Results 131 to 140 of about 8,174 (200)

A Fully‐Connected Chip‐Integrated Reconfigurable Mode‐Pairing Quantum Key Distribution Network

Laser &Photonics Reviews, EarlyView.
This work constructs a fully connected reconfigurable chip‐integrated quantum key distribution network via the mode‐pairing protocol. Hybrid ECL and WSS chips cut wavelength and port overhead, realizing arbitrary‐user key sharing with merely two wavelengths.
Xiao‐Lei Jiang, Wei Gao, Yan‐Yang Zhou, Yu Zhou, Yu‐Yao Guo, Yi‐Fei Lu, Yuan‐Chao Yu, Yang Wang, Jia‐Ji Li, Chun Zhou, Hong‐Wei Li, Liang‐Jun Lu, Lin‐Jie Zhou, Jian‐Ping Chen, Wan‐Su Bao   +14 more
wiley   +1 more source

Compact and programmable large-scale optical processor in free space. [PDF]

Light Sci Appl
Ammendola MG, Dehghan N, Scarfe L, D'Errico A, Di Colandrea F, Karimi E, Cardano F.   +6 more
europepmc   +1 more source

Side‐DBR Guided Exciton‐Polariton Lasing With Near‐Field Mode Imaging in an InGaN Thin‐Film Ridge Waveguide

Laser &Photonics Reviews, EarlyView.
Room‐temperature lower‐polariton‐like lasing is realized in an InGaN/GaN thin‐film ridge cavity with lateral distributed Bragg reflectors. Near‐field optical microscopy distinguishes the transverse side‐DBR‐confined mode from a longitudinal photon‐like mode, while simulations reproduce their spatial profiles.
Wai Yuen Fu, Yuk Fai Cheung, Hoi Wai Choi   +2 more
wiley   +1 more source

A novel diffuse liver nodule detector via integrating semantic edge features and probabilistic uncertainty modeling. [PDF]

Front Artif Intell
Tian L, Liu X, Shi Y, Ji Y, Wang S.
europepmc   +1 more source

Physics‐Informed Generative Machine Learning for Designing Crack‐Free γ′‐Strengthened Ni‐Based Superalloys for Laser Powder Bed Fusion

Materials Genome Engineering Advances, EarlyView.
This research proposes a physics‐informed generative machine learning framework to design SHA800, a crack‐free γ′‐strengthened nickel‐based superalloy for laser powder bed fusion, achieving a 43% γ′ volume fraction and 587 HV0.2 hardness. ABSTRACT Fabricating γ′‐strengthened nickel‐based superalloys via laser powder bed fusion (LPBF) faces significant ...
Kai Guo, Dongwei Guan, Tao Hu, Yaqing Hou, Xiaoqun Li, Zhongnan Bi, Songzhe Xu, Hao Yu, Wei Xu, Weidong Xuan, Chaoyue Chen, Zhongming Ren   +11 more
wiley   +1 more source

The Neuro-Cardiac Symbiotic Engine: A Multimodal Fusion Architecture for Cognitive State Decoding via High-Performance Computing. [PDF]

Life (Basel)
Bastidas-Benalcazar N, Calero-Apunte JA, Almeida-Galarraga D, Navas-Boada P, Alvarado-Cando O, Tirado-Espín A, Villalba-Meneses F, Carvajal Mora H, Orozco Garzón N.   +8 more
europepmc   +1 more source

Ecosystem retrogression enhances cross‐domain microbial stability and increases the genetic potential for nutrient cycling

mLife, EarlyView.
Abstract Ecosystem retrogression drives nutrient depletion, reduced productivity, and profound reorganization of soil microbial communities. Using amplicon sequencing and genome‐resolved metagenomics, we examined how cross‐domain microbial networks and functional gene potential respond to long‐term phosphorus and nitrogen limitation along the well ...
Javier A. Ceja‐Navarro, Dishant Patel, Garret Genco, Alyssa Byer, Daliang Ning, Kenneth H. Wan, Susan E. Celniker, Jizhong Zhou, Paul Dijkstra, Bruce A. Hungate, Jennifer Pett‐Ridge, Eoin L. Brodie   +11 more
wiley   +1 more source

TAGNN: topology-aware graph neural network framework for link prediction. [PDF]

Sci Rep
Liang M, Zhang H.
europepmc   +1 more source

Area‐restricted search under realistic constraints

Oikos, EarlyView.
Abstract Area‐restricted search (ARS) is one of the most influential and widely used concepts in foraging theory, capturing a simple rule by which animals intensify local search following a resource encounter. Because ARS performs well in many spatially structured environments, it serves as a basic model for interpreting movement patterns across taxa ...
Inon Scharf, Arik Dorfman
wiley   +1 more source

Emotion-driven front-end design of NEVs using an improved LSTM with LDA-based emotion mining. [PDF]

Sci Rep
Yu C, Qian Y, Li Y.
europepmc   +1 more source