Bit-field separation is an important part of gravity and magnetic data processing.In order to extract different levels of anomaly information better,this paper introduces the dual-tree complex wavelet multi-scale sepa...Bit-field separation is an important part of gravity and magnetic data processing.In order to extract different levels of anomaly information better,this paper introduces the dual-tree complex wavelet multi-scale separation to the processing of bit-field data firstly and uses the geological model of different buried depth to ve-rify its feasibility.Finally,the dual-tree complex wavelet is applied to the aeromagnetic anomaly in Jinchuan copper nickel mining area.The results show that the method can effectively separate the anomaly information of different scales and analyze the output results with relevant geological data.展开更多
Micro-and nano-to millimeter-scale magnetic matrix materials have gained widespread application due to their exceptional magnetic properties and favorable cost-effectiveness.With the rapid progress in condensed matter...Micro-and nano-to millimeter-scale magnetic matrix materials have gained widespread application due to their exceptional magnetic properties and favorable cost-effectiveness.With the rapid progress in condensed matter physics,materials science,and mineral separation technologies,these materials are now poised for new opportunities in theoretical research and development.This review provides a comprehensive analysis of these matrices,encompassing their structure,size,shape,composition,properties,and multifaceted applications.These materials,primarily composed of alloys of transition state metasl such as iron(Fe),cobalt(Co),titanium(Ti),and nickel(Ni),exhibit unique attributes like high magnetization rates,low eleastic modulus,and high saturation magnetic field strengths.Furthermore,the studies also delve into the complex mechanical interactions involved in the separation of magnetic particles using magnetic separator matrices,including magnetic,gravitational,centrifugal,and van der Waals forces.The review outlines how size and shape effects influence the magnetic behavior of matrices,offering new perspectives for innovative applications of magnetic matrices in various domains of materials science and magnetic separation.展开更多
In this paper, through a multi-scale separation of the aeromagnetic anomaly by wavelet transform technique, we reprocessed the aeromagnetic data collected 20 years ago in Beijing area and analyzed the aeromagnetic ano...In this paper, through a multi-scale separation of the aeromagnetic anomaly by wavelet transform technique, we reprocessed the aeromagnetic data collected 20 years ago in Beijing area and analyzed the aeromagnetic anomaly qualitatively, integrating geological structure features in the area. In particular, we studied the spatial distributions of the two main faults called Shunyi-Liangxiang fault and Banqiao-Babaoshan-Tongxian fault, which have cut and gone through the central Beijing area striking in NE and EW directions, respectively. The influences of these two faults on the earthquakes have also been discussed briefly.展开更多
The separation of propylene(C_(3)H_(6))and propane(C_(3)H_(8))presents a significant industrial challenge due to their similar molecular dimensions and physicochemical properties.Among various separation methods,molec...The separation of propylene(C_(3)H_(6))and propane(C_(3)H_(8))presents a significant industrial challenge due to their similar molecular dimensions and physicochemical properties.Among various separation methods,molecular sieving emerges as the most promising approach,but it will be significantly compromised at high temperatures due to the significant thermal motion.Here,we report a thermally robust zinc-based metal-organic framework(MOF)that can be synthesized on sub-kilogram scale and achieve exceptional C_(3)H_(6)/C_(3)H_(8) separation performances across a broad temperature range(298–353 K).Unlike conventional MOFs suffering from thermal lattice expansion to give poorer selectivity,this new MOF gives the adsorption capacity of C_(3)H_(6)essentially unchanged and that of C_(3)H_(8) negligible at elevated temperatures,outperforming most state-of-the-art adsorbents,in virtue of multiple hydrogen bonds at the aperture.Column breakthrough experiments confirmed the excellent separation capability,and showed no performance degradation over multi-round adsorption-desorption cycles at 353 K.This study addresses the critical challenge of the trade-off between temperature and selectivity in adsorptive separation,which offers new insights into the design of porous structures for highly effective separation at high temperatures.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun...Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric n...Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and dispos...The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.展开更多
This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow con...This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Developing advanced polymeric materials with enhanced mechanical properties and functionalities has been a long-standing goal in materials science.Recently,supramolecular polymeric materials(SPMs)have drawn increased ...Developing advanced polymeric materials with enhanced mechanical properties and functionalities has been a long-standing goal in materials science.Recently,supramolecular polymeric materials(SPMs)have drawn increased attention due to their unique properties and potential applications in self-healing,shape memory,sensors,and flexible electronics.Here,we develop an ionic cluster-optimized microphase separation strategy to enhance the toughening and energy dissipation capabilities of polydisulfide-based supramolecular polymers.The mechanical properties,including Young's modulus and toughness,are significantly improved by integrating the quadruple H-bonding 2-ureido-4-pyrimidone(UPy)induced microphase separation with iron(III)-to-carboxylate ionic clusters.By combining established chemical approaches with adjustable polymer phase ratios,it is revealed that the synergistic effect of these factors expands the interchain spacing,facilitates the formation of microphase domains,and enhances the tolerance of polythioctic acid-based polymers to external mechanical and thermal stimuli,meeting the practical requirements for industrial plastic applications.Moreover,the UPy-functionalized polymers incorporating iron carboxylate clusters exhibit good one-way shape memory behavior with practical applicability at a relatively low recovery temperature.Our work demonstrates a novel strategy for constructing industrially viable shape memory dynamic SPMs and paves the way for future innovations in developing SPMs.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
The removal of trace plutonium(Pu)from uranium products and organic wastes during spent nuclear fuel reprocessing remains a critical challenge,resulting in excessive plutonium content in uranium products and waste org...The removal of trace plutonium(Pu)from uranium products and organic wastes during spent nuclear fuel reprocessing remains a critical challenge,resulting in excessive plutonium content in uranium products and waste organic liquid.Currently,most organic ligands with selective separation functions are lipophilic,while research on water-soluble,highly selective ligands is relatively scarce,and there are also few reports on the single crystal of these ligands coordinating with plutonium.Herein,a hydrophilic multiamide ligand,N,N,N′,N″,N″-hexaethyl-nitrilotriacetamide(NTAamideC2),was synthesized and evaluated for its Pu(Ⅳ)back-extraction efficiency under harsh conditions.Systematic experiments revealed that NTAamideC2 achieved>99%Pu(Ⅳ)back-extraction rate within 15 min across a wide nitric acid concentration range(0-5 M),even with elevated dibutyl phosphate(DBP≤20000 ppm).Remarkably,the separation factor(SFPu/U)reached 767 at 1.5 M HNO_(3),demonstrating exceptional selectivity over uranium(Ⅵ).Spectrophotometric titration and DFT calculations confirmed the formation of 1:1 and 1:2 Pu(Ⅳ)-NTAamideC2 complexes,with log β values of 7.42±0.01 and 13.23±0.02,respectively.Single-crystal X-ray diffraction analysis of{[Pu_(2)(H_(2)O)_(2)(NTAamideC2)_(4)](H_(2)O)_(2)(NO_(3))(ClO_(4))_(7)}revealed a nine-coordinated PuO_(7)N_(2)geometry,where two NTAamideC2 molecules bind via six O and two N atoms.Compared to conventional agents(AHA/HSC),NTAamideC2 exhibited superior acid tolerance and selectivity,aligning with the CHON principle for sustainable nuclear waste management.This work provides a robust strategy for Pu(Ⅳ)removal in uranium purification cycles and advances fundamental insights into Pu coordination chemistry,offering significant potential for industrial nuclear fuel reprocessing.展开更多
Objective:To analyze the impact of maternal-infant separation on the physical and mental state of high-risk pregnancy patients and explore the clinical efficacy of targeted nursing interventions.Methods:A total of 80 ...Objective:To analyze the impact of maternal-infant separation on the physical and mental state of high-risk pregnancy patients and explore the clinical efficacy of targeted nursing interventions.Methods:A total of 80 high-risk pregnancy patients treated in our hospital from January 2023 to January 2024 were selected as the study subjects.These patients were randomly divided into an observation group and a control group(40 cases each)using a random number table.The control group received routine high-risk pregnancy nursing care,while the observation group received specialized maternal-infant separation nursing interventions in addition to routine care.The psychological and physiological states and nursing satisfaction of the two groups were compared before and after the intervention.Results:The SAS scores,SDS scores,and sleep quality scores of the observation group were significantly lower than those of the control group,with statistically significant differences(p<0.05).The incidence of postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the initiation time of lactation was significantly earlier than that in the control group,with both differences being statistically significant(p<0.05).The nursing satisfaction of the observation group was significantly higher than that of the control group(80%vs.32/40),with a statistically significant difference(p<0.05).Conclusion:Maternal-infant separation exacerbates anxiety and depression in high-risk pregnancy patients,reduces sleep quality,increases the risk of postpartum hemorrhage,and delays the initiation of lactation.Specialized nursing interventions for maternal-infant separation can improve the physical and mental state of high-risk pregnancy patients,reduce the incidence of postpartum complications,and enhance nursing satisfaction,making them worthy of clinical application and promotion.展开更多
Conventional electrolytic methods for separating chemically similar lanthanides(Ln)and actinides(An)are limited by thermodynamics and slow reaction kinetics,restricting their efficiency in rare-earth refining and nucl...Conventional electrolytic methods for separating chemically similar lanthanides(Ln)and actinides(An)are limited by thermodynamics and slow reaction kinetics,restricting their efficiency in rare-earth refining and nuclear fuel recycling.Herein,we report an electroextraction and oxidative back-extraction(EOB)strategy utilizing a LiCl-KCl-KAlCl_(4) molten salt that overcomes these limitations by leveraging divergent interfacial reactivity.The EOB process achieves an exceptional separation factor for Ln/An(>1000),while simultaneously increasing the separation rate by at least one order of magnitude.Through in-situ synchrotron radiation X-ray micro-computed tomography(SR-μCT)and X-ray diffraction(SR-XRD),we capture selective oxidation-induced destabilization of Ln-Al alloys while actinides retain phase stability-directly visualizing the electrochemical alloy transition mechanism.This research redefines the separation of f-block elements in molten salt systems and introduces a multimodal approach to investigating transient interfacial phenomena that are usually inaccessible to conventional metallurgical diagnostics under extreme conditions.展开更多
基金the National Key R&D Program of China(No.2016YFC0600505).
文摘Bit-field separation is an important part of gravity and magnetic data processing.In order to extract different levels of anomaly information better,this paper introduces the dual-tree complex wavelet multi-scale separation to the processing of bit-field data firstly and uses the geological model of different buried depth to ve-rify its feasibility.Finally,the dual-tree complex wavelet is applied to the aeromagnetic anomaly in Jinchuan copper nickel mining area.The results show that the method can effectively separate the anomaly information of different scales and analyze the output results with relevant geological data.
基金Project(52174245)supported by the National Natural Science Foundation of ChinaProject(2021J01640)supported by the Natural Science Foundation of Fujian Province,ChinaProject(BGRIMM-KJSKL2022-03)supported by Open Foundation of the State Key Laboratory of Mineral Processing,China。
文摘Micro-and nano-to millimeter-scale magnetic matrix materials have gained widespread application due to their exceptional magnetic properties and favorable cost-effectiveness.With the rapid progress in condensed matter physics,materials science,and mineral separation technologies,these materials are now poised for new opportunities in theoretical research and development.This review provides a comprehensive analysis of these matrices,encompassing their structure,size,shape,composition,properties,and multifaceted applications.These materials,primarily composed of alloys of transition state metasl such as iron(Fe),cobalt(Co),titanium(Ti),and nickel(Ni),exhibit unique attributes like high magnetization rates,low eleastic modulus,and high saturation magnetic field strengths.Furthermore,the studies also delve into the complex mechanical interactions involved in the separation of magnetic particles using magnetic separator matrices,including magnetic,gravitational,centrifugal,and van der Waals forces.The review outlines how size and shape effects influence the magnetic behavior of matrices,offering new perspectives for innovative applications of magnetic matrices in various domains of materials science and magnetic separation.
基金National Development and Reform Commission Project ″Experimental Detection of Urban Active Faults″ (2004-1138).
文摘In this paper, through a multi-scale separation of the aeromagnetic anomaly by wavelet transform technique, we reprocessed the aeromagnetic data collected 20 years ago in Beijing area and analyzed the aeromagnetic anomaly qualitatively, integrating geological structure features in the area. In particular, we studied the spatial distributions of the two main faults called Shunyi-Liangxiang fault and Banqiao-Babaoshan-Tongxian fault, which have cut and gone through the central Beijing area striking in NE and EW directions, respectively. The influences of these two faults on the earthquakes have also been discussed briefly.
基金supported by the National Natural Science Foundation of China(22475240,22090061,22488101)the State Key Laboratory of Catalysis(2024SKL-A-010)。
文摘The separation of propylene(C_(3)H_(6))and propane(C_(3)H_(8))presents a significant industrial challenge due to their similar molecular dimensions and physicochemical properties.Among various separation methods,molecular sieving emerges as the most promising approach,but it will be significantly compromised at high temperatures due to the significant thermal motion.Here,we report a thermally robust zinc-based metal-organic framework(MOF)that can be synthesized on sub-kilogram scale and achieve exceptional C_(3)H_(6)/C_(3)H_(8) separation performances across a broad temperature range(298–353 K).Unlike conventional MOFs suffering from thermal lattice expansion to give poorer selectivity,this new MOF gives the adsorption capacity of C_(3)H_(6)essentially unchanged and that of C_(3)H_(8) negligible at elevated temperatures,outperforming most state-of-the-art adsorbents,in virtue of multiple hydrogen bonds at the aperture.Column breakthrough experiments confirmed the excellent separation capability,and showed no performance degradation over multi-round adsorption-desorption cycles at 353 K.This study addresses the critical challenge of the trade-off between temperature and selectivity in adsorptive separation,which offers new insights into the design of porous structures for highly effective separation at high temperatures.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金financially supported byChongqingUniversity of Technology Graduate Innovation Foundation(Grant No.gzlcx20253267).
文摘Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金financial support from the National Key Research and Development Program of China(2022YFB3804905)National Natural Science Foundation of China(22375047,22378068,and 22378071)+1 种基金Natural Science Foundation of Fujian Province(2022J01568)111 Project(No.D17005).
文摘Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
基金Project(2022YFC2904103)supported by the National Key Research and Development Program of ChinaProjects(52374112,52274108)supported by the National Natural Science Foundation of China+1 种基金Projects(BX20220036,BX20230041)supported by the Postdoctoral Innovation Talents Support Program,ChinaProject(2232080)supported by the Beijing Natural Science Foundation,China。
文摘The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.
基金the Scientific Research Projects Unit of Erciyes University under contract no:FDS-2022-11532 and FOA-2025-14773.
文摘This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金supported by the National Natural Science Foundation of China(No.22375063)Science and Technology Commission of Shanghai Municipality(No.23JC140170O)the Fundamental Research Funds for the Central Universities.
文摘Developing advanced polymeric materials with enhanced mechanical properties and functionalities has been a long-standing goal in materials science.Recently,supramolecular polymeric materials(SPMs)have drawn increased attention due to their unique properties and potential applications in self-healing,shape memory,sensors,and flexible electronics.Here,we develop an ionic cluster-optimized microphase separation strategy to enhance the toughening and energy dissipation capabilities of polydisulfide-based supramolecular polymers.The mechanical properties,including Young's modulus and toughness,are significantly improved by integrating the quadruple H-bonding 2-ureido-4-pyrimidone(UPy)induced microphase separation with iron(III)-to-carboxylate ionic clusters.By combining established chemical approaches with adjustable polymer phase ratios,it is revealed that the synergistic effect of these factors expands the interchain spacing,facilitates the formation of microphase domains,and enhances the tolerance of polythioctic acid-based polymers to external mechanical and thermal stimuli,meeting the practical requirements for industrial plastic applications.Moreover,the UPy-functionalized polymers incorporating iron carboxylate clusters exhibit good one-way shape memory behavior with practical applicability at a relatively low recovery temperature.Our work demonstrates a novel strategy for constructing industrially viable shape memory dynamic SPMs and paves the way for future innovations in developing SPMs.
基金the National Key Research and Development Program of China (Grant No.2022YFF0711400)the National Space Science Data Center Youth Open Project (Grant No. NSSDC2302001)
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金supported by the China Institute of Atomic Energy。
文摘The removal of trace plutonium(Pu)from uranium products and organic wastes during spent nuclear fuel reprocessing remains a critical challenge,resulting in excessive plutonium content in uranium products and waste organic liquid.Currently,most organic ligands with selective separation functions are lipophilic,while research on water-soluble,highly selective ligands is relatively scarce,and there are also few reports on the single crystal of these ligands coordinating with plutonium.Herein,a hydrophilic multiamide ligand,N,N,N′,N″,N″-hexaethyl-nitrilotriacetamide(NTAamideC2),was synthesized and evaluated for its Pu(Ⅳ)back-extraction efficiency under harsh conditions.Systematic experiments revealed that NTAamideC2 achieved>99%Pu(Ⅳ)back-extraction rate within 15 min across a wide nitric acid concentration range(0-5 M),even with elevated dibutyl phosphate(DBP≤20000 ppm).Remarkably,the separation factor(SFPu/U)reached 767 at 1.5 M HNO_(3),demonstrating exceptional selectivity over uranium(Ⅵ).Spectrophotometric titration and DFT calculations confirmed the formation of 1:1 and 1:2 Pu(Ⅳ)-NTAamideC2 complexes,with log β values of 7.42±0.01 and 13.23±0.02,respectively.Single-crystal X-ray diffraction analysis of{[Pu_(2)(H_(2)O)_(2)(NTAamideC2)_(4)](H_(2)O)_(2)(NO_(3))(ClO_(4))_(7)}revealed a nine-coordinated PuO_(7)N_(2)geometry,where two NTAamideC2 molecules bind via six O and two N atoms.Compared to conventional agents(AHA/HSC),NTAamideC2 exhibited superior acid tolerance and selectivity,aligning with the CHON principle for sustainable nuclear waste management.This work provides a robust strategy for Pu(Ⅳ)removal in uranium purification cycles and advances fundamental insights into Pu coordination chemistry,offering significant potential for industrial nuclear fuel reprocessing.
文摘Objective:To analyze the impact of maternal-infant separation on the physical and mental state of high-risk pregnancy patients and explore the clinical efficacy of targeted nursing interventions.Methods:A total of 80 high-risk pregnancy patients treated in our hospital from January 2023 to January 2024 were selected as the study subjects.These patients were randomly divided into an observation group and a control group(40 cases each)using a random number table.The control group received routine high-risk pregnancy nursing care,while the observation group received specialized maternal-infant separation nursing interventions in addition to routine care.The psychological and physiological states and nursing satisfaction of the two groups were compared before and after the intervention.Results:The SAS scores,SDS scores,and sleep quality scores of the observation group were significantly lower than those of the control group,with statistically significant differences(p<0.05).The incidence of postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the initiation time of lactation was significantly earlier than that in the control group,with both differences being statistically significant(p<0.05).The nursing satisfaction of the observation group was significantly higher than that of the control group(80%vs.32/40),with a statistically significant difference(p<0.05).Conclusion:Maternal-infant separation exacerbates anxiety and depression in high-risk pregnancy patients,reduces sleep quality,increases the risk of postpartum hemorrhage,and delays the initiation of lactation.Specialized nursing interventions for maternal-infant separation can improve the physical and mental state of high-risk pregnancy patients,reduce the incidence of postpartum complications,and enhance nursing satisfaction,making them worthy of clinical application and promotion.
基金supported by the National Science Fund for Distinguished Young Scholars(21925603)the National Natural Science Foundation of China(22306185)the China Postdoctoral Science Foundation(2023M732032)。
文摘Conventional electrolytic methods for separating chemically similar lanthanides(Ln)and actinides(An)are limited by thermodynamics and slow reaction kinetics,restricting their efficiency in rare-earth refining and nuclear fuel recycling.Herein,we report an electroextraction and oxidative back-extraction(EOB)strategy utilizing a LiCl-KCl-KAlCl_(4) molten salt that overcomes these limitations by leveraging divergent interfacial reactivity.The EOB process achieves an exceptional separation factor for Ln/An(>1000),while simultaneously increasing the separation rate by at least one order of magnitude.Through in-situ synchrotron radiation X-ray micro-computed tomography(SR-μCT)and X-ray diffraction(SR-XRD),we capture selective oxidation-induced destabilization of Ln-Al alloys while actinides retain phase stability-directly visualizing the electrochemical alloy transition mechanism.This research redefines the separation of f-block elements in molten salt systems and introduces a multimodal approach to investigating transient interfacial phenomena that are usually inaccessible to conventional metallurgical diagnostics under extreme conditions.
