AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A...AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users.展开更多
The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF...The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF)argon plasma,and the temperatures were measured using a DPV-2000 monitor.A model combining the electromagnetism,thermal flow,and heat transfer characteristics of powder during in-flight heating in argon plasma was proposed.The melting processes of CeO_(2)powders of different diameters,with and without thermal resistance effect,were investigated.Results show that the heating process of CeO_(2)powder particles consists of three main stages,one of which is relevant to a dimensionless parameter known as the Biot number.When the Biot value≥0.1,thermal resistance increases significantly,especially for the larger powders.The predicted temperature of the particles at the outlet(1800–2880 K)is in good agreement with the experimental result.展开更多
Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone t...Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
Rechargeable Zn/Sn-air batteries have received considerable attention as promising energy storage devices.However,the electrochemical performance of these batteries is significantly constrained by the sluggish electro...Rechargeable Zn/Sn-air batteries have received considerable attention as promising energy storage devices.However,the electrochemical performance of these batteries is significantly constrained by the sluggish electrocatalytic reaction kinetics at the cathode.The integration of light energy into Zn/Sn-air batteries is a promising strategy for enhancing their performance.However,the photothermal and photoelectric effects generate heat in the battery under prolonged solar irradiation,leading to air cathode instability.This paper presents the first design and synthesis of Ni_(2)-1,5-diamino-4,8-dihydroxyanthraquinone(Ni_(2)DDA),an electronically conductiveπ-d conjugated metal-organic framework(MOF).Ni_(2)DDA exhibits both photoelectric and photothermal effects,with an optical band gap of~1.14 eV.Under illumination,Ni_(2)DDA achieves excellent oxygen evolution reaction performance(with an overpotential of 245 mV vs.reversible hydrogen electrode at 10 mA cm^(−2))and photothermal stability.These properties result from the synergy between the photoelectric and photothermal effects of Ni_(2)DDA.Upon integration into Zn/Sn-air batteries,Ni_(2)DDA ensures excellent cycling stability under light and exhibits remarkable performance in high-temperature environments up to 80℃.This study experimentally confirms the stable operation of photo-assisted Zn/Sn-air batteries under high-temperature conditions for the first time and provides novel insights into the application of electronically conductive MOFs in photoelectrocatalysis and photothermal catalysis.展开更多
Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challe...Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challenges for de-ployment on resource-constrained edge devices.Although post-training quantization(PTQ)provides a promising solution by reducing model precision with minimal calibration data,aggressive low-bit quantization typically leads to substantial perfor-mance degradation.To address this challenge,we present the truncated uniform-log2 quantizer and progressive bit-decline reconstruction method for vision Transformer quantization(TP-ViT).It is an innovative PTQ framework specifically designed for ViTs,featuring two key technical contributions:(1)truncated uniform-log2 quantizer,a novel quantization approach which effectively handles outlier values in post-Softmax activations,significantly reducing quantization errors;(2)bit-decline optimiza-tion strategy,which employs transition weights to gradually reduce bit precision while maintaining model performance under extreme quantization conditions.Comprehensive experiments on image classification,object detection,and instance segmenta-tion tasks demonstrate TP-ViT’s superior performance compared to state-of-the-art PTQ methods,particularly in challenging 3-bit quantization scenarios.Our framework achieves a notable 6.18 percentage points improvement in top-1 accuracy for ViT-small under 3-bit quantization.These results validate TP-ViT’s robustness and general applicability,paving the way for more efficient deployment of ViT models in computer vision applications on edge hardware.展开更多
AIM:To investigate the association between functionaloutcomes and postoperative patient satisfaction 5y aftersmall incision lenticule extraction(SMILE)and femtosecondlaser-assisted in situ keratomileusis(FS-LASIK).MET...AIM:To investigate the association between functionaloutcomes and postoperative patient satisfaction 5y aftersmall incision lenticule extraction(SMILE)and femtosecondlaser-assisted in situ keratomileusis(FS-LASIK).METHODS:This is a cross-sectional study.Thepatients underwent basic ophthalmic examinations,axiallength measurement,wide-field fundus photography,andaccommodation function testing.Behavioral habits datawere collected using a self-administered questionnaire,andvisual symptoms were assessed with the Quality of Vision(QoV)questionnaire.Postoperative satisfaction was alsorecorded.RESULTS:Totally 410 subjects[820 eyes,160males(39.02%)and 250 females(60.98%)]who hadundergone SMILE or FS-LASIK 5y ago were enrolled.Themean(standard deviation,SD)age of all patients was29.83y(6.69).The mean(SD)preoperative manifest SEwas-5.80(2.04)diopters(D;range:-0.88 to-13.75).Patient satisfaction at 5y after undergoing SMILE or FSLASIKwas 91.70%.Patients were categorized into twogroups:dissatisfied group and satisfied group.Significantdifferences were observed between the two groups in termsof age(P=0.012),sex(P=0.021),preoperative degreeof myopia(P=0.049),postoperative visual symptoms(frequency,P=0.043;severity,P<0.001;bothersome,P=0.018),difficulty driving at night(P=0.001),andaccommodative amplitude(AMP,P=0.020).Multivariateanalysis confirmed that female sex(P=0.024),severityof visual symptoms(P=0.009),and difficulty driving atnight(P=0.006)were significantly associated with lowersatisfaction.The dissatisfied group showed higher rates ofstarbursts,double or multiple images,and high myopia,but lower age.The frequency,severity,and bothersome ofdistortion exhibited decreased with increasing age.CONCLUSION:Patient satisfaction 5y after SMILEand FS-LASIK is high and stable.Difficulty driving at night,sex,and severity of visual symptoms are important factorsinfluencing patient satisfaction.Special attention should bepaid to younger highly myopic female patients,particularlythose with starbursts and double or multiple images.It is crucial to monitor postoperative visual outcomesand provide patients with comprehensive preoperativecounseling to enhance long-term satisfaction.展开更多
Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood s...Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood scenarios involving reflections,occlusions,or indistinct boundaries due to limited contextual modeling.To address these challenges,we propose a hybrid flood segmentation framework that integrates a Vision Transformer(ViT)encoder with a U-Net decoder,enhanced by a novel Flood-Aware Refinement Block(FARB).The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms.We evaluate our model on a UAV-acquired flood imagery dataset,demonstrating that the proposed ViTUNet+FARB architecture outperforms existing CNN and Transformer-based models in terms of accuracy and mean Intersection over Union(mIoU).Detailed ablation studies further validate the contribution of each component,confirming that the FARB design significantly enhances segmentation quality.To its better performance and computational efficiency,the proposed framework is well-suited for flood monitoring and disaster response applications,particularly in resource-constrained environments.展开更多
[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of ...[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value.Traditional manual measurements are inefficient,prone to human error,and may cause stress to sheep,limiting their suitability for precision sheep management.By summarizing the applications of sheep body size measurement technologies and analyzing their development directions,this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement.[Progress]This review synthesizes progress across three principal methodological paradigms:two-dimensional(2D)image-based techniques,three-dimensional(3D)point cloud-based approaches,and integrated 2D-3D fusion systems.2D methods,employing either handcrafted geometric features or deep learning-based keypoint detector algorithms,are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics.3D point-cloud approaches enable precise reconstruction of full animal morphology,supporting comprehensive body-size acquisition with higher accuracy,yet face challenges including high hardware costs,complex data workflows,and sensitivity to posture variability.Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds.Having been effectively validated in other livestock specise,e.g.,cattle and pigs,these fusion systems have demonstrated excellent performance,providing important technical references and practical insights for sheep body size measurement.[Conclusions and Prospects]Firstly,future research should focus on constructing large-scale,high-quality datasets for sheep body size measurement that encompass diverse breeds,growth stages,and environmental conditions,thereby enhancing model robustness and generalization.Secondly,the development of lightweight artificial intelligence models is essential.Techniques such as model compression,quantization,and algorithmic optimization can substantially reduce computational complexity and storage requirements,facilitating deployment in resource-constrained environments.Thirdly,the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition,filtering,registration,and segmentation,while promoting the integration of low-cost,high-resilience vision systems into practical farming scenarios.Fourthly,specific emphasis should be placed on improving the accuracy of curved-dimensional measurements,such as chest circumference,abdominal circumference,and shank circumference,through advances in pose standardization,refined 3D segmentation strategies,and multimodal data fusion.Finally,the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation,accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent,data-driven pasture management practices.展开更多
Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based met...Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based methods,tracking on a single CPU core,or parallelizing the computation across multiple cores via the message passing interface(MPI).Although these approaches work well for single-bunch tracking,scaling them to multiple bunches significantly increases the computational load,which often necessitates the use of a dedicated multi-CPU cluster.To address this challenge,alternative methods leveraging General-Purpose computing on Graphics Processing Units(GPGPU)have been proposed,enabling tracking studies on a standalone desktop personal computer(PC).However,frequent CPU-GPU interactions,including data transfers and synchronization operations during tracking,can introduce communication overheads,potentially reducing the overall effectiveness of GPU-based computations.In this study,we propose a novel approach that eliminates this overhead by performing the entire tracking simulation process exclusively on the GPU,thereby enabling the simultaneous processing of all bunches and their macro-particles.Specifically,we introduce MBTRACK2-CUDA,a Compute Unified Device Architecture(CUDA)ported version of MBTRACK2,which facilitates efficient tracking of single-and multi-bunch collective effects by leveraging the full GPU-resident computation.展开更多
The dried fruit of Forsythia suspensa(Oleaceae),also known as Forsythia,is a traditional Chinese medicinal herb known for its heat-clearing and detoxifying properties.It is used to disperse nodules,reduce swelling,rem...The dried fruit of Forsythia suspensa(Oleaceae),also known as Forsythia,is a traditional Chinese medicinal herb known for its heat-clearing and detoxifying properties.It is used to disperse nodules,reduce swelling,remove toxins,clear heat,and alleviate wind-heat syndromes.It also has hepatoprotective,anti-inflammatory,antiviral,antibacterial,anticancer,antioxidant,antiaging,and anti-obesity effects,as well as potential therapeutic effects on Alzheimer’s disease and diabetic nephropathy.It is used to treat scrofula,mastitis,wind-heat common cold,and other ailments.The review summarizes the chemical constituents and pharmacological effects of F.suspensa,aiming to provide a scientific foundation for its future development,research,and clinical utilization.展开更多
Kagome magnets are of growing interest due to their topological electronic structures and unconventional magnetic behavior.Here,we report on the anomalous Hall effect(AHE)in the kagome ferromagnet MgMn_(6)Sn_(6),which...Kagome magnets are of growing interest due to their topological electronic structures and unconventional magnetic behavior.Here,we report on the anomalous Hall effect(AHE)in the kagome ferromagnet MgMn_(6)Sn_(6),which has a Curie temperature of~290 K and an in-plane easy magnetization axis.Magnetotransport measurements show a positive magnetoresistance(MR)below 50 K,which becomes negative at higher temperatures.An intrinsic anomalous Hall conductivity of 114 S·cm^(-1)is observed in MgMn_(6)Sn_(6) single crystals,consistent with ab initio calculations.Moreover,theoretical predictions indicate that shifting the Fermi level(EF)upward by~70 meV could enhance the AHE to~528 S·cm^(-1).These results position MgMn_(6)Sn_(6) as a promising and tunable platform for exploring topological magnetism and related electronic phenomena.展开更多
AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy s...AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy subjects.METHODS:A cross-sectional comparative study was conducted,enrolling two cohorts:a PD group and a healthy control group.The PD group was recruited via non-random convenience sampling,while the control group was selected randomly from individuals without PD.All participants were screened according to predefined inclusion and exclusion criteria before undergoing a comprehensive optometric assessment,which included measurements of uncorrected visual acuity,corrected visual acuity,and objective and subjective refraction.Subsequently,binocular vision function evaluations were performed,covering NPC measurement,fusional vergence reserve assessment at both distance and near,saccadic eye movement testing,and versional eye movement and heterophoria assessment.RESULTS:A total of 42 PD patients and 41 healthy controls were included in the final analysis.The two groups were well-matched in terms of sex distribution[29 males(69.0%)in the PD group vs 29 males(70.7%)in the control group,P=0.867]and mean age(55.3±9.6y in the PD group vs 54.9±9.8y in the control group,P=0.866).The prevalence of abnormal versional eye movements was significantly higher in the PD group than in the control group(23.81%,95%CI:12.05%-39.45%vs 7.32%,95%CI:1.54%-19.92%;P=0.025).Near exophoria was more prevalent in PD patients(61.90%,95%CI:45.64%-76.43%)than in controls(17.07%,95%CI:7.15%-32.06%),with a significant difference[odds ratio(OR)=7.99;95%CI:2.83-21.99;P<0.001].The mean NPC was significantly greater(more receded)in the PD group than in the control group(9.01±3.74 cm vs 7.20±2.15 cm;P=0.007).A statistically significant positive correlation was observed between PD severity and NPC values(Pearson’s correlation coefficient=0.309;P=0.046).Except for distance baseout break and distance base-out recovery values,all other fusional vergence parameters were significantly lower in the PD group than in the control group(P<0.05).The mean saccadic test score was significantly lower in PD patients than in controls(3.29±0.57 vs 3.78±0.42;P<0.001).Among all fusional vergence indices,near base-in blur yielded the highest area under the curve(AUC=0.877),with a sensitivity of 69%and specificity of 90%,followed by distance base-out blur(AUC=0.824,sensitivity=97.6%,specificity=66.7%),near base-out blur(AUC=0.814,sensitivity=76.2%,specificity=72.7%),near base-out break(AUC=0.749,sensitivity=78.6%,specificity=67.6%),and near base-out recovery(AUC=0.749,sensitivity=95.2%,specificity=50%).CONCLUSION:PD is associated with significant binocular vision function impairment,with receded NPC and reduced near fusional vergence reserves being the most prominent disorders.These findings highlight the potential value of binocular vision assessment as a non-invasive biomarker for the early detection and clinical monitoring of PD.展开更多
基金Supported by Ongoing Research Funding Program(ORFFT-2025-054-1),King Saud University,Riyadh,Saudi Arabia.
文摘AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users.
基金National Natural Science Foundation of China(11875039)Shanxi Scholarship Council of China(2023-033)+2 种基金Fundamental Research Program of Shanxi Province(202303021221071)China Baowu Low Carbon Metallurgical Innovation Foundation(2022)2023 Anhui Major Industrial Innovation Plan Project。
文摘The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF)argon plasma,and the temperatures were measured using a DPV-2000 monitor.A model combining the electromagnetism,thermal flow,and heat transfer characteristics of powder during in-flight heating in argon plasma was proposed.The melting processes of CeO_(2)powders of different diameters,with and without thermal resistance effect,were investigated.Results show that the heating process of CeO_(2)powder particles consists of three main stages,one of which is relevant to a dimensionless parameter known as the Biot number.When the Biot value≥0.1,thermal resistance increases significantly,especially for the larger powders.The predicted temperature of the particles at the outlet(1800–2880 K)is in good agreement with the experimental result.
文摘Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
基金supported by the National Natural Science Foundation of China(No.62464010)Spring City Plan-Special Program for Young Talents(K202005007)+2 种基金Yunnan Talents Support Plan for Young Talents(XDYC-QNRC-2022-0482)Yunnan Local Colleges Applied Basic Research Projects(202101BA070001-138)Frontier Research Team of Kunming University 2023.
文摘Rechargeable Zn/Sn-air batteries have received considerable attention as promising energy storage devices.However,the electrochemical performance of these batteries is significantly constrained by the sluggish electrocatalytic reaction kinetics at the cathode.The integration of light energy into Zn/Sn-air batteries is a promising strategy for enhancing their performance.However,the photothermal and photoelectric effects generate heat in the battery under prolonged solar irradiation,leading to air cathode instability.This paper presents the first design and synthesis of Ni_(2)-1,5-diamino-4,8-dihydroxyanthraquinone(Ni_(2)DDA),an electronically conductiveπ-d conjugated metal-organic framework(MOF).Ni_(2)DDA exhibits both photoelectric and photothermal effects,with an optical band gap of~1.14 eV.Under illumination,Ni_(2)DDA achieves excellent oxygen evolution reaction performance(with an overpotential of 245 mV vs.reversible hydrogen electrode at 10 mA cm^(−2))and photothermal stability.These properties result from the synergy between the photoelectric and photothermal effects of Ni_(2)DDA.Upon integration into Zn/Sn-air batteries,Ni_(2)DDA ensures excellent cycling stability under light and exhibits remarkable performance in high-temperature environments up to 80℃.This study experimentally confirms the stable operation of photo-assisted Zn/Sn-air batteries under high-temperature conditions for the first time and provides novel insights into the application of electronically conductive MOFs in photoelectrocatalysis and photothermal catalysis.
基金supported by the National Natural Science Foundation of China(Nos.62301092 and 62301093).
文摘Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challenges for de-ployment on resource-constrained edge devices.Although post-training quantization(PTQ)provides a promising solution by reducing model precision with minimal calibration data,aggressive low-bit quantization typically leads to substantial perfor-mance degradation.To address this challenge,we present the truncated uniform-log2 quantizer and progressive bit-decline reconstruction method for vision Transformer quantization(TP-ViT).It is an innovative PTQ framework specifically designed for ViTs,featuring two key technical contributions:(1)truncated uniform-log2 quantizer,a novel quantization approach which effectively handles outlier values in post-Softmax activations,significantly reducing quantization errors;(2)bit-decline optimiza-tion strategy,which employs transition weights to gradually reduce bit precision while maintaining model performance under extreme quantization conditions.Comprehensive experiments on image classification,object detection,and instance segmenta-tion tasks demonstrate TP-ViT’s superior performance compared to state-of-the-art PTQ methods,particularly in challenging 3-bit quantization scenarios.Our framework achieves a notable 6.18 percentage points improvement in top-1 accuracy for ViT-small under 3-bit quantization.These results validate TP-ViT’s robustness and general applicability,paving the way for more efficient deployment of ViT models in computer vision applications on edge hardware.
基金Supported by Research and Transformation Application of Capital Clinical Diagnosis and Treatment Technology by Beijing Municipal Commission of Science and Technology(No.Z201100005520043).
文摘AIM:To investigate the association between functionaloutcomes and postoperative patient satisfaction 5y aftersmall incision lenticule extraction(SMILE)and femtosecondlaser-assisted in situ keratomileusis(FS-LASIK).METHODS:This is a cross-sectional study.Thepatients underwent basic ophthalmic examinations,axiallength measurement,wide-field fundus photography,andaccommodation function testing.Behavioral habits datawere collected using a self-administered questionnaire,andvisual symptoms were assessed with the Quality of Vision(QoV)questionnaire.Postoperative satisfaction was alsorecorded.RESULTS:Totally 410 subjects[820 eyes,160males(39.02%)and 250 females(60.98%)]who hadundergone SMILE or FS-LASIK 5y ago were enrolled.Themean(standard deviation,SD)age of all patients was29.83y(6.69).The mean(SD)preoperative manifest SEwas-5.80(2.04)diopters(D;range:-0.88 to-13.75).Patient satisfaction at 5y after undergoing SMILE or FSLASIKwas 91.70%.Patients were categorized into twogroups:dissatisfied group and satisfied group.Significantdifferences were observed between the two groups in termsof age(P=0.012),sex(P=0.021),preoperative degreeof myopia(P=0.049),postoperative visual symptoms(frequency,P=0.043;severity,P<0.001;bothersome,P=0.018),difficulty driving at night(P=0.001),andaccommodative amplitude(AMP,P=0.020).Multivariateanalysis confirmed that female sex(P=0.024),severityof visual symptoms(P=0.009),and difficulty driving atnight(P=0.006)were significantly associated with lowersatisfaction.The dissatisfied group showed higher rates ofstarbursts,double or multiple images,and high myopia,but lower age.The frequency,severity,and bothersome ofdistortion exhibited decreased with increasing age.CONCLUSION:Patient satisfaction 5y after SMILEand FS-LASIK is high and stable.Difficulty driving at night,sex,and severity of visual symptoms are important factorsinfluencing patient satisfaction.Special attention should bepaid to younger highly myopic female patients,particularlythose with starbursts and double or multiple images.It is crucial to monitor postoperative visual outcomesand provide patients with comprehensive preoperativecounseling to enhance long-term satisfaction.
基金supported by the National Research Foundation of Korea(NRF)grant funded by theKorea government(MSIT)(No.RS-2024-00405278)partially supported by the Jeju Industry-University Convergence District Project for Promoting Industry-Campus Cooperationfunded by the Ministry of Trade,Industry and Energy(MOTIE,Korea)[Project Name:Jeju Industry-University Convergence District Project for Promoting Industry-Campus Cooperation/Project Number:P0029950].
文摘Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood scenarios involving reflections,occlusions,or indistinct boundaries due to limited contextual modeling.To address these challenges,we propose a hybrid flood segmentation framework that integrates a Vision Transformer(ViT)encoder with a U-Net decoder,enhanced by a novel Flood-Aware Refinement Block(FARB).The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms.We evaluate our model on a UAV-acquired flood imagery dataset,demonstrating that the proposed ViTUNet+FARB architecture outperforms existing CNN and Transformer-based models in terms of accuracy and mean Intersection over Union(mIoU).Detailed ablation studies further validate the contribution of each component,confirming that the FARB design significantly enhances segmentation quality.To its better performance and computational efficiency,the proposed framework is well-suited for flood monitoring and disaster response applications,particularly in resource-constrained environments.
文摘[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value.Traditional manual measurements are inefficient,prone to human error,and may cause stress to sheep,limiting their suitability for precision sheep management.By summarizing the applications of sheep body size measurement technologies and analyzing their development directions,this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement.[Progress]This review synthesizes progress across three principal methodological paradigms:two-dimensional(2D)image-based techniques,three-dimensional(3D)point cloud-based approaches,and integrated 2D-3D fusion systems.2D methods,employing either handcrafted geometric features or deep learning-based keypoint detector algorithms,are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics.3D point-cloud approaches enable precise reconstruction of full animal morphology,supporting comprehensive body-size acquisition with higher accuracy,yet face challenges including high hardware costs,complex data workflows,and sensitivity to posture variability.Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds.Having been effectively validated in other livestock specise,e.g.,cattle and pigs,these fusion systems have demonstrated excellent performance,providing important technical references and practical insights for sheep body size measurement.[Conclusions and Prospects]Firstly,future research should focus on constructing large-scale,high-quality datasets for sheep body size measurement that encompass diverse breeds,growth stages,and environmental conditions,thereby enhancing model robustness and generalization.Secondly,the development of lightweight artificial intelligence models is essential.Techniques such as model compression,quantization,and algorithmic optimization can substantially reduce computational complexity and storage requirements,facilitating deployment in resource-constrained environments.Thirdly,the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition,filtering,registration,and segmentation,while promoting the integration of low-cost,high-resilience vision systems into practical farming scenarios.Fourthly,specific emphasis should be placed on improving the accuracy of curved-dimensional measurements,such as chest circumference,abdominal circumference,and shank circumference,through advances in pose standardization,refined 3D segmentation strategies,and multimodal data fusion.Finally,the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation,accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent,data-driven pasture management practices.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT)(No.RS-2022-00143178)the Ministry of Education(MOE)(Nos.2022R1A6A3A13053896 and 2022R1F1A1074616),Republic of Korea.
文摘Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based methods,tracking on a single CPU core,or parallelizing the computation across multiple cores via the message passing interface(MPI).Although these approaches work well for single-bunch tracking,scaling them to multiple bunches significantly increases the computational load,which often necessitates the use of a dedicated multi-CPU cluster.To address this challenge,alternative methods leveraging General-Purpose computing on Graphics Processing Units(GPGPU)have been proposed,enabling tracking studies on a standalone desktop personal computer(PC).However,frequent CPU-GPU interactions,including data transfers and synchronization operations during tracking,can introduce communication overheads,potentially reducing the overall effectiveness of GPU-based computations.In this study,we propose a novel approach that eliminates this overhead by performing the entire tracking simulation process exclusively on the GPU,thereby enabling the simultaneous processing of all bunches and their macro-particles.Specifically,we introduce MBTRACK2-CUDA,a Compute Unified Device Architecture(CUDA)ported version of MBTRACK2,which facilitates efficient tracking of single-and multi-bunch collective effects by leveraging the full GPU-resident computation.
文摘The dried fruit of Forsythia suspensa(Oleaceae),also known as Forsythia,is a traditional Chinese medicinal herb known for its heat-clearing and detoxifying properties.It is used to disperse nodules,reduce swelling,remove toxins,clear heat,and alleviate wind-heat syndromes.It also has hepatoprotective,anti-inflammatory,antiviral,antibacterial,anticancer,antioxidant,antiaging,and anti-obesity effects,as well as potential therapeutic effects on Alzheimer’s disease and diabetic nephropathy.It is used to treat scrofula,mastitis,wind-heat common cold,and other ailments.The review summarizes the chemical constituents and pharmacological effects of F.suspensa,aiming to provide a scientific foundation for its future development,research,and clinical utilization.
基金Project supported by the National Natural Science Foundation of China(Grant No.12204347)National Key Research and Development Program of China(Grant No.2022YFA1402600)the Fund from Beijing National Laboratory for Condensed Matter Physics(Grant No.2023BNLCMPKF011)。
文摘Kagome magnets are of growing interest due to their topological electronic structures and unconventional magnetic behavior.Here,we report on the anomalous Hall effect(AHE)in the kagome ferromagnet MgMn_(6)Sn_(6),which has a Curie temperature of~290 K and an in-plane easy magnetization axis.Magnetotransport measurements show a positive magnetoresistance(MR)below 50 K,which becomes negative at higher temperatures.An intrinsic anomalous Hall conductivity of 114 S·cm^(-1)is observed in MgMn_(6)Sn_(6) single crystals,consistent with ab initio calculations.Moreover,theoretical predictions indicate that shifting the Fermi level(EF)upward by~70 meV could enhance the AHE to~528 S·cm^(-1).These results position MgMn_(6)Sn_(6) as a promising and tunable platform for exploring topological magnetism and related electronic phenomena.
基金Supported by Mashhad University of Medical Sciences.
文摘AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy subjects.METHODS:A cross-sectional comparative study was conducted,enrolling two cohorts:a PD group and a healthy control group.The PD group was recruited via non-random convenience sampling,while the control group was selected randomly from individuals without PD.All participants were screened according to predefined inclusion and exclusion criteria before undergoing a comprehensive optometric assessment,which included measurements of uncorrected visual acuity,corrected visual acuity,and objective and subjective refraction.Subsequently,binocular vision function evaluations were performed,covering NPC measurement,fusional vergence reserve assessment at both distance and near,saccadic eye movement testing,and versional eye movement and heterophoria assessment.RESULTS:A total of 42 PD patients and 41 healthy controls were included in the final analysis.The two groups were well-matched in terms of sex distribution[29 males(69.0%)in the PD group vs 29 males(70.7%)in the control group,P=0.867]and mean age(55.3±9.6y in the PD group vs 54.9±9.8y in the control group,P=0.866).The prevalence of abnormal versional eye movements was significantly higher in the PD group than in the control group(23.81%,95%CI:12.05%-39.45%vs 7.32%,95%CI:1.54%-19.92%;P=0.025).Near exophoria was more prevalent in PD patients(61.90%,95%CI:45.64%-76.43%)than in controls(17.07%,95%CI:7.15%-32.06%),with a significant difference[odds ratio(OR)=7.99;95%CI:2.83-21.99;P<0.001].The mean NPC was significantly greater(more receded)in the PD group than in the control group(9.01±3.74 cm vs 7.20±2.15 cm;P=0.007).A statistically significant positive correlation was observed between PD severity and NPC values(Pearson’s correlation coefficient=0.309;P=0.046).Except for distance baseout break and distance base-out recovery values,all other fusional vergence parameters were significantly lower in the PD group than in the control group(P<0.05).The mean saccadic test score was significantly lower in PD patients than in controls(3.29±0.57 vs 3.78±0.42;P<0.001).Among all fusional vergence indices,near base-in blur yielded the highest area under the curve(AUC=0.877),with a sensitivity of 69%and specificity of 90%,followed by distance base-out blur(AUC=0.824,sensitivity=97.6%,specificity=66.7%),near base-out blur(AUC=0.814,sensitivity=76.2%,specificity=72.7%),near base-out break(AUC=0.749,sensitivity=78.6%,specificity=67.6%),and near base-out recovery(AUC=0.749,sensitivity=95.2%,specificity=50%).CONCLUSION:PD is associated with significant binocular vision function impairment,with receded NPC and reduced near fusional vergence reserves being the most prominent disorders.These findings highlight the potential value of binocular vision assessment as a non-invasive biomarker for the early detection and clinical monitoring of PD.
