International Journal of Computing Science and Mathematics (3 papers in press)

Regular Issues

• A Cross-modal Evolution Reasoning Framework for Mountainous Transmission Line Safety via Edge-deployed Vision-Language Models and Dynamic Knowledge Graphs  
  by Qi Cao, Song Yang, Hao Lin Li, Xin Qiang Wu, Ming Liu, Zhijun Qin, Feng Wang 
  Abstract: Mountainous transmission lines face compound hazards, while traditional monitoring suffers from semantic ambiguity and isolated multi-modal data. This paper proposes a cross-modal reasoning framework integrating edge vision-language models (Edge-VLM) and cross-modal dynamic knowledge graphs (CDKG). We design a lightweight Edge-VLM with decoupled contrastive loss and NF4 quantisation, reaching mAP@50=0.94 on Jetson Orin Nano. A semantic-guided binocular reconstruction achieves millimeter-level depth precision complying with power industry standards. CDKG fuses InSAR, meteorology and visual data via R-GCN for causal risk propagation. Tested on Jilin-Mount-24 dataset, the framework delivers a 10-hour advance early warning for geological disasters with full reasoning interpretability.
  Keywords: Transmission Line Inspection; Vision-Language Models; Knowledge Graphs; Edge Computing; Multi-modal Fusion; InSAR.
  DOI: 10.1504/IJCSM.2026.10080881
   
  
• Optimization and Simulation of Pickup and Transportation Scheduling for Digital Freight Platform  
  by Wenwen Zhang, Zixia Chen, Qianqian Wang, Haosheng Zhang, Xiaoping Chen 
  Abstract: This study addresses vehicle dispatching in pickup transportation for digital freight platforms, tackling multi-depot dynamic scheduling complexity and real-time response limitations. A novel KNN-GA two-stage hybrid algorithm is proposed, integrating K-nearest neighbours (KNNs) clustering for spatial decomposition and an improved Genetic Algorithm for route optimisation. The multi-objective model considers dispatching costs, temporal costs, and load factors. KNN preprocessing reduces search space complexity by 83.3%, while elite preservation and dynamic parameter adjustment enhance convergence. Experiments on automotive supply chain data demonstrate convergence to a fitness value of 0.015 within 30 iterations, with 26.5% computation time reduction (180 s vs. 245 s), 85.3% average load factor, and an 8.7% empty-running rate (p < 0.01, Cohens d = 1.82). The algorithm enables minute-level dynamic rescheduling for 50-vehicle fleets at $2.10 per AWS EC2 run, facilitating the transportation management system (TMS) integration via RESTful APIs. This hybrid integration of spatial clustering and evolutionary optimisation fills the research gap in closed-loop simulation for order-dispatch-pickup processes.
  Keywords: Fuzzy Cognitive Map (FCM); Ant Colony Algorithm (ACA); Niche Technique; Logistics Demand Forecasting.
  DOI: 10.1504/IJCSM.2026.10080885
   
  
• Collaborative Optimisation of Inspection Strategies and Predictive Maintenance Decisions for Hydropower Generators under Complex Operating Conditions  
  by Yingbing Ran, Xihui Zhang, Jiajin Yuan, Kongyun Chen, Yujie Zhao, Jian Wang 
  Abstract: Hydropower generators play a vital role in ensuring the stability and reliability of large-scale power systems. However, existing studies on condition monitoring and maintenance often treat inspection and maintenance decisions separately, overlooking the coupling between fault evolution, maintenance imperfection, and inspection timing. To address these challenges, this paper proposes a collaborative optimization framework for inspection strategies and predictive maintenance decisions of hydropower generators. An imperfect maintenance-aware reliability model based on the Weibull degradation law and residual defect accumulation is constructed to describe the nonlinear evolution of defect probability after maintenance. Moreover, a predictive maintenance optimization model integrating inspection intervals, repair thresholds, and costrisk trade-offs is developed, solved using a multi-objective evolutionary algorithm. Experimental validation using real hydropower station data demonstrates that the proposed framework achieves higher diagnostic accuracy. The results indicate that jointly optimizing inspection and maintenance decisions can significantly reduce operational risk and life-cycle costs.
  Keywords: Hydropower generator; collaborative optimization; inspection strategy; predictive maintenance decision.
  DOI: 10.1504/IJCSM.2026.10081030