Yang Liu¹, Biao Chen¹, Xin Liu², Chenxi Luo², Shihui Xiao^2,*

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 2127-2146, 2025, DOI:10.32604/fhmt.2025.070973 - 31 December 2025

Abstract This study evaluated corn kernel drying performance and quality changes using hot air drying (HAD) and infrared drying (ID) across temperatures ranging from 55°C to 80°C. Optimal drying parameters were determined by using the entropy weight method, with drying time, specific energy consumption, damage rate, fatty acids, starch, polyphenols, and flavonoids as indicators. Results demonstrated that ID significantly outperformed HAD, achieving drying times up to 20% shorter and reducing specific energy consumption and kernel damage by up to 79.3% and 66.7%, respectively, while also better preserving quality attributes. Both methods exhibited drying profiles characterized by More >

Buddhini Chaturika Jayasinghe¹, Neel Chaminda Withanage¹, Prabuddh Kumar Mishra^2,*

Revue Internationale de Géomatique, Vol.34, pp. 707-729, 2025, DOI:10.32604/rig.2025.067395 - 12 September 2025

Abstract Road Traffic Accidents (RTAs) pose significant threats to public safety and urban infrastructure. While numerous studies have addressed this issue in other countries, there remains a notable gap in localized RTA research in Sri Lanka. In this context, the present study investigates the spatial and temporal patterns of RTAs in the Matara urban area in 2023, with the goal of supporting evidence-based policy interventions. A suite of GIS-based spatial analysis techniques including hotspot analysis, kernel density estimation, GiZscore mapping, and spatial autocorrelation (Moran’s I = 0.36, p < 0.01) was applied to examine the distribution and… More >

Farhan A. Alenizi¹, Faten Khalid Karim^2,*, Alaa R. Al-Shamasneh³, Mohammad Hossein Shakoor⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1793-1829, 2025, DOI:10.32604/cmes.2025.066023 - 31 August 2025

Abstract Deep neural networks provide accurate results for most applications. However, they need a big dataset to train properly. Providing a big dataset is a significant challenge in most applications. Image augmentation refers to techniques that increase the amount of image data. Common operations for image augmentation include changes in illumination, rotation, contrast, size, viewing angle, and others. Recently, Generative Adversarial Networks (GANs) have been employed for image generation. However, like image augmentation methods, GAN approaches can only generate images that are similar to the original images. Therefore, they also cannot generate new classes of data.… More >

Amita Biswal, Dah-Jing Jwo^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 927-944, 2025, DOI:10.32604/cmes.2025.067299 - 31 July 2025

Abstract The extended Kalman filter (EKF) is extensively applied in integrated navigation systems that combine the global navigation satellite system (GNSS) and strap-down inertial navigation system (SINS). However, the performance of the EKF can be severely impacted by non-Gaussian noise and measurement noise uncertainties, making it difficult to achieve optimal GNSS/INS integration. Dealing with non-Gaussian noise remains a significant challenge in filter development today. Therefore, the maximum correntropy criterion (MCC) is utilized in EKFs to manage heavy-tailed measurement noise. However, its capability to handle non-Gaussian process noise and unknown disturbances remains largely unexplored. In this paper,… More >

Kamran^1,*, Farman Ali Shah¹, Kallekh Afef ², J. F. Gómez-Aguilar ³, Salma Aljawi⁴, Ioan-Lucian Popa^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3433-3462, 2025, DOI:10.32604/cmes.2025.064815 - 30 June 2025

Abstract In this article, we develop the Laplace transform (LT) based Chebyshev spectral collocation method (CSCM) to approximate the time fractional advection-diffusion equation, incorporating the Atangana-Baleanu Caputo (ABC) derivative. The advection-diffusion equation, which governs the transport of mass, heat, or energy through combined advection and diffusion processes, is central to modeling physical systems with nonlocal behavior. Our numerical scheme employs the LT to transform the time-dependent time-fractional PDEs into a time-independent PDE in LT domain, eliminating the need for classical time-stepping methods that often suffer from stability constraints. For spatial discretization, we employ the CSCM, where More >

Yuhan Xia¹, Xu Li¹, Ye Liu¹, Wenbo Zhou², Yiming Tang^1,3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 625-651, 2025, DOI:10.32604/cmc.2025.065358 - 09 June 2025

Abstract Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology. The combination of domain knowledge and fuzzy clustering algorithms has some problems, such as initialization sensitivity and information granule weight optimization. Therefore, we propose a weighted kernel fuzzy clustering algorithm based on a relative density view (RDVWKFC). Compared with the traditional density-based methods, RDVWKFC can capture the intrinsic structure of the data more accurately, thus improving the initial quality of the clustering. By introducing a Relative Density based Knowledge Extraction Method (RDKM) and adaptive weight optimization mechanism, we effectively solve the… More >

Seham M. AL-Mekhlafi^1,*, Kamal R. Raslan², Khalid K. Ali², Sadam. H. Alssad^2,3, Nehaya R. Alsenaideh⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1927-1953, 2025, DOI:10.32604/cmes.2025.063971 - 30 May 2025

Abstract This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations over four distinct time intervals. The model incorporates three key fractional derivatives: the Caputo-Fabrizio fractional derivative with a non-singular kernel, the Caputo proportional constant fractional derivative with a singular kernel, and the Atangana-Baleanu fractional derivative with a non-singular kernel. We analyze the stability of the core model and apply various numerical methods to approximate the proposed crossover model. To achieve this, the approximation of Caputo proportional constant fractional… More >

Stephan Rosswog^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1713-1741, 2025, DOI:10.32604/cmes.2025.062063 - 30 May 2025

Abstract We present and explore a new shock-capturing particle hydrodynamics approach. Our starting point is a commonly used discretization of smoothed particle hydrodynamics. We enhance this discretization with Roe’s approximate Riemann solver, we identify its dissipative terms, and in these terms, we use slope-limited linear reconstruction. All gradients needed for our method are calculated with linearly reproducing kernels that are constructed to enforce the two lowest-order consistency relations. We scrutinize our reproducing kernel implementation carefully on a “glass-like” particle distribution, and we find that constant and linear functions are recovered to machine precision. We probe our More >

Khaoula Ben Abdellafou¹, Kamel Zidi², Ahamed Aljuhani¹, Okba Taouali^1,*, Mohamed Faouzi Harkat³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3007-3023, 2025, DOI:10.32604/cmc.2025.063146 - 16 April 2025

Abstract Environmental protection requires identifying, investigating, and raising awareness about safeguarding nature from the harmful effects of both anthropogenic and natural events. This process of environmental protection is essential for maintaining human well-being. In this context, it is critical to monitor and safeguard the personal environment, which includes maintaining a healthy diet and ensuring plant safety. Living in a balanced environment and ensuring the safety of plants for green spaces and a healthy diet require controlling the nature and quality of the soil in our environment. To ensure soil quality, it is imperative to monitor and… More >

Rashid Ahmad¹, Asif Nawaz^1,*, Ghulam Mustafa¹, Tariq Ali¹, Mehdi Tlija², Mohammed A. El-Meligy^3,4, Zohair Ahmed⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4171-4194, 2024, DOI:10.32604/cmc.2024.056971 - 19 December 2024

Abstract Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively, predict potential criminal activities, and ensure public safety. Traditional methods of crime analysis often rely on manual, time-consuming processes that may overlook intricate patterns and correlations within the data. While some existing machine learning models have improved the efficiency and accuracy of crime prediction, they often face limitations such as overfitting, imbalanced datasets, and inadequate handling of spatiotemporal dynamics. This research proposes an advanced machine learning framework, CHART (Crime Hotspot Analysis and Real-time Tracking), designed to overcome these challenges. The proposed methodology… More >