Multiple Matrices Research Articles

Effective periodate activation by the ball-milled bimetallic zero-valent iron/manganese oxides catalyst for sulfamethoxazole degradation

Developing a green and efficient catalyst for oxidant activation to tackle refractory organics pollution is challenging for water purification. In this study, we prepared a zero-valent iron-based bimetallic catalyst (ZVI/Mn3O4) for periodate (PI) activation using a simple and convenient ball-milling method. Electrochemical characterization and theoretical calculations confirm that the synergy between Fe and Mn enables the ZVI/Mn3O4 system to exhibit higher electron transfer efficiency compared to its individual components. Consequently, the ZVI/Mn3O4/PI system demonstrated enhanced PI activation and significantly improved removal of sulfamethoxazole (SMX), with reaction rates being 18.61 to 57.01 times higher than the comparison systems. The influence of various physicochemical parameters on SMX removal was investigated, confirming the wide tolerance ZVI/Mn3O4/PI system towards multiple aqueous matrices. Furthermore, the scalability of this system for industrial applications was validated through the preparation of larger batches of catalysts (4.21 g per batch) and testing in a custom-designed magnetic plate continuous-flow reactor. Quenching experiments and electron spin resonance tests confirmed that •O2– and 1O2 were the primary reactive species responsible for SMX degradation, with no toxic iodine byproducts detected, attributed to the carefully designed catalyst, which optimized the interaction between PI and the catalyst. Based on these findings, a potential catalytic mechanism and degradation pathway were proposed, highlighting ZVI's role as an electron pump to enhance Mn3O4's activation of PI. Additionally, toxicity assessments via prediction, zebrafish cultivation, wheat germination, and E. coli culture confirmed the excellent detoxification capability of ZVI/Mn3O4/PI system for SMX degradation. Overall, this work provides a new research paradigm for the convenient manufacture of ZVI-based catalysts and the development of advanced techniques for PI activation in water purification.

Quaternion and Biquaternion Representations of Proper and Improper Transformations in Non-Cartesian Reference Systems

Quaternion and biquaternion symmetry transformations have been applied to non-Cartesian reference systems of direct and reciprocal crystal lattices. The transformations performed directly in the sets of crystal reference axes simplify the calculations, eliminate the need for orthogonalization, permit the use of crystallographic vectors for defining the directions of rotations and perform the computations directly in the crystal coordinates. The applications of the general quaternion transformations are envisioned for physical, chemical, crystallographic and engineering applications. The general quaternion multiplication rules for any symmetry-unrestricted lattices have been derived for the triclinic crystallographic system and have been applied to the biquaternion representations of all point-group symmetry elements, including the crystallographic hexagonal system. Cayley multiplication matrices for point-groups, based on the biquaternion symbols of proper and improper symmetry elements, have been exemplified.

A color image encryption algorithm based on a novel 4D hyperchaotic system and bit-level diffusion

As digital images are widely used in social media, medical, military and other fields, ensuring the privacy and security of image data has become a critical concern. Firstly, we propose a novel four-dimensional hyperchaotic system and validate that it exhibits a broad chaotic range, as demonstrated by bifurcation diagrams and Lyapunov exponent experiments. Additionally, simulated circuit diagrams verify the hardware feasibility of the proposed system. Secondly, we design a dynamic iterative scrambling (DIS) scheme that dynamically divides the image into multiple matrices for spatially indexed scrambling. Excellent substitution performance can be ensured by multiple iterations. In the diffusion stage, a multidirectional bit-level L-shaped (MDBL) scheme is proposed. Diffusion is conducted on the bit plane using a designed cross-multiplanar selection algorithm, which fuses the high and low bit planes, thereby enhancing the diffusion performance of MDBL. Thirdly, Based on the above concepts, a novel four-dimensional hyperchaotic system and an encryption algorithm based on bit-level diffusion are proposed. Finally, experimental results and security analyses demonstrate the effectiveness of the novel 4D hyperchaotic system and image encryption scheme. The proposed encryption scheme exhibits robust anti-interference capabilities and effectively safeguards image security.

Ternary Associativity and Ternary Lie Algebras at Cube Roots of Unity

We propose a new approach to extend the notion of commutator and Lie algebra to algebras with ternary multiplication laws. Our approach is based on the ternary associativity of the first and second kinds. We propose a ternary commutator, which is a linear combination of six triple products (all permutations of three elements). The coefficients of this linear combination are the cube roots of unity. We find an identity for the ternary commutator that holds due to the ternary associativity of either the first or second kind. The form of this identity is determined by the permutations of the general affine group GA(1,5)⊂S5. We consider this identity as a ternary analog of the Jacobi identity. Based on the results obtained, we introduce the concept of a ternary Lie algebra at cube roots of unity and provide examples of such algebras constructed using ternary multiplications of rectangular and three-dimensional matrices. We also highlight the connection between the structure constants of a ternary Lie algebra with three generators and an irreducible representation of the rotation group. The classification of two-dimensional ternary Lie algebras at cube roots of unity is proposed.

A-273 Rapid, Portable Invasive Fungal Infection Diagnostic and Candida Antimicrobial Susceptibility Testing

Abstract Background Traumatic wounds are associated with a higher risk of invasive fungal infections (IFI), which lead to a significant increase in amputation or death. Actionable treatment decisions are hampered because true infections can be difficult to differentiate from external wound contaminates, and tissue biopsy and quantification of fungal load is required to determine if treatment is necessary, which currently takes several days. GeneCapture is developing the CAPTURE 60-minute, portable infection identification platform to detect infectious levels of common fungi including the genera Aspergillus, Fusarium, and Scedosporium, the family Herpotrichiellaceae, and the order Mucorales. The company is also pursuing rapid identification and antimicrobial susceptibility testing for Candida species from multiple infection types. Methods This study seeks to determine the Limit of Detection (LoD), sensitivity and specificity of the CAPTURE ID assay for fungal pathogens. Since tissue biopsy samples must be used to assess the presence of IFI in wounds, GeneCapture has developed a manual process for the crude homogenization of muscle tissue to allow this matrix to be used as the starting sample for the ID assay. Mock samples were tested for inclusivity and exclusivity on a series of IFI ID panels against multiple strains of the included fungi, against fungi not yet specifically detected on the panel, and against bacteria that commonly infect wounds. While the LoD for yeasts may be determined in CFU/mL, there is no similar, easy-to-define metric for molds. GeneCapture has developed a quantitative PCR method to detect the single copy beta-tubulin gene to roughly correlate fungal dry mass and nuclei number to assay signal for molds cultured in Potato Dextrose Broth. Determining beta-tubulin copy numbers for extracts from mock samples then allowed detection of the progression of IFI over the course of infection. Lastly, antimicrobial susceptibility testing (AST) was performed on two species of Candida to validate utilizing GeneCapture’s rapid phenotypic AST method for fungal infections. Results To date, the CAPTURE ID panel has been tested against 27 included or excluded strains of fungi or bacteria with 97% sensitivity and 100% specificity. The current LoD for mold organisms varies from approximately 1,000 nuclei for Aspergillus species, to 100,000 nuclei for Fusarium species. Additional work to understand this range is underway. Lastly, we have correctly determined the susceptibility of Candida species using our novel approach within 4-6 hours. Conclusions GeneCapture’s ID and AST technology offer a unique solution to address the increasing prevalence of fungal infections. Our platform can identify the most common fungal components in a wound within 60 minutes of receiving a tissue biopsy sample. Candida infections occur across multiple matrices and rapidly identifying the species of Candida and then determining its phenotypic susceptibility profile will have a large impact on treating these difficult pathogens.

Quantification of Total Ketone Bodies in Biological Matrices Using Stable Isotope-Labeled Internal Standards and RP-UHPLC-MS/MS.

Acetoacetate (AcAc) and D-beta-hydroxybutyrate (D-βOHB), the two major ketone bodies found in circulation, are linked to multiple physiological and pathophysiological states. Therefore, analytical methodologies surrounding the quantification of total ketone body (TKB) concentrations in biological matrices are paramount. Traditional methods to quantify TKBs relied on indirect spectrophotometric assays with narrow dynamic ranges, which have been significantly improved upon by modern mass spectrometry (MS)-based approaches. However, the lack of stable isotope-labeled internal standards (ISs) for AcAc and the need to distinguish D-βOHB from its closely related structural and enantiomeric isomers pose significant obstacles. Here, we provide a protocol to synthesize and quantify a [13C] stable isotope-labeled IS for AcAc, which, in conjunction with a commercially available [2H] stable isotope-labeled IS for βOHB, allows TKBs to be measured across multiple biological matrices. This rapid (7min) analysis employs reverse phase ultra-high performance liquid chromatography (RP-UHPLC) coupled to tandem MS (MS/MS) to distinguish βOHB from three structural isomers using parallel reaction monitoring (PRM), providing excellent specificity and selectivity. Finally, a method is provided that distinguishes D-βOHB from L-βOHB using a simple one-step derivatization to produce the corresponding diastereomers, which can be chromatographically resolved using the same rapid RP-UHPLC separation with new PRM transitions. In summary, this method provides a rigorous analytical pipeline for the analysis of TKBs in biological matrices via leveraging two authentic stable isotope-labeled ISs and RP-UHPLC-MS/MS.

Robust Hashing with Deep Features and Meixner Moments for Image Copy Detection

Copy detection is a key task of image copyright protection. Most robust hashing schemes do not make satisfied performance of image copy detection yet. To address this, a robust hashing scheme with deep features and Meixner moments is proposed for image copy detection. In the proposed hashing, global deep features are extracted by applying tensor Singular Value Decomposition (t-SVD) to the three-order tensor constructed in the DWT domain of the feature maps calculated by the pre-trained VGG16. Since the feature maps in the DWT domain are slightly disturbed by digital operations, the constructed three-order tensor is stable and thus the desirable robustness is guaranteed. Moreover, since t-SVD can decompose a three-order tensor into multiple low-dimensional matrices reflecting intrinsic structure, the global deep feature calculation from the low-dimensional matrices can provide good discrimination. Local features are calculated by the block-based Meixner moments. As the Meixner moments are resistant to geometric transformation and can efficiently discriminate various images, the use of the block-based Meixner moments can make discriminative and robust local features. Hash is ultimately determined by quantifying and combining global deep features and local features. The results of extensive experiments on open image datasets demonstrate that the proposed robust hashing outperforms some state-of-the-art robust hashing schemes in terms of classification and copy detection performances.

Robust State Feedback Control with D-Admissible Assurance for Uncertain Discrete Singular Systems

This study addresses the state feedback control associated with D-admissible assurance for discrete singular systems subjected to parameter uncertainties in both the difference term and system matrices. Firstly, a refined analysis criterion of D-admissible assurance is presented, where the distinct form embraces multiple slack matrices and has lessened linear matrix inequalities (LMIs) constraints, which may be beneficial for reducing the conservatism of admissibility analysis. In consequence, by hiring the state feedback control, controller design issues with pole locations, which directly dominate the system performance, are mainly treated. For all the presented criteria can be formulated by the strict LMIs, they are thus suitably solved via current LMI solvers to conduct a state feedback controller with specific poles’ locations of system’s performance requirements. Finally, two numerical examples illustrate that the presented results are efficient and practicable.

Correlating light fields through disordered media across multiple degrees of freedom

Speckle patterns are inherent features of coherent light propagation through complex media. As a result of interference, they are sensitive to multiple experimental parameters such as the configuration of disorder or the propagating wavelength. Recent developments in wavefront shaping have made it possible to control speckle pattern statistics and correlations, for example using the concept of the transmission matrix. In this article, we address the problem of correlating scattered fields across multiple degrees of freedom. We highlight the common points between the specific techniques already demonstrated, and we propose a general framework based on the singular value decomposition of a linear combination of multiple transmission matrices. Following analytical predictions, we experimentally illustrate the technique on spectral and temporal correlations, and we show that both the amplitude and the phase of the field correlations can be tuned. Our work opens up new perspectives in speckle correlation manipulation, with potential applications in coherent control. Published by the American Physical Society 2024

Robust adaptive beamforming for cylindrical uniform conformal arrays based on low-rank covariance matrix reconstruction

Recently, conformal arrays have attracted considerable interest because such arrays can provide reduced radar cross-section and increased angle coverage. In this article, we devise a robust adaptive beamforming (RAB) approach using cylindrical uniform conformal array (CUCA). Firstly, we derive the minimum variance distortionless response (MVDR) beamformer for the CUCA by utilizing the noise subspace of interference covariance matrix (ICM) and steering vector (SV) of the signal-of-interest (SOI). Subsequently, the ICM is reconstructed by estimating the noise-free covariance matrix of the CUCA outputs and the interference projection matrix. Specifically, the noise-free covariance matrix can be regarded as multiple low-rank covariance matrices, and each low-rank matrix is reconstructed by formulating a nuclear norm minimization (NNM) problem. With the reconstructed covariance matrix, the 2-D DOAs of sources are determined by employing 2-D MUSIC spectrum to form the interference projection matrix. In addition, the SOI SV is estimated by solving a quadratically constrained quadratic programming (QCQP) problem. Numerical results demonstrate that the proposed approach is obviously superior to the existing RAB techniques.

Self-organizing maps with adaptive distances for multiple dissimilarity matrices

Self-organizing maps with adaptive distances for multiple dissimilarity matrices

Multi‐Environment Quantification of Parasite and Intermediate Host DNA on Pasture for Fine‐Scale Disease Risk Assessment

ABSTRACTParasite transmission occurs in complex environments comprising multiple matrices. Trematode parasites of ruminant livestock such as the liver fluke, Fasciola hepatica and the rumen fluke, Calicophoron daubneyi, show affinity with freshwater environments shared with their amphibious snail intermediate host, Galba truncatula. Isolation of environmental DNA (eDNA) from these parasites and their snail hosts in water draining from grazing land provides opportunities for improved molecular diagnostic detection and can help identify infection risks at farm level. The detection and quantification of eDNA from other environmental matrices has received less attention but would improve the understanding of parasite dynamics on pasture. Our study has considerably extended eDNA sampling methods for the detection of parasitic trematodes of ruminant livestock and their snail intermediate host by including for the first time the analysis of soil and herbage environmental samples alongside water collections. A droplet digital PCR (ddPCR) workflow was developed to detect parasite and snail eDNA from soil, herbage, and water collected from livestock farms. For the first time, C. daubneyi eDNA was isolated from agricultural soil alongside water samples and G. truncatula eDNA was detected in water, soil, and herbage samples. No environmental samples were positive for F. hepatica eDNA. Assessing multiple environmental matrices increased the number of positive sites. Future implementation of eDNA detection methods alongside traditional parasite diagnostics can underpin more holistic evaluations of the environmental components of parasite epidemiology and facilitate adaptation to changing disease patterns.

Follow-up Biomonitoring Study of Metal Exposure in Apprentice Welders in Montreal, Quebec, During Gas Metal Arc Welding (GMAW).

Welding activities are known to expose workers to metal fumes, but few studies have focused on assessing the internal exposure of apprentices in learning environments. This study aimed at determining internal doses of metals in apprentices performing gas metal arc welding (GMAW) during their training course. A total of 85 apprentice welders were assessed, and multi-elements were measured in urine, hair, fingernail, and toenail samples collected at the beginning of the program, and at the beginning and end of GMAW practical training. Concentrations of welding fumes and metals were also determined in personal respirable air samples. Serial measurements of metal concentrations in urine and hair, which reflect more recent exposure, showed an increase in arsenic (As), chromium (Cr), iron (Fe), and manganese (Mn) (and to a lesser extent nickel (Ni)) levels at the end of the GMAW process. Metal concentrations in fingernails and toenails showed a time-dependent increase in Fe, Mn, and Ni (and to a lesser extent cobalt (Co)) levels, reflecting cumulative exposure. Levels of Mn and Fe were high in personal air samples with respective median concentrations (95th percentiles) of 21 (300) and 230 (1900) µg/m3. Results show that even short-term exposure to welding fumes in a learning environment leads to a significant increase in absorbed metal doses, particularly for Fe and Mn. This follow-up study confirmed the interest and usefulness of measuring multi-elements in multiple matrices to assess internal exposure to welding fumes and its applicability to occupational or even population exposure to metals.

Alternating nonnegative least squares-incorporated regularized symmetric latent factor analysis for undirected weighted networks

An Undirected Weighted Network (UWN) can be precisely quantified as an adjacency matrix whose inherent characteristics are fully considered in a Symmetric Nonnegative Latent Factor (SNLF) model for its good representation accuracy. However, an SNLF model uses a sole latent factor matrix to precisely describe the topological characteristic of a UWN, i.e., symmetry, thereby impairing its representation learning ability. Aiming at addressing this issue, this paper proposes an Alternating nonnegative least squares-incorporated Regularized Symmetric Latent factor analysis (ARSL) model. First of all, equation constraints composed of multiple matrices are built in its learning objective for well describing the symmetry of a UWN. Note that it adopts an L2-norm-based regularization scheme to relax such constraints for making such a symmetry-aware learning objective solvable. Then, it designs an alternating nonnegative least squares-incorporated algorithm for optimizing its parameters efficiently. Empirical studies on four UWNs demonstrate that an ARSL model outperforms the state-of-the-art models in terms of representation accuracy, as well as achieves promising computational efficiency.

Revisiting “Rapid multiplication of rectangular matrices”

Revisiting “Rapid multiplication of rectangular matrices”

RSANMDA: Resampling based subview attention network for miRNA-disease association prediction

Many studies have demonstrated the importance of accurately identifying miRNA-disease associations (MDAs) for understanding disease mechanisms. However, the number of known MDAs is significantly fewer than the unknown pairs. Here, we propose RSANMDA, a subview attention network for predicting MDAs. We first extract miRNA and disease features from multiple similarity matrices. Next, using resampling techniques, we generate different subviews from known MDAs. Each subview undergoes multi-head graph attention to capture its features, followed by semantic attention to integrate features across subviews. Finally, combining raw and training features, we use a multilayer scoring perceptron for prediction. In the experimental section, we conducted comparative experiments with other advanced models on both HMDD v2.0 and HMDD v3.2 datasets. We also performed a series of ablation studies and parameter tuning exercises. Comprehensive experiments conclusively demonstrate the superiority of our model. Case studies on lung, breast, and esophageal cancers further validate our method's predictive capability for identifying disease-related miRNAs.

Norm convergence rate for multivariate quadratic polynomials of Wigner matrices

We study Hermitian non-commutative quadratic polynomials of multiple independent Wigner matrices. We prove that, with the exception of some specific reducible cases, the limiting spectral density of the polynomials always has a square root growth at its edges and prove an optimal local law around these edges. Combining these two results, we establish that, as the dimension N of the matrices grows to infinity, the operator norm of such polynomials q converges to a deterministic limit with a rate of convergence of N−2/3+o(1). Here, the exponent in the rate of convergence is optimal. For the specific reducible cases, we also provide a classification of all possible edge behaviors.

Data Completion-Guided Unified Graph Learning for Incomplete Multi-View Clustering

Due to its heterogeneous property, multi-view data has been widely concerned over single-view data for performance improvement. Unfortunately, some instances may be with partially available information because of some uncontrollable factors, for which the incomplete multi-view clustering (IMVC) problem is raised. IMVC aims to partition unlabeled incomplete multi-view data into their clusters by exploiting the heterogeneity of multi-view data and overcoming the difficulty of data loss. However, most existing IMVC methods like BSV, MIC, OMVC, and IVC tend to conduct basic completion processing on the input data, without taking advantage of the correlation between samples and information redundancy. To overcome the above issue, we propose one novel IMVC method named data completion-guided unified graph learning (DCUGL), which could complete the data of missing views and fuse multiple learned view-specific similarity matrices into one unified graph. Specifically, we first reduce the dimension of the input data to learn multiple view-specific similarity matrices. By stacking all view-specific similarity matrices, DCUGL constructs a third-order tensor with the low-rank constraint, such that sample correlation within and between views can be well explored. Finally, by dividing the original data into observed data and unobserved data, DCUGL can infer and complete the missing data according to the view-specific similarity matrices, and obtain a unified graph, which can be directly used for clustering. To solve the proposed model, we design an iterative algorithm, which is based on the alternating direction method of multipliers framework. The proposed model proves to be superior by benchmarking on six challenging datasets compared with state-of-the-art IMVC methods.

A‐prori layer height determination for wire arc additive manufacturing based on weld geometry

AbstractThe application of wire arc additive manufacturing (WAAM) for the production of large size components is currently limited, due to strong distortion and unprecise filling behavior. The resulting geometric and metallurgical irregularities pose a challenge to the process. The current approach of a layered structure cannot be adopted without adjustments when using wire arc additive manufacturing. Reasons include incompleteness, material accumulation and deformation. The combination of experimental weld geometry‐determination and its numerical estimation is presented here as solution to this challenge. The procedure is based on the measurement of a weld bead cross‐section‐area. By convolution of a path matrix with a weld‐geometry‐function, the planned path is filled with the seam geometry. Subsequent summation of multiple matrices results in a height profile showing discontinuities and accumulations. Further validation tests show a good agreement between the method and experimentally determined problem areas. The presented optimisation procedure can be extended with material parameters. A local compensation for deformation can be achieved.

Simultaneous detection of three hypoxia-inducible factor stabilizers-molidustat, roxadustat, and vadadustat-in multiple keratinized matrices and its application in a doping context.

In a doping case, a top athlete challenged an anti-doping rule violation, involving molidustat. Molidustat is a stabilizing agent of the hypoxia-inducible factor (HIF) recently developed. It is currently undergoing clinical trials for anemia associated with chronic kidney disease. HIF stabilizers are banned at all times by the World Anti-Doping Agency (class S2). Because of their pharmacological proprieties, these new drugs can enhance athletic performance. The athlete's defense wanted to analyze multiple keratinized matrices as they allow long-term investigations. Requests concerning HIF stabilizers are constantly growing. We have therefore developed a liquid chromatography coupled with tandem mass spectrometry method to identify and quantify three molecules of this class: molidustat, vadadustat, and roxadustat. Thirty milligrams of keratinized matrices were incubated in 1mL of pH8.4 diammonium hydrogen phosphate buffer for 16 h at 40°C with 1ng of testosterone-D3, used as internal standard. After extraction with ethyl acetate/diethyl ether (80/20), the organic phase was evaporated, and the dry residue was reconstituted in 30 μL of initial phase. The method was linear from 5 to 1000 pg/mg for the three analytes. Limits of quantification were 2, 0.5, and 5pg/mg for molidustat, roxadustat, and vadadustat, respectively. The analysis of the athlete's head hair (collected 1 month after the urine test) showed a concentration of molidustat of 135 pg/mg, and his beard hair and his fingernails clippings contained 55 and 40 pg/mg, respectively.