Wissal Saadellaoui¹, Samiha Kahlaoui¹, Kheiria Hcini¹, Abir Haddada¹, Noomene Sleimi^2,*, Roberta Ascrizzi³, Guido Flamini³, Fethia Harzallah-Skhiri⁴, Sondes Stambouli-Essassi¹

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 725-744, 2024, DOI:10.32604/phyton.2024.048110

Abstract In this work, we investigated aroma volatiles emanated by dry roots, stems, leaves, flowers, and fruits of Cardaria draba (L.) Desv. growing wild in Tunisia and its aerial part essential oils (EOs) composition. A total of 37 volatile organic compounds (96.7%–98.9%) were identified; 4 esters, 4 alcohols, 7 hydrocarbons, 12 aldehydes, 5 ketones, 1 lactone, 1 organosulfur compound, 2 organonitrogen compounds, and 1 acid. The hydrocarbons form the main group, representing 49.5%–84.6% of the total detected volatiles. The main constituent was 2,2,4,6,6-pentamethylheptane (44.5%–76.2%) reaching the highest relative percentages. Forty-two compounds were determined in the two fractions of EOs, representing 98.8%… More >

Ankita Gangwar¹, Vijaypal Singh Dhaka¹, Geeta Rani^2,*, Shrey Khandelwal¹, Ester Zumpano^3,4, Eugenio Vocaturo^3,4

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 117-142, 2024, DOI:10.32604/cmc.2024.048119

Abstract A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are one of the major causes… More >

Jiao Wang, Bin Wu^*, Hongying Zhang

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 143-160, 2024, DOI:10.32604/cmc.2023.046011

Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by… More >

Xue Wang^1,2, Weizhou Zhu^1,2, Zhao Cui^2,3, Xingguang Qian^2,3, Jinke Yang^1,2, Jianjun Jia^1,2,*, Yikun Wang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 325-337, 2024, DOI:10.32604/cmes.2024.048804

Abstract The gravitational wave spacecraft is a complex multi-input multi-output dynamic system. The gravitational wave detection mission requires the spacecraft to achieve single spacecraft with two laser links and high-precision control. Establishing one spacecraft with two laser links, compared to one spacecraft with a single laser link, requires an upgraded decoupling algorithm for the link establishment. The decoupling algorithm we designed reassigns the degrees of freedom and forces in the control loop to ensure sufficient degrees of freedom for optical axis control. In addressing the distinct dynamic characteristics of different degrees of freedom, a transfer function compensation method is used in… More >

Cyril Enault^*

Revue Internationale de Géomatique, Vol.33, pp. 77-92, 2024, DOI:10.32604/rig.2024.046591

Abstract La théorie égocentrée est aujourd’hui bien connue des éthologues et des psychologues mais moins diffusée chez les géographes car elle reste encore à l’état de théorie abstraite. Ce papier se propose dans un premier temps de rendre opérationnel cette approche dans le cadre de travaux géographiques à l’échelle de l’individu. Puis, elle envisage d’établir le lien entre l’échelle individu et l’échelle de la ville avec comme objectif de produire des cartes déformées de la ville. More > Graphic Abstract

Fei Wang, Qinpeng Meng, Jinchi Zhao, Xin Wang, Yuhong Liu, Qianru Zhang^*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 829-846, 2024, DOI:10.32604/fdmp.2023.026917

Abstract Buildings with large open spaces in which chemicals are handled are often exposed to the risk of explosions. Computational fluid dynamics is a useful and convenient way to investigate contaminant dispersion in such large spaces. The turbulent Schmidt number (Sc_t) concept has typically been used in this regard, and most studies have adopted a default value. We studied the concentration distribution for sulfur hexafluoride (SF₆) assuming different emission rates and considering the effect of Sc_t. Then we examined the same problem for a light gas by assuming hydrogen gas (H₂) as the contaminant. When SF₆ was considered as the contaminant… More >

Kai Chen^1,2, Jinwei Wang³, James Msughter Adeke^1,2, Guangjie Liu^1,2,*, Yuewei Dai^1,4

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3247-3265, 2024, DOI:10.32604/cmc.2024.046082

Abstract In recent years, various adversarial defense methods have been proposed to improve the robustness of deep neural networks. Adversarial training is one of the most potent methods to defend against adversarial attacks. However, the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training. This paper proposes a learnable distribution adversarial training method, aiming to construct the same distribution for training data utilizing the Gaussian mixture model. The distribution centroid is built to classify samples and constrain the distribution of the sample features. The natural and adversarial examples are… More >

Jean-Pierre Chery¹, Marie Gradeler¹, Vincent Bonnal^2,3,4

Revue Internationale de Géomatique, Vol.31, No.2, pp. 355-376, 2022, DOI:10.3166/RIG.31.355-376© 2022

Abstract Les besoins de valoriser et de partager des informations génériques et originales de différents projets de recherche, ont conduit à concevoir une plateforme géoweb dédiée, dénommée NAMO. Sa conception utilise des fonctionnalités ouvertes, libres et gratuites qui montrent leur souplesse dans une démarche agile et itérative. C’est en particulier le développement de deux dimensions de valorisation et de positionnement en géoweb 2.0 qui est souligné : la cartographie narrative et la modélisation systémique. Les dispositifs d’usage de l’information géographique, en particulier dans les démarches de co-construction et de science ouverte, peuvent ainsi être mieux outillés. More >

Cong Jin¹, Jinjie Huang^1,2,*, Yuanjian Chen¹, Yuqing Gong¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1547-1568, 2024, DOI:10.32604/cmc.2023.047489

Abstract In differentiable search architecture search methods, a more efficient search space design can significantly improve the performance of the searched architecture, thus requiring people to carefully define the search space with different complexity according to various operations. Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search. With this in mind, we propose a faster and more efficient differentiable architecture search method, AllegroNAS. Firstly, we introduce a more efficient search space enriched by the introduction of two redefined convolution modules. Secondly, we utilize a more efficient architectural parameter regularization… More >

Lanyao Zhang¹, Shichao Kan², Yigang Cen³, Xiaoling Chen¹, Linna Zhang^1,*, Yansen Huang^4,5

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1631-1648, 2024, DOI:10.32604/cmc.2024.046924

Abstract Unsupervised methods based on density representation have shown their abilities in anomaly detection, but detection performance still needs to be improved. Specifically, approaches using normalizing flows can accurately evaluate sample distributions, mapping normal features to the normal distribution and anomalous features outside it. Consequently, this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network (NF-BMR). It utilizes pre-trained Convolutional Neural Networks (CNN) and normalizing flows to construct discriminative source and target domain feature spaces. Additionally, to better learn feature information in both domain spaces, we propose the Bidirectional Mapping Residual Network (BMR), which maps sample features to these two spaces… More > Graphic Abstract