Xin Luan^1,*, Guoqing Su¹, Hailong Chen¹, Min Kuang^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2459-2473, 2024, DOI:10.32604/fdmp.2024.054621 - 28 October 2024

Abstract The proper selection of a relevant mixer generally requires an effective assessment of several models against the application requirements. This is a complex task, as traditional evaluation methods generally focus only on a single aspect of performance, such as pressure loss, mixing characteristics, or heat transfer. This study assesses a urea-based selective catalytic reduction (SCR) system installed on a ship, where the installation space is limited and the distance between the urea aqueous solution injection position and the reactor is low; therefore, the static mixer installed in this pipeline has special performance requirements. In particular,… More >

Yingnan Zhao^*, Yuyuan Ruan, Zhen Peng

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 549-566, 2024, DOI:10.32604/cmc.2024.056240 - 15 October 2024

Abstract As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set More >

Bo Hwan Kim^1,#, Sheikh Mansoor^2,#, Gyung Deok Han³, Ji Eun Park⁴, Wook Kim^1,*, Yong Suk Chung^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2339-2347, 2024, DOI:10.32604/phyton.2024.056439 - 30 September 2024

Abstract Recognizing the variation of genetic resources is the first step in selection. One of the most important variations in grain crops is the uniformity of seed grain weight, which can be converted into seed size. However, it has been challenging since it needs high labor costs and time to measure it on a large scale. The current study used an image analysis technique to measure the grain seed area of about 100 seeds per accession with 64 germplasm of Tartary buckwheat (Fagopyrum tataricum) to study variation among and within them. To understand the nature of variation, More >

Abdelghafour Doghbage^1,*, Safia Belhadj², Hassen Boukerker³, Jean Philippe Mevy⁴, Thierry Gauquelin⁴, Alain Tonetto⁵, Benbader Habib^1,6, Arezki Derridj⁷, Zahra Robã Bouabdelli¹, Walid Soufan⁸, Fathi Abdellatif Belhouadjeb¹

Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2383-2413, 2024, DOI:10.32604/phyton.2024.055528 - 30 September 2024

Abstract The Atlas pistachio tree is a typically Mediterranean species, which represents an important forest heritage in the arid and semi-arid regions of Algeria. It is deeply rooted in the local population’s culture, making it essential to better understand this species for its conservation and valorization. Through our work on 7 provenances of Pistacia atlantica distributed across different bioclimates in Algeria and based on 28 quantitative and qualitative leaf, trichome, and stomatal traits, it was revealed that the Atlas pistachio tree exhibits significant ecotypic variability linked to its habitat and a high adaptability to extreme conditions in… More >

M. Jayasree¹, K. A. Sunitha^2,*, A. Brindha¹, Punna Rajasekhar³, G. Aravamuthan³, G. Joselin Retnakumar¹

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 745-764, 2024, DOI:10.32604/iasc.2024.052983 - 06 September 2024

Abstract Identifying faces in non-frontal poses presents a significant challenge for face recognition (FR) systems. In this study, we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0° to ±90°. We initially selected the most suitable feature vector size by integrating the Dlib, FaceNet (Inception-v2), and “Support Vector Machines (SVM)” + “K-nearest neighbors (KNN)” algorithms. To train and evaluate this feature vector, we used two datasets: the “Labeled Faces in the Wild (LFW)” benchmark data and the “Robust… More >

Ain Raal^1,*, Anne Orav², Tetiana Ilina³, Alla Kovalyova⁴, Taras Koliadzhyn³, Yuliia Avidzba⁵, Oleh Koshovyi^1,4,*

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 2051-2062, 2024, DOI:10.32604/phyton.2024.052790 - 30 August 2024

Abstract Salvia officinalis L. (Lamiaceae) leaves and its essential oil is used for mouth and throat disorders, skin disorders, minor wounds, and gastrointestinal disorders, and is widely used worldwide. The research aimed to conduct a comparative study of the composition of S. officinalis essential oils from commercial samples, and their main chemotypes. The volatile constituents from S. officinalis leaves were investigated using gas chromatography (GC). The commercial samples of sage leaves were obtained from retail pharmacies in nine mainly European countries. The yield of essential oil in S. officinalis commercial leaves was between 10.0 and 24.8 mL/kg. The principal components More > Graphic Abstract

Dijun Rao^1,2,3,4, Min Huang^1,2,3,5, Xiuzhi Shi⁴, Zhi Yu^6,*, Zhengxiang He⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 187-217, 2024, DOI:10.32604/cmes.2024.051402 - 20 August 2024

Abstract The denoising of microseismic signals is a prerequisite for subsequent analysis and research. In this research, a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm (BWOA) optimized Variational Mode Decomposition (VMD) joint Wavelet Threshold Denoising (WTD) algorithm (BVW) is proposed. The BVW algorithm integrates VMD and WTD, both of which are optimized by BWOA. Specifically, this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited Intrinsic Mode Functions (BLIMFs). Subsequently, these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold… More >

Hongjiang Wang¹, Qingze Shen^2,*, Qin Dai¹, Yingcai Gao², Jing Gao², Tian Zhang^3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 625-642, 2024, DOI:10.32604/cmc.2024.051757 - 18 July 2024

Abstract Deep learning has emerged in many practical applications, such as image classification, fault diagnosis, and object detection. More recently, convolutional neural networks (CNNs), representative models of deep learning, have been used to solve fault detection. However, the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error. For this reason, an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection. YOLOv8 is a CNN-backed object detection model. Specifically, to reduce… More >

Jian Luo^1,*, Bo Xu¹, Tardi Tjahjadi², Jian Yi¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 235-261, 2024, DOI:10.32604/cmc.2024.050018 - 18 July 2024

Abstract Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes. This paper proposes a novel targeted 3-dimensional (3D) gait model (3DGait) represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model. The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing. The 3DGait recognition method involves 2-dimensional (2D) to 3DGait data learning based on 3D virtual samples, a semantic gait parameter estimation Long Short Time Memory (LSTM) network (3D-SGPE-LSTM), a feature fusion… More >

Weiming Huang^1,2, Baisong Liu^1,*, Zhaoliang Wang¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4449-4469, 2024, DOI:10.32604/cmc.2024.050389 - 20 June 2024

Abstract In the tag recommendation task on academic platforms, existing methods disregard users’ customized preferences in favor of extracting tags based just on the content of the articles. Besides, it uses co-occurrence techniques and tries to combine nodes’ textual content for modelling. They still do not, however, directly simulate many interactions in network learning. In order to address these issues, we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations. Specifically, we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles… More >