Ain Raal¹, Valeriia Kokitko², Vira Odyntsova², Anne Orav³, Oleh Koshovyi^1,4,*

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1365-1382, 2024, DOI:10.32604/phyton.2024.053754

Abstract Valeriana officinalis L. is a plant from the Caprifoliaceae family, which is widely distributed in various parts of the world, especially in Europe and Asia. All species of Valeriana are distinguished by their ability to synthesize essential oil, which has a powerful effect on the physiological and mental aspects of the human body. The aim was to study the qualitative and quantitative composition of essential oil from valerian roots, collected in different countries, using the gas chromatography method, and to establish marker compounds for valerian species. 13 samples of commercial roots with rhizomes of V. officinalis from nine… More >

Ramiro Reyes-Castro¹, Héctor G. Núñez-Palenius^1,*, Juan I. Valiente-Banuet², María E. Sosa-Morales¹, Blanca E. Orosco-Alcalá¹, Rafael Guzmán-Mendoza¹, Graciela M. L. Ruiz-Aguilar¹, Rogelio Costilla-Salazar¹

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1467-1484, 2024, DOI:10.32604/phyton.2024.051644

Abstract Bell pepper (Capsicum annuum L.), along with potato and tomato, is one of the three most cultivated vegetables in the world. Bell pepper is worldwide accepted due to its characteristics of color, smell, flavor, and texture. Bell pepper is also considered a nutritious food due to its vitamin and antioxidant contents. In Mexico, bell pepper production has a high value because it is grown for the international markets, particularly the United States of America. Nevertheless, the abscission of flowers and fruits is a factor that limits the yield, hinders the planning of activities, and causes a More >

Taghreed Alsufyani^1,*, Noura J. Alotaibi², Nour Houda M’sakni¹, Mona A. Almalki¹, Eman M. Alghamdi³

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1523-1542, 2024, DOI:10.32604/phyton.2024.050351

Abstract This study simplifies the complex relationship among grapevine plants, aphids, ladybirds, and ants, which is essential for effective pest management and ecological balance. This study investigated the impact of aphid attacks and the presence of ants and ladybirds on the volatile compounds profile released into the chemosphere of the community consisting of the common vine Vitis vinifera, the aphid Aphis illinoisensis, the ladybird Coccinella undecimpunctata-and the ant Tapinoma magnum. This study aims to analyze the volatile compounds emitted by the grapevine and surrounding insects in response to these intricate interactions. The extraction of volatile organic compounds (VOCs) was carried… More >

Yunjing Liu^1,, Erhu Zhang^1,2,,*, Jingjing Wang³, Guangfeng Lin², Jinghong Duan⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1633-1653, 2024, DOI:10.32604/cmc.2024.052611

Abstract Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue. Different from natural images, character images pay more attention to stroke information. However, existing models mainly consider pixel-level information while ignoring structural information of the character, such as its edge and glyph, resulting in reconstructed images with mottled local structure and character damage. To solve these problems, we propose a novel generative adversarial network (GAN) framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework, i.e., EDU-GAN. Unlike existing frameworks, the generator introduces the… More >

Chunwei Tian^1,2,3,4, Haoyang Gao^2,3, Pengwei Wang², Bob Zhang^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 105-118, 2024, DOI:10.32604/cmc.2024.052097

Abstract Generative adversarial networks (GANs) with gaming abilities have been widely applied in image generation. However, gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes. Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation. In this paper, we propose an enhanced GAN via improving a generator for image generation (EIGGAN). EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness… More >

Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

Li Li^*, Qing Zhang, Youran Kong

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 801-818, 2024, DOI:10.32604/cmc.2024.051916

Abstract Due to the diversity and unpredictability of changes in malicious code, studying the traceability of variant families remains challenging. In this paper, we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants. This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images. The method includes a lightweight classifier and a simulator. The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile, embedded, and other devices. The simulator utilizes… More >

Zhan Shi^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 973-994, 2024, DOI:10.32604/cmc.2024.051530

Abstract The convergence of Internet of Things (IoT), 5G, and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing. While generative adversarial networks (GANs) are instrumental in resource scheduling, their application in this domain is impeded by challenges such as convergence speed, inferior optimality searching capability, and the inability to learn from failed decision making feedbacks. Therefore, a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges. The proposed algorithm facilitates real-time, energy-efficient data processing by More >

Haiyue Li¹, Jing Xie², Jing Ke³, Ye Yuan¹, Xiaoyong Pan¹, Hongyi Xin⁴, Hongbin Shen^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 487-506, 2024, DOI:10.32604/cmc.2024.049640

Abstract Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue. Convolutional neural network (CNN) and generative adversarial network (GAN) are pivotal in medical image registration. However, existing methods often struggle with severe interference and deformation, as seen in histological images of conditions like Cushing’s disease. We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator in GAN. In this study, we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration. To… More >

Abdul Sattar Laghari¹, Mohammad Waqas Chandio¹, Laveet Kumar^2,*, Mamdouh El Haj Assad³

Energy Engineering, Vol.121, No.8, pp. 2023-2038, 2024, DOI:10.32604/ee.2024.051082

Abstract The selection of working fluid significantly impacts the geothermal ORC’s Efficiency. Using a mixture as a working fluid is a strategy to improve the output of geothermal ORC. In the current study, modelling and thermodynamic analysis of ORC, using geothermal as a heat source, is carried out at fixed operating conditions. The model is simulated in the Engineering Equation Solver (EES). An environment-friendly mixture of fluids, i.e., R245fa/R600a, with a suitable mole fraction, is used as the operating fluid. The mixture provided the most convenient results compared to the pure working fluid under fixed operating More >