This study presents a novel approach to improve fiber orientation estimation in diffusion tensor imaging (DTI), a widely used MRI technique for mapping brain white matter fibers (WMFs) by analyzing water diffusion patterns. Traditional DTI estimates the diffusion tensor matrix (DT-matrix) via linear regression, which effectively detects a single fiber per voxel but fails in regions with crossing fibers. To address this limitation, multicompartment mixture models have been introduced, typically assuming fixed eigenvalues ( 1 . 5 , 0 . 4 , 0 . 4 ) $$ \left(1.5,0.4,0.4\right) $$   μ $$ \upmu $$ m 2 $$ {}^2 $$ /ms based on normative WMF data. However, such fixed assumptions may lead to inaccuracies in regions with complex microstructures. In contrast, the proposed method dynamically computes the eigenvalues of the DT-matrix for each voxel, allowing for a voxel-specific characterization of diffusion properties. This adaptability accounts for spatial variability in fiber geometry, improving the accuracy of fiber orientation detection. Simulations and experiments on human and rat brain datasets demonstrate that the method achieves improved white matter reconstruction and reduced angular error compared with traditional DTI and fixed-eigenvalue models. By tailoring diffusion modeling to each voxel, this approach enhances neuroimaging analysis, refines tissue microstructure characterization, and advances the precision of diffusion MRI.

Vanadium oxide thin films exhibit temperature-driven electronic transitions desirable for sensing and microelectronic applications, yet their performance is often limited by thermal hysteresis, for which no robust morphology-based predictive descriptor currently exists. Conventional structural metrics, such as roughness or crystallinity, often fail to explain or anticipate electronic stability. Here, we demonstrate that multiscale morphological complexity, quantified through multifractal analysis, provides a physically meaningful indicator of electronic thermal stability, using scanning electron microscopy images and thermal hysteresis of mixed-phase vanadium oxide thin films deposited under systematically varied working pressures using Direct Current magnetron sputtering. While conventional roughness metrics and mono-fractal parameters remain insensitive to this behavior, multifractality strength captures heterogeneous local exponents scaling that links with enhanced thermal reversibility. These findings identify multifractal analysis as a morphological characterization tool that captures aspects of surface complexity relevant to thermal hysteresis, providing insight into the role of heterogeneous morphological organization in electronic stability.

Long‐term monitoring of blood glucose is of great significance for diabetes management and the construction of highly sensitive biosensors. Two‐dimensional (2D) nanomaterials such as graphene, transition metal dichalcogenides (TMDs), and MXenes are an excellent choice for non‐enzymatic glucose sensors due to the high surface area, tailored chemistry, and remarkable electronic properties. This review aims to encompass works by taking examples of flexible non‐enzymatic 2D material‐based glucose sensors in terms of design and sensing mechanisms, as well as performance investigations and integration into wearable devices. Non‐enzymatic glucose sensors are discussed. Non‐enzymatic glucose sensors detect glucose by direct electrochemical oxidation on functional 2D‐material electrodes, avoiding the problems of enzymatic instability. We discuss how 2D materials contribute to improved sensitivity and low detection limits by providing large active surface areas and fast electron transport. Key performance parameters such as sensitivity, linearity, limit of detection (LOD), selectivity, stability under mechanical stress, and long‐term operation are examined based on recent literature. Flexible and wearable glucose biosensors, such as sweat patches and textile‐based devices use 2D materials for real‐time non‐invasive monitoring. Printing, nanocomposite coatings, and fiber‐shaped structures integrate these materials into flexible electrodes. These sensors offer high sensitivity and stability, though challenges remain in biofluid reliability, scalable production, and biocompatibility. Continued advances could enable point‐of‐care and continuous health monitoring.

In recent years, quantum computing has achieved significant breakthroughs. Owing to the properties of qubits, image processing can be performed more efficiently and securely using quantum cryptographic techniques. However, existing quantum image encryption schemes often rely on low-dimensional chaotic systems with limited complexity, while conventional fixed-LSB steganography remains vulnerable to statistical analysis. To address these limitations, this paper introduces a novel quantum image encryption-steganography algorithm that incorporates a memristor-based Hopfield neural network (mHNN) with an optimal LSB-based quantum steganography. Theoretical investigation and experimental validation confirm that the model has two equilibrium points for a discrete parameter value and exhibits self-excited chaotic behavior. A multi-layer quantum diffusion mechanism is designed to improve diffusion and bolster robustness against chosen-plaintext and statistical attacks. This method incorporates quantum XOR operations by employing a quantum key image determined by a 2D semi-magic matrix. Consequently, quantum XOR diffusion is enabled by pseudo-random sequences produced by the proposed mHNN model. The encryption process is carried out using quantum CNOT and CCNOT gates. An optimal LSB-based quantum steganography scheme is employed for secure data hiding, substantially reducing the vulnerabilities linked to fixed-LSB techniques while maintaining superior visual fidelity. Experimental results demonstrate superior encryption efficiency, characterized by nearly ideal information entropy (>= 7.999) and high embedding imperceptibility, with PSNR values above 54 dB. A thorough comparison analysis validates that the proposed algorithm exceeds existing state-of-the-art quantum image encryption and steganography methods, highlighting its efficiency and potential for secure quantum image transmission.

This study proposes a hybrid diffusion MRI reconstruction framework combining Diffusion Tensor Imaging (DTI) and the multi-compartment non-central Wishart (MNCW) mixture model. The proposed framework uses an empirically selected fractional anisotropy (FA) threshold of 0.75 as a heuristic to distinguish between single- and multi-fiber voxels, enabling adaptive selection of the most appropriate reconstruction model for each voxel. Synthetic diffusion MRI datasets containing single-, two-, and three-fiber configurations were generated using 82 diffusion-sensitizing gradient directions with Rician noise. Voxels with FA ≥ 0.75 were reconstructed using DTI, while voxels with FA < 0.75 were analyzed using the MNCW model. Angular error and clustering behavior were evaluated under different thresholds. The method was further validated using rat optic chiasm and human brain datasets. The proposed framework reduced incorrect single-fiber detections and produced fewer disoriented voxels in complex fiber configurations compared with the conventional mixture model. A clustering threshold of 17 ^∘ provided stable reconstruction without merging distinct fibers. Real data experiments demonstrated anatomically consistent reconstruction of white matter structures. The hybrid framework improves orientation estimation by selectively applying mixture modeling only in voxels likely to contain multiple fibers, reducing over-segmentation, improving stability, and lowering computational complexity.

Monocular depth estimation (MDE) is a critical task for 3D scene understanding in applications such as autonomous driving, robotics, and intelligent transportation systems. This paper proposes a transformer-based hybrid architecture that integrates local and global feature fusion for accurate depth prediction from a single RGB image. The model utilises a Selective Feature Fusion (SFF) module to adaptively combine multi-scale contextual cues and an attention-supervised up-sampling block, thereby preserving spatial details while minimizing model complexity. Additionally, a depth-aware data augmentation strategy enhances generalization across diverse environments. Experimental evaluations on benchmark dataset NYU Depth V2 and VS13 (outdoor traffic scenes), demonstrate that the proposed model achieves good accuracy and robustness compared to existing approaches, while maintaining computational efficiency. These results confirm the effectiveness of combining transformer-based global encoding with lightweight convolutional decoding for high-quality, real-time monocular depth estimation.

This study presents a highly sensitive, enzyme-free cholesterol sensor based on Ti3C2Tx MXene nanosheets. The nanosheets were synthesized through in-situ etching using a LiF/HCl solution, and the sensing electrode was prepared by drop-casting the synthesized MXene onto a paper substrate. The quality and composition of the synthesized Ti3C2Tx MXene nanosheets were examined using FESEM, XRD, Raman spectroscopy, XPS, and EDS, which confirmed their successful synthesis. Electrochemical behavior was studied through electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The electrode's response to varying cholesterol concentrations in phosphate buffer solution (PBS) showed a strong linear relationship (R-2 approximate to 0.99) within the range of 1 to 250 nM. Additionally, the MXene-based electrode demonstrated excellent sensitivity (similar to 3.012 mF nM(-1) cm(-2)), a low detection limit (0.07 nM), outstanding flexibility (RSD = 3.3 %), high selectivity, reproducibility (RSD = 6.3 %), and remarkable cyclic stability (RSD = 2.1 %). The sensor demonstrated high accuracy in real sample analysis, with cholesterol recoveries of 93-95 % from diluted egg yolk, validating its practical applicability and minimal matrix interference. Furthermore, the study provides a comprehensive analysis of the capacitive detection mechanism that underpins the performance of the fabricated sensing electrode, highlighting its potential for real-time cholesterol monitoring in biomedical and healthcare applications.

For many years, metal complexes have piqued attention. Selenium atoms play key role in coordination of metal at the active sites of numerious metal biomolecules. The development of the field of bioinorganic chemistry has increased the interest in complexes of Selenium and serve as multifunctional properties. In view of contribution of studies on Selenium complexes containing ligands, we have prepared trinuclear complexes of Selenium (II) having formula [Se2NiMcBr2]. Selenium is an essential component of several measure metabolic pathways, including thyroid hormone metabolism antioxidant defence system and immune function. The decline in blood selenium concentration in the several part of the world as per WHO report has therefore several potential public health, particularly in related to the chronic disease. Selenium is the only one for which incorporation into protein is generaly encoded, as the constitutive part of the twenty one amino acid, selenocysteine. Anticancer properties of Selenium and its complexes are most important in medicinal chemistry. Being relevant to redox processes in living organism selenide in corporeted withing a drug structure have been most effective. Selenium available in biological system. It leads to multifunctional properties as fungicides, anticancerous agents, arrestors of human immuno deficiency virus infections.

Poly (vinylidene fluoride) (PVDF)-based composites are highly desirable for diverse applications, including wearable devices, energy harvesting, smart skin robotics, and health monitoring devices. However, enhancing the voltage sensitivity and output current density of PVDF-based composites remains critical for their practical use in energy harvesting and wearable device applications. To enhance the voltage sensitivity and output current density, polystyrene sulfonic acid (PSSA) ionic filler, and graphite (Gr) electronic filler are incorporated into a PVDF matrix, leading to the development of a polar ((3)-phase-based polymer composite sensor (PCS). The PVDF/ PSSA/Gr-based PCS with an optimized blend ratio of 80/05/15 exhibits a high sensitivity of 0.6 V/N, which is nearly 105 times higher than that of the pure PVDF sensor. Due to the high ionic-electronic conduction in PCS, the 80/05/15-based PCS generates an enhanced output current density of 0.02 A/cm2 with a tapping force of 7.8 N at frequency of 0.1 Hz, which is 1.5 x 106 times higher than that of the pure PVDF sensor. After being worn on the finger, the PCS successfully detects finger bending and generates an output voltage of up to 5 V. The study demonstrates the potential of PVDF/PSSA/Gr composite-based sensors for wearable sensing and energy harvesting applications.

An artificial synapse is integral to neuromorphic computing, a field poised to overcome the limitations of the traditional von Neumann architecture. Memristors, with their tunable, non-volatile resistive switching (RS) states, hold significant promise for acting as artificial synapses, facilitating both data storage and processing within the same physical unit. In this study, we report on memristive devices based on a hydrothermally synthesized MoSe2-ZnO nanoheterostructure, integrated between upper Ni/Ag and lower FTO electrodes, with a comprehensive investigation into their RS characteristics, synaptic functionalities, and potential for neuromorphic computing applications. The structural, compositional, and electronic properties of the MoSe2-ZnO nanoheterostructure were probed using XRD, Raman spectroscopy, FESEM, HRTEM, EDS, and XPS analyses. The fabricated Ag/MoSe2-ZnO/FTO memristor exhibited reliable analog resistive switching (ARS) behavior over a low operational voltage range (-1 V to +1 V). The device successfully emulated key synaptic functions, including potentiation and depression, under microsecond pulse stimuli (1 mu s) at multiple read voltages (0.2-0.6 V), closely replicating biological synaptic plasticity. Additionally, assessments of endurance, data retention, device-todevice (D2D), and cycle-to-cycle (C2C) reliability confirmed consistent analog switching behavior and stable operational performance. A mechanistic analysis revealed a hybrid resistive switching mechanism, involving both Ag+-based conductive filament formation/dissolution and charge trapping/detrapping within the MoSe2ZnO matrix. This dual-mode conduction was supported by double-logarithmic I-V analysis and energy band diagram illustrations, clarifying the role of interface dynamics and barrier modulation under bias.

The study presents an eco-friendly approach to synthesizing a reduced graphene oxide/carbon nanotube (RGO/CNT) nanocomposite using bamboo shoot extract as a natural reductant. The synthesized material was annealed at temperatures ranging from 500 degrees C to 800 degrees C. Raman spectroscopy revealed that the Id/Ig intensity ratio initially increased to 0.85 before decreasing to 0.82 with higher annealing temperatures. XRD analysis indicated an increase in d-spacing from 0.25 nm to 0.30 nm, facilitating greater CNT incorporation between graphene sheets, as confirmed by field emission scanning electron microscopy. Brunauer-Emmett-Teller (BET) surface area analysis showed an increase from 98 m(2)/g to 260 m(2)/g as the annealing temperature rose from 500 degrees C to 800 degrees C, likely due to the removal of oxygen-containing functional groups. Cyclic voltammetry (CV) analysis displayed broad redox peaks with a distorted rectangular shape, indicating the presence of both double-layer capacitance and pseudo-capacitance. The highest specific capacitance of 407.8 F/g was recorded for the RGO/CNT nanocomposite annealed at 800 degrees C. Additionally, Nyquist plot analysis demonstrated a reduction in charge transfer resistance from 17.00 Omega to 16.11 Omega with increasing annealing temperature.

This study presents a quantum secret sharing (QSS) protocol designed using Grover's search algorithm in a noisy environment. The proposed protocol utilizes Grover's three-particle quantum state. The proposed scheme is divided into secret information sharing and eavesdropping checking. The dealer prepares an encoded state by encoding the classical information as a marked state and shares the states' qubits between three participants. Using the amplitude-damping noise and the phase-damping noise as conventional noisy channels, it can be demonstrated that secret information can be conveyed between participants with some information lost. The security analysis shows the scheme is stringent against malicious participants or eavesdroppers. The simulation analysis is done on the cloud platform IBM-QE thereby showing the practical feasibility of the scheme. Finally, an application of the proposed scheme is demonstrated in visual cryptography using the GNEQR representation of images.

We obtained EuMnO3 3 films through a sol-gel process at varying temperatures, aiming to investigate spatial layout variations influenced by different sintering temperatures. Our results indicate that films adopt an orthorhombic structure when sintered between 800 degrees C and 850 degrees C, accompanied by a reduction in nanocrystal size during annealing. Surface analysis reveals that films processed at higher temperatures exhibit increased roughness, negative asymmetry, heightened kurtosis, elevated peak densities, and more distinct peak forms. Furthermore, these films display surfaces with greater anisotropy and lower spatial frequencies compared to those sintered at lower temperatures. Nevertheless, fractal analysis uncovers that both the EuMnO800 and EuMnO850 samples showcase heightened spatial complexity, a more uniform nanotexture, optimal surface porosity, and improved topographic consistency. The findings suggest that films sintered at 800 degrees C display a spatial arrangement with superior topographic qualities, rendering them potentially valuable for the advancement of semiconductor films derived from perovskite rare-earth oxides for various technological applications.

This study focuses on the impact of amplitude-damping noise, which diminishes the fidelity of the teleported quantum state on an existing cyclic quantum teleportation scheme [V. Verma, D. Yadav and D. K. Mishra, Opt. Quantum Electron. 53 (2021) 1]. The CQT scheme involves three participants performing a cyclic teleportation along with a controller to supervise the protocol. We also propose weak measurement and reversal measurement operations as potential solutions to combat the noise-induced decoherence and improve the fidelity of the existing CQT scheme. The simulation results showcase the improvements achieved in the fidelity calculation when implementing the potential solutions. The findings hold immense potential for quantum information communications in practical situations.

This study introduces a novel image encryption algorithm by combining the capabilities of a three-dimensional chaotic map (3D-ICPCM) and Hessenberg decomposition. By harnessing the Ikeda chaotic map and the Cosine polynomial chaotic map as seed maps, a new 3D chaotic map is developed, which becomes the pivotal element of the algorithm’s structure. The proposed method employs an approach to ensure robustness and security, integrating advanced methods of confusion and diffusion. Encryption involves three processes on image pixels: a bit-level confusion process using bit reversal and spiral permutation, and a diffusion process by applying the 3D-ICPCM sequence and Hessenberg decomposition for effective pixel value distribution. A series of comprehensive experiments were conducted to assess the proposed algorithm’s effectiveness. The subsequent research and comparative evaluations demonstrate that the proposed encryption system satisfies various security standards and surpasses the efficacy of recently introduced schemes.

This paper proposes a verifiable dynamic multi-dimensional quantum secret sharing scheme utilizing a generalized Hadamard gate. The dealer simultaneously distributes quantum and classical information to participants in a single distribution using a generalized Hadamard gate and a quantum SUM gate. To detect the malicious behavior of participants, the dealer prepares a sequence of checking particles. The participants retrieve the secret quantum state and classical information utilizing a generalized Hadamard gate and single-particle measurement. Additionally, the authenticity of secrets is ensured using a public hash function. While adding or removing participants, the dealer does not require assistance from other participants. The proposed protocol effectively thwarts eavesdroppers and participants from performing several types of attacks, including collusion, forgery, denial, and revoked dishonest participant attacks. The proposed protocol yields greater reliability, simplicity, versatility, and practicality.

This work proposes a $d$-dimensional quantum multi-secret sharing scheme with a cheat detection mechanism. The dealer creates multiple secrets and distributes the shares of these secrets using multi-access structures and a monotone span program. The dealer detects the cheating of each participant using the Black box's cheat detection mechanism. To detect the participants' deceit, the dealer distributes secret shares' shadows derived from a randomly invertible matrix $X$ to the participants, stored in the black box. The Black box identifies the participant's deceitful behavior during the secret recovery phase. Only honest participants authenticated by the Black box acquire their secret shares to recover the multiple secrets. After the Black box cheating verification, the participants reconstruct the secrets by utilizing the unitary operations and quantum Fourier transform. The proposed protocol is reliable in preventing attacks from eavesdroppers and participants. The scheme's efficiency is demonstrated in different noise environments: dit-flip noise, $d$-phase-flip noise, and amplitude-damping noise, indicating its robustness in practical scenarios. The proposed protocol provides greater versatility, security, and practicality.

This paper proposes a novel hybrid approach that combines a mixture of non-central Wishart distribution (MoNCW) model and a feed forward neural network (FFNN) to accurately estimate both the number and orientations of white matter fibers in biological tissues in brain. While the MoNCW model performs well in determining fiber orientations when the separation angle is greater than 50°, accurately clustering these orientations is still a significant challenge. To tackle this issue, the authors introduce a machine learning (ML) model that can precisely identify the number of fibers per voxel. The ML model is then integrated with the MoNCW model to improve the accuracy of fiber orientation estimation. The FFNN is trained using simulated datasets, which contain signal vectors for single, as well as two and three crossing fiber voxels, using a sequential model. The FFNN is particularly effective in solving classification problems, as it can process input data through multiple layers to produce output values that correspond to class labels. In this study, three classes are labelled as 1, 2, and 3, representing the number of fibers. By utilizing this hybrid approach, the accuracy of fiber number and orientation estimation in biological tissues is significantly improved, outperforming existing mixture models.

In this work we have adopted a simple one step bamboo shoot-assisted chemical synthesis of reduced graphene oxide/manganese oxide nanocomposite via a sono-chemical reduction of MnO2-into Mn2+ in aqueous medium. Here graphene oxide nanosheets were used as a support material and potassium permanganate as the precursor source for MnO2 deposition.The conformation of the nanocomposite was done using Raman, X-Ray diffraction analysis, UV-Visible spectroscopy and the morphology were visualized using field emission electron microscopy. Further the mass of the precursor was varied from 0.2 wt% to 0.8 wt% and its effect on the electrochemical properties were investigated. It was observed that with the rise in precursor mass the value of specific capacitance and specific surface area increases as calculated by cyclic vol-tammetry and Brunauer-Emmett-Teller (BET) surface area analyzer.The highest specific capacitance was found to be 827F/g for 0.8% precursor mass loading at a scan rate of 10 mV/s with a charge transfer resistance of 1.42 & omega; in 1 M KCl electrolyte which indicates a good synergistic effect between the EDLC and pseudocapacitive material.