International Journal of Information Quality (13 papers in press)

Regular Issues

• A concept for data quality assurance in materials science  
  by Patrick Klein, Stefan Wellsandt, Klaus-Dieter Thoben 
  Abstract: Materials science is a domain that creates large amounts of different experimental data due to the range of standardized and customized experiments. These data are an essential and valuable resource for researchers, provided they have high quality. Since data quality is a diffuse concept in materials science, it is challenging to assure it. This paper proposes a quality assurance concept for experimental data in materials science. We use a conceptual model to describe the main actors interacting with data quality. It uses existing international standards and scientific literature to identify relevant perspectives, terminology, and data quality characteristics for materials science. Furthermore, this paper outlines how an information system can support quality control by measuring quality and the visualization of quality measures, such as dataset completeness. Open challenges are selecting quality characteristics, finding the right level of automated quality measurement, and financing data quality control.
  Keywords: dataset; data management; experiment; financing; information quality; materials science; measurement; quality assurance; quality control; quality management.
  
  
• Ratings of Information Quality by Chinese College Students: An Update in the Context of a Changing Landscape  
  by Yi Guo, Barbara Klein, Chunyue Zhou 
  Abstract: The findings of a longitudinal study on user ratings of online and textual information sources are reported in this paper. Chinese university students were surveyed in 2017, and the results of the survey are compared to an earlier survey of Chinese university students that was done in 2012. Results show some similarities and some differences from those of prior studies, and these differences are discussed and interpreted through the lens of social and technological changes in China.
  Keywords: information quality; China; Internet; longitudinal research; social and technical change.
  
  
• Spatio-Temporal Attention LSTM Model for Tourist Behaviour Prediction and Smart Tourism Route Optimisation  
  by Hongling Wang 
  Abstract: To address insufficient tourist behaviour prediction accuracy and poor path planning in smart tourism, this paper proposes a CNN-LSTM-Attention joint model integrating CNN, LSTM, and attention mechanisms. Traditional methods struggle with spatiotemporal nonlinear features, dynamic congestion, and multi-objective optimisation- failing to accurately predict peak-hour passenger flow or generate personalised routes. This study uses an ST-CNN to extract local/global scenic spot transition features, combines CBAM for dynamic spatiotemporal weighting, designs a multi-task LSTM framework integrating personalised, environmental, and real-time congestion data, and develops a Pareto improvement-based path optimisation algorithm. Experimental results show the model outperforms existing methods in four indicators (ST-RMSE: 8.3, DTW: 9.3, PHR: 0.88, POG: 1.38), reducing spatiotemporal prediction error by 22.4% and improving path optimisation gain by 10.4%, offering an integrated solution for smart tourisms high-precision prediction and interpretable decision-making.
  Keywords: Convolutional neural networks; long short-term memory networks; attention mechanisms; spatiotemporal convolutional network.
  DOI: 10.1504/IJIQ.2026.10077816
   
  
• Meteorological information diagnosis and resource scheduling model based on multivariate Transformer  
  by Qiang Wan 
  Abstract: Meteorological decision making increasingly relies on multivariate, heterogeneous observations, where complex nonlinear spatiotemporal dependencies challenge conventional sequence models and static multi-task weighting. This study proposes a multivariate Transformer-based joint optimisation model for meteorological diagnosis and resource scheduling. The model integrates multi-head self-attention and residual position encoding to capture nonlinear spatiotemporal dependencies, while reinforcement learning dynamically adjusts task weights for balanced cross-task optimisation. Experimental results achieve 89.3% accuracy, 0.89 F1-score, and 0.92 AUC in meteorological diagnosis, alongside a 12.7% reduction in carbon emissions and an RMSE of 0.041 in scheduling. The model shows limited performance degradation under sensor noise and regional distribution shifts, and achieves an inference latency of 23 ms per sample across heterogeneous hardware platforms.
  Keywords: meteorological data; multivariate Transformer; meteorological diagnosis; resource scheduling.
  DOI: 10.1504/IJIQ.2026.10077866
   
  
• A Cyber-Physical-Aware MST-GNN for Power System Load Forecasting  
  by Xing Ma, Zhen Liu, Jian Ma, Xiaoyun Wang, Jiahao Guo 
  Abstract: With the increasing integration of renewable energy, power systems face great challenges in supply-demand balance due to complex spatiotemporal interactions. This study proposes a multi-scale temporal graph neural network (MST-GNN) for high-fidelity power load forecasting, which is a fundamental step toward collaborative cyber-physical optimization. It partitions power data into short-, medium-, and long-term scales, integrates adaptive GNN with dilated causal convolution to extract spatiotemporal dependencies, and adopts Hadamard fusion and physical constraint layers to enhance decision readiness. Experimental results on the PJM dataset show MST-GNN achieves a MAPE of 2.65% and an R2 of 0.943, outperforming conventional models. Ablation analysis confirms the effectiveness of key modules. This framework enhances the precision and interpretability of supply-demand balance prediction in renewable-integrated power systems.
  Keywords: Power system; supply-demand balance; graph neural network; load forecasting; temporal features.
  DOI: 10.1504/IJIQ.2026.10077867
   
  
• Graph-transformer-based spatiotemporal modelling for auxiliary teaching in PE classes  
  by Qingbing Li 
  Abstract: To address the neglect of inter-student interactive relationships in existing PE auxiliary teaching methods, this paper proposes a spatiotemporal modelling framework integrating graph convolutional networks (GCN) and frequency enhanced transformer (FEDformer). A complex-valued adjacency matrix characterises student interaction strength and skill transfer time lags for spatial feature extraction, while FEDformer captures temporal patterns of skill progression and teaching effectiveness. Experimental results show the model outperforms baselines with 91.2% teaching action recognition accuracy, 89.5% strategy recommendation precision, and 0.934 AUC for classroom engagement discrimination. Ablation studies and stability analysis confirm its synergistic effectiveness and robustness.
  Keywords: graph convolutional network; GCN; frequency enhanced transformer; PE optimisation; spatiotemporal modelling; group behaviour analysis.
  DOI: 10.1504/IJIQ.2026.10077994
   
  
• Optimisation Method for Intelligent Scheduling System of Coal Mine Auxiliary Transportation via Multi-Objective Reinforcement Learning  
  by Tao Lian, Haidong Hu, Can Guo, Yubao Guo, Nan Xu 
  Abstract: To address the low efficiency and insufficient practical constraint consideration in coal mine intelligent trackless auxiliary transportation scheduling, this study proposes a multi-objective twin delayed deep deterministic policy gradient algorithm (MOTD3). Integrating deep reinforcement learning, the algorithm optimizes three core objectives: minimizing transportation costs, electric locomotive robot waiting time, and transportation expected deviations, while accounting for constraints like loading/unloading capacities and time limits. MOTD3 adopts twin critic networks, target policy smoothing, and adaptive experience replay to enhance stability and exploration efficiency. Experimental results show it outperforms traditional evolutionary and reinforcement learning baselines, reducing transportation costs by 42.9% and improving locomotive utilization by 33.5%, offering impactful practical value for intelligent coal mine systems.
  Keywords: Deep reinforcement learning; Multi objective optimization; Intelligent trackless auxiliary transportation; Scheduling optimization.
  DOI: 10.1504/IJIQ.2026.10078394
   
  
• A Virtual Power Plant Electricity-Carbon Price Forecasting Model Based on Adaptive Learning Algorithms  
  by Gang Ma, Zhaohua Zhang, Yan Zhang, Juncheng Guang, Yiqun Zhou, Yawei Mu 
  Abstract: As a core platform for distributed energy integration, virtual power plants (VPPs) face dual uncertainties from deep electricity-carbon market coupling. To optimize energy allocation and mitigate low-carbon transition risks, this study proposes an electricity-carbon price forecasting framework based on adaptive learning algorithms. It adopts an exponential dynamic weighting strategy, integrates an enhanced zebra optimization algorithm with golden sine search and KNN feature selection to identify key price drivers, and uses a genetic algorithm to fine-tune BP neural network weight matrices and bias vectors for fitting complex nonlinear price patterns better. Experiments on GEFCom 2014 show MAE, MAPE, RMSE improved by 0.82%, 2.75%, 2.41% vs. benchmarks (XGBoost, BP-SAA, BP-PSOA). On EPE, it achieves MAE 0.12%, MAPE 0.52%, RMSE 1.13%, outperforming all comparators, verifying its high reliability in complex environments and supporting VPPs’ low-carbon risk mitigation and efficient operation.
  Keywords: Electricity-carbon price forecasting; BP neural network; Zebra optimization algorithm; Genetic algorithm; KNN feature selection.
  DOI: 10.1504/IJIQ.2026.10078395
   
  
• Differentiable Neural Architecture Search Variational Autoencoder Model for Vocal Style Transfer  
  by Jingjing Wang, Muhammad Attique Khan 
  Abstract: This study proposes the MUDAS-Net framework, which integrates Differentiable Neural Architecture Search (DARTS), Adaptive Instance Normalization (AdaIN), and a style embedding mechanism for efficient vocal style transfer. Firstly, MUDAS-Net leverages DARTS for automatic network architecture optimization, generating the optimal model architecture tailored for the vocal style transfer task, thereby enhancing the transfer performance. Then, the AdaIN technique is applied to finely adjust content and style information, enabling precise injection of the target style while preserving the melody and lyrics of the source audio. Finally, the style embedding module further enhances the model's adaptability to different singers and styles. Experimental results demonstrate that MUDAS-Net significantly outperforms traditional vocal style transfer methods on multiple benchmark datasets and real-world singing data, achieving excellent results in key metrics such as audio quality, style consistency, and naturalness. This study offers strong technical support for future personalized speech synthesis and voice style conversion technologies.
  Keywords: Singing Voice Style Transfer; DARTS; AdaIN; Neural Architecture Search.
  DOI: 10.1504/IJIQ.2026.10078715
   
  
• Sports Motion Trajectory Extraction Network Based on TCN-STA-GRU  
  by Jialin Li, Alireza Sharifi, Mohammadmahdi Safari 
  Abstract: Sports motion trajectory extraction is crucial for training guidance and tactical analysis, yet existing methods often struggle to capture discriminative trajectory patterns and model temporal dependencies in complex scenarios. This paper proposes a TCN-STA-GRU network for trajectory extraction and prediction. First, a multi-scale Temporal Convolutional Network (TCN) with dilated causal convolutions and residual connections captures motion cues across different temporal ranges. Second, a Spatio-Temporal Attention (STA) module highlights salient information in both space and time: the spatial branch localizes key positions, while the temporal branch emphasizes critical moments, suppressing background noise and irrelevant details. Finally, a Gated Recurrent Unit (GRU) performs deep temporal modeling via update and reset gates to learn long-term dependencies in trajectories. Experiments on three sports datasets (Basketball-D, Football-D, and Soccer-D) demonstrate superior performance. On Basketball-D, the proposed method achieves a 96.0% F1-score and a 1.89 average displacement error, confirming its effectiveness.
  Keywords: Motion trajectory extraction; Temporal Convolutional Network; Gated Recurrent Unit; Spatio-Temporal Attention Mechanism; Temporal feature modeling; Deep learning.
  DOI: 10.1504/IJIQ.2026.10078725
   
  
• Smart Contract-Driven Pricing Strategy for Web3 Crowdfunding Based on Multimodal Deep Clustering  
  by Xiang Chen, Kan Lu, Alpamis Kutlimuratov 
  Abstract: In response to the challenges posed by dynamic market fluctuations and contributor heterogeneity in Web3-based crowdfunding platforms, this study proposes a novel pricing strategy underpinned by multimodal deep clustering and smart contract automation. By integrating behavioral, financial, and social data from both on-chain and off-chain sources, the proposed framework employs autoencoder-based deep clustering and contrastive learning to segment contributors into latent pricing clusters. These segments inform a dynamic pricing policy executed through EVM-compatible smart contracts. Extensive experiments on simulated and benchmark datasets demonstrate superior clustering performance and pricing effectiveness, including increased funding completion rate (+13.3%), early-stage participation (+21.3%), and reduced price volatility. System-level evaluations further validate the model's feasibility in decentralized settings, maintaining sub-second latency and >94% contract execution success under high load. This research advances the theoretical and practical foundations of adaptive pricing strategies in decentralized economies by fusing AI-driven segmentation with blockchain-native automation.
  Keywords: Web3 crowdfunding; deep clustering; multimodal fusion; contrastive learning; dynamic pricing; smart contract.
  DOI: 10.1504/IJIQ.2026.10078728
   
  
• Community-Guided Cascade Fusion in Information Diffusion Prediction  
  by Yingting Lin, Fei Xiong, Zhiyuan Zhang 
  Abstract: The objective of information diffusion prediction is to estimate the probability that inactive users in a cascade will be activated. Existing studies either capture the sequence dependencies within the cascade or leverage user graph structures to predict future activations. However, existing methods overlook users’ positional roles and community structures, and typically rely on a single cascade structure, limiting their ability to jointly model sequential and non-sequential patterns. To address these limitations, we propose a Community-Guided Cascade Fusion Network (CGCF-Net). Specifically, we employ the Louvain algorithm to identify potential community relationships among users and compute the speed and position embeddings of users within each community to obtain their community representations. Furthermore, we leverage coverage attention mechanism and GRU to capture different structural representations, which are fused to obtain the final cascade representation. Extensive experiments on three public datasets verify the effectiveness of the proposed model.
  Keywords: information diffusion; social network analysis; attention; neural network; hypergraph; community analysis; data mining.
  DOI: 10.1504/IJIQ.2026.10078795
   
  
• Multimodal Data Mining in Learning Processes: Optimising the Allocation of Online English Teaching Resources  
  by Le Yang 
  Abstract: In English online teaching, the insufficient utilisation of multimodal data hinders the effectiveness of resource optimisation and allocation. Therefore, a research on optimising the allocation of English online teaching resources under multimodal data mining in the learning process is proposed. Principal component analysis and local outlier factor algorithm are used to process multimodal data, and feature mining and fusion are carried out through self-attention mechanism and graph neural network. Based on the feature fusion results, utilise long short-term memory networks to achieve resource demand prediction. Finally, the objective function for optimising the allocation of English online teaching resources is constructed, and the bat algorithm is used to solve it and obtain the optimal optimisation configuration scheme. The test results show that the proposed methods have a time consumption of less than 3.20 s, a maximum configuration accuracy of 0.97, and a skill mastery rate of over 83% for students.
  Keywords: English online teaching; Learning process; Multimodal data mining; optimized allocation of resources.
  DOI: 10.1504/IJIQ.2026.10078799