High-density Data Research Articles

Dynamic FeOx/FeWOx nanocomposite memristor for neuromorphic and reservoir computing.

Memristors are crucial in computing due to their potential for miniaturization, energy efficiency, and rapid switching, making them particularly suited for advanced applications such as neuromorphic computing and in-memory operations. However, these tasks often require different operational modes-volatile or nonvolatile. This study introduces a forming-free Ag/FeOx/FeWOx/Pt nanocomposite memristor capable of both operational modes, achieved through compliance current (CC) adjustment and structural engineering. Volatile switching occurs at low CC levels (<500 μA), transitioning to nonvolatile at higher levels (mA). Operating at extremely low voltages (<0.2 V), this memristor exhibits excellent uniformity, data retention, and multilevel switching, making it highly suitable for high-density data storage. The memristor successfully mimics fundamental biological synapse functions, exhibiting potentiation, depression, and spike-rate dependent plasticity (SRDP). It effectively emulates transitions from short-term memory (STM) to long-term memory (LTM) by varying pulse characteristics. Leveraging its volatile switching and STM features, the memristor proves ideal for reservoir computing (RC), where it can emulate dynamic reservoirs for sequence data classification. A physical RC system, implemented using digits 0 to 9, achieved a recognition rate of 93.4% in off-chip training with a deep neural network (DNN), confirming the memristor's effectiveness. Overall, the dual-mode switching capability of the Ag/FeOx/FeWOx/Pt memristor enhances its potential for AI applications, particularly in temporal and sequential data processing.

Gold on the horizon: unveiling the chemistry, applications and future prospects of 2D monolayers of gold nanoparticles (Au-NPs).

Noble 2D monolayers of gold nanoparticles (Au-NPs) have garnered significant attention due to their unique physicochemical properties, which are instrumental in various technological applications. This review delves into the intricate physical chemistry underlying the formation of Au-NP monolayers, highlighting key interactions such as electrostatic forces, van der Waals attractions, and ligand-mediated stabilization. The discussion extends to the size- and shape-dependent assembly processes of these NP monolayers, elucidating how nanoparticle dimensions and morphologies influence monolayer formation and stability. Moreover, the review explores the diverse interfaces-solid, liquid, and air-where Au-NP monolayers are employed, each presenting distinct advantages and challenges. In the realm of applications, Au-NP monolayers have shown remarkable promises. In memory devices, their ability to facilitate high-density data storage through enhanced electron transport mechanisms is examined. Biosensing applications benefit from the monolayers' exceptional sensitivity and specificity, which are crucial for detecting biomolecular interactions. Furthermore, the role of Au-NP monolayers in electrocatalysis is explored, with a focus on their catalytic efficiency and stability in various electrochemical reactions. Despite their potential, the deployment of Au-NP monolayers faces several challenges. The review addresses current limitations such as scalability, reproducibility, and long-term stability, proposing potential strategies to overcome these hurdles. Future prospects are also discussed, including the development of multifunctional monolayers and integration with other nanomaterials to enhance performance across different applications. In conclusion, while significant strides have been made in understanding and utilizing 2D Au-NP monolayers, ongoing research is imperative to fully exploit their capabilities. Addressing existing challenges through innovative approaches will pave the way for their widespread adoption in advanced technological applications.

Contribution of mine borehole data toward high-resolution stress mapping: An example from northern Bowen Basin, Australia

Most of in-situ stress data in the Australian continent comes from wellbore stress analysis in deep hydrocarbon reservoirs, and earthquake focal mechanism solutions near the Australian plate boundaries, where geophysical tools facilitate understanding of the present-day stress patterns. This resulted in a paucity of stress information in many other regions such as the northern Bowen Basin, which is an active mining province, but with low seismicity rates and limited deep petroleum exploration. The mining industry runs several hundred kilometres of image logs annually to characterise geotechnical attributes. These logs provide an image from the borehole wall, which facilitates analysis of stress-related borehole deformations for in-situ stress characterisation. This paper examines the orientation of horizontal in-situ stress using different types of image logs in mine boreholes across the northern Bowen Basin. Analyses of 128 km of image logs in 680 vertical boreholes resulted in the interpretation of 9046 pairs of stress-related indicators including 735 drilling induced fractures and 8311 borehole breakouts. Our comprehensive database comprises 890 quality-ranked data records for the orientation of maximum horizontal stress (SHmax), which makes the Bowen Basin as a basin with the highest data density in the world in terms of quality-ranked stress information according to the World Stress Map. Statistical analysis of SHmax orientation reveals that the mean SHmax orientation in northern Bowen Basin is N018° ± 16°. The results show that this orientation is consistent over long distances, which is in contrast with several eastern Australian basins. This uniform stress pattern agrees well with plate-scale geomechanical model predictions, which further highlights the impact of plate boundary forces in the contemporary stress pattern of this region. Detailed image log investigation did not show any systematic rotation of stress; however, some small-scale stress perturbations were observed in the vicinity of sharp stiffness contrasts and geological structures.

Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.

Dual-wavelength hologram based on dynamically adjustable cascading metasurface

With the advancement in nanotechnology, it is possible to manipulate light through groundbreaking technologies such as metasurface holography. This technology provides a powerful platform for manipulating light and its basic attributes. Here, we propose a cascading dynamic metasurface holographic system that composed of two metasurfaces with different functions. The first one can realize the functions of wavelength selection and polarization conversion by controlling the phase transition state of VO2. The other one can perform holographic displays. When the x-linear polarized lights with the frequency of 2.08 THz and 2.72 THz are incident on the system, different display output images can be observed with different polarization probes by modifying the phase transition state of VO2. This system has great potential in applications such as holographic display, camouflage, high-density data storage, and encryption.

Promoting quality in low-cost gas sensor devices for real-world applications

Recent advancements in metal oxide semiconductor sensors enable system integrators to make sensor devices without the traditional complications of operating barebone sensor components. Anyone, so it seems, can put together multi-sensor-systems by combining an integrated sensor subsystem with a small piece of digital infrastructure, resulting in low-cost sensor systems or sensor networks for all kinds of applications. With low energy consumption, the deployment of close meshed sensor networks is becoming a reality, promising high density data for big data models. However, data quality is not necessarily a feature of such devices, since the device output signals are heavily processed and the insight into the actual operating technique is black-boxed by the respective sensor manufacturer. High volume production of a sensor model requires widely applicable output information, yet this mainstreaming negatively impacts quality control efforts on the system level as well as niche applications that require specialized operation modes. This article exemplarily examines the measurement chain of typical metal oxide semiconductor sensor applications and deduces requirements for a technically sound advancement of the subject for research and commercial purposes. Equivalent considerations can be made for any low-cost sensor principle and their respective challenges. The conclusion urges all stakeholders participating in the development and marketing of sensor devices to advance a scientifically valid state of knowledge and to educate their customers and even the general public accordingly.

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis.

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

ICESat-2 noise filtering using a point cloud neural network

The ATLAS sensor onboard the ICESat-2 satellite is a photon-counting lidar (PCL) with a primary mission to map Earth's ice sheets. A secondary goal of the mission is to provide vegetation and terrain elevations, which are essential for calculating the planet's biomass carbon reserves. A drawback of ATLAS is that the sensor does not provide reliable terrain height estimates in dense, high-closure forests because only a few photons reach the ground through the canopy and return to the detector. This low penetration translates into lower accuracy for the resultant terrain model. Tropical forest measurements with ATLAS have an additional problem estimating top of canopy because of frequent atmospheric phenomena such as fog and low clouds that can be misinterpreted as top of the canopy. To alleviate these issues, we propose using a ConvPoint neural network for 3D point clouds and high-density airborne lidar as training data to classify vegetation and terrain returns from ATLAS. The semantic segmentation network provides excellent results and could be used in parallel with the current ATL08 noise filtering algorithms, especially in areas with dense vegetation. We use high-density airborne lidar data acquired along ICESat-2 transects in Central American forests as a ground reference for training the neural network to distinguish between noise photons and photons lying between the terrain and the top of the canopy. Each photon event receives a label (noise or signal) in the test phase, providing automated noise-filtering of the ATL03 data. The terrain and top of canopy elevations are subsequently aggregated in 100 m segments using a series of iterative smoothing filters. We demonstrate improved estimates for both terrain and top of canopy elevations compared to the ATL08 100 m segment estimates. The neural network (NN) noise filtering reliably eliminated outlier top of canopy estimates caused by low clouds, and aggregated root mean square error (RMSE) decreased from 7.7 m for ATL08 to 3.7 m for NN prediction (18 test profiles aggregated). For terrain elevations, RMSE decreased from 5.2 m for ATL08 to 3.3 m for the NN prediction, compared to airborne lidar reference profiles.

Optical Nonlinearity Enabled Super-Resolved Multiplexing Microscopy.

Optical multiplexing for nanoscale object recognition is of great significance within the intricate domains of biology, medicine, anti-counterfeiting, and microscopic imaging. Traditionally, the multiplexing dimensions of nanoscopy are limited to emission intensity, color, lifetime, and polarization. Here, a novel dimension, optical nonlinearity, is proposed for super-resolved multiplexing microscopy. This optical nonlinearity is attributable to the energy transitions between multiple energy levels of the doped lanthanide ions in upconversion nanoparticles (UCNPs), resulting in unique optical fingerprints for UCNPs with different compositions. A vortex beam is applied to transport the optical nonlinearity onto the imaging point-spread function (PSF), creating a robust super-resolved multiplexing imaging strategy for differentiating UCNPs with distinctive optical nonlinearities. The composition information of the nanoparticles can be retrieved with variations of the corresponding PSF in the obtained image. Four channels multiplexing super-resolved imaging with a single scanning, applying emission color and nonlinearity of two orthogonal imaging dimensions with a spatial resolution higher than 150nm (1/6.5λ), are demonstrated. This work provides a new and orthogonal dimension - optical nonlinearity - to existing multiplexing dimensions, which shows great potential in bioimaging, anti-counterfeiting, microarray assays, deep tissue multiplexing detection, and high-density data storage.

Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling

ABSTRACT Three-dimensional models of trees that correspond to the real-world forms of the trees on the ground are used in urban planning, solar power estimation, and other disciplines. Previous studies have focused on generating 3D tree models from high-density point cloud data such as Terrestrial Laser Scanning (TLS) data, which is expensive and limited to small spatial extents. However, there has been limited exploration of inexpensive solutions to model trees over large spatial extents. The goal of this study is to use widely available discrete return Airborne Laser Scanning (ALS) data along with field-captured tree photographs and Google Street View (GSV) images to develop 3D equivalents of trees over larger spatial extents. To this end, we designed a process to assign representative 3D models for individual trees in discrete return ALS point clouds. This study demonstrates the use of a Convolutional Neural Network (CNN) model and 3D models generated with Structure from Motion (SfM) for the realistic modelling of deciduous non-overlapping trees from discrete return ALS data. We classified and labelled the crown shapes of deciduous trees in a study area into four classes based on GSV images of trees. We delineated and segmented non-overlapping deciduous trees from ALS data and reduced them to 2D images using voxel point counts. Next, we trained a CNN architecture to match the 2D images to the corresponding classes observed from GSV images. For each class, we created a representative 3D tree model using field-captured circumnavigational photos of trees and SfM. To demonstrate 3D visualization using the 3D tree models, we created a 3D visualization of the trees surrounding a parking lot. The trained CNN model from this study can be used to classify non-overlapping deciduous trees from discrete return ALS data and subsequently visualize near-realistic 3D tree models of trees.

Structural, vibrational and magnetic properties of heat treated CuFe2O4 nanoparticles prepared by two different synthesis routes

The present study employed co-precipitation and high-energy ball milling techniques to synthesize nanoparticles of copper ferrite. Subsequently, the powders underwent air annealing at three distinct temperatures, namely 600 °C, 800 °C, and 1000 °C, for a duration of 3 h. The structural and magnetic properties of the generated compositions were assessed and examined by a range of techniques, including as X-ray diffraction (XRD), infrared (IR) spectroscopy, and vibrating sample magnetometer (VSM). Based on X-ray diffraction examinations, it has been observed that the co-precipitation process results in the production of copper ferrite nanoparticles of smaller size compared to those obtained using the milling procedure. The infrared absorption spectra demonstrated that the enhancement of the annealing temperature resulted in an augmentation of the structural integrity of milled copper ferrite. The results of magnetic investigations conducted at ambient temperature indicate that increasing the annealing temperature resulted in an enhancement of saturation magnetization. The copper ferrite material, which was synthesized using co-precipitation and subsequently subjected to annealing at a temperature of 1000 °C, had the most minimal coercivity value of 138 G and remanence value of 5.26 emu/g. In the context of high-density data storage devices, there is a strong preference for lower coercivity and remanence characteristics. It has been observed that the enhancement of structural and magnetic characteristics requires annealing at elevated temperatures.

The b-symbol weight hierarchy of the Kasami codes

The symbol-pair read channel was first proposed by Cassuto and Blaum. Later, Yaakobi et al. generalized it to the b-symbol read channel. It is motivated by the limitations of the reading process in high density data storage systems. One main task in b-symbol coding theory is to determine the b-symbol weight hierarchy of codes. In this paper, we study the b-symbol weight hierarchy of the Kasami codes, which are well known for their applications to construct sequences with optimal correlation magnitudes. The complete symbol-pair weight distribution of the Kasami codes is determined.

Genome-wide association and functional genomic analyses for various hoof health traits in North American Holstein cattle

Hoof diseases are a major welfare and economic issue in the global dairy cattle production industry, which can be minimized through improved management and breeding practices. Optimal genetic improvement of hoof health could benefit from a deep understanding of the genetic background and biological underpinning of indicators of hoof health. Therefore, the primary objectives of this study were to perform genome-wide association studies, using imputed high-density genetic markers data from North American Holstein cattle, for 8 hoof-related traits: digital dermatitis, sole ulcer, sole hemorrhage, white line lesion, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, and toe ulcer, and a hoof health index. De-regressed estimated breeding values from 25,580 Holstein animals were used as pseudo-phenotypes for the association analyses. The genomic quality control, genotype phasing, and genotype imputation were performed using the PLINK (version 1.9), Eagle (version 2.4.1), and Minimac4 software, respectively. The functional genomic analyses were performed using the GALLO R package and the DAVID platform. We identified 22, 34, 14, 22, 28, 33, 24, 43, and 15 significant markers for digital dermatitis, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, sole hemorrhage, sole ulcer, toe ulcer, white line lesion disease, and the hoof health index, respectively. The significant markers were located across all autosomes, except BTA10, BTA12, BTA20, BTA26, BTA27, and BTA28. Moreover, the genomic regions identified overlap with various previously reported quantitative trait loci for exterior, health, meat and carcass, milk, production, and reproduction traits. The enrichment analyses identified 44 significant gene ontology terms. These enriched genomic regions harbor various candidate genes previously associated with bone development, metabolism, and infectious and immunological diseases. These findings indicate that hoof health traits are highly polygenic and influenced by a wide range of biological processes.

40-Hz auditory steady-state response deficits are correlated with the severity of persistent auditory verbal hallucination in patients with schizophrenia

BackgroundAbnormal 40 Hz auditory steady-state response (ASSR) has been observed in some psychiatric disorders. Nevertheless, the role of 40 Hz ASSR in persistent auditory verbal hallucinations (pAVHs) schizophrenia (SCZ) is still unknown. This study aims to investigate whether the 40 Hz ASSR impairment is related to pAVHs and can detect pAVHs severity. MethodsWe analyzed high-density electroencephalography data that from 43 pAVHs patients (pAVH group), 20 moderate auditory verbal hallucinations patients (mid-AVH group), and 24 without auditory verbal hallucinations patients (non-AVH group). Event-related spectral perturbation and inter-trial phase coherence (ITPC) were calculated to quantify dynamic changes of the 40 Hz ASSR power and ITPC, respectively. ResultsFrontal-central, the 40 Hz ASSR power, and ITPC were significantly lower in the pAVH group than in the non-AVH group; There was no significant difference between the pAVH and mid-AVH group. The 40 Hz ASSR was significantly negatively correlated with the severity of pAVHs. The 40 Hz ASSR power, and ITPC could be used as a combinational marker to detect SCZ patients with and without pAVHs. ConclusionOur findings have shed light on the pathological mechanism of pAVHs in SCZ patients. These results can provide potential avenues for therapeutic intervention of pAVHs.

Design and analysis of a group-III nitride superlattice structure based semiconductor laser diode for blue region emission

The demand for high-power blue laser diodes (LDs) in the range above 2 W has been steadily increasing due to their applications in solid-state lighting, projection displays, high-density optical data storage and underwater communication. However, current designs face limitations in terms of achieving both high power output and efficiency. This work focuses on the design, development and numerical analysis of a blue LD utilizing group-III nitride superlattice structures. The present study aims to overcome design challenges by investigating the fundamental factors affecting the performance of blue LDs based on superlattice InGaN structures through careful device parameter optimization. The results show that our device successfully emits at around 430 nm wavelength and is capable of achieving a differential quantum efficiency of 46.91%, with a maximal optical power output of 2.18 W for 1.71 A of current for a strip width of 15 µm. However, when the strip width is increased to 20 µm, 4.6 W optical power is achieved with 3 A of injection current. Numerical studies are performed with several calibrated physics models and finite-difference time-domain techniques. Our results provide an insight into the potential of using superlattice group-III nitride structures to enhance the performance of blue LDs, opening up new possibilities for high-power and high-efficiency devices in the future.

Inspection of flatness and levelness of construction surface using terrestrial laser scanner according to ASTM E1155 standard

The flatness of the surface is one of the important indicators to access the quality of the concrete surface during and after the construction process. The terrestrial laser scanner with many outstanding advantages in accuracy, high point density and high data rate acquisition has become an effective tool for construction quality inspection. The most advantage of the ground laser scanner for assessment of floor flatness compared to traditional methods is so quickly data acquisition and able to measure when the concrete floor is still wet. This allows the contractor to identify and fix the errors without waiting for the floor to dry so it can reduce both costs and time for construction. However, in Vietnam today, there is still a lack of standards as well as technical guide to apply this technology in evaluating the flatness of the surface construction effectively. The article proposes a four-step procedure to access the flatness using a laser scanner based on the ASTM E1155 standard. The field work is carried out by using GLS 2200 scanner and uses the four-step procedure to evaluate the surface flatness of the construction. From experimental results, it can be confirmed that the above four-step proposed procedure is suitable to evaluate the flatness of the surface construction by laser scanner in Vietnam conditions. The article is an initial study from theoretical analysis, development of procedure and field work. Therefore, the article can be used as a technical guide for geodetic engineers in the quality control constructionas well as a reference for state management agencies in making standard for the assessment of surface flatness using terrestrial laser scanner.

Laser Ablation-Capillary Absorption Spectroscopy: A novel approach for high throughput and increased spatial resolution measurements of δ13C in plant-soil systems

Spatial and temporal heterogeneity of nutrient exchange within the rhizosphere is a topic of increasing interest, although challenging to study due to limits in existing analytical capabilities. Here, we developed and demonstrated a new approach applying laser ablation sample introduction to capillary absorption spectroscopy (LA-CAS) to characterize carbon isotopic distribution within plant tissues, rhizosphere, and soil. We exposed switchgrass plants to 13CO2 to allow tracing of 13C-labeled photosynthates within plant biomass and into the associated soil. The LA-CAS methods we describe leverage continuous measurements of a sample stream derived from laser ablation line scans (10–25 μm in width) over a sample surface which enables the user to produce an isotope map with a) higher data density or b) over larger spatial areas versus previously existing techniques. This versatility of LA-CAS is assessed through testing of a range of laser parameters (spot size, scan rates) on various materials (soil, plant biomass/tissues, and rhizosphere). We demonstrate the ability of LA-CAS to provide near instantaneous δ13C measurements over isotopically distinct surfaces to enable high spatially resolved mapping of 13C-labeled material within the rhizosphere. Applying LA-CAS analysis to plant biomass, we observed higher δ13C values concentrated within phloem structures, consistent with localized photosynthate transport. When mapping across the rhizosphere, 13C-enriched soil was typically present within 5–10 μm of root boundaries with a steep spatial increase in δ13C when the scan approached the middle of the root. As with all LA approaches, care is required to ensure accurate results as phenomena linked to ablation, combustion, and isotopic measurement can impart artifacts if not carefully controlled. Still, taken as a whole, our demonstrations highlight the increased sample throughput, improved data density, and enhanced δ13C capability of LA-CAS versus other LA techniques and emphasize the role this method can play in future plant and rhizosphere related studies.

Neanderthal introgression in SCN9A impacts mechanical pain sensitivity

The Nav1.7 voltage-gated sodium channel plays a key role in nociception. Three functional variants in the SCN9A gene (encoding M932L, V991L, and D1908G in Nav1.7), have recently been identified as stemming from Neanderthal introgression and to associate with pain symptomatology in UK BioBank data. In 1000 genomes data, these variants are absent in Europeans but common in Latin Americans. Analysing high-density genotype data from 7594 Latin Americans, we characterized Neanderthal introgression in SCN9A. We find that tracts of introgression occur on a Native American genomic background, have an average length of ~123 kb and overlap the M932L, V991L, and D1908G coding positions. Furthermore, we measured experimentally six pain thresholds in 1623 healthy Colombians. We found that Neanderthal ancestry in SCN9A is significantly associated with a lower mechanical pain threshold after sensitization with mustard oil and evidence of additivity of effects across Nav1.7 variants. Our findings support the reported association of Neanderthal Nav1.7 variants with clinical pain, define a specific sensory modality affected by archaic introgression in SCN9A and are consistent with independent effects of the Neanderthal variants on Nav1.7 function.

On Linear Perfect b-Symbol Codes over Finite Fields

Motivated by the application of high-density data storage technologies, Cassuto and Blaum introduced codes for symbol-pair read channels in 2011, and Yaakobi et al. generalized the coding framework to that for b-symbol read channels where b≥2 in 2016. In this paper, we establish a b-sphere-packing bound and present a recurrence relationship for the b-weight enumerator. We determine all parameters of linear perfect b-symbol e-error-correcting codes over Fq for e&lt;2b and show that for 2b≤e&lt;3b, there exist at most finite such codes for a given b, e, and q. We construct a family of linear perfect b-symbol b-error-correcting codes over Fq using constacyclic codes.

Role of interfacial layer on exchange-coupled magnetic properties of bi-magnetic nanostructures: An experimental and theoretical approach

Bi-magnetic hetero-nanostructures exhibiting significant exchange bias effects are highly desirable for technological advancement in spin valves, memory devices, data storage, biomedicine, and hyperthermia-based cancer treatment. As the interfacial spin interaction and the resulting anisotropy play a central role in these phenomena, investigating the tailored physical properties of hetero-structures of magnetically contrasting nanoparticles in different architectures is extremely useful. In this work, we report the formation of microspheres containing nano-aggregates of ferrimagnetic Fe3O4 and antiferromagnetic NiO phases. We demonstrate that this kind of bi-magnetic hetero-structures provide a conducive atmosphere for strong interfacial coupling giving rise to an extensive exchange bias effect (∼1.6 kOe). In addition, we also observe a substantial enhancement of the coercive field, indicating the onset of an additional unidirectional anisotropy under the field-cooled condition. Our first-principle density functional theory calculations reveal the role of cation intermixing at the interface of the two phases, being the source of an additional higher magneto-crystalline anisotropy than the original phases. This cation intermixing at the interface also causes a narrowed energy band, which was demonstrated by UV–Vis spectroscopy and first principle calculations. Having these technologically essential features with a tunable band gap, the embedded hetero-nanostructures are envisioned to cater to the requirement of high-density data storage and memory devices.