Shape-induced phenomena in the finite size antiferromagnets

It is of common knowledge that the direction of easy axis in the finite-size ferromagnetic sample is controlled by its shape. In the present paper we show that a similar phenomenon should be observed in the compensated antiferromagnets with strong magnetoelastic coupling. Destressing energy which originates from the long-range magnetoelastic forces is analogous to demagnetization energy in ferromagnetic materials and is responsible for the formation of equilibrium domain structure and anisotropy of macroscopic magnetic properties. In particular, crystal shape may be a source of additional uniaxial magnetic anisotropy which removes degeneracy of antiferromagnetic vector or artificial 4th order anisotropy in the case of a square cross-section sample. In a special case of antiferromagnetic nanopillars shape-induced anisotropy can be substantially enhanced due to lattice mismatch with the substrate. These effects can be detected by the magnetic rotational torque and antiferromagnetic resonance measurements.Comment: 7 pages, 5 figures, to appear in Phys. Rev. B, v.75, N17, 200

Humoral and protective response of Indian major carps to immersion vaccination with Aeromonas hydrophila

Fry of the Indian major carps, Catta catla (Ham.), Labeo rohita (Ham.) and Cirrhinus mrigala (Ham.) were immunized at 4 and 8 weeks post hatching (wph) by direct immersion in a suspension (10 super(8) cells ml super(-1))of heat inactivated Aeromonas hydrophila. Following the same procedure, booster dose was administered 20 days after the first immersion. Antibodies as well as protective response produced in both the groups after the first and the booster immersion were different and significant (P<0.05). No significant difference was found between the species in the two age groups. The specimens immunized 8 wph showed higher antibody titres and protection than the 4 wph group. C. catla had higher relative percent survival followed by L. rohita and C. mrigala

Ion-Acoustic Solitons in Bi-Ion Dusty Plasma

The propagation of ion-acoustic solitons in a warm dusty plasma containing two ion species is investigated theoretically. Using an approach based on the Korteveg-de-Vries equation, it is shown that the critical value of the negative ion density that separates the domains of existence of compressi- on and rarefaction solitons depends continuously on the dust density. A modified Korteveg-de Vries equation for the critical density is derived in the higher order of the expansion in the small parameter. It is found that the nonlinear coefficient of this equation is positive for any values of the dust density and the masses of positive and negative ions. For the case where the negative ion density is close to its critical value, a soliton solution is found that takes into account both the quadratic and cubic nonlinearities. The propagation of a solitary wave of arbitrary amplitude is investigated by the quasi-potential method. It is shown that the range of the dust densities around the critical value within which solitary waves with positive and negative potentials can exist simultaneously is relatively wide.Comment: 17 pages, 5 figure

A Physics-based Investigation of Pt-salt Doped Carbon Nanotubes for Local Interconnects

We investigate, by combining physical and electrical measurements together with an atomistic-to-circuit modeling approach, the conductance of doped carbon nanotubes (CNTs) and their eligibility as possible candidate for next generation back-end-of-line (BEOL) interconnects. Ab-initio simulations predict a doping-related shift of the Fermi level, which reduces shell chirality variability and improves electrical conductance up to 90% by converting semiconducting shells to metallic. Circuit-level simulations predict up to 88% signal delay improvement with doped vs. pristine CNT. Electrical measurements of Pt-salt doped CNTs provide up to 50% of resistance reduction which is a milestone result for future CNT interconnect technology

Resolving dichotomy in compact objects through continuous gravitational waves observation

More than two dozen soft gamma-ray repeaters (SGRs) and anomalous X-ray pulsars (AXPs) have been detected so far. These are isolated compact objects. Many of them are either found to be associated with supernova remnants or their surface magnetic fields are directly measured, confirming that they are neutron stars (NSs). However, it has been argued that some SGRs and AXPs are highly magnetized white dwarfs (WDs). Meanwhile, the existence of super-Chandrasekhar WDs has remained to be a puzzle. However, not even a single such massive WD has been observed directly. Moreover, some WD pulsars are detected in electromagnetic surveys and some of their masses are still not confirmed. Here we calculate the signal-to-noise ratio for all these objects, considering different magnetic field configurations and thereby estimate the required time for their detection by various gravitational wave (GW) detectors. For SGRs and AXPs, we show that, if these are NSs, they can hardly be detected by any of the GW detectors, while if they are WDs, Big Bang Observer (BBO), DECi-hertz Interferometer Gravitational wave Observatory (DECIGO) and Advanced Laser Interferometer Antenna (ALIA) would be able to detect them within a few days to a year of integration, depending on the magnetic field strength and its configuration. Similarly, if a super-Chandrasekhar WD has a dominant toroidal field, we show that even Laser Interferometer Space Antenna (LISA) and TianQin would be able to detect it within one year of integration. We also discuss how GWs can confirm the masses of the WD pulsars

A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection

Cytological evaluation through microscopic image analysis of fine needle aspiration cytology (FNAC) is pivotal in the initial screening of breast cancer. The sensitivity of FNAC as a screening tool relies on both image quality and the pathologist’s expertise. To enhance diagnostic accuracy and alleviate the pathologist’s workload, a computer-aided diagnosis (CAD) system was developed. A comparative study was conducted, assessing twelve candidate pre-trained models. Utilizing a locally gathered FNAC image dataset, three superior models-MobileNet-V2, DenseNet-121, and Inception-V3-were selected based on their training, validation, and testing accuracies. Further, these models underwent evaluation in four transfer learning scenarios to enhance testing accuracy. While the outcomes were promising, they left room for improvement, motivating us to create a novel deep convolutional neural network (CNN). The newly proposed model exhibited robust performance with testing accuracy at 85%. Our research concludes that the most lightweight, high-accuracy model is the one we propose. We’ve integrated it into our user-friendly Android App, “Breast Cancer Detection System,” in TensorFlow Lite format, with cloud database support, showcasing its effectiveness. Implementing an artificial intelligent (AI)-based diagnosis system with a user-friendly interface holds the potential to enhance early breast cancer detection using FNAC