Gradient Technique Research Articles

Advancements in MR hardware systems and magnetic field control: B0 shimming, RF coils, and gradient techniques for enhancing magnetic resonance imaging and spectroscopy.

High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.

Synchrotron intensity gradient revealing magnetic fields in galaxy clusters

Magnetic fields and their dynamical interplay with matter in galaxy clusters contribute to the physical properties and evolution of the intracluster medium. However, the current understanding of the origin and properties of cluster magnetic fields is still limited by observational challenges. In this article, we map the magnetic fields at hundreds-kpc scales of five clusters RXC J1314.4-2515, Abell 2345, Abell 3376, MCXC J0352.4-7401, and El Gordo using the synchrotron intensity gradient technique in conjunction with high-resolution radio observations from the Jansky Very Large Array (JVLA) and the Karoo Array Telescope (MeerKAT). We demonstrate that the magnetic field orientation of radio relics derived from synchrotron intensity gradient is in agreement with that obtained with synchrotron polarization. Most importantly, the synchrotron intensity gradient is not limited by Faraday depolarization in the cluster central regions and allows us to map magnetic fields in the radio halos of RXC J1314.4-2515 and El Gordo. We find that magnetic fields in radio halos exhibit a preferential direction along the major merger axis and show turbulent structures at higher angular resolution. The results are consistent with expectations from numerical simulations, which predict turbulent magnetic fields in cluster mergers that are stirred and amplified by matter motions.

DC Resistivity Inversion Using Conjugate Gradient and Maximum Likelihood Techniques with Hydrogeological Applications

DC Resistivity Inversion Using Conjugate Gradient and Maximum Likelihood Techniques with Hydrogeological Applications

Studying the theoretical basis and advantages and disadvantages of Logistic methods of the total horizontal Gradient

Edge detection is one of the most important steps in interpretation of magnetic and gravity data. In magnetic and gravity maps, it is difficult to distinguish adjacent sources due to their field superposition. Many different techniques have been used to determine the edges of sources. These techniques are based on vertical or horizontal gradients of magnetic and gravity data or combinations of them, and the edges of the geological structures are determined by maximum, minimum, or zero values in the output maps. One of the most popular techniques is the total horizontal gradient which is based on horizontal gradients of magnetic and gravity data. The capability of the total horizontal gradient technique in mapping the boundaries of deep bodies is very limited when competing with large-amplitude shallow bodies. Some enhanced modifications of the total horizontal gradient technique have been introduced to improve the boundary estimation results. These techniques are based on logistic functions and derivatives of the total horizontal gradient. In this study, we aim to estimate the effectiveness of the logistic filters of the total horizontal gradient. To obtain optimum results, these filters were tested on synthetic gravity and magnetic data and real magnetic data from the Zhurihe region (China). The findings show that the logistic filters can provide more accurate and sharper boundaries without false source edges than the total horizontal gradient. These techniques can determine the edges of shallow and deep structures at the same time. These results demonstrate that the logistic filters are useful tools for the qualitative interpretation of potential field data.

The conjugate gradient approach to solve two dimensions linear elliptic boundary value equations as a prototype of the reaction diffusion system

This paper presents a numerical approach to solve the 2-dimensional reaction-diffusion problem, a crucial model in physics and chemistry, with applications ranging from pattern formation to material science. Focusing on addressing a stationary linear elliptic problem within a rectangular domain, boundary conditions are determined through a finite-difference formulation. The Conjugate-Gradient Method is employed for the numerical solution, facilitating efficient computation. Key findings are elucidated: Firstly, the grid size for the symmetric matrix A is intricately linked to a bijective function, enabling the transition of indices to grid points. Notably, the solution to this elliptic problem exhibits a concave-up profile. Secondly, various solvers such as the Conjugate Gradient, Gauss-Seidel, and Jacobi techniques are viable, with the Conjugate Gradient method chosen for its superior accuracy, especially when considering computational efficiency. Moreover, the relationship between grid size and solution accuracy is explored, revealing a proportional dependence. Refinement of the grid leads to increased iteration counts but reduced implementation time, owing to the linearity of the function . The convergence criterion ensures high accuracy in solutions, as demonstrated in the provided figures.

An Optimized Inversion Method for Hyperspectral Image Fusion Based on a Hue–Intensity–Saturation, Wavelet, and Trust-Region Conjugate Gradient Method

In hyperspectral remote sensing, achieving high spatial resolution holds paramount importance for an array of applications, such as environmental monitoring, geographic mapping, and precision agriculture. Nevertheless, conventional hyperspectral images frequently grapple with the issue of restricted spatial resolution. We apply optimized inversion methods to hyperspectral image fusion and present an innovative approach for hyperspectral image fusion which combines the Hue–Intensity–Saturation (HIS) transform, the wavelet transform, and the Trust-Region Conjugate Gradient technique. This amalgamation not only refines spatial precision but also augments spectral faithfulness, which is a pivotal aspect for applications like precise object detection and classification. In the context of our investigation, we conducted a thorough validation of our proposed HIS, Wavelet, and Trust-Region Conjugate Gradient (TRCG-HW) method for image fusion using a comprehensive suite of evaluation metrics. These metrics encompassed the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), and Error Relative Global Accuracy Score (ERGAS). The findings incontrovertibly establish TRCG-HW as the preeminent method among those considered. Our study effectively tackles the pressing predicament of low spatial resolution encountered in hyperspectral imaging. This innovative paradigm harbors the potential to revolutionize high-resolution hyperspectral data acquisition, propelling the field of hyperspectral remote sensing forward and efficiently catering to crucial application.

Chip-In-Loop SNN Proxy Learning: a new method for efficient training of spiking neural networks.

The primary approaches used to train spiking neural networks (SNNs) involve either training artificial neural networks (ANNs) first and then transforming them into SNNs, or directly training SNNs using surrogate gradient techniques. Nevertheless, both of these methods encounter a shared challenge: they rely on frame-based methodologies, where asynchronous events are gathered into synchronous frames for computation. This strays from the authentic asynchronous, event-driven nature of SNNs, resulting in notable performance degradation when deploying the trained models on SNN simulators or hardware chips for real-time asynchronous computation. To eliminate this performance degradation, we propose a hardware-based SNN proxy learning method that is called Chip-In-Loop SNN Proxy Learning (CIL-SPL). This approach effectively eliminates the performance degradation caused by the mismatch between synchronous and asynchronous computations. To demonstrate the effectiveness of our method, we trained models using public datasets such as N-MNIST and tested them on the SNN simulator or hardware chip, comparing our results to those classical training methods.

Eco-friendly based HPLC and UV-spectrophotometric methods for simultaneous estimation of Efonidipine hydrochloride Ethanolate and Chlorthalidone in their dosage form

ObjectiveEfonidipine hydrochloride Ethanolate and Chlorthalidone combination was approved by CDSCO for Phase III clinical trial. This combination is utilized to pharmacological therapy that can effectively control hypertension situations. The present research includes an RP-HPLC and Simultaneous equation UV methods for the determination of drug combination in tablet dosage form. Furthermore, greenness assessment and statistical evaluation of developed methods. MethodsThe HPLC chromatography was performed using a gradient technique on a reversed-phase C18 column with mobile phase based and optimized depending on the polarity of the molecules. The Unesphere C18 column Agela Tech., (250 mm x 4.6 mm i.d, 5 μm,) as stationary phase and Acetonitrile: 0.2 M Potassium dihydrogen orthophosphate pH 4 (68: 32% v/v) as mobile phase. The Simultaneous equation UV method was developed and validated at 220 nm and 250 nm in methanol: distilled water (20:80, v/v). The greenness assessment tools were used to assess the ecological impact of the new UV spectrophotometric method and HPLC method. ResultsUV detection in RP-HPLC method at 220 nm for gradient elution was used to achieve chromatographic separation over the linearity 3.0–60.0 μg/mL and 10.0 - 200.0 μg/mL and for UV method 3.0–15.0 μg/mL and 10.0–50.0 μg/mL for Chlorthalidone (CLT) and Efonidipine (EFD) respectively.The value of% RSD within the limit indicated that the method is precise, accurate and% recovery shows that there is no interference from the excipients.Greenness assessment data confirmed the method's environmental friendliness in terms of solvent usage, chemical compounds, energy consumption, and trash creation. There were no spectral or chromatographic interferences from formulation excipients discovered. ConclusionThe analytical method validation parameters were found to be as per acceptance criteria. In terms of accuracy and precision, statistical testing employing an analysis of variance test did not find any significant differences between the two methods. To evaluate the greenness and whiteness of an analytical method, a reliable assessment of the health and environmental risk associated with chemical use is essential.

Refining DIIS algorithms for Si and GaAs solar cells: incorporation of weight regularization, conjugate gradient, and reverse automatic differentiation techniques.

Pivotal enhancements in electronic minimization algorithms, which are vital for the advancement of computational materials science, are introduced in this research. Our research is dedicated to refining the DIIS algorithm specifically for electronic structure calculations of silicon (Si) and gallium arsenide (GaAs) solar cells, aiming to enhance their efficiency and stability. We have enriched DIIS by integrating a weight regularization factor, significantly bolstering its convergence stability. This modification enhances iteration robustness and curtails the average iteration duration, thus streamlining the convergence process. Furthermore, we have incorporated the conjugate gradient (CG) algorithm to proficiently resolve symmetric positive definite residual matrices. This inclusion substantially accelerates the solution-finding process within the DIIS framework. A novel aspect of our research is the application of reverse automatic differentiation (AD), deployed in two distinct methodologies: the construction of the Jacobian matrix and direct chain rule application for gradient computation. These approaches involve sophisticated mathematical techniques that enhance computational precision and efficiency specifically for Si and GaAs solar cell materials in determining the optimal weights for residual combinations during DIIS iterations. The integration of these advanced methods into the DIIS algorithm not only augments its convergence stability but also ensures a substantial reduction in total computational time. Our findings demonstrate that the enhanced DIIS, CG-enhanced DIIS, and AD-integrated DIIS methods collectively lead to a more efficient electronic minimization process. This balance of stability and efficiency is crucial in high-performance computational materials science, particularly for complex systems analysis. The findings of this research represent a notable advancement in computational strategies for Si and GaAs solar cell materials, providing enhanced methodologies and insights that significantly improve the efficiency and stability of electronic structure calculations in these critical components of renewable energy technologies.

Finding the input features that reduce the entropy of a neural network’s prediction

In deep learning-based image classification, the entropy of a neural network’s output is often taken as a measure of its uncertainty. We introduce an explainability method that identifies those features in the input that impact most this uncertainty. Learning the corresponding features by straightforward backpropagation typically leads to results that are hard to interpret. We propose an extension of the recently proposed oriented, modified integrated gradients (OMIG) technique as an alternative to produce perturbations of the input that have a visual quality comparable to explainability methods from the literature but marks features that have a substantially higher impact on the entropy. The potential benefits of the modified OMIG method are demonstrated by comparison with current state-of-the-art explainability methods on several popular databases. In addition to a qualitative analysis of explainability results, we propose a metric for their quantitative comparison, which evaluates the impact of identified features on the entropy of a prediction.

Investigation of visible emission and luminescence center evolution in Pr–La:CaF2 single crystals for laser application

The study presents Pr,La:CaF2 single crystals grown using the temperature gradient technique, showing distinct Pr3+–La3+ cluster types, with two types of centers showing cubic symmetry.

Effective Surrogate Gradient Learning With High-Order Information Bottleneck for Spike-Based Machine Intelligence.

Brain-inspired computing technique presents a promising approach to prompt the rapid development of artificial general intelligence (AGI). As one of the most critical aspects, spiking neural networks (SNNs) have demonstrated superiority for AGI, such as low power consumption. Effective training of SNNs with high generalization ability, high robustness, and low power consumption simultaneously is a significantly challenging problem for the development and success of applications of spike-based machine intelligence. In this research, we present a novel and flexible learning framework termed high-order spike-based information bottleneck (HOSIB) leveraging the surrogate gradient technique. The presented HOSIB framework, including second-order and third-order formation, i.e., second-order information bottleneck (SOIB) and third-order information bottleneck (TOIB), comprehensively explores the common latent architecture and the spike-based intrinsic information and discards the superfluous information in the data, which improves the generalization capability and robustness of SNN models. Specifically, HOSIB relies on the information bottleneck (IB) principle to prompt the sparse spike-based information representation and flexibly balance its exploitation and loss. Extensive classification experiments are conducted to empirically show the promising generalization ability of HOSIB. Furthermore, we apply the SOIB and TOIB algorithms in deep spiking convolutional networks to demonstrate their improvement in robustness with various categories of noise. The experimental results prove the HOSIB framework, especially TOIB, can achieve better generalization ability, robustness and power efficiency in comparison with the current representative studies.

Magnetic Fields in Giant Filaments Probed by the Velocity Gradient Technique: Regular Magnetic Field Interrupted by Magnetization Gaps

We study the magnetic field structures in six giant filaments associated with the spiral arms of the Milky Way by applying the velocity gradient technique (VGT) to the 13CO spectroscopic data from the GRS, FUGIN, and SEDIGSM surveys. Unlike dust-polarized emission, the VGT allows us to separate the foreground and background using the velocity information, from which the orientation of the magnetic field can be reliably determined. We find that in most cases the magnetic fields stay aligned with the filament bodies, which are parallel to the disk midplane. Among these, G29, G47, and G51 exhibit smooth magnetic fields, and G24, G339, and G349 exhibit discontinuities. The fact that most filaments have magnetic fields that stay aligned with the Galactic disk midplane suggests that Galactic shear may be responsible for shaping the filaments. The fact that the magnetic field can stay regular at the resolution of our analysis (≲10 pc), where the turbulence crossing time is short compared to the shear time, suggests that turbulent motion cannot effectively disrupt the regular orientation of the magnetic field. The discontinuities found in some filaments can be caused by processes including filament reassembly, gravitational collapse, and stellar feedback.

Systematic surface bowing in 2D III-nitride monolayers.

In this article we report novel composite materials of bucky ball (C60 fullerene) and III-nitrides (BN, AlN, GaN, InN). The experimental feasibility of the novel composite materials is confirmed through negative binding energies and molecular dynamics simulations performed at 500 K. The structural properties of the novel composites are explored through density functional theory. An unusual phenomenon of surface bowing is observed in the 2D structure of the III-nitride monolayers due to the C60 sticking. This surface bowing systematically increases as one proceeds from BN → AlN → GaN → InN. The electron density difference and Hirshfeld charge density analysis show smaller charge transfer during the complexation, which is probably due to weak van der Waal's forces. The presence of van der Waal's forces is also confirmed by the Atom in Molecule analysis, Reduced Density Gradient Technique and Non-covalent Interaction analysis. This work provides a foundation for further theoretical and experimental studies of the novel phenomenon of systematic bowing in the 2D structure of III-nitride monolayers.

Effect of Microfluidic and Density gradient techniques to select Frozen-thawed Epididymal Sperm from Bighorn rams.

Gamete preservation of wild species is a priority in conservation programs. However, field techniques to recover sperm from deceased animals may impair its final quality.The cryopreservation process also has its challenges, and the minimal required quality parameters may differ depending on the intended use of the semen.Often, postmortem harvested and cryopreserved sperm may need to undergo a selection process to improve the final quality of the sample. Certain sperm selection techniques that employ centrifugal forces can increase the risk of damaging the cells. In contrast, microfluidics uses hydrostatic pressure and capillary forces, avoiding the need for costly equipment and minimizing cell damage.This study aims to compare sperm motion parameters, morphology, and acrosome integrity of cryopreserved-thawed epididymal bighorn ram sperm using two commercial selection techniques, VetCount Harvester® device (MFD) and BovipureTM (NidaCon International AB, Möndal, Sweden) density gradient centrifugation (DGC).Sperm parameters associated with quality were, progressive motile sperms (PMS), straight velocity (VSL), average path velocity (VAP), curvilinear velocity (VCL), linearity (LIN), straightness (STR), beat cross frequency (BCF), amplitude of lateral head deviation (ALH) and plasma membrane integrity (PMI). Computer Assisted Sperm Analyzer was used for the semen analysis. Eosin/nigrosine and Spermac stains were used to assess morphology and acrosome integrity, respectively. Two veterinarians counted the total number of morphologically normal sperm with complete acrosome.Mean sperm concentration was higher (P < 0.01) before filtration (129.56 ±53.9 ×106/ml) than DCG (68.42 ±29.18 ×106/ml) and MFD (52.94 ±28.76 ×106/ml). PMS was higher (P <0.01) after selection using the DGC (79.70 ± 11.11) and MFD (81.23 ±5.64) compared to the no sperm-selected controls (45.36 ±14.67). Sperm kinetics parameters (VCL, VSL, VAP, BCF, LIN, and STR) were higher in the DGC (P<0.01). However, ALH (P<0.01) was significantly higher in the MFD and Control. Morphological normal sperm, acrosome integrity, and plasma membrane integrity were higher (P<0.01) in samples processed with DGC and MFD.In conclusion, microfluidics and single-layer centrifugation improved motion parameters, viability, and proportion of morphologically normal sperm with intact acrosomes from frozen-thawed sperm harvested from Bighorn epididymis. The VetCount Harvester® microfluidic device was comparable to DGC and could be an alternative option for sperm selection. Further research is needed to explore the effect of selected sperm using this technique on artificial insemination, re-cryopreservation, and fertility outcomes.

Deuterium MR spectroscopy: potential applications in oncology research.

Metabolic imaging in clinical practice has long relied on PET with fluorodeoxyglucose (FDG), a radioactive tracer. However, this conventional method presents inherent limitations such as exposure to ionizing radiation and potential diagnostic uncertainties, particularly in organs with heightened glucose uptake like the brain. This review underscores the transformative potential of traditional deuterium MR spectroscopy (MRS) when integrated with gradient techniques, culminating in an advanced metabolic imaging modality known as deuterium MRI (DMRI). While recent advancements in hyperpolarized MRS hold promise for metabolic analysis, their widespread clinical usage is hindered by cost constraints and the availability of hyperpolarizer devices or facilities. DMRI, also denoted as deuterium metabolic imaging (DMI), represents a pioneering, single-shot, and noninvasive paradigm that fuses conventional MRS with nonradioactive deuterium-labelled substrates. Extensively tested in animal models and patient cohorts, particularly in cases of brain tumours, DMI's standout feature lies in its seamless integration into standard clinical MRI scanners, necessitating only minor adjustments such as radiofrequency coil tuning to the deuterium frequency. DMRI emerges as a versatile tool for quantifying crucial metabolites in clinical oncology, including glucose, lactate, glutamate, glutamine, and characterizing IDH mutations. Its potential applications in this domain are broad, spanning diagnostic profiling, treatment response monitoring, and the identification of novel therapeutic targets across diverse cancer subtypes.

Dynamic high‐temperature crystallization and processing properties of industrial soda–lime–silica glasses

AbstractIn situ dynamic crystallization properties of industrial soda–lime–silica glasses at realistic processing temperatures have not yet been explored. Hence, we collected in situ high‐temperature X‐ray diffraction patterns for 10 different industrially manufactured soda–lime–silica glasses as a function of temperature between 900 and 1200°C to investigate the phase relations in their devitrified melts. The high‐temperature X‐ray diffraction study was complemented by measuring the liquidus temperature of those glasses by the temperature gradient technique. A multiple variable regression analysis was applied to the experimental and modeled data to produce a predictive model for the rate of solidification and liquidus temperature based on glass composition. We have demonstrated that forms of quartz (SiO2) and Na2CaSiO4, which are not traditionally identified by room temperature X‐ray diffraction studies of commercial soda–lime–silica glasses, are the dominant crystalline phases at 800 and 900°C. Upon further heating, different forms of cristobalite become the primary phase field prior to the formation of X‐ray amorphous melts, irrespective of the glass composition. Sporadic unidentified as well as high‐temperature stable SiO2 polymorphs that are not recoverable to room temperature were also observed. In contrast to the literature, wollastonite (CaSiO3) and devitrite (Na2Ca3Si6O16), which are the main predictor variables in previously developed liquidus temperature models, were not observed prior to the formation of X‐ray amorphous glass melts, and hence their influence on liquidus temperature may be questionable. It was also found that the difference between glass processing and liquidus temperatures can be excessively high, and such large temperature differences can potentially be exploited and reduced to enable decreases in melting or processing temperatures of industrial soda–lime–silica glass melts.

An enhanced fletcher-reeves-like conjugate gradient methods for image restoration

Noise is an unavoidable aspect of modern camera technology, causing a decline in the overall visual quality of the images. Efforts are underway to diminish noise without compromising essential image features like edges, corners, and other intricate structures. Numerous techniques have already been suggested by many researchers for noise reduction, each with its unique set of benefits and drawbacks. Denoising images is a basic challenge in image processing. We describe a two-phase approach for removing impulse noise in this study. The adaptive median filter (AMF) for salt-and-pepper noise identifies noise candidates in the first phase. The second step minimizes an edge-preserving regularization function using a novel hybrid conjugate gradient approach. To generate the new improved search direction, the new algorithm takes advantage of two well-known successful conjugate gradient techniques. The descent property and global convergence are proven for the new methods. The obtained numerical results reveal that, when applied to image restoration, the new algorithms are superior to the classical fletcher reeves (FR) method in the same domain in terms of maintaining image quality and efficiency.

Efficient hybrid conjugate gradient techniques for vector optimization

Scalarization approaches transform vector optimization problems (VOPs) into single-objective optimization but have trade-offs: information loss, subjective weight assignments, and limited representation of the Pareto front. To address these limitations, alternative strategies like conjugate gradient (CG) techniques are valuable for their simplicity and less memory usage. The paper introduces three CG techniques for VOPs, including two CG techniques that satisfy sufficient descent conditions (SDC) without a line search. These two CG techniques are combined with the third CG technique, a variant of the Polak–Ribiére–Polyak (PRP) technique, resulting in two hybrid CG techniques. Global convergence of these hybrids is achieved, without convexity assumptions, under standard assumptions and Wolfe line search. Numerical analysis and comparisons with nonnegative PRP and Liu–Storey (LS) CG techniques showcase the implementation and effectiveness of our hybrid CG techniques. The results demonstrate the promise of our hybrids techniques.

Development of a New Numerical Conjugate Gradient Technique for Image Processing

We present a new iterative conjugate gradient technique for image processing. The technique is based on a new derivation of the conjugacy coefficient and develops a variant of the classical Fletcher-Reeves conjugate gradient method. The derivation exploits a quadratic function model. The new method is intended to minimize the presence of noise by utilizing the adaptive median filter (AMF) to reduce salt-and-pepper noise, while the adaptive center-weighted median filter (ACWMF) is used to reduce random-valued noise. The theoretical convergence properties of the method are proven and then tested on a basic set of images using MATLAB. The results show that the proposed algorithm is more efficient than the classical Fletcher-Reeves (FR) method, as measured by the signal-to-noise ratio (PSNR). The number of iterations and the number of function evaluations are also lower for the proposed method. The favorable performance of the new algorithm provides promise for deriving similar techniques that enhance the speed and efficiency of image-processing libraries.