International Journal of Computing Science and Mathematics (5 papers in press)

Regular Issues

• Artistic Mural Reconstruction via a GAN Network based on Sorting Loss  
  by Zhiqiang Chen, Jiaqi Liu, Cao Jianfang, Cunhe Peng 
  Abstract: To address severe mural image defects and the low-resolution and rough reconstruction details of mural reconstruction methods, which can lead to a reduction in mural artistry. This paper presents an artistic reconstruction method (RSSRGAN) for murals. This method adopts the architecture of generative adversarial networks and introduces an attention mechanism. First, channel separation is performed on the feature map obtained during preliminary feature extraction, and weight prediction is performed on the 64-dimensional features to construct the channel dependence between the mural feature maps to retain the high-frequency features lost by the murals in the LR space. Finally, mural textural features are retained to improve the artistic reconstruction effect. Compared with the four popular superresolution reconstruction baseline models, the proposed method achieves a peak signal-to-noise ratio (PSNR) increase of more than 0.58 and an increase in the structural similarity index (SSIM) of more than 0.025 on the mural dataset. Moreover, public dataset verification on the DIV2K dataset showed that the method achieved good reconstruction quality, in which the PSNR increased by more than 0.27 and the SSIM increased by more than 0.014. The RSSRGAN method has achieved significant improvements in mural image reconstruction and provides a new and effective method for artistic mural reconstruction.
  Keywords: mural protection; superresolution reconstruction; generative adversarial network; twin neural network; attention mechanism.
  DOI: 10.1504/IJCSM.2025.10075071
   
  
• Thermal Convection of a Oldroyd-B Nanofluid with Coriolis Effect  
  by Abhishek Singh, Mala Mala 
  Abstract: The investigation focuses on the onset of convection in a horizontal layer with the inclusion of Oldroyd-B nanofluid. The non-dimensional governing equations is solved using the normal mode technique, resulting in an eigenvalue problem. Analytical expression for Rayleigh number is obtained. Critical Rayleigh number values are determined for specific parameter settings. The influence of dimensionless parameters such as the Lewis number (Le), Prandtl number (Pr), Modified particle density increment (Nb), modified diffusivity ratio (Na), Taylor number (Ta), Nanoparticle Rayleigh number (Rn), and the relaxation times of the fluid 1 and 2 on the critical Rayleigh number is analysed. The results of the study indicate that the Taylor number (Ta) acts as a stabilising factor for the system, while the modified diffusivity ratio (Na) and Nanoparticle Rayleigh number (Rn) function as destabilising factors. Moreover, the critical Rayleigh number exhibits a non-monotonic dependency on the coefficients 1 and 2.
  Keywords: Oldroyd-B nanofluid; Linear stability analysis; Thermal convection.
  DOI: 10.1504/IJCSM.2025.10075311
   
  
• Modeling the Impacting of Extreme Snow and Ice Conditions on Energy Flow in Integrated Energy Systems  
  by Jian Wang, Hao Li, Zhanxi Zhang, Jieshan Shan, Fu Shen, Hongchun Shu, Yiming Han, Zilong Cai, Kaizheng Wang, Lei Kou 
  Abstract: This paper proposes a novel framework for analysing the dynamic effects of extreme snow and ice (S&I) conditions on energy flow within integrated energy systems (IES). First, an enhanced IES framework is developed to strengthen the coupling between electricity, heat, gas systems, energy hubs, and renewable energy sources. Second, an improved transmission line icing failure model is introduced, considering the variations in icing thickness and the breaking force. Monte Carlo simulations and numerical calculations are applied to assess the impacts of extreme S&I events on energy flow in IES. A 64-bus case study illustrates the significant operational differences and safety risks arising from such extreme weather conditions. The proposed IES framework provides a comprehensive view of complex energy interactions and underscores the need for resilient systems.
  Keywords: Integrated energy systems; dynamic energy flow; icing; transmission line ice failure.
  DOI: 10.1504/IJCSM.2025.10075665
   
  
• Deep Reinforcement Learning for Dynamic Cellular Manufacturing Systems with Deterioration Effect  
  by Mostafa Jafari, Amir Hossein Akbari 
  Abstract: This study presents an integrated framework for optimising machine layout and production planning in dynamic cellular manufacturing systems under uncertainty. The framework addresses key challenges including machine deterioration and breakdowns, order rejection, and tardiness costs, which are often treated separately in traditional approaches. A multi-objective mathematical model is developed to maximise profit, increase the number of accepted orders, and balance machine workloads to reduce failures. The solution employs a three-step hierarchical approach: heuristic machine-to-cell assignment, deep reinforcement learning for real-time order acceptance and scheduling while considering machine deterioration, and heuristic layout refinement. Computational results show that the proposed method accepts 2.63% more orders with a 5.7% profit reduction, enhancing customer attraction and competitiveness. Workload balancing decreases machine repairs by 11.5%, improving system stability and reducing maintenance costs. Despite an average profit loss of 9.77% due to machine deterioration, the framework significantly improves efficiency and operational resilience in dynamic manufacturing environments.
  Keywords: Cellular manufacturing System; Orde Acceptance and Scheduling; Deterioration effect; deep reinforcement learning.
  DOI: 10.1504/IJCSM.2025.10075736
   
  
• Multi-Stage Adaptive Firefly Algorithm with Enhanced Search  
  by Kefeng Li, Na Jin, Tang Jun, Cao Yiqing 
  Abstract: As a popular swarm intelligence optimization approach, firefly algorithm (FA) has exhibited excellent search capabilities in various optimization problems. However, FA still has some limitations. The search efficiency is sensitive to the step size factor, and the single search pattern results in slow convergence rate. To tackle these issues, this paper proposes a multi-stage adaptive FA with enhanced search (namely MSAFAES). First, a new adaptive parameter method is designed, in which the entire search is divided into two stages. Different parameters strategies are adopted for different search stages. Then, three types of search patterns are employed in the search process. To validate the performance of MSAFAES, ten well-known benchmark problems are tested. Computational results demonstrate the effectiveness of MSAFAES when compared with three other FA variants.
  Keywords: Firefly algorithm; multi-stage; adaptive; multi-strategy; optimization.
  DOI: 10.1504/IJCSM.2025.10075779