Neutralizing antibodies are essential tools in antiviral therapy and epidemic preparedness,capable of directlyinhibiting viral entry and limiting disease progression.However,traditional antibody discovery strategies—...Neutralizing antibodies are essential tools in antiviral therapy and epidemic preparedness,capable of directlyinhibiting viral entry and limiting disease progression.However,traditional antibody discovery strategies—suchas animal immunization or B cell isolation from infected individuals—are often hindered by biosafety concerns,lengthy development timelines,and limited adaptability during outbreaks.In the present study,we aimed toestablish a robust and rapid in vitro platform for the efficient isolation of neutralizing antibodies targetingconserved viral epitopes.We developed an epitope-guided negative screening strategy that integrates phagedisplay technology with rational antigen mutagenesis to exclude antibodies against variable regions whileenriching for those that recognize functionally constrained epitopes.When applied to the receptor-binding domainof severe acute respiratory syndrome coronavirus 2,this method enabled the identification of six neutralizingantibodies(one IgG and five nanobodies)exhibiting broad-spectrum neutralizing activity across multiple viralvariants.Notably,antibodies recognizing distinct epitopes demonstrated significant synergistic neutralizationwhen used in combination(P<0.05).This screening approach facilitates the rapid discovery of potent andmutation-resistant antibodies and holds promise for application to other emerging pathogens.Our findingsunderscore the potential of epitope-guided,in vitro platforms in expediting therapeutic antibody developmentunder conditions of high biosafety requirements.展开更多
The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and ot...The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and other risks.Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information.This survey reviews recent progress in detection methods,categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals.Passive detection is further divided into surface linguistic feature-based and language model-based methods,whereas active detection encompasses watermarking-based and semantic retrieval-based approaches.This taxonomy enables systematic comparison of methodological differences in model dependency,applicability,and robustness.A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks,particularly paraphrasing,which substantially compromises their effectiveness.Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization.By synthesizing current advances and limitations,this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.展开更多
In an era dominated by visual information,the display interface serves as a critical gateway between the human and digital worlds.The relentless pursuit of visual immersion has driven display technology from cinema sc...In an era dominated by visual information,the display interface serves as a critical gateway between the human and digital worlds.The relentless pursuit of visual immersion has driven display technology from cinema screens to smart-phones and now to virtual and augmented reality(VR/AR)headsets,progressively moving closer to the human eye.This evolution places unprecedented demands on pixel density,power efficiency,and form factor,pushing up against funda-mental physical and physiological limits.展开更多
The increasing significance of text data in power system intelligence has highlighted the out-of-distribution(OOD)problem as a critical challenge,hindering the deployment of artificial intelligence(AI)models.In a clos...The increasing significance of text data in power system intelligence has highlighted the out-of-distribution(OOD)problem as a critical challenge,hindering the deployment of artificial intelligence(AI)models.In a closed-world setting,most AI models cannot detect and reject unexpected data,which exacerbates the harmful impact of the OOD problem.The high similarity between OOD and indistribution(IND)samples in the power system presents challenges for existing OOD detection methods in achieving effective results.This study aims to elucidate and address the OOD problem in power systems through a text classification task.First,the underlying causes of OOD sample generation are analyzed,highlighting the inherent nature of the OOD problem in the power system.Second,a novel method integrating the enhanced Mahalanobis distance with calibration strategies is introduced to improve OOD detection for text data in power system applications.Finally,the case study utilizing the actual text data from power system field operation(PSFO)is conducted,demonstrating the effectiveness of the proposed OOD detection method.Experimental results indicate that the proposed method outperformed existing methods in text OOD detection tasks within the power system,achieving a remarkable 21.03%enhancement of metric in the false positive rate at 95%true positive recall(FPR95)and a 12.97%enhancement in classi-fication accuracy for the mixed IND-OOD scenarios.展开更多
The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pu...The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pursuit of advanced AR displays,particularly those capable of delivering immersive 3D experiences,is significantly hindered by the performance limitations of current hardware and the complexity of system integration.In this study,we present an innovative multi-focal plane AR display system that integrates a non-orthogonal polarization-multiplexing metasurface,freeform optical elements,and an OLED display screen.All optical elements are integrated into a single solid-state architecture,based on a joint optimization design approach of ray tracing and diffraction theory.The multi-focal plane AR visual effect is realized by the compact and multiplexing metasurface,which performs distinct phase functions across diverse polarization channels.Meanwhile,freeform surfaces offer ample design flexibility for the collaborative optimization of multi-focal plane imaging and the see-through systems.Followed by a mechanical design and prototype assembly,we demonstrate the system's capabilities in real-time and multi-focal plane display.The digital images at all virtual image distances seamlessly integrate with the real environment,fully exhibiting the system's high parallelism and real-time interactivity.With the innovative design concept and joint design method,we believe that our work will spur more innovative and compact intelligent solutions for AR displays and inject new vitality into hybrid optical systems.展开更多
Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame...Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.展开更多
Head-up displays(HUDs)are emerging as key components of intelligent vehicles,requiring wide-depth,large-area,and high-efficiency dynamic imaging,which remains difficult to realize with traditional refractive optics.Co...Head-up displays(HUDs)are emerging as key components of intelligent vehicles,requiring wide-depth,large-area,and high-efficiency dynamic imaging,which remains difficult to realize with traditional refractive optics.Computer-generated holography(CGH)with diffraction optics offers a promising solution to these technical demands.However,CGH optimization based on the fast Fourier transform(FFT)faces limitations such as zero-padding redundancy,coupled sampling intervals,and incompatible near-and farfield propagation models.Here,we report a holography-based multiplane HUD using a matrix multiplication(MM)-assisted diffraction algorithm that restructures the Fresnel integral into two sequential matrix operations,thus eliminating zero-padding and enabling fully decoupled sampling between object and image planes.Compared with FFT-based angular spectrum methods,the MM approach significantly improves computational speed and memory efficiency for hologram design,which is validated by demonstrating dual-plane holography with a size ratio exceeding 100:1 and unified reconstruction across Fresnel and Fraunhofer regimes within a single computation.A prototype HUD system is demonstrated successfully to exhibit multiple-plane holographic virtual images that can be mixed with real-world objects at three independent planes.The technique might be one of the potential candidates for next-generation intelligent vehicle displays.展开更多
White Cyphochilus insulanus beetles,exhibiting both environmental camouflage display and radiative cooling functions,serve as a good prototype for biomimetic fabrication.As inspired,this work presents a femtosecond(fs...White Cyphochilus insulanus beetles,exhibiting both environmental camouflage display and radiative cooling functions,serve as a good prototype for biomimetic fabrication.As inspired,this work presents a femtosecond(fs)laser-based biomimetic fabrication strategy that takes full use of the synthesized radiative cooling nanomaterials for a groundbreaking stimuli-responsive infrared(IR)impressionistic camouflage display.The proposed technique is capable of readily transforming various substrates(quartz glass and metals including Ti,Al,Zr,and W)into self-assembled porous networks(aerogels)consisting of oxygen-vacancy-rich oxide nanoparticles.Surprisingly,the emissions of all as-prepared porous particle-networks in the radiative-cooling long-wavelength infrared(LWIR)band are above 95%,with the SiO_(2) aerogels reaching a maximum of 99.6%.Benefiting from the far-from-equilibrium thermodynamic kinetics,metastable phases of anatase TiO_(2),tetragonal zirconia(t-ZrO_(2)),and monoclinic WO_(3)(Pc)are synthesizable,opening up opportunities for exploring their optical applications.Taking the low-temperature metastable phase WO_(3)(Pc)as representative for systematic studies,it is found that(1)the ratio WO_(3)(Pc)phase to that of room-temperature phase of WO_(3)(P2_(1)/n)can be tailored by modulation of processing parameters;(2)laser synthesized aerogels with hybrid phases of WO_(3)(Pc)and WO_(3)(P2_(1)/n)have a brighter visible whiteness,higher visible/nearinfrared(NIR)spectral selectivity than the natural prototype of white Cyphochilus insulanus beetles but with comparable LWIR emittance.White WO_(3) aerogel in situ deposited during flexibly fs laser artistic patterning can blur the painting features due to its radiative cooling effect,allowing a colorful impressionistic IR display in the heating mode.What's more,invisible painting features concealed by the white deposited WO_(3) aerogel are clearly/faintly distinguishable by introducing external stimuli of a human hand and sample heating,respectively,catalyzing progress in optical encryption and selectively stimuli-responsive decryption display in the infrared band.展开更多
This study compares the relative efficacy of the continuation task and the model-as-feedbackwriting (MAFW) task in EFL writing development. Ninety intermediate-level Chinese EFL learnerswere randomly assigned to a con...This study compares the relative efficacy of the continuation task and the model-as-feedbackwriting (MAFW) task in EFL writing development. Ninety intermediate-level Chinese EFL learnerswere randomly assigned to a continuation group, a MAFW group, and a control group, each with30 learners. A pretest and a posttest were used to gauge L2 writing development. Results showedthat the continuation task outperformed the MAFW task not only in enhancing the overall qualityof L2 writing, but also in promoting the quality of three components of L2 writing, namely, content,organization, and language. The finding has important implications for L2 writing teaching andlearning.展开更多
China’s environmental governance strategy provides a distinctive pathway for integrating sustainable development into national policy.Understanding its policy trajectory is essential for assessing China’s contributi...China’s environmental governance strategy provides a distinctive pathway for integrating sustainable development into national policy.Understanding its policy trajectory is essential for assessing China’s contribution to global sustainable development and the United Nations Sustainable Development Goals(SDGs).This study constructs a comprehensive database of 425 national environmental governance policy documents issued between 1978 and 2022 and applies Latent Dirichlet Allocation(LDA)modeling to examine the evolution of policy themes and discourse.The results show that China’s environmental governance has undergone four stages-initial exploration,detailed development,transformative leap,and diverse prosperity-reflecting a progressive shift toward more integrated and coordinated governance.Policy priorities have evolved from a primary focus on pollution control and energy transition to an emphasis on institutional construction and organizational reform,thereby strengthening alignment with the SDGs.This transformation is characterized by recurring developmental themes and increasingly preventive,forward-looking,and system-oriented governance approaches.Moreover,the co-evolution of policy concepts and implementation has driven a transition from localized,end-of-pipe responses to comprehensive governance frameworks,alongside a shift from normative guidance towards effectiveness-oriented policy design.By employing a data-driven text analysis approach,this study offers a systematic framework for tracing long-term policy evolution and assessing its implications for sustainable development.展开更多
With the rapid development of digital culture,a large number of cultural texts are presented in the form of digital and network.These texts have significant characteristics such as sparsity,real-time and non-standard ...With the rapid development of digital culture,a large number of cultural texts are presented in the form of digital and network.These texts have significant characteristics such as sparsity,real-time and non-standard expression,which bring serious challenges to traditional classification methods.In order to cope with the above problems,this paper proposes a new ASSC(ALBERT,SVD,Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model.Based on the framework of TextRCNN,the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding.Combined with the dual attention mechanism,the model’s ability to capture and model potential key information in short texts is strengthened.The Singular Value Decomposition(SVD)was used to replace the traditional Max pooling operation,which effectively reduced the feature loss rate and retained more key semantic information.The cross-entropy loss function was used to optimize the prediction results,making the model more robust in class distribution learning.The experimental results indicate that,in the digital cultural text classification task,as compared to the baseline model,the proposed ASSC-TextRCNN method achieves an 11.85%relative improvement in accuracy and an 11.97%relative increase in the F1 score.Meanwhile,the relative error rate decreases by 53.18%.This achievement not only validates the effectiveness and advanced nature of the proposed approach but also offers a novel technical route and methodological underpinnings for the intelligent analysis and dissemination of digital cultural texts.It holds great significance for promoting the in-depth exploration and value realization of digital culture.展开更多
Silks have various advantages compared with synthetic polymer fibers,such as sustainability,mechanical properties,luster,as well as air and humidity permeability.However,the functionalization of silks has not yet been...Silks have various advantages compared with synthetic polymer fibers,such as sustainability,mechanical properties,luster,as well as air and humidity permeability.However,the functionalization of silks has not yet been fully developed.Functionalization techniques that retain or even improve the sustainability of silk production are required.To this end,a low-cost,effective,and scalable strategy to produce TCSs by integrating yarn-spinning and continuous dip coating technique is developed herein.TCSs with extremely long length(>10 km),high mechanical performance(strength of 443.1 MPa,toughness of 56.0 MJ m−3,comparable with natural cocoon silk),and good interfacial bonding were developed.TCSs can be automatically woven into arbitrary fabrics,which feature super-hydrophobicity as well as rapid and programmable thermochromic responses with good cyclic performance:the response speed reached to one second and remained stable after hundreds of tests.Finally,applications of TCS fabrics in temperature management and dynamic textile displays are demonstrated,confirming their application potential in smart textiles,wearable devices,flexible displays,and human–machine interfaces.Moreover,combination of the fabrication and the demonstrated applications is expected to bridge the gap between lab research and industry and accelerate the commercialization of TCSs.展开更多
High-resolution non-emissive displays based on electrochromic tungsten oxides(WOx)are crucial for future near-eye virtual/augmented reality interactions,given their impressive attributes such as high environmental sta...High-resolution non-emissive displays based on electrochromic tungsten oxides(WOx)are crucial for future near-eye virtual/augmented reality interactions,given their impressive attributes such as high environmental stability,ideal outdoor readability,and low energy consumption.However,the limited intrinsic structure of inorganic materials has presented a significant challenge in achieving precise patterning/pixelation at the micron scale.Here,we successfully developed the direct photolithography for WOx nanoparticles based on in situ photo-induced ligand exchange.This strategy enabled us to achieve ultra-high resolution efficiently(line width<4μm,the best resolution for reported inorganic electrochromic materials).Additionally,the resulting device exhibited impressive electrochromic performance,such as fast response(<1 s at 0 V),high coloration efficiency(119.5 cm^(2) C^(−1)),good optical modulation(55.9%),and durability(>3600 cycles),as well as promising applications in electronic logos,pixelated displays,flexible electronics,etc.The success and advancements presented here are expected to inspire and accelerate research and development(R&D)in high-resolution non-emissive displays and other ultra-fine micro-electronics.展开更多
基金supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.22KJB310001 to W.G.)the Special Project of the Jiangsu Provincial Department of Science and Technology(Grant No.BE2023603 to W.G.)the Nanjing Medical University Science and Technology Development Foundation(Grant No.NMUB20210006 to W.G.).
文摘Neutralizing antibodies are essential tools in antiviral therapy and epidemic preparedness,capable of directlyinhibiting viral entry and limiting disease progression.However,traditional antibody discovery strategies—suchas animal immunization or B cell isolation from infected individuals—are often hindered by biosafety concerns,lengthy development timelines,and limited adaptability during outbreaks.In the present study,we aimed toestablish a robust and rapid in vitro platform for the efficient isolation of neutralizing antibodies targetingconserved viral epitopes.We developed an epitope-guided negative screening strategy that integrates phagedisplay technology with rational antigen mutagenesis to exclude antibodies against variable regions whileenriching for those that recognize functionally constrained epitopes.When applied to the receptor-binding domainof severe acute respiratory syndrome coronavirus 2,this method enabled the identification of six neutralizingantibodies(one IgG and five nanobodies)exhibiting broad-spectrum neutralizing activity across multiple viralvariants.Notably,antibodies recognizing distinct epitopes demonstrated significant synergistic neutralizationwhen used in combination(P<0.05).This screening approach facilitates the rapid discovery of potent andmutation-resistant antibodies and holds promise for application to other emerging pathogens.Our findingsunderscore the potential of epitope-guided,in vitro platforms in expediting therapeutic antibody developmentunder conditions of high biosafety requirements.
基金supported in part by the Science and Technology Innovation Program of Hunan Province under Grant 2025RC3166the National Natural Science Foundation of China under Grant 62572176the National Key R&D Program of China under Grant 2024YFF0618800.
文摘The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and other risks.Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information.This survey reviews recent progress in detection methods,categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals.Passive detection is further divided into surface linguistic feature-based and language model-based methods,whereas active detection encompasses watermarking-based and semantic retrieval-based approaches.This taxonomy enables systematic comparison of methodological differences in model dependency,applicability,and robustness.A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks,particularly paraphrasing,which substantially compromises their effectiveness.Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization.By synthesizing current advances and limitations,this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.
基金supported by the National Natural Science Foundation of China(Grant No.22105106)the Jiangsu Youth Science and Technology Talent Support Program(Grant No.JSTJ-2025-063)+1 种基金Nanjing Science and Technology Innovation Project for Overseas Students(Grant No.NJKCZYZZ2022-05)Start-up Funding from NUPTSF(Grant No.NY221003).
文摘In an era dominated by visual information,the display interface serves as a critical gateway between the human and digital worlds.The relentless pursuit of visual immersion has driven display technology from cinema screens to smart-phones and now to virtual and augmented reality(VR/AR)headsets,progressively moving closer to the human eye.This evolution places unprecedented demands on pixel density,power efficiency,and form factor,pushing up against funda-mental physical and physiological limits.
基金supported in part by the Science and Technology Project of the State Grid East China Branch(No.520800230008).
文摘The increasing significance of text data in power system intelligence has highlighted the out-of-distribution(OOD)problem as a critical challenge,hindering the deployment of artificial intelligence(AI)models.In a closed-world setting,most AI models cannot detect and reject unexpected data,which exacerbates the harmful impact of the OOD problem.The high similarity between OOD and indistribution(IND)samples in the power system presents challenges for existing OOD detection methods in achieving effective results.This study aims to elucidate and address the OOD problem in power systems through a text classification task.First,the underlying causes of OOD sample generation are analyzed,highlighting the inherent nature of the OOD problem in the power system.Second,a novel method integrating the enhanced Mahalanobis distance with calibration strategies is introduced to improve OOD detection for text data in power system applications.Finally,the case study utilizing the actual text data from power system field operation(PSFO)is conducted,demonstrating the effectiveness of the proposed OOD detection method.Experimental results indicate that the proposed method outperformed existing methods in text OOD detection tasks within the power system,achieving a remarkable 21.03%enhancement of metric in the false positive rate at 95%true positive recall(FPR95)and a 12.97%enhancement in classi-fication accuracy for the mixed IND-OOD scenarios.
基金funding provided by National Natural Science Foundation of China(U21A20140)National Key Research and Development Program of China(2021YFA1401200)+2 种基金Beijing Natural Science Foundation(JQ24028)Beijing Nova Program(20240484557)Synergetic Extreme Condition User Facility(SECUF).
文摘The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pursuit of advanced AR displays,particularly those capable of delivering immersive 3D experiences,is significantly hindered by the performance limitations of current hardware and the complexity of system integration.In this study,we present an innovative multi-focal plane AR display system that integrates a non-orthogonal polarization-multiplexing metasurface,freeform optical elements,and an OLED display screen.All optical elements are integrated into a single solid-state architecture,based on a joint optimization design approach of ray tracing and diffraction theory.The multi-focal plane AR visual effect is realized by the compact and multiplexing metasurface,which performs distinct phase functions across diverse polarization channels.Meanwhile,freeform surfaces offer ample design flexibility for the collaborative optimization of multi-focal plane imaging and the see-through systems.Followed by a mechanical design and prototype assembly,we demonstrate the system's capabilities in real-time and multi-focal plane display.The digital images at all virtual image distances seamlessly integrate with the real environment,fully exhibiting the system's high parallelism and real-time interactivity.With the innovative design concept and joint design method,we believe that our work will spur more innovative and compact intelligent solutions for AR displays and inject new vitality into hybrid optical systems.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R234)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB3607300)the National Natural Science Foundation of China(Grant Nos.62322512,62225506,and 12134013)+7 种基金the Fundamental Research Funds for the Central Universities(Grant Nos.WK2030000108 and WK2030000090)the CAS Project for Young Scientists in Basic Research(Grant No.YSBR-049)supported by the National Natural Science Foundation of China(Grant Nos.12174260 and 12574326)the Shanghai Rising-Star Program(Grant No.21QA1406400)the Shanghai Science and Technology Development Fund(Grant Nos.21ZR1443500 and 21ZR1443600)the support from the China Postdoctoral Science Foundation(Grant No.2023M743364)support from the Center for Micro and Nanoscale Research and Fabrication,University of Science and Technology of Chinasupported by the UPOLabs,which provided the experimental and technical support。
文摘Head-up displays(HUDs)are emerging as key components of intelligent vehicles,requiring wide-depth,large-area,and high-efficiency dynamic imaging,which remains difficult to realize with traditional refractive optics.Computer-generated holography(CGH)with diffraction optics offers a promising solution to these technical demands.However,CGH optimization based on the fast Fourier transform(FFT)faces limitations such as zero-padding redundancy,coupled sampling intervals,and incompatible near-and farfield propagation models.Here,we report a holography-based multiplane HUD using a matrix multiplication(MM)-assisted diffraction algorithm that restructures the Fresnel integral into two sequential matrix operations,thus eliminating zero-padding and enabling fully decoupled sampling between object and image planes.Compared with FFT-based angular spectrum methods,the MM approach significantly improves computational speed and memory efficiency for hologram design,which is validated by demonstrating dual-plane holography with a size ratio exceeding 100:1 and unified reconstruction across Fresnel and Fraunhofer regimes within a single computation.A prototype HUD system is demonstrated successfully to exhibit multiple-plane holographic virtual images that can be mixed with real-world objects at three independent planes.The technique might be one of the potential candidates for next-generation intelligent vehicle displays.
基金financial support received from the Shanghai Pujiang Program(23PJ1406500)。
文摘White Cyphochilus insulanus beetles,exhibiting both environmental camouflage display and radiative cooling functions,serve as a good prototype for biomimetic fabrication.As inspired,this work presents a femtosecond(fs)laser-based biomimetic fabrication strategy that takes full use of the synthesized radiative cooling nanomaterials for a groundbreaking stimuli-responsive infrared(IR)impressionistic camouflage display.The proposed technique is capable of readily transforming various substrates(quartz glass and metals including Ti,Al,Zr,and W)into self-assembled porous networks(aerogels)consisting of oxygen-vacancy-rich oxide nanoparticles.Surprisingly,the emissions of all as-prepared porous particle-networks in the radiative-cooling long-wavelength infrared(LWIR)band are above 95%,with the SiO_(2) aerogels reaching a maximum of 99.6%.Benefiting from the far-from-equilibrium thermodynamic kinetics,metastable phases of anatase TiO_(2),tetragonal zirconia(t-ZrO_(2)),and monoclinic WO_(3)(Pc)are synthesizable,opening up opportunities for exploring their optical applications.Taking the low-temperature metastable phase WO_(3)(Pc)as representative for systematic studies,it is found that(1)the ratio WO_(3)(Pc)phase to that of room-temperature phase of WO_(3)(P2_(1)/n)can be tailored by modulation of processing parameters;(2)laser synthesized aerogels with hybrid phases of WO_(3)(Pc)and WO_(3)(P2_(1)/n)have a brighter visible whiteness,higher visible/nearinfrared(NIR)spectral selectivity than the natural prototype of white Cyphochilus insulanus beetles but with comparable LWIR emittance.White WO_(3) aerogel in situ deposited during flexibly fs laser artistic patterning can blur the painting features due to its radiative cooling effect,allowing a colorful impressionistic IR display in the heating mode.What's more,invisible painting features concealed by the white deposited WO_(3) aerogel are clearly/faintly distinguishable by introducing external stimuli of a human hand and sample heating,respectively,catalyzing progress in optical encryption and selectively stimuli-responsive decryption display in the infrared band.
文摘This study compares the relative efficacy of the continuation task and the model-as-feedbackwriting (MAFW) task in EFL writing development. Ninety intermediate-level Chinese EFL learnerswere randomly assigned to a continuation group, a MAFW group, and a control group, each with30 learners. A pretest and a posttest were used to gauge L2 writing development. Results showedthat the continuation task outperformed the MAFW task not only in enhancing the overall qualityof L2 writing, but also in promoting the quality of three components of L2 writing, namely, content,organization, and language. The finding has important implications for L2 writing teaching andlearning.
基金supported by the Key Project of Jiangsu Social Science Fund and the Key Project of Jiangsu Research Center for Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era(Grant No.26ZXZA017).
文摘China’s environmental governance strategy provides a distinctive pathway for integrating sustainable development into national policy.Understanding its policy trajectory is essential for assessing China’s contribution to global sustainable development and the United Nations Sustainable Development Goals(SDGs).This study constructs a comprehensive database of 425 national environmental governance policy documents issued between 1978 and 2022 and applies Latent Dirichlet Allocation(LDA)modeling to examine the evolution of policy themes and discourse.The results show that China’s environmental governance has undergone four stages-initial exploration,detailed development,transformative leap,and diverse prosperity-reflecting a progressive shift toward more integrated and coordinated governance.Policy priorities have evolved from a primary focus on pollution control and energy transition to an emphasis on institutional construction and organizational reform,thereby strengthening alignment with the SDGs.This transformation is characterized by recurring developmental themes and increasingly preventive,forward-looking,and system-oriented governance approaches.Moreover,the co-evolution of policy concepts and implementation has driven a transition from localized,end-of-pipe responses to comprehensive governance frameworks,alongside a shift from normative guidance towards effectiveness-oriented policy design.By employing a data-driven text analysis approach,this study offers a systematic framework for tracing long-term policy evolution and assessing its implications for sustainable development.
基金funded by China National Innovation and Entrepreneurship Project Fund Innovation Training Program(202410451009).
文摘With the rapid development of digital culture,a large number of cultural texts are presented in the form of digital and network.These texts have significant characteristics such as sparsity,real-time and non-standard expression,which bring serious challenges to traditional classification methods.In order to cope with the above problems,this paper proposes a new ASSC(ALBERT,SVD,Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model.Based on the framework of TextRCNN,the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding.Combined with the dual attention mechanism,the model’s ability to capture and model potential key information in short texts is strengthened.The Singular Value Decomposition(SVD)was used to replace the traditional Max pooling operation,which effectively reduced the feature loss rate and retained more key semantic information.The cross-entropy loss function was used to optimize the prediction results,making the model more robust in class distribution learning.The experimental results indicate that,in the digital cultural text classification task,as compared to the baseline model,the proposed ASSC-TextRCNN method achieves an 11.85%relative improvement in accuracy and an 11.97%relative increase in the F1 score.Meanwhile,the relative error rate decreases by 53.18%.This achievement not only validates the effectiveness and advanced nature of the proposed approach but also offers a novel technical route and methodological underpinnings for the intelligent analysis and dissemination of digital cultural texts.It holds great significance for promoting the in-depth exploration and value realization of digital culture.
基金supported by the National Natural Science Foundation of China(Nos.51973116,U1832109,21935002,52003156)the Users with Excellence Program of Hefei Science Center CAS(2019HSC-UE003)+1 种基金the starting grant of ShanghaiTech UniversityState Key Laboratory for Modification of Chemical Fibers and Polymer Materials。
文摘Silks have various advantages compared with synthetic polymer fibers,such as sustainability,mechanical properties,luster,as well as air and humidity permeability.However,the functionalization of silks has not yet been fully developed.Functionalization techniques that retain or even improve the sustainability of silk production are required.To this end,a low-cost,effective,and scalable strategy to produce TCSs by integrating yarn-spinning and continuous dip coating technique is developed herein.TCSs with extremely long length(>10 km),high mechanical performance(strength of 443.1 MPa,toughness of 56.0 MJ m−3,comparable with natural cocoon silk),and good interfacial bonding were developed.TCSs can be automatically woven into arbitrary fabrics,which feature super-hydrophobicity as well as rapid and programmable thermochromic responses with good cyclic performance:the response speed reached to one second and remained stable after hundreds of tests.Finally,applications of TCS fabrics in temperature management and dynamic textile displays are demonstrated,confirming their application potential in smart textiles,wearable devices,flexible displays,and human–machine interfaces.Moreover,combination of the fabrication and the demonstrated applications is expected to bridge the gap between lab research and industry and accelerate the commercialization of TCSs.
基金supported by the National Key R&D Program of China(2022YFB3606501,2022YFB3602902)the Key projects of National Natural Science Foundation of China(62234004)+8 种基金the National Natural Science Foundation of China(U23A2092)Pioneer and Leading Goose R&D Program of Zhejiang(2024C01191,2024C01092)Innovation and Entrepreneurship Team of Zhejiang Province(2021R01003)Ningbo Key Technologies R&D Program(2022Z085),Ningbo 3315 Programme(2020A-01-B)YONGJIANG Talent Introduction Programme(2021A-038-B,2021A-159-G)“Innovation Yongjiang 2035”Key R&D Programme(2024Z146)Ningbo JiangBei District public welfare science and technology project(2022C07)the China National Postdoctoral Program for Innovative Talents(grant no.BX20240391)the China Postdoctoral Science Foundation(grant no.2023M743623).
文摘High-resolution non-emissive displays based on electrochromic tungsten oxides(WOx)are crucial for future near-eye virtual/augmented reality interactions,given their impressive attributes such as high environmental stability,ideal outdoor readability,and low energy consumption.However,the limited intrinsic structure of inorganic materials has presented a significant challenge in achieving precise patterning/pixelation at the micron scale.Here,we successfully developed the direct photolithography for WOx nanoparticles based on in situ photo-induced ligand exchange.This strategy enabled us to achieve ultra-high resolution efficiently(line width<4μm,the best resolution for reported inorganic electrochromic materials).Additionally,the resulting device exhibited impressive electrochromic performance,such as fast response(<1 s at 0 V),high coloration efficiency(119.5 cm^(2) C^(−1)),good optical modulation(55.9%),and durability(>3600 cycles),as well as promising applications in electronic logos,pixelated displays,flexible electronics,etc.The success and advancements presented here are expected to inspire and accelerate research and development(R&D)in high-resolution non-emissive displays and other ultra-fine micro-electronics.
