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
Using multi-directional forging temperature as the independent variable and adopting the dual-mode phase field crystal model,the nucleation modes,reaction mechanisms,and interactions between grain boundaries and dislo...Using multi-directional forging temperature as the independent variable and adopting the dual-mode phase field crystal model,the nucleation modes,reaction mechanisms,and interactions between grain boundaries and dislocations at different temperatures were investigated.Results show that a mapping relationship between process parameters and grain refinement/coarsening is established,and the optimal processing temperature coefficient is 0.23.Compared with the cases with processing temperature coefficient of 0.19,0.20,0.21,0.25,and 0.27,the refinement effect increases by 256.0%,146.0%,113.0%,6.7%,and 52.4%,respectively.Excessively high temperatures lead to grain coarsening due to dislocation annihilation,and the application of strain can reduce the actual melting point of materials.Even if the processing temperature does not exceed the theoretical melting point,remelting and crystallization may still occur,resulting in an overburning phenomenon that reduces internal defects and increases overall grain size.This research is of great significance for the actual forging process design.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
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
A deep-undercooling rapid-solidification technique combining cyclic superheating and molten glass purification was employed to successfully prepare Cu60Ni40 and Cu65Ni35 alloys at various undercooling levels.Furthermo...A deep-undercooling rapid-solidification technique combining cyclic superheating and molten glass purification was employed to successfully prepare Cu60Ni40 and Cu65Ni35 alloys at various undercooling levels.Furthermore,through precise compositional regulation by adjusting the Cu content and introducing Co,the Cu60Ni35Co5 alloy was obtained.The morphological evolution of the solidification front and the variation in solidification rate with undercooling were systematically investigated.By combining metallographic analysis,the BCT model,electron backscatter diffraction(EBSD),and transmission electron microscopy(TEM),the microstructural evolution and grain refinement mechanisms of the undercooled alloys were revealed.This work aims to establish the intrinsic relationship among undercooling,solidification behavior,and microstructure,thereby provides both experimental and theoretical foundations for a deeper understanding of the deep undercooling solidification mechanism and microstructural control.展开更多
The influences of silicon addition to commercially pure magnesium(CP Mg)and cooling rate during solidification on the as-cast microstructure and shear mechanical properties of Mg-Si alloys were systematically investig...The influences of silicon addition to commercially pure magnesium(CP Mg)and cooling rate during solidification on the as-cast microstructure and shear mechanical properties of Mg-Si alloys were systematically investigated.For this purpose,the Mg-0.6Si,Mg-1.34Si,and Mg-3Si(wt%)alloys were considered as hypoeutectic,eutectic,and hypereutectic alloys,respectively.By decreasing the geometrical modulus of the solidifying section(increasing cooling rate),remarkable grain refinement,refining the dendrite arm spacing(DAS),and modification of Mg_(2)Si particles were achieved.Moreover,the grain size was refined via Si addition in the hypoeutectic range,while coarsening of grain size at high Si concentrations was observed.The results of shear punch testing and hardness measurements demonstrated that the ultimate shear strength(USS)and hardness increased by increasing the cooling rate during solidification.Moreover,Si addition generally improved hardness,while the highest USS level was achieved for the eutectic alloy due to the fine grain size and strengthening effect of the eutectic constituent.However,regarding the hypereutectic Mg-3Si alloy that exhibited high hardness,the shear properties were inferior due to the detrimental effect of the primary Mg_(2)Si particles.Finally,the results were discussed with consideration of the relationship between strength and hardness,for which the critical effect of Si was clarified.展开更多
Nickel-rich cathodes(NRCs)hold great promise for next-generation high-energy lithium-ion batteries(LIBs)due to high specific energy and low cost.However,the higher Ni content exacerbates the instability issues associa...Nickel-rich cathodes(NRCs)hold great promise for next-generation high-energy lithium-ion batteries(LIBs)due to high specific energy and low cost.However,the higher Ni content exacerbates the instability issues associated with structural degradation and side reactions during electrochemical cycling.Herein,we demonstrate the possibility of preparing NRCs,typically Li Ni_(0.9)Co_(0.05)Mn_(0.05)O_(2)(NCM9055),with much-improved mechanical and chemical stability based on the surface coating of the hydroxide precursors.Specifically,a conformal nanoshell containing both Al^(3+)and W^(6+)was first deposited around the precursor particles,and the following high-temperature lithiation produced the targeted NCM9055 with favorable structural features,where Al3+existed as a bulk dopant to enhance the structural stability while the high-valent W^(6+)promoted the microstructural evolution into radially-architectured elongated primary particles.Such a structural engineering benefiting from the Al^(3+)/W^(6+)co-modification endowed the prepared NCM9055 cathode(NCM9055-Al W)with much-improved cycling stability,as revealed by a high-capacity retention of 98.0%after 100 cycles(tested at 0.5 C,4.3 V)as compared to only 79.0%for the pristine cathode without Al^(3+)/W^(6+).The NCM9055-15Al W cathode also showed a high-rate capability with extraordinary structural stability against mechanical failure.Our study highlighted the enormous potential of precursor multi-element treatment as an effective tool in structural refinement of NRCs to circumvent their stability challenge for their applications in high-energy LIBs.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
We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interp...We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interpretations in this evolving field.Given that much research time and financial investment is being given to the study of the effects of eccentric training in both athletic and clinical contexts,it is incumbent on our field to demonstrate whether eccentric contractions are a key(or the key)stimulus for sarcomerogenesis(increases in serial sarcomere number(SSN)).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
A 3D mathematical model was established to investigate the gas-liquid two-phase flow in Ruhrstahl-Heraeus(RH)vacuum refining process.The flow characteristics of molten steel were calculated using the coupled standard...A 3D mathematical model was established to investigate the gas-liquid two-phase flow in Ruhrstahl-Heraeus(RH)vacuum refining process.The flow characteristics of molten steel were calculated using the coupled standard k-εmodel and volume of fluid model.The bubble distribution was tracked by discrete phase model.Electromagnetic field was applied in the up-leg snorkel to enhance the effect of vacuum refining.The effect of swirling flow nozzles combined with electromagnetic stirring(EMS)on the flow characteristics of molten steel and bubble distribution was analyzed.The erosion of the up-leg snorkel was compared.The results show that when the swirling flow nozzles are used,the bubbles exhibit a distinct adherent rising behavior,and the refining efficiency decreases.In addition,the electromagnetic field can significantly improve the refining efficiency,but it brings stronger erosion to the up-leg snorkel.Nevertheless,when using the swirling flow nozzles combined with EMS,the refining performance is further optimized,and the erosion of the up-leg snorkel is also reduced due to its characteristic of bubble distribution.Compared to conventional nozzles,the mixing time was shortened by 16.2%,the recirculation rate increased by 12.5%.and the swirling intensity was strengthened by 8.9%.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 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 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
Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made re...Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.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
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(52375394,52275390,U23A20628,52305429)Major Project of Science and Technology in Shanxi(202301050201004)Natural Science Foundation of Shanxi Province(202403021222132)。
文摘Using multi-directional forging temperature as the independent variable and adopting the dual-mode phase field crystal model,the nucleation modes,reaction mechanisms,and interactions between grain boundaries and dislocations at different temperatures were investigated.Results show that a mapping relationship between process parameters and grain refinement/coarsening is established,and the optimal processing temperature coefficient is 0.23.Compared with the cases with processing temperature coefficient of 0.19,0.20,0.21,0.25,and 0.27,the refinement effect increases by 256.0%,146.0%,113.0%,6.7%,and 52.4%,respectively.Excessively high temperatures lead to grain coarsening due to dislocation annihilation,and the application of strain can reduce the actual melting point of materials.Even if the processing temperature does not exceed the theoretical melting point,remelting and crystallization may still occur,resulting in an overburning phenomenon that reduces internal defects and increases overall grain size.This research is of great significance for the actual forging process design.
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Funded by the Central Government-Guided Local Development Fund Project(No.YDZJSX2025D042)the Key R&D Program of Shanxi Province(No.202202150401018)+1 种基金the Basic Research Program of Shanxi Province(No.202503021211112)the State Key Laboratory of CAD/CG of Zhejiang University(No.A2325)。
文摘A deep-undercooling rapid-solidification technique combining cyclic superheating and molten glass purification was employed to successfully prepare Cu60Ni40 and Cu65Ni35 alloys at various undercooling levels.Furthermore,through precise compositional regulation by adjusting the Cu content and introducing Co,the Cu60Ni35Co5 alloy was obtained.The morphological evolution of the solidification front and the variation in solidification rate with undercooling were systematically investigated.By combining metallographic analysis,the BCT model,electron backscatter diffraction(EBSD),and transmission electron microscopy(TEM),the microstructural evolution and grain refinement mechanisms of the undercooled alloys were revealed.This work aims to establish the intrinsic relationship among undercooling,solidification behavior,and microstructure,thereby provides both experimental and theoretical foundations for a deeper understanding of the deep undercooling solidification mechanism and microstructural control.
文摘The influences of silicon addition to commercially pure magnesium(CP Mg)and cooling rate during solidification on the as-cast microstructure and shear mechanical properties of Mg-Si alloys were systematically investigated.For this purpose,the Mg-0.6Si,Mg-1.34Si,and Mg-3Si(wt%)alloys were considered as hypoeutectic,eutectic,and hypereutectic alloys,respectively.By decreasing the geometrical modulus of the solidifying section(increasing cooling rate),remarkable grain refinement,refining the dendrite arm spacing(DAS),and modification of Mg_(2)Si particles were achieved.Moreover,the grain size was refined via Si addition in the hypoeutectic range,while coarsening of grain size at high Si concentrations was observed.The results of shear punch testing and hardness measurements demonstrated that the ultimate shear strength(USS)and hardness increased by increasing the cooling rate during solidification.Moreover,Si addition generally improved hardness,while the highest USS level was achieved for the eutectic alloy due to the fine grain size and strengthening effect of the eutectic constituent.However,regarding the hypereutectic Mg-3Si alloy that exhibited high hardness,the shear properties were inferior due to the detrimental effect of the primary Mg_(2)Si particles.Finally,the results were discussed with consideration of the relationship between strength and hardness,for which the critical effect of Si was clarified.
基金supported by the National Key R&D Program of China(Grant No.2022YFB2404402)the National Natural Science Foundation of China(Grant Nos.22025507,22421001,and 22409200)+1 种基金the Strategic Priority Research Program of the Chinese Academy of SciencesGrant No.XDB 1040200。
文摘Nickel-rich cathodes(NRCs)hold great promise for next-generation high-energy lithium-ion batteries(LIBs)due to high specific energy and low cost.However,the higher Ni content exacerbates the instability issues associated with structural degradation and side reactions during electrochemical cycling.Herein,we demonstrate the possibility of preparing NRCs,typically Li Ni_(0.9)Co_(0.05)Mn_(0.05)O_(2)(NCM9055),with much-improved mechanical and chemical stability based on the surface coating of the hydroxide precursors.Specifically,a conformal nanoshell containing both Al^(3+)and W^(6+)was first deposited around the precursor particles,and the following high-temperature lithiation produced the targeted NCM9055 with favorable structural features,where Al3+existed as a bulk dopant to enhance the structural stability while the high-valent W^(6+)promoted the microstructural evolution into radially-architectured elongated primary particles.Such a structural engineering benefiting from the Al^(3+)/W^(6+)co-modification endowed the prepared NCM9055 cathode(NCM9055-Al W)with much-improved cycling stability,as revealed by a high-capacity retention of 98.0%after 100 cycles(tested at 0.5 C,4.3 V)as compared to only 79.0%for the pristine cathode without Al^(3+)/W^(6+).The NCM9055-15Al W cathode also showed a high-rate capability with extraordinary structural stability against mechanical failure.Our study highlighted the enormous potential of precursor multi-element treatment as an effective tool in structural refinement of NRCs to circumvent their stability challenge for their applications in high-energy LIBs.
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We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interpretations in this evolving field.Given that much research time and financial investment is being given to the study of the effects of eccentric training in both athletic and clinical contexts,it is incumbent on our field to demonstrate whether eccentric contractions are a key(or the key)stimulus for sarcomerogenesis(increases in serial sarcomere number(SSN)).
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support from the National Natural Science Foundation of China(No.52174305).
文摘A 3D mathematical model was established to investigate the gas-liquid two-phase flow in Ruhrstahl-Heraeus(RH)vacuum refining process.The flow characteristics of molten steel were calculated using the coupled standard k-εmodel and volume of fluid model.The bubble distribution was tracked by discrete phase model.Electromagnetic field was applied in the up-leg snorkel to enhance the effect of vacuum refining.The effect of swirling flow nozzles combined with electromagnetic stirring(EMS)on the flow characteristics of molten steel and bubble distribution was analyzed.The erosion of the up-leg snorkel was compared.The results show that when the swirling flow nozzles are used,the bubbles exhibit a distinct adherent rising behavior,and the refining efficiency decreases.In addition,the electromagnetic field can significantly improve the refining efficiency,but it brings stronger erosion to the up-leg snorkel.Nevertheless,when using the swirling flow nozzles combined with EMS,the refining performance is further optimized,and the erosion of the up-leg snorkel is also reduced due to its characteristic of bubble distribution.Compared to conventional nozzles,the mixing time was shortened by 16.2%,the recirculation rate increased by 12.5%.and the swirling intensity was strengthened by 8.9%.
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supported by the National Key Research and Development of China(No.2022YFB2503400).
文摘Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.
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.
