Amal Hkiri^1,*, Mouna Karmani¹, Omar Ben Bahri², Ahmed Mohammed Murayr², Fawaz Hassan Alasmari², Mohsen Machhout¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 227-247, 2024, DOI:10.32604/cmc.2023.047087

Abstract The RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem. Despite its significance, RPL’s susceptibility to attacks remains a concern. This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static and mobile network environments. We employ the Random Direction Mobility Model (RDM) for mobile scenarios within the Cooja simulator. Our systematic evaluation focuses on critical performance metrics, including Packet Delivery Ratio (PDR), Average End to End Delay (AE2ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption… 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 >

Hyun Kwon^1,*, Sanghyun Lee²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 249-263, 2024, DOI:10.32604/cmc.2023.042466

Abstract Deep neural networks perform well in image recognition, object recognition, pattern analysis, and speech recognition. In military applications, deep neural networks can detect equipment and recognize objects. In military equipment, it is necessary to detect and recognize rifle management, which is an important piece of equipment, using deep neural networks. There have been no previous studies on the detection of real rifle numbers using real rifle image datasets. In this study, we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient. The proposed method was designed to improve the recognition rate of a specific… More >

Rana Muhammad Zulqarnain¹, Hafiz Khalil ur Rehman², Imran Siddique³, Hijaz Ahmad^4,5, Sameh Askar⁶, Shahid Hussain Gurmani^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1863-1892, 2024, DOI:10.32604/cmes.2023.031480

Abstract This research is devoted to diagnosing water-borne infectious diseases caused by floods employing a novel diagnosis approach, the Einstein hybrid structure of q-rung orthopair fuzzy soft set. This approach integrates parts of fuzzy logic and soft set theory to develop a robust alternative for disease detection in stressful situations, especially in areas affected by floods. Compared to the traditional intuitionistic fuzzy soft set and Pythagorean fuzzy soft set, the q-rung orthopair fuzzy soft set (q-ROFSS) adequately incorporates unclear and indeterminate facts. The major objective of this investigation is to formulate the q-rung orthopair fuzzy soft Einstein hybrid weighted average (q-ROFSEHWA)… More >

Baixuan Tang^1,2,#, Xiaolong Xu^1,2,#, Fei Dai³, Song Wang^4,*

Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 35-55, 2023, DOI:10.32604/iasc.2023.036715

Abstract Person re-identification (ReID) aims to recognize the same person in multiple images from different camera views. Training person ReID models are time-consuming and resource-intensive; thus, cloud computing is an appropriate model training solution. However, the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments, leading to significant communication overheads. This paper proposes a federated person ReID method with model-contrastive learning (MOON) in an edge-cloud environment, named FRM. Specifically, based on federated partial averaging, MOON warmup is added to correct the local training of individual edge servers and improve the model’s… More >

Kaiyue Hong^1,2, Yasmina Radani², Waqas Ahmad², Ping Li³, Yuming Luo^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 45-61, 2024, DOI:10.32604/phyton.2023.046389

Abstract Carbon monoxide (CO) and nitric oxide (NO) are signal molecules that enhance plant adaptation to environmental stimuli. Auxin is an essential phytohormone for plant growth and development. CO and NO play crucial roles in modulating the plant’s response to iron deficiency. Iron deficiency leads to an increase in the activity of heme oxygenase (HO) and the subsequent generation of CO. Additionally, it alters the polar subcellular distribution of Pin-Formed 1 (PIN1) proteins, resulting in enhanced auxin transport. This alteration, in turn, leads to an increase in NO accumulation. Furthermore, iron deficiency enhances the activity of ferric chelate reductase (FCR), as… More >

Tian-Jiao Wei¹, Guang Li¹, Yan-Ru Cui¹, Jiao Xie¹, Xing-Ai Gao¹, Xing Teng¹, Xin-Ying Zhao¹, Fa-Chun Guan^1,*, Zheng-Wei Liang^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 29-43, 2024, DOI:10.32604/phyton.2023.046078

Abstract Soil salinization is the main factor that threatens the growth and development of plants and limits the increase of yield. It is of great significance to study the key soil environmental factors affecting plant root traits to reveal the adaptation strategies of plants to saline-alkaline-stressed soil environments. In this study, the root biomass, root morphological parameters and root mineral nutrient content of two alfalfa cultivars with different sensitivities to alkaline stress were analyzed with black soil as the control group and the mixed saline-alkaline soil with a ratio of 7:3 between black soil and saline-alkaline soil as the saline-alkaline treatment… More >

Jing Liu^1,*, Defeng Shu¹, Zilong Tan¹, Mei Ma¹, Huanhuan Yang¹, Ning Guo^1,2, Shipeng Li¹, Dayong Cui^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 63-79, 2024, DOI:10.32604/phyton.2023.045940

Abstract INDETERMINATE-DOMAIN proteins (IDDs) are a plant-specific transcription factor family characterized by a conserved ID domain with four zinc finger motifs. Previous studies have demonstrated that IDDs coordinate a diversity of physiological processes and functions in plant growth and development, including floral transition, plant architecture, seed and root development, and hormone signaling. In this review, we especially summarized the latest knowledge on the functions and working models of IDD members in Arabidopsis, rice, and maize, particularly focusing on their role in the regulatory network of biotic and abiotic environmental responses, such as gravity, temperature, water, and pathogens. Understanding these mechanisms underlying… More >

Jie Huang^*, Rongbin Jiang, Xiaobo Sun, Yingyong Shuai

Journal of Renewable Materials, Vol.12, No.1, pp. 187-201, 2024, DOI:10.32604/jrm.2023.044232

Abstract The reuse of waste recycled concrete from harsh environments has become a research hotspot in the field of construction. This study investigated the repair effect of carbonation treatment on second-generation recycled fine aggregate (SRFA) obtained from recycled fine aggregate concrete (RFAC) subjected to freeze-thaw (FT) cycles. Before and after carbonation, the properties of SRFA were evaluated. Carbonated second-generation recycled fine aggregate (CSRFA) at five substitution rates (0%, 25%, 50%, 75%, 100%) to replace SRFA was used to prepare carbonated second-generation recycled fine aggregate concrete (CSRFAC). The water absorption, porosity and mechanical properties of CSRFAC were tested, and its frost-resisting durability… More >

Ismail Shah^1,2, Jing Li^1,3,4,*, Nauman Khan⁵, Hamad R. Almujibah⁶, Muhammad Mudassar Rehman², Ali Raza⁷, Yun Peng^3,4

Journal of Renewable Materials, Vol.12, No.1, pp. 167-186, 2024, DOI:10.32604/jrm.2023.031503

Abstract Recycled steel fiber reinforced concrete is an innovative construction material that offers exceptional mechanical properties and durability. It is considered a sustainable material due to its low carbon footprint and environmental friendly characteristics. This study examines the key influencing factors that affect the behavior of this material, such as the steel fiber volume ratio, recycled aggregate replacement rate, concrete strength grade, anchorage length, and stirrup constraint. The study investigates the bond failure morphology, bond-slip, and bond strength constitutive relationship of steel fiber recycled concrete. The results show that the addition of steel fibers at 0.5%, 1.0%, and 1.5% volume ratios… More >