Abderraouf Guelzim^1,2,*, Baraka Achraf Chakir³, Aziz Ettahir¹, Anas Mbarki^1,*

Frontiers in Heat and Mass Transfer, Vol.21, pp. 125-140, 2023, DOI:10.32604/fhmt.2023.041832

Abstract The Bleve is an explosion involving both the rapid vaporization of liquid and the rapid expansion of vapor in a vessel. The loss of containment results in a large fireball if the stored chemical is flammable. In order to predict the damage generated by a Bleve, several authors propose analytical or semi-empirical correlations, which consist in predicting the diameter and the lifetime of the fireballs according to the quantity of fuel. These models are based on previous experience, which makes their validity arbitrary in relation to the initial conditions and the nature of the product concerned. The article delves into… More > Graphic Abstract

Hongyan Xia, Jin Zhu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1637-1661, 2024, DOI:10.32604/cmes.2023.029576

Abstract Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to cope with the development trend of large-scale ships. In order to improve the solution efficiency of the existing space-time network (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guided vehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balance constraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added to acquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added to… More >

Muhammad Farman^1,2,4, Ali Akgül^3,9,*, Mir Sajjad Hashemi⁵, Liliana Guran^6,7, Amelia Bucur^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1385-1403, 2024, DOI:10.32604/cmes.2023.028803

Abstract New fractional operators, the COVID-19 model has been studied in this paper. By using different numerical techniques and the time fractional parameters, the mechanical characteristics of the fractional order model are identified. The uniqueness and existence have been established. The model’s Ulam-Hyers stability analysis has been found. In order to justify the theoretical results, numerical simulations are carried out for the presented method in the range of fractional order to show the implications of fractional and fractal orders. We applied very effective numerical techniques to obtain the solutions of the model and simulations. Also, we present conditions of existence for… More >

Yuyuan Xiang, Yangli Jia^*, Xiangliang Zhang, Zhenling Zhang

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 97-114, 2023, DOI:10.32604/cmc.2023.040751

Abstract Event extraction stands as a significant endeavor within the realm of information extraction, aspiring to automatically extract structured event information from vast volumes of unstructured text. Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data. Although researchers have proposed various methods to accomplish this task, most existing event extraction models cannot address these challenges because they are only applicable to text scenarios. To solve the above issues, this paper proposes a multi-modal event extraction method based on knowledge fusion. Specifically, for event-type… More >

Yih Bing Chu^*, Zhi Min Lim, Bryan Keane, Ping Hao Kong, Ahmed Rafat Elkilany, Osama Hisham Abusetta

Journal of Cyber Security, Vol.5, pp. 33-46, 2023, DOI:10.32604/jcs.2023.045422

Abstract The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud, particularly in credit card transactions. Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising from potentially fraudulent activities. However, a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations. While sampling techniques can significantly reduce computational time, the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed. Such… More >

Borja Bordel Sánchez^1,*, Ramón Alcarria², Tomás Robles¹

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3189-3218, 2023, DOI:10.32604/cmc.2023.039466

Abstract Cyber-Physical Systems are very vulnerable to sparse sensor attacks. But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely. Therefore, in this paper, we propose a new non-linear generalized model to describe Cyber-Physical Systems. This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and random effects in the physical and computational worlds. Besides, the digitalization stage in hardware devices is represented too. Attackers and most critical sparse sensor attacks are described through a stochastic process. The reconstruction and protection mechanisms are based on a weighted… More >

Jingyao Liu^1,2, Qinghe Feng⁴, Jiashi Zhao^2,3, Yu Miao^2,3, Wei He², Weili Shi^2,3, Zhengang Jiang^2,3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2649-2665, 2023, DOI:10.32604/cmc.2023.038891

Abstract The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method was used for feature extraction… More >

YANG GU^1,#,*, MINGGUANG BI^2,#, DEHUI CHEN³, NING NI⁴, JIANMING CHEN^1,*

BIOCELL, Vol.47, No.9, pp. 2069-2080, 2023, DOI:10.32604/biocell.2023.029989

Abstract Background: The underlying mechanism of sciatic nerve injury (SNI) is a common motor functional disorder, necessitates further research. Methods: A rat model of SNI was established, with the injury group subjected to compressive injury of the right sciatic nerve exposed at the midpoint of the thigh and the sham surgery group undergoing the same surgical procedure. An oxygen-glucose deprivation model was employed to simulate in vitro SNI in PC12 cells. Following data acquisition and quality control, differentially expressed proteins (DEPs) in each model were identified through differential analysis, and enrichment analysis was used to explore the potential functions and pathways… More >

Tao Yin¹, Changgen Peng^2,*, Weijie Tan³, Dequan Xu⁴, Hanlin Tang⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 827-843, 2024, DOI:10.32604/cmes.2023.029039

Abstract In the assessment of car insurance claims, the claim rate for car insurance presents a highly skewed probability distribution, which is typically modeled using Tweedie distribution. The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset, when the data is provided by multiple parties, training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge. To address this issue, this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos. The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection… More >

Baydaa Abdul Kareem^1,2, Salah L. Zubaidi^2,3, Nadhir Al-Ansari^4,*, Yousif Raad Muhsen^2,5

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.027954

Abstract Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance… More > Graphic Abstract