QINGQING GUO^1,2, JIAYUE QI^1,2, XIAOQIANG LIANG², ZIGANG ZHAO², JIA BAI², FANG XIE^2,#,*, CHENGXIN LI^1,2,#,*

BIOCELL, Vol.47, No.11, pp. 2435-2443, 2023, DOI:10.32604/biocell.2023.043126

Abstract Introduction: Verruca vulgaris is one of the most common low-risk HPV infections and is characterized by excessive proliferation of keratinocytes. Currently, very little genetic information is available regarding verruca vulgaris in the Chinese population. This study aimed to obtain comprehensive transcript information of verruca vulgaris by RNA sequencing. Methods: High-throughput sequencing was performed on three fresh verruca vulgaris samples and adjacent normal skin on the Illumina sequencing platform. The transcriptomes were analyzed using bioinformatics and the differentially expressed genes (DEGs) were verified by immunohistochemistry. Verruca vulgaris exhibited a unique molecular signature. Results: In total, 1,643 DEGs were identified in verruca… More >

Lifan Chen^1,*, Ning Guo¹, Zhongxuan Yang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09709

Abstract A multiscale approach [1] that couples the finite-element method (FEM) and the discrete-element method (DEM) is employed to model and analyse the earthquake fault rupture-soil-foundation interaction (FR-SFI) problem. In the approach, the soil constitutive responses are obtained from DEM solutions of representative volume elements (RVEs) embedded at the FEM integration points so as to effectively bypass the phenomenological hypotheses in conventional FEM simulations. The fault rupture surfaces and shear localization patterns under normal faults with or without foundation atop have been well captured by the multiscale approach and verified with available centrifuge experimental [2] and numerical results [3]. By examining… More >

Min-Jeong Kim¹, Byeong-Uk Jeon¹, Hyun Yoo², Kyungyong Chung^3,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2371-2386, 2023, DOI:10.32604/iasc.2023.040310

Abstract With the increasing number of digital devices generating a vast amount of video data, the recognition of abnormal image patterns has become more important. Accordingly, it is necessary to develop a method that achieves this task using object and behavior information within video data. Existing methods for detecting abnormal behaviors only focus on simple motions, therefore they cannot determine the overall behavior occurring throughout a video. In this study, an abnormal behavior detection method that uses deep learning (DL)-based video-data structuring is proposed. Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models. The… More >

Nian Wang, Mingjie Zhang, Sulaiman Alsaleem, Lesley M. Wright, Je-Chin Han^*

Frontiers in Heat and Mass Transfer, Vol.13, pp. 1-13, 2019, DOI:10.5098/hmt.13.9

Abstract This study investigates turbine blade, leading edge cooling from normal or tangential impinging jets. These jets impinging on a semi-cylindrical, inner surface are constrained to discharge in a single direction. The downstream jets are affected by the crossflow originating from the upstream jets. To understand the thermal flow physics, numerical simulations are performed using the realizable k- turbulence model. Both the experimental and numerical results show crossflow is more detrimental to normal impinging jets than the tangential jets. Furthermore, with a significant temperature drop across the jet plate, designers must correctly interpret jet impingement results. More >

Dawei Liang^1,2,4, Meng Wu^1,*, Yan Hu³

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 279-293, 2023, DOI:10.32604/cmc.2023.035279

Abstract As an indispensable part of identity authentication, offline writer identification plays a notable role in biology, forensics, and historical document analysis. However, identifying handwriting efficiently, stably, and quickly is still challenging due to the method of extracting and processing handwriting features. In this paper, we propose an efficient system to identify writers through handwritten images, which integrates local and global features from similar handwritten images. The local features are modeled by effective aggregate processing, and global features are extracted through transfer learning. Specifically, the proposed system employs a pre-trained Residual Network to mine the relationship between large image sets and… More >

Yong Chen^1,*, Chao Xu¹, Jing Selena He², Sheng Xiao³

Journal of Quantum Computing, Vol.4, No.2, pp. 75-84, 2022, DOI:10.32604/jqc.2022.031312

Abstract With the rapid development of information technology, audit objects and audit itself are more and more inseparable from software. As an important means of software security audit, code security audit will become an important aspect of future audit that cannot be ignored. However, the existing code security audit is mainly based on source code, which is difficult to meet the audit needs of more and more programming languages and binary commercial software. Based on the idea of normalized transformation, this paper constructs a cross language code security audit framework (CLCSA). CLCSA first uses compile/decompile technology to convert different high-level programming… More >

Zhipeng Bin¹, Huihuang Zhao^1,2,*, Xiaoman Liang^1,2, Wenli Chen¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 971-983, 2023, DOI:10.32604/iasc.2023.037270

Abstract The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images. In this study, the Face Swapping Attention Network (FSA-Net) is proposed to generate photorealistic face swapping. The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint (cheek, mouth, eye, nose, etc.), which causes artifacts and makes the generated face silhouette non-realistic. To address this problem, a novel reinforced multi-aware attention module, referred to as RMAA, is proposed for handling facial fusion and expression occlusion flaws. The framework includes two stages. In the first stage, a novel attribute encoder is proposed… More >

S. R. Deepa¹, M. Subramoniam^2,*, R. Swarnalatha³, S. Poornapushpakala², S. Barani²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 745-761, 2023, DOI:10.32604/iasc.2023.034211

Abstract The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and Tompkins algorithm is employed to… More >

Shang Zhang¹, Jie Wang², Benjamin Rose⁵, Yushan Yang³, Qingfeng Ding¹, Bengang Zhang^4,*, Chunlei Dong^2,*

Journal of Renewable Materials, Vol.11, No.6, pp. 2881-2891, 2023, DOI:10.32604/jrm.2023.028160

Abstract To study the static bending creep properties of glass fiber reinforced wood, glass fiber reinforced poplar (GFRP) specimens were obtained by pasting glass fiber on the upper and lower surfaces of Poplar (Populus euramevicana, P), the performance of Normal Creep (NC) and Mechanical Sorptive Creep (MSC) of GFRP and their influencing factors were tested and analyzed. The test results and analysis show that: (1) The MOE and MOR of Poplar were increased by 17.06% and 10.00% respectively by the glass fiber surface reinforced composite. (2) The surface reinforced P with glass fiber cloth only exhibits the NC pattern of wood… More >

Sana Zahir¹, Rafi Ullah Khan¹, Mohib Ullah¹, Muhammad Ishaq¹, Naqqash Dilshad², Amin Ullah^3,*, Mi Young Lee^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2741-2754, 2023, DOI:10.32604/csse.2023.037706

Abstract The analysis of overcrowded areas is essential for flow monitoring, assembly control, and security. Crowd counting’s primary goal is to calculate the population in a given region, which requires real-time analysis of congested scenes for prompt reactionary actions. The crowd is always unexpected, and the benchmarked available datasets have a lot of variation, which limits the trained models’ performance on unseen test data. In this paper, we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene. The proposed model consists of encoder and decoder networks comprising batch-free normalization layers… More >