Luda Chen¹, Kuangzhu Bao², Ying Chen², Jingang Hao^2,*, Jianfeng He^1,3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 409-427, 2024, DOI:10.32604/cmc.2024.048507

Abstract Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion… More >

Xiaojie Liang^1,2, Wei An², Yuekun Li², Yajun Wang², Xiaoya Qin², Yanhong Cui¹, Shuchai Su^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 467-486, 2024, DOI:10.32604/phyton.2024.047628

Abstract

Goji berry (Lycium barbarum L.) is substantially dependent on nitrogen fertilizer application, which can significantly enhance fruit yield and Goji berry industrial development in Ningxia, China. This study aimed to analyze the functions of differential nitrogen application rates including low (N1), medium (N2), and high (N3) levels in soil microbial community structure (bacterial and fungal) at 2 diverse soil depths (0–20, 20–40 cm) through high-throughput sequencing technology by targeting 16S RNA gene and ITS1 & ITS2 regions. All the observed physicochemical parameters exhibited significant improvement (p < 0.05) with increased levels of nitrogen and the highest values for most parameters… More >

Chao Chen^1,2, Yankun Chen³, Haohai Jin⁴, Li Chen^5,*, Zhisong Liu³, Haozhe Sun⁴, Junchi Hong⁴, Haonan Wang⁴, Shiyu Fang⁴, Xin Zhang²

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 113-114, 2024, DOI:10.32604/iasc.2024.051760

Abstract This article has no abstract. More >

Keqiang Wang¹, Pengyang Zhao¹, Changjian Wang², Zimeng Zhang¹, Yu Zhang¹, Jia Lu¹, Zedong Yang^2,*

Energy Engineering, Vol.121, No.4, pp. 1091-1108, 2024, DOI:10.32604/ee.2023.044016

Abstract The rising frequency of extreme disaster events seriously threatens the safe and secure operation of the regional integrated electricity-natural gas system (RIENGS). With the growing level of coupling between electric and natural gas systems, it is critical to enhance the load restoration capability of both systems. This paper proposes a coordinated optimization strategy for resilience-enhanced RIENGS load restoration and repair scheduling and transforms it into a mixed integer second-order cone programming (MISOCP) model. The proposed model considers the distribution network reconfiguration and the coordinated repair strategy between the two systems, minimizing the total system load loss cost and repair time.… More >

Perla Patricia Ochoa-García¹, Jaime Sánchez-Salas², Ricardo Trejo-Calzada¹, Jesús Josafath Quezada-Rivera², Fabián García-González^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 371-386, 2024, DOI:10.32604/phyton.2024.048338

Abstract The genus Argemone L. (Papaveraceae) is found widely distributed in Mexico’s Chihuahuan Desert (CD). Some species of this genus are of phytochemical or ethnobotanical interest. They are inedible plants considered as scrubs. To date they have not been broadly studied; thus, their ecology is, to our knowledge, unknown. The present work was centered around carrying out a morphometric analysis and the determination of minerals in the soil and seeds of the wild populations of Argemone at sites belonging to two ecoregions of the CD in Mexico. In April 2021 and April 2022, seeds of Argemone spp., and soil samples were… More >

Jingyu Li^1,2, Mushui Wang^1,2,*, Zhaoyuan Wu^1,3, Guizhen Tian^1,2, Na Zhang^1,2, Guangchen Liu^1,2

Energy Engineering, Vol.121, No.3, pp. 619-641, 2024, DOI:10.32604/ee.2023.044862

Abstract Given the “double carbon” objective and the drive toward low-carbon power, investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors. However, further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen (P2H) technology, focusing on participating in combined carbon-electricity market transactions. This study introduces an innovative Electro-Hydrogen Regional Energy System (EHRES) in this context. This system integrates renewable energy sources, a P2H system, cogeneration units, and energy storage devices. The core purpose of this integration is to optimize renewable energy… More > Graphic Abstract

Aysha Naseer¹, Nouf Abdullah Almujally², Saud S. Alotaibi³, Abdulwahab Alazeb⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1381-1398, 2024, DOI:10.32604/cmc.2023.042963

Abstract Object segmentation and recognition is an imperative area of computer vision and machine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features. The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks (ANNs). The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label them based on their characteristics. Then, two distinct kinds of features are obtained from the segmented images to help identify the objects… More >

Jiawei Shi, Jiye Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2175-2195, 2024, DOI:10.32604/cmes.2023.043539

Abstract Combining the detached eddy simulation (DES) method and Ffowcs Williams-Hawkings (FW-H) equation, the effect of bogie cavity end wall inclination on the flow field and aerodynamic noise in the bogie region is numerically studied. First, the simulation is conducted based on a simplified cavity-bogie model, including five cases with different inclination angles of the front and rear walls of the cavity. By comparing and analyzing the flow field and acoustic results of the five cases, the influence of the regularity and mechanism of the bogie cavity end wall inclination on the flow field and the aerodynamic noise of the bogie… More >

Deepak Kumar¹, Vinay Kukreja¹, Ayush Dogra^1,*, Bhawna Goyal², Talal Taha Ali³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2097-2121, 2023, DOI:10.32604/cmc.2023.044287

Abstract Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20% every year. The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust disease identification. In the proposed… More >

Wenqiu Zhu^1,2, Yongsheng Li^1,2, Qiang Liu^1,2,*, Zhigao Zeng^1,2

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1373-1392, 2023, DOI:10.32604/cmc.2023.037216

Abstract Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs and uncropped IBs. To alleviate… More >