Liqiu Ren, Zhanying Li^*, Xueyu He, Lingyan Kong, Yinghao Zhang

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2829-2845, 2024, DOI:10.32604/cmc.2024.047028

Abstract For underwater robots in the process of performing target detection tasks, the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model, which is prone to issues like error detection, omission detection, and poor accuracy. Therefore, this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7) underwater target detection algorithm. To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase, we have added a Convolutional Block Attention Module (CBAM) to the backbone network. The Reparameterization Visual Geometry Group (RepVGG) module is inserted into the… More >

Haipeng Sun^1,2, Xueyan Ding^1,2,*, Jian Sun^1,2, Hua Yu³, Jianxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2721-2739, 2024, DOI:10.32604/cmc.2024.046449

Abstract Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition, we present a deep learning-based approach for Yi character detection and recognition. In the detection stage, an improved Differentiable Binarization Network (DBNet) framework is introduced to detect Yi characters, in which the Omni-dimensional Dynamic Convolution (ODConv) is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features, thereby improving the accuracy of Yi character detection. Then, the feature pyramid network fusion module is used to further extract Yi character image features, improving target recognition… More >

Yunhong Zhang^1,2,*, Yonghui Yang^1,2, Jiawei Mao^1,2

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 185-212, 2024, DOI:10.32604/phyton.2023.046811

Abstract Alginate oligosaccharides (AOS) enhance drought resistance in wheat (Triticum aestivum L.), but the definite mechanisms remain largely unknown. The physiological and transcriptome responses of wheat seedlings treated with AOS were analyzed under drought stress simulated with polyethylene glycol-6000. The results showed that AOS promoted the growth of wheat seedlings and reduced oxidative damage by improving peroxidase and superoxide dismutase activities under drought stress. A total of 10,064 and 15,208 differentially expressed unigenes (DEGs) obtained from the AOS treatment and control samples at 24 and 72 h after dehydration, respectively, were mainly enriched in the biosynthesis of secondary metabolites (phenylpropanoid biosynthesis,… More >

Jingtao Hu¹, Wenjing Chen¹, Yanyan Duan¹, Yingjing Ru¹, Wenqing Cao¹, Pingwei Xiang², Chengzhi Huang², Li Zhang², Jingsheng Chen¹, Liping Gan^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 227-245, 2024, DOI:10.32604/phyton.2024.046521

Abstract Abiotic stress, including flooding, seriously affects the normal growth and development of plants. Mulberry (Morus alba), a species known for its flood resistance, is cultivated worldwide for economic purposes. The transcriptomic analysis has identified numerous differentially expressed genes (DEGs) involved in submergence tolerance in mulberry plants. However, a comprehensive analyses of metabolite types and changes under flooding stress in mulberry remain unreported. A non-targeted metabolomic analysis utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) was conducted to further investigate the effects of flooding stress on mulberry. A total of 1,169 metabolites were identified, with 331 differentially accumulated metabolites (DAMs) exhibiting up-regulation in… More >

Dongmei Chen¹, Peipei Cao¹, Lijie Yan¹, Huidong Chen¹, Jia Lin¹, Xin Li², Lin Yuan³, Kaihua Wu^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 261-275, 2024, DOI:10.32604/phyton.2024.046331

Abstract Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea. Traditional tea-picking machines may compromise the quality of the tea leaves. High-quality teas are often handpicked and need more delicate operations in intelligent picking machines. Compared with traditional image processing techniques, deep learning models have stronger feature extraction capabilities, and better generalization and are more suitable for practical tea shoot harvesting. However, current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks. We propose a tea shoot instance segmentation model based on multi-scale mixed attention… More >

Jialin Yu^1,*, Xiaodi Zhang², Qi Zhong¹, Jian Feng¹

Energy Engineering, Vol.121, No.3, pp. 789-806, 2024, DOI:10.32604/ee.2023.043299

Abstract With a further increase in energy flexibility for customers, short-term load forecasting is essential to provide benchmarks for economic dispatch and real-time alerts in power grids. The electrical load series exhibit periodic patterns and share high associations with metrological data. However, current studies have merely focused on point-wise models and failed to sufficiently investigate the periodic patterns of load series, which hinders the further improvement of short-term load forecasting accuracy. Therefore, this paper improved Autoformer to extract the periodic patterns of load series and learn a representative feature from deep decomposition and reconstruction. In addition, a novel multi-factor attention mechanism… More >

Jian Pang^1,3, Tingting Mao², Wenyu Jia³, Xiaoli Jia^3,*, Peisong Dai², Haibo Huang^1,2,*

Sound & Vibration, Vol.58, pp. 59-80, 2024, DOI:10.32604/sv.2024.046247

Abstract Currently, the inexorable trend toward the electrification of automobiles has heightened the prominence of road noise within overall vehicle noise. Consequently, an in-depth investigation into automobile road noise holds substantial practical importance. Previous research endeavors have predominantly centered on the formulation of mechanism models and data-driven models. While mechanism models offer robust controllability, their application encounters challenges in intricate analyses of vehicle body acoustic-vibration coupling, and the effective utilization of accumulated data remains elusive. In contrast, data-driven models exhibit efficient modeling capabilities and can assimilate conceptual vehicle knowledge, but they impose stringent requirements on both data quality and quantity. In… More >

TIANKAI DI^1,2,#, ZHIFEI ZHOU^3,#, FEN LIU^4,#, YUJIANG CHEN^5,*, LULU WANG^1,*

BIOCELL, Vol.48, No.1, pp. 33-45, 2024, DOI:10.32604/biocell.2023.031638

Abstract Autophagy is a widespread biological process that controls cellular growth, survival, development, and death. Circadian rhythm is a recurring reaction of living organisms and behaviors to variations in surrounding brightness and obscurity. Most of the fundamental physiological processes in mammals, such as the sleep-wake pattern and the rhythm of nutrition and energy metabolism, are governed by circadian rhythms. Research has indicated that autophagy exhibits a specific circadian pattern in both normal and abnormal conditions. Autophagy can modulate circadian rhythms by breaking down proteins that regulate the circadian clock. The potential regulatory connection between the two has been a popular subject… More >

Yongwang Yuan¹, Xiangwei Liu^2,3,*, Ke Lu^1,3

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1227-1252, 2024, DOI:10.32604/cmc.2023.046937

Abstract Predictive Business Process Monitoring (PBPM) is a significant research area in Business Process Management (BPM) aimed at accurately forecasting future behavioral events. At present, deep learning methods are widely cited in PBPM research, but no method has been effective in fusing data information into the control flow for multi-perspective process prediction. Therefore, this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion. Firstly, the first layer BERT network learns the correlations between different category attribute data. Then, the attribute data is integrated into a weighted event-level feature vector and input into the second layer… More >

Chengjun Wang^1,2, Fan Ding^2,*, Yiwen Wang¹, Renyuan Wu¹, Xingyu Yao², Chengjie Jiang¹, Liuyi Ling¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1481-1501, 2024, DOI:10.32604/cmc.2023.046876

Abstract The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots. Real-time identification of strawberries in an unstructured environment is a challenging task. Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy. To this end, the present study proposes an Efficient YOLACT (E-YOLACT) algorithm for strawberry detection and segmentation based on the YOLACT framework. The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism, pyramid squeeze shuffle attention (PSSA), for efficient feature extraction. Additionally, an attention-guided context-feature pyramid network (AC-FPN) is… More >