors further highlight the limitations of indirect decompression and factors that may predict unsuccessful outcomes including bony foraminal stenosis,severe central canal stenosis,and osteoporosis.As a result,failure of indirect decompression can lead to persistent pain,radiculopathy and unsatisfied patients.Spinal surgeons may be left to reimage patients and consider additional procedures with direct decompression.
