Kernel Structure Research Articles

Isomerism in Molecular Metal Carbonyl Clusters

The present microreview focuses on different typologies of isomerism documented along the years for metal carbonyl clusters (MCCs), and outlines their analogies to other classes of ligand‐protected molecular clusters and nanoclusters. Isomerism in molecular MCCs is discussed within two main categories, that is, surface isomerism and core isomerism. The first Section presents some representative examples of surface isomerism involving inorganic (carbonyls and hydrides) and organic ligands, as well as isomerism due to ML fragments decorating the cluster surface. The second Section focuses on three major categories of core isomerism, that is: (1) isomers that mainly differ on M‐M distances; (2) isomers displaying different structures of the metal kernels; (3) isomers possessing almost identical metal kernels and ligand shells, but differing for the positions of different types of metal atoms within the metal kernel. The third Section briefly discusses two related and very rare cases of isomerism, that is, polymerisation and coordination isomerism. General conclusions are outlined in the final Section.

Explicit and approximate solutions for a classical hyperbolic fragmentation equation using a hybrid projected differential transform method

Population balance equations are widely used to study the evolution of aerosols, colloids, liquid–liquid dispersion, raindrop fragmentation, and pharmaceutical granulation. However, these equations are difficult to solve due to the complexity of the kernel structures and initial conditions. The hyperbolic fragmentation equation, in particular, is further complicated by the inclusion of double integrals. These challenges hinder the analytical solutions of number density functions for basic kernel classes with exponential initial distributions. To address these issues, this study introduces a new approach combining the projected differential transform method with Laplace transform and Padé approximants to solve the hyperbolic fragmentation equation. This method aims to provide accurate and efficient explicit solutions to this challenging problem. The approach's applicability is demonstrated through rigorous mathematical derivation and convergence analysis using the Banach contraction principle. Additionally, several numerical examples illustrate the accuracy and robustness of this new method. For the first time, new analytical solutions for number density functions are presented for various fragmentation kernels with gamma and other initial distributions. This method significantly enhances solution quality over extended periods using fewer terms in the truncated series. The solutions are compared and verified against the finite volume method and the homotopy perturbation method, showing that the coupled approach not only estimates number density functions accurately but also captures integral moments with high precision. This research advances computational methods for particle breakage phenomena, offering potential applications in various industrial processes and scientific disciplines.

Self-selective receptive field network for person re-identification

Person Re-identification (Re-ID) technology aims to solve the matching problem of the same pedestrians at different times and places, which has important application value in the field of public safety. At present, most scholars focus on designing complex models to improve the accuracy of Re-ID, but the high complexity of the model further restricts the practical application of Re-ID algorithm. To solve the above problems, this paper designs a lightweight Self-selective Receptive Field (SRF) block instead of directly designing complex models. Specifically, the module can be plug-and-play on the general backbone network, so as to significantly improve the performance of Re-ID while effectively controlling the amount of its own parameter and calculation: (1) the SRF block encodes pedestrian targets and image contexts at different scales by constructing pyramidal convolution group and allows the module to independently select the size of the receptive field through training by means of self-adaptive weighting; (2) in order to reduce the complexity of SRF block, we introduce a "channel scaling factor" and design a "grouped convolution operation" by constraining the channels of the feature map and changing the structure of the convolution kernel respectively. Experiments on multiple datasets show that SRF Network (SRFNet) for Re-ID can achieve a good balance between performance and complexity, which fully demonstrates the effectiveness of SRF block.

Toward compound fault diagnosis via EMAGAN and large kernel augmented few-shot learning

Bearings are essential in machinery. Damage to them can cause financial losses and safety risks at industrial sites. Therefore, it is necessary to design an accurate diagnostic model. Although many bearing fault diagnosis methods have been proposed recently, they still cannot meet the requirements of high-accurate prediction of bearing faults. There are several challenges in this: 1) In practical settings, gathering sufficient and balanced sample data for training diagnostic network models proves challenging. 2) The damage to bearings in real industrial production sites is not singular, and compound faults are also a huge challenge for diagnostic networks. To address these issues, this study introduces a novel fault diagnosis model called EMALKNet that integrates DCGAN with Efficient Multi-Scale Attention (EMAGAN) and RepLKNet-XL, enhancing the detection and analysis of bearing faults in industrial machinery. This model employs EMAGAN to explore the underlying distribution of raw data, thereby enlarging the fault sample pool and enhancing the model’s diagnostic capabilities; The large kernel structure of RepLKNet-XL is different from the current mainstream small kernel and has stronger representation extraction ability. The proposed method has been validated on the Paderborn University dataset and the Huazhong University of Science and Technology dataset.

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage, partially initialised with an ECG template, and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template in the input signal, prior to filtering these matches with the subsequent convolutional layers and selecting peaks corresponding to the ground-truth ECG. The latent representation of R-peak information is then fed into a R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross-validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Overall, this Deep-Match framework serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its interpretable operation, the acceptance of deep learning models in e-Health.

Beta Brownian motion

Brownian motion (BM) is the paradigmatic model of diffusion. Transcending from diffusion to anomalous diffusion, the principle Gaussian generalizations of BM are Scaled BM (SBM) and Fractional BM (FBM). In the sub/super diffusivity regimes: SBM is characterized by aging/anti-aging, and FBM is characterized by anti-persistence/persistence. BM is neither aging/anti-aging, nor persistent/anti-persistent. Within the realm of diffusion, a recent Gaussian generalization of BM, Weird BM (WBM), was shown to display aging/anti-aging and persistence/anti-persistence. This paper introduces and explores the anomalous-diffusion counterpart of WBM—termed Beta BM (BBM) due to its inherent beta-function kernel structure—and shows that: the weird behaviors of WBM become even weirder when elevating to BBM. Indeed, BBM displays a rich assortment of anomalous behaviors, and an even richer assortment of combinations of anomalous behaviors. In particular, the BBM anomalous behaviors include aging/anti-aging and persistence/anti-persistence—which BBM displays in both the sub and super diffusivity regimes. So, anomalous behaviors that are unattainable by the prominent models of SBM and FBM are well attainable by the BBM model.

Asymptotics of the confluent hypergeometric process with a varying external potential in the super-exponential region

In this paper, we investigate a determinantal point process on the interval [Formula: see text], associated with the confluent hypergeometric kernel. Let [Formula: see text] denote the trace class integral operator acting on [Formula: see text] with the confluent hypergeometric kernel. Our focus is on deriving the asymptotics of the Fredholm determinant [Formula: see text] as [Formula: see text], while simultaneously [Formula: see text] in a super-exponential region. In this regime of double scaling limit, our asymptotic result also gives us asymptotics of the eigenvalues [Formula: see text] of the integral operator [Formula: see text] as [Formula: see text]. Based on the integrable structure of the confluent hypergeometric kernel, we derive our asymptotic results by applying the Deift–Zhou nonlinear steepest descent method to analyze the related Riemann–Hilbert problem.

PSIII-16 Effects of dietary corn origin (U.S., Argentina, and Brazil) on pig growth performance

Abstract Corn is an important global commodity. Understanding its nutritional value and practically utilizing different corn sources and varieties in swine diets is important to U.S. corn and swine production. Globally, the U.S. “dent” corn variety is presumed to be of lesser quality compared with “harder” flint-type corn (the preferred varieties in Argentina and Brazil) due to differences in whole kernel structure when shipped long distances. In fact, official work from the US Grains Council indicates that US corn produces more ethanol per bushel when compared with flint-type varieties of corn. This indicates a greater concentration of starch in US corn and increased starch content should mean a greater quantity of energy and nutrients. Therefore, swine producers globally benefit from understanding the feeding value of different varieties and origins of corn. The objective of this study was to assess the performance of pigs fed corn from three exporting countries: U.S., Argentina (ARG), and Brazil (BRZ). Two similar studies were conducted comparing the effect of 3 corn sources on late nursery pig growth performance. Study 1 used 96 nursery pigs [body weight (BW) = 8.8 ± 1.7 kg] split between 24 pens and study 2 used 114 nursery pigs (8.0 ± 1.3 kg) split between 24 pens. Three experimental diets were formulated with corn sources from: 1) U.S.; 2) ARG; and, 3) BRZ. The U.S. corn diet was considered the control diet and was balanced to meet or exceed all nutrient requirements for the pigs. The U.S. corn was replaced with ARG corn and BRZ corn on a kg for kg basis in the diets. Pigs were assigned in a randomized block design based on initial body weights (BW) and sex. Feed and water were offered ad libitum over a 28-d period, pig BW and feed disappearance were measured each week. Average daily gain (ADG), average daily feed intake (ADFI), and feed efficiency [gain to feed ratio (G:F)] were determined after weeks 1, 2, and 4. Performance data were analyzed using PROC MIXED procedure in SAS considering dietary treatment as a main effect and pen as experimental unit. There were no significant differences (P > 0.05) in BW, ADFI, ADG, and feed efficiency between the three corn sources throughout all weeks and the overall 28 d. Overall performance values for the three treatments were: US corn ADG (0.66 kg), ADFI (0.91 kg), and G:F (0.72): Argentina corn, ADG (0.65 kg), ADFI (0.92), and G:F (0.70); and Brazil corn, ADG (0.65), ADFI (0.92), and G:F (0.71). In conclusion, the value of US corn is similar in nutrient content and nutritional quality as demonstrated by equivalent performance in nursery pigs. The aesthetics of whole kernels are not indicative of feeding value to swine.

Dilated Heterogeneous Convolution for Cell Detection and Segmentation Based on Mask R-CNN.

Owing to the variable shapes, large size difference, uneven grayscale, and dense distribution among biological cells in an image, it is very difficult to accurately detect and segment cells. Especially, it is a serious challenge for some microscope imaging devices with limited resources owing to a large number of learning parameters and computational burden when using the standard Mask R-CNN. In this work, we propose a mask R-DHCNN for cell detection and segmentation. More specifically, Dilation Heterogeneous Convolution (DHConv) is proposed by designing a novel convolutional kernel structure (i.e., DHConv), which integrates the strengths of the heterogeneous kernel structure and dilated convolution. Then, the traditional homogeneous convolution structure of the standard Mask R-CNN is replaced with the proposed DHConv module to it adapt to shape and size differences encountered in cell detection and segmentation tasks. Finally, a series of comparison and ablation experiments are conducted on various biological cell datasets (such as U373, GoTW1, SIM+, and T24) to verify the effectiveness of the proposed method. The results show that the proposed method can obtain better performance than some state-of-the-art methods in multiple metrics (including AP, Precision, Recall, Dice, and PQ) while maintaining competitive FLOPs and FPS.

Probabilistic identification method for seismic failure modes of reinforced concrete beam-column joints using Gaussian process with deep kernel

Identifying the seismic failure modes of beam-column joints (BCJs) is crucial for the safety and integrity of reinforced concrete (RC) buildings or structures withstanding seismic forces. However, traditional identification methods fail to provide any indication about the uncertainties within their predictions, which is beneficial for evaluating, interpreting and improving these predictions. This study develops a probabilistic identification method for seismic failure modes of BCJs using Gaussian process (GP) with a deep kernel, which integrates the representational power of deep neural networks with the flexible structure of kernel functions to accurately represent the evolution characteristics of seismic failure modes of BCJs. Analysis results demonstrated the potential of the proposed method for improving the classification accuracy of traditional GPs, as well as its superiority over the prediction accuracy of traditional shear-resistance design methods and machine learning techniques. Furthermore, the proposed method also provides an efficient approach to estimate the uncertainties within their predictions for seismic failure modes of BCJs.

Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning

Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.

Heterogeneous interface engineering of bionic corn-structured ternary nanocomposites for excellent low-frequency microwave absorption

Achieving strong low-frequency microwave absorption is an important research direction, and heterogeneous interface engineering is emerging as a promising strategy. In this work, drawing inspiration from corn, bionic corn-structured ternary nanocomposites were designed and prepared. The obtained Co2NiO4/Fe2O3/Fe3O4 ternary nanocomposites mimic corn kernel structures, and create abundant heterogeneous interfaces, large specific surface area (SSA), and pores. These features effectively enhance interface polarization, optimize impedance matching, and also induce low-frequency natural resonance and high-frequency eddy current loss. The internal defects in the material can promote dipole polarization. The heterogeneous interface engineering of the corn-structured ternary nanocomposites enhances the synergistic effect of dielectric and magnetic loss in the low-frequency region. This optimization achieves an excellent microwave absorption capability of −55.16 dB at a low frequency of 4.40 GHz. Furthermore, radar cross section (RCS) simulations also verified its immense potential for practical applications. This work fabricates an original bionic nanostructure and proposes a performance optimization strategy that will provide useful guidance for the design and fabrication of low-frequency microwave absorbers.

The variance and correlations of the divisor function in Fq[T]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {F}}_q [T]$$\\end{document}, and Hankel matrices

We prove an exact formula for the variance of the divisor function over short intervals in A:=Fq[T]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {A}}:= {\\mathbb {F}}_q [T]$$\\end{document}, where q is a prime power; and for correlations of the form d(A)d(A+B)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d(A) d(A+B)$$\\end{document}, where we average both A and B over certain intervals in A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {A}}$$\\end{document}. We also obtain an exact formula for correlations of the form d(KQ+N)d(N)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d(KQ+N) d (N)$$\\end{document}, where Q is prime and K and N are averaged over certain intervals with degN≤degQ-1≤degK\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\ extrm{deg}\\,}}N \\le {{\\,\ extrm{deg}\\,}}Q -1 \\le {{\\,\ extrm{deg}\\,}}K$$\\end{document}; and we demonstrate that d(KQ+N)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d(KQ+N)$$\\end{document} and d(N) are uncorrelated. We generalize our results to σz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma _z$$\\end{document} defined by σz(A):=∑E∣A|A|z\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma _z (A):= \\sum _{E \\mid A} |A |^z$$\\end{document} for all monics A∈A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A \\in {\\mathcal {A}}$$\\end{document}. Our approach is to use the orthogonality relations of additive characters on Fq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {F}}_q$$\\end{document} to translate the problems to ones involving the ranks of Hankel matrices over Fq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {F}}_q$$\\end{document}. We prove several results regarding the rank and kernel structure of these matrices, thus demonstrating their number-theoretic properties. We also discuss extending our method to other divisor sums, such as those involving dk\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_k$$\\end{document}.

Expressive mortality models through Gaussian process kernels

AbstractWe develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance prior best matching the spatio-temporal dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA) on synthetic case studies to validate the ability of the GA to recover APC structure and on real-life national-level datasets from the Human Mortality Database. Our machine learning-based analysis provides novel insight into the presence/absence of Cohort effects in different populations and into the relative smoothness of mortality surfaces along the Age and Year dimensions. Our modeling work is done with the PyTorch libraries in Python and provides an in-depth investigation of employing GA to aid in compositional kernel search for GP surrogates.

A Systemic Functional Linguistic Analysis of Spousal Conflictual Language in a Nigerian Play

This paperx-rayed language use drawn from excerpt of utterances by the spouses (characters) in the play, Clark’s The Wives’ Revolt anchoring on the Hallidayan Systemic functional linguisticas the theoretical framework known for analysing language structure and function. The purpose of this study is to reveal the experiential representation of conflictual language of the spouses in J.P. Clark’s Wives’ Revoltsthrough its transitivity processes. Twenty – nine (29) excerpts that depict the spousal conflictual lexico-grammatical forms are purposively selected from these plays after thorough readings and they are represented accordingly for easier referencing during analysis in order to determine the frequencies of the transitivity features, their roles, functions, and meanings. A qualitative method is used for data analysis. Results from descriptive analysis showed that spouses applied four (material, mental relational and verbal processes) out of six Hallidayan’s transitivity process types to express their experiences/actions feelings for each other, negative image of themselves, and their central point of argument during their conflictual interactivity. The result showed that experiential is construed through dominance of material process, followed by mental, relational, and verbal. The prevalence of the material is found to be as a result of spouses’ more of physical actions in their relations. Again, transitive verb which transfers action to an object is also found more prevalent and is used more by the male spouse. This reveals male dominance of power over his female counterpart. The occurrence of the transitive processes will help the reader to agree with the kernel structure of the conflicting issues.

Operational Status Evaluation of Smart Electricity Meters Using Gaussian Process Regression With Optimized-ARD Kernel

Operational Status Evaluation (OSE) is essential for the health management of Smart Electricity Meters (SEM). This paper develops a machine learning-enabled OSE method for SEM. Specifically, the Gaussian Process Regression (GPR) is developed for modeling analysis, where an Optimized Automatic Relevance Determination (OARD) kernel is first used. Though the conventional ARD kernel structure can capture the potential mapping relationship between running time, temperature, humidity, and Measurement Error (ME), it cannot extract the influence degree of different stresses on the ME. To address this problem, an OARD kernel structure is proposed to identify the contribution of each part in ARD structure to the target data. Furthermore, the quartiles line instead of 95% confidence interval is exploited to enhance the performance of GPR for long-term prediction. Combining the two above improvements, a novel OSE model is established for the future-oriented long-term operation of the SEM. It is the first-known data-driven application that utilizes the GPR with OARD kernel to perform OSE for SEM. Actual SEM datasets collected from both dry and hot region are used for model validation and prediction. The results demonstrate that the proposed GPR model with OARD Matern52 kernel outperforms other conventional kernel approaches in the aspect of interpretability. More importantly, the operational status of the SEM in future can be assessed via the proposed OSE framework.

Mutations in starch biosynthesis genes affect chloroplast development in wheat pericarp

Starch bioengineering in cereals has produced a plethora of genotypes with new nutritional and technological functionalities. Modulation of amylose content from 0 to 100% was inversely correlated with starch digestibility and promoted a lower glycemic index in food products. In wheat, starch mutants have been reported to exhibit various side effects, mainly related to the seed phenotype. However, little is known about the impact of altered amylose content and starch structure on plant metabolism. Here, three bread wheat starch mutant lines with extreme phenotypes in starch branching and amylose content were used to study plant responses to starch structural changes. Omics profiling of gene expression and metabolic patterns supported changes, confirmed by ultrastructural analysis in the chloroplast of the immature seeds. In detail, the identification of differentially expressed genes belonging to functional categories related to photosynthesis, chloroplast and thylakoid (e.g. CURT1), the alteration in the accumulation of photosynthesis-related compounds, and the chloroplast alterations (aberrant shape, grana stacking alteration, and increased number of plastoglobules) suggested that the modification of starch structure greatly affects starch turnover in the chloroplast, triggering oxidative stress (ROS accumulation) and premature tissue senescence. In conclusion, this study highlighted a correlation between starch structure and chloroplast functionality in the wheat kernel.

Data-Driven Joint Fault Diagnosis Based on RMK-ASSA and DBSKNet for Blast Furnace Iron-Making Process

Blast furnace iron-making process (BFIP) is one of the most critical procedures in the iron and steel industry where timely detection and accurate classification of faults have always been of core focus. However, the coupling effects of system’s nonlinear and nonstationary characteristics often cause process consistent underlying information to be buried, allowing accurate extraction to be a significant challenge. This also complicates the development of BFIP fault diagnosis model. Therefore, we propose a novel data-driven joint fault diagnosis strategy that employs regularized mutual kernel analytic stationary subspace analysis (RMK-ASSA) and deep broad stationary kernel network (DBSKNet) to eliminate this interference. To develop this method, we first construct an RMK-ASSA approach to address the poor modeling accuracy caused by standard analytic stationary subspace analysis (ASSA)’s inability to handle complex process nonlinearity. Global and local kernels are utilized to account for multiple nonlinearities in BFIP data. The weight of different nonlinear data is calculated by regularized principal component analysis, and the main information is imported into ASSA to obtain more robust and accurate modeling results by eliminating the interference of redundant noise. Subsequently, we design a DBSKNet-based classifier to implement the fault diagnosis task. This network further considers the nonlinearity by boosting kernel structure in depth and width while distinguishing the respective contributions of different kernels to fault diagnosis results. Finally, a double-layer loop parameter optimization algorithm is used for optimizing. Simulated cases and practical BFIP tests validate that RMK-ASSA eliminates the negative impact caused by nonstationary data and that the proposed joint fault diagnosis strategy outperforms other methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —BFIP’s nonlinear and nonstationary coupling properties pose unique challenges in eliminating distractions, constructing fault classifiers and accurately detecting process anomalies. To tackle these challenges, this paper proposes a joint fault diagnosis strategy based on RMK-ASSA and DBSKNet. RMK-ASSA effectively estimates nonlinear consistent features, while DBSKNet mines rich deep nonlinear information, accurately distinguishing variations in BFIP data under different working conditions. Experimental results demonstrate that this data-driven strategy can perform high-quality fault diagnosis, enabling field engineers to execute operations efficiently.

On Solutions of a System of Nonlinear Integral Equations of Convolution Type on the Entire Real Line

We consider a special system of integral equations of convolution type with a monotone convex nonlinearity naturally arising when searching for stationary or limit states in various dynamic models of applied nature, for example, in models of the spread of epidemics, and prove theorems stating the existence or absence of a nontrivial bounded solution with limits at +@ depending on the values of these limits and on the structure of the matrix kernel of the system. We also study the uniqueness of such a solution assuming that it exists. Specific examples of systems whose parameters satisfy the conditions stated in our theorems are given.

Generative and Discriminative Learning for Lung X-Ray Analysis Based on Probabilistic Component Analysis.

The paper presents a hybrid generative/discriminative classification method aimed at identifying abnormalities, such as cancer, in lung X-ray images. The proposed method involves a generative model that performs generative embedding in Probabilistic Component Analysis (PrCA). The primary goal of PrCA is to model co-existing information within a probabilistic framework, with the intent to locate the feature vector space for X-ray data based on a defined kernel structure. A kernel-based classifier, grounded in information-theoretic principles, was employed in this study. The performance of the proposed method is evaluated against nearest neighbour (NN) classifiers and support vector machine (SVM) classifiers, which use a diagonal covariance matrix and incorporate normal linear and non-linear kernels, respectively. The method is found to achieve superior accuracy, offering a viable solution to the class of problems presented. Accuracy rates achieved by the kernels in the NN and SVM models were 95.02% and 92.45%, respectively, suggesting the method's competitiveness with state-of-the-art approaches.