Guohui Song^1,2, Yongbin Wang^1,*, Jianfei Li¹, Hongbin Hu¹

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 275-295, 2023, DOI:10.32604/iasc.2023.041873

Abstract Recommending high-quality news to users is vital in improving user stickiness and news platforms’ reputation. However, existing news quality evaluation methods, such as clickbait detection and popularity prediction, are challenging to reflect news quality comprehensively and concisely. This paper defines news quality as the ability of news articles to elicit clicks and comments from users, which represents whether the news article can attract widespread attention and discussion. Based on the above definition, this paper first presents a straightforward method to measure news quality based on the comments and clicks of news and defines four news quality indicators. Then, the dataset… More >

Zhiheng He¹, Hui Guo², Houguang Liu^1,*, Yu Zhao^1,3, Zipeng Zhang¹, Shanguo Yang¹

Sound & Vibration, Vol.58, pp. 47-58, 2024, DOI:10.32604/sv.2024.045470

Abstract When designing and optimizing the hull of vehicles, their sound quality needs to be considered, which greatly depends on the psychoacoustic parameters. However, the traditional psychoacoustic calculation method does not consider the influence of the real human ear anatomic structure, even the loudness which is most related to the auditory periphery. In order to introduce the real physiological structure of the human ear into the evaluation of vehicle sound quality, this paper first carried out the vehicle internal noise test to obtain the experimental samples. Then, the physiological loudness was predicted based on an established human ear physiological model, and… More >

Jingjing Zhou¹, Yungneng Lin^2,*, Tingting Huang¹

International Journal of Mental Health Promotion, Vol.26, No.1, pp. 39-49, 2024, DOI:10.32604/ijmhp.2023.045203

Abstract The mental health of young people, a significant public health concern worldwide, has deteriorated during the COVID-19 pandemic. Despite the subsiding of the epidemic, the issue remains unresolved in the post-pandemic era, specifically in China. In response, numerous art museums have stepped up to provide long-term therapeutic experiences and comprehensive mental health support. While these institutions offer a variety of services and programs aimed at enhancing the psychological well-being of their visitors, a standardized method for assessing their impact is lacking. This study, therefore, employed the Generic Wellbeing Questionnaire (GWQ) as a tool to evaluate the decrease in psychological anxiety… More >

Lalit Mohan Patnaik^1,5, Wenfeng Wang^2,3,4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1203-1215, 2024, DOI:10.32604/cmes.2023.029451

Abstract This article reviews the theory of fairness in AI–from machine learning to federated learning, where the constraints on precision AI fairness and perspective solutions are also discussed. For a reliable and quantitative evaluation of AI fairness, many associated concepts have been proposed, formulated and classified. However, the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness. The privacy worries induce the data unfairness and hence, the biases in the datasets for evaluating AI fairness are unavoidable. The imbalance between algorithms’ utility and humanization has further reinforced such worries.… More >

Bilal Khan¹, Saifullah Jan^1,*, Wahab Khan¹, Muhammad Imran Chughtai²

Journal on Big Data, Vol.6, pp. 1-20, 2024, DOI:10.32604/jbd.2023.046223

Abstract The extraction, transformation, and loading (ETL) process is a crucial and intricate area of study that lies deep within the broad field of data warehousing. This specific, yet crucial, aspect of data management fills the knowledge gap between unprocessed data and useful insights. Starting with basic information unique to this complex field, this study thoroughly examines the many issues that practitioners encounter. These issues include the complexities of ETL procedures, the rigorous pursuit of data quality, and the increasing amounts and variety of data sources present in the modern data environment. The study examines ETL methods, resources, and the crucial… More >

Elisa Listo^1,#, Nicola Martini^2,#, Stefano Salvadori³, Elisa Valenti³, Nicola Stagnaro¹, Gianluca Trocchio⁴, Chiara Marrone⁵, Alberto Clemente⁶, Francesca Raimondi^7,*, Pierluigi Festa⁵, Lamia Ait Ali^5,8,*

Congenital Heart Disease, Vol.18, No.6, pp. 627-638, 2023, DOI:10.32604/chd.2023.030312

Abstract Background: The assessment of Fontan circuit’s flow is traditionally evaluated by multiple through-plane phase-contrast MRI acquisitions (2D flow), while recently, a single volumetric 4D-flow MRI acquisition is emerging as a comprehensive tool for the hemodynamic evaluation in congenital heart diseases. Purpose: To compare 2D and 4D-flow MRI measurements in patients after Fontan palliation and to evaluate parameters affecting potential disagreement. Methods: 39 patients after Fontan palliation (23 males, age 22 ± 11 years) who underwent cardiac MRI with 2D and 4D-flow MRI acquisition were included in the study. In all patients, blood flow quantification in the Fontan circuit and aorta… More > Graphic Abstract

Shan Xu^1,*, Huadu Tang¹, Ding Wang¹, Ruiguang Zhu¹, Liwei Wang¹, Shengwang Hao¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.4, pp. 1-3, 2023, DOI:10.32604/icces.2023.09930

Abstract Damage evaluation is an important index for the evaluation of buildings health. To provide a rapid crack evaluation in practical applications, a crack identification and damage evaluation via deep learning framework is proposed in this paper. We built a combined dataset from Kaggle and site photos. A pre-trained U-net model is used to perform the training of model. With updated weights, the identification of cracks could be performed on non-labelled photos. More >

A. C. Filankembo Kava^*, B. C. Ndjengue Bengono, P. L. Nzamba Bissielou, C. Nziengui Tirogo, A. Kabena, T. Mpami, E. Belembaogo

Psycho-Oncologie, Vol.17, No.4, pp. 267-273, 2023, DOI:10.32604/po.2023.044512

Abstract Aim: Oncologists are particularly prone to developing burnout syndrome due to the demanding task of caring for cancer patients. Undiagnosed and incorrectly managed, burnout can have a negative impact on professional performance. The objective of the study is to measure the frequency of burnout among nursing staff at the Institut de Cancérologie d’Akanda (ICA) and to assess the main risk factors. Procedure: We conducted a cross-sectional study at the ICA during the month of January 2022. Burnout was assessed using the Maslach Burnout Inventory (MBI). Results: Between the 42 participants, there was a female predominance (57.1%) with a male to… More >

MESFER AL SHAHRANI, REEM GAHTANI, MOHAMMAD ABOHASSAN, MOHAMMAD ALSHAHRANI, YASSER ALRAEY, AYED DERA, MOHAMMAD RAJEH ASIRI, PRASANNA RAJAGOPALAN^*

Oncology Research, Vol.32, No.2, pp. 251-259, 2024, DOI:10.32604/or.2023.043139

Abstract Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation, adhesion, angiogenesis, and metastasis. Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations. Hence, dual inhibition strategies are recommended to increase potency and reduce cytotoxicity. In this study, we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities. Diversity-based High-throughput Virtual Screening (D-HTVS) was used to screen the whole ChemBridge small molecular library against EGFR and… More > Graphic Abstract

Jiamin Xiang¹, Ying Zhang¹, Xiaohua Cao^1,*, Zhigang Zhou²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3443-3466, 2023, DOI:10.32604/cmc.2023.045120

Abstract This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles (AGVs) under the composite operation mode. The multi-objective model aims to minimize the maximum completion time, the total distance covered by AGVs, and the distance traveled while empty-loaded. The improved hybrid algorithm combines the improved genetic algorithm (GA) and the simulated annealing algorithm (SA) to strengthen the local search ability of the algorithm and improve the stability of the calculation results. Based on the characteristics of the composite operation mode, the authors introduce the combined coding and parallel decoding… More >