Yanjing Cao¹, Chenchen Xu^1,2, Qi Li¹, Shan Lu^1,2,*, Jing Xiao^1,*

International Journal of Mental Health Promotion, Vol.26, No.4, pp. 261-269, 2024, DOI:10.32604/ijmhp.2024.043729

Abstract Background: Valid and reliable measures of depressive symptoms are crucial for understanding risk factors, outcomes, and interventions across rural and urban settings. Despite this need, the longitudinal invariance of these measures over time remains understudied. This research explores the structural components of the Center for Epidemiological Studies Depression Scale (CES-D) and examines its consistency across various living environments and temporal stability in a cohort of Chinese teenagers. Method: In the initial phase, 1,042 adolescents furnished demographic details and undertook the CES-D assessment. After a three-month interval, 967 of these participants repeated the CES-D evaluation. The study employed Confirmatory factor analysis… More >

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 >

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 >

Dan Xu^*, Jiale Ru, Jinlong Shi

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 85-104, 2024, DOI:10.32604/cmc.2023.045258

Abstract Video salient object detection (VSOD) aims at locating the most attractive objects in a video by exploring the spatial and temporal features. VSOD poses a challenging task in computer vision, as it involves processing complex spatial data that is also influenced by temporal dynamics. Despite the progress made in existing VSOD models, they still struggle in scenes of great background diversity within and between frames. Additionally, they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term duration. We propose a multi-stream temporal enhanced network (MSTENet) to address these problems. It… More >

Zhigang Zhao¹, Xiong Luo^1,2,3,*, Maojian Chen^1,2,3, Ling Ma¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513

Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… More >

Yanlei Xu, Xue Cong, Yuting Zhai, Zhiyuan Gao, Helong Yu^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3019-3037, 2023, DOI:10.32604/iasc.2023.040330

Abstract To solve inefficient water stress classification of spinach seedlings under complex background, this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt (NCAM+MobileNetXt) network. Firstly, this study reconstructed the Sandglass Block to effectively increase the model accuracy; secondly, this study introduced the group convolution module and a two-dimensional adaptive average pool, which can significantly compress the model parameters and enhance the model robustness separately; finally, this study innovatively proposed the Normalization-based Channel Attention Module (NCAM) to enhance the image features obviously. The experimental results showed that the classification accuracy of N-MobileNetXt… More >

Jaehwan Lee¹, Sangoh Park^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 171-186, 2023, DOI:10.32604/csse.2023.038139

Abstract Recently, various mobile apps have included more features to improve user convenience. Mobile operating systems load as many apps into memory for faster app launching and execution. The least recently used (LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low. However, the LRU-based cached app termination does not distinguish between frequently or infrequently used apps. The app launch performance degrades if LRU terminates frequently used apps. Recent studies have suggested the potential of using users’ app usage patterns to predict the next app launch and address the limitations of… More >

Tieming Wang^a , Fuyong Su^b,*

Frontiers in Heat and Mass Transfer, Vol.20, pp. 1-4, 2023, DOI:10.5098/hmt.20.12

Abstract This paper analyzes the power generation technology of coke dry quenching (CDQ) waste heat boiler, and compares the exergy efficiency of medium temperature medium pressure boiler and high temperature high pressure boiler. The scheme of high temperature ultrahigh pressure primary intermediate reheat boiler to further improve the power generation efficiency of CDQ waste heat is put forward, and the exergy efficiency is analyzed. The bottleneck problem of further improving power generation efficiency by CDQ waste heat power generation and the exergy efficiency limit under the current process conditions are obtained. More >

Leilei Li¹, Yansheng Fu², Dongjie Zhu^2,*, Xiaofang Li³, Yundong Sun², Jianrui Ding², Mingrui Wu², Ning Cao^4,*, Russell Higgs⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3295-3307, 2023, DOI:10.32604/iasc.2023.035257

Abstract The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms. Image and text descriptions added to the knowledge graph enrich the node information, which accounts for the advantage of the multi-modal knowledge graph. In the field of cross-modal retrieval platforms, multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational information provided by knowledge graphs. The representation learning method is significant to the application of multi-modal knowledge graphs. This paper proposes a distributed collaborative vector retrieval platform (DCRL-KG) using the multimodal knowledge graph VisualSem… More >

Jhulan Kumar^1,2, Surender Singh Saini^1,2, Divya Agrawal^1,2, Vinod Karar^1,2,*, Aman Kataria²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2411-2423, 2023, DOI:10.32604/iasc.2023.034203

Abstract Flying an aircraft in low visibility is still a challenging task for the pilot. It requires precise and accurate situational awareness (SA) in real-time. A Head-up Display (HUD) is used to project collimated internal and external flight information on a transparent screen in the pilot’s forward field of view, which eliminates the change of eye position between Head-Down-Display (HDD) instruments and outer view through the windshield. Implementation of HUD increases the SA and reduces the workload for the pilot. But to provide a better flying capability for the pilot, projecting extensive information on HUD causes human factor issues that reduce… More >