Results 61 to 70 of about 474,494 (357)

Peptide Sequencing With Single Acid Resolution Using a Sub‐Nanometer Diameter Pore

Advanced Functional Materials, EarlyView.
To sequence a single molecule of Aβ1−42–sodium dodecyl sulfate (SDS), the aggregate is forced through a sub‐nanopore 0.4 nm in diameter spanning a 4.0 nm thick membrane. The figure is a visual molecular dynamics (VMD) snapshot depicting the translocation of Aβ1−42–SDS through the pore; only the peptide, the SDS, the Na+ (yellow/green) and Cl− (cyan ...
Apurba Paul, Archith Rayabharam, Lisa Almonte, Eveline Rigo, Joshy Joseph, Ashutosh Kumar, Zhuxin Dong, Narayana Aluru, Gregory Timp   +8 more
wiley   +1 more source

CFDI: Coordinated false data injection attack in active distribution network

IET Generation, Transmission & Distribution
The active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability.
Yang Liu, Chenyang Yang, Nanpeng Yu, Jiazhou Wang, Jue Tian, Hao Huang, Yadong Zhou, Ting Liu   +7 more
doaj   +1 more source

False Data Injection Detection in Nuclear Systems Using Dynamic Noise Analysis

IEEE Access
The integration of nuclear reactors into the smart electric grid dictates the digitalization of Instrumentation and Control (I&C) systems, to support advanced networking capabilities (e.g., remote, semi-autonomous operation).
Konstantinos Gkouliaras, Vasileios Theos, Zachery Dahm, William Richards, Konstantinos Vasili, Stylianos Chatzidakis   +5 more
doaj   +1 more source

Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid

IEEE Access, 2017
Internet of Things (IoT) technologies have been broadly applied in smart grid for monitoring physical or environmental conditions. Especially, state estimation is an important IoT-based application in smart grid, which is used in system monitoring to get
Ruzhi Xu, Rui Wang, Zhitao Guan, Longfei Wu, Jun Wu, Xiaojiang Du   +5 more
doaj   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

From In‐Silico Optimized Microfabrication to Experimental Validation: Engineering a Tridimensional Epi‐Intraneural Interface

Advanced Functional Materials, EarlyView.
An epi‐intraneural interface is developed through in silico optimization and a novel tridimensional microfabrication pipeline. The device integrates penetrating and epineural contacts on a flexible substrate. Mechanical, electrochemical, and in vivo testing in rat and pig reveal robust implantation, low‐threshold activation, and site‐dependent ...
Federico Ciotti, Andrea Cimolato, Noemi Gozzi, Pietro Palopoli, Luca Brugnoli, Natalija Katic Secerovic, Rebecca Gallivan, Alexander Shokurov, Carlo Menon, Johannes Weichart, Ignacio Delgado‐Martinez, Weiguo Song, Stavros Zanos, Xavier Navarro, Stanisa Raspopovic   +14 more
wiley   +1 more source

Filtering Schemes for Injected False Data in Wsn

IOSR Journal of Computer Engineering, 2013
Wireless sensor networking is an emerging technology, which supports many emerging applications due to their low cost, small size and unethered communication over short distances. Sensor nodes are deployed in open hostile environment in WSN applications. An adversary can easily compromise sensor nodes due to their unattended nature.
openaire   +1 more source

Investigating Man-in-the-Middle-based False Data Injection in a Smart Grid Laboratory Environment [PDF]

, 2021
Ömer Sen, Dennis van der Velde, Philipp Linnartz, Immanuel Hacker, Martin Henze, Michaël Andres, Andreas Ulbig   +6 more
openalex   +1 more source

PRELIVE: A Framework for Predicting Lipid Nanoparticles In Vivo Efficacy and Reducing Reliance on Animal Testing

Advanced Functional Materials, EarlyView.
PREdicting LNP In Vivo Efficacy (PRELIVE) framework enables the prediction of lipid nanoparticle (LNPs) organ‐specific delivery through dual modeling approaches. Composition‐based models using formulation parameters and protein corona‐based models using biological fingerprints both achieve high predictive accuracy across multiple organs.
Belal I. Hanafy, Chuan‐En Lu, Kai Liu, Audrey Gallud   +3 more
wiley   +1 more source

A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids

IEEE Access, 2017
False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge
Yi Wang, Mahmoud M. Amin, Jian Fu, Heba B. Moussa   +3 more
doaj   +1 more source