Purpose:In this paper,we attempt to use query refinements to identify users' search intents and seek a method for intent clustering based on real world query data.Design/methodology/approach:An experiment has been...Purpose:In this paper,we attempt to use query refinements to identify users' search intents and seek a method for intent clustering based on real world query data.Design/methodology/approach:An experiment has been conducted to analyze selected search sessions from the American Online(AOL) query logs with a two-stage approach.The first stage is to identify underlying intent by combining query co-occurrence information with query expression similarity.The work in the second stage is to cluster identified results by constructing query vectors through performing random walks on a Markov graph.Findings:Average correctness for identifying search intent is 0.74.Precision,recall,F-score values for intent clustering are 0.73,0.72 and 0.71,respectively.The results indicate that combining session co-occurrence information and query expression similarity can further filter noises and our clustering method is more suitable for sparse data.Research limitations:We use the time-out threshold(15-minutc) method to group queries in one session,but a user may have multiple search goals at the same time and the multi-task behavior of a user is hard to capture in a session defined based on time notions.Practical implications:This study provides insights into the ways of understanding users' search intents by analyzing their queries and refinements from a new perspective.The results will help search engine developers to identify user intents.Originality/value:We propose a new method to identify users' search intents by combining session co-occurrence information and query expression similarity,and a new method for clustering sparse data.展开更多
Low-density short-duration pulsed current-assisted aging treatment was applied to the Ti-6Al-4V-0.5Mo-0.5Zr alloy subjected to different solution treatments.The results show that numerous α_(p) phases redissolve into...Low-density short-duration pulsed current-assisted aging treatment was applied to the Ti-6Al-4V-0.5Mo-0.5Zr alloy subjected to different solution treatments.The results show that numerous α_(p) phases redissolve into the new β phase during the pulsed current-assisted aging process,and then the newly formed β phase is mainly transformed into the β_(t) phase,with occasional transition to new α_(p) phase,leading to a remarkable grain refinement,especially for the lamellarαs phases.In comparison to conventional aging treatment,the pulsed current-assisted aging approach achieves a significant enhancement in strength without degrading ductility,yielding an excellent mechanical property combination:a yield strength of 932 MPa,a tensile strength of 1042 MPa,and an elongation of 12.2%.It is primarily ascribed to the increased fraction of β_(t) phases,the obvious grain refinement effect,and the slip block effect induced by the multiple-variantαs colonies distributed within β_(t) phases.展开更多
The effect of trace addition of 0.1wt%Y on the grain refinement and mechanical properties of Al-2.2Li-1.5Cu-0.5Mg-1Zn-0.2Zr-0.2Sc alloys at as-cast and heat-treated states was investigated.Results show that the additi...The effect of trace addition of 0.1wt%Y on the grain refinement and mechanical properties of Al-2.2Li-1.5Cu-0.5Mg-1Zn-0.2Zr-0.2Sc alloys at as-cast and heat-treated states was investigated.Results show that the addition of 0.1wt%Y into the Al-2.2Li-1.5Cu-0.5Mg-1Zn-0.2Zr-0.2Sc alloys can elevate the nucleation temperature of the Al_(3)(Sc,Zr)phase,leading to the preferential precipitation of the Al_(3)(Sc,Zr)phase and increasing the amount of Al_(3)(Sc,Zr)phase in the matrix.Al_(3)(Sc,Zr)phase can also act as a heterogeneous nucleation site in theα-Al matrix to promote nucleation and refine grains.The addition of element Y changes the precipitation phase characteristics at the grain boundaries in the as-cast alloy,which changes the distribution characteristics of secondary phases from initially continuous and coarse strip-like distribution at grain boundaries into the discontinuous dot-like and rod-like distribution.Besides,the size of secondary phases becomes smaller and their amount increases.Under the combined effects of grain refinement strengthening and precipitation strengthening,the Al-2.2Li-1.5Cu-0.5Mg-1Zn-0.2Zr-0.2Sc-0.1Y alloy after 175℃/10 h aging treatment achieves an ultimate tensile strength of 412 MPa and an elongation of 6.3%.Compared with those of the alloy without Y addition,the ultimate tensile strength and elongation of the added alloy increase by 16.1%and 53.7%,respectively.展开更多
Refinements to inequalities on inner product spaces are presented. In this respect, inequali-ties dealt with in this paper are: Cauchy’s inequality, Bessel’s inequality, Fan-Todd’s inequality and Fan-Todd’s determ...Refinements to inequalities on inner product spaces are presented. In this respect, inequali-ties dealt with in this paper are: Cauchy’s inequality, Bessel’s inequality, Fan-Todd’s inequality and Fan-Todd’s determinantal inequality. In each case, a strictly increasing function is put for-ward, which lies between the smaller and the larger quantities of each inequality. As a result. an improved condition for equality of the Fan-Todd’s determinantal inequality is deduced.展开更多
At the start of the new year,Cao Xiucheng,Chairman of Henan No.2 Textile Machinery Co.,Ltd.,was on his way to visit clients when he kept receiving urgent calls from the Xinyang production base regarding order scheduli...At the start of the new year,Cao Xiucheng,Chairman of Henan No.2 Textile Machinery Co.,Ltd.,was on his way to visit clients when he kept receiving urgent calls from the Xinyang production base regarding order scheduling.It turned out that since the end of 2025,the company had successively secured bulk spindle orders from overseas clients in Bangladesh and other countries,coupled with continuous urgent requests for orders from domestic manufacturers.Faced with such a production peak right at the beginning of the year,Mr.Cao Xiucheng admitted,“It was truly unexpected.”展开更多
Hard tissue repair materials that balance high strength with low modulus are highly promising,representing a transformative focus in applied biomaterials research.In this study,Ti-Nb alloys with high performance are p...Hard tissue repair materials that balance high strength with low modulus are highly promising,representing a transformative focus in applied biomaterials research.In this study,Ti-Nb alloys with high performance are prepared by a low-cost process for orthopedic applications.Phase composition,modulus,compressive strength and recovery properties are effectively manipulated by tailoring trace amounts of interstitial oxygen.With increasing oxygen concentration in sintered Ti-Nb alloys,theβ(body centered cubic)phase was stabilized due to the lattice distortion.The elastic modulus declined from 91 to 24 GPa.The compressive strength slightly decreased from 1595 to 1404 MPa and yield strength increased from 760 to 904 MPa.Additionally,the recovery properties were enhanced by the interstitial oxygen as a shape memory alloy.The utilization of trace oxygen serves to modulate the thermoelastic martensitic transformation in Ti-Nb alloys,thereby obtaining appropriate mechanical properties.A notable reduction in modulus is achieved while maintaining high strength,which facilitates the development of orthopedic implants capable of withstanding more complex forces.展开更多
Confucius’imminent birth is heralded by the appearance of the qilin.The mythical one-horned animal came to his mother at the door and cast out of its mouth a jade tablet bearing an inscription saying that she would g...Confucius’imminent birth is heralded by the appearance of the qilin.The mythical one-horned animal came to his mother at the door and cast out of its mouth a jade tablet bearing an inscription saying that she would give birth to“the son of the refinement of water,and that he would succeed the Zhou Dynasty,but as a king without a throne(su wang).”Stunned,Yan Zhengzai–Confucius’mother–tied an embroidered ribbon around the horn of the qilin,and the animal stayed for two nights.展开更多
Knowledge-based VisualQuestion Answering(VQA)requires the integration of visual information with external knowledge reasoning.Existing approaches typically retrieve information from external corpora and rely on pretra...Knowledge-based VisualQuestion Answering(VQA)requires the integration of visual information with external knowledge reasoning.Existing approaches typically retrieve information from external corpora and rely on pretrained language models for reasoning.However,their performance is often hindered by the limited capabilities of retrievers and the constrained size of knowledge bases.Moreover,relying on image captions to bridge the modal gap between visual and language modalities can lead to the omission of critical visual details.To address these limitations,we propose the Reflective Chain-of-Thought(ReCoT)method,a simple yet effective framework inspired by metacognition theory.ReCoT effectively activates the reasoning capabilities ofMultimodal Large LanguageModels(MLLMs),providing essential visual and knowledge cues required to solve complex visual questions.It simulates a metacognitive reasoning process that encompasses monitoring,reflection,and correction.Specifically,in the initial generation stage,an MLLM produces a preliminary answer that serves as the model’s initial cognitive output.During the reflective reasoning stage,this answer is critically examined to generate a reflective rationale that integrates key visual evidence and relevant knowledge.In the final refinement stage,a smaller language model leverages this rationale to revise the initial prediction,resulting in amore accurate final answer.By harnessing the strengths ofMLLMs in visual and knowledge grounding,ReCoT enables smaller language models to reason effectively without dependence on image captions or external knowledge bases.Experimental results demonstrate that ReCoT achieves substantial performance improvements,outperforming state-of-the-art methods by 2.26%on OK-VQA and 5.8%on A-OKVQA.展开更多
Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challeng...Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challenging.This study presents the Wavelet-Guided Transformer U-Net(WGT-UNet)model,a new hybrid net-work that combines Convolutional Neural Networks(CNNs),Discrete Wavelet Transform(DWT),and Transformer architectures.The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure.Thus,low and high frequency components are separated at each stage using DWT,suppressing structural noise and making linear objects more prominent.The developed design is supported by multi-component hybrid cost functions that simultaneously solve class imbalance,edge sharpness,structural integrity,and spatial regularity issues.Furthermore,high segmentation success has been achieved in producing sharp boundaries and continuous line structures with the DWT-guided attention mechanism.Experiments conducted on the TTPLA dataset reveal that the version using the ConvNeXt backbone outperforms the current state-of-the-art approaches with an F1-Score of 79.33%and an Intersection over Union(IoU)value of 68.38%.The models and visual outputs of the developed method and all compared models can be accessed at https://github.com/burhanbarakli/WGT-UNET.展开更多
Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task...Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task-driven two-stage(macro–micro)architecture that restructures the SOD process around superpixel representations.In the proposed approach,a“split-and-enhance”principle,introduced to our knowledge for the first time in the SOD literature,hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions.At the macro stage,the image is partitioned into content-adaptive superpixel regions,and each superpixel is represented by a high-dimensional region-level feature vector.These representations define a regional decomposition problem in which superpixels are assigned to three classes:background,object interior,and transition regions.Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context.At the micro stage,the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions.The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway.Subsequently,channel-aware fusion blocks adaptively combine information from these two sources,producing sharper and more stable object boundaries.Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods.On the PASCAL-S and DUT-OMRON datasets,SPSALNet exhibits a clear performance advantage across all key metrics,and it ranks first on accuracy-oriented measures on HKU-IS.On the challenging DUT-OMRON benchmark,SPSALNet reaches a MAE of 0.034.Across all datasets,it preserves object boundaries and regional structure in a stable and competitive manner.展开更多
Understanding the temperature dependent deformation behavior of Mg alloys is crucial for their expanding use in the aerospace sector.This study investigates the deformation mechanisms of hot-rolled AZ61 Mg alloy under...Understanding the temperature dependent deformation behavior of Mg alloys is crucial for their expanding use in the aerospace sector.This study investigates the deformation mechanisms of hot-rolled AZ61 Mg alloy under uniaxial tension along rolling direction(RD)and transverse direction(TD)at-50,25,50,and 150℃.Results reveal a transition from high strength with limited elongation at-50℃ to significant softening and maximum ductility at 150℃.TD samples consistently showed 2%-6%higher strength than RD;however,this yield anisotropy diminished at 150℃ due to the shift from twinning to thermally activated slip and recovery.Fractography indicated a change from semi-brittle to fully ductile fracture with increasing temperature.Electron backscattered diffraction(EBSD)analysis confirmed twinning-driven grain refinement at low temperatures,while deformation at high temperatures involved grain elongation along shear zones,enabling greater strain accommodation before material failure.展开更多
Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and comple...Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.展开更多
The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on e...The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.展开更多
The effect of temperature on molten zone length was investigated through simulation to optimize the control of molten zone length during the experimental process. The temperature gradient distribution within the molte...The effect of temperature on molten zone length was investigated through simulation to optimize the control of molten zone length during the experimental process. The temperature gradient distribution within the molten zone during zone refining was simulated using COMSOL Multiphysics software and experimentally validated. The simulated molten zone length showed good agreement with the actual measured length. The experimental study of tellurium purification by zone refining was conducted under the following conditions: three passes of zone refining, a hydrogen flow rate of 0.5 L/min, and molten zone movement speeds of 0.5 and 1.0 mm/min. The results demonstrated that the removal efficiencies of impurities such as Ca and Cu exceeded 95%, while the removal efficiency of phosphorus (P) reached over 70%. And the purity of tellurium reached 6N.展开更多
The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,...The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,building models from scratch is computationally expensive and requires large datasets.This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image.The core idea is to fine-tune a pre-trained model on specific object categories using new,unseen data,resulting in specialized versions of the model that are better adapted to reconstruct particular objects.The proposed approach utilizes a three-phase pipeline comprising image acquisition,3D reconstruction,and refinement.After ensuring the quality of the input image,a ResNet50 model is used for object recognition,directing the image to the corresponding category-specific model to generate a voxel-based representation.The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing.An experimental study,using the Pix2Vox model and the Pascal3D dataset,has been conducted to evaluate and validate the effectiveness of the proposed approach.Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories,with substantial gains in Intersection over Union(IoU)scores.Visual assessments confirm improvements in geometric detail and surface realism.These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.展开更多
Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although la...Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.展开更多
Fang Chuxiong’s works combine the meticulousness of fine brushwork with the elegance of freehand brushstrokes,making him a distinct figure in the contemporary Chinese art circle.Fang Chuxiong is a representative figu...Fang Chuxiong’s works combine the meticulousness of fine brushwork with the elegance of freehand brushstrokes,making him a distinct figure in the contemporary Chinese art circle.Fang Chuxiong is a representative figure in the field of contemporary Chinese flower-and-bird painting.His artwork masterfully integrates the elements of nature,the charm of living creatures,the subtleties of brushwork,and the vitality of color,exhibiting refined taste and a vibrant style.展开更多
Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine.Here,we provide a detailed atlas of 2,920 plasma proteins linking to diseases(406 prevalent and 660 incid...Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine.Here,we provide a detailed atlas of 2,920 plasma proteins linking to diseases(406 prevalent and 660 incident)and 986 health-related traits in 53,026 individuals(median follow-up:14.8 years)from the UK Biobank,representing the most comprehensive proteome profiles to date.This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations.展开更多
The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is develop...The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is developed.In this representation,an expansion center is adopted within the neighborhood of the sources.Then a high-frequency electromagnetic scattering evaluation algorithm is formulated,combining the refined physical optics(PO)and equivalent edge current(EEC)algorithm.The modified method not only retains the conciseness and efficiency of the standard code but also can be directly used in the near field(NF)scattering estimation.Afterwards,two basic concepts of the angular glint are briefly introduced and formulated.The proposed procedure makes preparation for the computation of NF linear deviation.Numerical examples demonstrate the accuracy and efficiency of the NF scattering prediction algorithm.The angular glint characteristics in near-field scenarios are also presented and analyzed in the final section.展开更多
Laser remelting(LR)was used as an auxiliary post-treatment process for the Ti6Al4V titanium alloys fabricated by laser powder bed fusion(LPBF).Optical microscope(OM),scanning electron microscope(SEM)and electron back ...Laser remelting(LR)was used as an auxiliary post-treatment process for the Ti6Al4V titanium alloys fabricated by laser powder bed fusion(LPBF).Optical microscope(OM),scanning electron microscope(SEM)and electron back scattering diffraction(EBSD)observations showed that the grains in melted zone(MZ)transformed into equiaxial grains with an average size of 1.31μm,and the grains in heat affected zone(HAZ)were refined.Moreover,the texture intensity dropped significantly from 13.86 to 6.35 in MZ and 10.79 in HAZ.The temperature gradient(G)to solidification rate(R)ratio decreased when the laser scanning speed slowed down to a certain extent in the LR process,which effectively improved the highly preferred orientation and filled the hole defects in the surface of LPBF-Ti6Al4V.Furthermore,the hardness,wear resistance and corrosion resistance of the surface of the LPBF samples were improved by LR treatment.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.:71173164)the National Key Technology R&D Program of the Ministry of Science and Technology of China(GrantNo.:2012BAH33F03)
文摘Purpose:In this paper,we attempt to use query refinements to identify users' search intents and seek a method for intent clustering based on real world query data.Design/methodology/approach:An experiment has been conducted to analyze selected search sessions from the American Online(AOL) query logs with a two-stage approach.The first stage is to identify underlying intent by combining query co-occurrence information with query expression similarity.The work in the second stage is to cluster identified results by constructing query vectors through performing random walks on a Markov graph.Findings:Average correctness for identifying search intent is 0.74.Precision,recall,F-score values for intent clustering are 0.73,0.72 and 0.71,respectively.The results indicate that combining session co-occurrence information and query expression similarity can further filter noises and our clustering method is more suitable for sparse data.Research limitations:We use the time-out threshold(15-minutc) method to group queries in one session,but a user may have multiple search goals at the same time and the multi-task behavior of a user is hard to capture in a session defined based on time notions.Practical implications:This study provides insights into the ways of understanding users' search intents by analyzing their queries and refinements from a new perspective.The results will help search engine developers to identify user intents.Originality/value:We propose a new method to identify users' search intents by combining session co-occurrence information and query expression similarity,and a new method for clustering sparse data.
基金National Key Research and Development Program of China(2021YFB3700801)。
文摘Low-density short-duration pulsed current-assisted aging treatment was applied to the Ti-6Al-4V-0.5Mo-0.5Zr alloy subjected to different solution treatments.The results show that numerous α_(p) phases redissolve into the new β phase during the pulsed current-assisted aging process,and then the newly formed β phase is mainly transformed into the β_(t) phase,with occasional transition to new α_(p) phase,leading to a remarkable grain refinement,especially for the lamellarαs phases.In comparison to conventional aging treatment,the pulsed current-assisted aging approach achieves a significant enhancement in strength without degrading ductility,yielding an excellent mechanical property combination:a yield strength of 932 MPa,a tensile strength of 1042 MPa,and an elongation of 12.2%.It is primarily ascribed to the increased fraction of β_(t) phases,the obvious grain refinement effect,and the slip block effect induced by the multiple-variantαs colonies distributed within β_(t) phases.
基金National Natural Science Foundation of China(52071065)Fundamental Research Funds for the Central Universities(N2007007)+2 种基金Joint Fund of Henan Province Science and Technology R&D Program(N225200810040)High-Level Talent Research Start-Up Project Funding of Henan Academy of Sciences(N242017003)Liaoning Provincial Department of Education Basic Research Projects for Colleges and Universities(LJ212410142093)。
文摘The effect of trace addition of 0.1wt%Y on the grain refinement and mechanical properties of Al-2.2Li-1.5Cu-0.5Mg-1Zn-0.2Zr-0.2Sc alloys at as-cast and heat-treated states was investigated.Results show that the addition of 0.1wt%Y into the Al-2.2Li-1.5Cu-0.5Mg-1Zn-0.2Zr-0.2Sc alloys can elevate the nucleation temperature of the Al_(3)(Sc,Zr)phase,leading to the preferential precipitation of the Al_(3)(Sc,Zr)phase and increasing the amount of Al_(3)(Sc,Zr)phase in the matrix.Al_(3)(Sc,Zr)phase can also act as a heterogeneous nucleation site in theα-Al matrix to promote nucleation and refine grains.The addition of element Y changes the precipitation phase characteristics at the grain boundaries in the as-cast alloy,which changes the distribution characteristics of secondary phases from initially continuous and coarse strip-like distribution at grain boundaries into the discontinuous dot-like and rod-like distribution.Besides,the size of secondary phases becomes smaller and their amount increases.Under the combined effects of grain refinement strengthening and precipitation strengthening,the Al-2.2Li-1.5Cu-0.5Mg-1Zn-0.2Zr-0.2Sc-0.1Y alloy after 175℃/10 h aging treatment achieves an ultimate tensile strength of 412 MPa and an elongation of 6.3%.Compared with those of the alloy without Y addition,the ultimate tensile strength and elongation of the added alloy increase by 16.1%and 53.7%,respectively.
文摘Refinements to inequalities on inner product spaces are presented. In this respect, inequali-ties dealt with in this paper are: Cauchy’s inequality, Bessel’s inequality, Fan-Todd’s inequality and Fan-Todd’s determinantal inequality. In each case, a strictly increasing function is put for-ward, which lies between the smaller and the larger quantities of each inequality. As a result. an improved condition for equality of the Fan-Todd’s determinantal inequality is deduced.
文摘At the start of the new year,Cao Xiucheng,Chairman of Henan No.2 Textile Machinery Co.,Ltd.,was on his way to visit clients when he kept receiving urgent calls from the Xinyang production base regarding order scheduling.It turned out that since the end of 2025,the company had successively secured bulk spindle orders from overseas clients in Bangladesh and other countries,coupled with continuous urgent requests for orders from domestic manufacturers.Faced with such a production peak right at the beginning of the year,Mr.Cao Xiucheng admitted,“It was truly unexpected.”
基金Project(52501069)supported by the National Natural Science Foundation of ChinaProject(GZC20233172)supported by the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(CPSF)Project(21B0121)supported by Hunan Provincial Education Department,China。
文摘Hard tissue repair materials that balance high strength with low modulus are highly promising,representing a transformative focus in applied biomaterials research.In this study,Ti-Nb alloys with high performance are prepared by a low-cost process for orthopedic applications.Phase composition,modulus,compressive strength and recovery properties are effectively manipulated by tailoring trace amounts of interstitial oxygen.With increasing oxygen concentration in sintered Ti-Nb alloys,theβ(body centered cubic)phase was stabilized due to the lattice distortion.The elastic modulus declined from 91 to 24 GPa.The compressive strength slightly decreased from 1595 to 1404 MPa and yield strength increased from 760 to 904 MPa.Additionally,the recovery properties were enhanced by the interstitial oxygen as a shape memory alloy.The utilization of trace oxygen serves to modulate the thermoelastic martensitic transformation in Ti-Nb alloys,thereby obtaining appropriate mechanical properties.A notable reduction in modulus is achieved while maintaining high strength,which facilitates the development of orthopedic implants capable of withstanding more complex forces.
文摘Confucius’imminent birth is heralded by the appearance of the qilin.The mythical one-horned animal came to his mother at the door and cast out of its mouth a jade tablet bearing an inscription saying that she would give birth to“the son of the refinement of water,and that he would succeed the Zhou Dynasty,but as a king without a throne(su wang).”Stunned,Yan Zhengzai–Confucius’mother–tied an embroidered ribbon around the horn of the qilin,and the animal stayed for two nights.
基金supported by the National Natural Science Foundation of China(Nos.62572017,62441232,62206007)R&D Program of Beijing Municipal Education Commission(KZ202210005008).
文摘Knowledge-based VisualQuestion Answering(VQA)requires the integration of visual information with external knowledge reasoning.Existing approaches typically retrieve information from external corpora and rely on pretrained language models for reasoning.However,their performance is often hindered by the limited capabilities of retrievers and the constrained size of knowledge bases.Moreover,relying on image captions to bridge the modal gap between visual and language modalities can lead to the omission of critical visual details.To address these limitations,we propose the Reflective Chain-of-Thought(ReCoT)method,a simple yet effective framework inspired by metacognition theory.ReCoT effectively activates the reasoning capabilities ofMultimodal Large LanguageModels(MLLMs),providing essential visual and knowledge cues required to solve complex visual questions.It simulates a metacognitive reasoning process that encompasses monitoring,reflection,and correction.Specifically,in the initial generation stage,an MLLM produces a preliminary answer that serves as the model’s initial cognitive output.During the reflective reasoning stage,this answer is critically examined to generate a reflective rationale that integrates key visual evidence and relevant knowledge.In the final refinement stage,a smaller language model leverages this rationale to revise the initial prediction,resulting in amore accurate final answer.By harnessing the strengths ofMLLMs in visual and knowledge grounding,ReCoT enables smaller language models to reason effectively without dependence on image captions or external knowledge bases.Experimental results demonstrate that ReCoT achieves substantial performance improvements,outperforming state-of-the-art methods by 2.26%on OK-VQA and 5.8%on A-OKVQA.
文摘Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challenging.This study presents the Wavelet-Guided Transformer U-Net(WGT-UNet)model,a new hybrid net-work that combines Convolutional Neural Networks(CNNs),Discrete Wavelet Transform(DWT),and Transformer architectures.The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure.Thus,low and high frequency components are separated at each stage using DWT,suppressing structural noise and making linear objects more prominent.The developed design is supported by multi-component hybrid cost functions that simultaneously solve class imbalance,edge sharpness,structural integrity,and spatial regularity issues.Furthermore,high segmentation success has been achieved in producing sharp boundaries and continuous line structures with the DWT-guided attention mechanism.Experiments conducted on the TTPLA dataset reveal that the version using the ConvNeXt backbone outperforms the current state-of-the-art approaches with an F1-Score of 79.33%and an Intersection over Union(IoU)value of 68.38%.The models and visual outputs of the developed method and all compared models can be accessed at https://github.com/burhanbarakli/WGT-UNET.
文摘Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task-driven two-stage(macro–micro)architecture that restructures the SOD process around superpixel representations.In the proposed approach,a“split-and-enhance”principle,introduced to our knowledge for the first time in the SOD literature,hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions.At the macro stage,the image is partitioned into content-adaptive superpixel regions,and each superpixel is represented by a high-dimensional region-level feature vector.These representations define a regional decomposition problem in which superpixels are assigned to three classes:background,object interior,and transition regions.Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context.At the micro stage,the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions.The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway.Subsequently,channel-aware fusion blocks adaptively combine information from these two sources,producing sharper and more stable object boundaries.Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods.On the PASCAL-S and DUT-OMRON datasets,SPSALNet exhibits a clear performance advantage across all key metrics,and it ranks first on accuracy-oriented measures on HKU-IS.On the challenging DUT-OMRON benchmark,SPSALNet reaches a MAE of 0.034.Across all datasets,it preserves object boundaries and regional structure in a stable and competitive manner.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea Program(No.RS-2025-02603127,Innovation Research Center for Zero-carbon Fuel Gas Turbine Design,Manufacture,and Safety)。
文摘Understanding the temperature dependent deformation behavior of Mg alloys is crucial for their expanding use in the aerospace sector.This study investigates the deformation mechanisms of hot-rolled AZ61 Mg alloy under uniaxial tension along rolling direction(RD)and transverse direction(TD)at-50,25,50,and 150℃.Results reveal a transition from high strength with limited elongation at-50℃ to significant softening and maximum ductility at 150℃.TD samples consistently showed 2%-6%higher strength than RD;however,this yield anisotropy diminished at 150℃ due to the shift from twinning to thermally activated slip and recovery.Fractography indicated a change from semi-brittle to fully ductile fracture with increasing temperature.Electron backscattered diffraction(EBSD)analysis confirmed twinning-driven grain refinement at low temperatures,while deformation at high temperatures involved grain elongation along shear zones,enabling greater strain accommodation before material failure.
基金supported by the National Natural Science Foundation of China(No.62203163)the Scientific Research Project of Hunan Provincial Education Department(No.24A0519)+1 种基金the Hunan Provincial Natural Science Foundation(No.2025JJ60407)the Postgraduate Scientific Research Innovation Project of Hunan Province(No.CX2024100).
文摘Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.
基金supported by the National Key Research and Development Program of China(No.2023YFB3712401),the National Natural Science Foundation of China(No.52274301)the Aeronautical Science Foundation of China(No.2023Z0530S6005)the Ningbo Yongjiang Talent-Introduction Programme(No.2022A-023-C).
文摘The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.
基金financial support from the National Key Research and Development Program of China(No.2023YFC2907904)the National Natural Science Foundation of China(Nos.52374364,52104355,52074363)+1 种基金National Sustainable Development Agenda Innovation Demonstration Zones:Provincial Special“Open Competition”Project in Chenzhou,China(No.2022sfq57)Postdoctoral Innovation Talent Support Program,China(No.BX20230438)。
文摘The effect of temperature on molten zone length was investigated through simulation to optimize the control of molten zone length during the experimental process. The temperature gradient distribution within the molten zone during zone refining was simulated using COMSOL Multiphysics software and experimentally validated. The simulated molten zone length showed good agreement with the actual measured length. The experimental study of tellurium purification by zone refining was conducted under the following conditions: three passes of zone refining, a hydrogen flow rate of 0.5 L/min, and molten zone movement speeds of 0.5 and 1.0 mm/min. The results demonstrated that the removal efficiencies of impurities such as Ca and Cu exceeded 95%, while the removal efficiency of phosphorus (P) reached over 70%. And the purity of tellurium reached 6N.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia—under supervision Energy,Industry,and Advanced Technologies Research Center,Taibah University,Madinah,Saudi Arabia with grant number(12979-iau-2023-TAU-R-3-1-EI-).
文摘The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,building models from scratch is computationally expensive and requires large datasets.This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image.The core idea is to fine-tune a pre-trained model on specific object categories using new,unseen data,resulting in specialized versions of the model that are better adapted to reconstruct particular objects.The proposed approach utilizes a three-phase pipeline comprising image acquisition,3D reconstruction,and refinement.After ensuring the quality of the input image,a ResNet50 model is used for object recognition,directing the image to the corresponding category-specific model to generate a voxel-based representation.The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing.An experimental study,using the Pix2Vox model and the Pascal3D dataset,has been conducted to evaluate and validate the effectiveness of the proposed approach.Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories,with substantial gains in Intersection over Union(IoU)scores.Visual assessments confirm improvements in geometric detail and surface realism.These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.
基金supported by the Key Program of National Natural Science Foundation of China(Grant No.41930650)Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42301310).
文摘Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.
文摘Fang Chuxiong’s works combine the meticulousness of fine brushwork with the elegance of freehand brushstrokes,making him a distinct figure in the contemporary Chinese art circle.Fang Chuxiong is a representative figure in the field of contemporary Chinese flower-and-bird painting.His artwork masterfully integrates the elements of nature,the charm of living creatures,the subtleties of brushwork,and the vitality of color,exhibiting refined taste and a vibrant style.
文摘Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine.Here,we provide a detailed atlas of 2,920 plasma proteins linking to diseases(406 prevalent and 660 incident)and 986 health-related traits in 53,026 individuals(median follow-up:14.8 years)from the UK Biobank,representing the most comprehensive proteome profiles to date.This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations.
文摘The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is developed.In this representation,an expansion center is adopted within the neighborhood of the sources.Then a high-frequency electromagnetic scattering evaluation algorithm is formulated,combining the refined physical optics(PO)and equivalent edge current(EEC)algorithm.The modified method not only retains the conciseness and efficiency of the standard code but also can be directly used in the near field(NF)scattering estimation.Afterwards,two basic concepts of the angular glint are briefly introduced and formulated.The proposed procedure makes preparation for the computation of NF linear deviation.Numerical examples demonstrate the accuracy and efficiency of the NF scattering prediction algorithm.The angular glint characteristics in near-field scenarios are also presented and analyzed in the final section.
基金supported by the National Natural Science Foundation of China(No.51871243)the National Key Laboratory of Strength and Structural Integrity,China(No.ASSIKFJJ202304001)+3 种基金the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China(No.PBSKL2022C01)the Guangdong-Hong Kong-Macao Joint Laboratory for Neutron Scattering Science and Technology,China(No.HT-CSNS-DG-CD-0092/2021)the Shock and Vibration of Engineering Materials and Structures Key Laboratory of Sichuan Province,China(No.22kfgk06)the Hubei Longzhong Laboratory,China(No.2022KF-08)。
文摘Laser remelting(LR)was used as an auxiliary post-treatment process for the Ti6Al4V titanium alloys fabricated by laser powder bed fusion(LPBF).Optical microscope(OM),scanning electron microscope(SEM)and electron back scattering diffraction(EBSD)observations showed that the grains in melted zone(MZ)transformed into equiaxial grains with an average size of 1.31μm,and the grains in heat affected zone(HAZ)were refined.Moreover,the texture intensity dropped significantly from 13.86 to 6.35 in MZ and 10.79 in HAZ.The temperature gradient(G)to solidification rate(R)ratio decreased when the laser scanning speed slowed down to a certain extent in the LR process,which effectively improved the highly preferred orientation and filled the hole defects in the surface of LPBF-Ti6Al4V.Furthermore,the hardness,wear resistance and corrosion resistance of the surface of the LPBF samples were improved by LR treatment.
