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 >

Junqing Bai¹, Qiuchao Dai^1,*, Yingying Li²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5083-5103, 2024, DOI:10.32604/cmc.2024.050921

Abstract To support the explosive growth of Information and Communications Technology (ICT), Mobile Edge Computing (MEC) provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge. However, resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications. To address the difficulty of running computationally intensive applications on resource-constrained clients, a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper. Then a user benefit function EoU (Experience of Users) is… More >

Suyi Liu^1,*, Jianning Chi¹, Chengdong Wu¹, Fang Xu^2,3,4, Xiaosheng Yu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4471-4489, 2024, DOI:10.32604/cmc.2024.049450

Abstract In recent years, semantic segmentation on 3D point cloud data has attracted much attention. Unlike 2D images where pixels distribute regularly in the image domain, 3D point clouds in non-Euclidean space are irregular and inherently sparse. Therefore, it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space. Most current methods either focus on local feature aggregation or long-range context dependency, but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks. In this paper, we propose a Transformer-based… More >

Jie Ma¹, Pengcheng Yue², Haozheng Yu¹, Yuqing Zhang³, Youwen Zhang¹, Cuiping Li³, Junhui Li^3,*, Wenwen Qin³, Yong Guo¹

Energy Engineering, Vol.121, No.7, pp. 1823-1846, 2024, DOI:10.32604/ee.2024.048225

Abstract In recent years, the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing, but the peak regulation capacity of the power grid in the three north regions of China is limited, resulting in insufficient local wind power consumption capacity. Therefore, this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid's wind power consumption capacity. The objective of the upper model is to minimize the peak-valley difference of the system load, which is mainly to optimize the system… More >

Yuanjun Dai, Haonan Li, Baohua Li^*

Energy Engineering, Vol.121, No.6, pp. 1607-1636, 2024, DOI:10.32604/ee.2024.047255

Abstract This paper addresses the micro wind-hydrogen coupled system, aiming to improve the power tracking capability of micro wind farms, the regulation capability of hydrogen storage systems, and to mitigate the volatility of wind power generation. A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction, the hydrogen storage state division interval, and the daily scheduled output of wind power generation. The control strategy maximizes the power tracking capability, the regulation capability of the hydrogen storage system, and the fluctuation of the joint output of the wind-hydrogen… More >

Daisuke Ishihara^*, Syunnosuke Nozaki, Tomoya Niho, Naoto Takayama

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1371-1386, 2024, DOI:10.32604/cmes.2024.049694

Abstract The inverse and direct piezoelectric and circuit coupling are widely observed in advanced electro-mechanical systems such as piezoelectric energy harvesters. Existing strongly coupled analysis methods based on direct numerical modeling for this phenomenon can be classified into partitioned or monolithic formulations. Each formulation has its advantages and disadvantages, and the choice depends on the characteristics of each coupled problem. This study proposes a new option: a coupled analysis strategy that combines the best features of the existing formulations, namely, the hybrid partitioned-monolithic method. The analysis of inverse piezoelectricity and the monolithic analysis of direct piezoelectric More >

Deng Yang¹, Chong Zhou^1,*, Xuemeng Wei², Zhikun Chen³, Zheng Zhang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1563-1593, 2024, DOI:10.32604/cmes.2024.048049

Abstract In classification problems, datasets often contain a large amount of features, but not all of them are relevant for accurate classification. In fact, irrelevant features may even hinder classification accuracy. Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate. Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter, but the results obtained depend on the value of the parameter. To eliminate this parameter’s influence, the problem can be reformulated as a multi-objective optimization problem. The… More >

Muchang Rao, Hang Qin^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2647-2672, 2024, DOI:10.32604/cmc.2024.050380

Abstract More devices in the Intelligent Internet of Things (AIoT) result in an increased number of tasks that require low latency and real-time responsiveness, leading to an increased demand for computational resources. Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension. However, the effective allocation of resources for task execution within fog environments, characterized by limitations and heterogeneity in computational resources, remains a formidable challenge. To tackle this challenge, in this study, we integrate fog computing and cloud computing. We begin by establishing a fog-cloud environment… More >

Di Wang, Yuefei Zhu, Jinlong Fei^*, Maohua Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2253-2276, 2024, DOI:10.32604/cmc.2024.049504

Abstract Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target… More >

Shuyi Li, Hongchao Hu^*, Xiaohan Yang, Guozhen Cheng, Wenyan Liu, Wei Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2331-2359, 2024, DOI:10.32604/cmc.2024.047275

Abstract Adversarial distillation (AD) has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training. However, fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation. Additionally, the reliability of guidance from static teachers diminishes as target models become more robust. This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation (LDAS&ET-AD). Firstly, a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation. A strategy model is introduced to produce attack strategies that… More >