976 research outputs found
First-order nature of the spin-reorientation phase transition in SmCr O3
No full text© 2022 American Physical Society.The ever expected canted antiferromagnetic (CAFM) Pb′n′m:Γ4(Gx,Ay,FZ;FZR) to Pbn′m′:Γ2(Fx,Cy,GZ;FxR, CyR) spin reorientation phase transition (SRPT) has only recently been confirmed through high-resolution time-of-flight neutron scattering studies by Sau et al. [Phys. Rev. B 103, 144418 (2021)10.1103/PhysRevB.103.144418]. Despite several studies on SmCrO3, the nature of its SRPT still remains debatable. In the present study, we revisit the issue through dc M(T) and ac-susceptibility, χac(T), measurements. Repeated cycle field-cooled-cooling and field-cooled-warming dc M(T) measurements clearly expose a temperature point differentiating the regimes of continuous and discontinuous parts of the SRPT. The discontinuous part has a tiny but clear hysteresis in M(T), confirming the first-order nature of the SRPT with supercooling (T∗) and superheating (T∗∗) temperatures to be ∼33 and ∼36K, respectively. The hysteresis in the M(T) is strongly supported by the occurrence of hysteresis in the nondispersing peaks in χac(T), measured using a 3 Oe ac signal amplitude during cooling and heating under zero dc-bias. Below SRPT, the complete reversibility of M(T) and χac(T) confirms the second-order nature of the Sm3+ ordering at TN2, which arises due to independent Sm3+-Sm3+ interaction. Similarly, the absence of hysteresis in M(T) as well as in χac(T), across the paramagnetic to CAFM Γ4 phase transition, proves the second-order nature of this phase transition.11Nsciescopu
Noncollinear magnetic order, in-plane anisotropy, and magnetoelectric coupling in a pyroelectric honeycomb antiferromagnet NiMoO
No full textNiMoO is a pyroelectric honeycomb antiferromagnet
exhibiting peculiar changes of its electric polarization at magnetic
transitions. NiMoO stands out from the isostructural magnetic
compounds, showing an anomalously low magnetic transition temperature and
unique magnetic anisotropy. We determine the magnetic structure of
NiMoO utilizing high-resolution powder and single-crystal
neutron diffraction. A noncollinear stripy antiferromagnetic order is found in
the honeycomb planes. The magnetic space group is \textit{Pna}2. The
in-plane magnetic connection is of the stripy type both for the -plane and
the -axis spin components. This is a simpler connection than the one
proposed previously. The ferromagnetic interlayer order of the -axis spin
components in our model is also distinct. The magnetic anisotropy of
NiMoO is characterized by orientation-dependent magnetic
susceptibility measurements on a single crystal, consistent with neutron
diffraction analysis. The local magnetoelectric tensor analysis using our
magnetic models provides new insights into its magnetoelectric coupling and
polarization. Thus, our results deliver essential information for understanding
both the unusual magnetoelectric properties of NiMoO and the
prospects for observation of exotic nonreciprocal, Hall, and magnonic effects
characteristic to this compound family.Comment: 28 pages, 23 figures, 14 table
A systematic framework for categorising IoT device fingerprinting mechanisms
No full textThe popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one’s network. However, there are many challenges faced in the task of fingerprinting IoT devices, mainly due to the huge variety of the devices involved. At the same time, the task can potentially be improved by applying machine learning techniques for better accuracy and efficiency. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carried out an extensive literature review of existing papers on fingerprinting IoT devices – paying close attention to those with machine learning features. This is followed by an extraction of important and comparable features among the mechanisms outlined in those papers. As a result, we came up with a key set of terminologies that are relevant both in the fingerprinting context and in the IoT domain. This enabled us to construct a framework called IDWork, which can be used for categorising existing IoT fingerprinting mechanisms in a way that will facilitate a coherent and fair comparison of these mechanisms. We found that the majority of the IoT fingerprinting mechanisms take a passive approach – mainly through network sniffing – instead of being intrusive and interactive with the device of interest. Additionally, a significant number of the surveyed mechanisms employ both static and dynamic approaches, in order to benefit from complementary features that can be more robust against certain attacks such as spoofing and replay attacks
First person – Shweta Yadav
No full textABSTRACT
First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Shweta Yadav is the first author on ‘RDGBα localization and function at membrane contact sites is regulated by FFAT–VAP interactions’, published in Journal of Cell Science. Shweta is a post-doctoral associate in the laboratory of Prof. Juan Botas at Baylor College of Medicine, Texas, USA, investigating neurodegenerative diseases.</jats:p
Mitigating IoT Botnet DDoS Attacks through MUD and eBPF based Traffic Filtering
No full textAs the prevalence of Internet-of-Things (IoT) devices becomes more and more dominant, so too do the associated management and security challenges. One such challenge is the exploitation of vulnerable devices for recruitment into botnets, which can be used to carry out Distributed Denial-of-Service (DDoS) attacks. The recent Manufacturer Usage Description (MUD) standard has been proposed as a way to mitigate this problem, by allowing manufacturers to define communication patterns that are permitted for their IoT devices, with enforcement at the gateway home router. In this paper, we present a novel integrated system implementation that uses a MUD manager (osMUD) to parse an extended set of MUD rules, which also allow for rate-limiting of traffic and for setting appropriate thresholds. Additionally, we present two new backends for MUD rule enforcement, one based on eBPF and the other based on the Linux standard iptables. The evaluation results reported show that these techniques are feasible and effective in protecting against attacks, with minimal impact on legitimate traffic and on the home gateway
The hired farm labor market: some recent evidence from Oregon
No full textT.M. Hammonds, R. Yadav, and C. Vathana.This archived document is maintained by the State Library of Oregon as part of the Oregon Documents Depository Program. It is for informational purposes and may not be suitable for legal purposes.Includes bibliographical references (pages 22-23).Mode of access: Internet from the Oregon Government Publications Collection.Text in English
“Discovery of the Wave-Edge Rectangle (WER) and its Area Formula”
No full textTitle: The Wave-Edge Rectangle (WER): A Newly Identified Geometric Structure and Its Area Formula
Author: Yadvendra Singh Yadav
Date: 2 December 2025
Summary:
I discovered a new geometric shape called the Wave-Edge Rectangle (WER), formed by replacing all four straight sides of a rectangle with equal semicircular arcs. I derived a general area formula:
=2(4+(+)
Enhancement of security using structured phase masked in optical image encryption on Fresnel transform domain
No full textDistributed Federated Learning in Manufacturer Usage Description (MUD) Deployment Environments
No full textIl costante avanzamento dei dispositivi Internet of Things (IoT) in diversi ambienti, ha provocato la necessità di nuovi meccanismi di sicurezza e monitoraggio in una rete. Tali dispositvi sono spesso considerati fonti di vulnerabilità sfruttabili da malintenzionati per accedere alla rete o condurre altri attacchi. Questo è dovuto alla natura stessa dei dispositivi, ovvero offrire servizi aventi a che fare con dati sensibili (p.es. videocamere) seppur con risorse molto limitate. Una soluzione in questa direzione, è l'impiego della specifica Manufacturer Usage Description (MUD), che impone al maufacturer dei dispositivi di fornire dei file contenenti un particolare pattern di comunicazione che i dispositivi da lui prodotti dovranno adottare. Tuttavia, tale specifica riduce solo parzialmente le suddette vulnerabilità. Infatti, diventa inverosimile definire un pattern di comunicazione per dispositivi IoT aventi un traffico di rete molto generico (p.es. Alexa). Perciò, è di grande interesse studiare un sistema di anomaly detection basato su tecniche di machine learning, che riesca a colmare tali vulnerabilità.
In questo lavoro, verranno esplorate tre prototipi di implementazione della specifica MUD, che si concluderà con la scelta di una tra queste. Successivamente, verrà prodotta una Proof-of-Concept uniforme a tale specifica, contenente un'ulteriore entità in grado di fornire maggiore autorità all'amministratore di rete in quest'ambiente. In una seconda fase, verrà analizzata un'architettura distribuita che riesca ad effettuare learning di anomalie direttamente sui dispositivi sfruttando il concetto di Federated Learning, il che significa garantire la privacy dei dati. L'idea fondamentale di questo lavoro è quindi quella di proporre un'architettura basata su queste due nuove tecnologie, in grado di ridurre al minimo vulnerabilità proprie dei dispositivi IoT in un ambiente distribuito garantendo il più possibile la privacy dei dati
Study of dielectric relaxation processes of Ba0.90Mn0.10TiO3 ceramic using electric modulus spectroscopy
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