Elimination Method Research Articles

Pre-softening HIP treatment enabled crack-healing and superior mechanical properties for René 142 superalloy fabricated via laser powder bed fusion

The generation of defects, such as cracks and pores, presents significant challenges for high-strength metals and alloys fabricated by the quick-emerging additive manufacturing technology, and subsequent post-processing treatments are often necessary before their practical applications. In this work, a novel heat treatment approach, involving a pre-softening treatment before hot isostatic pressing (HIP), is developed to facilitate the crack-healing in René 142 superalloy produced through laser powder bed fusion. Results demonstrate that René 142 alloy exhibits a propensity for severe cracking across a wide range of printing parameters, primarily in the form of solidification cracks and liquation cracks. These cracks are formed mainly due to a wide solidification range, the presence of a liquid film, and the concentration of residual stress. The pre-softening solution heat treatment significantly reduces dislocation density and residual stress levels, and the subsequent HIP together leads to a defect-free, dense structure for René 142 superalloy. Consequently, the René 142 alloy processed by the pre-softening HIP treatment achieves an excellent combination of yield strength (850 MPa), ultimate tensile strength (1227 MPa), and elongation (13.7 %), with pseudo-equiaxed grains (120–150 μm) and square γ′ precipitates (approximately 540 nm). These findings provide valuable insights for exploring crack elimination methods in other nickel-based superalloys fabricated through additive manufacturing.

Computer-Assisted Histopathological Calculation Analysis of the Sciatic Nerve of Diabetic Neuropathy Rat Model

Histopathology is the science that studies the signs of disease by studying the structural and functional changes that occur in cells using certain types of dyes such as hematoxylin and eosin (H&E). Traditionally histopathological testing is carried out using semi-quantitative methods. A more advanced method is done by taking photos digitally, and then digital photos are quantified with the help of software such as ImageJ using plug-in tools. Recent advances in digital pathology require the development of more efficient computerized image analysis such as the Gaussian adaptive threshold method. This research aims to compare the calculation results of computer-assisted digitalization of histopathology using the ImageJ plugin manual method with automatic calculations using Gaussian adaptive threshold to quantify the amount of sciatic nerve cell damage in the Diabetic peripheral neuropathy (DPN) rat model. In this study, two image analysis methods were used to test their ability to measure the amount of cell damage in the sciatic nerve of normal rats using a model of diabetic neuropathy. The first method uses the ImageJ plugin manual. The second method is the Gaussian adaptive threshold method. The ImageJ plugin manual method obtained a cell abnormality value of 213 cells. Meanwhile, with the Gaussian adaptive threshold method, a value of 204 cells was obtained. The calculation results of the two methods show an insignificant difference between the methods p >0.05. This study presents a computerized morphometric image analysis method with the potential for pathology digitalization applications.

Thermal analysis of hybrid nanoliquid contains iron-oxide (Fe3O4) and copper (Cu) nanoparticles in an enclosure

The major aim of this work is to analyze the thermophysical properties of hybrid nanoliquid inside a square lid driven enclosure under the effect of applied magnetic field and mixed convection. The Hybrid nanofluid is considered a suspension of Iron-oxide (Fe3O4) and Copper (Cu) nanoparticles into water. The cavity walls have varying properties such as top adiabatic wall is moving continuously while the lower wall is maintained at a high constant temperature, in addition, the vertical walls are kept at a low fixed temperature. The magnetic field is applied to the right vertical wall in a horizontal direction. Additionally, the effects of buoyancy forces caused by temperature gradients and shear forces caused by continuous lid motion are considered. The mathematical modelling of problem with all assumptions yields the nonlinear partial differential system. First this system is transferred into non-dimensional form and then transformed system is solved by using Galerkin finite element method (GFEM) along with Gaussian elimination method. Results for isotherms, streamlines and average Nusselt number are obtained versus Richardson number (0.1 ≤ Ri ≤ 15), Grashof number (10 ≤ Gr ≤ 10000), Hartmann number (0 ≤ Ha ≤ 100), volume fractions of solid nanoparticles (0.005 ≤ ϕ1 ≤ 0.02) and (0.005 ≤ ϕ2 ≤ 0.02). It is observed that the Richardson number and Grashof number have significant impacts on both flow velocity and temperature. In addition, concentration of hybrid nanoparticles improves the heat transfer performance. The obtained results may be crucial for thermal management in the cooling of electronics, heat exchangers, solar thermal collectors, industrial operations, medical equipment, HVAC systems, food processing, and renewable energy systems.

Nonlinearly preconditioned semismooth Newton algorithms for nonlinear nonsmooth systems

We aim to develop efficient and robust algorithms for nonsmooth nonlinear systems arising from complementarity problems. The semismooth Newton algorithm is popular due to its reliability and efficiency. However, it struggles with issues with imbalanced nonlinearities of the problems, leading to degraded convergence rates or failure despite help from the globalization techniques like linesearch or trust region. We introduce a right nonlinearly preconditioned semismooth Newton algorithm to address this difficulty. The critical success ingredient is that before each global Newton update, a nonlinear preconditioning step implicitly removes the so-called ‘bad components’ causing trouble via nonlinear subspace correction, inspired by Gaussian elimination but adapted nonlinearly to balance system nonlinearities. Additionally, our method integrates with a domain decomposition framework, enhancing parallelism. Numerical results on two classes of problems demonstrate significantly improved convergence over standard semismooth Newton methods.

Equivariant score-based generative diffusion framework for 3D molecules

BackgroundMolecular biology is crucial for drug discovery, protein design, and human health. Due to the vastness of the drug-like chemical space, depending on biomedical experts to manually design molecules is exceedingly expensive. Utilizing generative methods with deep learning technology offers an effective approach to streamline the search space for molecular design and save costs. This paper introduces a novel E(3)-equivariant score-based diffusion framework for 3D molecular generation via SDEs, aiming to address the constraints of unified Gaussian diffusion methods. Within the proposed framework EMDS, the complete diffusion is decomposed into separate diffusion processes for distinct components of the molecular feature space, while the modeling processes also capture the complex dependency among these components. Moreover, angle and torsion angle information is integrated into the networks to enhance the modeling of atom coordinates and utilize spatial information more effectively.ResultsExperiments on the widely utilized QM9 dataset demonstrate that our proposed framework significantly outperforms the state-of-the-art methods in all evaluation metrics for 3D molecular generation. Additionally, ablation experiments are conducted to highlight the contribution of key components in our framework, demonstrating the effectiveness of the proposed framework and the performance improvements of incorporating angle and torsion angle information for molecular generation. Finally, the comparative results of distribution show that our method is highly effective in generating molecules that closely resemble the actual scenario.ConclusionThrough the experiments and comparative results, our framework clearly outperforms previous 3D molecular generation methods, exhibiting significantly better capacity for modeling chemically realistic molecules. The excellent performance of EMDS in 3D molecular generation brings novel and encouraging opportunities for tackling challenging biomedical molecule and protein scenarios.

Master–slave elimination scheme for arbitrary smooth nonlinear multi-point constraints

Nonlinear multi-point constraints are essential in modeling various engineering problems, for example in the context of (a) linking individual degrees of freedom of multiple nodes to model nonlinear joints, (b) coupling different element types in finite element analysis, (c) enforcing various types of rigidity in parts of the mesh and (d) considering deformation-dependent Dirichlet boundary conditions. One method for addressing constraints is the master–slave elimination, which offers the benefit of reducing the problem dimension as opposed to Lagrange multipliers and the penalty method. However, the existing master–slave elimination method is limited to linear constraints. In this paper, we introduce a new master–slave elimination method for handling arbitrary smooth nonlinear multi-point constraints in the system of equations of the discretized system. We present a rigorous mathematical derivation of the method. Within this method, new constraints can be easily considered as an item of a “constraint library”; i.e. no case-by-case-programming is required. In addition to the theoretical aspects, we also provide helpful remarks on the efficient implementation. Among others, we show that the new method results in a reduced computational complexity compared to the existing methods. The study also places emphasis on comparing the new approach with existing methods via numerical examples. We have developed innovative benchmarks which encompass all relevant computational properties, and provide analytical and reference solutions. Our findings demonstrate that our new method is as accurate, robust and flexible as the Lagrange multipliers, and more efficient due to the reduction of the total number of degrees of freedom, which is particularly advantageous when a large number of constraints have to be considered.

FIRE 6.5: Feynman integral reduction with new simplification library

FIRE is a program which performs integration-by-parts (IBP) reductions of Feynman integrals. Originally, the C++ version of FIRE relies on the computer algebra system Fermat by Robert Lewis to simplify rational functions. We present an upgrade of FIRE which incorporates a new library FUEL initially described in a separate publication, which enables a flexible choice of third-party computer algebra systems as simplifiers, as well as efficient communications with some of the simplifiers as C++ libraries rather than through Unix pipes. We achieve significant speedups for IBP reductions of Feynman integrals involving many kinematic variables, when using an open source backend based on FLINT newly added in this work, or the Symbolica backend developed by Ben Ruijl as a potential successor of FORM. Program summaryProgram title: FIRE, version 6.5 (FIRE 6.5)CPC Library link to program files:https://doi.org/10.17632/cy6k69pb3y.2Developer's repository link:https://gitlab.com/feynmanintegrals/fire.gitLicensing provisions: GPLv2Programming language:Wolfram Mathematica 8.0 or higher, C++17Supplementary material: See linked repository for installation instructions.Journal reference of previous version: Comput. Phys. Commun. 247 (2020) 106877Does the new version supersede the previous version?: Yes.Reasons for the new version: The new version no longer relies on a single computer algebra system, Fermat [1], but instead allows a flexible choice of several systems, some of which offer higher performance, especially when the number of variables is large.Summary of revisions: A new library FUEL [2] is used as a core component of the new version of FIRE to access different computer algebra systems as simplifiers of rational function expressions. Since the first release of FUEL described elsewhere, FUEL version 1.0 here has been enhanced with a new backend based on the open source library FLINT [3] that provides highly performant simplification of rational functions.Nature of problem: Feynman integrals of a given family are reduced to a finite set of master integrals, by solving linear equations arising from integration by parts, using Gaussian elimination. The coefficients of the linear equations are generally rational functions in kinematic variables and the spacetime dimension, and the simplification of such rational functions during Gaussian elimination is a key task that is improved in this upgrade of FIRE.Solution method: Computer algebra systems with state-of-the-art capabilities for polynomial GCD computations are used as simplification backends, or simplifiers in short. Due to the design of FIRE, text strings are used as the exchange format for rational functions before and after simplification. A fast C++ parser is written to parse strings into the internal format of an external simplifer, FLINT [3], with state-of-the-art performance for multivariate polynomial calculations. Similarly, the simplifier Symbolica [4] has high performance in both GCD computations and parsing, and has been integrated into FIRE.

Enhancement of waterborne pathogen removal by functionalized biochar with ε-polylysine ″dynamic arms″: Potential application in ultrafiltration system

Widespread outbreaks of threatening infections caused by unknown pathogens and water transmission have spawned the development of adsorption methods for pathogen elimination. We proposed a biochar functionalization strategy involving ε-polylysine (PLL), a bio-macromolecular poly(amino acid)s with variable folding conformations, as a "pathogen gripper" on biochar. PLL was successfully bridged onto biochar via polydopamine (PDA) crosslinking. The extension of electropositive side chains within PLL enables the capture of both nanoscale viruses and micrometer-scale bacteria in water, achieving excellent removal performances. This functionalized biochar was tentatively incorporated into ultrafiltration (UF) system, to achieve effective and controllable adsorption and retention of pathogens, and to realize the transfer of pathogens from membrane surface/pore to biochar surface as well as flushing water. The biochar-amended UF systems presents complete retention (∼7 LRV) and hydraulic elution of pathogens into membrane flushing water. Improvements in removal of organics and anti-fouling capability were observed, indicating the broken trade-off in UF pathogen removal dependent on irreversible fouling. Chemical characterizations revealed adsorption mechanisms encompassing electrostatic/hydrophobic interactions, pore filling, electron transfer, chemical bonding and secondary structure transitions. Microscopic and mechanical analyses validated the mechanisms for rapid adsorption and pathogen lysis. Low-concentration alkaline solution for used biochar regeneration, facilitated the deprotonation and transformation of PLL side chain to folded structures (α-helix/β-sheet). Biochar regeneration process also promoted the effective detachment/inactivation of pathogens and protection of functional groups on biochar, corroborated by physicochemical inspection and molecular dynamics simulation. The foldability of poly(amino acid)s acting like dynamic arms, significantly contributed to pathogen capture/desorption/inactivation and biochar regeneration. This study also inspires future investigation for performances of UF systems amended by poly(amino acid)s-functionalized biochar under diverse pressure, temperature, reactive oxygen species of feeds and chemical cleaning solutions, with far-reaching implications for public health, environmental applications of biochar, and UF process improvement.

Application and characterization of digital Sallen–Key filter in nuclear spectrum smoothing

In the nuclear spectrum analysis processing, spectrum smoothing can remove the statistical fluctuation in the spectrum, which is beneficial for peak detection and peak area calculation. In this work, a spectrum smoothing algorithm is proposed based on digital Sallen–Key filter, which contains four parameters (m, n, k, D). The amplitude–frequency response curve of Sallen–Key filter is deduced and the filtering performance is analyzed. Meanwhile, the effects of the four parameters on the shape of the smoothed spectrum are explored: D affects the counts and peak areas of the spectrum, and the peak area can be corrected by the peak area correction function S’. The parameters of m, n and k affect the peak position after smoothing, making the peak position shift to the right, and the peak position correction function P’ can be used to correct the peak position, when n¿2, the spectrum data appear negative after smoothing, when k¿2, the smoothed spectrum broadening degree is greater than 20%. Smoothness (R), noise smoothing factor (NSF), spectrum count ratio before and after smoothing (PER), and comprehensive evaluation factor (Q) are used to evaluate the smoothing effect of the algorithm. The parameters of the algorithm are optimally selected: about the gamma spectrum of 137Cs and 60Co, the optimal parameters are m=1.5 n=2 k=2 D=1, about the characteristic X-ray spectrum of Fe and quasi-geological sample (TiMnFeNiCuZn), the optimal parameters are m=1.1 n=1.1 k=1.3 D=1. Based on Sallen–Key smoothing method, Fourier transform method, Gaussian function method, wavelet transformation method, center of gravity method and least squares method, the gamma spectrum of 137Cs is smoothed and denoised in this paper. The results show that the Sallen–Key method has better spectrum denoising effect (R=0.6056) and comprehensive performance indicators (Q=0.6104), which can be further applied for the smoothing of nuclear spectrum data.

Dynamic Gaussian mutation beetle swarm optimization method for large-scale weapon target assignment problems

The weapon target assignment is a crucial issue for firepower resources optimization in modern warfare. Such a problem is complicated, multi-constrained, strongly nonlinear, NP-complete and the existing studies did not consider the suitability between different weapons and targets. In this paper, a novel weapon target assignment model is established that involves the weapon-target suitability and is closer to the real combat scenarios. Then, in view of that the conventional weapon target assignment methods are difficult to be applied in the large-scale problems efficiently, this work proposes a dynamic Gaussian mutation beetle swarm optimization algorithm with rule-based chaotic initialization. With the assistance of the dynamic parameter adjustment strategies and Gaussian mutation, the improved algorithm has fast convergence speed and high convergence accuracy, and it can solve the weapon target assignment problems with excellent optimization capabilities. Besides, the rule-based chaotic initialization strategy is embedded in this algorithm to generate high-quality population with better diversity. Finally, two comparative simulation cases of different initialization methods and algorithms for solving the large-scale weapon target assignment problems are designed. The results demonstrate that the proposed approach can provide more superior assignment schemes than its competitors with enhanced efficiency.

Developing a Model to Predict Self-Reported Student Performance during Online Education Based on the Acoustic Environment

In recent years, great developments in online university education have been observed, favored by advances in ICT. There are numerous studies on the perception of academic performance in online classes, influenced by aspects of a very diverse nature; however, the acoustic environment of students at home, which can certainly affect the performance of academic activities, has barely been evaluated. This study assesses the influence of the home acoustic environment on students’ self-reported academic performance. This assessment is performed by calculating prediction models using the Recursive Feature Elimination method with 40 initial features and the following classifiers: Random Forest, Gradient Boosting, and Support Vector Machine. The optimal number of predictors and their relative importance were also evaluated. The performance of the models was assessed by metrics such as the accuracy and the area under the receiver operating characteristic curve (ROC_AUC-score). The model with the smallest optimal number of features (with 14 predictors, 9 of them about the perceived acoustic environment) and the best performance achieves an accuracy of 0.7794; furthermore, the maximum difference for the same algorithm between using 33 and 14 predictors is 0.03. Consequently, for simplicity and the ease of interpretation, models with a reduced number of variables are preferred.

Diagnostic significance of multisequence MRI radiomics models in distinguishing benign and malignant spinal fractures

PurposeTo investigate the diagnostic value of multi-sequence magnetic resonance imaging (MRI) in benign and malignant spinal fractures. Materials and methodsMRI data of patients with pathologically confirmed malignant vertebral fractures were retrospectively collected and compared with those with benign vertebral fractures. The image omics features of T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) were extracted respectively, and were randomly divided into training and test sets at 8:2. The training set adopted recursive feature elimination method and minimum absolute contraction and selection operator (LASSO) regression to screen the variables. Three image omics models, T1WI, T2WI and the combination of two sequences, were constructed by logistic regression, and the diagnostic efficiency of each model was verified by the test set. The receiver operating curve evaluated the diagnostic efficiency of the model. ResultsA total of 111 vertebrae with fracture were included in 97 patients, 55 with benign fracture and 56 with malignant fracture. In the training set, the AUC, sensitivity and specificity of the T1WI model were 0.892, 88.9% and 81.4%, respectively. In T2WI model, the results were 0.924, 82.2% and 95.3%, respectively. The combined models of the two sequences were 0.934, 90.0% and 87.5%, respectively. In the test set, the AUC, sensitivity and specificity of T1WI model were 0.877, 80.0% and 92.3%, respectively. In T2WI model, the results were 0.923, 90.0% and 92.3%, respectively. The combined models were 0.933, 100.0% and 87.5%, respectively. The diagnostic efficiency of the two-sequence combined image omics model was better than that of the radiologist. ConclusionThe combined model, incorporating T1WI and T2WI, outperformed single-sequence models in terms of diagnostic accuracy. The integration of multisequence radiomic features provided enhanced texture information, contributing to superior accuracy and efficiency in diagnosing malignant spinal fractures.

Enhancing stability analysis of open-pit slopes via integrated 3D numerical modeling and data monitoring

The slope at Zhahanur open-pit mine (China) has experienced significant and prolonged deformation, posing a considerable risk to life and property within the mining area. To analyse the deformation and failure mechanism of the rock slope, geotechnical investigations, numerical simulations, and long-term movement monitoring were undertaken. In order to improve the efficiency of slope pretreatment, a point elimination method was proposed for the slope surface, and a 3D geological model of mining area was established. A systematic analysis was performed on monitoring data from surface slope radar and underground inclinometers, enabling the identification of the potential sliding surface and deformation pattern of northern slope in a rational manner. Additionally, through three-dimensional numerical simulation, the feasibility of the mining scheme that incorporates waste dump inside the pit was explored. These simulations accurately captured the varying deformation patterns at different levels and exhibited good agreement with available field monitoring data. The findings revealed a composite multi-stage slope failure pattern primarily controlled by weak layers. The safety factors associated to two competing slip surfaces were determined to be 1.20 and 1.15, respectively. To ensure the safe extraction of coal resources from the deformed slope, a combined mining scheme of in-pit dumping and mining is proposed in the study. The proposed scheme was shown to meet the production needs, with a minimum safety factor of 1.08 for rock slope under the mining scheme. By integrating various field monitoring techniques with numerical simulations, a collaborative monitoring scheme was devised to effectively and promptly identify potential sliding surface and implement necessary mining and control measures to ensure slope stability. This comprehensive approach provides valuable insights into the deformation behavior of the rock slope and facilitates the implementation of proactive measures to mitigate risks associated with slope instability.

Gaussian-kernel weighted neighborhood preserving embedding algorithm and its application in fault detection

Fault detection in industrial processes is essential for enhancing production safety. Despite the application of the neighborhood preserving embedding (NPE) algorithm in fault detection as a manifold learning technique, a notable limitation exists-NPE overlooks local geometric structure, leading to suboptimal fault detection and occasional false alarms. This paper introduces the Gaussian kernel weighted NPE (KW-NPE) algorithm to address this challenge. Specifically designed for precise weight assignment in local structures, KW-NPE strategically employs the Gaussian kernel method to project the spatial neighborhood set and capture comprehensive local structural characteristics. The weight assignment, dependent on feature values, enhances the retention of intrinsic structure during dimensionality reduction. A novel objective function further augments this process.To assess performance, a comprehensive composite index is introduced in a case study, amalgamating the false alarm rate and fault detection rate. The effectiveness of the KW-NPE algorithm is demonstrated through extensive simulations and its application to the Tennessee Eastman process dataset, highlighting its superiority over conventional approaches.

Universal Gaussian elimination hardware for cryptographic purposes

Universal Gaussian elimination hardware for cryptographic purposes

Influence of theoretical and semi-empirical peak models on the efficiency calculation in chiral chromatography

Height equivalent to theoretical plate (H) equations, such as the van Deemter or Knox-Saleem equations, and other efficiency vs. linear velocity equations (u), provide kinetic insights into chromatographic separations phenomena and column performance. In enantioselective separations, the peak shape of the two enantiomers can differ significantly and are often asymmetric. The peak efficiency calculations heavily impact these efficiency-flow profiles, leading to erroneous estimations of eddy diffusion, longitudinal diffusion, and mass transfer terms. In this work, new asymmetric peak functions are employed for modeling enantiomer peaks based on the Haarhoff-Van der Linde function, its generalized variant (GHVL), once Generalized Asymmetric Gaussian (AGN), and Twice Generalized Gaussian (TGN). The new models (AGN, TGN, and GHVL) incorporate higher statistical moments besides the zeroth, first, and second moments to account for two-sided asymmetry (fronting or tailing). The fit results are compared with the traditional efficiency calculation methods endorsed by official pharmacopeia and numerical estimation of moments from the raw data. Enantiomeric separations of ibuprofen and dl-homophenylalanine were chosen as probe molecules. The results demonstrate that non-linear least squares fitted functions provide better estimations of peak efficiency data even in the presence of high noise. In particular, the generalized models consistently offered the best quality fits for various peak shapes in chiral separations. Conversely, the half-height Gaussian method greatly overpredicted skewed peak efficiencies. This investigation reveals that the commonly held assumptions of peak shape and numerical integration of raw data are highly insufficient for chiral chromatography. The impact of asymmetry on plate height should not be overlooked when accurate data from efficiency-flow rate curves is derived. We advocate for the broader adoption of these new generalized peak (AGN, TGN, GHVL) models because they provide robustness at various SNRs that account for right or left asymmetry while accurately representing peak geometry.

Formation mechanism and elimination method of joint defects in lattice structures printed by metal droplet deposition manufacturing

Benefiting from the dense inner microstructure and smooth bead-like surface morphology, metal droplet deposition manufacturing (MDDM) is advantageous in manufacturing small metallic lattice structures, which have great potential in advanced aerospace, energy, and robotics applications. However, the improper top-layer droplet distance leads to joint defects in the printed lattices and deteriorates the forming accuracy. Moreover, if the substrate temperature and inclined angle increase, joint defects may occur even if the top-layer droplets are at an ideal distance due to droplet oscillation. This work proposes a novel method to eliminate joint defects by re-placing the joint region droplets to an ideal center distance. To this end, the dynamics of top-layer droplets (impact, spread, recoil, and oscillation) were investigated by combining morphology analysis and high-speed photography (Ts = 673 K, θ = 45°). A theoretical model was established to obtain the parameter range of defect-free joints through a series of bifurcate lattice printing experiments. Then, the morphology and generation mechanism of cumulative joint defects in the lattices at higher temperatures and angles (Ts = 773 K, θ = 60°) were analyzed. A parameter window of joint morphology with different values of inclined angle, substrate temperature, and top-layer droplet distance was established for predicting potential defects in the printing trace. Next, a novel method was proposed to remove redundant droplets and re-place top-layer droplet coordinates into the ideal center distance. Furthermore, the printing strategy was expanded to multi-truss lattices using three-dimensional transformation matrixes. Finally, multiple cells bifurcate, triangular, and pyramid lattices were successfully printed with perfect joints. The height deviation of joint regions is <5 % of the droplet diameter, verifying the feasibility of the proposed strategy. This work represents a further step towards MDDM printing of high-quality truss lattice structures and other complex 3D architectures.

Understanding the effects of microbubble concentration on localization accuracy in super-resolution ultrasound imaging.

Super-resolution ultrasound (SRUS) through localising and tracking of microbubbles (MBs) can achieve sub-wavelength resolution for imaging microvascular structure and flow dynamics in deep tissuein vivo. The technique assumes that signals from individual MBs can be isolated and localised accurately, but this assumption starts to break down when the MB concentration increases and the signals from neighbouring MBs start to interfere. The aim of this study is to gain understanding of the effect of MB-MB distance on ultrasound images and their localisation. Ultrasound images of two MBs approaching each other were synthesised by simulating both ultrasound field propagation and nonlinear MB dynamics. Besides the distance between MBs, a range of other influencing factors including MB size, ultrasound frequency, transmit pulse sequence, pulse amplitude and localisation methods were studied. The results show that as two MBs approach each other, the interference fringes can lead to significant and oscillating localisation errors, which are affected by both the MB and imaging parameters. When modelling a clinical linear array probe operating at 6 MHz, localisation errors between 20 and 30μm (∼1/10 wavelength) can be generated when MBs are ∼500μm (2 wavelengths or ∼1.7 times the point spread function (PSF)) away from each other. When modelling a cardiac probe operating at 1.5 MHz, the localisation errors were as high as 200μm (∼1/5 wavelength) even when the MBs were more than 10 wavelengths apart (2.9 times the PSF). For both frequencies, at smaller separation distances, the two MBs were misinterpreted as one MB located in between the two true positions. Cross-correlation or Gaussian fitting methods were found to generate slightly smaller localisation errors than centroiding. In conclusion, caution should be taken when generating and interpreting SRUS images obtained using high agent concentration with MBs separated by less than 1.7 to 3 times the PSF, as significant localisation errors can be generated due to interference between neighbouring MBs.

Waveform Optimization Control of an Active Neutral Point Clamped Three-Level Power Converter System

Currently, the escalating integration of renewable energy sources is causing a steady weakening of grid strength. When grid strength is weak, interactions between inverters or those between inverters and grid line impedance can provoke widespread oscillations in the power system. Additionally, the diverse DC voltage application characteristics of power converter systems (PCS) may lead to over-modulation, generating narrow pulse issues that further impact control of the midpoint potential balance. Existing dead-time elimination methods are highly susceptible to current polarity judgments, rendering them ineffective in practical use. PCS, due to inherent dead-time effects, midpoint potential imbalances in three-level topologies, and narrow pulses, can elevate low-order harmonic content in the output voltage, ultimately distorting grid-connected currents. This is particularly susceptible to causing resonance in weak grids. To enhance the output voltage waveform of PCS, this article introduces a comprehensive compensation control strategy that combines dead-time elimination, midpoint potential balance, and narrow pulse suppression, all based on an active neutral point clamped (ANPC) three-level topology. This strategy gives precedence to dead-time elimination and calculates the upper and lower limits of the zero-sequence available for midpoint potential balance while fully compensating for narrow pulses. By prioritizing dead-time elimination, followed by narrow pulse suppression and finally midpoint potential balance, this method decouples the coupling between these three factors. The effectiveness of the proposed method is validated through semi-physical simulations.

The Bhargava Greedoid as a Gaussian Elimination Greedoid

Inspired by Manjul Bhargava's theory of generalized factorials, Grinberg and Petrov have defined the Bhargava greedoid - a greedoid (a matroid-like set system on a finite set) assigned to any "ultra triple" (a somewhat extended variant of a finite ultrametric space). Here we show that the Bhargava greedoid of a finite ultra triple is always a Gaussian elimination greedoid over any sufficiently large (e.g., infinite) field; this is a greedoid analogue of a representable matroid. We find necessary and sufficient conditions on the size of the field to ensure this.