Jinyang Yu^1,2, Xiao Zhang^1,2,3,*, Jinjiang Wang^1,2, Yuchen Zhang^1,2, Yulong Shi^1,2, Linxuan Su^1,2, Leijie Zeng^1,2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2159-2179, 2024, DOI:10.32604/cmc.2024.047340

Abstract The proliferation of Internet of Things (IoT) systems has resulted in the generation of substantial data, presenting new challenges in reliable storage and trustworthy sharing. Conventional distributed storage systems are hindered by centralized management and lack traceability, while blockchain systems are limited by low capacity and high latency. To address these challenges, the present study investigates the reliable storage and trustworthy sharing of IoT data, and presents a novel system architecture that integrates on-chain and off-chain data manage systems. This architecture, integrating blockchain and distributed storage technologies, provides high-capacity, high-performance, traceable, and verifiable data storage and access. The on-chain system,… More >

Zhiqing Dong¹, Zhao Zhang^1,*, Hongyan Zhou², Xuebo Chen²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1977-1993, 2024, DOI:10.32604/cmc.2024.047233

Abstract With the advent of the information security era, it is necessary to guarantee the privacy, accuracy, and dependable transfer of pictures. This study presents a new approach to the encryption and compression of color images. It is predicated on 2D compressed sensing (CS) and the hyperchaotic system. First, an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security. Then, the processed images are concurrently encrypted and compressed using 2D CS. Among them, chaotic sequences replace traditional random measurement matrices to increase the system’s security. Third, the processed images are re-encrypted using a combination of… More >

S. Abinaya^*, K. Uttej Kumar

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2269-2286, 2024, DOI:10.32604/cmc.2024.047167

Abstract A Recommender System (RS) is a crucial part of several firms, particularly those involved in e-commerce. In conventional RS, a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences. Nowadays, businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’ preferences. On the other hand, the collaborative filtering (CF) algorithm utilizing AutoEncoder (AE) is seen to be effective in identifying user-interested items. However, the cost of these computations increases nonlinearly as the number of items and users increases. To triumph over the… More >

Sang-min Lee, Namgi Kim^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1897-1914, 2024, DOI:10.32604/cmc.2023.046346

Abstract Recommendation Information Systems (RIS) are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet. Graph Convolution Network (GCN) algorithms have been employed to implement the RIS efficiently. However, the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process. To address this issue, we propose a Weighted Forwarding method using the GCN (WF-GCN) algorithm. The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning. By applying the WF-GCN… More >

Ali Hamid Farea^1,*, Omar H. Alhazmi¹, Kerem Kucuk²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1525-1545, 2024, DOI:10.32604/cmc.2023.045794

Abstract While emerging technologies such as the Internet of Things (IoT) have many benefits, they also pose considerable security challenges that require innovative solutions, including those based on artificial intelligence (AI), given that these techniques are increasingly being used by malicious actors to compromise IoT systems. Although an ample body of research focusing on conventional AI methods exists, there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures. To contribute to this nascent research stream, a novel AI-driven security system denoted as “AI2AI” is presented in this work. AI2AI employs AI techniques to… More >

Mingzhao Han¹, Susilawati Kasim^1,*, Zhongming Yang², Xi Deng², Md Kamal Uddin¹, Noor Baity Saidi³, Effyanti Mohd Shuib¹

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 213-226, 2024, DOI:10.32604/phyton.2024.047150

Abstract Drought stress is a major factor affecting plant growth and crop yield production. Plant extracts as natural biostimulants hold great potential to strengthen plants to overcome drought impacts. To explore the effect of Polygonum minus extract (PME) in enhancing drought tolerance in plants, a study was set up in a glasshouse environment using 10 different treatment combinations. PME foliar application were designed in CRD and effects were closely observed related to the growth, physiology, and antioxidant system changes in maize (Zea mays L.) under well-watered and drought conditions. The seaweed extract (SWE) was used as a comparison. Plants subjected to… More >

Shijie Lin¹, Zimo Wang¹, Hongbo Zhu², Conghui Wang³, Hongfeng Wang², Dawei Zhang¹, Tianbing Gou¹, Guangdao Bao¹, Ye Luo¹, Huaijiang He¹, Zhonghui Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 277-289, 2024, DOI:10.32604/phyton.2024.046989

Abstract Tilia amurensis is an economically valuable broadleaf tree species in Northeast China. The production of high-quality T. amurensis varieties at commercial scales has been greatly limited by the low germination rates. There is thus a pressing need to develop an organogenesis protocol for in vitro propagation of T. amurensis to alleviate a shortage of high-quality T. amurensis seedlings. Here, we established a rapid in vitro propagation system for T. amurensis from mature zygotic embryos and analyzed the effects of plant growth regulators and culture media in different stages. We found that Woody plant medium (WPM) was the optimal primary culture… More >

Zhifen Shi^1,3, Fumei Pan^1,3, Xiaotian Kong², Jiaqi Lang³, Mingyan Ye³, Qian Wu⁴, Guangzhi Wang^1,*, Liang Han^5,*, Nong Zhou^3,*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 247-260, 2024, DOI:10.32604/phyton.2024.046452

Abstract Fritillaria taipaiensis P. Y. Li is a widely used medicinal herb in treating pulmonary diseases. In recent years, its wild resources have become scarce, and the demand for efficient artificial cultivation has significantly increased. This article is the first to apply phosphate solubilizing bacteria isolated from the rhizosphere soil of F. taipaiensis P. Y. Li to the cultivation process of F. taipaiensis P. Y. Li. The aim is to identify suitable reference strains for the artificial cultivation and industrial development of F. taipaiensis P. Y. Li by examining the effects of various phosphate solubilizing bacteria and their combinations on photosynthesis,… More >

Hang Cui¹, Hongbo Ren^1,*, Qiong Wu^1,2, Hang Lv¹, Qifen Li^1,2, Weisheng Zhou³

Energy Engineering, Vol.121, No.3, pp. 581-601, 2024, DOI:10.32604/ee.2023.047470

Abstract Cascading faults have been identified as the primary cause of multiple power outages in recent years. With the emergence of integrated energy systems (IES), the conventional approach to analyzing power grid cascading faults is no longer appropriate. A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance. In this study, an innovative analysis method for cascading faults in integrated heat and electricity systems (IHES) is proposed. It considers the degradation characteristics of transmission and energy supply components in the system to address the impact of component aging on cascading faults. Firstly, degradation models for the current carrying… More >

Huaxing Zhao¹, Yanbo Che^1,*, Gang Wen², Yijing Chen³

Energy Engineering, Vol.121, No.3, pp. 643-660, 2024, DOI:10.32604/ee.2023.045204

Abstract Long-term use in challenging natural conditions is possible for photovoltaic modules, which are extremely prone to failure. Failure to diagnose and address faults in Photovoltaic (PV) power systems in a timely manner can cause permanent damage to PV modules and, in more serious cases, fires. Therefore, research into photovoltaic module defect detection techniques is crucial for the growth of the photovoltaic sector as well as for maintaining national economic prosperity and ensuring public safety. Considering the drawbacks of the current real-time and historical data-based methods for monitoring distributed PV systems, this paper proposes a method for monitoring PV systems at… More >