LU ZHANG^*, JINGUO CHU^*, YUSHAN YU

Oncology Research, Vol.32, No.4, pp. 703-716, 2024, DOI:10.32604/or.2023.030988

Abstract Background: Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC). Methods: We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using consensus clustering to identify subgroups… More >

Seoyun Kim^1,#, Hyerim Yu^2,#, Jeewoo Yoon^1,3, Eunil Park^1,2,*

Computer Systems Science and Engineering, Vol.48, No.2, pp. 413-429, 2024, DOI:10.32604/csse.2023.041575

Abstract Given the increasing number of countries reporting degraded air quality, effective air quality monitoring has become a critical issue in today’s world. However, the current air quality observatory systems are often prohibitively expensive, resulting in a lack of observatories in many regions within a country. Consequently, a significant problem arises where not every region receives the same level of air quality information. This disparity occurs because some locations have to rely on information from observatories located far away from their regions, even if they may be the closest available options. To address this challenge, a novel approach that leverages machine… More >

Arvind Pattamatta^a,*, Ghanshyam Singh^b

Frontiers in Heat and Mass Transfer, Vol.3, No.2, pp. 1-11, 2012, DOI:10.5098/hmt.v3.2.3005

Abstract The present study deals with the assessment of different turbulence models for heated wall jet flow. The velocity field and thermal characteristics for isothermal and uniform heat flux surfaces in the presence of wall jet flow have been predicted using different turbulence models and the results are compared against the experimental data of Wygnanski et al. (1992), Schneider and Goldstein (1994), and AbdulNour et al. (2000). Thirteen different turbulence models are considered for validation, which include the Standard k-ε (SKE), Realizable k-ε (RKE), shear stress transport (SST), Sarkar & So (SSA), v ² -f, Reynolds stress Model (RSM), and Spalart… More >

Frantisek Kavicka^a,*, Jana Dobrovska^b, Karel Stransky^a, Bohumil Sekanina^a, Josef Stetina^a

Frontiers in Heat and Mass Transfer, Vol.3, No.2, pp. 1-9, 2012, DOI:10.5098/hmt.v3.2.3002

Abstract Corundo-baddeleyit material (CBM) – EUCOR – is a heat- and wear-resistant material even at extreme temperatures. This article introduces an original numerical model of solidification and cooling of this material in a non-metallic mold. The second, cooperating model of chemical heterogeneity and its application on EUCOR samples prove that the applied method of measuring the chemical heterogeneity provides the detailed quantitative information on the material structure and makes it possible to analyze the solidification process. The verification of both numerical models was conducted on a real cast 350 x 200 x 400 mm block. More >

Wenfeng Zheng¹, Siyu Lu¹, Zhuohang Cai¹, Ruiyang Wang¹, Lei Wang², Lirong Yin^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2729-2745, 2024, DOI:10.32604/cmes.2023.046692

Abstract In the field of natural language processing (NLP), there have been various pre-training language models in recent years, with question answering systems gaining significant attention. However, as algorithms, data, and computing power advance, the issue of increasingly larger models and a growing number of parameters has surfaced. Consequently, model training has become more costly and less efficient. To enhance the efficiency and accuracy of the training process while reducing the model volume, this paper proposes a first-order pruning model PAL-BERT based on the ALBERT model according to the characteristics of question-answering (QA) system and language model. Firstly, a first-order network… More >

Yifan Huang¹, Zikang Zhou^1,2, Mingyu Li¹, Xuedong Luo^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3147-3165, 2024, DOI:10.32604/cmes.2024.045947

Abstract Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management. In this study, Tuna Swarm Optimization (TSO), Whale Optimization Algorithm (WOA), and Cuckoo Search (CS) were used to optimize two hyperparameters in support vector regression (SVR). Based on these methods, three hybrid models to predict peak particle velocity (PPV) for bench blasting were developed. Eighty-eight samples were collected to establish the PPV database, eight initial blasting parameters were chosen as input parameters for the prediction model, and the PPV was the output parameter. As predictive performance evaluation indicators, the coefficient of determination (R²), root mean square… More >

S. Dickson¹, S. Padmasekaran¹, Pushpendra Kumar^2,*, Kottakkaran Sooppy Nisar³, Hamidreza Marasi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2265-2287, 2024, DOI:10.32604/cmes.2023.030286

Abstract This research examines the transmission dynamics of the Omicron variant of COVID-19 using SEIQI_cRVW and SQIRV models, considering the delay in converting susceptible individuals into infected ones. The significant delays eventually resulted in the pandemic’s containment. To ensure the safety of the host population, this concept integrates quarantine and the COVID-19 vaccine. We investigate the stability of the proposed models. The fundamental reproduction number influences stability conditions. According to our findings, asymptomatic cases considerably impact the prevalence of Omicron infection in the community. The real data of the Omicron variant from Chennai, Tamil Nadu, India, is used to validate the… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for analyzing the conversation progress and… More >

Fatma Harby¹, Mansor Alohali², Adel Thaljaoui^2,3,*, Amira Samy Talaat⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2689-2719, 2024, DOI:10.32604/cmc.2024.046623

Abstract Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state. The examination of the emotional states of speakers holds significant importance in a range of real-time applications, including but not limited to virtual reality, human-robot interaction, emergency centers, and human behavior assessment. Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs. Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients (MFCCs) due to their ability to capture the periodic nature of audio… More >

Bayi Xu¹, Lei Sun^2,*, Xiuqing Mao², Chengwei Liu³, Zhiyi Ding²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1995-2022, 2024, DOI:10.32604/cmc.2023.046478

Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models, we apply various preprocessing… More >