Qian Tao, Guiqi Han, Bujin Ma, Hongmei Jia, Can Zhao, Wenshang Li, Zhuyun Yan^*

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1297-1310, 2024, DOI:10.32604/phyton.2024.051923

Abstract Fritillaria cirrhosa D. Don (Liliaceae) is an endangered perennial bulbous plant and its dry bulb is a valuable medicinal material with antitussive and expectorant effects. Nevertheless, lack of resources and expensive prices make it difficult to meet clinical needs. This study presents a regeneration system aimed at overcoming the challenge of inadequate supply in F. cirrhosa, focusing on: (1) callus induction, (2) bulblets and adventitious bud induction, and (3) artificial seed production. Callus development was achieved in 84.93% on Murashige and Skoog (MS) medium fortified with 1.0 mg·L picloram. The optimal medium for callus differentiation into regenerated… More >

Junhui Yan¹, Yawen Wang¹, Qiong Li¹, Yu Zhou², Xu Wang^2,*, Li Luo^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1237-1248, 2024, DOI:10.32604/phyton.2024.051517

Abstract Rhizobia induces nitrogen-fixing nodules in legumes used in agricultural production, providing a direct source of combined nitrogen to leguminous crops. Small peptides, such as CLAVATA3/EMBRYO SURROUNDING REGION peptides (CLE), are known to regulate the formation and development of nitrogen-fixing nodules in legumes. Root meristem growth factor (RGF) peptides from Medicago truncatula not only regulate root development but also modulate nodulation symbiosis with Sinorhizobium meliloti. However, the impact of RGF peptides from one leguminous species on the others remains unclear. In this study, we investigate the effects of the RGF family peptide MtRGF6p from M. truncatula on nodulation symbiosis… More >

Chuang Liu^1,2, Jing Xie², Hao Liu², Can Zhong², Gen Pan², Shuihan Zhang², Jian Jin^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1221-1236, 2024, DOI:10.32604/phyton.2024.050759

Abstract Medicinal plants have aroused considerable interest as an alternative to chemical drugs due to the beneficial effects of their active secondary metabolites. However, the extensive use of chemical fertilizers and pesticides in pursuit of yield has caused serious pollution to the environment, which is not conducive to sustainable development in the field of medicinal plants. Microbial fertilizers are a type of “green fertilizer” containing specific microorganisms that can improve the soil microbial structure, enhance plant resistance to biological and abiotic stresses, and increase the yield of medicinal plants. The root exudates of medicinal plants attract… More >

Yuting Zhou¹, Xuemei Yang¹, Junping Yin^2,3,4,*, Shiqi Liu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5313-5333, 2024, DOI:10.32604/cmc.2024.052060

Abstract Gliomas have the highest mortality rate of all brain tumors. Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’ survival rates. This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net), which effectively combines global features and local features. The network framework consists of three parallel layers: The global feature extraction layer, the local feature extraction layer, and the multi-scale feature fusion layer. A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy. In the local feature… More >

Jingjing Nie^1,*, Zhenhua Chen²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4763-4779, 2024, DOI:10.32604/cmc.2024.051181

Abstract The Internet of Medical Things (IoMT) is an application of the Internet of Things (IoT) in the medical field. It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems, which is essential in smart healthcare. However, Personal Health Records (PHRs) are normally kept in public cloud servers controlled by IoMT service providers, so privacy and security incidents may be frequent. Fortunately, Searchable Encryption (SE), which can be used to execute queries on encrypted data, can address the issue above. Nevertheless, most existing SE schemes cannot solve the vector dominance threshold… More >

Zhenyu Qian¹, Yizhang Jiang¹, Zhou Hong¹, Lijun Huang², Fengda Li³, KhinWee Lai⁶, Kaijian Xia^4,5,6,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, DOI:10.32604/cmc.2024.050920

Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction… More > Graphic Abstract

Xiuyang Meng^1,2, Chunling Wang^1,2,*, Jingran Yang^1,2, Mairui Li^1,2, Yue Zhang^1,2, Luo Wang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4259-4281, 2024, DOI:10.32604/cmc.2024.050325

Abstract Suicide has become a critical concern, necessitating the development of effective preventative strategies. Social media platforms offer a valuable resource for identifying signs of suicidal ideation. Despite progress in detecting suicidal ideation on social media, accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge. To tackle this, we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships (TCNN-SN). This model enhances predictive performance by leveraging social network relationship features More >

Jing Gao^*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158

Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

Monerah Alawadh^*, Ahmed Barnawi

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4995-5015, 2024, DOI:10.32604/cmc.2024.048762

Abstract Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant… More >

Mingyuan Li^1,*, Yuanli Gu¹, Qingqiao Geng², Hongru Yu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3877-3896, 2024, DOI:10.32604/cmc.2024.047765

Abstract This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors. The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Convolutional Long Short Term Memory Neural Network (ConvLSTM) to predict short-term taxi travel demand. The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components, capturing sequence characteristics at different time scales and frequencies. Based on the sample entropy value of components, secondary processing of more… More >