The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.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
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
To enhance the efficiency of wastewater biotreatment with microalgae, the effects of physical parameters need to be investigated and optimized. In this regard, the individual and interactive effects of temperature, p ...To enhance the efficiency of wastewater biotreatment with microalgae, the effects of physical parameters need to be investigated and optimized. In this regard, the individual and interactive effects of temperature, p H and aeration rate on the performance of biological removal of nitrate and phosphate by Chlorella vulgaris were studied by response surface methodology(RSM). Furthermore, a multi-objective optimization technique was applied to the response equations to simultaneously find optimal combinations of input parameters capable of removing the highest possible amount of nitrate and phosphate. The optimal calculated values were temperature of 26.3 °C, pH of 8 and aeration rate of 4.7 L·min^(-1). Interestingly, under the optimum condition, approximately 85% of total nitrate and 77% of whole phosphate were removed after 48 h and 24 h, respectively, which were in excellent agreement with the predicted values. Finally, the effect of baffle on mixing performance and, as a result, on bioremoval efficiency was investigated in Stirred Tank Photobioreactor(STP) by means of Computational Fluid Dynamics(CFD). Flow behavior indicated substantial enhancement in mixing performance when the baffle was inserted into the tank. Obtained simulation results were validated experimentally. Under the optimum condition, due to proper mixing in baffled STP, nitrate and phosphate removal increased up to 93% and 86%,respectively, compared to unbaffled one.展开更多
Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by ...Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.展开更多
Current research primarily focuses on emerging organic pollutants,with limited attention to emerging inorganic pollutants (EIPs).However,due to advances in detection technology and the escalating environmental and hea...Current research primarily focuses on emerging organic pollutants,with limited attention to emerging inorganic pollutants (EIPs).However,due to advances in detection technology and the escalating environmental and health challenges posed by pollution,there is a growing interest in treating waters contaminated with EIPs.This paper explores biochar characteristics and modification methods,encompassing physical,chemical,and biological approaches for adsorbing EIPs.It offers a comprehensive review of research advancements in employing biochar for EIPs remediation in water,outlines the adsorption mechanisms of EIPs by biochar,and presents an environmental and economic analysis.It can be concluded that using biochar for the adsorption of EIPs in wastewater exhibits promising potential.Nonetheless,it is noteworthy that certain EIPs like Au(III),Rh(III),Ir(III),Ru(III),Os(III),Sc(III),and Y(III),have not been extensively investigated regarding their adsorption onto biochar.This comprehensive review will catalyze further inquiry into the biochar-based adsorption of EIPs,addressing current research deficiencies and advancing the practical implementation of biochar as a potent substrate for EIP removal from wastewater streams.展开更多
To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-cap...To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.展开更多
The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-...The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-ZnSO_(4) solution,employing seed-induced nucleation methods.Analysis of the iron removal rate,residue structure,morphology,and elemental composition involved ICP,XRD,FT-IR,and SEM.The existing state of zinc was investigated by combining step-by-step dissolution using hydrochloric acid.Concurrently,iron removal tests were extended to industrial solutions to assess the influence of seeds and solution pH on zinc loss and residue yield.The results revealed that seed addition increased the iron removal rate by 3%,elevated the residual iron content by 6.39%,and mitigated zinc loss by 29.55%in the simulated solution.Seed-induced nucleation prevented excessive nuclei formation,fostering crystal stable growth and high crystallinity.In addition,the zinc content of surface adsorption and crystal internal embedding in the residue was determined,and the zinc distribution on the surface was dense.In contrast,the total amount of zinc within the crystal was higher.The test results in the industrial solution demonstrated that the introduction of seeds expanded the pH range for goethite formation and growth,and the zinc loss per ton of iron removed was reduced by 50.91 kg(34.12%)and the iron residue reduced by 0.17 t(8.72%).展开更多
Wigner-Ville distribution(WVD)is widely used in the field of signal processing due to its excellent time-frequency(TF)concentration.However,WVD is severely limited by the cross-term when working with multicomponent si...Wigner-Ville distribution(WVD)is widely used in the field of signal processing due to its excellent time-frequency(TF)concentration.However,WVD is severely limited by the cross-term when working with multicomponent signals.In this paper,we analyze the property differences between auto-term and cross-term in the one-dimensional sequence and the two-dimensional plane and approximate entropy and Rényi entropy are employed to describe them,respectively.Based on this information,we propose a new method to achieve adaptive cross-term removal by combining seeded region growing.Compared to other methods,the new method can achieve cross-term removal without decreasing the TF concentration of the auto-term.Simulation and experimental data processing results show that the method is adaptive and is not constrained by the type or distribution of signals.And it performs well in low signal-to-noise ratio environments.展开更多
Atomic surfaces are strictly required by high-performance devices of diamond.Nevertheless,diamond is the hardest material in nature,leading to the low material removal rate(MRR)and high surface roughness during machin...Atomic surfaces are strictly required by high-performance devices of diamond.Nevertheless,diamond is the hardest material in nature,leading to the low material removal rate(MRR)and high surface roughness during machining.Noxious slurries are widely used in conventional chemical mechanical polishing(CMP),resulting in the possible pollution to the environment.Moreover,the traditional slurries normally contain more than four ingredients,causing difficulties to control the process and quality of CMP.To solve these challenges,a novel green CMP for single crystal diamond was developed,consisting of only hydrogen peroxide,diamond abrasive and Prussian blue(PB)/titania catalyst.After CMP,atomic surface is achieved with surface roughness Sa of 0.079 nm,and the MRR is 1168 nm·h^(-1).Thickness of damaged layer is merely 0.66 nm confirmed by transmission electron microscopy(TEM).X-ray photoelectron spectroscopy,electron paramagnetic resonance and TEM reveal that·OH radicals form under ultraviolet irradiation on PB/titania catalyst.The·OH radicals oxidize diamond,transforming it from monocrystalline to amorphous atomic structure,generating a soft amorphous layer.This contributes the high MRR and formation of atomic surface on diamond.The developed novel green CMP offers new insights to achieve atomic surface of diamond for potential use in their high-performance devices.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed...Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.展开更多
The application of photocatalytic technology in algae killing is limited by the non-floatability and difficulty in recycling of the photocatalysts.Loading photocatalyst on magnetic or floatable carriers is the most po...The application of photocatalytic technology in algae killing is limited by the non-floatability and difficulty in recycling of the photocatalysts.Loading photocatalyst on magnetic or floatable carriers is the most popular method for overcoming the above inadequacies.In this work,a CdZnS/TiO_(2) membrane photocatalyst with adjustable suspended depth(include floating)and flexible assembly is designed,which is less prone to dislodgement due to in situ synthesis and has a wider range of applicability than previously reported photocatalysts.The photocatalytic removal of Microcystis aeruginosa revealed that the suspended depth and distribution format of the CdZnS/TiO_(2) membrane photocatalysts have striking effects on the photocatalytic removal performance of Microcystis aeruginosa,the photocatalytic removal efficiency of CdZnS/TiO_(2)-2 membrane photocatalysts for Microcystis aeruginosa could reach to 98.6%in 60 min when the photocatalysts assembled in the form of 3×3 arrays suspended at a depth of 2 cm from the liquid surface.A tiny amount of TiO_(2) loading allows the formation of Z-Scheme heterojunction,resulting in accelerating the separation efficiency of photogenerated carriers,preserving the photogenerated electrons and holes with stronger reduction and oxidation ability and inhabiting the photo-corrosion of CdZnS.展开更多
Mercury removal from coal combustion flue gas remains a significant challenge for environmental protection due to the lack of cost-effective sorbents.In this study,a series of red mud(RM)-based sorbents impregnated wi...Mercury removal from coal combustion flue gas remains a significant challenge for environmental protection due to the lack of cost-effective sorbents.In this study,a series of red mud(RM)-based sorbents impregnated with sodium halides(NaBr and NaI)are presented to capture elemental mercury(Hg^(0))from flue gas.The modified RM underwent comprehensive characterization,including analysis of its textural qualities,crystal structure,chemical composition,and thermal properties.The results indicate that the halide impregnation substantially impacts the surface area and pore size of the RM.Hg^(0) removal performance was evaluated on a fixed-bed reactor in simulated flue gas(consisting of N_(2),O_(2),CO_(2),NO and SO_(2),etc.)on a modified RM.At an optimal adsorption temperature of 160℃,NaI-modified sorbent(RMI5)offers a removal efficiency of 98%in a mixture of gas,including O_(2),NO and HCl.Furthermore,pseudo-second-order model fitting results demonstrate the chemisorption mechanism for the adsorption of Hg^(0) in kinetic investigations.展开更多
As a non-contact ultra-precision machining method,abrasive water jet polishing(AWJP)has signi-ficant application in optical elements processing due to its stable tool influence function(TIF),no subsurface damage and s...As a non-contact ultra-precision machining method,abrasive water jet polishing(AWJP)has signi-ficant application in optical elements processing due to its stable tool influence function(TIF),no subsurface damage and strong adaptability to workpiece shapes.In this study,the effects of jet pressure,nozzle diameter and impinging angle on the distribution of pressure,velocity and wall shear stress in the polishing flow field were systematically analyzed by computational fluid dynamics(CFD)simulation.Based on the Box-Behnken experimental design,a response surface regression model was constructed to investigate the influence mech-anism of process parameters on material removal rate(MRR)and surface roughness(Ra)of fused silica.And experimental results showed that increasing jet pressure and nozzle diameter significantly improved MRR,consistent with shear stress distribution revealed by CFD simulations.However,increasing jet pressure and impinging angle caused higher Ra values,which was unfavorable for surface quality improvement.Genetic algorithm(GA)was used for multi-objective optimization to establish Pareto solutions,achieving concurrent optimization of polishing efficiency and surface quality.A parameter combination of 2 MPa jet pressure,0.3 mm nozzle diameter,and 30°impinging angle achieved MRR of 169.05μm^(3)/s and Ra of 0.50 nm.Exper-imental verification showed prediction errors of 4.4%(MRR)and 3.8%(Ra),confirming the model’s reliabil-ity.This parameter optimization system provides theoretical basis and technical support for ultra-precision polishing of complex curved optical components.展开更多
The subcortical visual pathway is generally thought to be involved in dangerous information processing,such as fear processing and defensive behavior.A recent study,published in Human Brain Mapping,shows a new functio...The subcortical visual pathway is generally thought to be involved in dangerous information processing,such as fear processing and defensive behavior.A recent study,published in Human Brain Mapping,shows a new function of the subcortical pathway involved in the fast processing of non-emotional object perception.Rapid object processing is a critical function of visual system.Topological perception theory proposes that the initial perception of objects begins with the extraction of topological property(TP).However,the mechanism of rapid TP processing remains unclear.The researchers investigated the subcortical mechanism of TP processing with transcranial magnetic stimulation(TMS).They find that a subcortical magnocellular pathway is responsible for the early processing of TP,and this subcortical processing of TP accelerates object recognition.Based on their findings,we propose a novel training approach called subcortical magnocellular pathway training(SMPT),aimed at improving the efficiency of the subcortical M pathway to restore visual and attentional functions in disorders associated with subcortical pathway dysfunction.展开更多
Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dens...Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dense small objects is challenging.展开更多
Copper is a strategic metal that plays an important role in many industries.In copper metallurgy,electrolytic refining is essential to obtain high-purity copper.However,during the electrolytic refining process,impurit...Copper is a strategic metal that plays an important role in many industries.In copper metallurgy,electrolytic refining is essential to obtain high-purity copper.However,during the electrolytic refining process,impurities such as arsenic are introduced into the electrolyte,which significantly affect the subsequent production and quality of copper products.This paper first discusses the sources,forms,and transformation pathways of arsenic in copper electrolyte during the electrolytic process,then reviews various arsenic removal technologies in detail,including electrowinning,adsorption,solvent extraction,ion exchange,membrane filtration,and precipitation.Particular emphasis is placed on electrowinning,which is the most widely used and mature among these arsenic removal techniques.The paper evaluates these methods based on arsenic removal efficiency,cost effectiveness,technical maturity,environmental friendliness,and operation simplicity.In addition,the paper explores future trends in copper electrolyte purification,focusing on waste reduction at source,resource utilization,intelligent digitalization,and innovations in materials and processes.This review aims to provide researchers and practitioners with a comprehensive and in-depth reference on arsenic removal methods in copper electrolytes.展开更多
To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,a...To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB.展开更多
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
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.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
文摘To enhance the efficiency of wastewater biotreatment with microalgae, the effects of physical parameters need to be investigated and optimized. In this regard, the individual and interactive effects of temperature, p H and aeration rate on the performance of biological removal of nitrate and phosphate by Chlorella vulgaris were studied by response surface methodology(RSM). Furthermore, a multi-objective optimization technique was applied to the response equations to simultaneously find optimal combinations of input parameters capable of removing the highest possible amount of nitrate and phosphate. The optimal calculated values were temperature of 26.3 °C, pH of 8 and aeration rate of 4.7 L·min^(-1). Interestingly, under the optimum condition, approximately 85% of total nitrate and 77% of whole phosphate were removed after 48 h and 24 h, respectively, which were in excellent agreement with the predicted values. Finally, the effect of baffle on mixing performance and, as a result, on bioremoval efficiency was investigated in Stirred Tank Photobioreactor(STP) by means of Computational Fluid Dynamics(CFD). Flow behavior indicated substantial enhancement in mixing performance when the baffle was inserted into the tank. Obtained simulation results were validated experimentally. Under the optimum condition, due to proper mixing in baffled STP, nitrate and phosphate removal increased up to 93% and 86%,respectively, compared to unbaffled one.
文摘Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.
基金support from the earmarked fund for XJARS(No.XJARS-06)the Bingtuan Science and Technology Program(Nos.2021DB019,2022CB001-01)+1 种基金the National Natural Science Foundation of China(No.42275014)the Guangdong Foundation for Program of Science and Technology Research,China(No.2023B1212060044)。
文摘Current research primarily focuses on emerging organic pollutants,with limited attention to emerging inorganic pollutants (EIPs).However,due to advances in detection technology and the escalating environmental and health challenges posed by pollution,there is a growing interest in treating waters contaminated with EIPs.This paper explores biochar characteristics and modification methods,encompassing physical,chemical,and biological approaches for adsorbing EIPs.It offers a comprehensive review of research advancements in employing biochar for EIPs remediation in water,outlines the adsorption mechanisms of EIPs by biochar,and presents an environmental and economic analysis.It can be concluded that using biochar for the adsorption of EIPs in wastewater exhibits promising potential.Nonetheless,it is noteworthy that certain EIPs like Au(III),Rh(III),Ir(III),Ru(III),Os(III),Sc(III),and Y(III),have not been extensively investigated regarding their adsorption onto biochar.This comprehensive review will catalyze further inquiry into the biochar-based adsorption of EIPs,addressing current research deficiencies and advancing the practical implementation of biochar as a potent substrate for EIP removal from wastewater streams.
基金supported by the Shanghai Science and Technology Innovation Action Plan High-Tech Field Project(Grant No.22511100601)for the year 2022 and Technology Development Fund for People’s Livelihood Research(Research on Transmission Line Deep Foundation Pit Environmental Situation Awareness System Based on Multi-Source Data).
文摘To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.
基金Project(2018YFC1900403) supported by the National Key Research and Development Program of ChinaProject(CX20210197) supported by the Postgraduate Scientific Research Innovation Project of Hunan Province,China+1 种基金Project(202206370103) supported by the China Scholarship CouncilProject(2021zzts0115) supported by the Fundamental Research Funds for the Central Universities,China。
文摘The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-ZnSO_(4) solution,employing seed-induced nucleation methods.Analysis of the iron removal rate,residue structure,morphology,and elemental composition involved ICP,XRD,FT-IR,and SEM.The existing state of zinc was investigated by combining step-by-step dissolution using hydrochloric acid.Concurrently,iron removal tests were extended to industrial solutions to assess the influence of seeds and solution pH on zinc loss and residue yield.The results revealed that seed addition increased the iron removal rate by 3%,elevated the residual iron content by 6.39%,and mitigated zinc loss by 29.55%in the simulated solution.Seed-induced nucleation prevented excessive nuclei formation,fostering crystal stable growth and high crystallinity.In addition,the zinc content of surface adsorption and crystal internal embedding in the residue was determined,and the zinc distribution on the surface was dense.In contrast,the total amount of zinc within the crystal was higher.The test results in the industrial solution demonstrated that the introduction of seeds expanded the pH range for goethite formation and growth,and the zinc loss per ton of iron removed was reduced by 50.91 kg(34.12%)and the iron residue reduced by 0.17 t(8.72%).
基金Supported by the National Natural Science Foundation of China(62201171).
文摘Wigner-Ville distribution(WVD)is widely used in the field of signal processing due to its excellent time-frequency(TF)concentration.However,WVD is severely limited by the cross-term when working with multicomponent signals.In this paper,we analyze the property differences between auto-term and cross-term in the one-dimensional sequence and the two-dimensional plane and approximate entropy and Rényi entropy are employed to describe them,respectively.Based on this information,we propose a new method to achieve adaptive cross-term removal by combining seeded region growing.Compared to other methods,the new method can achieve cross-term removal without decreasing the TF concentration of the auto-term.Simulation and experimental data processing results show that the method is adaptive and is not constrained by the type or distribution of signals.And it performs well in low signal-to-noise ratio environments.
基金financial support from the National Key Research and Development Program of China(2018YFA0703400)the Fundamental Research Funds for the Provincial Universities of Zhejiang(GK239909299001021)+1 种基金the Ninth China Association for Science and Technology Youth Talent Lift Project Support Plan(KYZ015324002)the Changjiang Scholars Program of Chinese Ministry of Education。
文摘Atomic surfaces are strictly required by high-performance devices of diamond.Nevertheless,diamond is the hardest material in nature,leading to the low material removal rate(MRR)and high surface roughness during machining.Noxious slurries are widely used in conventional chemical mechanical polishing(CMP),resulting in the possible pollution to the environment.Moreover,the traditional slurries normally contain more than four ingredients,causing difficulties to control the process and quality of CMP.To solve these challenges,a novel green CMP for single crystal diamond was developed,consisting of only hydrogen peroxide,diamond abrasive and Prussian blue(PB)/titania catalyst.After CMP,atomic surface is achieved with surface roughness Sa of 0.079 nm,and the MRR is 1168 nm·h^(-1).Thickness of damaged layer is merely 0.66 nm confirmed by transmission electron microscopy(TEM).X-ray photoelectron spectroscopy,electron paramagnetic resonance and TEM reveal that·OH radicals form under ultraviolet irradiation on PB/titania catalyst.The·OH radicals oxidize diamond,transforming it from monocrystalline to amorphous atomic structure,generating a soft amorphous layer.This contributes the high MRR and formation of atomic surface on diamond.The developed novel green CMP offers new insights to achieve atomic surface of diamond for potential use in their high-performance devices.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
文摘Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.
基金financially supported by the Natural Science Foundation of ShanDong(Nos.ZR2023QD152 and ZR2021MD002).
文摘The application of photocatalytic technology in algae killing is limited by the non-floatability and difficulty in recycling of the photocatalysts.Loading photocatalyst on magnetic or floatable carriers is the most popular method for overcoming the above inadequacies.In this work,a CdZnS/TiO_(2) membrane photocatalyst with adjustable suspended depth(include floating)and flexible assembly is designed,which is less prone to dislodgement due to in situ synthesis and has a wider range of applicability than previously reported photocatalysts.The photocatalytic removal of Microcystis aeruginosa revealed that the suspended depth and distribution format of the CdZnS/TiO_(2) membrane photocatalysts have striking effects on the photocatalytic removal performance of Microcystis aeruginosa,the photocatalytic removal efficiency of CdZnS/TiO_(2)-2 membrane photocatalysts for Microcystis aeruginosa could reach to 98.6%in 60 min when the photocatalysts assembled in the form of 3×3 arrays suspended at a depth of 2 cm from the liquid surface.A tiny amount of TiO_(2) loading allows the formation of Z-Scheme heterojunction,resulting in accelerating the separation efficiency of photogenerated carriers,preserving the photogenerated electrons and holes with stronger reduction and oxidation ability and inhabiting the photo-corrosion of CdZnS.
基金supported by the National Natural Science Foundation of China(22278066,21776039)the National Key R&D Program of China(2023YFB4103001)The Fundamental Research Funds for the Central Universities(DUT2021TB03).
文摘Mercury removal from coal combustion flue gas remains a significant challenge for environmental protection due to the lack of cost-effective sorbents.In this study,a series of red mud(RM)-based sorbents impregnated with sodium halides(NaBr and NaI)are presented to capture elemental mercury(Hg^(0))from flue gas.The modified RM underwent comprehensive characterization,including analysis of its textural qualities,crystal structure,chemical composition,and thermal properties.The results indicate that the halide impregnation substantially impacts the surface area and pore size of the RM.Hg^(0) removal performance was evaluated on a fixed-bed reactor in simulated flue gas(consisting of N_(2),O_(2),CO_(2),NO and SO_(2),etc.)on a modified RM.At an optimal adsorption temperature of 160℃,NaI-modified sorbent(RMI5)offers a removal efficiency of 98%in a mixture of gas,including O_(2),NO and HCl.Furthermore,pseudo-second-order model fitting results demonstrate the chemisorption mechanism for the adsorption of Hg^(0) in kinetic investigations.
文摘As a non-contact ultra-precision machining method,abrasive water jet polishing(AWJP)has signi-ficant application in optical elements processing due to its stable tool influence function(TIF),no subsurface damage and strong adaptability to workpiece shapes.In this study,the effects of jet pressure,nozzle diameter and impinging angle on the distribution of pressure,velocity and wall shear stress in the polishing flow field were systematically analyzed by computational fluid dynamics(CFD)simulation.Based on the Box-Behnken experimental design,a response surface regression model was constructed to investigate the influence mech-anism of process parameters on material removal rate(MRR)and surface roughness(Ra)of fused silica.And experimental results showed that increasing jet pressure and nozzle diameter significantly improved MRR,consistent with shear stress distribution revealed by CFD simulations.However,increasing jet pressure and impinging angle caused higher Ra values,which was unfavorable for surface quality improvement.Genetic algorithm(GA)was used for multi-objective optimization to establish Pareto solutions,achieving concurrent optimization of polishing efficiency and surface quality.A parameter combination of 2 MPa jet pressure,0.3 mm nozzle diameter,and 30°impinging angle achieved MRR of 169.05μm^(3)/s and Ra of 0.50 nm.Exper-imental verification showed prediction errors of 4.4%(MRR)and 3.8%(Ra),confirming the model’s reliabil-ity.This parameter optimization system provides theoretical basis and technical support for ultra-precision polishing of complex curved optical components.
文摘The subcortical visual pathway is generally thought to be involved in dangerous information processing,such as fear processing and defensive behavior.A recent study,published in Human Brain Mapping,shows a new function of the subcortical pathway involved in the fast processing of non-emotional object perception.Rapid object processing is a critical function of visual system.Topological perception theory proposes that the initial perception of objects begins with the extraction of topological property(TP).However,the mechanism of rapid TP processing remains unclear.The researchers investigated the subcortical mechanism of TP processing with transcranial magnetic stimulation(TMS).They find that a subcortical magnocellular pathway is responsible for the early processing of TP,and this subcortical processing of TP accelerates object recognition.Based on their findings,we propose a novel training approach called subcortical magnocellular pathway training(SMPT),aimed at improving the efficiency of the subcortical M pathway to restore visual and attentional functions in disorders associated with subcortical pathway dysfunction.
基金supported in part by the National Science Foundation of China(52371372)the Project of Science and Technology Commission of Shanghai Municipality,China(22JC1401400,21190780300)the 111 Project,China(D18003)
文摘Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dense small objects is challenging.
基金Project(52174385)supported by the National Natural Science Foundation of ChinaProjects(2023YFC3904003,2023YFC3904004,2023YFC390400501)supported by the National Key R&D Program of China。
文摘Copper is a strategic metal that plays an important role in many industries.In copper metallurgy,electrolytic refining is essential to obtain high-purity copper.However,during the electrolytic refining process,impurities such as arsenic are introduced into the electrolyte,which significantly affect the subsequent production and quality of copper products.This paper first discusses the sources,forms,and transformation pathways of arsenic in copper electrolyte during the electrolytic process,then reviews various arsenic removal technologies in detail,including electrowinning,adsorption,solvent extraction,ion exchange,membrane filtration,and precipitation.Particular emphasis is placed on electrowinning,which is the most widely used and mature among these arsenic removal techniques.The paper evaluates these methods based on arsenic removal efficiency,cost effectiveness,technical maturity,environmental friendliness,and operation simplicity.In addition,the paper explores future trends in copper electrolyte purification,focusing on waste reduction at source,resource utilization,intelligent digitalization,and innovations in materials and processes.This review aims to provide researchers and practitioners with a comprehensive and in-depth reference on arsenic removal methods in copper electrolytes.
文摘To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB.
