International Journal of Information and Communication Technology (6 papers in press)

Regular Issues

• Open Access

  Three dimensional temperature field modelling and simulation for the deicing of post insulators ( Free Full-text Access )

  by Shenli Wang, Song Xie, Di Zhang, Jun Wu, Yi Shi, Jin Li, Zhenqiang Liao 
  Abstract: The icing of pillar insulators in actual substation operating environments can lead to potential hazards such as power outages. Effective deicing of pillar insulators is of great significance. As an emerging deicing method, jet heating deicing lacks existing models. This study innovatively explores the temperature field distribution through advanced numerical simulation methods based on COMSOL, unlike traditional methods that rely mainly on experimental measurements or simple theoretical models. It further describes the process of establishing a three-dimensional model in detail. Through simulation analysis, the three-dimensional temperature field distributions of post insulators under different working conditions or heat source parameters are obtained, visually presenting the change trends and distribution characteristics of temperature. The research results provide a theoretical basis for in-depth understanding of the heat transfer mechanism during the deicing process of post insulators.
  Keywords: pillar insulator; three-dimensional temperature field distribution; distribution characteristics; heat transfer mechanism.
  DOI: 10.1504/IJICT.2025.10074671
   
  

• Open Access

  Development of an instructional model for Korean translation in multilingual classroom contexts ( Free Full-text Access )

  by Rong Rong, Xiaojian Liu 
  Abstract: Multilingual classroom contexts pose significant pedagogical challenges, particularly when students have diverse native languages and varying levels of Korean proficiency. This study introduces a data-driven instructional model for Korean translation education that employs machine learning to address learner diversity. The model evaluates translation outputs, identifies learner-specific error patterns, and personalises instruction based on three key variables: native language influence, historical translation accuracy, and individual learning progression. A dataset of Korean translation tasks was collected from university students representing six L1 backgrounds Chinese, Vietnamese, Arabic, Russian, Japanese, and Spanish. Texts were pre-processed through tokenisation, lemmatisation, and POS tagging, with Word2Vec embeddings used for feature extraction. The proposed Sparrow Search Optimiser Tuned Attention-based Sequence-to-Sequence (SSO-Attn-Seq2Seq) model demonstrated substantial improvements, achieving 8891% across accuracy, precision, recall, and F1-score. Results highlight its adaptability in handling idiomatic expressions and syntactic variation, providing a scalable solution for multilingual Korean language education.
  Keywords: multilingual classroom settings; grammatical variations; languages; SSO-Attn-Seq2Seq.
  DOI: 10.1504/IJICT.2025.10074672
   
  

• Open Access

  Real-time detection of Business English grammar errors driven by transfer learning ( Free Full-text Access )

  by Zhenxin Fang, Zhenyu Song 
  Abstract: Improving the grammatical accuracy of Business English writing is crucial, but general grammar checking tools often struggle to adapt to professional contexts. This study proposes a real-time grammar error detection method based on BERT transfer learning, aimed at enhancing performance in business scenarios. Methodologically, the BERT-base pre-trained model is directly utilised to capture general language features. To meet real-time requirements, a lightweight model inference architecture was designed. Experimental results show that the model fine-tuned for the business domain achieves an accuracy rate of 89.2% and an F1 score of 0.842. The improvements are particularly significant in detecting formal expressions and complex sentence structures specific to business texts. This study demonstrates that combining BERT-based transfer learning with fine-tuning using small yet representative domain-specific datasets can effectively enhance the practicality and accuracy of grammar error detection in Business English.
  Keywords: transfer learning; Business English; grammar error detection; BERT.
  DOI: 10.1504/IJICT.2025.10074621
   
  

• Open Access

  Energy efficiency analysis and optimisation strategies for green building design based on gravitational search algorithm ( Free Full-text Access )

  by Yaxi Gong, Yingyi Ma, Shanshan Cheng 
  Abstract: As people's requirements for energy saving and emission reduction continue to increase, the issue of energy consumption in buildings has received more and more attention. How to efficiently optimise the energy consumption of green buildings has become an important research goal in the field of energy consumption analysis and architectural design. This study, aiming at the energy consumption problem in green buildings, designs a method based on gravitational search algorithm (GSA) to optimise energy consumption. First, sensor data of equipment in the building is collected. Then, a multi-objective optimisation model is constructed to ensure that the final goal is the lowest energy consumption without reducing comfort. The final experimental results show that the overall building energy use decreased by 33.8% because the GSA algorithm can effectively reduce the overall energy consumption of building equipment and meets the requirements for energy consumption optimisation in green buildings.
  Keywords: green buildings; gravitational search algorithm; GSA; energy consumption analysis; multi-objective optimisation model.
  DOI: 10.1504/IJICT.2025.10074623
   
  

• Open Access

  Identification of translation bias in Chinese-Korean Confucian texts based on pre-trained language models ( Free Full-text Access )

  by Zhengfeng Huang 
  Abstract: Confucian classics hold a foundational position in the history of Sino-Korean cultural exchange. However, machine translation of these texts often leads to semantic distortion and cultural bias. This paper proposes an automated bias identification framework based on the pre-trained cross-lingual model x-language model-robustly optimised bidirectional encoder representations from transformers pretraining approach. Through a multi-task architecture integrates contrastive learning, semantic role labelling, and context-aware alignment, our method effectively identifies and quantifies semantic, cultural, and grammatical deviations in translated Confucian texts. Experimental results on multiple public available corpora demonstrate that the proposed approach achieves an F1-score of 0.83 and accuracy of 85%, outperforming existing baselines in both metrics, especially in identifying culturally specific terms and nuanced expressions (F1 = 0.86 for cultural bias). This research provides valuable methodological insights for evaluating classical text translation quality and supports the accurate dissemination and digital preservation of Confucian cultural heritage.
  Keywords: pre-trained language models; PLMs; Chinese-Korean translation; Confucian texts; bias identification; cross-language processing.
  DOI: 10.1504/IJICT.2025.10074595
   
  

• Open Access

  A spatio-temporal transformer predictive model for elderly-oriented tourism via attention mechanism ( Free Full-text Access )

  by Jiya Sun 
  Abstract: To address the issue that current models for predicting the potential of retirement destinations overlook the spatio-temporal correlations between influencing factors, this paper first selects the influencing factors of retirement destination potential and designs an improved empirical mode decomposition algorithm to decompose these factors, obtaining the individual mode components. Then, the characteristics of each mode component are captured, and the spatio-temporal dependencies are unified through an adaptive embedding mechanism. Subsequently, a temporal self-attention module is designed to capture temporal dependencies, and a spatial self-attention mechanism is implemented to model geographical relationships. Feature fusion is achieved using a multi-head attention mechanism, and the prediction results are output through a feedforward neural network. Experimental outcome indicates that the prediction accuracy of the suggested model improves by 2.7%-11.8% compared to the baseline model, validating the superiority of the suggested model.
  Keywords: potential prediction; spatiotemporal transformer; empirical mode decomposition; EMD; attention mechanism.
  DOI: 10.1504/IJICT.2025.10074622