532 research outputs found
Offset effect on the S-Bend structure losses and optimization of its size for integrated optics
The S-Bend structures are heavily exploited to join optical components. Reducing the power loss caused by the curve is the main objective in the design step of these components. However integrated optical circuits require S-Bend waveguide to be low loss and compact sized. In this paper, we present a contribution to link the curved structure to the straight waveguide by using the simulated bend function available in the Beam propagation tool of the Rsoft commercial software package. Simulation results confirm that this approach allows a reduction of the size of the curved structure with offset with relatively minimum of losses for photonic field
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide
range of smart services and innovative applications. Such a context urges a
heavy usage of Machine Learning (ML) techniques, particularly Deep Learning
(DL), to foster innovation and ease the deployment of intelligent network
functions/operations, which are able to fulfill the various requirements of the
envisioned 6G services. Specifically, collaborative ML/DL consists of deploying
a set of distributed agents that collaboratively train learning models without
sharing their data, thus improving data privacy and reducing the
time/communication overhead. This work provides a comprehensive study on how
collaborative learning can be effectively deployed over 6G wireless networks.
In particular, our study focuses on Split Federated Learning (SFL), a technique
recently emerged promising better performance compared with existing
collaborative learning approaches. We first provide an overview of three
emerging collaborative learning paradigms, including federated learning, split
learning, and split federated learning, as well as of 6G networks along with
their main vision and timeline of key developments. We then highlight the need
for split federated learning towards the upcoming 6G networks in every aspect,
including 6G technologies (e.g., intelligent physical layer, intelligent edge
computing, zero-touch network management, intelligent resource management) and
6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous
systems). Furthermore, we review existing datasets along with frameworks that
can help in implementing SFL for 6G networks. We finally identify key technical
challenges, open issues, and future research directions related to SFL-enabled
6G networks
ΠΠΆΠ΅ΡΠΏΠΈΠ»ΠΈΡΡ ΠΠ½Π³ΡΠ»Π΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ΄ΡΠΊΡΡ ΠΈΡ Π³ΠΈΠΏΠ΅ΡΠ³Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π² ΡΠ²ΡΠ·ΠΈ Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ ΡΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΏΠ΅ΡΠ΅ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΡΡΠ΄Π½ΠΎΠΎΠ±ΠΎΠ³Π°ΡΠΈΠΌΡΡ ΠΆΠ΅Π»Π΅Π·Π½ΡΡ ΡΡΠ΄
Mineralogical, geochemical and spectroscopic characteristics of hardly enrichable ferruginous quartzites of the Krivoy Rog iron ore basin are discussed. The results of the study of mineral and chemical composition, assortment and content of microelements, and crystallochemistry of iron are considered. The results of pioneer experiments on thermo magnetization of the ore in order to create a scientific background for improving the processing technology of different iron ore and iron aluminum materials.ΠΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΠΌΠΈΠ½Π΅ΡΠ°Π»ΠΎΠ³ΠΎ-Π³Π΅ΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ ΡΠΏΠ΅ΠΊΡΡΠΎΡΠΊΠΎΠΏΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΡΡΠ΄Π½ΠΎΠΎΠ±ΠΎΠ³Π°ΡΠΈΠΌΡΡ
ΠΆΠ΅Π»Π΅Π·ΠΈΡΡΡΡ
ΠΊΠ²Π°ΡΡΠΈΡΠΎΠ² ΠΈΠ· ΠΡΠΈΠ²ΠΎΡΠΎΠΆΡΠΊΠΎΠ³ΠΎ ΠΆΠ΅Π»Π΅Π·ΠΎΡΡΠ΄Π½ΠΎΠ³ΠΎ Π±Π°ΡΡΠ΅ΠΉΠ½Π°. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΠΌΠΈΠ½Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΈ Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π°, Π°ΡΡΠΎΡΡΠΈΠΌΠ΅Π½ΡΠ° ΠΈ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ ΠΌΠΈΠΊΡΠΎΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ², ΠΊΡΠΈΡΡΠ°Π»Π»ΠΎΡ
ΠΈΠΌΠΈΠΈ ΠΆΠ΅Π»Π΅Π·Π°. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ Π΄Π°Π½Π½ΡΠ΅ ΠΏΠΈΠΎΠ½Π΅ΡΡΠΊΠΈΡ
ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ² ΠΏΠΎ ΡΠ΅ΡΠΌΠΎΠΎΠΌΠ°Π³Π½ΠΈΡΠΈΠ²Π°Π½ΠΈΡ ΡΡΠ΄ Π² ΡΠ΅Π»ΡΡ
ΡΠΎΠ·Π΄Π°Π½ΠΈΡ Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠ»ΠΎΠΊ Π΄Π»Ρ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΏΠ΅ΡΠ΅ΡΠ°Π±ΠΎΡΠΊΠΈ Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π²ΡΠ±ΡΠ°ΠΊΠΎΠ²ΡΠ²Π°ΡΡΠ΅Π³ΠΎΡΡ ΠΆΠ΅Π»Π΅Π·ΠΎΡΡΠ΄Π½ΠΎΠ³ΠΎ ΠΈ ΠΆΠ΅Π»Π΅Π·ΠΎΠ°Π»ΡΠΌΠΈΠ½ΠΈΠ΅Π²ΠΎΠ³ΠΎ ΡΡΡΡΡ
One-pot total chemical synthesis of human Ξ±-synuclein
Post-translational modifications (PTMs) regulate key aspects of the physiological and pathogenic properties of Parkinson's disease-associated presynaptic protein Ξ±-synuclein. We herein describe a one-pot total chemical synthesis that should enable site-specific introduction of single or multiple PTMs or small molecule probes essentially at any site within the protein
Switch peptide via Staudinger reaction
A new transformation based on the Staudinger reaction is described, and its application in the design of a novel switch element to control peptide folding is demonstrated. We found that the azide switch is activated rapidly in water to promote acyl transfer using tris(2-carboxyethyl)phosphine hydrochloride (TCEP) via the Staudinger reaction. Our findings expand the repertoire of uses of the Staudinger reaction in chemical biology and the number of available triggers for use in switch peptides
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