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An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter's parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system's input and the IIR filter's output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value. [ABSTRACT FROM AUTHOR]

The input layer, hidden layer, and output layer are three models of neural processors that comprise feedforward neural networks. In this paper, an enhanced tunicate swarm algorithm based on a differential sequencing alteration operator (ETSA) with symmetric cooperative swarms is presented to train feedforward neural networks. The objective is to accomplish minimum classification errors and the most appropriate neural network layout by regulating the layers' connection weights and neurons' deviation thresholds according to the transmission error between the anticipated input and the authentic output. The TSA mimics jet motorization and swarm scavenging to mitigate directional collisions and to maintain the greatest solution that is customized and regional. However, the TSA exhibits the disadvantages of low computational accuracy, a slow convergence speed, and easy search stagnation. The differential sequencing alteration operator has adaptable localized extraction and search screening to broaden the identification scope, enrich population creativity, expedite computation productivity, and avoid search stagnation. The ETSA integrates exploration and exploitation to mitigate search stagnation, which has sufficient stability and flexibility to acquire the finest solution. The ETSA was distinguished from the ETTAO, EPSA, SABO, SAO, EWWPA, YDSE, and TSA by monitoring seventeen alternative datasets. The experimental results confirm that the ETSA maintains profound sustainability and durability to avoid exaggerated convergence, locate the acceptable transmission error, and equalize extraction and prospection to yield a faster convergence speed, superior calculation accuracy, and greater categorization accuracy. [ABSTRACT FROM AUTHOR]

The North China type cucumber, characterized by its dense spines and top flowers, is susceptible to damage during the grading process, affecting its market value. Moreover, traditional manual grading methods are time-consuming and labor-intensive. To address these issues, this paper proposes a cucumber quality grader based on machine vision and deep learning. In the electromechanical aspect, a novel fixed tray type grading mechanism is designed to prevent damage to the vulnerable North China type cucumbers during the grading process. In the vision grading algorithm, a new convolutional neural network is introduced named MassNet, capable of predicting cucumber mass using only a top-view image. After obtaining the cucumber mass prediction, mass grading is achieved. Experimental validation includes assessing the electromechanical performance of the grader, comparing MassNet with different models in predicting cucumber mass, and evaluating the online grading performance of the integrated algorithm. Experimental results indicate that the designed cucumber quality grader achieves a maximum capacity of 2.3 t/hr. In comparison with AlexNet, MobileNet, and ResNet, MassNet demonstrates superior cucumber mass prediction, with a MAPE of 3.9% and RMSE of 6.7 g. In online mass grading experiments, the grading efficiency of the cucumber quality grader reaches 93%. [ABSTRACT FROM AUTHOR]

OGG1 is the first enzyme in the base excision repair pathway (BER) responsible for repairing 8-oxoguanine DNA lesions. Recent studies found that OGG1 may also be involved in epigenetic regulation. In this study, we focused on the roles of OGG1 in histone modification. First, to study the effects of OGG1 on histone modification, the protein levels of symmetric dimethylation of histone H4 arginine-3 (H4R3me2s) were determined by western blot analysis following the knockdown or overexpression of OGG1. Second, the molecular mechanisms by which OGG1 regulates H4R3me2s were assessed by co-immunoprecipitation (CO-IP) assays in mouse embryonic fibroblast (MEF) wild-type (WT) and Ogg −/− cells. Finally, to verify the regulation of H4R3me2s by OGG1 on specific genes, chromatin immunoprecipitation (CHIP) was performed on MEF WT and Ogg −/− cells. We found that OGG1 affects PRMT5 binding on histone H4 and the formation of H4R3me2s via PRMT5. The methylation level of H4R3me2s was dramatically decreased in MEF Ogg −/− cells compared to WT cells. Knockdown of OGG1 by siRNA led to a decrease in H4R3me2s, while overexpression of OGG1 increased the level of H4R3me2s. OGG1 also interacted with PRMT5 and histone H4, and the interaction between PRMT5 and histone H4 was reduced in MEF Ogg −/− cells. Our data not only illustrate the important roles of OGG1 in histone modification, but also reveal the mechanism by which OGG1 affects PRMT5 binding on H4R3 resulting in the symmetrical dimethylation of histone H4 arginine-3. [ABSTRACT FROM AUTHOR]