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Fourfold anisotropic magnetoresistance in antiferromagnetic epitaxial thin films of MnPtxPd1-x
No full textAntiferromagnets are emerging as promising alternatives to ferromagnets in spintronics applications. A key feature of antiferromagnets is their anisotropic magnetoresistance (AMR), which has the potential to serve as a sensitive marker for the antiferromagnetic order parameter. However, the underlying origins of this behavior remains poorly understood, particularly in thin film geometries. In this study, we report the observation of AMR in epitaxial thin films of the collinear L10 antiferromagnet MnPtxPd1-x. In the thicker films, AMR is dominated by a noncrystalline twofold component, which emerges from domain reconfiguration and spin canting under applied magnetic field. As the film thickness is reduced, however, a crystalline fourfold component emerges, accompanied by the appearance of uncompensated magnetic moment, which strongly modifies the magnetotransport properties in the thinner films. We demonstrate that interfacial interactions lead to a large density of states (DOS) at the Fermi level. This enhanced DOS, combined with disorder in the thinner films, stabilizes the uncompensated moment and results in a fourfold modulation of the DOS as the N & eacute;el vector rotates, explaining the observed AMR behavior
Machine learning-accelerated design of near-infrared TADF emitters beyond conventional scaffolds
No full textKinship on the move: reeth, rented spaces, and ration cards among transgender people in a parivar in Kerala
No full textNon-linear dynamical approaches for characterizing multi-sector climate impacts under irreducible uncertainty
No full textInternal climate variability (ICV) remains a major source of uncertainty in climate projections, complicating impact assessments across critical sectors, especially at stakeholder-relevant scales. Given that ICV emerges from the nonlinear interactions of the climate system, we argue that nonlinear dynamical (NLD) approaches can improve its characterization, providing physically interpretable insights that strengthen adaptation strategies and support multisector decision-making. However, despite their suitability for such problems, NLD approaches remain largely underutilized in the analysis of initial condition large ensembles (LEs). We argue that a diverse suite of NLD approaches offers a promising pathway for systematically extracting robust insights from LEs. If effectively applied and systematically integrated, these methods could fully harness the potential of LEs, uncovering underlying patterns and variability across ensemble members to refine fundamental insights from climate projections. This will help bridge the gap between complex climate dynamics and practical resilience strategies, ensuring that decision-makers, resource managers, and infrastructure planners have a more reliable foundation for navigating irreducible uncertainty
Formation pathways of particulate NO3− and sources of its precursor over the northwest India: Insights through dual isotopes
No full textNOx plays a vital role in tropospheric ozone formation, OH radical recycling, and acts as a precursor to the formation of particulate nitrate (pNO3-), a major reactive nitrogen species. pNO3- mainly forms via four pathways: oxidation of NO2 by OH (P1), N2O5 hydrolysis (P2), reactions with VOCs (P3), and ClO (P4). However, studies on its sources and formation mechanisms are limited. This study uses dual isotopes (δ18O and δ15N) of pNO3- to explore the sources of NOx and dominant pNO3- formation pathways over Patiala, a semi-urban site in the northwestern Indo-Gangetic Plain (IGP), during a large-scale paddy residue burning. Day-time δ15N and δ18O averaged −5.0 ± 2.4 ‰ and 52.1 ± 6.2 ‰, while night-time values were −0.13 ± 5.7 ‰ and 60.0 ± 8.4 ‰, respectively, reflecting enhanced nighttime partitioning due to cooler temperatures. Further, P1 (79.6 ± 7.2 %) and P2 (16.1 ± 7.5 %) dominated pNO3- formation; P3 and P4 were negligible (x were traffic exhaust (38 ± 18 %), biomass burning (29 ± 18 %), followed by emissions from coal-fired power plants (20 ± 11 %) and soil (13 ± 9 %). Our study, the first of its kind over India provide valuable insight into NOx transformation processes under specific seasonal and emission conditions. While these results improve the understanding of pNO3- formation and may aid in refining regional NOx inventories, they are representative of the particular location and time frame of sampling and may not reflect source contributions in other regions or during periods without episodic biomass burning influence
Microstructural evolution and hydration mechanisms of GGBS activated with 5M sodium hydroxide
No full textAI-Driven Gait Classification Using Portable Wearable Sensors: Advances and Case Study
No full textThis chapter explores the latest advancements in AI-driven gait classification. The work mainly focuses on developing and implementing techniques for characterizing and measuring human gait using a wearable portable sensor system. By leveraging deep learning (DL) and machine learning (ML) models, we aim to accurately classify different types of gait activity performed by the user. The chapter also presents a review of recent advancements in gait classification, focusing on machine learning and deep learning techniques. A case study is discussed to illustrate the practical applications and benefits of these technologies. For the case study, we collect the gait data using IMU (Inertial Measurement Unit) sensor attached on right shank of a subject. The data is collected from 20 young subjects, who performed six gait activities: (i) walking uphill, (ii) walking downhill, (iii) walking on ground, (iv) climbing upstairs, (v) climbing downstairs, and (vi) standing, all at a speed comfortable to them. We train and test multiple ML and DL models for gait classification, presenting comprehensive experimental results with performance evaluation. Overall, this chapter explores advancements in gait classification with a focus on wearable sensor systems and machine learning techniques, along with a case study relevant to gait rehabilitation applications
Quantification of Enhanced Fire Severity in Modern Buildings
No full textModern materials used in the interior as well as exterior of a building pose new fire safety challenges. From an interior perspective, the traditional materials such as natural wood and fabrics are being increasingly replaced by modern synthetic alternatives, which are typically petroleum-based and are hence, more susceptible to fire. Similarly, from an exterior perspective, current building fa�ade systems are predominantly based on combustible materials such as aluminum composite panels as opposed to the traditional non-combustible materials such as terracotta, concrete, and brick masonry. Modern materials offer significantly improved performance in certain aspects compared to their traditional alternatives�for instance, most petroleum-based polymers are much better thermal insulators compared to concrete or masonry and hence provide significantly better energy efficiency to a building. However, these modern materials are also significantly more susceptible to being vehicles of fire spread and hence pose challenges to the designers and engineers, where they have to find a balance in this pareto optimal choice of modern versus traditional. The current work examines this issue�and presents methods to quantify fire severity of modern buildings both from interior and exterior perspectives. It is clear that the downside of modern materials is too prominent to be ignored, and some of the estimation methods must be factored in the modern building design philosophy. � 2021 Elsevier B.V., All rights reserved
SIGN REGULAR MATRICES AND VARIATION DIMINUTION: SINGLE-VECTOR TESTS AND CHARACTERIZATIONS, FOLLOWING SCHOENBERG, GANTMACHER-KREIN, AND MOTZKIN
No full textVariation diminution (VD) is a fundamental property in total positivity theory, first studied in 1912 by Fekete-Pólya for one-sided Pólya frequency sequences, followed by Schoenberg, and by Motzkin who characterized sign regular (SR) matrices using VD and some rank hypotheses. A classical theorem by Gantmacher-Krein characterized the strictly sign regular (SSR) m × n matrices for m > n using this property. In this article we strengthen these results by characterizing all m × n SSR matrices using VD. We further characterize strict sign regularity of a given sign pattern in terms of VD together with a natural condition motivated by total positivity. We then refine Motzkin's characterization of SR matrices by omitting the rank condition and specifying the sign pattern. This concludes a line of investigation on VD started by Fekete-Pólya [Rend. Circ. Mat. Palermo 34 (1912), pp. 89-120] and continued by Schoenberg [Math. Z. 32 (1930), pp. 321-328], Motzkin [Beiträge zur Theorie der linearen Ungleichungen, PhD Dissertation, Jerusalem, 1936], Gantmacher-Krein [Oscillyacionye matricy i yadra i malye kolebaniya mehaniceskih sistem, Gosudarstv. Izdat. Tehn.-Teor. Lit., Moscow-Leningrad, 1950], Brown-Johnstone-MacGibbon [J. Amer. Statist. Assoc. 76 (1981), pp. 824-832], and Choudhury [Bull. Lond. Math. Soc. 54 (2022), pp. 791-811; Bull. Sci. Math. 186 (2023), p. 21]. In fact we show stronger characterizations, by employing single test vectors with alternating sign coordinates - i.e., lying in the alternating bi-orthant. We also show that test vectors chosen from any other orthant will not work