Ultra-thin crystalline silicon stands as a cornerstone material in the foundation of modern micro and nano electronics.Despite the proliferation of various materials including oxide-based,polymer-based,carbon-based,an...Ultra-thin crystalline silicon stands as a cornerstone material in the foundation of modern micro and nano electronics.Despite the proliferation of various materials including oxide-based,polymer-based,carbon-based,and two-dimensional(2D)materials,crystal silicon continues to maintain its stronghold,owing to its superior functionality,scalability,stability,reliability,and uniformity.Nonetheless,the inherent rigidity of the bulk silicon leads to incompatibility with soft tissues,hindering the utilization amid biomedical applications.Because of such issues,decades of research have enabled successful utilization of various techniques to precisely control the thickness and morphology of silicon layers at the scale of several nanometres.This review provides a comprehensive exploration on the features of ultra-thin single crystalline silicon as a semiconducting material,and its role especially among the frontier of advanced bioelectronics.Key processes that enable the transition of rigid silicon to flexible form factors are exhibited,in accordance with their chronological sequence.The inspected stages span both prior and subsequent to transferring the silicon membrane,categorized respectively as on-wafer manufacturing and rigid-to-soft integration.Extensive guidelines to unlock the full potential of flexible electronics are provided through ordered analysis of each manufacturing procedure,the latest findings of biomedical applications,along with practical perspectives for researchers and manufacturers.展开更多
Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel ...Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.展开更多
TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,th...TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping,data tampering,and device impersonation.While digital signatures are indispensable for ensuring authenticity and non-repudiation,conventional schemes such as RSA and ECCare vulnerable to quantumalgorithms,jeopardizing long-termtrust in IIoT deployments.This study proposes a lightweight,stateless,hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT.The design introduces two key optimizations:(1)Forest ofRandomSubsets(FORS)onDemand,where subset secret keys are generated dynamically via a PseudoRandom Function(PRF),thereby minimizing storage overhead and eliminating key-reuse risks;and(2)Winternitz One-Time Signature Plus(WOTS+)partial hash-chain caching,which precomputes intermediate hash values at edge gateways,reducing device-side computations,latency,and energy consumption.The architecture integrates a multi-layerMerkle authentication tree(Merkle tree)and role-based delegation across sensors,gateways,and a Signature Authority Center(SAC),supporting scalable cross-site deployment and key rotation.Froma theoretical perspective,we establish a formal(Existential Unforgeability under Chosen Message Attack)EUF-CMA security proof using a game-based reduction framework.The proof demonstrates that any successful forgerymust reduce to breaking the underlying assumptions of PRF indistinguishability,(second)preimage resistance,or collision resistance,thus quantifying adversarial advantage and ensuring unforgeability.On the implementation side,our design achieves a balanced trade-off between postquantum security and lightweight performance,offering concrete deployment guidelines for real-time industrial systems.In summary,the proposed method contributes both practical system design and formal security guarantees,providing IIoT with a deployable signature substrate that enhances resilience against quantum-era threats and supports future extensions such as device attestation,group signatures,and anomaly detection.展开更多
To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.T...To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.This source is based on spontaneous four-wave mixing(SFWM)in a piece of shallow-ridge silicon waveguide.Theoretical analysis shows that the waveguide dispersion could be tailored by adjusting the ridge width,enabling broadband photon pair generation by SFWM across C band and O band.The spontaneous Raman scattering(SpRS)in silicon waveguides is also investigated experimentally.It shows that there are two regions in the spectrum of generated photons from SpRS,which could be used to achieve cross-band photon pair generation.A chip of shallow-ridge silicon waveguide samples with different ridge widths has been fabricated,through which cross-band photon pair generation is demonstrated experimentally.The experimental results show that the source can be achieved using dispersion-optimized shallow-ridge silicon waveguides.This cross-band quantum light source provides a way to develop new fiber-based quantum communication functions utilizing both C band and O band and extends applications of quantum networks.展开更多
Scalability remains a major challenge in building practical fault-tolerant quantum computers.Currently,the largest number of qubits achieved across leading quantum platforms ranges from hundreds to thousands.In atom a...Scalability remains a major challenge in building practical fault-tolerant quantum computers.Currently,the largest number of qubits achieved across leading quantum platforms ranges from hundreds to thousands.In atom arrays,scalability is primarily constrained by the capacity to generate large numbers of optical tweezers,and conventional techniques using acousto-optic deflectors or spatial light modulators struggle to produce arrays much beyond∼10,000 tweezers.Moreover,these methods require additional microscope objectives to focus the light into micrometer-sized spots,which further complicates system integration and scalability.Here,we demonstrate the experimental generation of an optical tweezer array containing 280×280 spots using a metasurface,nearly an order of magnitude more than most existing systems.The metasurface leverages a large number of subwavelength phase-control pixels to engineer the wavefront of the incident light,enabling both large-scale tweezer generation and direct focusing into micron-scale spots without the need for a microscope.This result shifts the scalability bottleneck for atom arrays from the tweezer generation hardware to the available laser power.Furthermore,the array shows excellent intensity uniformity exceeding 90%,making it suitable for homogeneous single-atom loading and paving the way for trapping arrays of more than 10,000 atoms in the near future.展开更多
Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and colla...Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.展开更多
ABSTRACT:Federated Learning(FL)enables collaborative medical model training without sharing sensitive patient data.However,existing FL systems face increasing security risks from post quantum adversaries and often inc...ABSTRACT:Federated Learning(FL)enables collaborative medical model training without sharing sensitive patient data.However,existing FL systems face increasing security risks from post quantum adversaries and often incur nonnegligible computational and communication overhead when encryption is applied.At the same time,training high performance AI models requires large volumes of high quality data,while medical data such as patient information,clinical records,and diagnostic reports are highly sensitive and subject to strict privacy regulations,including HIPAA and GDPR.Traditional centralized machine learning approaches therefore pose significant challenges for cross institutional collaboration in healthcare.To address these limitations,Federated Learning was introduced to allow multiple institutions to jointly train a global model while keeping local data private.Nevertheless,conventional cryptographicmechanisms,such as RSA,are increasingly inadequate for privacy sensitive FL deployments,particularly in the presence of emerging quantum computing threats.Homomorphic encryption,which enables computations to be performed directly on encrypted data,provides an effective solution for preserving data privacy in federated learning systems.This capability allows healthcare institutions to securely perform collaborative model training while remaining compliant with regulatory requirements.Among homomorphic encryption techniques,NTRU,a lattice based cryptographic scheme defined over polynomial rings,offers strong resistance against quantum attacks by relying on the hardness of the Shortest Vector Problem(SVP).Moreover,NTRU supports limited homomorphic operations that are sufficient for secure aggregation in federated learning.In this work,we propose an NTRU enhanced federated learning framework specifically designed for medical and healthcare applications.Experimental results demonstrate that the proposed approach achieves classification performance comparable to standard federated learning,with final accuracy consistently exceeding 0.93.The framework introduces predictable encryption latency on the order of hundreds of milliseconds per training round and a fixed ciphertext communication overhead per client under practical deployment settings.In addition,the proposed systemeffectivelymitigatesmultiple security threats,including quantum computing attacks,by ensuring robust encryption throughout the training process.By integrating the security and homomorphic properties of NTRU,this study establishes a privacy preserving and quantumresistant federated learning framework that supports the secure,legal,and efficient deployment of AI technologies in healthcare,thereby laying a solid foundation for future intelligent healthcare systems.展开更多
Adhesively bonded joints are widely used in modern lightweight structures due to their high strengthto-weight ratio and design flexibility.However,the reliable non-destructive evaluation of bond integrity remains a si...Adhesively bonded joints are widely used in modern lightweight structures due to their high strengthto-weight ratio and design flexibility.However,the reliable non-destructive evaluation of bond integrity remains a significant challenge.This study presents a numerical investigation of adhesively bonded joints with different adhesive properties using ultrasonic guided waves.The main focus of the investigation is to evaluate the feasibility of using guided waves to assess bond integrity,particularly for detecting challenging weak bonds.For this purpose,a theoretical analysis of dispersion curves was conducted,revealing that the S0 Lamb wave mode is significantly sensitive to variations in adhesive properties in the 300-700 kHz frequency range.Finite element modelling was used to analyse the propagation of guided waves in two scenarios:an adhesively bonded aluminum structure and a more complex configuration-adhesively bonded lap joints.The Short-Time Fourier Transform(STFT)was used to process the obtained results and determine the group velocities of guided waves.By analysing the group velocity characteristics,their dependence on the adhesive properties was identified.In the first scenario,a clear separation of S0 modes from A0 modes was observed in the STFT analysis,with a decrease in group velocity as adhesive stiffness increased.For the more complex lap joint scenario,the separation between A0 and S0 modes was less distinct.However,the analysis of the average group velocity shows a dependence of average group velocity on adhesive properties.This is similar to the first scenario.There is a decrease in average group velocity as adhesive stiffness increases.The results obtained demonstrate that guided wavebased methods have a high potential for non-destructive evaluation of adhesively bonded structures,including the detection of weak bonds.展开更多
Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pe...Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pest and disease detectionmust overcome three fundamental challenges:feature degradation in dense vegetation environments,limited detection capability for sub-32×32 pixel targets,and inadequate bounding box regression for irregular pest morphologies.This study proposes YOLOv12-KMA,a novel detection framework that addresses these limitations through four synergistic architectural innovations,specifically optimized for agricultural environments.First,we introduce efficient multi-head attention(C3K2-EMA),which reduces noise interference by 41%through selective regional attention while maintaining O(k⋅n⋅d)computational complexity vs.O(n2⋅d)for standard attention.Second,we develop A2C2f-KAN modules embedding Kolmogorov-Arnold networks(KAN)with B-spline activation functions,achieving 15%better feature representation for small targets without global distortion.Third,we propose minimum point distance intersection over union(MPDIoU)loss that resolves aspect ratio degeneration issues in complete intersection over union(CIoU),accelerating convergence by 23%for irregular pest shapes.Fourth,we implement the dynamic sampling(DySample)module that reduces computational overhead by 72%while preserving 94%feature fidelity compared to conventional interpolation methods.Comprehensive validation on 8742 annotated agricultural images demonstrates significant improvements:2.6 percentage point increase in mean average precision(mAP)@0.5(91.0%→93.6%),3.2 percentage point gain in mAP@0.5:0.95,with precision and recall improvements of 4.8%and 2.4%,respectively.Statistical analysis confirms significance(p<0.001)with large effect sizes(η2=0.78).The optimized architecture maintains real-time performance at 159 frames per second(FPS)on consumer hardware,enabling practical deployment in precision agriculture monitoring systems.展开更多
A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By...A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By applying the theory of generalized Brewster complex wave impedance matching,five distinct unit cell designs are developed to attain more than95%absorption rate for dual-polarized incident waves within five angular ranges:0°-30°,30°-50°,50°-60°,60°-70°,and 70°-80°.To optimally reduce the RCS of a cambered platform,the five types of units can be evenly distributed on the surface based on the local incident angles of plane waves originating from the target airspace.As an illustrative example,the leading edge of an airfoil is taken into account,and experimental measurements validate the efficiency of the proposed structure.Specifically,the absorbing surface achieves more than 10 dB of RCS reduction in the frequency ranges from 5-10 GHz(about66.7%relative bandwidth)for dual polarizations.展开更多
Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been...Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been extensively studied for diagnosing malignancy and stroke.In recent years,the emerging exploration of chemical exchange saturation transfer magnetic resonance imaging for detecting pathological changes in neurodegenerative diseases has opened up new possibilities for early detection and repetitive scans without ionizing radiation.This review serves as an overview of chemical exchange saturation transfer magnetic resonance imaging with detailed information on contrast mechanisms and processing methods and summarizes recent developments in both clinical and preclinical studies of chemical exchange saturation transfer magnetic resonance imaging for Alzheimer’s disease,Parkinson’s disease,multiple sclerosis,and Huntington’s disease.A comprehensive literature search was conducted using databases such as PubMed and Google Scholar,focusing on peer-reviewed articles from the past 15 years relevant to clinical and preclinical applications.The findings suggest that chemical exchange saturation transfer magnetic resonance imaging has the potential to detect molecular changes and altered metabolism,which may aid in early diagnosis and assessment of the severity of neurodegenerative diseases.Although promising results have been observed in selected clinical and preclinical trials,further validations are needed to evaluate their clinical value.When combined with other imaging modalities and advanced analytical methods,chemical exchange saturation transfer magnetic resonance imaging shows potential as an in vivo biomarker,enhancing the understanding of neuropathological mechanisms in neurodegenerative diseases.展开更多
The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware...The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.展开更多
The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr...The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.展开更多
Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft conti...Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.展开更多
Accurate blood pressure(BP)monitoring is essential for preventing and managing cardiovascular disease.Advancements in materials science,medicine,flexible electronic,and artificial intelligence(AI)have enabled cuffless...Accurate blood pressure(BP)monitoring is essential for preventing and managing cardiovascular disease.Advancements in materials science,medicine,flexible electronic,and artificial intelligence(AI)have enabled cuffless,unobtrusive BP monitoring systems,offering an alternative to traditional sphygmomanometers.However,extending these advances to real-world cardiovascular care particularly in resource-limited settings remains challenging due to constraints in computational resources,power efficiency,and deployment scalability.This review presents a comprehensive synthesis of AI-enhanced wearable BP monitoring,emphasizing its potential for personalized,scalable,and accessible healthcare.We systematically analyze the end-to-end system architecture,from mechano-electric sensing principles and AI-based estimation models to edge-aware deployment strategies tailored for low-resource environments.We further discuss clinical validation metrics and implementation barriers and prospective strategies.To bridge lab-to-field translation,we propose an innovative"sensor-model-deployment-assessment"co-design framework.This roadmap highlights how AI-enhanced BP technologies can support proactive hypertension control and promote cardiovascular health equity on a global scale.展开更多
Space exploration is significant for scientific innovation,resource utilization,and planetary security.Space exploration involves several systems including satellites,space suits,communication systems,and robotics,whi...Space exploration is significant for scientific innovation,resource utilization,and planetary security.Space exploration involves several systems including satellites,space suits,communication systems,and robotics,which have to function under harsh space conditions such as extreme temperatures(−270 to 1650℃),microgravity(10^(-6)g),unhealthy humidity(<20%RH or>60%RH),high atmospheric pressure(~1450 psi),and radiation(4000–5000 mSv).Conventional energy-harvesting technologies(solar cells,fuel cells,and nuclear energy),that are normally used to power these space systems have certain limitations(e.g.,sunlight dependence,weight,degradation,big size,high cost,low capacity,radioactivity,complexity,and low efficiency).The constraints in conventional energy resources have made it imperative to look for non-conventional yet efficient alternatives.A great potential for enhancing efficiency,sustainability,and mission duration in space exploration can be offered by integrating triboelectric nanogenerators(TENGs)with existing energy sources.Recently,the potential of TENG including energy harvesting(from vibrations/movements in satellites and spacecraft),self-powered sensing,and microgravity,for multiple applications in different space missions has been discussed.This review comprehensively covers the use of TENGs for various space applications,such as planetary exploration missions(Mars environment monitoring),manned space equipment,In-orbit robotic operations/collision monitoring,spacecraft’s design and structural health monitoring,Aeronautical systems,and conventional energy harvesting(solar and nuclear).This review also discusses the use of self-powered TENG sensors for deep space object perception.At the same time,this review compares TENGs with conventional energy harvesting technologies for space systems.Lastly,this review talks about energy harvesting in satellites,TENG-based satellite communication systems,and future practical implementation challenges(with possible solutions).展开更多
Owing to the exceptional optoelectronic properties,metal halide perovskites have emerged as leading semiconductor materials for next-generation display technologies,providing perovskite light-emitting diodes(Pe LEDs)g...Owing to the exceptional optoelectronic properties,metal halide perovskites have emerged as leading semiconductor materials for next-generation display technologies,providing perovskite light-emitting diodes(Pe LEDs)great potential for high-quality color displays with a wide color gamut and pure color emission.Although laboratory-scale Pe LEDs have achieved neartheoretical efficiencies,challenges such as achieving uniform large-area films,improving material stability,and enhancing patterning precision remain barriers to commercialization.This review presents a systematic analysis of scalable manufacturing and precision patterning strategies for Pe LEDs,focusing on their applications in large-area lighting and full-color displays.Fabrication methods are categorized into film deposition techniques(spin-coating,blade-coating,and thermal evaporation)and patterning strategies,including top-down(photolithography,laser/e-beam lithography,and nanoimprinting)and bottom-up(patterned crystal growth,inkjet printing,and electrohydrodynamic jet printing)approaches.In this review,we discuss the advantages and limitations of each strategy,highlight current challenges,and outlook possible pathways towards scalable,high-performance Pe LEDs for advanced optoelectronic applications.展开更多
Over the past decade,the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks.Traditional detection techniques,while still in use,often fall short when...Over the past decade,the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks.Traditional detection techniques,while still in use,often fall short when confronted with modern threats that use advanced evasion strategies.This systematic review critically examines recent developments in malware detection,with a particular emphasis on the role of artificial intelligence(AI)and machine learning(ML)in enhancing detection capabilities.Drawing on literature published between 2019 and 2025,this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore,SpringerLink,ScienceDirect,and ACM Digital Library.In doing so,it explores the evolution of malware,evaluates detection methods,assesses the quality and limitations of widely used datasets,and identifies key challenges facing the field.Unlike existing surveys,this work offers a structured comparison of AI-driven frameworks and provides a detailed account of emerging techniques such as hybrid detection frameworks and image-based analysis.The findings indicate that AIbased models trained on diverse,high-quality datasets consistently outperform conventional methods,particularly when supported by feature engineering,explainable AI and a multi-faceted strategy.The review concludes by outlining future research directions,including the need for standardized datasets,enhanced adversarial robustness,and the integration of privacy-preserving mechanisms in malware detection systems.展开更多
Simple models are proposed for the calculation of refractive index n and electronic polarizability α of AⅠBⅢC2Ⅵ and AⅡBⅣC2Ⅴ compounds of groups of chalcopyrite semiconductors from their energy gap data. The val...Simple models are proposed for the calculation of refractive index n and electronic polarizability α of AⅠBⅢC2Ⅵ and AⅡBⅣC2Ⅴ compounds of groups of chalcopyrite semiconductors from their energy gap data. The values family and 12 compounds of AⅡBⅣC2Ⅴ family are calculated for the work. The proposed models are applicable for the whole range of energy gap materials. The calculated values are compared with the available experimental and reported values. A fairly good agreement between them is obtained.展开更多
In this work, a Fe-based nanocrystalline microwire of 20 mm in length and 25 μm in diameter was placed in the center of a 316 stainless steel pipe. The pipe was 500 μm in diameter and a little shorter than the micro...In this work, a Fe-based nanocrystalline microwire of 20 mm in length and 25 μm in diameter was placed in the center of a 316 stainless steel pipe. The pipe was 500 μm in diameter and a little shorter than the microwire. A series of voltages were applied on the pipe to study the influence of the electrical field on the Giant-Magneto-Impedance(GMI) effect of the microwire. Experimental results showed that the electronic field between the wire and the pipe reduced the hysteresis of the GMI effect. The results were explained based on equivalent circuit and eddy current consumptions analysis.展开更多
基金support received from National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT)(RS-2024-00353768)the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT)(RS-2025-02217919)+1 种基金funded by the Yonsei Fellowshipfunded by Lee Youn Jae and the KIST Institutional Program Project No.2E31603-22-140 (KJY).
文摘Ultra-thin crystalline silicon stands as a cornerstone material in the foundation of modern micro and nano electronics.Despite the proliferation of various materials including oxide-based,polymer-based,carbon-based,and two-dimensional(2D)materials,crystal silicon continues to maintain its stronghold,owing to its superior functionality,scalability,stability,reliability,and uniformity.Nonetheless,the inherent rigidity of the bulk silicon leads to incompatibility with soft tissues,hindering the utilization amid biomedical applications.Because of such issues,decades of research have enabled successful utilization of various techniques to precisely control the thickness and morphology of silicon layers at the scale of several nanometres.This review provides a comprehensive exploration on the features of ultra-thin single crystalline silicon as a semiconducting material,and its role especially among the frontier of advanced bioelectronics.Key processes that enable the transition of rigid silicon to flexible form factors are exhibited,in accordance with their chronological sequence.The inspected stages span both prior and subsequent to transferring the silicon membrane,categorized respectively as on-wafer manufacturing and rigid-to-soft integration.Extensive guidelines to unlock the full potential of flexible electronics are provided through ordered analysis of each manufacturing procedure,the latest findings of biomedical applications,along with practical perspectives for researchers and manufacturers.
文摘Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.
文摘TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping,data tampering,and device impersonation.While digital signatures are indispensable for ensuring authenticity and non-repudiation,conventional schemes such as RSA and ECCare vulnerable to quantumalgorithms,jeopardizing long-termtrust in IIoT deployments.This study proposes a lightweight,stateless,hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT.The design introduces two key optimizations:(1)Forest ofRandomSubsets(FORS)onDemand,where subset secret keys are generated dynamically via a PseudoRandom Function(PRF),thereby minimizing storage overhead and eliminating key-reuse risks;and(2)Winternitz One-Time Signature Plus(WOTS+)partial hash-chain caching,which precomputes intermediate hash values at edge gateways,reducing device-side computations,latency,and energy consumption.The architecture integrates a multi-layerMerkle authentication tree(Merkle tree)and role-based delegation across sensors,gateways,and a Signature Authority Center(SAC),supporting scalable cross-site deployment and key rotation.Froma theoretical perspective,we establish a formal(Existential Unforgeability under Chosen Message Attack)EUF-CMA security proof using a game-based reduction framework.The proof demonstrates that any successful forgerymust reduce to breaking the underlying assumptions of PRF indistinguishability,(second)preimage resistance,or collision resistance,thus quantifying adversarial advantage and ensuring unforgeability.On the implementation side,our design achieves a balanced trade-off between postquantum security and lightweight performance,offering concrete deployment guidelines for real-time industrial systems.In summary,the proposed method contributes both practical system design and formal security guarantees,providing IIoT with a deployable signature substrate that enhances resilience against quantum-era threats and supports future extensions such as device attestation,group signatures,and anomaly detection.
基金supported by the Quantum Science and Technology-National Science and Technology Major Project (Grant No.2024ZD0302502 for WZ)the National Natural Science Foundation of China(Grant No.92365210 for WZ)+1 种基金Tsinghua Initiative Scientific Research Program (for WZ)the project of Tsinghua University-Zhuhai Huafa Industrial Share Company Joint Institute for Architecture Optoelectronic Technologies (JIAOT,for YH)。
文摘To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.This source is based on spontaneous four-wave mixing(SFWM)in a piece of shallow-ridge silicon waveguide.Theoretical analysis shows that the waveguide dispersion could be tailored by adjusting the ridge width,enabling broadband photon pair generation by SFWM across C band and O band.The spontaneous Raman scattering(SpRS)in silicon waveguides is also investigated experimentally.It shows that there are two regions in the spectrum of generated photons from SpRS,which could be used to achieve cross-band photon pair generation.A chip of shallow-ridge silicon waveguide samples with different ridge widths has been fabricated,through which cross-band photon pair generation is demonstrated experimentally.The experimental results show that the source can be achieved using dispersion-optimized shallow-ridge silicon waveguides.This cross-band quantum light source provides a way to develop new fiber-based quantum communication functions utilizing both C band and O band and extends applications of quantum networks.
基金supported by the National Natural Science Foundation of China (Grant No.92576208)Tsinghua University Initiative Scientific Research Program+1 种基金Beijing Science and Technology Planning ProjectTsinghua University Dushi Program。
文摘Scalability remains a major challenge in building practical fault-tolerant quantum computers.Currently,the largest number of qubits achieved across leading quantum platforms ranges from hundreds to thousands.In atom arrays,scalability is primarily constrained by the capacity to generate large numbers of optical tweezers,and conventional techniques using acousto-optic deflectors or spatial light modulators struggle to produce arrays much beyond∼10,000 tweezers.Moreover,these methods require additional microscope objectives to focus the light into micrometer-sized spots,which further complicates system integration and scalability.Here,we demonstrate the experimental generation of an optical tweezer array containing 280×280 spots using a metasurface,nearly an order of magnitude more than most existing systems.The metasurface leverages a large number of subwavelength phase-control pixels to engineer the wavefront of the incident light,enabling both large-scale tweezer generation and direct focusing into micron-scale spots without the need for a microscope.This result shifts the scalability bottleneck for atom arrays from the tweezer generation hardware to the available laser power.Furthermore,the array shows excellent intensity uniformity exceeding 90%,making it suitable for homogeneous single-atom loading and paving the way for trapping arrays of more than 10,000 atoms in the near future.
基金supported by the National Natural Science Foundation of China Youth Fund(No.62101579)。
文摘Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.
文摘ABSTRACT:Federated Learning(FL)enables collaborative medical model training without sharing sensitive patient data.However,existing FL systems face increasing security risks from post quantum adversaries and often incur nonnegligible computational and communication overhead when encryption is applied.At the same time,training high performance AI models requires large volumes of high quality data,while medical data such as patient information,clinical records,and diagnostic reports are highly sensitive and subject to strict privacy regulations,including HIPAA and GDPR.Traditional centralized machine learning approaches therefore pose significant challenges for cross institutional collaboration in healthcare.To address these limitations,Federated Learning was introduced to allow multiple institutions to jointly train a global model while keeping local data private.Nevertheless,conventional cryptographicmechanisms,such as RSA,are increasingly inadequate for privacy sensitive FL deployments,particularly in the presence of emerging quantum computing threats.Homomorphic encryption,which enables computations to be performed directly on encrypted data,provides an effective solution for preserving data privacy in federated learning systems.This capability allows healthcare institutions to securely perform collaborative model training while remaining compliant with regulatory requirements.Among homomorphic encryption techniques,NTRU,a lattice based cryptographic scheme defined over polynomial rings,offers strong resistance against quantum attacks by relying on the hardness of the Shortest Vector Problem(SVP).Moreover,NTRU supports limited homomorphic operations that are sufficient for secure aggregation in federated learning.In this work,we propose an NTRU enhanced federated learning framework specifically designed for medical and healthcare applications.Experimental results demonstrate that the proposed approach achieves classification performance comparable to standard federated learning,with final accuracy consistently exceeding 0.93.The framework introduces predictable encryption latency on the order of hundreds of milliseconds per training round and a fixed ciphertext communication overhead per client under practical deployment settings.In addition,the proposed systemeffectivelymitigatesmultiple security threats,including quantum computing attacks,by ensuring robust encryption throughout the training process.By integrating the security and homomorphic properties of NTRU,this study establishes a privacy preserving and quantumresistant federated learning framework that supports the secure,legal,and efficient deployment of AI technologies in healthcare,thereby laying a solid foundation for future intelligent healthcare systems.
基金supported by the Research Council of Lithuania(LMTLT),agreement no.S-MIP-22-5.
文摘Adhesively bonded joints are widely used in modern lightweight structures due to their high strengthto-weight ratio and design flexibility.However,the reliable non-destructive evaluation of bond integrity remains a significant challenge.This study presents a numerical investigation of adhesively bonded joints with different adhesive properties using ultrasonic guided waves.The main focus of the investigation is to evaluate the feasibility of using guided waves to assess bond integrity,particularly for detecting challenging weak bonds.For this purpose,a theoretical analysis of dispersion curves was conducted,revealing that the S0 Lamb wave mode is significantly sensitive to variations in adhesive properties in the 300-700 kHz frequency range.Finite element modelling was used to analyse the propagation of guided waves in two scenarios:an adhesively bonded aluminum structure and a more complex configuration-adhesively bonded lap joints.The Short-Time Fourier Transform(STFT)was used to process the obtained results and determine the group velocities of guided waves.By analysing the group velocity characteristics,their dependence on the adhesive properties was identified.In the first scenario,a clear separation of S0 modes from A0 modes was observed in the STFT analysis,with a decrease in group velocity as adhesive stiffness increased.For the more complex lap joint scenario,the separation between A0 and S0 modes was less distinct.However,the analysis of the average group velocity shows a dependence of average group velocity on adhesive properties.This is similar to the first scenario.There is a decrease in average group velocity as adhesive stiffness increases.The results obtained demonstrate that guided wavebased methods have a high potential for non-destructive evaluation of adhesively bonded structures,including the detection of weak bonds.
文摘Smart pest control is crucial for building farmresilience andensuringsustainable agriculture inthe faceof climate change and environmental challenges.To achieve effective intelligent monitoring systems,agricultural pest and disease detectionmust overcome three fundamental challenges:feature degradation in dense vegetation environments,limited detection capability for sub-32×32 pixel targets,and inadequate bounding box regression for irregular pest morphologies.This study proposes YOLOv12-KMA,a novel detection framework that addresses these limitations through four synergistic architectural innovations,specifically optimized for agricultural environments.First,we introduce efficient multi-head attention(C3K2-EMA),which reduces noise interference by 41%through selective regional attention while maintaining O(k⋅n⋅d)computational complexity vs.O(n2⋅d)for standard attention.Second,we develop A2C2f-KAN modules embedding Kolmogorov-Arnold networks(KAN)with B-spline activation functions,achieving 15%better feature representation for small targets without global distortion.Third,we propose minimum point distance intersection over union(MPDIoU)loss that resolves aspect ratio degeneration issues in complete intersection over union(CIoU),accelerating convergence by 23%for irregular pest shapes.Fourth,we implement the dynamic sampling(DySample)module that reduces computational overhead by 72%while preserving 94%feature fidelity compared to conventional interpolation methods.Comprehensive validation on 8742 annotated agricultural images demonstrates significant improvements:2.6 percentage point increase in mean average precision(mAP)@0.5(91.0%→93.6%),3.2 percentage point gain in mAP@0.5:0.95,with precision and recall improvements of 4.8%and 2.4%,respectively.Statistical analysis confirms significance(p<0.001)with large effect sizes(η2=0.78).The optimized architecture maintains real-time performance at 159 frames per second(FPS)on consumer hardware,enabling practical deployment in precision agriculture monitoring systems.
基金supported by the National Key Research and Development Program of China(2023YFB3907304-3)the National Natural Science Foundation of China(NSFC)(62271050)。
文摘A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By applying the theory of generalized Brewster complex wave impedance matching,five distinct unit cell designs are developed to attain more than95%absorption rate for dual-polarized incident waves within five angular ranges:0°-30°,30°-50°,50°-60°,60°-70°,and 70°-80°.To optimally reduce the RCS of a cambered platform,the five types of units can be evenly distributed on the surface based on the local incident angles of plane waves originating from the target airspace.As an illustrative example,the leading edge of an airfoil is taken into account,and experimental measurements validate the efficiency of the proposed structure.Specifically,the absorbing surface achieves more than 10 dB of RCS reduction in the frequency ranges from 5-10 GHz(about66.7%relative bandwidth)for dual polarizations.
基金supported by The University of Hong Kong,China(109000487,109001694,204610401,and 204610519)National Natural Science Foundation of China(82402225)(to JH).
文摘Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been extensively studied for diagnosing malignancy and stroke.In recent years,the emerging exploration of chemical exchange saturation transfer magnetic resonance imaging for detecting pathological changes in neurodegenerative diseases has opened up new possibilities for early detection and repetitive scans without ionizing radiation.This review serves as an overview of chemical exchange saturation transfer magnetic resonance imaging with detailed information on contrast mechanisms and processing methods and summarizes recent developments in both clinical and preclinical studies of chemical exchange saturation transfer magnetic resonance imaging for Alzheimer’s disease,Parkinson’s disease,multiple sclerosis,and Huntington’s disease.A comprehensive literature search was conducted using databases such as PubMed and Google Scholar,focusing on peer-reviewed articles from the past 15 years relevant to clinical and preclinical applications.The findings suggest that chemical exchange saturation transfer magnetic resonance imaging has the potential to detect molecular changes and altered metabolism,which may aid in early diagnosis and assessment of the severity of neurodegenerative diseases.Although promising results have been observed in selected clinical and preclinical trials,further validations are needed to evaluate their clinical value.When combined with other imaging modalities and advanced analytical methods,chemical exchange saturation transfer magnetic resonance imaging shows potential as an in vivo biomarker,enhancing the understanding of neuropathological mechanisms in neurodegenerative diseases.
基金co-supported by the National Natural Science Foundation of China(Grant Nos.62222404,T2450054,62304084,62504087,62361136587 and 92248304)the National Key Research and Development Plan of China(Grant No.2021YFB3601200)+3 种基金the Major Program of Hubei Province(Grant No.2023BAA009)the Research Grants Council of Hong Kong Postdoctoral Fellowship Scheme(Grant No.PDFS2223-4S06)the China Postdoctoral Science Foundation funded project(Grant No.2025M770530)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20250136).
文摘The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.
文摘The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.
基金supported in part by the Innovation and Technology Commission of Hong Kong,China(ITS/136/20,ITS/234/21,MHP/096/22,ITS/235/22)Multi-Scale Medical Robotics Center,InnoHK,China(8312051)+1 种基金Research Grants Council(RGC) of Hong Kong,China(CUHK 14217822,CUHK14207823,AoE/E-407/24-N)The Chinese University of Hong Kong(CUHK) Direct Grant。
文摘Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.
基金the Chinese University of Hong Kong for providing research resources and institutional support
文摘Accurate blood pressure(BP)monitoring is essential for preventing and managing cardiovascular disease.Advancements in materials science,medicine,flexible electronic,and artificial intelligence(AI)have enabled cuffless,unobtrusive BP monitoring systems,offering an alternative to traditional sphygmomanometers.However,extending these advances to real-world cardiovascular care particularly in resource-limited settings remains challenging due to constraints in computational resources,power efficiency,and deployment scalability.This review presents a comprehensive synthesis of AI-enhanced wearable BP monitoring,emphasizing its potential for personalized,scalable,and accessible healthcare.We systematically analyze the end-to-end system architecture,from mechano-electric sensing principles and AI-based estimation models to edge-aware deployment strategies tailored for low-resource environments.We further discuss clinical validation metrics and implementation barriers and prospective strategies.To bridge lab-to-field translation,we propose an innovative"sensor-model-deployment-assessment"co-design framework.This roadmap highlights how AI-enhanced BP technologies can support proactive hypertension control and promote cardiovascular health equity on a global scale.
基金supported by Swedish Research Council(Vetenskapsradet,2023-04962).
文摘Space exploration is significant for scientific innovation,resource utilization,and planetary security.Space exploration involves several systems including satellites,space suits,communication systems,and robotics,which have to function under harsh space conditions such as extreme temperatures(−270 to 1650℃),microgravity(10^(-6)g),unhealthy humidity(<20%RH or>60%RH),high atmospheric pressure(~1450 psi),and radiation(4000–5000 mSv).Conventional energy-harvesting technologies(solar cells,fuel cells,and nuclear energy),that are normally used to power these space systems have certain limitations(e.g.,sunlight dependence,weight,degradation,big size,high cost,low capacity,radioactivity,complexity,and low efficiency).The constraints in conventional energy resources have made it imperative to look for non-conventional yet efficient alternatives.A great potential for enhancing efficiency,sustainability,and mission duration in space exploration can be offered by integrating triboelectric nanogenerators(TENGs)with existing energy sources.Recently,the potential of TENG including energy harvesting(from vibrations/movements in satellites and spacecraft),self-powered sensing,and microgravity,for multiple applications in different space missions has been discussed.This review comprehensively covers the use of TENGs for various space applications,such as planetary exploration missions(Mars environment monitoring),manned space equipment,In-orbit robotic operations/collision monitoring,spacecraft’s design and structural health monitoring,Aeronautical systems,and conventional energy harvesting(solar and nuclear).This review also discusses the use of self-powered TENG sensors for deep space object perception.At the same time,this review compares TENGs with conventional energy harvesting technologies for space systems.Lastly,this review talks about energy harvesting in satellites,TENG-based satellite communication systems,and future practical implementation challenges(with possible solutions).
基金supported by 14th Five-Year Plan Key R&D Plan,Ministry of Science and Technology of the People’s Republic of China,2024YFB3409002National Natural Science Foundation of China,12302142+4 种基金HKUSTHKUST(GZ)Collaborative Research Scheme,G035Yangcheng Scholars Research Project-Leading Talent Training Project,2024312156Guangzhou-HKUST(GZ)Joint Funding Scheme,2023A03J0157Guangzhou Basic and Applied Basic Research Project,2024A04J4765Shenzhen Basic Research Project,JCYJ20220530114417040。
文摘Owing to the exceptional optoelectronic properties,metal halide perovskites have emerged as leading semiconductor materials for next-generation display technologies,providing perovskite light-emitting diodes(Pe LEDs)great potential for high-quality color displays with a wide color gamut and pure color emission.Although laboratory-scale Pe LEDs have achieved neartheoretical efficiencies,challenges such as achieving uniform large-area films,improving material stability,and enhancing patterning precision remain barriers to commercialization.This review presents a systematic analysis of scalable manufacturing and precision patterning strategies for Pe LEDs,focusing on their applications in large-area lighting and full-color displays.Fabrication methods are categorized into film deposition techniques(spin-coating,blade-coating,and thermal evaporation)and patterning strategies,including top-down(photolithography,laser/e-beam lithography,and nanoimprinting)and bottom-up(patterned crystal growth,inkjet printing,and electrohydrodynamic jet printing)approaches.In this review,we discuss the advantages and limitations of each strategy,highlight current challenges,and outlook possible pathways towards scalable,high-performance Pe LEDs for advanced optoelectronic applications.
文摘Over the past decade,the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks.Traditional detection techniques,while still in use,often fall short when confronted with modern threats that use advanced evasion strategies.This systematic review critically examines recent developments in malware detection,with a particular emphasis on the role of artificial intelligence(AI)and machine learning(ML)in enhancing detection capabilities.Drawing on literature published between 2019 and 2025,this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore,SpringerLink,ScienceDirect,and ACM Digital Library.In doing so,it explores the evolution of malware,evaluates detection methods,assesses the quality and limitations of widely used datasets,and identifies key challenges facing the field.Unlike existing surveys,this work offers a structured comparison of AI-driven frameworks and provides a detailed account of emerging techniques such as hybrid detection frameworks and image-based analysis.The findings indicate that AIbased models trained on diverse,high-quality datasets consistently outperform conventional methods,particularly when supported by feature engineering,explainable AI and a multi-faceted strategy.The review concludes by outlining future research directions,including the need for standardized datasets,enhanced adversarial robustness,and the integration of privacy-preserving mechanisms in malware detection systems.
文摘Simple models are proposed for the calculation of refractive index n and electronic polarizability α of AⅠBⅢC2Ⅵ and AⅡBⅣC2Ⅴ compounds of groups of chalcopyrite semiconductors from their energy gap data. The values family and 12 compounds of AⅡBⅣC2Ⅴ family are calculated for the work. The proposed models are applicable for the whole range of energy gap materials. The calculated values are compared with the available experimental and reported values. A fairly good agreement between them is obtained.
文摘In this work, a Fe-based nanocrystalline microwire of 20 mm in length and 25 μm in diameter was placed in the center of a 316 stainless steel pipe. The pipe was 500 μm in diameter and a little shorter than the microwire. A series of voltages were applied on the pipe to study the influence of the electrical field on the Giant-Magneto-Impedance(GMI) effect of the microwire. Experimental results showed that the electronic field between the wire and the pipe reduced the hysteresis of the GMI effect. The results were explained based on equivalent circuit and eddy current consumptions analysis.
